https://orcid.org/0000-0001-7806-6665
Figures
Abstract
Objectives
This systematic review aimed to evaluate the association between smartphone addiction and sleep in medical students. The secondary outcomes included the prevalence of smartphone addiction, duration and purpose of its use, prevalence of poor sleep, duration and quality of sleep.
Methods
The authors searched PubMed, Cochrane Library, Embase, PsycINFO and CINAHL databases, from inception of each database to October 2022. Quantitative studies in the English language on smartphone addiction and sleep in students studying Western Medicine were included. The Rayyan application was used for title-abstract screening, and Joanna Briggs Institute (JBI) critical appraisal checklist to assess the risk of bias. Heterogeneity tests and meta-synthesis of data were performed using the meta-package in R software. Data on the activities used on the smartphone was synthesized qualitatively
Results
A total of 298 abstracts were initially assessed for inclusion eligibility: 16 of them were eventually appraised, covering 9466 medical students comprising 3781 (39.9%) males and 5161 (54.5%) females. Meta-correlation between the Smartphone Addiction Scale Short Version (SAS-SV) and Pittsburgh Sleep Quality Index (PSQI) was 0.30 (95%CI = 0.24–0.36), and 0.27 (95% CI = 0.18–0.36) for SAS-SV and sleep duration. The meta-analytic estimation of smartphone addiction prevalence was 39% (95%CI = 0.30–0.50), and score using SAS-SV was 31.11 (95%CI = 29.50–32.72). The mean duration of smartphone daily used was 4.90 hours (95%CI = 3.72–6.08). The meta-analytic estimation on prevalence of poor sleep was 57% (95%CI = 0.48–0.66), and the meta-mean of PSQI and duration of sleep was 5.95 (95%CI = 4.90–7.00) and 5.62h (95%CI = 4.87–6.36) respectively. Medical students used their smartphones mostly for text messaging, followed by photo-sharing or social networking. Its usage for medical education remains unclear.
Citation: Leow MQH, Chiang J, Chua TJX, Wang S, Tan NC (2023) The relationship between smartphone addiction and sleep among medical students: A systematic review and meta-analysis. PLoS ONE 18(9): e0290724. https://doi.org/10.1371/journal.pone.0290724
Editor: Humayun Kabir, McMaster University, CANADA
Received: January 5, 2023; Accepted: August 14, 2023; Published: September 15, 2023
Copyright: © 2023 Leow et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data has been reposited into Figshare (citation: Leow MQH, Chiang J, Wang S, Chua T, Tan NC. Use of smartphone in Medical Students. 2023. Figshare dataset. https://doi.org/10.6084/m9.figshare.23605977.v1)
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Smartphones have become an inextricable part of daily life with the advancement of mobile technology and digitization, with 6.64 billion (83.32%) of the global population owning at least one device in 2022 [1]. In Singapore, 5.17 million people use smartphones, with its usage achieving 100% in youths aged 15 to 24 years old [2]. From 2019 to 2021, its local population aged 16–64 years spent an average of 3.6 hours on the smartphone internet daily [3].
Smartphone addiction was defined as users underestimating the amount of time spent on their smartphones, or being unable to regulate their use, resulting in negative consequences in daily life [4, 5]. Overuse of smartphones can result in addiction [6]. A multi-national study by conducted by Pew research center to understand the activities performed on a smartphone [7], and found that social activities were most common (i.e. sending text messages taking pictures or videos, and posting on social media), followed by seeking health and education related information.
Smartphone addiction can result in poor sleep quality, reduced sleep duration, and daytime dysfunction [8–11]. An association between smartphone addiction and poor sleep quality in university students and young adults has been observed [12, 13]. In a cross-sectional study which involved 1043 young adults aged 18–30 years in London, 68.7% of them had both smartphone addiction and poor sleep, reflecting a prevalent problem [13].
Poor sleep can impact both mental and physical health. Poor sleep quality increases risk for anxiety, suicide and depression [14–16]. In the longer-term, poor or inadequate sleep may result in musculoskeletal disorders such as cervical disc degeneration or hand joint inflammation [17], or chronic diseases such as hypertension, obesity, type-2 diabetes mellitus, cardiovascular disease, neurodegeneration, and dementia [18]. Compared with students from other degree courses such as law and economics, who also faced high academic demands, medical students had poorer sleep [19]. Poor sleep quality in medical students can also affect academic performance [20].
