Factors Related to Pain and Disability Outcomes After an Internet-Delivered or Physiotherapist-Led Exercise Program for Individuals With Chronic Whiplash Symptoms: Secondary Analysis of a Randomized Controlled Study
Bibliographic record
Abstract
Background: A neck-specific exercise program has shown sustained clinically important changes in pain and disability for approximately 50% of individuals with chronic whiplash-associated disorders (WAD). However, there is limited information about factors related to treatment response. Objective: The aim of this study is to identify factors related to changes in disability, neck pain, and physical function after a neck-specific exercise program delivered in 2 different ways for individuals with persistent WAD grade II or III, and to investigate whether any factors could predict those with clinically improved versus not improved disability, pain, and physical function. Methods: This was a planned secondary analysis of a multicenter prospective randomized controlled trial. Participants (n=140) with persistent (between 6 mo and 5 y from injury) WAD grade II or III were randomized into a 12-week, internet-based neck-specific exercise program (NSEIT) with 4 physiotherapy visits or the same exercise program supervised by a physiotherapist (NSE) twice per week for 12 weeks. Multivariate data analyses and orthogonal partial least squares (OPLS) models were used to investigate change in psychological and physiological factors (independent factors) related to change in the dependent factors: neck-related disability measured with the Neck Disability Index (NDI), neck pain intensity measured with a visual analogue scale, and physical function measured with the Patient-Specific Functional Scale (PSFS). Outcomes were measured at baseline and at 3-month and 15-month follow-up. OPLS discriminant analysis was used to investigate differences between the two groups (NSEIT and NSE) by studying the change scores of the dependent and independent factors. OPLS discriminant analysis was also used to investigate whether background variables and baseline measurements of the independent factors could predict clinically significant improvement in the dependent factors NDI, neck pain, and PSFS. Results: There were no significant differences between the groups. In both NSEIT and NSE, improvements in the following independent factors were related to improvements in NDI, pain, and PSFS at 3-month and 15-month follow-up: anxiety, depression, cognitive failures, pain catastrophizing, self-efficacy, fear avoidance beliefs, cervical range of motion, headache, and symptom satisfaction (R2=0.31-0.37; Q2=0.25-0.30; cross-validated ANOVA P<.001). No significant OPLS models could be built to distinguish clinically improved versus nonimproved patients as assessed by NDI, neck pain, or PSFS. Conclusions: Improvements in both psychological and physiological factors were related to improvements in disability, neck pain, and physical function after 12 weeks of NSEIT or NSE. The results indicate that these factors are interrelated and can be improved both with NSEIT and NSE. Known risk factors for poor outcomes of neck disability in WAD, such as low self-efficacy, fear avoidance beliefs, depressive symptoms, and catastrophizing, were improved, and we need to examine other factors not included in this study that can identify those who are not improved after NSEIT or NSE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".