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Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

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An automated image-based dietary assessment application: a pilot study.

Lachlan Lee^1,2, Rhiane Bishop², James Stanley³

¹Department of Medicine, University of Otago Wellington, Wellington, New Zealand.

Journal of Nutritional Science

|December 1, 2025

Summary

This summary is machine-generated.

A new app using computer vision for dietary assessment shows promising results. Its energy intake estimates are comparable to the validated 24-hour recall method, aiding nutritional research.

Keywords:

AEE, Activity related energy expenditure DIT, Diet-induced thermogenesis Diet Dietary assessment EE, Total energy expenditure EIapp, Energy Intake estimated by the App EIrecall, Energy Intake estimated by the 24-hour recalls ENMO, Euclidean Norm Minus One Energy intake Mobile applications Nutrition REE, Resting energy expenditure VO2, Oxygen consumption

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Area of Science:

Nutrition Science
Computer Vision
Health Informatics

Background:

Accurate dietary assessment is crucial for understanding diet-health relationships.
Image-based methods offer potential for improved dietary assessment reliability.
Existing methods like 24-hour recalls have limitations.

Purpose of the Study:

To evaluate the accuracy of an iOS application using computer vision for estimating energy intake (EI).
To compare the app's EI estimates against energy expenditure (EE) and a validated 24-hour recall method (EI_recall).

Main Methods:

Development of an iOS application utilizing computer vision for food identification from images.
Participants recorded 7-day food intake using the application.
Comparison of application-derived EI (EI_app) with two 24-hour recalls (EI_recall).
Comparison of EI_app and EI_recall with EE measured via indirect calorimetry and accelerometry.

Main Results:

Both EI_app and EI_recall were lower than estimated EE, indicating potential underreporting by both methods.
The mean bias between EI_app and EI_recall was not statistically significant (783 kJ, p = 0.33).
This suggests comparable performance between the app and the traditional 24-hour recall.

Conclusions:

The developed image-based dietary assessment application provides energy intake estimates comparable to the validated 24-hour recall.
This technology holds potential for enhancing the reliability and efficiency of dietary assessment in nutritional research.
Further validation and application in diverse populations are warranted.