Self-reported dietary assessments have long been a limiting factor in advancing the field of precision nutrition. This is due to challenges such as unrecorded eating episodes, recall bias and portion-size estimation errors (1) . We developed a system using customised wearable cameras and reasoning-enabled large vision–language models (LVLMs) to create a fully automated pipeline facilitating scalable and objective dietary assessment. Beyond reducing user burden, passive capture can record brief or opportunistic eating episodes that are typically missed, while the use of LVLMs improves identification across heterogeneous contexts. However, the feasibility, privacy safeguards, and quantitative performance of such systems remain underexplored. This study aims to evaluate the LVLM-enabled passive system’s performance in real-world deployments, focusing on its ability to accurately capture and analyze dietary intake. A feasibility study was conducted at two centres, Hammersmith Hospital and the University of Reading (2) , where thirty UK participants wore customised cameras side-mounted on glasses (STM32 microcontroller; 128-GB SD card; rechargeable) throughout waking hours whilst consuming two highly-controlled, standardised diets; one of which was compliant with UK healthy eating guidelines and the other was not. Each diet was consumed over four study days, during which participants remained in the facility and consumed meals provided by the study team. The model outputs were benchmarked against a dietitian-verified reference menu and the weights of food portions consumed. The preprocessing pipeline was first applied to blur faces and screens in captured images for privacy protection. The LVLM-based pipeline then performed three tasks: (i) extracting eating episodes; (ii) recognising food items across heterogeneous settings; and (iii) context-aware portion-size estimation, using cues from containers, utensils, and hands to mitigate monocular visual scale ambiguity (3) . Any eating sessions lacking captured images were excluded from subsequent analyses. Data passively captured with wearable cameras from 30 participants (Hammersmith, n=15; Reading, n=15) over eight study days yielded 2.08 million raw images at Hammersmith and 2.15 million at Reading. After privacy filtering and removal of redundant frames, 0.49% and 0.46% of images were retained from each site, respectively. Overall, food-item recall from passively captured imagery was 82% (95% CI 81–84%). Portion-size estimation showed a mean absolute error of 44.7 g (95% CI 42.2–47.3 g) for food items and 70.4 mL (95% CI 67.4–73.4 mL) for beverages against weighed consumed portions. This feasibility study provides foundational evidence for LVLM-enabled, passive, camera-based dietary monitoring and supports progression to real-world deployment. These feasibility results support further multi-site validation, inclusion of metrics beyond recall (e.g., energy and macronutrient assessment), and assessment of performance across settings (home vs out-of-home) and subgroups to capture nutrient intake at population-level and enhance precision nutrition approaches.

Cardiovascular disease (CVD) is a leading cause of death in the UK, and cardiac rehabilitation (CR) is recommended post-myocardial infarction to improve outcomes. However, uptake remains low due to referral barriers, logistical challenges, and limited resources. Poor diet is a modifiable risk factor for CVD, yet access to tailored nutrition support within CR is limited. NICE guidance recommends personalised dietary advice as part of CR (1) , and remote delivery of personalised nutrition may offer a scalable solution to improve diet quality and support equitable CR access. eNutriCardio is a novel web-based personalised nutrition advice (PNA) tool adapted from the validated eNutri web-app (2) , originally developed for the general population (3) . This study investigated the effectiveness of eNutriCardio in improving diet quality in UK NHS patients offered CR. Patients eligible for CR completed a validated food frequency questionnaire (FFQ) via eNutriCardio shortly after their cardiac event or procedure. Participants were randomised to receive either usual care (including CR programme) (control) or usual care plus eNutriCardio PNA (intervention). The PNA was tailored to individual FFQ responses and an 11-component diet quality score (DQS), providing food-based recommendations to improve their three lowest scoring DQS components, where a higher score reflects greater adherence to UK dietary guidance. Intervention participants received coaching emails at weeks 2, 4 and 8, prompting participants to set and reflect on healthy eating intentions. After 12 weeks, both groups repeated the FFQ and provided feedback. An independent samples t-test compared change in overall DQS between groups (primary outcome). Targeted analysis (paired samples t-tests) explored whether change in DQS differed between PNA components that intervention participants intended to change versus components they either did not intend to change nor received advice about. The study was registered with ClinicalTrials.gov (ID: NCT05449769) and conducted according to the principles outlined in the Declaration of Helsinki. Twenty-seven participants were included in the present analysis (intervention: n=13; control: n=14). Baseline characteristics were not significantly different between groups: mean age 61 years (SD 8), 82% male, and mean BMI 27.9 kg/m 2 (SD 5.4). Intervention participants showed a 54% greater increase in total DQS (+21.2 points, out of 100) compared with control participants (+9.8 points) after 12 weeks (p=0.036). Within the intervention group, changes in DQS component scores were 80% higher for components participants intended to change versus those without intentions or advice (p≤0.001). No significant difference in BMI change was observed. All intervention participants agreed that eNutriCardio PNA helped them eat healthier and should be offered to all CR-eligible patients. Adding eNutriCardio PNA to usual care improved diet quality significantly more than usual care alone in NHS patients offered CR. This digital tool may enhance CR by offering a remote, personalised nutrition solution for patients unable to attend in-person programmes.

