Validation of a smartphone app to map social networks of proximity

Abstract

Social network analysis is a prominent approach to investigate interpersonal relationships. Most studies use self-report data to quantify the connections between participants and construct social networks. In recent years smartphones have been used as an alternative to map networks by assessing the proximity between participants based on Bluetooth and GPS data. While most studies have handed out specially programmed smartphones to study participants, we developed an application for iOS and Android to collect Bluetooth data from participants’ own smartphones. In this study, we compared the networks estimated with the smartphone app to those obtained from sociometric badges and self-report data. Participants (n = 21) installed the app on their phone and wore a sociometric badge during office hours. Proximity data was collected for 4 weeks. A contingency table revealed a significant association between proximity data (ϕ = 0.17, p<0.0001), but the marginal odds were higher for the app (8.6%) than for the badges (1.3%), indicating that dyads were more often detected by the app. We then compared the networks that were estimated using the proximity and self-report data. All three networks were significantly correlated, although the correlation with self-reported data was lower for the app (ρ = 0.25) than for badges (ρ = 0.67). The scanning rates of the app varied considerably between devices and was lower on iOS than on Android. The association between the app and the badges increased when the network was estimated between participants whose app recorded more regularly. These findings suggest that the accuracy of proximity networks can be further improved by reducing missing data and restricting the interpersonal distance at which interactions are detected.

Automated sensing of daily activity: A new lens into development

“…For example, radio‐frequency signals and infrared beams can be used to can detect close contact (1–5 feet) as well as directionality, which can be used to detect face‐to‐face contact between individuals (Olguín et al, ). In contrast, Bluetooth radio waves are relatively far‐ranging but provide relatively coarse spatial resolution (e.g., distinguishing whether participants are 1–2, 3–4 vs. 5–10 feet from one another) (Boonstra, Larsen, Townsend, & Christensen, ; Montanari, Nawaz, Mascolo, & Sailer, ; Osmani, Carreras, Matic, & Saar, ). However, Bluetooth is an omnidirectional signal and thus cannot be used to detect face‐to‐face contact.…”

“…However, Bluetooth is an omnidirectional signal and thus cannot be used to detect face‐to‐face contact. While it is technically possible to obtain Bluetooth scans every 5–20 s, on most cell phones it is only feasible to log scans once every five minutes or so due to battery constraints (Boonstra et al, ).…”

Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions (Preprint)

“…For example, we used push notifications and the Significant-Change Location Service to successfully increase data completeness on iOS devices compared to previous studies showing a significant disparity between Android and iOS data acquisition rates [20,21]. With the current version of the app, we no longer have a significant difference between Android and iOS data completeness.…”

“…We developed Socialise as a native application in Java for Android and Objective-C for iOS in order to collect passive data (Bluetooth and GPS) and Ecological Momentary Assessment (EMA). Building on the results of a previous validation and feasibility study [20,21], we have implemented several changes to improve scanning rates on iOS and here we tested Socialise version v0.2.…”

Are the different layers of a social network conveying the same information?

“…These data have been widely used to investigate the structure of social networks, the patterns of social interactions and social theories, such as the strength of weak ties [7], homophily patterns (the tendency of individuals to have social links with similar individuals, with respect to gender, nationality, social class, etc.[10]) [11][12][13][14][15][16], mechanisms of link formation and persistence [11,12,17], social strategies linked to limited attention capacity [8], etc.In most cases, such studies are based on data describing one specific layer of the multilayer network characterizing social interactions, and consider this layer as a proxy of "the" social network of the population under study. Several research groups have, however, managed in recent years to access simultaneously more than one layer of interactions in various population groups, showing in each case that these layers are correlated but not equivalent [4,[18][19][20][21][22][23][24]. For instance, a comparison between face-to-face contacts measured by sensors and friendship relations obtained through surveys has shown that the distribution of contact durations are broad both for pairs of friends and pairs of non-friends, even if the longest contacts occur between friends [20].…”