Prospective Associations of Accelerometer‐Measured Machine‐Learned Sedentary Behavior With Death Among Older Women: The OPACH Study

Abstract

Background Sedentary behavior is a recognized mortality risk factor. The novel and validated convolutional neural network hip accelerometer posture algorithm highly accurately classifies sitting and postural changes compared with accelerometer count cut points. We examined the prospective associations of convolutional neural network hip accelerometer posture–classified total sitting time and mean sitting bout duration with all‐cause and cardiovascular disease (CVD) death. Methods and Results Women (n=5856; mean±SD age, 79±7 years; 33% Black women, 17% Hispanic or Latina women, 50% White women) in the Women's Health Initiative Objective Physical Activity and Cardiovascular Health (OPACH) Study wore the ActiGraph GT3X+ for ~7 days from May 2012 to April 2014 and were followed through February 19, 2022 for all‐cause and CVD death. The convolutional neural network hip accelerometer posture algorithm classified total sitting time and mean sitting bout duration from GT3X+ output. Over follow‐up (median, 8.4 years; range, 0.1–9.9), there were 1733 deaths (632 from CVD). Adjusted Cox regression hazard ratios (HRs) comparing women in the highest total sitting time quartile (>696 min/d) to those in the lowest (15 minutes) to the shortest (<9.3 minutes) were 1.43 (95% CI; 1.23–1.66; P‐trend<0.001) for all‐cause death and 1.52 (95% CI; 1.18–1.96; P‐trend<0.001) for CVD death. Apparent nonlinear associations for total sitting time suggested higher all‐cause death (P nonlinear=0.009) and CVD death (P nonlinear=0.008) risk after ~660 to 700 min/d. Conclusions Higher total sitting time and longer mean sitting bout duration are associated with higher all‐cause and CVD mortality risk among older women. These data support interventions aimed at reducing both total sitting time and interrupting prolonged sitting.

Keywords

• aging
• epidemiology
• public health
• women's health