Evidence map›Paper›PMID 33805471›Full record

ReviewDiagnostics (Basel, Switzerland)2021

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges.

Nora El-Rashidy, Shaker El-Sappagh, S M Riazul Islam, Hazem M El-Bakry, Samir Abdelrazek

Open access · goldAbstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 3 pooled it
24.4field-weighted citation impact, top 1% 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

57 citing papers in PubMed, 3 syntheses or guidelines pooled it, 234 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Trial
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Review
  16. Review
  17. Article
  18. Review
  19. Article
  20. 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

5 authors at 4 institutions in 3 countries.

Nora El-RashidyMachine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt.ORCID 0000-0001-8177-9439
Shaker El-SappaghCentro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain.
S M Riazul IslamDepartment of Computer Science and Engineering, Sejong University, Seoul 05006, Korea.ORCID 0000-0003-2968-9561
Hazem M El-BakryInformation Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura 13518, Egypt.
Samir AbdelrazekInformation Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura 13518, Egypt.ORCID 0000-0002-1943-2861
Mansoura University · EGKafrelsheikh University · EGSejong University · KRUniversidade de Santiago de Compostela · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.

Indexed as

AIclinical-decision support systemcloud computingelectronic healthelectronic health recordinternet of thingsremote patient monitoringwireless body area network

Identifiers

PMID33805471
PMCPMC8067150
OpenAlexW3151989229

What Socratic 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 Socratic graph.