  Notifications are one of the most prevailing mechanisms on smartphones and
personal computers to convey timely and important information. Despite these
benefits, smartphone notifications demand individuals' attention and can cause
stress and frustration when delivered at inopportune timings. This paper
investigates the effect of individuals' smartphone usage behavior and mood on
notification response time. We conduct an in-the-wild study with more than 18
participants for five weeks. Extensive experiment results show that the
proposed regression model is able to accurately predict the response time of
smartphone notifications using current user's mood and physiological signals.
We explored the effect of different features for each participant to choose the
most important user-oriented features in order to to achieve a meaningful and
personalised notification response prediction. On average, our regression model
achieved over all participants an MAE of 0.7764 ms and RMSE of 1.0527 ms. We
also investigate how physiological signals (collected from E4 wristbands) are
used as an indicator for mood and discuss the individual differences in
application usage and categories of smartphone applications on the response
time of notifications. Our research sheds light on the future intelligent
notification management system.
