Defining Accelerometer Nonwear Time to Maximize Detection of Sedentary Time in Youth

in Pediatric Exercise Science

Click name to view affiliation

Kelli L. Cain University of California, San Diego

Search for other papers by Kelli L. Cain in
Current site
Google Scholar
PubMed
Close
*
,
Edith Bonilla University of California, San Diego

Search for other papers by Edith Bonilla in
Current site
Google Scholar
PubMed
Close
*
,
Terry L. Conway University of California, San Diego

Search for other papers by Terry L. Conway in
Current site
Google Scholar
PubMed
Close
*
,
Jasper Schipperijn University of Southern Denmark

Search for other papers by Jasper Schipperijn in
Current site
Google Scholar
PubMed
Close
*
,
Carrie M. Geremia University of California, San Diego

Search for other papers by Carrie M. Geremia in
Current site
Google Scholar
PubMed
Close
*
,
Alexandra Mignano University of California, San Diego

Search for other papers by Alexandra Mignano in
Current site
Google Scholar
PubMed
Close
*
,
Jacqueline Kerr University of California, San Diego

Search for other papers by Jacqueline Kerr in
Current site
Google Scholar
PubMed
Close
*
, and
James F. Sallis University of California, San Diego

Search for other papers by James F. Sallis in
Current site
Google Scholar
PubMed
Close
*
Restricted access

Purpose: The present study examined various accelerometer nonwear definitions and their impact on detection of sedentary time using different ActiGraph models, filters, and axes. Methods: In total, 61 youth (34 children and 27 adolescents; aged 5–17 y) wore a 7164 and GT3X+ ActiGraph on a hip-worn belt during a 90-minute structured sedentary activity. Data from GT3X+ were downloaded using the Normal filter (N) and low-frequency extension (LFE), and vertical axis (V) and vector magnitude (VM) counts were examined. Nine nonwear definitions were applied to the 7164 model (V), GT3X+LFE (V and VM), and GT3X+N (V and VM), and sedentary estimates were computed. Results: The GT3X+LFE-VM was most sensitive to movement and could accurately detect observed sedentary time with the shortest nonwear definition of 20 minutes of consecutive “0” counts for children and 40 minutes for adolescents. The GT3X+N-V was least sensitive to movement and required longer definitions to detect observed sedentary time (40 min for children and 90 min for adolescents). VM definitions were 10 minutes shorter than V definitions. LFE definitions were 40 minutes shorter than N definitions in adolescents. Conclusion: Different nonwear definitions are needed for children and adolescents and for different model-filter-axis types. Authors need to consider nonwear definitions when comparing prevalence rates of sedentary behavior across studies.

Cain, Bonilla, Conway, Geremia, Mignano, Kerr, and Sallis are with the Dept. of Family Medicine and Public Health, University of California, San Diego, CA. Schipperijn is with the Research Unit for Active Living, Dept. of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark.

Address author correspondence to Kelli L. Cain at kcain@ucsd.edu.
  • Collapse
  • Expand
  • 1.

    ActiGraph R&D and Software Departments. ActiLife 5: User’s manual. Pensacola, FL: ActiGraph. 2011 [cited 2017 Oct 24]. Available from: http://dl.theactigraph.com/ActiLife5-PUB10DOC10-H.pdf

    • Search Google Scholar
    • Export Citation
  • 2.

    Alhassan S, Robinson TN. Objectively measured physical activity and cardiovascular disease risk factors in African American girls. Ethn Dis. 2008;18(4):4216. PubMed

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Audrey S, Bell S, Hughes R, Campbell R. Adolescent perspectives on wearing accelerometers to measure physical activity in population-based trials. Eur J Public Health. 2012;23(3):47580. doi:10.1093/eurpub/cks081

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Brychta RJ, Klonoff M, McMahon GC, et al. Influence of Actigraph filter settings on detecting low and high intensity movements. Proceedings of the International Congress on Physical Activity and Public Health; May 2010. Toronto (Canada).