BACKGROUND:Digital dietary assessment tools are highly beneficial for nutrition research and personalized interventions. OBJECTIVE:This paper describes the development and evaluation of eNutriFFQv2.0, an updated online food frequency questionnaire designed to reflect current diets in the United Kingdom (UK). Updates included modernized food lists based on recent UK population surveys, food composition tables, and food portion photos to improve accuracy and user experience. METHODS:To assess reproducibility, UK adults completed the FFQ twice, 14 days apart; validity was evaluated against a 3-d weighed food record in a sub-sample. Multiple statistical methods were used. After excluding participants with unfeasible energy intakes, 87 participants completed the reproducibility and 53 the evaluation. RESULTS:The final eNutriFFQv2.0 captured 164 items and estimated intake for 56 nutrients and 6 food groups. Agreement with the WFR was acceptable to good for 25 out of the 29 nutrients analyzed (weighted kappa 0.21-0.77), with ≤10% misclassification into opposite quartiles for most nutrients. Bland-Altman plots showed good agreement for energy (176 kcal/d higher in FFQ1) and macronutrient estimates. Reproducibility was good for 24 out of the 29 nutrients analyzed (weighted kappa 0.58-0.85) with <5% misclassification. Mean bias for estimates of carbohydrate, fat, and protein was small (0.0-0.7). Energy estimates were 209 kcal/d (10.7%) higher in the first compared with the second completion of the FFQ. CONCLUSIONS:These findings demonstrate that eNutriFFQv2.0 is a valid and reliable tool for assessing nutrient intake in UK adults, offering a practical, scalable solution for research and public health in the context of digital health and personalized dietary interventions.

Insufficient dietary fibre intake can increase chronic disease risk with identification of effective strategies considered a public health priority. However, there are no UK-based dietary fibre intake tools to estimate consumption habits. This study addresses this gap by developing a novel dietary fibre screening tool (SCREEN-IT) for the UK population and explores validity, reproducibility and usability insights. SCREEN-IT was developed based on key dietary fibre-rich food categories in the UK diet. The validity of SCREEN-IT (10-item) was tested against a UK-based Food Frequency Questionnaire (eNutri-FFQ; 157-item) seven-days apart (n = 70; 55.1 ± 18.7 years). SCREEN-IT reproducibility (n = 155; 51.7 ± 18.2 years) was evaluated on two occasions (four-weeks apart) and usability (System Usability Scale; SUS) was assessed. Intra-class correlation coefficients, percentage difference, quartile agreement, weighted kappa and Bland-Altman plots were used to quantify agreement and extent of bias. Agreement between methods (SCREEN-IT vs. eNutri-FFQ) was “acceptable to good agreement”; higher dietary fibre estimates from eNutri-FFQ (mean bias: −3.91 g/d). SCREEN-IT was quick to complete (< 5-min) with higher SUS than eNutri-FFQ (83.4 vs. 76.8/100). SCREEN-IT was reproducible on re-test (“acceptable to good agreement”), mean bias close to zero (−0.04 g/d), high usability (84.9/100) and received positive feedback (easy-to-use, functional, thought-provoking, enjoyable). SCREEN-IT was successfully developed with favourable validity, reproducibility and usability feedback. It was considered a suitable tool to estimate dietary fibre intake for the UK population. This novel tool could help raise dietary fibre awareness by promoting relevant food sources in a quick and easy way to increase future intake.

