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

Predicting human interruptibility with sensors, in

A person seeking someone else’s attention is normally able to quickly assess how interruptible they are. This assessment allows for behavior we perceive as natural, socially appropriate, or simply polite. On the other hand, today’s computer systems are almost entirely oblivious to the human world they operate in, and typically have no way to take into account the interruptibility of the user. This paper presents a Wizard of Oz study exploring whether, and how, robust sensor-based predictions of interruptibility might be constructed, which sensors might be most useful to such predictions, and how simple such sensors might be. The study simulates a range of possible sensors through human coding of audio and video recordings. Experience sampling is used to simultaneously collect randomly distributed self-reports of interruptibility. Based on these simulated sensors, we construct statistical models predicting human interruptibility and compare their predictions with the collected self-report data. The results of these models, although covering a demographically limited sample, are very promising, with the overall accuracy of several models reaching about 78%. Additionally, a model tuned to avoiding unwanted interruptions does so for 90 % of its predictions, while retaining 75 % overall accuracy

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

Engagement-aware computing: Modelling user engagement from mobile contexts

In this paper, we examine the potential of using mobile context to model user engagement. Taking an experimental approach, we systematically explore the dynamics of user engagement with a smartphone through three different studies. Specifically, to understand the feasibility of detecting user engagement from mobile context, we first assess an EEG artifact with 10 users and observe a strong correlation between automatically detected engagement scores and user's subjective perception of engagement. Grounded on this result, we model a set of application level features derived from smartphone usage of 10 users to detect engagement of a usage session using a Random Forest classifier. Finally, we apply this model to train a variety of contextual factors acquired from smartphone usage logs of 130 users to predict user engagement using an SVM classifier with a F1-Score of 0.82. Our experimental results highlight the potential of mobile contexts in designing engagement-aware applications and provide guidance to future explorations

Decomposing responses to mobile notifications

Notifications from mobile devices frequently prompt us with information, either to merely inform us or to elicit a reaction. This has led to increasing research interest in considering an individual’s interruptibility prior to issuing notifications, in order for them to be positively received. To achieve this, predictive models need to be built from previous response behaviour where the individual’s interruptibility is known. However, there are several degrees of freedom in achieving this, from different definitions in what it means to be interruptible and a notification to be successful, to various methods for collecting data, and building predictive models. The primary focus of this thesis is to improve upon the typical convention used for labelling interruptibility, an area which has had limited direct attention. This includes the proposal of a flexible framework, called the decision-on-information-gain model, which passively observes response behaviour in order to support various interruptibility definitions. In contrast, previous studies have largely surrounded the investigation of influential contextual factors on predicting interruptibility, using a broad labelling convention that relies on notifications being responded to fully and potentially a survey needing to be completed. The approach is supported through two in-the-wild studies of Android notifications, one with 11,000 notifications across 90 users, and another with 32,000,000 across 3000 users. Analysis of these datasets shows that: a) responses to notifications is a decisionmaking process, whereby individuals can be reachable but not receptive to their content, supporting the premise of the approach; b) the approach is implementable on typical Android devices and capable of adapting to different notification designs and user preferences; and c) the different labels produced by the model are predictable using data sources that do not require invasive permissions or persistent background monitoring; however there are notable performance differences between different machine learning strategies for training and evaluation

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