Consumers' Preferences and Willingness to Pay for Personalised Nutrition

The Evolving Landscape of Discrete Choice Experiments in Health Economics: A Systematic Review

INTRODUCTION

Stakeholder preference evaluations are increasingly emphasized in healthcare policy and health technology assessment. Discrete choice experiments (DCEs) are the most common method for quantifying preferences among patients, the public, and healthcare professionals. While prior reviews (1990-2017) have examined DCE trends, no comprehensive synthesis exists for studies published since 2018. This updated review (2018-2023) provides critical insights into evolving methodologies and global trends in health-related DCEs.

METHODS

A systematic search (2018-2023) of Medline, Embase, and Web of Science identified relevant studies. Studies were screened for inclusion and data were extracted, including details on DCE design and analysis. To enable trend comparisons, the search strategy and extraction items aligned with previous reviews.

RESULTS

Of 2663 identified papers, 1279 met the inclusion criteria, reflecting a significant rise in published DCEs over time. DCEs were conducted globally, with a remarkable increase in publications from Asia and Africa compared with previous reviews. Experimental designs and econometric models have advanced, continuing prior trends. Notably, most recent DCEs were administered online.

DISCUSSION

The rapid growth of DCE applications underscores their importance in health research. While the methodology is advancing rapidly, it is crucial that researchers provide full transparency in reporting their methods, particularly in detailing experimental designs and validity tests, which are too often overlooked. Key recommendations include improving reporting of experimental designs, applying validity tests, following good practices for presenting benefit-risk attributes, and adopting open science practices. Ensuring methodological rigor will maximize the impact and reproducibility of DCE research in health economics.

Data in Personalized Nutrition: Bridging Biomedical, Psycho-behavioral, and Food Environment Approaches for Population-wide Impact

Personalized nutrition (PN) represents an approach aimed at delivering tailored dietary recommendations, products, or services to support both prevention and treatment of nutrition-related conditions and to improve individual health using genetic, phenotypic, medical, nutritional, and other pertinent information. However, current approaches have yielded limited scientific success in improving diets or in mitigating diet-related conditions. In addition, PN currently caters to a specific subgroup of the population rather than having a widespread impact on diet and health at a population level. Addressing these challenges requires integrating traditional biomedical and dietary assessment methods with psycho-behavioral, and novel digital and diagnostic methods for comprehensive data collection, which holds considerable promise in alleviating present PN shortcomings. This comprehensive approach not only allows for deriving personalized goals ("what should be achieved") but also customizing behavioral change processes ("how to bring about change"). We herein outline and discuss the concept of "Adaptive Personalized Nutrition Advice Systems," which blends data from 3 assessment domains: 1) biomedical/health phenotyping; 2) stable and dynamic behavioral signatures; and 3) food environment data. Personalized goals and behavior change processes are envisaged to no longer be based solely on static data but will adapt dynamically in-time and in-situ based on individual-specific data. To successfully integrate biomedical, behavioral, and environmental data for personalized dietary guidance, advanced digital tools (e.g., sensors) and artificial intelligence-based methods will be essential. In conclusion, the integration of both established and novel static and dynamic assessment paradigms holds great potential for transitioning PN from its current focus on elite nutrition to a widely accessible tool that delivers meaningful health benefits to the general population.

The effectiveness of personalised food choice advice tailored to an individual's socio-demographic, cognitive characteristics, and sensory preferences

Personalised dietary advice has become increasingly popular, currently however most approaches are based on an individual's genetic and phenotypic profile whilst largely ignoring other determinants such as socio economic and cognitive variables. This paper provides novel insights by testing the effectiveness of personalised healthy eating advice concurrently tailored to an individual's socio-demographic group, cognitive characteristics, and sensory preferences. We first used existing data to build a synthetic dataset based on information from 3654 households (Study 1a), and then developed a cluster model to identify individuals characterised by similar socio-demographic, cognitive, and sensory aspects (Study 1b). Finally, in Study 2 we used the characteristics of 8 clusters to build 8 separate personalised food choice advice and assess their ability to motivate the increased consumption of fruit and vegetables and decreased intakes of saturated fat and sugar. We presented 218 participants with either generic UK Government "EatWell" advice, advice that was tailored to their allocated cluster (matched personalised), or advice tailored to a different cluster (unmatched personalised). Results showed that, when compared to generic advice, participants that received matched personalised advice were significantly more likely to indicate they would change their diet. Participants were similarly motivated to increase vegetable consumption and decrease saturated fat intake when they received unmatched personalised advice, potentially highlighting the power of providing alternative food choices. Overall, this study demonstrated that the power of personalizing food choice advice, based on a combination of individual characteristics, can be more effective than current approaches in motivating dietary change. Our study also emphasizes the viability of addressing population health through automatically delivered web-based personalised advice.

Community Health Worker Implementation and Cost Analysis of a Plant-Based Nutrition Program

OBJECTIVE

This study describes program implementation through a research-restaurant partnership and assesses participant satisfaction, program costs, and percent body weight changes.

METHODS

Participants (n = 60) in a virtual synchronous (n = 43) or virtual asynchronous (n = 17) 12-week plant-based nutrition program received restaurant vouchers. Class satisfaction data were collected weekly. Assessments were completed at baseline, 3 months, and 9 months, along with interviews (n = 13) between 3 and 9 months. The costing approach estimated costs per participant. Interviews were coded using a content analysis and constant comparative method.

RESULTS

Participants rated the intervention favorably. Program costs were $198.63/participant, and participants' willingness to pay postintervention was $101.50 ± $63.90. Participants shared satisfaction with course content, the restaurant partnership, and suggestions for future delivery. No changes in participants' percent body weight were observed between 3 and 9 months (P = 0.98), indicating maintenance of 3-month weight loss.

CONCLUSIONS AND IMPLICATIONS

A research-restaurant partnership successfully implemented a nutrition program and generated positive feedback. With the lifting of coronavirus disease 2019 pandemic restrictions, future research can now test alternative implementation methods (in person vs online) in other restaurants.

Forgetting how we ate: personalised nutrition and the strategic uses of history

Personalised nutrition (PN) has emerged over the past twenty years as a promising area of research in the postgenomic era and has been popularized as the new big thing out of molecular biology. Advocates of PN claim that previous approaches to nutrition sought general and universal guidance that applied to all people. In contrast, they contend that PN operates with the principle that "one size does not fit all" when it comes to dietary guidance. While the molecular mechanisms studied within PN are new, the notion of a personal dietary regime guided by medical advice has a much longer history that can be traced back to Galen's "On Food and Diet" or Ibn Sina's (westernized as Avicenna) "Canon of Medicine". Yet this history is either wholly ignored or misleadingly appropriated by PN proponents. This (mis)use of history, we argue helps to sustain the hype of the novelty of the proposed field and potential commodification of molecular advice that undermines longer histories of food management in premodern and non-Western cultures. Moreover, it elides how the longer history of nutritional advice always happened in a heavily moralized, gendered, and racialized context deeply entwined with collective technologies of power, not just individual advice. This article aims at offering a wider appreciation of this longer history to nuance the hype and exceptionalism surrounding contemporary claims.

Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition

Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user's needs. In this article, we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.