Detecting Receptivity for mHealth Interventions in the Natural Environment

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 adaptiveComment: This paper is currently under submission. Please contact the authors for more detai

Detecting Receptivity for mHealth Interventions in the Natural Environment

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\u27s 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

Detecting Receptivity for mHealth Interventions in the Natural Environment

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

AwareNotifications: Multi-Device Semantic Notification Handling with User-Defined Preferences

With the increase of connected devices and online services, the number of notifications received by each person is growing. Although notifications are useful to inform users about important information such as new messages and events, the continuous interruptions, the notification duplication, and the rigid delivery can be sources of discomfort. To overcome these issues, we present AwareNotifications, an intelligent system based on user-defined preferences to manage multi-device notifications. AwareNotifications is powered by Semantic Web technologies. By directly exploiting user preferences in the semantic reasoning process, the system is able to identify suitable device(s), modality, and moment(s) to deliver the incoming user notifications. We evaluated AwareNotifications in a user study with 15 participants, in which we compared our system with the "traditional" notification delivery system. The study confirms the perceived effectiveness of AwareNotifications, and provides insights to further improve the system

ForgetMeNot: Active Reminder Entry Support for Adults with Acquired Brain Injury

Smartphone reminding apps can compensate for memory impairment after acquired brain injury (ABI). In the absence of a caregiver, users must enter reminders themselves if the apps are going to help them. Poor memory and apathy associated with ABI can result in failure to initiate such configuration behaviour and the benefits of reminder apps are lost. ForgetMeNot takes a novel approach to address this problem by periodically encouraging the user to enter reminders with unsolicited prompts (UPs). An in situ case study investigated the experience of using a reminding app for people with ABI and tested UPs as a potential solution to initiating reminder entry. Three people with severe ABI living in a post-acute rehabilitation hospital used the app in their everyday lives for four weeks to collect real usage data. Field observations illustrated how difficulties with motivation, insight into memory difficulties and anxiety impact reminder app use in a rehabilitation setting. Results showed that when 6 UPs were presented throughout the day, reminder-setting increased, showing UPs are an important addition to reminder applications for people with ABI. This study demonstrates that barriers to technology use can be resolved in practice when software is developed with an understanding of the issues experienced by the user group

Understanding receptivity to interruptions in mobile human-computer interaction

Interruptions have a profound impact on our attentional orientation in everyday life. Recent advances in mobile information technology increase the number of potentially disruptive notifications on mobile devices by an increasing availability of services. Understanding the contextual intricacies that make us receptive to these interruptions is paramount to devising technology that supports interruption management. This thesis makes a number of contributions to the methodology of studying mobile experiences in situ, understanding receptivity to interruptions, and designing context-sensitive systems. This thesis presents a series of real-world studies that investigate opportune moments for interruptions in mobile settings. In order to facilitate the study of the multi-faceted ways opportune moments surface from participants' involvement in the world this thesis develops: - a model of the contextual factors that interact to guide receptivity to interruptions, and - an adaptation of the Experience-Sampling Method (ESM) to capture behavioural response to interruptions in situ. In two naturalistic experiments, participants' experiences of being interrupted on a mobile phone are sampled as they go about their everyday lives. In a field study, participants' experiences are observed and recorded as they use a notification-driven mobile application to create photo-stories in a theme park. Experiment 1 explores the effects of content and time of delivery of the interruption. The results show that receptivity to text messages is significantly affected by message content, while scheduling one's own interruption times in advance does not improve receptivity over randomly timed interruptions. Experiment 2 investigates the hypothesis that opportune moments to deliver notifications are located at the endings of episodes of mobile interaction such as texting and calling. This notification strategy is supported by significant effects in behavioural measures of receptivity, while self-reports and interviews reveal complexities in the subjective experience of the interruption. By employing a mixed methods approach of interviews, observations and an analysis of system logs in the field study, it is shown that participants appreciated location-based notifications as prompts to foreground the application during relative 'downtimes' from other activities. However, an unexpected quantity of redundant notifications meant that visitors soon habituated to and eventually ignored them, which suggests careful, sparing use of notifications in interactive experiences. Overall, the studies showed that contextual mediation of the timing of interruptions (e.g. by phone activity in Experiment 2 and opportune places in the field study) is more likely to lead to interruptions at opportune moments than when participants schedule their own interruptions. However, momentary receptivity and responsiveness to an interruption is determined by the complex and situated interactions of local and relational contextual factors. These contextual factors are captured in a model of receptivity that underlies the interruption process. The studies highlight implications for the design of systems that seek to manage interruptions by adapting the timing of interruptions to the user's situation. In particular, applications to manage interruptions in personal communication and pervasive experiences are considered

Exploring smartphone keyboard interactions for Experience Sampling Method driven probe generation

Keyboard interaction patterns on a smartphone is the input for many intelligent emotion-aware applications, such as adaptive interface, optimized keyboard layout, automatic emoji recommendation in IM applications. The simplest approach, called the Experience Sampling Method (ESM), is to systematically gather self-reported emotion labels from users, which act as the ground truth labels, and build a supervised prediction model for emotion inference. However, as manual self-reporting is fatigue-inducing and attention-demanding, the self-report requests are to be scheduled at favorable moments to ensure high fidelity response. We, in this paper, perform fine-grain keyboard interaction analysis to determine suitable probing moments. Keyboard interaction patterns, both cadence, and latency between strokes, nicely translate to frequency and time domain analysis of the patterns. In this paper, we perform a 3-week in-the-wild study (N = 22) to log keyboard interaction patterns and self-report details indicating (in)opportune probing moments. Analysis of the dataset reveals that time-domain features (e.g., session length, session duration) and frequency-domain features (e.g., number of peak amplitudes, value of peak amplitude) vary significantly between opportune and inopportune probing moments. Driven by these analyses, we develop a generalized (all-user) Random Forest based model, which can identify the opportune probing moments with an average F-score of 93%. We also carry out the explainability analysis of the model using SHAP (SHapley Additive exPlanations), which reveals that the session length and peak amplitude have strongest influence to determine the probing moments