Country,
Year
Summary of outcomes.
Author and Reference | Outcomes | Specific Outcome | Quality |
---|---|---|---|
HYE-JIN KIM [ ] | Accident | Fair | |
Yeon-Jin Kim [ ] | Depression and anxiety | Fair | |
DEOKJONG LEE [ ] | Gray matter abnormalities | Fair | |
JeonHyeong Lee [ ] | Musculoskeletal problems | Fair | |
Kyung Eun Lee [ ] | Anxiety | Fair | |
Yeon-Seop Lee [ ] | Carpal tunnel syndrome | Poor | |
Mi Jung Rho [ ] | Mental health problems were related to problematic smartphone use: (1) self-control (66%), (2) anxiety (25%), (3) depression (7%), and (4) dysfunctional impulsivities (3%) | Psychiatric symptoms | Fair |
Aljohara A. Alhassan [ ] | Factors associated with higher depression scores were high school-educated users (β = −2.03, adj. = 0.01) compared to the university educated group and users with higher smart phone addiction scores (β = 0.194, adj. < 0.001). | Depression | Fair |
Alosaimi, F. D. [ ] | Risk of sedentary behavior | Fair | |
Dalia El-Sayed [ ] | Depression and trait anxiety | Good | |
Jon D. Elhai [ ] | Anxiety | Good | |
Yuanming Hu [ ] | Lower white matter integrity | Fair | |
Jon D. Elhai [ ] | COVID-19 anxiety | Good | |
Linbo Zhuang [ ] | cervical disc degeneration | Good | |
Yasemin P. Demir [ ] | less than 0.05); a strong positive correlation between MPPUS and ESS (r = 0.675, less than 0.05); and a negative correlation between MPPUS and 24-h MQoLQ (r = −0.508, less than 0.05) | Increased headache duration, poor sleep quality | Fair |
KADİR DEMİRCİ [ ] | Depression, anxiety, and daytime dysfunction | Fair | |
Ayse Gokce [ ] | Increased smoking | Fair | |
Betul Ozcan [ ] | Poor sleep quality | Good | |
S HariPriya [ ] | Poor sleep quality, less physical activity | Good | |
Hsien-Yuan Lane [ ] | Psychological distress, poor sleep quality | Good | |
Anna Maria [ ] | Social anxiety | Fair | |
Jon D. Elhai [ ] | Worry and anger | Good | |
Matteo Megna [ ] | Psoriatic arthritis | Fair | |
Arunrat TangmunkongvorakulI [ ] | < 0.001) | Psychological well-being | Fair |
Zaheer Hussain [ ] | Anxiety | Good | |
MILES RICHARDSON [ ] | Connectedness with nature and anxiety | Fair | |
Asem A. Alageel [ ] | Insomnia, depression, adult ADHD | Fair |
3.2.1. mental health.
As outlined in Table 2 , mental health was associated with SA in fourteen studies [ 22 , 25 , 27 , 28 , 30 , 31 , 33 , 36 , 40 , 41 , 42 , 45 , 46 , 47 ]. Depression and anxiety were the most common mental health conditions associated with SA [ 22 , 25 , 28 , 30 , 31 , 33 , 36 , 41 , 45 , 47 ]. Several depression measures were used; however, the Beck Depression Inventory was the most common measure used [ 28 , 30 , 36 , 40 ]. Alhassan et al. (2018) revealed that less-educated people and young adult users of the smartphone were at high risk of depression. Another study [ 28 ] found that the groups who were classified as smartphone-addicted had an increased risk of depression (relative risk 1.337; p < 0.001) and anxiety (relative risk 1.402; p < 0.001) [ 28 ]. Miles Richardson et al. (2018) found that problematic smartphone use (PSU) was positively related to anxiety [ 46 ].
Social anxiety was also associated with SA [ 41 ]. For instance, a study conducted in China during COVID-19 reported that COVID-19 anxiety was associated with the severity of problematic smartphone use [ 33 ].
Interestingly, female participants were more susceptible to SA [ 36 ] and showed significantly higher dependence on smartphones than men [ 25 ]. Further, a study conducted among university students in Thailand demonstrated that not only were female students more likely to be smartphone addicted, but smartphone addiction among female participants was likely to be negatively associated with psychological well-being [ 44 ].
