why is the placebo effect a problem in experiments

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Placebo Effect – What It Is and How It Works

The placebo effect is the phenomenon where a subject experiences an effect from an inactive substance or fake treatment, which is called a placebo . While not all people experience the placebo effect (certainly not in all situations), there are genuine therapeutic effects of placebos. Here is a look at what the placebo effect is, why it occurs, and how scientists and health professionals use it.

• A placebo is a fake treatment, which can have genuine therapeutic value, called the placebo effect.
• Examples of placebos include sugar pills and saline solution injections.
• The placebo effect helps providing relief from depression, pain, and certain other conditions.
• Overall, the placebo effect occurs because any treatment (real or a placebo) affects the brain, which responds to the stimulus and produces a physiological effect.

Placebo vs Placebo Effect

The placebo effect is a therapeutic benefit or apparent side effect from a placebo. A placebo, in turn, is a substance or treatment that has no effect. Alternatively, it is a treatment with the exact composition of inactive ingredients or the same steps as the therapy, minus the active substance or procedure.

Examples of placebos include sugar pills, consumable liquids or solids, saline injections, and fake surgeries.

The Nocebo Effect

Sometimes the placebo effect refers to any response to a fake treatment. However, other scientists refer to a therapeutic or beneficial response as the placebo effect and side effects or a negative response as the nocebo effect (negative placebo). The nocebo effect also includes withdrawal symptoms some patients experience after discontinuing a placebo treatment.

Uses of Placebos

The primary use of a placebo is in scientific research and drug testing. A researcher administers the placebo to a control group , while the experimental group receives the treatment. Assuming the placebo is identical to the treatment in every respect except the active ingredient or treatment, this type of experiment identifies the efficacy of the treatment with a high degree of confidence. Also, using a placebo makes double blind experiments possible.

However, when you compare the outcomes for an experimental group, placebo group, and a control group that receives no treatment whatsoever, then the placebo effect becomes apparent. This type of study also reveals “inactive ingredients” that aren’t actually inactive. The placebo effect does not influence the outcomes of all studies, but it is a major factor in others.

Situations Where Placebos Work

So, knowing that the placebo effect is a real phenomenon, scientists and medical professionals studied the effectiveness of placebos. In some situations, a placebo is an effective treatment, even when people know they are taking a placebo. Placebos have an effect on:

• Irritable bowel syndrome
• Sleep disorders

Studies indicate some people taking a placebo for a stimulant experience increased heart rate and blood pressure, while those taking a placebo for a depressant experience the opposite effects.

How the Placebo Effect Works

There is no single definitive mechanism for how the placebo effect works. Multiple factors likely play a role:

• Expectation : Basically, what we believe we will experience from a treatment plays a part in the actual effect. So, if you think an injection will hurt, it probably will. Or, if you think a pill (real or placebo) helps a condition, then it likely does. Even if you know a treatment is a placebo, receiving care from a health professional aids in a positive response.
• Conditioning : Conditioning is a learned response or association between two events. For example, in one study, rats drank a saccharin-sweetened beverage containing the immunosuppressant cyclophosphamide. After three days of conditioning, rats given the saccharin beverage minus the cyclophosphamide still displayed suppressed immune responses.
• Genetics : Some subjects are genetically predisposed to respond to placebos. For example, in one study, people carrying a gene coding for higher levels of the neurotransmitter dopamine were more likely to experience the placebo effect than those with a gene for lower dopamine production.

Studies indicate that the brain controls a variety of responses that manifest as the placebo effect. Physiological processes subject to placebos include pain response, depression, insulin secretion, immunosuppression, symptoms of Parkinson’s disease, and serum iron levels. Brain imaging shows a placebo for pain relief activates several regions of the nervous system, including the spinal cord, amygdala, nucleus accumbens, and anterior cingulate, insular, orbitofrontal, and prefrontal cortices in the brain.

• Ader, R.; Cohen, N. (1975). “Behaviorally conditioned immunosuppression”. Psychosomatic Medicine . 37 (4): 333–40. doi: 10.1097/00006842-197507000-00007
• Eippert, F.; Bingel, U.; Schoell, E.D.; et al. (2009). “Activation of the opioidergic descending pain control system underlies placebo analgesia”. Neuron . 63 (4):533-543. doi: 10.1016/j.neuron.2009.07.014
• Gross, Liza (2017). “Putting placebos to the test”. PLOS Biology . 15 (2): e2001998. doi: 10.1371/journal.pbio.2001998
• Häuser, W.; Hansen, E.; Enck, P. (June). “Nocebo phenomena in medicine: their relevance in everyday clinical practice”. Deutsches Ärzteblatt International . 109 (26): 459–65. doi: 10.3238/arztebl.2012.0459
• Khan, A.; Redding, N.; Brown, W.A. (2008). “The persistence of the placebo response in antidepressant clinical trials”. Journal of Psychiatric Research . 42 (10): 791–6. doi: 10.1016/j.jpsychires.2007.10.004
• Price, D.D.; Finniss, D.G.; Benedetti, F. (2008). “A comprehensive review of the placebo effect: recent advances and current thought”. Annual Review of Psychology . 59 (1): 565–90. doi: 10.1146/annurev.psych.59.113006.095941

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The Placebo Effect: Fake Treatment, Real Response

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The mind can trick you into believing that a fake treatment has real therapeutic results, a phenomenon known as the placebo effect. In some cases, placebos can exert an influence powerful enough to mimic the effects of real medical treatments.

In this phenomenon, some people experience a benefit after the administration of an inactive lookalike substance or treatment. This substance, or placebo, has no known medical effect and can be in the form of a pill (sugar pill), injection (saline solution), or consumable liquid.

In most cases, the person does not know that the treatment they're receiving is actually a placebo. Instead, they believe they've received the real treatment. The placebo is designed to seem exactly like the real treatment, yet the substance has no actual effect on the condition it purports to treat.

The placebo effect is much more than just positive thinking , however. When this occurs, many people have no idea they are responding to what is essentially a sugar pill. Placebos are often used in medical research to help doctors and scientists discover and understand the physiological and psychological effects of new medications.

Here's why the placebo effect is important, how it happens, and why it works.

Placebo vs. Placebo Effect

It is important to note that a "placebo" and the "placebo effect" are different things. The term placebo refers to the inactive substance itself, while the term placebo effect refers to any effects of taking a medicine that cannot be attributed to the treatment itself.

Causes of the Placebo Effect

Although researchers know that the placebo effect is real, they do not yet fully understand how and why it occurs. Various factors might contribute to this phenomenon.

Hormonal Response

One possible explanation is that taking the placebo triggers a release of endorphins. Endorphins have a structure similar to that of morphine and other opiate painkillers and act as the brain's own natural painkillers.

Researchers have demonstrated the placebo effect in action using brain scans, showing that areas with many  opiate  receptors were activated in both the placebo and treatment groups. Naloxone is an opioid antagonist that blocks both natural endorphins and opioid drugs. After people received naloxone, placebo pain relief was reduced.

Conditioning

Other possible explanations include classical conditioning , or when you form an association between two stimuli resulting in a learned response. In some cases, a placebo can be paired with an actual treatment until it evokes the desired effect.  

For example, if you're regularly given the same arthritis pill to relieve stiff, sore joints, you may begin to associate that pill with pain relief. If you're given a placebo that looks similar to your arthritis pill, you may still believe it provides pain relief because you've been conditioned to do so.

Expectation

Expectations, or what we believe we will experience, have been found to play a significant role in the placebo effect. People who are highly motivated and expect the treatment to work may be more likely to experience a placebo effect.

A prescribing physician's enthusiasm for treatment can even impact how a patient responds. If a doctor seems very positive that a treatment will have a desirable effect, a patient may be more likely to see benefits from taking the drug. This demonstrates that the placebo effect can even take place when a patient is taking real medications to treat an illness.

Verbal, behavioral, and social cues can contribute to a person's expectations of whether the medication will have an effect.

• Behavioral : The act of taking a pill or receiving an injection to improve your condition
• Social : Reassuring body language, eye contact, and speech from a doctor or nurse
• Verbal : Listing to a health care provider talk positively about treatment

Genes may also influence how people respond to placebo treatments. Some people are genetically predisposed to respond more to placebos. One study found that people with a gene variant that codes for higher levels of the brain chemical dopamine are more prone to the placebo effect than those with the low-dopamine version. People with the high-dopamine version of this gene also tend to have higher levels of pain perception and reward-seeking.

The Nocebo Effect

Conversely, individuals can experience more symptoms or side effects as a response to a placebo, a response that is sometimes referred to as the " nocebo effect ." For example, a patient might report having headaches, nausea, or dizziness in response to a placebo.

The placebo effect can be used in a variety of ways, including in medical research and psychology research to learn more about the physiological and psychological effects of new medications.

In Medical Research

In medical research, some people in a study may be given a placebo, while others get the new treatment being tested. The purpose of doing this is to determine the effectiveness of the new treatment. If participants taking the actual drug demonstrate a significant improvement over those taking the placebo, the study can help support the claim for the drug's effectiveness.

When testing new medications or therapies, scientists want to know if the new treatment works and if it's better than what's already available. Through their research, they learn the sort of side effects the new treatment might produce, which patients may benefit the most, and if the potential benefits outweigh the risks.

By comparing the effects of a treatment to a placebo, researchers hope to be able to determine if the effects of the medicine are due to the treatment itself or caused by some other variable.

In Psychology Experiments

In a psychology experiment, a placebo is an inert treatment or substance that has no known effects. Researchers might utilize a placebo control group , which is a group of participants who are exposed to the placebo or fake independent variable . The impact of this placebo treatment is then compared to the results of the  experimental group .

Even though placebos contain no real treatment, researchers have found they can have a variety of both physical and psychological effects. Participants in placebo groups have displayed changes in heart rate, blood pressure, anxiety levels, pain perception, fatigue, and even brain activity. These effects point to the brain's role in health and well-being.

Benefits of Using a Placebo

The major advantage of using a placebo when evaluating a new drug is that it weakens or eliminates the effect that expectations can have on the outcome. If researchers expect a certain result, they may unknowingly give clues to participants about how they should behave. This can affect the results of the study.

To minimize this, researchers sometimes conduct what is known as a double-blind study . In this type of study, neither the study participants nor the researchers know who is getting the placebo and who is getting the real treatment. By minimizing the risk of these subtle biases influencing the study, researchers are better able to look at the effects of the drug and the placebo.

One of the most studied and strongest placebo effects is in the reduction of pain. According to some estimates, approximately 30% to 60% of people will feel that their pain has diminished after taking a placebo pill.

For example, imagine that a participant has volunteered for a study to determine the effectiveness of a new headache drug. After taking the drug, she finds that her headache quickly dissipates, and she feels much better. However, she later learns that she was in the placebo group and that the drug she was given was just a sugar pill.

Placebo Effect Outcomes

While placebos can affect how a person feels, studies suggest that they do not have a significant impact on underlying illnesses. A major review of more than 150 clinical trials involving placebos found that placebos had no major clinical effects on illnesses. Instead, the placebo effect had a small influence on patient-reported outcomes, particularly of perceptions of nausea and pain.

However, another review conducted nearly 10 years later found that in similar populations, both placebos and treatments had similar effects. The authors concluded that placebos, when used appropriately, could potentially benefit patients as part of a therapeutic plan.

• Depression : The placebo effect has been found to impact people with major depression disorder. In one study, participants who weren’t currently taking any other medication were given placebo pills labeled as either fast-acting antidepressants or placebo for one week. After the week, the researchers took PET scans and told the participants they were receiving an injection to improve mood. Participants who took the placebo labeled as an antidepressant as well as the injection reported decreased depression symptoms and increased brain activity in areas of the brain linked to emotion and stress regulation.
• Pain management : A small 2014 study tested the placebo effect on 66 people with episodic migraine, who were asked to take an assigned pill—either a placebo or Maxalt (rizatriptan), which is a known migraine medication—and rate their pain intensity. Some people were told the pill was a placebo, some were told it was Maxalt, and others were told it could be either. Researchers found that the expectations set by the pill labeling influenced the participants responses. Even when Maxalt was labeled as a placebo, participants gave it the same rating as a placebo that was labeled Maxalt.
• Symptom relief : The placebo effect has also been studied on cancer survivors who experience cancer-related fatigue. Participants received three weeks of treatment, either their regular treatment or a pill labeled as a placebo. The study found that the placebo (despite being labeled as such) was reported to improve symptoms while taking the medication and three weeks after discontinuation.

A Word From Verywell

The placebo effect can have a powerful influence on how people feel, but it is important to remember that they are not a cure for an underlying condition.

Healthcare providers aren't allowed to use placebos in actual practice without informing patients (this would be considered unethical care), which reduces or eliminates the desired placebo effect.

However, by using placebos in research, during which they don't have to inform the participant, scientists are able to get a better idea of how treatments impact patients and whether new medications and treatment approaches are safe and effective.

Eippert F, Bingel U, Schoell ED, et al. Activation of the opioidergic descending pain control system underlies placebo analgesia .  Neuron . 2009;63(4):533-543. doi:10.1016/j.neuron.2009.07.014

Bąbel P. Classical conditioning as a distinct mechanism of placebo effects .  Front Psychiatry . 2019;10:449. doi:10.3389/fpsyt.2019.00449

Brown WA. Expectation, the placebo effect and the response to treatment .  R I Med J (2013) . 2015;98(5):19-21.

Hall KT, Lembo AJ, Kirsch I, et al. Catechol-O-methyltransferase val158met polymorphism predicts placebo effect in irritable bowel syndrome . PLoS One . 2012;7(10):e48135. doi:10.1371/journal.pone.0048135

Colloca L. The placebo effect in pain therapies . Annu Rev Pharmacol Toxicol . 2019;59:191-211. doi:10.1146/annurev-pharmtox-010818-021542

Hróbjartsson A, Gøtzsche PC. Placebo interventions for all clinical conditions . Cochrane Database Syst Rev . 2004;(3):CD003974. doi:10.1002/14651858.CD003974.pub2

Howick J, Friedemann C, Tsakok M, et al. Are treatments more effective than placebos? A systematic review and meta-analysis . PLoS One . 2013;8(5):e62599. doi:10.1371/journal.pone.0062599

Peciña M, Bohnert ASB, Sikora M, et al. Association between placebo-activated neural systems and antidepressant responses: Neurochemistry of placebo effects in major depression .  JAMA Psychiatry . 2015;72(11):1087. doi:10.1001/jamapsychiatry.2015.1335

Kam-Hansen S, Jakubowski M, Kelley JM, et al. Altered placebo and drug labeling changes the outcome of episodic migraine attacks . Science Translational Medicine . 2014;6(218):218ra5-218ra5. doi:10.1126/scitranslmed.3006175

Hoenemeyer TW, Kaptchuk TJ, Mehta TS, Fontaine KR. Open-label placebo treatment for cancer-related fatigue: A randomized-controlled clinical trial .  Sci Rep . 2018;8(1):2784. doi:10.1038/s41598-018-20993-y

Weiner IB, Craighead WE.  The Corsini Encyclopedia of Psychology, Volume 3 . Hoboken, NJ: John Wiley & Sons. 2010.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Article Contents

A formal definition of the placebo effect, common approaches to estimating placebo effects require untenably strong assumptions, an improved framework for estimating placebo effects, acknowledgments.

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Editorial: Demystifying the Placebo Effect

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Eleanor J Murray, Editorial: Demystifying the Placebo Effect, American Journal of Epidemiology , Volume 190, Issue 1, January 2021, Pages 2–9, https://doi.org/10.1093/aje/kwaa162

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Placebo effects are a tantalizing concept that have captured public attention since at least 1955, when Henry Beecher ( 1 ) cataloged evidence across 15 large studies (total n  = 1,082 patients) and concluded that 35.5.% (±2.2%) of individuals “respond” to placebo. The results presented by Beecher seem plausible at first read. The goal of placebos in medical research is often described as “to distinguish pharmacological effects from the effects of suggestion … and to obtain an unbiased assessment of the result of experiment” ( 1 , p. 1602). Inherent in this goal is a claim that all medical interventions might have some causal effect that operates via psychological mechanisms and that the strength of such an effect is independent of the mode of intervention ( 2 , 3 ). However, without clearly articulating the nature of this causal effect, we cannot refute or support the existence of this effect.

The counterfactual or potential outcome framework for causal inference ( 4 ) states that an exposure has a causal effect on an outcome if the expected value of the outcome had everyone received that exposure or treatment is on average different from the expected value of the outcome had those same individuals received some other exposure or treatment ( 4 ). A key element of this framework often missing from the conversation about placebo effects is the requirement to specify the other exposure or treatment of interest. As we shall see, the omission of an explicit nonplacebo comparison group for the placebo group introduces unrealistic assumptions, confusion, and spurious findings into the definition and estimation of the placebo effect and “placebo response.”

Although the placebo effect is not often of clinical interest to epidemiologists, understanding what is meant by this concept, and how biases can provide misleading inference, can help shed light on potential sources of bias in other single-group studies that might be more consequential. For example, in recent months, a desire to rapidly identify effective treatments for COVID-19 has resulted in a number of single-arm medication “trials” wherein all individuals are given an exploratory treatment, and “response” to this treatment is assessed based on some outcome measure such as disease biomarker levels at the end of follow-up ( 5 ). Similarly, it is not uncommon for researchers who compare the change in levels of some variable over time within a particular exposure group to attribute the results of this comparison to a causal effect of the exposure of interest. However, these outcome-change score studies are subject to many of the sources of bias that I describe here for the placebo effect ( 6 ).

In addition, because belief in placebo effects might lead patients to seek out potentially ineffective alternative medicine treatments ( 7 ), studies that inappropriately define the placebo effect or that do not sufficiently address sources of bias in estimating this effect could contribute to an erosion of public trust in pharmaceuticals ( 8 ). Finally, for many outcomes, such as mortality, placebos likely have no effect, and therefore, when the placebo effect can be validly confirmed to be absent, placebos might be useful as negative control exposures ( 9 ).

Let |$Y$| denote the outcome at the end of follow-up. This might be a binary or time-to-event variable like mortality, or a continuous variable such as pain level. Let |$Z$| represent random assignment. Conventionally, |$Z=1$| denotes the therapeutic treatment arm, and |$Z=0$| denotes the placebo arm. It might also be of interest to denote actual treatment received as |$X$|⁠ , where |$X=0$| for individuals who receive the placebo regardless of treatment assignment. Finally, let |$\mathbf{L}$| represent all other covariates of interest.

Now, we define |${Y}^z$| as the counterfactual outcome that would have been observed for someone randomized to |$Z=z$| under the assumption of consistency. This assumption is valid when we have a clearly defined intervention, such as assignment to placebo. As such, |${Y}^{z=0}$| is the counterfactual outcome that would have been observed for someone randomized to placebo. |${Y}^{z=1}$| is commonly used to denote the counterfactual outcome that would have been observed for someone randomized to the therapeutic treatment. Here, we are interested in placebo versus something other than the therapeutic treatment, so we will denote |${Y}^{z=-1}$| as the counterfactual outcome under some other nontherapeutic exposure, including no exposure. Note that although |${Y}^{z=0}$| and |${Y}^{z=-1}$| are defined for all individuals, at maximum, only one of these outcomes is ever actually observable for any given individual.

Similarly, we define |${Y}^{x=0}$| as the outcome that would be observed when someone receives placebo regardless of randomization, |${Y}^{x=-1}$| when someone receives the nonplacebo control, and joint counterfactuals such as |${Y}^{(z=0,x=0)}$| when someone is randomized to and receives placebo.

Finally, we define |$t$| as an indicator of follow-up time, with |$t=0$| at baseline and |$t=\tau$| at the end of follow-up.

Given that causal effects are defined with respect to a comparison exposure, the “placebo effect” as a single entity does not actually exist—any effect of placebo must include a specification of some nonplacebo control group of interest (i.e., |$Z=-1$|⁠ ), even if that group is hypothetical. That is, we cannot talk about the causal effect of placebo; we must talk about the causal effect of placebo versus something else.

Placebos have been hypothesized to have causal effects via suggestion or other psychological mechanisms, via behavioral change, and via heretofore unknown physiological or pharmacological mechanisms ( 1 , 7 , 8 , 10 , 11 ). The ideal comparison group for a given placebo will depend on the mechanism of interest under study ( Figure 1 ). For example, if the placebo of interest is a sham surgery hypothesized to operate via a psychological mechanism (e.g., reassurance), an appropriate nonplacebo control might be “no surgery.” Alternately, we might want to know the placebo effect if everyone received sham surgery compared with everyone being required to wait for some defined time period before surgery. Importantly, even if both effects are nonzero, they might have different magnitudes or different directions.

Possible mechanisms through which a placebo might act upon an outcome. The appropriate choice of nonplacebo control will depend on which mechanism is of interest. The nonplacebo control should be hypothesized not to operate via the mechanism of interest.

We now formalize the 2 categories of placebo effects.

First, we can defined an “intention-to-treat placebo effect” as a comparison between the average outcome that would have been observed if everyone in the trial had been randomized to placebo versus if everyone in the trial had been randomized to the nonplacebo control.

Second, we can define the “per-protocol placebo effect” (i.e., the effect of receiving placebo) as a comparison between the average outcome that would have been observed if everyone in the trial had been randomized to placebo and actually received it versus if everyone in the trial had been randomized to some other nonplacebo control and actually received that control treatment. The per-protocol placebo effect most closely matches the concept of “placebo response” ( 12 ) in incorporating the idea of receiving placebo, not just being assigned to placebo.

On the absolute scale, using the notation and definitions above, we could write these effects as:

Intention-to-treat effect of placebo versus nonplacebo control: |$E[{Y}^{z=0}]-E[{Y}^{z=-1}]$|⁠ .

Per-protocol effect of placebo versus nonplacebo control: |$E[{Y}^{z=0,x=0}]-E[{Y}^{z=-1,x=-1}]$|⁠ .

Four hypothetical trajectories for a health outcome among individuals assigned to placebo over time. A) The outcome does not change over time for placebo-arm participants. B) The outcome is cyclic over time for placebo-arm participants. C) The outcome decreases over time for placebo-arm participants. D) The outcome increases over time for placebo-arm participants. In all, the blue dotted line represents the observed outcome trajectory for individuals in the placebo arm; the solid gray line represents the counterfactual outcome trajectory for the placebo arm, had they not been given placebo, that is assumed when the analysis uses the average outcome at the end of follow-up in the placebo arm as an estimator of the placebo effect; and the red dashed line represents the counterfactual outcome trajectory for the placebo arm, had they not been given placebo, that is assumed when the analysis uses the average difference between end of follow-up and baseline in the placebo arm as the estimator.

Three common approaches to estimating the placebo effect are: 1) the outcome at the end of follow-up in the placebo arm (for example, % reporting symptoms below some threshold) ( 13 ); 2) outcome change from baseline to the end of follow-up in the placebo arm ( 12 , 14–16 ); and 3) comparison of placebo adherers and placebo nonadherers with no or minimal adjustment for confounding ( 17–20 ). Under very strong assumptions, these approaches could provide an estimate of a placebo effect ( Figure 2 ). However, in nearly all cases these assumptions will be unreasonable. I briefly explain the required assumptions and why they are inappropriate ( Table 1 ).

In addition to the specific assumptions discussed below, these all further require the assumption of no, or noninformative, loss to follow-up, and well-defined causal questions, including clear specification of what is meant by both “placebo” and “nonplacebo control.”

Intention-to-treat placebo effect estimation using outcome at end of follow-up

Estimating an intention-to-treat placebo effect using the outcome at the end of follow-up requires the strongest assumptions. Because a causal effect is by definition the contrast between 2 counterfactual outcomes, this method implicitly assumes that the counterfactual outcome under the (unspecified) nonplacebo control would have been exactly zero.

To see why, remember that our goal is to estimate a comparison such as |$E[{Y}^{z=0}]-E[{Y}^{z=-1}]$|⁠ . Under the consistency assumption, the observed outcome among individuals assigned to placebo is equal to the counterfactual outcome they would have had, had they been assigned to placebo, and randomization ensures that the exchangeability assumption is met—that is, the counterfactual outcome under placebo observed among those assigned to placebo is equal on average to the counterfactual outcome that would have been observed if everyone had been assigned to placebo (i.e., |$E[Y|Z=0]=E[{Y}^{z=0}]$|⁠ ). Therefore, in order for our estimator, |$E[Y|Z=0]$|⁠ , to return a valid estimate of our causal effect of interest, |$E[{Y}^{z=0}]-E[{Y}^{z=-1}]$|⁠ , we must assume that the counterfactual outcome if everyone had received the nonplacebo control is exactly zero (i.e., |$E[{Y}^{z=-1}]=0$|⁠ ). Table 1 gives a proof for this intuition.

Note, this is true regardless of what zero represents. For example, for a beneficial outcome 0 might reflect complete symptom resolution, whereas for a harmful outcome it might represent 0% survival.

Intention-to-treat placebo effect estimation using change since baseline

Estimating an intention-to-treat placebo effect by comparing change in the outcome from baseline to the end of follow-up among individuals assigned to placebo is often called “placebo response” ( 18 ). Under this approach, the intention-to-treat effect of assignment to placebo versus a nonplacebo control, |$E[{Y_{t=\tau}}^{z=0}]-E[{Y_{t=\tau}}^{z=-1}]$|⁠ , is estimated using the observed difference in outcome measurements at baseline versus at the end of follow-up among the placebo-arm participants: |$E[{Y}_t|Z=0,t=\tau ]-E[{Y}_t|Z=0, t=0]$|⁠ .

