The Challenge of Incomplete Data in Accelerometer Studies: Characteristics of Nonparticipation and Noncompliance in a Nationwide Sample of Adolescents and Young Adults in Germany

in Journal of Physical Activity and Health

Click name to view affiliation

Kristin Manz Robert Koch Institute, Berlin, Germany

Search for other papers by Kristin Manz in
Current site
Google Scholar
PubMed Close
https://orcid.org/0000-0002-1213-516X *
,
Alexander Burchartz Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany

Search for other papers by Alexander Burchartz in
Current site
Google Scholar
PubMed Close
https://orcid.org/0000-0003-1338-5395
,
Claudia Niessner Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany

Search for other papers by Claudia Niessner in
Current site
Google Scholar
PubMed Close
https://orcid.org/0000-0003-2094-0836
,
Simon Kolb Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany

Search for other papers by Simon Kolb in
Current site
Google Scholar
PubMed Close
https://orcid.org/0000-0002-4059-8823
,
Anja Schienkiewitz Robert Koch Institute, Berlin, Germany

Search for other papers by Anja Schienkiewitz in
Current site
Google Scholar
PubMed Close
https://orcid.org/0000-0003-3821-9772
, and
Gert B.M. Mensink Robert Koch Institute, Berlin, Germany

Search for other papers by Gert B.M. Mensink in
Current site
Google Scholar
PubMed Close
https://orcid.org/0000-0001-6268-5998
DOI:
https://doi.org/10.1123/jpah.2022-0443
Keywords:
selection bias; measurement; population studies
First Published Online:
01 Feb 2023
In Print:
Volume 20: Issue 3
Page Range:
226–238
Restricted access

Background: Incomplete data due to nonparticipation and noncompliance with the study protocol can bias the results of studies. We investigated how a nationwide accelerometer sample of adolescents and young adults is affected by such incomplete data. Methods: We analyzed cross-sectional data from 6465 participants (11–31 y old) who participated in a national health survey in Germany (KiGGS Wave 2; 2014–2017). The data included information about the participation in the measurement of physical activity using accelerometers, compliance with the wear-time protocol, and sociodemographic and health-related variables. Multivariable regression analyses were conducted to detect factors associated with incomplete data. Results: Of the total sample, 78.0% participated in the accelerometer part of the study, and 83.5% of the participants with data available complied with the wear-time protocol. In 11- to 17-year-olds, the likelihood of having incomplete accelerometer data was higher in boys, older adolescents, adolescents with a lower sociodemographic status, adolescents with overweight, adolescents not participating in organized sport, adolescents not speaking only German at home, current smokers, and adolescents having a higher soft drink consumption. In 18- to 31-year-olds, the likelihood of having incomplete accelerometer data was higher in men, adults with a lower educational level, adults not speaking only German at home, and adults who smoke. Conclusions: Our results suggest that accelerometer samples are biased such that participants with more beneficial health behaviors provide complete accelerometer data more often. This knowledge should be used to design effective recruitment strategies and should be considered when interpreting results of accelerometer studies.

  • Collapse
  • Expand

Journal of Physical Activity and Health

Cover Journal of Physical Activity and Health
  • 1.

    Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet Glob Health. 2018;6(10):e1077–e1086. PubMed ID: 30193830 doi:10.1016/S2214-109X(18)30357-7

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

    Steene-Johannessen J, Hansen BH, Dalene KE, et al. Variations in accelerometry measured physical activity and sedentary time across Europe — Harmonized analyses of 47,497 children and adolescents. Int J Behav Nutr Phys Act. 2020;17(1):38. PubMed ID: 32183834 doi:10.1186/s12966-020-00930-x

    • Search Google Scholar
    • Export Citation
  • 3.

