John Staudenmayer

Professor

Statistical methods to estimate physical activity. Statistical methodology development: measurement error correction methods, non-parametric / statistical learning methods, Bayesian data analysis, smoothing

Current Research
I am statistician, and my students and I have collaborated extensively with researchers from kinesiology and public health to develop statistical methods to estimate various aspects of physical activity from accelerometers and other devices. We have also developed novel statistical methodology for measurement error correction, non-parametric / statistical learning, and Bayesian data analysis.

Recent and current projects include:

Free-living physical activity and sedentary behavior: novel accelerometer data processing methods
Physical activity calibration in individuals with movement limitations
Dimension reduction methods for hierarchical functional data
Calibration and measurement error in physical activity recall surveys and accelerometers
Behavioral database architecture for the storage, analysis, and reporting of biosignal datasets.
Use of objective physical activity monitors and accelerometers in clinical trials
Transdisciplinary research on energetics and cancer

Academic Background

BA Williams College, 1992
PhD Cornell University, 2000
Postdoctoral Training: Harvard School of Public Health, 2000-2001

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Publications

Kozey-Keadle, S, Lyden, K, Libertine, A, Viskochil, R, Staudenmayer, J, Braun, B, Freedson PS. (2014+). The independent and combined effects of exercise training and reducing sedentary behavior on cardiometabolic risk factors. Applied Physiology, Nutrition, and Metabolism. Published on the web 07 January 2014.

http://dx.doi.org/10.1139%2Fapnm-2013-0379

Ellis, K, Godbole, S, Marshall, S, Lanckriet, G, Staudenmayer, J and Kerr, J. (2014). Identifying active travel behaviors in challenging environments using GPS, accelerometers, and machine learning algorithms. Front. Public Health. Volume 2.

http://dx.doi.org/10.3389%2Ffpubh.2014.00036

Lyden, K, Kozey-Keadle, S, Staudenmayer, J, Freedson, PS. (2014). A method to estimate free-living active and sedentary behavior from an accelerometer. Medicine and Science in Sports and Exercise. 46(2): 386-97.

http://dx.doi.org/10.1249/MSS.0b013e3182a42a2d

John D, Jeffer S, Staudenmayer J, Mavilia M, Freedson PS. (2013). Comparison of Raw Acceleration from the GENEA and ActiGraph™ GT3X+ activity monitors. Sensors. 13(11): 14754-63.

http://dx.doi.org/10.3390/s131114754

John, D.; Staudenmayer, J.; Freedson, P. (2013). Simple to complex modeling of breathing volume using a motion sensor. The Science of the Total Environment, 454-455, 184-8.

http://dx.doi.org/10.1016/j.scitotenv.2013.02.092

Liu S, Gao RX, John D, Staudenmayer J, Freedson P. (2013). Tissue artifact removal from respiratory signals based on empirical mode decomposition. Ann Biomed Eng. 41(5): 1003-15.

http://dx.doi.org/10.1007/s10439-013-0742-5

Liu S, Gao R, He Q, Staudenmayer J, Freedson P. (2012). Improved regression models for ventilation estimation based on chest and abdomen movements. Physiol Meas. 33(1): 79-93.

http://dx.doi.org/10.1088/0967-3334/33/1/79

Staudenmayer J, Zhu W, Catellier DJ. (2012). Statistical considerations in the analysis of accelerometry-based activity monitor data. Med Sci Sports Exerc. 44(1 Suppl 1): S61-7.

http://dx.doi.org/10.1249/MSS.0b013e3182399e0f

Lyden K, Kozey Keadle SL, Staudenmayer JW, Freedson PS. (2012). Validity of two wearable monitors to estimate breaks from sedentary time. Med Sci Sports Exerc, 44: 2243-2252.

http://dx.doi.org/10.1249/MSS.0b013e318260c477

Kozey-Keadle S, Libertine A, Staudenmayer J, Freedson P. (2012). The Feasibility of Reducing and Measuring Sedentary Time among Overweight, Non-Exercising Office Workers. J Obes. 282-303.

http://dx.doi.org/10.1155/2012/282303

Kozey-Keadle, S Libertine A, Lyden K, Staudenmayer J, Freedson P. (2011). Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc, 43(8): 1561-67.

http://dx.doi.org/10.1249/MSS.0b013e31820ce174

Freedson, PS, Lyden, K, Kozey-Keadle, S, Staudenmayer, J. (2011). Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: Validation on an independent sample. Journal of Applied Physiology, 111: 1804-1812.

http://dx.doi.org/10.1152/japplphysiol.00309.2011

Staudenmayer J, Pober D, Crouter S, Bassett DR Jr, Freedson, PS. (2009). An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. Journal of Applied Physiology, 107: 1300-1307.

http://dx.doi.org/10.1152/japplphysiol.00465.2009

Hasson, R, Haller, J, Pober, D, Staudenmayer, J, Freedson, PS. (2009). Validity of the Omron HJ-112 pedometer during treadmill walking. Med Sci Sports Exerc, 41(4): 805-809.

http://dx.doi.org/10.1249/MSS.0b013e31818d9fc2

Pober D, Staudenmayer J, Raphael C, Freedson P. (2006). Development of a novel analytical technique to assess physical activity using accelerometers. Med Sci Sports Exerc, 38:1626-1634.

http://dx.doi.org/10.1249/01.mss.0000227542.43669.45

John Staudenmayer

Institute for Applied Life Sciences