Smartphone addiction can have profound impacts on medical students’ mental health [21] and academic performance [22–24]. Zhong et al. [21] conducted a systematic review on the impact of smartphone addiction among Asian medical students on their mental health. From the nine studies from 2014 to 2019, 41.9% of Asian medical students had smartphone addiction. Smartphone addiction also positively correlated with poor sleep quality, stress, anxiety, depression, neuroticism, and poor general health. In medical students, inadequate sleep can cause emotional exhaustion, and the latter has a statistically significant relationship with decreased academic performance [22]. Smartphone addiction resulted in poorer academic performance in two studies on university students [23, 24]. Conversely, Boonluksiri [25] reported that even though smartphone addiction was associated with increased napping during class among medical students, their academic performance grading was unaffected.
Both poor sleep and smartphone addiction can result in poor health and academic performance. Thus, this systematic review aimed to evaluate the association between smartphone addiction and sleep in medical students. The secondary outcomes included the prevalence of smartphone addiction, duration of smartphone use, prevalence of poor sleep, duration of sleep, quality of sleep using a sleep survey, and type of activities medical students perform on the smartphone.
Methods
Literature selection
The systematic review was carried out according to the PRISMA statement and reported with the PRISMA checklist [26]. A priori protocol is registered on PROSPERO (CRD42022370847). Quantitative studies which used an observational design (cohort, case-control) or descriptive studies design were included. The review excluded publications covering reports, expert opinion papers, narratives, commentaries. Eligibility criteria included English studies which focused on smartphone addiction and sleep in students training in undergraduate or postgraduate Western Medicine.
Information sources & search strategy
A three-step search strategy was utilized in this review. An initial limited search of MEDLINE and CINAHL was undertaken followed by analysis of the text words contained in the title and abstract, and of the index terms used to describe the articles. A second extensive search using all identified keywords and index terms was then undertaken across all included databases to further identify articles. During the conduct of the search, consideration was given to the diverse terminology used and the spelling of keywords as it might influence the identification of relevant studies. Finally, a hand search of the reference lists and bibliographies of retrieved articles was conducted to search for additional published studies not located through the databases that were used. Only published English papers were included.
A literature search of the databases was conducted from the date of inception of each database to October 2022. The electronic databases included were PubMed, Cochrane Library, Embase, PsycINFO and CINAHL (Cumulative Index to Nursing and Allied Health Literature). A search strategy based on the MeSH headings for these terms were used: ‘medical students’, ‘smartphone usage’, ‘screentime’, ‘smartphone addiction scale’, ‘problematic use of mobile phones’, ‘mobile phone dependence’, ‘app usage tracker’, ‘android application quality time’, ‘sleep quality’, and ‘sleep duration’. A combination of these terms were used for all the databases. The search strategy is in Appendix 1 in S1 File.
Quality assessment and characteristics of included studies
Two reviewers were involved throughout the process of title-abstract screening, full-text screening and appraisal of the selected studies. Blinding between the two reviewers was ensured throughout the process (ML and JC) by conducting the assessments separately. Any disagreements between the two reviewers was resolved through discussion with a third reviewer (NCT).
The Rayyan application was used for title-abstract screening, followed by extraction of the full-text papers for review. Papers which meet the review’s inclusion criteria were appraised. The Joanna Briggs Institute (JBI) critical appraisal checklist (observational design or descriptive design tool for the respective types of studies) was used to assess the risk of bias. Decisions were recorded on Microsoft excel spreadsheet.
All the relevant data from the studies included were entered into an excel spreadsheet (authors, year of publication, country, study design, study setting, participants’ characteristics, number of participants, participants’ demographics, outcome measures, and results of study). For studies with missing data, the study team contacted the authors for unreported data or additional details.
Sleep could be measured by sleep duration, or a sleep tool such as the Pittsburgh Sleep Quality Index (PSQI), Athens Insomnia Scale (AIS), Insomnia Severity Index (ISI), or the PROMIS Sleep Disturbance scale. Smartphone addiction could be measured scales such as the Smartphone Addiction Scale (SAS), Cell-Phone Overuse Scale (COS), Problematic Mobile Phone Use Questionnaire (PMPU-Q) and Android Application Quality Time.
Synthesis methods
The meta package in the R software was used for meta-synthesis of data [27, 28]. The main objective was to evaluate the association between smartphone addiction and sleep in medical students. Correlation coefficient was used for pooling of data. For studies which reported the odds ratio, this value was converted into a correlation coefficient to allow pooling of data [29].
