Assessment of the Popularity and Perceived Effectiveness of Smartphone Tools That Track and Limit Smartphone Use: Survey Study and Machine Learning Analysis
We aimed to assess the popularity and perceived effectiveness of smartphone tools that track and limit smartphone use. We also aimed to explore how a set of variables related to mental health, smartphone use, and smartphone addiction may influence the use of these tools. First, we conducted a web-ba...
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| Vydané v: | Journal of medical Internet research Ročník 24; číslo 10; s. e38963 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Toronto
Journal of Medical Internet Research
20.10.2022
Gunther Eysenbach MD MPH, Associate Professor JMIR Publications |
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| ISSN: | 1438-8871, 1439-4456, 1438-8871 |
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| Abstract | We aimed to assess the popularity and perceived effectiveness of smartphone tools that track and limit smartphone use. We also aimed to explore how a set of variables related to mental health, smartphone use, and smartphone addiction may influence the use of these tools. First, we conducted a web-based survey in a representative sample of 1989 US-based adults using the crowdsourcing platform Prolific. Second, we used machine learning and other statistical tools to identify latent user classes; the association between latent class membership and demographic variables; and any predictors of latent class membership from covariates such as daily average smartphone use, social problems from smartphone use, smartphone addiction, and other psychiatric conditions. Smartphone tools that monitor and control smartphone use were popular among participants, including parents targeting their children; for example, over two-thirds of the participants used sleep-related tools. Among those who tried a tool, the highest rate of perceived effectiveness was 33.1% (58/175). Participants who experienced problematic smartphone use were more likely to be younger and more likely to be female. Finally, 3 latent user classes were uncovered: nonusers, effective users, and ineffective users. Android operating system users were more likely to be nonusers, whereas younger adults and females were more likely to be effective users. The presence of psychiatric symptoms did not discourage smartphone tool use. If proven effective, tools that monitor and control smartphone use are likely to be broadly embraced. Our results portend well for the acceptability of mobile interventions in the treatment of smartphone-related psychopathologies and, potentially, non–smartphone-related psychopathologies. Better tools, targeted marketing, and inclusive design, as well as formal efficacy trials, are required to realize their potential. |
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| AbstractList | Background Problematic smartphone use, like problematic internet use, is a condition for which treatment is being sought on the web. In the absence of established treatments, smartphone-provided tools that monitor or control smartphone use have become increasingly popular, and their dissemination has largely occurred without oversight from the mental health field. Objective We aimed to assess the popularity and perceived effectiveness of smartphone tools that track and limit smartphone use. We also aimed to explore how a set of variables related to mental health, smartphone use, and smartphone addiction may influence the use of these tools. Methods First, we conducted a web-based survey in a representative sample of 1989 US-based adults using the crowdsourcing platform Prolific. Second, we used machine learning and other statistical tools to identify latent user classes; the association between latent class membership and demographic variables; and any predictors of latent class membership from covariates such as daily average smartphone use, social problems from smartphone use, smartphone addiction, and other psychiatric conditions. Results Smartphone tools that monitor and control smartphone use were popular among participants, including parents targeting their children; for example, over two-thirds of the participants used sleep-related tools. Among those who tried a tool, the highest rate of perceived effectiveness was 33.1% (58/175). Participants who experienced problematic smartphone use were more likely to be younger and more likely to be female. Finally, 3 latent user classes were uncovered: nonusers, effective users, and ineffective users. Android operating system users were more likely to be nonusers, whereas younger adults and females were more likely to be effective users. The presence of psychiatric symptoms did not discourage smartphone tool