Children’s Compliance With Wrist-Worn Accelerometry Within a Cluster-Randomized Controlled Trial: Findings From the Healthy Lifestyles Programme

in Pediatric Exercise Science
Restricted access

Purchase article

USD $24.95

Student 1 year subscription

USD $68.00

1 year subscription

USD $90.00

Student 2 year subscription

USD $129.00

2 year subscription

USD $168.00

Purpose: The purpose of this study was to assess children’s compliance with wrist-worn accelerometry during a randomized controlled trial and to examine whether compliance differed by allocated condition or gender. Methods: A total of 886 children within the Healthy Lifestyles Programme trial were randomly allocated to wear a GENEActiv accelerometer at baseline and 18-month follow-up. Compliance with minimum wear-time criteria (≥10 h for 3 weekdays and 1 weekend day) was obtained for both time points. Chi-square tests were used to determine associations between compliance, group allocation, and gender. Results: At baseline, 851 children had usable data, 830 (97.5%) met the minimum wear-time criteria, and 631 (74.1%) had data for 7 days at 24 hours per day. At follow-up, 789 children had usable data, 745 (94.4%) met the minimum wear-time criteria, and 528 (67%) had complete data. Compliance did not differ by gender (baseline: χ2 = 1.66, P = .2; follow-up: χ2 = 0.76, P = .4) or by group at follow-up (χ2 = 2.35, P = .13). Conclusion: The use of wrist-worn accelerometers and robust trial procedures resulted in high compliance at 2 time points regardless of group allocation, demonstrating the feasibility of using precise physical activity monitors to measure intervention effectiveness.

Price and Hillsdon are with the Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, Exeter, Devon, United Kingdom. Wyatt, Lloyd, Abraham, and Dean are with the University of Exeter Medical School, University of Exeter, Exeter, Devon, United Kingdom. Creanor is with the Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth University, Plymouth, United Kingdom.

Address author correspondence to Lisa Price at L.R.S.Price@exeter.ac.uk.
Pediatric Exercise Science
Article Sections
References
  • 1.

    Audrey SBell SHughes RCampbell R. Adolescent perspectives on wearing accelerometers to measure physical activity in population-based trials. Eur J Public Health. 2012;23:47580. doi:10.1093/eurpub/cks081

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Booth JNLeary SDJoinson CNess ARTomporowski PDBoyle JMReilly JJ. Associations between objectively measured physical activity and academic attainment in adolescents from a UK cohort. Br J Sports Med. 2014;48;26570. doi:10.1136/bjsports-2013-092334

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

    Catellier DJHannan PJMurray DM. Imputation of missing data when measuring physical activity by accelerometry. Med Sci Sports Exerc. 2005;37 Suppl:55562. doi:10.1249/01.mss.0000185651.59486.4e

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

    Cole TJFreeman JVPreece MA. British 1990 growth reference centiles for weight, height, body mass index and head circumference fitted by maximum penalized likelihood. Stat Med. 1998;17:40729. PubMed doi:10.1002/(SICI)1097-0258(19980228)17:4<407::AID-SIM742>3.0.CO;2-L

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Colley RGarriguet DJanssens ICraig CClarke JTremblay MS. Physical activity of Canadian children and youth: accelerometer results from the 2007 to 2009 Canadian Health Measures Survey. Health Rep. 2011;21:634.

    • Search Google Scholar
    • Export Citation
  • 6.

    Da Silva ICMVan Hees VTRamires Vet al. Physical activity levels in three Brazilian birth cohorts as assessed with raw triaxial wrist accelerometry. Int J Epidemiol. 2014;43:195968. PubMed doi:10.1093/ije/dyu203

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Esliger DHall J. Accelerometry in children. In: Craig RMindell JHirani V editors. Health Survey for England: Physical Activity and Fitness. Leeds, UK: The NHS Information Centre for Health and Social Care; 2008 pp. 15973

    • Search Google Scholar
    • Export Citation
  • 8.

    Fairclough SJNoonan RRowlands AVVan Hees VKnowles ZBoddy LM. Wear compliance and activity in children wearing wrist and hip mounted accelerometers. Med Sci Sports Exerc. 2016;48:24553. PubMed doi:10.1249/MSS.0000000000000771

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

    Griffiths LJCortina-Borja MSera Fet al. How active are our children? Findings from the Millennium Cohort Study. BMJ Open. 2013;3:e002893. PubMed doi:10.1136/bmjopen-2013-002893

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

    Herrmann SDBarreira TVKang MAinsworth BE. Impact of accelerometer wear time on physical activity data: a NHANES semisimulation data approach. Br J Sports Med. 2014;48:27282. doi:10.1136/bjsports-2012-091410

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

    Hildebrand Mvan Hees VTHansen BHEkelun U. Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. Med Sci Sports Exerc. 2014;46:181624. PubMed doi:10.1249/MSS.0000000000000289

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

    Howie EKMcVeigh JAStraker LM. Comparison of compliance and intervention outcomes between hip- and wrist-worn accelerometers during a randomized crossover trial of an active video games intervention in children. J Phys Act Health. 2016;13:9649. PubMed doi:10.1123/jpah.2015-0470