Consistency and randomization ensure that the observed outcome at the end of follow-up among individuals assigned to placebo is a valid estimate of the counterfactual outcome at the end of follow-up if all individuals had been assigned to placebo (i.e., |$E[{Y}_t|Z=0,t=\tau ]=E[{Y_{t=\tau}}^{z=0}]$|⁠ ). Now, we no longer assume the outcome in the nonplacebo control group at the end of follow-up is exactly zero. Instead, we make the slightly less strong, but still potentially unreasonable, assumption that the average observed outcome in the placebo group at the start of follow-up would be exactly equal to the average counterfactual outcome in the nonplacebo control group at the end of follow-up (i.e., |$E[{Y}_t|Z=0,t=0]=E[{Y_{t=\tau}}^{z=-1}]$|⁠ ). That is, we assume that if the individuals assigned to the placebo arm had not been assigned to the placebo arm but instead to some other control group, their outcome values would have on average remained unchanged over the entire follow-up duration.

For some conditions, this might be a reasonable assumption. For example, if the outcome is performance on some skill-based test, values might be expected to be on average unchanged when no intervention is delivered. However, when the outcome is disease progression or symptom severity, it is often common for the outcome to worsen, improve, or fluctuate naturally over time in the absence of any medical intervention ( Figure 2 ). Such natural changes will likely violate the assumption required for using change among placebo-arm participants as an estimator of the placebo effect. This is also a problem for analyzing change trajectories when exposures other than the placebo are of interest ( 6 , 21 )

Furthermore, in many randomized trials, the value of the outcome measurement at baseline is used as part of the trial eligibility criteria—that is, only individuals who meet some cutoff for severity will be allowed to enroll in the trial. In these cases, natural fluctuations in disease symptoms will necessarily mean that enrolled individuals are more likely to be chosen if they are at or near their peak symptom value, and any second measurement time point will be expected to show a change in outcome through the simple process of regression to the mean ( 22 , 23 ).

Potential Estimators for the Placebo Effect and Their Assumptions a

a Variable definitions: |$Y$| denotes the outcome at the end of follow-up. |$Z$| represents random assignment, with |$Z=0$| for the placebo arm and |$Z=-1$| for the nonplacebo comparator arm. |$A$| denotes actual treatment received, where |$X=0$| for individuals who receive the placebo regardless of treatment assignment. |$\mathbf{L}$| represents all other covariates of interest. Superscripts indicate counterfactuals.

b Partial exchangeability means that the counterfactual outcome under a given treatment assignment, |${Y}^z$|⁠ , is independent of the actual treatment assignment received, for individuals within the placebo arm |$(Z=0)$|⁠ . Similarly, partial consistency means that the counterfactual outcome under a given treatment assignment, |${Y}^z$|⁠ , is equal to the observed outcome for individuals actually assigned to the placebo arm |$Z=0$|⁠ .

c Exchangeability means that the counterfactual outcome under a given treatment assignment |${Y}^z$|⁠ , is independent of the actual treatment assignment for all values of |$Z=z$|⁠ . Similarly, consistency means that the counterfactual outcome under a given treatment assignment, |${Y}^z$|⁠ , is equal to the observed outcome for individuals actually assigned to the placebo arm |$Z=0$|⁠ .

d Conditional exchangeability means that the counterfactual outcome under a given treatment assignment |${Y}^z$|⁠ , is independent of the actual treatment assignment, conditional on some set of measured covariates, |$\mathbf{L}$|⁠ , for all values of |$Z=z$|⁠ .

e Joint exchangeability means that the counterfactual outcome under a given combination of treatment assignment and adherence level, |${Y}^{z,x}$|⁠ , is jointly independent of the actual assignment and adherence levels, |$Z$| and |$X$| for all |$Z=z$| and |$X=x$|⁠ .

f Joint conditional exchangeability means that the counterfactual outcome under a given combination of treatment assignment and adherence level, |${Y}^{z,x}$|⁠ , is jointly independent of the actual assignment and adherence levels, |$Z$| and |$X$| for all |$Z=z$| and |$X=x$|⁠ , conditional on some set of measured covariates, |$\mathbf{L}$|⁠ .

Per-protocol placebo effect estimation assuming no confounding exists

Finally, a third common approach is to compare the outcome at the end of follow-up among individuals in the placebo arm who adhere to their assigned placebo protocol with the outcome among individuals in the placebo arm who do not adhere to their assigned placebo protocol. Under some assumptions, this can provide an estimate of the per-protocol effect of placebo versus nonplacebo control. In fact, this approach is not in itself an unreasonable method in that the placebo nonadherers might, in many cases, provide a reasonable estimate of the outcome expected from a control group that had received all study care except the specific placebo medication (i.e., a nonplacebo control group).

However, most implementations of this approach in the literature have made the strong assumption that adherence or nonadherence to placebo is either entirely random, or is predicted only by baseline (prerandomization) covariates regardless of the duration of treatment ( 17 , 20 , 24 , 25 ). This is an extremely strong assumption of no confounders (common causes) of the adherence-outcome relationship, and when violated it can lead to extremely large estimates of the placebo effect even where none exist ( 9 , 20 , 26–28 ).

The approaches described above have been widely used to estimate placebo effects, with little recognition or discussion of the required assumptions to endow these estimates with a causal interpretation ( 12 , 14–16 ). For example, a recent meta-analysis of placebo-arm “response rates” for ulcerative colitis found 64 studies that reported rates of symptom response in the placebo arm and concluded that placebo response rate was highest among those trials with the most stringent symptom inclusion criteria ( 29 ). Furthermore, a search of the National Center for Biotechnology Information’s PubMed for “placebo effect” returns over 6,000 articles, but less than 50 of these also mention “causal” or “causal inference.”

The required causal assumptions for estimating a placebo effect using the methods described above are strong and likely unrealistic for almost all randomized trials. However, this does not mean that placebo effects cannot be validly estimated. Instead, researchers interested in the causal effects of placebo versus nonplacebo controls should use methods that rely on weaker, potentially more reasonable, assumptions, beginning with a clear identification of the causal question of interest, hypothesized mechanism of placebo effect, and selection of appropriate nonplacebo control group.

Intention-to-treat placebo effect estimation using an explicit nonplacebo control group

When the intention-to-treat placebo effect is of interest, the approach that makes the fewest assumptions, and is therefore the most likely to provide a valid effect estimate, is to design a trial in which individuals can be randomized to both placebo control and nonplacebo control arms ( 3 ). Such a trial could also include an active treatment arm but need not, as with a recent randomized trial of placebo versus study visits only for assessing potential psychological benefits of placebo in irritable bowel disease symptom relief ( 10 ). This intention-to-treat placebo effect might still need to be adjusted for loss to follow-up, but it can otherwise be validly obtained via a simple comparison of the outcome at the end of follow-up in the 2 control arms ( 30 ). In fact, this is the only approach guaranteed to validly estimate the intention-to-treat effect of placebo versus an explicit nonplacebo control.

Intention-to-treat placebo effect estimation assuming confounders are known and measured

Alternatively, the intention-to-treat placebo effect could, in some cases, be estimated by comparing the outcome among individuals randomly assigned to the placebo arm and individuals who were eligible to participate in the randomized trial but who declined to participate or were not contacted for enrollment. This comparison no longer has the guarantee of validity, because individuals who enrolled in the trial might be systematically different from individuals who did not enroll in the trial. However, with careful adjustment for all variables that both predict trial enrollment and are prognostic for the outcome, an estimate of the intention-to-treat placebo effect could be obtained.

This estimate makes the somewhat stronger assumption that all confounders for trial participation and the outcome are known, measured, and appropriately accounted for in the analysis (for example, via standardization). While there might be scenarios under which this assumption is not plausible, it is much less strong that the previously discussed common assumptions that the nonplacebo control outcome is on average exactly zero or equal to the baseline value in the placebo arm.

This method also requires assumptions about positivity or overlap. That is, individuals selected as members of the control group must have been eligible to be trial participants. Individuals who were rejected from the trial due to failure to meet inclusion requirements or because they met 1 or more exclusion criteria should therefore not be included in the comparison group. In addition, if all individuals with a specific set of covariates had refused invitations to participate in the trial, despite having been eligible, then there will also not be positivity, and individuals with these same characteristics should also be excluded from the control group.

Per-protocol placebo effect estimation assuming confounders are known and measured

Finally the per-protocol placebo effect could be estimated from a trial in which individuals are randomized to both placebo and nonplacebo controls, and adherence to each is collected. The per-protocol placebo effect could also be estimated by comparing placebo-arm adherers and placebo-arm nonadherers whenever the nonplacebo control of interest is similar to the experience of placebo-arm nonadherers (for worked examples see Murray and Hernán ( 26 , 27 )). Both of these approaches require the assumption that all confounders for adherence and the outcome are known, measured, and appropriately adjusted for in the analysis.

For trials where intervention and control care is delivered only once (so-called point interventions), prerandomization or baseline confounders will be sufficient, and any analytical method that accounts for these confounders can provide an unbiased estimate of the per-protocol placebo effect. However, in many trials, interventions and controls are delivered at multiple time points (sustained interventions). In these cases, confounding should be assessed throughout follow-up. Whenever any confounders are suspected to also be caused by prior adherence to placebo, adjustment must be made using g-methods, which can account for adherence-confounder feedback ( 30 , 31 ).

The terms “placebo effect” and “placebo response” are generally used to imply that, through some psychological or biological process or some change in other health or risk behaviors, individuals who take a placebo treatment experience improvement in health outcomes relative to what would have been expected if they had not taken placebo ( 2 , 3 , 11 , 13 , 23 ). This implies that a per-protocol placebo effect is of most interest. However, there are several key methodological biases which prohibit the interpretability of many placebo effect estimates.

First and foremost, the placebo effect cannot be defined without reference to a clear nonplacebo control. The placebo effect of a sugar pill versus no study contact might be different from the placebo effect of the same sugar pill versus regular study visits. The appropriate control group will depend on the mechanism of interest for the specific health condition, placebo, population, and outcome of interest.

Second, many reported placebo effects rely on extremely strong assumptions about the expected value of the outcome among the study individuals if they had received a hypothetical nonplacebo control, rather than collecting data on a control group of individuals who do receive a nonplacebo control. When these assumptions are incorrect, we might falsely conclude there is no placebo effect even though a placebo effect does exist, or the existence of a placebo effect when there is truly no placebo effect. Furthermore, in the absence of an explicit nonplacebo control group, regression to the mean is an extremely likely source of spurious conclusions about the size and direction of the placebo effect ( 23 ).

Finally, even when explicit control groups are used to estimate what would have happened to placebo-arm participants if they had not received placebo, control for confounding both at baseline and postrandomization might be needed ( 30 ). It is not uncommon for researchers to assume no confounding at all or to assume only baseline confounding despite lengthy follow-up and sustained placebo treatment. Table 1 summarizes the required assumptions for the methods discussed in this paper to provide valid estimates of causal effects.

The concepts presented in this paper apply to causal inference in applications other than the placebo effect. Whenever the goal is to estimate a causal effect, clear causal estimands and questions with explicit comparison groups must be specified. Assumptions about the relationships between available observed data and the desired counterfactual contrast must be made, and analyses of change in outcomes, single-group outcomes, or outcomes adjusted only for confounders at a single time point are likely to be biased whenever the strong assumptions described in this work are violated.

Placebo effects are in many ways an oxymoron, and yet many people believe that they exist, at least for self-reported or subjective health outcomes. However, without more rigorous definition and investigation of these effects, the existence of placebo effects cannot be scientifically supported or refuted. Investigators interested in the possible psychological, behavioral, or physiological effects of placebos should clarify the reference group of interest, use an appropriate study design to reduce the reliance on unreasonable assumptions, and estimate both intention-to-treat and per-protocol placebo effects with appropriate control for confounding and loss to follow-up, following the same guidelines as the investigation of any other causal effect of a medical intervention.

Author affiliation: Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts (Eleanor J. Murray).

This work was partly supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant R21 HD098733).

This work arose from work presented at “The Placebome in Clinical Trials and Medicine” Exploratory Seminar at the Radcliffe Institute for Advanced Study in 2019.

Conflict of interest: none declared.

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The weird power of the placebo effect, explained

Yes, the placebo effect is all in your mind. And it’s real.

by Brian Resnick

Illustrations by Javier Zarracina

Over the last several years, doctors noticed a mystifying trend: Fewer and fewer new pain drugs were getting through double-blind placebo control trials, the gold standard for testing a drug’s effectiveness.

In these trials, neither doctors nor patients know who is on the active drug and who is taking an inert pill. At the end of the trial, the two groups are compared. If those who actually took the drug report significantly greater improvement than those on placebo, then it’s worth prescribing.

When researchers started looking closely at pain-drug clinical trials, they found that an average of 27 percent of patients in 1996 reported pain reduction from a new drug compared to placebo. In 2013, it was 9 percent.

What this showed was not that the drugs were getting worse, but that “the placebo response is growing bigger over time,” but only in the US, explains Jeffrey Mogil, the McGill University pain researcher who co-discovered the trend. And it’s not just growing stronger in pain medicine. Placebos are growing in strength in antidepressants and anti-psychotic studies as well.

“The placebo effect is the most interesting phenomenon in all of science,” Mogil says. “It’s at the precise interface of biology and psychology,” and is subject to everything from the drug ads we see to our interactions with health care providers to the length of a clinical trial.

Scientists have been studying this incredibly complex interface in great detail over the past 15 years, and they’re finding that sugar pills are stranger and more useful than we’ve previously imagined. The new science of placebo is bringing new understanding to why alternative treatments — like acupuncture and reiki — help some people. And it could also potentially allow us to one day prescribe smaller doses of pain drugs to help address the opioid crisis currently ravaging America.

Most instructively, the science finds that since we can’t separate a medicine from the placebo effect, shouldn’t we use it to our advantage?

There is no one placebo response. It’s a family of overlapping psychological phenomena.

Belief is the oldest medicine known to man.

For millennia, doctors, caregivers, and healers had known that sham treatments made for happy customers. Thomas Jefferson himself marveled at the genius behind the placebo. “One of the most successful physicians I have ever known has assured me that he used more bread pills, drops of colored water, powders of hickory ashes than of all other medicines put together,” Jefferson wrote in 1807. “It was certainly a pious fraud.”

These days, placebo — Latin for “I shall please” — is much more than a pious fraud.

As Ted Kaptchuk at Harvard, who is regarded as one of the world’s leading experts on placebo, put it to me in a recent interview , the study of the placebo effect is about “finding out what is it that’s usually not paid attention to in medicine — the intangible that we often forget when we rely on good drugs and procedures. The placebo effect is a surrogate marker for everything that surrounds a pill. And that includes rituals, symbols, doctor-patient encounters.”

And it’s not just one thing. “I see the placebo effect as a kind of loose family of different phenomena that are just yoked together by this term,” says Franklin Miller, a retired NIH bioethicist who has edited a volume on the subject. “Sooner or later we’ll get rid of the term,” he says, and talk more specifically about each of its components.

The family of placebo effects ranges from the common sense to some head scratchers. Let’s start off with the simplest.

1) Regression to the mean

When people first go to a doctor or start on a clinical trial, their symptoms might be particularly bad (why else would they have sought treatment?). But in the natural course of an illness, symptoms may get better all on their own. In depression clinical studies, for instance, researchers find around one-third of patients get better without drugs or placebo. In other words, time itself is a kind of placebo that heals.

2) Confirmation bias

A patient may hope to get better when they’re in treatment, so they will change their focus. They’ll pay closer attention to signs that they’re getting better and ignore signs that they’re getting worse. (Relatedly, there’s the Hawthorne effect: We change our behavior when we know we’re being watched.)

But as we’ve seen, the placebo effect is more than just bias. There’s also:

3) Expectations and learning

The placebo response is something we learn via cause and effect. When we take an active drug, we often feel better. That’s a memory we revisit and recreate when on placebo.

Luana Colloca , a physician and researcher at University of Maryland, has conducted a number of studies on this phenomenon. And they typically go like this: She’ll often hook up a study participant to an electroshock machine. For each strong, painful shock, she’ll flash a red light on a screen the participant is looking at. For mild shocks, she’ll flash a green light. By the end of the experiment, when the participants see the green light, they feel less pain, even when the shocks are set to the highest setting.

The lesson: We get cues about how we should respond to pain — and medicine — from our environments.

Take morphine, a powerful drug that acts directly on neurochemical receptors in the brain. You can become addicted to it. But its analgesic powers grow when we know we’re taking it, and know a caring professional is giving it to us.

Studies show that post-operative patients whose painkillers are distributed by a hidden robot pump at an undisclosed time need twice as much drug to get the same pain-relieving effect as when the drug is injected by a nurse they could see. So awareness that you’re being given something that’s supposed to relieve pain seems to impact perception of it working.

The research also suggests that fake surgeries — where doctors make some incisions but don’t actually change anything — are an even stronger placebo than pills. A 2014 systematic review of surgery placebos found that the fake surgery led to improvements 75 percent of the time. In the case of surgeries to relieve pain, one meta-review found essentially no difference in outcomes between the real surgeries and the fake ones.

There is such thing as the nocebo effect: where negative expectations make people feel worse. Some researchers think this is what’s fueling the gluten-free diet fad. People have developed a negative expectation that eating gluten will make them feel bad. And so it does, even though they may not have any biological gluten sensitivity.

4) Pharmacological conditioning

This is where things get a little weird.

Colloca has conducted many studies where for several days, a patient will be on a drug to combat pain or deal with the symptoms of Parkinson’s disease. Then one day, she’ll surreptitiously switch the patient over to a placebo. And lo and behold, they still feel healing effects.

On that fifth day, it seems the placebo triggers a similar response in the brain as the real drug. “You can see brain locations associated with chronic pain and chronic psychiatric disease” acting like there are drugs in the system, she says. For instance, Colloca has found that individual neurons in the brains of patients with Parkinson’s disease will still respond to placebos as though they are actual anti-Parkinson’s drugs after such conditioning has taken place.

What’s going on here? Learning. Just like Pavlov’s dogs learned to associate the sound of a bell with food and would start to salivate in anticipation, our brains learn to associate taking a pill with relief, and start to produce the brain chemicals to kick-start that relief.

This pharmacological conditioning only works if the drug is acting on a process that the brain can do naturally. “You can condition pain relief because there are endogenous pain-relieving mechanisms,” Miller says. Painkillers activate the opioid system in the brain. Taking a pill you think is a painkiller can activate that system (to a lesser degree).

And some studies do suggest that the placebo effect’s powers may possibly move beyond the brain.

In a 2012 study , participants were given a sweet drink along with a pill that contained an immune suppressant drug for a few days. Without notice, the drug was swapped with placebo on one of the trial days. And their bodies still showed a decreased immune response. Their bodies had learned to associate the sweet drink with decreased production of interleukin, a key protein in our immune systems, which is produced in many cells outside the brain .

Results like these show “we are talking about a neurobiological phenomenon,” Colloca says.

5) Social learning

When study participants see another patient get relief from a placebo treatment (like in the electroshock experiment described above), they have a greater placebo response when they’re hooked up to the machine.

6) A human connection

Irritable bowel syndrome is an incredibly hard condition to treat. People with it live with debilitating stomach cramps, and there are few effective treatments. And doctors aren’t sure of the underlying biological cause.

It’s the type of ailment that’s sometimes derided as “all in their head,” or a diagnosis given when all others fail. In the early 2000s, Harvard’s Ted Kaptchuk and colleagues conducted an experiment to see if usually intangible traits like warmth and empathy help make patients feel better.

In the experiment, 260 participants were split into three groups. One group received sham acupuncture from a practitioner who took extra time asking the patient about their life and struggles. He or she took pains to say things like, “I can understand how difficult IBS must be for you.” A second group got sham acupuncture from a practitioner who did minimal talking. A third group was just put on a waiting list for treatment.

The warm, friendly acupuncturist was able to produce better relief of symptoms. “These results indicate that such factors as warmth, empathy, duration of interaction, and the communication of positive expectation might indeed significantly affect clinical outcome,” the study concluded.

This may be the least-understood component of placebo: It’s not just about pills. It’s about the environment a pill is taken in. It’s about the person who gave it to you — and the rituals and encounters associated with them.

What placebos can, and can’t, do

Placebos seem to have the greatest power over symptoms that lie at the murky boundary between the physical and psychological.

A 2010 systematic review looked at 202 drug trials where a placebo group was compared to patients who received neither placebo nor active drug. And it found that placebos seem to move the needle on pain, nausea, asthma, and phobias, with more inconsistent results for outcomes like smoking, dementia, depression*, obesity, hypertension, insomnia, and anxiety. (*Separate literature review on depression meds does find an effect of placebo compared with no treatment.)

“It seems like placebo taps into a family of psychological and brain processes that’s very much something we evolved for,” says Tor Wager, a University of Colorado Boulder neuroscientist who has co-authored many of the key papers on the neuroscience of placebo. “Take pain as an example. If you step on something sharp, there’s pain in your foot. Now, how should you respond to it? Well, if you are running from an attack, you don’t even want to feel that. You keep going.”

Another way to think about it: Placebos tweak our experience of symptoms, not their underlying causes.

A 2011 study elegantly illustrates this. In the experiment, asthma patients were randomly sorted into three groups: One group received an inhaler with albuterol, a drug that opens the airways. Another group got an inhaler with a placebo. A third group got “sham” acupuncture (meaning the needles were withdrawn before they touched the skin). A fourth got nothing. The study authors evaluated lung function on two metrics: self-report from the patients on their asthma symptoms, and an objective measure of lung functioning.

If you go by self-report, it looks like the placebo, albuterol, and sham acupuncture are all equally effective.

The objective measure, however, shows only the albuterol improved airflow. (FEV is a measure of lung function.)

Which isn’t to say that the self-reported improvement on placebo doesn’t matter. In many illnesses, patients would love a greater opportunity to ignore their symptoms.

“In all the objectively measurable illnesses, like cancer, even heart disease, there are components of it that are not [objectively measurable],” Kaptchuk says. And it’s those symptoms that are the prime targets to treat with placebo.

Placebo can only help symptoms that can be modulated by the mind. “There are real limits to what you can condition,” Miller says. You can’t, for example, condition the cancer-killing effects of chemotherapy. Our bodies don’t produce cancer-killing chemicals.

There’s evidence that placebos actually release opioids in the brain

Over the past 15 years, scientists have made some of their most interesting discoveries looking at how placebos have a powerful impact on the brain.

“When I first started studying placebo effects, it kind of seemed like magic — for some reason, your brain mimicked a drug response,” Wager says. “The biggest change in this field in the last 15 years is that neuroscientists are beginning to uncover the underlying neural mechanisms that create the placebo response.”

Placebos, researchers have found, actually prompt the release of opioids and other endorphins (chemicals that reduce pain) in the brain. Other findings:

• Drugs that negate the effects of opioids — such as naloxone — also counteract the placebo effect, which shows that placebos are indeed playing on the brain’s natural pain management circuitry.
• The periaqueductal gray matter, a region of the brain key for pain management, shows increased activity under placebo. Regions of the spinal cord that respond to pain show decreased activity under placebo, which suggests either the sensation of pain or our perception of it is diminished under placebo.
• Patients with Alzheimer’s disease start to show a diminished placebo response. It’s probably due to the degradation of their frontal lobes, the area of the brain that helps direct our subjective experience of the world.

Our understanding of all this is far from complete, Wager says. For one, researchers still don’t completely understand how the brain processes pain. A lot of the brain regions implicated in the placebo response also play a role in emotions. So we don’t yet know if placebo is actually reducing our sensation of pain, or just our interpretation of it. (Also, as with a lot of neuroscience studies, a brain area might “light up” in an experiment, but it’s really, really hard to know what exactly is going on.)

“So really, what we should be concluding from those studies is something like ‘placebo affects the pain you report,’” Wager says. “What does pain mean to you? That’s a decision that’s made in your brain in different circuits, and that’s essential to placebo.”

You can tell people they’re taking a sugar pill for their illness, and they’ll still feel better

Kaptchuk has studied the placebo effect for decades, and something always bothered him: deception. Placebo studies have long relied on double-blind procedures. It ensures scientific rigor but keeps patients in the dark about what they’re actually taking.

“About five years ago, I said to myself, ‘I’m really tired [of] doing research that people say is about deception and tricking people,’” he says.

So he wanted to see: Could he induce a placebo response even when he told patients they were on placebo?

His own randomized controlled trials found that giving patients open-label placebos — sugar pills that the doctors admit are sugar pills — improved symptoms of certain chronic conditions that are among the hardest for doctors to treat, including irritable bowel syndrome and lower back pain . And he wonders if chronic fatigue — a hard-to-define, hard-to-treat, but still debilitating condition — will be a good future target for this research.

“Our patients tell us it’s nuts,” he says. “The doctors think it’s nuts. And we just do it. And we’ve been getting good results.”

Kaptchuk’s work adds a few new mysteries to the placebo effect. For one, he says that the placebo effect doesn’t require patient expectations for a positive outcome to work. “All my patients are people who have been to many doctors before. They don’t have positive expectations about getting better,” he says. “They’ve been to 10 doctors already.”

Colloca has a different interpretation of his results. She says there’s a difference between belief and expectation, so while the patients may not believe the pill will work, they still unconsciously expect it to.

That’s because, she says, they still have a deep-seated conditioned memory for what it means to take a pill. They have a conditioned memory for what it means to be in the care of another person. And that memory is indeed an expectation that can kick-start the analgesic effect in the brain. They don’t have to be aware it’s happening.

