Trial reportJMIR mHealth and uHealth2022
Investigating When, Which, and Why Users Stop Using a Digital Health Intervention to Promote an Active Lifestyle: Secondary Analysis With A Focus on Health Action Process Approach-Based Psychological Determinants.
Trial report in JMIR mHealth and uHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03274271 (A Parallel-group Randomized Trial to Compare the Efficacy of Different Behaviour Change Techniques in the e- and M-health Intervention 'MyPlan 2.0'), which is not on this map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Parallel-group Randomized Trial to Compare the Efficacy of Different Behaviour Change Techniques in the e- and M-health Intervention 'MyPlan 2.0'
Who cites it
20 citing papers in PubMed, 46 citations in OpenAlex.
- App-Based Physical Activity Intervention Among Women With Prior Hypertensive Pregnancy Disorder: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Effective Communication Supported by an App for Pregnant Women: Quantitative Longitudinal Study.JMIR human factors · 2024Trial
- Integrating digital health and remote monitoring: emerging trends in cardiac rehabilitation research for chronic heart failure.Frontiers in cardiovascular medicine · 2026Article
- Implementation process of community-based lifestyle interventions supported by technology for elderly population: qualitative evaluation of six European regions case studies.Frontiers in digital health · 2026Article
- The Effectiveness of Theory Based Educational Intervention on Health Literacy, Medication Adherence and Self-Manag.Journal of preventive medicine and hygiene · 2025Article
- Development and evaluation of the COntextualised and Personalised Physical activity and Exercise Recommendations (COPPER) Ontology.The international journal of behavioral nutrition and physical activity · 2025Article
- Early Attrition Prediction for Web-Based Interpretation Bias Modification to Reduce Anxious Thinking: A Machine Learning Study.JMIR mental health · 2024Article
- Methodological Challenges in Randomized Controlled Trials of mHealth Interventions: Cross-Sectional Survey Study and Consensus-Based Recommendations.Journal of medical Internet research · 2024Article
- Discovering what young adults want in electronic interventions aimed at reducing alcohol-related consequences.Alcohol, clinical & experimental research · 2024Article
- Participatory development of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED).BMC public health · 2024Article
- Exploring the Influence of YouTube on Digital Health Literacy and Health Exercise Intentions: The Role of Parasocial Relationships.Behavioral sciences (Basel, Switzerland) · 2024Article
- Identifying app components that promote physical activity: a group concept mapping study.PeerJ · 2024Article
- Patient motivation as a predictor of digital health intervention effects: A meta-epidemiological study of cancer trials.PloS one · 2024Article
- Understanding Attrition in Text-Based Health Promotion for Fathers: Survival Analysis.JMIR formative research · 2023Article
- Using the Person-Based Approach to Develop a Digital Intervention Targeting Diet and Physical Activity in Pregnancy: Development Study.JMIR formative research · 2023Article
- Article
- Towards more personalized digital health interventions: a clustering method of action and coping plans to promote physical activity.BMC public health · 2022Article
- Nonusage Attrition of Adolescents in an mHealth Promotion Intervention and the Role of Socioeconomic Status: Secondary Analysis of a 2-Arm Cluster-Controlled Trial.JMIR mHealth and uHealth · 2022Article
- Review
- Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDigital health interventions have gained momentum to change health behaviors such as physical activity (PA) and sedentary behavior (SB). Although these interventions show promising results in terms of behavior change, they still suffer from high attrition rates, resulting in a lower potential and accessibility. To reduce attrition rates in the future, there is a need to investigate the reasons why individuals stop using the interventions. Certain demographic variables have already been related to attrition; however, the role of psychological determinants of behavior change as predictors of attrition has not yet been fully explored.
objectiveThe aim of this study was to examine when, which, and why users stopped using a digital health intervention. In particular, we aimed to investigate whether psychological determinants of behavior change were predictors for attrition.
methodsThe sample consisted of 473 healthy adults who participated in the intervention MyPlan 2.0 to promote PA or reduce SB. The intervention was developed using the health action process approach (HAPA) model, which describes psychological determinants that guide individuals in changing their behavior. If participants stopped with the intervention, a questionnaire with 8 question concerning attrition was sent by email. To analyze when users stopped using the intervention, descriptive statistics were used per part of the intervention (including pre- and posttest measurements and the 5 website sessions). To analyze which users stopped using the intervention, demographic variables, behavioral status, and HAPA-based psychological determinants at pretest measurement were investigated as potential predictors of attrition using logistic regression models. To analyze why users stopped using the intervention, descriptive statistics of scores to the attrition-related questionnaire were used.
resultsThe study demonstrated that 47.9% (227/473) of participants stopped using the intervention, and drop out occurred mainly in the beginning of the intervention. The results seem to indicate that gender and participant scores on the psychological determinants action planning, coping planning, and self-monitoring were predictors of first session, third session, or whole intervention completion. The most endorsed reasons to stop using the intervention were the time-consuming nature of questionnaires (55%), not having time (50%), dissatisfaction with the content of the intervention (41%), technical problems (39%), already meeting the guidelines for PA/SB (31%), and, to a lesser extent, the experience of medical/emotional problems (16%).
conclusionsThis study provides some directions for future studies. To decrease attrition, it will be important to personalize interventions on different levels, questionnaires (either for research purposes or tailoring) should be kept to a minimum especially in the beginning of interventions by, for example, using objective monitoring devices, and technical aspects of digital health interventions should be thoroughly tested in advance.
trial registrationClinicalTrials.gov NCT03274271; https://clinicaltrials.gov/ct2/show/NCT03274271. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-019-3456-7.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.