Personalized Nutrition Advice Research Articles

Preferences and willingness to pay for personalized nutrition interventions: Discrete choice experiments in Europe and the United States

This study gives insight into what intervention-related factors are crucial for using personalized nutrition (PN) interventions, as well as what the general population is willing to pay for PN. This was done by focusing on two different types of PN (i.e., PN advice and personalized meals) in two discrete choice experiments (DCEs). The DCEs were conducted in four European countries and the United States, including at least 500 respondents per country aged 18–65 years. Panel mixed multinomial logit models were used to evaluate the preferences. Results show that for both types of PN in all countries, the total expenditure on nutrition was the most crucial factor when choosing a PN intervention. The participation rate for specific hypothetical scenario’s varied but was considered high overall (maximum 81 % for ‘PN advice’ and 87 % for ‘personalized meals’ in Spain). Moreover, highest willingness to pay estimates were found for six kilograms of weight loss. For example, Polish respondents were willing to spend an extra 25.78 euros per week for ‘personalized meals’ for a 4-month period to lose six kilograms. Our models showed preference heterogeneity between, but also within, the different countries. In conclusion, this study showed that people seem willing to pay for and participate in PN interventions. Since PN interventions may improve health outcomes, policymakers should consider subsidizing some of the costs, financially incentivizing PN interventions or introducing commitment lotteries to encourage uptake. More research is needed to study heterogeneity in preferences.

Perspective: A Conceptual Framework for Adaptive Personalized Nutrition Advice Systems.

Nearly all approaches to personalized nutrition (PN) use information such as the gene variants of individuals to deliver advice that is more beneficial than a generic "1-size-fits-all" recommendation. Despite great enthusiasm and the increased availability of commercial services, thus far, scientific studies have only revealed small to negligible effects on the efficacy and effectiveness of personalized dietary recommendations, even when using genetic or other individual information. In addition, from a public health perspective, scholars are critical of PN because it primarily targets socially privileged groups rather than the general population, thereby potentially widening health inequality. Therefore, in this perspective, we propose to extend current PN approaches by creating adaptive personalized nutrition advice systems that are tailored to the type and timing of personalized advice for individual needs, capacities, and receptivity in real-life food environments. These systems encompass a broadening of current PN goals (i.e., what should be achieved) to incorporate "individual goal preferences" beyond currently advocated biomedical targets (e.g., making sustainable food choices). Moreover, they cover the "personalization processes of behavior change" by providing in situ, "just-in-time" information in real-life environments (how and when to change), which accounts for individual capacities and constraints (e.g., economic resources). Finally, they are concerned with a "participatory dialog between individuals and experts" (e.g., actual or virtual dieticians, nutritionists, and advisors) when setting goals and deriving measures of adaption. Within this framework, emerging digital nutrition ecosystems enable continuous, real-time monitoring, advice, and support in food environments from exposure to consumption. We present this vision of a novel PN framework along with scenarios and arguments that describe its potential to efficiently address individual and population needs and target groups that would benefit most from its implementation.

Design Issues in Personalized Nutrition Advice Systems.

The current health status of the general public can substantially benefit from a healthy diet. Using a personalized approach to initiate healthy dietary behavior seems to be a promising strategy, as individuals differ in terms of health status, subsequent dietary needs, and their desired behavior change support. However, providing personalized advice to a wide audience over a long period is very labor-intensive. This bottleneck can possibly be overcome by digitalizing the process of creating and providing personalized advice. An increasing number of personalized advice systems for different purposes is becoming available in the market, ranging from systems providing advice about just a single parameter to very complex systems that include many variables characterizing each individual situation. Scientific background is often lacking in these systems. In designing a personalized nutrition advice system, many design questions need to be answered, ranging from the required input parameters and accurate measurement methods (sense), type of modeling techniques to be used (reason), and modality in which the personalized advice is provided (act). We have addressed these topics in this viewpoint paper, and we have demonstrated the feasibility of setting up an infrastructure for providing personalized dietary advice based on the experience of 2 practical applications in a real-life setting.

Metabotyping: a tool for identifying subgroups for tailored nutrition advice.