Some doctors wonder if placebos can be integrated into mainstream medicine

The researchers I spoke to for this story are overall optimistic that these discoveries can be used in the clinical settings. There’s a lot of work left to do here, and certainly some of the findings are easier to implement than others. For instance, we could start with reminding doctors that they can relieve pain simply by being warm and caring to their patients.

Colloca wonders if the placebo effect can also be harnessed so that the millions living with chronic pain can feel the same therapeutic effects with a lower dosage of opioid treatments that are both ineffective and deadly .

The NIH’s Miller says it’s too soon to start prescribing placebos, or using the effect, to decrease the dosage of a drug. For one, most of these studies are short-term and conducted with healthy volunteers, not actual patients.

“There’s still lot we don’t know,” he says. Like side effects: Just as a placebo can mimic a drug, it can also mimic a side effect. “We haven’t done the kinds of studies that will indicate that you can maintain therapeutic benefit at lower side effect burden.”

More broadly, Kaptchuk says, for years researchers have seen the placebo as a hurdle to clear to produce good medicine. But placebo is not just a hurdle. “It’s basically the water that medicine swims in,” he says. “I would like to see the bottom line of my research change the art of medicine into the science of medicine.”

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• Published: 24 December 2022

Placebo and nocebo effects: from observation to harnessing and clinical application

• Yiheng Tu   ORCID: orcid.org/0000-0002-3495-9549 1 , 2 ,
• Libo Zhang 1 , 2 &
• Jian Kong 3  

Translational Psychiatry volume  12 , Article number:  524 ( 2022 ) Cite this article

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Placebo and nocebo effects are salubrious benefits and negative outcomes attributable to non-specific symbolic components. Leveraging advanced experimental and analytical approaches, recent studies have elucidated complicated neural mechanisms that may serve as a solid basis for harnessing the powerful self-healing and self-harming capacities and applying these findings to improve medical practice and minimize the unintended exacerbation of symptoms in medical practice. We review advances in employing psychosocial, pharmacological, and neuromodulation approaches to modulate/harness placebo and nocebo effects. While these approaches show promising potential, translating these research findings into clinical settings still requires careful methodological, technical, and ethical considerations.

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Introduction.

Placebo and nocebo effects are essential components of clinical practice and efficacy research [ 1 ]. They occur in both experimental and clinical contexts when a pure inert treatment is administered on its own or as part of active treatments. A significant proportion of clinical improvement, particularly the subjective symptom relief, may be attributable to placebo effects [ 2 , 3 ]. In contrast, nocebo effects are a major concern for clinical care since patients are often non-compliant, make unnecessary medical visits, and take additional medications to counteract adverse effects that are actually nocebo effects [ 4 , 5 , 6 ]. Placebo and nocebo effects have been observed in a plethora of conditions including pain, Parkinson’s disease, depression, anxiety disorders, immunologic responses, cardiovascular functions, and sleep disorders [ 7 ].

In recent years, considerable efforts have been made to conceptualize placebo and nocebo effects in a broad variety of disciplines from clinical sciences to cognitive neuroscience and social sciences. The improved understanding of placebo and nocebo effects has built a basis for the crucial next step: shifting from understanding their biopsychosocial mechanisms through systematic observation to modulating placebo and nocebo effects through experimental paradigms/designs or brain stimulation methods. The proposed research focus shift echoes the growing interest in optimizing placebo effects to improve therapeutic outcomes and minimizing nocebo effects to avoid unintended exacerbation of symptoms in medical practice.

This review discusses recent advances in placebo and nocebo research in moving from observation to experimental mechanistic modulation and finally to clinical practice. We first briefly survey key mechanisms involved in the placebo and nocebo effects. Based on these mechanisms, we then discuss recent attempts at modulating these effects using psychosocial, pharmacological, and neuromodulation approaches. Finally, we discuss approaches and challenges to harness these effects ethically and effectively in clinical settings. We will focus on placebo analgesia and nocebo hyperalgesia as the majority of placebo and nocebo research centers on pain; we will also highlight mechanistic heterogeneity of placebo and nocebo effects in other domains/conditions as appropriate.

Behavioral and neural bases for harnessing placebo and nocebo effects

Behavioral bases of placebo and nocebo effects.

Expectations and learning are two frequently studied behavioral mechanisms associated with placebo and nocebo effects [ 1 , 8 ]. Numerous studies have demonstrated that expectations of receiving treatment induce placebo and nocebo effects [ 9 , 10 ]. Expectations can be generated through verbal information [ 11 , 12 ], which involves the provision of direct information about the efficacy of treatment. Alternatively, expectations can be created by associative learning, especially classic conditioning, which repetitively pairs a neutral cue with an active treatment [ 13 ]. Conditioning-based expectations have been shown to exert strong effects on pain (i.e., placebo analgesia and nocebo hyperalgesia) [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ] and other subjective symptoms including emotion, Parkinson’s disease, and depression [ 22 ].

Aside from learning from direct experience (i.e., classic conditioning), observational conditioning may also produce placebo and nocebo effects. Studies have demonstrated that participants will feel less pain after receiving treatment if they see this treatment is effective in others [ 23 , 24 , 25 ]. Similarly, cue-based expectations can be learned from observing others’ painful experiences, and in turn modulate subsequent subjective perception [ 15 , 26 ]. Although most of these studies have focused on the reduction of pain, the effects of social learning may be generalized to other modalities [ 27 ], for example, psychogenic illness [ 28 , 29 ]. Therefore, social learning is a major routine for the transmission of placebo/nocebo effects and produces substantial effects on the associated brain processes.

Recent placebo research showed operant conditioning as a new mechanism of placebo effects [ 30 ]. In operant conditioning, responses, instead of cues, are reinforced. In a recent study [ 31 ], participants’ pain ratings of identical electrocutaneous pain stimuli preceded by visual cues were either verbally rewarded or punished. Placebo analgesia was successfully established by rewarding low ratings and punishing high ratings, and, interestingly, seemed resistant to extinction. Another study revealed that operant conditioning generated greater placebo analgesia than classical conditioning and that mechanical pain-induced brain activities in the ipsilateral S1 and contralateral lingual gyrus were reduced more by operant conditioning [ 32 ]. These findings revealed that patients can learn placebo analgesia as a result of operant learning. Altogether, these studies suggest that expectations can be finely tuned by different forms of learning and, thus, may provide flexible and alternative ways to induce placebos in medical practice.

Apart from expectations and learning, other behavioral bases of placebo and nocebo effects have also been proposed. For example, the desire for pain relief has been suggested as a key contributor to placebo analgesia [ 33 ]. In an early study, combined with expectations, the desire for pain relief explained approximately 80% of the variance of placebo analgesia in irritable bowel syndrome patients [ 34 ]. However, perhaps due to limited sample sizes and specific painful stimuli used, desire did not exert a unique influence on placebo effects, but interacted with expectations to modulate placebo analgesia [ 34 ]. Reducing negative emotions also have been posited to mediate placebo effects [ 35 , 36 ]. Some studies have demonstrated that induced fear of pain could weaken the magnitude of placebo analgesia [ 37 ]. However, compared with expectations and learning, these behavioral bases are still under-investigated and require further research.

Neural responses underlying placebo and nocebo effects

Earlier studies have shown that placebo analgesia can be blocked by naloxone, indicating that the endogenous opioid and descending pain modulatory system (DPMS) play a crucial role in placebo analgesia [ 38 ]. Key regions in the DPMS originate in the cingulate cortex and prefrontal cortex (PFC) and project directly and indirectly to the periaqueductal gray (PAG), and the PAG in turn sends projections to the rostroventral medulla (RVM) and spinal cord. Recent brain imaging studies provide further support for the involvement of the DPMS in mediating placebo analgesia and nocebo hyperalgesia [ 39 , 40 ]. Functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies have shown placebo-related activity increases in brain regions including the cingulate cortex, ventromedial PFC (vmPFC), dorsolateral PFC (DLPFC), anterior insula, PAG, and RVM [ 39 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. Nevertheless, it is still not clear at which stage the DPMS inhibits the noxious signal input. Two participant-level meta-analyses of 20 functional neuroimaging studies in 603 participants have confirmed that placebo analgesia only has small effects on the ‘neurologic pain signature’, a machine learning-derived functional imaging correlates of pain [ 48 ], but is likely to act at the level of several brain networks beyond nociception that may be important for the emotions, decision making, and behaviors surrounding pain [ 49 , 50 ]. Furthermore, the release of endogenous opioids in the DPMS could be relatively slow, so they are less likely to mediate cue-based expectations on pain, which is expected to be transient and reversible [ 9 ]. Thus, the DPMS may be just one of the mechanisms underlying placebo analgesia.

The reward system is another likely neural underpinning of placebo analgesia since symptom reduction (decreased suffering) is a special case of reward. Furthermore, the expectation is closely related to the activation of tegmental or prefrontal dopaminergic neurons that project to the dorsal and ventral striatum/nucleus accumbens (VS/NAc) [ 51 , 52 , 53 ]. Across studies, placebo analgesia increased fMRI responses [ 54 , 55 ], opioid [ 44 , 45 , 56 ], and dopamine [ 44 ] activities in the NAc during pain. Importantly, the isolated brain regions (e.g., NAc and PFC) in the reward system form the mesocortical (originates from the ventral tegmental area [VTA] and projects primarily to the frontal lobe, e.g., rostral anterior cingulate cortex [rACC]) and mesolimbic pathways (originates from the VTA and projects primarily to the ventral striatum, e.g., NAc) to encode expectancy effects on pain (i.e., placebo analgesia and nocebo hyperalgesia), and to explain individual differences of the magnitudes of placebo and nocebo effects [ 14 ]. Apart from placebo analgesia, the reward system is also implicated in placebo effects in Parkinson’s disease. An early PET study showed that placebos trigger the release of endogenous dopamine in the striatum [ 51 ]. Later studies reconfirmed the involvement of dopamine release in the dorsal and ventral striatum in placebo effects in Parkinson’s disease [ 57 ].

It is still under debate whether placebo and nocebo effects are engaged in the same brain network with opposite activity directions [ 58 ]. Some studies have suggested that the DPMS and reward system might be essential for both placebo and nocebo effects [ 14 , 44 , 59 , 60 , 61 ]. They elicit opposite responses of endogenous opioid neurotransmission in the DPMS and of dopamine neurotransmission in the reward system [ 44 ]. Others suggest that placebo and nocebo effects recruit different neural circuitry and release distinct substances (e.g., cholecystokinin for nocebo effects) [ 62 , 63 ]. A recent meta-analysis also showed placebo-specific concordance in the ventral striatum and nocebo-specific concordance in the posterior insula and dorsal ACC [ 64 ]. Overall, placebo and nocebo effects may be associated with both shared and distinct mechanisms/pathways.

Harnessing placebo and nocebo effects using psychosocial, pharmacological, and neuromodulation approaches

Psychosocial approaches.

Based on the psychological mechanisms introduced above, modulating expectations, learning, and social interactions are three major psychosocial approaches to harnessing placebo and nocebo effects (shown in Fig. 1A ). Expectation manipulation can be easily achieved by altering external characteristics of placebo treatments, such as brand names (generic vs. branded) and value information (expensive vs. cheap). Compared with generic tablets (e.g., generic Ibuprofen), branded ones (e.g., Nurofen) not only have a greater efficacy but also produce fewer side effects [ 65 ]. This phenomenon may be explained by individuals’ perceiving generic drugs as less effective and of poorer quality [ 66 ]. The price tag of treatment also conveys information about its value or quality, suggesting a role of price in placebo effects [ 67 , 68 ]. This was recently confirmed by a study using two placebo creams (high vs. low price), showing the higher-priced placebo treatment led to enhanced pain relief, which was associated with fMRI responses in the NAc, vmPFC, and ventral tegmental area [ 69 ]. The effect of price on placebo magnitude and brain activity also occurs in Parkinson’s disease [ 70 ]. Interestingly, higher-priced medications may also lead to an increase in perceived side effects (i.e., nocebo effects), suggesting that participants may infer that expensive medication contains a more potent and effective agent and consequently produces more side effects [ 71 ].

A Psychosocial approaches, including valuable information and enhanced conditioning (i.e., verbal suggestion precedes conditioning), modulate the reward system (e.g., ventromedial prefrontal cortex [vmPFC], nucleus accumbens [NAc], and ventral tegmental area [VTA]). Trustful doctor–patient relationships rely on the brain-to-brain coupling in the temporoparietal junction (TPJ), insula, and ventrolateral prefrontal cortex (vlPFC). B Intranasally administered oxytocin/vasopressin travels to the brain via olfactory and trigeminal nerve fibers and may modulate placebo-related brain activities in the anterior cingulate cortex (ACC), NAc, hypothalamus, amygdala, hippocampus, and brainstem. C Neuromodulational approaches including transcranial magnetic stimulation (TMS) and electrical stimulation (tES) target key regions in the prefrontal cortex (e.g., dorsolateral prefrontal cortex [DLPFC] and orbitofrontal cortex [OFC]) to modulate the reward system and descending pain modulation system (e.g., periaqueductal gray [PAG] and rostral ventromedial medulla [RVM]) to harness placebo effects.

Although classical conditioning can induce significant placebo and nocebo effects, it is suggested that the combination of conditioning and verbal suggestion/instruction brings stronger effects [ 12 ]. Interestingly, the chronology of verbal suggestion and conditioning, as well as their congruence, influence the magnitudes of placebo and nocebo effects [ 72 ]. Participants may have stronger placebo analgesia when the verbal suggestion proceeds rather than follows conditioning, while the order of the procedures does not affect the magnitude of nocebo hyperalgesia.

Minimizing nocebo effects is of profound clinical implications. One intriguing yet thorny issue in nocebo hyperalgesia is that it seems not subject to extinction once established through classical conditioning [ 73 , 74 ]. However, a recent study showed that nocebo hyperalgesia can be attenuated by classical extinction and counterconditioning with a larger trial number, where conditioned aversive cues are later paired with positive stimuli [ 75 ]. Interestingly, conditioning-induced nocebo itch can even be reversed by counterconditioning [ 76 ].

Social interaction between healthcare providers and patients typically occurs in medical treatments. In particular, the doctor-patient relationship is critical in maximizing treatment beliefs/expectations and other non-specific treatment effects, and, thus, enhancing the placebo effects and the total treatment efficacy [ 13 , 77 ]. Two early studies demonstrated that a supportive doctor–patient relationship is a robust component of the placebo effect (i.e., boosts patients’ expectations towards the treatment) and can enhance therapeutic effects in large single-blind randomized clinical trials [ 78 , 79 ]. A recent effort has been made to modulate and study how doctors’ expectations can be transmitted to patients and affect clinical outcomes [ 80 ]. The study highlights the importance of healthcare providers’ behavior and cognitive mindsets in affecting clinical interactions.

It is worth noting that while the behaviors of the doctor may influence the patient’s expectancy/belief, there is no guarantee that these manipulations would be effective. For instance, researchers applied a context manipulation model [ 78 ] to test if enhancing the doctor–patient relationship could increase the expectancy and treatment effect of acupuncture on chronic low back pain (cLBP). Results showed no significant differences between the high- and low-context groups in both back pain severity and expectancy scores [ 81 ]. These findings suggest gaining patients’ trust and enhancing expectancy is a complicated process, and warmness and empathy may be just two of several factors that can influence their expectations.

Pharmacological approaches

The pharmacological approaches have gained interest due to their potential in modulating key psychosocial components of placebo and nocebo effects (Fig. 1B ).

Oxytocin is a peptide hormone and neuropeptide that plays a role in empathy, trust, and social learning. Recently, studies have shown oxytocin can promote social cognition and learning [ 82 , 83 ], enhance empathy levels [ 84 ], and reduce stress and anxiety [ 85 ]. Thus, some researchers hypothesized oxytocin may enhance the placebo effect. An earlier study found oxytocin can enhance verbally induced placebo analgesia in males [ 86 ]. Nevertheless, several recent studies report insignificant modulatory effects of oxytocin on placebo analgesia and nocebo hyperalgesia [ 87 , 88 , 89 ]. Therefore, experimental evidence supporting the role of oxytocin for placebo and nocebo effects is mixed, at least in the field of pain.

Vasopressin is another potential candidate to modulate placebo effects. The brain distribution of oxytocin receptors overlaps with those of arginine vasopressin, and studies suggest that vasopressin can regulate conciliatory behaviors and social communication in females [ 90 , 91 ]. A previous study found that vasopressin agonizts boosted placebo effects in women but had no effect in men [ 92 ].

Two aspects of these pharmacological studies on placebo/nocebo effects are noteworthy. One is that most of them recruited healthy participants, not patients [ 93 ]. As a result, it remains an open question whether and how these findings can be translated to clinical application. The other is that they induced placebo or nocebo effects mainly by verbal instructions. Neurotransmitters like oxytocin and vasopressin influence the outcome of conditioning [ 94 , 95 ], so it is of interest to investigate whether conditioning-induced placebo and nocebo effects can be pharmacologically modulated.

Neuromodulation approaches

The past decade has witnessed a growing interest (or rediscovery, as the concept of brain stimulation has existed for over one hundred years) in the modulation of human behavior and cognition by noninvasive brain stimulation (NIBS). These methods allow researchers non-invasively alter neural activity/excitability (enhancing or inhibiting) to affect behaviors [ 96 ]. Two NIBS methods have emerged in both basic and clinical contexts: transcranial magnetic stimulation (TMS), which sends pulses to increase cortical excitability due to long-term potentiation or to inhibit cortical excitability due to long-term depression, and transcranial electrical stimulation, which passes low-intensity electrical currents through the cortex and de- or hyperpolarizes neuronal membrane potentials to alter cortical excitability.

Given the important role of the brain in placebo and nocebo effects, it is a natural next step to apply NIBS tools to modulate the placebo effect as well as investigate the mechanism of placebo/nocebo effects to elucidate the causal role of certain brain regions (Fig. 1C ). For instance, across neuroimaging studies, the most consistent placebo-related brain responses in the PFC are observed in the DLPFC, vlPFC, and vmPFC (including the rACC and pregenual anterior cingulate cortex, and orbitofrontal cortex [OFC]) [ 14 , 18 , 19 , 40 , 41 , 55 , 61 , 67 , 97 ]. The converging findings in the PFC bring valuable targets for harnessing placebo and nocebo effects through directly modulating brain responses in these precise locations.

In an early study, investigators found that low-frequency repetitive TMS (rTMS) at the DLPFC (aiming to transiently disrupt left and right DLPFC function) could block expectation-induced placebo analgesia as measured by pain threshold and tolerance increases [ 98 ]. The following study investigated the modulation effect of single-session transcranial direct current stimulation (tDCS) at the right DLPFC and showed that placebo and nocebo effects could only be observed in participants who received anodal tDCS (to enhance neuronal excitability) but not in those who received cathodal tDCS (to inhibit neuronal excitability) [ 99 ]. In a more recent study, we found that multi-session (three sessions) repeated tDCS at the left OFC and right DLPFC could boost placebo and blunt nocebo effects, as well as modulate brain activity and connectivity associated with placebo analgesia and nocebo hyperalgesia, respectively [ 97 ].

These findings together not only demonstrate the feasibility of harnessing placebo and nocebo effects through changing brain excitability with NIBS but also suggest how experimentally altered neural activity causally affects placebo and nocebo effects. Nevertheless, caution must be exercised when applying NIBS to modulate specific brain areas or networks, because the NIBS-induced effects are more complex than their computational models and these effects are subject to stimulation protocols (e.g., current intensity [ 100 ], stimulation duration [ 101 ]) and individual’s brain characteristics (e.g., the orientation of the axons in relation to the current flow [ 102 ], baseline brain state [ 103 ]).

A noticeable limitation of NIBS is that the targeted areas are generally limited to cortical regions due to low penetrance [ 96 ]. On the other hand, abundant subcortical areas (e.g., the NAc and PAG) and complicated brain networks are involved in placebo/nocebo effects. Most of them cannot be directly modulated via NIBS. Deep brain stimulation can reach subcortical areas but are undesirable for many patients due to its invasiveness. Two strategies may be adopted to partially overcome this limitation of NIBS. One is to target multiple brain regions simultaneously; the other is to modulate areas that are hubs of placebo-related brain networks or exhibit strong connectivity with subcortical areas. Future studies may test the effectiveness of these strategies in the context of placebo and nocebo effects.

Translating basic research findings into clinical treatment settings

General principles in clinical practice.

Translating basic research findings of placebo and nocebo effects into clinical treatment settings is a high-stake issue. To maximize the salubrious placebo effects as well as minimize the detrimental nocebo effects in clinical care, some generally agreed-upon guidelines for utilizing placebo and nocebo effects in clinical practice have been recently suggested [ 104 ]. Based on these guidelines, general principles that health care providers can use to elicit placebo effects and reduce nocebo effects include:

The modulating expectation is always helpful to induce placebo effects in medical practice. Healthcare providers may point out directly that a drug or treatment is effective if its efficacy has already been proven. Informally explaining the mechanisms of treatments and placebo effects may also be of benefit. Such knowledge promotes trust in the treatment and boosts positive expectations toward efficacy.

Increasing knowledge mitigates nocebo effects. Misattribution of accidental experiences or preexisting symptoms to treatments will amplify nocebo effects [ 105 ]. Demystifying nocebo effects by increasing knowledge is thus a crucial step to preemptively nullify misattribution. Indeed, recent studies have shown that informing participants with weekly headaches about nocebo effects reduced the nocebo side effects they experienced [ 106 ], and providing timing information minimized nocebo effects because they often occurred when individuals expected them to occur [ 107 ].

Improving the communication style to build a supportive relationship between patients and physicians triggers placebo effects [ 77 ]. On the other hand, a cold, indifferent, impatient, or hostile relationship induces nocebo effects [ 108 ]. Nevertheless, enhancing expectancy and gaining patients’ trust is a complicated process. The supportive relationship may only work for some individuals. In addition, “exaggerated” positive information and “over” supportive relationships should be used with caution to avoid ethical concerns.

Applying the learning model in clinical treatment

Experimental studies have demonstrated that a conditioning-like manipulation model can produce greater placebo effects compared to verbal suggestion/instruction alone [ 109 ]. In addition, this model can also enhance the effect of active treatments on experimental pain [ 54 , 110 ]. Nevertheless, few studies have applied the expectancy manipulation model in longitudinal treatment in patient populations due to the difficulty in modulating chronic pain intensity compared to experimental pain. To overcome this challenge, investigators have applied an expectancy manipulation model using experimental heat pain to enhance subjects’ expectation of acupuncture analgesia (on heat pain), and then confirmed that this enhanced expectation improved the treatment effect of acupuncture on chronic pain caused by knee osteoarthritis (KOA) [ 111 ]. This study demonstrated the feasibility of applying the expectancy manipulation model in clinical settings, which may shed light on improving treatment effects.

Observational learning and operant conditioning can also be considered when clinicians interact with patients to induce placebo effects [ 23 , 30 ]. Arguably, they can be more easily applied than classical conditioning in clinical treatment, since observational conditioning involves only indirect information about treatment effectiveness from other individuals and operant conditioning requires only appropriate reinforcement like a verbal reward. A recent clinical trial has proved the role of observational learning in enhancing placebo analgesia in cLBP patients [ 112 ]. However, operant conditioning has only recently been put forth as a new mechanism of placebo effects [ 30 ], and no clinical studies have empirically examined its ability to produce placebo effects in patients. Further research is thus in need to test the clinical applicability of operant learning.

Variability of placebo effects

It has long been acknowledged that placebo effects exhibit large individual variabilities [ 113 ]. Clinical application of placebo effects has to account for these variabilities. Demographical, psychological, and biological factors have all been linked to individual variabilities in placebo effects.

Sex and race matter for placebo effects. Females and white populations seem to experience larger placebo effects [ 114 , 115 ]. Another set of important predictors of placebo effects is psychological factors, e.g., expectation, trait optimism, desire for control [ 116 ], emotional distress, and maladaptive cognitive appraisals of pain [ 117 ]. In clinical studies, patients susceptible to a placebo effect can be identified by assessing pretreatment positive and negative expectations [ 118 , 119 ], and prior therapeutic experience via conditioning [ 120 ]. Brain activity or brain structures have also been used to predict placebo effects. Stronger placebo effects have been associated with a more efficient reward system (e.g., NAc responses to reward cues [ 121 ], gray matter densities of the NAc and PFC [ 14 , 122 ], regional homogeneity of NAc [ 123 ]) and frontoparietal network functional connectivity [ 21 ]. Importantly, using machine learning and fMRI, studies were able to identify placebo responders and predict the magnitude of placebo effects in patients with KOA [ 124 ], major depression [ 125 ], and cLBP [ 81 , 126 ].

