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

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+ ac-celerometers 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, educa-tion, 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-trend<0.001. The HR (95% CI) for each 1-hour increment in DLM was 0.86 (0.80– 0.92) with evidence of a linear dose-response association (p non-linear>0.09). 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.