Attrition of older adults in web-based health interventions
Survival analysis within an observational cohort study
Bibliographic Data
| ID | 4835943 |
|---|---|
| Authors | Marian Zm Hurmuz-Bodde (0000-0003-0070-5521, Roessingh Research and Development, The Netherlands), Stephanie M Jansen-Kosterink (0000-0002-2095-7104, Roessingh Research and Development, The Netherlands), Hermie J Hermens (Roessingh Research and Development, The Netherlands), Hermie Hermens (0000-0002-3065-3876, Roessingh Research and Development), Lex Van Velsen (0000-0003-0599-8706, Roessingh Research and Development, The Netherlands) |
| Year | 2025 |
| Volume | 30 |
| Issue | 8 |
| Pages | 1768-1779 |
| Publication date | 2025-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Health Psychology (JOURNAL) |
| Journal identifiers | ISSN: 1359-1053 • E-ISSN: 1461-7277 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/13591053241274097 |
| PMID | 39276083 |
| OpenAlex | W4402540364 |
| Language | EN |
| References cited | 27 |
To identify demographics and personal motivation types that predict dropping out of eHealth interventions among older adults. We conducted an observational cohort study. Participants completed a pre-test questionnaire and got access to an eHealth intervention, called Stranded, for 4 weeks. With survival and Cox-regression analyses, demographics and types of personal motivation were identified that affect drop-out. Ninety older adults started using Stranded. 45.6% participants continued their use for 4 weeks. 32.2% dropped out in the first week and 22.2% dropped out in the second or third week. The final multivariate Cox-regression model which predicts drop-out, consisted of the variables: perceived computer skills and level of external regulation. Predicting the chance of dropping out of an eHealth intervention is possible by using level of self-perceived computer skills and level of external regulation (externally controlled rewards or punishments direct behaviour). Anticipating to these factors can improve eHealth adoption
Affect (linguistics · Attrition · Cohort · Cohort study · Demographics · eHealth · Health care · Health psychology · Intervention (counseling · Observational study · Proportional hazards model · Psychological intervention · Public health · Digital Mental Health Interventions · Impact of Technology on Adolescents · Medicine · Mobile Health and mHealth Applications · Nursing · Psychology · Clinical Psychology · Demography · Gerontology · Internal Medicine
| Citation velocity | historical |
|---|---|
| Highly cited | No |