ArticleJMIR mHealth and uHealth2024
Influencing Factors and Implementation Pathways of Adherence Behavior in Intelligent Personalized Exercise Prescription: Qualitative Study.
Article in JMIR mHealth and uHealth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Efficacy of an Intelligent and Integrated Older Adult Care Model on Quality of Life Among Home-Dwelling Older Adults: Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- Prescribed Rest Intervals in Home-Based Exercise Therapy and Rehabilitation: A Narrative Review and Conceptual Framework for Temporal Fidelity.Healthcare (Basel, Switzerland) · 2026Review
- Healthcare consumers' acceptance and use of digital health technology in LMICs and the role of self-efficacy and facilitating conditions: a systematic review and meta-analysis.The Lancet regional health. Western Pacific · 2026Article
- Identifying patient profiles based on protection motivation theory to predict exercise adherence in patients with lumbar disc herniation: a latent profile analysis.BMC musculoskeletal disorders · 2026Article
- Understanding the factors influencing participant engagement and adherence in exercise referral in the City of Manchester.BMJ open sport & exercise medicine · 2026Article
- Health Motivation as a Predictor of mHealth Engagement Across BMI: Cross-Sectional Survey.JMIR mHealth and uHealth · 2025Article
- A Gamification mHealth Intervention to Enhance Adherence to Personalized Exercise for Older Adults with Chronic Diseases: A Randomized Controlled Trial Protocol.Patient preference and adherence · 2025Article
- Ageing Arteries and Aerobic Exercise: Bridging Evidence and Innovation in Vascular Health.Pulse (Basel, Switzerland)Article
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Authors and funding
8 authors.
Funding
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Abstract
backgroundPersonalized intelligent exercise prescriptions have demonstrated significant benefits in increasing physical activity and improving individual health. However, the health benefits of these prescriptions depend on long-term adherence. Therefore, it is essential to analyze the factors influencing adherence to personalized intelligent exercise prescriptions and explore the intrinsic relationship between individual behavioral motivation and adherence. This understanding can help improve adherence and maximize the effectiveness of such prescriptions.
objectiveThis study aims to identify the factors influencing adherence behavior among middle-aged and older community residents who have been prescribed personalized exercise regimens through an electronic health promotion system. It also explores how these factors affect the initiation and maintenance of adherence behavior.
methodsWe used purposive sampling to conduct individual, face-to-face semistructured interviews based on the Transtheoretical Model (TTM) with 12 middle-aged and older community residents who had been following personalized exercise regimens for 8 months. These residents had received detailed exercise health education and guidance from staff. The interviews were recorded, transcribed verbatim, and analyzed using NVivo software through grounded theory. We then applied the TTM and multibehavioral motivation theory to analyze the factors influencing adherence. Additionally, the relationship between behavioral motivations and adherence was explored.
resultsUsing the behavior change stages of the TTM, open coding yielded 21 initial categories, which were then organized into 8 main categories through axial coding: intrinsic motivation, extrinsic motivation, benefit motivation, pleasure motivation, achievement motivation, perceived barriers, self-regulation, and optimization strategies. Selective coding further condensed these 8 main categories into 3 core categories: "multitheory motivation," "obstacle factors," and "solution strategies." Using the coding results, a 3-level model of factors influencing adherence to intelligent personalized exercise prescriptions was developed. Based on this, an implementation path for promoting adherence to intelligent personalized exercise prescriptions was proposed by integrating the model with the TTM.
conclusionsAdherence to personalized exercise prescriptions is influenced by both facilitating factors (eg, multibehavioral motivation, optimization strategies) and obstructive factors (eg, perceived barriers). Achieving and maintaining adherence is a gradual process, shaped by a range of motivations and factors. Personalized solutions, long-term support, feedback mechanisms, and social support networks are essential for promoting adherence. Future efforts should focus on enhancing adherence by strengthening multibehavioral motivation, optimizing solutions, and addressing barriers to improve overall adherence.
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