Conference Paper

Using context-aware computing to reduce the perceived burden of interruptions from mobile devices

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Abstract

The potential for sensor-enabled mobile devices to proactively present information when and where users need it ranks among the greatest promises of ubiquitous computing. Unfortunately, mobile phones, PDAs, and other computing devices that compete for the user's attention can contribute to interruption irritability and feelings of information overload. Designers of mobile computing interfaces, therefore, require strategies for minimizing the perceived interruption burden of proactively delivered messages. In this work, a context-aware mobile computing device was developed that automatically detects postural and ambulatory activity transitions in real time using wireless accelerometers. This device was used to experimentally measure the receptivity to interruptions delivered at activity transitions relative to those delivered at random times. Messages delivered at activity transitions were found to be better received, thereby suggesting a viable strategy for context-aware message delivery in sensor-enabled mobile computing devices.

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... This information is then used to infer the most appropriate time to send a notification. Several studies have shown that notifications sent based on time, users' activity, location, mobile application usage, cognitive context or ringer mode are more likely to be accepted and acted upon by users [9,15,22,30,31,33,39,40,43,44,48]. ...
... The most prominent factors that impact or are associated with interruptibility are time [30,33,39,43,50,53], physical activity [22,24,30,33,34,36,53], location [8,9,13,30,39,43,46,48], application usage [33,34,36,40,44], and ringer mode [8,31,33,40,50]. ...
... One of the significant features for detecting available moments is physical activity. It has been found that users are more interruptible during activity transitions [22,24]. In recent works [30,33], activity type (e.g., walking, standing, running) obtained from mobile devices has been used for an interruptibility management system. ...
Article
Understanding in which circumstances office workers take rest breaks is important for delivering effective mobile notifications and make inferences about their daily lifestyle, e.g., whether they are active and/or have a sedentary life. Previous studies designed for office workers show the effectiveness of rest breaks for preventing work-related conditions. In this paper, we propose a hybrid personalised model involving a kernel density estimation model and a generalised linear mixed model to model office workers’ available moments for rest breaks during working hours. We adopt the experience-based sampling method through which we collected office workers’ responses regarding their availability through a mobile application with contextual information extracted by means of the mobile phone sensors. The experiment lasted 10 workdays and involved 19 office workers with a total of 528 responses. Our results show that time, location, ringer mode, and activity are effective features for predicting office workers’ availability. Our method can address sparse sample issues for building individual predictive behavioural models based on limited and unbalanced data. In particular, the proposed method can be considered as a potential solution to the “cold-start problem”, i.e., the negative impact of the lack of individual data when a new application is installed.
... Pejovic et al. [46] considered four physical activities related to interruptibility (being still, being on foot, being on a bicycle, and being inside a vehicle) and observed that the degree of interruptibility differs for each activity. Ho et al. [21] found that a user is likely to be interruptible when transitioning between two physical activities (e.g., from sitting to walking). Based on these observations, we aim to find whether physical movements signal opportune moments for smart speakers to engage with users. ...
... ESM Trigger Strategies: In our study, ESM prompts were delivered randomly or when movements were detected. Previous studies [21] reported that users were likely to be interruptible to mobile devices delivering messages during activity transition moments (e.g. from sitting to walking); these messages were more positively received during activity transitions than when delivered at random times. We wanted to investigate if the transition period between physical activities is an opportune time to interrupt users. ...
... Users were generally more interruptible after entering a room and were transitioning between physical activities, but were less interruptible during departure; participants were interruptible in 96% of cases upon returning to their rooms, but only 35% of departures were reported as interruptible. High interruptibility during activity transition is consistent with previous research [21,46]. During activity transitions, as the users were not occupied with any activities, they could easily shift their attention to the smart speakers [67]. ...
Article
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Increasing number of researchers and designers are envisioning a wide range of novel proactive conversational services for smart speakers such as context-aware reminders and restocking household items. When initiating conversational interactions proactively, smart speakers need to consider users' contexts to minimize disruption. In this work, we aim to broaden our understanding of opportune moments for proactive conversational interactions in domestic contexts. Toward this goal, we built a voice-based experience sampling device and conducted a one-week field study with 40 participants living in university dormitories. From 3,572 in-situ user experience reports, we proposed 19 activity categories to investigate contextual factors related to interruptibility. Our data analysis results show that the key determinants for opportune moments are closely related to both personal contextual factors such as busyness, mood, and resource conflicts for dual-tasking, and the other contextual factors associated with the everyday routines at home, including user mobility and social presence. Based on these findings, we discuss the need for designing context-aware proactive conversation management features that dynamically control conversational interactions based on users' contexts and routines.
... The app was implemented for the Android mobile operating system, embraces the Android Material design patterns [24] to ensure "contemporary" look and feel. It relies on mobile sensing to detect activity breakpoints (further elaborated in Section 3.1) in order to deliver messages at the most appropriate times [25]. Sample screens of our applications are displayed in Figure 1. ...
... Finally, previous research has examined the importance of the timing of prompts [27] with the goal of recognizing the "right" moments to send the message. While numerous factors may affect a user's readiness to react to, and indeed even notice, a notification, a breakpoint, such as change in activity, e.g., from sitting to walking or from walking to sitting, is often found to be a favorable moment for interrupting a user [25]. Thus, in our application we harness a built-in Google Activity Recognition classifier to detect activity transitions in a battery-friendly manner and, in case sufficient time has passed since the last notification (1.5 h), a new message is delivered. ...
Article
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The growing ubiquity of smartphones and the ease of creating and distributing applications render the mobile platform an attractive means for facilitating positive behavior change at scale. Within the smartphone as a behavior change support system, mobile notifications play a critical role as they enable timely and relevant information distribution. In this paper we describe our preliminary investigation of the persuasiveness of mobile notifications delivered within a real-world behavior change intervention mobile app, which enabled users to set goals and define tasks related to those goals. The application aimed to motivate the users with notifications belonging to one of two groups—tailored and non-tailored, seeing them as sparks in the Fogg Behavior Model and personalizing them according to the users’ Big Five personality traits. Results indicate that customized messages may work for some individuals while working poorly for others. When analyzing users as a single group, no significant differences were observed, but when proceeding with the analysis on the individual level we found seven users whose personality traits notifications interact with in interesting ways. Our results offer two general insights: (1) Using personality-tailored messaging in a dynamic mobile domain as opposed to a static domain leads to different outcomes, and it seems that there is no one-to-one mapping between domains; (2) A major reason for most of our hypotheses being false may be that messages that are deemed as persuasive on their own are not what persuades people to perform an action. Unlike the clear-cut findings observed in other domains, we discover a rather nuanced relationship between the personalization and persuasiveness that calls for further exploration at the individual participant level.
... Various studies have focused on user behavior and user state to control the timing of notifications. Some studies attempted to control notifications by acquiring user actions from web camera information, mouse movement, keyboard typing information, and page or application switching timing [12] [2] [7] [10]. Furthermore, a study developed a middleware, "Attelia," that detected the breakpoints and delays in notification timing [16]. ...
... There are several methods for obtaining user behavior based on the information obtained from a web camera [12], mouse movement [2], or keyboard timing [7]. User behavior can be obtained from the page/application timing switch [10]. Furthermore, a middleware called "Attelia" has been used to delay the notification timing by detecting the breakpoints [16]. ...
