We report from the Do Not Disturb Challenge where 30 volunteers disabled notification alerts for 24 hours across all devices. The effect of the absence of notifications on the participants was isolated through an experimental study design: we compared self-reported feedback from the day without notifications against a baseline day. The evidence indicates that notifications have locked us in a dilemma: without notifications, participants felt less distracted and more productive. But, they also felt no longer able to be as responsive as expected, which made some participants anxious. And, they felt less connected with one's social group. In contrast to previous reports, about two third of the participants expressed the intention to change how they manage notifications. Two years later, half of the participants are still following through with their plans.
Notifications & Availability On mobile phones, the largest chunk of notifications originate from messaging applications @cite_31 @cite_19 @cite_8 , such as SMS, WhatsApp, or Facebook Messenger. On such communication channels, people are assumed to be constantly co-present, and thus, constantly available for conversation @cite_18 . On average, notifications from messengers are attended within minutes @cite_26 @cite_36 @cite_35 @cite_12 @cite_8 , and people maintain this levels of attentiveness for large parts of their wake time @cite_36 . Consequently, notification-enabled computed mediated communication plays a crucial role [..] in the fragmentation of the working day @cite_10 . We hypothesize that the absence of notifications will affect the participants' ability to maintain the usual level of attentiveness.
@cite_35 @cite_18 @cite_26 @cite_8 @cite_36 @cite_19 @cite_31 @cite_10 @cite_12
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Mobile instant messaging (e.g., via SMS or WhatsApp) often goes along with an expectation of high attentiveness, i.e., that the receiver will notice and read the message within a few minutes. Hence, existing instant messaging services for mobile phones share indicators of availability, such as the last time the user has been online. However, in this paper we not only provide evidence that these cues create social pressure, but that they are also weak predictors of attentiveness. As remedy, we propose to share a machine-computed prediction of whether the user will view a message within the next few minutes or not. For two weeks, we collected behavioral data from 24 users of mobile instant messaging services. By the means of machine-learning techniques, we identified that simple features extracted from the phone, such as the user's interaction with the notification center, the screen activity, the proximity sensor, and the ringer mode, are strong predictors of how quickly the user will attend to the messages. With seven automatically selected features our model predicts whether a phone user will view a message within a few minutes with 70.6 accuracy and a precision for fast attendance of 81.2 Notifications on mobile phones alert users about new messages, emails, social network updates, and other events. However, little is understood about the nature and effect of such notifications on the daily lives of mobile users. We report from a one-week, in-situ study involving 15 mobile phones users, where we collected real-world notifications through a smartphone logging application alongside subjective perceptions of those notifications through an online diary. We found that our participants had to deal with 63.5 notifications on average per day, mostly from messengers and email. Whether the phone is in silent mode or not, notifications were typically viewed within minutes. Social pressure in personal communication was amongst the main reasons given. While an increasing number of notifications was associated with an increase in negative emotions, receiving more messages and social network updates also made our participants feel more connected with others. Our findings imply that avoiding interruptions from notifications may be viable for professional communication, while in personal communication, approaches should focus on managing expectations. Social norm has it that people are expected to respond to mobile phone messages quickly. We investigate how attentive people really are and how timely they actually check and triage new messages throughout the day. By collecting more than 55,000 messages from 42 mobile phone users over the course of two weeks, we were able to predict people's attentiveness through their mobile phone usage with close to 80 accuracy. We found that people were attentive to messages 12.1 hours a day, i.e. 84.8 hours per week, and provide statistical evidence how very short people's inattentiveness lasts: in 75 of the cases mobile phone users return to their attentive state within 5 minutes. In this paper, we present a comprehensive analysis of attentiveness throughout each hour of the day and show that intelligent notification delivery services, such as bounded deferral, can assume that inattentiveness will be rare and subside quickly. It is well documented that interacting with a mobile phone is associated with poorer performance on concurrently performed tasks because limited attentional resources must be shared between tasks. However, mobile phones generate auditory or tactile notifications to alert users of incoming calls and messages. Although these notifications are generally short in duration, they can prompt task-irrelevant thoughts, or mind wandering, which has been shown to damage task performance. We found that cellular phone notifications alone significantly disrupted performance on an attention-demanding task, even when participants did not directly interact with a mobile device during the task. The magnitude of observed distraction effects was comparable in magnitude to those seen when users actively used a mobile phone, either for voice calls or text messaging. (PsycINFO Database Record Keywords: Driver