Muscular and Postural Demands of Using a Massage Chair and Massage Table
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the difference in muscular and postural demands of performing manual therapy using a massage chair and a massage table. METHODS: Twelve female senior massage therapy students performed two 10-minute regional back massages on a fully clothed client using both a massage chair and massage table. The root mean square was used to determine the mean activation from the electromyographic signal collected from 8 upper extremity muscles. Integrated electromyography was used to compare activation between the 14 massage techniques used. Eight electromagnetic motion capture sensors were attached: the head, trunk, and upper arm, forearm, and hand bilaterally to track segment kinematics and determine total time spent in different postures. RESULTS: There was higher activation in lumbar erector spinae when using the table and anterior deltoid when using the chair. The anterior deltoid showed a significant condition x period interaction for mean muscle activation for 6 of the 14 massage techniques. The therapists spent significantly more time in mild trunk flexion when using the massage table and significantly more time in severe radial deviation and mild shoulder flexion when using the massage chair. CONCLUSIONS: The chair and table were more demanding of the anterior deltoid and lumbar erector spinae, respectively. Therapists adopted trunk and wrist postures that would increase the risk of upper extremity injury while using either the massage chair or table.
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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 | 0.004 |
| 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) | 0.004 | 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".