Enhancing the Effectiveness of Digital Health Interventions in Managing Chronic Musculoskeletal Pain

Abstract

Chronic musculoskeletal (MSK) pain is characterised by diverse presentations and can have extensive impacts on individual well-being, while also imposing significant economic burden on healthcare systems and society. Managing chronic MSK pain requires a holistic approach that acknowledges the heterogeneity of pain experience and begins to move away from a one-size-fits-all solution to developing personalised pathways. Digital technologies offer great potential to transform healthcare delivery by overcoming geographical and time barriers, streamlining health data management, and enabling the collection of individual-specific data. Although digital health interventions (DHIs) have demonstrated promising results in managing chronic pain, standardised interventions often fail to accommodate individual variations. This thesis explored opportunities to optimise intervention delivery that can better address pain heterogeneity and advance personalised pain management. Adopting an interdisciplinary approach including health psychology, epidemiology, and digital health, four studies were conducted. First, a scoping review (Chapter 2) explored just-in-time adaptive interventions (JITAIs) as a promising approach to realise personalised interventions for behaviour change. This review provided guidance on optimising intervention timing and tailoring support. Following this, a focus-group type study (Chapter 3) with stakeholders explored perceived needs and challenges of personalised interventions, emphasising the importance of timely care and monitoring of pain triggers. Subsequently, two quantitative studies focused on advancing personalised pain management. A clustering analysis (Chapter 4), using the UK Biobank database, identified five distinct chronic pain subgroups based on age, sex, and number of pain sites. This analysis was a preliminary step in informing persona development for DHIs that provide targeted support at the subgroup level. Finally, a within-person analysis (Chapter 5), using a mHealth dataset from the Quality of Life, Sleep and Rheumatoid Arthritis study, investigated the dynamics of pain flares and their associated factors. This analysis highlighted the value of daily patient-generated health data in identifying pain patterns. This study suggested that sleep patterns and emotional distress were associated with pain flare occurrence. The integration of technology into chronic pain management offers transformative opportunities but also presents challenges in understanding the mechanisms of personalised interventions. While the potential of DHIs is evident, future research needs to incorporate a proactive stance in developing personalised pain management, while also prioritising scalability and adaptability to ensure interventions are accessible and effective for diverse patient needs.

Date of Award	27 Jan 2025
Original language	English
Awarding Institution	The University of Manchester
Supervisor	John Mcbeth (Supervisor), Lis Cordingley (Supervisor), Christopher Arden Armitage (Supervisor) & Pauline Whelan (Supervisor)