Estimates of Physical Activity in Older Adults Using the ActiGraph Low-Frequency Extension Filter

in Journal for the Measurement of Physical Behaviour
View More View Less
  • 1 The University of Kansas
  • | 2 The University of Kansas Alzheimer’s Disease Center
Restricted access

Purchase article

USD  $24.95

Student 1 year online subscription

USD  $38.00

1 year online subscription

USD  $51.00

Student 2 year online subscription

USD  $73.00

2 year online subscription

USD  $97.00

As a default setting, many body-worn research-grade activity monitors rely on software algorithms developed for young adults using waist-worn devices. ActiGraph offers the low-frequency extension (LFE) filter, which reduces the movement threshold to capture low acceleration activity, which is more common in older adults. It is unclear how this filter changes activity estimates and whether it is appropriate for all older adults. The authors compared activity estimates with and without the LFE filter on wrist-worn devices in a sample of 34 older adults who wore the ActiGraph GT9X on their nondominant wrist for 7 days in a free-living environment. The authors used participant characteristics to predict discrepancy in step count estimates generated with and without the LFE filter to determine which individuals are most accurately characterized. Estimates of steps per minute were higher (M = 21, SD = 1), and more activity was classified as moderate to vigorous intensity (M = 5.03%, SD = 3.92%) with the LFE filter (M = 11, SD = 1; M = 4.27%, SD = 3.52%) versus without the LFE filter (all ps < .001). The findings suggest that axes-based variables should be interpreted with caution when generated with wrist-worn data, and future studies should develop separate wrist and waist-worn standard estimates in older adults. Participation in a greater amount of moderate to vigorous intensity physical activity predicted a larger discrepancy in step counts generated with and without the filter (p < .009), suggesting that the LFE filter becomes increasingly inappropriate for use in highly active older individuals.

Hicks, Laffer, and Watts are with the Department of Psychology, The University of Kansas, Lawrence, KS, USA. Meyer and Watts are with The University of Kansas Alzheimer’s Disease Center, Fairway, KS, USA.

Hicks (hilary_hicks@ku.edu) is corresponding author.
  • ActiGraph. (2018, November 8). Low frequency extension explained. [Online support post]. Retrieved from https://actigraphcorp.force.com/support/s/article/Low-Frequency-Extension-Explained

    • Search Google Scholar
    • Export Citation
  • Barnett, D.W., Barnett, A., Nathan, A., Van Cauwenberg, J., Cerin, E., & Council on Environmental and Physical Activity (CEPA)-Older Adults Working Group. (2017). Built environmental correlates of older adults’ total physical activity and walking: A systematic review and meta-analysis. International Journal of Behavioral Nutrition and Physical Activity, 14, 103. doi:10.1186/s12966-017-0558-z

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57, 289300. doi:10.1111.j.2517-6161.1995.tb02031.x

    • Search Google Scholar
    • Export Citation
  • Cain, K.L., Conway, T.L., Adams, M.A., Husak, L.E., & Sallis, J.F. (2013). Comparison of older and newer generations of ActiGraph accelerometers with the normal filter and the low frequency extension. International Journal of Behavioral Nutrition and Physical Activity, 10, 16. Retrived from http://www.ijbnpa.org/content/10/1/51.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choi, L., Liu, Z., Matthews, C.E., & Buchowski, M.S. (2011). Validation of accelerometer wear and nonwear time classification algorithm. Medicine & Science in Sports & Exercise, 43, 357364. PubMed ID: 20581716 doi:10.1249/MSS.0b013e3181ed61a3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chow, J.J., Thom, J.M., Wewege, M.A., Ward, R.E., & Parmenter, B.J. (2017). Accuracy of step count measured by physical activity monitors: The effect of gait speed and anatomical placement site. Gait & Posture, 57, 199203. PubMed ID: 28666177 doi:10.1016/j.gaitpost.2017.06.012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clevenger, K.A., Pfeiffer, K.A., & Montoye, A.H.K. (2020). Cross-generational comparability of hip- and wrist-worn ActiGraph GT3X+, wGT3X-BT, and GT9X accelerometers during free-living in adults. Journal of Sports Sciences, 38(24), 27942802. doi:10.1080/02640414.2020.1801320

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cole, R.J., Kripke, D.F., Gruen, W., Mullaney, D.J., & Gillin, J.C. (1992) Automatic sleep/wake identification from wrist activity. Sleep, 15, 461469. PubMed ID: 1455130 doi:10.1093/sleep/15.5.461

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Diaz, K.M., Howard, V.J., Hutto, B., Colabianchi, N., Vena, J.E., Blair, S.N., & Hooker, S.P. (2016). Patterns of sedentary behavior in US middle-age and older adults: The REGARDS study. Medicine & Science in Sports & Exercise, 48, 430438. PubMed ID: 26460633 doi:10.1249/MSS.0000000000000792

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feito, Y., Garner, H.R., & Bassett, D.R. (2015). Evaluation of ActiGraph’s low-frequency filter in laboratory and free-living environments. Medicine & Science in Sports & Exercise, 47(1), 211217. doi:10.1249/MSS.0000000000000395

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feito, Y., Hornbuckle, L.M., Reid, L.A., & Crouter, S.E. (2017). Effect of ActiGraph’s low frequency extension for estimating steps and physical activity intensity. PLoS One, 12, e0188242. doi:10.1371/journal.pone.0188242

    • 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, 777781. PubMed ID: 9588623 doi:10.1097/00005768-199805000-00021

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fry, A., Littlejohns, T.J., Sudlow, C., Doherty, N., Adamska, L., Sprosen, T.,… Allen, N.E. (2017). Comparison of sociodemographic and health-related characteristics of UK biobank participants with those of the general population. American Journal of Epidemiology, 186, 10261034. PubMed ID: 28641372 doi:10.1093/aje/kwx246