Placebo effects in patients and healthy individuals

Patients and healthy individuals differ considerably in many ways. For example, chronic pain patients typically suffer from anxiety and depression [ 127 ]. More importantly, the neural underpinnings of placebo effects are impaired in some diseases like chronic back pain [ 128 ] and fibromyalgia [ 129 ]. A crucial issue is then whether findings based on healthy individuals can be generalized to patients. A meta-analysis has shown that the magnitude of placebo analgesia in studies with healthy participants was smaller than in studies with patients, but the difference was not statistically significant [ 130 ]. Recent studies directly comparing chronic pain patients and healthy controls also found that the magnitude of placebo analgesia was comparable between healthy controls and fibromyalgia, osteoarthritis, and chronic orofacial pain patients [ 120 , 131 , 132 ]. These findings suggest that chronic pain may not significantly affect patients’ susceptibility to placebo effects, even though it may impair neural pathways key to placebo analgesia. One explanation is that these impaired areas and pathways are not necessary for placebo analgesia, since multiple distributed neural networks are involved in placebo effects. However, it still remains an open question whether disease impairs the ability of psychosocial, pharmacological, and neuromodulation approaches to modulate placebo and nocebo effects. Due to shared psychological and neural mechanisms, it is reasonable to assume that placebo and nocebo effects can also be modulated similarly in patients and healthy people. Nevertheless, future research needs to directly test the feasibility of boosting placebo effects and blunting nocebo effects in patient populations to harness these effects in clinical settings.

Considerations in clinical application

When applying placebo/nocebo effects, one must be sensitive to the clinical issues involved. A placebo lies not in the drug or procedure itself, but in the patient’s own mind (or brain). Persuading the patients that a placebo treatment works may involve deception and violation of their autonomy. One arguably less concerning approach for harnessing placebo/nocebo effects is to adopt open-label placebo treatments, in which the inertness of the treatment and the efficacy of placebos are revealed explicitly. Randomized clinical trials of open-label placebos in different conditions, including patients with irritable bowel syndrome [ 133 ], cLBP [ 134 ], cancer-related fatigue [ 135 ], and episodic migraine [ 136 ], have demonstrated the therapeutic efficacy of open-label placebos. A recent meta-analysis of 13 open-label placebo clinical trials found a significant overall effect of open-label placebos as compared to no treatment but also cautioned that current studies were still immature [ 137 ].

One potential issue for open-label placebo treatment is that the power of the verbal suggestion (informing the participants that studies have shown placebos can also produce treatment effect) may fade with wide application of the open-label placebo treatment, as open-label placebos may tend to be less effective than real medication. One solution to this issue is to combine active treatments with placebos [ 138 ]. Exploiting the power of placebo effects to boost, not replace the efficacy of active interventions, could induce fewer clinical concerns. Dose-extending placebos may raise fewer clinical problems than pure placebos. By interspersing placebos between real medications, dose-extending placebos not only induce placebo effects, but also have many practical advantages such as cutting medication intake, reducing medication dependence, and likely decreasing financial costs for patients [ 139 ]. Since potent treatments are also used, dose-extending placebos are presumably less clinically problematic. Combining open-label placebos with dose-extending placebos (i.e., open-label dose-extending placebos) further reduces clinical concerns. Admittedly, even this combination of two clinically less concerning placebos is not perfect. However, surveys have shown that a fair proportion of health providers prescribe placebos in real clinical settings [ 140 , 141 ]. Since placebo and nocebo effects are almost inevitable, the real question is not whether clinicians should apply these effects, but how they can make use of current findings to better apply placebo and nocebo effects while keeping ethical considerations in mind.

Open questions and conclusion

In this review, we surveyed important advances in understanding behavioral and neural mechanisms of placebo and nocebo effects and employing psychosocial, pharmacological, and neuromodulation approaches to harness these effects and discussed the challenges to applying these findings to medical practice. The mechanistic heterogeneity of placebo and nocebo effects in different domains is worth noting. Up till now, most placebo and nocebo studies have come from pain. Nevertheless, general psychological (e.g., conditioning, expectations) and brain mechanisms (e.g., the DLPFC, reward system) identified from pain-related studies may be not specific and could be shared with placebo effects across other domains/symptoms [ 27 ].

Using psychosocial approaches to modulate placebo and nocebo effects might be low-cost and easy to do in clinical applications. However, it is noteworthy that psychosocial approaches may be dependent on an individual’s different preferences or personality in gaining expectancy/belief (e.g., someone may prefer warm conversations with the doctor, while others may prefer less conversation), and therefore the modulatory effects may vary considerably across individuals. How to build a trustful doctor–patient relationship in the clinical setting should be carefully characterized and studied based on different populations rather than a general style/suggestion.

Although promising, findings concerning the efficacy of pharmacological approaches are somewhat mixed [ 87 ]. Current efforts mainly focus on modulating pain-related placebo and nocebo effects. It is worth testing if oxytocin and vasopressin can modulate other placebo and nocebo effects. For example, since oxytocin can promote social trust in humans [ 142 ], its role in modulating a trustful doctor-patient relationship should be tested in future studies.

Ideally, it is desirable to simultaneously enhance placebo and inhibit nocebo effects by changing brain excitability. As mentioned above, placebo and nocebo effects have both shared and distinct mechanisms. Achieving these two aims at once would thus be challenging. Indeed, an effective brain target for simultaneously harnessing these two effects is still inconclusive and needs experimental validation. However, it is worth trying to target with NIBS presumably shared brain areas (e.g., the DPMS and reward system) underlying placebo analgesia and nocebo hyperalgesia [ 44 ].

In summary, placebo and nocebo effects are powerful, pervasive, and common in cognitive neuroscience and clinical practice. Moving from native observation to experimental mechanistic manipulation, and finally utilizing the effects wisely in clinical practice may lead to the improvement of therapeutic outcomes and minimization of unintended exacerbation of symptoms.

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Acknowledgements

YT is supported by the ‘Sci-Tech Innovation 2030’ Brain Science and Brain-Inspired Intelligence Technology Research by the Ministry of Science and Technology of China (2022ZD0206400), the National Natural Science Foundation of China (32171078), Young Elite Scientist Sponsorship Program by the China Association for Science and Technology (E1KX0210), Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (E0CX52 and E2CX4015). We thank Sierra Hodges for proofreading the paper. The figure was created with BioRender.com .

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Tu, Y., Zhang, L. & Kong, J. Placebo and nocebo effects: from observation to harnessing and clinical application. Transl Psychiatry 12 , 524 (2022). https://doi.org/10.1038/s41398-022-02293-2

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Why are placebos used in research.

Placebos are used in medical research for new medication or therapies, called clinical trials. In these trials some people are given a placebo, while others are given the new medication being tested.

The purpose is to determine how effective the new medication is: if it benefits people beyond a predefined threshold as compared to the placebo, it’s considered effective and not the result of a placebo effect .

Frequently asked questions: Research bias

Perception bias is a problem because it prevents us from seeing situations or people objectively. Rather, our expectations, beliefs, or emotions interfere with how we interpret reality. This, in turn, can cause us to misjudge ourselves or others. For example, our prejudices can interfere with whether we perceive people’s faces as friendly or unfriendly.

The bandwagon effect is a type of cognitive bias . It describes the tendency of people to adopt behaviors or opinions simply because others are doing so, regardless of their own beliefs.

Belief bias and confirmation bias are both types of cognitive bias that impact our judgment and decision-making.

Confirmation bias relates to how we perceive and judge evidence. We tend to seek out and prefer information that supports our preexisting beliefs, ignoring any information that contradicts those beliefs.

Belief bias describes the tendency to judge an argument based on how plausible the conclusion seems to us, rather than how much evidence is provided to support it during the course of the argument.

The availability heuristic can influence our perception of risk in everyday life. One common example occurs when we are considering buying insurance. The sharp increase in purchases of flood insurance in the aftermath of flood events illustrates this phenomenon.

Witnessing such events, knowing someone who was personally affected, or extensive media coverage can make us more aware of floods (or make floods more “available” to us). This can change our risk perception, even though statistically there may not be a change in the probabilities of future flooding.

Heuristics are mental shortcuts or rules of thumb that help people reduce the time and effort required to make a decision. An example of a heuristic in psychology is the availability heuristic (or availability bias ). It involves relying on information that comes to mind quickly, (i.e., information that is available to us).

Although both are common types of cognitive bias , they refer to different ways of processing information.

• The availability bias (or availability heuristic ) refers to the tendency people have to rely on information that is easier to recall when faced with a decision.
• Confirmation bias is the tendency to selectively search for or interpret information in a way that confirms one’s preconceived ideas.

In other words, the availability heuristic gives preference to information that is easy to recall, while confirmation bias gives preference to information that aligns with our existing beliefs. Even though they are different, they both cause us to focus on only a subset of information.

In survey research , such as political polling, the way questions are worded or the order in which answers are presented can influence how respondents answer the questions. This is called the framing effect .

For example, if voters are asked to select which of two candidates they plan to vote for, the order in which the candidates are listed affects the percentage of respondents selecting each candidate. Recognizing the potential for research bias , researchers typically rotate which major candidate is listed first and which is listed second.

The framing effect is often used in advertising to positively influence consumer choice.

One common type of frame is “ gain framing. ” This shows consumers how they are going to benefit from a product or service. For example, dental care product advertisements use gain framing to display the benefits of using their product: white teeth, healthy gums, fresh breath, etc.

Apart from the obvious benefits, ads using the framing effect often imply other benefits, such as how a better-looking smile makes one more attractive to potential dating partners.

Because of the framing effect , the way information is presented to us influences how attractive a proposition is.

Suppose you are considering joining a gym. A membership at $500 per year sounds like a considerable investment and might prevent you from signing up immediately. However, if they tell you it costs just $1.37 per day and emphasize that this is less than the cost of a cup of coffee, you might think it’s a great offer, even though in reality both offers cost you the same.

The opposite of implicit bias is explicit bias , or conscious bias. This refers to preferences, opinions, and attitudes of which people are generally consciously aware. In other words, explicit bias is expressed openly and deliberately.

The opposite of optimism bias is pessimism bias. Optimism bias occurs when we overestimate our chances of experiencing positive events in our lives, while pessimism bias occurs when we overestimate our chance of experiencing negative events.

For example, pessimism bias could cause someone to think they are going to fail an exam, even though they are well prepared and usually get good grades.

A positive illusion is a form of self-deception under which people have inflated, favorable attitudes about themselves or others close to them.

The most common positive illusions involve:

• Exaggerating one’s positive traits
• Overestimating one’s degree of control in life
• Harboring overly optimistic beliefs about future events (also called optimism bias ).

The planning fallacy refers to people’s tendency to underestimate the resources needed to complete a future task, despite knowing that previous tasks have also taken longer than planned.

For example, people generally tend to underestimate the cost and time needed for construction projects. The planning fallacy occurs due to people’s tendency to overestimate the chances that positive events, such as a shortened timeline, will happen to them. This phenomenon is called optimism bias .

Myside bias is a type of cognitive bias where individuals process information in a way that favors their prior beliefs and attitudes. It occurs when people search for, interpret, and recall information that confirms their opinions, and refute opinions different from their own—such as selecting news sources that agree with one’s political affiliation, while ignoring any opposing arguments from other sources.

Myside bias is closely related to confirmation bias . Although some researchers use the terms interchangeably, others use myside bias to refer to the tendency of processing information that supports one’s own position.

Cognitive bias is an umbrella term used to describe the different ways in which our beliefs and experiences impact our judgment and decision making. These preconceptions are “mental shortcuts” that help us speed up how we process and make sense of new information.

However, this tendency may lead us to misunderstand events, facts, or other people. Cognitive bias can be a source of research bias .

Some common types of cognitive bias are:

• Anchoring bias
• Framing effect
• Actor–observer bias
• Availability heuristic
• Belief bias
• Confirmation bias
• The halo effect
• The Baader–Meinhof phenomenon

A real-life example of perception bias is the false consensus effect . Because we spend most of our time with friends, family, and colleagues who share the same opinions or values we do, we are often misled to believe that the majority of people think or act in ways similar to us. This explains, for instance, why some people take office supplies home: they may genuinely feel that this behavior is more common than it really is.

Selective perception is the unconscious process by which people screen, select, and notice objects in their environment. During this process, information tends to be selectively perceived in ways that align with existing attitudes, beliefs, and goals.

Although this allows us to concentrate only on the information that is relevant for us at present, it can also lead to perception bias . For example, while driving, if you become hyper-focused on reaching your exit on a highway, your brain may filter visual stimuli so that you can only focus on things you need to notice in order to exit the highway. However, this can also cause you to miss other things happening around you on the road.

Correspondence bias and fundamental attribution error were often seen as interchangeable in the past. However, researchers have recently proposed that there is a subtle difference between the two.

• Correspondence bias refers to the fact that behavior is often viewed as a reflection of a person’s character. In other words, we believe that a person’s behavior reflects stable internal qualities, even though it was actually caused by the situation. The fundamental attribution error refers to the idea that people fundamentally ignore or underestimate situational influences on others’ behavior.
• Although people often commit the fundamental attribution error, they do not necessarily fall for correspondence bias at the same time. Only when we take the fundamental attribution error one step further and judge a person’s character from their actions do we display correspondence bias.

Correspondence bias is a problem because it can cause us to make incorrect judgments about other people’s behaviors. This can lead to misunderstandings that can negatively affect our relationship with them. When we overlook the situation and jump to conclusions about an individual’s character, it is also easier to justify reacting to them aggressively.

In a wider social context, if we ignore the situational factors that might have pushed someone to behave a certain way, we may also ignore systemic factors, like discrimination. For example, some people attribute poverty and unemployment to individuals rather than to social conditions.

A real-life example of correspondence bias is how we think about people who cut in line. For example, you are waiting in line at the airport and someone cuts in front of you at the security checkpoint. Because of correspondence bias, your immediate reaction is to feel annoyed and think that the person must be entitled and rude. In reality, this person may never cut into lines and they are doing this only because they are about to miss their plane, which they are taking to visit a sick family member.

The opposite of normalcy bias is overreaction or worst-case scenario bias . This happens when people exaggerate the likelihood of negative outcomes or consequences when faced with a threat warning. In other words, people jump to the worst possible conclusion, no matter how improbable it is. For instance panic-buying of toilet paper, face masks, and food in the early days of the COVID-19 outbreak are examples of overreaction.

Normalcy bias and optimism bias are closely related as they both influence our risk perception. However, they are two separate phenomena.

• Normalcy bias denotes our tendency to minimize or ignore threat warnings and to believe that nothing can seriously disrupt our everyday life.
• Optimism bias , on the other hand, denotes the tendency to overestimate the likelihood of positive events and underestimate the likelihood of negative events.

Although normalcy bias and optimism bias are distinct types of bias, they may reinforce each other. For instance, an individual who receives a hurricane alert may underestimate how serious it is (normalcy bias) and may also think that even if the hurricane affects their area, nothing bad will happen to them personally (optimism bias).

Normality bias (or normalcy bias ) is the tendency to underestimate the likelihood or impact of a potential hazard, based on the belief that things will continue as they have in the past. For example, you hear a sudden noise and think it must be fireworks. However, in reality it’s a gunshot. Instead of finding a safe spot, you go about your business because your brain “normalizes” the noise.

Vividness bias is important because it can affect our decisions and negotiations. It causes us to assign more weight to vivid information, like a perception of prestige, rather than other factors that, upon greater reflection, are more important to us. As a result, we get distracted and lose sight of our goals and priorities.

A real-life example of vividness bias can often be observed in the outcome of business negotiations. Price is usually the most vivid information, while other aspects, such the complexity of implementation, or the time needed to complete the project, might be ignored.

The vividness effect in communication is the persuasive impact that vivid information is thought to have on opinions and behaviors. In other words, information that is vivid, concrete, dramatic, etc., is more likely to capture our attention and sway us into believing or doing one thing rather than another. On the contrary, information that is dull or abstract is not so effective. The vividness effect relates to the vividness bias .

Attribution is a term describing the inferences people make when trying to explain the causes of certain events, the behavior of others, or their own behavior. Because these inferences are based not only on objective facts but also on our mental state, emotions, and past experiences, attributions can be distorted and lead to bias.

An example of such bias is hostile attribution bias , or the tendency to attribute negative intentions to others, especially when their intentions are unclear.

To measure hostile attribution bias , studies typically present participants with a hypothetical situation in which an individual is provoked by a peer whose behavior is purposely ambiguous. Participants are then asked to indicate the intent of the peer. This can be done through videos, pictures, audio, vignettes, or staged interactions (with actors).

Two important considerations when choosing the format are ecological validity (i.e., the extent to which the results are generalizable to a real-life setting) and social desirability bias (i.e., participants may not have wanted to report hostile attributions).

A funnel plot shows the relation between a study’s effect size and its precision. It is a scatter plot of the treatment effects estimated from individual studies (horizontal axis) against sample size (vertical axis).

Asymmetry in the funnel plot, measured using regression analysis , is an indication of publication bias . In the absence of bias, results from small studies will scatter widely at the bottom of the graph, with the spread narrowing among larger studies.

The idea here is that small studies are more likely to remain unpublished if their results are nonsignificant or unfavorable, whereas larger studies get published regardless. This leads to asymmetry in the funnel plot.

Confirmation bias is the tendency to search, interpret, and recall information in a way that aligns with our pre-existing values, opinions, or beliefs. It refers to the ability to recollect information best when it amplifies what we already believe. Relatedly, we tend to forget information that contradicts our opinions.

Although selective recall is a component of confirmation bias, it should not be confused with recall bias.

On the other hand, recall bias refers to the differences in the ability between study participants to recall past events when self-reporting is used. This difference in accuracy or completeness of recollection is not related to beliefs or opinions. Rather, recall bias relates to other factors, such as the length of the recall period, age, and the characteristics of the disease under investigation.

Although there is no definite answer to what causes the placebo effect , researchers propose a number of explanations such as the power of suggestion, doctor-patient interaction, classical conditioning, etc.

Common types of selection bias are:

• Sampling bias or ascertainment bias
• Attrition bias
• Volunteer or self-selection bias
• Survivorship bias
• Nonresponse bias
• Undercoverage bias

Response bias is a general term used to describe a number of different conditions or factors that cue respondents to provide inaccurate or false answers during surveys or interviews. These factors range from the interviewer’s perceived social position or appearance to the the phrasing of questions in surveys.

Nonresponse bias occurs when the people who complete a survey are different from those who did not, in ways that are relevant to the research topic. Nonresponse can happen because people are either not willing or not able to participate.

Observer bias occurs when the researcher’s assumptions, views, or preconceptions influence what they see and record in a study, while actor–observer bias refers to situations where respondents attribute internal factors (e.g., bad character) to justify other’s behavior and external factors (difficult circumstances) to justify the same behavior in themselves.

Research bias affects the validity and reliability of your research findings , leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population (i.e., the sample) and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

Social desirability bias is a type of response bias that occurs when survey respondents provide answers according to society’s expectations, rather than their own beliefs or experiences.

It is especially likely to occur in self-report questionnaires , as well as in any type of behavioral research, particularly if the participants know they’re being observed. This research bias can distort your results, leading to over-reporting of socially desirable behaviors or attitudes and under-reporting of socially undesirable behaviors or attitudes.

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

The observer-expectancy effect is often used synonymously with the Pygmalion or Rosenthal effect .

You can use several tactics to minimize observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure interrater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardize your observation procedures to make sure they are structured and clear.

It’s impossible to completely avoid observer bias in studies where data collection is done or recorded manually, but you can take steps to reduce this type of bias in your research .

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

If you have a small amount of attrition bias , you can use some statistical methods to try to make up for it.

Multiple imputation involves using simulations to replace the missing data with likely values. Alternatively, you can use sample weighting to make up for the uneven balance of participants in your sample.

To avoid attrition , applying some of these measures can help you reduce participant dropout by making it easy and appealing for participants to stay.

• Provide compensation (e.g., cash or gift cards) for attending every session
• Minimize the number of follow-ups as much as possible
• Make all follow-ups brief, flexible, and convenient for participants
• Send participants routine reminders to schedule follow-ups
• Recruit more participants than you need for your sample (oversample)
• Maintain detailed contact information so you can get in touch with participants even if they move

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalize your findings to the original population that you sampled from, so your external validity is compromised.

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa .

Some attrition is normal and to be expected in research. However, the type of attrition is important because systematic bias can distort your findings. Attrition bias can lead to inaccurate results because it affects internal and/or external validity .

Attrition bias is the selective dropout of some participants who systematically differ from those who remain in the study.

Some groups of participants may leave because of bad experiences, unwanted side effects, or inadequate incentives for participation, among other reasons. Attrition is also called subject mortality, but it doesn’t always refer to participants dying!

Demand characteristics are aspects of experiments that may give away the research purpose to participants. Social desirability bias is when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

You can control demand characteristics by taking a few precautions in your research design and materials.

Use these measures:

• Deception: Hide the purpose of the study from participants
• Between-groups design : Give each participant only one independent variable treatment
• Double-blind design : Conceal the assignment of groups from participants and yourself
• Implicit measures: Use indirect or hidden measurements for your variables

Demand characteristics are a type of extraneous variable that can affect the outcomes of the study. They can invalidate studies by providing an alternative explanation for the results.

These cues may nudge participants to consciously or unconsciously change their responses, and they pose a threat to both internal and external validity . You can’t be sure that your independent variable manipulation worked, or that your findings can be applied to other people or settings.

In research, demand characteristics are cues that might indicate the aim of a study to participants. These cues can lead to participants changing their behaviors or responses based on what they think the research is about.

Demand characteristics are common problems in psychology experiments and other social science studies because they can cause a bias in your research findings .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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The Pervasive Problem With Placebos in Psychology: Why Active Control Groups Are Not Sufficient to Rule Out Placebo Effects

Affiliations.

• 1 Department of Psychology, Florida State University [email protected].
• 2 Psychology Department & Beckman Institute for Advanced, Science and Technology, University of Illinois at Urbana-Champaign.
• 3 Department of Psychology, Florida State University.
• PMID: 26173122
• DOI: 10.1177/1745691613491271

To draw causal conclusions about the efficacy of a psychological intervention, researchers must compare the treatment condition with a control group that accounts for improvements caused by factors other than the treatment. Using an active control helps to control for the possibility that improvement by the experimental group resulted from a placebo effect. Although active control groups are superior to "no-contact" controls, only when the active control group has the same expectation of improvement as the experimental group can we attribute differential improvements to the potency of the treatment. Despite the need to match expectations between treatment and control groups, almost no psychological interventions do so. This failure to control for expectations is not a minor omission-it is a fundamental design flaw that potentially undermines any causal inference. We illustrate these principles with a detailed example from the video-game-training literature showing how the use of an active control group does not eliminate expectation differences. The problem permeates other interventions as well, including those targeting mental health, cognition, and educational achievement. Fortunately, measuring expectations and adopting alternative experimental designs makes it possible to control for placebo effects, thereby increasing confidence in the causal efficacy of psychological interventions.

Keywords: demand characteristics; intervention design; placebo effect; research methods.

© The Author(s) 2013.

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Mind Over Matter: The Power of Placebo

Don't dismiss the placebo effect as self-deception..

Posted January 27, 2022 | Reviewed by Vanessa Lancaster

• Placebo effects are genuine physiological and cognitive shifts, similar to or greater than the results of “real” interventions.
• Placebos impacts a dazzling breadth of areas, and they can work even when recipients know they’re getting placebos.
• The placebo effect has implications for our everyday lives, including improving moods, boosting energy, and building good habits.

The placebo effect is the phenomenon of our expectations resulting in real physical and mental changes. It has frustrated, intrigued, and perplexed researchers at least as far back as the 1700s.

More recently, placebos have been found to impact a dazzling breadth of areas such as pain, nausea, asthma, erectile dysfunction, blood pressure, speed, strength, endurance, depression , focus, and phobias.

Here are just a few fascinating examples:

• Luana Colloca and team found that patients whose painkillers were given to them without their knowledge needed twice as much of the drug to get the same effect.
• Surgeon Bruce Moseley gave half of his patients real arthroscopies (knee surgery) and the other half placebo surgery. He found that the fake surgery worked as well as the real surgery in more than half of his 50 trials.
• Christina Draganich and Kristi Erdal made participants think they had gotten more sleep than they did. This false belief improved their performance on language and math tests.
• Liron Rozenkrantz and team told participants that sniffing a special odor would boost their creativity , even though it was merely a placebo smell. After sniffing the scent , participants demonstrated greater creativity.

How do placebos work?

It’s important to note that the impacts of placebos are not imaginary . It’s not a matter of lying to ourselves or pretending to see results that aren’t there. Placebo effects are genuine physiological and cognitive shifts – similar to or greater than the results of “real” interventions.

As a case in point, researchers Jon Levine, Newton Gordon, and Howard Fields gave patients in pain either morphine (a strong painkiller), a placebo, or naloxone (a drug that blocks the endorphins that morphine triggers). They found that naloxone blocked the painkilling effect of placebos , demonstrating that placebos release actual endorphins in the same way that a drug like morphine can.

So, what makes these sci-fiesque mind-over-matter results possible? While the placebo’s underlying mechanisms are still a bit of a mystery and likely a combination of multiple factors, here are two leading explanations:

• Conscious expectations (AKA the confirmation bias plus self-fulfilling prophecy): when we anticipate something, we look for small hints that confirm our beliefs. The more evidence we gather, the stronger our beliefs become, and the more closely our expectations approach reality. For example, You take a piano lesson and are told that you are naturally gifted . Now each time you practice, you notice how quickly you learn, which builds your confidence , which makes you learn even faster.
• Unconscious expectations (AKA classical conditioning ): when a stimulus is paired repeatedly with a response, just the sight, smell, or even thought of the stimulus can trigger the response. For example, Your manager has complained about your work several times, causing your stress levels to rise. Now just the sound of their voice saying “good morning” makes your stomach hurt and your palms sweat. There is no actual threat in their greeting, but your body prepares for more criticism.