    Guthold R, Stevens GA, Riley LM, Bull FC. Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1.6 million participants. Lancet Child Adolesc Health. 2020;4(1):23–35. PubMed ID: 31761562 doi:10.1016/S2352-4642(19)30323-2

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

    Dowd KP, Szeklicki R, Minetto MA, et al. A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study. Int J Behav Nutr Phys Act. 2018;15(1):15. PubMed ID: 29422051 doi:10.1186/s12966-017-0636-2

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

    Howie EK, Straker LM. Rates of attrition, non-compliance and missingness in randomized controlled trials of child physical activity interventions using accelerometers: a brief methodological review. J Sci Med Sport. 2016;19(10):830–836. PubMed ID: 26874648 doi:10.1016/j.jsams.2015.12.520

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

    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. 2013;23(3):475–480. PubMed ID: 23132872 doi:10.1093/eurpub/cks081

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

    Scott JJ, Rowlands AV, Cliff DP, Morgan PJ, Plotnikoff RC, Lubans DR. Comparability and feasibility of wrist- and hip-worn accelerometers in free-living adolescents. J Sci Med Sport. 2017;20(12):1101–1106. PubMed ID: 28501418 doi:10.1016/j.jsams.2017.04.017

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

    Tackney MS, Cook DG, Stahl D, Ismail K, Williamson E, Carpenter J. A framework for handling missing accelerometer outcome data in trials. Trials. 2021;22(1):379. PubMed ID: 34090494 doi:10.1186/s13063-021-05284-8

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

    Jakobsen JC, Gluud C, Wetterslev J, Winkel P. When and how should multiple imputation be used for handling missing data in randomised clinical trials — A practical guide with flowcharts. BMC Med Res Methodol. 2017;17(1):162. PubMed ID: 29207961 doi:10.1186/s12874-017-0442-1

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

    Roth MA, Mindell JS. Who provides accelerometry data? Correlates of adherence to wearing an accelerometry motion sensor: the 2008 health survey for England. J Phys Act Health. 2013;10(1):70–78. PubMed ID: 22398686 doi:10.1123/jpah.10.1.70

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

    Loprinzi PD, Cardinal BJ, Crespo CJ, Brodowicz GR, Andersen RE, Smit E. Differences in demographic, behavioral, and biological variables between those with valid and invalid accelerometry data: implications for generalizability. J Phys Act Health. 2013;10(1):79–84. PubMed ID: 22398390 doi:10.1123/jpah.10.1.79

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

    Rich C, Cortina-Borja M, Dezateux C, et al. Predictors of non-response in a UK-wide cohort study of children’s accelerometer-determined physical activity using postal methods. BMJ Open. 2013;3(3):e002290. doi:10.1136/bmjopen-2012-002290

    • Search Google Scholar
    • Export Citation
  • 13.

    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):S98–S111. doi:10.1123/jpah.5.s1.s98

    • Search Google Scholar
    • Export Citation
  • 14.

    Lange M, Hoffmann R, Mauz E, et al. KiGGS wave 2 longitudinal component — Data collection design and developments in the numbers of participants in the KiGGS cohort. J Health Monit. 2018;3(1):92–106. doi:10.17886/RKI-GBE-2018-035

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

    Mauz E, Lange M, Houben R, et al. Cohort profile: KiGGS cohort longitudinal study on the health of children, adolescents and young adults in Germany. Int J Epidemiol. 2019;49(2):375. doi:10.1093/ije/dyz231

    • Search Google Scholar
    • Export Citation
  • 16.

    Burchartz A, Manz K, Anedda B, et al. Measurement of physical activity and sedentary behavior by accelerometry among a nationwide sample from the KiGGS and MoMo study: study protocol. JMIR Res Protoc. 2020;9(7):e14370. PubMed ID: 32459648 doi:10.2196/14370

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

    Migueles JH, Cadenas-Sanchez C, Ekelund U, 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(9):1821–1845. PubMed ID: 28303543 doi:10.1007/s40279-017-0716-0

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

    Burchartz A, Anedda B, Auerswald T, et al. Assessing physical behavior through accelerometry. State of the science, best practices and future directions. Psychol Sport Exerc. 2020;49:101703. doi:10.1016/j.psychsport.2020.101703

    • Search Google Scholar
    • Export Citation
  • 19.

