Dietary Assessment Methods and Health Applications

Dietary assessment encompasses a suite of methods designed to quantify food and nutrient intake in individuals and populations. Traditional approaches include food diaries, 24-hour dietary recalls and food frequency questionnaires, each with inherent strengths and limitations in terms of respondent burden, recall bias and cost. Biomarker analyses—such as urinary or blood markers—offer objective measures but are constrained by expense and invasiveness. Recent advances harness digital technology to improve accuracy, reduce participant burden and enable real-time data capture. Smartphone applications and wearable sensors now permit image-based or near-real-time recording of meals, while remote digital cohorts facilitate continuous monitoring of diet, activity and physiological metrics in free-living settings. Machine learning algorithms can enrich these data, for example by predicting unreported micronutrient content from label information. Together, these innovations broaden our capacity to track dietary exposures, strengthen associations with health outcomes—such as weight change, cardiometabolic risk and microbiota modulation—and underpin personalised nutrition strategies at scale. Practical applications range from tailored nutritional counselling and public health surveillance to clinical interventions aimed at noncommunicable disease prevention and management. As methods converge, hybrid approaches combining self-report, digital capture and objective biomarkers promise more robust insights into diet–disease relationships globally.

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Dietary Assessment Methods and Health Applications publication trend

The graph below shows the total number of articles in dietary assessment methods and health applications across all publications each year (not limited to Nature Index journals).

Technical terms

24-hour dietary recall: A retrospective interview-based method in which participants recount all foods and beverages consumed in the previous 24 hours.

Digital cohort: A study design in which participants provide continuous or repeated measurements—such as diet, activity and biomarkers—via digital platforms in their everyday environments.

Dietary biomarkers: Objective indicators, often measured in blood or urine, that reflect the intake or metabolism of specific nutrients or food components.

Image-based dietary assessment: The use of photographs or video captures of meals to estimate portion sizes and nutrient composition, often aided by software analysis.

Machine learning prediction: Computational models that infer unreported or missing nutritional information—such as micronutrient values—from existing data sources like food labels or composition databases.

References

1. Food & You: A digital cohort on personalized nutrition. PLOS Digital Health (2023).
2. Validation of the smartphone-based dietary assessment tool “Traqq” for assessing actual dietary intake by repeated 2-h recalls in adults: comparison with 24-h recalls and urinary biomarkers. American Journal of Clinical Nutrition (2023).
3. Predicting Unreported Micronutrients From Food Labels: Machine Learning Approach. Journal of Medical Internet Research (2023).
4. Short-Term Effect of a Health Promotion Intervention Based on the Electronic 12-Hour Dietary Recall (e-12HR) Smartphone App on Adherence to the Mediterranean Diet Among Spanish Primary Care Professionals: Randomized Controlled Clinical Trial. JMIR mHealth and uHealth (2024).