Compensatory Exercise with Dual-Task using Kommo® for People with Sedentary Work
Bibliographic record
Abstract
Background: Sedentary work increases the risk of back pain and reduces physical capacity. Cognitive-motor (dual-task) training can influence attention, postural control, and pain perception. Objective: To evaluate the feasibility of a group compensatory exercise program using the Kommo® bar (dual-task approach) in sedentary workers and to assess its effects on spinal mobility and back pain. Methods: Fifteen participants (11 women, 4 men; aged 20–59 years, mean age 41.8; average sitting time 8.6 h/day) completed an 8-week intervention (one 60-minute group session per week). Spinal mobility was assessed using Čepoj’s distance, Ott’s distance (inclination and reclination), Schober’s distance, Stibor’s distance, and Thomayer’s test. Functional mobility was evaluated with the Five Times Sit-to-Stand test and the Timed Up and Go test (including the cognitive variant, TUGcog). Pain was assessed using the Visual Analogue Scale (VAS) and the McGill Pain Questionnaire. Data were analyzed with paired t-tests, after testing for normality with the Shapiro–Wilk test. Results: All participants completed the program without complications (0% dropout). Significant improvements were observed in thoracic spine mobility (Ott, Stibor), functional mobility (5×STS, TUG, TUGcog), and perceived back pain (VAS, McGill). Conclusion: An 8week compensatory exercise program with dual-task elements using the Kommo® bar is feasible and shows promise in reducing back pain and improving mobility in sedentary workers. Larger controlled studies are warranted.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".