Diet Behavior Change Techniques in Type 2 Diabetes: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE Dietary behavior is closely connected to type 2 diabetes. The purpose of this meta-analysis was to identify behavior change techniques (BCTs) and specific components of dietary interventions for patients with type 2 diabetes associated with changes in HbA1c and body weight. RESEARCH DESIGN AND METHODS The Cochrane Library, CINAHL, Embase, PubMed, PsycINFO, and Scopus databases were searched. Reports of randomized controlled trials published during 1975–2017 that focused on changing dietary behavior were selected, and methodological rigor, use of BCTs, and fidelity and intervention features were evaluated. RESULTS In total, 54 studies were included, with 42 different BCTs applied and an average of 7 BCTs used per study. Four BCTs—“problem solving,” “feedback on behavior,” “adding objects to the environment,” and “social comparison”—and the intervention feature “use of theory” were associated with >0.3% (3.3 mmol/mol) reduction in HbA1c. Meta-analysis revealed that studies that aimed to control or change the environment showed a greater reduction in HbA1c of 0.5% (5.5 mmol/mol) (95% CI −0.65, −0.34), compared with 0.32% (3.5 mmol/mol) (95% CI −0.40, −0.23) for studies that aimed to change behavior. Limitations of our study were the heterogeneity of dietary interventions and poor quality of reporting of BCTs. CONCLUSIONS This study provides evidence that changing the dietary environment may have more of an effect on HbA1c in adults with type 2 diabetes than changing dietary behavior. Diet interventions achieved clinically significant reductions in HbA1c, although initial reductions in body weight diminished over time. If appropriate BCTs and theory are applied, dietary interventions may result in better glucose control.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".