Accelerometer Calibration: The Importance of Considering Functionality

in Journal for the Measurement of Physical Behaviour
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  • 1 University of Wisconsin-Milwaukee
  • 2 Saginaw Valley State University
  • 3 Marquette University
  • 4 University of Massachusetts Amherst
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Purpose: To compare the accuracy and precision of a hip-worn accelerometer to predict energy cost during structured activities across motor performance and disease conditions. Methods: 118 adults self-identifying as healthy (n = 44) and those with arthritis (n = 23), multiple sclerosis (n = 18), Parkinson’s disease (n = 17), and stroke (n = 18) underwent measures of motor performance and were categorized into groups: Group 1, usual; Group 2, moderate impairment; and Group 3, severe impairment. The participants completed structured activities while wearing an accelerometer and a portable metabolic measurement system. Accelerometer-predicted energy cost (metabolic equivalent of tasks [METs]) were compared with measured METs and evaluated across functional impairment and disease conditions. Statistical significance was assessed using linear mixed effect models and Bayesian information criteria to assess model fit. Results: All activities’ accelerometer counts per minute (CPM) were 29.5–72.6% less for those with disease compared with those who were healthy. The predicted MET bias was similar across disease, −0.49 (−0.71, −0.27) for arthritis, −0.38 (−0.53, −0.22) for healthy, −0.44 (−0.68, −0.20) for MS, −0.34 (−0.58, −0.09) for Parkinson’s, and −0.30 (−0.54, −0.06) for stroke. For functional impairment, there was a graded reduction in CPM for all activities: Group 1, 1,215 CPM (1,129, 1,301); Group 2, 789 CPM (695, 884); and Group 3, 343 CPM (220, 466). The predicted MET bias revealed similar results across the Group 1, −0.37 METs (−0.52, −0.23); Group 2, −0.44 METs (−0.60, −0.28); and Group 3, −0.33 METs (−0.55, −0.13). The Bayesian information criteria showed a better model fit for functional impairment compared with disease condition. Conclusion: Using functionality to improve accelerometer calibration could decrease variability and warrants further exploration to improve accelerometer prediction of physical activity.

Strath, Cho, Swartz, Keenan, and Martinez are with the Department of Kinesiology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA. Rowley is with the Department of Kinesiology, Saginaw Valley State University, MI, USA. Hyngstrom is with the Department of Physical Therapy, Marquette University, Milwaukee, WI, USA. Staudenmayer is with the Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, USA.

Strath (sstrath@uwm.edu) is corresponding author.
  • Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effect models using lme4. Journal of Statistical Software, 67(1), 148. doi:10.18637/jss.v067.i01

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, K.O., Wood-Dauphinee, S.L., Williams, J.I., & Maki, B. (1992). Measuring balance in the elderly: Validation of an instrument. Canadian Journal of Public Health, 83(Suppl. 2), S7–S11. PubMed ID: 1468055

    • Search Google Scholar
    • Export Citation
  • Bianchim, M.S., McNarry, M.A., Larun, L.& Mackintosh, K.A. (2019). Calibration and validation of accelerometry to measure physical activity in adult clinical groups: A systematic review. Preventive Medicine Reports, 16, 101001. PubMed ID: 31890467 doi:10.1016/j.pmedr.2019.101001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bohannon, R.W. (1997). Comfortable and maximum walking speed of adults aged 20–79 years: Reference values and determinants. Age and Ageing, 26(1), 1519. PubMed ID: 9143432 doi:10.1093/ageing/26.1.15

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, M., Sinacore, D.R., Binder, E.F., & Kohrt, W.M. (2000). Physical and performance measures for the identification of mild to moderate frailty. The Journals of Gerontology, Series A: Biological Sciences & Medical Sciences, 55(6), M350M355. PubMed ID: 10843356 doi:10.1093/gerona/55.6.M350

