Effects of practicing yoga on alexisomia: an open-label trial
Bibliographic record
Abstract
BACKGROUND: Alexisomia refers to difficulties in the awareness and expression of somatic feelings. This idea was proposed by Dr. Yujiro Ikemi as a characteristic observed in patients with psychosomatic diseases and is based on his observations that patients with psychosomatic diseases have difficulty in the awareness and expression of not only their emotions, i.e., alexithymia, but also somatic feelings and sensations, i.e., alexisomia. He also proposed that treating alexisomia is important in the treatment of psychosomatic diseases and that yoga might help improve alexisomia. However, no study has investigated if yoga actually affects alexisomia. This open-label pilot study investigated whether practicing yoga in a class results in change in patients with alexisomia and alexithymia. METHODS: The Shitsu-taikan-sho Scale (STSS) and the Toronto Alexithymia Scale (TAS-20) were administered to 305 participants, including 64 healthy participants, 111 participants who had subjective symptoms without abnormal findings, and 130 participants with chronic diseases. Participants were tested before and 3 months after attending yoga classes. RESULTS: Yoga practice reduced the STSS and the TAS-20 difficulty in identifying feelings (DIF) subscale scores. Multiple linear regression indicated that a reduction in the TAS-20 DIF subscale scores predicted a decrease in the STSS score, whereas reductions in the STSS difficulty in identifying bodily feelings (DIB) and the lack of health management based on bodily feelings (LHM) subscale scores predicted a decrease in the TAS-20 scores. CONCLUSION: We found that regular yoga practice improves alexisomia. Yoga-induced improvement of alexisomia may be mediated, at least in part, by an improvement of DIF in alexithymia. Yoga would be a promising therapeutic approach to improve alexisomia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".