Choice of Processing Method for Wrist-Worn Accelerometers Influences Interpretation of Free-Living Physical Activity Data in a Clinical Sample

in Journal for the Measurement of Physical Behaviour
Restricted access

Purchase article

USD $24.95

Student 1 year subscription

USD $37.00

1 year subscription

USD $50.00

Student 2 year subscription

USD $71.00

2 year subscription

USD $93.00

Wrist-worn accelerometers are increasingly used to assess free-living physical activity (PA), but the implications of different processing methods are not well characterized. To advance research in this area it is important to better understand how choice of processing method influences estimates of free-living PA behavior. This study compared PA profiles resulting from processing wrist-worn data collected under free-living conditions using four different methods in a clinical sample of 160 women with chronic pain, a condition for which PA serves as a treatment. Participants wore monitors on their non-dominant wrist for 7 days and completed a self-report PA measure. Processing methods were Hildebrand linear, a modified nonlinear Hildebrand, Staudenmayer linear, and Staudenmayer random forest. Using each method, minutes per day in sedentary, light, and moderate-to-vigorous PA (MVPA) were estimated and individuals were classified as meeting PA guidelines based on their accumulated MVPA. Comparisons of outcomes among processing methods and with self-reported PA were made using repeated measures ANOVA, correlations, and kappa statistics. With few exceptions, estimated time at each intensity differed significantly across processing methods and with self-report (p < .001). Correlations between methods ranged widely (ρrange = 0.09 to 1.00) and showed inconsistent agreement for classifying individuals as meeting PA guidelines (κrange: −0.02 to 0.90). Thus, choice of processing method significantly influenced conclusions regarding free-living PA. Researchers and clinicians should exercise caution when interpreting accelerometer activity data and comparing across existing studies using different processing methods when examining how PA influences clinical conditions.

Ellingson and Welk are with the Iowa State University, Ames, IA. Hibbing is with the University of Tennessee–Knoxville. Dailey is with St. Ambrose University, Davenport, IA. Rakel, Sluka, and Frey-Law are with the University of Iowa, Iowa City, IA. Crofford is with the Vanderbilt University, Nashville, TN.

