Multi-Stage Receptivity Model for Mobile Just-In-Time Health Intervention

Abstract

A critical aspect of mobile just-in-time (JIT) health intervention is proper delivery timing, which correlates with successfully promoting target behaviors. Despite extensive prior studies on interruptibility, however, our understanding of the receptivity of mobile JIT health intervention is limited. This work extends prior interruptibility models to capture the JIT intervention process by including multiple stages of conscious and subconscious decisions. We built BeActive, a mobile intervention system for preventing prolonged sedentary behaviors, and we collected users' responses to a given JIT support and relevant contextual factors and cognitive/physical states for three weeks. Using a multi-stage model, we systematically analyzed the responses to deepen our understanding of receptivity using a mixed methodology. Herein, we identify the key factors relevant to each stage outcome and show that the receptivity of JIT intervention is nuanced and context-dependent. We propose several practical design implications for mobile JIT health intervention and context-aware computing.

A Multi-perspective Analysis of Social Context and Personal Factors in Office Settings for the Design of an Effective Mobile Notification System

“…As stated earlier, we made an eort to understand and model knowledge workers' responsiveness to the questions related to their work engagement/challenge, and their attentional states. As seen in a very recent study [13], focus level of users can be eective on perception of the mobile notications and adherence to health interventions sent via mobile phones. In this regard, we carried out a data collection study among oce workers capturing their in-situ engagement/challenge levels.…”

“…They found that the duration of Facebook use and e-mail/calendar use are negatively related to engagement levels, whereas face-to-face interaction are positively related. A very recent work [13] investigated the eects of focus on the adherence to physical activity. They found that when individuals have a high focus on the ongoing task, they are less likely to notice the mobile intervention, or comply with the intervention.…”

“…Our study diers from the previous work from a methodological point of view since it is based on the evaluation of work engagement and challenge, which are important to understand the availability of oce workers for delivering notications [13,41]. We measured in-situ work engagement and challenge levels, i.e., we captured the engagement and challenge levels when employees were currently working with a mobile application.…”

“…More specically, health interventions have been increasingly delivered via mobile phones, thanks to their ubiquity and the possibility of inferring contextual information by means of the embedded sensors. Numerous studies have been focusing on when and how to deliver intervention messages eectively [13,35,54,67]. In an oce setting, there are many factors that can aect the responsiveness of individuals to these messages, in particular their engagement and challenge levels [41,80].…”

When Do Drivers Interact with In-Vehicle Well-being Interventions?

“…JITAI relies strongly on the concepts of states-of-vulnerability, i.e., moments with increased risk of negative health outcomes when a participant needs an intervention, and states-of-receptivity, i.e., moments where a person is able to receive, process, and use the intervention provided. In the UbiComp and HCI communities, there has been a growing research in sensing vulnerable conditions (like stress [25,45,46] and depression [44,67]), and understanding receptivity to interventions [11,39,47,59].…”