An integrated framework for using mobile sensing to understand response to mobile interventions among breast cancer patients

Abstract

Abstract Despite the proliferation of mobile interventions that are delivered through smartphones, and their potential impact on promoting mental health in chronic health populations, little research has examined how to leverage the advanced technological capabilities of smartphones to also monitor response to app-based interventions. Current methods used to evaluate response to digital interventions rely on static and retrospective self-report measures to infer crucial behavioral and affective patterns, which provide little contextual information and limits generalizability. Importantly, this approach can also increase burden among chronic health populations that are already experiencing a heavy treatment load. Using data collected from 7 recently diagnosed breast cancer patients undergoing active cancer treatment (average time from initial diagnosis to study enrollment is 13 days), we propose a framework for integrating self-report surveys and fine-grained mobile sensing data. Preliminary results demonstrate the feasibility and utility of this framework to reduce burden and improve detection of mood in response to an app-based intervention among chronic health populations.