Reachable but not receptive: enhancing smartphone interruptibility prediction by modelling the extent of user engagement with notifications

Smartphone notifications frequently interrupt our daily lives, often at inopportune moments. We propose the decision-on-information-gain model, which extends the existing data collection convention to capture a range of interruptibility behaviour implicitly. Through a six-month in-the-wild study of 11,346 notifications, we find that this approach captures up to 125% more interruptibility cases. Secondly, we find different correlating contextual features for different behaviour using the approach and find that predictive models can be built with >80% precision for most users. However we note discrepancies in performance across labelling, training, and evaluation methods, creating design considerations for future systems

Tools to Improve Interruption Management

Interruptions carry a high cost, especially to software developers. To prevent unnecessary interruptions, several technologies are being explored that can help manage the timing of interruptions, such as displaying the interruptibility of a worker to their peers. Relatively simple algorithms utilizing computer interaction data have been created and used successfully in the workplace, while technology using bio-metric emotion recognition to detect the interruptibility of a user is also being developed

Forecasting User Attention During Everyday Mobile Interactions Using Device-Integrated and Wearable Sensors

Visual attention is highly fragmented during mobile interactions, but the erratic nature of attention shifts currently limits attentive user interfaces to adapting after the fact, i.e. after shifts have already happened. We instead study attention forecasting -- the challenging task of predicting users' gaze behaviour (overt visual attention) in the near future. We present a novel long-term dataset of everyday mobile phone interactions, continuously recorded from 20 participants engaged in common activities on a university campus over 4.5 hours each (more than 90 hours in total). We propose a proof-of-concept method that uses device-integrated sensors and body-worn cameras to encode rich information on device usage and users' visual scene. We demonstrate that our method can forecast bidirectional attention shifts and predict whether the primary attentional focus is on the handheld mobile device. We study the impact of different feature sets on performance and discuss the significant potential but also remaining challenges of forecasting user attention during mobile interactions.Comment: 13 pages, 9 figure

Interruptibility prediction for ubiquitous systems: conventions and new directions from a growing field

When should a machine attempt to communicate with a user? This is a historical problem that has been studied since the rise of personal computing. More recently, the emergence of pervasive technologies such as the smartphone have extended the problem to be ever-present in our daily lives, opening up new opportunities for context awareness through data collection and reasoning. Complementary to this there has been increasing interest in techniques to intelligently synchronise interruptions with human behaviour and cognition. However, it is increasingly challenging to categorise new developments, which are often scenario specific or scope a problem with particular unique features. In this paper we present a meta-analysis of this area, decomposing and comparing historical and recent works that seek to understand and predict how users will perceive and respond to interruptions. In doing so we identify research gaps, questions and opportunities that characterise this important emerging field for pervasive technology

Sensing and indicating interruptibility in office workplaces

In office workplaces, interruptions by co-workers, emails or instant messages are common. Many of these interruptions are useful as they might help resolve questions quickly and increase the productivity of the team. However, knowledge workers interrupted at inopportune moments experience longer task resumption times, lower overall performance, more negative emotions, and make more errors than if they were to be interrupted at more appropriate moments. To reduce the cost of interruptions, several approaches have been suggested, ranging from simply closing office doors to automatically measuring and indicating a knowledge worker’s interruptibility - the availability for interruptions - to co-workers. When it comes to computer-based interruptions, such as emails and instant messages, several studies have shown that they can be deferred to automatically detected breakpoints during task execution, which reduces their interruption cost. For in-person interruptions, one of the most disruptive and time-consuming types of interruptions in office workplaces, the predominant approaches are still manual strategies to physically indicate interruptibility, such as wearing headphones or using manual busy lights. However, manual approaches are cumbersome to maintain and thus are not updated regularly, which reduces their usefulness. To automate the measurement and indication of interruptibility, researchers have looked at a variety of data that can be leveraged, ranging from contextual data, such as audio and video streams, keyboard and mouse interaction data, or task characteristics all the way to biometric data, such as heart rate data or eye traces. While studies have shown promise for the use of such sensors, they were predominantly conducted on small and controlled tasks over short periods of time and mostly limited to either contextual or biometric sensors. Little is known about their accuracy and applicability for long-term usage in the field, in particular in office workplaces. In this work, we developed an approach to automatically measure interruptibility in office workplaces, using computer interaction sensors, which is one type of contextual sensors, and biometric sensors. In particular, we conducted one lab and two field studies with a total of 33 software developers. Using the collected computer interaction and biometric data, we used machine learning to train interruptibility models. Overall, the results of our studies show that we can automatically predict interruptibility with high accuracy of 75.3%, improving on a baseline majority classifier by 26.6%. An automatic measure of interruptibility can consequently be used to indicate the status to others, allowing them to make a well-informed decision on when to interrupt. While there are some automatic approaches to indicate interruptibility on a computer in the form of contact list applications, they do not help to reduce in-person interruptions. Only very few researchers combined the benefits of an automatic measurement with a physical indicator, but their effect in office workplaces over longer periods of time is unknown. In our research, we developed the FlowLight, an automatic interruptibility indicator in the form of a traffic-light like LED placed on a knowledge worker's desk. We evaluated the FlowLight in a large-scale field study with 449 participants from 12 countries. The evaluation revealed that after the introduction of the FlowLight, the number of in-person interruptions decreased by 46% (based on 36 interruption logs), the awareness on the potential harm of interruptions was elevated and participants felt more productive (based on 183 survey responses and 23 interview transcripts), and 86% remained active users even after the two-month study period ended (based on 449 online usage logs). Overall, our research shows that we can successfully reduce in-person interruption cost in office workplaces by sensing and indicating interruptibility. In addition, our research can be extended and opens up new opportunities to further support interruption management, for example, by the integration of other more accurate biometric sensors to improve the interruptibility model, or the use of the model to reduce self-interruptions

Exploring the State-of-Receptivity for mHealth Interventions

Recent advancements in sensing techniques for mHealth applications have led to successful development and deployments of several mHealth intervention designs, including Just-In-Time Adaptive Interventions (JITAI). JITAIs show great potential because they aim to provide the right type and amount of support, at the right time. Timing the delivery of a JITAI such as the user is receptive and available to engage with the intervention is crucial for a JITAI to succeed. Although previous research has extensively explored the role of context in users’ responsiveness towards generic phone notiications, it has not been thoroughly explored for actual mHealth interventions. In this work, we explore the factors afecting users’ receptivity towards JITAIs. To this end, we conducted a study with 189 participants, over a period of 6 weeks, where participants received interventions to improve their physical activity levels. The interventions were delivered by a chatbot-based digital coach ś Ally ś which was available on Android and iOS platforms. We deine several metrics to gauge receptivity towards the interventions, and found that (1) several participant-speciic characteristics (age, personality, and device type) show signiicant associations with the overall participant receptivity over the course of the study, and that (2) several contextual factors (day/time, phone battery, phone interaction, physical activity, and location), show signiicant associations with the participant receptivity, in-the-moment. Further, we explore the relationship between the efectiveness of the intervention and receptivity towards those interventions; based on our analyses, we speculate that being receptive to interventions helped participants achieve physical activity goals, which in turn motivated participants to be more receptive to future interventions. Finally, we build machine-learning models to detect receptivity, with up to a 77% increase in F1 score over a biased random classiier