Analysis of the 24-h activity cycle: An illustration examining the association with cognitive function in the Adult Changes in Thought study

Abstract

The 24-h activity cycle (24HAC) is a new paradigm for studying activity behaviors in relation to health outcomes. This approach inherently captures the interrelatedness of the daily time spent in physical activity (PA), sedentary behavior (SB), and sleep. We describe three popular approaches for modeling outcome associations with the 24HAC exposure. We apply these approaches to assess an association with a cognitive outcome in a cohort of older adults, discuss statistical challenges, and provide guidance on interpretation and selecting an appropriate approach. We compare the use of the isotemporal substitution model (ISM), compositional data analysis (CoDA), and latent profile analysis (LPA) to analyze 24HAC. We illustrate each method by exploring cross-sectional associations with cognition in 1,034 older adults (Mean age = 77; Age range = 65–100; 55.8% female; 90% White) who were part of the Adult Changes in Thought (ACT) Activity Monitoring (ACT-AM) sub-study. PA and SB were assessed with thigh-worn activPAL accelerometers for 7-days. For each method, we fit a multivariable regression model to examine the cross-sectional association between the 24HAC and Cognitive Abilities Screening Instrument item response theory (CASI-IRT) score, adjusting for baseline characteristics. We highlight differences in assumptions and the scientific questions addressable by each approach. ISM is easiest to apply and interpret; however, the typical ISM assumes a linear association. CoDA uses an isometric log-ratio transformation to directly model the compositional exposure but can be more challenging to apply and interpret. LPA can serve as an exploratory analysis tool to classify individuals into groups with similar time-use patterns. Inference on associations of latent profiles with health outcomes need to account for the uncertainty of the LPA classifications, which is often ignored. Analyses using the three methods did not suggest that less time spent on SB and more in PA was associated with better cognitive function. The three standard analytical approaches for 24HAC each have advantages and limitations, and selection of the most appropriate method should be guided by the scientific questions of interest and applicability of each model’s assumptions. Further research is needed into the health implications of the distinct 24HAC patterns identified in this cohort.

The Association of the 24-Hour Activity Cycle Profiles With Cognition in Older Adults With Mild Cognitive Impairment: A Cross-Sectional Study

“…Only a few cross-sectional studies have investigated cognition and 24-HAC composition in older adults ( 28 , 65–68 ), with equivocal evidence. Wu and colleagues ( 28 ) investigated whether the daily composition of sitting, standing, and stepping (no sleep data) was associated with global cognition among 1 034 cognitively unimpaired older adults (+65 years).…”

“…Compositional data analysis techniques address limitations caused by multicollinearity due to the inclusion of all 24-HAC behaviors within a single model ( 26 ). It involves expressing time spent (or proportion of time) engaging in different time-use behaviors during a finite period (eg, 1 440 min or 100%) in relative terms as a set of isometric log-ratio coordinates ( 28 ). These coordinates contain all the relative information regarding the 24-HAC time-use behaviors.…”

“…The percentage of time engaging in the remaining two behaviors was coded in the numerator (+1) and the denominator (−1). The isometric log-ratio transforms each behavior’s composition to a unique behavior-1 vector on a new coordinate system where each new coordinate is a log-ratio that falls along the real line ( 28 ). We chose the following coordinates (1) the first coordinate included all relative information regarding MVPA versus the geometric mean of % of time spent engaging in SB, light PA, and sleep; (2) the second coordinate represented SB versus geometric mean of light PA and sleep; and (3) the last coordinate included light PA versus sleep.…”

“…We chose the following coordinates (1) the first coordinate included all relative information regarding MVPA versus the geometric mean of % of time spent engaging in SB, light PA, and sleep; (2) the second coordinate represented SB versus geometric mean of light PA and sleep; and (3) the last coordinate included light PA versus sleep. This set of isometric log-ratios was also adopted by Wu and colleagues ( 28 ). The isometric log-ratios transformations of the 24-HAC behaviors were expressed using equations (1)–(3), where the numbers preceding the log-ratios are normalizing constants necessary for the desirable mathematical properties of the transformed coordinates:…”

“…This approach considers the compositions (ie, the proportion of time) of each 24-HAC behavior as its fundamental unit of analysis. Latent profile analysis can then identify groups of individuals with distinct 24-HAC activity patterns (ie, activity profiles) ( 28 ). To the best of our knowledge, studies have yet to examine what types of 24-HAC activity profiles exist in older adults with MCI or whether these activity profiles differ in cognition.…”