Physical Activity Monitor Research

A key focus in our lab is on improving the validity and utility of measures of physical activity and considerable work is aimed at the use of accelerometry-based activity monitors. Some specific lines of research are summarized below and on the associated pages. 

Overview of Accelerometry-Based ResEaRch

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A key area of work in our lab is focused on improving the accuracy and utility of physical activity assessments. Our lab has conducted a range of validity and reliability studies and has contributed to guiding best practices in the field for processing and interpreting data from accelerometry - based monitors. A list of papers led by members of our team is included here but we have active collaborations with a number of other colleagues that conduct similar work. 

Bodymedia Sensewear Armband

Our lab conducted pioneering work with the SenseWear armband monitor which utilized multiple heat detecting sensors in addition to a tri-axial accelerometer to assess physical activity, The multi-sensor configuration and pattern recognition methods developed by Bodymedia provided advantages over other competing technologies. We conducted foundational research with the Sensewear and contributed to algorithm evaluation and refinement. We documented the advantages of this technology over other monitors using the doubly-labelled method as well as with portable indirect calorimetry methods.  The Sensewear became accepted as perhaps the most valid research grade tool. While it is no longer commercially available, our lab still uses it in studies as a de-facto "alloyed standard" for comparisons with other monitors and assessments.

Evaluation of Consumer-Based Monitoring Devices

Our team has contributed to advancing research on the accuracy and utility of many consumer - based activity monitors. Early research compared the accuracy of different monitors - most of which are not commercially available anymore (e.g. FitBit (Force / Flex), Direct Life, Nike Fuelband, Jawbone UP, Basis, Larklife, Garmin VivoFit, Magellan Echo, Mio Alpha, Misfit Shine, Polar Loop etc...). Click here for more info.

Work with consumer monitors has evolved to focus more on Smartwatches and we are continuing to do this work through our FLASH project. See the link on the FLASH Project for more information.

Calibration of Report-Based Measures 

Our lab has conducted pioneering research on measurement error in physical activity self-report and has advanced applications for calibration methods to improve the accuracy and utility of report-based measures.

Emphasis in the field has been on refining the accuracy and utility of monitor-based methods for estimating energy expenditure (EE) and time spent in moderate to vigorous physical activity (MVPA) or sedentary behavior (SB). The objectivity of monitor-based measures is a considerable advantage, but report-based tools are more practical and provide valuable information about the context of physical activity behavior. ​Thus, our approach has been to try to harmonize the methods to capitalize on the strengths of both approaches. Click here to access a list of papers on report-based measures or see the information below about specific calibration-based work.
  • Information on the foundational PAMS study is available at this link.
  • Information on the applications of calibration in the Youth Activity Profile are at this link

Access to R Code files

Our lab supports open-source processing methods and will be working to post updates on our data processing methods online so that they can be easily accessed and used. Click here for more info.