Chapter
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The various notifications received on computers while being used interrupt users’ reading experience, negatively affect their emotions, and increase the cognitive load. Many studies have focused on user behavior and user state to control the notification timing. Blinking has been applied as a type of physical activity to detect users’ interests and emotions, detect driver fatigue, and design interactive robots. In this study, we focused on breakpoints while reading text information depending on the blink frequency during concentrated reading as a method to control notification timing. We constructed a system that controls notification timing based on the detected breakpoint. In the experiment, we simulated a real reading environment using the prototype of the system. We evaluated the detection times of the breakpoints and the effectiveness of the system. Although we have not proven the hypothesis that the expected breakpoint is detected based on the blink frequency, we found that the users’ browsing experience was improved when they used the control system.
... We observed that people from different age groups tend to perceive and react phone checking behavior differently. While middle-aged participants (41-55) consider this behavior as a harmful habit and think that it should be modified, young adult participants (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) thought that it has no negative impact on their lives. For young adults, this was not a big and general problem. ...
... While trying to manage smartphone notifications which distract the users during social interaction (preservers), designers should generate adaptive solutions that can change according to the context [19]. For example, while a user is in a business meeting, notifications related with other kind of things such as nonserious and nonurgent content should be detected and filtered. ...
Conference Paper
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Excessive smartphone use has negative effects on our social relations as well as on our mental and psychological health. Most of the previous work to avoid these negative effects is based on a top-down approach such as restricting or limiting users' use of smartphones. Diverging from previous work, we followed a bottom-up approach to understand the practice of smartphone use in public settings from the users' perspective. We conducted observations in four coffeehouses, six focus group sessions with 46 participants and three design workshops with 15 designers. We identified five themes that help better understand smartphone use behavior in public settings and four alternative design approaches to mediate this behavior, namely enlighteners, preventers, supporters, and compliers. We discuss the implications of these themes and approaches for designing future interactive technologies aimed at mediating excessive smartphone use behavior.
... Such smart garments providing the computing functions through embedded display, input device and other PC components, military uniforms providing the functions for communicating and surviving, and medical clothes checking patients' vital signals have been developed by various organizations such as academic institute, military, government and private industries [2,8,16]. By convergence-smart-fashion platform that develops the co-existence of the functionalities of fashion and engineering technologies, fashion products, especially garments, can provide potential benefits on mobility (portability), connectivity and physical sensing that could be fully user-oriented [11,17,18,19]. In many researches on the wearable technology, sensing from body, system interface and augmenting fashion's fundamental roles are considered as the research and development (hereunder, R&D) topics which could make use of benefits of the clothing forms [11,17,18]. ...
... By convergence-smart-fashion platform that develops the co-existence of the functionalities of fashion and engineering technologies, fashion products, especially garments, can provide potential benefits on mobility (portability), connectivity and physical sensing that could be fully user-oriented [11,17,18,19]. In many researches on the wearable technology, sensing from body, system interface and augmenting fashion's fundamental roles are considered as the research and development (hereunder, R&D) topics which could make use of benefits of the clothing forms [11,17,18]. ...
Article
Full-text available
The attempts of combining fashion and technology together to provide digital functionalities at the closest distance users has been continued based on context-aware computing and wearable sensing. Through such convergence, the productive benefits of user-oriented computing and the expansion of traditional fashion functionality can be achieved. In this study, we aim to investigate the optimized way for the development of convergence-smart-fashion prototypes that provide user-oriented multi-functionality to increase the potential features of fashion and widen the application scope of related technologies. Through research and development (R&D), we developed four convergence prototypes which could provide four different functions: 1) Rear-detection, which detects vehicles or people approaching from rear-side and warns the user through vibrations. 2) Bluetooth hands-free provides remote-control functions, such as phone-call and sound-streaming. 3) Vital-signal monitoring, which measures and displays user's heart-beat rates and body heat through a built-in-screen and smartphone application for a user's health-care. The development was proceeded based on the following steps: the determination of the basic usability as a garment and its related practical digital functions, the minimization of the size of the system modules which could be easily assembled and disassembled to ease washability, and the system maintenance, which could help to diversify the usage of convergence fashion.
... However, people are using these devices and they are being used in a way that encourages multitasking behaviors (Oulasvirta, Tamminen, Roto, & Kuorelahti, 2005;Böhmer, Hecht, Schöning, Krüger, & Bauer, 2011;Oulasvirta, Rattenbury, Ma, & Raita, 2012). Multitasking on mobile devices has received some research attention in recent years (Ho & Intille, 2005;Okoshi, Ramos, Nozaki, Nakazawa, Dey, & Tokuda, 2015b;Okoshi et al., 2015a). Users download numerous apps on their smartphones and some are constantly interrupting users (Ho & Intille, 2005). ...
... Multitasking on mobile devices has received some research attention in recent years (Ho & Intille, 2005;Okoshi, Ramos, Nozaki, Nakazawa, Dey, & Tokuda, 2015b;Okoshi et al., 2015a). Users download numerous apps on their smartphones and some are constantly interrupting users (Ho & Intille, 2005). Performance effects of multitasking on mobile devices may not be the same as computer-based multitasking. ...
Conference Paper
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Research has shown that people use smartphones in a way that encourages constant multitasking. While previous studies have determined that multitasking with technology has a negative impact on performance of a primary task, we examine how constant task switching specifically with mobile devices affects users. Our experiment examines discretionary task interleaving with smartphones and the effects this has on performance. The results demonstrate that while any amount of multitasking negatively affects performance on a primary task, these effects are lessened when switching at a breakpoint between subtasks rather than during a subtask.
... Kern et al. [39] showed that users' personal and social interruptibility could be determined with an accuracy of up to 91%. Ho et al. [27] used wireless accelerometers to detect postural and ambulatory activity transitions in real time with an average accuracy of 91.2%; their results showed that participants were more receptive to the delivered messages. Haapalainen et al. [26] used psycho-physiological sensors and found electrocardiogram median absolute deviation and median heat fux measurements were the most accurate at distinguishing between low and high levels of cognitive load, with an overall accuracy of 80% when used together. ...
... For the sensor data, we focused on the features that captured users' movement in VR. Previous research found that interruptibility could be predicted through velocity [27,39,41,76] and the rotation of the device users wear [70,88]; as such, we used the velocity and angular velocity of the HMD and the controllers, as well as the rotation of the HMD, as features. We also used the angle between the HMD and gaze, as well as gaze-shift speed over time, as features since previous research has found that diferent tasks afect eye-head dynamics [13] and gaze-shift dynamics [14]. ...
... Previous studies have shown that users' receptivity to notifications is influenced by many factors: (1) informational qualities of the notifications, e.g. interest, entertainment, relevance and actionability [242]; (2) mobile usage, such as the time of the interruption and the type of app pushing the notification [243,242]; (3) demographics, such as personality traits [244]; and (4) personal dynamics, such as location [245], transitions between activities [246] and social roles [247]. ...
... Okoshi et al. [289,290,291] examined breakpoints in physical activities and app usage and found that notifications delivered at breakpoints, denoted as transitions between apps and physical activities, could lower individuals' mental burden. Ho and Intille [246] also suggested that notifications delivered during activity transitions produced more favourable outcomes than those delivered randomly. As described earlier, we used the Android Google API to record the current physical activities of the participants. ...
