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Record W4406218566 · doi:10.1002/alz.087540

Sleep health, cognitive function, and self‐management skills in middle‐aged adults

2024· article· en· W4406218566 on OpenAlexaboutno aff
Minjee Kim, Fangyu Yeh, Pauline Zheng, Mary Kwasny, Julia Yoshino‐Benavente, Laura M. Curtis, Stacy Cooper Bailey, Morgan Bonham, Britney Sun, Han Q Luu, Patrick Cecil, Prophecy Agyare, Phyllis C. Zee, Michael S. Wolf

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsSleep (system call)CognitionGerontologyPsychologyFunction (biology)Developmental psychologyMedicineComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Poor sleep health has been associated with worse cognitive and health outcomes in older adults. Less is known about this relationship in midlife. Thus, we aimed to investigate the relationship between self‐reported sleep health, cognitive function, and performance on common health tasks among middle‐aged adults, as sleep may be a modifiable target to address later life risk of cognitive decline. Method English‐speaking adults aged 35‐64 were recruited from an academic general internal medicine practice and federally qualified health centers in the greater Chicagoland area. Multidimensional sleep health (regularity, satisfaction, alertness, timing, efficiency, and duration) was measured by the RU‐SATED questionnaire. Global cognitive function was measured using the Montreal Cognitive Assessment (MoCA); age‐ and education‐adjusted Z‐scores were calculated. Cognitive impairment was defined as MoCA Z‐score lower than one standard deviation below population mean. Performance on common health tasks (e.g. comprehension of print health material, recall of spoken instructions, dosing medications, recall of multimedia education) were assessed using the Comprehensive Health Activities Scale (CHAS). We examined the association between sleep health, cognitive impairment, and health task performance using univariate and multivariable logistic regression. Covariates, selected a priori, included age, sex, number of chronic conditions, and depressive symptoms. Result A total of 310 participants (mean age 51.2 ± 8.1; 66% female; 39% non‐Hispanic Black, 32% non‐Hispanic White, 21% Hispanic; 54% with 2+ chronic conditions) were included in analyses. The median sleep health score was 8 (interquartile range: 6‐10) and cognitive impairment was found in 11.3%. Poorer sleep health was significantly associated with cognitive impairment after adjusting for a priori covariates (adjusted odds ratio, 0.98; 95% CI, 0.96‐0.99; P = 0.007). Both poorer sleep health (ß, 1.11; 95% CI, 0.22‐1.99; P = 0.014) and cognitive impairment (ß, ‐27.7; CI, (‐34.3)‐(‐21.1); P<0.001) were independently associated with poorer performance on health tasks, after adjusting for a priori covariates. Conclusion Poorer self‐reported sleep health in midlife was associated with a greater likelihood of cognitive impairment, whereas both poorer sleep health and cognitive impairment were associated with lower self‐management abilities to navigate healthcare. Future studies should examine whether sleep‐targeted intervention in midlife can mitigate cognitive later‐life decline and poorer health outcomes.

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)0.001
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.271
Teacher spread0.259 · 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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