Skip to main content
SpringerLink
Log in

Hot Deck Multiple Imputation for Handling Missing Accelerometer Data

  • Published:
Statistics in Biosciences Aims and scope Submit manuscript

Abstract

Missing data due to non-wear are common in accelerometer studies measuring physical activity and sedentary behavior. Accelerometer outputs are high-dimensional time-series data that are episodic and often highly skewed, presenting unique challenges for handling missing data. Common methods for missing accelerometry either are ad-hoc, require restrictive parametric assumptions, or do not appropriately impute bouts. This study developed a flexible hot-deck multiple imputation (MI; i.e., “replacing” missing data with observed values) procedure to handle missing accelerometry. For each missing segment of accelerometry, “donor pools” contained observed segments from either the same or different participants, and ten imputed segments were randomly drawn from the donor pool according to selection weights, where the donor pool and selection weight depended on variables associated with non-wear and/or accelerometer-based measures. A simulation study of 2550 women compared hot deck MI to two standard methods in the field: available case (AC) analysis (i.e., analyzing all observed accelerometry with no restriction on wear time or number of days) and complete case (CC) analysis (i.e., analyzing only participants that wore the accelerometer for ≥ 10 h for 4–7 days). This was repeated using accelerometry from the entire 24-h day and daytime (10am–8pm) only, and data were missing at random. For the entire 24-h day, MI produced less bias and better 95% confidence interval (CI) coverage than AC and CC. For the daytime only, MI produced less bias and better 95% CI coverage than AC; CC produced similar bias and 95% CI coverage, but longer 95% CIs than MI.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Trost SG, Pate RR, Freedson PS, Sallis JF, Taylor WC (2000) Using objective physical activity measures with youth: how many days of monitoring are needed? Med Sci Sports Exerc 32(2):426–431

    Article  Google Scholar 

  2. Evenson KR, Terry JW Jr (2009) Assessment of differing definitions of accelerometer nonwear time. Res Q Exerc Sport 80(2):355–362

    Article  Google Scholar 

  3. Choi L, Liu Z, Matthews CE, Buchowski MS (2011) Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc 43(2):357–364

    Article  Google Scholar 

  4. Choi L, Ward SC, Schnelle JF, Buchowski MS (2012) Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. Med Sci Sports Exerc 44(10):2009–2016

    Article  Google Scholar 

  5. Little RJA, Rubin DB (2002) Statistical analysis with missing data, 2nd edn. Wiley, Hoboken, NJ

    Book  MATH  Google Scholar 

  6. Rubin DB (1987) Multiple imputation for nonresponse in surveys. Wiley, New York

    Book  MATH  Google Scholar 

  7. Schafer JL (1997) Analysis of incomplete multivariate data. Chapman & Hall, New York

    Book  MATH  Google Scholar 

  8. van Buuren S (2007) Multiple imputation of discrete and continuous data by fully conditional specification. Stat Methods Med Res 16:219–242

    Article  MathSciNet  MATH  Google Scholar 

  9. Andridge RR, Little RJA (2010) A review of hot deck imputation for survey non-response. Int Stat Rev 78(1):40–64

    Article  Google Scholar 

  10. Catellier DJ, Hannan PJ, Murray DM, Addy CL, Conway TL, Yang S, Rice JC (2005) Imputation of missing data when measuring physical activity by accelerometry. Med Sci Sports Exerc 37(11):S555–S562

    Article  Google Scholar 

  11. Lee PH (2013) Data imputation for accelerometer-measured physical activity: the combined approach. Am J Clin Nutr 97:965–971

    Article  Google Scholar 

  12. Lee JA, Gill J (2018) Missing value imputation for physical activity data measured by accelerometer. Stat Methods Med Res 27(2):490–506

    Article  MathSciNet  Google Scholar 

  13. LaCroix AZ, Rillamas-Sun E, Buchner D et al (2017) The objective physical activity and cardiovascular disease health in older women (OPACH) study. BMC Public Health 17:192

    Article  Google Scholar 

  14. Anderson GL, Manson J, Wallace R et al (2003) Implementation of the Women’s Health Initiative study design. Ann Epidemiol 13:S5–S17

    Article  Google Scholar 

  15. Hays J, Hunt JR, Hubbell FA, Anderson GL, Limacher M, Allen C, Rossouw JE (2003) The Women’s Health Initiative recruitment methods and results. Ann Epidemiol 13:S18–S77

    Article  Google Scholar 

  16. Evenson KR, Wen F, Herring AH et al (2015) Calibrating physical activity intensity for hip-worn accelerometry in women age 60 to 91 years: the Women’s Health Initiative OPACH Calibration Study. Prev Med Rep 2:750–756

