Grant Number:	1R01CA121005-01A1 Interpret this number
Primary Investigator:	Freedson, Patty
Organization:	University Of Massachusetts Amherst
Project Title:	Novel Analytic Techniques to Assess Physical Activity
Fiscal Year:	2006

DESCRIPTION (provided by applicant): 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.




A Review of Statistical Analyses on Physical Activity Data Collected from Accelerometers.
Authors: Zhang Y. , Li H. , Keadle S.K. , Matthews C.E. , Carroll R.J. .
Source: Statistics in biosciences, 2019; 11(2), p. 465-476.
EPub date: 2019-06-28.
PMID: 32863980
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Physical activity classification with dynamic discriminative methods.
Authors: Ray E.L. , Sasaki J.E. , Freedson P.S. , Staudenmayer J. .
Source: Biometrics, 2018 Dec; 74(4), p. 1502-1511.
EPub date: 2018-06-19.
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The activPALTM Accurately Classifies Activity Intensity Categories in Healthy Adults.
Authors: Lyden K. , Keadle S.K. , Staudenmayer J. , Freedson P.S. .
Source: Medicine and science in sports and exercise, 2017 May; 49(5), p. 1022-1028.
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Performance of Activity Classification Algorithms in Free-Living Older Adults.
Authors: Sasaki J.E. , Hickey A.M. , Staudenmayer J.W. , John D. , Kent J.A. , Freedson P.S. .
Source: Medicine and science in sports and exercise, 2016 May; 48(5), p. 941-50.
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Methods to assess an exercise intervention trial based on 3-level functional data.
Authors: Li H. , Kozey Keadle S. , Staudenmayer J. , Assaad H. , Huang J.Z. , Carroll R.J. .
Source: Biostatistics (Oxford, England), 2015 Oct; 16(4), p. 754-71.
EPub date: 2015-05-18.
PMID: 25987650
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Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements.
Authors: Staudenmayer J. , He S. , Hickey A. , Sasaki J. , Freedson P. .
Source: Journal of applied physiology (Bethesda, Md. : 1985), 2015-08-15; 119(4), p. 396-403.
EPub date: 2015-06-25.
PMID: 26112238
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Hierarchical functional data with mixed continuous and binary measurements.
Authors: Li H. , Staudenmayer J. , Carroll R.J. .
Source: Biometrics, 2014 Dec; 70(4), p. 802-11.
EPub date: 2014-08-18.
PMID: 25134936
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Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors.
Authors: Sarkar A. , Mallick B.K. , Staudenmayer J. , Pati D. , Carroll R.J. .
Source: Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2014-10-01; 23(4), p. 1101-1125.
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A method to estimate free-living active and sedentary behavior from an accelerometer.
Authors: Lyden K. , Keadle S.K. , Staudenmayer J. , Freedson P.S. .
Source: Medicine and science in sports and exercise, 2014 Feb; 46(2), p. 386-97.
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Patterns of accelerometer-assessed sedentary behavior in older women.
Authors: Shiroma E.J. , Freedson P.S. , Trost S.G. , Lee I.M. .
Source: JAMA, 2013-12-18; 310(23), p. 2562-3.
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Classification accuracy of the wrist-worn gravity estimator of normal everyday activity accelerometer.
Authors: Welch W.A. , Bassett D.R. , Thompson D.L. , Freedson P.S. , Staudenmayer J.W. , John D. , Steeves J.A. , Conger S.A. , Ceaser T. , Howe C.A. , et al. .
Source: Medicine and science in sports and exercise, 2013 Oct; 45(10), p. 2012-9.
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Energy cost of common activities in children and adolescents.
Authors: Lyden K. , Keadle S.K. , Staudenmayer J. , Freedson P. , Alhassan S. .
Source: Journal of physical activity & health, 2013 Jan; 10(1), p. 62-9.
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Accuracy of accelerometer regression models in predicting energy expenditure and METs in children and youth.
Authors: Alhassan S. , Lyden K. , Howe C. , Kozey Keadle S. , Nwaokelemeh O. , Freedson P.S. .
Source: Pediatric exercise science, 2012 Nov; 24(4), p. 519-36.
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Biomechanical examination of the 'plateau phenomenon' in ActiGraph vertical activity counts.
Authors: John D. , Miller R. , Kozey-Keadle S. , Caldwell G. , Freedson P. .
Source: Physiological measurement, 2012 Feb; 33(2), p. 219-30.
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Statistical considerations in the analysis of accelerometry-based activity monitor data.
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Assessment of physical activity using wearable monitors: recommendations for monitor calibration and use in the field.
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Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample.
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A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations.
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Accelerometer output and MET values of common physical activities.
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Errors in MET estimates of physical activities using 3.5 ml x kg(-1) x min(-1) as the baseline oxygen consumption.
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Accelerometer prediction of energy expenditure: vector magnitude versus vertical axis.
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An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer.
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