Trial reportJournal of medical Internet research2022
Relative Validation of an Artificial Intelligence-Enhanced, Image-Assisted Mobile App for Dietary Assessment in Adults: Randomized Crossover Study.
Trial report in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07348432 (The diabEAT Study), which is not on this map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The diabEAT Study: Insulin dElivery Technologies And eaTing Behaviours in People With Type 1 Diabetes
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Validity and accuracy of artificial intelligence-based dietary intake assessment methods: a systematic review.The British journal of nutrition · 2025Pooled it
- Inflammatory Cytokine Responses to 6 Wks of Increased Dairy Intake in Individuals with Overweight and Obesity-A Randomized Crossover Trial.The Journal of nutrition · 2026Trial
- Exploring relationships with food after dietary intervention in patients with colorectal cancer: a qualitative analysis from the Protein Recommendations to Increase Muscle (PRIMe) trial.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Trial
- Calorie-Counting Apps for Monitoring and Managing Calorie Intake in Adults Living With Weight-Related Chronic Diseases: Decade-Long Scoping Review (2013-2024).JMIR mHealth and uHealth · 2026Article
- Advances and opportunities in measuring dietary intake: from omics to AI.Nature metabolism · 2026Review
- Precision nutrition in diabetic foot ulcers: multimodal artificial intelligence for personalized metabolic management.Frontiers in nutrition · 2026Review
- User Preferences for an Image-Assisted Dietary Recall: Qualitative Study Comparing 3 Dietary Assessment Methods.JMIR human factors · 2025Article
- Efficacy of FiberMore, an AI-Based mHealth Intervention to Increase Dietary Fiber Intake Among Type 2 Diabetes Patients: Protocol for a Pilot Randomized Controlled Trial.JMIR research protocols · 2025Article
- AI-Driven Personalized Nutrition: Integrating Omics, Ethics, and Digital Health.Molecular nutrition & food research · 2025Review
- Article
- The Use of Artificial Intelligence (AI) to Support Dietetic Practice Across Primary Care: A Scoping Review of the Literature.Nutrients · 2025Article
- Minimum days estimation for reliable dietary intake information: findings from a digital cohort.European journal of clinical nutrition · 2025Article
- Multidisciplinary Care in a Public University Family Medicine Group in Québec (Canada): Data on Patients' Follow-Up and Cardiometabolic Risk Management.Healthcare (Basel, Switzerland) · 2025Article
- Novel multimodal intervention for surgical prehabilitation on functional recovery and muscle characteristics in patients with non-small cell lung cancer: study protocol for a randomised controlled trial (MMP-LUNG).BMJ open respiratory research · 2025Article
- Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools-a scoping review of potential applications.Frontiers in nutrition · 2025Review
- From Fetus to Eight: the CHILD Cohort Study.American journal of epidemiology · 2024Article
- Reliability Issues of Mobile Nutrition Apps for Cardiovascular Disease Prevention: Comparative Study.JMIR mHealth and uHealth · 2024Article
- Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Photos for Dietary Assessment.Nutrients · 2023Article
- Article
- Impact of glycemic variability on cognitive impairment, disordered eating behaviors and self-management skills in patients with type 1 diabetes: study protocol for a cross-sectional online study, the Sugar Swing study.BMC endocrine disorders · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThorough dietary assessment is essential to obtain accurate food and nutrient intake data yet challenging because of the limitations of current methods. Image-based methods may decrease energy underreporting and increase the validity of self-reported dietary intake. Keenoa is an image-assisted food diary that integrates artificial intelligence food recognition. We hypothesized that Keenoa is as valid for dietary assessment as the automated self-administered 24-hour recall (ASA24)-Canada and better appreciated by users.
objectiveWe aimed to evaluate the relative validity of Keenoa against a 24-hour validated web-based food recall platform (ASA24) in both healthy individuals and those living with diabetes. Secondary objectives were to compare the proportion of under- and overreporters between tools and to assess the user's appreciation of the tools.
methodsWe used a randomized crossover design, and participants completed 4 days of Keenoa food tracking and 4 days of ASA24 food recalls. The System Usability Scale was used to assess perceived ease of use. Differences in reported intakes were analyzed using 2-tailed paired t tests or Wilcoxon signed-rank test and deattenuated correlations by Spearman coefficient. Agreement and bias were determined using the Bland-Altman test. Weighted Cohen κ was used for cross-classification analysis. Energy underreporting was defined as a ratio of reported energy intake to estimated resting energy expenditure <0.9.
resultsA total of 136 participants were included (mean 46.1, SD 14.6 years; 49/136, 36% men; 31/136, 22.8% with diabetes). The average reported energy intakes (kcal/d) were 2171 (SD 553) in men with Keenoa and 2118 (SD 566) in men with ASA24 (P=.38) and, in women, 1804 (SD 404) with Keenoa and 1784 (SD 389) with ASA24 (P=.61). The overall mean difference (kcal/d) was -32 (95% CI -97 to 33), with limits of agreement of -789 to 725, indicating acceptable agreement between tools without bias. Mean reported macronutrient, calcium, potassium, and folate intakes did not significantly differ between tools. Reported fiber and iron intakes were higher, and sodium intake lower, with Keenoa than ASA24. Intakes in all macronutrients (r=0.48-0.73) and micronutrients analyzed (r=0.40-0.74) were correlated (all P<.05) between tools. Weighted Cohen κ scores ranged from 0.30 to 0.52 (all P<.001). The underreporting rate was 8.8% (12/136) with both tools. Mean System Usability Scale scores were higher for Keenoa than ASA24 (77/100, 77% vs 53/100, 53%; P<.001); 74.8% (101/135) of participants preferred Keenoa.
conclusionsThe Keenoa app showed moderate to strong relative validity against ASA24 for energy, macronutrient, and most micronutrient intakes analyzed in healthy adults and those with diabetes. Keenoa is a new, alternative tool that may facilitate the work of dietitians and nutrition researchers. The perceived ease of use may improve food-tracking adherence over longer periods.
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