use. Conclusions If proven effective, tools that monitor and control smartphone use are likely to be broadly embraced. Our results portend well for the acceptability of mobile interventions in the treatment of smartphone-related psychopathologies and, potentially, non–smartphone-related psychopathologies. Better tools, targeted marketing, and inclusive design, as well as formal efficacy trials, are required to realize their potential. We aimed to assess the popularity and perceived effectiveness of smartphone tools that track and limit smartphone use. We also aimed to explore how a set of variables related to mental health, smartphone use, and smartphone addiction may influence the use of these tools. First, we conducted a web-based survey in a representative sample of 1989 US-based adults using the crowdsourcing platform Prolific. Second, we used machine learning and other statistical tools to identify latent user classes; the association between latent class membership and demographic variables; and any predictors of latent class membership from covariates such as daily average smartphone use, social problems from smartphone use, smartphone addiction, and other psychiatric conditions. Smartphone tools that monitor and control smartphone use were popular among participants, including parents targeting their children; for example, over two-thirds of the participants used sleep-related tools. Among those who tried a tool, the highest rate of perceived effectiveness was 33.1% (58/175). Participants who experienced problematic smartphone use were more likely to be younger and more likely to be female. Finally, 3 latent user classes were uncovered: nonusers, effective users, and ineffective users. Android operating system users were more likely to be nonusers, whereas younger adults and females were more likely to be effective users. The presence of psychiatric symptoms did not discourage smartphone tool use. If proven effective, tools that monitor and control smartphone use are likely to be broadly embraced. Our results portend well for the acceptability of mobile interventions in the treatment of smartphone-related psychopathologies and, potentially, non–smartphone-related psychopathologies. Better tools, targeted marketing, and inclusive design, as well as formal efficacy trials, are required to realize their potential. Problematic smartphone use, like problematic internet use, is a condition for which treatment is being sought on the web. In the absence of established treatments, smartphone-provided tools that monitor or control smartphone use have become increasingly popular, and their dissemination has largely occurred without oversight from the mental health field.BACKGROUNDProblematic smartphone use, like problematic internet use, is a condition for which treatment is being sought on the web. In the absence of established treatments, smartphone-provided tools that monitor or control smartphone use have become increasingly popular, and their dissemination has largely occurred without oversight from the mental health field.We aimed to assess the popularity and perceived effectiveness of smartphone tools that track and limit smartphone use. We also aimed to explore how a set of variables related to mental health, smartphone use, and smartphone addiction may influence the use of these tools.OBJECTIVEWe aimed to assess the popularity and perceived effectiveness of smartphone tools that track and limit smartphone use. We also aimed to explore how a set of variables related to mental health, smartphone use, and smartphone addiction may influence the use of these tools.First, we conducted a web-based survey in a representative sample of 1989 US-based adults using the crowdsourcing platform Prolific. Second, we used machine learning and other statistical tools to identify latent user classes; the association between latent class membership and demographic variables; and any predictors of latent class membership from covariates such as daily average smartphone use, social problems from smartphone use, smartphone addiction, and other psychiatric conditions.METHODSFirst, we conducted a web-based survey in a representative sample of 1989 US-based adults using the crowdsourcing platform Prolific. Second, we used machine learning and other statistical tools to identify latent user classes; the association between latent class membership and demographic variables; and any predictors of latent class membership from covariates such as daily average smartphone use, social problems from smartphone use, smartphone addiction, and other psychiatric conditions.Smartphone tools that monitor and control smartphone use were popular among participants, including parents targeting their children; for example, over two-thirds of the participants used sleep-related