Benno Krachler, Anna Söderholm, Fanny Ekman, Frida Lindberg, Joakim Lindbäck, Johan Nilsson Sommar, Eva-Lotta Glader, Bernt Lindahl
{"title":"Intensive Lifestyle Intervention for Cardiometabolic Prevention Implemented in Healthcare: Higher Risk Predicts Premature Dropout","authors":"Benno Krachler, Anna Söderholm, Fanny Ekman, Frida Lindberg, Joakim Lindbäck, Johan Nilsson Sommar, Eva-Lotta Glader, Bernt Lindahl","doi":"10.1177/15598276241259961","DOIUrl":null,"url":null,"abstract":"AimsPatient characteristics and treatment setting are potential predictors of premature dropout from lifestyle interventions, but their relative importance is unknown.MethodsFrom the quality registry of the unit for behavioral medicine, Umeå University hospital, we identified 2589 patients who had been enrolled in a multimodal lifestyle intervention for cardiometabolic risk reduction between 2006 and 2015. Baseline characteristics predicting dropout before 1-year follow-up were selected by a stepwise logistic regression algorithm.ResultsBetter physical health and older age predicted full participation, with odds ratios for premature dropout (ORs) of .44 (95% confidence interval (CI) .31-.63), and .47 (95% CI .34-.65) in the highest compared to the lowest quartile, respectively. Odds of premature dropout were also lower among female participants, .71 (95% CI .58-.89). Premature dropout was predicted by higher BMI, snuffing tobacco, and smoking, with ORs of 1.53 (95% CI 1.13-2.08) in the highest compared to the lowest quartile of BMI, 1.37 (95% CI 1.03-1.81) comparing snuff user with non-users and 2.53 (95% CI 1.79-3.61) comparing smokers with non-smokers. Odds ratio for premature dropout among inpatients compared with outpatients was .84 (95% CI .68-1.04).ConclusionHigher risk at baseline predicts premature dropout.","PeriodicalId":47480,"journal":{"name":"American Journal of Lifestyle Medicine","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Lifestyle Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/15598276241259961","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
AimsPatient characteristics and treatment setting are potential predictors of premature dropout from lifestyle interventions, but their relative importance is unknown.MethodsFrom the quality registry of the unit for behavioral medicine, Umeå University hospital, we identified 2589 patients who had been enrolled in a multimodal lifestyle intervention for cardiometabolic risk reduction between 2006 and 2015. Baseline characteristics predicting dropout before 1-year follow-up were selected by a stepwise logistic regression algorithm.ResultsBetter physical health and older age predicted full participation, with odds ratios for premature dropout (ORs) of .44 (95% confidence interval (CI) .31-.63), and .47 (95% CI .34-.65) in the highest compared to the lowest quartile, respectively. Odds of premature dropout were also lower among female participants, .71 (95% CI .58-.89). Premature dropout was predicted by higher BMI, snuffing tobacco, and smoking, with ORs of 1.53 (95% CI 1.13-2.08) in the highest compared to the lowest quartile of BMI, 1.37 (95% CI 1.03-1.81) comparing snuff user with non-users and 2.53 (95% CI 1.79-3.61) comparing smokers with non-smokers. Odds ratio for premature dropout among inpatients compared with outpatients was .84 (95% CI .68-1.04).ConclusionHigher risk at baseline predicts premature dropout.
目的患者特征和治疗环境是生活方式干预过早退出的潜在预测因素,但它们的相对重要性尚不清楚。方法我们从于默奥大学医院行为医学科的质量登记册中确定了2589名患者,这些患者在2006年至2015年间参加了降低心脏代谢风险的多模式生活方式干预。结果较好的身体健康状况和较大的年龄预示着完全参与,最高四分位数与最低四分位数相比,过早退出的几率比(ORs)分别为.44(95% 置信区间 (CI) .31-.63)和.47(95% CI .34-.65)。女性参与者过早辍学的几率也较低,为 0.71 (95% CI .58-.89)。预测过早辍学的因素包括较高的体重指数、吸食烟草和吸烟,与体重指数最低的四分位数相比,体重指数最高的四分位数的OR值为1.53(95% CI 1.13-2.08),吸食烟草者与非吸食者的OR值为1.37(95% CI 1.03-1.81),吸烟者与非吸烟者的OR值为2.53(95% CI 1.79-3.61)。住院患者与门诊患者相比,过早辍学的风险比为 0.84 (95% CI 0.68-1.04)。