Musculoskeletal problems.
The effect of SA on the musculoskeletal system was identified in four studies [ 24 , 26 , 34 , 43 ] ( Table 2 ). Among those studies, two studies reported cervical problems [ 24 , 34 ], one study demonstrated nerve thickness [ 26 ], and one study showed psoriatic arthritis [ 43 ]. Lee et al. (2014) compared cervical spine repositioning errors in different smartphone addiction groups and revealed that there were significant differences between non-addicted, moderately addicted, and severely addicted groups; the severe smartphone addict group showed the largest changes in posture, the cervical repositioning errors of flexion (3.2 ± 0.8), extension (4.9 ± 1.1), right lateral flexion (3.9 ± 1.0), and left lateral flexion (4.1 ± 0.7). [ 24 ]. A study conducted among 2438 young patients suffering from chronic neck pain found that cervical disc degeneration was more likely to be associated with SA [ 34 ]. Another study conducted among university students revealed that excess smartphone use can cause nerve injury [ 26 ]. Megna et al. (2018) found that SA was linked to higher signs of inflammation in the musculoskeletal structures of hand joints.
Five studies showed an association between smartphone addiction and sleep quality [ 29 , 35 , 38 , 39 , 40 ]. The Pittsburgh Sleep Quality Index (PSQI) was used in all five studies ( Table 1 ). A study conducted by Fahad et al. (2016) among 2367 university students reported 43% of the participants had decreased their sleeping hours due to SA, and 30% of the participants had an unhealthy lifestyle including weight gain, reduced exercise, and the consumption of more fast food when diagnosed with SA [ 29 ]. Another study conducted among migraine patients reported that SA can increase headache duration and decrease sleep quality [ 35 ].
One study conducted by Hye-Jin Kim et al. (2017) revealed that SA is associated with different types of accidents, such as traffic accidents; falls/slips; bumps/collisions; being trapped in the subway, impalement, cuts, and exit wounds; and burns or electric shocks [ 21 ]. The study found that self-reported experience of accidents was significantly associated with SA [ 21 ].
Two studies reported the neurological effect of SA [ 23 , 32 ]; one study found alterations in white matter integrity [ 32 ] and another study reported smaller grey matter volume [ 23 ]. Hu et al. (2017) used a high-resolution magnetic resonance imaging technique to identify white matter integrity in young adults with SA and found that smartphone-addicted participants had significantly lower white matter integrity [ 32 ]. Lee et al. (2019) found that smartphone-addicted participants had significantly smaller grey matter volume (GMV) in the right lateral orbitofrontal cortex (OFC) [ 23 ].
In recent years, several articles have examined the role of smartphone addiction and associated health outcomes among the adult population, however, substantial gaps still remain. To the best of our knowledge, no previous systematic review has been conducted to summarize these findings among this cohort. Our review is the first systematic review that utilizes empirical evidence from the last decades that demonstrates the relationship between smartphone addiction and health outcomes among adults. Interestingly, studies conducted in different parts of the world showed similar effects on health outcomes as a result of smartphone addiction. Hence, the consistency across the studies strengthens the study findings, emphasizing the association between SA and health outcomes.
Our findings suggest that depression and anxiety are significantly linked with smartphone addiction. One national USA survey found that 46% of smartphone owners believed they could not live without their phones [ 48 ]. Overuse patterns of smartphones involves a tendency to check notifications all the time, and such behavior patterns can induce “reassurance seeking” which broadly includes symptoms such as depression and anxiety [ 49 ]. This “reassurance seeking” pathway corresponds to those individuals whose smartphone use is driven by the necessity to maintain relationships and obtain reassurance from others. Bilieux and colleagues explained this reassurance-seeking behavior with the theoretical model of “problematic mobile phone use” [ 50 ]. In addition, this checking behavior is related to the next pathway, the “fear of missing out” (FOMO). One study found that FOMO mediated relations between both depression and anxiety severity with SA [ 51 ].
