Physical measures of physical functioning as prognostic factors in predicting outcomes for neck pain: Protocol for a
Rabea Begum1,2, Paul Parikh1,2, David Walton1,2
1School of Physical Therapy, Western University, London, Ontario, Canada.
Introduction:
Neck pain is a common musculoskeletal condition that contributes to significant global burden. Evidence on physical measures of functioning as prognostic factors for neck pain remains limited. The objective of this study is to investigate whether physical measures of physical functioning as prognostic factors predict outcomes for people with neck pain.
Methods:
This protocol for a prospective longitudinal cohort study will recruit approximately 156 adults with neck pain of any duration. Physical measures of physical functioning including impairment, performance, and activity measures in a real environment, will be assessed as prognostic factors. Outcomes at 12-month follow-up will include pain, disability, and overall changes, assessed using the Numeric Pain Rating Scale (NPRS), Neck Disability Index (NDI), and Global Rating of Change (GROC), respectively. Hierarchical regression analyses will be conducted separately for each physical measure and outcome. Physical measures will first be entered alone to examine the initial unadjusted (bivariate) association with the outcome, followed by adjustment for covariates (age, sex and baseline NPRS/NDI). Additional contribution will be evaluated using changes in explained variance ΔR2 and F-change statistics. Physical measures demonstrating additional contribution will subsequently be entered into the final penalized multivariable regression model using least absolute shrinkage and selection operator (LASSO) regression to reduce overfitting and identify the most relevant prognostic factors. Sensitivity analyses comparing penalized and non-penalized multivariable regression models will also be conducted to examine the consistency and stability of the findings. Prognostic effects will be reported as beta coefficients (β) or odds ratios (OR) with 95% confidence intervals (CIs). Each model will be internally validated using bootstrapping, with model performance evaluated using calibration and discrimination.
Discussion:
This study will provide new insights into how physical measures of physical functioning relate to outcomes for neck pain. The resulting prognostic models have the potential to strengthen clinical decision-making, support personalized care, and guide the development of targeted interventions for people with neck pain.
Registration Number:
The protocol has prospectively registered on the Open Science Framework (OSF). The link is https://doi.org/10.17605/OSF.IO/HG6PJ.