distraction; Language: en As smartphones increasingly pervade our daily lives, people are ever more interrupted by alerts and notifications. Using both correlational and experimental methods, we explored whether such interruptions might be causing inattention and hyperactivity-symptoms associated with Attention Deficit Hyperactivity Disorder (ADHD) even in people not clinically diagnosed with ADHD. We recruited a sample of 221 participants from the general population. For one week, participants were assigned to maximize phone interruptions by keeping notification alerts on and their phones within their reach sight. During another week, participants were assigned to minimize phone interruptions by keeping alerts off and their phones away. Participants reported higher levels of inattention and hyperactivity when alerts were on than when alerts were off. Higher levels of inattention in turn predicted lower productivity and psychological well-being. These findings highlight some of the costs of ubiquitous connectivity and suggest how people can reduce these costs simply by adjusting existing phone settings. telephone or IM, which provided mediated co-presence when one was in the vicinity of a landline phone or PC, today’s mobile devices provide constant co-presence. Instant messaging is often combined with text messaging or proprietary messaging clients (e.g., BlackBerry Messenger, Apple’s iMessage, etc.), such that today’s teens send hundreds of messages per day to stay in touch with family and friends [3]. People are assumed to be constantly co-present, and thus, constantly available for conversation. This is taking a toll. There is evidence that all of these opportunities for interaction cause overload and stress. A recent Pew report suggests that nearly a third of respondents periodically turn off their phones just to take a break from all of the interaction opportunities they provide [4]. Sherri Turkle’s recent book suggests we are paying more attention to our devices than to each other [5]. Others report feeling disco-presence (i.e., being on the phone at the same time) could offer a similar experience. And one could inquire about another’s availability for mediated co-presence by calling in a more modern sense; namely, by dialing the phone and waiting for a response on the other end [1]. Instant messaging (IM) and chat systems, which became very popular in the late 1990s and early 2000s, changed this process again [2]. With these systems, one had a list of contacts who were co-present at any given time. Co-presence, for many users of these systems, suggested availability for conversation and was often reason enough to start one. The contact list made co-presence even easier to coordinate. More recently, there has been a larger socio-technical shift that we believe has significant consequences for how people coordinate around communication. In contrast to Historically, the problem of coordinating opportunities for real-time communication has been dominated by the problem of co-presence. Before there was any interactive media, people had to be in the same place to talk. Visiting somebody’s house (“calling,” in the early sense) to see if they were available or scheduling a time to meet were both good ways of coordinating. Early media such as the telephone changed this process a bit, in that people no longer needed to be physically co-present to talk. Mediated Understanding Unavailability in a World of Constant Connection Notifications are a core feature of mobile phones. They inform users about a variety of events. Users may take immediate action or ignore them depending on the importance of a notification as well as their current context. The nature of notifications is manifold, applications use them both sparsely and frequently. In this paper we present the first large-scale analysis of mobile notifications with a focus on users' subjective perceptions. We derive a holistic picture of notifications on mobile phones by collecting close to 200 million notifications from more than 40,000 users. Using a data-driven approach, we break down what users like and dislike about notifications. Our results reveal differences in importance of notifications and how users value notifications from messaging apps as well as notifications that include information about people and events. Based on these results we derive a number of findings about the nature of notifications and guidelines to effectively use them. Smartphones are considered to be "always on, always connected" but mobile users are not always attentive and responsive to incoming communication. We present a mixed methods study investigating how mobile users use ringer modes for managing interruption by and awareness of incoming communication, and how these practices and locales affect their attentiveness and responsiveness. We show that mobile users have diverse ringer mode usage, but they switch ringer modes mainly for three purposes: avoiding interruption, preventing the phone from disrupting the environment, and noticing important notifications. In addition, without signals of notifications, users are less likely to immediately attend to notifications, but they are not less responsive to those they have attended. Finally, ringer mode switches, attentiveness, and responsiveness are all correlated with certain locales. We discuss implications from these findings, and suggest how future CMC tools and notification services take different purposes for using ringer modes and locales into consideration. Smartphones have become an indispensable part of everyday life. By this time, push notifications are at the core of many apps, proactively pushing new content to users. These notifications may raise awareness, but also have the downside of being disruptive. In this paper we present a laboratory study investigating users' attitudes towards notifications and how they deal with notification settings on their smartphones. Permission requests for sending push notifications