Classifiers for Accelerometer-Measured Behaviors in Older Women

Abstract

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 field. 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. Conclusions Free-living algorithms for walking and sedentary time yielded high levels of accuracy and concurrent validity and can be applied to existing accelerometer data from older women.

Usefulness of Wearable Cameras as a Tool to Enhance Chronic Disease Self-Management: Scoping Review (Preprint)

“…Eight had between 20 -50 participants [8,9,13,20,24,[29][30][31][32][33]36,37] and only three studies (13%) [26][27][28] had more than 50 participants (maximum = 84). Participants were predominantly healthy adults (n= 15, 65%) [8,[10][11][12][13]20,22,23,[25][26][27][28]33,35,36], with some studies including athletes (n = 4, 17%) [24,[29][30][31] and school children (n = 2, 9%) [9,34].…”

“…Studies including data analysis were predominantly feasibility or pilot studies (n = 13, 54%) [9][10][11]16,20,[22][23][24][25][26][27][28][29], followed by validation studies (n = 4, 17%) [8,28,30,31] and methodological studies (n = 5, 21%) [32][33][34][35][36]. There was one randomised controlled trial [37] conducted with acquired brain injury patients where camera images formed part of a health intervention.…”

“…There was one randomised controlled trial [37] conducted with acquired brain injury patients where camera images formed part of a health intervention. The majority of the studies were conducted in the United States of America (n = 7, 29%) [22,25,27,29,31,32,36], the United Kingdom (n = 6, 25%) [9][10][11][12][13][14], and Ireland (n = 4, 13%) [24,30,33,35]. Three studies [23,26,28] were international multicentre studies, while the remaining studies were from New Zealand [8,20,34] and Spain [37].…”

“…Studies classified as validation [8,28,30,31] compared the validity of wearable cameras either against, or in combination with other measurement approaches, including self-reported diaries, questionnaires, as well as objective measurement techniques such as accelerometers and doubly labelled water. For the methodological papers [32][33][34][35][36], outcomes focused on testing software or analytic approaches to classify and analyse generated images.…”

“…Participants were predominantly healthy adults (n= 15, 65%) [8,[10][11][12][13]20,22,23,[25][26][27][28]33,35,36], with some studies including athletes (n = 4, 17%) [24,[29][30][31] and school children (n = 2, 9%) [9,34]. One study used a sample of older women to examine physical activity and sedentary time [32]. No studies identified in our review recruited participants with a chronic disease; however one study including participants with an acquired brain injury [37].…”