Evaluating the Feasibility of Measuring Travel to School Using a Wearable Camera

Abstract

BackgroundThe school journey is often studied in relation to health outcomes in children and adolescents. Self-report is the most common measurement tool.PurposeTo investigate the error on self-reported journey duration in adolescents, using a wearable digital camera (Microsoft SenseCam).MethodsDuring March–May 2011, participants (n=17; aged 13–15 years) from four schools wore wearable cameras to and from school for 1 week. The device automatically records time-stamped, first-person point-of-view images, without any action from the wearer. Participants also completed a researcher-administered self-report travel survey over the same period. Analysis took place in November 2011. Within- and between-subjects correlation coefficients and Bland-Altman 95% limits of agreement were derived, accounting for the multiple observations per individual.ResultsSelf-report data were collected for 150 journey stages and SenseCam data for 135 (90%) of these. The within-subjects correlation coefficient for journey duration was 0.89 (95% CI=0.84, 0.93). The between-subjects correlation coefficient was 0.92 (95% CI=0.79, 0.97). The mean difference (bias) between methods at the whole sample level was small (10 seconds per journey, 95% CI= −33, 53). The wide limits of agreement (±501 seconds, 95% CI= −491, 511) reveal large random error.ConclusionsCompared to direct observation from images, self-reported journey duration is accurate at the mean group level but imprecise at the level of the individual participant.

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

“…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].…”

“…The next most common use of wearable cameras was to augment measurement of physical activity-related behaviours, including travel behaviours such as cycling and using public transport [14,28]. Kelly et al [14] tested the feasibility of measuring travel to school in 2012, followed by validating travel data against a travel diary in 2014 [28] with 84 adults using a variety of transportation modes.…”

“…Kelly et al [14] tested the feasibility of measuring travel to school in 2012, followed by validating travel data against a travel diary in 2014 [28] with 84 adults using a variety of transportation modes. Both studies found that self-reported journey data was accurate at the group level but imprecise at the individual level.…”

“…In 21 studies (81%) participants wore a camera for between three to seven days; in one study, participants wore the cameras for seven weeks [37]. Studies classified as primary or secondary data analyses used wearable cameras to measure, observe or validate specific behaviours related to dietary intake (n = 10, 38%) [8][9][10][11]20,[22][23][24][25]39], physical activity (n =5, 17%) [26,27,30,32,40], a diverse range of daily activities such as travel, work and exposure to food marketing (n = 5, 17%) [12,[33][34][35]37], sedentary behaviour (n = 4, 15%) [13,29,31,36] and travel behaviours such as walking, cycling or motorised transport (n = 2, 8%) [14,28].…”

Novel technology to help understand the context of physical activity and sedentary behaviour

“…Wearable cameras have previously been used to assess active travel and to augment accelerometer measured time spent sedentary and in physical activity (Kelly et al 2011, Kelly et al 2012b, Doherty et al 2013a, Kelly et al 2012a, Doherty et al 2012. Given the wide range of information that can be extracted from an image, wearable cameras offer the potential to simultaneously assess a number of contextual factors; however, image coding can be laborious on the researcher.…”