ArticleJournal of medical Internet research2018
How Users Experience and Use an eHealth Intervention Based on Self-Regulation: Mixed-Methods Study.
Article in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
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Who cites it
16 citing papers in PubMed, 30 citations in OpenAlex.
- 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.JMIR mHealth and uHealth · 2022Trial
- Requirements for (web-based) physical activity interventions targeting adults above the age of 65 years - qualitative results regarding acceptance and needs of participants and non-participants.BMC public health · 2020Trial
- Efficacy of a Self-Regulation-Based Electronic and Mobile Health Intervention Targeting an Active Lifestyle in Adults Having Type 2 Diabetes and in Adults Aged 50 Years or Older: Two Randomized Controlled Trials.Journal of medical Internet research · 2019Trial
- Identifying Influential Theories in Human-Computer Interaction Within Health Informatics: A Systematic Review.Health science reports · 2025Review
- Identifying app components that promote physical activity: a group concept mapping study.PeerJ · 2024Article
- Stakeholder Perceptions towards a Mobile Application for Community-Led Monitoring of Tuberculosis Services in Metro Manila, Philippines: A Qualitative Study.Acta medica Philippina · 2024Article
- Building on Existing Classifications of Behavior Change Techniques to Classify Planned Coping Strategies: Physical Activity Diary Study.JMIR formative research · 2023Article
- Setting Physical Activity Goals with a Virtual Coach: Vicarious Experiences, Personalization and Acceptance.Journal of medical systems · 2023Article
- Towards more personalized digital health interventions: a clustering method of action and coping plans to promote physical activity.BMC public health · 2022Article
- Enrollment and Retention of Participants in Remote Digital Health Studies: Scoping Review and Framework Proposal.Journal of medical Internet research · 2022Article
- User Perceptions of eHealth and mHealth Services Promoting Physical Activity and Healthy Diets: Systematic Review.JMIR human factors · 2022Review
- Effectiveness of the mHealth intervention 'MyDayPlan' to increase physical activity: an aggregated single case approach.The international journal of behavioral nutrition and physical activity · 2021Article
- The potential of artificial intelligence in enhancing adult weight loss: a scoping review.Public health nutrition · 2021Article
- Engagement, Acceptability, Usability, and Preliminary Efficacy of a Self-Monitoring Mobile Health Intervention to Reduce Sedentary Behavior in Belgian Older Adults: Mixed Methods Study.JMIR mHealth and uHealth · 2020Article
- Acceptability and feasibility of the mHealth intervention 'MyDayPlan' to increase physical activity in a general adult population.BMC public health · 2020Article
- Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundeHealth interventions show stronger effects when informed by solid behavioral change theories; for example, self-regulation models supporting people in translating vague intentions to specific actions have shown to be effective in altering health behaviors. Although these theories inform developers about which behavioral change techniques should be included, they provide limited information about how these techniques can be engagingly implemented in Web-based interventions. Considering the high levels of attrition in eHealth, investigating users' experience about the implementation of behavior change techniques might be a fruitful avenue.
objectiveThe objective of our study was to investigate how users experience the implementation of self-regulation techniques in a Web-based intervention targeting physical activity and sedentary behavior in the general population.
methodsIn this study, 20 adults from the general population used the intervention for 5 weeks. Users' website data were explored, and semistructured interviews with each of the users were performed. A directed content analysis was performed using NVivo Software.
resultsThe techniques "providing feedback on performance," "action planning," and "prompting review of behavioral goals" were appreciated by users. However, the implementation of "barrier identification/problem solving" appeared to frustrate users; this was also reflected by the users' website data-many coping plans were of poor quality. Most users were well aware of the benefits of adopting a more active way of living and stated not to have learned novel information. However, they appreciated the provided information because it reminded them about the importance of having an active lifestyle. Furthermore, prompting users to self-monitor their behavioral change was not sufficiently stimulating to make users actually monitor their behavior.
conclusionsIteratively involving potential end users offers guidance to optimally adapt the implementation of various behavior change techniques to the target population. We recommend creating short interventions with a straightforward layout that support users in creating and evaluating specific plans for action.
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Registered trials
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.