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- W2890747653 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." @default.
- W2890747653 created "2018-09-27" @default.
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- W2890747653 date "2018-09-18" @default.
- W2890747653 modified "2023-10-18" @default.
- W2890747653 title "People Like Me" @default.
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- W2890747653 doi "https://doi.org/10.1145/3264917" @default.
- W2890747653 hasPublicationYear "2018" @default.
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