Construct validity and reliability of a real-time multidimensional smartphone app to assess pain in children and adolescents with cancer
Bibliographic record
Abstract
We evaluated the construct validity (including responsiveness), reliability, and feasibility of the Pain Squad multidimensional smartphone-based pain assessment application (app) in children and adolescents with cancer, using 2 descriptive studies with repeated measures. Participants (8-18 years) undergoing cancer treatment were drawn from 4 pediatric cancer centers. In study 1, 92 participants self-reported their level of pain twice daily for 2 weeks using the Pain Squad app to assess app construct validity and reliability. In study 2, 14 participants recorded their level of pain twice a day for 1 week before and 2 weeks after cancer-related surgery to determine app responsiveness. Participants in both studies completed multiple measures to determine the construct validity and feasibility of the Pain Squad app. Correlations between average weekly pain ratings on the Pain Squad app and recalled least, average, and worst weekly pain were moderate to high (0.43-0.68). Correlations with health-related quality of life and pain coping (measured with PedsQL Inventory 4.0, PedsQL Cancer Module, and Pain Coping Questionnaire) were -0.46 to 0.29. The app showed excellent internal consistency (α = 0.96). Pain ratings changed because of surgery with large effect sizes between baseline and the first week postsurgery (>0.85) and small effect sizes between baseline and the second week postsurgery (0.13-0.32). These findings provide evidence of the construct validity, reliability, and feasibility of the Pain Squad app in children and adolescents with cancer. Use of real-time data capture approaches should be considered in future studies of childhood cancer pain. A video accompanying this abstract is available online as Supplemental Digital Content at http://links.lww.com/PAIN/A169.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".