Using Video Cameras to Assess Physical Activity and Other Well-Being Behaviors in Urban Environments: Feasibility, Reliability, and Participant Reactivity Studies

Abstract

BackgroundUnobtrusive observation is a promising method for assessing physical activity and other well-being behaviors (eg, social interactions) in urban environments, without participant burden and biases associated with self-report. However, current methods require multiple in-person observers. Using video cameras instead could allow for more accurate observations at lower cost and with greater flexibility in scheduling. ObjectiveThis research aimed to test the feasibility of using stationary wireless video cameras to observe physical activity and other well-being behaviors, and to assess its reliability and potential participant reactivity. MethodsAcross 3 cross-sectional studies, 148 hours of video recordings were collected from 6 outdoor public spaces in Manchester, United Kingdom. The videos were coded by 3 researchers using MOHAWk (Method for Observing Physical Activity and Wellbeing)—a validated in-person observation tool for assessing physical activity, social interactions, and people taking notice of the environment. Inter- and intrarater reliabilities were assessed using intraclass correlation coefficients (ICCs). Intercept surveys were conducted to assess public awareness of the cameras and whether they altered their behavior due to the presence of cameras. ResultsThe 148 hours of video recordings were coded in 85 hours. Interrater reliability between independent coders was mostly “excellent” (ICCs>0.90; n=36), with a small number of “good” (ICCs>0.75; n=2), “moderate” (ICCs=0.5-0.75; n=3), or “poor” (ICCs<0.5; n=1) ICC values. Reliability decreased at night, particularly for coding ethnic group and social interactions, but remained mostly “excellent” or “good.” Intrarater reliability within a single coder after a 2-week interval was “excellent” for all but 1 code, with 1 “good” ICC value for assessing vigorous physical activity, indicating that the coder could reproduce similar results over time. Intrarater reliability was generally similar during the day and night, apart from ICC values for coding ethnic group, which reduced from “excellent” to “good” at night. Intercept surveys with 86 public space users found that only 5 (5.8%) participants noticed the cameras used for this study. Importantly, all 5 said that they did not alter their behavior as a result of noticing these cameras, therefore, indicating no evidence of reactivity. ConclusionsCamera-based observation methods are more reliable than in-person observations and do not produce participant reactivity often associated with self-report methods. This method requires less time for data collection and coding, while allowing for safe nighttime observation without the risk to research staff. This research is a significant first step in demonstrating the potential for camera-based methods to improve natural experimental studies of real-world environmental interventions. It also provides a rigorous foundation for developing more scalable automated computer vision algorithms for assessing human behaviors.