Agreement of Step-Based Metrics From ActiGraph and ActivPAL Accelerometers Worn Concurrently Among Older Adults

in Journal for the Measurement of Physical Behaviour

Click name to view affiliation

Eric T. HydeHerbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA

Search for other papers by Eric T. Hyde in
Current site
Google Scholar
PubMedClose
*
,
Steve NguyenHerbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA

Search for other papers by Steve Nguyen in
Current site
Google Scholar
PubMedClose
,
Fatima Tuz-ZahraHerbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA

Search for other papers by Fatima Tuz-Zahra in
Current site
Google Scholar
PubMedClose
,
Christopher C. MooreDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Search for other papers by Christopher C. Moore in
Current site
Google Scholar
PubMedClose
,
Mikael Anne Greenwood-HickmanKaiser Permanente Washington Health Research Institute, Seattle, WA, USA

Search for other papers by Mikael Anne Greenwood-Hickman in
Current site
Google Scholar
PubMedClose
,
Rod L. WalkerKaiser Permanente Washington Health Research Institute, Seattle, WA, USA

Search for other papers by Rod L. Walker in
Current site
Google Scholar
PubMedClose
,
Loki NatarajanHerbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA

Search for other papers by Loki Natarajan in
Current site
Google Scholar
PubMedClose
,
Dori RosenbergKaiser Permanente Washington Health Research Institute, Seattle, WA, USA

Search for other papers by Dori Rosenberg in
Current site
Google Scholar
PubMedClose
, and
John BellettiereHerbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA

Search for other papers by John Bellettiere in
Current site
Google Scholar
PubMedClose
DOI:
https://doi.org/10.1123/jmpb.2022-0001
Keywords:
cadence; wearable devices; gait; physical activity
First Published Online:
11 Oct 2022
In Print:
Volume 5: Issue 4
Page Range:
242–251
Restricted access

Purpose: Our study evaluated the agreement of mean daily step counts, peak 1-min cadence, and peak 30-min cadence between the hip-worn ActiGraph GT3X+ accelerometer, using the normal filter (AGN) and the low frequency extension (AGLFE), and the thigh-worn activPAL3 micro (AP) accelerometer among older adults. Methods: Nine-hundred and fifty-three older adults (≥65 years) were recruited to wear the ActiGraph device concurrently with the AP for 4–7 days beginning in 2016. Using the AP as the reference measure, device agreement for each step-based metric was assessed using mean differences (AGN − AP and AGLFE − AP), mean absolute percentage error (MAPE), and Pearson and concordance correlation coefficients. Results: For AGN − AP, the mean differences and MAPE were: daily steps −1,851 steps/day and 27.2%, peak 1-min cadence −16.2 steps/min and 16.3%, and peak 30-min cadence −17.7 steps/min and 24.0%. Pearson coefficients were .94, .85, and .91 and concordance coefficients were .81, .65, and .73, respectively. For AGLFE − AP, the mean differences and MAPE were: daily steps 4,968 steps/day and 72.7%, peak 1-min cadence −1.4 steps/min and 4.7%, and peak 30-min cadence 1.4 steps/min and 7.0%. Pearson coefficients were .91, .91, and .95 and concordance coefficients were .49, .91, and .94, respectively. Conclusions: Compared with estimates from the AP, the AGN underestimated daily step counts by approximately 1,800 steps/day, while the AGLFE overestimated by approximately 5,000 steps/day. However, peak step cadence estimates generated from the AGLFE and AP had high agreement (MAPE ≤ 7.0%). Additional convergent validation studies of step-based metrics from concurrently worn accelerometers are needed for improved understanding of between-device agreement.

  • Collapse
  • Expand

Journal for the Measurement of Physical Behaviour

Cover Journal for the Measurement of Physical Behaviour

  • 2018 Physical Activity Guidelines Advisory Committee. (2018). 2018 physical activity guidelines advisory committee scientific report.

