Feasibility and Acceptability of a Virtual “Coping with Brain Fog” Intervention for Improving Cognitive Functioning in Young Adults with Cancer
Bibliographic record
Abstract
Purpose:Cancer-related cognitive deficits (CRCDs) are common among young adults (YAs) (ages: 18–39) with cancer and can be debilitating. We aimed to determine the feasibility and acceptability of a virtual Coping with Brain Fog intervention among YAs with cancer. Our secondary aims were to explore the intervention's effect on cognitive functioning and psychological distress. Methods:This prospective feasibility study involved eight weekly, 90-minute virtual group sessions. Sessions focused on psychoeducation on CRCD, memory skills, task management, and psychological well-being. The primary outcomes were feasibility and acceptability of the intervention evaluated through attendance (>60% not missing >2 consecutive sessions) and satisfaction (Client Satisfaction Questionnaire [CSQ] score >20). Secondary outcomes included the following: cognitive functioning (Functional Assessment of Cancer Therapy-Cognitive Function [FACT-Cog] Scale) and symptoms of distress (Patient-Reported Outcomes Measurement Information System [PROMIS] Short Form–Anxiety/Depression/Fatigue) and participants' experiences using semistructured interviews. Paired t-tests and summative content analysis were used for quantitative and qualitative data analyses. Results:Twelve participants (five male, mean age = 33 years) were enrolled. All but one participant met feasibility criteria of not missing >2 consecutive sessions (11/12 = 92%). The mean CSQ score was 28.1 (standard deviation 2.5). Significant improvement in cognitive function as measured by FACT-Cog Scale was observed postintervention (p < 0.05). Ten participants adopted strategies from the program to combat CRCD, and eight reported CRCD symptom improvement. Conclusion:A virtual Coping with Brain Fog intervention is feasible and acceptable for the symptoms of CRCD among YAs with cancer. The exploratory data indicate subjective improvement in cognitive function, and will inform the design and implementation of a future clinical trial.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".