Real-World Evidence of a Hospital-Linked Digital Health App for the Control of Hypertension and Diabetes Mellitus in South Korea: Nationwide Multicenter Study
Bibliographic record
Abstract
BACKGROUND: Digital health care apps have been widely used for managing chronic conditions such as diabetes mellitus and hypertension, providing promising prospects for enhanced health care delivery, increased patient engagement, and improved self-management. However, the impact of integrating these apps within hospital systems for managing such conditions still lacks conclusive evidence. OBJECTIVE: We aimed to investigate the real-world effectiveness of using hospital-linked digital health care apps in lowering blood pressure (BP) and blood glucose levels in patients with hypertension and diabetes mellitus. METHODS: Nationwide multicenter data on demographic characteristics and the use of a digital health care app from 233 hospitals were collected for participants aged 20 to 80 years in South Korea between August 2021 and June 2022. We divided the participants into 2 groups: 1 group consisted of individuals who exclusively used the digital health app (control) and the other group used the hospital-linked digital health app. All the patients participated in a 12-week digital health care intervention. We conducted a comparative analysis to assess the real-world effectiveness of the hospital-linked digital health app. The primary outcome was the differences in the systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG) level, and postprandial glucose (PPG) level between baseline and 12 weeks. RESULTS: A total of 1029 participants were analyzed for the FBG level, 527 participants were analyzed for the PPG level, and 2029 participants for the SBP and DBP were enrolled. After 12 weeks, a hospital-linked digital health app was found to reduce SBP (-5.4 mm Hg, 95% CI -7.0 to -3.9) and DBP (-2.4 mm Hg, 95% CI -3.4 to -1.4) in participants without hypertension and FBG level in all participants (those without diabetes, -4.4 mg/dL, 95% CI -7.9 to -1.0 and those with diabetes, -3.2 mg/dL, 95% CI -5.4 to -1.0); however, there was no statistically significant difference compared to the control group (using only digital health app). Specifically, participants with diabetes using a hospital-linked digital health app demonstrated a significant decrease in PPG after 12 weeks (-10.9 mg/dL, 95% CI -31.1 to -5.3) compared to those using only a digital health app (P=.006). CONCLUSIONS: Hospital-linked digital interventions have greatly improved glucose control for diabetes compared with using digital health technology only. These hospital-linked digital health apps have the potential to offer consumers and health care professionals cost-effective support in decreasing glucose levels when used in conjunction with self-monitoring.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".