Managing Smartphone Interruptions through Adaptive Modes and Modulation of Notifications

Smartphones are capable of alerting their users to different kinds of digital interruption using different modalities and with varying modulation. Smart notification is the capability of a smartphone for selecting the user's preferred kind of alert in particular situations using the full vocabulary of notification modalities and modulations. It therefore goes well beyond attempts to predict if or when to silence a ringing phone call. We demonstrate smart notification for messages received from a document retrieval system while the user is attending a meeting. The notification manager learns about their notification preferences from users' judgements about videos of meetings. It takes account of the relevance of the interruption to the meeting, whether the user is busy and the sensed location of the smartphone. Through repeated training, the notification manager learns to reliably predict the preferred notification modes for users and this learning continues to improve with use

The influence of concurrent mobile notifications on individual responses

Notifications on mobile devices punctuate our daily lives to provide information and prompt for further engagement. Investigations into the cognitive processes involved in consuming notifications are common across the literature, however most research to date investigates notifications in isolation of one another. In reality, notifications often coexist together, forming a “stack”, however the behavioural implications of this on the response towards individual notifications has received limited attention. Through an in-the-wild study of 1,889 Android devices, we observe user behaviour in a stream of 30 million notifications from over 6,000 applications. We find distinct strategies for user management of the notification stack within usage sessions, beyond the behaviour patterns observable from responses to individual notifications. From the analysis, we make recommendations for collecting and reporting data from mobile applications to improve validity through timely responses, and capture potential confounding features

Using mobile phones in pub talk

We present the findings from a study of how people interleave mobile phone use with conversation in pubs. Our findings, informed by ethnomethodology and conversation analysis, unpack the interactional methods through which groups of people in pubs occasioned, sustained, and disengaged from mobile device use during conversation with friends. Fundamentally, the work that is done consists of various methods of accounting for mobile device use, and displaying involvement in social interaction while the device is used. We highlight multiple examples of the nuanced ways in which interleaving is problematic in interaction, and relate our findings to the CSCW and HCI literature on collocated interaction. We conclude by considering avenues for future research, and discuss how we may support or disrupt interleaving practices through design to overcome the highlighted interactional troubles

Using mobile phones in pub talk

We present the findings from a study of how people interleave mobile phone use with conversation in pubs. Our findings, informed by ethnomethodology and conversation analysis, unpack the interactional methods through which groups of people in pubs occasioned, sustained, and disengaged from mobile device use during conversation with friends. Fundamentally, the work that is done consists of various methods of accounting for mobile device use, and displaying involvement in social interaction while the device is used. We highlight multiple examples of the nuanced ways in which interleaving is problematic in interaction, and relate our findings to the CSCW and HCI literature on collocated interaction. We conclude by considering avenues for future research, and discuss how we may support or disrupt interleaving practices through design to overcome the highlighted interactional troubles

Why are smartphones disruptive? An empirical study of smartphone use in real-life contexts

Notifications are one of the core functionalities of smartphones. Previous research suggests they can be a major disruption to the professional and private lives of users. This paper presents evidence from a mixed-methods study using first-person wearable video cameras, comprising 200 h of audio-visual first-person, and self-confrontation interview footage with 1130 unique smartphone interactions (N = 37 users), to situate and analyse the disruptiveness of notifications in real-world contexts. We show how smartphone interactions are driven by a complex set of routines and habits users develop over time. We furthermore observe that while the duration of interactions varies, the intervals between interactions remain largely invariant across different activity and location contexts, and for being alone or in the company of others. Importantly, we find that 89% of smartphone interactions are initiated by users, not by notifications. Overall this suggests that the disruptiveness of smartphones is rooted within learned user behaviours, not devices

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

A framework for intelligent mobile notifications

Mobile notifications provide a unique mechanism for real-time information delivery systems to users in order to increase its effectiveness. However, real-time notification delivery to users via mobile phones does not always translate into users' awareness about the delivered information because these alerts might arrive at inappropriate times and situations. Moreover, notifications that demand users' attention at inopportune moments are more likely to have adverse effects and become a cause of potential disruption rather than proving beneficial to users. In order to address these challenges it is of paramount importance to devise intelligent notification mechanisms that monitor and learn users' behaviour for maximising their receptivity to the delivered information and adapt accordingly. The central goal of this dissertation is to build a framework for intelligent notifications that relies on the awareness of users' context and preferences. More specifically, we firstly investigate the impact of physical and cognitive contextual features on users' attentiveness and receptivity to notifications. Secondly, we construct and evaluate a series of models for predicting opportune moments to deliver notifications and mining users' notification delivery preferences in different situations. Finally, we design and evaluate a model for anticipating the right device notifications in cross-platform environments