Predictors for Reporting of Dietary Assessment Methods in Food-based Randomized Controlled Trials over a Ten-year Period

The effects of MIND diet on depression, anxiety, quality of life and metabolic and hormonal status in obese or overweight women with polycystic ovary syndrome: a randomised clinical trial

Polycystic ovary syndrome (PCOS) is a common hormonal disorder in women of reproductive age, associated with increased risks of metabolic disorders, depression and reduced quality of life. This study examined the impact of the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet on mental health, PCOS-related quality of life (PCOSQ), anthropometric indices, hirsutism, and metabolic/hormonal parameters in women with PCOS. Total of 52 women with PCOS, aged between 18 and 45 years, were randomly assigned to either the low calorie MIND diet group or the control group. Anthropometric data, depression and anxiety scores, serum levels of gonadotropins (luteinizing hormone [LH], follicle-stimulating hormone [FSH]), PCOSQ, and Ferriman-Gallwey score were evaluated before and after the intervention. Outcomes were assessed at baseline and at the end of the 8-week follow-up period. After 8 weeks, the MIND diet significantly reduced depression (β -3·03; 95 % CI: [-5·36, -0·70]; P = 0·011) and anxiety (β -3·54; 95 % CI: [-6·60, -0·49]; P = 0·024) scores compared with the control group. The MIND diet group exhibited significant increases in the emotion (β 0·63; 95 % CI: [0·18, 1·08]; P = 0·007) and body hair (β 0·65; 95 % CI: [0·03, 1·28]; P = 0·04) domains of the PCOSQ compared to the control group. The changes in other parameters did not show significant differences between the two groups. Both the MIND diet and control groups showed improvements in weight and BMI, with a slight advantage for the MIND diet group. These findings suggest that the MIND diet may be beneficial for improving mental health and some aspects of PCOSQ in women with PCOS.

Perceptions and Experiences of People with Obesity and Type 2 Diabetes Around Appetite and Eating Behaviors: A Qualitative Study

INTRODUCTION

Chronic weight management and treatments for type 2 diabetes (T2D) involve a combination of lifestyle-based (diet, exercise) and pharmaceutical interventions. In people with obesity or T2D, understanding the impact of drivers/triggers on appetite and eating behaviors can be crucial to successful medical management. This study aimed to characterize perceptions and experiences regarding appetite and eating behaviors among people with obesity or T2D and identify drivers/triggers of food choices.

METHODS

This non-interventional, cross-sectional, qualitative study utilized semi-structured concept elicitation interviews to explore the perceptions of people with obesity and/or T2D around appetite, eating behaviors and drivers/triggers of food choices. Adult US residents (≥ 18 years) with stable body weight (± 5 kg) in the 3 months preceding participation were included in the study.

RESULTS

Forty-five participants (obesity: n = 15; overweight: n = 10; T2D: n = 20) were interviewed. Interviews were audio-recorded and transcribed verbatim for analysis. A subset of participants described eating behaviors on smartphone-based app tasks over 5 days. Most (> 96%) discussed the influence of hunger, cravings and satiety on food choices. Participants identified 22 drivers/triggers (including health, 95.6%; culture/heritage, 93.3%; location, 91.1%; stress, 88.8%). Participants also discussed associations between drivers/triggers and eating behavior concepts (appetite, hunger, cravings, satiety, motivation/determination). A conceptual model illustrating eating behavior concepts and related drivers/triggers was developed. The concept elicitation interviews identified a multitude of drivers and triggers and characterized the association of such drivers/triggers with seven core patient-reported concepts encompassing eating behaviors.

CONCLUSION

The findings build upon existing models of factors influencing food choices. Findings confirm prior research regarding impact of drivers/triggers on food choice in people with obesity and T2D and indicate underlying disease state does not appear to influence eating behaviors in people with stable body weight.

Female Athlete Triad and Relative Energy Deficiency in Sport (REDs): Nutritional Management

The female athlete triad (TRIAD) is a spectrum of disorders involving low energy availability (LEA), low bone mineral density, and menstrual disorders. It is increasingly common to use the term 'relative energy deficiency in sport' (RED), emphasising the extensive impact of LEA on the body. The aim of this narrative review was to gather original research encompassing female athletes across various sports as well as to collect findings on the potential of a nutrition-focused approach to prevent or treat the aforementioned disorders. A comprehensive search was conducted in PubMed and Scopus. Several challenges were identified regarding the adequacy of the energy availability, protein, and carbohydrate requirements in the diets of female athletes. Moreover, insufficient intake of vitamin D has been observed across all athlete groups studied. This insufficiency also extends to the average requirement for Ca, Mg, the Ca/P ratio, Zn, and Fe. To address those concerns, a nutritional approach is proposed in the latter part of this review. The factors that can improve the absorption of micronutrients have also been discussed. The TRIAD/REDs affect an ever-growing number of women and require appropriate therapeutic management, particularly through nutritional care. Therefore, cooperation within an interdisciplinary team comprising a physician, nutritionist, physiotherapist, and psychologist is crucial.

The validity of technology-based dietary assessment methods in childhood and adolescence: a systematic review

Technology-based dietary assessment has promising benefits on improving accuracy and reducing cost of dietary data collection. The validity of technology-based tools in children/adolescents was examined. A systematic literature search was performed to identify studies published till September 2019. In total, 26 studies with 29 validation-related estimations were selected; 13 web-based 24-hdietary recalls (image assisted; n = 12, drop-down food list; n = 13), 4 mobile applications and 3 web-based dietary records (image based; n = 2 and image assisted; n = 1, drop-down food list; n = 3) and 6 web-based food frequency questionnaires (FFQs) (image assisted; n = 3). Fourteen studies were addressed to adolescents, ten studies to children (principally 9-11 years old) and two studies to mixed study samples. Validation was mostly performed through 24-h dietary recalls and dietary records while in some cases objective methods were used. Image-assisted 24-h dietary recalls presented good level of agreement with paper-based methods, yet principally in adolescents. Mobile-application dietary records with image-based facilities and drop-down food lists were found to accurately record dietary intake even when compared with objective methods. FFQs, mainly examined in adolescents, had good level of agreement with the "gold standard" dietary records method. The validity of technology-based methods could be supported yet various technical/methodological issues need better clarification.

