Sleep disturbance underlies the co-occurrence of trauma and pediatric chronic pain: a longitudinal examination
Bibliographic record
Abstract
Epidemiological and cross-sectional studies have shown that post-traumatic stress disorder symptoms (PTSS) are common and impairing in youth with chronic pain. Yet, the co-occurrence of PTSS and pediatric chronic pain has not been examined longitudinally, which has limited understanding of theoretically proposed mechanisms (eg, sleep disturbance) underlying the PTSS-pain relationship over time. This longitudinal study aimed to fill this gap. Participants included 138 youth (Mage = 14.29, 75% girls) referred to a tertiary-level outpatient chronic pain program and one of their parents. At baseline, youth reported their pain intensity and interference, PTSS, and subjective sleep disturbances (ie, sleep quality and insomnia). Youth and parents completed semistructured diagnostic interviews to determine the child's post-traumatic stress disorder diagnostic status, and youth completed an objective assessment of sleep patterns for 7 days using actigraphy. At 3-month follow-up, youth once again completed the diagnostic interview and reported their pain intensity, pain interference, and PTSS. Partially latent cross-lagged structural equation panel models revealed that, controlling for pain intensity, pain interference and PTSS co-occurred at baseline, but not at follow-up (while controlling for baseline levels). Higher levels of baseline PTSS were predictive of increases in pain interference at follow-up. Furthermore, subjective sleep disturbances mediated the relationship between baseline PTSS and follow-up pain interference. These findings lend support to conceptual models of PTSS-pain co-occurrence and highlight a critical need to assess and address trauma and sleep disturbances in youth with chronic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".