Gabriella M. Harari

TL;DR: A Dartmouth term lifecycle is identified in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns, while stress appreciably rises while positive affect, sleep, conversation and activity drops off.

TL;DR: The lessons from the first wave of smartphone-sensing research are drawn to highlight areas of opportunity for psychological research, present practical considerations for designing smartphone studies, and discuss the ongoing methodological and ethical challenges associated with research in this domain.

TL;DR: It is shown that there are a number of important behavioral factors automatically inferred from smartphones that significantly correlate with term and cumulative GPA, including time series analysis of activity, conversational interaction, mobility, class attendance, studying, and partying.

TL;DR: A review of recent studies focused on measuring human behavior using smartphones and their embedded mobile sensors is provided in this article, which describes the daily behaviors captured using these methods, which include movement behaviors (physical activity, mobility patterns), social behaviors (face-to-face encounters, computer-mediated communications), and other daily activities (non-mediated and mediated activities).

TL;DR: Cross-validated results reveal that specific patterns in behaviors in the domains of 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity are distinctively predictive of the Big Five personality traits.