... Different factors that may affect the interruption, for example, cognitive, social and environmental factors, have been studied in previous works. 2,[5][6][7] Specifically, Grandhy and Jones 2 executed a field study where it was shown that who is calling, as well as the social and cognitive contexts of the call had an influence on interruption management practices in everyday cell phone calls. Ho and Intille 5 discuss 11 factors that influence a person's interruption at a given moment, among others: the activity and social engagement of the user, and the utility of the message. ...
Article
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The inclusion of the Internet of Things in healthcare is producing numerous automatic notifications for health professionals. These notifications must be delivered in the right moment and in the right way to be appropriately attended, and at the same time, ensuring no important task is interrupted. In this work, we have applied a human-centred design method to deal with this issue. By collaborating with health professionals in Belgium, we have designed and validated DELICATE, a conceptual framework that categorizes the different attention needs for each notification, and links them with the delivery mechanisms that are more appropriate for each particular context. As an aid for designers, we also define methodological guidelines to clearly determine how DELICATE can be used to develop a notification system. Finally, as a proof-of-concept validation of the framework, we have implemented it in an Android application and tested it using real scenarios. This validation has shown that DELICATE can be used to design a notification system that delivers kind healthcare notifications.
... In addition to the user's context, researchers have investigated the user's physical activity and have found signiicant correlation between activity and interruptibility, e.g. [14,21,32,38]. Okoshi et al. found that breakpoints between two diferent activities, e.g. ...
Article
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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 notifications, it has not been thoroughly explored for actual mHealth interventions. In this work, we explore the factors affecting 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 define several metrics to gauge receptivity towards the interventions, and found that (1) several participant-specific characteristics (age, personality, and device type) show significant 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 significant associations with the participant receptivity, in-the-moment. Further, we explore the relationship between the effectiveness 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 classifier.
... Iqbal and Bailey [158] demonstrate that optimising interruption mid-task reduces resumption lag. Finally, Ho and Intille [146] demonstrate that messages delivered when participants transition to a new activity are better received. In our study, we aim to provide new insights into the accuracy of collected ESM responses. ...
Thesis
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The widespread availability of technologically-advanced mobile devices has brought researchers the opportunity to observe human life in day-to-day circumstances. Rather than studying human behaviour through extensive surveys or in artificial laboratory situations, this research instrument allows us to systematically capture human life in naturalistic settings. Mobile devices can capture two distinct data streams. First, the data from sensors embedded within these devices can be appropriated to construct the context of study participants. Second, participants can be asked to actively and repeatedly provide data on phenomena which cannot be reliably collected using the aforementioned sensor streams. This method is known as Experience Sampling. Researchers employing this method ask participants to provide observations multiple times per day, across a range of contexts, and to reflect on current rather than past experiences. This approach brings a number of advantages over existing methods, such as the ability to observe shifts in participant experiences over time and context, and reducing reliance on the participant’s ability to accurately recall past events. As the onus of data collection lies with participants rather researchers, there is a firm reliance on the reliability of participant contributions. While previous work has focused on increasing the number of participant contributions, the quality of these contributions has remained relatively unexplored. This thesis focuses on improving the quality and quantity of participant data collected through mobile Experience Sampling. Assessing and subsequently improving the quality of participant responses is a crucial step towards increasing the reliability of this increasingly popular data collection method. Previous recommendations for researchers are based primarily on anecdotal evidence or personal experience in running Experience Sampling studies. While such insights are valuable, it is challenging to replicate these recommendations and quantify their effect. Furthermore, we evaluate the application of this method in light of recent developments in mobile devices. The opportunities and challenges introduced by smartphone-based Experience Sampling studies remain underexplored in the current literature. Such devices can be utilised to infer participants’ context and optimise questionnaire scheduling and presentation to increase data quality and quantity. By deploying our studies on these devices, we explore the opportunities of mobile sensing and interaction in the context of mobile Experience Sampling studies. Our findings illustrate the feasibility of assessing and quantifying participant accuracy through the use of peer assessment, ground truth questions, and the assessment of cognitive skills. We empirically evaluate these approaches across a variety of study goals. Furthermore, our results provide recommendations on study design, motivation and data collection practices, and appropriate analysis techniques of participant data concerning response accuracy. Researchers can use our findings to increase the reliability of their data, to collect participant responses more evenly across different contexts in order to reduce the potential for bias, and to increase the total number of collected responses. The goal of this thesis is to improve the collection of human-labelled data in ESM studies, thereby strengthening the role of smartphones as valuable scientific instruments. Our work reveals a clear opportunity in the combination of human and sensor data sensing techniques for researchers interested in studying human behaviour in situ.
... [26,27,31], context-aware systems that would defer notification until opportune moments (e.g. [19,21,43,46], content-aware filtering of notifications based on importance (e.g. [12,59], and ambient notification systems (e.g. ...
... From a technological view, it includes remote monitoring, remote consultation, and assistive technologies. This provides exciting possibilities in the health care sector neither for diagnostic equipment or communication devices [11]. Contemporary telemedicine systems have suffi ciently advanced so as to relay the medical data over large distances within a reasonable delay. ...
Conference Paper
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The massive penetration of mobile devices into the everyday life's actions was made possible by the tremendous growth in the emerging computation paradigm called pervasive computing. This approach envisions a conglomeration of small smart devices as sensors, signal conditioners, wearable computers, mobile phones, tablets, notebook computers, hand-held devices etc. seamlessly integrated and scattered in the environment, according to IoT concept. Pervasive health care focuses explicitly on the use of pervasive computing technology for developing tools and procedures that put the patient at the center of the health care process. From a technological stand point, it includes remote monitoring, remote consultation, and assistive technologies. The main focus in this paper is presentation of our approach in design of these complex distributed systems as a part of improved patient's conditions for living
... Similarly, their interaction with computers (which applications they use and how long) might also reveal boredom [7,41]. The context and activity of users are other important factors [31,45,55,70]. When people are moving or during an activity transition, they are more likely to respond to messages. ...
Article
In this study, we investigate the effects of social context, personal and mobile phone usage on the inference of work engagement/challenge levels of knowledge workers and their responsiveness to well-being related notifications. Our results show that mobile application usage is associated to the responsiveness and work engagement/challenge levels of knowledge workers. We also developed multi-level (within- and between-subjects) models for the inference of attentional states and engagement/challenge levels with mobile application usage indicators as inputs, such as the number of applications used prior to notifications, the number of switches between applications, and application category usage. The results of our analysis show that the following features are effective for the inference of attentional states and engagement/challenge levels: the number of switches between mobile applications in the last 45 minutes and the duration of application usage in the last 5 minutes before users’ response to ESM messages.
... Additional important research questions in the development of the HeartSteps activity suggestion component concern the potential effects of habituation (Rankin et al., 2009) and/or treatment burden (Clawson et al., 2015;Eysenbach, 2005;Ho & Intille, 2005;Klasnja et al., 2008;Shaw et al., 2013;Yardley et al., 2016). If individuals habituate to the activity suggestions or find the intervention burdensome, the causal effect would be expected to deteriorate over time. ...
Preprint
Although there is much excitement surrounding the use of mobile and wearable technology for the purposes of delivering interventions as people go through their day-to-day lives, data analysis methods for constructing and optimizing digital interventions lag behind. Here, we elucidate data analysis methods for primary and secondary analyses of micro-randomized trials (MRTs), an experimental design to optimize digital just-in-time adaptive interventions. We provide a definition of causal "excursion" effects suitable for use in digital intervention development. We introduce the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators for digital interventions from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as SAS or R. Throughout we use HeartSteps, an MRT designed to increase physical activity among sedentary individuals, to illustrate potential primary and secondary analyses.