  • ActiGraph Corp. (2017). ActiGraph white paper: Low frequency extension filter. https://s3.amazonaws.com/actigraphcorp.com/wp-content/uploads/2017/11/26205810/Low-Frequency-Extension-Filter.pdf

    • Search Google Scholar
    • Export Citation

  • Bassett, D.R., Toth, L.P., LaMunion, S.R., & Crouter, S.E. (2017). Step counting: A review of measurement considerations and health-related applications. Sports Medicine, 47(7), 1303–1315. https://doi.org/10.1007/s40279-016-0663-1

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bland, J.M., & Altman, D.G. (2007). Agreement between methods of measurement with multiple observations per individual. The Journal of Biopharmaceutical Statistics, 17(4), 571–582. https://doi.org/10.1080/10543400701329422

    • Crossref
    • 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. The International Journal of Behavioral Nutrition and Physical Activity, 10, Article 51. https://doi.org/10.1186/1479-5868-10-51

    • 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(2), 357–364. https://doi.org/10.1249/MSS.0b013e3181ed61a3

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Choi, L., Ward, S.C., Schnelle, J.F., & Buchowski, M.S. (2012). Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. Medicine & Science in Sports & Exercise, 44(10), 2009–2016. https://doi.org/10.1249/MSS.0b013e318258cb36

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Crowley, P., Skotte, J., Stamatakis, E., Hamer, M., Aadahl, M., Stevens, M.L., Rangul, V., Mork, P.J., & Holtermann, A. (2019). Comparison of physical behavior estimates from three different thigh-worn accelerometers brands: A proof-of-concept for the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS). International Journal of Behavioral Nutrition and Physical Activity, 16(1), 65. https://doi.org/10.1186/s12966-019-0835-0

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dall, P.M., Skelton, D.A., Dontje, M.L., Coulter, E.H., Stewart, S., Cox, S.R., Shaw, R.J., Čukić, I., Fitzsimons, C.F., & Greig, C.A. (2018). Characteristics of a protocol to collect objective physical activity/sedentary behavior data in a large study: Seniors USP (understanding sedentary patterns). Journal for the Measurement of Physical Behaviour, 1(1), 26–31. https://doi.org/10.1123/jmpb.2017-0004

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Edwardson, C.L., Winkler, E.A.H., Bodicoat, D.H., Yates, T., Davies, M.J., Dunstan, D.W., & Healy, G.N. (2017). Considerations when using the activPAL monitor in field-based research with adult populations. Journal of Sport and Health Science, 6(2), 162–178. https://doi.org/https://doi.org/10.1016/j.jshs.2016.02.002

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ekelund, U., Tarp, J., Fagerland, M.W., Johannessen, J.S., Hansen, B.H., Jefferis, B.J., Whincup, P.H., Diaz, K.M., Hooker, S., Howard, V.J., Chernofsky, A., Larson, M.G., Spartano, N., Vasan, R.S., Dohrn, I.M., Hagströmer, M., Edwardson, C., Yates, T., Shiroma, E.J., … Lee, I.M. (2020). Joint associations of accelerometer measured physical activity and sedentary time with all-cause mortality: A harmonised meta-analysis in more than 44000 middle-aged and older individuals. British Journal of Sports Medicine, 54(24), 1499–1506. https://doi.org/10.1136/bjsports-2020-103270

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ekelund, U., Tarp, J., Steene-Johannessen, J., Hansen, B.H., Jefferis, B., Fagerland, M.W., Whincup, P., Diaz, K.M., Hooker, S.P., Chernofsky, A., Larson, M.G., Spartano, N., Vasan, R.S., Dohrn, I.M., Hagströmer, M., Edwardson, C., Yates, T., Shiroma, E., Anderssen, S.A., & Lee, I.M. (2019). Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: Systematic review and harmonised meta-analysis. British Medical Journal, 366, Article l4570. https://doi.org/10.1136/bmj.l4570

    • 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(11), Article e0188242. https://doi.org/10.1371/journal.pone.0188242

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hall, K.S., Hyde, E.T., Bassett, D.R., Carlson, S.A., Carnethon, M.R., Ekelund, U., Evenson, K.R., Galuska, D.A., Kraus, W.E., Lee, I.M., Matthews, C.E., Omura, J.D., Paluch, A.E., Thomas, W.I., & Fulton, J.E. (2020). Systematic review of the prospective association of daily step counts with risk of mortality, cardiovascular disease, and dysglycemia. International Journal of Behavioral Nutrition and Physical Activity, 17(1), Article 78. https://doi.org/10.1186/s12966-020-00978-9

    • Crossref
    • Search Google Scholar
    • Export Citation

  • 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 Sciences, 29(6), 627–633. https://doi.org/10.1080/02640414.2010.549499

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hergenroeder, A.L., Barone Gibbs, B., Kotlarczyk, M.P., Kowalsky, R.J., Perera, S., & Brach, J.S. (2018). Accuracy of objective physical activity monitors in measuring steps in older adults. Gerontology and Geriatric Medicine 4, Article 2333721418781126. https://doi.org/10.1177/2333721418781126

