Investigating the Effects of Mood & Usage Behaviour on Notification Response Time

TL;DR

This study explores how users' mood and usage behavior influence notification response times on smartphones, demonstrating a personalized prediction model that could enhance notification management systems.

Contribution

It introduces a regression model that accurately predicts notification response times based on mood and physiological signals, highlighting personalized factors affecting response behavior.

Findings

Model achieved MAE of 0.7764 ms and RMSE of 1.0527 ms

Physiological signals from wristbands can indicate mood impacts

Individual differences significantly affect notification response times

Abstract

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.