on iOS don't inform the user about the nature of notifications of this app, leaving the user to make a rather uninformed choice on whether to accept or deny. We show that requests including explanations are significantly more likely to be accepted. Our results further indicate that apart from being disruptive, notifications may create stress due to information overload. Notification settings, once assigned a preset, are rarely changed, although not necessarily matching the favored one. Modern technologies not only provide a variety of communication modes (e.g., texting, cell phone conversation, and online instant messaging), but also detailed electronic traces of these communications between individuals. These electronic traces indicate that the interactions occur in temporal bursts. Here, we study intercall duration of communications of the 100,000 most active cell phone users of a Chinese mobile phone operator. We confirm that the intercall durations follow a power-law distribution with an exponential cutoff at the population level but find differences when focusing on individual users. We apply statistical tests at the individual level and find that the intercall durations follow a power-law distribution for only 3,460 individuals (3.46 ). The intercall durations for the majority (73.34 ) follow a Weibull distribution. We quantify individual users using three measures: out-degree, percentage of outgoing calls, and communication diversity. We find that the cell phone users with a power-law duration distribution fall into three anomalous clusters: robot-based callers, telecom fraud, and telephone sales. This information is of interest to both academics and practitioners, mobile telecom operators in particular. In contrast, the individual users with a Weibull duration distribution form the fourth cluster of ordinary cell phone users. We also discover more information about the calling patterns of these four clusters (e.g., the probability that a user will call the cr-th most contact and the probability distribution of burst sizes). Our findings may enable a more detailed analysis of the huge body of data contained in the logs of massive users. Desktop notifications are designed to provide awareness of information while a user is attending to a primary task. Unfortunately the awareness can come with the price of disruption to the focal task. We review results of a field study on the use and perceived value of email notifications in the workplace. We recorded users' interactions with software applications for two weeks and studied how notifications or their forced absence influenced users' quest for awareness of new email arrival, as well as the impact of notifications on their overall task focus. Results showed that users view notifications as a mechanism to provide passive awareness rather than a trigger to switch tasks. Turing off notifications cause some users to self interrupt more to explicitly monitor email arrival, while others appear to be able to better focus on their tasks. Users acknowledge notifications as disruptive, yet opt for them because of their perceived value in providing awareness. A notification represents the proactive delivery of information to a user and reduces the need to visually scan or repeatedly check an external information source. At the same time, notifications often interrupt user tasks at inopportune moments, decreasing productivity and increasing frustration. Controlled studies have shown that linking notification delivery to the perceptual structure of a user's tasks can reduce these interruption costs. However, in these studies, the scheduling was always performed manually, and it was not clear whether it would be possible for a system to mimic similar techniques. This article contributes the design and implementation of a novel system called Oasis that aligns notification scheduling with the perceptual structure of user tasks. We describe the architecture of the system, how it detects task structure on the fly without explicit knowledge of the task itself, and how it layers flexible notification scheduling policies on top of this detection mechanism. The system also includes an offline tool for creating customized statistical models for detecting task structure. The value of our system is that it intelligently schedules notifications, enabling the reductions in interruption costs shown within prior controlled studies to now be realized by users in everyday desktop computing tasks. It also provides a test bed for experimenting with how notification management policies and other system functionalities can be linked to task structure. With increasing prevalence of mobile wireless devices with WiFi and Bluetooth capability, new applications that can make use of limited contact opportunities when the devices are physically close are emerging. Proximity-based social networking, and location specific dissemination of advertisements and events, are some such applications. Discovering such services is a challenging problem due to energy budget limitations, user mobility, and nonuniformity and the time-varying nature of energy budgets across users. It is important to rapidly discover such mobile services to make use of limited contact opportunities. To support such applications, we seek to design a localized discovery scheme that can minimize the expected contact latency between mobile phones with limited energy budgets. All the existing neighbor discovery schemes assume lack of any time synchronization. However, in practice sufficiently accurate time synchronization can be achieved with existing time synchronization techniques. We propose Recursive Binary Time Partitioning (RBTP), a scheme that determines how the devices should wake up and sleep to achieve minimal contact latency with other nearby devices. RBTP achieves provable performance bound and outperforms state-of-the-art asynchronous protocols for smartphones. When compared with the optimum scheme, the contact latency is shown to be within a factor of (9 8) in the expected case and 2 in the worst case. 