The Effectiveness of a Mobile Health Care App and Human Coaching Program in Primary Care Clinics: Pilot Multicenter Real-World Study
Bibliographic record
Abstract
BACKGROUND: As the global burden of chronic conditions increases, their effective management is a concern. Although the need for chronic disease management using mobile self-management health care apps is increasing, there are still many barriers to their practical application in the primary care field. OBJECTIVE: This study evaluated the effectiveness of primary care services combining a mobile self-management health care app with human coaching for patients with chronic diseases in the current primary care system. METHODS: A total of 110 patients (mean age 53.2, SD 9.2 years; 64 of 110, 58.2% female) with hypertension, diabetes, dyslipidemia, or metabolic syndrome who visited one of 17 participating primary care clinics from September to November 2020 were included in this study. All participants recorded data regarding changes in body weight, sleep conditions, quality of life, depression, anxiety, stress, BMI, waist circumference, blood sugar levels, blood pressure, and blood lipids levels. The app user group (n=65) used a mobile self-management health care app with human coaching for 12 weeks, and the control group (n=45) underwent conventional self-managed health care. RESULTS: Patients in the app user group reported significantly more weight loss than those in the control group-the body weight of the app user group decreased by 1.43 kg (95% CI -2.07 to -0.79) and that of the control group decreased by 0.13 kg (95% CI -0.67 to 0.41; P=.002). The weight loss was markedly greater after using the app for 9 weeks than that when used for 4 weeks or 5-8 weeks (P=.002). Patients in the app user group reported better sleep quality (P=.04) and duration (P=.004) than those in the control group. CONCLUSIONS: The combination of primary care clinics and a mobile self-management health care app with human coaching results in better management of chronic conditions. This study shows that the primary care services combining a mobile self-management health care app with human coaching are effective in the current primary care system. An implication of this study is the possibility that a mobile self-management health care app with human coaching is a treatment option in the current primary 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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".