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hickey, A., John, D., Sasaki, J.E., Mavilia, M., & Freedson, P. (2016). Validity of activity monitor step detection is related to movement patterns. Journal of Physical Activity and Health, 13(2), 145–153. https://doi.org/10.1123/jpah.2015-0203

    • Crossref
    • Search Google Scholar
    • Export Citation

  • John, D., Morton, A., Arguello, D., Lyden, K., & Bassett, D. (2018). “What is a step?” Differences in how a step is detected among three popular activity monitors that have impacted physical activity research. Sensors, 18(4), Article 1206. https://doi.org/10.3390/s18041206

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Koo, T.K., & Li, M.Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kooiman, T.J., Dontje, M.L., Sprenger, S.R., Krijnen, W.P., van der Schans, C.P., & de Groot, M. (2015). Reliability and validity of ten consumer activity trackers. BMC Sports Science, Medicine and Rehabilitation, 7, Article 24. https://doi.org/10.1186/s13102-015-0018-5

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Korpan, S.M., Schafer, J.L., Wilson, K.C., & 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(3), 377–382. https://doi.org/10.1123/japa.2014-0033

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kraus, W.E., Janz, K.F., Powell, K.E., Campbell, W.W., Jakicic, J.M., Troiano, R.P., Sprow, K., Torres, A., & Piercy, K.L. (2019). Daily step counts for measuring physical activity exposure and its relation to health. Medicine & Science in Sports & Exercise, 51(6), 1206–1212. https://doi.org/10.1249/mss.0000000000001932

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kukull, W.A., Higdon, R., Bowen, J.D., McCormick, W.C., Teri, L., Schellenberg, G.D., van Belle, G., Jolley, L., & Larson, E.B. (2002). Dementia and Alzheimer disease incidence: A prospective cohort study. Archives of Neurology, 59(11), 1737–1746. https://doi.org/10.1001/archneur.59.11.1737

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lee, I.-M., Shiroma, E.J., Kamada, M., Bassett, D.R., Matthews, C.E., & Buring, J.E. (2019). Association of step volume and intensity with all-cause mortality in older women. JAMA Internal Medicine, 179(8), 1105–1112. https://doi.org/10.1001/jamainternmed.2019.0899

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Maula, A., LaFond, N., Orton, E., Iliffe, S., Audsley, S., Vedhara, K., & Kendrick, D. (2019). Use it or lose it: A qualitative study of the maintenance of physical activity in older adults. BMC Geriatrics, 19(1), Article 349. https://doi.org/10.1186/s12877-019-1366-x

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Moore, C.C., McCullough, A.K., Aguiar, E.J., Ducharme, S.W., & Tudor-Locke, C. (2020). Toward harmonized treadmill-based validation of step-counting wearable technologies: A scoping review. Journal of Physical Activity and Health,  17(8), 1–13. https://doi.org/10.1123/jpah.2019-0205

    • Search Google Scholar
    • Export Citation

  • Newman, A.B., Sanders, J.L., Kizer, J.R., Boudreau, R.M., Odden, M.C., Zeki Al Hazzouri, A., & Arnold, A.M. (2016). Trajectories of function and biomarkers with age: The CHS all stars study. International Journal of Epidemiology, 45(4), 1135–1145. https://doi.org/10.1093/ije/dyw092

    • Search Google Scholar
    • Export Citation

  • Ortlieb, S., Dias, A., Gorzelniak, L., Nowak, D., Karrasch, S., Peters, A., Kuhn, K.A., Horsch, A., Schulz, H., & Group, K.S. (2014). Exploring patterns of accelerometry-assessed physical activity in elderly people. International Journal of Behavioral Nutrition and Physical Activity, 11(1), Article 28. https://doi.org/10.1186/1479-5868-11-28

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Paluch, A.E., Gabriel, K.P., Fulton, J.E., Lewis, C.E., Schreiner, P.J., Sternfeld, B., Sidney, S., Siddique, J., Whitaker, K.M., & Carnethon, M.R. (2021). Steps per day and all-cause mortality in middle-aged adults in the coronary artery risk development in young adults study. JAMA Network Open, 4(9), Article e2124516. https://doi.org/10.1001/jamanetworkopen.2021.24516

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Park, S., Marcotte, R.T., Toth, L.P., Paulus, P., Lauricella, L.P., Kim, A.H., Crouter, S.E., Springer, C.M., Staudenmayer, J.W., & Bassett, D.R. (2021). Free-living validation and harmonization of 10 wearable step count monitors. Translational Journal of the American College of Sports Medicine, 6(4), Article e000172. https://doi.org/10.1249/tjx.0000000000000172

