JMIR Publications

ABSTRACT

Background:

Personalized intelligent exercise prescriptions have shown significant effects in increasing individual physical activity and improving individual health levels. However, the health benefits of personalized intelligent exercise prescriptions rely on individuals' long-term adherence behaviors. Therefore, it is crucial to analyze the factors influencing individual adherence to personalized intelligent exercise prescriptions and further explore the intrinsic correlation between individual psychological motivation and adherence behaviors, aiming to enhance individual adherence to such prescriptions.

Objective:

This study aims to analyze the adherence behavior of community residents who received personalized intelligent exercise prescriptions from an electronic health promotion system, and to explore the relationship between their psychological motivations and adherence behaviors towards these prescriptions.

Methods:

This study employed purposive sampling to conduct individual, face-to-face semi-structured interviews with 12 community residents who had been prescribed personalized intelligent exercise for at least 8 months. The interviews utilized the trans-theoretical model and the multi-theory motivation model. Participants received detailed explanations and exercise guidance from staff after being provided with exercise health education materials. The interviews were recorded, transcribed verbatim, and analyzed using qualitative analysis software NVivo with three-level coding. The coding results were utilized to analyze the adherence status and influencing factors of personalized intelligent exercise prescriptions, and to further explore the relationship between community residents' psychological motivations and adherence behaviors towards exercise prescriptions.

Results:

Open coding yielded 21 initial categories, which were then organized into 8 main categories via axial coding: intrinsic motivation, extrinsic motivation, benefit motivation, pleasure motivation, achievement motivation, perceived barriers, self-regulation, and optimization strategies. Selective coding further condensed the 8 main categories into three core categories: "multi-theory motivation," "obstacle factors," and "solution strategies." Using the coding results, a model depicting factors influencing adherence behavior to personalized intelligent exercise prescriptions was developed. Subsequently, a pathway model for fostering adherence behavior to personalized intelligent exercise prescriptions was proposed by integrating it with the trans-theoretical model.

Conclusions:

While most community residents exhibit good adherence to personalized intelligent exercise prescriptions, such adherence behavior is influenced by both facilitating factors (multi-theory motivation, solution strategies) and hindering factors (perceived barriers). Furthermore, the development and sustenance of individual adherence to personalized intelligent exercise prescriptions is not instantaneous but rather entails a gradual progression across stages, influenced by diverse motivational and other factors. Hence, future endeavors aimed at enhancing adherence to exercise prescriptions from a multi-theory motivation perspective should also prioritize optimizing solution strategies and mitigating barriers to facilitate the attainment and perpetuation of adherence behavior to exercise prescriptions.