Understanding the Longitudinal Characteristics of Chronic Pain in Arthritis: The Role of Intensive Longitudinal Methods in Analysing Pain and the Attributable Burden of Persistent Pain on Treatment and Health Outcomes

Abstract

Chronic pain is a common sequela of rheumatic and musculoskeletal diseases (RMDs) worldwide. The health care and societal costs related to the burden of chronic pain are insurmountable. Chronic pain is regarded as a symptom of utmost importance to individuals living with arthritis. Chronic pain, to everyone, is unique, complex, and multidimensional, and for many, it can adversely impact on the individual’s day-to-day physical functioning and psychosocial states. In a typical rheumatology clinic, and usually with a standard 3-month or 6-month follow-up timeframe, patients are often asked to discuss their pain relating to their arthritis since the last visit, which is highly prone to recall bias and selective memory. The lived experience of pain in arthritis is highly variable and can be unpredictable at times, especially during disease flare or with other co-existing pain-related comorbidities, such as fibromyalgia (FM). Summarising the ebb and flow of pain may not necessarily reflect the real-time pain impact. Capturing these pain symptoms in real-time may provide the window of opportunity to intervene and to help better manage their pain when symptomatic. Furthermore, discordance between the retrospective summary of pain experience and the actual realtime impact of pain can result in unintended consequences of unnecessary treatment escalation and poor pain management. Data collection of repeated measures of symptoms and co-occurring events captured over time is well established, although it is often an onerous task and can be unappealing and intrusive. The implementation of mobile health (mHealth) in clinical practice and research has unfolded many potentials to capture temporally rich patient-reported outcomes (PROs) in real-time, when compared to traditional methods of data collection. In the context of using smartphones in capturing realtime pain symptoms in arthritis, this type of mHealth data creates a unique platform to explore novel methods in examining pain trajectory and pain variability in RMDs. Such granular real-time data may provide opportunities to explore the key components of capturing pain ‘flare’, often defined as the momentary state of heightened/significant pain level and is usually accompanied by other pain-related symptoms. Traditionally, pain in arthritis is understood as a form of nociceptive pain signalling output in the nervous system, often attributed to the disease activity such as synovial joint inflammation. Yet, despite adequately treated arthritis, PROs such as persistent pain and fatigue exist in some individuals, highlighting the roles of nociplastic central pain processing and the inter-relationship with psychosocial and socioeconomic factors. Despite treatment advances in disease modifying anti-rheumatic drugs (DMARDs) use in rheumatoid arthritis (RA), persistent pain remains a treatment conundrum to patients and clinicians, even in those with an absence of or low level of disease inflammation. Globally, the mortality gap in RA is high. Concerningly, chronic pain is known to increase mortality in the wider population, posing an important research question on whether mortality in RA is accelerated if the trajectory of health status is downtrending in those with persistent pain.