Exploring the Feasibility and Initial Impact of an mHealth-Based Disease Management Program for Chronic Ischemic Heart Disease: Formative Study
Bibliographic record
Abstract
BACKGROUND: Ischemic heart disease (IHD) is a leading cause of morbidity and mortality worldwide, requiring innovative management strategies. Traditional disease management programs often struggle to maintain patient engagement and ensure long-term adherence to lifestyle modifications and treatment plans. Mobile health (mHealth) technologies have emerged as a promising approach to address these challenges by providing continuous, personalized support and monitoring. However, the reported use and effectiveness of mHealth in the management of chronic diseases, such as IHD, have not been fully explored. OBJECTIVE: The primary aim of this study was to evaluate the feasibility and initial impact of an mHealth-based disease management program on coronary risk factors, specifically focusing on low-density lipoprotein cholesterol (LDL-C) levels, in individuals with chronic IHD. This formative study assessed changes in LDL-C and other metabolic health indicators over a 6-month period to determine the initial impact of the program on promoting cardiovascular health and lifestyle modification. METHODS: This study was conducted using data from 266 individuals enrolled in an mHealth-based disease management program between December 2018 and October 2022. Eligibility was based on a documented history of IHD, with participants undergoing a comprehensive cardiac risk assessment before enrollment. The program included biweekly telephone sessions, health tracking via a smartphone app, and regular progress reports to physicians. The study measured change in LDL-C levels as the primary outcome, with secondary outcomes including body weight, triglyceride levels, and other metabolic health indicators. Statistical analysis used paired 2-tailed t tests and stratified analyses to assess the impact of the program. RESULTS: Participants experienced a significant reduction in LDL-C, with LDL-C levels decreasing from a mean of 98.82 (SD 40.92) mg/dL to 86.62 (SD 39.86) mg/dL (P<.001). The intervention was particularly effective in individuals with high baseline LDL-C levels. Additional improvements were seen in body weight and triglyceride levels, suggesting a broader impact on metabolic health. Program adherence and engagement metrics suggested high participant satisfaction and compliance. CONCLUSIONS: The results of this study suggest that the mHealth-based disease management program is feasible and has an initial positive impact on reducing LDL-C levels and improving metabolic health in individuals with chronic IHD. However, the study design does not allow for a definitive conclusion regarding whether mHealth-based disease management programs are more effective than traditional face-to-face care. Future studies are needed to further validate these findings and to examine the comparative effectiveness of these interventions in more detail.
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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 | 0.042 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".