Yoga Therapy in Cancer Care via Telehealth During the COVID-19 Pandemic
Bibliographic record
Abstract
Background: Yoga is an evidence-based mind-body practice known to improve physical and mental health in cancer patients. We report on the processes and patient-reported outcomes of one-on-one yoga therapy (YT) consultations delivered via telehealth. Methods: For patients completing a YT consultation between March 2020 and October 2021, we examined demographics, reasons for referral, and self-reported symptom burden before and after one YT session using the Edmonton Symptom Assessment Scale (ESAS). Changes in ESAS symptom and subscale scores [physical distress (PHS), psychological distress (PSS), and global distress (GDS)] were evaluated by Wilcoxon signed-rank test. Descriptive statistics summarized the data. Results: Ninety-seven initial YT consults were completed, with data evaluated for 95 patient encounters. The majority were women (83.2%) and white (75.8%), The mean age for females was 54.0 and for males was 53.4; the most common diagnosis was breast cancer (48%), 32.6% had metastatic disease, and nearly half (48.4%) were employed full-time. Mental health (43.0%) was the most common reason for referral, followed by fatigue (13.2%) and sleep disturbances (11.7%). The highest symptoms at baseline were sleep disturbance (4.3), followed by anxiety (3.7) and fatigue (3.5). YT lead to clinically and statistically significant reductions in PHS (mean change = −3.1, P < .001) and GDS (mean change = −5.1, P < .001) and significant reductions in PSS (mean change = −1.6, P < .001). Examination of specific symptom scores revealed clinically and statistically significant reductions in anxiety (mean change score −1.34, P < .001) and fatigue (mean change score −1.22, P < .001). Exploratory analyses of patients scoring ≥1 for specific symptoms pre-YT revealed clinically and statistically significant improvements in almost all symptoms and those scoring ≥4 pre-YT. Conclusions: As part of an integrative oncology outpatient consultation service, a single YT intervention delivered via telehealth contributed to a significant improvement in global, physical, and psychosocial distress. Additional research is warranted to explore the long-term sustainability of the improvement in symptoms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.105 | 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".