Research exploring the influence of diet on the skin microbiome and its role in skin aging is limited (1,2) . This study aimed to investigate age-related changes in skin microbiome and physiological parameters across adulthood, and to examine associations with habitual diet, anthropometry, demographics, and use of skin care products or medications. Institutional ethical approval was obtained (0617-2025-Apr-HSET). Adults were recruited across 5 age groups through word of mouth, flyers and social media. Exclusion criteria included ongoing intensive medical treatment (e.g., chemotherapy), current or recent smoking (stopped <1 year ago), and allergies to tape strips and/or swabs. Anthropometric measures included weight (kg), body mass index (BMI) (kg/m2), waist circumference (cm) and body fat (%). Habitual dietary intake over the proceeding 4 weeks was assessed using the validated eNutri web app (3) capturing fluid, food, nutrient and supplement intake. Skin physiological measures were conducted on the non-dominant forearm using validated instruments to measure barrier function (indicated by lower trans-epidermal water loss [TEWL]), stratum corneum cohesion (indicated by protein removed from stratum corneum), skin hydration and skin surface pH. Skin swabs from the inner elbow were used for DNA extraction and 16S metagenomic sequencing. Bacterial diversity (observed species, Shannon index), abundance, and phylum level composition were determined. Statistical analysis included Pearson’s or Spearman’s correlation coefficients, Mann–Whitney U and Kruskal–Wallis tests for non-parametric comparisons, and general linear models to identify predictors of skin physiological parameters and skin microbiome. Forty-one adults, aged 20-92 years, 30 female, 11 male (median BMI 24.9kgm2, IQR 6.5) completed the study. There was a significant negative correlation between TEWL and age (r=-0.431, p=0.012), and alcohol intake (% total energy) (r=-0.325, p=0.002) whereas iron intake (mg/d) was positively associated with TEWL (r=0.311, p=0.028). Skin barrier function was significantly higher in females (p=0.008). This indicates that older age, higher alcohol consumption, lower iron intake, and female sex may be associated with improved skin barrier function. There were also significant negative correlations between protein removal from the stratum corneum and both age (r=-0.371, p=0.007) and fruit intake (g/day) (r=-0.480, p=0.013), indicating stronger cohesion in younger individuals and those with higher fruit intake. Skin hydration was negatively associated with body weight (r=-0.343, p=0.020) and positively associated with dietary fibre intake (AOAC g/d) (r=0.468, p=0.001). Additionally, body weight (kg) was positively associated with skin microbiome abundance (r=0.536, p=0.002) and negatively associated with microbial diversity (observed species) (r=-.513, p<0.001). This study shows it is feasible to explore how age, anthropometry, and diet relate to skin physiology and microbiome in healthy adults. Our findings suggest that these factors influence different aspects of skin health in distinct ways. A larger powered cross-sectional study is warranted to validate these findings.

