Evidence map›Paper›PMID 37701636›Full record

ArticleAdvanced materials technologies2023

Add-on soft electronic interfaces for continuous cuffless blood pressure monitoring.

Myeong Namkoong, Balaji Baskar, Lakhvir Singh, Heng Guo, Justin McMurray, Kimberly Branan, Md Saifur Rahman, Chin-To Hsiao, Josh Kuriakose, Joanna Hernandez and 4 more

Abstract read
In one paragraph

Article in Advanced materials technologies, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Ion-Pair-Tuned Ionogels for Broad-Range Linear Pressure Sensing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. 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

14 authors.

Myeong NamkoongDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA.
Balaji BaskarDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA.
Lakhvir SinghDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA.
Heng GuoDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA.
Justin McMurrayDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA.
Kimberly BrananDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA.
Md Saifur RahmanDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA.
Chin-To HsiaoDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA.
Josh KuriakoseDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA.
Joanna HernandezDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA.
Arda A ArikanThe Awty International School, Houston, TX 77055, USA.
Luis E Garza-RiveraSharyland Advanced Academy, Mission, TX 78572, USA.
Gerard L CotéDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA.
Limei TianDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA.

Funding

Refreshable Biosensors for Continuous Renal Function MonitoringR21EB029064 · NIBIB · TEXAS ENGINEERING EXPERIMENT STATION · PI TIAN, LIMEI · 2020 to 2020
$589k
NIBIB NIH HHS R21 EB029064
6 · The paper itself

Abstract

Continuous monitoring of arterial blood pressure is clinically important for the diagnosis and management of cardiovascular diseases. Soft electronic devices with skin-like properties show promise in a wide range of applications, including the human-machine interface, the Internet of things, and health monitoring. Here, we report the use of add-on soft electronic interfaces to address the connection challenges between soft electrodes and rigid data acquisition circuitry for bioimpedance monitoring of cardiac signals, including heart rate and cuffless blood pressure. Nanocomposite films in add-on electrodes provide robust electrical and mechanical contact with the skin and the rigid circuitry. We demonstrate bioimpedance sensors composed of add-on electrodes for continuous blood pressure monitoring with high accuracy. Specifically, the bioimpedance collected with add-on nanocomposite electrodes shows a signal-to-noise ratio of 37.0 dB, higher than the ratio of 25.9 dB obtained with standard silver/silver chloride (Ag/AgCl gel) electrodes. Although the sample set is low, the continuously measured systolic and diastolic blood pressure offer accuracy of -2.0 ± 6.3 mmHg and -4.3 ± 3.9 mmHg, respectively, confirming the grade A performance based on the IEEE standard. These results show promise in bioimpedance measurements with add-on soft electrodes for cuffless blood pressure monitoring.

Indexed as

bioimpedanceConductive nanocompositecontinuous monitoring of blood pressureelectrophysiological sensorssoft electronics

Identifiers

PMID37701636
PMCPMC10495086

What OpenQuestion holds

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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.