Fear Avoidance Beliefs Predict Disability in Older Adults With Chronic Low Back Pain
Bibliographic record
Abstract
OBJECTIVES: To determine whether fear avoidance beliefs (FABs) in older adults with chronic low back pain (CLBP) are significantly associated with gait speed decline and/or self-report of greater disability. DESIGN: Cross-sectional analysis. SETTING: An academic medical center (single site). PARTICIPANTS: Two hundred English-speaking participants aged 65 years and older with CLBP every day or almost every day of moderate or greater intensity for ≥3 months. MAIN OUTCOME MEASUREMENTS: The physical activity portion of the FAB questionnaire assessed FABs. Disability was measured with gait speed and the Roland Morris Questionnaire. Covariates measured included age, gender, body mass index, chronic disease (Cumulative Illness Rating Scale), depression (Geriatric Depression Scale), and pain (McGill Pain Questionnaire Short Form). RESULTS: FABs were significantly associated with the Roland Morris Questionnaire (P < .0001) and gait speed (P = .002) after controlling for all covariates. CONCLUSION: FABs related to physical activity in older adults with CLBP were significantly associated with both self-reported and performance-based disability after controlling for known confounders. Previous studies have reported similar associations between self-reported measures of disabling back pain and FABs. Ours is the first study to examine the relationship between FAB and gait speed, a powerful predictor of morbidity and mortality. Future work should examine whether targeting fear avoidance in addition to other psychosocial measures in older adults with CLBP improves gait speed and functional independence.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".