Pooling of data was also performed for the prevalence of smartphone addiction and poor sleep, mean values obtained from the duration of sleep and smartphone use, and the scores from the sleep tool and smartphone addiction tool. If a study provided a median score, this was converted to mean and standard deviation based on a formula by Hozo and colleagues in 2005 [30]. Both the mean and standardised mean difference (SMD) were reported. The random-effect model was used if the heterogeneity (I2 statistic) was higher than 50%; otherwise, the fixed-effect model would be used [31]. Funnel plot asymmetry tests was conducted for outcomes which included more than 10 studies to ensure sufficient power for distinguishing chance from real asymmetry [32, 33].
The types of activities and smartphone applications used by the medical students were presented in percentages.
Results
Identified studies and quality assessment
Three hundred and ninety papers were initially identified through a search of the literature from the database, of which 93 were duplicates. One additional paper was found using hand search. 298 abstracts were reviewed, and 261 papers were removed after evaluation of the abstracts. 37 studies were retrieved for detailed examination. 24 papers were excluded for the following reasons: smartphone addiction was not measured in six papers, smartphone addiction was not measured with a tool in five papers, no sleep outcomes were measured in three papers, sleep outcomes measured did not meet objectives of the review in three papers, and four papers did not provide data to meet the review’s objectives. Sixteen papers were critically appraised using the Joanna Briggs Institute (JBI) critical appraisal checklist for descriptive studies, and all papers were included in this review (Fig 1). The study data was reposited in Figshare [34].
Fig 2 presents the appraisal using JBI checklist. Of the 16 papers reviewed, 13 had low risk of bias, and three had moderate risk for bias. Five studies used a random sampling method, while the other 11 studies used convenience sampling. The inclusion criteria were clearly defined for all the studies. Confounding factors were identified in 15 papers. All studies assessed outcomes using objective criteria, and if comparisons were made, there was sufficient description of the groups. All studies used a cross-section design, with no follow-up. Outcomes were measured in a reliable way for all papers, with appropriate statistical analyses being used.
Profiles of the study population
The studies were published between 2016 and 2022, and conducted in India (n = 6), China (n = 5), Indonesia (n = 1), Iran (n = 1), United States of America (n = 1), Saudi Arabia (n = 1) and Serbia (n = 1). Among the combined population of 9466 medical students, 3781 (39.9%) were males and 5161 (54.5%) were females. Fourteen studies surveyed undergraduate medical students, and graduate medical students were the study populations of the remaining two studies. The profile of the study population is in Table 1.
Sleep.
A sleep tool was used to measure sleep in 15 studies, including the PSQI (n = 11), AIS (n = 2), ISI (n = 1) and PROMIS (n = 1). One study measured sleep duration. The mean sleep duration from two studies is 5.62 (95%CI = 4.87–6.36), with a high heterogeneity of 98% (Fig 3A). The prevalence of poor sleep was 0.57 (95%CI = 0.48–0.66), with high heterogeneity of 97% (Fig 3B). A meta-mean of sleep score using the PSQI by six studies was 5.95 (95%CI = 4.90–7.00), with a high heterogeneity of 97%. When the non-Asian countries were excluded [35, 36], the heterogeneity was still high (97.6%), and random effects of 0.55 (95% CI = 0.43–0.66) (Fig 3C). Results from the SMD also showed high heterogeneity (97%), and random effects was -0.06 (95% CI = -0.35–0.22) (Fig 3D).
a. Mean sleep duration. b. Prevalence of poor sleep. c. Mean sleep score using the Pittsburgh Sleep Quality Index (PSQI). d. Standardised mean difference of sleep score using the Pittsburgh Sleep Quality Index (PSQI).
