Feasibility and acceptability of a mindfulness app-based intervention among patients with metastatic renal cell carcinoma: a multinational study
Bibliographic record
Abstract
BACKGROUND: Patients with metastatic renal cell carcinoma (mRCC) experience emotional distress and limited supportive care access. This study assesses a mindfulness app's feasibility, acceptability, and preliminary efficacy in improving emotional symptoms, trait mindfulness, and overall quality of life for patients with mRCC on immunotherapy. METHODS: This multinational study recruited patients with mRCC undergoing immunotherapy from Brazil and the United States. Participants were required to engage in mindfulness app-based activities for 20-30 min daily, at least 4 days per week, over a 4-week period. Assessments were conducted at weeks 0, 2, 4, and 12 to evaluate emotional symptoms (PROMIS-Anxiety and Depression, Fear of Cancer Recurrence-7), fatigue (Brief Fatigue Inventory), trait mindfulness (Mindfulness Attention Awareness Scale), and quality of life (Functional Assessment of Chronic Illness Therapy-General). Self-reported data were used to assess adherence. Linear mixed-effects models were used to evaluate changes over time for the measured outcomes. RESULTS: Among 50 patients with mRCC, the feasibility of this intervention was demonstrated; 96% of patients were assessed at week 4, with high adherence rates reported by 75% of patients. Participants expressed positive feedback on the smartphone-based approach. Significant improvements were observed in emotional symptoms, fatigue, and quality of life scores from baseline to post-intervention (P = .001 for each), suggesting the positive impact of this intervention. CONCLUSION: Our findings provide encouraging evidence for the feasibility and acceptability of a mindfulness app-based intervention among patients with mRCC. This intervention may offer a viable and accessible means of providing psychosocial support to patients with mRCC.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".