Personal Health Coaching as a Type 2 Diabetes Mellitus Self-Management Strategy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
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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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 | 0.028 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".