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Accepted for/Published in: JMIR mHealth and uHealth

Date Submitted: Apr 17, 2024
Open Peer Review Period: Apr 29, 2024 - Jun 24, 2024
Date Accepted: Nov 11, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Influencing Factors and Implementation Pathways of Adherence Behavior in Intelligent Personalized Exercise Prescription: Qualitative Study

Xu X, Zhang G, Xia Y, Xie H, Ding Z, Wang H, Ma Z, Sun T

Influencing Factors and Implementation Pathways of Adherence Behavior in Intelligent Personalized Exercise Prescription: Qualitative Study

JMIR Mhealth Uhealth 2024;12:e59610

DOI: 10.2196/59610

PMID: 39636668

PMCID: 11659695

Influencing Factors and Implementation Pathways of Adherence Behavior in Intelligent Personalized Exercise Prescription: Qualitative Study

  • Xuejie Xu; 
  • Guoli Zhang; 
  • Yuxin Xia; 
  • Hui Xie; 
  • Zenghui Ding; 
  • Hongyu Wang; 
  • Zuchang Ma; 
  • Ting Sun

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 identify the factors influencing adherence behaviors among middle-aged and elderly middle-aged and elderly community residents prescribed personalized exercise regimens via an electronic health promotion system. It also examines how these factors impact the initiation and maintenance of adherence behaviors.

Methods:

We used purposive sampling to conduct face-to-face semi-structured interviews with 12 middle-aged and elderly community residents who had been following personalized exercise regimens for 8 months. These residents received detailed exercise health education and guidance from staff. Interviews were recorded, transcribed verbatim, and analyzed using NVivo software through grounded theory. We then used the trans-theoretical model and multi-behavioral motivation theory to analyze adherence factors. The relationship between psychological motivation and adherence behavior was also explored.

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:

Adherence to personalized exercise prescriptions is influenced by both facilitating factors (multi-behavioral motivation, optimization strategies) and obstructive factors (perceived barriers). Achieving and maintaining adherence is a gradual process, influenced by various motivations and factors. Personalized solutions, long-term support, feedback mechanisms, and social support networks are crucial for promoting adherence. Future efforts should enhance adherence by focusing on multi-behavioral motivation, optimizing solutions, and reducing barriers to improve overall adherence.


 Citation

Please cite as:

Xu X, Zhang G, Xia Y, Xie H, Ding Z, Wang H, Ma Z, Sun T

Influencing Factors and Implementation Pathways of Adherence Behavior in Intelligent Personalized Exercise Prescription: Qualitative Study

JMIR Mhealth Uhealth 2024;12:e59610

DOI: 10.2196/59610

PMID: 39636668

PMCID: 11659695

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