Published Research 

  1. Duhamahoro, J., Lamoureux, N. R., Hibbing, P. R., Taylor, M. A., & Welk, G. J. (2025). Comparative Validity of Two Thigh-Worn Activity Monitors in Free-Living Conditions. Journal for the Measurement of Physical Behaviour, 8(1).
  2. Hibbing PR, Lamoureux NR, Matthews CE, & Welk GJ. (2021 Protocol and data description: The free-living activity study for health (FLASH). Journal for the Measurement of Physical Behaviour, 1 (aop), 1-8.
  3. Welk, G. J., Beyler, N. K., Kim, Y., & Matthews, C. E. (2017). Calibration of Self-Report Measures of Physical Activity and Sedentary Behavior. Medicine and science in sports and exercise.
  4. Ellingson, L. D., Hibbing, P. R., Kim, Y., Frey-Law, L. A., Saint-Maurice, P. F., & Welk, G. J. (2017). Lab-based validation of different data processing methods for wrist-worn ActiGraph accelerometers in young adults. Physiological Measurement, 38(6), 1045.
  5. Kim, Y., Hibbing, P., Saint-Maurice, P. F., Ellingson, L. D., Hennessy, E., Wolff-Hughes, D. L., & Welk, G. J. (2017). Surveillance of youth physical activity and sedentary behavior with wrist accelerometry. American Journal of Preventive Medicine, 52(6), 872-879.
  6. Ellingson, L. D., Schwabacher, I. J., Kim, Y., Welk, G. J., & Cook, D. B. (2016). Validity of an Integrative Method for Processing Physical Activity Data. Medicine and Science in Sports and Exercise, 48(8):1629-38.
  7. Hibbing, P. R., Kim, Y., Saint-Maurice, P. F., & Welk, G. J. (2016). Impact of activity outcome and measurement instrument on estimates of youth compliance with physical activity guidelines: a cross-sectional study. BMC Public Health, 16(1), 1.
  8. Tucker, J. M., Welk, G. J., Beyler, N. K., & Kim, Y. (2016). Associations Between Physical Activity and Metabolic Syndrome: Comparison Between Self-Report and Accelerometry. American Journal of Health Promotion, 30(3):155-62.
  9. Lee, J. M., Kim, Y., Bai, Y., Gaesser, G. A., & Welk, G. J. (2016). Validation of the SenseWear Mini Armband in Children during Semi-Structure Activity Settings. Journal of Science and Medicine in Sport, 19(1):41-5. PMID: 25459233
  10. Kim, Y., Welk, G. J., Braun, S. I., & Kang, M. (2015). Extracting objective estimates of sedentary behavior from accelerometer data: measurement considerations for surveillance and research applications. PloS one, 10(2), e0118078.
  11. Kim, Y., & Welk, G. J. (2015). Criterion Validity of Competing Accelerometry-based Activity Monitoring Devices. Medicine and Science in Sports and Exercise. 47(11):2456-63.
  12. Laurson, K. R., Welk, G. J., & Eisenmann, J. C. (2015). Estimating physical activity in children: impact of pedometer wear time and metric. Journal of Physical Activity & Health, 12(1), 124-131.
  13. Kim, Y., Crouter, S. E., Lee, J. M., Dixon, P. M., Gaesser, G. A., & Welk, G. J. (2014). Comparisons of prediction equations for estimating energy expenditure in youth. Journal of Science and Medicine in Sport. doi: 10.1016/j.jsams.2014.10.002. PMID: 25459235
  14. Calabro, M. A., Kim, Y., Franke, W. D., Stewart, J. M., & Welk, G. J. (2014). Objective and subjective measurement of energy expenditure in older adults: a doubly labeled water study. European Journal of Clinical Nutrition. doi:10.1038/ejcn.2014.241 PMID: 25351651
  15. Calabró, M. A., Lee, J. M., Saint-Maurice, P. F., Yoo, H., & Welk, G. J. (2014). Validity of physical activity monitors for assessing lower intensity activity in adults. International Journal of Behavioral Nutrition and Physical Activity, 11(1), 119. PMID: 25260625
  16. Calabro, M.A., Stewart, J.M., Welk, G.J. (2013). Validation of pattern-recognition monitors in children using doubly labeled water. Medicine and Science in Sports and Exercise. 45(7): 1313-1322. PMID: 23299766
  17. Smith KM, Lanningham-Foster LM, Welk GJ, Campbell CG. (2012). Validity of the SenseWear® Armband to predict energy expenditure in pregnant women. Medicine and Science in Sports and Exercise. 2012 Oct;44(10):2001-8. PMID: 22617395
  18. Welk, G.J., McClain, J., & Ainsworth, B.E. (2012). Protocols for evaluating equivalency of accelerometry-based activity monitors. Medicine and Science in Sports and Exercise. 44: S39-49. PMID: 22157773.
  19. Tucker J.M., Welk, G.J. & Beyler, N.K. (2011). Physical activity in U.S.: adults compliance with the Physical Activity Guidelines for Americans. American Journal of Preventive Medicine. 40(4):454-61. PMID:  21406280.
  20. Harrington, D.M., Welk, G.J. & Donnelly, A.E. (2011). Validation of MET estimates and step measurement using the ActivPAL physical activity logger. Journal of Sports Science. 29(6):627-33. PMID: 21360402
  21. Rowe, D. A., Welk, G. J., Heil, D. P., Mahar, M. T., Kemble, C. D., Calabro, M. A. et al. (2011). Stride rate recommendations for moderate-intensity walking. Medicine and Science in Sports and Exercise. 43, 312-318. PMID: 20543754
  22. Johannsen, D.L., Calabro, M.A., Stewart, J., Franke, W., Rood, J.C. and Welk, G.J. (2010). Accuracy of armband monitors for measuring daily energy expenditure in healthy adults. Medicine and Science in Sports and Exercise. Vol. 42, No. 11, pp. 2134-2140, PMID: 20386334
  23. Calabro, M.A., Welk, G.J., Eisenmann. (2009). Validation of the Sensewear Pro armband monitor algorithms in children. Medicine and Science in Sports and Exercise. Sep;41(9):1714-20.PMID: 19657300
  24. Welk, G.J., Eisenmann, J.C., Trost, S., Schaben, J.W., Dale, D.D. (2007) Calibration of the Biotrainer Pro Activity Monitor for Youth using Receiver Operator Characteristic (ROC) Curves. Pediatric Exercise Science, 19, 145-158. PMID: 17603138
  25. Welk, G.J., McClain, J., Eisenmann, J.C., Wickel, E.E., Beier, S. & Flakol, P. (2007). Field Validation of the MTI and BodyMedia monitors usingthe IDEEA monitor. Obesity, 15, 918-928. PMID: 17426327

Calibration of Self-Report Measures

The refinement of methods for processing monitors-based data is central to our related research on calibration of report-based measures. This work is most actively applied in the development and refinement of the Youth Activity Profile (see link)