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Comparative Study
. 2024 Aug 5;16(15):2573.
doi: 10.3390/nu16152573.

Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care

Xinyi Li  1   2 , Annabelle Yin  1   2 , Ha Young Choi  1   2 , Virginia Chan  1   2 , Margaret Allman-Farinelli  1   2 , Juliana Chen  1   2
Affiliations
Comparative Study

Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care

Xinyi Li et al. Nutrients. .

Abstract

For artificial intelligence (AI) to support nutrition care, high quality and accuracy of its features within smartphone applications (apps) are essential. This study evaluated popular apps' features, quality, behaviour change potential, and comparative validity of dietary assessment via manual logging and AI. The top 200 free and paid nutrition-related apps from Australia's Apple App and Google Play stores were screened (n = 800). Apps were assessed using MARS (quality) and ABACUS (behaviour change potential). Nutritional outputs from manual food logging and AI-enabled food-image recognition apps were compared with food records for Western, Asian, and Recommended diets. Among 18 apps, Noom scored highest on MARS (mean = 4.44) and ABACUS (21/21). From 16 manual food-logging apps, energy was overestimated for Western (mean: 1040 kJ) but underestimated for Asian (mean: -1520 kJ) diets. MyFitnessPal and Fastic had the highest accuracy (97% and 92%, respectively) out of seven AI-enabled food image recognition apps. Apps with more AI integration demonstrated better functionality, but automatic energy estimations from AI-enabled food image recognition were inaccurate. To enhance the integration of apps into nutrition care, collaborating with dietitians is essential for improving their credibility and comparative validity by expanding food databases. Moreover, training AI models are needed to improve AI-enabled food recognition, especially for mixed dishes and culturally diverse foods.

Keywords: apps; artificial intelligence; dietary assessment; dietetic practice; dietitian; food; mHealth; mobile phone; nutrition care process; smartphone.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Flow chart of included and excluded apps for evaluation and analysis.
Figure 2
Figure 2
Scores for MARS section A–D (A: Engagement, B: Functionality, C: Aesthetics and D: Information) for nutrition-related apps (n = 18). Ranked from highest to lowest overall MARS score.
Figure 3
Figure 3
ABACUS category scores and overall scores for nutrition-related apps (n = 18).
Figure 4
Figure 4
Energy difference based on three-day mean energy output from manual food-logging apps (n = 16) compared to three-day food records for the three diet types: (a) Western diet, (b) Asian diet, and (c) Recommended diet based on the Australian Dietary Guideline recommendations.
See this image and copyright information in PMC

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