A wide range of technologies, such as smartphones, wearables (e.g., Fitbit, Apple Watch), and medical devices use alerts to inspire actions of users. Potentially useful alerts come at the cost of alert fatigue whereby individuals ignore alerts over time. For example, several physical activity interventions use alerts to inspire activity; notifications work initially but with diminished efficacy over time. Ignoring alerts is problematic in a variety of domains. For example, notification fatigue reduces the potency of interventions (e.g., notifications to inspire walking) and can be a safety risk in other areas such as in hospitals where notification fatigue can lead providers to ignore safety alerts (e.g., cross-drug interaction) provided by the electronic medical record. There is a need for novel solutions for reducing alert fatigue. Location, digital traces, and other data enable inference of states when a person would desire/need alerts, henceforth labeled just-in-time states, but more advanced analytics are needed. For example, a suggestion to walk (e.g., SMS saying, ?Want to go for a walk??) may only produce the desired outcome when a person?s state (e.g., low stress) and context (e.g., no meetings, nice weather) align such that the person appreciates the notification (what we label receptivity) and can act on it (what we label opportunity). Estimating the likelihood that a given moment is a just-in-time state requires not only data but also an approach to manage the multivariate, dynamic, idiosyncratic, and multi-timescale nature of the problem. Returning to the walking example, stress, weather, and location change dynamically with each influencing the likelihood that a notification will inspire walking. In our work, results suggest idiosyncrasy in the factors that predict steps: some people walk more when stressed, others less, and still others are not influenced by stress. Further, just-in-time notifications cannot be viewed in a vacuum and, instead, are often part of a more long-term process, such as sustained engagement in a health behavior, thus making it a multi-timescale problem.

The purpose of this work is to develop a just-in-time state estimation strategy and to stage a multi-timescale controller for walking as a concrete use-case of a control systems approach to counteract alert fatigue. Previous work (IIS-1449751) translated control systems techniques to ones suitable for just-in-time behavioral interventions and ongoing work (HeartSteps) provides ample data to stage our approach and a technology platform to evaluate it. Secondary analyses of our HeartSteps datasets will be conducted using a control engineering approach for inferring just-in-time states for walking. These models will be linked with a previously created daily timescale model and a multi-timescale hybrid model predictive controller to support decision-making about when to send notifications and personalized daily step goals to support accumulation of walking bouts into meaningful change, which will be evaluated in a n=50 cohort study.