Progress has been made in developing and using accelerometer-based motion sensors for physical activity research. However, traditional methods of processing activity monitor data do not provide sufficient accuracy to satisfy current trends in the use of objective physical activity data in the research arena.
The aims of this proposal address this weakness in accelerometer- based PA assessment methodologies:
The specific aims are: 1) To develop and validate novel methods to process Actigraph accelerometer data to improve estimates of PA using powerful modern classification methods (classification trees, discriminant analyses, hidden Markov models, neural networks, regression splines, and support vector machines); 2) To compare these classification methods and traditional approaches for assessing PA in a controlled setting; 3) To compare the classification methods and traditional approaches for quantifying PA in free living PA conditions and to select a recommended method; and 4) To correct for measurement error in summary estimates of habitual PA from the novel classification methods and traditional approaches for quantifying PA. Our uniquely qualified multidisciplinary research group will address these aims by first developing innovative classification methods to identify specific activities in a laboratory setting, and then validating the models using data collected from known activities performed in both controlled laboratory environments and free- living situations. Based on the results of these studies, the classification methods will be refined, and estimates of PA behavior will be adjusted using statistical measurement error methods to derive more accurate estimates of PA. We have chosen the classification methods to include publicly available """"""""off-the shelf"""""""" classification methods that others can easily use. The resulting data processing programs will be implemented in popular commercial software packages and made freely available. The results of the proposed investigations will move the field of PA assessment forward by providing innovative approaches to derive more accurate and detailed estimates of PA using a popular accelerometer-based PA monitor. This systematic approach will provide information leading to a clearer understanding of the dose-response relationship between PA and health and the physiological basis of this relationship. ? ? ?
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| 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 : |
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| Li, Haocheng; Staudenmayer, John; Carroll, Raymond J (2014) Hierarchical functional data with mixed continuous and binary measurements. Biometrics 70:802-11 |
| 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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| Lyden, Kate; Keadle, Sarah Kozey; Staudenmayer, John et al. (2013) Energy cost of common activities in children and adolescents. J Phys Act Health 10:62-9 |
| Shiroma, Eric J; Freedson, Patty S; Trost, Stewart G et al. (2013) Patterns of accelerometer-assessed sedentary behavior in older women. JAMA 310:2562-3 |
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