Physician step prescription and monitoring to improve <scp>ARTERial</scp> health ( <scp>SMARTER</scp> ): A randomized controlled trial in patients with type 2 diabetes and hypertension
Bibliographic record
Abstract
AIMS: There are few proven strategies to enhance physical activity and cardiometabolic profiles in patients with type 2 diabetes and hypertension. We examined the effects of physician-delivered step count prescriptions and monitoring. METHODS: Participants randomized to the active arm were provided with pedometers and they recorded step counts. Over a 1-year period, their physicians reviewed their records and provided a written step count prescription at each clinic visit. The overall goal was a 3000 steps/day increase over 1 year (individualized rate of increase). Control arm participants were advised to engage in physical activity 30 to 60 min/day. We evaluated effects on step counts, carotid femoral pulse wave velocity (cfPWV, primary) and other cardiometabolic indicators including haemoglobin A1c in diabetes (henceforth abbreviated as A1c) and Homeostasis Model Assessment-Insulin Resistance (HOMA-IR) in participants not receiving insulin therapy. RESULTS: A total of 79% completed final evaluations (275/347; mean age, 60 years; SD, 11). Over 66% of participants had type 2 diabetes and over 90% had hypertension. There was a net 20% increase in steps/day in active vs control arm participants (1190; 95% CI, 550-1840). Changes in cfPWV were inconclusive; active vs control arm participants with type 2 diabetes experienced a decrease in A1c (-0.38%; 95% CI, -0.69 to -0.06). HOMA-IR also declined in the active arm vs the control arm (ie, assessed in all participants not treated with insulin; -0.96; 95% CI, -1.72 to -0.21). CONCLUSIONS: A simple physician-delivered step count prescription strategy incorporated into routine clinical practice led to a net 20% increase in step counts; however, this was below the 3000 steps/day targeted increment. While conclusive effects on cfPWV were not observed, there were improvements in both A1c and insulin sensitivity. Future studies will evaluate an amplified intervention to increase impact.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".