Agreement Between StepWatch3 and ActiGraph wGT3X+ for Measuring Step-Based Metrics in People With Peripheral Artery Disease

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Pierre Jéhannin
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Alexis Le Faucheur
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Ségolène Chaudru
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Aline Taoum
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Guillaume Mahé
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Pierre-Yves de Müllenheim
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The authors investigated the agreement between StepWatch3™ (SW3) and ActiGraph™ wGT3X+ monitors for measuring step-based metrics in patients with peripheral artery disease and older adults. In 23 patients with peripheral artery disease and 38 older participants, the authors compared the metrics obtained during an outdoor (400-m track) walking session (step count) and a 7-day free-living period (step count and 60/30/5/1-min maximal or peak step accumulation) using the SW3 (ankle) and the wGT3X+ (hip) with the low-frequency extension filter enabled (wGT3X+/LFE) or not (wGT3X+/N). During outdoor walking session, agreement was high, particularly for wGT3X+/LFE: correlations ≥.98, median absolute percentage errors <1%, and significant equivalence using a ± 15% equivalence zone or narrower. In free living, no wGT3X+ method was equivalent to SW3 for step count. The wGT3X+/LFE was equivalent to SW3 regarding all step accumulation metrics using a ± 20% equivalence zone or narrower, with median absolute percentage errors <11%. The wGT3X+/LFE method is the best option for comparisons with SW3 in peripheral artery disease and older adults.

Jéhannin and Mahé are with the Vascular Medicine Unit, University Hospital, Rennes, France. Le Faucheur, Taoum, and Mahé are with the University of Rennes 2, M2S–EA 7470, Rennes, France. Chaudru and Mahé are with the Clinical Investigation Center, University of Rennes 1, Rennes, France. de Müllenheim is with the Institute of Physical Education and Sports Sciences (IFEPSA), West Catholic University (UCO), Les Ponts-de-Cé, France.

de Müllenheim (pydemull@uco.fr) is corresponding author.

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  • Abraham, P., Noury-Desvaux, B., Gernigon, M., Mahe, G., Sauvaget, T., Leftheriotis, G., & Le Faucheur, A. (2012). The inter- and intra-unit variability of a low-cost GPS data logger/receiver to study human outdoor walking in view of health and clinical studies. PLoS One, 7(2), e31338. PubMed ID: 22363623 doi:10.1371/journal.pone.0031338

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baguley, T. (2012). Calculating and graphing within-subject confidence intervals for ANOVA. Behavior Research Methods, 44(1), 158175. PubMed ID: 21858605 doi:10.3758/s13428-011-0123-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bassett, D.R., Jr., 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), 13031315. PubMed ID: 28005190 doi:10.1007/s40279-016-0663-1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chaudru, S., Jehannin, P., de Müllenheim, P.-Y., Klein, H., Jaquinandi, V., Mahe, G., & Le Faucheur, A. (2019). Using wearable monitors to assess daily walking limitations induced by ischemic pain in peripheral artery disease. Scandinavian Journal of Medicine & Science in Sports, 29(11), 18131826. PubMed ID: 31271680 doi:10.1111/sms.13511

    • 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), 20092016. PubMed ID: 22525772 doi:10.1249/MSS.0b013e318258cb36

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Carvalho, M. (2018). spearmanCI: Jackknife Euclidean/empirical likelihood inference for Spearman rho. R package version 1.0.

  • de Müllenheim, P.-Y., Chaudru, S., Mahé, G., Prioux, J., & Le Faucheur, A. (2016). Clinical interest of ambulatory assessment of physical activity and walking capacity in peripheral artery disease. Scandinavian Journal of Medicine & Science in Sports, 26(7), 716730. PubMed ID: 26173488 doi:10.1111/sms.12512

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeShaw, K.J., Ellingson, L., Bai, Y., Lansing, J., Perez, M., & Welk, G. (2018). Methods for activity monitor validation studies: An example with the Fitbit Charge. Journal for the Measurement of Physical Behaviour, 1(3), 130135. doi:10.1123/jmpb.2018-0017

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dixon, P.M., Saint-Maurice, P.F., Kim, Y., Hibbing, P., Bai, Y., & Welk, G.J. (2018). A primer on the use of equivalence testing for evaluating measurement agreement. Medicine & Science in Sports & Exercise, 50(4), 837845. PubMed ID: 29135817 doi:10.1249/MSS.0000000000001481

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Downs, J., Leonard, H., Jacoby, P., Brisco, L., Baikie, G., & Hill, K. (2015). Rett syndrome: Establishing a novel outcome measure for walking activity in an era of clinical trials for rare disorders. Disability and Rehabilitation, 37(21), 19921996. PubMed ID: 25495774 doi:10.3109/09638288.2014.993436

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feito, Y., Bassett, D.R., & Thompson, D.L. (2012). Evaluation of activity monitors in controlled and free-living environments. Medicine & Science in Sports & Exercise, 44(4), 733741. PubMed ID: 21904249 doi:10.1249/MSS.0b013e3182351913

