Smartphone‐based Ecological Momentary Assessment to study “scanxiety” among Adolescent and Young Adult survivors of childhood cancer: A feasibility study
Bibliographic record
Abstract
OBJECTIVE: Scan-related anxiety ("scanxiety") refers to the fear, stress, and anxiety in anticipation of tests and scans in follow-up cancer care. This study assessed the feasibility of Ecological Momentary Assessment (EMA) for real-world, real-time capture of scanxiety using patients' personal smartphone. METHODS: Adolescent and Young Adult survivors of childhood cancer were prompted to complete EMA surveys on a smartphone app three times per day for 11 days (33 surveys total) around their routine surveillance scans. Participants provided structured feedback on the EMA protocol. RESULTS: Thirty out of 46 contacted survivors (65%) enrolled, exceeding the preregistered feasibility cutoff of 55%. The survey completion rate (83%) greatly exceeded the preregistered feasibility cutoff of 65%. Participants generally found the smartphone app easy and enjoyable to use and reported low levels of distress from answering surveys. Participants reported significantly more daily fear of cancer recurrence (FCR) and negative affect in the days before compared to the days after surveillance scans, aligning with the expected trajectory of scanxiety. Participants who reported greater FCR and scanxiety using comprehensive measures at baseline also reported significantly more daily FCR around their surveillance scans, indicating validity of EMA items. Bodily threat monitoring was prospectively and concurrently associated with daily FCR, thus warranting further investigation as a risk factor for scanxiety. CONCLUSIONS: Findings indicate the feasibility, acceptability, and validity of EMA as a research tool to capture the dynamics and potential risk factors for scanxiety.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".