Effects of Comprehensive Physical Exercises Combined With Cognitive Functional Therapy on Women With Low Back Pain
Bibliographic record
Abstract
Objectives: This study examined how women with low back pain (LBP) responded to comprehensive physical exercises (CPEs) combined with cognitive functional therapy (CFT) in terms of pain, disability and postural sway (PS).Methods: A total of 45 women with non-specific chronic LBP (NSCLBP) participated in this quasi-experimental study.The participants were randomly assigned to the CPEs, CPEs combined with CFT and control group (CG) (15 participants per group).The McGill pain questionnaire, Roland-Morris disability questionnaire, and Zebris platform were used to assess pain, disability and PS in anteroposterior (AP) and mediolateral (ML) directions with eyes open and closed.For eight weeks, the participants engaged in both CPEs and CPEs combined with CFT.The CG continued their usual routine.The multivariate analysis of variance statistical test with repeated measures design was used to analyze the data (P<0.05). Results:The results revealed that after eight weeks, the patient's pain and disability significantly decreased in both intervention groups compared to the CG (P<0.001).The CPEs combined with CFT (P<0.001) and the CPEs group (P<0.001)compared to the CG showed a significant decline in AP sways with eyes open.The AP with EC and ML with eyes open demonstrated a significant reduction in the CPEs group with CFT versus the CPEs and CG (P<0.05).The ML oscillations with eyes closed revealed a significant reduction in the CPEs group compared to the CG (P=0.005).Discussion: The CPEs combined with the CFT group showed better results in postural control ability, especially when the eyes were closed which may be a consequence of CFT.Therefore, it is recommended to use CPEs combined with CFT to reduce pain, disability and PS in women with NSCLBP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".