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimm, E.K., Swartz, A.M., Hart, T., Miller, N.E., & Strath, S.J. (2012). Comparison of the IPAQ-Short Form and accelerometry predictions of physical activity in older adults. Journal of Aging and Physical Activity, 20, 6479. PubMed ID: 22190120 doi:10.1123/japa.20.1.64

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harvey, J.A., Chastin, S.F.M., & Skelton, D.A. (2013). Prevalence of sedentary behavior in older adults: A systematic review. International Journal of Environmental Research and Public Health, 10(12), 66456661. PubMed ID: 24317382 doi:10.3390/ijerph10126645

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, X.Z., & Baker, D.W. (2005). Differences in leisure-time, household, and work-related physical activity by race, ethnicity, and education. Journal of General Internal Medicine, 20, 259266. PubMed ID: 15836530 doi:10.1111/j.1525-1497.2005.40198.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kerr, J., Marinac, C.R., Ellis, K., Godbole, S., Hipp, A., Glanz, K., … Berrigan, D. (2017). Comparison of accelerometry methods for estimating physical activity. Medicine & Science in Sports & Exercise, 49, 617624. PubMed ID: 27755355 doi:10.1249/MSS.0000000000001124

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Korpan, S.M., Schafer, J.L., Wilson, K.C.S., & Webber, S.C. (2015). Effect of ActiGraph GT3X+ position and algorithm choice on step count accuracy in older adults. Journal of Aging and Physical Activity, 23, 377382. PubMed ID: 25102469 doi:10.1123/japa.2014-0033

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loprinzi, P.D., & Smith, B. (2017). Comparison between wrist-worn and waist-worn accelerometry. Journal of Physical Activity and Health, 14(7), 539545. PubMed ID: 28290761 doi:10.1123/jpah.2016-0211

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mandigout, S., Lacroix, J., Perrochon, A., Svoboda, Z., Aubourg, T., & Vuillerme, N. (2019). Comparison of step count assessed using wrist- and hip-worn Actigraph GT3X in free-living conditions in young and older adults. Frontiers in Medicine, 6, 252. PubMed ID: 31828072 doi:10.3389/fmed.2019.00252

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matthews, C.E. (2005). Calibration of accelerometer output for adults. Medicine & Science in Sports & Exercise, 37, S512S522. doi:10.1249/01.mss.0000185659.11982.3d

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montoye, A.H.K., Clevenger, K.A., Pfeiffer, K.A., Nelson, M.B., Bock, J.M., Imboden, M.T., & Kaminsky, L.A. (2020). Development of cut-points for determining activity intensity from a wrist-worn ActiGraph accelerometer in free-living adults. Journal of Sports Sciences, 38(22), 25692578. doi:10.1080/02640414.2020.1794244

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montoye, A.H.K., Nelson, M.B., Bock, J.M., Imboden, M.T., Kaminsky, L.A., Mackintosh, K.A., … Pfeiffer, K.A. (2018). Raw and count data comparability of hip-worn ActiGraph GT3X+ and Link accelerometers. Medicine & Science in Sports & Exercise, 50, 11031112. PubMed ID: 29283934 doi:10.1249/MSS.0000000000001534

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sasaki, J.E., John, D., & Freedson, P.S. (2011). Validation and comparison of ActiGraph activity monitors. Journal of Science and Medicine in Sport, 14, 411416. PubMed ID: 21616714 doi:10.1016/j.jsams.2011.04.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tedesco, S., Sica, M., Ancillao, A., Timmons, S., Barton, J., & O’Flynn, B. (2019). Validity evaluation of the Fitbit Charge2 and the Garmin Vivosmart HR+ in free-living environments in an older adult cohort. Journal of Medical Internet Research Mhealth Uhealth, 7(6), e13084. doi:10.2196/13084

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toth, L.P., Park, S., Springer, C.M., Feyerabend, M.D., Steeves, J.A., & Bassett, D.R. (2018). Video-recorded validation of wearable step counters under free-living conditions. Medicine & Science in Sports & Exercise, 50(6), 3151322. doi:10.1249/MSS.0000000000001569

    • Search Google Scholar
    • Export Citation
  • Troiano, R.P., Berrigan, D., Dodd, K.W., Masse, L.C., Tilert, T., & McDowell, M. (2008). Physical activity in the United States measured by accelerometer. Medicine & Science in Sports & Exercise, 40, 181188. PubMed ID: 18091006 doi:10.1249/mss.0b013e31815a51b3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tudor-Locke, C., Barreira, T.V., & Schuna, J.M. (2015). Comparison of step outputs for waist and wrist accelerometer attachment sites. Medicine & Science in Sports & Exercise, 47(4), 839842. doi:10.1249/MSS.0000000000000476

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tudor-Locke, C., Craig, C.L., Aoyagi, Y., Bell, R.C., Croteau, K.A., De Bourdeaudhuij, I., … Blair, S.N. (2011). How many steps/day are enough? For older adults and special populations. International Journal of Behavioral Nutrition and Physical Activity, 8, 80. doi:10.1186/1479-5868-8-80

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tudor-Locke, C., Washington, T.L., & Hart, T.L. (2009). Expected values for steps/day in special populations. Preventive Medicine, 49(1), 311. PubMed ID: 19409409 doi:10.1016/j.ypmed.2009.04.012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wallén, M.B., Nero, H., Franzén, E., & Hagströmer, M. (2014). Comparison of two accelerometer filter settings in individuals with Parkinson’s disease. Physiological Measurement, 35, 22872296. PubMed ID: 25340812 doi:10.1088/0967-3334/35/11/2287

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 123 123 64
Full Text Views 7 7 4
PDF Downloads 5 5 2