Implementation and preliminary effectiveness of a real‐time pain management smartphone app for adolescents with cancer: A multicenter pilot clinical study
Bibliographic record
Abstract
BACKGROUND: Pain in adolescents with cancer (12-18 years) is common and negatively impacts health-related quality of life (HRQL). The Pain Squad+ smartphone app, which provides adolescents with real-time pain self-management support, was developed to address this issue. This study evaluated the implementation of the app to inform a future randomized controlled trial (RCT) and obtain treatment effect estimates for pain intensity, pain interference, HRQL, and self-efficacy. PROCEDURE: A one-group baseline/poststudy design with 40 adolescents recruited from two pediatric tertiary care centers was used. Baseline questionnaires were completed and adolescents used the app at least twice daily for 28 days, receiving algorithm-informed self-management advice depending on their reported pain. A nurse received alerts in response to sustained pain and contacted adolescents to assist in pain care. Poststudy questionnaires were completed. Descriptive analyses, with exploratory inferential testing conducted on health outcome data, were used to address study aims. RESULTS: Most (40/52; 77%) eligible adolescents participated. Two participants withdrew participation. Intervention fidelity was impacted by technical difficulties (occurring for 15% of participants) and a prolonged time for nurse contact in the event of sustained pain. Adherence to pain reporting was 68.8 ± 38.1%. Outcome measure completion rates were high and the intervention was acceptable to participants. Trends in improvements in pain intensity, pain interference, and HRQL were significant, with effect sizes of 0.23-0.67. CONCLUSIONS: Implementation of Pain Squad+ is feasible and the app appears to improve pain-related outcomes for adolescents with cancer. A multicenter RCT will be undertaken to examine app effectiveness.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".