Diet quality scores (DQSs) are widely used in research to quantify diet quality based on adherence to population-based dietary recommendations and favourable dietary patterns. Many DQSs exist (e.g. Healthy Eating Index and Mediterranean Diet Score), but there are no recognised DQSs developed for a UK population (1) . The evidence-based DQS-UK-2022 was developed to quantify diet quality in UK adults (2) . This research aims to explore its relationship with CVD risk in UK adults. Cross-sectional data from the UK’s National Diet and Nutrition Survey (NDNS) rolling programme (2008-2014) (3,4) were used (n=4738 adults). Markers of CVD risk included blood pressure, anthropometric measurements, fasting serum lipids, C-reactive protein (CRP), haemoglobin A1C, fasting glucose, and homocysteine (n=2387 for blood biomarkers). The DQS-UK-2022 was calculated for dietary intake data (nutrients and food groups) from estimated diet diaries collected as part of NDNS. The DQS includes 6 adequacy components (fruits, vegetables, wholegrains, dairy, nuts/pulses and oily fish, where greater intakes scored higher), 4 moderation components (sodium, alcohol, red/processed meats and free sugars, where lower intakes scored higher) and a mixed component (saturated fatty acids (SFA) and ratio of unsaturated to SFA) 2. Each component had absolute cut-offs broadly based on current UK dietary recommendations. Intakes above/below the maximum/minimum cut-offs scored 10 or 0 points, respectively, with intermediate intakes scored proportionately using linear interpolation. Component scores were summed and scaled to a total score out of 100, with higher DQS-UK-2022 scores indicating greater adherence to UK dietary recommendations. Participants with unfeasible energy intakes (<600 and >4500 kcal/d) were excluded (n=34). DQS scores were divided into quintiles (Q1: ≤35 points; Q5: ≥57 points). All continuous variables were log transformed to normalise data. Associations between DQS quintiles and CVD risk markers were assessed using analysis of covariance (ANCOVA) adjusted for age, sex and ethnicity. Where significant (p<0.05), a comparison between Q1 and Q5 was conducted, with Bonferroni correction for all potential pairwise comparisons. Results are presented as estimated marginal means ±SD. Those in Q5 vs Q1 had significantly lower body mass index (26.9 ±0.2 vs 28.0 ±0.2 kg/m2), waist circumference (90.7 ±0.5 vs 95.1 ±0.5 cm), waist-to-hip ratio (0.86 ±0.03 vs 0.89 ±0.03), serum triacylglycerol (1.15 ±0.04 vs 1.50 ±0.04 mmol/l), total cholesterol to high density lipoprotein cholesterol (HDL-C) ratio (3.52 ±0.06 vs 3.91 ±0.06), CRP (2.67 ±0.32 vs 4.52 ±0.34 mg/l), homocysteine (9.0 ±0.2 vs 11.5 ±0.2 μmol/l), systolic blood pressure (125 ±1 vs 128 ±1 mmHg) and diastolic blood pressure (72.5 ±0.4 vs 75.0 ±0.5 mmHg) and higher HDL-C (1.54 ±0.02 vs 1.44 ±0.02 mmol/l) (ANCOVA: p≤0.007 for all). Higher scores from the novel DQS-UK-2022 were associated with lower CVD risk factors. To strengthen these findings, prospective associations between the DQS-UK-2022 and CVD incidence/mortality will be investigated.

