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

in Pediatric Exercise Science
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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.
  • 1.

    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;23:475–80. doi:10.1093/eurpub/cks081

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

    Booth JN, Leary SD, Joinson C, Ness AR, Tomporowski PD, Boyle JM, Reilly JJ. Associations between objectively measured physical activity and academic attainment in adolescents from a UK cohort. Br J Sports Med. 2014;48;265–70. doi:10.1136/bjsports-2013-092334

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

    Catellier DJ, Hannan PJ, Murray DM. Imputation of missing data when measuring physical activity by accelerometry. Med Sci Sports Exerc. 2005;37 Suppl:555–62. doi:10.1249/01.mss.0000185651.59486.4e

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

    Cole TJ, Freeman JV, Preece MA. British 1990 growth reference centiles for weight, height, body mass index and head circumference fitted by maximum penalized likelihood. Stat Med. 1998;17:407–29. 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 R, Garriguet D, Janssens I, Craig C, Clarke J, Tremblay MS. Physical activity of Canadian children and youth: accelerometer results from the 2007 to 2009 Canadian Health Measures Survey. Health Rep. 2011;21:63–4.

    • Search Google Scholar
    • Export Citation
  • 6.

    Da Silva ICM, Van Hees VT, Ramires V, et al. Physical activity levels in three Brazilian birth cohorts as assessed with raw triaxial wrist accelerometry. Int J Epidemiol. 2014;43:1959–68. PubMed doi:10.1093/ije/dyu203

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

    Esliger D, Hall J. Accelerometry in children. In: Craig R, Mindell J, Hirani V, editors. Health Survey for England: Physical Activity and Fitness. Leeds, UK: The NHS Information Centre for Health and Social Care; 2008, pp. 159–73

    • Search Google Scholar
    • Export Citation
  • 8.

    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;48:245–53. PubMed doi:10.1249/MSS.0000000000000771

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

    Griffiths LJ, Cortina-Borja M, Sera F, et 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 SD, Barreira TV, Kang M, Ainsworth BE. Impact of accelerometer wear time on physical activity data: a NHANES semisimulation data approach. Br J Sports Med. 2014;48:272–82. doi:10.1136/bjsports-2012-091410

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

    Hildebrand M, van Hees VT, Hansen BH, Ekelun U. Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. Med Sci Sports Exerc. 2014;46:1816–24. PubMed doi:10.1249/MSS.0000000000000289

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

    Howie EK, McVeigh JA, Straker 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;13:964–9. PubMed doi:10.1123/jpah.2015-0470

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

    Jago R, Anderson CB, Baranowski T, Watson K. Adolescent patterns of physical activity, differences by gender, day and time of day. Am J Prev Med. 2005;28:447–52. doi:10.1016/j.amepre.2005.02.007

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

    Jago R, Edwards MJ, Sebire SJ, et 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 M, Rowe DA, Barreira TV, Robinson TS, Mahar MT. Individual information-centered approach for handling physical activity missing data. Res Q Exerc Sport. 2009;80:131–7. PubMed doi:10.1080/02701367.2009.10599546

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

    Lloyd J, Creanor S, Price L, et 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 KT, Sigurgeirsson I, Sveinsson T, Johannsson E. Assessment of a two-year school-based physical activity intervention among 7– 9 year old children. Int J Behav Nutr Phys Act. 2011;8:138–49. doi:10.1186/1479-5868-8-138

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

    Montori VM, Guyatt GH. Intention-to-treat principle. CMAJ. 2001;165:1339–41. PubMed

  • 19.

    Phillips LRS, Parfitt G, Rowlands A. Calibration of the GENEA accelerometer for assessment of physical activity intensity in children. J Sci Med Sport. 2013;16:124–8. PubMed doi:10.1016/j.jsams.2012.05.013

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

    Rennie KL, Wareham 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:S30–8.

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

    Rowlands AV, 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. 219–22.

    • Search Google Scholar
    • Export Citation
  • 22.

    Rowlands AV, Yates T, Davis M, Khunti K, Edwardson CL. Raw accelerometer data analysis with GGIR R-package: does accelerometer brand matter? Med Sci Sports Exerc. 2016;48:1935–41. PubMed doi:10.1249/MSS.0000000000000978

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

    Sabia S, Van Hees VT, Shipley MJ, et al. Associations between questionnaire- and accelerometer-assessed physical activity: the role of sociodemographic factors. Am J Epidemiol. 2014;179:781–90. PubMed doi:10.1093/aje/kwt330

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

    Toftager M, Kristensen PL, Oliver M, et 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 R, Berrigan D, Dodd K, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40:181–8. PubMed doi:10.1249/mss.0b013e31815a51b3

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

    Trost SG, Mciver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37  Suppl:S531–43. doi:10.1249/01.mss.0000185657.86065.98

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

    Trost SG, Pate RR, Freedson PS, Sallis J, Taylor WC. Using objective physical activity measures with youth: how many days of monitoring are needed? Med Sci Sports Exerc. 2000;32:426–31. PubMed doi:10.1097/00005768-200002000-00025

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

    Tudor-Locke C, Barreira TV, Schuna JM Jr, et 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 VT, Fang Z, Langford J, et 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;117:738–44. PubMed doi:10.1152/japplphysiol.00421.2014

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

    van Hees VT, Fang Z, Zhao JH, Heywood J, Mirkes E, Sabia 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 VT, Gorzelniak L, Leon ECD, et 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 K, Lloyd J, Abraham C, et 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
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