Considering depression as a risk factor for early disengagement in a 12-month eHealth weight loss intervention
Bibliographic Data
| ID | 4228461 |
|---|---|
| Authors | Lisa Hurley (0000-0003-2217-0492, The University of North Carolina at Chapel Hill, USA), Lex Hurley, Nisha Gottfredson O''Shea (0000-0002-4765-7179), Nisha Gottfredson O’shea (Penn State Cancer Institute), Christopher Sciamanna (0000-0002-1568-341X, RTI International), Deborah F Tate (0000-0002-4915-5308, The University of North Carolina at Chapel Hill, USA) |
| Year | 2025 |
| Pages | 13591053251348987-13591053251348987 |
| Publication date | 2025-06-29 |
| 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 | 10.1177/13591053251348987 |
| PMID | 40583318 |
| OpenAlex | W4411782270 |
| Language | EN |
| References cited | 39 |
Depression is often described as likely associated with engagement and risk of disengagement (i.e. non-usage attrition) in digital behavior change interventions, but is rarely studied in-depth outside of mental health-specific programs; thus, we sought to quantify its influence on disengagement risk. Data come from 363 adults (mean age = 51.86, SD = 10.86; 70.3% female) randomized to the intervention groups of the LoseNowPA eHealth weight management intervention. Kaplan-Meier and Cox proportional-hazards modeling were applied on the outcome of cessation of logins before program end at 12 months. Overall, mild to moderate depression symptoms were associated with an increased risk of early disengagement from this eHealth intervention, controlling for sociodemographic covariates ( p < 0.0001). Subclinical levels of depression can still exert meaningful influence on participant risk of disengagement in digital weight loss interventions, which can limit their effectiveness. Future interventions may wish to consider tailoring on depression symptoms to possibly preserve engagement
Attrition · Depression (economics · Disengagement theory · eHealth · Health care · Health psychology · Intervention (counseling · Mental health · Obesity · Psychiatry · Psychological intervention · Public health · Weight loss · Digital Mental Health Interventions · Impact of Technology on Adolescents · Medicine · Mobile Health and mHealth Applications · Nursing · Psychology · Clinical Psychology · Gerontology · Internal Medicine
The Law of Attrition
Negative self-efficacy and goal effects revisited.
Conceptualising engagement with digital behaviour change interventions
Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions
Self-Efficacy
Understanding attrition from international internet health interventions
Body mass index and depressive symptoms in middle aged and older adults
Subjective mood and energy levels of healthy weight and overweight/obese healthy adults on high-and low-glycemic load experimental diets
Assessing the Public Health Impact of the mHealth App Business
| Citation velocity | historical |
|---|---|
| Highly cited | No |