Smartphone addiction
Smartphone addiction was most commonly measured using the SAS-SV (n = 12). Other scales used included the COS (n = 1), PMPU-Q (n = 1), AAQT (n = 1), and MPAI (n = 1). The duration of smartphone daily used was described in 3 studies, with a meta mean of 4.90 (95%CI = 3.72–6.08) and high heterogeneity of 98% (Fig 4A). The overall prevalence of smartphone addiction in this study was 0.39 (95%CI = 0.30–0.50), with high heterogeneity of 99% (Fig 4B). Funnel plot test showed no publication bias (t = -1.25, p = 0.24). Excluding the non-Asian countries [35, 37], the prevalence was higher at 0.46% but the heterogeneity remained high at 96%. A meta-mean of smartphone addiction score using SAS-SV by five studies was 31.11 (95%CI = 29.50–32.72), with high heterogeneity of 87% (Fig 4C). Results from the SMD also showed high heterogeneity (94%), and random effects was 0.32 (95% CI = -0.12–0.51) (Fig 4D).
a. Duration of smartphone use daily.b Prevalence of smartphone addiction/overuse. c Smartphone addiction score using the Smartphone Addiction Scale Short Version (SAS-SV). d. Standardised mean difference of smartphone addiction score using the Smartphone Addiction Scale Short Version (SAS-SV).
Relationship between smartphone addiction and sleep duration
Chatterjee11 reported a significant relationship between reduced sleep duration and increased duration of smartphone use (r = 0.18, p<0.001). Two studies analyzed the association between Smartphone addiction using SAS-SV and sleep duration. The overall correlation was 0.27 (95% CI = 0.18–0.36), with a low heterogeneity between studies of 0% (Fig 5A).
a. Correlation between Smartphone Addiction Scale Short Version (SAS-SV) and sleep duration. b. Correlation between Smartphone Addiction Scale Short Version (SAS-SV) and Pittsburgh Sleep Quality Index (PSQI).
All studies except those by Nasution [38] and Ibrahim [36] tested the association between smartphone addiction and sleep scores. Six studies used both SAS-SV tool to measure smartphone addiction and PSQI to measure sleep, with either odds ratio or correlation coefficient values provided. The random effect of the correlation was 0.30 (95%CI = 0.24–0.36), with moderate heterogeneity of 54% (Fig 5B).
Activities that students perform on smartphone
Three studies described the type of smartphone applications used (Table 2) [35, 36, 39]. The applications included Whatsapp, Instagram, Line, Snapchat, Viper, Tango, Path, Facebook, and YouTube. Whatsapp was the most common application used in all three studies. Instagram and Facebook were used in two studies. Text messaging was the most common activity performed on the smartphone in all three studies. This was followed by photo sharing or social networking.
Discussion
This systematic review investigated the association between sleep and smartphone addiction in medical students. The studies reported a significant correlation between sleep scores and smartphone addiction scores, sleep duration and smartphone addiction scores, and duration of sleep and smartphone usage. The overall correlation between SAS-SV and PSQI in this study was considerate low (r = 0.3). A previous study reported that the general population with problematic smartphone usage had a 2.19 odds ratio (estimated correlation coefficient = 0.211) of having poor sleep quality compared to those without [16]. Compared to the general population, this study found a higher correlation between smartphone addiction and sleep in medical students.
The prevalence of poor sleep scores in this review was 57%, which meant that over half of the medical students had poor sleep. This finding was almost similar to a previous study which described that 55% of medical students had poor sleep [40]. The mean PSQI in this review was 5.95, which was similar compared with to a systematic review study which reported a PSQI score of 6.1 in medical students [41]. Poor sleep seems pervasive amongst medical students. It can be attributed to stress, high study load, longer study times, studying prior to sleep, irregular work/rest schedules and anxiety about their studies and results [42–44]. Medical students were more anxious about their studies and spent less time on leisure activities compared to students in law and economics [42]. Poor sleep can also be attributed to low levels of sleep knowledge [45–47]. Earlier bedtimes and higher sleep duration during weekdays was associated with excellent academic performance [48]. However, Cvejic et al. [49] alluded that consistently short but unrefreshing sleep was associated with achieving higher academic grades.
The prevalence of smartphone addiction in this systematic review was 39%. This was lower than a previous study solely on Asian medical students which reported a prevalence of 41.9% [21]. When non-Asian countries (USA and Iran) were excluded, the prevalence was 46%, suggesting higher prevalence of smartphone addiction in Asian medical students compared to medical students from other regions. Higher prevalence of smartphone addiction in medical students was noted when compared to those studying other healthcare related subject. For example, the prevalence of smartphone addiction in nursing students was lower at 22% [50].
