Evidence map›Paper›PMID 35849429›Full record

ArticleJMIR public health and surveillance2022

Use of Facial Morphology to Determine Nutritional Status in Older Adults: Opportunities and Challenges.

Wesley Tay, Rina Quek, Bhupinder Kaur, Joseph Lim, Christiani Jeyakumar Henry

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 22% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Observational
  4. Article
  5. Review
  6. Review
  7. 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 1 institution in 1 country.

Wesley TayClinical Nutrition Research Centre, Singapore Institute of Food and Biotechnology Innovation, Agency for Science, Technology and Research, Singapore, Singapore.ORCID 0000-0002-2610-4428
Rina QuekClinical Nutrition Research Centre, Singapore Institute of Food and Biotechnology Innovation, Agency for Science, Technology and Research, Singapore, Singapore.ORCID 0000-0002-7904-6014
Bhupinder KaurClinical Nutrition Research Centre, Singapore Institute of Food and Biotechnology Innovation, Agency for Science, Technology and Research, Singapore, Singapore.ORCID 0000-0002-0428-2530
Joseph LimClinical Nutrition Research Centre, Singapore Institute of Food and Biotechnology Innovation, Agency for Science, Technology and Research, Singapore, Singapore.ORCID 0000-0002-7912-8343
Christiani Jeyakumar HenryClinical Nutrition Research Centre, Singapore Institute of Food and Biotechnology Innovation, Agency for Science, Technology and Research, Singapore, Singapore.ORCID 0000-0002-1602-0140
Agency for Science, Technology and Research · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Undiagnosed malnutrition is a significant problem in high-income countries, which can reduce the quality of life of many individuals, particularly of older adults. Moreover, it can also inflate the costs of existing health care systems because of the many metabolic complications that it can cause. The current methods for assessing malnutrition can be cumbersome. A trained practitioner must be present to conduct an assessment, or patients must travel to facilities with specialized equipment to obtain their measurements. Therefore, digital health care is a possible way of closing this gap as it is rapidly gaining traction as a scalable means of improving efficiency in the health care system. It allows for the remote monitoring of nutritional status without requiring the physical presence of practitioners or the use of advanced medical equipment. As such, there is an increasing interest in expanding the range of digital applications to facilitate remote monitoring and management of health issues. In this study, we discuss the feasibility of a novel digital remote method for diagnosing malnutrition using facial morphometrics. Many malnutrition screening assessments include subjective assessments of the head and the face. Facial appearance is often used by clinicians as the first point of qualitative indication of health status. Hence, there may be merit in quantifying these subtle but observable changes using facial morphometrics. Modern advancements in artificial intelligence, data science, sensors, and computing technologies allow facial features to be accurately digitized, which could potentially allow these previously intuitive assessments to be quantified. This study aims to stimulate further discussion and discourse on how this emerging technology can be used to provide real-time access to nutritional status. The use of facial morphometrics extends the use of currently available technology and may provide a scalable, easily deployable solution for nutritional status to be monitored in real time. This will enable clinicians and dietitians to keep track of patients remotely and provide the necessary intervention measures as required, as well as providing health care institutions and policy makers with essential information that can be used to inform and enable targeted public health approaches within affected populations.

Indexed as

MalnutritionNutritional StatusAgedArtificial IntelligenceHealth StatusHumansQuality of Life3D scansdigital healthdigital nutritionfacial morphologyfacial recognitionmalnutritionmobile phonepublic health nutritiontelemonitoring

Identifiers

PMID35849429
PMCPMC9345026
OpenAlexW4285727517

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.