for evaluating wrist-worn, consumer-grade devices that measure heart rate were recently published.47


Research-Grade Wearables


Research-grade devices provide access to raw sensor data, which may be collected under structured laboratory conditions, semistructured conditions, or simulated free-living conditions. Researchers use these data in conjunction with a criterion measure of energy expenditure (indirect calorimetry) and activity (direct observa- tion) to develop analytical methods for estimating energy expenditure, quantifying activity intensity, and classifying physical activity type. In most cases, these meth- ods are peer-reviewed and published, which allows for independent validation. In a recent trend, researchers have been publishing raw data and open-source code in order to improve the validation process; however, some research-grade devices (eg, activPAL) have proprietary algorithms included in their software. The development process as outlined results in analytical methods that are valid for the population used in collecting the original data, for the activities performed in the original data collection, and for data collected using the same parameters (eg, device attachment site, sampling frequency, device brand).2


The use of research-grade devices for


assessing physical activity is always evolving. While these data provide a great deal of analytical flexibility, they require technical expertise to process and interpret. Health care practitioners should consult an expert in the field before implementing research-grade wearable devices in a project in order to ensure that the most cur- rent devices and methods are being employed. Although the hip and wrist are the most common attachment sites, several


others are effective; thigh-worn devices, such as the activPAL, are rapidly gaining in popularity.48


The latter trend is especially notable, given the differences between


sedentary behavior and physical activity, which must be considered when deciding how to measure the two. Specifically, sedentary behavior involves both an energy expenditure component (energy expenditure of 1.5 METs or less) and a postural component (seated or lying posture, excluding sleep),49,50


whereas physical activity


has only an energy expenditure component (eg, energy expenditure of 3 METs or more for moderate-to-vigorous physical activity). Thus, the assessment of sedentary behavior generally requires more detail than the assessment of physical activity, and this can lead to challenges when deciding which device to use. Posture detection is most accurate with thigh-worn devices,51,52


whereas


energy expenditure is more accurately captured by monitors worn on other parts of the body (eg, ActiGraph devices worn on the hip or wrist).53,54


Multiple monitors


can be worn simultaneously to ensure that both variables are adequately captured, but doing so places a greater burden on the patient; thus, trade-offs may need to be made between logistical and empirical factors.55 Multiple-monitor arrays are not the only promising path toward optimizing


the assessment of both posture and energy expenditure. Algorithms such as the Convolutional Neural Network (CNN) Hip Accelerometer Posture, or CHAP, method have shown that deep learning can be useful for increasing the conver- gence of posture estimates from hip-worn and thigh-worn monitors.56


Gyroscope


sensors may also hold unique promise for assessing sedentary behavior, beyond what has been achieved to date with traditional accelerometer data.57


However,


gyroscopes have a short battery life that currently precludes their large-scale use, again requiring trade-offs between logistical and empirical factors.


124 SECTION 2: Interprofessional Assessment


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