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryden, P.J., & Roy, E.A. (2005). A new method of administering the Grooved Pegboard Test: Performance as a function of handedness and sex. Brain and Cognition, 58(3), 258268. PubMed ID: 15963376 doi:10.1016/j.bandc.2004.12.004

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Compher, C., Frankenfield, D., Keim, N., & Roth-Yousey, L. (2006). Best practice methods to apply to measurement of resting metabolic rate in adults: A systematic review. Journal of the American Dietetic Association, 106(6), 881903. PubMed ID: 16720129 doi:10.1016/j.jada.2006.02.009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Copeland, J.L., & Esliger, D.W. (2009). Accelerometer assessment of physical activity in active, healthy older adults. Journal of Aging and Physical Activity, 17(1), 1730. PubMed ID: 19299836 doi:10.1123/japa.17.1.17

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evenson, K.R., Wen, F., Herring, A.H., Di, C., Lamonte, M.J., Tinker, L.F., . . . Buchner, D.M. (2015). Calibrating physical activity intensity for hip-worn accelerometry in women age 60 to 91 years: The Women’s Health Initiative OPACH Calibration Study. Preventive Medicine Reports, 2, 750756. PubMed ID: 26527313 doi:10.1016/j.pmedr.2015.08.021

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Freedson, P.S., Melanson, E., & Sirard, J. (1998). Calibration of the Computer Science and Applications, Inc. accelerometer. Medicine & Science in Sports & Exercise, 30(5), 777781. PubMed ID: 9588623 doi:10.1097/00005768-199805000-00021

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamilton, D.M., Haennel, R.G., King, S., Wessel, J., Bhambhani, Y., Maikala, R., . . . Maksymowych, W. (2000). Validity and reliability of the 6-minute walk test in a cardiac rehabilitation population. Journal of Cardiopulmonary Rehabilitation, 20(3), 156164. PubMed ID: 10860197 doi:10.1097/00008483-200005000-00003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hyngstrom, A.S., Cho, C.C., Berrios Barillas, R., Joshi, M., Rowley, T.R., Keenan, K.G., . . . Strath, S.J. (2020). Idenitification of latent classes of motor performance in a heterogeneous population of adults. Archives of Rehabilitation Research and Clinical Translation. 2(4). doi:10.1016/j.arrct.2020.100080

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jeng, B., Cederberg, K.L.J., Lai, B., Sasaki, J.E., Bamman, M.M., & Motl, R.W. (2020). Accelerometer output and its association with energy expenditure in persons with mild-to-moderate Parkinson’s disease. PLoS One, 15(11), e0232136. PubMed ID: 33175904 doi:10.1371/journal.pone.0242136

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Katch, V.L., McArdle, W.D., & Katch, F.I. (2011). Energy expenditure during rest and physical activity. In W.D. McArdle, F.I. Katch, & V.L. Katch (Eds.), Essentials of exercise physiology (pp. 237262). Baltimore, MD: Lippincott Williams & Wilkins.

    • Search Google Scholar
    • Export Citation
  • Keadle, S.K., Lyden, K.A., Strath, S.J., Staudenmayer, J.W., & Freedson, P.S. (2019). A framework to evaluate devices that assess physical behavior. Exercise and Sport Sciences Reviews, 47(4), 206214. PubMed ID: 31524786 doi:10.1249/JES.0000000000000206

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lanza, S.T., Collins, L.M., Lemmon, D.R., & Schafer, J.L. (2007). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Medeling, 14(4), 671694. PubMed ID: 19953201 doi:10.1080/10705510701575602

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mathiowetz, V., Volland, G., Kashman, N., & Weber, K. (1985). Adult norms for the box and block test of manual dexterity. Am J Occup Ther, 39, 386391.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McLaughlin, J.E., King, G.A., Howley, E.T., Bassett, D.R., & Ainsworth, B.E. (2001). Validation of the Cosmed K4b2 portable metabolic system. International Journal of Sports Medicine, 22(4), 280284. PubMed ID: 11414671 doi:10.1055/s-2001-13816

