Evidence map›Paper›PMID 38965318›Full record

ArticleScientific reports2024

A smartphone application toward detection of systolic hypertension in underserved populations.

Cederick Landry, Vishaal Dhamotharan, Mark Freithaler, Alisse Hauspurg, Matthew F Muldoon, Sanjeev G Shroff, Anand Chandrasekhar, Ramakrishna Mukkamala

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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  3. Review
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  5. Article
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

8 authors.

Cederick Landry *Department of Bioengineering, University of Pittsburgh, 408 Benedum Hall, 3700 O'Hara Street, Pittsburgh, PA, 15261, USA.
Vishaal Dhamotharan *Department of Bioengineering, University of Pittsburgh, 408 Benedum Hall, 3700 O'Hara Street, Pittsburgh, PA, 15261, USA.
Mark FreithalerDepartment of Bioengineering, University of Pittsburgh, 408 Benedum Hall, 3700 O'Hara Street, Pittsburgh, PA, 15261, USA.
Alisse HauspurgDepartment of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Matthew F MuldoonDivision of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Sanjeev G ShroffDepartment of Bioengineering, University of Pittsburgh, 408 Benedum Hall, 3700 O'Hara Street, Pittsburgh, PA, 15261, USA.
Anand ChandrasekharDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Ramakrishna MukkamalaDepartment of Bioengineering, University of Pittsburgh, 408 Benedum Hall, 3700 O'Hara Street, Pittsburgh, PA, 15261, USA. rmukkamala@pitt.edu.

Funding

Cardiovascular Bioengineering Training ProgramT32HL076124 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SANJEEV G SHROFF · 2005 to 2026
$7.0M
A Smartphone-Based Device for Cuff-Less and Calibration-Free Blood Pressure MonitoringR01HL146470 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MUKKAMALA, RAMAKRISHNA · 2019 to 2022
$1.1M
Natural Sciences and Engineering Research Council of Canada PDF 568003-2022NHLBI NIH HHS HL146470NHLBI NIH HHS R01 HL146470NHLBI NIH HHS T32 HL076124
6 · The paper itself

Abstract

High systolic blood pressure (BP) is the most important modifiable risk factor for cardiovascular disease. Managing systolic hypertension is especially difficult in underserved populations wherein access to cuff BP devices is limited. We showed that ubiquitous smartphones without force sensing can be converted into absolute pulse pressure (PP) monitors. The concept is for the user to perform guided thumb and hand maneuvers with the phone to induce cuff-like actuation and allow built-in sensors to make cuff-like measurements for computing PP. We developed an Android smartphone PP application. The 'app' could be learned by volunteers and yielded PP with total error < 8 mmHg against cuff PP (N = 24). We also analyzed a large population-level database comprising adults less than 65 years old to show that PP plus other basic information can detect systolic hypertension with ROC AUC of 0.9. The smartphone PP app could ultimately help reduce the burden of systolic hypertension in underserved populations and thus health disparities.

Indexed as

HypertensionMobile ApplicationsSmartphoneAdultAgedBlood PressureBlood Pressure DeterminationFemaleHumansIsolated Systolic HypertensionMaleMiddle AgedVulnerable Populations

Identifiers

PMID38965318
PMCPMC11224237

What OpenQuestion holds

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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.