Women’s Improvements in Cardiorespiratory Fitness Following Cardiac Rehabilitation Differ by Body Mass Index Category
Bibliographic record
Abstract
Background Improving women's cardiovascular outcomes requires optimizing cardiorespiratory fitness (CRF), as higher CRF predicts improved mortality in people with cardiovascular disease (CVD). As such, increasing CRF is a key goal of cardiac rehabilitation (CR). This study assesses the potential influence of body habitus, assessed by body mass index (BMI), on improvements in CRF in women with CVD. Methods Women (18+ years) diagnosed with CVD who completed a 12-week exercise-based CR program between 1996 and 2016 were included in this retrospective analysis. Women completed a symptom-limited graded exercise test before CR and at CR completion to determine CRF via peak metabolic equivalents (METs). Women were categorized by baseline BMI: normal = 18.5 to 24.9 kg/m 2 , overweight = 25.0 to 29.9 kg/m 2 , and obese ≥ 30 kg/m 2 . Mixed analysis of covariance (ANCOVA) was performed to evaluate the impact of BMI classification on ΔMETs at 12 weeks. Results Data from 1313 women (mean age = 62 ± 11 years) were analyzed. Results from mixed ANCOVA indicated a significant time (pre-CR, 12 weeks) by BMI category interaction (F [2,1307] = 3.20, P = 0.041, ƞp 2 = 0.005). Follow-up analyses of variance (ANOVAs) showed significant improvements in ΔMETs in women with normal and overweight BMI categories (standard mean difference =1.03, n = 454 and 0.92, n = 461, respectively, P < 0.001). However, ΔMETs among women classified as obese was nonsignificant using a Bonferroni-adjusted alpha of 0.017 (standardized mean difference [SMD] = 0.79, P = 0.028; n = 398). Conclusions A 12-week exercise-based CR program increased CRF in women classified as normal or overweight by BMI, whereas those with obesity did not realize similar improvements. Women with obesity may need tailored strategies to increase their improvements in CRF in CR. Clinical Trial Registration REB18-0083.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".