Virtual Mindfulness-Based Intervention for Hemodialysis Patients During COVID-19 for Chronic Pain, Stress, Anxiety, and Depression
Bibliographic record
Abstract
Introduction: Up to 50% of hemodialysis (HD) patients experience stress, anxiety, depression and chronic pain. With COVID-19, these symptoms are often exacerbated, and healthcare services are harder to access due to distancing measures and staff shortages. Mindfulness-based interventions (MI) are effective in reducing these symptoms. As part of our institution's standard clinical practice, we offered a virtually-delivered adapted Mindfulness-Based Stress Reduction (MBSR) program to patients during their HD sessions given by an MBSR-certified psychologist. Case Description: A 35-year-old female, on HD since age 6, received five 20-40 minutes individual sessions of the virtual adapted MBSR program over 3 weeks. Perceived stress (Perceived Stress Scale), anxiety (Generalized Anxiety Disorder-7), depression (Patient Health Questionnaire-9) and chronic pain (Questionnaire de Saint-Antoine which is a french adapted version of the McGill pain questionary) levels were measured prior to starting the program, and 2 weeks after the last session. Over 5 weeks, the patient's stress decreased by 1 point (PSS = 17; PSS = 16, both moderate), anxiety decreased by 50% (GAD-7 = 14, moderate; GAD-7 = 7, mild), depression decreased by 15 points (PHQ-9 = 15, moderately severe; PHQ-9 = 0, none/mild), and chronic pain decreased by 19 points (QDSA = 22, moderate; QDSA = 3, mild). The patient also reported successful withdrawal from her restless-legs syndrome and insomnia medications, due to the MBSR breathing techniques she learnt for pain-management and sleep. Using the same MBSR techniques, 18 months after the program, she reported continuing self-management of her insomnia, chronic pain, and restless-legs syndrome without medication and feeling capable of coping with new health challenges, managing difficult emotions, and being able to calm and detach herself from worries and negative self-talk. Discussion: This case illustrates that an adapted MI delivered during HD sessions: 1) may help in managing symptoms of chronic pain, sleep disorders, anxiety and depression, 2) can be delivered virtually, 3) may be a viable short-term and long-term non-pharmacological alternative to managing symptoms in HD patients, for which polypharmacy is a high safety concern.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".