    Sherar LB, Griew P, Esliger DW, et al. International children’s accelerometry database (ICAD): design and methods. BMC Public Health. 2011;11(1):485. PubMed ID: 21693008 doi:10.1186/1471-2458-11-485

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

    Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Comparative study review. Med Sci Sports Exerc. 2005;37(11)(suppl 1):S531–S543. PubMed ID: 16294116 doi:10.1249/01.mss.0000185657.86065.98

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

    Lampert T, Hoebel J, Kuntz B, Müters S, Kroll LE. Socioeconomic status and subjective social status measurement in KiGGS Wave 2. J Health Monit. 2018;3(1):108–125. PubMed ID: 35586179

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

    Brauns H, Scherer S, Steinmann S. The CASMIN educational classification in international comparative research. In: Hoffmeyer-Zlotnik JHP, Wolf C, Eds. Advances in Cross-National Comparison: A European Working Book for Demographic and Socio-Economic Variables. Springer; 2003.

    • Search Google Scholar
    • Export Citation
  • 23.

    World Health Organization. Global Recommendations on Physical Activity for Health. 2010.

  • 24.

    World Health Organization. WHO Guidelines on Physical Activity and Sedentary Behaviour. 2020.

  • 25.

    Finger JD, Tafforeau J, Gisle L, et al. Development of the European health interview survey — Physical activity questionnaire (EHIS-PAQ) to monitor physical activity in the European Union. Arch Public Health. 2015;73(1):59. PubMed ID: 26634120 doi:10.1186/s13690-015-0110-z

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

    Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol use disorders identification test. Arch Intern Med. 1998;158(16):1789–1795. doi:10.1001/archinte.158.16.1789

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

    Haftenberger M, Heuer T, Heidemann C, Kube F, Krems C, Mensink GB. Relative validation of a food frequency questionnaire for national health and nutrition monitoring. Nutr J. 2010;9(1):36. PubMed ID: 20840739 doi:10.1186/1475-2891-9-36

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

    Kromeyer-Hauschild K, Wabitsch M, Kunze D, et al. Perzentile für den Body-mass-Index für das Kindes- und Jugendalter unter Heranziehung verschiedener deutscher Stichproben. Monatsschr Kinderheilkd. 2001;149(8):807–818. doi:10.1007/s001120170107

    • Search Google Scholar
    • Export Citation
  • 29.

    Schmitz R, Thamm M, Ellert U, Kalcklosch M, Schlaud M. Verbreitung häufiger Allergien bei Kindern und Jugendlichen in Deutschland: Ergebnisse der KiGGS-Studie - Erste Folgebefragung (KiGGS Welle 1). Bundesgesundblatt Gesundforschung Gesundschutz Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz. 2014;57(7):771–778. PubMed ID: 24950826 doi:10.1007/s00103-014-1975-7

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

    O’Brien WJ, Shultz SP, Firestone RT, George L, Breier BH, Kruger R. Exploring the challenges in obtaining physical activity data from women using hip-worn accelerometers. Eur J Sport Sci. 2017;17(7):922–930. PubMed ID: 28504054 doi:10.1080/17461391.2017.1323952

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

    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):245–253. PubMed ID: 26375253 doi:10.1249/MSS.0000000000000771

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

    Marcotte RT, Petrucci GJ Jr, Cox MF, Freedson PS, Staudenmayer JW, Sirard JR. Estimating sedentary time from a hip- and wrist-worn accelerometer. Med Sci Sports Exerc. 2020;52(1):225–232. PubMed ID: 31343523 doi:10.1249/MSS.0000000000002099

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

    da Silva IC, van Hees VT, Ramires VV, et al. Physical activity levels in three Brazilian birth cohorts as assessed with raw triaxial wrist accelerometry. Int J Epidemiol. 2014;43(6):1959–1968. PubMed ID: 25361583 doi:10.1093/ije/dyu203

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

    Oh AY, Caporaso A, Davis T, et al. Effect of incentive amount on U.S. adolescents’ participation in an accelerometer data collection component of a national survey. Field Methods. 2021;33(3):219–235. PubMed ID: 34326708 doi:10.1177/1525822X21989841

    • Search Google Scholar
    • Export Citation
  • 35.