tools. Among those who tried a tool, the highest rate of perceived effectiveness was 33.1% (58/175). Participants who experienced problematic smartphone use were more likely to be younger and more likely to be female. Finally, 3 latent user classes were uncovered: nonusers, effective users, and ineffective users. Android operating system users were more likely to be nonusers, whereas younger adults and females were more likely to be effective users. The presence of psychiatric symptoms did not discourage smartphone tool use.RESULTSSmartphone tools that monitor and control smartphone use were popular among participants, including parents targeting their children; for example, over two-thirds of the participants used sleep-related tools. Among those who tried a tool, the highest rate of perceived effectiveness was 33.1% (58/175). Participants who experienced problematic smartphone use were more likely to be younger and more likely to be female. Finally, 3 latent user classes were uncovered: nonusers, effective users, and ineffective users. Android operating system users were more likely to be nonusers, whereas younger adults and females were more likely to be effective users. The presence of psychiatric symptoms did not discourage smartphone tool use.If proven effective, tools that monitor and control smartphone use are likely to be broadly embraced. Our results portend well for the acceptability of mobile interventions in the treatment of smartphone-related psychopathologies and, potentially, non-smartphone-related psychopathologies. Better tools, targeted marketing, and inclusive design, as well as formal efficacy trials, are required to realize their potential.CONCLUSIONSIf proven effective, tools that monitor and control smartphone use are likely to be broadly embraced. Our results portend well for the acceptability of mobile interventions in the treatment of smartphone-related psychopathologies and, potentially, non-smartphone-related psychopathologies. Better tools, targeted marketing, and inclusive design, as well as formal efficacy trials, are required to realize their potential. BackgroundProblematic smartphone use, like problematic internet use, is a condition for which treatment is being sought on the web. In the absence of established treatments, smartphone-provided tools that monitor or control smartphone use have become increasingly popular, and their dissemination has largely occurred without oversight from the mental health field. ObjectiveWe aimed to assess the popularity and perceived effectiveness of smartphone tools that track and limit smartphone use. We also aimed to explore how a set of variables related to mental health, smartphone use, and smartphone addiction may influence the use of these tools. MethodsFirst, we conducted a web-based survey in a representative sample of 1989 US-based adults using the crowdsourcing platform Prolific. Second, we used machine learning and other statistical tools to identify latent user classes; the association between latent class membership and demographic variables; and any predictors of latent class membership from covariates such as daily average smartphone use, social problems from smartphone use, smartphone addiction, and other psychiatric conditions. ResultsSmartphone tools that monitor and control smartphone use were popular among participants, including parents targeting their children; for example, over two-thirds of the participants used sleep-related tools. Among those who tried a tool, the highest rate of perceived effectiveness was 33.1% (58/175). Participants who experienced problematic smartphone use were more likely to be younger and more likely to be female. Finally, 3 latent user classes were uncovered: nonusers, effective users, and ineffective users. Android operating system users were more likely to be nonusers, whereas younger adults and females were more likely to be effective users. The presence of psychiatric symptoms did not discourage smartphone tool use. ConclusionsIf proven effective, tools that monitor and control smartphone use are likely to be broadly embraced. Our results portend well for the acceptability of mobile interventions in the treatment of smartphone-related psychopathologies and, potentially, non–smartphone-related psychopathologies. Better tools, targeted marketing, and inclusive design, as well as formal efficacy trials, are required to realize their potential. |
| Audience | Academic |
| Author | Khan, Riaz Vera Cruz, Germano Rochat, Lucien Aboujaoude, Elias Courtois, Robert Ben Brahim, Farah Khazaal, Yasser |
