Evidence map›Paper›PMID 27347946›Full record

ReviewSensors (Basel, Switzerland)2016

Electronic Noses for Well-Being: Breath Analysis and Energy Expenditure.

Julian W Gardner, Timothy A Vincent

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.1field-weighted citation impact, top 14% 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

10 citing papers in PubMed, 29 citations in OpenAlex.

  1. Article
  2. Review
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  4. Article
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  8. Article
  9. Review
  10. Artificial intelligence-Developments in medicine in the last two years.Chronic diseases and translational medicine · 2019
    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

2 authors at 1 institution in 1 country.

Julian W GardnerSchool of Engineering, University of Warwick, Coventry, CV4 7AL, UK. j.w.gardner@warwick.ac.uk.
Timothy A VincentSchool of Engineering, University of Warwick, Coventry, CV4 7AL, UK. T.A.Vincent@warwick.ac.uk.
University of Warwick · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The wealth of information concealed in a single human breath has been of interest for many years, promising not only disease detection, but also the monitoring of our general well-being. Recent developments in the fields of nano-sensor arrays and MEMS have enabled once bulky artificial olfactory sensor systems, or so-called "electronic noses", to become smaller, lower power and portable devices. At the same time, wearable health monitoring devices are now available, although reliable breath sensing equipment is somewhat missing from the market of physical, rather than chemical sensor gadgets. In this article, we report on the unprecedented rise in healthcare problems caused by an increasingly overweight population. We first review recently-developed electronic noses for the detection of diseases by the analysis of basic volatile organic compounds (VOCs). Then, we discuss the primary cause of obesity from over eating and the high calorific content of food. We present the need to measure our individual energy expenditure from our exhaled breath. Finally, we consider the future for handheld or wearable devices to measure energy expenditure; and the potential of these devices to revolutionize healthcare, both at home and in hospitals.

Indexed as

Electronic NoseEnergy MetabolismBreath TestsExhalationGasesHumansVolatile Organic CompoundsGasesVolatile Organic Compoundsbreath analysisenergy expenditureVOCwell-being

Identifiers

PMID27347946
PMCPMC4970001
OpenAlexW2465452631

What OpenQuestion 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 OpenQuestion graph.