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 rapid deployment of millions of mobile sensors and smartphones has resulted in a demand for opportunistic encounter-based networking to support mobile social networking applications and proximity-based gaming. However, the success of these emerging networks is limited by the lack of effective and energy efficient neighbor discovery protocols. While probabilistic approaches perform well for the average case, they exhibit long tails resulting in high upper bounds on neighbor discovery time. Recent deterministic protocols, which allow nodes to wake up at specific timeslots according to a particular pattern, improve on the worst case bound, but do so by sacrificing average case performance. In response to these limitations, we have designed Searchlight, a highly effective asynchronous discovery protocol that is built on three basic ideas. First, it leverages the constant offset between periodic awake slots to design a simple probing-based approach to ensure discovery. Second, it allows awake slots to cover larger sections of time, which ultimately reduces total awake time drastically. Finally, Searchlight has the option to employ probabilistic techniques with its deterministic approach that can considerably improve its performance in the average case when all nodes have the same duty cycle. We validate Searchlight through analysis and real-world experiments on smartphones that show considerable improvement (up to 50 ) in worst-case discovery latency over existing approaches in almost all cases, irrespective of duty cycle symmetry. A common assumption in studies of interruptions is that one is focused in an activity and then distracted by other stimuli. We take the reverse perspective and examine whether one might first be in an attentional state that makes one susceptible to communications typically associated with distraction. We explore the confluence of multitasking and workplace communications from three temporal perspectives -- prior to an interaction, when tasks and communications are interleaved, and at the end of the day. Using logging techniques and experience sampling, we observed 32 employees in situ for five days. We found that certain attentional states lead people to be more susceptible to particular types of interaction. Rote work is followed by more Facebook or face-to-face interaction. Focused and aroused states are followed by more email. The more time in email and face-fo-face interaction, and the more total screen switches, the less productive people feel at the day's end. We present the notion of emotional homeostasis along with new directions for multitasking research. 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. Smartphone users might be interrupted while interacting with an application, either by intended or unintended circumstances. In this paper, we report on a large-scale observational study that investigated mobile application interruptions in two scenarios: (1) intended back and forth switching between applications and (2) unintended interruptions caused by incoming phone calls. Our findings reveal that these interruptions rarely happen (at most 10 of the daily application usage), but when they do, they may introduce a significant overhead (can delay completion of a task by up to 4 times). We conclude with a discussion of the results, their limitations, and a series of implications for the design of mobile phones. In this work, we discover the daily location-driven routines that are contained in a massive real-life human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines that characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16-month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group's activities, identified with a methodology based on the Latent Dirichlet Allocation topic model, include “going to work late”, “going home early”, “working nonstop” and “having no reception (phone off)” at different times over varying time-intervals. We also detect routines which are characteristic of users, with a methodology based on the Author-Topic model. With the routines discovered, and the two methods of characterizing days and users, we can then perform various tasks. We use the routines discovered to determine behavioral patterns of users and groups of users. For example, we can find individuals that display specific daily routines, such as “going to work early” or “turning off the mobile (or having no reception) in the evenings”. We are also able to characterize daily patterns by determining the topic structure of days in addition to determining whether certain routines occur dominantly on weekends or weekdays. Furthermore, the routines discovered can be used to rank users or find subgroups of users who display certain routines. We can also characterize users based on their entropy. We compare our method to one based on clustering using K-means. Finally, we analyze an individual's routines over time to determine regions with high variations, which may correspond to specific events. We describe a study on the influence of instant messaging (IM) on ongoing computing tasks. The study both replicates and extends earlier work on the cost of sending notifications at different times and the sensitivity of different tasks to interruption. We investigate alternative hypotheses about the nature of disruption for a list evaluation task, an activity identified as being particularly costly to interrupt. Our findings once again show the generally disruptive effects of IM, especially during fast, stimulus-driven search tasks. In addition, we show that interruptions coming early during a search task are more likely to result in the user forgetting the primary task goal than interruptions that arrive later on. These findings have implications for the design of user interfaces and notification policies that minimize the disruptiveness of notifications.