Comparison and validation of accelerometer wear time and non-wear time algorithms for assessing physical activity levels in children and adolescents

Jérémy Vanhelst, Florian Vidal, Elodie Drumez, Laurent Béghin, Jean-Benoît Baudelet, Stéphanie Coopman, Frédéric Gottrand, Jérémy Vanhelst, Florian Vidal, Elodie Drumez, Laurent Béghin, Jean-Benoît Baudelet, Stéphanie Coopman, Frédéric Gottrand

Abstract

Background: Accelerometers are widely used to measure sedentary time and daily physical activity (PA). However, data collection and processing criteria, such as non-wear time rules might affect the assessment of total PA and sedentary time and the associations with health variables. The study aimed to investigate whether the choice of different non-wear time definitions would affect the outcomes of PA levels in youth.

Methods: Seventy-seven healthy youngsters (44 boys), aged 10-17 years, wore an accelerometer and kept a non-wear log diary during 4 consecutives days. We compared 7 published algorithms (10, 15, 20, 30, 60 min of continuous zeros, Choi, and Troiano algorithms). Agreements of each algorithm with the log diary method were assessed using Bland-Altmans plots and by calculating the concordance correlation coefficient for repeated measures.

Results: Variations in time spent in sedentary and moderate to vigorous PA (MVPA) were 30 and 3.7%. Compared with the log diary method, greater discrepancies were found for the algorithm 10 min (p < 0.001). For the time assessed in sedentary, the agreement with diary was excellent for the 4 algorithms (Choi, r = 0.79; Troiano, r = 0.81; 30 min, r = 0.79; 60 min, r = 0.81). Concordance for each method was excellent for the assessment of time spent in MVPA (> 0.86). The agreement for the wear time assessment was excellent for 5 algorithms (Choi r = 0.79; Troiano r = 0.79; 20 min r = 0.77; 30 min r = 0.80; 60 min r = 0.80).

Conclusions: The choice of non-wear time rules may considerably affect the sedentary time assessment in youth. Using of appropriate data reduction decision in youth is needed to limit differences in associations between health outcomes and sedentary behaviors and may improve comparability for future studies. Based on our results, we recommend the use of the algorithm of 30 min of continuous zeros for defining non-wear time to improve the accuracy in assessing PA levels in youth.

Trial registration: NCT02844101 (retrospectively registered at July 13th 2016).

Keywords: Activity monitor; Algorithms; Free living conditions; Wear time; Young.

Conflict of interest statement

Ethics approval and consent to participate

The study was approved by the Research Ethics Committee of the University of Lille (Comité Protection des Personnes, Nord Ouest IV, Lille, France). All procedures were performed according to the ethical standards of the Helsinki Declaration of 1975, as revised in 2008, and European Good Clinical Practice. All parents/guardians signed an informed consent form, and the adolescents agreed to participate in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Bland and Altman plots for concordance between algorithms with the log diary method in the assessment of time spent in sedentary activities (a Troiano algorithm, b Choi algorithm, c 60 min algorithm, d 30 min algorithm, e 20 min algorithm, f 15 min algorithm, g 10 min algorithm)
Fig. 2
Fig. 2
Percentage of participants meeting the recommendation of minimum of 10 h wearing time according to algorithms used

