Standardised and Objective Dietary Intake Assessment Tool (SODIAT): Protocol of a dual-site dietary intervention study to integrate dietary assessment methods

Abstract

Introduction Current dietary assessment methods face challenges in accurately capturing individuals’ dietary habits, undermining the efficacy of public health strategies. The ‘Standardised and Objective Dietary Intake Assessment Tool’ (SODIAT)-1 study aims to assess the effectiveness of three emerging technologies (urine and capillary blood biomarkers, and wearable camera technology) and two online self-reporting dietary assessment tools to monitor dietary intake. Methods This randomised controlled crossover trial will recruit 30 participants (aged 18-70 years and BMI of 20-30 kg/m2) from Imperial College London and the University of Reading. Exclusion criteria include recent weight change, food allergies/intolerances, following restrictive diets, certain health conditions and medication use. Interested volunteers will be directed to an online screening questionnaire via REDCap and eligible participants will attend a pre-study visit. Volunteers will consume, in a random order, two highly-controlled diets (compliant and non-compliant with UK guidelines) for four days each. Each study arm will be separated by at least one-week. During each test period, dietary intake will be monitored continuously using wearable cameras and self-recorded using eNutri (food frequency questionnaire) and Intake24 (24-hour dietary recall). Urine and capillary blood samples will be collected for biomarker analysis. Data analysis will assess the accuracy of dietary reporting across these methods using Lin’s concordance correlation coefficient. Discussion and ethical considerations This study introduces a novel approach to dietary assessment, addressing the limitations of traditional methods by reducing misreporting and enhancing inclusivity, particularly for underrepresented populations with literacy or language barriers. However, challenges persist, such as variability in biomarker data due to failure to adhere to sample storage requirements and the practicalities of continuously wearing cameras. To protect privacy, participants will be instructed to remove cameras at inappropriate times, and artificial intelligence will be used to blur all images captured apart from food.