Impact of Massage Therapy on Fatigue, Pain, and Spasticity in People with Multiple Sclerosis: a Pilot Study
Bibliographic record
Abstract
Background: Multiple sclerosis (MS) is a chronic, immune-mediated, inflammatory disease that leads to fatigue, pain, and spasticity, as well as other sensorimotor and cognitive changes. Often traditional medical approaches are ineffective in alleviating these disruptive symptoms. Although about one-third of surveyed individuals report they use massage therapy (MT) as an adjunct to medical treatment, there is little empirical evidence that MT is effective for symptom management in people with MS.Purpose: To measure the effects of MT on fatigue, pain, spasticity, perception of health, and quality of life in people with MS.Setting: Not-for-profit long-term care facility.Participants: Twenty-four of 28 enrolled individuals with MS (average age = 47.38, SD = 13.05; 22 female) completed all MT sessions and outcome assessments.Research Design: Nonrandomized, pre–post pilot study.Intervention: Standardized MT routine one time a week for six weeks.Main Outcome Measure(s): Modified Fatigue Index Scale (MFIS), MOS Pain Effects Scale (MOS Pain), and Modified Ashworth Scale (MAS). Secondary outcome measures: Mental Health Inventory (MHI) and Health Status Questionnaire (HSQ).Results: There was a significant improvement in MFIS (p < .01), MOS Pain (p < .01), MHI (p < .01), and HSQ (p < .01), all with a large effect size (ES) (Cohen’s d = -0.76, 1.25, 0.93, -1.01, respectively). There was a significant correlation betweenchange scores on the MFIS and the MOS Pain (r = 0.532, p < .01), MHI (r = -0.647, p < .01), and subscales of the HSQ (ranging from r = -0.519, to -0.619, p < .01).Conclusions: MT as delivered in this study is a safe and beneficial intervention for management of fatigue and pain in people with MS. Decreasing fatigue and pain appears to correlate with improvement in quality of life, which is meaningfulfor people with MS who have a chronic disease resulting in long-term health care needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".