Evidence map›Paper›PMID 38466691›Full record

ArticlePloS one2024

Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing.

Imran Shafi, Sadia Din, Siddique Farooq, Isabel de la Torre Díez, Jose Breñosa, Julio César Martínez Espinosa, Imran Ashraf

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

7 authors.

Imran ShafiCollege of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan.
Sadia DinTexas A&M University at Qatar, Ar-Rayyan, Qatar.
Siddique FarooqNational Centre for Robotics and Automation (NCRA), National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Isabel de la Torre DíezDepartment of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Valladolid, Spain.
Jose BreñosaUniversidad Europea del Atlántico, Santander, Spain.
Julio César Martínez EspinosaUniversidad Europea del Atlántico, Santander, Spain.
Imran AshrafDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South Korea.ORCID 0000-0002-8271-6496

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the outbreak of the COVID-19 pandemic, social isolation and quarantine have become commonplace across the world. IoT health monitoring solutions eliminate the need for regular doctor visits and interactions among patients and medical personnel. Many patients in wards or intensive care units require continuous monitoring of their health. Continuous patient monitoring is a hectic practice in hospitals with limited staff; in a pandemic situation like COVID-19, it becomes much more difficult practice when hospitals are working at full capacity and there is still a risk of medical workers being infected. In this study, we propose an Internet of Things (IoT)-based patient health monitoring system that collects real-time data on important health indicators such as pulse rate, blood oxygen saturation, and body temperature but can be expanded to include more parameters. Our system is comprised of a hardware component that collects and transmits data from sensors to a cloud-based storage system, where it can be accessed and analyzed by healthcare specialists. The ESP-32 microcontroller interfaces with the multiple sensors and wirelessly transmits the collected data to the cloud storage system. A pulse oximeter is utilized in our system to measure blood oxygen saturation and body temperature, as well as a heart rate monitor to measure pulse rate. A web-based interface is also implemented, allowing healthcare practitioners to access and visualize the collected data in real-time, making remote patient monitoring easier. Overall, our IoT-based patient health monitoring system represents a significant advancement in remote patient monitoring, allowing healthcare practitioners to access real-time data on important health metrics and detect potential health issues before they escalate.

Indexed as

Cloud ComputingInternet of ThingsHumansInformation Storage and RetrievalMonitoring, PhysiologicPandemics

Identifiers

PMID38466691
PMCPMC10927143

What OpenQuestion holds

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LicenceCC BY
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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.