The Rutgers Well-being Study

With more than 5 billion active phone subscriptions, and related trends in objective-self, smart health, and sensor-based analysis, human lives are being captured at scale and resolution never possible before. This data allows us to study multiple human phenomena and understand human behavior in ways not possible before. An ability to understand human behavior including activity levels, location patterns, sleep, consumption, and communication and social interaction allows for highly detailed and personalized data collection in a far more granular, unobtrusive, and affordable way than earlier.

This research project is investigating the interconnections between human social and mobility behaviors and general wellbeing, self-esteem, and mental health. Specifically, the project is investigating how socio-mobile data (such as GPS locations and call logs) from users’ personal mobile devices can be used to understand the interplay between socio-mobile behavior and wellbeing, and how any changes in user wellbeing can be automatically detected via this data, which may potentially lead to future appropriate interventions.

In this study, detailed socio-mobile logs for about 60 participants were collected for 10 weeks on Rutgers campus during Spring 2015. The same participants were asked to fill in a number of surveys related to wellbeing. This project has resulted in multiple publications on the aspects of predicting cooperation and privacy needs. 

Related Publications.

(1) Bati, G. F., & Singh, V. K. (2021). Altrumetrics: Inferring Altruism Propensity Based on Mobile Phone Use PatternsIEEE Transactions on Big Data. 7 (2), 397-406. DOI: 10.1109/TBDATA.2018.2873346.

(2) Ghosh, I., & Singh, V. (2020). Phones, privacy, and predictions: A study of phone logged data to predict privacy attitudes of individualsOnline Information Review. 44(2), 483-502. DOI:10.1108/OIR-03-2018-0112

(3) Singh, V. K., Goyal, R., & Wu, S. (2018). Riskalyzer: Inferring Individual Risk-Taking Propensity Using Phone MetadataProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(1), 34.

(4) Bati, G. F., & Singh, V. K. (2018). “Trust Us”: Mobile Phone Use Patterns Can Predict Individual Trust Propensity. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 330). ACM. Length: 14 pages. 

(5) Singh, V. K., & Long, T. (2018). Automatic assessment of mental health using phone metadata. Proceedings of the Association for Information Science and Technology55(1), 450-459.

(6) Singh, V. K., & Ghosh, I. (2017). Inferring Individual Social Capital Automatically via Phone LogsProceedings of the ACM on Human-Computer Interaction, 1(CSCW), 95. Length: 12 pages.

(7) Singh, V. K., & Jain, A. (2017). Toward harmonizing self-reported and logged social data for understanding human behavior. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 2233-2238). ACM.

(8) Singh, V. K., & Agarwal, R. R. (2016). Cooperative phoneotypes: exploring phone-based behavioral markers of cooperation. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 646-657). ACM.