Evidence map›Paper›PMID 29888145›Full record

ArticleIEEE journal of translational engineering in health and medicine2018

A Community-Based IoT Personalized Wireless Healthcare Solution Trial.

Philip A Catherwood, David Steele, Mike Little, Stephen Mccomb, James Mclaughlin

Open access · goldAbstract read
In one paragraph

Article in IEEE journal of translational engineering in health and medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
9.8field-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

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 136 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Requirements, Deployments, and Challenges of LoRa Technology: A Survey.Computational intelligence and neuroscience · 2023
    Review
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Future Smart Connected Communities to Fight COVID-19 Outbreak.Internet of things (Amsterdam, Netherlands) · 2021
    Article
  11. Article
  12. Article
  13. Article
  14. Security in IoMT Communications: A Survey.Sensors (Basel, Switzerland) · 2020
    Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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

5 authors at 2 institutions in 2 countries.

Philip A CatherwoodNIBECUlster UniversityNewtownabbeyBT37 0QBU.K.
David SteeleNIBECUlster UniversityNewtownabbeyBT37 0QBU.K.
Mike LittleRFproximityBelfastBT3 9DTU.K.
Stephen MccombNIBECUlster UniversityNewtownabbeyBT37 0QBU.K.
James MclaughlinNIBECUlster UniversityNewtownabbeyBT37 0QBU.K.
University of Ulster · GBBioproximity (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents an advanced Internet of Things point-of-care bio-fluid analyzer; a LoRa/Bluetooth-enabled electronic reader for biomedical strip-based diagnostics system for personalized monitoring. We undertake test simulations (technology trial without patient subjects) to demonstrate potential of long-range analysis, using a disposable test 'key' and companion Android app to form a diagnostic platform suitable for remote point-of-care screening for urinary tract infection (UTI). The 868 MHz LoRaWAN-enabled personalized monitor demonstrated sound potential with UTI test results being correctly diagnosed and transmitted to a remote secure cloud server in every case. Tests ranged over distances of 1.1-6.0 Km with radio path losses from 119-141 dB. All tests conducted were correctly and robustly received at the base station and relayed to the secure server for inspection. The UTI test strips were visually inspected for correct diagnosis based on color change and verified as 100% accurate. Results from testing across a number of regions indicate that such an Internet of Things medical solution is a robust and simple way to deliver next generation community-based smart diagnostics and disease management to best benefit patients and clinical staff alike. This significant step can be applied to any type of home or region, particularly those lacking suitable mobile signals, broadband connections, or even landlines. It brings subscription-free long-range bio-telemetry to healthcare providers and offers savings on regular clinician home visits or frequent clinic visits by the chronically ill. This paper highlights practical hurdles in establishing an Internet of Medical Things network, assisting informed deployment of similar future systems.

Indexed as

Clinical diagnosticsInternet of ThingsLoRaremote healthcaresensor networksurinary Tract Infection

Identifiers

PMID29888145
PMCPMC5991865
OpenAlexW2800928961

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.