# Evaluation scale and behavioral model construction for intention to use postpartum exercise rehabilitation mobile application based on user experience

**Authors:** Zhiyuan Wang, Zheng Wang, Rong Deng, Meng Xia, Liming Huang

PMC · DOI: 10.3389/fpsyg.2025.1575049 · Frontiers in Psychology · 2025-05-30

## TL;DR

This study identifies key factors influencing postpartum women's intention to use exercise rehabilitation mobile apps and develops an evaluation scale and behavioral model to improve app design and user experience.

## Contribution

The study introduces a new evaluation scale and behavioral model for postpartum exercise rehabilitation mobile app usage based on user experience.

## Key findings

- Five key factors were identified that influence the intention to use postpartum exercise rehabilitation mobile apps.
- Exercise safety assurance, physical activity tracking, emotional social support, and health benefits significantly affect user intention.
- Dialogue support was found to have no direct effect on the intention to use these apps.

## Abstract

Physical exercise is a widely recognized and practical approach to postnatal rehabilitation. In recent years, its delivery through mobile applications has become increasingly prevalent due to their accessibility and affordability. These mobile applications are crucial in supporting postpartum women in restoring physical and mental well-being and promoting sustainable health behaviors. The intention to use such mobile applications is a key determinant of user behavior and offers valuable guidance for designing and improving digital health services. Despite this, current research has largely overlooked the intention to use postpartum exercise rehabilitation mobile applications from the user experience perspective. This gap has contributed to a limited understanding of the specific needs of postpartum women, which are frequently underestimated or disregarded in the design process.

This study aims to identify the key factors influencing postpartum women’s use of postpartum exercise rehabilitation mobile applications and to develop corresponding evaluation scales and behavioral models related to their intention to use. The ultimate goal is to promote the health and well-being of postpartum women while offering a theoretical foundation for future research and design practices in relevant domains.

A mixed-methods approach was employed, integrating qualitative and quantitative research techniques. Initially, user interviews, open-ended questionnaires, and a review of existing literature were conducted to identify the factors that postpartum women consider most important when evaluating postpartum exercise rehabilitation mobile applications. These factors were then synthesized to develop an evaluation scale measuring users’ intention to use. Subsequently, exploratory factor analysis and linear regression analysis were applied to identify the key influencing factors, examine their relationships with intention to use, and construct a behavioral model reflecting the determinants of user intention.

300 valid questionnaires were collected and used for factor analysis and linear regression analysis. The study identified five key factors. The standardized path coefficients between these factors and the intention to use were 0.254, 0.205, 0.198, 0.015, and 0.142, respectively. All factors, except factor 4 (p = 0.77), demonstrated statistically significant relationships with intention to use (p < 0.05). These findings indicate that exercise safety assurance, physical activity tracking, emotional social support, and health benefits significantly influence users’ intention to use postpartum exercise rehabilitation mobile applications. In contrast, dialogue support does not have a direct effect.

Postpartum women using exercise rehabilitation mobile applications prioritized the practical features of these tools, particularly the safety and efficacy of the exercise regimens and the emotional and social support provided. These findings highlight the importance of aligning functional benefits with users’ health objectives.

## Full text

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## Figures

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## References

113 references — full list in the complete paper: https://tomesphere.com/paper/PMC12162496/full.md

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Source: https://tomesphere.com/paper/PMC12162496