Statistical considerations in the analysis of accelerometry-based activity monitor data

Medicine and Science in Sports and Exercise

Volumn 44, Issue SUPPL. 1, 2012, Pages

  Staudenmayer, John ^a   Zhu, Weimo ^b   Catellier, Diane J ^c  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^c IL   (United States)

Author keywords

accelerometry; calibration studies; measurement error; physical activity energy expenditure

Indexed keywords

ARTICLE; ENERGY METABOLISM; HUMAN; INSTRUMENTATION; METHODOLOGY; MONITORING; MOTOR ACTIVITY; PHYSIOLOGY; STATISTICS;

ENERGY METABOLISM; HUMANS; MONITORING, PHYSIOLOGIC; MOTOR ACTIVITY;

EID: 83455178799     PISSN: 01959131     EISSN: 15300315     Source Type: Journal    
DOI: 10.1249/MSS.0b013e3182399e0f     Document Type: Article

References (28)

1. Bassett DR Jr, Ainsworth BE, Swartz AM, Strath SJ, O'Brien WL, King GA. Validity of four motion sensors in measuring moderate intensity physical activity. Med Sci Sports Exerc. 2000;32(9 suppl): S471-80.
2. Bonomi AG, Plasqui G, Goris AH, Westerterp KR. Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer. J Appl Physiol. 2009;107(3): 655-61.
3. Brage S, Brage N, Franks PW, et al. Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure. J Appl Physiol. 2004;96(1):343-51. (Pubitemid 38032019)
4. Buonaccorsi JP. Measurement Error: Models, Methods, and Applications. New York (NY): CRC; 2010.
5. Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM. Measurement Error in Nonlinear Models: A Modern Perspective. 2nd ed. New York (NY): CRC; 2006.
6. Catellier DJ, Hannan PJ, Murray DM, et al. Imputation of missing data when measuring physical activity by accelerometry. Med Sci Sports Exerc. 2005;37(11 suppl):S555-62. (Pubitemid 41691818)
7. Crouter SE, Clowers KG, Bassett DR Jr. A novel method for using accelerometer data to predict energy expenditure. J Appl Physiol. 2006;100(4):1324-31. (Pubitemid 43671491)
8. Ferrari P, Friedenreich C, Matthews CE. The role of measurement error in estimating levels of physical activity. Am J Epidemiol. 2007;166(7):832-40. (Pubitemid 47428902)
9. Fleiss JL. Statistical Methods for Rates and Proportions. 2nd ed. New York (NY): John Wiley; 1981.
10. Freedson P, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc. 1998;30(5):777-81. (Pubitemid 28211005)
11. Hastie T, Tibshirani R, Friedman JH. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed., New York (NY): Springer; 2009.
12. Hendelman D, Miller K, Baggett C, Debold E, Freedson P. Validity of accelerometry for the assessment of moderate intensity physical activity in the field. Med Sci Sports Exerc. 2000;32(9 suppl): S442-9.
13. Hopkins WG. Percent effects from log-transformed variables. In: A New View of Statistics [Internet]. 2003. Available from: http://sportsci.org/ resource/stats/logtrans.html.
14. Kang M, Zhu W, Tudor-Locke C, Ainsworth B. Experimental determination of effectiveness of an individual information-centered approach in recovering step-count missing data. Meas Phys Educ Exerc Sci. 2005;9:233-50. (Pubitemid 41713507)
15. Kiani K, Snijders CJ, Gelsema ES. Recognition of daily life motor activity classes using an artificial neural network. Arch Phys Med Rehabil. 1998;79(2):147-54. (Pubitemid 28084168)
16. Kipnis V, Subar AF, Midthune D, et al. Structure of dietary measurement error: results of the OPEN biomarker study. Am J Epidemiol. 2003;158(1):14-21. (Pubitemid 36745326)
17. Kozey SL, Lyden K, Howe CA, Staudenmayer JW, Freedson PS. Accelerometer output and MET values of common physical activities. Med Sci Sports Exerc. 2010;42(9):1776-84.
18. Little RJA, Rubin DB. Statistical Analysis with Missing Data. New York (NY): J. Wiley & Sons; 1987.
19. Paul DR, Kramer M, Stote KS, et al. Estimates of adherence and error analysis of physical activity data collected via accelerometry in a large study of free-living adults. BMC Med Res Methodol. 2008;8;38.
20. Preece SJ, Goulermas JY, Kenney LP, Howard D, Meijer K, Crompton R. Activity identification using body-mounted sensors-a review of classification techniques. Physiol Meas. 2009;30(4): R1-33.
21. Rothney MP, Neumann M, Beziat A, Chen KY. An artificial neural network model of energy expenditure using nonintegrated acceleration signals. J Appl Physiol. 2007;103(4):1419-27. (Pubitemid 47605982)
22. Staudenmayer J, Pober D, Crouter S, Bassett DR Jr, Freedson PS. An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J Appl Physiol. 2009;107(4):1300-7.
23. Sugar EA, Wang CY, Prentice RL. Logistic regression with exposure biomarkers and flexible measurement error. Biometrics. 2007;63(1):143-51.
24. Swartz AM, Strath SJ, Bassett DR Jr, O'Brien WL, King GA, Ainsworth BE. Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. Med Sci Sports Exerc. 2000; 32(9 suppl):S450-6.
25. Tapia EM, Intille SS, Haskell W, et al. Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In: Proceedings of the International Symposium on Wearable Computers. New York: IEEE Press; 2007. p. 37-40. (Pubitemid 351424263)
26. Troiano RP. A timely meeting: objective measurement of physical activity. Med Sci Sports Exerc. 2005;37(11 suppl):S487-9. (Pubitemid 41691811)
27. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181-8.
28. Zakeri I, Adolph AL, Puyau MR, Vohra FA, Butte NF. Multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents. J Appl Physiol. 2010;108: 128-36.