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

Engagement with and Adherence to Digital Health Interventions, Law of Attrition (497)

Published on in Vol 21, No 9 (2019): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13617, first published .
Predicting Dropouts From an Electronic Health Platform for Lifestyle Interventions: Analysis of Methods and Predictors

Predicting Dropouts From an Electronic Health Platform for Lifestyle Interventions: Analysis of Methods and Predictors

Predicting Dropouts From an Electronic Health Platform for Lifestyle Interventions: Analysis of Methods and Predictors

Authors of this article:

Daniel Hansen Pedersen1 Author Orcid Image ;   Marjan Mansourvar2 Author Orcid Image ;   Camilla Sortsø1 Author Orcid Image ;   Thomas Schmidt2 Author Orcid Image
Article Authors Cited by (42) Tweetations (8) Metrics

Journals

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  2. Brusniak K, Arndt H, Feisst M, Haßdenteufel K, Matthies L, Deutsch T, Hudalla H, Abele H, Wallwiener M, Wallwiener S. Challenges in Acceptance and Compliance in Digital Health Assessments During Pregnancy: Prospective Cohort Study. JMIR mHealth and uHealth 2020;8(10):e17377 View
  3. GONÇALVES M, BEDENDO A, ANDRADE A, NOTO A. Factors associated with adherence to a web-based alcohol intervention among college students. Estudos de Psicologia (Campinas) 2021;38 View
  4. Annesi J. Exercise Amounts and Short- to Long-Term Weight Loss: Psychological Implications for Behavioral Treatments of Obesity. Research Quarterly for Exercise and Sport 2021;92(4):851 View
  5. Rubin D, Rich Severin , Arena R, Bond S. Leveraging technology to move more and sit less. Progress in Cardiovascular Diseases 2021;64:55 View
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  7. Bremer V, Chow P, Funk B, Thorndike F, Ritterband L. Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach. Journal of Medical Internet Research 2020;22(10):e17738 View
  8. Himle J, Weaver A, Zhang A, Xiang X. Digital Mental Health Interventions for Depression. Cognitive and Behavioral Practice 2022;29(1):50 View
  9. Goh Y, Ow Yong Q, Tam W. Effects of online stigma‐reduction programme for people experiencing mental health conditions: A systematic review and meta‐analysis. International Journal of Mental Health Nursing 2021;30(5):1040 View
  10. Bevens W, Weiland T, Gray K, Neate S, Nag N, Simpson-Yap S, Reece J, Yu M, Jelinek G. The Feasibility of an Online Educational Lifestyle Program for People With Multiple Sclerosis: A Randomised Controlled Trial. SSRN Electronic Journal 2021 View
  11. Ramos L, Blankers M, van Wingen G, de Bruijn T, Pauws S, Goudriaan A. Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning. Frontiers in Psychology 2021;12 View
  12. Moshe I, Terhorst Y, Paganini S, Schlicker S, Pulkki-Råback L, Baumeister H, Sander L, Ebert D. Predictors of Dropout in a Digital Intervention for the Prevention and Treatment of Depression in Patients With Chronic Back Pain: Secondary Analysis of Two Randomized Controlled Trials. Journal of Medical Internet Research 2022;24(8):e38261 View
  13. Butler S, Sculley D, Santos D, Fellas A, Gironès X, Singh-Grewal D, Coda A. Effectiveness of eHealth and mHealth Interventions Supporting Children and Young People Living With Juvenile Idiopathic Arthritis: Systematic Review and Meta-analysis. Journal of Medical Internet Research 2022;24(2):e30457 View
  14. Godziuk K, Prado C, Quintanilha M, Forhan M. Acceptability and preliminary effectiveness of a single-arm 12-week digital behavioral health intervention in patients with knee osteoarthritis. BMC Musculoskeletal Disorders 2023;24(1) View
  15. Kupila S, Venäläinen M, Suojanen L, Rosengård-Bärlund M, Ahola A, Elo L, Pietiläinen K. Weight Loss Trajectories in Healthy Weight Coaching: Cohort Study. JMIR Formative Research 2022;6(3):e26374 View
  16. Bevens W, Weiland T, Gray K, Neate S, Nag N, Simpson-Yap S, Reece J, Yu M, Jelinek G. The Feasibility of a Web-Based Educational Lifestyle Program for People With Multiple Sclerosis: A Randomized Controlled Trial. Frontiers in Public Health 2022;10 View
  17. Woldamanuel Y, Rossen J, Andermo S, Bergman P, Åberg L, Hagströmer M, Johansson U. Perspectives on Promoting Physical Activity Using eHealth in Primary Care by Health Care Professionals and Individuals With Prediabetes and Type 2 Diabetes: Qualitative Study. JMIR Diabetes 2023;8:e39474 View
  18. Tahsin F, Tracy S, Chau E, Harvey S, Loganathan M, McKinstry B, Mercer S, Nie J, Ramsay T, Thavorn K, Palen T, Sritharan J, Steele Gray C. Exploring the relationship between the usability of a goal-oriented mobile health application and non-usage attrition in patients with multimorbidity: A blended data analysis approach. DIGITAL HEALTH 2021;7 View
