We present CrowdWalk, a mobile app that leverages the wisdom of the crowd to produce location-based “walking challenges”, and thus attempts to assist behavior change through highlighting opportunities for physical activity. CrowdWalk infers users’ location and presents a list of walking activities that can be initiated from one’s current location. For instance, as users enter a building CrowdWalk may suggest taking the stairs. When entering a supermarket, users may be challenged to leave their shopping cart behind while walking back and forth to gather shopping items.
Activities are contributed by users and are ordered by proximity and popularity. They are displayed within an “activity indicator circle”, depicting general information on an activity (such as name and description) as well as its contribution towards goal completion (e.g. walking around the campus will contribute an additional 2km towards your 15km daily goal, see Fig 1). Additional tips and comments are displayed, either provided by users (e.g. sharing experiences on a certain walking activity) or by the system (e.g. tips on the how to complete a certain walking activity). Users are further presented with a map (see Fig 2) view pinpointing the concrete location of an activity that reassures them of the accuracy of their location inference and allows browsing nearby activities.
Individuals often struggle to move from the intention of attaining healthy lifestyles to the set goal. Long-lasting behaviors are hard to achieve, and despite the initial premises, effects of self-monitoring have been found to wear off with time . With the design of CrowdWalk, we aim at fostering an alternative approach to the dominant narrative of quantification. Although Crowdwalk records user’s walking distance, the goal is not to evaluate performance, but rather to raise awareness on the frequency and contribution of individual walking activities. Our future work aims at deploying CrowdWalk in the wild with the goal of assessing the quality and quantity of walking challenges generated by users and the impact these have on physical activity and habit formation.
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