Self-management of Chronic Conditions Using mHealth Interventions in Korea: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Population aging has increased the burden of chronic diseases globally. mHealth is often cited as a viable solution to enhance the management of chronic conditions. In this study, we conducted a systematic review of mHealth interventions for the self-management of chronic diseases in Korea, a highly-connected country with a high chronic care burden. METHODS: Five databases were searched for relevant empirical studies that employed randomized controlled trial (RCT) or quasi-experimental methods published in English or Korean from the years 2008 to 2018. The selected studies were reviewed according to the PRISMA guidelines. The selected studies were classified using the Individual and Family Self-Management Theory conceptual framework. RESULTS: Sixteen studies met the inclusion criteria, 9 of which were targeted towards diabetes management, and 7 of which were RCTs. Other target diseases included hypertension, stroke, asthma, and others. mHealth interventions were primarily delivered through smartphone applications, mobile phones connected to a monitoring device, and short message services (SMS). Various self-management processes were applied, including providing social influence and support, and facilitating self-monitoring and goal setting. Eleven studies showed mHealth interventions to be effective in improving self-management behaviors, biomarkers, or patient-reported outcome measures associated with chronic diseases. CONCLUSIONS: While the number of identified studies was not large, none reported negative impacts of mHealth on selected outcomes. Future studies on mHealth should design interventions with a greater variety of targeted functions and should adopt more rigorous methodologies to strengthen the evidence for its effectiveness in chronic disease management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it