Relationship Among Energy Balance, Health Outcome, and 24-hour Time-Use Lifestyle Behaviors
Principal Investigator
Name
Yaozong He
Degrees
Ph.D. Student
Institution
University of Illinois at Urbana-Champaign
Position Title
Ph.D. Student
Email
About this CDAS Project
Study
IDATA
(Learn more about this study)
Project ID
IDATA-34
Initial CDAS Request Approval
Mar 2, 2020
Title
Relationship Among Energy Balance, Health Outcome, and 24-hour Time-Use Lifestyle Behaviors
Summary
To fight with the pandemic of chronic diseases, the most effective and economical way is lifestyle intervention. The most important part of health behavior change is to fully understand what to do, why to do it, and how to do it in detail. The lifestyle intervention prescriptions are given according to the guidelines of the diet, physical activities (PA), and sleep. However, the guidelines only provide suggestions on the daily or weekly totals without details about the time-use over 24-hour and the interactions between these three types of behaviors. As more evidence has accumulated, the different time-use patterns of diet, PA (including sedentary behavior), and sleep result in different health outcomes even when the aggregations of each category over 24-hour or 7-day period remain the same (de Cabo et al. 2019; Pedišić et al. 2017). However, most past research studies focused only on either diet or PA, and sleep but not combining all three of them together and, as a result, the impact of the patterns and interactions of these behaviors throughout the 24-hour period has not been examined carefully.
To better understand the relationship between time-use behavior and the health outcomes, this study will primarily focus on the patterns of the three types of behaviors over 24-hour. Eating brings the calorie intake; PA, and sleep mark the energy expenditure; PA is further broken down into sedentary behavior, light-intensity physical activity, moderate-intensity physical activity, and vigorous-intensity physical activity, four sub-categories. We would use compositional data analysis for the research (Chastin et al., 2015; Dumuid et al., 2017; Solans et al., 2019). ASA24 provides time markers, calorie intake, and food groups consumption for diet information; ACT24, ActiGraph, PA Log, and HRM will give details about PA and sleep behaviors.
The study will then investigate the energy intake and expenditure process along with the patterns we identified to see if specific patterns are associated with lower calorie intake, shorter sedentary behavior period, higher PA level, higher quality sleep, and better health outcomes. Doubly-labeled water (DLW) in IDATA will serve as the golden standard of energy expenditure measurement, and estimated maximum volume of oxygen (VO2 max), BMI, waist circumference, hip circumference and other results from fitness tests, demographic information will contribute to the health outcome data.
The study will consider the variation of patterns over time, to see if there is any difference between workdays and weekends, or between different seasons, to provide us with a better understanding of certain environmental influences on health behaviors, by using other datasets from IDATA study.
To better understand the relationship between time-use behavior and the health outcomes, this study will primarily focus on the patterns of the three types of behaviors over 24-hour. Eating brings the calorie intake; PA, and sleep mark the energy expenditure; PA is further broken down into sedentary behavior, light-intensity physical activity, moderate-intensity physical activity, and vigorous-intensity physical activity, four sub-categories. We would use compositional data analysis for the research (Chastin et al., 2015; Dumuid et al., 2017; Solans et al., 2019). ASA24 provides time markers, calorie intake, and food groups consumption for diet information; ACT24, ActiGraph, PA Log, and HRM will give details about PA and sleep behaviors.
The study will then investigate the energy intake and expenditure process along with the patterns we identified to see if specific patterns are associated with lower calorie intake, shorter sedentary behavior period, higher PA level, higher quality sleep, and better health outcomes. Doubly-labeled water (DLW) in IDATA will serve as the golden standard of energy expenditure measurement, and estimated maximum volume of oxygen (VO2 max), BMI, waist circumference, hip circumference and other results from fitness tests, demographic information will contribute to the health outcome data.
The study will consider the variation of patterns over time, to see if there is any difference between workdays and weekends, or between different seasons, to provide us with a better understanding of certain environmental influences on health behaviors, by using other datasets from IDATA study.
Aims
• Explore the time-use pattern of diet, PA, and sleep over a 24-hour period.
• Examine the energy intake and expenditure flow over the time-use pattern.
• Analyze the variation of time-use patterns over the workday, holiday, and seasonal changes throughout the year.
• Investigate if the dietary patterns including food groups and their proportions in each eating occasion would influence or predict the time interval or total calorie intake for the next eating occasion when taking the PA between these two eating occasions into account.
• Research the relationship between time-use patterns and health outcomes, to see if certain combined patterns would predict better health outcomes.
Collaborators
Weimo Zhu, Ph.D., University of Illinois at Urbana-Champaign