| AuthorAffiliation | 3 Addiction Division, Department of Psychiatry University Hospitals of Geneva Geneva Switzerland 4 Department of Psychology Tours University Tours France 5 Addiction Psychiatry Foederatio Medicorum Helveticorum Geneva Switzerland 1 Stanford University School of Medicine Department of Psychiatry and Behavioral Sciences Stanford, CA United States 6 Lausanne University Lausanne Switzerland 2 Department of Psychology University of Picardie Jules Verne Amiens France |
| AuthorAffiliation_xml | – name: 4 Department of Psychology Tours University Tours France – name: 6 Lausanne University Lausanne Switzerland – name: 2 Department of Psychology University of Picardie Jules Verne Amiens France – name: 5 Addiction Psychiatry Foederatio Medicorum Helveticorum Geneva Switzerland – name: 1 Stanford University School of Medicine Department of Psychiatry and Behavioral Sciences Stanford, CA United States – name: 3 Addiction Division, Department of Psychiatry University Hospitals of Geneva Geneva Switzerland |
| Author_xml | – sequence: 1 givenname: Elias orcidid: 0000-0003-1216-8313 surname: Aboujaoude fullname: Aboujaoude, Elias – sequence: 2 givenname: Germano orcidid: 0000-0002-8297-6933 surname: Vera Cruz fullname: Vera Cruz, Germano – sequence: 3 givenname: Lucien orcidid: 0000-0002-9992-2720 surname: Rochat fullname: Rochat, Lucien – sequence: 4 givenname: Robert orcidid: 0000-0002-3557-5264 surname: Courtois fullname: Courtois, Robert – sequence: 5 givenname: Farah orcidid: 0000-0002-0269-0848 surname: Ben Brahim fullname: Ben Brahim, Farah – sequence: 6 givenname: Riaz orcidid: 0000-0002-4520-7465 surname: Khan fullname: Khan, Riaz – sequence: 7 givenname: Yasser orcidid: 0000-0002-8549-6599 surname: Khazaal fullname: Khazaal, Yasser |
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| Cites_doi | 10.1007/s11469-017-9787-2 10.1080/01488370802678983 10.1002/j.2051-5545.2010.tb00278.x 10.5498/wjp.v6.i1.143 10.1016/j.jpeds.2015.12.009 10.1016/j.psychres.2020.112822 10.1016/j.tele.2011.11.001 10.1016/j.comppsych.2008.08.011 10.4088/jcp.v69n0316 10.1023/A:1010933404324 10.1017/S109285291800127X 10.2114/jpa2.25.377 10.1016/j.jesp.2017.01.006 10.4065/83.2.226 10.9734/jsrr/2015/12245 10.1017/CBO9780511499531 10.1016/j.chb.2010.08.011 10.1177/0095798420930932 |
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| Copyright | COPYRIGHT 2022 Journal of Medical Internet Research 2022. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Elias Aboujaoude, Germano Vera Cruz, Lucien Rochat, Robert Courtois, Farah Ben Brahim, Riaz Khan, Yasser Khazaal. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.10.2022. Distributed under a Creative Commons Attribution 4.0 International License Elias Aboujaoude, Germano Vera Cruz, Lucien Rochat, Robert Courtois, Farah Ben Brahim, Riaz Khan, Yasser Khazaal. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.10.2022. 2022 |
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| Language | English |
| License | Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
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| Snippet | We aimed to assess the popularity and perceived effectiveness of smartphone tools that track and limit smartphone use. We also aimed to explore how a set of... Background Problematic smartphone use, like problematic internet use, is a condition for which treatment is being sought on the web. In the absence of... Background: Problematic smartphone use, like problematic internet use, is a condition for which treatment is being sought on the web. In the absence of... Problematic smartphone use, like problematic internet use, is a condition for which treatment is being sought on the web. In the absence of established... BACKGROUND: Problematic smartphone use, like problematic internet use, is a condition for which treatment is being sought on the web. In the absence of... BackgroundProblematic smartphone use, like problematic internet use, is a condition for which treatment is being sought on the web. In the absence of... |
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| SubjectTerms | Acceptability Addictions Addictive behaviors Adults Age Averages Behavior Crowdsourcing Dissemination Drug use Effectiveness Efficacy Females Health status Human health and pathology Internet Intervention Life Sciences Machine learning Marketing Membership Mental disorders Mental health Operating systems Original Paper Parent-child relations Polls & surveys Popularity Psychiatric symptoms Psychology, Pathological Psychotherapy Questionnaires Sleep Smart phones Smartphones Social problems Sociodemographics Surveys Telemedicine Treatment methods Variables Young adults |
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| Title | Assessment of the Popularity and Perceived Effectiveness of Smartphone Tools That Track and Limit Smartphone Use: Survey Study and Machine Learning Analysis |
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| Volume | 24 |
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