... One of the commonly adopted approaches is based on devising smart ESM strategies, which identify the opportune probing moments, minimizing the user interruption. This approach leverages on different on-device sensors to infer user context and issue the probes at favorable moments, when the high-quality self-reports can be elicited [28,9,20,1,7,10]. Additionally, attempts have been made to improve the participant retention rate and data quality based on incentives, which can be in different forms like monetary, providing community benefit, increasing reputation, rewarding participants [17,11,6]. ...
Conference Paper
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In an Experience Sampling Method (ESM) based emotion self-report collection study, engaging participants for a long period is challenging due to the repetitiveness of answering self-report probes. This often impacts the self-report collection as participants dropout in between or respond with arbitrary responses. Self-reflection (or commonly known as analyzing past activities to operate more efficiently in the future) has been effectively used to engage participants in logging physical, behavioral, or psychological data for Quantified Self (QS) studies. This motivates us to apply self-reflection to improve the emotion self-report collection procedure. We design, develop, and deploy a self-reflection interface and augment it with a smartphone keyboard-based emotion self-report collection application. The interface provides feedback to the users regarding the relation between typing behavior and self-reported emotions. We validate the proposed approach using a between-subject study, where one group (control group) is not exposed to the self-reflection interface and the other group (study group) is exposed to it. Our initial results demonstrate that using self-reflection it is possible to engage the participants in the long-term and collect more self-reports.
... For instance, Bailey et al. [5] suggested that an attention-aware system that defers presenting peripheral information until coarse boundaries are reached during task execution could mitigate the negative impact of such information's arrival. Some studies have used system-usage features to predict opportune moments for notification [17][18][19], while others have investigated mobile activity [15,24] and mental workload [28] for this purpose. Users have also been found to perceive varying levels of disruption depending on what tasks they are performing [12,13], and that mobile interruptibility is influenced by their levels of task engagement [40,45]. ...
... Recognizing surfaces based on their vibration signatures is useful as it can enable tagging of different locations without requiring any additional hardware such as Near Field Communication (NFC) tags. Such tagging of locations can provide us with indirect information about the user activities and intentions without any dedicated infrastructure, based on which we can enable useful services such as context aware notifications/alarms [5]. For example, a user can set their phone to automatically go into silent mode when it is placed on their bed side table. ...
Preprint
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Recognizing surfaces based on their vibration signatures is useful as it can enable tagging of different locations without requiring any additional hardware such as Near Field Communication (NFC) tags. However, previous vibration based surface recognition schemes either use custom hardware for creating and sensing vibration, which makes them difficult to adopt, or use inertial (IMU) sensors in commercial off-the-shelf (COTS) smartphones to sense movements produced due to vibrations, which makes them coarse-grained because of the low sampling rates of IMU sensors. The mainstream COTS smartphones based schemes are also susceptible to inherent hardware based irregularities in vibration mechanism of the smartphones. Moreover, the existing schemes that use microphones to sense vibration are prone to short-term and constant background noises (e.g. intermittent talking, exhaust fan, etc.) because microphones not only capture the sounds created by vibration but also other interfering sounds present in the environment. In this paper, we propose VibroTag, a robust and practical vibration based sensing scheme that works with smartphones with different hardware, can extract fine-grained vibration signatures of different surfaces, and is robust to environmental noise and hardware based irregularities. We implemented VibroTag on two different Android phones and evaluated in multiple different environments where we collected data from 4 individuals for 5 to 20 consecutive days. Our results show that VibroTag achieves an average accuracy of 86.55% while recognizing 24 different locations/surfaces, even when some of those surfaces were made of similar material. VibroTag's accuracy is 37% higher than the average accuracy of 49.25% achieved by one of the state-of-the-art IMUs based schemes, which we implemented for comparison with VibroTag.
... Many researchers already investigated interruptibility, especially in terms of finding opportune moments to deliver smartphone notifications [45,53]. They found that interruptibility depends on different features such as the user activity [26], being in company [45], location [14], and engagement with the smartphone [16], but also on the content of notification [34] such as its importance [18,47]. Such features are also known as situationally-induced impairments [48]. ...
... Many studies have found a significant correlation between physical activity and interruptibility [33,38,14,25]. The type of physical activity was also found to be significant; for example, people driving in a vehicle replied more slowly than people walking outside [38]. ...
Preprint
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JITAI is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user's receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach -- Walkie -- that provided physical-activity interventions and motivated participants to achieve their step goals. The Walkie app included two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive
... J. Ho et al. proposed a context-awareness algorithm that determined when and what information to present would not make flawless decisions on mobile devices with heavy communication traffic [29]. We can find many similar applications of context-awareness based on smartphone sensors, e.g., [30]- [32]. ...
Article
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Voice Assistants (VAs) are increasingly popular for human-computer interaction (HCI) smartphones. To help users automatically conduct various tasks, these tools usually come with high privileges and are able to access sensitive system resources. A comprised VA is a stepping stone for attackers to hack into users’ phones. Prior work has experimentally demonstrated that VAs can be a promising attack point for HCI tools. However, the state-of-the-art approaches require ad-hoc mechanisms to activate VAs that are non-trivial to trigger in practice and are usually limited to specific mobile platforms. To mitigate the limitations faced by the state-of-the-art, we propose a novel attack approach, namely Vaspy, which crafts the users’ “activation voice” by silently listening to users’ phone calls. Once the activation voice is formed, Vaspy can select a suitable occasion to launch an attack. Vaspy embodies a machine learning model that learns suitable attacking times to prevent the attack from being noticed by the user. We implement a proof-of-concept spyware and test it on a range of popular Android phones. The experimental results demonstrate that this approach can silently craft the activation voice of the users and launch attacks. In the wrong hands, a technique like Vaspy can enable automated attacks to HCI tools. By raising awareness, we urge the community and manufacturers to revisit the risks of VAs and subsequently revise the activation logic to be resilient to the style of attacks proposed in this work.
... are inherently ubiquitous and tend to remain within reach of their owners throughout the day [19], the potential for disruption from mobile push-notifications is much higher than from other notification sources such as desktop computers [15]. For this reason, recent research [1,3,7,8,[11][12][13]18] has been conducted on mobile Notification Management Systems (NMS) which aim to block or delay notifications which are not seen as useful or desired, while still allowing important notifications to be delivered immediately. Currently the majority of these state-of-the-art systems are trained using real user notification data collected in-the-wild and implement some form of supervised learning. ...
... Many studies have found a significant correlation between physical activity and interruptibility [14,26,33,39]. The type of physical activity was also found to be significant; for example, people driving in a vehicle replied more slowly than people walking outside [39]. ...
Article
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Just-In-Time Adaptive Intervention (JITAI) is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user’s receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach – Ally – that provided physical-activity interventions and motivated participants to achieve their step goals. We extended the original Ally app to include two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive model increased over the course of the study.
... Fischer et al. showed that participants react faster to probes when they are delivered immediately after completing a task on mobile (e.g., reading a text message) [7]. Ho et al. demonstrated that placing the probe between two physical activities (like sitting and walking) may attract user attention quickly [14]. In [25], authors demonstrated that last survey response, phone's ringer mode can be leveraged to identify suitable probing moments. ...
