Implementation of Digital Device–Assisted and Nurse-Led Case Management to Promote Self-Management in Adults With Noncommunicable Diseases: Protocol for a Single-Arm Intervention Study
Bibliographic record
Abstract
Background: Self-management plays a vital role in noncommunicable disease prevention and control. However, it has been challenging for patients and their caregivers to identify how much their lifestyle affects their health and what level of effort they should make to reduce cardiovascular disease (CVD) risks in everyday life. Therefore, knowing their own CVD risk and daily health-related situations will provide relevant information for self-management by those at risk. The need to help individuals understand their relevant information creates an opportunity to investigate whether and how to implement a combined digital and nurse-led self-management intervention in a real-world community setting. Objective: This study aims to evaluate the effectiveness of a combined approach combining digital device support, including a smartwatch, a mobile app, and a salt meter, with nurse-led case management, on self-management behaviors and clinical outcomes. Methods: This study uses a combination of a nurse-led self-management with a digital and mobile health innovative approach, including tailored small group face-to-face education sessions, a smartwatch, a smartphone health app, and a salt meter, to increase the self-management behaviors to reduce vascular risk through designing and testing an integrated community-based strategy targeted at adults and older adults at risk of CVD in Thailand. The study uses a single-arm pretest-posttest design to assess the intervention's effects. The intervention will consist of the following components: (1) an interactive face-to-face education session; (2) a real-time knowing your numbers strategy using a smartwatch, a smartphone health app, and a salt meter; (3) a mindfulness-based stress management strategy using Somporn Kantaradusdi-Triamchaisri technique meditation healing exercise; and (4) a self-management diary. Quantitative data will be collected using a smartwatch, a salt meter, a food diary, and questionnaires at baseline and at the end of week 6. Clinical outcomes will be assessed at baseline, primary end point (wk 12), and secondary end point (wk 24). Results: This study, funded in January 2025, will involve 45 patients. We received ethical approval on May 31, 2024, and began recruitment for participation in May 2025. Researchers will collect, analyze, and synthesize to evaluate the study procedure. We expect to complete data collection by December 2025, with the first results submitted for publication in March 2026. Conclusions: The implementation of a combined digital device and nurse-led case management may identify the use of digital health to support self-management and improve vascular health. The findings of this study will provide insights for a large-scale randomized controlled trial and for ongoing improvements in the noncommunicable disease care system.
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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 | 0.026 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".