This application addresses the broad Challenge Area (01) Behavior, Behavioral Change, and Prevention and specific Challenge Area 01-HL-101: Develop innovative technologies and measurements to assess and provide real-time feedback on behavioral and environmental exposures for disease onset and progression for heart, lung, and blood disease. In October, 2008 the US Department of Health and Human Services issued the first-ever federally mandated Physical Activity Guidelines for Americans. The Guidelines reflect the view of the Physical Activity Guidelines Advisory Committee (PAGAC) and are based on an extensive review of the scientific literature on physical activity (PA) and health. In their report, the PAGAC points out the limited knowledge of the dose-response relationship between PA and health, and identifies poor measures of PA exposure as a major contributing factor to this gap in knowledge. Our application directly addresses this issue by applying innovative technologies to measure PA dose in a free- living environment. We will use these technologies to examine if habitual PA performed outside of purposeful exercise influences biomarkers of cardiovascular health. Although insufficient PA clearly correlates with an increased risk for cardiovascular disease (CVD), research evidence is equivocal regarding the effects of training on CVD risk factors (e.g. insulin action, triglycerides, blood pressure, and cholesterol). Research suggests increases in sedentary behavior may negate the benefits of training however this idea has not been explored experimentally. Our application will consider habitual free-living PA as a possible mechanism mediating the relationship between training and risk factors for cardiovascular disease. In order to elucidate the relationship between PA and biomarkers of cardiovascular disease risk, it is critical that valid, objective measures are used to quantify PA. We propose to use novel analytic techniques known as artificial neural networks (ANN) to process accelerometer-based measurements of PA. The first part of this project (Aim 1) will examine the ANN's sensitivity to change in PA dose by applying the ANN technique to distinguish three distinct patterns of habitual PA - Sedentary, Moderately Active, and Very Active. These three conditions represent common activity patterns that impact health. Accurately assessing changes to habitual PA levels that are relevant to public health will advance the field by further establishing a technique for application in population surveillance research and detection of changes in PA consequent to an intervention. The second part of this project (Aim 2) will apply the ANN methodology to examine the effect of free-living activity and inactivity levels, performed outside of training, on insulin action, blood pressure, triglycerides, cholesterol, and cardiorespiratory fitness following a 12-week exercise training trial in previously sedentary individuals with an elevated risk for CVD. Results from this study have the potential to impact how clinical exercise trials are conducted (e.g. require objective monitoring of PA outside of an exercise training trial) and how exercise is prescribed (e.g. reducing sedentary time AND maintaining sufficient PA). The Physical Activity Guidelines Advisory Committee advocates improved measures of physical activity exposure in order to elucidate the relationship between physical activity dose and health. To address this challenge we will apply and validate innovative accelerometer-based technologies for measuring physical activity to assess its sensitivity to detecting changes in dose of physical activity and to monitor activity outside of a training program designed to improve cardiorespiratory fitness and biomarkers of cardiovascular disease risk. Through improved measures of physical activity this project will promote a better understanding of how the dose of physical activity affects selected health outcomes.

Public Health Relevance

The Physical Activity Guidelines Advisory Committee advocates improved measures of physical activity exposure in order to elucidate the relationship between physical activity dose and health. To address this challenge we will apply and validate innovative accelerometer-based technologies for measuring physical activity to assess its sensitivity to detecting changes in dose of physical activity and to monitor activity outside of a training program designed to improve cardiorespiratory fitness and biomarkers of cardiovascular disease risk. Through improved measures of physical activity this project will promote a better understanding of how the dose of physical activity affects selected health outcomes.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
NIH Challenge Grants and Partnerships Program (RC1)
Project #
5RC1HL099557-02
Application #
7937759
Study Section
Special Emphasis Panel (ZRG1-PSE-J (58))
Program Officer
Boyington, Josephine
Project Start
2009-09-30
Project End
2012-07-31
Budget Start
2010-08-01
Budget End
2012-07-31
Support Year
2
Fiscal Year
2010
Total Cost
$490,558
Indirect Cost
Name
University of Massachusetts Amherst
Department
Other Health Professions
Type
Schools of Public Health
DUNS #
153926712
City
Amherst
State
MA
Country
United States
Zip Code
01003
Li, Haocheng; Staudenmayer, John; Wang, Tianying et al. (2018) Three-part joint modeling methods for complex functional data mixed with zero-and-one-inflated proportions and zero-inflated continuous outcomes with skewness. Stat Med 37:611-626
Keadle, Sarah Kozey; Sampson, Joshua N; Li, Haocheng et al. (2017) An Evaluation of Accelerometer-derived Metrics to Assess Daily Behavioral Patterns. Med Sci Sports Exerc 49:54-63
Lyden, Kate; Keadle, Sarah Kozey; Staudenmayer, John et al. (2017) The activPALTM Accurately Classifies Activity Intensity Categories in Healthy Adults. Med Sci Sports Exerc 49:1022-1028
Lyden, Kate; Keadle, Sarah Kozey; Staudenmayer, John et al. (2016) The activPAL TM Accurately Classifies Activity Intensity Categories in Healthy Adults. Med Sci Sports Exerc :
Hickey, Amanda; John, Dinesh; Sasaki, Jeffer E et al. (2016) Validity of Activity Monitor Step Detection Is Related to Movement Patterns. J Phys Act Health 13:145-53
Lyden, Kate; Keadle, Sarah Kozey; Staudenmayer, John et al. (2015) Discrete features of sedentary behavior impact cardiometabolic risk factors. Med Sci Sports Exerc 47:1079-86
Lyden, Kate; Petruski, Natalia; Staudenmayer, John et al. (2014) Direct observation is a valid criterion for estimating physical activity and sedentary behavior. J Phys Act Health 11:860-3
Kozey-Keadle, Sarah; Staudenmayer, John; Libertine, Amanda et al. (2014) Changes in sedentary time and physical activity in response to an exercise training and/or lifestyle intervention. J Phys Act Health 11:1324-33
Kozey Keadle, Sarah; Lyden, Kate; Staudenmayer, John et al. (2014) The independent and combined effects of exercise training and reducing sedentary behavior on cardiometabolic risk factors. Appl Physiol Nutr Metab 39:770-80
Lyden, Kate; Keadle, Sarah Kozey; Staudenmayer, John et al. (2014) A method to estimate free-living active and sedentary behavior from an accelerometer. Med Sci Sports Exerc 46:386-97

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