    Article  Google Scholar 

  17. McHorney CA, Ware JE, Raczek AE (1993) The MOS 36-item Short-Form Health Survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs. Med Care 31:247–263

    Article  Google Scholar 

  18. Ware JE, Sherbourne CD (1992) The MOS 36-item Short-Form Health Survey (SF-36): I. Conceptual framework and item selection. Med Care 30:473–483

    Article  Google Scholar 

  19. Meyer AM, Evenson KR, Morimoto L, Siscovick D, White E (2009) Test-retest reliability of the WHI physical activity questionnaire. Med Sci Sports Exerc 41(3):530–538

    Article  Google Scholar 

  20. Neuhouser ML, Di C, Tinker LF et al (2013) Physical activity assessment: biomarkers and self-report of activity-related energy expenditure in the WHI. Am J Epidemiol 177(6):576–585

    Article  Google Scholar 

  21. LaMonte MJ, Lewis CE, Buchner DM, Evenson KR, Rillamas-Sun E, Di C, Lee I-M, Bellettiere J, Stefanick ML, Eaton CB, Howard BV, Bird C, LaCroix AZ (2017) Both light intensity and moderate-to-vigorous physical activity measured by accelerometry are favorably associated with cardiometabolic risk factors in older women: the Objective Physical Activity and Cardiovascular Health (OPACH) Study. J Am Heart Assoc 6:e007064

    Google Scholar 

  22. Little RJA (1988) A test of missing completely at random for multivariate data with missing values. J Am Stat Assoc 83(404):1198–1202

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the following investigators in the WHI Program: Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland) Jacques Rossouw, Shari Ludlam, Joan McGowan, Leslie Ford, and Nancy Geller. Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, WA) Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg. Investigators and Academic Centers: (Brigham and Women’s Hospital, Harvard Medical School, Boston, MA) JoAnn E. Manson; (MedStar Health Research Institute/Howard University, Washington, DC) Barbara V. Howard; (Stanford Prevention Research Center, Stanford, CA) Marcia L. Stefanick; (The Ohio State University, Columbus, OH) Rebecca Jackson; (University of Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson; (University at Buffalo, Buffalo, NY) Jean Wactawski-Wende; (University of Florida, Gainesville/Jacksonville, FL) Marian Limacher; (University of Iowa, Iowa City/Davenport, IA) Jennifer Robinson; (University of Pittsburgh, Pittsburgh, PA) Lewis Kuller; (Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker; (University of Nevada, Reno, NV) Robert Brunner; (University of Minnesota, Minneapolis, MN) Karen L. Margolis. Women’s Health Initiative Memory Study: (Wake Forest University School of Medicine, Winston-Salem, NC) Mark Espeland.

The authors thank Fang Wen for her assistance with the data. This research was funded by grants from National Heart, Lung, and Blood Institute, National Institutes of Health (NIH), Grant R01HL105065 (PI: LaCroix) and contracts #HHSN268201100046C, #HHSN268201100001C, #HHSN268201100002C, #HHSN268201100003C, #HHSN268201100004C, and #HHSN271201100004C. The WHI program is funded by the National Heart, Lung, and Blood Institute, NIH, through contracts #HHSN268201600018C, #HHSN268201600001C, #HHSN268201600002C, #HHSN268201600003C, and #HHSN268201600004C. The content is solely the responsibility of the authors and does not necessarily represent the views of the NIH. The results of the present study do not constitute endorsement by the authors of the products described in this paper. The results of this study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.

Author information

Authors and Affiliations

  1. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, USA

    Nicole M. Butera & Siying Li

  2. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, USA

    Kelly R. Evenson

  3. Biostatistics and Biomathematics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA

    Chongzhi Di

  4. Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, USA

    David M. Buchner

  5. Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, USA

    Michael J. LaMonte

  6. Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, USA

    Andrea Z. LaCroix

  7. Department of Statistical Science and Global Health, Duke University, Durham, USA

    Amy Herring

Authors
  1. Nicole M. Butera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siying Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kelly R. Evenson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chongzhi Di
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David M. Buchner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Michael J. LaMonte
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andrea Z. LaCroix
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Amy Herring
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicole M. Butera.

Ethics declarations

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 78 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Butera, N.M., Li, S., Evenson, K.R. et al. Hot Deck Multiple Imputation for Handling Missing Accelerometer Data. Stat Biosci 11, 422–448 (2019). https://doi.org/10.1007/s12561-018-9225-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12561-018-9225-4

Keywords

Search

Navigation