... Recognizing the activity a human is doing at any given moment is a technique that has a wide range of areas of application. From health and care applications [1][2][3] to automation and context-awareness [4][5][6], sports [7,8], social media [9] and even security surveillance [10]. Dependent on the use case, the applicability of a certain approach in regards to sensors, attributes or models can vary. ...
Article
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Human Activity Recognition (HAR) is a field with many contrasting application domains, from medical applications to ambient assisted living and sports applications. With ever-changing use cases and devices also comes a need for newer and better HAR approaches. Machine learning has long been one of the predominant techniques to recognize activities from extracted features. With the advent of deep learning techniques that push state of the art results in many different domains like natural language processing or computer vision, researchers have also started to build deep neural nets for HAR. With this increase in complexity, there also comes a necessity to compare the newer approaches to the previous state of the art algorithms. Not everything that is new is also better. Therefore, this paper aims to compare typical machine learning models like a Random Forest (RF) or a Support Vector Machine (SVM) to two commonly used deep neural net architectures, Convolutional Neural Nets (CNNs) and Recurrent Neural Nets (RNNs). Not only in regards to performance but also in regards to the complexity of the models. We measure complexity as the memory consumption, the mean prediction time and the number of trainable parameters of the models. To achieve comparable results, the models are all tested on the same publicly available dataset, the UCI HAR Smartphone dataset. With this combination of prediction performance and model complexity, we look for the models achieving the best possible performance/complexity tradeoff and therefore being the most favourable to be used in an application. According to our findings, the best model for a strictly memory limited use case is the Random Forest with an F1-Score of 88.34%, memory consumption of only 0.1 MB and mean prediction time of 0.22 ms. The overall best model in terms of complexity and performance is the SVM with a linear kernel with an F1-Score of 95.62%, memory consumption of 2 MB and a mean prediction time of 0.47 ms. The two deep neural nets are on par in terms of performance, but their increased complexity makes them less favourable to be used.
... The authors find that notifications delivered at breakpoints denoted as transitions between applications and physical activities can lower the individuals' mental burden. Ho and Intille [23] also suggest that notifications delivered during activity transitions produce more favorable outcomes than those delivered randomly. The number of different activities detected was also used as a feature in the first stage classification. ...
Preprint
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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.
... However, interruptive messages result in productivity loss, increased stress, and time pressure [151], implying that less opportune delivery of intervention may lead to a low level of intervention adherence [152]. A user's behavioral routines can be leveraged to find opportune moments (e.g., activity transition times) [153]. A user's contextual model based on temporal and location contexts can be further used to define the complex rules for delivery timing. ...
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With the advent of Digital Therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis. In this work, the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets, to investigate contextual patterns associated with DTx usage, and to establish the (causal) relationship of DTx engagement and behavioral adherence. This review of the key components of data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx.
... Furthermore, prior works studied infuences of transition lags due to task interruptions on users' emotions, cognition, and contextual determinants and reported that interruptions could induce negative feelings (e.g., stress [46], annoyance [3], anxiety [4]) and increase workload [46] in a variety of environments (e.g., ofce work [75] and driving [35]). In addition, many studies have reported that task interruptions are infuenced by various contextual factors, such as changes in physical activity [25], changes in conversation [33], calendar information [71], messages from diferent social relationships [49], and diference in personality traits [50]. ...
... Plusieurs raisons pourraient expliquer ce constat. Des auteurs et autrices ont par exemple souligné qu'un des freins à l'adoption des chatbots réside dans la difficulté de ces derniers à identifier le contexte de la conversation (Ho & Intille, 2005 ;Coniam, 2014 ;Q. V. Liao et al., 2016). ...
Article
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In what way are chatbots new resources for humans? What are the conditions for their successful implementation? How do chatbots transform human activity? To answer these questions, this paper investigates how four chatbots were implemented and utilized in a professional context based on real user activity. We first show that the current design approach does not take sufficient account of the real activity and its multiple determinants, or of the users' point of view on their activity. Yet these interactive devices participate in a pre-existing socio-technical system made up of a diversity of subjects engaged in activities with multiple purposes. Through the prism of instrumental geneses, we therefore propose to identify how these chatbots can help or hinder human activity, so as to document, for design purposes, the conditions leading to the emergence of chatbot uses. After a synthetic presentation of the empirical results, we discuss the relevance of the instrumental approach when characterizing the contributions and limitations of chatbots. Finally, we argue that a design perspective supporting the postulate of human-machine asymmetry is prolific in supporting a design approach that is more focused on human power to act than on innovation.
... In contrast, because of the physical absence of the interrupter notification interruptions are a kind of flexible with the inclusion of many options for the user. So, the handling of the interruption can be negotiated (Ho and Intille 2005). In Fig. 1, three states in which a user might interact with notification in a normal way are shown. ...
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This paper devises the issue of machine learning rule-based methodology for uncovering the behavior-based rules of respective smart-phone users for purpose of providing context wise individualized notification services. Nowadays, the number of notifications arrived at an inappropriate moment of time or carried unrelated material, which can cause disruption. Previously Rule-based Classifier and Association Rule Mining (ARM) Techniques have been used to solve those problems. However, the classifier approach processes accuracy and reliability problems because of small data instances. ARM creates a vast number of redundant rules, which are pointless for creating context-aware decisions. Redundant rules can make the approach non-efficient and also make the dataset unnecessarily large, making decision-based problems more complicated. For those problems solution in this article, we propose a new Behavioral Adversarial Traversal Tree approach for extracting user behavioral rules with respect to different contexts. A real-world dataset is collected to make this approach more relevant. The Proposed approach effectively identifies and removes the redundant rules with individual behavior-oriented time slots, which are used in the proposed approach to make it more exact and efficient. Our experiments and comparisons on each individual contextual dataset exhibit that the following rule discovery approach is more adequate and more exact for context-aware notification services.
... Balancing intrusiveness and noticeability and finding appropriate moments for notification delivery constitute popular research topics in the field of human-computer interaction (HCI) [18,19,20], in which the exploration on auditory notifications makes an important part and has also been covered in previous ICAD conferences [8,11]. To provide auditory notifications effectively but in a less distracting manner, Kilander and Lönnqvist [8] proposed to render an artificial audio ambience using pre-designed sound signals (e.g., a thunderstorm sequence), and they added reverb effect to create an ambient atmosphere. ...
Conference Paper
Less intrusive information delivery has been a popular research topic for auditory displays. While most research has addressed this issue by creating new notification cues such as rendering ambient soundscapes or modifying background music, we present a novel method to gently deliver artificial notification sounds that have been commonly used in digital devices and for popular applications. We propose to play a notification sound by embedding it into the music that a user is listening to, after changing the musical timbre, amplitude, tempo, and octave of the notification to match these features of the music. To implement this concept, we extend a melody extraction algorithm for notification timbre transfer, and we present a pipeline that algorithmically selects a proper time spot and harmoniously embeds the notification into music. To validate our design concept, we present a user study comparing our method with the standard method of playing notification sounds on digital devices. Through an extensive analysis of 96 tasks performed by 32 participants, we demonstrate that our method can deliver notification sounds in a less intrusive but adequately noticeable manner and is preferred by most participants.
... Choosing the opportune moments to prompt participants while adhering to the overall study protocol can be a way to mitigate these challenges. Numerous works examined how to do this by leveraging the capabilities of smartphones to gather contextual information [18,[35][36][37][38]. ...
