Improved health-related quality of life (HR-QOL) with use of an online mindfulness tool in patients with metastatic renal cell carcinoma (mRCC) receiving immunotherapy (IO).
Bibliographic record
Abstract
660 Background: Previous randomized studies have shown the benefit of interventions to increase mindfulness in multiple cancer types, including prostate cancer (Chambers et al JCO 2017), but limited data exists in mRCC. We sought to determine the effect of an app-based mindfulness intervention on anxiety, fear of cancer progression (FCR), fatigue and HR-QOL in this population. Methods: Eligible patients had mRCC, were receiving IO, had measurable symptoms of anxiety or FCR, had a smartphone with internet access and had not participated in a mindfulness program in the past 5 years. Patients were recruited in Brazil across 7 private centers and one academic center in the US. We evaluated the Mindfulness-Based Cancer Survivorship Journey, a program within the Am Mindfulness smartphone app (AmDTx). Patients used AmDTx for 20-30 minutes each day for a minimum of 4 days per week over a period of 4 weeks. Patients were assessed at baseline (T1) and at weeks 2 (T2), 4 (T3) and 12 (T4) using the PROMIS-Anxiety, FCR-7, Brief Fatigue Inventory (BFI), and Functional Assessment of Chronic Illness Therapy-General (FACT-G). RM-ANOVA was used to test the effect of time on symptoms and on HR-QOL. Results: A total of 41 patients were recruited; median age was 59 (range, 36-79) and patients were predominantly male (70%), white (61%), married (75%) and well educated (65% had at least a college degree). Most patients were receiving nivolumab/ipilimumab (44%), nivolumab (22%) or axitinib/pembrolizumab (9%). Symptoms of anxiety significantly decreased from 21.6 + 4.8 to 12.5 + 5.1 (P=0.001). Similar findings were found for FCR (MT1=21.4 to MT4=13.5, P=0.001) and fatigue (MT1=32.0 to MT4=19.4, P=0.001). Notably, HR-QOL increased from 81.1 + 13.4 to 92.7 + 14.9 (P=0.001). No significant differences were identified based on disease characteristics or type of therapy. Conclusions: The current study suggests that smartphone-based mindfulness intervention could improve HR-QOL and decreased FCR, anxiety and fatigue. This low-cost, easily accessible intervention may provide an important alternative to in-person psychosocial support for patients with mRCC and should be assessed in randomized trials in this disease. Funding: Kure It Cancer Research: 2020 Barry Hoeven Memorial Kidney Cancer Research Grant (PI: C D Bergerot).
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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 | 0.001 |
| 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) | 0.001 | 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".