Pilot Crossover Trial of Reiki Versus Rest for Treating Cancer-Related Fatigue
Bibliographic record
Abstract
Fatigue is an extremely common side effect experienced during cancer treatment and recovery. Limited research has investigated strategies stemming from complementary and alternative medicine to reduce cancer-related fatigue. This research examined the effects of Reiki, a type of energy touch therapy, on fatigue, pain, anxiety, and overall quality of life. This study was a counterbalanced crossover trial of 2 conditions: (1) in the Reiki condition, participants received Reiki for 5 consecutive daily sessions, followed by a 1-week washout monitoring period of no treatments, then 2 additional Reiki sessions, and finally 2 weeks of no treatments, and (2) in the rest condition, participants rested for approximately 1 hour each day for 5 consecutive days, followed by a 1-week washout monitoring period of no scheduled resting and an additional week of no treatments. In both conditions, participants completed questionnaires investigating cancer-related fatigue (Functional Assessment of Cancer Therapy Fatigue subscale [FACT-F]) and overall quality of life (Functional Assessment of Cancer Therapy, General Version [FACT-G]) before and after all Reiki or resting sessions. They also completed a visual analog scale (Edmonton Symptom Assessment System [ESAS]) assessing daily tiredness, pain, and anxiety before and after each session of Reiki or rest. Sixteen patients (13 women) participated in the trial: 8 were randomized to each order of conditions (Reiki then rest; rest then Reiki). They were screened for fatigue on the ESAS tiredness item, and those scoring greater than 3 on the 0 to 10 scale were eligible for the study. They were diagnosed with a variety of cancers, most commonly colorectal (62.5%) cancer, and had a median age of 59 years. Fatigue on the FACT-F decreased within the Reiki condition (P=.05) over the course of all 7 treatments. In addition, participants in the Reiki condition experienced significant improvements in quality of life (FACT-G) compared to those in the resting condition (P <.05). On daily assessments (ESAS) in the Reiki condition, presession 1 versus postsession 5 scores indicated significant decreases in tiredness (P <.001), pain (P <.005), and anxiety (P<.01), which were not seen in the resting condition. Future research should further investigate the impact of Reiki using more highly controlled designs that include a sham Reiki condition and larger sample sizes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".