Search Results

Background: Application of machine learning for classifying human behavior is increasingly common as access to raw accelerometer data improves. The aims of this scoping review are (1) to examine if machine-learning techniques can accurately identify human activity behaviors from raw accelerometer data and (2) to summarize the practical implications of these machine-learning techniques for future work. Methods: Keyword searches were performed in Scopus, Web of Science, and EBSCO databases in 2018. Studies that applied supervised machine-learning techniques to raw accelerometer data and estimated components of physical activity were included. Information on study characteristics, machine-learning techniques, and key study findings were extracted from included studies. Results: Of the 53 studies included in the review, 75% were published in the last 5 years. Most studies predicted postures and activity type, rather than intensity, and were conducted in controlled environments using 1 or 2 devices. The most common models were support vector machine, random forest, and artificial neural network. Overall, classification accuracy ranged from 62% to 99.8%, although nearly 80% of studies achieved an overall accuracy above 85%. Conclusions: Machine-learning algorithms demonstrate good accuracy when predicting physical activity components; however, their application to free-living settings is currently uncertain.

Physical activity (PA) and social support have known benefits for the well-being and health of older adults, and social support is associated with PA behavior and positive affective experiences in PA contexts. The aim of this study was to synthesize qualitative research conducted on the experiences of social support related to PA among older adults (age ≥55 years). Following meta-study methodology, the authors searched nine databases and extracted information from 31 studies. Results were synthesized in terms of common themes and in light of theoretical and methodological perspectives used. The qualitative literature identifies supportive behaviors and social network outcomes which may be useful for informing how best to support older adults to be physically active. This literature rarely reflected the experiences of vulnerable populations, and future research should aim to further understand supportive behaviors which enable older adults to overcome barriers and challenges to being physically active.