Predictors of physical activity levels one year after the start of cardiac rehabilitation
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».