ArticleDigital health
Protocol of a mixed-methods evaluation of Perfect Fit: A personalized mHealth intervention with a virtual coach to promote smoking cessation and physical activity in adults.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Reinforcement learning for proposing smoking cessation activities that build competencies: Combining two worldviews in a virtual coach.BMC medical informatics and decision making · 2025Trial
- Evaluation of the Feasibility and Acceptability of Perfect Fit, a Virtual Coach-Based mHealth Intervention for Smoking Cessation and Physical Activity in Adults: Mixed Methods Study.JMIR human factors · 2026Article
- A practical step-by-step approach for patient and public involvement in eHealth intervention research: Lessons learned from three case projects.Internet interventions · 2026Article
- Effectiveness of digital and mobile-based interventions on sleep quality among nurses: a systematic review and meta-analysis.Frontiers in digital health · 2026Review
- Article
- Psychological, economic, and ethical factors in human feedback for a chatbot-based smoking cessation intervention.NPJ digital medicine · 2025Article
- Digital future-self interventions to promote physical activity: perspectives of minimally active middle-aged and older adults.Journal of imagery research in sport and physical activity · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Adopting healthy behavior is vital for preventing chronic diseases. Mobile health (mHealth) interventions utilizing virtual coaches (i.e., artificial intelligence conversational agents) can offer scalable and cost-effective solutions. Additionally, targeting multiple unhealthy behaviors, like low physical activity and smoking, simultaneously seems beneficial. We developed Perfect Fit, an mHealth intervention with a virtual coach providing personalized feedback to simultaneously promote smoking cessation and physical activity. Through innovative methods (e.g., sensor technology) and iterative development involving end-users, we strive to overcome challenges encountered by mHealth interventions, such as shortage of evidence-based interventions and insufficient personalization. This paper outlines the content of Perfect Fit and the protocol for evaluating its feasibility, acceptability, and preliminary effectiveness, the role of participant characteristics, and the study's feasibility. Methods: A single-arm, mixed-method, real-world evaluation study will be conducted in the Netherlands. We aim to recruit 100 adult daily smokers intending to quit within 6 weeks. The personalized intervention will last approximately 16 weeks. Primary outcomes include Perfect Fit's feasibility and acceptability. Secondary outcomes are preliminary effectiveness and study feasibility, and we will measure participant characteristics. Quantitative data will be collected through questionnaires administered at baseline, post-intervention and 2, 6, and 12 months post-intervention. Qualitative data will be gathered via semi-structured interviews post-intervention. Data analysis will involve descriptive analyses, generalized linear mixed models (quantitative) and the Framework Approach (qualitative), integrating quantitative and qualitative data during interpretation. Conclusions: This study will provide novel insight into the potential of interventions like Perfect Fit, as a multiple health behavior change strategy. Findings will inform further intervention development and help identify methods to foster feasibility and acceptability. Successful mHealth interventions with virtual coaches will prevent chronic diseases and promote public health.
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