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ArticleHypertension research : official journal of the Japanese Society of Hypertension2021

Differences between home blood pressure and strictly measured office blood pressure and their determinants in Japanese men.

Sayaka Kadowaki, Takashi Kadowaki, Atsushi Hozawa, Akira Fujiyoshi, Takashi Hisamatsu, Atsushi Satoh, Hisatomi Arima, Sachiko Tanaka, Sayuki Torii, Keiko Kondo and 7 more

Abstract read
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In one paragraph

Article in Hypertension research : official journal of the Japanese Society of Hypertension, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Annual reports on hypertension research 2020.Hypertension research : official journal of the Japanese Society of Hypertension · 2022
    Review
  4. Article
  5. Home and office blood pressure: time to look at the individual patient.Hypertension research : official journal of the Japanese Society of Hypertension · 2021
    Article
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Sayaka KadowakiDepartment of Public Health, Shiga University of Medical Science, Shiga, Japan. sayakado@belle.shiga-med.ac.jp.
Takashi KadowakiDepartment of Public Health, Shiga University of Medical Science, Shiga, Japan.
Atsushi HozawaDepartment of Preventive Medicine and Epidemiology, Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan.
Akira FujiyoshiDepartment of Hygiene, Wakayama Medical University, Wakayama, Japan.
Takashi HisamatsuDepartment of Public Health, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
Atsushi SatohDepartment of Preventive Medicine and Public Health, Faculty of Medicine, Fukuoka University, Fukuoka, Japan.
Hisatomi ArimaDepartment of Preventive Medicine and Public Health, Faculty of Medicine, Fukuoka University, Fukuoka, Japan.
Sachiko TanakaDepartment of Biostatistics, Shiga University of Medical Science, Shiga, Japan.
Sayuki ToriiDepartment of Public Health, Shiga University of Medical Science, Shiga, Japan.
Keiko KondoCenter for Epidemiologic Research in Asia, Shiga University of Medical Science, Shiga, Japan.
Aya KadotaDepartment of Public Health, Shiga University of Medical Science, Shiga, Japan.
Kamal MasakiDepartment of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii Manoa, Honolulu, HI, USA.
Tomonori OkamuraDepartment of Preventive Medicine and Public Health, School of Medicine, Keio University, Tokyo, Japan.
Takayoshi OhkuboDepartment of Hygiene and Public Health, Teikyo University School of Medicine, Tokyo, Japan.
Katsuyuki MiuraDepartment of Public Health, Shiga University of Medical Science, Shiga, Japan.
Hirotsugu UeshimaDepartment of Public Health, Shiga University of Medical Science, Shiga, Japan.
SESSA Research Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conventional office blood pressure (OBP) and home blood pressure (HBP) measurements are often inconsistent. The purpose of this research was (1) to test whether strictly measured OBP values with sufficient rest time before measurement (st-OBP) is comparable to HBP at the population level and (2) to ascertain whether there are particular determinants for the difference between HBP and st-OBP at the individual level. Data from a population-based group of 1056 men aged 40-79 years were analyzed. After a five-min rest, st-OBP was measured twice. HBP was measured after a 2-min rest every morning for seven consecutive days. To determine factors related to ΔSBP (HBP minus st-OBP measurements), multiple linear regression analyses and analyses of covariance were performed. While st-OBP and HBP were comparable (136.5 vs. 137.2 mmHg) at the population level, ΔSBP varied with a standard deviation of 13.5 mmHg. Smoking was associated with a larger ΔSBP regardless of antihypertensive usage, and BMI was associated with a larger ΔSBP in participants using antihypertensive drugs. The adjusted mean ΔSBP in the highest BMI tertile category was 4.6 mmHg in participants taking antihypertensive drugs. st-OBP and HBP measurements were comparable at the population level, although the distribution of ΔSBP was considerably broad. Smokers and obese men taking antihypertensive drugs had higher HBP than st-OBP, indicating that their blood pressure levels are at risk of being underestimated. Therefore, this group would benefit from the addition of HBP measurements.

Indexed as

Blood Pressure DeterminationAntihypertensive AgentsBlood PressureBlood Pressure Monitoring, AmbulatoryHumansHypertensionJapanMaleAntihypertensive AgentsGeneral populationHome blood pressureOffice blood pressure

Identifiers

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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.