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

Chevance, Guillaume; Baretta, Dario; Romain, Ahmed Jérôme; Godino, Job G.; Bernard, Paquito

doi:10.1007/s10865-021-00254-6

Cited by 13 publications

(13 citation statements)

References 53 publications

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“…Sleep duration was significantly decreased following HIIE. This was supported by a day-to-day study that indicated there was a negative association between steps and sleep duration [ 63 ]. However, some studies suggested that exercises were an effective strategy to improve sleep duration.…”

Section: Discussionmentioning

confidence: 69%

Effects of Low-Volume High-Intensity Interval Exercise on 24 h Movement Behaviors in Inactive Female University Students

Wiltshire

Baker

et al. 2022

IJERPH

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The purpose of this study was to examine if low-volume, high-intensity interval exercise (HIIE) was associated with changes in 24-h movement behaviors. A quasi-experimental study design was used. We collected accelerometry data from 21 eligible participants who consistently wore an ActiGraph for a period of two-weeks. Differences in behaviors were analyzed using a paired t-test and repeated measures analysis of variance. Regression analysis was used to explore relationships with factors that impacted changes. The results indicated a compensatory increase in sedentary time (ST) (4.4 ± 6.0%, p < 0.01) and a decrease in light-intensity physical activity (LPA) (−7.3 ± 16.7%, p < 0.05). Meanwhile, moderate-intensity physical activity (MPA), vigorous-intensity physical activity (VPA), and total physical activity (TPA) increased following exercise (p < 0.001). Sleep duration and prolonged sedentary time were reduced (p < 0.05). Exercise intensity and aerobic capacity were associated with changes in ST. The results from the study indicate that participating in a low-volume HIIE encouraged participants who were previously inactive to become more active. The observations of increases in ST may have displaced a prolonged sitting time. The decrease in sleeping time observed may be reflecting an increased sleep quality in connection with increased higher-intensity PA.

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Section: Discussionmentioning

confidence: 69%

Effects of Low-Volume High-Intensity Interval Exercise on 24 h Movement Behaviors in Inactive Female University Students

Wiltshire

Baker

et al. 2022

IJERPH

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“…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.…”

Section: Methodsmentioning

confidence: 99%

“…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.…”

Section: Methodsmentioning

confidence: 99%

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

2022

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Background Heart rate (HR), especially at nighttime, is an important biomarker for cardiovascular health. It is known to be influenced by overall physical fitness, as well as daily life physical or psychological stressors like exercise, insufficient sleep, excess alcohol, certain foods, socialization, or air travel causing physiological arousal of the body. However, the exact mechanisms by which these stressors affect nighttime HR are unclear and may be highly idiographic (i.e. individual-specific). A single-case or “ n-of-1” observational study (N1OS) is useful in exploring such suggested effects by examining each subject's exposure to both stressors and baseline conditions, thereby characterizing suggested effects specific to that individual. Objective Our objective was to test and generate individual-specific N1OS hypotheses of the suggested effects of daily life stressors on nighttime HR. As an N1OS, this study provides conclusions for each participant, thus not requiring a representative population. Methods We studied three healthy, nonathlete individuals, collecting the data for up to four years. Additionally, we evaluated model-twin randomization (MoTR), a novel Monte Carlo method facilitating the discovery of personalized interventions on stressors in daily life. Results We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate. Conclusions This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available.

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“…For example, one can examine varying the exposure to factors that impact the physiologic sleep drive to infer the APTE of that factor on sleep. Chevance et al (2021) [46] examined such associations, but stopped short of attempting to draw causal inferences. A reasonable treatment phase or period could be one day, given that periods of sleep, by their very nature, tend to "wash out" the effect of increased sleep pressure resulting from increased levels of waking activity.…”

Section: Why Study Sleep?mentioning

confidence: 99%

“…However, simply getting more sleep leaves one less time to take steps the following day. Hence, raw step count is often associated with sleep duration [46], regardless of how it might affect sleep duration (i.e., our APTE of interest).…”

Section: Data-generating Proceduresmentioning

confidence: 99%

Model-Twin Randomization (MoTR): A Monte Carlo Method for Estimating the Within-Individual Average Treatment Effect Using Wearable Sensors

Daza¹,

Schneider²

2022

Preprint

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Temporally dense single-person "small data" have become widely available thanks to mobile apps and wearable sensors. Many caregivers and self-trackers want to use these data to help a specific person change their behavior to achieve desired health outcomes. Ideally, this involves discerning possible causes from correlations using that person's own observational time series data. In this paper, we estimate within-individual average treatment effects of physical activity on sleep duration, and vice-versa. We introduce the model twin randomization (MoTR; "motor") method for analyzing an individual's intensive longitudinal data. Formally, MoTR is an application of the g-formula (i.e., standardization, back-door adjustment) under serial interference. It estimates stable recurring effects, as is done in n-of-1 trials and single case experimental designs. We compare our approach to standard methods (with possible confounding) to show how to use causal inference to make better personalized recommendations for health behavior change, and analyze 222 days of Fitbit sleep and steps data for one of the authors.

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

Cited by 13 publications

References 53 publications

Effects of Low-Volume High-Intensity Interval Exercise on 24 h Movement Behaviors in Inactive Female University Students

Effects of Low-Volume High-Intensity Interval Exercise on 24 h Movement Behaviors in Inactive Female University Students

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

Model-Twin Randomization (MoTR): A Monte Carlo Method for Estimating the Within-Individual Average Treatment Effect Using Wearable Sensors

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