Combining Wearable Proximity Sensing and Digital Time Diaries for Longitudinal Network Data Collection

Observing how social interactions in nature change over time and space is a major challenge. This study introduces the SocialScope project, which aims to develop an innovative approach to longitudinal data collection on social networks by integrating three technologies. The first is a novel dual-radio proximity wearable sensor combining Bluetooth and ultra-wideband (UWB) radios, capturing the distance between individuals over time, therefore allowing the reconstruction of proximity networks from these spatial and temporal patterns [1]. The second is a smartphone application called iLog, which combines user self-reported information (e.g., time diary) and data passively collected from smartphone sensors [2]. Digital diaries provide repeated snapshots of participants' daily behaviours, minimizing reliance on retrospective recall and improving ecological validity. Smartphone sensors, e.g., GPS, allow us to track subjects' daily activities and social and spatial interactions. The third is a close-ended questionnaire. Here we explore some early evidence on how people adjust interpersonal distances in everyday contexts based on gender, nationality, and age attributes. Eighteen college students living on the same dormitory floor wore the proximity sensors for 15 days, and continuously measured distances during real-life encounters every 15 seconds. At the same time, the app installed on the subjects' smartphones gathered time diary information every 30 minutes (Where are you? Who are you with? What are you doing? What is your mood?) and continuous information from the smartphone's sensors. The analysis is based on a total of 431,329 distances detected with the tag, with a total of 6,299 interactions lasting 90 seconds or longer, and 4,681 self-reported time diary information.