Use of the CAREN system as a treatment adjunct for Canadian Armed Forces members with chronic non-specific low back pain: a pilot study
Bibliographic record
Abstract
Introduction: Non-specific chronic low back pain (cLBP) is a significant concern for the Canadian Armed Forces (CAF). Some CAF members develop pain-related fear of movement that is difficult to treat. The goal of this pilot study was to test the feasibility of applying a virtual reality exercise program using the Computer Assisted Rehabilitation Environment (CAREN) with CAF members with cLBP and kinesiophobia. Methods: Participants were randomized into one of two treatment groups or a control group. The treatment groups received 10 CAREN sessions over 2.5 weeks (high intensity) or over 5 weeks (low intensity), and the control group received exercise instruction alone. A standard CAREN protocol using five applications was used to encourage trunk movement and stability. Results: Eleven participants were recruited. Both CAREN treatment protocols were feasible, with 100% attendance, and CAREN performance continued to improve over the 10 sessions with no plateau. CAREN participants showed stable or slightly improved scores on outcome measures from baseline to post-intervention. Participants showed no consistency on the physical performance measures, with ceiling effects or variability noted between participants. Discussion: Significant inferences regarding effectiveness cannot be made because of the low number of participants. The CAREN treatment protocol was shown to be feasible and well tolerated at these intensities. The outcome measures that show the most promise for future study include the Oswestry Disability Index, Fear of Daily Activities Questionnaire, Edgren Side Step Test, and 30-second chair stand. Future randomized controlled trials to study the effectiveness of the CAREN intervention should include military-relevant outcomes and cost efficiency.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".