Article
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The Experience Sampling Method (ESM) is gaining ground for collecting self-reported data from human participants during daily routines. An important methodological challenge is to sustain sufficient response rates, especially when studies last longer than a few days. An obvious strategy is to deliver the experiential questions on a device that study participants can access easily at different times and contexts (e.g., a smartwatch). However, responses may still be hampered if the prompts are delivered at an inconvenient moment. Advances in context sensing create new opportunities for improving the timing of ESM prompts. Specifically, we explore how physiological sensing on commodity-level smartwatches can be utilized in triggering ESM prompts. We have created Experiencer, a novel ESM smartwatch platform that allows studying different prompting strategies. We ran a controlled experiment (N=71) on Experiencer to study the strengths and weaknesses of two sampling regimes. One group (N=34) received incoming notifications while resting (e.g., sedentary), and another group (N=37) received similar notifications while being active (e.g., running). We hypothesized that response rates would be higher when experiential questions are delivered during lower levels of physical activity. Contrary to our hypothesis, the response rates were found significantly higher in the active group, which demonstrates the relevance of studying dynamic forms of experience sampling that leverage better context-sensitive sampling regimes. Future research will seek to identify more refined strategies for context-sensitive ESM using smartwatches and further develop mechanisms that will enable researchers to easily adapt their prompting strategy to different contextual factors.
... Several researchers have tried to identify context factors, create context frameworks and models for different application areas (e.g. Dey and Abowd 1999, , Shilit and Theimer 1994, Petrelli et al. 2001, Pascoe 1998, Schmidt 2001, Hall 1976, Becker and Nicklas 2004, Jung et al. 2005, Raptis et al. 2005, Ho and Intille 2005, Hinze and Buchanan 2005, Mihalic et al. 2006. A reflection can also be found in Murer et al. 2015. ...
Chapter
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Austria has a mature RTI funding system today. Being a late-comer in terms of institutionalisation, Austria developed councils and agencies as the standard organisational models since the 1960s. Institutional path dependencies, fragmentation and contradicting claims of autonomy and political steering led to persistent tensions. Recent changes were therefore driven by the need for better coordination and rebalancing principal-agent relations.
... However, data is captured about work experiences in order to support learning; rather than data being captured about learning experiences and processes. Secondly, this research contributes to research on adaptive and contextualized reflection guidance (Fischer et al., 1993;McCall et al., 1990;Kocielnik et al., 2018); and more broadly to research on context-aware prompting (e.g., Ho & Intille, 2005;Pejovic & Musolesi, 2014). ...
Article
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Professional and lifelong learning are a necessity for workers. This is true both for re-skilling from disappearing jobs, as well as for staying current within a professional domain. AI-enabled scaffolding and just-in-time and situated learning in the workplace offer a new frontier for future impact of AIED. The hallmark of this community’s work has been i) data-driven design of learning technology and ii) machine-learning enabled personalized interventions. In both cases, data are the foundation of AIED research and data-related ethics are thus central to AIED research. In this paper we formulate a vision how AIED research could address data-related ethics issues in informal and situated professional learning. The foundation of our vision is a secondary analysis of five research cases that offer insights related to data-driven adaptive technologies for informal professional learning. We describe the encountered data-related ethics issues. In our interpretation, we have developed three themes: Firstly, in informal and situated professional learning, relevant data about professional learning – to be used as a basis for learning analytics and reflection or as a basis for adaptive systems - is not only about learners. Instead, due to the situatedness of learning, relevant data is also about others (colleagues, customers, clients) and other objects from the learner’s context. Such data may be private, proprietary, or both. Secondly, manual tracking comes with high learner control over data. Thirdly, learning is not necessarily a shared goal in informal professional learning settings. From an ethics perspective, this is particularly problematic as much data that would be relevant for use within learning technologies hasn’t been collected for the purposes of learning. These three themes translate into challenges for AIED research that need to be addressed in order to successfully investigate and develop AIED technology for informal and situated professional learning. As an outlook of this paper, we connect these challenges to ongoing research directions within AIED – natural language processing, socio-technical design, and scenario-based data collection - that might be leveraged and aimed towards addressing data-related ethics challenges.
... Okoshi et al. [82] determined accurate application-specific break points, during which the user can be interrupted while she is using an application. Ho et al. [49] determined when the user is transitioning from one physical activity to another (e.g., from sitting to walking) using body-worn accelerometers and used those moments to deliver notifications. These prior systems do not consider the importance of the notification, nor do they consider the possibility of interrupting an activity when user has spare attention to handle a new secondary task. ...
Thesis
We live in a world where mobile computing systems are increasingly integrated with our day-to-day activities. People use mobile applications virtually everywhere they go, executing them on mobile devices such as smartphones, tablets, and smart watches. People commonly interact with mobile applications while performing other primary tasks such as walking and driving (e.g., using turn-by-turn directions while driving a car). Unfortunately, as an application becomes more mobile, it can experience resource scarcity (e.g., poor wireless connectivity) that is atypical in a traditional desktop environment. When critical resources become scarce, the usability of the mobile application deteriorates significantly. In this dissertation, I create system support that enables users to interact smoothly with mobile applications when wireless network connectivity is poor and when the user’s attention is limited. First, I show that speculative execution can mitigate user-perceived delays in application responsiveness caused by high-latency wireless network connectivity. I focus on cloud-based gaming, because the smooth usability of such application is highly dependent on low latency. User studies have shown that players are sensitive to as little as 60 ms of additional latency and are aggravated at latencies in excess of 100ms. For cloud-based gaming, which relies on powerful servers to generate high-graphics quality gaming content, a slow network frustrates the user, who must wait a long time to see input actions reflected in the game. I show that by predicting the user’s future gaming inputs and by performing visual misprediction compensation at the client, cloud-based gaming can maintain good usability even with 120 ms of network latency. Next, I show that the usability of mobile applications in an attention-limited environment (i.e., driving a vehicle) can be improved by automatically checking whether interfaces meet best-practice guidelines and by adding attention-aware scheduling of application interactions. When a user is driving, any application that demands too much attention is an unsafe distraction. I first develop a model checker that systematically explores all reachable screens for an application and determines whether the application conforms to best-practice vehicular UI guidelines. I find that even well- known vehicular applications (e.g., Google Maps and TomTom) can often demand too much of the driver’s attention. Next, I consider the case where applications run in the background and initiate interactions with the driver. I show that by quantifying the driver’s available attention and the attention demand of an interaction, real-time scheduling can be used to prevent attention overload in varying driving conditions.
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While mobile instant messaging (MIM) facilitates ubiquitous interpersonal communication, its constant connectivity could build the expectation of an immediate response to messages, and its notifications flood could cause interruptions at inopportune moments. We examine two design concepts for MIM-private status sharing and sender-controlled notifications-that aim to lower the pressure for an immediate reply and reduce unnecessary interruptions by untimely notifications. Private status sharing reactively reveals a customized status with a selected partner(s) only when the partner has sent a message. Sender-controlled notifications give senders the control of choosing whether to send a notification for their own messages. We built MyButler, an Android app prototype that instantiates these two concepts and integrated it with KakaoTalk, a commercial MIM app. During a two-week field study with 11 pairs (5 couples and 6 friend pairs), participants expressed themselves through a total of 210 different statuses, 64.3% of which indicated the current activity or task of the user. Participants reported that private status sharing enabled them to explain their unavailability and relieved the pressure and expectations for timely attendance. We reveal more findings on the types of privately shared statuses and their roles in MIM communication; the in-situ behaviors and patterns of using sender-controlled notifications; and the motivations of MIM users in choosing whether to alert their messages. In terms of message notifications, senders chose to send 25.4% of the messages without any notification. We found that senders' decisions to alert are affected by the receiver's status, their own status to chat, and the possibility of message content exposure to others through notifications. Based on our findings, we draw insights into how the concepts of private status sharing and sender-controlled notifications can be applied in future designs and explorations.