Diet is a major determinant of life course outcomes, yet the accurate measurement of an individual’s dietary intake remains a persistent challenge (1) . Intake biomarkers measured in urine and blood using advanced analytical tools, have proven to be a robust and objective method of measuring the intake of particular dietary components(2-3). The “Standardised and Objective Dietary Intake Assessment Tool” (SODIAT)-1 study aimed to determine the effectiveness of using a combination of dietary assessment technologies, to accurately measure an individual’s dietary intake (4) . A randomised controlled crossover trial recruited 33 UK adults (Male; 14, Female;19) with a median age of 43 years (IQR; 29, 56), across two sites; University of Reading and Hammersmith Hospital, Imperial College London. Participants consumed two 4-day controlled diets, one designed to be compliant and one non-compliant with recommended UK dietary guidance. For each 4-day period participants consumed two separate menu plans, on alternating days. Participants collected nine spot urine samples; First Morning Voids on days 1-5 and Bed Time on days 1-4. Fasted capillary blood samples were self-collected by the participants on days 1, 2 and 4 using the OneDraw blood collection system. Urine samples were analysed by Ultra High Performance Liquid Chromatography UHPLC Triple Quadrupole Mass Spectrometry using a panel of previously validated intake biomarkers (2) . Dried blood samples were analysed using UHPLC High Resolution Mass Spectrometry for lipid biomarkers of dietary intake. All biomarkers were measured as absolute concentrations. Supervised machine learning (Random Forest) was used to determine the extent of discrimination between study diets. Classification accuracies were calculated as mean values from 1000 randomised resamples. Supervised models consisting of 83 urinary biomarkers yielded classification accuracies of 0.975 [95% CI; 0.973 - 0.978], the addition of 154 lipid biomarkers from dried blood samples enabled perfect classification; 1.000 [95% CI; 0.999 - 1.000]. Following recursive feature elimination, the model was reduced to a total of 47 biomarkers, with no loss in classification performance. Top ranked features in the optimal model consisted of triacylglycerols (TAG:53_2, TAG:50_2, TAG:49_1) which sufficiently discriminated between dairy and sugar containing components, and urinary markers of the intake of meat (3-Methyl-histidine, L-Anserine), wholegrains (DHPPA-3-Sulfate) and high anti-oxidant containing components (Protocatechuic acid, Hippuric acid). Biomarkers from spot urine samples and capillary blood samples can provide objective measurements of the intake of dietary components commonly consumed in the UK. Combining urinary and lipid biomarkers into single models, improves performance while extending the coverage of detectable dietary components. These models can serve as a foundation for scalable and objective reporting of dietary intake in free living populations.

Although links between dietary patterns (DPs) and cardiometabolic disease (CMD) risk markers have been identified in UK populations, these studies often rely on less quantitative measures of dietary assessment and include only a limited number of risk markers. This cross-sectional analysis aimed to identify DP in self-reported disease-free adults using weighed diet diaries and explore relationships with a broad range of CMD risk factors and diet quality. Data were collated from five studies conducted in adults living in the UK (2009-2019) and DPs were a posteriori extracted from habitual dietary intake data using principal component analysis. Associations between quartiles (Q) of adherence to the DPs with CMD risk markers, nutrient intakes and the Alternative Healthy Eating Index (AHEI-2010) were evaluated using ANCOVA. In our cohort [n = 646, 58.4

A key aim of the FNS-Cloud project (grant agreement no. 863059) was to overcome fragmentation within food, nutrition and health data through development of tools and services facilitating matching and merging of data to promote increased reuse. However, in an era of increasing data reuse, it is imperative that the scientific quality of data analysis is maintained. Whilst it is true that many datasets can be reused, questions remain regarding whether they should be, thus, there is a need to support researchers making such a decision. This paper describes the development and evaluation of the FNS-Cloud data quality assessment tool for dietary intake datasets. Markers of quality were identified from the literature for dietary intake, lifestyle, demographic, anthropometric, and consumer behavior data at all levels of data generation (data collection, underlying data sources used, dataset management and data analysis). These markers informed the development of a quality assessment framework, which comprised of decision trees and feedback messages relating to each quality parameter. These fed into a report provided to the researcher on completion of the assessment, with considerations to support them in deciding whether the dataset is appropriate for reuse. This quality assessment framework was transformed into an online tool and a user evaluation study undertaken. Participants recruited from three centres (N = 13) were observed and interviewed while using the tool to assess the quality of a dataset they were familiar with. Participants positively rated the assessment format and feedback messages in helping them assess the quality of a dataset. Several participants quoted the tool as being potentially useful in training students and inexperienced researchers in the use of secondary datasets. This quality assessment tool, deployed within FNS-Cloud, is openly accessible to users as one of the first steps in identifying datasets suitable for use in their specific analyses. It is intended to support researchers in their decision-making process of whether previously collected datasets under consideration for reuse are fit their new intended research purposes. While it has been developed and evaluated, further testing and refinement of this resource would improve its applicability to a broader range of users.

