Specificity of a Back Muscle Exercise Machine in Healthy and Low Back Pain Subjects
Bibliographic record
Abstract
PURPOSE: To determine whether dynamic back muscle endurance exercises in a semisitting position induce more fatigue in back muscles than that in hip extensors in healthy controls as well as in patients with nonspecific chronic low back pain. METHODS: Sixteen healthy volunteers and 18 volunteers with nonspecific chronic low back pain performed trunk flexion-extension cycles until exhaustion at 60% of their strength in a machine designed for back exercise in a semisitting position with knees' angle at 135 degrees . The number of cycles and perceived muscle fatigue (Borg CR-10 scale) at five areas (upper and lower back, gluteus, hamstrings, and quadriceps) were used as fatigue criteria. EMG signals were recorded bilaterally on four back muscles, two hip extensors (gluteus maximus and biceps femoris), and the vastus medialis. The slope values of the instantaneous median frequency values computed over time were retained as EMG indices of fatigue. RESULTS: The number of cycles was equivalent in healthy controls (n = 23 +/- 13) and patients with back pain (n = 27 +/- 16). EMG indices of fatigue disclosed evidence of muscle fatigue in all the back muscles and the vastus medialis, contrary to hip extensors. EMG revealed significantly more muscle fatigue of lower back muscles, which was further corroborated by the Borg scale assessment. No between-group difference was obtained in any EMG comparison. CONCLUSION: These results showed that this type of exercise machine can specifically train the back muscles, and this as much in subjects with nonspecific chronic low back pain as in healthy controls. This has implications for the training of back muscle endurance, especially in patients with back pain for whom poor back muscle endurance is sometimes of concern.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".