Effect of Dance Movement Therapy on Cancer-Related Fatigue in Breast Cancer Patients Undergoing Radiation Therapy: A Pre-post Intervention Study
Bibliographic record
Abstract
Introduction Dance movement therapy (DMT) is a movement-based psychosocial intervention that incorporates the therapeutic components of dance movements and group psychotherapy. DMT, also known as creative movement therapy (CMT) is a psychotherapy used as a complementary therapy in cancer care. It helps in enhancing mood, emotions, self-expression and helps to rebuild self-confidence. Besides, it allows the patients to recognise their own strengths and weaknesses as well as helps to improve physical capabilities. Methods By simple random sampling method, 30 breast cancer patients were recruited at Pravara Rural Hospital, Loni, Maharashtra, India. The participants were in the age range of 30-60 years based on the inclusion and exclusion criteria. Pre-intervention scores of cancer-related fatigue (CRF) were taken using the Brief Fatigue Inventory (BFI) scale and intervention was given for 45 minutes each day for 5 days a week, over a span of 2 weeks. Thereafter, post-intervention assessment was done and the scores were noted. Pre-intervention and post-intervention scores were compared using paired t-test. Results The mean and standard deviation (SD) of pre- and post-BFI scores derived by using paired t-test was 73.76 (8.6) and 69.33 (9.8), respectively, with a p-value of < 0.001, which is highly significant. Conclusion The results of the present study revealed that DMT seems to be effective in reducing some amount of CRF in breast cancer patients undergoing radiation therapy. Besides, it turned out to be an engaging, entertaining and cost-effective approach. The investigation showed that DMT appears to be beneficial in reducing the side effects of radiation therapy such as pain, stress, anxiety and fear, giving a psychotherapeutic relief but did not completely remove the persistent fatigue experienced by the breast cancer patients. Thus, further investigation with long-term follow-up is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".