APPLIED SCIENCES: Commentary
In the article, “Estimating Activity and Sedentary Behavior from an Accelerometer on the Hip or Wrist” (3), the authors provide evidence that a hip-worn accelerometer estimates activity energy expenditure and classifies activity type and sedentary behavior more accurately than a wrist-worn accelerometer.
This study is an important and timely contribution improving our understanding of how accelerometer placement affects the accuracy in estimating physical activity and sedentary behavior metrics. As acknowledged by the authors, this conclusion is based on using simple analytic procedures and summary data from a derived acceleration measure that eliminates much of the raw acceleration signal features that could potentially improve the estimates. Other studies using the raw acceleration signal have shown that the wrist-worn monitor provides reasonably accurate and precise estimates of activity type (4), energy expenditure, activity intensity, and sedentary behavior (1).
Use of a derived summary measure from the accelerometer has other limitations. Average accelerometer count values may lead to activity intensity misclassification (2). Specifically, similar average activity counts for activities with different energy expenditure levels and similar energy expenditure levels with different activity counts have been reported. Nevertheless, the authors used these derived accelerometer counts since accelerometer data are typically reported in this manner. Additional evidence about the performance of newer machine learning processing of the raw acceleration signal is necessary to ultimately determine the utility of wrist-worn accelerometers.
The authors point out that a wrist-worn accelerometer may be particularly inaccurate for detecting sedentary behaviors since arm movements may occur during sedentary time (i.e., seated and talking with arm movements). It is possible, however, that advanced computational methods may be able to detect these kinds of arm movements and differentiate them from arm activity where energy expenditure is increased.
There are some advantages to using a wrist-worn accelerometer. NHANES is currently collecting data using an accelerometer worn on the wrist so that the monitor is worn 24 h·d−1, making it possible to collect sleep measures. In addition, wear compliance is improved with a monitor worn on the wrist. In the first NHANES hip-worn accelerometer study (2003–2006), 40%–70% of participants (compliance varied by age group) had 6+ d of data with 10 h·d−1 of wear time. In the current NHANES wrist-worn accelerometer study (2011–2012), compliance has been 70%–80% (6+ d of data) with a median wear time of 21–22 h·d−1 (R. Troiano and J. McClain, personal communication).
Traditionally, cut points developed and validated in laboratory calibration studies were applied to the data collected in the field. This approach has limitations and likely leads to field-based activity intensity misclassification. Using advanced computation methods applied to the raw acceleration signal may improve estimates of free-living physical activity and sedentary behavior measures. To address this issue, algorithms should first be developed using a broad range of activities that are most prevalent in day-to-day activity. Second, cross validation of the algorithms in the free-living setting is needed where activities do not occur in fixed time intervals and are often performed in sporadic and unpredictable patterns. This strategy will ensure a fair comparison of findings across studies by eliminating the strong influence of activity type on prediction accuracy. While this new method may challenge the traditional research paradigm of calibrating activity monitors exclusively in controlled conditions, it can maximally harness the potential of advanced data processing techniques that adapt and improve prediction. This will be particularly important when there is variability in acceleration patterns due to random movements when activities are performed in natural settings. The adaptability of these techniques combined with the richness of the raw acceleration signal may help to discern different activities and identify transitions between activities. However, there is no question that the computation power demands and data storage requirements will be significant. Collaboration with experts in mathematics, statistics, computer science, and engineering is essential for success in finding solutions to these challenges.
1. Esliger DW, Rowlands AV, Hurst TL, Catt M, Murray P, Eston RG. Validation of the GENEA accelerometer. Med Sci Sports Exerc
. 2011; 43 (6): 1085–93.
2. Lyden K, Kozey SL, Staudenmayer JW, Freedson PS. A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations. Eur J Appl Physiol
. 2011; 111: 187–201.
3. Rosenberger ME, Haskell WL, Albinali F, Mota S, Nawyn J, Intille S. Estimating activity and sedentary behavior from an accelerometer on the hip or wrist. Med Sci Sports Exerc
. 2013; 45 (5): 964–75.
4. Zhang S, Rowlands AV, Murray P, Hurst TL. Physical activity classification using the GENEA wrist-worn accelerometer. Med Sci Sports Exerc
. 2012; 44 (4): 742–8.