Developing a Tailored eHealth Self-Management Intervention for Patients With Chronic Kidney Disease in China: Intervention Mapping Approach
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is a major public health concern. Adequate self-management skills are vital to reduce CKD burden, optimize patient health outcomes, and control health care expenditures. Using eHealth to support CKD self-management has the potential to promote healthy behaviors and improve health outcomes of patients with CKD. However, knowledge of the implementation of such interventions in general, and in China specifically, is still limited. OBJECTIVE: This study aims to develop a tailored eHealth self-management intervention for patients with CKD in China based on the Dutch Medical Dashboard (MD) eHealth self-management intervention. METHODS: We used an intervention mapping approach. In phase 1, a systematic review and 2 qualitative studies were conducted to examine the needs, beliefs, and perceptions of patients with CKD and health care professionals regarding CKD self-management and eHealth interventions. Afterward, key factors gathered from the aforementioned studies were categorized following the 5 domains of the Consolidated Framework for Implementation Research (CFIR). In phase 2, we specified program outcomes, performance objectives, determinants, theory-based methods, and practical strategies. Knowledge obtained from previous results was combined to complement core components of the MD self-management intervention and adapt them for Chinese patients with CKD. Additionally, the CFIR-Expert Recommendations for Implementing Change Matching Tool was pragmatically used to generate a list of potential implementation strategies to address the key factors influencing the implementation of eHealth CKD self-management interventions, and implementation strategies were discussed and finalized with the intervention monitoring group. RESULTS: An overview of the CFIR domains showed the essential factors influencing the implementation of eHealth CKD self-management interventions in Chinese settings, including "knowledge and beliefs" in the domain "individual characteristics," "quality and advantage of eHealth intervention" in the domain "intervention characteristics," "compatibility" in the domain "inner setting," and "cultural context" in the domain "outer setting." To ensure the effectiveness of the Dutch MD-based self-management intervention, we did not change the core self-management intervention components of MD that underlie its effectiveness, such as self-monitoring. We identified surface-level cultural adaptations involving customizing intervention content, messages, and approaches to the observable cultural characteristics of the local population to enhance the intervention's appeal, receptivity, and feasibility, such as providing video or voice call options to support interactions with health care professionals. Furthermore, the adapted modules such as Knowledge Center and My Self-Monitoring were developed in a mobile health app. CONCLUSIONS: Our study resulted in the delivery of a culturally tailored, standardized eHealth self-management intervention for patients with CKD in China that has the potential to optimize patients' self-management skills and improve health status and quality of life. Moreover, our study's research approach and results can inform future research on the tailoring and translation of evidence-based, eHealth self-management interventions to various contexts. TRIAL REGISTRATION: ClinicalTrials.gov NCT04212923; https://classic.clinicaltrials.gov/ct2/show/NCT04212923.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".