Related Experiment Video
Updated: Jun 8, 2026
14:56
An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
22.1K
Multidomain evaluation and data-driven approaches to predict recurrent neck pain (END-RNP): a study protocol for a
Valter Devecchi1, Bernard Liew2, Jonathan Price3,4
1Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK.
BMJ Open
|February 27, 2026
Summary
Neck pain (NP) is a common disability. The End Recurrent Neck Pain (END-RNP) study predicts frequent and severe NP episodes using physical, psychological, and social factors in pain-free individuals.
Area of Science:
- Musculoskeletal health
- Epidemiology of chronic pain
- Rehabilitation science
Background:
- Neck pain (NP) is a significant global health issue, causing widespread disability and increasing economic burden.
- Recurrent NP affects millions, yet factors predicting new or more severe episodes remain poorly understood.
- Persistent physical, psychological, and social changes between episodes may contribute to recurrence.
Purpose of the Study:
- To identify physical, psychological, and social factors that predict the frequency and severity of new neck pain episodes within a 12-month period.
- To develop predictive models for recurrent neck pain using data from individuals in symptom remission.
- To inform personalized prevention strategies for recurrent neck pain.
Main Methods:
- A multicentre prospective cohort study (END-RNP) involving 300 adults with recurrent NP and 48 controls.
- Baseline laboratory assessments of physical (kinematics, strength, endurance, activation), pain processing, and psychosocial factors in a pain-free state.
- 12-month follow-up with bi-weekly online questionnaires on NP episodes, intensity, interference, and healthcare use; repeat lab tests for acute episodes.
Main Results:
- Prediction models will be developed using penalized regression based on baseline measurements differentiating recurrent NP from controls or showing within-subject changes.
- Models will predict (i) number of days with NP (linear regression) and (ii) NP severity (ordinal regression).
- Internal validation will assess model performance, stability, and clinical utility using bootstrap resampling.
Conclusions:
- The END-RNP study will yield clinical prediction tools to identify individuals at high risk for frequent and severe recurrent NP.
- These tools will support the development of personalized prevention strategies for recurrent neck pain.
- Findings will aid in managing the growing global burden of neck pain disability.