References

    1. Pedersen BK, Saltin B. Evidence for prescribing exercise as therapy in chronic disease. Scand J Med Sci Sports. 2006;16:3–63. doi: 10.1111/j.1600-0838.2006.00520.x.
    1. Hallal PC, Victora CG, Azevedo MR, Wells JC. Adolescent physical activity and health: a systematic review. Sports Med. 2006;36:1019–1030. doi: 10.2165/00007256-200636120-00003.
    1. Saunders TJ, Vallance JK. Screen time and health indicators among children and youth: current evidence, limitations and future directions. Appl Health Econ Health Policy. 2017;15:323–331. doi: 10.1007/s40258-016-0289-3.
    1. Sirard JR, Pate RR. Physical activity assessment in children and adolescents. Sports Med. 2001;31:439–454. doi: 10.2165/00007256-200131060-00004.
    1. McLellan G, Arthur R, Buchan DS. Wear compliance, sedentary behaviour and activity in free-living children from hip-and wrist-mounted ActiGraph GT3X+ accelerometers. J Sports Sci. 2018;5:1–7.
    1. Brage S, Wareham N, Wedderkopp N, Andersen L, Ekelund U, Froberg K. Features of the metabolic syndrome are associated with objectively measured physical activity and fitness in Danish children: the European youth heart study. Diabetes Care. 2004;27:2141–2148. doi: 10.2337/diacare.27.9.2141.
    1. Ekelund U, Yngve A, Brage S, Westerterp K, Sjostrom M. Body movement and physical activity energy expenditure in children and adolescents: how to adjust for differences in body size and age. Am J Clin Nutr. 2004;79:851–856. doi: 10.1093/ajcn/79.5.851.
    1. Riddoch C, Andersen L, Wedderkopp N, Harro M, Klasson-Heggebo L, Sardinha L, et al. Physical activity levels and patterns of 9- and 15-yr-old European children. Med Sci Sports Exerc. 2004;36:86–92. doi: 10.1249/01.MSS.0000106174.43932.92.
    1. Schmidt MD, Freedson PS, Chasan-Taber L. Estimating physical activity using the CSA accelerometer and a physical activity log. Med Sci Sports Exerc. 2003;35:1605–1611. doi: 10.1249/01.MSS.0000084421.97661.17.
    1. Ruiz JR, Ortega FB, Martínez-Gómez D, Labayen I, Moreno LA, De Bourdeaudhuij I, et al. Objectively measured physical activity and sedentary time in European adolescents: the HELENA study. Am J Epidemiol. 2011;174:173–184. doi: 10.1093/aje/kwr068.
    1. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40:181–188. doi: 10.1249/mss.0b013e31815a51b3.
    1. Rousham E, Clarke P, Gross H. Significant changes in physical activity among pregnant women in the UK as assessed by accelerometry and self-reported activity. Eur J Clin Nutr. 2005;60:393–400. doi: 10.1038/sj.ejcn.1602329.
    1. Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011;43:357–364. doi: 10.1249/MSS.0b013e3181ed61a3.
    1. Toftager M, Kristensen PL, Oliver M, Duncan S, Christiansen LB, Boyle E, et al. Accelerometer data reduction in adolescents: effects on sample retention and bias. Int J Behav Nutr Phys Act. 2013;10:140. doi: 10.1186/1479-5868-10-140.
    1. Choi L, Ward SC, Schnelle JF, Buchowski MS. Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. Med Sci Sports Exerc. 2012;44:2009–2016. doi: 10.1249/MSS.0b013e318258cb36.
    1. Evenson KR, Terry JW., Jr Assessment of differing definitions of accelerometer nonwear time. Res Q Exerc Sport. 2009;80:355–362. doi: 10.1080/02701367.2009.10599570.
    1. Mâsse LC, Fuemmeler BF, Anderson CB, Matthews CE, Trost SG, Catellier DJ, et al. Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. Med Sci Sports Exerc. 2005;37:S544–S554. doi: 10.1249/01.mss.0000185674.09066.8a.
    1. Mailey EL, Gothe NP, Wójcicki TR, Szabo AN, Olson EA, Mullen SP, et al. Influence of allowable interruption period on estimates of accelerometer wear time and sedentary time in older adults. J Aging Phys Act. 2014;22:255–260. doi: 10.1123/japa.2013-0021.