Qualitative synthesis revealed that text messaging was the most common activity performed on the smartphone, followed by photo sharing or social networking. Whatsapp was the most common text messaging platform. However, results from this review did not reflect the use of smartphone for academic purposes. A study conducted at an Indian medical college reported that 90% of the medical students across all years of studying identified smartphones to be useful for medical education, communication and instant access to information during bedside teaching [51]. A significant proportion of medical students used smartphone applications for online textbooks (70%), medical podcasts (60%), medical calculator (75%), online lecture (50%) and notes taking (45%) [52, 53]. However, smartphone applications such as Whatsapp had no impact on some medical students’ academic performance, but resulted in smartphone addiction and reduced sleep duration [54]. These applications could also cause superficial learning and distraction from textbooks [55].
Strength and weakness of included studies
This systematic review aimed to include global studies. However, the majority of the studies were conducted in India and China, which are Asian countries. There was a lack of representation in studies conducted in the Western region. The studies covered largely undergraduate medical students, with a lack of studies which included graduate medical students to allow for comparison between both groups. The studies were largely descriptive cross-sectional surveys, with a high risk of bias. High heterogeneity was found in the results for both sleep and smartphone addiction, which could limit the reliability of the studies. The PSQI and SAS-SV were most commonly used in the studies. The remaining studies which deployed different sleep scales and smartphone addiction scales limited the pooling of data. None of the studies revealed the length of time which students used their phone to read medical materials or attend lessons remotely.
Implications for policy, practice and research
Smartphone addiction correlated with poor sleep, with a high prevalence for both outcomes. It is important to create awareness on how overuse of smartphone can lead to smartphone addiction and reduced sleep, and to encourage medical students engage in physical activities instead of spending all their free time on the smartphone. Inculcating habits such as setting the phone to silent, do not disturb, or airplane mode during bedtime can help to prevent use and sleep disruptions. A systematic review recommended that exercise interventions, psychological interventions, cognitive behavioral therapy, and education program could be used to address smartphone addiction [56, 57]. For psychological interventions, Lan and colleagues [58] proposed that mindfulness helped to alleviate smartphone addiction by reducing desire for smartphone use and relieve their discomfort when required to leave their smartphone through cognitive reconstruction. Cognitive behavioral therapy may also be able to lower the desire to use a smartphone among people with smartphone addiction by reducing depression, impulsivity, anxiety [59, 60].
Future research on the use of smartphone in medical students can endeavor to uncover the number of hours the students spent studying on their phones. Longitudinal cohort studies can be conducted to determine if there is relationship between students’ academic performance with smartphone addiction and sleep.
Conclusion
The prevalence of poor sleep and smartphone addiction in Medical students was 57% and 39% respectively, with a low correlation of 0.3. The prevalence of smartphone addiction in Asian medical students was 46%. They commonly use their smartphones for text messaging, photo sharing or social networking, with Whatsapp being the most common text messaging platform.
Acknowledgments
The authors appreciate Winnie Seah and Winnie Ong from the SingHealth-Duke NUS Family Medicine Academic Clinical Programme secretariat for their facilitation during the execution of the systematic review. They would also like to thank Rebecca Lavanie David for her guidance in reviewing the search strategy, and Ding Xuan Ng for assisting with producing the figure image.
References
- 1.
How Many Smartphones Are In the World? [Internet]. Statista. 2022. Available from: https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/
- 2.
Müller J. Smartphone usage among residents in Singapore 2016–2020, by age group [Internet]. Statista. 2021. Available from: https://www.statista.com/statistics/1266889/singapore-smartphone-usage-by-age-group/#:~:text=In 2020%2C 100 percent of,groups in Singapore since 2016.
- 3.
Average daily time spent on mobile internet among internet users in Singapore from 3rd quarter 2019 to 3rd quarter 2021 [Internet]. Statista. 2022. Available from: https://www.statista.com/statistics/1327789/singapore-daily-time-spent-on-mobile-internet/
- 4. Billieux J. Problematic Use of the Mobile Phone: A Literature Review and a Pathways Model. Current Psychiatry Reviews. 2012;8(4):299–307.
- 5. Lin YH, Lin YC, Lee YH, et al. Time distortion associated with smartphone addiction: Identifying smartphone addiction via a mobile application (App). Journal of Psychiatric Research. 2015;65:139–45. pmid:25935253
- 6. Kwon M, Lee JY, Won WY, et al. Development and Validation of a Smartphone Addiction Scale (SAS). Public Library of Science ONE. 2013;8(2):e56936. pmid:23468893
- 7.