Chapter
The growing use of mobile devices that are available everywhere can blur the boundaries between life domains work and life. The increasing number of notifications on smartphones leads to interruptions that might be unrelated to the current life domain and task and are therefore disruptive. Despite some tools there is a gap between the preferred and the actual separation of life domains. In this paper we show how concepts from the field of boundary management can be applied for notification management on mobile devices. We present a formal model of the semantic structure of life domains, which is based on the concepts of integration and segmentation from boundary theory. We introduce an app for the management of notifications on Android smartphones that leverages on this formal model. In a field study we evaluated the app with real-life notifications. The results show a significant reduction of the gap between actual and preferred boundary management.
Chapter
The need to better understand the role of context has emerged after the revolution of mobile computing, as such devices are used in heterogeneous circumstances. However, it is difficult to say what context of use in mobile human-computer interaction actually means. This study summarises past research in mobile contexts of use and not only provides a deeper understanding of the characteristics associated with it, but also indicates a path for future research. This article presents an extensive and systematic literature review of more than 100 papers published in five high-quality journals and one main conference in the field of HCI during the years 2000-2007. The authors’ results show that context of use is still explored as a relatively static phenomenon in mobile HCI. Its most commonly mentioned characteristics are linked to social, physical, and technical components, while transitions between the contexts were rarely listed. Based on this review, a descriptive model of context of use for mobile HCI (CoU-HMCI) summarising five components, their subcomponents and descriptive properties is presented. The model can help both practitioners and academics to identify broadly relevant contextual factors when designing, experimenting with, and evaluating, mobile contexts of use.
Chapter
Intervention authors can likely increase the effectiveness of mobile health interventions (MHIs) by determining conditions under which individuals are able to receive, process, and use support. In this chapter, we will therefore first introduce and motivate the relevance of receptivity to MHIs. Second, we will describe the anatomy of an “ideal” MHI before key processes involved in the detection and prediction of receptivity to MHIs are discussed. Thereafter, we will review research on receptivity and summarize factors that may carry relevant signals for determining receptive states and thus, offer guidance for the design of receptivity-capable MHIs. We will conclude with challenges intervention authors and engineers face and offer opportunities for future work.
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Voice assistants, such as Amazon's Alexa and Google Home, increasingly find their way into consumer homes. Their functionality, however, is currently limited to being passive answer machines rather than proactively engaging users in conversations. Speakers' proactivity would open up a range of important application scenarios, including health services, such as checking in on patient states and triggering medication reminders. It remains unclear how passive speakers should implement proactivity. To better understand user perceptions, we ran a 3-week field study with 13 participants where we modified the off-the-shelf Google Home to become proactive. During the study, our speaker proactively triggered conversations that were essentially Experience Sampling probes allowing us to identify when to engage users. Applying machine-learning, we are able to predict user responsiveness with a 71.6% accuracy and find predictive features. We also identify self-reported factors, such as boredom and mood, that are significantly correlated with users' perceived availability. Our prototype and findings inform the design of proactive speakers that verbally engage users at opportune moments and contribute to the design of proactive application scenarios and voice-based experience sampling studies.
Chapter
Mobile ecological momentary assessments (mEMAs) require substantial user efforts to complete, resulting in low user compliance. One major source of incompliance is triggering mEMAs at inopportune moments. In this work, we propose a framework for implementing adaptive mEMAs using reinforcement learning (RL) to address the timing and context challenge, aiming to improve long term response compliance. To effectively model user state, we also propose a two-level user model with both momentary and routine state features. A novel k-routine mining algorithm is developed to extract routine state from passive sensing data. Using real mobile sensing data collected from 220 participants for over two weeks, we show that our proposed RL strategies consistently outperform the baseline methods including a random strategy and a supervised strategy in user compliance.
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Mobile learning (M-learning) is currently popular. Although M-learning has a positive effect on the interest, attitude, initiative, and learning of learners, it comes with the negative effect of distraction. Exploring the factors that influence M-learning concentration have become a popular research subject. Environment (quiet and noisy) and posture (sitting, standing, and moving) are two typical factors that directly influence the concentration of mobile learners. This study focuses on the influence of environment and posture on concentration in M-learning. A total of 120 college students were randomly divided into two environment groups, namely, the quiet and noisy environments. The two groups were subjected to M-learning experiments by adopting sitting, standing, and moving postures. This study shows that (1) interaction effects on M-learning concentration existed between environment and posture and a quiet environment and a sitting posture enable learners to concentrate better; and (2) no interaction effects on M-learning achievement existed between environment and posture, while environment and posture alone had a significant effect on learning achievement. This study presents several implications of the M-learning practice, deficiencies of the research, and the direction of future studies in this field.
Chapter
Quality of life (QoL) is a subjective term often determined by various aspects of living, such as personal well-being, health, family, and safety. QoL is challenging to capture objectively but can be anticipated through a person’s emotional state; especially positive emotions indicate an increased QoL and may be a potential indicator for other QoL aspects (such as health, safety). Affective computing is the study of technologies that can quantitatively assess human emotions from external clues. It can leverage different modalities including facial expression, physiological responses, or smartphone usage patterns and correlate them with the person’s life quality assessments. Smartphones are emerging as a main modality, mostly because of their ubiquitous availability and use throughout daily life activities. They include a plethora of onboard sensors (e.g., accelerometer, gyroscope, GPS) and can sense different user activities passively (e.g., mobility, app usage history). This chapter presents a research study (here referred to as the TapSense study) that focuses on assessing the individual’s emotional state from the smartphone usage patterns. In this TapSense study, the keyboard interaction of n = 22 participants was unobtrusively monitored for 3 weeks to determine the users’ emotional state (i.e., happy, sad, stressed, relaxed) using a personalized machine learning model. TapSense can assess emotions with an average AUCROC of 78%(±7% std). We summarize the findings and reflect upon these in the context of the potential developments within affective computing at large, in the long term, indicating a person’s quality of life.
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The goal of this in-the-wild study was to understand how different patient, provider, and environment contexts affected the use of a tablet-based checklist in a dynamic medical setting. Fifteen team leaders used the digital checklist in 187 actual trauma resuscitations. The measures of checklist interactions included the number of unchecked items and the number of notes written on the checklist. Of the 10 contexts we studied, team leaders’ arrival after the patient and patients with penetrating injuries were both associated with more unchecked items. We also found that the care of patients with external injuries contributed to more notes written on the checklist. Finally, our results showed that more experienced leaders took significantly more notes overall and more numerical notes than less experienced leaders. We conclude by discussing design implications and steps that can be achieved with context-aware computing towards adaptive checklists that meet the needs of dynamic use contexts.