    • Search Google Scholar
    • Export Citation
  • 5.

    Cain KL, Conway TL, Adams MA, Husak LE, Sallis JF. Comparison of older and newer generations of ActiGraph accelerometers with the normal filter and the low frequency extension. Int J Behav Nutr Phys Act. 2013;10:51. PubMed doi:10.1186/1479-5868-10-51

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Cain KL, Geremia CM. Accelerometer data collection and scoring manual for adult & senior studies [Internet]. San Diego, CA: San Diego State University; 2012 [cited 2017 Oct 24]. Available from: http://www.ipenproject.org/documents/methods_docs/Accelerometer_Data_Collection_and_Scoring_Manual_Updated_June2012.pdf

    • Search Google Scholar
    • Export Citation
  • 7.

    Cain KL, Sallis JF, Conway TL, Van Dyck D, Calhoon L. Using accelerometers in youth physical activity studies: a review of methods. J Phys Act Health. 2013;10:43750. PubMed doi:10.1123/jpah.10.3.437

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Chen KY, Bassett DR. The technology of accelerometry-based activity monitors: current and future. Med Sci Sports Exerc. 2005;37 Suppl 1:490500. doi:10.1249/01.mss.0000185571.49104.82

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Chinapaw MJ, Proper KI, Brug J, van Mechelen W, Singh AS. Relationship between young peoples’ sedentary behaviour and biomedical health indicators: a systematic review of prospective studies. Obes Rev. 2011;12:e62132. PubMed doi:10.1111/j.1467-789X.2011.00865.x

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Choi L, Liu Z, Matthews C, Buchowski M. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011;43(2):35764. PubMed doi:10.1249/MSS.0b013e3181ed61a3

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Choi L, Ward SC, Schnelle JF, Buchowski MS. Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. Med Sci Sports Exerc. 2012;44(10):200916. doi:10.1249/MSS.0b013e318258cb36

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Colley R, Gorber SC, Tremblay MS. Quality control and data reduction procedures for accelerometry-derived measures of physical activity. Health Rep. 2010;21(1):639. PubMed

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Computer Science and Applications, Inc. Activity Monitor Operator’s Manual. Model 7164. Shalimar, FL: Computer Science and Applications, Inc; 1995.

    • Search Google Scholar
    • Export Citation
  • 14.

    Deforche B, Bourdeaudhuij BI, D’hondt E, Cardon G. Objectively measured physical activity, physical activity related personality and body mass index in 6- to 10-yr-old children: a cross-sectional study. Int J Behav Nutr Phys Act. 2009;6:25. PubMed doi:10.1186/1479-5868-6-25

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Dietz WH, Gortmaker SL. Do we fatten our children at the TV set? Obesity and television viewing in children and adolescents. Pediatrics. 1985;75:80712. PubMed

    • Search Google Scholar
    • Export Citation
  • 16.

    Ekelund U, Brage S, Froberg K, et al. TV viewing and physical activity are independently associated with metabolic risk in children: the European Youth Heart Study. PLoS Med. 2006;3:e488. PubMed doi:10.1371/journal.pmed.0030488

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Ekelund U, Sardinha LB, Andersson SA, et al. Associations between objectively assessed physical activity and indicators of body fatness in 9- and 10-yr old children: a population based study from 4 distinct regions in Europe (the European Youth Heart Study). Am J Clin Nutr. 2004;80:58490. PubMed

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18.

