Nonlinear analysis of ambulatory activity patterns in community-dwelling older adults

Abstract

BackgroundThe natural ambulatory activity patterns of older adults are not well understood. User-worn monitors illuminate patterns of ambulatory activity and generate data suitable for analysis using measures derived from nonlinear dynamics.MethodsAmbulatory activity data were collected continuously from 157 community-dwelling older adults for 2 weeks. Participants were separated post hoc into groups based on the mean number of steps per day: highly active (steps ≥ 10,000), moderately active (5,000 ≤ steps < 10,000 steps), and inactive (steps <5,000 steps). Detrended fluctuation analysis (DFA), entropy rate (ER), and approximate entropy (ApEn) were used to examine the complexity of daily time series composed of 1-minute step count values. Coefficient of variation was used to examine time series variability. Between-group differences for each parameter were evaluated using analysis of variance.ResultsAll groups displayed patterns of fluctuating step count values containing complex temporal structure. DFA, ER, and ApEn parameter values increased monotonically and significantly with increasing activity level (p