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Prospective memory lapses, which involve forgetting to perform intended actions, affect independent living in older adults. Although memory training using smartphone applications could address them, users are sometimes unaware of available times for training or forget about it, presenting a need for proactive prompts. Existing applications mostly provide time-based prompts and prompts based on users' cognitive contexts remain an under-explored area. We developed Prompto, a conversational memory coach that detects physiological signals to suggest training sessions when users are relaxed and potentially more receptive. Our study with 21 older adults showed that users were more receptive to prompts and memory training under low cognitive load than under high cognitive load. Interviews and an in-the-wild deployment of Prompto indicated that majority of users appreciated the concept, found it helpful and were likely to respond to its prompts. We contribute towards developing technologies with cognitive context-aware prompting based on users' physiological readings.
Chapter
Learning on a mobile device in everyday settings makes users particularly susceptible for interruptions. Guidance (memory) cues can be implemented to support users in resuming a learning task after a distraction. These cues can take a wide range of forms and designs and, to work effectively, need to be carefully adapted to the mobile learning use case. In this work, we present a structured in-depth literature review on task resumption support for mobile devices. In particular, we propose a design space based on 30 carefully chosen publications to highlight well-evaluated design ideas as well as currently underrepresented research directions. Furthermore, we evaluate the causes of interruptions in the domain of mobile learning and derive design ideas for task resumption support on mobile devices. To this end, we conducted two focus groups with HCI experts (\(N=4\)) and users of mobile learning applications (\(N=3\)). Based on the literature review, focus groups, and further related work, we discuss ideas and research gaps for task resumption cues in mobile learning. We derive six design guidelines to support researchers and designers of mobile learning applications and emphasize promising research directions and open questions.
Chapter
This chapter offers a conceptual framework that ties together two domains of design decisions for digital therapeutics—those related to intervention design (i.e., which components or treatments to include) and those related to study design (i.e., how to test whether components or treatments work as intended). This framework is intended to help researchers in identifying and testing key digital therapeutics design considerations across the intervention development lifespan—that is, from formative work through longer-term implementation. The framework encourages intervention developers to make design decisions by considering three motivating questions: first, what health outcomes are we trying to impact; second, what are we trying to learn; and third, cross-cutting both prior questions to inform potential evidence, how much is at stake in terms of health impacts if we get this decision wrong? We consider how these questions can help to inform design decisions across three phases of the intervention lifespan—preparation and formative work, optimization of intervention components, and evaluation of effectiveness and implementation—to maximize the likelihood of positive health impact and accumulation of evidence about the intervention's functioning.
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The significance of Attentional User Interfaces that help create computing and communication systems to sense and reason about human attention by fusing together information from multiple streams, is discussed. Attentional cues are central in decisions about when to initiate or to make an effective contribution to a conversation or project. Computers with an ability to track and to understand attentional patterns among people engaged in conversations can provide new kinds of services and facilities. It is noted that continuing refinement of methods for recognizing, reasoning and communicating about attention will change in a qualitative manner the way working with computing systems is perceived.
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We introduce utility-directed procedures for mediating the flow of potentially distracting alerts and communications to computer users. We present models and inference procedures that balance the context-sensitive costs of deferring alerts with the cost of interruption. We describe the challenge of reasoning about such costs under uncertainty via an analysis of user activity and the content of notifications. After introducing principles of attention-sensitive alerting, we focus on the problem of guiding alerts about email messages. We dwell on the problem of inferring the expected criticality of email and discuss work on the Priorities system, centering on prioritizing email by criticality and modulating the communication of notifications to users about the presence and nature of incoming email.
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SenSay is a context-aware mobile phone that adapts to dynamically changing environmental and physiological states. In addition to manipulating ringer volume, vibration, and phone alerts, SenSay can provide remote callers with the ability to communicate the urgency of their calls, make call suggestions to users when they are idle, and provide the caller with feedback on the current status of the SenSay user. A number of sensors including accelerometers, light, and microphones are mounted at various points on the body to provide data about the user's context. A decision module uses a set of rules to analyze the sensor data and manage a state machine composed of uninterruptible, idle, active and normal states. Results from our threshold analyses show a clear delineation can be made among several user states by examining sensor data trends. SenSay augments its contextual knowledge by tapping into applications such as electronic calendars, address books, and task lists.
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In this paper we propose to use context information obtained from body-worn sensors to mediate notifications for a wearable computer In particular we introduce a model which uses two axes, namely personal and social interruptability of the user in order to decide both whether or not to notify the user and to decide which notification modality to use. Rather than to model and recognize the complete context of the user we argue that personal and social interruptability can be derived directly from various sensors by the combination of tendencies. First experimental results show the feasibility of the approach using acceleration, audio, and location sensors.
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As users continue offloading more control and responsibility to the computer, coordinating the asynchronous interactions between the user and computer is becoming increasingly important. Without proper coordination, an application attempting to gain the user's attention risks interrupting the user in the midst of performing another task. To justify why an application should avoid interrupting the user whenever possible, we designed an experiment measuring the disruptive effect of an interruption on a user's task performance. The experiment utilized six Web-based task categories and two categories of interruption tasks. The results of the experiment demonstrate that: (i) a user performs slower on an interrupted task than a non-interrupted task, (ii) the disruptive effect of an interruption differs as a function of the task category, and (iii) different interruption tasks cause similar disruptive effects on task performance. These results empirically validate the need to better coordinate user interactions among applications that are competing for the user's attention
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Just because something is new and clever does not necessarily mean that it is good. The utility of a technology must be evaluated strictly in terms of whether it actually helps people succeed. A new technology that is useful for isolated tasks may carry an invisible and costly sideeffect like interrupting people. However, the side-effect of human interruption by machine only manifests itself in real world contexts where people normally perform several complex heterogeneous tasks in parallel. The telephone and email are examples. They are useful by themselves, but in a real work environment they also create annoying interruptions. A newer and more problematic example is the technology of semi-autonomous computer systems such as intelligent agents. These systems can be assigned to do useful things in the background while their human users work on other tasks. However, delegating a task requires supervising a task; and whenever an intelligent agent must initiate an interaction with its us...
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The majority of reminder systems are inflexible; reminders are issued at static, prespecified times. To be effective, cognitive orthotics should reason about what reminders should be issued and when. This paper describes the personalized cognitive orthotic (PCO), a system that uses plan-based reasoning to attain flexibility. PCO relies on local search techniques to generate high-quality reminder plans based on knowledge of the user's plans and her typical behavior. PCO is being developed in concert with other technologies aimed at improved plan management, including systems that update a user's plans and track action execution. We describe the PCO as it is implemented in the Nursebot application: where it provides timely and relevant reminders to elderly people who have cognitive decline that necessitates assistance in managing their daily activities.
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: 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. Keywords: Notifications, user study, interruptions, information overload, divided attention 1 Introduction With the adv...
Predicting M. Czerwinski. Scope: providing awareness of multiple notifications at a glance
  • S E Hudson
  • J Fogarty
  • C G Atkeson
  • D Avrahami
  • J Forlizzi
  • S Kiesler
  • J C Lee
  • J Yang
S.E. Hudson, J. Fogarty, C.G. Atkeson, D. Avrahami, J. Forlizzi, S. Kiesler, J.C. Lee, and J. Yang. Predicting M. Czerwinski. Scope: providing awareness of multiple notifications at a glance. In Proceedings of AVI 2002, ACM Conference on Advanced Visual Interfaces. ACM Press, 2002.