Silver L, Huang C. Social activities, information seeking on subjects like health and education top the list of mobile activities [Internet]. Pew Research Centre. 2019. Available from: https://www.pewresearch.org/internet/2019/08/22/social-activities-information-seeking-on-subjects-like-health-and-education-top-the-list-of-mobile-activities/
- 8. Nowreen N, Ahad F. Effect of smartphone usage on quality of sleep in medical students. National Journal of Physiology, Pharmacy and Pharmacology. 2018;8(9):1366.
- 9. Randjelović P, Stojiljković N, Radulović N, et al. The association of smartphone usage with subjective sleep quality and daytime sleepiness among medical students. Biological Rhythm Research. 2019;50(6):857–65.
- 10. Kumar V, Chandrasekaran V, Brahadeeswari H. Prevalence of smartphone addiction and its effects on sleep quality: A cross-sectional study among medical students. Industrial Psychiatry Journal. 2019;28(1):82. pmid:31879452
- 11. Chatterjee S, Kar SK. Smartphone Addiction and Quality of Sleep among Indian Medical Students. Psychiatry (New York). 2021;84(2):182–91. pmid:33856961
- 12. Kaya F, Bostanci Daştan N, Durar E. Smart phone usage, sleep quality and depression in university students. International Journal of Social Psychiatry. 2021;67(5):407–14. pmid:32969293
- 13. Sohn SY, Krasnoff L, Rees P, Kalk NJ, Carter B. The Association Between Smartphone Addiction and Sleep: A UK Cross-Sectional Study of Young Adults. Frontiers in Psychiatry. 2021;12:629407. pmid:33737890
- 14. Marcon G, Massaro Carneiro Monteiro G, Ballester P, et al. Who attempts suicide among medical students? Acta Psychiatrica Scandinavica. 2020;141(3):254–64. pmid:31797353
- 15. Barahona-Correa JE, Aristizabal-Mayor JD, Lasalvia P, Ruiz ÁJ, Hidalgo-Martínez P. Sleep disturbances, academic performance, depressive symptoms and substance use among medical students in Bogota, Colombia. Sleep Science. 2018;11(4):260–8. pmid:30746044
- 16. Yang J, Fu X, Liao X, Li Y. Association of problematic smartphone use with poor sleep quality, depression, and anxiety: A systematic review and meta-analysis. Psychiatry Research. 2020;284:112686. pmid:31757638
- 17. Ratan ZA, Parrish AM, Zaman S Bin, Alotaibi MS, Hosseinzadeh H. Smartphone addiction and associated health outcomes in adult populations: A systematic review. International Journal of Environmental Research and Public Health. 2021;18(22):12257.
- 18. Worley SL. The extraordinary importance of sleep: The detrimental effects of inadequate sleep on health and public safety drive an explosion of sleep research. P and T. 2018;43(12):758–63. pmid:30559589
- 19. Azad MC, Fraser K, Rumana N, et al. Sleep disturbances among medical students: A global perspective. Journal of Clinical Sleep Medicine. 2015;11(1):69–74. pmid:25515274
- 20. Seoane HA, Moschetto L, Orliacq F, et al. Sleep disruption in medicine students and its relationship with impaired academic performance: A systematic review and meta-analysis. Sleep Med Rev. 2020;53:101333. pmid:32485517
- 21. Zhong Y, Ma H, Liang YF, et al. Prevalence of smartphone addiction among Asian medical students: A meta-analysis of multinational observational studies. International Journal of Social Psychiatry. 2022;68(6):1171–83. pmid:35422151
- 22. Pagnin D, de Queiroz V. Influence of burnout and sleep difficulties on the quality of life among medical students. SpringerPlus. 2015;4(1):1–7.
- 23. Rozgonjuk D, Saal K, Täht K. Problematic smartphone use, deep and surface approaches to learning, and social media use in lecture. International Journal of Environmental Research and Public Health. 2018;15(1):92.
- 24. Samaha M, Hawi NS. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers in Human Behavior. 2016;57:321–5.
- 25. Boonluksiri P. Effect of Smartphone Overuse on Sleep Problems in Medical Students. The Asia Pacific Scholar. 2018;3(2):25–8.
- 26. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Public Library of Science Medicine. 2009;6(7):e1000097.
- 27.
R Core team. R: A language and environment for statistical computing. R foundation for statistical computer, Vienna, Austria; 2018. URL http://www.R-project.org/.
- 28. Balduzzi S., Rücker G., & Schwarzer G. (2019). How to perform a meta-analysis with R: a practical tutorial. Evidence-based mental health, 22(4), 153–160. pmid:31563865
- 29.
