Comparing the effects of motor control exercises and PNF exercises on postural control, strength, endurance, and proprioception in women with chronic nonspecific low back pain
Bibliographic record
Abstract
Background: Low back pain is a common debilitating condition and a major clinical and socio-economic problem in the most industrialized and non-industrialized countries.Aim: The aim of this study was to compare the effect of motor control exercises and PNF exercises on postural control, strength, endurance and proprioception in women with chronic non-specific low back pain.Materials and Methods: Forty-five women with non-specific chronic low back pain selected by convenience sampling and randomly divided into three groups of 15 (motor control exercises, PNF exercises, and control group). The pre-test included posture control, flexor and extensor muscle strength of the trunk, trunk muscle endurance, and proprioception using Y balance, dynamometer, McGill, and Goniometer tests, respectively. The subjects of the experimental groups performed the training program for 8 weeks under the supervision of the instructor and according to the training protocol. Then, the post-test was performed. Paired t-test and analysis of covariance at the significance level of 0.05 were used to collect data.Results: The results showed that motor control and PNF exercises improved proprioception, postural control, endurance, and strength of trunk flexor and extensor muscles in women with non-specific chronic low back pain (α≤0.05). The results also showed that there was no significant difference between the effects of motor control and PNF exercises on proprioception, postural control, flexor muscle endurance, and extensor muscle strength (α≥0.05).Discussion: Motor control and PNF exercises are effective in improving the proprioception, postural control, endurance, and strength of flexor and extensor muscles of the trunk with non-specific chronic low back pain, and both training methods are effective in treating chronic non-specific low back pain.
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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 | 0.001 |
| 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".