A Series of Case Reports Regarding the Use of Massage Therapy to Improve Sleep Quality in Individuals with Post-Traumatic Stress Disorder (PTSD)
Bibliographic record
Abstract
BACKGROUND: Post-traumatic stress disorder (PTSD) is a common mental health diagnosis in Canada with prevalence estimated at about 2.4% in the general population. Previous studies have suggested massage therapy may be able to reduce the symptoms of PTSD. One of the symptoms commonly experienced is difficulty falling or staying asleep. No previously published massage therapy research has specifically assessed sleep symptoms of PTSD. OBJECTIVES: The research question was, "For individuals who have PTSD as a result of experiencing traumatic events, does MT have an effect on sleep quality?" METHODS: A prospective series of case reports describing 10-week MT treatment plans provided by Registered Massage Therapists at Sutherland-Chan Clinic's Belleville location. Three individuals with PTSD were recruited using promotional posters in the community. Treatment focused on improving sleep quality and followed a pragmatic treatment protocol using light to moderate pressure. Outcomes were measured using a sleep diary, Pittsburgh Sleep Quality Index, and the Leeds Sleep Evaluation Questionnaire. RESULTS: Data collected at baseline and throughout the series showed inconsistent improvement and worsening of symptoms amongst participants. Treatment was well tolerated and attended. No harmful incidents were noted. CONCLUSION: For these participants, MT did not predictably impact sleep quality. It is possible, as the underlying cause of poor sleep quality was unlikely resolved, the participants did not have a significant change in their sleep quality. This differs from findings of previous studies in which MT improved sleep for patients with poor sleep quality due to exposure to traumatic events. There is need for further understanding of how MT affects sleep.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".