  19. Yang M, Duan Y, Liang W, Peiris D, Baker J. Effects of Face-to-Face and eHealth Blended Interventions on Physical Activity, Diet, and Weight-Related Outcomes among Adults: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health 2023;20(2):1560 View
  20. Jakob R, Harperink S, Rudolf A, Fleisch E, Haug S, Mair J, Salamanca-Sanabria A, Kowatsch T. Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review. Journal of Medical Internet Research 2022;24(5):e35371 View
  21. Linnet J, Hertz S, Jensen E, Runge E, Tarp K, Holmberg T, Mathiasen K, Lichtenstein M. Days between sessions predict attrition in text-based internet intervention of Binge Eating Disorder. Internet Interventions 2023;31:100607 View
  22. Daniore P, Nittas V, von Wyl V. Enrollment and Retention of Participants in Remote Digital Health Studies: Scoping Review and Framework Proposal. Journal of Medical Internet Research 2022;24(9):e39910 View
  23. Bricker J, Miao Z, Mull K, Santiago-Torres M, Vock D. Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials. Journal of Medical Internet Research 2023;25:e43629 View
  24. Oinas-Kukkonen H, Pohjolainen S, Agyei E. Mitigating Issues With/of/for True Personalization. Frontiers in Artificial Intelligence 2022;5 View
  25. Ilie G, David Harold Rutledge R. Reply to Mauricio Plata, Cesar Diaz Ritter, and Nicolás Badillo’s Letter to the Editor re: Gabriela Ilie, Ricardo Rendon, Ross Mason, et al. A Comprehensive 6-mo Prostate Cancer Patient Empowerment Program Decreases Psychological Distress Among Men Undergoing Curative Prostate Cancer Treatment: A Randomized Clinical Trial. Eur Urol. In press. https://doi.org/10.1016/j.eururo.2023.02.009. European Urology 2023;84(1):e26 View
  26. Giebel G, Speckemeier C, Abels C, Plescher F, Börchers K, Wasem J, Blase N, Neusser S. Problems and Barriers Related to the Use of Digital Health Applications: Scoping Review. Journal of Medical Internet Research 2023;25:e43808 View
  27. Brankovic A, Hendrie G, Baird D, Khanna S. Predicting Disengagement to Better Support Outcomes in a Web-Based Weight Loss Program Using Machine Learning Models: Cross-Sectional Study. Journal of Medical Internet Research 2023;25:e43633 View
  28. Zantvoort K, Scharfenberger J, Boß L, Lehr D, Funk B. Finding the Best Match — a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions. Journal of Healthcare Informatics Research 2023;7(4):447 View
  29. Ekpezu A, Wiafe I, Oinas-Kukkonen H. Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review. JMIR AI 2023;2:e46779 View
  30. Vouzis E, Maglogiannis I. Prediction of Early Dropouts in Patient Remote Monitoring Programs. SN Computer Science 2023;4(5) View
  31. Yang X, Xiang Z, Zhang J, Song Y, Guo E, Zhang R, Chen X, Chen L, Gao L. Development and feasibility of a theory-guided and evidence-based physical activity intervention in pregnant women with high risk for gestational diabetes mellitus: a pilot clinical trial. BMC Pregnancy and Childbirth 2023;23(1) View
  32. von Itzstein M, Gwin M, Gupta A, Gerber D. Telemedicine and Cancer Clinical Research. The Cancer Journal 2024;30(1):22 View
  33. Packer T, Austin N, Lehman M, Douglas S, Plow M. Factors influencing how informal caregivers of people with multiple sclerosis access and use a curated intervention website: Analysis from an RCT. DIGITAL HEALTH 2024;10 View
  34. Yang M, Duan Y, Lippke S, Liang W, Su N. A blended face-to-face and eHealth lifestyle intervention on physical activity, diet, and health outcomes in Hong Kong community-dwelling older adults: a study protocol for a randomized controlled trial. Frontiers in Public Health 2024;12 View
  35. Zantvoort K, Hentati Isacsson N, Funk B, Kaldo V. Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions. DIGITAL HEALTH 2024;10 View
  36. van den Bekerom L, van Gestel L, Schoones J, Bussemaker J, Adriaanse M. Health behavior interventions among people with lower socio-economic position: a scoping review of behavior change techniques and effectiveness. Health Psychology and Behavioral Medicine 2024;12(1) View
  37. Knutzen S, Christensen D, Cairns P, Damholdt M, Amidi A, Zachariae R. Efficacy of eHealth Versus In-Person Cognitive Behavioral Therapy for Insomnia: Systematic Review and Meta-Analysis of Equivalence. JMIR Mental Health 2024;11:e58217 View
  38. Hurmuz-Bodde M, Jansen-Kosterink S, Hermens H, van Velsen L. Attrition of older adults in web-based health interventions: Survival analysis within an observational cohort study. Journal of Health Psychology 2024 View
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Books/Policy Documents

  1. Mansourvar M, Wiil U, Nøhr C. Emerging Technologies in Computing. View
  2. Enders K, Haller N. Digitalisierung und Innovation im Sport und in der Sportwissenschaft. View