This paper is in the following e-collection/theme issue:

Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2111) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (997) mHealth for Data Collection and Research (1196) Problematic Internet Use, Online Gambling and Game Addiction (190) Mobile Health (mhealth) (3464) Machine Learning (2076)

Published on in Vol 24, No 10 (2022): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38963, first published .
Assessment of the Popularity and Perceived Effectiveness of Smartphone Tools That Track and Limit Smartphone Use: Survey Study and Machine Learning Analysis

Assessment of the Popularity and Perceived Effectiveness of Smartphone Tools That Track and Limit Smartphone Use: Survey Study and Machine Learning Analysis

Assessment of the Popularity and Perceived Effectiveness of Smartphone Tools That Track and Limit Smartphone Use: Survey Study and Machine Learning Analysis

Authors of this article:

Elias Aboujaoude1 Author Orcid Image ;   Germano Vera Cruz2 Author Orcid Image ;   Lucien Rochat3 Author Orcid Image ;   Robert Courtois4 Author Orcid Image ;   Farah Ben Brahim4 Author Orcid Image ;   Riaz Khan5 Author Orcid Image ;   Yasser Khazaal6 Author Orcid Image
Article Authors Cited by (15) Tweetations (9) Metrics

Journals

  1. Rahmillah F, Tariq A, King M, Oviedo-Trespalacios O. Evaluating the Effectiveness of Apps Designed to Reduce Mobile Phone Use and Prevent Maladaptive Mobile Phone Use: Multimethod Study. Journal of Medical Internet Research 2023;25:e42541 View
  2. Gao W, Hu Y, Ji J, Liu X. Relationship between depression, smartphone addiction, and sleep among Chinese engineering students during the COVID-19 pandemic. World Journal of Psychiatry 2023;13(6):361 View
  3. Muehlensiepen F, Petit P, Knitza J, Welcker M, Vuillerme N. Prediction of the acceptance of telemedicine among rheumatic patients: a machine learning-powered secondary analysis of German survey data. Rheumatology International 2024;44(3):523 View
  4. Ben Brahim F, Courtois R, Vera Cruz G, Khazaal Y. Predictors of compulsive cyberporn use: A machine learning analysis. Addictive Behaviors Reports 2024;19:100542 View
  5. Nagata J, Paul A, Yen F, Smith-Russack Z, Shao I, Al-shoaibi A, Ganson K, Testa A, Kiss O, He J, Baker F. Associations between media parenting practices and early adolescent screen use. Pediatric Research 2025;97(1):403 View
  6. 毛 心. Application of the Random Forest Model in Predicting Smartphone Addiction among First-Year College Students. Advances in Psychology 2024;14(10):30 View
  7. Khazaal Y, Vera Cruz G. Le bien-être numérique, un enjeu de santé mentale technologique : la place du téléphone intelligent. Santé mentale au Québec 2024;49(2):127 View
  8. Rochat L, Cruz G, Aboujaoude E, Courtois R, Brahim F, Khan R, Khazaal Y. Problematic smartphone use in a representative sample of US adults: Prevalence and predictors. Addictive Behaviors 2025;162:108228 View
  9. Karila L, Scher N, Draghi C, Lichte D, Darmon I, Boudabous H, Lamallem H, Bauduceau O, Bollet M, Toledano A. Understanding Problematic Smartphone and Social Media Use Among Adults in France: Cross-Sectional Survey Study. JMIR Mental Health 2025;12:e63431 View
  10. Andrade L, Viñán-Ludeña M. Mapping research on ICT addiction: a comprehensive review of Internet, smartphone, social media, and gaming addictions. Frontiers in Psychology 2025;16 View
  11. Suh Y, Yoo J. Risk level prediction for problematic internet use: A digital health perspective. Internet Interventions 2025;41:100863 View
  12. Vera Cruz G, Liberacka-Dwojak M, Wiłkość-Dębczyńska M, Aktaş Terzioğlu M, Farchione T, Lecomte T, Ingram S, Khan R, Khazaal Y. Perceived Digital Well-Being Scale in the United States and United Kingdom: Psychometric Validation Study. JMIR Mental Health 2025;12:e78334 View
  13. Peñafiel Mora V, Granda M, Parra O. Technology-based interventions to address internet addictive behaviors: systematic review. Discover Mental Health 2025;5(1) View
  14. Ali A, Hosain M, Siddik M, Hasan M, Habib M, Kabir M, Rahman M, Shanto P, Hasan N, Mahmud A. Classifying Internet Addiction Using Machine Learning Approach: A Study Among Adolescents in Bangladesh. Public Health Challenges 2025;4(4) View

Books/Policy Documents

  1. Graziani P, Romo L. Soigner les Addictions par les TCC. View