Day-to-day associations between sleep and physical activity: a set of person-specific analyses in adults with overweight and obesity

Abstract

The objective of the present study was to estimate whether physical activity (i.e., step counts) on one day was associated with both sleep quality (i.e., sleep efficiency) and quantity (i.e., total sleep time) the following night and to examine to what extent sleep on one night was associated with physical activity the next day. We collected data continuously with a Fitbit Charge 3 among 33 young adults with overweight and obesity participating to a weight management program. A total of 7094 days and nights were analyzed (215 days on average per participants). Person-specific models (i.e., participants were not pooled together) were conducted to test the bi-directional associations for each participant separately. Results from linear regressions and Generalized Additive Models (GAMs) were compared to account for potential non-linearities in associations. Results suggest an absence of association between steps and sleep efficiency in the two directions for most participants [i.e., these associations were not significant for 31 (94%) and 32 (97%) of the participants]. More heterogeneous results were observed for the association between steps and total sleep time, with 19 participants (58%) showing a negative association between total sleep time and next day steps, and 9 (27%) showing a negative association between steps and next day total sleep time (results of the linear regressions). Taken together, these results suggest a potential conflicting association between total sleep time and physical activity in the daily lives of young adults with overweight and obesity.

What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data

“…With respect to our hypotheses, 71 investigated a number of these that we relied on in forming our a priori hypotheses in Subsection “A Priori Hypotheses.” True to our exploratory goals, these are broad in scope and do not specify exact quantities, but rather directions (e.g. increasing or introducing X causes Y to decrease).…”

“…increasing or introducing X causes Y to decrease). Rather, we created our a priori hypotheses based on both the findings of 71 and our own experiences and reflections.…”

“…time frame of available data). These are the time series linear models fit by, 71 combinations of which might together comprise a vector autoregression. “Granger” refers to so-called “Granger causality,” which by causal inference definition is in fact only an association/correlation/prediction of one time series with another time series—not a causal effect of one time series on another, as we are attempting to estimate in this study.…”

“…We also included calendar-based control variables in our models, following the examples in 71 Table 1 (e.g. weekend indicator).…”

Exploring person-specific associations of situational motivation and readiness with leisure-time physical activity effort and experience

“…Existing research using EMA, however, has been criticized for reducing the richness of data in order to conduct traditional statistical approaches (e.g., linear regression, multilevel modeling; [ 21 ]) for generalizing relationships across the sample. Few studies to date have explicitly demonstrated evidence for heterogeneity in within-person associations specific to physical activity [ 22 , 23 ], which were limited to quantitative characteristics of performed physical activity (e.g., duration, intensity, steps per day) that influence physiological adaptations. Beyond that, experiential factors must be accounted for when assessing person-specific associations, as contemporary models and theories in exercise psychology [ 24 – 27 ] identify affective and evaluative responses arising from physical activity as determinants of future intentions and behavior, in general.…”