Chronic Pain Management in Musculoskeletal Disorders

Chronic pain associated with musculoskeletal disorders represents a complex clinical challenge that affects mobility, quality of life and socioeconomic participation on a global scale. Persistent pain often arises from a combination of structural tissue changes, central and peripheral sensitisation, psychological factors and environmental influences. Contemporary management strategies embrace a biopsychosocial model, integrating pharmacological interventions—including judicious use of analgesics and adjuvant medications—with non-pharmacological approaches such as exercise therapy, patient education and cognitive-behavioural techniques. Rehabilitation programmes tailored to individual need combine strength and flexibility training with strategies to enhance self-efficacy and functional capacity. Advances in personalised medicine have spurred the development of data-driven classification systems that stratify patients according to multidimensional profiles, allowing more precise targeting of therapies. Decision support systems employing machine learning are beginning to inform triage and treatment selection by analysing electronic health records and referral data. Multidisciplinary and interprofessional collaboration underpins effective care delivery, ensuring that physiotherapists, rheumatologists, pain specialists and psychological therapists share expertise. Despite progress, barriers persist in translating evidence into routine practice, including variability in outcome measurement, limited access to specialised services and the need for enhanced clinician and patient education. Future research aims to refine mechanistic understanding, optimise intervention timing and harness digital health tools to support self-management and continuous monitoring.

Research from Nature Portfolio

A recent pilot study introduced a data-driven classification of non-specific chronic low back pain by integrating nervous system metrics, lumbar spinal tissue assessments and psychosocial variables. Using dimensionality reduction and fuzzy c-means clustering, two distinct patient sub-groups emerged—those with normal psychosocial profiles and those with impaired profiles characterised by elevated anxiety, depressive symptoms and reduced self-efficacy. Machine learning models then accurately classified patients into these clusters, achieving low error rates within the chronic pain cohort. This multidimensional taxonomy has potential to guide personalised rehabilitation pathways and inform the development of targeted interventions that address both tissue and psychosocial drivers of chronic pain.

Chronic Pain Management in Musculoskeletal Disorders publication trend

The graph below shows the total number of articles in chronic pain management in musculoskeletal disorders across all publications each year (not limited to Nature Index journals).

Technical terms

Biopsychosocial model: An approach to health that considers biological, psychological and social factors in the development and management of chronic pain.

Decision support system (DSS): A computer-based tool that integrates diverse patient data to aid clinicians in selecting optimal treatment pathways.

Interprofessional rehabilitation: A collaborative treatment programme involving professionals from multiple disciplines to deliver coordinated care.

Machine learning classification: The use of algorithms to group patients into categories based on patterns within multidimensional datasets.

Self-efficacy: An individual’s belief in their capacity to execute behaviours necessary to manage their pain and maintain function.

Numeric Rating Scale (NRS): A patient-reported measure of pain intensity, typically ranging from zero (no pain) to ten (worst possible pain).

References

1. Natural Language Processing of Referral Letters for Machine Learning–Based Triaging of Patients With Low Back Pain to the Most Appropriate Intervention: Retrospective Study. Journal of Medical Internet Research (2024).
2. Towards data-driven biopsychosocial classification of non-specific chronic low back pain: a pilot study. Scientific Reports (2023).
3. Prognostic factors for pain chronicity in low back pain: a systematic review. PAIN Reports (2021).