Diet-related diseases are the leading cause of death globally and strategies to tailor effective nutrition advice are required. Personalised nutrition advice is increasingly recognised as more effective than population-level advice to improve dietary intake and health outcomes. A potential tool to deliver personalised nutrition advice is metabotyping which groups individuals into homogeneous subgroups (metabotypes) using metabolic profiles. In summary, metabotyping has been successfully employed in human nutrition research to identify subgroups of individuals with differential responses to dietary challenges and interventions and diet–disease associations. The suitability of metabotyping to identify clinically relevant subgroups is corroborated by other fields such as diabetes research where metabolic profiling has been intensely used to identify subgroups of patients that display patterns of disease progression and complications. However, there is a paucity of studies examining the efficacy of the approach to improve dietary intake and health parameters. While the application of metabotypes to tailor and deliver nutrition advice is very promising, further evidence from randomised controlled trials is necessary for further development and acceptance of the approach.

Can a mobile application deliver appropriate, acceptable and usable personalised nutrition advice to UK adults

Can a mobile application deliver appropriate, acceptable and usable personalised nutrition advice to UK adults

Short-chain fatty acids are key mediators of the favorable effects of the Mediterranean diet on intestinal barrier integrity: data from the randomized controlled LIBRE trial

The Mediterranean diet is associated with the prevention of diabetes, cardiovascular disease, and cancer, all of which are linked to intestinal barrier impairment. Here, we hypothesize that the Mediterranean diet, possibly via the induction of short-chain fatty acids (SCFAs), improves intestinal barrier integrity. Furthermore, we aim to establish novel personalized nutrition advice based on machine learning algorithms. We studied 260 women with intestinal barrier impairment. The women were allocated to follow either a Mediterranean diet or a control diet for 3 mo. Intestinal permeability was assessed by measuring lipopolysaccharide binding protein (LBP) in plasma and zonulin in feces. SCFA concentrations were analyzed in feces. Bi- and multivariate analyses and machine learning algorithms (random forest classification) were conducted. Particularly in the intervention group, adherence to the Mediterranean diet increased, whereas plasma LBP and fecal zonulin concentrations decreased (all q<0.001 for the intervention group, all q<0.1 for control group). In the intervention group, fecal SCFA concentrations increased (propionate+19%; butyrate+44%; both q<0.001). Multivariate analyses showed that adherence to the Mediterranean diet was associated with SCFA concentrations (all q<0.001) and inversely associated with LBP and zonulin concentrations (all q<0.02). Mediation analyses identified propionate and butyrate as the key mechanistic link between diet and intestinal permeability integrity. Accordingly, using baseline SCFA data, we could predict the effect of the Mediterranean diet on intestinal permeability using a machine learning algorithm (receiver operating characteristic AUC: 0.78-0.96). Our data suggest that SCFAs are key mediators for the relation between diet and gut health. Assessment of SCFAs may form a basis for personalized nutrition in future clinical care. These results need to be verified in larger studies powered for this purpose, comprising different study populations. The trial was registered at clinicaltrials.gov as NCT02087592 and NCT02516540.

Personalized nutrition for people with diabetes and at risk of diabetes has begun

Food and nutrition are essential parts for the management of blood glucose of patients with diabetes and other metabolic diseases. The results of recent human clinical studies have shown that the blood glucose levels change differently in different people in response to the same standardized meals. This phenomenon shows the challenges to find a one-size-fits-all approach to combat diabetes. With the development of technologies, personalized nutrition/precision nutrition has gradually become more practical in order to treat individual diabetes. The aim of this review article is to summarize personalized nutrition's progress and potential in treating the diabetes epidemic. We have searched PubMed to identify relevant articles and found that personalized nutrition on multiple factors associated with an individual has started to draw attention to scientific communities. Two seminal studies have shown that healthy adults show differential responses of postprandial blood glucose levels to the same standardized meals. Human clinical trials have started to integrate sensor technologies such as continuous glucose monitoring and personal data such as genomic sequences and microbiome to provide personalized nutrition advice, and shown promises in intervention and management of type 2 diabetes. It appears that the interplays of diets and genomes, gut microbiome, gut transit time, insulin sensitivity, cultural, social, and economic factors should all be considered to create a personalized treatment for an individual's chronic metabolic disease. This probably can be achieved through the integration of personalized nutrition and personalized food intervention with the development of technologies and advances in food and nutrition sciences. More research should be anticipated soon.

Interaction of polygenic variants specific for abdominal obesity risk with energy metabolism in large Korean cohorts.

