Evidence map›Paper›PMID 37310958›Full record

ArticlePLOS digital health2023

Classification of blood pressure during sleep impacts designation of nocturnal nondipping.

Bobak J Mortazavi, Josefa L Martinez-Brockman, Baylah Tessier-Sherman, Matthew Burg, Mary Miller, Zhale Nowroozilarki, O Peter Adams, Rohan Maharaj, Cruz M Nazario, Maxine Nunez and 2 more

Open access · goldAbstract read
In one paragraph

Article in PLOS digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 21% of its field
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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Trial
  2. Review
  3. Observational
  4. Review
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

12 authors at 7 institutions in 5 countries.

Bobak J MortazaviDepartment of Computer Science & Engineering, Texas A&M University, College Station, Texas, United States of America.ORCID https://orcid.org/0000-0002-2655-2095
Josefa L Martinez-BrockmanEquity Research and Innovation Center, Yale School of Medicine, New Haven, Connecticut, United States of America.
Baylah Tessier-ShermanEquity Research and Innovation Center, Yale School of Medicine, New Haven, Connecticut, United States of America.ORCID https://orcid.org/0000-0002-8697-672X
Matthew BurgSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
Mary MillerEquity Research and Innovation Center, Yale School of Medicine, New Haven, Connecticut, United States of America.
Zhale NowroozilarkiDepartment of Computer Science & Engineering, Texas A&M University, College Station, Texas, United States of America.
O Peter AdamsDepartment of Family Medicine, Faculty of Medical Sciences, University of the West Indies, Cave Hill, Barbados.
Rohan MaharajDepartment of Paraclinical Sciences, University of the West Indies, Saint Augustine, Trinidad.ORCID https://orcid.org/0000-0001-8319-8187
Cruz M NazarioDepartment of Biostatistics and Epidemiology, Graduate School of Public Health, University of Puerto Rico, San Juan, Puerto Rico.
Maxine NunezSchool of Nursing, University of the Virgin Islands, US Virgin Islands.
Marcella Nunez-SmithEquity Research and Innovation Center, Yale School of Medicine, New Haven, Connecticut, United States of America.
Erica S SpatzYale/Yale New Haven Health System Corporation Center for Outcomes Research and Evaluation, New Haven, Connecticut, United States of America.ORCID https://orcid.org/0000-0002-1557-7713
Yale University · USTexas A&M University · USUniversity of Puerto Rico System · PRUniversity of the Virgin Islands · VIUniversity of the West Indies · TTUniversity of the West Indies · BBYale New Haven Health System · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Yale Transdisciplinary Collaborative Center for Health Disparities Research (YALE-TCC)U54MD010711 · NIMHD · YALE UNIVERSITY · PI NUNEZ-SMITH, MARCELLA · 2016 to 2021
$14.4M
Institutional Career Development CoreKL2TR001862 · NCATS · YALE UNIVERSITY · PI CANTLEY, LLOYD G, EDELMAN, E. JENNIFER · 2016 to 2025
$12.2M
Eastern Caribbean Health Outcomes Research Network (ECHORNU24MD006938 · NIMHD · YALE UNIVERSITY · PI NUNEZ-SMITH, MARCELLA · 2011 to 2016
$5.7M
NCATS NIH HHS KL2 TR001862NCATS NIH HHS UL1 TR001863NIMHD NIH HHS U24 MD006938NIMHD NIH HHS U54 MD010711
6 · The paper itself

Abstract

The identification of nocturnal nondipping blood pressure (< 10% drop in mean systolic blood pressure from awake to sleep periods), as captured by ambulatory blood pressure monitoring, is a valuable element of risk prediction for cardiovascular disease, independent of daytime or clinic blood pressure measurements. However, capturing measurements, including determination of wake/sleep periods, is challenging. Accordingly, we sought to evaluate the impact of different definitions and algorithms for defining sleep onset on the classification of nocturnal nondipping. Using approaches based upon participant self-reports, applied definition of a common sleep period (12 am -6 am), manual actigraphy, and automated actigraphy we identified changes to the classification of nocturnal nondipping, and conducted a secondary analysis on the potential impact of an ambulatory blood pressure monitor on sleep. Among 61 participants in the Eastern Caribbean Health Outcomes Research Network hypertension study with complete ambulatory blood pressure monitor and sleep data, the concordance for nocturnal nondipping across methods was 0.54 by Fleiss' Kappa (depending on the method, 36 to 51 participants classified as having nocturnal nondipping). Sleep quality for participants with dipping versus nondipping was significantly different for total sleep length when wearing the ambulatory blood pressure monitor (shorter sleep duration) versus not (longer sleep duration), although there were no differences in sleep efficiency or disturbances. These findings indicate that consideration of sleep time measurements is critical for interpreting ambulatory blood pressure. As technology advances to detect blood pressure and sleep patterns, further investigation is needed to determine which method should be used for diagnosis, treatment, and future cardiovascular risk.

Identifiers

PMID37310958
PMCPMC10263317
OpenAlexW4380537074

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.