International journal of cardiology · 2023
Factors that predict compliance in a virtual cardiac rehabilitation program.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Eichner Natalie Z M, Zhu Qiuyu Martin, Granados Adelita, Berry Natalia C, Saha Sudip K
Аннотация
BACKGROUND: Despite the well-established benefits of cardiac rehabilitation (CR) for patients with cardiovascular disease (CVD), participation in CR remain low. Virtual CR programs present a unique opportunity to promote utilization. To date, few virtual CR cohorts have been analyzed for compliance. This study aims to determine factors that predict compliance within a large virtual CR program in the United States.
METHODS: We analyzed 1409 patients enrolled in the Kaiser Permanente Mid-Atlantic States Virtual CR program that consists of 12 CR sessions via telephone. Demographic characteristics, as well as body weight, blood pressure, HbA1c level, and smoking status were collected at admission. Patients were further classified by CVD diagnosis codes. Compliance was defined as at least 75% (9/12 sessions) attendance. Data was analyzed using simple and multiple regression models with significance defined as P < 0.05.
RESULTS: Age was the single strongest predictor for virtual CR compliance (adjusted R2 = 0.58; P < 0.001), and non-compliant patients were younger. HbA1C level, CVD diagnosis codes, and smoking status each moderately predicted compliance (adjusted R2 = 0.48, 0.42, and 0.31, respectively; P < 0.001). Smoking and HbA1C level combined in a multiple regression model significantly improved prediction of compliance (adjusted R2 = 0.79, P < 0.01). Sex, baseline weight or hypertension were not significant predictors of CR compliance.
CONCLUSIONS: Age, diabetes, CVD diagnoses, smoking status at admission are independent predictors of compliance in a large virtual CR program. Targeted intervention could be designed accordingly to improve CR compliance.
METHODS: We analyzed 1409 patients enrolled in the Kaiser Permanente Mid-Atlantic States Virtual CR program that consists of 12 CR sessions via telephone. Demographic characteristics, as well as body weight, blood pressure, HbA1c level, and smoking status were collected at admission. Patients were further classified by CVD diagnosis codes. Compliance was defined as at least 75% (9/12 sessions) attendance. Data was analyzed using simple and multiple regression models with significance defined as P < 0.05.
RESULTS: Age was the single strongest predictor for virtual CR compliance (adjusted R2 = 0.58; P < 0.001), and non-compliant patients were younger. HbA1C level, CVD diagnosis codes, and smoking status each moderately predicted compliance (adjusted R2 = 0.48, 0.42, and 0.31, respectively; P < 0.001). Smoking and HbA1C level combined in a multiple regression model significantly improved prediction of compliance (adjusted R2 = 0.79, P < 0.01). Sex, baseline weight or hypertension were not significant predictors of CR compliance.
CONCLUSIONS: Age, diabetes, CVD diagnoses, smoking status at admission are independent predictors of compliance in a large virtual CR program. Targeted intervention could be designed accordingly to improve CR compliance.