Ellingson (ellingl@iastate.edu) is corresponding author.
Journal for the Measurement of Physical Behaviour
Article Sections
References
  • AtkinA.J.GorelyT.ClemesS.A.YatesT.EdwardsonC.BrageS. . . . BiddleS.J.H. (2012). Methods of Measurement in epidemiology: Sedentary Behaviour. International Journal of Epidemiology 41(5) 14601471. PubMed ID: 23045206 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • BakraniaK.YatesT.RowlandsA.V.EsligerD.W.BunnewellS.SandersJ. . . . EdwardsonC.L. (2016). Intensity thresholds on raw acceleration data: Euclidean norm minus one (ENMO) and mean amplitude deviation (MAD) approaches. PLoS One 11(10) e0164045. PubMed ID: 27706241 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • BassettD.R. (2012). Device-based monitoring in physical activity and public health research. Physiological Measurement 33(11) 17691783. PubMed ID: 23110847 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • BassettD.R.RowlandsA. & TrostS.G. (2012). Calibration and validation of wearable monitors. Medicine & Science in Sports & Exercise 44(1 Suppl. 1) S32S38. PubMed ID: 22157772 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • BidondeJ.BuschA.J.BathB. & MilosavljevicS. (2014). Exercise for adults with fibromyalgia: An umbrella systematic review with synthesis of best evidence. Current Rheumatology Reviews 10(1) 4579. PubMed ID: 25229499. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • BuchanD.S.McSeveneyF. & McLellanG. (2019). A comparison of physical activity from Actigraph GT3X+ accelerometers worn on the dominant and non-dominant wrist. Clinical Physiology and Functional Imaging 39(1) 5156. PubMed ID: 30058765 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Celis-MoralesC.A.Perez-BravoF.IbañezL.SalasC.BaileyM.E.S. & GillJ.M.R. (2012). Objective vs. self-reported physical activity and sedentary time: Effects of measurement method on relationships with risk biomarkers. PLoS One 7(5) e36345. PubMed ID: 22590532 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CraigC.L.MarshallA.L.SjöströmM.BaumanA.E.BoothM.L.AinsworthB.E. . . . OjaP. (2003). International physical activity questionnaire: 12-country reliability and validity. Medicine & Science in Sports & Exercise 35(8) 13811395. PubMed ID: 12900694 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Da SilvaI.C.van HeesV.T.RamiresV.V.KnuthA.G.BielemannR.M.EkelundU. . . . HallalP.C. (2014). Physical activity levels in three Brazilian birth cohorts as assessed with raw triaxial wrist accelerometry. International Journal of Epidemiology 43(6) 19591968. PubMed ID: 25361583 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DohertyA.JacksonD.HammerlaN.PlötzT.OlivierP.GranatM.H. . . . WarehamN.J. (2017). Large scale population assessment of physical activity using wrist worn accelerometers: The UK biobank study. PLoS One 12(2) e0169649. PubMed ID: 28146576 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • EllingsonL.D.HibbingP.R.KimY.Frey-LawL.A.Saint-MauriceP.F. & WelkG.J. (2017). Lab-based validation of different data processing methods for wrist-worn ActiGraph accelerometers in young adults. Physiological Measurement 38(6) 10451060. PubMed ID: 28481750 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • EllingsonL.D.SchwabacherI.J.KimY.WelkG.J. & CookD.B. (2016). Validity of an integrative method for processing physical activity data. Medicine & Science in Sports & Exercise 48(8) 16291638. PubMed ID: 27015380 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • EllingsonL.D.ShieldsM.R.StegnerA.J. & CookD.B. (2012). Physical activity, sustained sedentary behavior, and pain modulation in women with fibromyalgia. The Journal of Pain 13(2) 195206. PubMed ID: 22245361 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • EllisK.KerrJ.GodboleS.LanckrietG.WingD. & MarshallS. (2014). A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers. Physiological Measurement 35(11) 21912203. PubMed ID: 25340969 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • FreedsonP.S. & JohnD. (2013). Comment on “estimating activity and sedentary behavior from an accelerometer on the hip and wrist”. Medicine & Science in Sports & Exercise 45(5) 962963. PubMed ID: 23594509 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • FreedsonP.S.MelansonE. & SirardJ. (1998). Calibration of the Computer Science and Applications, Inc. accelerometer. Medicine & Science in Sports & Exercise 30(5) 777781. PubMed ID: 9588623. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • GonzálezK.FuentesJ. & MárquezJ.L. (2017). Physical inactivity, sedentary behavior and chronic diseases. Korean Journal of Family Medicine 38(3) 111115. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • HibbingP.R.EllingsonL.D.DixonP.M. & WelkG.J. (2018). Adapted sojourn models to estimate activity intensity in youth: A suite of tools. Medicine & Science in Sports & Exercise 50(4) 846854. PubMed ID: 29135657 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • HibbingP.R.KimY.Saint-MauriceP.F. & WelkG.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 16223. PubMed ID: 26939783 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • HildebrandM.HansenB.H.van HeesV.T. & EkelundU. (2016). Evaluation of raw acceleration sedentary thresholds in children and adults. Scandinavian Journal of Medicine & Science in Sports. doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • HildebrandM.VAN HeesV.T.HansenB.H. & EkelundU. (2014). Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. Medicine & Science in Sports & Exercise 46(9) 18161824. PubMed ID: 24887173 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPAQ scoring protocol - International Physical Activity Questionnaire. (2005). Retrieved from https://sites.google.com/site/theipaq/scoring-protocol

    • Export Citation
  • KeadleS.K.ShiromaE.J.FreedsonP.S. & LeeI.-M. (2014). Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study. BMC Public Health 141210. PubMed ID: 25421941 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • KeimN.L.BlantonC.A. & KretschM.J. (2004). America’s obesity epidemic: Measuring physical activity to promote an active lifestyle. Journal of the American Dietetic Association 104(9) 13981409. PubMed ID: 15354157 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • KimY. & WelkG.J. (2015). Criterion validity of competing accelerometry-based activity monitoring devices. Medicine & Science in Sports & Exercise 47(11) 24562463. PubMed ID: 25910051 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • LandisJ.R. & KochG.G. (1977). The measurement of observer agreement for categorical data. Biometrics 33(1) 159174. PubMed ID: 843571. doi:

  • LeeP.H.MacfarlaneD.J.LamT.H. & StewartS.M. (2011). Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): A systematic review. The International Journal of Behavioral Nutrition and Physical Activity 8115. PubMed ID: 22018588 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • LydenK.KeadleS.K.StaudenmayerJ. & FreedsonP.S. (2014). A method to estimate free-living active and sedentary behavior from an accelerometer. Medicine & Science in Sports & Exercise 46(2) 386397. PubMed ID: 23860415 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McLoughlinM.J.ColbertL.H.StegnerA.J. & CookD.B. (2011). Are women with fibromyalgia less physically active than healthy women? Medicine & Science in Sports & Exercise 43(5) 905912. PubMed ID: 20881881 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MiguelesJ.H.Cadenas-SanchezC.EkelundU.Delisle NyströmC.Mora-GonzalezJ.LöfM. . . . OrtegaF.B. (2017). Accelerometer data collection and processing criteria to assess physical activity and other outcomes: A systematic review and practical considerations. Sports Medicine (Auckland N.Z.) 47(9) 18211845. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MontoyeA.H.K.BegumM.HenningZ. & PfeifferK.A. (2017). Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data. Physiological Measurement 38(2) 343357. PubMed ID: 28107205 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NoehrenB.DaileyD.L.RakelB.A.VanceC.G.T.ZimmermanM.B.CroffordL.J. & SlukaK.A. (2015). Effect of transcutaneous electrical nerve stimulation on pain, function, and quality of life in fibromyalgia: A double-blind randomized clinical trial. Physical Therapy 95(1) 129140. PubMed ID: 25212518 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • OwenN.SparlingP.B.HealyG.N.DunstanD.W. & MatthewsC.E. (2010). Sedentary behavior: Emerging evidence for a new health risk. Mayo Clinic Proceedings. Mayo Clinic 85(12) 11381141. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • PerruchoudC.BuchserE.JohanekL.M.AminianK.Paraschiv-IonescuA. & TaylorR.S. (2014). Assessment of physical activity of patients with chronic pain. Neuromodulation: Journal of the International Neuromodulation Society 17(Suppl. 1) 4247. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • PrinceS.A.AdamoK.B.HamelM.E.HardtJ.Connor GorberS. & TremblayM. (2008). A comparison of direct versus self-report measures for assessing physical activity in adults: A systematic review. The International Journal of Behavioral Nutrition and Physical Activity 556. PubMed ID: 18990237 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • RothneyM.P.BrychtaR.J.MeadeN.N.ChenK.Y. & BuchowskiM.S. (2010). Validation of the ActiGraph two-regression model for predicting energy expenditure. Medicine & Science in Sports & Exercise 42(9) 17851792. PubMed ID: 20142778 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • RothneyM.P.SchaeferE.V.NeumannM.M.ChoiL. & ChenK.Y. (2008). Validity of physical activity intensity predictions by ActiGraph, Actical, and RT3 accelerometers. Obesity (Silver Spring Md.) 16(8) 19461952. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • RowlandsA.V.CliffD.P.FaircloughS.J.BoddyL.M.OldsT.S.ParfittG. . . . BeetsM.W. (2016). Moving forward with backward compatibility: Translating wrist accelerometer data. Medicine & Science in Sports & Exercise 48(11) 21422149. PubMed ID: 27327029 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • SabiaS.van HeesV.T.ShipleyM.J.TrenellM.I.Hagger-JohnsonG.ElbazA. . . . Singh-ManouxA. (2014). Association between questionnaire- and accelerometer-assessed physical activity: The role of sociodemographic factors. American Journal of Epidemiology 179(6) 781790. PubMed ID: 24500862 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • StaudenmayerJ.HeS.HickeyA.SasakiJ. & FreedsonP. (2015). Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements. Journal of Applied Physiology (Bethesda Md.: 1985) 119(4) 396403. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • SylviaL.G.BernsteinE.E.HubbardJ.L.KeatingL. & AndersonE.J. (2014). Practical guide to measuring physical activity. Journal of the Academy of Nutrition and Dietetics 114(2) 199208. PubMed ID: 24290836 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • TroianoR.P.McClainJ.J.BrychtaR.J. & ChenK.Y. (2014). Evolution of accelerometer methods for physical activity research. British Journal of Sports Medicine 48(13) 10191023. PubMed ID: 24782483 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • U. S. Department of Health and Human Services. (2018). 2018 Physical Activity Guidelines Advisory Committee Scientific Report. Washington DC: Office of Disease Prevention and Health Promotion.

    • Search Google Scholar
    • Export Citation
  • Vähä-YpyäH.VasankariT.HusuP.MänttäriA.VuorimaaT.SuniJ. & SievänenH. (2015). Validation of cut-points for evaluating the intensity of physical activity with accelerometry-based mean amplitude deviation (MAD). PLoS One 10(8) e0134813. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van HeesV.T.FangZ.LangfordJ.AssahF.MohammadA.da SilvaI.C.M. . . . BrageS. (2014). Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: An evaluation on four continents. Journal of Applied Physiology (Bethesda Md.: 1985) 117(7) 738744. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van HeesV.T.GorzelniakL.Dean LeónE.C.EderM.PiasM.TaherianS. . . . BrageS. (2013). Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PLoS One 8(4) e61691. PubMed ID: 23626718 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • WhiteT.WestgateK.WarehamN.J. & BrageS. (2016). Estimation of physical activity energy expenditure during free-living from wrist accelerometry in UK adults. PLoS One 11(12) e0167472. PubMed ID: 27936024 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
Article Metrics
All Time Past Year Past 30 Days
Abstract Views 59 59 18
Full Text Views 5 5 4
PDF Downloads 3 3 1
Altmetric Badge
PubMed
Google Scholar