Accelerometer‐Derived Daily Life Movement Classified by Machine Learning and Incidence of Cardiovascular Disease in Older Women: The OPACH Study

Abstract

Background Current physical activity guidelines focus on volume and intensity for CVD prevention rather than common behaviors responsible for movement, including those for daily living activities. We examined the associations of a machine‐learned, accelerometer‐measured behavior termed daily life movement (DLM) with incident CVD. Methods and Results Older women (n=5416; mean age, 79±7 years; 33% Black, 17% Hispanic) in the Women’s Health Initiative OPACH (Objective Physical Activity and Cardiovascular Health) study without prior CVD wore ActiGraph GT3X+ accelerometers for up to 7 days from May 2012 to April 2014 and were followed for physician‐adjudicated incident CVD through February 28th, 2020 (n=616 events). DLM was defined as standing and moving in a confined space such as performing housework or gardening. Cox models estimated hazard ratios (HR) and 95% CI, adjusting for age, race and ethnicity, education, alcohol use, smoking, multimorbidity, self‐rated health, and physical function. Restricted cubic splines examined the linearity of the DLM‐CVD dose‐response association. We examined effect modification by age, body mass index, Reynolds Risk Score, and race and ethnicity. Adjusted HR (95% CIs) across DLM quartiles were: 1.00 (reference), 0.68 (0.55–0.84), 0.70 (0.56–0.87), and 0.57 (0.45–0.74); p‐trend0.09). There was no evidence of effect modification by age, body mass index, Reynolds Risk Score, or race and ethnicity. Conclusions Higher DLM was independently associated with a lower risk of CVD in older women. Describing the beneficial associations of physical activity in terms of common behaviors could help older adults accumulate physical activity.

Keywords

• aging
• cardiovascular disease
• epidemiology
• lifestyle
• machine learning
• primary prevention