From our results, it is evident that musculoskeletal pain and insomnia are the two most common physical problems related to SA. Fingers, cervical, back, and shoulder problems are most commonly linked to excessive smartphone usage. Prolonged use of smartphones can cause defective postures such as forwarding head posture, which can produce injuries to the cervical spine and cause cervical pain [ 52 ]. Numerous studies found De Quervain tenosynovitis (characterized by pain in the wrist over the radio styloid process—the thumb side of wrist) was associated with different electronic devices like gaming controllers, tablets, and smartphones [ 53 , 54 ]. Texting and chatting through smartphones have been considered a risk factor for De Quervain tenosynovitis [ 55 ].
Poor sleep quality and difficulty in falling asleep or maintaining sleep has been identified as one of the negative consequences of SA, which is similar to our results [ 56 , 57 ]. Moreover, in line with our finding, another systematic review revealed that SA is related to poorer sleep quality [ 58 ]. One study found that 75% of the young adults (age < 30 years) take their phones to bed, which may increase the likelihood of poor sleep quality [ 59 ]. Smartphone addicts are unsuccessful at controlling their smartphone use, even in bed. Again, fear of missing out could be the reason of taking phones in the beds as they do not want to miss any notification [ 60 , 61 ]. In addition, blue light emitted by smartphones can have a negative effect on circadian rhythms, leading to negative sleep consequences, such as going to sleep later than intended and thus reducing overall sleep time [ 62 ].
The neurological effect of SA is not clear yet from this review. However, currently neuroimaging studies play an important role in understanding the complexity of addictive behavior [ 63 ], as they can assess any pathological change in the brain. Two studies in this review reported the negative changes in grey matter and white matter integrity in the brain with the assistance of neuroimaging ( Table 2 ), which is similar to the neuropathy caused by substance abuse [ 64 , 65 ] and Internet addiction [ 66 , 67 ]. However, the modest sample size and the lack of a clinical evaluation are the potential limitations of these studies [ 23 , 32 ].
This review indicates that smartphone addiction shares similar features with substance abuse. A consistent relationship has been demonstrated between SA and physical and mental health symptoms, including depression, anxiety, musculoskeletal problems, and poor sleep. However, smartphones have become a part of daily life, facilitating work, education, or entertainment. Therefore, it is important not only to utilize the advantages of the smartphone but also to reduce the negative consequences. To address SA in a proper way, a validated definition and consistent diagnostic criteria of SA is required. The findings from this research suggest that healthcare providers and policymakers should recognize the problem and take necessary steps in raising community awareness about SA and its physical and mental impact.
This systematic review has several limitations. First, all of the selected studies were cross-sectional ( Table 1 ), therefore drawing conclusions about causal directions of associations is not possible. Secondly, all the papers were excluded if not in the English language; however, SA has received attention in Asian and European countries, and findings may have been published in other languages. This may lead to exclusion of studies conducted in diverse cultures and may bias the results of the review. Thirdly, most of the studies that were qualified to be included in this review were performed in developed countries, which may question the generalizability our findings to developing countries. Finally, most of the outcomes were reported over less than one year of follow-up. No standard scale and cut-off scores were used for the determination of smartphone addiction.
The current review describes the effect of smartphones on health outcomes in the adult population. Although the diagnostic criteria and effect of smartphone addiction are yet to be fully established, this review provides invaluable findings about the health impact of smartphone addiction and has significant implications for policy and decision makers. There is a need for more longitudinal studies to validate and strengthen this review’s findings.
The following are available online at https://www.mdpi.com/article/10.3390/ijerph182212257/s1 , Table S1. Electronic search strategy.
Z.A.R. conceptualized and designed the study, conducted initial searches, assessed the eligibility of the retrieved papers in the titles/abstracts and full text. S.B.Z. and M.S.A. independently reviewed all the retrieved abstracts and selected eligible papers. Z.A.R., A.-M.P., S.B.Z., M.S.A. and H.H. critically assessed the eligible studies and extracted data. Z.A.R. analyzed and interpreted the data and drafted the manuscript. All authors critically reviewed the manuscript. A.-M.P. and H.H. reviewed and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.
This research received no funding.
Informed consent statement, data availability statement, conflicts of interest.
Authors declared no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Home — Essay Samples — Information Science and Technology — Smartphone — The impact of smartphone on daily life
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Introduction, signs and symptoms of smartphone addiction, the impact of smartphone addiction, works cited, impacts in the physical health, impacts in the psychological health.
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