Related Experiment Video
Updated: May 9, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A mini-program for high-risk of stroke population management using the intervention mapping approach: design,
Mengxia Chen1,2, Dongmei Li3, Bing Yu1,4
1Education and Scientific Research Department of Clinical Nursing, The First Affiliated Hospital of Naval Medical University, Changhai Hospital, 200433, Shanghai, China.
Background:
Improving health behavior is an effective and practical method for preventing stroke. While effectively managing modifiable risk factors-especially among high-risk populations-remains a persistent challenge in clinical practice due to the scarcity of management channels and tools. Mobile health (mHealth) is a promising approach to ensure healthcare is more accessible and affordable. However, the intervention and application development processes are often not theory-driven and well-described, creating challenges in explaining why and how the intervention would work.
Objectives:
This study aims to describe the development and evaluation of a WeChat-based mini-program using Intervention Mapping (IM).
Methods:
We followed the first 4 steps of the Intervention Mapping (IM) process to develop the UHEALTH mini-program. (1) Needs assessment was conducted via semi-structured interviews with 16 SHP and 14 health providers. (2) A matrix of change objectives was developed using the COM-B framework to identify target behaviors and determinants. (3) Theory-based intervention methods and practical applications were designed. (4) Usability was evaluated using the mHealth App Usability Questionnaire (MAUQ), and qualitative interviews were conducted to gather user feedback. Quantitative data from the MAUQ were analyzed using descriptive statistics (mean ± standard deviation), while qualitative interview data were analyzed using thematic analysis to identify key user experiences and improvement suggestions.
Results:
The UHEALTH mini-program was developed with a multi-layer architecture, featuring comprehensive modules for patients (e.g., risk assessment, medication reminders, and online consultation) and health providers (e.g., patient management and message monitoring). Usability evaluation showed high satisfaction, with total MAUQ scores of 1.79 ± 0.28 for health providers and 2.23 ± 0.59 for SHP, indicating good feasibility and system usability. Qualitative interviews further confirmed that the mini-program effectively facilitated two-way communication and improved management efficiency compared to traditional methods.
Conclusion:
We designed, developed, and evaluated UHEALTH-a comprehensive mini-program for stroke high-risk populations (SHP)-to improve their health behaviors using the Intervention Mapping (IM) framework. UHEALTH demonstrates promising potential for feasibility and high levels of user-reported usability.
