The relationship between smartphone addiction and musculoskeletal pain prevalence among young population: a cross-sectional study

Mustafaoğlu, Rüstem; Yasacı, Zeynal; Zirek, Emrah; Griffiths, Mark D.; Ozdincler, And Arzu Razak

doi:10.3344/kjp.2021.34.1.72

Cited by 94 publications

(78 citation statements)

References 33 publications

(47 reference statements)

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“…Previous research has shown PMPU to be associated with individual health status and to not only cause physical symptoms such as musculoskeletal pain [28] and increase the risk of traffic injuries [29] but also induce mental health problems [30]. Regarding physical and mental health conditions related to PMPU, depressive symptoms have been widely investigated by scholars.…”

Section: Pmpu and Depressive Symptomsmentioning

confidence: 99%

Longitudinal relationships among problematic mobile phone use, bedtime procrastination, sleep quality and depressive symptoms in Chinese college students: a cross-lagged panel analysis

Cui

Yin

et al. 2021

BMC Psychiatry

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Background Cross-sectional and longitudinal studies have found that problematic mobile phone use, bedtime procrastination, sleep quality, and depressive symptoms are strongly associated. However, studies are inconsistent regarding whether problematic mobile phone use predicts depressive symptoms or vice versa, and sleep factors have been infrequently focused on in this regard. In addition, few studies have examined the longitudinal associations and directions of effects between these factors. Therefore, this study aims to explore the longitudinal relationship among problematic mobile phone use, bedtime procrastination, sleep quality, and depressive symptoms in college students. Methods Overall, 1181 college students completed questionnaires on problematic mobile phone use, bedtime procrastination, sleep quality, and depressive symptoms at two time points 12 months apart. A cross-lagged model was used to examine the longitudinal relationship between these factors. Results Cross-lagged analyses showed significant bidirectional relationships of problematic mobile phone use with bedtime procrastination and depressive symptoms. Additionally, there were also significant bidirectional relationships of sleep quality with bedtime procrastination and depressive symptoms. Problematic mobile phone use predicted subsequent sleep quality one-way, and bedtime procrastination predicted subsequent depressive symptoms one-way. Conclusions This study further expands our understanding of the longitudinal and bidirectional relationships among problematic mobile phone use, bedtime procrastination, sleep quality and depressive symptoms and helps school mental health educators design targeted interventions to reduce problematic mobile phone use, sleep problems, and depressive symptoms among college students.

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Section: Pmpu and Depressive Symptomsmentioning

confidence: 99%

Longitudinal relationships among problematic mobile phone use, bedtime procrastination, sleep quality and depressive symptoms in Chinese college students: a cross-lagged panel analysis

Cui

Yin

et al. 2021

BMC Psychiatry

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“…In relation to the rst cell phone acquisition, it was found that 74.1% (n = 343) of the students with a smartphone dependency acquired it for the rst time more than 6 years ago. A similar study in 249 participants found that 42.6% of the students (n=106) presented signi cant results between the time of acquisition of the smartphone and the addiction to the smartphone (p = 0.027) [52].…”

Section: Discussionmentioning

confidence: 78%

Machine-Learning Predictive Models For Dependency On Smartphones Based On Risk Factors

Giraldo-Jiménez

Gaviria-Chavarro

Urrutia-Valdés

et al. 2021

Preprint

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Background: Machine learning techniques allow highly accurate prediction of different tasks by measuring the event probabilities. This research proposes a prediction model for dependency on smartphones based on machine learning techniques. Methods: We performed an analytical observational study with a retrospective case–control approach; the different classification methods used were decision tree, random forest, logistic regression, and support vector machine. The sample demographic included 1228 students from a private university in Cali. The tests were 1) smartphone dependency assessment and 2) the Nordic musculoskeletal symptoms questionnaire. Results: It was found that some of the variables related to smartphone dependency are academic curriculum, school, marital status, socioeconomic status, rules, discussions, and discrimination. Conclusions: The support vector machine model evidences highest prediction precision for smartphone dependency, obtained through the stratified-k-fold cross-validation technique.

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“…Um estudo com populações jovens na Turquia relatou que o vício em smartphones tem uma relação significativa com dores musculoesqueléticas no pescoço, punhos e mãos, parte superior das costas e ombros. 15 Além disso, o móvel ou IGD ocorre com base no tipo de jogos / gênero. 16 O presente estudo, entretanto, mostra que a prevalência de IGD entre estudantes universitários é muito baixa (1.8%), o que é comparável à taxa de prevalência de (3.1%) entre adolescentes de Taiwan.…”

Section: Discussionunclassified

Predictors of Mobile Video Gaming on Musculoskeletal Pain among University Students in Selangor, Malaysia

2021

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INTRODUCTION: Mobile video gaming among university students has increased rapidly, more than before the COVID-19 pandemic. This is very concerning as this could spark various problems, such as musculoskeletal pain and gaming disorders. OBJECTIVES: The present study is to identify the predictors of mobile video gaming on musculoskeletal pain among university students in Selangor, Malaysia. PARTICIPANTS AND METHODS: This study was conducted online using a self-reported online questionnaire via Google Form and sent to university students in Selangor, Malaysia. Participants' gaming addiction was measured using the Ten Item Internet Gaming Disorder Test (IGDT-10) questionnaire, and the prevalence of musculoskeletal pain was assessed by the Modified Nordic Musculoskeletal Questionnaire (MNMQ). The data was analyzed using SPSS version 25. A descriptive and binomial linear regression test was used to predict the variables. The statistical significance was set at p < 0.05, and odds ratios were calculated with confidence intervals of 95%. RESULTS: The prevalence of Internet Gaming Disorder among university students in Selangor, Malaysia is 1.8% (n=3). The neck region (74.2%) was the most commonly reported body region with musculoskeletal pain, followed by the shoulder region (60.7 %), lower back region (55.8 %), and upper back region (50.9 %). The body position was the only predictor of mobile video gaming with musculoskeletal pain (p = 0.002) in the lower back region. CONCLUSION: According to the findings of this study, the prevalence of Internet Gaming Disorder (IGD) among university students was low and not addicted to gaming in the Covid-19 lockdown. We also found that participants who sat while playing mobile video games were more likely to develop low back pain. However, one of the limiting factors could be prolonged sitting in virtual classes during the lockdown, which causes low back pain.

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The relationship between smartphone addiction and musculoskeletal pain prevalence among young population: a cross-sectional study

Cited by 94 publications

References 33 publications

Longitudinal relationships among problematic mobile phone use, bedtime procrastination, sleep quality and depressive symptoms in Chinese college students: a cross-lagged panel analysis

Longitudinal relationships among problematic mobile phone use, bedtime procrastination, sleep quality and depressive symptoms in Chinese college students: a cross-lagged panel analysis

Machine-Learning Predictive Models For Dependency On Smartphones Based On Risk Factors

Predictors of Mobile Video Gaming on Musculoskeletal Pain among University Students in Selangor, Malaysia

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