Evidence map›Paper›PMID 36644664›Full record

ArticleDigital health

MyDietCam: Development and usability study of a food recognition integrated dietary monitoring smartphone application.

Nadine Alvina Kong, Foong Ming Moy, Shu Hwa Ong, Ghalib Ahmed Tahir, Choo Kiong Loo

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Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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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

8 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

5 authors.

Nadine Alvina KongDepartment of Social and Preventive Medicine, University of Malaya Faculty of Medicine, Kuala Lumpur, Malaysia.ORCID https://orcid.org/0000-0003-0207-9675
Foong Ming MoyDepartment of Social and Preventive Medicine, University of Malaya Faculty of Medicine, Kuala Lumpur, Malaysia.
Shu Hwa OngDivision of Nutrition & Dietetics, International Medical University School of Health Sciences, Kuala Lumpur, Malaysia.
Ghalib Ahmed TahirDepartment of Artificial Intelligence, University of Malaya Faculty of Computer Science and Information Technology, Kuala Lumpur, Malaysia.ORCID https://orcid.org/0000-0002-6726-994X
Choo Kiong LooDepartment of Artificial Intelligence, University of Malaya Faculty of Computer Science and Information Technology, Kuala Lumpur, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Diet monitoring has been linked with improved eating habits and positive health outcomes such as prevention of obesity. However, this is often unsustainable as traditional methods place a high burden on both participants and researchers through pen and paper recordings and manual nutrient coding respectively. The digitisation of dietary monitoring has greatly reduced these barriers. This paper proposes a diet application with a novel food recognition feature with a usability study conducted in the real world. Methods: This study describes the development of a mobile diet application (MyDietCam) targeted at healthy Malaysian adults. Focus group discussions (FGD) were carried out among dietitians and potential users to determine ideal features in a diet application. Thirty participants were recruited from a local university to log their meals through MyDietCam for six days and submit the Malay mHealth Application Usability Questionnaire (M-MAUQ) at the end of the study. Results: The findings from the FGD led to the implementation of the main features: individualised recommendations, food logging through food recognition to reduce steps for data entry and provide detailed nutrient analyses through visuals. An average overall usability score of 5.13 out of a maximum of seven was reported from the M-MAUQ which is considered acceptable. Conclusion: The development of a local (Malaysian) mobile diet application with acceptable usability may be helpful in sustaining the diet monitoring habit to improve health outcomes. Future work should focus on improving the issues raised before testing the effectiveness of the application for improving health outcomes.

Indexed as

cameradiet qualityhealthy eatinglifestylemobile health (mHealth)nutritional assessmentobesityself-monitoringusabilityweight

Identifiers

PMID36644664
PMCPMC9834938

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.