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

    Jago RAnderson CBBaranowski TWatson K. Adolescent patterns of physical activity, differences by gender, day and time of day. Am J Prev Med. 2005;28:44752. doi:10.1016/j.amepre.2005.02.007

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Jago REdwards MJSebire SJet al. Bristol girls dance project (BGDP): protocol for a cluster randomised controlled trial of an after-school dance programme to increase physical activity among 11–12 year old girls. BMC Public Health. 2013;13:1003. PubMed doi:10.1186/1471-2458-13-1003

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

    Kang MRowe DABarreira TVRobinson TSMahar MT. Individual information-centered approach for handling physical activity missing data. Res Q Exerc Sport. 2009;80:1317. PubMed doi:10.1080/02701367.2009.10599546

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Lloyd JCreanor SPrice Let al. Trial baseline characteristics of a cluster randomised controlled trial of a school-located obesity prevention programme; the Healthy Lifestyles Programme (HeLP) trial. BMC Public Health. 2017;17:291. PubMed doi:10.1186/s12889-017-4196-9

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

    Magnusson KTSigurgeirsson ISveinsson TJohannsson E. Assessment of a two-year school-based physical activity intervention among 7– 9 year old children. Int J Behav Nutr Phys Act. 2011;8:13849. doi:10.1186/1479-5868-8-138

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

    Montori VMGuyatt GH. Intention-to-treat principle. CMAJ. 2001;165:133941. PubMed

  • 19.

    Phillips LRSParfitt GRowlands A. Calibration of the GENEA accelerometer for assessment of physical activity intensity in children. J Sci Med Sport. 2013;16:1248. PubMed doi:10.1016/j.jsams.2012.05.013

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

    Rennie KLWareham NJ. The assessment of physical activity in individuals and populations: why try to be more precise about how physical activity is assessed? Int J Obes Relat Metab Disord. 1998;22 Suppl 2:S308.

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

    Rowlands AVEsliger DWEady JEston RG. Empirical evidence to inform decisions regarding identification of non-wear periods from accelerometer habitual physical activity data. In: Bacquet GBethoin S editors. Children and Exercise XXV. London, UK: Routledge; 2010 pp. 21922.

    • Search Google Scholar
    • Export Citation
  • 22.

    Rowlands AVYates TDavis MKhunti KEdwardson CL. Raw accelerometer data analysis with GGIR R-package: does accelerometer brand matter? Med Sci Sports Exerc. 2016;48:193541. PubMed doi:10.1249/MSS.0000000000000978

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

    Sabia SVan Hees VTShipley MJet al. Associations between questionnaire- and accelerometer-assessed physical activity: the role of sociodemographic factors. Am J Epidemiol. 2014;179:78190. PubMed doi:10.1093/aje/kwt330

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

    Toftager MKristensen PLOliver Met al. Accelerometer data reduction in adolescents: effects on sample retention and bias. Int J Behav Nutr Phys Act. 2013;10:140. PubMed doi:10.1186/1479-5868-10-140

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

    Troiano RBerrigan DDodd KMasse LCTilert TMcDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40:1818. PubMed doi:10.1249/mss.0b013e31815a51b3

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

    Trost SGMciver KLPate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37  Suppl:S53143. doi:10.1249/01.mss.0000185657.86065.98

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

    Trost SGPate RRFreedson PSSallis JTaylor WC. Using objective physical activity measures with youth: how many days of monitoring are needed? Med Sci Sports Exerc. 2000;32:42631. PubMed doi:10.1097/00005768-200002000-00025

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

    Tudor-Locke CBarreira TVSchuna JM Jret al. Improving wear time compliance with a 24-hour waist worn accelerometer protocol in the international study of childhood obesity lifestyle and environment (ISCOLE). Int J Behav Nutr Phys Act. 2015;12:11. doi:10.1186/s12966-015-0172-x

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

    van Hees VTFang ZLangford Jet al. Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. J Appl Physiol. 2014;117:73844. PubMed doi:10.1152/japplphysiol.00421.2014

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

    van Hees VTFang ZZhao JHHeywood JMirkes ESabia S. Package ‘GGIR’: raw accelerometer data analysis [Internet] [cited 2015 Aug 9]. Available from: https://cran.r-project.org/web/packages/GGIR/index.html

    • Export Citation
  • 31.

    van Hees VTGorzelniak LLeon ECDet al. Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PLoS ONE. 2013;8:e61691. doi:10.1371/journal.pone.0061691

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

    Wyatt KLloyd JAbraham Cet al. The Healthy Lifestyles Programme (HeLP), a novel school-based intervention to prevent obesity in school children: study protocol for a randomised controlled trial. Trials. 2013;14:95. PubMed doi:10.1186/1745-6215-14-95

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
Article Metrics
All Time Past Year Past 30 Days
Abstract Views 53 53 9
Full Text Views 7 7 0
PDF Downloads 4 4 0
Altmetric Badge
PubMed
Google Scholar