    Ruiz JR, Ortega FB, Martínez-Gómez D, et al. Objectively measured physical activity and sedentary time in European adolescents: the HELENA study. Am J Epidemiol. 2011;174(2):173–184. PubMed ID: 21467152 doi:10.1093/aje/kwr068

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

    Bringolf-Isler B, Schindler C, de Hoogh K, et al. Association of objectively measured and perceived environment with accelerometer-based physical activity and cycling: a Swiss population-based cross-sectional study of children. Int J Public Health. 2019;64(4):499–510. PubMed ID: 30701279 doi:10.1007/s00038-019-01206-3

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

    Loyen A, Clarke-Cornwell AM, Anderssen SA, et al. Sedentary time and physical activity surveillance through accelerometer pooling in four European countries. Sports Med. 2017;47(7):1421–1435. PubMed ID: 27943147 doi:10.1007/s40279-016-0658-y

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

    Colley RC, Garriguet D, Janssen I, Craig CL, 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;22(1):15–23. PubMed ID: 21510586

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

    Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exercise. 2008;40(1):181–188. PubMed ID: 18091006 doi:10.1249/mss.0b013e31815a51b3

    • Search Google Scholar
    • Export Citation
  • 40.

    De Goede IHA, Branje SJT, Meeus WHJ. Developmental changes in adolescents’ perceptions of relationships with their parents. J Youth Adolesc. 2009;38(1):75–88. PubMed ID: 19636793 doi:10.1007/s10964-008-9286-7

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41.

    Cato MS, Wyka K, Ferris EB, et al. Correlates of accelerometry non-adherence in an economically disadvantaged minority urban adult population. J Sci Med Sport. 2020;23(8):746–752. PubMed ID: 32085979 doi:10.1016/j.jsams.2020.01.013

    • Search Google Scholar
    • Export Citation
  • 42.

    Evenson KR, Sotres-Alvarez D, Deng Y, et al. Accelerometer adherence and performance in a cohort study of US Hispanic adults. Med Sci Sports Exercise. 2015;47(4):725–734. PubMed ID: 25137369 doi:10.1249/MSS.0000000000000478

    • Search Google Scholar
    • Export Citation
  • 43.

    Lee PH, Macfarlane DJ, Lam TH. Factors associated with participant compliance in studies using accelerometers. Gait Posture. 2013;38(4):912–917. PubMed ID: 23688408 doi:10.1016/j.gaitpost.2013.04.018

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Galea S, Tracy M. Participation rates in epidemiologic studies. Ann Epidemiol. 2007;17(9):643–653. PubMed ID: 17553702 doi:10.1016/j.annepidem.2007.03.013

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

    van Sluijs EM, Skidmore PM, 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(1):388. PubMed ID: 19014571 doi:10.1186/1471-2458-8-388

    • Search Google Scholar
    • Export Citation
  • 46.

    Costa BGGd, Lopes MVV, Malheiros LEA, Sasaki JE, Silva KSd. Correlates of compliance with hip-worn accelerometer protocol in adolescents. Rev Bras Ativ Fís Saúde Revista Brasileira deAtividade Física & Saúde. 2019;24:1–8.

    • Search Google Scholar
    • Export Citation
  • 47.

    Dragano N, Wahrendorf M, Müller K, Lunau T. Arbeit und gesundheitliche Ungleichheit. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz. 2016;59(2):217–227. PubMed ID: 26661590 doi:10.1007/s00103-015-2281-8

    • PubMed
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 840 840 49
Full Text Views 31 31 2
PDF Downloads 51 51 2