Increase in Daily Steps After an Exercise Specialist Led Lifestyle Intervention for Adults With Type 2 Diabetes in Primary Care: A Controlled Implementation Trial
Bibliographic record
Abstract
OBJECTIVE: To determine the effectiveness of an exercise specialist led lifestyle program for adults with type 2 diabetes in primary care. METHODS: Eligible participants from 4 primary care networks in Alberta, Canada were assigned to either a lifestyle program or a control group. The program targeted increased daily walking through individualized daily pedometer step goals for the first 3 months and brisk walking speed, along with substitution of low-relative to high-glycemic index foods over the next 3 months. The outcomes were daily steps, diet, and clinical markers, and were compared using random effects models. RESULTS: 198 participants were enrolled (102 in the intervention and 96 in the control). For all participants, (51% were women), mean age 59.5 (SD 8.3) years, A1c 6.8% (SD 1.1), BMI 33.6 kg/m(2) (SD 6.5), systolic BP 125.6 mmHg (SD 16.2), glycemic index 51.7 (4.6), daily steps 5879 (SD 3130). Daily steps increased for the intervention compared with the control at 3-months (1292 [SD 2698] vs. 418 [SD 2458] and 6-months (1481 [SD 2631] vs. 336 [SD 2712]; adjusted P = .002). No significant differences were observed for diet or clinical outcomes. CONCLUSIONS: A 6-month lifestyle program delivered in primary care by an exercise specialist can be effective for increasing daily walking among adults with recently diagnosed type 2 diabetes. This short-term increase in daily steps requires longer follow-up to estimate the potential impact on health outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".