Objective We conducted a laboratory-based calibration study to determine relevant cutpoints for a hip-worn accelerometer among women ≥60 years, considering both type and filtering of counts. Methods Two hundred women wore an ActiGraph GT3X+ accelerometer on their hip while performing eight laboratory-based activities. Oxygen uptake was measured using an Oxycon portable calorimeter. Accelerometer data were analyzed in 15-second epochs for both normal and low frequency extension (LFE) filters. Receiver operating characteristic (ROC) curve analyses were used to calculate cutpoints for sedentary, light (low and high), and moderate to vigorous physical activity (MVPA) using the vertical axis and vector magnitude (VM) counts. Results Mean age was 75.5 years (standard deviation 7.7). The Spearman correlation between oxygen uptake and accelerometry ranged from 0.77 to 0.85 for the normal and LFE filters and for both the vertical axis and VM. The area under the ROC curve was generally higher for VM compared to the vertical axis, and higher for cutpoints distinguishing MVPA compared to sedentary and light low activities. The VM better discriminated sedentary from light low activities compared to the vertical axis. The area under the ROC curves were better for the LFE filter compared to the normal filter for the vertical axis counts, but no meaningful differences were found by filter type for VM counts. Conclusion The cutpoints derived for this study among women ≥60 years can be applied to ongoing epidemiologic studies to define a range of physical activity intensities.

Key Points Question Is light physical activity associated with reduced risk of heart disease in older women? Findings In this cohort study of 5861 women, the highest quartile of light physical activity was associated with a 42% reduced risk of myocardial infarction or coronary death and a 22% reduced risk of incident cardiovascular disease events compared with the lowest quartile of light physical activity. These reduced risks persisted after adjustment for sociodemographic, behavioral, and health status variables, as well as moderate to vigorous physical activity. Meaning This study suggests that all daily life physical activity has a role in the prevention of coronary heart disease and cardiovascular disease in older women.

Background Reproductive factors provide an early window into a woman’s coronary heart disease (CHD) risk, however their contribution to CHD risk stratification is uncertain. Methods and Results In the Women’s Health Initiative Observational Study, we constructed Cox proportional hazards models for CHD including age, pregnancy status, number of live births, age at menarche, menstrual irregularity, age at first birth, stillbirths, miscarriages, infertility ≥ 1 year, infertility cause, and breastfeeding. We next added each candidate reproductive factor to an established CHD risk factor model. A final model was then constructed with significant reproductive factors added to established CHD risk factors. Improvement in C-statistic, net reclassification index (or NRI with risk categories of <5%, 5–<10%, and ≥10% 10-year risk of CHD) and integrated discriminatory index (IDI) were assessed. Among 72,982 women [n=4607 CHD events, median follow-up=12.0 (IQR=8.3–13.7) years, mean (SD) age 63.2 (7.2) years], an age-adjusted reproductive risk factor model had a C-statistic of 0.675 for CHD. In a model adjusted for established CHD risk factors, younger age at first birth, number of still births, number of miscarriages and lack of breastfeeding were positively associated with CHD. Reproductive factors modestly improved model discrimination (C-statistic increased from 0.726 to 0.730; IDI=0.0013, p-value < 0.0001). Net reclassification for women with events was not improved (NRI events=0.007, p-value=0.18); and for women without events was marginally improved (NRI non-events=0.002, p-value=0.04) Conclusions Key reproductive factors are associated with CHD independently of established CHD risk factors, very modestly improve model discrimination and do not materially improve net reclassification.