Evidence map›Paper›PMID 39749351›Full record

ArticleSleep epidemiology2024

Correlates of multidimensional sleep in premenopausal women: The BioCycle study.

Xinrui Wu, Galit Levi Dunietz, Kerby Shedden, Ronald D Chervin, Erica C Jansen, Xiru Lyu, Louise M O'Brien, Ana Baylin, Jean Wactawski-Wende, Enrique F Schisterman and 1 more

Abstract read
In one paragraph

Article in Sleep epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Xinrui WuDepartment of Statistics, University of Michigan, Ann Arbor, MI, United States.
Galit Levi DunietzDivision of Sleep Medicine, Department of Neurology, University of Michigan, Ann Arbor, MI, United States.
Kerby SheddenDepartment of Statistics, University of Michigan, Ann Arbor, MI, United States.
Ronald D ChervinDivision of Sleep Medicine, Department of Neurology, University of Michigan, Ann Arbor, MI, United States.
Erica C JansenDepartment of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, United States.
Xiru LyuDepartment of Statistics, University of Michigan, Ann Arbor, MI, United States.
Louise M O'BrienDivision of Sleep Medicine, Department of Neurology, University of Michigan, Ann Arbor, MI, United States.
Ana BaylinDepartment of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, United States.
Jean Wactawski-WendeDepartment of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, United States.
Enrique F SchistermanDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Sunni L MumfordDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Funding

Strategic Vision & Impact on Environmental HealthP30ES017885 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI AMY J SCHULZ · 2011 to 2026
$21.3M
Associations of Sleep Duration and Dynamics with Biomarkers of Cardiometabolic Diseases; A micro-longitudinal ApproachK01HL144914 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DUNIETZ, GALIT LEVI · 2019 to 2023
$537k
NHLBI NIH HHS K01 HL144914NICHD NIH HHS HHSN275201100002CNICHD NIH HHS HHSN275201100002GNIEHS NIH HHS P30 ES017885
6 · The paper itself

Abstract

Purpose: To identify sleep dimensions (characteristics) that co-occur in premenopausal women. The second aim was to examine associations between multiple dimensions of sleep and a set of demographic, lifestyle, and health correlates. The overarching goal was to uncover patterns of poor-sleep correlates that might inform interventions to improve sleep health of women in this age group. Methods: The BioCycle Study included 259 healthy women aged 18-44y recruited between 2005 and 2007 from Western New York. Participants reported sleep data through daily diaries and questionnaires that were used to create five sleep health dimensions (duration, variability, timing, latency, and continuity). We used multivariate analysis - canonical correlation methods - to identify links among dimensions of sleep health and patterns of demographic, psychological, and occupational correlates. Results: Two distinct combinations of sleep dimensions were identified. The first - primarily determined by low variability in nightly sleep duration, low variability in bedtime (timing), greater nocturnal awakening, and less sleep onset latency - was distinguished from the second - primarily determined by sleep duration.The first combination of sleep dimensions was associated with older age and higher parity, fewer depressive symptoms, and higher stress level. The second combination of sleep dimensions was associated with perception of longer sleep duration as optimal, lower parity, not engaging in shift work, older age, lower stress level, higher prevalence of depressive symptoms, and White race. Conclusion: Among premenopausal women, we demonstrated distinct patterns of sleep dimensions that co-occur and vary by demographic, health, and lifestyle correlates. These findings shed light on the correlates of sleep health vulnerabilities among young women.

Indexed as

BedtimeInsomniaSleepSleep healthSleep variabilityWomen’s health

Identifiers

PMID39749351
PMCPMC11694500

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.