Participation Metrics for 

Accelerometer-Based Research 

Christopher Antoun 

Alexander Wenz 

2021 FCSM Research and 

Policy Conference
Motivation

• Accelerometers used by researchers to measure physical 

activity

• Common devices: Fitbit, AppleWatch, GENEActiv

• Non-participation is a key challenge

• Occurs at two hierarchical levels:

• Sample members: missing because they do not participate

• Measurements: missing due to non-wear (among participants)

• Unlike with surveys, no standards exist for computing 

participation rates

• Efforts to examine the level of non-participation bias are rare

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Our aims

• Conceptual aim

• Propose definitions and formulas for calculating participation rates in 

accelerometer-based studies

• Propose methods for assessing non-participation bias

• Empirical aim

• Illustrate these concepts using data from National Health and Nutrition 

Examination Survey (NHANES) 2011-2012 and 2013-2014

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Basic steps in accelerometer-based 

studies

1. Implement screening

2. Invite eligible individuals to participate in study

3. Provide accelerometer devices

4. Collect devices

5. Extract and process data

5a. Measurements aggregated into periods (e.g., 1 minute, 5 minutes)

5b. Classification of wear vs non-wear periods using statistical algorithm

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Sample members

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Complication (1 of 2) 

• Sample members that do not participate in screening step 

• Eligibility is unknown

• Unclear whether they should be included in denominator of 

participation rate

• Addressing this issue: as in surveys, estimate proportion that 

are eligible using information from other cases

• Or present two participation rates: one including all cases of unknown 

eligibility in the denominator and one excluding these cases

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Complication (2 of 2) 

• Individuals do not wear devices for the full study period

• Unclear what amount of wear-time is sufficient to be deemed a 

participant

• Addressing this issue: develop a priori definition of what 

constitutes “sufficient” wear-time in a particular study

• For example: ≥72 of 168 hours; ≥5 of 7 days with at least 10 hrs/day

• Participant inclusion criteria vary across studies

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Assessing non-participation bias

• Compare characteristics of participants and non-participants

• If sample is recruited from respondents to a previous survey, can use 

self-report information for both groups

• Self reports of physical activity capture information directly about the 

behavior of interest

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Illustration: NHANES 2011-2012 

and 2013-2014

• Cross-sectional study of U.S. general population

• Participants first interviewed in homes, subsequently examined 

in Mobile Examination Center (MEC), then given accelerometer

• Data source: Actigraph GT3X+ (waterproof) accelerometer

• 80 Hz raw data aggregated to 1-minute measurement periods

• Ages 3 yrs + (6+ for 2011)

• Protocol:

• 24-hour wear requested over 7 complete days

• Return device by mail ($40 incentive paid upon receipt)

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Participation rates

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Step

2011 NHANES 2013 NHANES

N Rate N Rate

Study Samplea 7,821 8,913

Returned Device (w/ readable data) 6,917 7,776

Adherent Participantsb 6,467 7,114

Participation rate 83% 80%

a Responded to household interview; examined in Medical Examination Center; eligible for accelerometer study

b Algorithm-estimated wear time of at least 10+ hours on 4+ days of the 7-day study period.
Assessment of non-participation bias

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Step

2011 NHANES 2013 NHANES

N Average 

Weekly PA

(METs)

N Average 

Weekly PA

(METs)

Study Sample - BENCHMARK 6,549 3,097 6,979 3,156

Adherent Participants 5,385 3,060 5,597 3,103

Overall Discrepancy -37 (-1%) -53 (-2%)

Analysis restricted to participants aged 12+

MET: Unit of energy expenditure. Computed using NHANES responses as follows: 

METs = (Mins Moderate PA x 4.0) + (Mins of Vigorous PA x 8.0)
Measurements

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Assessing non-wear bias

• In practice, no information is available about physical activity 

during non-wear periods 

• Addressing this issue: 

• Replace these periods with the average of wear periods from other

participants at the same time of day.

• Then compare characteristics of wear and non-wear periods. 

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Discussion and Conclusion

• Estimates in accelerometer-based studies are based on data 

that exclude: (level 1) non-participants and (level 2) non-wear 

periods among participants 

• To compute participation rates, we divided cases into highlevels groups -> more fine-grained categories can be used

• Our indicators of non-participation bias have limitations

• Self-report data from different reference period; contains some amount 

of measurement error 

• Actual values of non-wear periods are generally unknowable

• Potential next step is to extend to other data sets

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Thank You!

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Chris Antoun

University of Maryland

JPSM and iSchool

antoun@umd.edu

Alex Wenz

University of Mannheim

School of Social Sciences

a.wenz@uni-mannheim.de