A fascinating twist? The placebo effect occurs even when you know it’s a placebo – a phenomenon known as the ‘open placebo’ effect. This research is relatively nascent, but researchers have already found that open placebos successfully alleviate symptoms for people with irritable bowel syndrome, back pain, and hay fever. The familiar act of taking a pill alone might be enough for many to see positive changes.

How can we make placebos work for us?

Most people view the placebo effect as either frustrating or fascinating, but what if we could put placebos to good use beyond the confines of the laboratory? After all, if no trickery is required for placebos to do their magic, why not find ways to placebo ourselves? So, without further ado:

Here are six strategies to harness the power of the placebo in your everyday life:

• Partner with your placebos: If there’s something you do that skeptics question, whether it be chromatherapy or acupuncture, don’t worry too much about whether these solutions are “proven” to work. As long as they are safe and affordable, placebo research shows us that what works is what works for you .
• Act “as if”: This concept comes from relationship therapist Michele Weiner-Davis , who encourages kicking off a positive chain of events by pretending that it’s already begun. Are you dreading your workout? For just a few minutes, act as if you’re really looking forward to it. Are you trapped in a boring conversation? Act as if you’re fascinated. This strategy lets you shift your expectations manually – no pills needed.
• Dwell on what’s working: While it’s important to acknowledge and address our negative feelings, it’s also incredibly easy to dwell on what’s going wrong. Just by deliberately shifting our attention to the positive, we can build on what’s working. For example, researchers Alia Crum and Ellen Langer found that people who were told they got exercise just by doing their regular jobs actually burned more calories and lowered their blood pressure without changing their daily activities.
• Connect with the why: Placebo research reveals the extent to which our beliefs shape our reality. One way to leverage this phenomenon is to deepen our beliefs around why the things we do are important. For a burst of motivation , pause to ask yourself why the thing you’re doing matters, whether it’s your work, your art, your fitness rituals, or time spent with loved ones. The clearer that “why” feels, the greater return you’ll see on the time you invest.
• Create (elaborate) rituals: Another way to trigger the placebo effect is to train yourself into unconscious associations through classical conditioning. Pair two things over and over until your brain expects one thing to follow the other. In other words: create rituals. For example, if you have a hard time falling asleep – spray lavender mist into the air right before you get into bed. Do this over and over again until the mere scent of lavender makes you sleepy. Placebo research shows us that the more elaborate the placebo, the stronger the effect. Four placebo pills have a larger effect than two, and placebo injections are more effective than placebo pills. So, for best results, make your rituals elaborate or unusual. Perhaps adding a gesture, word, song, or special pillow along with that lavender mist will get you sleeping even faster.
• Prescribe a magic “pill:” Perhaps the strangest (yet still effective) strategy on this list is to simply prescribe yourself a placebo to achieve your desired result. Decide on something (safe) you’ll eat, drink, do, or wear to signal that you’re entering a certain state. For example, a single blueberry can become your focus pill. A hat turned backward can be your creativity pill. A tug on your earlobe can become your empathy pill. Odd as it sounds, pairing the action and the intention again and again will function as your very own open placebo.

Placebo effect research is an important illustration of the vast potential of our minds. Placebos are not a replacement for validated medical interventions. But in a society that trains us to believe that our happiness depends on everything from pills to cosmetics to shopping sprees, placebos remind us how much power exists within us.

This article summarizes research and ideas from the Placebo & Nocebo Effect episode of our podcast, Talk Psych to Me .

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Tania Luna is an author, researcher, educator, and co-host of the podcast Talk Psych to Me.

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Double-Blind Experimental Study And Procedure Explained

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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What is a Blinded Study?

• Binding, or masking, refers to withholding information regarding treatment allocation from one or more participants in a clinical research study, typically in randomized control trials .
• A blinded study prevents the participants from knowing about their treatment to avoid bias in the research. Any information that can influence the subjects is withheld until the completion of the research.
• Blinding can be imposed on any participant in an experiment, including researchers, data collectors, evaluators, technicians, and data analysts. 
• Good blinding can eliminate experimental biases arising from the subjects’ expectations, observer bias, confirmation bias, researcher bias, observer’s effect on the participants, and other biases that may occur in a research test.
• Studies may use single-, double- or triple-blinding. A trial that is not blinded is called an open trial.

Double-Blind Studies

Double-blind studies are those in which neither the participants nor the experimenters know who is receiving a particular treatment.

Double blinding prevents bias in research results, specifically due to demand characteristics or the placebo effect.

Demand characteristics are subtle cues from researchers that can inform the participants of what the experimenter expects to find or how participants are expected to behave.

If participants know which group they are assigned to, they might change their behavior in a way that would influence the results. Similarly, if a researcher knows which group a participant is assigned to, they might act in a way that reveals the assignment or influences the results.

Double-blinding attempts to prevent these risks, ensuring that any difference(s) between the groups can be attributed to the treatment. 

On the other hand, single-blind studies are those in which the experimenters are aware of which participants are receiving the treatment while the participants are unaware.

Single-blind studies are beneficial because they reduce the risk of errors due to subject expectations. However, single-blind studies do not prevent observer bias, confirmation bias , or bias due to demand characteristics.

Because the experiments are aware of which participants are receiving which treatments, they are more likely to reveal subtle clues that can accidentally influence the research outcome.

Double-blind studies are considered the gold standard in research because they help to control for experimental biases arising from the subjects’ expectations and experimenter biases that emerge when the researchers unknowingly influence how the subjects respond or how the data is collected.

Using the double-blind method improves the credibility and validity of a study .

Example Double-Blind Studies

Rostock and Huber (2014) used a randomized, placebo-controlled, double-blind study to investigate the immunological effects of mistletoe extract. However, their study showed that double-blinding is impossible when the investigated therapy has obvious side effects. 

Using a double-blind study, Kobak et al. (2005) found that S t John’s wort ( Hypericum perforatum ) is not an efficacious treatment for anxiety disorder, specifically OCD.

Using the Yale–Brown Obsessive–Compulsive Scale (Y-BOCS), they found that the mean change with St John’s wort was not significantly different from the mean change found with placebo. 

Cakir et al. (2014) conducted a randomized, controlled, and double-blind study to test the efficacy of therapeutic ultrasound for managing knee osteoarthritis.

They found that all assessment parameters significantly improved in all groups without a significant difference, suggesting that therapeutic ultrasound provided no additional benefit in improving pain and functions in addition to exercise training.

Using a randomized double-blind study, Papachristofilou et al. (2021) found that whole-lung LDRT failed to improve clinical outcomes in critically ill patients admitted to the intensive care unit requiring mechanical ventilation for COVID-19 pneumonia.

Double-Blinding Procedure

Double blinding is typically used in clinical research studies or clinical trials to test the safety and efficacy of various biomedical and behavioral interventions.

In such studies, researchers tend to use a placebo. A placebo is an inactive substance, typically a sugar pill, that is designed to look like the drug or treatment being tested but has no effect on the individual taking it. 

The placebo pill was given to the participants who were randomly assigned to the control group. This group serves as a baseline to determine if exposure to the treatment had any significant effects.

Those randomly assigned to the experimental group are given the actual treatment in question. Data is collected from both groups and then compared to determine if the treatment had any impact on the dependent variable.

All participants in the study will take a pill or receive a treatment, but only some of them will receive the real treatment under investigation while the rest of the subjects will receive a placebo. 

With double blinding, neither the participants nor the experimenters will have any idea who receives the real drug and who receives the placebo. 

For Example

A common example of double-blinding is clinical studies that are conducted to test new drugs.

In these studies, researchers will use random assignment to allocate patients into one of three groups: the treatment/experimental group (which receives the drug), the placebo group (which receives an inactive substance that looks identical to the treatment but has no drug in it), and the control group (which receives no treatment).

Both participants and researchers are kept unaware of which participants are allocated to which of the three groups.

The effects of the drug are measured by recording any symptoms noticed in the patients.

Once the study is unblinded, and the researchers and participants are made aware of who is in which group, the data can be analyzed to determine whether the drug had effects that were not seen in the placebo or control group, but only in the experimental group. 

Double-blind studies can also be beneficial in nonmedical interventions, such as psychotherapIes.

Reduces risk of bias

Double-blinding can eliminate, or significantly reduce, both observer bias and participant biases.

Because both the researcher and the subjects are unaware of the treatment assignments, it is difficult for their expectations or behaviors to influence the study.

Results can be duplicated

The results of a double-blind study can be duplicated, enabling other researchers to follow the same processes, apply the same test item, and compare their results with the control group.

If the results are similar, then it adds more validity to the ability of a medication or treatment to provide benefits. 

It tests for three groups

Double-blind studies usually involve three groups of subjects: the treatment group, the placebo group, and the control group.

The treatment and placebo groups are both given the test item, although the researcher does not know which group is getting real treatment or placebo treatment.

The control group doesn’t receive anything because it serves as the baseline against which the other two groups are compared.

This is an advantage because if subjects in the placebo group improved more than the subjects in the control group, then researchers can conclude that the treatment administered worked.

Applicable across multiple industries

Double-blind studies can be used across multiple industries, such as agriculture, biology, chemistry, engineering, and social sciences.

Double-blind studies are used primarily by the pharmaceutical industry because researchers can look directly at the impact of medications. 

Disadvantages

Inability to blind.

In some types of research, specifically therapeutic, the treatment cannot always be disguised from the participant or the experimenter. In these cases, you must rely on other methods to reduce bias.

Additionally, imposing blinding may be impossible or unethical for some studies. 

Double-blinding can be expensive because the researcher has to examine all the possible variables and may have to use different groups to gather enough data. 

Small Sample Size

Most double-blind studies are too small to provide a representative sample. To be effective, it is generally recommended that double-blind trials include around 100-300 participants.

Studies involving fewer than 30 participants generally can’t provide proof of a theory. 

Negative Reaction to Placebo

In some instances, participants can have adverse reactions to the placebo, even producing unwanted side effects as if they were taking a real medication. 

It doesn’t reflect real-life circumstances

When participants receive treatment or medication in a double-blind placebo study, each individual is told that the item in question might be real medication or a placebo.

This artificial situation does not represent real-life circumstances because when a patient receives a pill after going to the doctor in the real-world, they are told that the product is actual medicine intended to benefit them.

When situations don’t feel realistic to a participant, then the quality of the data can decrease exponentially.

What is the difference between a single-blind, double-blind, and triple-blind study?

In a single-blind study, the experimenters are aware of which participants are receiving the treatment while the participants are unaware.

In a double-blind study, neither the patients nor the researchers know which study group the patients are in. In a triple-blind study, neither the patients, clinicians, nor the people carrying out the statistical analysis know which treatment the subjects had.

Is a double-blind study the same as a randomized clinical trial?

Yes, a double-blind study is a form of a randomized clinical trial in which neither the participants nor the researcher know if a subject is receiving the experimental treatment, a standard treatment, or a placebo.

Are double-blind studies ethical?

Double blinding is ethical only if it serves a scientific purpose. In most circumstances, it is unethical to conduct a double-blind placebo controlled trial where standard therapy exists.

What is the purpose of randomization using double blinding?

Randomization with blinding avoids reporting bias, since no one knows who is being treated and who is not, and thus all treatment groups should be treated the same. This reduces the influence of confounding variables and improves the reliability of clinical trial results.

Why are double-blind experiments considered the gold standard?

Randomized double-blind placebo control studies are considered the “gold standard” of epidemiologic studies as they provide the strongest possible evidence of causality.

Additionally, because neither the participants nor the researchers know who has received what treatment, double-blind studies minimize the placebo effect and significantly reduce bias.

Can blinding be used in qualitative studies?

Yes, blinding is used in qualitative studies .

Cakir, S., Hepguler, S., Ozturk, C., Korkmaz, M., Isleten, B., & Atamaz, F. C. (2014). Efficacy of therapeutic ultrasound for the management of knee osteoarthritis: a randomized, controlled, and double-blind study. American journal of physical medicine & rehabilitation , 93 (5), 405-412.

Kobak, K. A., Taylor, L. V., Bystritsky, A., Kohlenberg, C. J., Greist, J. H., Tucker, P., … & Vapnik, T. (2005). St John’s wort versus placebo in obsessive–compulsive disorder: results from a double-blind study. International Clinical Psychopharmacology , 20 (6), 299-304.

Papachristofilou, A., Finazzi, T., Blum, A., Zehnder, T., Zellweger, N., Lustenberger, J., … & Siegemund, M. (2021). Low-dose radiation therapy for severe COVID-19 pneumonia: a randomized double-blind study. International Journal of Radiation Oncology* Biology* Physics , 110 (5), 1274-1282. Rostock, M., & Huber, R. (2004). Randomized and double-blind studies–demands and reality as demonstrated by two examples of mistletoe research. Complementary Medicine Research , 11 (Suppl. 1), 18-22.

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Placebo Effects: Biological, Clinical and Ethical Advances

Damien g finniss.

a University of Sydney Pain Management and Research Institute, Royal North Shore Hospital, St Leonards, NSW, Australia

Ted J Kaptchuk

b Osher Research Center, Harvard Medical School, Boston, Massachusetts

Franklin Miller

c Department of Bioethics, National Institutes of Health, Bethesda, Maryland

Fabrizio Benedetti

d Department of Neuroscience, University of Turin Medical School, and National Institute of Neuroscience, Corso Raffaello 30, I-10125 Turin, Italy

For many years, placebos have been conceptualised by their inert content and their use as controls in clinical trials and treatments in clinical practice. Recent research demonstrates that placebo effects are genuine psychobiological phenomenon attributable to the overall therapeutic context, and that placebo effects can be robust in both laboratory and clinical settings. Evidence has also emerged that placebo effects can exist in clinical practice, even if no placebo is given. Further promotion and integration of laboratory and clinical research will allow advances in the ethical harnessing of placebo mechanisms that are inherent in routine clinical care and the potential use of treatments to primarily promote placebo effects.

Introduction

The notion of something called “placebo” started with St. Jerome's incorrect rendering of the first word of the ninth line of the 116 psalm, where instead of translating the Hebrew “I will walk before the Lord,” he wrote “I will please the Lord.” By the thirteenth century, when hired mourners waited for Vespers for the Dead to begin, they often repetitively chanted the ninth line, and received the name of “placebos” to describe their fake behavior ( 1 ). Indeed, in the 14 th century, in the Canterbury Tales , Chaucer named his sycophant, flattering courtier Placebo. Placebo controls, which entailed administrating fake procedures to separate the effects of imagination from reality, began in the 16 th century with “progressive” Catholic efforts to discredit “right-wing” exorcisms ( 2 ). These controls were then applied in medical experiments, beginning in 1784 with the Franklin commission's debunking of the psychic force of mesmerism/animal magnetism ( 3 ). The use of the word “placebo” in a medical context to describe innocuous treatments to make a patient comfortable dates to at least the end of the 18 th century ( 4 ). These earlier nefarious connections undoubtedly led to the tainted notion of placebos and placebo effects that has been retained until very recently ( 1 ). Mainstream interest in placebo effects only began with the widespread adoption of the placebo controlled randomized controlled trial (RCT) after World War II, as it was quickly noticed that people improved; sometimes dramatically, in placebo control arms ( 5 ). Henry Beecher popularized this observation in his famous proto-meta-analysis which claimed that about 35% of patients responded positively to placebo treatment ( 6 ). Beecher, however, encouraged an inflated conception of the “powerful placebo” because he failed to distinguish the genuine placebo response from other confounding factors. Since this time, there has been increasing interest in investigating placebo effects by rigorous research methods, especially in the last ten years.

Conceptual Background

The association of placebo effects with RCTs has caused confusion because the response in the placebo arm is not necessarily a genuine psychosocial response to the simulation of treatment. In fact, the observed response to placebo in RCTs may reflect natural course of disease, fluctuations in symptoms, regression to the mean, response bias with respect to the patient reporting of subjective symptoms and other concurrent treatments. Furthermore, a traditional focus on the “inert” content of a placebo has led to difficulties in defining and understanding placebo effects ( 7 , 8 ), let alone applying them in a clinical research and practice ( 9 ).

Much of the controversy surrounding placebo effects relates to how they are conceptualized and then defined. Generally, a placebo is seen as an inert substance or procedure and the placebo effect (or response) is something that follows administration of a placebo. The paradox in this statement lies with the fact that if something is “inert’, it by definition is unable to elicit an effect, and therefore placebos can't elicit placebo effects ( 7 , 8 ). This can be further confused with terminology such as “active” ( 10 ), “true” and “perceived” placebos ( 11 ), which are all attempts to better conceptualise placebo effects, and other terms such as context effects ( 12 , 13 ) and meaning responses ( 7 ) which have shifted the focus from the use of the word. Nevertheless, the “placebo” terminology, despite its defects, is too engrained in the scientific literature to replace it at this time, especially in the absence of a satisfactory alternative.

To obviate these confusions and better understand placebo effects in clinical trials and practice, it is necessary to reconceptualise placebos and placebo effects, shifting the focus from the “inert” content of a placebo or sham procedure to what the placebo intervention, consisting of a simulated treatment and the surrounding clinical context is actually doing to the patient. Accumulated evidence indicates that the placebo effect is a genuine psychobiological phenomenon attributable to the overall therapeutic context ( 9 , 14 ). This psychosocial context surrounding the patient can be comprised of both individual patient and clinician factors, and the interaction between the patient, clinician and treatment environment. The latter represents the many factors involved in a treatment context (such as the specific nature of the treatment and the way it is administered) and the “Doctor-Patient Relationship”, which is a term that encompasses a host of factors that constitute the therapeutic interaction ( Panel 2 )( 12 ). The placebo intervention is designed to simulate a therapeutic context such that the effect following this intervention, the placebo effect, is attributable to the way in which this context affects the patient brain, body and behaviour ( 9 ). When an active treatment is given, the overall response is the result of the treatment itself and the context in which it is given. Such a conceptualization allows for progression in thinking about the many factors which make up the psychosocial context around a patient and how these factors, and the mechanisms by which they operate can be enhanced in clinical practice.

Contributions of the psychosocial context surrounding the patient (or placebo component of a given therapy) to the overall response

A key shift in the emerging mechanistic understanding of placebo effects is the recognition that there is not one placebo effect but many ( 14 - 16 )( Figure 1 ). These mechanisms can be broadly discussed from psychological and neurobiological viewpoints.

The principal placebo mechanisms that have been unraveled across different medical conditions and systems/apparatuses

Psychological mechanisms

From the psychological viewpoint, a multitude of mechanisms contribute to placebo effects. These include expectations, conditioning, learning, memory, motivation, somatic focus, reward, anxiety reduction and meaning ( 9 , 17 ). Whilst there is a growing amount of research into these mechanisms, two principal mechanisms are well supported.

One principal mechanism involves expectancy: expectations of future responses following administration of a placebo ( 18 ). Many experiments have used simple verbal cues as modulators of expectancies ( 19 - 21 ). For example, a research subject receiving experimentally induced pain is given a topical placebo cream in the context of two different cues: the first that the cream is inert and will have no effect and the second, that the cream is a powerful pain killer ( 19 ). This paradigm demonstrates that such verbal cues can manipulate expectations and mediate placebo effects, including placebo analgesic effects in experimental ( 21 ) and clinical pain ( 22 ); and placebo induced changes in motor performance in Parkinson's Disease ( 23 , 24 ) and changes in emotions ( 25 ) and brain responses in addiction ( 26 ). Furthermore, the presence of a conditioning protocol to increase expectations results in larger placebo analgesic responses, demonstrating that expectation can both mediate and modulate placebo effects ( 20 , 27 , 28 ) as well as interact with other constructs such as desire and emotion ( 9 , 22 ).

Another principal mechanism of placebo effects involves classical conditioning ( 29 ). Repeated associations between a neutral stimulus and an active drug (unconditioned stimulus) can result in the ability of the neutral stimulus by itself to elicit a response characteristic of the unconditioned stimulus. Classical conditioning mechanisms have been demonstrated in both animal ( 30 - 32 ) and human studies ( 27 , 28 , 33 , 34 ), although it has been difficult to exclude any cognitive component (such as expectancy) in humans ( 35 , 36 ). Despite this issue, conditioning mechanisms in humans are supported by the fact that placebo effects are higher in magnitude after a conditioning protocol (even if an expectation mechanism is present) ( 20 ) and have been demonstrated to mediate placebo induced changes in unconscious physiological processes such as hormone secretion ( 33 ) and immune responses ( 34 ).

The interaction between expectancy and conditioning mechanisms remains an area for further research, which may be particularly relevant to exploring the clinical implications of these mechanisms. Although classical conditioning, manifesting an automatic unconscious mechanism, exists in humans, it can also be conceptualized as a complex process including cognitive components and derived from previous experience of either positive or negative therapeutic outcomes ( 37 ). Accordingly, conditioning and expectancy are certainly entangled in the occurrence of placebo effects in clinical practice. The most reasonable interpretation of recent literature is that expectancy is first, conditioning follows and is dependent on the success of the first encounter. This leads to the possibility that the first encounter could be critical for the development of subsequent robust placebo responses: the higher the expectancy, the greater the placebo effect, and, potentially the greater the conditioning effects associated with future drug intake.

In addition to classical conditioning, other learning processes such as past experiences and social observation have been shown to mediate placebo effects ( 38 ). For example, observation of a demonstrator simulating responsiveness to a therapy resulted in placebo effects in subjects that were similar in magnitude to a classical conditioning protocol ( 39 ), indicating the presence of multiple placebo effects mediated by expectations and different types of learning.

Neurobiological Mechanisms

Looking at placebo mechanisms from the neurobiological viewpoint further emphasizes the fact that there are multiple placebo effects. It also demonstrates that placebo effects can be manifested in different physiological systems in healthy volunteers and in patients with a host of different clinical conditions ( Figure 2 ).

Social stimuli around the treatment may activate, through expectation and/or conditioning mechanisms, a number of receptorial pathways in different diseases and therapeutic interventions (the involvement of 5-HT receptors in hormonal responses and depression is not definitive). These receptors are the same to which different drugs bind, thus indicating that psychosocial factors are capable of modulating the action of drugs. This interference has profound implications for our understanding of drug action: when a drug is given, the very act of administering it (i.e., the psychosocial context) may perturb the system and change the response to the drug. From : Benedetti (2008) Annu Rev Pharmacol Toxicol

Most research into the neurobiology of placebo responsiveness has addressed placebo analgesia; accordingly, the neurobiology of placebo effects is commonly considered in terms of opioid and non-opioid mechanisms ( 40 , 41 ). Several studies have demonstrated that placebo effects can be completely ( 42 - 44 ) or partially reversed ( 45 ) by the opioid antagonist naloxone, supporting the involvement of endogenous opioids in some placebo analgesic effects ( 46 ). Furthermore, placebo analgesic effects are likely to be inhibited by the peptide cholecystokinin (CCK) ( 44 ), for they are potentiated when a CCK antagonist is administered ( 47 ). Taken together, these studies demonstrate that some placebo mechanisms operate by altering the activity of both CCK and endogenous opioids ( 48 ). Interestingly, several studies have demonstrated the bodily region specificity of placebo effects ( 19 , 21 , 49 ), which are reversed by naloxone ( 21 ), indicating highly specific endogenous opioid mediated placebo analgesic responses, rather than a more generalized opioid release, such as increased opioid concentration in the cerebrospinal fluid ( 50 ). These results have been confirmed and extended by brain imaging techniques such as positron emission tomography (PET) ( 51 , 52 ) and functional magnetic resonance imaging (fMRI)( 53 - 55 ), one of which demonstrated placebo induced brain changes which were similar to those seen with opioid drug administration ( 56 ). Opioid mediated placebo responses also extend beyond pain pathways. Some studies have found that placebo induced respiratory depression (a conditioned placebo side effect) ( 57 ) and decreased heart rate and β-adrenergic activity ( 58 ) can be reversed by naloxone, demonstrating the involvement of opioid mechanisms on other physiological processes, such as respiratory and cardiovascular function.

Not all placebo effects are mediated by opioids. Growing evidence illustrates that many placebo effects are mediated by other mechanisms, such as the release of different neurotransmitters and neuromodulators. For example, in one study the placebo response in subjects who had prior conditioning with an opioid drug was reversed by naloxone; however, there was no reversal in those who had a non-opioid drug. Accordingly, completely different placebo mechanisms can be produced depending on the drug used in a conditioning protocol ( 20 ).

Although other medical conditions have been investigated from a neurobiological perspective, the placebo mechanisms in these conditions are little understood compared to pain and analgesia. For example, placebo administration to Parkinson patients induces dopamine release in the striatum ( 23 , 59 ), and changes in basal ganglia and thalamic neuron firing ( 60 , 61 ). Changes in metabolic activity in the brain following placebo administration in depression ( 62 ) and following expectation manipulations in addiction ( 26 ) have also been described.

Less research has been devoted to the nocebo effect, a phenomenon that is opposite to the placebo effect. This is mainly due to ethical limitations, as nocebo administration involves the induction of negative expectations. CCK has been shown to play a key role in nocebo hyperalgeisa, and this occurs through anticipatory anxiety mechanisms ( 63 - 65 ). A deactivation of dopamine has also been found in the nucleus accumbens during nocebo hyperalgesia ( 66 ), which indicates the involvement of different neurotransmitters. Furthermore, a neuroimaging study of nocebo effects has demonstrated brain activation different from placebo effects, including the hippocampus and regions involved with anticipatory anxiety ( 67 ).

Implications for Clinical Practice

Understanding how placebo effects work clinically in relevant patient populations over time has not kept pace with the recent mechanistic research, which mostly has involved laboratory experiments performed over short durations with healthy subjects. In the case of clinical populations, the study of longer-term placebo responsiveness has been limited to RCTs; however, these studies rarely include no-treatment groups to control for natural history and regression to the mean, making it difficult to discern a genuine placebo effect. There have been several meta-analyses which have attempted to address the presence and magnitude of placebo effects in RCTs, including some studies where no-treatment control groups were used. These analyses concluded that placebo effects are small and limited to subjective outcomes when placebos are used as a control condition in RCTs ( 68 - 70 ). However, placebo effects are much larger in studies which investigate placebo mechanisms ( 71 , 72 ). This finding is not at all surprising given that the mechanistic experiments employ controlled manipulations in verbal instructions and context that may be more representative of normal clinical practice than a clinical trial setting. To this extent, it is important bridge this gap by looking at placebo research from basic science, clinical trial and ethical perspectives in an attempt to better understand how placebo effects operate in the clinical setting.

A three week single blind RCT with irritable bowel syndrome (IBS) patients (n=262), investigated whether placebo effects can be disaggregated into two main components (placebo ritual alone and placebo ritual + supportive patient-practitioner relationship) and then progressively combined to produce clinically significant improvements as compared to no treatment controls ( 73 ). The placebo ritual consisted of a validated placebo acupuncture device, which was used in both “treatment” arms ( 74 ). Instead of penetrating the skin, the needle telescopes up the shaft of the needle handle. The supportive patient-relationship, used only in one arm, was prospectively scripted and included attention, warmth, confidence and thoughtful silence. At the three week outcome, in the supportive + ritual group, 62% of patients reported adequate relief (AR) on a validated IBS measure, while 44% reported AR with dummy ritual alone and the no treatment group reported 28% AR (p<0.001) The results were similar with the other three validated IBS measures used. The effect size of 62% AR was comparable to the improvement seen in RCTs of alosetron for IBS ( 75 ). The outcomes were similar after an additional three week follow-up. In addition to demonstrating that genuine placebo effects can be statistically and clinically significant over time in clinical populations, this trial demonstrated that placebo effects can be incrementally added in a manner resembling a graded dose escalation of component factors. Interestingly, in a separate analysis of the study, it was found that patient extraversion, agreeableness, and openness to experience were associated with placebo responses, features only seen in the supportive relationship + ritual treatment arm and not the ritual treatment alone arm ( 76 ). Significant practitioner effects were also observed. Future integration of such study designs in clinical RCTs with mechanistic laboratory work will allow for better understanding of these placebo mechanisms and how they can be augmented in practice.

Several RCTs have sought to study whether different vehicles of placebo ritual produce different effects ( 77 ). The largest such study compared treatment with placebo acupuncture with treatment with an oral pill in 270 patients with chronic arm pain due to repetitive use ( 78 ). At two weeks, the first primary end point, patients taking the pills had greater improvement in ability to function (primarily related to less disturbed sleep because of pain) compared to needle (p<0.05) while there was no difference in pain. At the end of the study (6 weeks) those on needle treatment had a significant reduction in pain compared to the pill group (p<0.001). Depending on the complaint and the length of time administered, different placebos had different effects. Differential nocebo effects were also observed. Patients in the placebo pill group were told they might have the adverse effects (e.g. drowsiness) of a medication (amitriptyline) and the placebo needle group was informed about the side effects of acupuncture. While 30% of people in both placebo groups reported adverse effects, these effects were entirely different and mimicked the information provided in the informed consent.

Some commentators have suggested that alternative therapies with elaborate rituals and distinct environmental cues can have pronounced and clinically significant placebo effects ( 79 , 80 ). Recent RCTs of acupuncture, while not primarily designed to study placebo effects, provided results that support this hypothesis. A series of large acupuncture trials conducted in Germany compared acupuncture according to traditional Chinese medicine, sham acupuncture (superficial needling at non-acupuncture points) and either no-treatment (wait list) groups or those receiving usual clinical care. Conditions studied included migraine ( 81 ), tension headaches ( 82 ), chronic low back pain ( 83 , 84 ) and osteoarthritis of the knee ( 85 ). Generally, across the various trials, there was no difference between verum and sham acupuncture, but those in both of these groups experienced substantially greater symptom improvement than no-treatment and usual care control groups ( 86 ). Supporting the hypothesis that acupuncture works by means of a placebo effect, Linde et al (2007) showed that in four of these German acupuncture RCTs (n=864) for migraine, osteoarthritis of the knee, migraine and tension headache, for which only one trial showed superiority over placebo, positive expectations influenced analgesic responses, doubling the likelihood of positive outcome ( 87 ). These expectancy effects lasted for one year in duration. A more recent study in chronic low back pain (n=640) again showed that eight weeks of tooth pick simulation sham acupuncture plus usual care had clinically meaningful improvements compared to usual care alone, and such effects also lasted one year ( 88 ).

Placebo Effects are Inherent in Clinical Practice – even when no placebo is given

Some of the clearest evidence supporting the involvement of placebo effects in clinical care comes in the form of the open-hidden paradigm ( Figure 3 ). In this experimental paradigm a treatment is given in a routine manner (the open administration), where the psychosocial context surrounding treatment administration is present, and a hidden manner, where the treatment is given without the patient's knowledge. In the case of drug therapy, the open administration mimics normal clinical care, where the doctor injects a drug in full view of the patient with verbal and contextual interactions. In the hidden administration, the drug is infused by a computer pump in the absence of the clinician and the therapeutic context. Patients receiving hidden administration are aware that at some stage they will receive a drug but they do not experience the expectation component or other contextual factors surrounding the administration. Because the hidden administration removes the psychosocial context of treatment, this paradigm defines the placebo component as the difference between open and hidden administrations, although no placebo is given ( 89 , 90 ).

In routine clinical practice, any treatment has a specific and a non-specific effect. The non-specific effect may come from the mere knowledge that a treatment is being given. The effectiveness of the active treatment can be assessed either by eliminating its specific effect (placebo study) or by eliminating the non-specific effects (hidden treatment). From: Colloca et al. (2004) Lancet Neurol

The open-hidden paradigm has been used in several clinical settings. In pain, hidden administrations of five commonly used painkillers (morphine, buprenorphine, tramadol, ketorolac, metamizol) have been demonstrated to be markedly less effective than open administrations ( 89 , 91 , 92 ).This included experiments in healthy volunteers (where pain ratings were higher in the hidden group) and in patients with postoperative pain (where the dose required to reduce pain by 50% was much higher in the hidden administration) ( 91 ). These results have been reproduced for drug administration for anxiety and deep brain stimulation for Parkinson's Disease ( 92 , 93 ). A slightly different methodology has been employed in addiction, where the absence of an expectation component with stimulant drug administration results in reduced regional brain glucose metabolism and verbal reports of efficacy ( 26 ). Taken together, the open-hidden paradigm demonstrates that the overall outcome of a therapy combines the specific pharmacological or physiological action of the therapy and the psychosocial context in which it is delivered. The latter represents the placebo component, based on expectancy.

The open-hidden paradigm has also provided a means of exploring the interaction between placebo effects and responses to active therapies, something that has not been possible in standard RCT's designed to evaluate treatment efficacy, as in this case one can only compare the response to placebo against the response to the index intervention without understanding the interaction between the two. In this case, it is worth describing a clinical trial performed in 1995, where the cholecystokinin (CCK) antagonist proglumide was shown to be better than placebo, which was in turn better than no-treatment for post-operative pain ( 47 ). According to methodology used in classical clinical trials, these results would indicate that proglumide is a good analgesic which acts on pain pathways, whereas placebo reduces pain by activating placebo analgesic mechanisms (through expectancy pathways). However, this conclusion proved to be erroneous, as a hidden injection of proglumide was completely ineffective. If the drug was an effective modulator of pain pathways, such a difference between open and hidden administration should not be seen. In this instance, the drug achieves a response by interacting with and enhancing placebo mechanisms (expectancy pathways), not by acting on pain pathways, and therefore it is only effective when combined with the placebo mechanisms inherent in the clinical encounter. This is the best example that placebo mechanisms can interact with treatments such as drug therapy, even if no placebo is given, as every therapy is administered in a therapeutic context, rich with potential to activate and modulate placebo mechanisms, many of which can act on similar biochemical pathways to the actual drug ( Figure 2 ).

Expectations can be modulated to improve therapy

A short term experimental trial in 2001 has advanced our understanding of the clinical implications of modulating placebo effects in routine clinical care. In this trial, studying post-operative pain over several days, investigators used intravenous saline (a placebo) as a background infusion in addition to an routine analgesic therapy (buprenorphine on request) ( 94 ). One group was told that the administration was simply a rehydrating solution (natural history control group), and another group was told that it was a powerful painkiller (maximized placebo context). Patients received normal analgesic therapy throughout the course of the trial and overall analgesic intake was monitored. The clear differences in the context (primarily expectation of benefit) of the intravenous administration resulted in significant differences in drug intake. The group who believed the solution was assisting in analgesia took 33% less active analgesic for the same pain control, demonstrating an important clinical effect and the potential for using placebo effects in conjunction with an active therapy to reduce overall drug intake. There was also a third group included which followed the same methodology but were given the instructions that “the solution may or may not be a powerful painkiller”, representing the classic double blind instructions characteristic of placebo-controlled trials and adding an element of doubt as to the effectiveness of the therapy. In this group, patients took 20% less analgesic medication, which was not as powerful as the certainty involved in the maximized placebo context.

Similar modulations in short-term placebo effects have also been found in more recent studies in patients suffering from irritable bowel syndrome ( 22 , 95 ). In these studies, patients were exposed to a painful stimulus (rectal distention balloon) under two conditions; local anaesthetic and placebo administration. In one study, patients were told that they “may receive an active or a placebo agent” ( 95 ), whereas in the second, they were told that “the agent you have been given is known to significantly reduce pain in some patients” ( 22 ). The more subtle changes in instructions and expectations affected the magnitude of placebo responses, whereby in the second trial (more certain instructions) placebo responses were larger.

Physician expectations seem to matter also. One small double-blinded trial conducted some time ago involved administration of a placebo in patients with post-operative dental pain ( 96 ). Patients were divided into two groups and were told that they could receive a drug which would increase their pain (naloxone), decrease their pain (fentanyl) or have no effect (placebo). In contrast, the clinicians were told that in one of the groups, there was no chance of administering an active analgesic drug, and to this extent it was the clinicians who were manipulated and not the patients. The placebo response was dramatically less in the group where the clinicians believed that no analgesic therapy could be given, demonstrating that clinicians' beliefs can affect placebo effects. Interestingly, the double-blind nature of the study suggests that alterations in clinicians' beliefs may have altered the therapeutic context (and placebo effect) in more subtle ways, as the patients were not aware of the different information given to the clinicians.

Loss of placebo mechanisms reduce therapeutic efficacy

Loss of placebo mechanisms has been shown to have significant clinical ramifications. For example, a recent study used an open-hidden design in Alzheimer's Disease ( 97 ). It showed that the placebo component (difference between open and hidden administrations) was correlated with cognitive status and functional connectivity between brain regions. Reductions in both cognitive status and functional connectivity correlated to reduced placebo mechanisms and reduced overall analgesic effect, so much so that an increase in dose was required for the same level of analgesia. This signifies the importance of not only attempting to maximize placebo components of therapies, but assessing situations where loss of placebo mechanisms may require increased therapeutic dosage.

Ethical principles of enhancing placebo effects in clinical care

Any ethical evaluation of efforts to promote placebo effects in clinical practice first requires knowledge as to the clinical relevance and significance of placebo effects. The evidence reviewed in the previous section suggests the potential for placebo interventions and the therapeutic context to promote clinically important symptomatic relief. Nevertheless, more studies of placebo effects in specific clinical settings are required before the use of treatments with the primary aim to promote placebo responses can be recommended as evidence-based practice.

A second important ethical consideration relates to whether and how placebo effects can be promoted without deception. Since it has been demonstrated that placebo effects are inherent in routine clinical care, and that the psychosocial context surrounding the patient (including the patient-Doctor interaction and the therapeutic ritual) can be augmented to improve these placebo effects, it is ethically sound, not to mention clinically relevant, to provide a supportive clinical encounter that relieves anxiety and promotes positive expectations along with honest disclosure of the expected benefits of a medically indicated therapy. To this extent, routine conscious attempts to identify and exploit features of the clinical encounter to augment placebo effects represent one ethical (non-deceptive) means of applying the understanding of placebo mechanisms to improving clinical outcomes.

Whether it is ethical to recommend a treatment primarily to produce a placebo effect is a more complicated and controversial question. Most studies of the placebo effect have employed deception in the administration of “inert” placebos as a key element of experimental design. Whereas the use of deception in research poses its own ethical issues ( 98 ), the problem of deception in clinical practice raises even stronger concerns. To recommend or administer a placebo intervention to a patient presented deceptively as a therapy with specific efficacy for the patient's condition violates informed consent and threatens the trust that is central to clinical practice ( 99 ). Recent data indicate that the administration of sugar pills and saline injections is in fact very low ( 100 , 101 ), but that clinicians commonly prescribe various active treatments with the primary intent of promoting a placebo response or complying with the wishes of the patient. The available evidence suggests that the practice of disclosure to patients regarding such placebo treatments is deceptive or at least not sufficiently transparent.

Can the recommendation for a treatment intended to promote the placebo effect be made without deception and also without undermining its therapeutic potential? Consider, for example, the case of a clinician who recommends treatment with acupuncture for a patient with chronic low back pain who has not been helped by standard medical therapy. Aware of the results of the recent acupuncture trials, described above, this clinician thinks that acupuncture may work by promoting a placebo response. The clinician might provide the following disclosure to the patient: “I recommend that you try acupuncture. Several large studies have shown that traditional acupuncture is not better than a fake acupuncture treatment, but that both of these produce considerably greater symptom improvement in patients with chronic low back pain condition as compared with those patients who receive no treatment or conventional medical therapy. Although the specific type of needling doesn't appear to make any difference, it is likely that acupuncture works by a psychological mechanism that promotes self-healing, known as the placebo effect.” On its face, this disclosure appears honest. A patient who received this disclosure and subsequently got better after undergoing acupuncture might nonetheless develop a false belief about why it worked. This does not mean, however, that the patient has been deceived by his physician.

Can it be ethical for clinicians to prescribe “inert” placebos with a disclosure that the treatment being administered (a placebo) “has been shown to be effective by altering pain transmission in similar ways to other treatments”? As is the case with most of the studies of the placebo effect ( 98 ), an element of deception is involved, and in this example the element of deception relates to a lack of full disclosure of the content of the placebo and the complete reason for why it is being given: that is, not only to modulate pain transmission but to do so through a placebo effect. Therefore, as is the case with the example of acupuncture, completely eliminating deception would involve additional disclosure that the placebo had no active medicine in it and would be working through psychological mechanisms that promote self healing. It is not known how such disclosure might affect placebo responses, and with the exclusion of two small trials in patients with various mild psychiatric symptoms (and without a no-treatment control group) ( 102 , 103 ) there has been no research to answer this important question. It is therefore important for clinicians who are recommending treatments for the primary intention of maximizing placebo effects to be aware of the ethical implications of different types of disclosure and the potential for deception. Clinically-focused research is needed to explore non-deceptive techniques for administering treatments aimed at promoting placebo effects.

Conclusions

It is evident that placebo effects are real and that they have therapeutic potential. Laboratory evidence supports the existence of numerous placebo mechanisms and effects in both healthy volunteers and patients with a variety of medical conditions. Furthermore, clinically relevant evidence demonstrates that placebo effects can have meaningful therapeutic effects, by virtue of magnitude and duration, in different patient populations. Although substantial progress has been made in understanding placebo effects, considerable scientific work remains to be done in both laboratory experiments and translational clinical trial research, with the ultimate aim of harnessing placebo effects to improve patient care.

Acknowledgments

Grants from Istituto San Paolo and Regione Piemonte (Turin, Italy) and Volkswagen Foundation (Hannover, Germany) to Fabrizio Benedetti. Grant # K24 AT004095 from National Center for Complementary Medicine (NCCAM) at NIH to Ted Kaptchuk.

• Pain Management

What Is the Placebo Effect?

A placebo is anything that seems to be a "real" medical treatment -- but isn't. It could be a pill, a shot, or some other type of "fake" treatment. What all placebos have in common is that they do not contain an active substance meant to affect health.

How Are Placebos Used?

Researchers use placebos during studies to help them understand what effect a new drug or some other treatment might have on a particular condition.

For instance, some people in a study might be given a new drug to lower cholesterol. Others would get a placebo. None of the people in the study will know if they got the real treatment or the placebo.

Researchers then compare the effects of the drug and the placebo on the people in the study. That way, they can determine the effectiveness of the new drug and check for side effects.

Sometimes a person can have a response to a placebo. The response can be positive or negative. For instance, the person's symptoms may improve. Or the person may have what appears to be side effects from the treatment. These responses are known as the "placebo effect."

There are some conditions in which a placebo can produce results even when people know they are taking a placebo. Studies show that placebos can have an effect on conditions such as:

• Sleep disorders
• Irritable bowel syndrome

In one study involving asthma, people using a placebo inhaler did no better on breathing tests than sitting and doing nothing. But when researchers asked for people's perception of how they felt, the placebo inhaler was reported as being as effective as medicine in providing relief.

How Does the Placebo Effect Work?

Research on the placebo effect has focused on the relationship of mind and body. One of the most common theories is that the placebo effect is due to a person's expectations. If a person expects a pill to do something, then it's possible that the body's own chemistry can cause effects similar to what a medication might have caused.

For instance, in one study, people were given a placebo and told it was a stimulant. After taking the pill, their pulse rate sped up, their blood pressure increased, and their reaction speeds improved. When people were given the same pill and told it was to help them get to sleep , they experienced the opposite effects.

Experts also say that there is a relationship between how strongly a person expects to have results and whether or not results occur. The stronger the feeling, the more likely it is that a person will experience positive effects. There may be a profound effect due to the interaction between a patient and healthcare provider.

The same appears to be true for negative effects. If people expect to have side effects such as headaches , nausea , or drowsiness, there is a greater chance of those reactions happening.

The fact that the placebo effect is tied to expectations doesn't make it imaginary or fake. Some studies show that there are actual physical changes that occur with the placebo effect. For instance, some studies have documented an increase in the body's production of endorphins, one of the body's natural pain relievers.

One problem with the placebo effect is that it can be difficult to distinguish from the actual effects of a real drug during a study. Finding ways to distinguish between the placebo effect and the effect of treatment may help improve the treatment and lower the cost of drug testing. And more study may also lead to ways to use the power of the placebo effect in treating disease.

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• Open access
• Published: 11 September 2024

Why is Antactic krill (Euphasia superba) oil on the spotlight? A review

• Fereidoon Shahidi   ORCID: orcid.org/0000-0002-9912-0330 1 &
• Abrehem Abad 1  

Food Production, Processing and Nutrition volume  6 , Article number:  88 ( 2024 ) Cite this article

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Antarctic krill ( Euphausia superba ) oil is attracting more interest for its nutritional as well as functional potentials. Nevertheless, its potential as new and innovative food component remains largely unexplored. This review aims to outline the chemical composition, extraction methods, and health advantages of krill oil, offering insights for its utilization and provides evidence why it is now on the spotlight. Krill oil presents a distinctive fat profile, rich in lipid classes, with phospholipids (PLs) comprising a significant portion (38.93—79.99%) with high levels of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Additionally, it includes several minor bioactive components like astaxanthin, tocopherols, sterols, flavonoids, and vitamin A. Various extraction technics, including solvent and solvent-free extraction, enzyme-assisted pretreatment extraction, super/subcritical fluid extraction, significantly influence both output as well as standard of the resulting product. Furthermore, the oil had been linked to a number of health advantages, including prevention of cardiovascular disease (CVD), anti-inflammatory effects, support for women's physiology, anticancer activities, as well as neuroprotection, among others. Despite the commercial availability of krill oil products as dietary supplement, there is a scarcity of studies exploring the underlying molecular mechanisms responsible for its various biological activities. Despite this, apply krill oil as an innovative food ingredient has not been thoroughly investigated. This review consolidates information on the chemical composition, extraction techniques, possible health advantages, as well as existing uses as applications, aiming to offer insights for its complete exploitation. In addition, it attempts to unravel the fundamental molecular mechanisms that being investigated to deeply understand how krill oil produces various biological effects.

Graphical Abstract

Introduction

Krill, scientifically known as Euphausia superba , is a tiny crustacean found in the seas of the Antarctic Ocean, its also holds significant ecological importance as a primary food source for many fish species (Zhou et al.,  2021 ). Although quantifying krill biomass poses challenges, estimates suggest approximately 379 million metric tons. In an effort to preserve the marine ecosystem, the agency of Antarctic Marine Living has implemented the catch maximum per season which is < 619,000 tons (Zeb et al.,  2021 ). However, the actual catch less than 250,000 tons, which is below the allowed limits, likely due to challenges in preserving krill due to its delicate nature (Fuxing et al.,  2017 ). Krill composition has a water content of 77.9—83.1%, lipids from 0.5 to 3.6%, protein from 11.9 to 15.4%, chitin at 2%, along with carbohydrates, and 3% ash (Xie et al.,  2017 ).

Krill oil (KO) finds its way in the aquaculture sectors and as a dietary supplement to enhance health. This is attributed to its nutritional profile, that is high in omega-3 fatty acids as triacylglycerols (TAGs) also phospholipids (PLs), astaxanthin, and vitamins (A and E) (Cicero and Colletti,  2015 ).

Krill has a lipid content of 0.5–3.6% (Xie et al.,  2019 ), notably phospholipids (PLs) that account for 30 to 65% of the total. Unlike fish oils (FO), which primarily occur as TAGs, the oil has a significant portion of phosphatidylcholine. With roughly 40% of its overall fatty acids are due to EPA (C20:5) and DHA (C22:6) (Ramprasath et al.,  2013 ). The C20:5 and C22:6 in KO exhibit various beneficial pharmacological properties, hence serve a major role in the management of several chronic conditions like CVD as well as inflammatory diseases (Cicero et al.,  2012 ; Costanzo et al.,  2016 ). Moreover, they contribute to cancer prevention and improve the gut health (Saravanan et al.,  2010 ). Research findings indicate that C20:5 and C22:6 obtained from KO shown better bioaccessibility when compared to different n-3 PUFA sources and varieties (Rossmeisl et al.,  2012 ).

Krill oil received approval from the U.S. Food and Drug Administration (FDA) in 2008, designated as Generally Recognized as Safe (GRAS) status. It was also granted approval as a novel food by the European Food Safety Authority (EFSA) in 2009 and was granted authorization in China in 2014. Additionally, EFSA approved the use of KO for pregnant women in 2014.

This write up offers a comprehensive account of krill oil with regard to its chemical composition, bioavailability, health benefits, mechanisms of action, extraction methods (both traditional and unconventional), and existing applications. Additionally, it explores the future prospects of krill oil as nutraceutical and why it has captured the spotlight.

Composition of Antarctic krill oil ( Euphausia superba )

Lipid class composition.

Unlike typical edible oils, which predominantly comprising mostly TAGs (over 95%) (Shahidi and Abad,  2019 ; Shahidi et al.,  2020 ), krill oil consists of a broader range of lipid classes. Phospholipids constitute the primary class, followed by TAGs, DAGs, MAGs, FFAs, and other constituents (Ahmmed et al.,  2020 ; Phleger et al.,  2002 ). Various factors affect the composition of each lipid portion in krill oil. These include year-to-year environmental changes, seasonal fluctuations, the maturity level of KO, and storage conditions, transportation means, and preparation techniques (Xie et al.,  2019 ). For instance, dehydrating krill through hot air yields higher amounts of free fatty acids (Gang et al.,  2019 ). Moreover, several studies have employed different analytical methods to analyze the composition of krill oil, thus comparison of the results may not always be straightforward (Han and Liu,  2019 ).

Numerous studies have documented varying lipid compositions in different krill samples. Krill oils typically exhibit a substantial content of PLs, varying from 39.89 to 80.69%, which can vary based on the sample type as well as the analytical method employed (Table  1 ). Discrepancies in lipid compositions observed in different years and regions may be due to fluctuations in feeding behavior (Phleger et al.,  2002 ). Clarke ( 1980 ) noted that oil extracted from krill ovarian tissues had higher phospholipid levels compared to that from muscle tissues, similar to that observed in fish (Takama et al.,  1994 ). Thus, females tended to possess higher PL levels than males due to the presence of developing ovaries (Kołakowska,  1991 ). Moreover, the extracted KO's PL content was greatly impacted by the extraction solvent selection, with mix of ethanol and isopropanol extraction resulting in elevated levels of phospholipids compared to solvents like hexane and acetone (Xie et al.,  2017 ).

Table 2 provides an overview of the phospholipid (PL) composition of Antarctic krill oil, indicating that phosphatidylcholine (PC) constitutes the majority, 44.58 to 99.80%, followed by phosphatidylethanolamine (PE) at 0.20 to 24.74%. Additionally, lysophosphatidylcholine (LPC) is also present in significant proportions, possibly attributed to PC hydrolysis due to either incorrect storage or preparation of KO (Lim et al.,  2015 ). Other PLs types such as lysophosphatidylethanolamine, phosphatidylinositol, phosphatidylserine, cardiolipin, sphingomyelin, phosphatidic acid, and phosphatidylglycerol have also been observed in smaller amounts, typically not surpassing 15%, in certain studies (Fricke et al.,  1984 ; Kołakowska,  1991 ). PLs, particularly PC, have long been used as food additives and nutritional supplements, mainly derived from sources such as egg yolk, plant oils, and milk products (Chen et al.,  2023 ). The abundance of PC in KO provides a promising marine source for supplying PLs.

