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Record W4410501607 · doi:10.1093/sleep/zsaf090.1211

1211 Sleep Outcomes and Relationships with Anxiety and Depression Symptoms in Youth with and Without Cystic Fibrosis

2025· article· en· W4410501607 on OpenAlexaff
Jordana McMurray, Kim Widger, Anne L. Stephenson, Robyn Stremler

Bibliographic record

VenueSLEEP · 2025
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsAnxietyDepression (economics)Sleep (system call)Cystic fibrosisClinical psychologyMedicinePsychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction The adolescent and young adult (AYA) developmental period is a time of risk for both sleep and mental health concerns which may be amplified for those living with cystic fibrosis (CF). This study aimed to better understand sleep, sleep disturbance, and relationships to anxiety and depression symptoms in AYA with CF. Methods This study examined whether significant differences exist between AYA with CF and healthy controls, aged 14-25, in actigraphic (TST, SE, WASO, number of night awakenings) or self-reported sleep outcomes (PSQI, PROMIS-SD, PROMIS-SRI, sleep diary); anxiety or depression symptoms (STAI-S, CES-D); and examined whether anxiety or depression symptoms were associated with sleep outcomes in AYA with CF. Semi-structured interviews were conducted with a subset of purposively sampled participants with CF aiming to capture maximum variation in sleep experience as defined by the PSQI. Disease characteristics were also collected from the CF National Data Registry for all CF participants. Quantitative analysis: linear regression models were used to determine differences between groups and to examine associations between sleep and mental health outcomes. Qualitative analysis: interviews were audio-recorded, de-identified, and transcribed verbatim. Data was analyzed through content analysis, independently by two researchers using qualitative descriptive methods. Results This study (n=86; n=45 with CF, n=41 without CF) found no significant difference between AYA with CF and healthy controls 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 (p< 0.001) and depression (p< 0.001) symptoms, but actigraphically-measured sleep was not. The qualitative arm (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. Despite potential improvements, AYA with CF still report unique sleep challenges. Support (if any)

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.013
GPT teacher head0.253
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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