Evaluation of an online cardiometabolic and weight loss program: a mixed methods study
Bibliographic record
Abstract
Risk factors contributing to cardiovascular diseases (CVD) can be addressed through behavior modification, including changes in diet and physical activity. In 2021, The Wellness Institute (WI), located at Seven Oaks General Hospital, created a virtual cardiometabolic risk reduction program in response to COVID-19 pandemic public health restrictions, encompassing virtual health coaching and lifestyle education. The objective was to evaluate the acceptability, adherence, efficacy, and engagement of the WI online cardiometabolic and weight loss program. The study followed a mixed methods quasi-experimental design. A total of 93 participants enrolled. Quantitative measures including anthropometrics, blood chemistry, and lifestyle were assessed for changes via paired t tests at baseline and 16 weeks. Qualitatively, short answer questionnaires and three focus groups were completed to understand participants’ experiences and program acceptability. We combined qualitative and quantitative data for analysis. Seventy-three participants (64 females, 87.7%) completed the final study visit (age 58 ± 11 years, weight 98 ± 20.9 kg). Of those, 98% attended all coaching sessions, would recommend the program, and reported the coaching sessions helped them stay motivated. A reduction in weight (5 ± 9 kg, p < 0.001), systolic blood pressure (6 ± 10 mmHg, p < 0.001), and diastolic blood pressure (4 ± 8 mmHg, p < 0.001) were observed. Lifestyle factors also improved, including increases in physical activity ( p < 0.05). Most participants adhered to the program and found it acceptable. Completion was associated with an improvement in weight and blood pressure. These findings highlight the potential of virtual programming to contribute to improving lifestyle and health. Clinicaltrials.gov (ID# NCT04784624CT).
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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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".