A novel ecological momentary assessment app for the investigation of daily cognitive functioning in breast cancer survivors: a feasibility study
Bibliographic record
Abstract
PURPOSE: Breast cancer survivors often experience cancer-related cognitive impairment (CRCI), such as problems with memory and attention. However, typical neuropsychological test batteries are unable to capture the day-to-day variability of cognition and may be underestimating CRCI. The present study aims to assess the feasibility, usability, and validity of a novel ecological momentary assessment (EMA) app of cognition. METHODS: Nineteen breast cancer survivors 6-36-month post-chemotherapy and 28 healthy controls completed the NIH Toolbox Cognition Battery. Subsequently, participants completed the EMA app (once a day, for 30 days) comprising four cognitive tasks assessing processing speed, working memory, inhibition, and attention. At the conclusion of the app, participants completed a usability questionnaire on which content analysis was performed. Feasibility was assessed against eight criteria, including accessibility, app compliance, and technical smoothness. Convergent construct validity was assessed using Spearman's correlation analyses between the NIH toolbox and the EMA app. RESULTS: Five of eight feasibility criteria were met, including accessibility, app motivation, participation rate, drop-out, and data collection. Additionally, our content analyses revealed four themes important to usability: self-development, altruism, engagement, and functionality. Majority of the EMA tasks were moderately positively correlated with the corresponding constructs of the NIH toolbox tasks (R's range 0.55-0.64), indicating better performance on the EMA app coincided with better performance on the NIH toolbox. CONCLUSIONS: Our findings show the app was accessible, participants were motivated to complete sessions, and our tasks showed good construct validity. IMPLICATIONS FOR CANCER SURVIVORS: Our novel EMA app can be used as a comprehensive cognitive measure in cancer survivors.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".