Background/Objectives: A balanced nutritious diet is vital during pregnancy for both the mother and the baby. The aims of this longitudinal study were to (1) determine any differences in macro- and micronutrient intakes in a group of UK women during pregnancy (and in the post-partum period) who were overweight or obese (BMI mean (SD) 31.1 (2.9)) at antenatal booking appointment compared with women who were within the ideal BMI range (BMI mean (SD) 22.1 (1.9)) and (2) determine the proportion of women who met the Harmonized Average Requirements (H-AR) during pregnancy. Methods: Forty-two participants attended four clinic visits: three during pregnancy, one in each trimester (V1, V2, and V3), and one 12 weeks post-partum (V4). Dietary intake was assessed by 24 h diet recall and analysed using DietPlan6. Results: There were no differences in energy and macronutrient intakes between overweight/obese and lean women. During pregnancy, the overweight/obese women consumed a mean (SD) of 3238 (941) sodium (mg per day), which was approximately 10% higher compared to 2934 (732) sodium (mg per day) in the lean group (p = 0.015). Dietary and supplemental intakes of the sodium to potassium ratio was 21% higher in overweight/obese women compared to the lean women, p = 0.0031 (mean (SD) of 1.17 (0.35) versus 0.93 (0.28), respectively). Virtually all women did not meet the H-AR for niacin, folate, and vitamin D through dietary intake alone. Conclusions: The ‘eat better and not more’ message during pregnancy is supported.

Introduction Current dietary assessment methods struggle to accurately capture individuals’ dietary habits. 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, wearable camera technology) and two online self-reporting dietary assessment tools to monitor dietary intake. Methods This randomised controlled crossover trial was conducted at two sites (Hammersmith Hospital and the University of Reading) and aimed to recruit 30 UK participants (aged 18-70 years, BMI 20-30 kg/m2). Exclusion criteria included recent weight change, food allergies/intolerances, restrictive diets, certain health conditions and medication use. Volunteers completed an online screening questionnaire via REDCap and eligible participants attended a pre-study visit. Participants consumed, in a random order, two highly-controlled diets (compliant/non-compliant with UK guidelines) for four consecutive days, separated by at least one-week. Dietary intake was monitored daily using wearable cameras and self-recorded using Intake24 (24HR). Two versions of the online eNutri FFQ were completed: at baseline to assess habitual diet and on day 4 of each test period to record food intake. Urine and capillary blood samples were collected for biomarker analysis. Data analysis will assess dietary reporting accuracy across these methods using Lin’s concordance correlation coefficient. Discussion and ethical considerations The SODIAT project introduced a novel approach to dietary assessment, aiming to address the limitations like misreporting and inclusivity. However, challenges persist, such as variability in biomarker data due to failure to follow sample storage requirements and the practicalities of wearing cameras throughout the day. To protect privacy, participants removed cameras at inappropriate times, and AI removed non-food related images and blurred faces/device screens captured on the images. The accuracy of the tools in a highly-controlled setting will be evaluated in this study. Future studies are planned to validate these tools further in free-living and minority populations.

Students' diets often change when leaving home and starting university due to increased responsibility for their diet and finances. However, there is limited qualitative research with students at UK universities about how their diets change during the transition to, and whilst at university and the reasons for these changes. The aim of this study was to qualitatively explore three topics: 1) specific dietary changes reported by students at UK universities, 2) reasons for these dietary changes and 3) how students can be supported to eat more healthily. Fifteen students (100% female, 54% white) across different academic years (60% undergraduate and 40% postgraduate) from the Universities of Reading and Hertfordshire were recruited. Four online focus groups were conducted, ranging from groups of 2 to 6 participants, using a semi-structured topic guide. Discussions were recorded and professionally transcribed. Transcripts were coded and themes derived for each research topic using qualitative analysis software. After joining university, dietary changes commonly reported by the students included either increased or decreased fruit and vegetable intake, increased snacking behaviour, and increased alcohol and convenience food consumption. Common reasons for changes included limited budget, time management struggles, a lack of cooking skills, and peer influence. Students suggested that reduced cost of healthy foods on campus and cooking classes to learn new skills could help them to adopt a healthier diet. These suggestions could be used to guide future healthy eating interventions for university students.