    1. Keadle SK, Shiroma EJ, Freedson PS, Lee IM. Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study. BMC Public Health. 2014;14:1210. doi: 10.1186/1471-2458-14-1210.
    1. Banda JA, Haydel KF, Davila T, Desai M, Bryson S, Haskell WL, et al. Effects of varying epoch lengths, Wear time algorithms, and activity cut-points on estimates of child sedentary behavior and physical activity from accelerometer data. PLoS One. 2016;11:e0150534. doi: 10.1371/journal.pone.0150534.
    1. Aadland E, Andersen LB, Anderssen SA, Resaland GK. A comparison of 10 accelerometer non-Wear time criteria and logbooks in children. BMC Pub Health. 2018;18:323. doi: 10.1186/s12889-018-5212-4.
    1. Chinapaw MJ, de Niet M, Verloigne M, De Bourdeaudhuij I, Brug J, Altenburg TM. From sedentary time to sedentary patterns: accelerometer data reduction decisions in youth. PLoS One. 2014;9:e111205. doi: 10.1371/journal.pone.0111205.
    1. Janssen XL, Basterfield KN, Parkinson MS, Pearce JK, Reilly AJ, Adamson JJ, et al. Objective measurement of sedentary behavior: impact of non-Wear time rules on changes in sedentary time. BMC Pub Health. 2015;15:515. doi: 10.1186/s12889-015-1847-6.
    1. Esliger DW, Copeland JL, Barnes JD, Tremblay MS. Standardizing and optimizing the use of accelerometer data for free-living physical activity monitoring. J Phys Act Health. 2005;(3):366–83.
    1. Santos-Lozano A, Marín PJ, Torres-Luque G, Ruiz JR, Lucía A, Garatachea N. Technical variability of the GT3X accelerometer. Med Eng Phys. 2012;34:787–790. doi: 10.1016/j.medengphy.2012.02.005.
    1. Santos-Lozano A, Torres-Luque G, Marín PJ, Ruiz JR, Lucia A, Garatachea N. Intermonitor variability of GT3X accelerometer. Int J Sports Med. 2012;33:994–999. doi: 10.1055/s-0032-1312580.
    1. Romanzini M, Petroski EL, Ohara D, Dourado AC, Reichert FF. Calibration of ActiGraph GT3X, Actical and RT3 accelerometers in adolescents. Eur J Sport Sci. 2014;14:91–99. doi: 10.1080/17461391.2012.732614.
    1. Rich C, Geraci M, Griffiths L, Sera F, Dezateux C, Cortina-Borja M. Quality control methods in accelerometer data processing: defining minimum wear time. PLoS One. 2013;24:e67206. doi: 10.1371/journal.pone.0067206.
    1. Carrasco J, Phillips B, Puig-Martinez J, King T, Chinchilli V. Estimation of the concordance correlation coefficient for repeated measures using SAS and R. Comput Methods Prog Biomed. 2013;109:293–304. doi: 10.1016/j.cmpb.2012.09.002.
    1. Bland JM, Altman DG. Agreement between methods of measurement with multiple observations per individual. J Biopharm Stat. 2007;17:571–582. doi: 10.1080/10543400701329422.
    1. Fleiss JL. The design and analysis of clinical experiments. Hoboken, NJ. USA: John Wiley & Sons, Inc.; 1986. Reliability of measurement; pp. 1–32.
    1. 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;10:437–450. doi: 10.1123/jpah.10.3.437.
    1. Migueles JH, Cadenas-Sanchez C, Ekelund U, Delisle Nyström C, Mora-Gonzalez J, Löf M, Labayen I, et al. Accelerometer data collection and processing criteria to assess physical activity and other outcomes: a systematic review and practical considerations. Sports Med. 2017;47:1821–1845. doi: 10.1007/s40279-017-0716-0.
    1. Corder K, Sharp SJ, Atkin AJ, Griffin SJ, Jones AP, Ekelund U, et al. Change in objectively measured physical activity during the transition to adolescence. Br J Sports Med. 2015;49:730–736. doi: 10.1136/bjsports-2013-093190.
    1. Ortega FB, Konstabel K, Pasquali E, Ruiz JR, Hurtig-Wennlöf A, Mäestu J, et al. Objectively measured physical activity and sedentary time during childhood, adolescence and young adulthood: a cohort study. PLoS One. 2013;8:e60871. doi: 10.1371/journal.pone.0060871.

Source: PubMed

Sponsor e collaboratori

Condizioni mediche

Interventi farmacologici

3
Sottoscrivi