Background
People with physical long term health conditions (LTCs) often benefit from digital cognitive behavioural therapy [1], yet many do not start or stay engaged [2]. Orbi is an illness-tailored CBT app that supports psychological wellbeing in LTCs, based on the transdiagnostic model of psychological adjustment in LTCs [3]. This project will link passive smartphone and optional wearable data with app usage and patient reported outcomes to understand how people use Orbi and how use relates to changes in symptoms and quality of life. Passive data may include screen time, mobility patterns, communication metadata, activity, sleep, and heart rate variability. Insights will guide targeted support that helps more people begin and complete treatment.
Novelty and Importance
The project combines real world passive sensing with engagement modelling and equitable design. It will develop and validate models to predict disengagement. It will then use co-design methodology to develop tailored support protocols to improve uptake and retention in under-served and seldom-heard groups. This work moves beyond one size fits all approaches and aims to reduce avoidable differences in access and outcomes.
Aims and Objectives
The aim is to improve uptake and sustained engagement with Orbi by predicting early risk and delivering adaptive, culturally responsive support that strengthens clinical outcomes. Objectives are to:
1. Build an integrated dataset that links passive signals, app usage, and outcomes. To develop and validate early risk models and identify modifiable drivers of disengagement with tests of calibration, transportability, and fairness.
2. Co design and evaluate content and engagement supports that address identified barriers, with a specific focus on underserved groups.
3. Run a randomised adaptive evaluation that tests effects on initiation, sustained engagement, completion, and changes in anxiety, depression, fatigue, and quality of life.
4. Examine behavioural mechanisms and moderators that inform future personalisation.
1. Picariello F, Hulme K, Seaton N, Hudson JL, Norton S, Wroe A, Moss-Morris R. A randomized controlled trial of a digital cognitive-behavioral therapy program (COMPASS) for managing depression and anxiety related to living with a long-term physical health condition. Psychological medicine. 2024 Jun;54(8):1796-809.
2. Kaveladze BT, Wasil AR, Bunyi JB, Ramirez V, Schueller SM. User experience, engagement, and popularity in mental health apps: secondary analysis of app analytics and expert app reviews. JMIR Hum Factors. 2022;9(1):e30766.
3. Carroll S, Moon Z, Hudson J, Hulme K, Moss-Morris R. An evidence-based theory of psychological adjustment to long-term physical health conditions: applications in clinical practice. Biopsychosocial Science and Medicine. 2022 Jun 1;84(5):547-59.

