Effect of the Implementation of a Multiple-Behavior Self-Monitoring Intervention on Dietary Intake in Type 2 Diabetes: Secondary Data Analysis
Bibliographic record
Abstract
BACKGROUND: An electronic diary embedded in a mobile device to monitor lifestyle can be as effective as traditional methods. However, the efficacy of self-monitoring multiple behaviors for dietary intake has not been well studied in people with diabetes. OBJECTIVE: This study aimed to compare the effect of using technology-assisted self-monitoring versus paper diaries on changes in dietary intake. METHODS: This is a secondary analysis of data collected from 39 people with type 2 diabetes as part of a 3-month pilot clinical trial. Changes in energy intake and the contribution of total fat intake and total carbohydrate intake to total calories (%) from baseline to after intervention (3 months) were evaluated. RESULTS: In total, 26 (67%) of the 39 participants preferred mobile diaries over paper diaries. Participants in the mobile diary group showed slightly higher self-monitoring adherence. Linear mixed modeling results indicated a significant overall decrease in total energy intake (P=.005), dietary fat intake (P=.01), and carbohydrate intake (P=.08) from baseline to 3 months. No significant group differences were detected (P>.05). CONCLUSIONS: The implementation of a 3-month, multiple-behavior, self-monitoring intervention in Diabetes Self-Management Education programs has resulted in successful reduction in dietary intake (energy, fat, and carbohydrate), whichever self-monitoring method is chosen by participants according to their preferences. Long-term studies are needed to confirm our findings on dietary intake and examine other behavioral and disease outcomes that require monitoring.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".