    • 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. PubMed ID: 24870583 doi:10.1249/MSS.0000000000000395

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frimenko, R., Goodyear, C., & Bruening, D. (2015). Interactions of sex and aging on spatiotemporal metrics in non-pathological gait: A descriptive meta-analysis. Physiotherapy, 101(3), 266272. PubMed ID: 25702092 doi:10.1016/j.physio.2015.01.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gardner, A.W., Montgomery, P.S., Ritti-Dias, R.M., & Forrester, L. (2010). The effect of claudication pain on temporal and spatial gait measures during self-paced ambulation. Vascular Medicine, 15(1), 2126. PubMed ID: 19783569 doi:10.1177/1358863X09106836

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gardner, A.W., Parker, D.E., Montgomery, P.S., Khurana, A., Ritti-Dias, R.M., & Blevins, S.M. (2010). Gender differences in daily ambulatory activity patterns in patients with intermittent claudication. Journal of Vascular Surgery, 52(5), 12041210. PubMed ID: 20692790 doi:10.1016/j.jvs.2010.05.115

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gardner, A.W., Parker, D.E., Montgomery, P.S., Scott, K.J., & Blevins, S.M. (2011). Efficacy of quantified home-based exercise and supervised exercise in patients with intermittent claudication: A randomized controlled trial. Circulation, 123(5), 491498. PubMed ID: 21262997 doi:10.1161/CIRCULATIONAHA.110.963066

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gernigon, M., Le Faucheur, A., Noury-Desvaux, B., Mahe, G., Abraham, P., & Post-GPS Study Coinvestigators Group. (2014). Applicability of global positioning system for the assessment of walking ability in patients with arterial claudication. Journal of Vascular Surgery, 60(4), 973981.e1. doi:10.1016/j.jvs.2014.04.053

    • 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, 233372141878112. doi: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), 145153. PubMed ID: 26107045 doi: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), 1206. doi:10.3390/s18041206

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karabulut, M., Crouter, S.E., & Bassett, D.R., Jr. (2005). Comparison of two waist-mounted and two ankle-mounted electronic pedometers. European Journal of Applied Physiology, 95(4), 335343. doi:10.1007/s00421-005-0018-3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Korkmaz, S., Goksuluk, D., & Zararsiz, G. (2014). MVN: An R package for assessing multivariate normality. The R Journal, 6(2), 151162. doi:10.32614/RJ-2014-031

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28(5), 126. doi:10.18637/jss.v028.i05

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Le Faucheur, A., Abraham, P., Jaquinandi, V., Bouye, P., Saumet, J.L., & Noury-Desvaux, B. (2007). Study of human outdoor walking with a low-cost GPS and simple spreadsheet analysis. Medicine & Science in Sports & Exercise, 39(9), 15701578. PubMed ID: 17805090 doi:10.1249/mss.0b013e3180cc20c7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Le Faucheur, A., Abraham, P., Jaquinandi, V., Bouye, P., Saumet, J.L., & Noury-Desvaux, B. (2008). Measurement of walking distance and speed in patients with peripheral arterial disease: A novel method using a global positioning system. Circulation, 117(7), 897904. PubMed ID: 18250268 doi:10.1161/CIRCULATIONAHA.107.725994

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leicht, A.S., & Crowther, R.G. (2007). Pedometer accuracy during walking over different surfaces. Medicine & Science in Sports & Exercise, 39(10), 18471850. PubMed ID: 17909414 doi:10.1249/mss.0b013e3181405b9f

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leicht, A.S., & Crowther, R.G. (2009). Influence of non-level walking on pedometer accuracy. Journal of Science and Medicine in Sport, 12(3), 361365. PubMed ID: 18356103 doi:10.1016/j.jsams.2008.01.007

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mangiafico, S.S. (2016). Summary and analysis of extension program evaluation in R, version 1.18.1. Retrieved from https://rcompanion.org/

    • Search Google Scholar
    • Export Citation
  • McDermott, M.M., Liu, K., Guralnik, J.M., Criqui, M.H., Spring, B., Tian, L., … Rejeski, W.J. (2013). Home-based walking exercise intervention in peripheral artery disease: A randomized clinical trial. JAMA, 310(1), 5765. PubMed ID: 23821089 doi:10.1001/jama.2013.7231

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Migueles, J.H., Cadenas-Sanchez, C., Ekelund, U., Delisle Nystrom, C., Mora-Gonzalez, J., Lof, M., … Ortega, F.B. (2017). Accelerometer data collection and processing criteria to assess physical activity and other outcomes: A systematic review and practical considerations. Sports Medicine, 47(9), 18211845. PubMed ID: 28303543 doi:10.1007/s40279-017-0716-0

