  The 24-hour activity cycle (24HAC) is a new paradigm for studying activity
behaviors in relation to health outcomes. This approach captures the
interrelatedness of the daily time spent in physical activity (PA), sedentary
behavior (SB), and sleep. We illustrate and compare the use of three popular
approaches, namely isotemporal substitution model (ISM), compositional data
analysis (CoDA), and latent profile analysis (LPA) for modeling outcome
associations with the 24HAC. We apply these approaches to assess an association
with a cognitive outcome, measured by CASI item response theory (IRT) score, in
a cohort of 1034 older adults (mean [range] age = 77 [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. We highlight differences in assumptions between the
three approaches, discuss statistical challenges, and provide guidance on
interpretation and selecting an appropriate approach. ISM is easiest to apply
and interpret; however, the typical ISM model assumes a linear association.
CoDA specifies a non-linear association through isometric logratio
transformations that are more challenging to apply and interpret. LPA can
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. The selection of
the most appropriate method should be guided by the scientific questions of
interest and the applicability of each model's assumptions. The analytic
results did not suggest that less time spent on SB and more in PA was
associated with better cognitive function. Further research is needed into the
health implications of the distinct 24HAC patterns identified in this cohort.
