Abstract
BACKGROUND: Personalized 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. OBJECTIVE: This 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. METHODS: We 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. RESULTS: Using 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. CONCLUSIONS: Adherence 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.