Cleophas TJ, Zwinderman AH. Transforming Odds Ratios into Correlation Coefficients. In: Modern Meta-Analysis. Springer, Cham; 2017.
- 30. Hozo SP, Djulbegovic B, Hozo I. Estimating the mean and variance from the median, range, and the size of a sample. BioMed Central Medical Research Methodology. 2005;5:13. pmid:15840177
- 31. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. British Medical Journal. 2003;327(7414):557–60. pmid:12958120
- 32. Sterne JAC, Sutton AJ, Ioannidis JPA, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. British Medical Journal. 2011;343:d4002. pmid:21784880
- 33. Higgins JPT, Thomas J, Chandler J, et al. Cochrane handbook for systematic reviews of interventions. Cochrane Handbook for Systematic Reviews of Interventions. 2019;1–694.
- 34. Leow MQH, Chiang J, Wang S, Chua T, Tan NC. Use of smartphone in Medical Students. 2023. Figshare dataset. https://doi.org/10.6084/m9.figshare.23605977.v1
- 35. Mohammadbeigi A, Absari R, Valizadeh F, et al. Sleep quality in medical students; the impact of over-use of mobile cell-phone and social networks. Journal of Research in Health Sciences. 2016;16(1):46–50. pmid:27061997
- 36. Ibrahim NK, Baharoon BS, Banjar WF, et al. Mobile phone addiction and its relationship to sleep quality and academic achievement of medical students at King Abdulaziz University, Jeddah, Saudi Arabia. Journal of Research in Health Sciences. 2018;18(3):e00420. pmid:30270211
- 37. Brubaker JR, Beverly EA. Burnout, perceived stress, sleep quality, and smartphone use: A survey of osteopathic medical students. Journal of the American Osteopathic Association. 2020;120(1):6–17. pmid:31904778
- 38. Nasution FA, Amin MM, Effendy E. Factors Associated with Smartphone Addiction during COVID-19 Pandemic Lockdown in Students of Faculty of Medicine in North Sumatera. Open Access Macedonian Journal of Medical Sciences. 2022;10(T7):59–62.
- 39. Dharmadhikari S, Harshe S, Bhide P. Prevalence and correlates of excessive smartphone use among medical students: A cross-sectional study. Indian Journal of Psychological Medicine. 2019;41(6):549–55. pmid:31772442
- 40. Jahrami H, Dewald-Kaufmann J, Faris MAI, et al. Prevalence of sleep problems among medical students: a systematic review and meta-analysis. Journal of Public Health (Germany). 2020;28(5):605–22.
- 41. Rao WW, Li W, Qi H. et al. Sleep quality in medical students: a comprehensive meta-analysis of observational studies. Sleep Breath, 2020;24:1151–65. pmid:32072469
- 42. Preišegolavičiute E, Leskauskas D, Adomaitiene V. Associations of quality of sleep with lifestyle factors and profile of studies among Lithuanian students. Medicina. 2010;46(7):482–9. pmid:20966622
- 43. Feng Z, Diao Y, Ma H, et al. Mobile phone addiction and depression among Chinese medical students: the mediating role of sleep quality and the moderating role of peer relationships. BioMed Central Psychiatry. 2022;22(1):567. pmid:35999533
- 44. Almojali AI, Almalki SA, Alothman AS, Masuadi EM, Alaqeel MK. The prevalence and association of stress with sleep quality among medical students. Journal of Epidemiology and Global Health. 2017;7(3):169–74. pmid:28756825
- 45. Luo M, Feng Y, Li T. Sleep medicine knowledge, attitudes, and practices among medical students in Guangzhou, China. Sleep and Breathing. 2013;17(2):687–93. pmid:22752711
- 46. Shankar P. Knowledge, attitude and behavior towards sleep among fourth semester medical undergraduates in Pokhara, Nepal. Journal of Clinical and Diagnostic Research. 2008;2(5):1074–80.