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murrow, J.R., Brizendine, J.T., Djire, B., Young, H.-J., Rathbun, S., Nilsson, K.R., Jr., & McCully, K.K. (2019). Near infrared spectroscopy-guided exercise training for claudication in peripheral arterial disease. European Journal of Preventive Cardiology, 26(5), 471480. PubMed ID: 30152245 doi:10.1177/2047487318795192

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noury-Desvaux, B., Abraham, P., Mahe, G., Sauvaget, T., Leftheriotis, G., & Le Faucheur, A. (2011). The accuracy of a simple, low-cost GPS data logger/receiver to study outdoor human walking in view of health and clinical studies. PLoS One, 6(9), e23027. PubMed ID: 21931593 doi:10.1371/journal.pone.0023027

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parsons, T.J., Sartini, C., Ellins, E.A., Halcox, J.P., Smith, K.E., Ash, S., … Jefferis, B.J. (2016). Objectively measured physical activity and sedentary behaviour and ankle brachial index: Cross-sectional and longitudinal associations in older men. Atherosclerosis, 247, 2834. PubMed ID: 26854973 doi:10.1016/j.atherosclerosis.2016.01.038

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riley, R.D., Ensor, J., Snell, K.I.E. , Harrell, F.E., Jr., Martin, G.P., Reitsma, J.B., … van Smeden, M. (2020). Calculating the sample size required for developing a clinical prediction model. The BMJ, 368, m441. PubMed ID: 32188600 doi:10.1136/bmj.m441

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sandroff, B.M., Motl, R.W., Pilutti, L.A., Learmonth, Y.C., Ensari, I., Dlugonski, D., … Riskin, B.J. (2014). Accuracy of StepWatch and ActiGraph accelerometers for measuring steps taken among persons with multiple sclerosis. PLoS One, 9(4), e93511. PubMed ID: 24714028 doi:10.1371/journal.pone.0093511

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shepherd, E.F., Toloza, E., McClung, C.D., & Schmalzried, T.P. (1999). Step activity monitor: Increased accuracy in quantifying ambulatory activity. Journal of Orthopaedic Research, 17(5), 703708. PubMed ID: 10569479 doi:10.1002/jor.1100170512

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Song, P., Rudan, D., Zhu, Y., Fowkes, F.J.I., Rahimi, K., Fowkes, F.G.R., & Rudan, I. (2019). Global, regional, and national prevalence and risk factors for peripheral artery disease in 2015: An updated systematic review and analysis. Lancet Global Health, 7(8), e1020e1030. PubMed ID: 31303293 doi:10.1016/S2214-109X(19)30255-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taoum, A., Chaudru, S., de Mullenheim, P.-Y., Congnard, F., Emily, M., Noury-Desvaux, B., … Le Faucheur, A. (2021). Comparison of activity monitors accuracy in assessing intermittent outdoor walking. Medicine & Science in Sports & Exercise, 53(6), 13031314. PubMed ID: 33731660 doi:10.1249/MSS000000000002587

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tew, G.A., Humphreys, L., Crank, H., Hewitt, C., Nawaz, S., Al-Jundi, W., … Gorely, T. (2015). The development and pilot randomised controlled trial of a group education programme for promoting walking in people with intermittent claudication. Vascular Medicine, 20(4), 348357. PubMed ID: 25858012 doi:10.1177/1358863X15577857

    • 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), 13151322. PubMed ID: 29381649 doi:10.1249/MSS.0000000000001569

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Treacy, D., Hassett, L., Schurr, K., Chagpar, S., Paul, S.S., & Sherrington, C. (2017). Validity of different activity monitors to count steps in an inpatient rehabilitation setting. Physical Therapy, 97(5), 581588. PubMed ID: 28339904 doi:10.1093/ptj/pzx010

    • 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), 839842. doi:10.1249/mss.0000000000000476

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tudor-Locke, C., Schuna, J.M., Jr., Han, H.O., Aguiar, E.J., Green, M.A., Busa, M.A., … Johnson, W.D. (2017). Step-based physical activity metrics and cardiometabolic risk: NHANES 2005–2006. Medicine & Science in Sports & Exercise, 49(2), 283291. PubMed ID: 27669450 doi:10.1249/MSS.0000000000001100

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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), 170177. PubMed ID: 22895373 doi:10.1249/MSS.0b013e31826c2cf1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webber, S.C., & St. John, P.D. (2016). Comparison of ActiGraph GT3X+ and StepWatch step count accuracy in geriatric rehabilitation patients. Journal of Aging and Physical Activity, 24(3), 451458. PubMed ID: 26751505 doi:10.1123/japa.2015-0234

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Welk, G.J., Bai, Y., Lee, J.M., Godino, J., Saint-Maurice, P.F., & Carr, L. (2019). Standardizing analytic methods and reporting in activity monitor validation studies. Medicine & Science in Sports & Exercise, 51(8), 17671780. PubMed ID: 30913159 doi:10.1249/MSS.0000000000001966

    • Crossref
    • Search Google Scholar
    • Export Citation
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