Skip to main content

Determining Minimum Wear Time for Mobile Sensor Technology


Part 1 in the DIA Study Endpoint Community Working Group on Mobile Sensor Technology (MST) series addresses considerations that may be useful when determining the minimum wear time associated with mobile sensor use to ensure reliable estimation of the clinical endpoint under consideration. What constitutes a minimum valid data set is a dilemma facing those using MSTs in clinical studies. If this alignment does not occur, the integrity of the data collected and conclusions drawn from these data may be in incorrect. While study participants should consent to engage with MSTs as defined in a protocol, participant behavior or technology lapses may result in capturing incomplete data. Drawing from the literature, we review what constitutes a minimum data set, the risks associated with missing data, alignment with the clinical endpoint(s) and goals of a study, as well as managing patient burden.

This is a preview of subscription content, access via your institution.


  1. Catellier DJ, Hannan PJ, Murray DM, et al. Imputation of mission data when measuring physical activity by accelerometry. Med Sci Sports Exerc. 2005;37(11):S555–62.

    Article  Google Scholar 

  2. Herrmann SD, Barreira TV, Kang M, Ainsworth BE. How many hours are enough? Accelerometer wear time may provide bias in daily activity estimates. J Phys Activity Health. 2013;10:742–9.

    Article  Google Scholar 

  3. Chen C, Jerome GJ, Laferriere D, Young DR, Vollmer WM. Procedures used to standardize data collected by RT3 triaxial accelerometers in a large-scale weight-loss trial. J Phys Act Health. 2009;6(3):354–9.

    Article  Google Scholar 

  4. Orendurff MS, Schoen JA, Bernatz GC, Segal AD. How humans walk, bout duration, steps per bout, rest duration. J Rehabil Res Dev. 2007;45(7):1077–89.

    Article  Google Scholar 

  5. Drenowatz C, Gribben N, Wirth MD, Hand GA, Shook RP, Burgess S, Blair SN. The association of physical activity during weekdays and weekend with body composition in young adults. J Obes. 2016;2016:8236439.

    Article  Google Scholar 

  6. Hart TL, Swartz AM, Cashin SE, Strath SJ. How many days of monitoring predict physical activity and sedentary behaviour in older adults? Int J Behav Nutr Phys Act. 2011;8:62.

    Article  Google Scholar 

  7. Doherty A, Jackson D, Hammerla N, et al. Large scale population assessment of physical activity using wrist worn accelerometers: the UK Biobank study. PLoS ONE. 2017;12(2):e0169649.

    Article  Google Scholar 

  8. American Academy of Sleep Medicine. 2014. International classification of sleep disorders.

  9. Byrom B, Rowe DA. Measuring free-living physical activity in COPD patients: deriving methodology standards for clinical trials through a review of research studies. Contemp Clin Trials. 2016;47:172–84.

    Article  Google Scholar 

  10. Herrmann SD, Barreira TV, Kang M, Ainsworth BE. Impact of accelerometer wear time on physical activity data: a NHANES semi-simulation data approach. Br J Sports Med. 2014;48(3):278–82.

    Article  Google Scholar 

  11. Little RJ, D’Agostino R, Cohen ML, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med. 2012;367:1355–60.

    Article  CAS  Google Scholar 

  12. Kang H. The prevention and handling of the missing data. Korean J Anesthesiol. 2013;64(5):402–6.

    Article  Google Scholar 

  13. Dziura JD, Post LA, Zhao Q, Fu Z, Peduzzi P. Strategies for dealing with missing data in clinical trials: from design to analysis. Yale J Biol Med. 2013;86:343–58.

    PubMed  PubMed Central  Google Scholar 

  14. Rubin DB. Inference and missing data. Biometrika. 1976;63:581–92.

    Article  Google Scholar 

  15. Belin TR. Missing data: what a little can do and what researchers can do in response. Am J Ophthalmol. 2009;148(6):820–2.

    Article  Google Scholar 

  16. E6(R2) Good Clinical Practice: Integrated Addendum to ICH E6 (R1) Guidance for Industry. US Department of Health and Human Services, Food and Drug Administration. March 2018. Accessed 13 May 2020.

  17. TransCelerate Biopharma, Inc. Risk-based quality management: quality tolerance limits and risk reporting. 2017.

  18. Perry B, Geoghegan C, Lin L, et al. Patient preferences for using mobile technologies in clinical trials. Contemp Clin Trials Commun. 2019;15:100399.

    Article  Google Scholar 

Download references


There were no funding sources. No financial support was received.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Marie McCarthy MSc, MBA.

Ethics declarations

Conflict of interest

There were no conflicts of interest.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

McCarthy, M., Bury, D.P., Byrom, B. et al. Determining Minimum Wear Time for Mobile Sensor Technology. Ther Innov Regul Sci 55, 33–37 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: