Sleep Outcomes and Mental Health in Adolescents and Young Adults with Cystic Fibrosis
Bibliographic record
Abstract
Background: Sleep has established relationships with emotional well-being. The adolescent and young adult (AYA) developmental period is a known time of risk for both sleep and mental health concerns and these concerns may be amplified for those living with cystic fibrosis (CF). Objective: To better understand sleep, sleep disturbance, and relationships to anxiety and depression symptoms in AYA with CF. Design: A multi-method, multiple study design included a systematic review, a cross-sectional comparative quantitative study, and a qualitative descriptive study. Data was collected in Toronto, Ontario, Canada from March 2023-February 2024. Methods: In the systematic review we examined what was currently known about actigraphic and self-reported sleep in young people with CF. The results were utilized to inform the development of two subsequent studies which were conducted concurrently. In the quantitative study we examined actigraphic and self-reported sleep outcomes and relationships to anxiety and depression symptoms in AYA with CF as compared to a healthy comparator group. In the qualitative study we collected interview data and explored the experience of sleep and factors affecting sleep from the perspective of AYA with CF. Results: The quantitative study (n=86; n=45 with CF, n=41 without CF) found no significant difference between AYA with CF and a healthy comparator group in actigraphically-measured or self-reported sleep outcomes, or in anxiety or depression symptoms. In participants with CF, self-reported sleep quality was significantly associated with both anxiety and depression symptoms, but actigraphically-measured sleep was not. The qualitative study (n=19) found that AYA with CF reported that CF symptoms, CF treatments, anxiety, changes in health status, and initiation of elexacaftor/tezacaftor/ ivacaftor (ETI) interfere with sleep. While, positioning, good sleep hygiene, breathing therapies, and being stable on ETI were described as benefitting sleep. AYA’s with CF also discussed a desire to engage in discussions about sleep with their CF care teams. Conclusion: Within the current context of improving treatments and care, sleep and mental health outcomes may be improving for AYA with CF. However, despite potential improvements, AYA with CF still report unique sleep challenges. These findings have important implications for sleep assessment and future interventional research.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".