Personal Health Coaching as a Type 2 Diabetes Mellitus Self-Management Strategy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
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Bibliographic record
Abstract
OBJECTIVE: Personal health coaching (PHC) programs have become increasingly utilized as a type 2 diabetes mellitus (T2DM) self-management intervention strategy. This article evaluates the impact of PHC programs on glycemic management and related psychological outcomes. DATA SOURCES: Electronic databases (CINAHL, MEDLINE, PubMed, PsycINFO, and Web of Science). STUDY INCLUSION AND EXCLUSION CRITERIA: Randomized controlled trials (RCT) published between January 1990 and September 2017 and focused on the effectiveness of PHC interventions in adults with T2DM. DATA EXTRACTION: Using prespecified format guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. DATA SYNTHESIS: Quantitative synthesis for primary (ie, hemoglobin A1c [HbA1c]) and qualitative synthesis for selected psychological outcomes. RESULTS: Meta-analyses of 22 selected publications showed PHC interventions favorably impact HbA1c levels in studies with follow-ups at ≤3 months (-0.32% [95% confidence interval, CI = -0.55 to -0.09%]), 4 to 6 months (-0.50% [95% CI = -0.65 to -0.35%], 7 to 9 months (-0.66% [95% CI = -1.04 to -0.28%]), and 12 to 18 months (-0.24% [95% CI = -0.38 to -0.10%]). Subsequent subgroup analyses led to no conclusive patterns, except for greater magnitude of effect size in studies with conventional (2-arm) RCT design. CONCLUSIONS: The PHC appears effective in improving glycemic control. Further research is required to assess the effectiveness of specific program components, training, and supervision approaches and to determine the cost-effectiveness of PHC interventions.
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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 | 0.071 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.061 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it