Purpose: Machine learning methods could better improve detection of specific types of physical activities and sedentary behaviors from accelerometer data. No studies in older populations have developed and tested algorithms for walking and sedentary time in free-living daily life. Our goal was to rectify this gap by leveraging access to data from two studies in older women. Methods: In study 1, algorithms were developed and tested in a sample of older women (N = 39; age range = 55-96) in the

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... ld. Women wore accelerometers and SenseCam (ground truth annotation) devices for 7 days yielding 3,191 hours and 320 days of data. Images were annotated and time matched to accelerometer data and random forest classifiers labeled behaviors (sitting, riding in a vehicle, standing still, standing moving, walking/running). In study 2, we examined the concurrent validity of the algorithms using accelerometer data from an observed 400 meter walk test (2983 minutes of data available) and 6 days of wearing both accelerometers and global positioning systems (GPS) devices in a sample of 222 women (age range = 67-100; 313,290 minutes of data available). Analyses included sensitivity, specificity balanced accuracy, and precision, as appropriate, averaged over each test participant at the minute level for each behavior. Results: In study 1, the algorithms had 82.2% balanced accuracy. In study 2, the classifier had 87.9% accuracy for predicting walking. Overall machine learning classifiers and GPS had 88.6% agreement.