Predicting pain outcomes after digital care in chronic spinal pain: the roles of disability, work impairment, and occupation in a secondary analysis of a prospective clinical study - Report - MDSpire
Advertisement
Predicting pain outcomes after digital care in chronic spinal pain: the roles of disability, work impairment, and occupation in a secondary analysis of a prospective clinical study
Predicting Pain Outcomes After Digital Care for Chronic Spinal Pain
Overview
This study analyzed 13,330 adults with chronic spinal pain undergoing a digital care program (DCP) to identify predictors of post-treatment pain outcomes. Baseline disability and job type significantly influenced pain levels after treatment, while work impairment did not. These findings were consistent across neck and low back pain patients.
Background
Chronic spinal pain is a leading cause of disability and productivity loss globally, with substantial healthcare and indirect costs. Multimodal rehabilitation is the first-line treatment, but patient responses vary widely. Digital care programs offer scalable, accessible treatment options, yet predictors of outcomes in digital settings remain underexplored. Understanding factors such as disability, work impairment, and occupation can enhance personalized care and prognosis.
Data Highlights
Predictor
Effect on Last Pain Score (β)
Standard Error
P-value
Baseline Disability
0.30
0.02
<.001
Business Occupations vs Goods-Producing
-0.18
0.07
0.015
Business Occupations vs Healthcare/Education
-0.14
0.04
0.001
Work Impairment (WPAI Overall and Activity)
Not significant
–
–
Key Findings
Higher baseline disability scores predicted higher pain levels after digital treatment (β=0.30, P<.001).
Occupation type influenced outcomes: business-related jobs had higher pain scores compared to goods-producing and healthcare/education jobs.
Work impairment measures (WPAI Overall and Activity) were not significant predictors after adjusting for covariates.
Predictor effects were consistent regardless of pain location (neck vs low back).
The final predictive model explained 21.3% of the variance in post-treatment pain scores.
Clinical Implications
Incorporating baseline disability and occupation type into routine screening can improve prediction of pain outcomes following digital rehabilitation for chronic spinal pain. This enables clinicians to better tailor treatment plans and manage patient expectations. Work impairment measures may be less useful as prognostic tools in this context.
Conclusion
Baseline disability and job type are important predictors of pain outcomes after digital care for chronic spinal pain, while work impairment is not. Integrating these factors into clinical workflows may enhance personalized rehabilitation strategies.
