Back to Search Start Over

People Like Me

People Like Me

Authors :
Lauren Wilcox
Clayton Feustel
Bongshin Lee
Shyamak Aggarwal
Source :
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2:1-21
Publication Year :
2018
Publisher :
Association for Computing Machinery (ACM), 2018.

Abstract

Increases in data complexity in personal informatics systems require new ways of contextualizing personal data to facilitate meaningful reflection. An emerging approach for providing such context includes augmenting one's personal data with the data of others "like them" to help individuals make sense of their data. However, we do not yet understand how an individual's self-reflection process is affected when the data of others is made available. In this paper, we investigate how people reflect on three types of personal data when presented alongside a large set of aggregated data of multiple cohorts. We conducted personal and cohort data reviews using a subset of participants from a mobile-sensing study that collected physical activity, digital social activity, and perceived stress, from 47 students over three weeks. Participants preferred to use characteristics of the data (e.g., maxima, minima) and graphical presentation (e.g., appearance of trends) along with demographic identities (e.g., age, gender) when relating to cohorts. We further characterize how participants incorporated cohort data into their self-reflection process, and conclude with discussion of the implications for personal informatics systems that leverage the data of "people like me" to enable meaningful reflection.

Details

ISSN :
24749567
Volume :
2
Database :
OpenAIRE
Journal :
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Accession number :
edsair.doi...........e7069f6030c7cd98ff8e2bd622ac86a2
Full Text :
https://doi.org/10.1145/3264917