John Staudenmayer

Summary

Affiliation: University of Massachusetts
Country: USA

Publications

  1. ncbi Measurement error in a random walk model with applications to population dynamics
    John Staudenmayer
    Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts 01003, USA
    Biometrics 62:1178-89. 2006
  2. ncbi A study of indoor carbon dioxide levels and sick leave among office workers
    Theodore A Myatt
    Department of Environmental Health, Harvard School of Public Health, 665 Huntington Ave, Boston, MA 02115, USA
    Environ Health 1:3. 2002
  3. ncbi Segmented regression in the presence of covariate measurement error in main study/validation study designs
    John Staudenmayer
    Department of Mathematics and Statistics, University of Massachusetts, Lederle Graduate Research Tower, Amherst, Massachusetts 01003, USA
    Biometrics 58:871-7. 2002
  4. ncbi Statistical considerations in the analysis of accelerometry-based activity monitor data
    John Staudenmayer
    Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 010003, USA
    Med Sci Sports Exerc 44:S61-7. 2012
  5. ncbi An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer
    John Staudenmayer
    Dept of Mathematics and Statistics, Univ of Massachusetts, Lederle Graduate Research Center, Amherst, MA 01003, USA
    J Appl Physiol 107:1300-7. 2009
  6. ncbi Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample
    Patty S Freedson
    Dept of Kinesiology, University of Massachusetts, Amherst, Massachusetts, USA
    J Appl Physiol 111:1804-12. 2011
  7. ncbi Validity of the Omron HJ-112 pedometer during treadmill walking
    Rebecca E Hasson
    Department of Kinesiology, University of Massachusetts, Amherst, MA, USA
    Med Sci Sports Exerc 41:805-9. 2009
  8. ncbi Errors in MET estimates of physical activities using 3.5 ml x kg(-1) x min(-1) as the baseline oxygen consumption
    Sarah Kozey
    Dept of Kinesiology, University of Massachusetts, Amherst, MA, USA
    J Phys Act Health 7:508-16. 2010
  9. ncbi The Feasibility of Reducing and Measuring Sedentary Time among Overweight, Non-Exercising Office Workers
    Sarah Kozey-Keadle
    Department of Kinesiology, University of Massachusetts, Amherst, MA 01003, USA
    J Obes 2012:282303. 2012
  10. ncbi Development of novel techniques to classify physical activity mode using accelerometers
    David M Pober
    Department of Exercise Science, Exercise Physiology Laboratory, University of Massachusetts, Amherst, MA 01003, USA
    Med Sci Sports Exerc 38:1626-34. 2006

Collaborators

  • Patty Freedson
  • Kate Lyden
  • Weimo Zhu
  • DONNA L SPIEGELMAN
  • Sarah Kozey-Keadle
  • David M Pober
  • Amanda Libertine
  • Sarah Kozey
  • Rebecca E Hasson
  • John P Buonaccorsi
  • Theodore A Myatt
  • Jeannie Haller
  • Maximo Carreras
  • Christopher Raphael
  • Kate Adams
  • Stephen N Rudnick
  • Donald K Milton
  • Michael Walters

Detail Information

Publications12

  1. ncbi Measurement error in a random walk model with applications to population dynamics
    John Staudenmayer
    Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts 01003, USA
    Biometrics 62:1178-89. 2006
    ..We also examine the practical implications of the methods by using them to analyze two existing population dynamics data sets...
  2. ncbi A study of indoor carbon dioxide levels and sick leave among office workers
    Theodore A Myatt
    Department of Environmental Health, Harvard School of Public Health, 665 Huntington Ave, Boston, MA 02115, USA
    Environ Health 1:3. 2002
    ..This study was undertaken to explore this association...
  3. ncbi Segmented regression in the presence of covariate measurement error in main study/validation study designs
    John Staudenmayer
    Department of Mathematics and Statistics, University of Massachusetts, Lederle Graduate Research Tower, Amherst, Massachusetts 01003, USA
    Biometrics 58:871-7. 2002
    ....
  4. ncbi Statistical considerations in the analysis of accelerometry-based activity monitor data
    John Staudenmayer
    Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 010003, USA
    Med Sci Sports Exerc 44:S61-7. 2012
    ....
  5. ncbi An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer
    John Staudenmayer
    Dept of Mathematics and Statistics, Univ of Massachusetts, Lederle Graduate Research Center, Amherst, MA 01003, USA
    J Appl Physiol 107:1300-7. 2009
    ..This novel approach of applying ANNs for processing Actigraph accelerometer data is promising and shows that we can successfully estimate activity METs and identify activity type using ANN analytic procedures...
  6. ncbi Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample
    Patty S Freedson
    Dept of Kinesiology, University of Massachusetts, Amherst, Massachusetts, USA
    J Appl Physiol 111:1804-12. 2011
    ..We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type...
  7. ncbi Validity of the Omron HJ-112 pedometer during treadmill walking
    Rebecca E Hasson
    Department of Kinesiology, University of Massachusetts, Amherst, MA, USA
    Med Sci Sports Exerc 41:805-9. 2009
    ..The purpose of this investigation was to examine the validity of step counts measured with the Omron HJ-112 pedometer and to assess the effect of pedometer placement...
  8. ncbi Errors in MET estimates of physical activities using 3.5 ml x kg(-1) x min(-1) as the baseline oxygen consumption
    Sarah Kozey
    Dept of Kinesiology, University of Massachusetts, Amherst, MA, USA
    J Phys Act Health 7:508-16. 2010
    ..To compare intensity misclassification and activity MET values using measured RMR (measMET) compared with 3.5 ml x kg(-1) x min(-1) (standMET) and corrected METs [corrMET = mean standMET x (3.5 / Harris-Benedict RMR)] in subgroups...
  9. ncbi The Feasibility of Reducing and Measuring Sedentary Time among Overweight, Non-Exercising Office Workers
    Sarah Kozey-Keadle
    Department of Kinesiology, University of Massachusetts, Amherst, MA 01003, USA
    J Obes 2012:282303. 2012
    ..These data describe ST measurement properties from wearable monitors and self-report tools to inform sample-size estimates for future ST interventions...
  10. ncbi Development of novel techniques to classify physical activity mode using accelerometers
    David M Pober
    Department of Exercise Science, Exercise Physiology Laboratory, University of Massachusetts, Amherst, MA 01003, USA
    Med Sci Sports Exerc 38:1626-34. 2006
    ....
  11. ncbi Validation of wearable monitors for assessing sedentary behavior
    Sarah Kozey-Keadle
    Department of Kinesiology, University of Massachusetts, Amherst, MA, USA
    Med Sci Sports Exerc 43:1561-7. 2011
    ..The purpose of this study was to examine the validity of commercially available monitors to assess SB...
  12. ncbi Modeling observation error and its effects in a random walk/extinction model
    John P Buonaccorsi
    Department of Mathematics and Statistics and Graduate Program in Organismic and Evolutionary Biology Lederle Graduate Research Tower, University of Massachusetts, Amherst MA, 01003 9305, USA
    Theor Popul Biol 70:322-35. 2006
    ..This discussion recognizes that the bias and variance in observation errors may change over time, the result of changing sampling effort or dependence on the underlying population being sampled...