IntroductionPhysical activity (PA) and sedentary behavior (SB) are important behavioral variables that are associated with many key short- and long-term health indices. Objective and highly accurate methods of measuring PA and SB are needed in order to better understand the relationships of PA and SB with various health outcomes, determine population levels of PA and SB, identify and target groups at high risk of having low PA or high SB, and assess the effectiveness of interventions aimed to increase PA and reduce SB in populations. Of the available measurement tools, accelerometer-based activity monitors have gained popularity due to their blend of feasibility for use and relatively high accuracy for assessing PA (by identifying specific activity types), SB, and energy expenditure (EE). However, little research has been done to compare the accuracy of accelerometers placed on different parts of the body, and current data modeling methods are either 1) simple to use but lack accuracy or 2) highly accurate but highly complex. Therefore, the purpose of this dissertation was 1) to develop accurate and relatively simple data processing and modeling methods for accelerometer data and 2) to compare accelerometers located on the right hip, right thigh, and both wrists for classification of activity type and prediction of SB and EE.MethodsHealthy adults (n=44) were recruited to participate in a 90-minute simulated free-living protocol. For the protocol, participants performed 14 activities for between 3-10 minutes, with order, duration, and intensity of activities left up to participants. Participants wore a portable metabolic analyzer (for a criterion measure of EE) and four accelerometers, which were placed on the right hip, right thigh, and both wrists. The order and timing of the activities performed during the protocol was recorded by a trained research assistant (for a criterion measure of activity type and SB). Machine learning algorithms (i.e., artificial neural networks) were created by extracting simple-to-compute features from the data from each of the four accelerometers in order to classify activity type and predict SB and EE. Accuracy of the four accelerometers for each outcome variable was assessed by comparing predictions from the accelerometers to the actual values obtained by the criterion measures. Additionally, we processed, cleaned, and extracted features of the accelerometer data in Microsoft Excel and created the artificial neural networks using R software, thereby accomplishing our goal of using simple methods to create machine learning algorithms to model accelerometer data. ResultsOverall, the thigh accelerometer provided the highest predictive accuracy for EE, although both the wrists and hip accelerometers also provided highly accurate EE predictions. For recognition of activity type, the wrist accelerometers achieved the highest accuracy while the hip accelerometer had the lowest accuracy. Finally, for prediction of SB, the hip and left wrist accelerometers provided the highest accuracy while the right wrist accelerometer provided the lowest accuracy. DiscussionOur study highlights the strengths and weaknesses of accelerometers placed on the hip, thigh, and wrists for prediction of activity type, SB, and EE. These findings suggest that single accelerometers can be used for accurate measurement of PA, SB, and EE, although the optimal accelerometer placement site will depend on the specific research question. Further research should be conducted in a true free-living setting with a more diverse population, different sets of activities, and when using other types of machine learning to mode the accelerometer data.
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