iNNOV Sensing

A pilot cognitive-behavioral internet intervention to detect, treat, and monitor anxiety and depression in breast cancer survivors based on multimodal active and passive sensing data



Despite the efficacy of psychosocial interventions in minimizing psychosocial morbidity in breast cancer survivors (BCS), the delivery of Interventions is limited by physical, organizational, and individual barriers, which contribute to a mental healthcare treatment gap in cancer settings. Digital Phenotyping enhanced Internet Interventions may provide remarkable opportunities to overcome these limitations. By enabling the dynamic collection and assessment of multimodal data, these interventions may be used to refine diagnostic processes, improve condition monitoring for actionable outcomes, such as early signs of relapse, and tailor interventions, configuring a disruptive healthcare delivery model.

However, limited research has been conducted on translating digital phenotyping signals into clinically actionable digital phenotypes or prediction models capable of better explaining, assessing, and tailoring internet interventions to BCS. Moreover, little is known about their acceptability, feasibility, and efficacy in BCS. This proposal intends to bridge these research gaps by applying a Machine Learning approach leveraging data from smartphones and fitness trackers in BCS to predict their health outcomes while undergoing an internet intervention named iNNOV Breast Cancer (iNNOVBC).
AIMS: 1) To explore the acceptability and feasibility of iNNOVBC and the collection of passively sensed smartphone and wearable data in BCS; 2) To develop an Intelligent solution to assess anxiety and depression, detect changes in its severity, and predict its course in BCS and; 3) To assess the preliminary efficacy of INNOVBC in improving anxiety and depression, and psychological flexibility, fatigue, insomnia, sexual dysfunction, and Health-Related Quality of Life (HRQoL) in BCS when compared to treatment as usual (TAU) in a waiting-list control group (WLC).

METHODS: To attain these goals, we will conduct a two-arm, parallel, open-label, multicentre, waiting list pilot feasibility RCT involving at least 50 BCS. Passive sensor data will be collected using smartphones and fitness trackers throughout the trial. The primary outcomes of this research will be anxiety and depression. Secondary outcomes include psychological flexibility, fatigue, insomnia, sexual dysfunction, and HRQoL. The study will be conducted in Portugal at Instituto Português de Oncologia do Porto Francisco Gentil (CI-IPOP) and Centro Hospitalar Universitário de São João (CHUSJ).



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