Predictors of physical activity levels one year after the start of cardiac rehabilitation
Bibliographic record
Abstract
Abstract Introduction Physical activity levels often decline after cardiac rehabilitation (CR) completion and a significant number of patients remain physically inactive. While CR participation is associated with a 32% risk reduction in all-cause mortality, this effect is expected to increase if patients maintain an active lifestyle. Predicting physical activity levels after CR helps to identify patients at risk of relapsing into an inactive lifestyle. Purpose To identify patient characteristics that predict their objectively assessed physical activity level (PAL) one year after the start of a CR programme. Methods We used data from the SmartCare-CAD clinical trial, in which 300 patients with coronary artery disease entering phase 2 outpatient CR were randomised between May 2016 and July 2018 to centre-based CR with supervised training or telerehabilitation with relapse prevention. Follow-up was 12 months. PAL was calculated using accelerometer and heart rate sensor data. For the current analysis, patients in the intervention and control group were pooled, as no significant between-group difference in PAL was observed in the response over time. We performed univariate regression analysis to identify possible predictors (p<0.20) for PAL at 12 months, followed by a multiple regression analysis. Results Patients with both baseline and 12-month PAL data available (n=206) were included in the analysis (89% male, mean age 61.0 ± 9.7 years). Univariate analysis revealed 4 predictors of PAL at 12 months: higher baseline PAL, higher baseline percentage of expected exercise capacity and greater increase in exercise capacity at 3 months were associated with higher 12-month PAL levels, whereas higher educational level was associated with a lower 12-month PAL level. The overall regression model including these 4 predictors was statistically significant (adjusted R² = 0.202, F(4, 195) = 13.61, p = < 0.001), with all variables being independent predictors (Table 1). Other baseline characteristics (i.e. age, sex, BMI, health literacy, comorbidity index, working status and treatment allocation) were not related to 12-month PAL levels. Conclusion Predicting physical activity levels one year after the start of cardiac rehabilitation is partly possible, with 20% of the variability being explained by our regression model. Predictive characteristics for a low PAL at 12 months were low PAL at baseline, low baseline exercise capacity and little improvement of exercise capacity during CR, with PAL at baseline being of most influence. Surprisingly, high educational level was also associated with a lower PAL at 12 months. These findings help identify patients that may benefit from personalised CR programmes and extended guidance.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".