Trial reportJMIR mHealth and uHealth2020
Validity and Usability of a Smartphone Image-Based Dietary Assessment App Compared to 3-Day Food Diaries in Assessing Dietary Intake Among Canadian Adults: Randomized Controlled Trial.
Trial report in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07169123 (Protease Regulation and Impact of Sodium as Mechanisms of Inflammation in IBD), which is not on this map. Cited by 51 papers, 1 of them a synthesis that pooled 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.
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.
Protease Regulation and Impact of Sodium as Mechanisms of Inflammation in IBD
Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Validity and accuracy of artificial intelligence-based dietary intake assessment methods: a systematic review.The British journal of nutrition · 2025Pooled it
- Trial
- Acceptability and Effectiveness of a Fully Web-Based Nutrition and Exercise Program for Individuals With Chronic Disease During COVID-19: Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- An Image-Recognition Dietary Assessment App for Adolescents With Obesity: Pilot Randomized Controlled Trial.JMIR formative research · 2024Trial
- Skeletal muscle mass, strength, and physical performance gains are similar between healthy postmenopausal women and postmenopausal breast cancer survivors after 12 weeks of resistance exercise training.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Trial
- Relative Validation of an Artificial Intelligence-Enhanced, Image-Assisted Mobile App for Dietary Assessment in Adults: Randomized Crossover Study.Journal of medical Internet research · 2022Trial
- Adaptation and Evaluation of Traqq-Spain: An Ecological Momentary Dietary Assessment Smartphone App for the Spanish Adult Population.Current developments in nutrition · 2026Article
- Impact of Nutrition Education on Dietary Intake and Body Composition Among Czech University Students Studying Nutrition and Food.Healthcare (Basel, Switzerland) · 2026Article
- Advances and opportunities in measuring dietary intake: from omics to AI.Nature metabolism · 2026Review
- Article
- Development of an intelligent food nutrition recognition and nutrient intake assessment system in hospital settings.Frontiers in nutrition · 2026Article
- AI-Driven Personalized Nutrition: Integrating Omics, Ethics, and Digital Health.Molecular nutrition & food research · 2025Review
- AI powered dietary proportion assessment for improving accuracy and practicality of the balanced meal plate model.Scientific reports · 2025Article
- Development and Evaluation of a Smartphone App-Based Rapid 25-Hydroxy Vitamin D Test.Diagnostics (Basel, Switzerland) · 2025Article
- Evaluation of the Accuracy, Usability, and User Perspectives of the Ecological Momentary Dietary Assessment App Traqq Among Dutch Adolescents: Protocol for a Mixed Methods Study.JMIR research protocols · 2025Article
- The Use of Artificial Intelligence (AI) to Support Dietetic Practice Across Primary Care: A Scoping Review of the Literature.Nutrients · 2025Article
- Role of Nutrition in Cognitive Development and Academic Performance During Adolescence: A Comprehensive Review.Cureus · 2025Review
- Reliability and Validity of Nutrient Assessment Applications for Canadian Endurance Athletes: MyFitnessPal and Cronometer.Journal of human nutrition and dietetics : the official journal of the British Dietetic Association · 2025Observational
- Minimum days estimation for reliable dietary intake information: findings from a digital cohort.European journal of clinical nutrition · 2025Article
- Smartphones as Catalysts for Synergistic Nutrition: A New Era in Bioactive Detection, Personalization, and Food System Intelligence.Food science & nutrition · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate dietary assessment is needed in studies that include analysis of nutritional intake. Image-based dietary assessment apps have gained in popularity for assessing diet, which may ease researcher and participant burden compared to traditional pen-to-paper methods. However, few studies report the validity of these apps for use in research. Keenoa is a smartphone image-based dietary assessment app that recognizes and identifies food items using artificial intelligence and permits real-time editing of food journals.
objectiveThis study aimed to assess the relative validity of an image-based dietary assessment app - Keenoa - against a 3-day food diary (3DFD) and to test its usability in a sample of healthy Canadian adults.
methodsWe recruited 102 participants to complete two 3-day food records. For 2 weeks, on 2 non-consecutive days and 1 weekend day, in random order, participants completed a traditional pen-to-paper 3DFD and the Keenoa app. At the end of the study, participants completed the System Usability Scale. The nutrient analyses of the 3DFD and Keenoa data before (Keenoa-participant) and after they were reviewed by dietitians (Keenoa-dietitian) were analyzed using analysis of variance. Multiple tests, including the Pearson coefficient, cross-classification, kappa score, % difference, paired t test, and Bland-Altman test, were performed to analyze the validity of Keenoa (Keenoa-dietitian).
resultsThe study was completed by 72 subjects. Most variables were significantly different between Keenoa-participant and Keenoa-dietitian (P<.05) except for energy, protein, carbohydrates, fiber, vitamin B1, vitamin B12, vitamin C, vitamin D, and potassium. Significant differences in total energy, protein, carbohydrates, % fat, saturated fatty acids, iron, and potassium were found between the 3DFD and Keenoa-dietitian data (P<.05). The Pearson correlation coefficients between the Keenoa-dietitian and 3DFD ranged from .04 to .51. Differences between the mean intakes assessed by the 3DFD and Keenoa-dietitian were within 10% except for vitamin D (misclassification rate=33.8%). The majority of nutrients were within an acceptable range of agreement in the Bland-Altman analysis; no agreements were seen for total energy, protein, carbohydrates, fat (%), saturated fatty acids, iron, potassium, and sodium (P<.05). According to the System Usability Scale, 34.2% of the participants preferred using Keenoa, while 9.6% preferred the 3DFD.
conclusionsThe Keenoa app provides acceptable relative validity for some nutrients compared to the 3DFD. However, the average intake of some nutrients, including energy, protein, carbohydrates, % fat, saturated fatty acids, and iron, differed from the average obtained using the 3DFD. These findings highlight the importance of verifying data entries of participants before proceeding with nutrient analysis. Overall, Keenoa showed better validity at the group level than the individual level, suggesting it can be used when focusing on the dietary intake of the general population. Further research is recommended with larger sample sizes and objective dietary assessment approaches.
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