Digital Technology Distraction for Acute Pain in Children: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Digital distraction is being integrated into pediatric pain care, but its efficacy is currently unknown. OBJECTIVE: To determine the effect of digital technology distraction on pain and distress in children experiencing acutely painful conditions or procedures. DATA SOURCES: Medline, Embase, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, Institute of Electrical and Electronics Engineers Xplore, Ei Compendex, Web of Science, and gray literature sources. STUDY SELECTION: Quantitative studies of digital technology distraction for acutely painful conditions or procedures in children. DATA EXTRACTION: Performed by 1 reviewer with verification. Outcomes were child pain and distress. RESULTS: There were 106 studies (n = 7820) that reported on digital technology distractors (eg, virtual reality and video games) used during common procedures (eg, venipuncture, dental, and burn treatments). No studies reported on painful conditions. For painful procedures, digital distraction resulted in a modest but clinically important reduction in self-reported pain (standardized mean difference [SMD] −0.48; 95% confidence interval [CI] −0.66 to −0.29; 46 randomized controlled trials [RCTs]; n = 3200), observer-reported pain (SMD −0.68; 95% CI −0.91 to −0.45; 17 RCTs; n = 1199), behavioral pain (SMD −0.57; 95% CI −0.94 to −0.19; 19 RCTs; n = 1173), self-reported distress (SMD −0.49; 95% CI −0.70 to −0.27; 19 RCTs; n = 1818), observer-reported distress (SMD −0.47; 95% CI −0.77 to −0.17; 10 RCTs; n = 826), and behavioral distress (SMD −0.35; 95% CI −0.59 to −0.12; 17 RCTs; n = 1264) compared with usual care. LIMITATIONS: Few studies directly compared different distractors or provided subgroup data to inform applicability. CONCLUSIONS: Digital distraction provides modest pain and distress reduction for children undergoing painful procedures; its superiority over nondigital distractors is not established. Context, preferences, and availability should inform the choice of distractor.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".