Evidence map›Paper›PMID 42008265›Full record

ArticleJAMA network open2026

Objectively Measured Daytime Napping Patterns and All-Cause Mortality in Older Adults.

Chenlu Gao, Ruixue Cai, Xi Zheng, Arlen Gaba, Lei Yu, Aron S Buchman, David A Bennett, Lei Gao, Kun Hu, Peng Li

Abstract read
In one paragraph

Article in JAMA network open, 2026. 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

10 authors.

Chenlu GaoDepartment of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts.
Ruixue CaiDepartment of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts.
Xi ZhengDepartment of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts.
Arlen GabaDivision of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts.
Lei YuRush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois.
Aron S BuchmanRush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois.
David A BennettRush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois.
Lei GaoDepartment of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts.
Kun HuDepartment of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts.
Peng LiDepartment of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts.

Funding

Sleep and Circadian Rhythm Biomarkers of Postoperative Neurocognitive Recovery (SLEEP-POD2)R01AG093843 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI Lei Gao · 2026 to 2026
$1.0M
Harnessing Sleep and Biological Rhythms to Improve Postoperative RecoveryR35GM160254 · NIGMS · MASSACHUSETTS GENERAL HOSPITAL · PI Lei Gao · 2025 to 2026
$908k
NIA NIH HHS R01 AG093843NIGMS NIH HHS R35 GM160254
6 · The paper itself

Abstract

Importance: Daytime napping is common among older adults, but evidence linking objective daytime nap patterns to mortality is limited, as prior studies relied largely on self-report. Some daytime nap characteristics, such as timing and variability of nap duration across days, are also understudied. Objective: To test whether actigraphy-measured daytime nap duration, frequency, variability, and timing are associated with all-cause mortality. Design, Setting, and Participants: This prospective cohort study used data from the population-based Rush Memory and Aging Project, in which the analytic baseline was initiated in August 2005. Participants in this current study were community-dwelling adults aged 56 years or older in northern Illinois, with up to 19 years of follow-up. Data for this study were gathered until April 2025. Main Outcome and Measures: Daytime napping, defined as sleeping between 9 am and 7 pm and assessed by up to 14 days of actigraphy data. Cox proportional hazards regression models were used to estimate associations between baseline daytime nap characteristics (duration, frequency, variability in duration across days, and timing) and subsequent all-cause mortality. Results: Among 1338 participants over a mean (SD) follow-up of 8.30 (4.78) years (mean [SD] participant age, 81.40 [7.39] years; 1018 [76.0%] female), 926 (69.2%) died at a mean (SD) of 7.54 (4.52) years (range, 0.08-19.08 years) after analytic baseline. Participants wore the actigraphy device for a mean (SD) of 9.58 (1.22) days. After adjusting for all covariates, longer daytime nap duration (adjusted hazard ratio [AHR] per 1-hour increase, 1.13; 95% CI, 1.04-1.23; P = .005) and higher nap frequency (AHR per additional daily nap, 1.07; 95% CI, 1.02-1.13; P = .003) at baseline were associated with increased mortality. Morning nappers had higher mortality risk compared with early afternoon nappers (AHR, 1.30; 95% CI, 1.03-1.64; P = .03). Variability in daytime nap duration was not associated with mortality after covariate adjustment (AHR per 1-hour increase, 1.01; 95% CI, 0.89-1.14; P = .93). Conclusions and Relevance: In this prospective cohort study of community-dwelling older adults, longer and more frequent daytime napping, especially in the morning, were associated with higher all-cause mortality. The findings suggest incorporating wearable device-based daytime nap assessments into clinical and public health practices may provide novel opportunities for early risk identification of health conditions.

Indexed as

MortalitySleepActigraphyAgedAged, 80 and overCause of DeathFemaleHumansIllinoisIndependent LivingMaleMiddle AgedProportional Hazards ModelsProspective StudiesSleep Duration

Identifiers

PMID42008265
PMCPMC13096975

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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.