Dietary assessment methods play a crucial role in evaluating individuals’ and communities’ dietary intake (1). Among these methods, Food Frequency Questionnaires (FFQs) are common in epidemiological dietary surveys. However, with the rapid advancement of technology and increased internet usage globally, innovative digital tools to assess dietary intake have emerged (2). Collaborative efforts between tool developers and dietitians are vital for leveraging technology effectively and advancing evidence-based nutrition practice. Thus, the aim of this study was to explore the: 1) perceived advantages and disadvantages of FFQs, 2) challenges and benefits associated with transitioning from traditional paper-based to web-based FFQs, and 3) opportunities and challenges of integrating a range of new technologies, from established digital tools such as web-based or smartphone applications to more futuristic options such as artificial intelligence and biosensors, into dietary assessment practices and research among researchfocused dietitians with PhD.Seven dietitians from Turkey with extensive experience in using dietary assessment methods were selected using purposive sampling. One-to-one semi-structured interviews were conducted using a topic guide via Microsoft Teams and transcribed verbatim into text-based records. They examined advantages and challenges of paper-based and web-based FFQs, ranked a list of predefined features for FFQ development, and provided insights into technology integration, addressing both the benefits and challenges of incorporating new tools. Preliminary thematic analysis was conducted using NVivo12 software to identify common themes.Participants had an average of 18 years (6-49 years) of experience in nutritional research. Common challenges identified by the group included the absence of validated FFQs in Turkish (n = 5), necessitating the use of FFQs alongside other methods like 24-hour recall (n = 4). General diet representation was commonly appreciated, while all participants deemed paper-based semiquantitative FFQs time-consuming. The top priorities for FFQ enhancement included a semiquantitative feature for nutrient intake calculation, inclusion of portion size photos, and evidencebased development using national diet survey data. None of the participants had employed any digital dietary assessment tools in their research endeavors. However, there was a consensus recognizing the potential benefits and drawbacks of using technology in dietary assessment. Participants highlighted the efficiency (n = 7), increased flexibility in data collection (n = 6), and heightened accuracy (n = 5) associated with technology-based assessment methods as significant advantages. Common concerns included users’ lack of proficiency with technology (n = 6), potential challenges related to the cost of research and development (n = 4), and considerations surrounding data privacy and ethical breaches (n = 3), particularly the unauthorized recording of sensitive information by artificial intelligence in camera-based technology.The insights from Turkish dietitians highlight the necessity for validated Turkish web-based FFQs. While technology-based dietary assessment tools offer research benefits, addressing integration barriers is crucial. These findings will contribute to the development of web-based FFQs and more futuristic dietary assessment tools, advancing evidence-based nutrition practice.

