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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Bibliographic Details
Published in:Journal of medical Internet research Vol. 24; no. 10; p. e38963
Main Authors: Aboujaoude, Elias, Vera Cruz, Germano, Rochat, Lucien, Courtois, Robert, Ben Brahim, Farah, Khan, Riaz, Khazaal, Yasser
Format: Journal Article
Language:English
Published: Toronto Journal of Medical Internet Research 20.10.2022
Gunther Eysenbach MD MPH, Associate Professor
JMIR Publications
Subjects:
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
United States > US
Crowdsourcing
telemedicine
smartphone addiction
Humans
Internet Addiction Disorder
Male
internet gaming disorder
internet addiction
smartphone tools
Machine Learning
telepsychiatry
mobile phone
digital mental health interventions
Adult
Female
Surveys and Questionnaires
Smartphone
Child
social media
ISSN:1438-8871, 1439-4456, 1438-8871
Online Access:Get full text
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Summary: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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ISSN:1438-8871
1439-4456
1438-8871
DOI:10.2196/38963