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Motl, R.W., Snook, E.M., Agiovlasitis, S., & Suh, Y. (2009). Calibration of accelerometer output for ambulatory adults with multiple sclerosis. Archives of Physical Medicine and Rehabilitation, 90(10), 17781784. PubMed ID: 19801071 doi:10.1016/j.apmr.2009.03.020

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Müller, S., Scealy, J.L., & Welsh, A.H. (2013). Model selection in linear mixed models. Statistical Science, 28(2), 135167. doi:10.1214/12-STS410

  • Nero, H., Benka Wallén, M., Franzén, E., Ståhle, A., & Hagströmer, M. (2015). Accelerometer cut points for physical activity assessment of older adults with Parkinson’s disease. PLoS One, 10(9), e0135899. PubMed ID: 26332765 doi:10.1371/journal.pone.0135899

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pandyan, A.D., Johnson, G.R., Price, C.I., Curless, R.H., Barnes, M.P., & Rodgers, H. (1999). A review of the properties and limitations of the Ashworth and modified Ashworth Scales as measures of spasticity. Clinical Rehabilitation, 13(5), 373383. PubMed ID: 10498344 doi:10.1191/026921599677595404

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peterson Kendall, F., Kendall McCreary, E., Provance, P., Rodgers, M., & Romani, W. (2005). Muscles testing and function with posture and pain (5th ed.). North America: Lippincott Williams and Wilkins.

    • Search Google Scholar
    • Export Citation
  • Rowley, T.W., Cho, C., Swartz, A.M., Staudenmayer, J., Hyngstrom, A., Keenan, K.G., . . . Strath, S.J. (2019). Energy cost of slow and normal gait speeds in low and normally functioning adults. American Journal of Physical Medicine & Rehabilitation, 98(11), 976981. PubMed ID: 31135461 doi:10.1097/PHM.0000000000001228

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sandroff, B.M., Motl, R.W., & Suh, Y. (2012). Accelerometer output and its association with energy expenditure in persons with multiple sclerosis. Journal of Rehabilitation Research and Development, 49(3), 467475. PubMed ID: 22773205 doi:10.1682/JRRD.2011.03.0063

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sandroff, B.M., Riskin, B.J., Agiovlasitis, S., & Motl, R.W. (2014). Accelerometer cut-points derived during over-ground walking in persons with mild, moderate, and severe multiple sclerosis. Journal of the Neurological Sciences, 340(1–2), 5057. PubMed ID: 24635890 doi:10.1016/j.jns.2014.02.024

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serra, M.C., Balraj, E., DiSanzo, B.L., Ivey, F.M., Hafer-Macko, C.E., Treuth, M.S., & Ryan, A.S. (2017). Validating accelerometry as a measure of physical activity and energy expenditure in chronic stroke. Topics in Stroke Rehabilitation, 24(1), 1823. PubMed ID: 27322733 doi:10.1080/10749357.2016.1183866

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shumway-Cook, A., Baldwin, M., Polissar, N.L., & Gruber, W. (1997). Predicting the probability for falls in community-dwelling older adults. Physical Therapy, 77(8), 812819. PubMed ID: 9256869 doi:10.1093/ptj/77.8.812

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Staudenmayer, J., He, S., Hickey, A., Sasaki, J., & Freedson, P. (2015). Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements. Journal of Applied Physiology, 119(4), 396403. PubMed ID: 26112238 doi:10.1152/japplphysiol.00026.2015

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Staudenmayer, J., Pober, D., Crouter, S., Bassett, D., & Freedson, P. (2009). An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. Journal of Applied Physiology, 107(4), 13001307. PubMed ID: 19644028 doi:10.1152/japplphysiol.00465.2009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • R Core Team. (2019). A language and environment for statistical computing. R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/

    • Search Google Scholar
    • Export Citation
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