Bedtime digital media use, sleep and fatigue among survivors of childhood cancer, their siblings and healthy control sibling pairs
Bibliographic record
Abstract
Bedtime digital media use (BDM) is linked to poor sleep and fatigue in many populations. Pediatric cancer patients have been observed to engage in BDM in clinical settings, but it is unknown whether BDM rates are higher in this population or how this impacts their sleep and fatigue during treatment and into survivorship. The goal of this study was to evaluate patterns of BDM and its relationship with sleep and fatigue in a sample of pediatric cancer survivors and to compare these patterns with children from their own family (i.e. siblings) and children from unaffected families (i.e. healthy matched controls and siblings of controls). Ninety-nine children (4 groups: 24 acute lymphoblastic leukemia survivors, 13 survivor siblings, 33 controls, 29 control siblings) ages 8–18 were recruited from a long-term survivor clinic at a large children’s hospital and via community advertisements. Survivors were 2–7 years post-treatment (M = 4.80 years). Children’s BDM was parent-reported. Children completed 7 consecutive days of sleep actigraphy and the PedsQL Multidimensional Fatigue Scale. Most survivors (66.67%) engaged in BDM; smartphones were the most common medium. BDM patterns were equivalent across survivors, their siblings, controls, and control siblings. Statistical trends suggested that BDM was associated with fewer minutes of sleep and greater fatigue for all children; these relationships were equivalent across groups. BDM was common among survivors, but usage was not different from their own siblings or compared to healthy control children and sibling pairs. This study underscores the importance of assessing bedtime digital media use in childhood cancer survivors, although other factors impacting sleep should be explored. Clinicians should emphasize established recommendations for healthy media use and sleep habits in pediatric oncology settings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".