    Evenson K, Terry X. Assessment of differing definitions of accelerometer nonwear time. Res Q Exerc Sport. 2009;80(2):35562. doi:10.1080/02701367.2009.10599570

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Fairclough SJ, Noonan R, Rowlands AV, van Hees V, Knowles Z, Boddy LM. Wear compliance and activity in children wearing wrist and hip-mounted accelerometers. Med Sci Sports Exerc. 2016;48(2):24553. PubMed doi:10.1249/MSS.0000000000000771

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Grant PM, Ryan CG, Tigbe WW, Granat MH. The validation of a novel activity monitor in the measurement of posture and motion during everyday activities. Br J Sports Med. 2006;40:9927. PubMed doi:10.1136/bjsm.2006.030262

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Grydeland M, Hansen BH, Ried-Larsen M, Kolle E, Anderssen SA. Comparison of three generations of ActiGraph activity monitors under free-living conditions: do they provide comparable assessments of overall physical activity in 9-year old children? BMC Sports Sci Med Rehabil. 2014;6(1):26. doi:10.1186/2052-1847-6-26

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Jago R, Wedderkopp N, Kristensen PL, Møller NC, Andersen LB, Cooper AR, Froberg K. Six-year change in youth physical activity and effect on fasting insulin and HOMA-IR. Am J Prev Med. 2008;35(6):55460. PubMed doi:10.1016/j.amepre.2008.07.007

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Janssen I, Katzmarzyk PT, Boyce WF, et al. Comparison of overweight and obesity prevalence in school-aged youth from 34 countries and their relationships with physical activity and dietary patterns. Obes Rev. 2005;6(2):12332. PubMed doi:10.1111/j.1467-789X.2005.00176.x

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    John D, Freedson P. ActiGraph and Actical physical activity monitors: a peek under the hood. Med Sci Sports Exerc. 2012;44 Suppl 1:S86. doi:10.1249/MSS.0b013e3182399f5e

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Kerr J, Marshall SJ, Godbole S, et al. Using the SenseCam to improve classifications of sedentary behavior in free-living settings. Am J Prev Med. 2013;44(3):2906. PubMed doi:10.1016/j.amepre.2012.11.004

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Kozey SL, Lyden K, Howe CA, Staudenmayer JW, Freedson PS. Accelerometer output and MET values of common physical activities. Med Sci Sports Exerc. 2010;42(9):177684. PubMed doi:10.1249/MSS.0b013e3181d479f2

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Kozey SL, Staudenmayer JW, Troiano RP, Freedson PS. Comparison of the ActiGraph 7164 and the ActiGraph GT1M during self-paced locomotion. Med Sci Sports Exerc. 2010;42(5):9716.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28.

    Mannini A, Intille SS, Rosenberger M, Sabatini AM, Haskell W. Activity recognition using a single accelerometer placed at the wrist or ankle. Med Sci Sports Exerc. 2013;45(11):2193203. PubMed doi:10.1249/MSS.0b013e31829736d6

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29.

    Mark AE, Janssen I. Relationship between screen time and metabolic syndrome in adolescents. J Public Health. 2008;30(2):15360. doi:10.1093/pubmed/fdn022

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30.

    Masse LC, Fuemmeler BF, Anderson B, Matthews CE, Trost SG, Catellier DJ, Treuth M. Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. Med Sci Sports Exerc. 2005;37 Suppl 11:S54454. PubMed doi:10.1249/01.mss.0000185674.09066.8a

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Mathias RL, Brønd JC, Brage S, Hansen BH, Grydeland M, Andersen LB, Møller NC. Mechanical and free living comparisons of four generations of the Actigraph activity monitor. Int J Behav Nutr Phys Act. 2012;9:113. doi:10.1186/1479-5868-9-113

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32.

    Matthews CE, Chen KY, Freedson PS, Buchowski MS, Beech BM, Pate RR, Troiano RP. Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am J Epidemiol. 2008;167(7):87581. PubMed doi:10.1093/aje/kwm390

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Mattocks C, Ness A, Leary S, et al. Use of accelerometers in a large field-based study of children: protocols, design issues, and effects on precision. J Phys Act Health. 2008;5 Suppl 1:S98111. doi:10.1123/jpah.5.s1.s98

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 34.

    Nader PR, Bradley RH, Houts RM, McRitchie SL, O’Brien M. Moderate-to-vigorous physical activity from ages 9 to 15 years. JAMA. 2008;300(3):295305. PubMed doi:10.1001/jama.300.3.295

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.