- 47. Mahendran R, Subramaniam M, Chan YH. Medical students’ behaviour, attitudes and knowledge of sleep medicine. Singapore Medical Journal. 2004;45(12):587–9. pmid:15568121
- 48. Bahammam AS, Alaseem AM, Alzakri AA, Almeneessier AS, Sharif MM. The relationship between sleep and wake habits and academic performance in medical students: A cross-sectional study. BioMed Central Medical Education. 2012;12(1):61. pmid:22853649
- 49. Cvejic E, Huang S, Vollmer-Conna U. Can you snooze your way to an ‘A’? Exploring the complex relationship between sleep, autonomic activity, wellbeing and performance in medical students. Australian and New Zealand Journal of Psychiatry. 2018;52(1):39–46. pmid:28649873
- 50. Osorio-Molina C, Martos-Cabrera MB, Membrive-Jiménez MJ, et al. Smartphone addiction, risk factors and its adverse effects in nursing students: A systematic review and meta-analysis. Nurse Education Today. 2021;98(8):104741. pmid:33485161
- 51. Gavali MY, Khismatrao DS, Gavali Y V., Patil KB. Smartphone, the new learning aid amongst medical students. Journal of Clinical and Diagnostic Research. 2017;11(5):JC05–8.
- 52. Wallace S, Clark M, White J. “It’s on my iPhone”: Attitudes to the use of mobile computing devices in medical education, a mixed-methods study. British Medical Journal Open. 2012;2(4):e001099.
- 53. Latif MZ, Hussain I, Saeed R, Qureshi MA, Maqsood U. Use of smart phones and social media in medical education: Trends, advantages, challenges and barriers. Acta Informatica Medica. 2019;27(2):133–8. pmid:31452573
- 54. Alkhalaf AM, Tekian A, Park YS. The impact of WhatsApp use on academic achievement among Saudi medical students. Medical Teacher. 2018;40(sup1):S10–4. pmid:29909715
- 55. Lee Ventola C. Mobile devices and apps for health care professionals: Uses and benefits. Pharmacy and Therapeutics. 2014;39(5):356–64. pmid:24883008
- 56. Liu S, Xiao T, Yang L, Loprinzi PD. Exercise as an alternative approach for treating smartphone addiction: A systematic review and meta-analysis of random controlled trials. International Journal of Environmental Research and Public Health. 2019;16(20):3912. pmid:31618879
- 57. Esan O, Esan A, Folasire A. Mental health and wellbeing of medical students in Nigeria: a systematic review. International Review of Psychiatry. 2019;31(7–8):661–72. pmid:31646912
- 58. Lan Y, Ding JE, Li W, et al. A pilot study of a group mindfulness-based cognitive-behavioral intervention for smartphone addiction among university students. Journal of Behavioral Addictions. 2018;7(4):1171–6. pmid:30418075
- 59. An JS, Jung HN, Kim JM. Cognitive Behavioral Therapy for College Students with ADHD Tendencies. Korean Journal of Health Psychology. 2016;21(4):699–718.
- 60. Malinauskas R, Malinauskiene V. A meta-analysis of psychological interventions for Internet/smartphone addiction among adolescents. Journal of Behavioral Addictions. 2019;8(4):613–24. pmid:31891316
- 61. Zhang C, Zeng P, Tan J, et al. Relationship of Problematic Smartphone Use, Sleep Quality, and Daytime Fatigue Among Quarantined Medical Students During the COVID-19 Pandemic. Frontiers In Psychiatry. 2021;12:755059. pmid:34858229
- 62. Dhamija S, Shailaja B, Chaudhari B, Chaudhury S, Saldanha D. Prevalence of smartphone addiction and its relation with sleep disturbance and low self- esteem among medical college students. Industrial Psychiatry Journal. 2021;30(Suppl 1):S189–94. pmid:34908688
- 63. Song Y, Sznajder K, Cui C, Yang Y, Li Y. Anxiety and its relationship with sleep disturbance and problematic smartphone use among Chinese medical students during COVID-19 home confinement ‐ A structural equation model analysis. Journal of Affective Disorders. 2022;296:315–21. pmid:34600968
- 64. Chen B, Liu F, Ding S, et al. Gender differences in factors associated with smartphone addiction: A cross-sectional study among medical college students. BioMed Central Psychiatry. 2017;17(1):341. pmid:29017482
- 65. Liu H, Zhou Z, Huang L, et al. Prevalence of smartphone addiction and its effects on subhealth and insomnia: a cross-sectional study among medical students. BioMed Central Psychiatry. 2022;22(1):305. pmid:35488216
- 66. Telgote SA, Ghogare AS, Khadse V, Karwande SG. Smartphone Addiction and its Impact on Insomnia among the Undergraduate Medical Students of a Teaching Hospital of Maharashtra, India- A Cross-sectional Study. Journal of Clinical and Diagnostic Research. 2021;15(12):VC01–5.