Poor dietary habits are associated with the development of non-communicable diseases(1). Most strategies implemented to enhance population diet quality follow a “one-size-fits-all” standardised approach, often neglecting individual preferences and requirements. Evidence suggests that personalised nutrition (PN) advice, tailored to an individual, can improve dietary intakes(2). This research investigates participants’ subjective feedback from the EatWellUK-2 randomised control trial that compared PN advice versus general dietary guidance, both delivered via eNutri, a web app, developed at the University of Reading. eNutri delivers automated food-based nutrition advice tailored to the user based on their dietary intake recorded by a food frequency questionnaire (FFQ)(3).Participants were disease-free UK adults (>18y) who were randomised to the PN or control group. Participants completed the eNutri FFQ, then automatically received via the app either advice tailored to their dietary intake (PN group) or general population advice based on the UK’s Eatwell Guide (control). Following a 12-week intervention, the FFQ was repeated and participants completed a feedback questionnaire containing open-text and Likert questions assessing agreement with statements (strongly disagree, disagree, neutral, agree and strongly agree). MannWhitney U tests compared the mean ranks between the PN and control groups for each question. Data are presented as percentages of participants who felt positively about each statement based on “agreed” plus “strongly agreed” responses. The study received ethical approval from the University of Reading Research Ethics Committee (08/19) and was registered at ClinicalTrials.gov (NCT03897972).Participants (90% female) had a mean (SD) age of 46 (15) y and BMI of 25.8 (6.1) kg/m2. When asked about the advice received, the responses of the PN group (n = 54) were more positive that “it encouraged me to eat healthily, even if only for short time” (p = 0.045, PN 55.6% vs control 41.8%) and “it was clear what changes I needed to make to improve my diet” (p = 0.011, PN 73.6% vs C 51.8%). When asked to provide an “app review” of eNutri, the control group (n = 55) described their advice as “too general” and “readily available elsewhere”, while the PN group said it contained “varied and realistic examples”, and the advice was “very clear”, “helpful” in relation to choosing “better foods to eat” and allowed people to “assess your own goals”.Participant feedback favoured PN over general population advice as it provided encouragement and clarity about which dietary changes would benefit each individual. Together with the quantitative results from the EatWellUK-2 study, which are still being analysed, these findings will help to assess eNutri’s potential as a useful tool to encourage UK adults to adopt healthier dietary behaviours.

University students often make less healthful dietary choices whilst at university however, do not typically receive advice and support to help them eat more healthily(1,2). A tool which could be provided to students to promote more favourable dietary behaviours is the eNutri web-based app which includes a food frequency questionnaire (FFQ) and delivers automated personalised nutrition advice (PNA) and a diet quality score (DQS) consisting of 11 food/nutrient components(3). The PNA includes scores and general advice for each component and, for the user’s three lowest scoring components, recommends which foods to eat more/less frequently to improve their DQS. As part of a 4-week intervention study, we aimed to explore the perceptions of the eNutri PNA in UK university students.As part of this intervention, 14 students from the Universities of Reading and Hertfordshire completed the eNutri FFQ and received their PNA. At the end of the study, they rated how much they agreed with statements about the perceived value and benefit (if any) of the eNutri PNA tool, on a 6-point scale ranging from strongly disagree to strongly agree. The percentage of respondents reported is the total number who responded “somewhat agree”, “agree”, or “strongly agree” to each statement.Of the 14 students, 79% were female with a mean age of 25y (range = 18-37y) and mean BMI of 24.7kg/m2 (range = 19.4-31.9kg/m2). At baseline, the average importance of a healthy diet to the participants (n = 13) was rated at 7.2 out of 10 (with 0 being ‘not important at all’ and 10 being ‘very important’). In total, 57% of respondents indicated that they felt they ‘were eating a healthier diet because of the eNutri advice received’ and only 14% reported that ‘the advice did not motivate them to make changes to their diet’. Furthermore, 64% of respondents indicated that the ‘eNutri PNA gave them confidence in their ability to make changes to their diet’ and that it ‘supported them to do so’. Half of the students agreed that ‘they would want to use eNutri long term to track their progress and receive regular PNA’. In addition, 79% agreed that ‘eNutri should be offered to all university students to help them make healthier food choices’, and that if eNutri was offered to them for free by their university, ‘it would be a valuable student benefit’ and they ‘would want to use it again’.In general, university students indicated the eNutri PNA tool supported them to eat healthier and providing access to the wider student population would be beneficial to encourage healthy eating at university. These findings along with the quantitative data from the PNA intervention which is currently being analysed will support the development of larger, suitably-powered studies to confirm these findings.

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