Evaluation of the dietary intake data coding process in a clinical setting: Implications for research practice

BACKGROUND

High quality dietary intake data is required to support evidence of diet-disease relationships exposed in clinical research. Source data verification may be a useful quality assurance method in this setting. The present pilot study aimed to apply source data verification to evaluate the quality of the data coding process for dietary intake in a clinical trial and to explore potential barriers to data quality in this setting.

METHODS

Using a sample of 20 cases from a clinical trial, source data verification was conducted between three sets of data derived documents: transcripts of audio-recorded diet history interviews, matched paper-based diet history forms and outputs from nutrition analysis software. The number of cases and rates of discrepancies between documents were calculated. A total of five in-depth interviews with dietitians collecting and coding dietary data were thematically analysed.

RESULTS

Some 2024 discrepancies were identified. The highest discrepancy rate was 57.49%, and occurred between diet history interviews and nutrition analysis software outputs. Sources of the discrepancies included both quantities and frequencies of food intake. The highest discrepancy rate was for the food group "vegetable products and dishes". In-depth interviews implicated recall bias of trial participants as a cause of discrepancies, but dietitians also acknowledged a possible subconscious influence of having to code reported foods into nutrition analysis software programs.

CONCLUSION

The accuracy of dietary intake data appeared to depend on the level of detailed food data required. More support for participants on reporting consumption, and incorporating supportive tools to guide estimates of food quantities may facilitate a more consistent coding process and improve data quality. This pilot study offers a novel method and an overview of dietary intake data coding measurement errors. These findings may warrant further investigation in a larger sample.

Food and functional dyspepsia: a systematic review

BACKGROUND

Functional dyspepsia (FD) is a debilitating functional gastrointestinal disorder characterised by early satiety, post-prandial fullness or epigastric pain related to meals, which affects up to 20% of western populations. A high dietary fat intake has been linked to FD and duodenal eosinophilia has been noted in FD. We hypothesised that an allergen such as wheat is a risk factor for FD and that withdrawal will improve symptoms of FD. We aimed to investigate the relationship between food and functional dyspepsia.

METHODS

Sixteen out of 6451 studies identified in a database search of six databases met the inclusion criteria of studies examining the effect of nutrients, foods and food components in adults with FD or FD symptoms.

RESULTS

Wheat-containing foods were implicated in FD symptom induction in six studies, four of which were not specifically investigating gluten and two that were gluten-specific, with the implementation of a gluten-free diet demonstrating a reduction in symptoms. Dietary fat was associated with FD in all three studies that specifically measured this association. Specific foods reported as inducing symptoms were high in either natural food chemicals, high in fermentable carbohydrates or high in wheat/gluten. Caffeine was associated with FD in four studies, although any association with alcohol was uncertain.

CONCLUSIONS

Wheat and dietary fats may play key roles in the generation of FD symptoms and reduction or withdrawal eased symptoms. Randomised trials investigating the roles of gluten, FODMAPs (fermentable oligosaccharide, disaccharide, monosaccharide and polyols) and high fat ingestion and naturally occurring food chemicals in the generation of functional dyspepsia symptoms are warranted and further investigation of the mechanisms is now required.

First-Stage Development and Validation of a Web-Based Automated Dietary Modeling Tool: Using Constraint Optimization Techniques to Streamline Food Group and Macronutrient Focused Dietary Prescriptions for Clinical Trials

BACKGROUND

Standardizing the background diet of participants during a dietary randomized controlled trial is vital to trial outcomes. For this process, dietary modeling based on food groups and their target servings is employed via a dietary prescription before an intervention, often using a manual process. Partial automation has employed the use of linear programming. Validity of the modeling approach is critical to allow trial outcomes to be translated to practice.

OBJECTIVE

This paper describes the first-stage development of a tool to automatically perform dietary modeling using food group and macronutrient requirements as a test case. The Dietary Modeling Tool (DMT) was then compared with existing approaches to dietary modeling (manual and partially automated), which were previously available to dietitians working within a dietary intervention trial.

METHODS

Constraint optimization techniques were implemented to determine whether nonlinear constraints are best suited to the development of the automated dietary modeling tool using food composition and food consumption data. Dietary models were produced and compared with a manual Microsoft Excel calculator, a partially automated Excel Solver approach, and the automated DMT that was developed.

RESULTS

The web-based DMT was produced using nonlinear constraint optimization, incorporating estimated energy requirement calculations, nutrition guidance systems, and the flexibility to amend food group targets for individuals. Percentage differences between modeling tools revealed similar results for the macronutrients. Polyunsaturated fatty acids and monounsaturated fatty acids showed greater variation between tools (practically equating to a 2-teaspoon difference), although it was not considered clinically significant when the whole diet, as opposed to targeted nutrients or energy requirements, were being addressed.

CONCLUSIONS

Automated modeling tools can streamline the modeling process for dietary intervention trials ensuring consistency of the background diets, although appropriate constraints must be used in their development to achieve desired results. The DMT was found to be a valid automated tool producing similar results to tools with less automation. The results of this study suggest interchangeability of the modeling approaches used, although implementation should reflect the requirements of the dietary intervention trial in which it is used.

Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping

Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work.