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      Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence

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          Abstract

          Background

          Over the past decade, smartphone use has become widespread amongst today’s children and young people (CYP) which parallels increases in poor mental health in this group. Simultaneously, media concern abounds about the existence of ‘smartphone addiction’ or problematic smartphone use. There has been much recent research concerning the prevalence of problematic smartphone use is in children and young people who use smartphones, and how this syndrome relates to mental health outcomes, but this has not been synthesized and critically evaluated.

          Aims

          To conduct a systematic review and meta-analysis to examine the prevalence of PSU and quantify the association with mental health harms.

          Methods

          A search strategy using Medical Subject Headings was developed and adapted for eight databases between January 1, 1st 2011 to October 15th 2017. No language restriction was applied. Of 924 studies identified, 41 were included in this review, three of which were cohort studies and 38 were cross sectional studies. The mental health outcomes were self-reported: depression; anxiety; stress; poor sleep quality; and decreased educational attainment, which were synthesized according to an a priori protocol.

          Results

          The studies included 41,871 CYP, and 55% were female. The median prevalence of PSU amongst CYP was 23.3% (14.0–31.2%). PSU was associated with an increased odds of depression (OR = 3.17;95%CI 2.30–4.37; I 2 = 78%); increased anxiety (OR = 3.05 95%CI 2.64–3.53; I 2 = 0%); higher perceived stress (OR = 1.86;95%CI 1.24–2.77; I 2 = 65%); and poorer sleep quality (OR = 2.60; 95%CI; 1.39–4.85, I 2 = 78%).

          Conclusions

          PSU was reported in approximately one in every four CYP and accompanied by an increased odds of poorer mental health. PSU is an evolving public health concern that requires greater study to determine the boundary between helpful and harmful technology use. Policy guidance is needed to outline harm reduction strategies.

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          Most cited references62

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          The age of adolescence

          Adolescence is the phase of life stretching between childhood and adulthood, and its definition has long posed a conundrum. Adolescence encompasses elements of biological growth and major social role transitions, both of which have changed in the past century. Earlier puberty has accelerated the onset of adolescence in nearly all populations, while understanding of continued growth has lifted its endpoint age well into the 20s. In parallel, delayed timing of role transitions, including completion of education, marriage, and parenthood, continue to shift popular perceptions of when adulthood begins. Arguably, the transition period from childhood to adulthood now occupies a greater portion of the life course than ever before at a time when unprecedented social forces, including marketing and digital media, are affecting health and wellbeing across these years. An expanded and more inclusive definition of adolescence is essential for developmentally appropriate framing of laws, social policies, and service systems. Rather than age 10-19 years, a definition of 10-24 years corresponds more closely to adolescent growth and popular understandings of this life phase and would facilitate extended investments across a broader range of settings.
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            The Smartphone Addiction Scale: Development and Validation of a Short Version for Adolescents

            Objective This study was designed to investigate the revised and short version of the smartphone addiction scale and the proof of its validity in adolescents. In addition, it suggested cutting off the values by gender in order to determine smartphone addiction and elaborate the characteristics of smartphone usage in adolescents. Method A set of questionnaires were provided to a total of 540 selected participants from April to May of 2013. The participants consisted of 343 boys and 197 girls, and their average age was 14.5 years old. The content validity was performed on a selection of shortened items, while an internal-consistency test was conducted for the verification of its reliability. The concurrent validity was confirmed using SAS, SAPS and KS-scale. Receiver operating characteristics analysis was conducted to suggest cut-off. Results The 10 final questions were selected using content validity. The internal consistency and concurrent validity of SAS were verified with a Cronbach's alpha of 0.911. The SAS-SV was significantly correlated with the SAS, SAPS and KS-scale. The SAS-SV scores of gender (p<.001) and self-evaluation of smartphone addiction (p<.001) showed significant difference. The ROC analysis results showed an area under a curve (AUC) value of 0.963(0.888–1.000), a cut-off value of 31, sensitivity value of 0.867 and specificity value of 0.893 in boys while an AUC value of 0.947(0.887–1.000), a cut-off value of 33, sensitivity value of 0.875, and a specificity value of 0.886 in girls. Conclusions The SAS-SV showed good reliability and validity for the assessment of smartphone addiction. The smartphone addiction scale short version, which was developed and validated in this study, could be used efficiently for the evaluation of smartphone addiction in community and research areas.
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              Relationship of Smartphone Use Severity with Sleep Quality, Depression, and Anxiety in University Students

              Background and Aims The usage of smartphones has increased rapidly in recent years, and this has brought about addiction. The aim of the current study was to investigate the relationship between smartphone use severity and sleep quality, depression, and anxiety in university students. Methods In total, 319 university students (203 females and 116 males; mean age = 20.5 ± 2.45) were included in the study. Participants were divided into the following three groups: a smartphone non-user group (n = 71, 22.3%), a low smartphone use group (n = 121, 37.9%), and a high smartphone use group (n = 127, 39.8%). All participants were evaluated using the Pittsburgh Sleep Quality Index, Beck Depression Inventory, Beck Anxiety Inventory; moreover, participants other than those in the smartphone non-user group were also assessed with the Smartphone Addiction Scale. Results The findings revealed that the Smartphone Addiction Scale scores of females were significantly higher than those of males. Depression, anxiety, and daytime dysfunction scores were higher in the high smartphone use group than in the low smartphone use group. Positive correlations were found between the Smartphone Addiction Scale scores and depression levels, anxiety levels, and some sleep quality scores. Conclusion The results indicate that depression, anxiety, and sleep quality may be associated with smartphone overuse. Such overuse may lead to depression and/or anxiety, which can in turn result in sleep problems. University students with high depression and anxiety scores should be carefully monitored for smartphone addiction.
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                Author and article information

                Contributors
                seiyon.sohn@kcl.ac.uk
                p.rees@ucl.ac.uk
                bethany.wildridge@kcl.ac.uk
                nicola.kalk@kcl.ac.uk
                44 77177 06161 , ben.carter@kcl.ac.uk
                Journal
                BMC Psychiatry
                BMC Psychiatry
                BMC Psychiatry
                BioMed Central (London )
                1471-244X
                29 November 2019
                29 November 2019
                2019
                : 19
                : 356
                Affiliations
                [1 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Institute of Psychiatry Psychology and Neuroscience, King’s College London, ; London, UK
                [2 ]ISNI 0000000121901201, GRID grid.83440.3b, Institute of Child Health, , University College London, ; London, UK
                [3 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Department of Addictions, , Institute of Psychiatry Psychology and Neuroscience, King’s College London, ; London, UK
                [4 ]ISNI 0000 0000 9439 0839, GRID grid.37640.36, South London and Maudsley NHS Foundation Trust, ; London, UK
                [5 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Department of Biostatistics, and Health Informatics, , Institute of Psychiatry, Psychology and Neuroscience, King’s College London, ; Denmark Hill, De Crespigny Park, London, SE5 8AF UK
                [6 ]ISNI 0000 0004 1936 8868, GRID grid.4563.4, Cochrane Skin Group, School of Medicine, , Nottingham University, Nottingham, ; Nottinghamshire, UK
                Author information
                http://orcid.org/0000-0003-0318-8865
                Article
                2350
                10.1186/s12888-019-2350-x
                6883663
                31779637
                cb1c0edc-6a57-448c-92eb-8323b292e4cb
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 12 April 2019
                : 31 October 2019
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Clinical Psychology & Psychiatry
                problematic smartphone usage,anxiety,depression,sleep,educational attainment

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