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Updated: Jul 4, 2026
Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
Published on: March 7, 2019
Estimates of adherence and error analysis of physical activity data collected via accelerometry in a large study of
David R Paul1, Matthew Kramer, Kim S Stote
1Diet and Human Performance Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, United States Department of Agriculture, 307B Center Rd., Beltsville, MD 20705, USA. dpaul@uidaho.edu
Background:
Activity monitors (AM) are small, electronic devices used to quantify the amount and intensity of physical activity (PA). Unfortunately, it has been demonstrated that data loss that occurs when AMs are not worn by subjects (removals during sleeping and waking hours) tend to result in biased estimates of PA and total energy expenditure (TEE). No study has reported the degree of data loss in a large study of adults, and/or the degree to which the estimates of PA and TEE are affected. Also, no study in adults has proposed a methodology to minimize the effects of AM removals.
Methods:
Adherence estimates were generated from a pool of 524 women and men that wore AMs for 13 - 15 consecutive days. To simulate the effect of data loss due to AM removal, a reference dataset was first compiled from a subset consisting of 35 highly adherent subjects (24 HR; minimum of 20 hrs/day for seven consecutive days). AM removals were then simulated during sleep and between one and ten waking hours using this 24 HR dataset. Differences in the mean values for PA and TEE between the 24 HR reference dataset and the different simulations were compared using paired t-tests and/or coefficients of variation.
Results:
The estimated average adherence of the pool of 524 subjects was 15.8 +/- 3.4 hrs/day for approximately 11.7 +/- 2.0 days. Simulated data loss due to AM removals during sleeping hours in the 24 HR database (n = 35), resulted in biased estimates of PA (p < 0.05), but not TEE. Losing as little as one hour of data from the 24 HR dataset during waking hours results in significant biases (p < 0.0001) and variability (coefficients of variation between 7 and 21%) in the estimates of PA. Inserting a constant value for sleep and imputing estimates for missing data during waking hours significantly improved the estimates of PA.
Conclusion:
Although estimated adherence was good, measurements of PA can be improved by relatively simple imputation of missing AM data.