    Owen CG, Nightingale CM, Rudnicka AR, Cook DG, Ekelund U, Whincup PH. Ethnic and gender differences in physical activity levels among 9-10-year-old children of white European, South Asian and African-Caribbean origin: the Child Heart Health Study in England (CHASE Study). Int J Epidemiol. 2009;38(4):108293. PubMed doi:10.1093/ije/dyp176

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 36.

    Owen N, Healy GN, Mathews CE, Dunstan DW. Too much sitting: the population-health science of sedentary behavior. Exerc Sport Sci Rev. 2010;38:10513. PubMed doi:10.1097/JES.0b013e3181e373a2

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Pearson N, Biddle SJH. Sedentary behavior and dietary intake in children, adolescents, and adults: a systematic review. Am J Prev Med. 2011;41(2):17888. PubMed doi:10.1016/j.amepre.2011.05.002

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Ridgers ND, Salmon J, Ridley K, O’Connell E, Arundell L, Timperio A. Agreement between activPAL and ActiGraph for assessing children’s sedentary time. Int J Behav Nutr Phys Act. 2012;9:15. PubMed doi:10.1186/1479-5868-9-15

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39.

    Rosenberger ME, Haskell WL, Albinali F, Mota S, Nawyn J, Intille S. Estimating activity and sedentary behavior from an accelerometer on the hip or wrist. Med Sci Sports Exerc. 2013;45(5):96475. PubMed doi:10.1249/MSS.0b013e31827f0d9c

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Rowlands A, Esliger DW, Eady J, Eston RG. Empirical evidence to inform decisions regarding identification of non-wear periods from accelerometer habitual physical activity data. In: Bacquet G, Bethoin S, editors. Children and Exercise XXV. London, UK: Routledge; 2010, pp. 21922.

    • Search Google Scholar
    • Export Citation
  • 41.

    Rowlands AV, Pilgrim EL, Eston RG. Seasonal changes in children’s physical activity: an examination of group changes, intra-individual variability and consistency in activity pattern across season. Ann Hum Biol. 2009;36(4):36378. PubMed doi:10.1080/03014460902824220

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Schaefer SE, Loan MV, German JB. A feasibility study of wearable activity monitors for pre-adolescent school-age children. Prev Chronic Dis. 2014;11:130262. doi:10.5888/pcd11.130262

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 43.

    Sedentary Behaviour Research Network. Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours.”  Appl Physiol Nutr Metab. 2012;37(3):5402. doi:10.1139/h2012-024

    • Search Google Scholar
    • Export Citation
  • 44.

    Sirard JR, Forsyth A, Oakes JM, Schmitz KH. Accelerometer test-retest reliability by data processing algorithms: results from the Twin Cities Walking Study. J Phys Act Health. 2011;8:66874. PubMed doi:10.1123/jpah.8.5.668

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008, 40(1):1818. PubMed doi:10.1249/mss.0b013e31815a51b3

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46.

    van Sluijs EMF, Skidmore PML, Mwanza K, et al. Physical activity and dietary behaviour in a population-based sample of British 10-year old children: the SPEEDY study (sport, physical activity and eating behaviour: environmental determinants in young people). BMC Public Health. 2008;8:388. PubMed doi:10.1186/1471-2458-8-388

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 47.

    Wanner M, Martin BW, Meier F, Probst-Hensch N, Kriemler S. Effects of filter choice in GT3X accelerometer assessments of free-living activity. Med Sci Sports Exerc. 2013;45(1):1707. PubMed doi:10.1249/MSS.0b013e31826c2cf1

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 48.

    Winkler EAG, Paul A, Clark BK, Matthews CE, Owen NG, Healy GN. Identifying sedentary time using automated estimates of accelerometer wear time. Br J Sports Med. 2011;46(6):43642. PubMed doi:10.1136/bjsm.2010.079699

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 2049 604 6
Full Text Views 25 3 0
PDF Downloads 23 6 0