Self-management of Chronic Conditions Using mHealth Interventions in Korea: A Systematic Review
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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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".