A Primary Care-Based Exercise Intervention to Improve Metabolic Risk Factors of Cardiovascular Disease
Bibliographic record
Abstract
Prescribing exercise can be an efficacious and cost-effective way for primary care physicians to lower their patients' risk of cardiometabolic disease. Despite the expected benefits, program supervision and monitoring patient compliance remains a challenge, especially in rural settings. Remote monitoring technology could be a way of addressing these issues by increasing patient accountability without requiring more physician visits. PURPOSE: The purpose of the current randomized control trial was to examine the effect of a physician-supervised exercise prescription and the use of remote monitoring technology on blood sugar and lipid profiles in participants with metabolic syndrome. METHODS: Eighty-one men and women (mean age = 57) with metabolic syndrome in rural southwestern Ontario were randomized to a control group (n = 39; exercise prescription and paper-based exercise log) or intervention group (n = 44; exercise prescription and remote blood pressure, blood glucose, and exercise monitoring technology). Fasting blood was collected at baseline, 12, and 24 weeks for serum levels of total cholesterol (TCHL), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), HbA1c and glucose (BG). Using SPSS Version 19.0, a one-way repeated measures ANOVA was used to assess for significant changes across time and between groups. RESULTS: A significant decrease in LDL was observed (p = 0.027) in both groups with time as the main effect. There was a trend towards decreased TCHL (p = 0.063) with time as the main effect. A trend towards increased HDL (p = 0.093) was demonstrated in the intervention group only. There were no significant changes in BG, HbA1c, or TG across time or between groups (p > 0.05). CONCLUSION: An exercise program with self-monitoring technology may help to improve HDL in patients with metabolic syndrome. In addition, an exercise prescription with either self-monitoring technology or a paper-based exercise log may help to improve LDL and TCHL. However, these are preliminary results at the 6-month mark of a one-year study and only include results from half of the study's total sample. As such, final results may differ from those presented here. Thank-you to CIHR for their generous funding.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".