Lipid fraction (TAGs and PLs)

As already noted, KO composition consists primarily phospholipids (PLs) followed by triacylglycerols (Abad and Shahidi,  2023 ; Ahmmed et al.,  2020 ). These components significantly contribute to in absorption, and metabolism (Zhang et al.,  2021 ).

More than 64 molecular species of triacylglycerols have been identified in krill oil, with carbon number (CN) 42 to 60 and with one to eleven double bonds. Among these, the primary prevalent TAG species are 16:0/16:0/18:1, 14:0/16:0/18:1, 16:0/18:1/18:1, and 16:0/16:1/16:1 (Castro-Gómez et al.,  2015 ), constituting relative proportions of 4.8, 8, 5.5, and 6% of all TAG species found, in that order. This observation is consistent with the primary fatty acid profiles of TAG that contain 14:0, 16:0, 16:1, and 18:1. While the TAG fraction in krill oil contains relatively low levels of C20:5 and C22:6, the majority of residues are EPA found in the sn-1,3 positions, whereas C22:6 residues are located mainly in the sn-2 position (Fuller et al.,  2020 ). This distribution pattern mirrors that observed in FO (Akanbi et al.,  2013 ; Standal et al.,  2009 ). Moreover, another study found that about 21% of n-3 PUFAs were in TAGs fraction of krill oil, notably found in the sn -2 location (Araujo et al.,  2014 ).

Choline-containing PLs are dominant in krill oil. Using HPLC–ESI–MS, Winther et al. ( 2011 ) 69 PL species in krill oil have choline-head groups, comprising 60 PC species and nine LPC species. Notably, PC (16:0/22:6), PC (16:0/18:1), also PC (16:0/20:5) were the species with the highest abundance based on relative intensity, consistent with findings reported by Castro-Gómez et al. ( 2015 ) as well as Le Grandois et al. ( 2009 ). Although differing numbers of PC and LPC species were reported, it was discovered that about seven species of PC have omega-3 fatty acid in both two positions ( sn -1 and sn -2). These species included PC, (18:4/22:6), (18:4/20:5), (20:5/22:6) (20:5/20:5), (20:5/23:5), (20:5/22:5), as well as (22:6/22:6) (Winther et al.,  2011 ), highlighting the prevalence of omega-3 fatty acid within PC molecules. Furthermore, NMR analysis (Hupfeld,  2018 ) indicated that in contrast to the first position ( sn -1), the majority of omega-3 fatty acids in krill phospholipids were located in the sn -2 positions.

Fatty acid composition

Krill oil is rich in PUFAs, including C20:5 and C22:6, together with high amounts of C14:0, C16:1, C16:0, C18:1, and C20:1(Sun et al.,  2018 ). In addition, n-3 PUFAs, in particular C20:5 and C22:6 derived from dietary lipids, are recognized for their vital role in health (Marventano et al.,  2015 ). While other marine oil including fish known for their high C20:5 and C22:6 content and have traditionally been used as supplements of n-3 PUFAs, similar composition could be offered by KO. Table 3 provides a comparative overview of the composition of fatty acid in KO (Xie et al.,  2018 ) along with other marine oils including algal oil (DHASCO) (Abuzaytoun and Shahidi,  2006 ), cod liver oil (Dalheim et al.,  2021 ), as well as tuna and menhaden oils (Codex Standard 329–2017, WHO, Food and Agriculture Organization of the United Nations,  2017 ). Furthermore, KO has similar levels of C20:5 and C22:6 as other marine oils, although a significant portion of these fatty acids in KO are linked to PLs rather than TAGs found in other oil like fish.

The levels of C20:5 and C22:6 in krill oil are similar to what been found in FO (Table  3 ), but occurring mainly in the PLs rather than TAGs. Clarke ( 1980 ) found that the PLs portion of KO exhibited substantially greater proportions of PUFAs, as well as n-3 PUFAs, along with lower concentrations of monounsaturated and saturated fatty acids. Specifically, 31.13% of C20:5 and 14.87% of C22:6 been identified in PL portion, compared to about 3.17% of C20:5 and 1.5% of C22:6 in TAG portion. Those findings were supported by several additional research (Cicero and Colletti,  2015 ; Laidlaw et al.,  2014 ), which indicate that KO with elevated PL levels have higher amount of C20:5 and C22:6 (Sun et al.,  2019 ). The latest studies have indicated that omega-3 fatty acid that located in PLs demonstrate notably enhanced bioavailability compared to other omega-3 that located in TAGs (Jiang et al.,  2020 ). Consequently, krill oil may offer superior bioavailability of C20:5 and C22:6 compared to FO.

Minor components

Astaxanthin.

Astaxanthin, consider as a primary carotenoid present in certain marine organisms as well as algae, exhibits potent antioxidant properties as well as significant biological advantages (Table  4 ) (Ambati et al.,  2014 ). Miki ( 1991 ) highlighted astaxanthin's antioxidant potency that is ten times stronger than zeaxanthin, lutein, canthaxanthin, and β-carotene; furthermore, hundred times superior to alpha-tocopherol. Moreover, existence of astaxanthin responsable the deep red color in KO (Zeng et al.,  2024 ). The astaxanthin content in krill oilKO varies from 4 to 500 mg/100 g, also is affected by factors such as extraction techniques and analytical methods (Ali-Nehari et al.,  2012 ; Sun et al.,  2017a ; Tandyet al.,  2009 ). Extraction by acetone solvent has been shown to yield krill oil with higher levels of astaxanthin (Ahmadkelayeh and Hawboldt,  2020 ).

In KO astaxanthin predominantly exists as form of fatty acid esters. Foss et al. ( 1987 ) reported that astaxanthin diesters (51%), monoesters (43%), as well as free astaxanthin (6%), which coincides with findings of other studies (Lambertsen and Braekkan,  1971 ; Yamaguchi et al.,  1983 ) and similar to that of other shellfish. Subsequently, research has identified C14:0, C16:0, C16:1, C18:1, C20:0, C20:5, as well as C22:6 as primary fatty acids present as astaxanthin esters form (Cao et al.,  2023 ). Furthermore, astaxanthin was found to exist as three astaxanthin isomers in KO, namely all- trans , 9- cis , and 13- cis astaxanthin, with the all- trans isomer being most abundant.

KO have a considerable proportion of sterols, ranging from 2.3 to 3.9% of total lipids, mainly as cholesterol and desmosterol (Table  1 ) (Huenerlage et al.,  2016 ). Cholesterol constitutes approximately 81.31—82.57% of total sterols at concentrations ranging from 1895 to 3196 mg/100 g (Colletti et al.,  2021 ). These concentrations exceed those found in certain fish oils like tuna oil (204 mg/100 g) and hoki oil (515 mg/100 g) (Huenerlage et al.,  2016 ), also those present in egg yolk (1181 mg/100 g) (Albalat et al.,  2016 ). Since diet that including high cholesterol is associated with CVD (Nissinen et al.,  2008 ), concerns have been expressed regarding the consumption of krill oil. Bruheim et al. ( 2017 ) suggested that employing one solvent for extraction like ethanol may end to reduced cholesterol levels in KO in contrast to ethanol–water mixtures. Nonetheless, there is a need to explore novel extraction techniques to further mitigate cholesterol amounts in KO.

Desmosterol, recognized as the forerunner to cholesterol which constitutes 1.70—18.63% of all sterols (Fricke et al.,  1984 ). Additionally, Phleger et al. ( 2002 ), identified several other sterols in KO like brassica-sterol brassica-sterol (0.5—1.7%), 24-nordehydrocholesterol (0.1—1.7%), 24-methylenecholesterol (0.1—0.4%), transdehydrocholesterol (1.1—1.5%), and stanols (0.1—0.2%). Minor discrepancies in sterol composition may arise from variations in krill diet over different years, as crustaceans rely heavily on dietary sources or phytosterol dealkylation for sterol acquisition, rather than de novo synthesis (Xie et al.,  2018 ).

Vitamin E encompasses all tocopherols and tocotrienols, comprising four pairs of homologues (α-, β-, γ-, δ-), each possessing antioxidant properties and biological advantages, with α-tocopherol being the most potent (Valk and Hornstra,  2000 ). Similar to many other marine organisms (Ackman and Cormier,  1967 ), α-tocopherol predominates in krill oil, ranging from 14.74 to 63.0 mg/100 (Xie et al.,  2017 ; Tilseth,  2010 ). Some studies have also identified γ-tocopherol vary in concentration from 0.25 to 3.67 mg/100; however, traces of δ-tocopherol from 0 to 0.65 mg/100 g (Sun et al.,  2018 ; Xie et al.,  2017 ). Typically, in KO over 90% of tocopherols exist as α-tocopherol. Tocopherols in KO may enhance antioxidant capacity also potentially synergize with some bioactive constituents.

Vitamin A, crucial for human nutrition, is essential for both immune function as well as the management of certain infectious diseases (Mayo-Wilson et al.,  2011 ). According to wet weight basis, frozen krill normally contains 0.11 mg/100 g of vitamin A, (Suzuki and Shibata,  1990 ). a nutrient that is fat-soluble and may concentrate in the oil during the lipid extraction process. Xie et al. ( 2017 ) reported vitamin A contents of 16.40—28.55 mg/100 g of krill oil, with variations attributed to the extraction solvents used. Tilseth ( 2010 ) noted that oil extracted from cookedd krill had a vitamin A content of about 18 mg/100 g. Krill oil has a higher content of vitamin A than some FO, like menhaden oil (0.1–0.6 mg/100 g) as well as tuna oil (11.09 mg/100 g), but less than the 99.76 mg/100 g found in hoki oil.

Flavonoids exhibit various biological activities, including antioxidant, antibacterial, immunomodulatory, antitumor, also anti-inflammatory properties (Ullah et al.,  2020 ). While fruits, vegetables, and grains are primary sources of flavonoids (Merken and Beecher,  2000 ; Shahidi and Yeo,  2018 ), krill oil contains a novel flavonoid, 6,8-di- C -glucosyl luteolin. Sampalis ( 2013 ) patented a KO extract containing approximately 40% phospholipids (PLs) and about 7 mg/100 mL of flavonoids. This extract demonstrated efficacy in protecting the skin against harmful ultraviolet B (UVB) radiation also in improving dyslexia and abnormal motor function. According to certain research (Omar et al.,  2011 ), flavonoids' ability to function as antioxidants is increased when they are C -glycosylated at particular locations as well as their antidiabetic properties (Matsuda et al.,  2003 ). However, information about the characteristics of flavonoids found in KO are currently unavailable.

Whole krill possesses significant levels of minerals that are necessary for bone health, including magnesium, calcium, and phosphorus 360, 1322 1140 mg/100 g respectively, that fulfill the recommended amount for adults (Colletti et al.,  2021 ). Even though processing of krill may lead to loss of some minerals, using the Sampalis ( 2011 ) patented process, a krill lipid extract enriched with multiple minerals including potassium, calcium, selenium, and zinc may be obtained. Moreover, KO also have a great amount of fluoride which is 2,400 mg/kg (Soevik and Braekkan,  1979 ). However, fluoride is recognized as a global health concern (Barbier et al.,  2010 ). While fluoride in krill predominantly accumulates in the exoskeleton, there is a potential chance that it gets released in deceased krill. Hence, careful consideration of fluoride transfer is essential during krill oil extraction to prevent excessive fluoride levels in the oil. Typically, removing the exoskeleton from krill prior to extract the oil yields KO with low concentration of fluoride < 0.5 mg/kg, whereas extract by use whole body of krill exhibit a great concentration of fluoride from 3—5 mg/kg (Bruheim et al.,  2016 ; Jansson et al.,  2018 ).

Extraction methods

Krill oil extraction uses dried material as well as fresh krill (Katevas et al.,  2014 ; Ronen et al.,  2017 ). High concentrations of active proteolytic enzymes in krill allow for quick autolysis following catch. Therefore, it is imperative to commence on-board processing as soon as krill are captured in order to extract oil from fresh krill (Beaudoin et al.,  2004 ). The krill biomass serves as a more suitable material for on-shore krill oil extraction, particularly in regions lacking onboard or offshore processing capabilities (Yoshitomi et al.,  2003 ). Various extraction techniques, such as solvent extraction, mechanical pressing (nonsolvent extraction), enzyme-assisted extraction, and super/subcritical fluid extraction, are well-documented for krill oil extraction (see Table  5 ). Every approach has pros and disadvantages of its own, which are discussed below.

Traditional extraction methods

Solvent extraction.

Solvent extraction, a traditional method in oil production (Abad and Shahidi,  2017 , 2021 ), remains prevalent for krill oil production. Since one type of solvent cannot effectively extract all of the lipids from krill because of presence of different lipid classes with differing polarities, Xie et al. ( 2017 ) found that alcohols like ethanol as well as isopropanol could extract substantial volumes of PLs from krill meal but yield KO has less minor components. Conversely, acetone efficiently extracts minor components but fails to fully extract PLs. Hexane is widely used in oil extraction from seeds and is cost-effective with high extraction efficiency (Abad and Shahidi,  2020a , 2020b ); as such, it shows moderate capabilities to extract PLs as well as minor components (Li et al.,  2013 ). Combining solvents such a polar with a nonpolar can help balance the extraction efficiencies of PLs as well as minor component (Ronen et al.,  2017 ). Although the Folch method (Folch et al.,  1957 ) is widely applied to extract lipids from animal tissues with high lipid (Bruheim et al.,  2016 ), its commercial feasibility is limited due to the solvent’s toxicity such as chloroform as well as methanol.

Presently, the most used technique for extracting KO involves a two-step process using ethanol and acetone (Beaudoin et al.,  2004 ) which yields better lipid extraction (2.62%) compared to a single solvent extraction (acetone, 2.15%). Alternatively, a simpler one-step strategy, using a mixture of ethanol and acetone (1:1, v/v), can also achieve high lipid yields (Gigliotti et al.,  2011 ). Furthermore, Yin et al. ( 2015 ) found that combining solvent extraction with extrusion pretreatment enhances lipid extraction efficiency from krill. Defatted krill remains a valuable resource for protein recovery (Chen et al.,  2009 ), including enzymatic hydrolysis for producing peptides (Zhao et al.,  2013 ) or by fermentation (Sun and Mao,  2016 ). While solvent extraction is cost-effective and scalable, it necessitates large quantities of solvents, thus posing potential environmental concerns. Moreover, the process takes a lot of time also labor-intensive.

Mechanical pressing (solvent-free extraction)

Solvent-free extraction, unlike solvent-based methods, does not rely on organic solvents for extracting KO. Mechanical pressing, which is known as classic solvent-free extraction technique, is commonly used for oilseeds with great content of oil including sesame oil 49 to 58% and sunflower oil 40 to 43% (Khan and Hanna,  1983 ). Although less efficient compared to solvent extraction, mechanical pressing is often employed to remove the majority of the oil before recovering the remaining oil via solvent extraction. Fresh krill is not inherently suited for conventional mechanical pressing due to its relatively and 17.24% in krill meal (Yin et al.,  2015 ) using this method, fresh or thawed material should be ground as slurry in fluid medium, facilitating lipid release during subsequent mechanical disruption procedures, followed by oil recovery using centrifugation (Larsen et al.,  2007 ). However, the resultant slurry during grinding could lead to emulsification because of nature of phospholipids, hence complicating the removal of the fat off the mixture. To address this concern, Katevas et al. ( 2014 ) introduced an alternative method, which includes cooking, drain, and centrifuging. The approach allows simultaneous extraction of PLs-enriched KO as well as neutral lipid-enriched KO. It is worth noting that the initial cooking step take place at 90 °C with no agitation to prevent emulsification. Additionally, it is preferable to process krill when fresh, because ice crystals that grow as a result of freezing might harm krill tissues, resulting in emulsification during processing and yielding low-quality products.

Solvent-free extraction offers the advantage of providing a safer and more environmentally friendly process compared to solvent extraction methods. However, it presents significant drawbacks such as the need for investing in equipment purchase and high energy requirements. Additionally, the high operating temperatures involved may lead to product oxidation. Furthermore, solvent-free extraction may not efficiently extract all oil that exist in krill, as indicated by Katevas et al. ( 2014 ) who reported a yield of only 2.1%. Consequently, some krill manufacturers opt to use mechanical separation consider as very first step to extract a portion of oil while at the same time producing krill meal. Thereafter, some techniques like solvent extraction or supercritical fluid extraction are employed to extract oil from the remaining krill meal (Tilseth et al.,  2015 ).

Other extraction techniques

Enzymatic extraction.

Enzyme pretreatment represents an efficient method for releasing bound compounds and increasing lipid yield during the extraction process (Dom´ınguez et al.,  1994 ). By using specific enzymes, the extractability of oil can be improved. Moreover, the gentle nature of this process guarantees better-quality meal and oil. These advantages render enzyme pretreatment an attractive option for KO extraction.

Oil has been extracted from raw krill using proteases, as demonstrated by Bruheim et al. ( 2016 ). The typical process involves disintegrating the krill into small particles, followed by the addition of water then heating. Subsequently, enzymes are added to hydrolyze resultant material, after which the enzymes are deactivated. The solids, primarily the exoskeleton, are removed and then PL-protein complex is separated then dried. KO is then extracted from this complex (Bruheim et al.,  2016 ). It is worth noting that the remove the exoskeleton from material can lead to a reduction in fluoride content in the resulting products. Lee ( 2014 ) patented an alternative enzyme assisted extraction method by using ultra high-pressure reactor ranging from 10 to 300 MPa to liquefy krill and assure effective interaction with enzyme (proteases). Following undergoing enzymatic processing for a duration of 4 to 24 h, the krill that had turned into liquid was thereafter subjected to filtration in order to separate the resulting filtrate from the solid residue known as sludge via centrifugation. moreover, the astaxanthin-enriched oils were separated from sludge using another solvent such as ethanol.

This method's main benefit is gentle operating conditions, that enable the extraction of high-quality protein and oil from krill at the same time. Additionally, the enzymatic hydrolysis process facilitates the recovery useful byproduct like krill peptides. These peptides are gaining attention and recognized as bioactive compounds in functional foods and nutraceuticals, that have positive effects on health and low the risk of disease. Nevertheless, importantly, the longer hydrolysis time restricts the enzymes' potential for large-scale industrial applications, Furthermore, the high coast compared to other extraction method.

Supercritical fluid extraction

Lipid extraction via supercritical extraction has attracted a lot of attention recently for its solvent-free nature, environmental friendliness, and gentle operating conditions. Among supercritical solvents, supercritical carbon dioxide (SC-CO 2 ) is preferred for its chemical inertness, safety, non-toxicity, and moderate critical properties (Friedrich and Pryde,  1984 ). Despite its advantages, SC-CO 2 is not optimal for extracting all krill lipids, particularly phospholipids (PLs) (Yamaguchi et al.,  1986 ). Nevertheless, extracting lipids using SC-CO 2 yields good quality as well as more thermally stable proteins from krill compared to the traditional solvent extraction methods. The addition of ethanol at 5 to 20% in SC-CO2 could enhance PL solubility, thereby improving lipid recovery. However, because ethanol is liquid at ambient temperature, use it not be ideal. on the other hand, for commercial of extraction by supercritical fluid remains limited because of the restricted processing capacity and expensive high-pressure equipment (Bruheim et al.,  2018 ).

Although it works at lower pressure and temperature levels than supercritical extraction, subcritical fluid extraction has many of the same benefits. Liu et al. ( 2015 ) reported that propane as well as butane are primary subcritical fluids used in extraction due to their colorless nature also easy removal from the extracted products. Extracting krill oil using subcritical butane at 30 °C and 0.3—0.8 MPa conditions (Xie et al.,  2017 ) yielded similar oil quantity and quality as hexane but in a faster process with less solvent usage. A study by Sun et al. ( 2018 ) showed that KO extracted with subcritical butane contained great levels of tocopherols also astaxanthin while maintaining lower oxidation level compared to solvent extraction methods. However, similar to supercritical fluid extraction is also not yet cost-effective for routine applications.

Health benefits of Antarctic krill oil

Krill oil contains a number of nutrients and bioactives like C20:5, C22:6, PLs, astaxanthin, vitamin A, also tocopherols (vitamin E), all of which contribute to human health support. Multiple research studies have examined the potential health benefits of Antarctic krill oil, encompassing cardiovascular disease prevention, anti-inflammatory activities, potential anti-cancer properties, effects on diabetes and obesity, neuroprotection, and benefits for women's physiology. These findings are summarized in Table  6 .

Cardiovascular health

CVD is recognized as a significant worldwide health challenge and a leading cause of mortality among adults and the elderly. Research by Harris et al. ( 1988 ), also Rizos et al. ( 2012 ) has indicated that incorporating n-3PUFAs into the diet can help mitigate CVD risks. Fish oil consumption, for having a high n-3 PUFA content, is widely acknowledged for its positive impact on CVD prevention. Studies are presently underway to investigate any possible connection between the use of KO and CVD prevention.

Elevation of triacylglycerols (TAG), total cholesterol (TC), also low-density lipoprotein cholesterol (LDL-C) usually linked to increased risk of CVD disease and are commonly considered as CVD risk indicators. Papakonstantinou et al. ( 2013 ) have demonstrated this association. Several studies (Batetta et al.,  2009 ; Hals et al.,  2017 ; Sun et al.,  2017b ; Zhu et al.,  2008 ) utilizing animal models have assessed the impact KO on CVD risk factor in both tissues and blood. Through eight weeks feeding trial, where mice were supplemented with 1.25, 2.50, or 5.0% KO in their diet, significant reductions in hepatic TAG and TC levels were observed, along with a decrease in serum TAG levels in mice fed with diet contains lots of fat (Tandy et al.,  2009 ). Additionally, in the diet that has a highest dosage of 5%, rise in serum adiponectin level was noted in mice fed with krill oil, supporting its anti-atherogenic properties (Lu et al.,  2008 ).

In another investigation, revealed that KO supplementation (5% in the diet) caused the drop in serum LDL-C after twelve weeks (0.45 mol/L) and TC (reaching 2.50 mol/L) levels in mice fed with diet contains lots of fat, compared to control group (0.65 mol/L, 3.70 mol/L, respectively). Additionally, Zhu et al. ( 2008 ) also Batetta et al. ( 2009 ) showed that KO can lower TAGs, TC, as well as LDL levels in mice with metabolic dysfunction induced by diet contains lots of fat. Similar findings observed in a separate study, using cynomolgus monkeys as a model Hals et al. ( 2017 ), where KO effectively improved various CVD risk factors, including HDL-C, LDL, TC, TAG, apolipoprotein, as well as A1, apolipoprotein B100 in dyslipidemic nonhuman primates with diabetes type 2.

Further support for the preventive effects of KO against CVD has emerged from human clinical trials conducted by, Cicero et al. ( 2016 ), also Rundblad et al. ( 2017 ). Bunea et al. ( 2004 ) examined the relationship between the consumption of KO and level of lipid in blood in 120 hyperlipidemic patients with moderately very high level of TC as well as high TAGs. Patients receiving 1- 3 g/day of KO for 3 months exhibited significantly elevated levels of HDL also decreased levels of glucose in blood, TC, LDL, as well as TAG compared to others were given placebo. In a similar vein, Berge et al. ( 2014 ) noted reduced risk of CVD in 300 adults with extremely high or high fasting serum TAG levels after consuming KO capsules. An about 10 percentage reduction in serum TAG level (relative to the placebo group) was observed in subjects administered krill oil at doses ranging from 0.5 – 4.0 g/day for 3 months. Similar improvements in lipid profiles following KO treatment were observed in overweight subjects as well as healthy individuals with fasting serum TAG levels range from 1.3 to 4.0 mmol/l (Rundblad et al.,  2017 ).

Anti-inflammatory properties

Chronic inflammation is strongly linked to numerous illnesses, including inflammatory bowel disease, asthma, psoriasis, and rheumatoid arthritis (Barnes and Karin,  1997 ). Additionally, systemic inflammation might be contributed to development of exacerbated like atherosclerosis, obesity, cachexia, osteoporosis, as well as anorexia (Gan,  2004 ; Monteiro and Azevedo,  2010 ). Hence, it is crucial to focus on controlling inflammation for overall health improvement. The anti-inflammatory properties of KO have been validated through in vivo as well as vitro studies, as detailed in Table  6 .

In laboratory experiments, it has been demonstrated that krill oil can markedly reduce the tumor necrosis factor α (TNF-α). This reduction is achieved by blocking the attachment of lipopolysaccharide (LPS) to toll-like receptor 4 (TLR4) (Bonaterra et al.,  2017 ) in LPS induced inflammatory human acute monocytic leukemia cell line THP-1, in a manner that depends on the dosage of KO used. At a concentration of 49 μg/ml in the medium, KO totally prevented the irrevocable of LPS to TLR4 and decreased TNF-α production by 75%. Similarly, Batetta et al. ( 2009 ) observed reduced TNF-α release in LPS-treated peritoneal macrophages from obese Zucker rats, given KO supplements in their diets compared to control. The n-3PUFAs induced modifications in the endocannabinoid (EC) system which affects these anti-inflammatories.