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ried-Larsen, M., Brønd, J.C., Brage, S., Hansen, B.H., Grydeland, M., Andersen, L.B., & Møller, N.C. (2012). Mechanical and free living comparisons of four generations of the Actigraph activity monitor. International Journal of Behavioral Nutrition and Physical Activity, 9, Article 113. https://doi.org/10.1186/1479-5868-9-113

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rosenberg, D., Walker, R., Greenwood-Hickman, M.A., Bellettiere, J., Xiang, Y., Richmire, K., Higgins, M., Wing, D., Larson, E.B., Crane, P.K., & LaCroix, A.Z. (2020). Device-assessed physical activity and sedentary behavior in a community-based cohort of older adults. BMC Public Health, 20(1), Article 1256. https://doi.org/10.1186/s12889-020-09330-z

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ryan, C.G., Grant, P.M., Tigbe, W.W., & Granat, M.H. (2006). The validity and reliability of a novel activity monitor as a measure of walking. British Journal of Sports Medicine, 40(9), 779–784. https://doi.org/10.1136/bjsm.2006.027276

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Saint-Maurice, P.F., Troiano, R.P., Bassett, D.R., Jr., Graubard, B.I., Carlson, S.A., Shiroma, E.J., Fulton, J.E., & Matthews, C.E. (2020). Association of daily step count and step intensity with mortality among US adults. The Journal of the American Medical Association, 323(12), 1151–1160. https://doi.org/10.1001/jama.2020.1382

    • 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), 1315–1322. https://doi.org/10.1249/mss.0000000000001569

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tudor-Locke, C., & Aguiar, E.J. (2019). Toward comprehensive step-based physical activity guidelines: Are we ready? Kinesiology Review, 8(1), 25–31. https://doi.org/10.1123/kr.2018-0065

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tudor-Locke, C., Barreira, T.V., & Schuna, J.M., Jr. (2015). Comparison of step outputs for waist and wrist accelerometer attachment sites. Medicine & Science in Sports & Exercise, 47(4), 839–842. https://doi.org/10.1249/mss.0000000000000476

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tudor-Locke, C., Han, H., Aguiar, E.J., Barreira, T.V., Schuna, J.M., Jr., Kang, M., & Rowe, D.A. (2018). How fast is fast enough? Walking cadence (steps/min) as a practical estimate of intensity in adults: A narrative review. British Journal of Sports Medicine, 52(12), 776–788. https://doi.org/10.1136/bjsports-2017-097628

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tudor-Locke, C., Johnson, W.D., & Katzmarzyk, P.T. (2009). Accelerometer-determined steps per day in US adults. Medicine & Science in Sports & Exercise, 41(7), 1384–1391. https://doi.org/10.1249/MSS.0b013e318199885c

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tudor-Locke, C., & Rowe, D.A. (2012). Using cadence to study free-living ambulatory behaviour. Sports Medicine, 42(5), 381–398. https://doi.org/10.2165/11599170-000000000-00000

    • Crossref
    • Search Google Scholar
    • Export Citation

  • U.S. Department of Health and Human Services. (2018). Physical activity guidelines for Americans (2nd ed.).

  • Wanner, M., Martin, B.W., Meier, F., Probst-Hensch, N., & Kriemler, S. (2013). Effects of filter choice in GT3X accelerometer assessments of free-living activity. Medicine & Science in Sports & Exercise, 45(1), 170–177. https://doi.org/10.1249/MSS.0b013e31826c2cf1

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ware, J.E., Jr. (2000). SF-36 health survey update. Spine, 25(24), 3130–3139. https://doi.org/10.1097/00007632-200012150-00008

  • Ware, J.E., Jr., & Sherbourne, C.D. (1992). The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Medical Care, 30(6), 473–483.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wijndaele, K., Westgate, K., Stephens, S.K., Blair, S.N., Bull, F.C., Chastin, S.F., Dunstan, D.W., Ekelund, U., Esliger, D.W., Freedson, P.S., Granat, M.H., Matthews, C.E., Owen, N., Rowlands, A.V., Sherar, L.B., Tremblay, M.S., Troiano, R.P., Brage, S., & Healy, G.N. (2015). Utilization and harmonization of adult accelerometry data: Review and expert consensus. Medicine & Science in Sports & Exercise, 47(10), 2129–2139. https://doi.org/10.1249/mss.0000000000000661

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 650 650 74
Full Text Views 19 19 4
PDF Downloads 23 23 5