The endocannabinoids (ECs) derived from n-3PUFAs have been shown to possess anti-inflammatory properties (Calder,  2009 ). Additionally, KO effectively reduced the mRNA expression levels of pro-inflammatory cytokines such as interleukin-8 (IL-8) and TNF-α in inflammatory cells exposed to LF82 bacteria or cytomix. Treatment with about 250 (mg/L) of krill oil in the medium also inhibited bacterial adhesion/invasion in epithelial cells as well as promoted wound healing. Those findings provide support for the health benefits associated with KO, particularly in the process of mitigating epithelial restitution and enhancing intestinal barrier integrity (Costanzo et al.,  2016 ).

The main focus of in vivo investigations has been on investigating the anti-inflammatory effects of krill oil on conditions such as arthritis or colitis in both humans and mice, as summarized in Table  6 . One study investigated the anti-inflammatory properties of KO using an experimental model of collagen-induced arthritis in mice (DBA/1) (Ierna et al.,  2010 ). They found that administering KO at a daily dosage approximately 0.45 g of C20:5 and C22:6/100 g of diet for 8 weeks (in mice) improved arthritis pathology. This improvement was evidenced by cartilage erosion, synovial membrane thickening, and reductions in cell influx. Moreover, krill oil demonstrated potential in mitigating inflammation in a rat model of colitis. Rats supplemented with KO at amount of 4.9% in diet for one month exhibited preserved colon length and favorable changes in prostaglandin (PG) and interleukin (IL) levels associated with inflammation.

The plasma level of CRP notably increases during inflammatory states and serves as a marker for multiple forms of inflammation (Young et al.,  1991 ). Deutsch ( 2007 ) observed that everyday intake of 300 mg of KO over 2 weeks led to a significant decrease in CRP concentration and relief of arthritic symptoms in patients with chronic inflammatory conditions. Similarly, another study reported that supplementing with 500 mg of KO two time a day for one month resulted in a significant reduction in high-sensitivity CRP levels in plasma, decreased from 2.15 to 0.43 mg/L in overweight subjects (Cicero et al.,  2016 ).

Anti-cancer

Cancer has emerged as a primary cause of death worldwide in both developed and developing nations (Jemal et al.,  2011 ) The worldwide prevalence of cancer is steadily increasing due to factors such as population growth, aging, as well as lifestyle habits, including smoking, also food rich in fat, sugar, and salt (Torre et al.,  2015 ) While chemotherapy as well as radiotherapy known as crucial in cancer treatment to slow down disease progression or inducing apoptosis to halt tumor formation, they are often come with undesirable consequences like diarrhea, myelosuppression, mucositis, as well as dermatitis (Jayathilake et al.,  2016 ). There is an increasing interest in exploring the potential of dietary factors to modulate apoptosis as a means of anticancer therapy (Block et al.,  1992 ). Although there are relatively few experimental studies examining the in vivo anticancer effect of KO in the literature, several in vitro studies have evaluated its impact on the growth of certain cancer cell lines (Xie et al.,  2019 ).

Colon cancer ranks as the second most common cause of cancer related deaths the United States (Jemal et al.,  2005 ). Research indicates that KO exhibits dose- duration of treatment effects on the growth of colon cancer cells, specifically SW480 cells (Jemal et al.,  2005 ). Application of KO at dose of 20 μg/ml in Dulbecco’s modified Eagle’s medium for two days led to a 29.9% suppression of SW480 cell growth (Zhu et al.,  2008 ). Jayathilake et al. ( 2016 ) investigated impact of KO free fatty acid (FFA) extracts on cell proliferation and apoptosis in three human colon adenocarcinoma cell lines. Treatment use 0.12 μL of FFA from KO in 100 μl of DMEM for two inhibited the proliferation of HCT-15 as well as SW-480 cells. Additionally, The FFA extract elicited markedly elevated levels of apoptosis in all three colon cell lines (Su et al.,  2018 ) compared to control. Furthermore, the anticancer activity of FFA that extracted from KO was validated in human osteosarcoma cells, where the inhibitory effect of 1.89 μM of FFA from KO comparable to 0.5 to 1.0 μM of doxorubicin which is commonly used anticancer drug (Su et al.,  2018 ).

Zheng et al. ( 2017 ) conducted research to isolate and identify the trans (E) -configuration of certain FAs detected in KO, such as C20:5 and C22:6. They discovered that these FAs exhibited significantly stronger inhibitory effects on the growth of various cancer cell lines (including K562, PC-3, HL60, MCF-7, and U937) compared to C20:5 and C22:6 from FO. Additionally, astaxanthins as well as tocopherols have shown anti-cancer effects (Constantinou et al.,  2008 ; Rao et al.,  2013 ). It is plausible that the combined effects of the bioactive compounds in KO contribute to its potent anti-cancer capabilities. However, further in vivo studies are necessary to elucidate the underlying molecular mechanisms and validate these anti-cancer effects.

Anti-diabetic and anti-obesity effects

Imbalanced intake of energy could disrupt the endocannabinoid (EC) system, leading to excessive accumulation of visceral fat and reduced release of adiponectin, thereby the chances of type 2 diabetes and obesity. Anandamide (AEA) and 2-arachidonoylglycerol (2-AG) are the primary ECs studied, known for their roles in regulating fat as well as glucose metabolism (Di Marzo,  2008 ). Obese people's tissues have been found to contain elevated quantities of 2-AG and AEA (Batetta et al.,  2009 ; Di Marzo et al.,  2010 ). Diets supplemented with KO have been shown to reduce the concentration of AEA as well as 2-AG in various tissues, including the kidneys, heart, as well as adipose tissues in high fat-fed C57BL/6 mice (Piscitelli et al., 2011 ). Furthermore, krill oil diets decreased body weight gain in obese mice (Sun et al.,  2017b ; Yang et al.,  2016 ) also hyperlipidemic rats (Zhu et al.,  2008 ). The n-3PUFAs diminished the biosynthesis of arachidonic acid and its integration into phospholipids, possibly decreasing the quantity available of biosynthetic precursors for anandamide as well as 2-arachidonoylglycerol (Matias et al.,  2008 ). Effect of the anti-obesity associated with KO are likely due to its high content of n-3 PUFAs. Maki et al. ( 2009 ) also, observed that one month of KO supplementation at amount of 2 g/day led to increased the plasma level of C20:5 and C22:6 in obese as well as overweight individuals.

Insulin resistance caused by fat is a prevalent issue. Ivanova et al. ( 2015 ) demonstrated that consuming a diet supplemented with KO, has about 600 mg of n-3PUFAs daily for one month, resulted in reduced the fasting blood glucose levels also improved the glucose tolerance in obese rabbits (New Zealand with rabbits). Similarly, healthy subjects experienced a decrease in fasting blood glucose after consuming about 4 g per day of KO for two months, indicating its potential as an anti-diabetic agent (Rundblad et al.,  2017 ) suggested that n-3PUFAs from KO had improved insulin sensitivity as well as secretion and altered the expression level of key enzymes involved in β-oxidation and lipogenesis in muscles as well as liver (Ivanova et al.,  2015 ).

Neuroprotective effects

Alzheimer’s disease (AD) is a neurological condition that progresses over time, frequently seen in the elderly (Francis et al.,  1999 ). It manifests as a gradual decline in cognitive function, often accompanied by behavioral changes like wandering, aggression as well as depression, significantly affecting both patients and their caregivers' quality of life. Various studies in animal and human models have explored the neuroprotective properties of KO (Table  6 ).

Using the Aversive Light Stimulus Avoidance Test (ALSAT), the Unavoidable ALSAT, as well as the Forced Swimming Test (FST), rats were treated with 1.25 g/100 g of food containing KO for around two months exhibited a positive impact on memory processes and learning. Additionally, rats supplemented with krill oil showed elevated expression levels of mRNA for brain-derived neurotrophic factor (Bdnf), a gene linked to neuronal growth also differentiation in the hippocampus. These results are consistent with those of Tome-Carneiro ( 2018 ). Moreover, Cheong et al. ( 2017 ) noted a correlation between KO consumption and alterations in the proteome of aged mice's brain tissues. They observed that giving elderly mice KO at doses ranging from 150 to 600 mg/kg daily for seven weeks dramatically changed the expression levels of 28 different proteins in their brain regions. Notably, the group receiving KO showed a significant increase in the expression levels of Celsr3 as well as Ppp1r1b mRNA, that linked to working memory, brain development, and learning acquisition. KO was found to enhance oxidative stress biomarkers in serum like malondialdehyde (MDA) as well as superoxide dismutase (SOD). Furthermore, Li et al. ( 2018 ) demonstrated that KO has a beneficial impact on Alzheimer's disease in animal model using senescence-accelerated prone mouse strain 8 mice, providing evidence for its preventive properties. Over three months, using supplement have 1% of KO in the diet effectively improved the memory capabilities and learning while alleviating nervousness in SAMP8 old mice, as determined through the open field test as well as Morris water. The accumulation of β-amyloid (Aβ) is implicated in cognitive decline as well as AD pathology. Moreover, KO mitigated the accumulation of Aβ in the hippocampus, along with reducing oxidative stress in the brain.

The decline in estrogen levels among aging women may heighten risk of AD (Janicki and schupf,  2010 ). Research conducted on ovariectomized rats have revealed significantly reduced levels of serotonin, insulin growth factor, estrogen, and dopamine, alongside alterations in the gene expression of amyloid precursor protein, glycogen synthase-3beta, Bdnf, as well as selective AD indicator-1, all of which are associated with AD in rats. Mansour et al. ( 2017 ) observed that supplementation with KO at 200 mg/kg per day for 8 weeks resulted in the normalization of all these parameters in ovariectomized rats, suggesting the effects of KO in inhibition of AD development and neurodegeneration in old women.

Human studies have also provided evidence supporting the favorable impacts of KO on cognitive function. Oxyhemoglobin, which is mostly linked to cerebral blood flow, acts as a measure of regional brain function activation during cognitive tasks (Hibino et al.,  2013 ). The P300 event-related potential is a cognitive component utilized to objectively evaluate neuroelectrical activity-linked cognitive behaviors as well as activities (Alvarenga et al.,  2005 ; Hansenne,  2000 ). Konagai et al. ( 2013 ) assessed the effects of dietary KO on cognitive function in more than 45 healthy elderly males during calculation and memory tasks by monitoring oxyhemoglobin variations as well as P300 event-related potential components in the cerebral cortex. Following the administration of KO about 1.98 g/day for three months, subjects exhibited important alterations in oxyhemoglobin concentration during working memory tasks as well as reduced differential value of P300 latency during calculation tasks compared to the control. These findings demonstrate the positive effects of KO in enhancing cognitive function among the elderly.

Women’s physiology

Premenstrual syndrome (PMS) is a cyclic disorder commonly experienced by young as well as middle aged women during the luteal phase of menstruation, characterized by psychological, emotional, also behavioral symptoms (Dickerson et al.,  2003 ; Stevinson and Ernst,  2001 ). While the exact cause of PMS is unclear, around 75% of women encounter some PMS symptoms (Barnhart et al.,  1995 ) during their reproductive years. Various dietary supplements, such as multivitamin/mineral supplements, vitamins (A, E, and B 6 ), and minerals (magnesium and calcium) have been suggested for alleviating certain PMS symptoms (Bendich,  2000 ; Dickerson et al.,  2003 ). Additionally, it has been noted thatn-3 PUFAs (Sohrabi et al.,  2013 ) may help reduce both psychiatric as well as somatic of PMS.

Krill oil as source of n-3 PUFAs as well as vitamins (E and A), has demonstrated beneficial effects in managing both emotional and physical symptoms associated with PMS. Individuals who took KO soft gels for three menstrual cycles reported decreased usage of pain relievers for menstrual pain as well as lower scores on the self assessment questionnaire for PMS, based on the American College of Obstetricians and Gynecologists (ACOG) diagnostic criteria for PMS, which ranged from zero to no symptoms to ten for unbearable. Moreover, KO exhibited greater efficacy in managing PMS and dysmenorrhea compared to FO (Sampalis et al.,  2003 ). This superior performance is attributed to the unique profile of krill oil which includes a combination of phospholipids, n-3 PUFAs as well as antioxidative substances. The n-3 PUFAs linked with PLs in KO are believed to offer higher bioavailability than TAGs in FO, thus potentially playing a more active role in regulating emotional symptoms (Sampalis et al.,  2003 ; Schuchardt et al.,  2011 ). Nevertheless, additional inquiries are required to elucidate the underlying mechanisms involved.

Postmenopausal women commonly encounter cerebrovascular dysfunction as a result of estrogen deficiency (Serock et al.,  2008 ). Key regulatory components like KCa channels, KATP channels, as well as Na + /Ca 2+ exchanger 1 (NCX1) are crucial in maintaining cerebral blood flow autoregulation; however, they are susceptible to disruption in cases of ovarian dysfunction. In a study involving ovariectomized rats, the administration of KO (providing 182 mg EPA + 118 mg DHA sourced from KO) for a duration of 2 weeks was found to beneficially regulate the expression of NCX1 mRNA, KCa channels, as well as KATP channels in the basilar artery, leading to an enhancement in cerebral blood circulation. The results indicate that KO may serve as a beneficial supplement for women who gone through menopause (Sakai et al.,  2014 ).

Effect on depression

The impact of KO supplementation on cognitive function as well as depression like behaviors was assessed through both preclinical and clinical studies. One of the initial investigations in this area involved a one and half month trial conducted on rats which received either krill oil at a dosage of 0.2 g/rat per day, imipramine at 20 mg/kg per day (utilized as a reference drug for antidepressant effects), or a placebo. Following the treatment period, cognitive abilities were evaluated using the ALSAT, while the potential antidepressant effects were assessed through FST and the Unavoidable Aversive Light Stimulus Test (UALST). The rats treated with krill oil demonstrated a notable ability to differentiate between active as well as inactive levers in the ALSAT test from the initial day of training. Furthermore, rats receiving KO and imipramine showed high improvements in behavioral aspects, including reduced level in the UALST test from the third day onwards as well as decreased immobility time in the FST test. Moreover, study examined the expression of Bdnf (Zadeh-Ardabili et al.,  2019 ), which was observed to be elevated in the hippocampus of rats treated with KO.

Zadeh-Ardabili et al. ( 2019 ) conducted a study involving mice subjected to treatments with FO, KO, vitamin B12, imipramine, or over two weeks, beginning after seven days of exposure to the Chronic Unpredictable Stress (CUS) paradigm overnight procedure. During the CUS procedures, mice were exposed to stress overnight using 10W LED light at a frequency of 15 Hz for 12 h over 21 days. The potential therapeutic benefits of the treatments on depression were assessed using the tail suspension test (TST) as well as FST. After the animals were sacrificed, oxidation markers were assessed in the brain tissue. Both KO and FO were high decreased immobility factors as well as increased climbing and swimming time, similar to the effects observed with imipramine. Moreover, both KO and FO reduced levels of MDA and hydrogen peroxide, decreased catalase activity, increased glutathione peroxidase levels, and increased superoxide dismutase activities as well as glutathione levels in hippocampal tissue (Mendoza et al.,  2018 ).

In another pre-clinical study, Mendoza et al. ( 2018 ) examined the impact of krill oil on restraint stress in mice following reduced mobility. The study investigated the effects of KO on the response to restraint stress in mice after experiencing limited mobility. Following 14 days of handling and acclimation, the mice were immobilized for one month, and then behavioral test took place for seven days. Over the course of the one month study, mice orally received either PBS, nicotine derivative cotinine about 5 mg, or combination of cotinine with 140 mg/kg of KO. Although cotinine by itself reduced the loss of memory deficiencies and the behaviors associated with anxiety and depression, cotinine in combination with KO proved to be more beneficial. This underscores the role of KO in mechanisms related to depression (van der Wurff et al.,  2016 ). Subsequently, these authors conducted, over the course of one year employed a double-blind, randomized, and controlled methodology to investigate the impact of KO supplementation on the learning and cognitive function of teenagers, mental well-being, also visual processing. The study involved 260 adolescents aged 13 to 15 years, divided into two cohorts. The first cohort initially received 400 mg/day of C20:5 and C22:6 or a placebo, with dose increased to 800 mg of C20:5 and C22:6 per day after 12 weeks. The second cohort started directly with 800 mg of C20:5 and C22:6 per day (Zheng et al.,  2017 ). The efficacy of these treatments was evaluated through omega-3 index finger-prick blood measurements, using the Centre for Epidemiologic Studies Depression Scale, and the Rosenberg Self-Esteem questionnaire. The authors did not find any evidence supporting the effectiveness of KO in reducing depressive feeling (van der Wurff et al.,  2020 ).

Exercise and bodily performance

Krill oil enhances exercise performance and reduces oxidative stress and inflammation, leading to the initiation of several clinical trials. One of the initial studies involved a small double blind trial conducted on 16 members of Polish National Rowing Team. Participants were divided into 2 groups, first one had received 1 g of KO per day for one and half month, while the other received a placebo. Various parameters were assessed before, after 1 min, and after 24 h of exercise, with the latter representing maximum effort after rowing 2000 m. Exercise increased levels of certain markers including superoxide dismutase, TNF-α, and TBARS, which indicate lipid oxidation. While there were generally no significant differences between the control and KO groups in most parameters, during the recovery period, TBARS levels kept rising in the control, while the KO group displayed notably lower levels of lipid oxidation (Da Boit et al.,  2015 ). Thus, krill oil supplementation helped reducing exercise-induced free radical mediated injuries.

The effects of KO on exercise performance as well as post effort immune function were investigated in small randomized clinical trial involving 37 athletes (Average age of 25.8 years). Participants, in two groups, first one has receiving about 2 g/day of KO for one and half month. On the other hand, second group receiving a placebo. A cycling time test was conducted before and after the supplementation period, during which blood samples were collected for five time test as follow: prior to supplementation, immediately post-exercise, after 1 h, after 3 h, as well as at rest. The results showed that after one and half month of supplementation, athletes who received krill oil showed significant increases in peripheral blood mononuclear cell IL-2 production and natural killer cell cytotoxic activity 3 h post exercise (Georges et al.,  2018 ). Based on these findings, additional research examined the potential of KO to increase body mass. In vitro experiments utilized C2C12 rat myoblasts (skeletal muscle) treated with KO, PC derived from soy, or control. Only KO was capable of stimulating the mTOR pathway. Subsequently, a double-blind, placebo controlled clinical trial was conducted on resistance-trained athletes who received 3 g per day of KO or placebo over two months resistance trained program. The findings showed no significant differences in complete blood count, comprehensive metabolic panel, and urine analysis between the two groups. Nevertheless, KO supplementation resulted in an increase in lean body mass by approximately 2.2% compared to the baseline (Barenie et al.,  2022 ).

In another study, a specific mixed formulation called ESPO-572®, consisting of 75% PCSO-524® (mussel oil) at 600 mg/day and 25% KO for 26 days, was found to alleviate exercise-induced muscle damage as well as cytokine-induced tissue degradation in untrained men who underwent a running test As choline is associated with maintaining muscle function and exercise performance, a reduction in choline level may occur after high resistance or high intensity exercise. To investigate whether krill oil could offer any protective effect against this choline loss, Storsve et al. ( 2020 ) conducted a study involving 47 triathletes randomly divided into 2 groups. First one received 4 g per day of KO called SuperbaBoost™ for 40 days leading up to the race, while the other group received a placebo. Blood test results showed high decrease in choline levels after the race; however, athletes in KO group had higher choline levels compared to those in placebo group. Thus, krill oil supplementation may help mitigating the negative effects on exercise performance, particularly during high-resistance activities by preventing choline decline (Ibrahim et al.,  2016 ).

Comparison with fish oil (bioavailability and bioaccessibility)

When KO and FO supplements compared, it has been found that KO had more pronounced effects in managing cognitive function (Konagai et al.,  2013 ), PMS (Sampalis et al.,  2003 ), and hyperlipidemia (Bunea et al.,  2004 ). Several studies carried out by Ulven et al. ( 2011 ), Rossmeisl et al. ( 2012 ), Ramprasath et al. ( 2013 ), and Laidlaw et al. ( 2014 ) attributed the superior performance of KO to its higher bioavailability of EPA and DHA in phospholipid form. However, these studies failed to administer identical doses of EPA and DHA from KO and FO as well as the existing differences arising from other components present, thus additional work is needed to verify these findings. In this connection, problems noted may be exemplified by the work of Ramprasath et al. ( 2013 ) who found that KO can elevate plasma n-3 PUFA concentrations more effectively than fish oil, but used daily amount of EPA and DHA of 777 mg for KO group and about 664 mg for FO group. In addition, Ulven et al. ( 2011 ) used dosages of EPA and DHA of around 543 mg for KO group with ratio of EPA (1.74) also 864 mg for FO with ratio o EPA/DHA (1.12). As already mentioned, most studies did not account for the minor components present in two olis (Bunea et al.,  2004 ; Konagai et al.,  2013 ; Sampalis et al.,  2003 ). Meanwhile, Köhler et al. ( 2015 ) reported that EPA and DHA in krill meal had a lower bioavailability compared to KO, but similar to FO. Thus, EPA and DHA in phospholipid form alone may not fully explain the superior performance of KO.

The exact mechanism of superior effect of KO compared to FO effect remains elusive. This may arise from the fact that KO contain high concentration of various biological active components such as astaxanthin, tocopherols, and vitamin A, hence the effects may be multifactorial. Therefore, more investigation is required to comprehend the underlying mechanisms and to use better controlled human trials to determine the performance as well as the efficacy of KO and FO after prolonged administration (Tillander et al.,  2014 ).

Applications and future perspectives of KO

Production and use of KO has emerged as a highly appealing within the food industry. Recognized as unique food ingredient, KO shows promise for applications in food, pharmaceutical, and nutraceuticals because of its wide range of health advantages.

Presently, KO products can be easily obtained as supplements in various forms including capsules, soft gels, tablets, and gummies. There are leading global producers of krill oil, with their products enjoying popularity in European and American health markets. Patents and patent applications for krill oil highlight its potential in preventing inflammation, CVD, PMS, cognitive diseases, and enhancing brain function. Some KO products are formulated with additional beneficial additives including various carotenoids, conjugated linoleic acid, and vitamin D, in order to offer enhanced benefits (Rockway,  2006 ; Derohanes et al.,  2018 ). For instance, a combination of KO, vitamin D, and probiotic Lactobacillus reuteri is proposed to alleviate gut inflammation by promoting epithelial restitution and modulation the gut microbiota (Costanzo et al.,  2018 ). Additionally, intranasal administration of KO along with cotinine (a product found in tobacco) exhibits potential in treating depressive symptoms associated with recurrent associative trama memories in patients with posttraumatic stress disorder (Alvarez-Ricartes et al.,  2018 ).

While numerous studies have explored the various functionalities as well as commercial applications of KO only a limited number have detailed molecular mechanisms involved in its diverse activities. For instance, Xie et al. ( 2018 ) observed significant differences in antioxidant activity among krill oils with distinct chemical compositions. Nevertheless, the majority of studies that examined the health advantages in KO (Table  6 ) do not provide detailed information abut the composition of KO which used in these studies. Therefore, future research must clarify the relationship between the content and health benefits of KO in order to facilitate the development of more specialized and a variety of useful KO products with specific health benefits. This would accelerate broader application of KO by taking advantage of its multicomponent feature.

Krill oil derived from Antarctic krill ( Euphausia superba ) garnered growing interest due to its unique benefits. This contribution explored the chemical composition, health benefits, extraction methods as well as some of current application of KO. As noted, KO is abundant in EBA, DHA, tocopherols, vitamin A, astaxanthin, and flavonoids, all of which offer significant health benefits. Notably, a substantial portion of EPA and DHA in KO exists in the phospholipid (PL) form, prompting extensive research comparing its benefits with those of fish oil. Four primary extraction technologies are employed in KO production, namely solvent extraction, solvent-free extraction, enzyme-assisted pretreatment extraction, as well as super/subcritical fluid extraction, each with its own advantages as well as limitations.

At present, commercially available KO products are utilized as supplements. Numerous researches inclusive in vivo as well as in vitro experiments, have indicated that KO offers a range of benefits, including CVD, anti-inflammatory, anti-cancer, anti-obesity and antidiabetic effects, neuroprotective effects, benefits for women’s physiology, and effect on depression. In spite of that, the precise mechanisms underlying these effects require further investigation. It is widely acknowledged that the functionalities of product are closely linked to its chemical composition. Hence, further research is imperative to help understanding of the intricate relationship between the chemical composition and functional properties of KO. This enhanced knowledge would not only enable the refinement of extraction techniques but also empower the creation of a wider range of KO products to address the requirements of specific markets and health objectives.

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Acknowledgements

Fereidoon Shahidi thanks the NSERC of Canada for Grant. Abrehem abad thank Libyan Ministry of Higher Education & Scientific Research provided a scholarship as well as thnk Food and Drug Control Center- Libya.

The author FS thank the Natural Science and Engineering Research Council (NSERC) of Canada for support in the form of a Discovery Grant (RGPIN-2016–04468). The author AA achnowledges a scholarship from the Libyan Ministry of Higher Education and Scientific Research as well as Food and Drug Control Center – Libya.

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Conceptualization, AA and FS; AA wrote the original draft; Preparation of the final version and editing, FS; supervision, FS; Both authors have read and agreed to the publication of the manuscript.

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Shahidi, F., Abad, A. Why is Antactic krill (Euphasia superba) oil on the spotlight? A review. Food Prod Process and Nutr 6 , 88 (2024). https://doi.org/10.1186/s43014-024-00260-6

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Received : 20 May 2024

Accepted : 25 June 2024

Published : 11 September 2024

DOI : https://doi.org/10.1186/s43014-024-00260-6

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