We examined the hypothesis that the genetic variants associated with abdominal obesity (AO) risk, excluding skeletal muscle mass impact, interact with lifestyle characteristics of adults aged >40 years in three cohorts from the Korean Genome and Epidemiology Study. Participants from a large city hospital-based cohort, excluding those with cancers, thyroid diseases, chronic kidney disease or brain-related diseases, were divided into AO (case; n=17 545) and control (n=36 283) using the cut-offs for waist circumference of 90 cm for men and 85 cm for women. The genetic variants affecting AO risk were chosen from a genome-wide association study in this cohort with obesity-related covariates, including and excluding skeletal muscle mass as a covariate. The genetic variants were confirmed in the other two cohorts. The interaction between the genetic variants was identified by generalised multifactor dimensionality reduction analysis (GMDR). The polygenic risk scores (PRS)-nutrient interactions were determined. Abdominal obesity was associated with SEC16B_rs543874, KCNQ5_rs2796052, CDKAL1_rs9356744, BDNF_rs6265, FTO_rs1421085, MC4R_rs17782313 and GIPR_rs1444988703 adjusting for covariates excluding LBM. However, the best model with 5 single nucleotide polymorphisms (SNP) contained COL3A1_rs3106801, ADAMTS3_rs13105983, KCNQ5_rs2796044, ZFHX3_rs9938769 and MIR17HG_rs7318578 from GMDR when including LBM in covariates. These genetic variants were specific for AO risk. The expression quantitative trait locus (eQTL) analysis showed that the SNP effects were associated with the corresponding gene expression. The high PRS with the 5 SNP model was positively associated with an increased risk of AO of 1.639 (1.463-1.836). The PRS interacted with the age of menarche, energy intake and physical activity. In conclusion, adults with a high PRS, particularly those experiencing early menarche, may particularly benefit from not exceeding energy requirements and from regular physical exercise in order to prevent abdominal obesity. This finding can be applied to personalised nutrition advice to prevent abdominal obesity.

Effectiveness of Web-Based Personalized Nutrition Advice for Adults Using the eNutri Web App: Evidence From the EatWellUK Randomized Controlled Trial.

BackgroundEvidence suggests that eating behaviors and adherence to dietary guidelines can be improved using nutrition-related apps. Apps delivering personalized nutrition (PN) advice to users can provide individual support at scale with relatively low cost.ObjectiveThis study aims to investigate the effectiveness of a mobile web app (eNutri) that delivers automated PN advice for improving diet quality, relative to general population food-based dietary guidelines.MethodsNondiseased UK adults (aged >18 years) were randomized to PN advice or control advice (population-based healthy eating guidelines) in a 12-week controlled, parallel, single-blinded dietary intervention, which was delivered on the web. Dietary intake was assessed using the eNutri Food Frequency Questionnaire (FFQ). An 11-item US modified Alternative Healthy Eating Index (m-AHEI), which aligned with UK dietary and nutritional recommendations, was used to derive the automated PN advice. The primary outcome was a change in diet quality (m-AHEI) at 12 weeks. Participant surveys evaluated the PN report (week 12) and longer-term impact of the PN advice (mean 5.9, SD 0.65 months, after completion of the study).ResultsFollowing the baseline FFQ, 210 participants completed at least 1 additional FFQ, and 23 outliers were excluded for unfeasible dietary intakes. The mean interval between FFQs was 10.8 weeks. A total of 96 participants were included in the PN group (mean age 43.5, SD 15.9 years; mean BMI 24.8, SD 4.4 kg/m2) and 91 in the control group (mean age 42.8, SD 14.0 years; mean BMI 24.2, SD 4.4 kg/m2). Compared with that in the control group, the overall m-AHEI score increased by 3.5 out of 100 (95% CI 1.19-5.78) in the PN group, which was equivalent to an increase of 6.1% (P=.003). Specifically, the m-AHEI components nuts and legumes and red and processed meat showed significant improvements in the PN group (P=.04). At follow-up, 64% (27/42) of PN participants agreed that, compared with baseline, they were still following some (any) of the advice received and 31% (13/42) were still motivated to improve their diet.ConclusionsThese findings suggest that the eNutri app is an effective web-based tool for the automated delivery of PN advice. Furthermore, eNutri was demonstrated to improve short-term diet quality and increase engagement in healthy eating behaviors in UK adults, as compared with population-based healthy eating guidelines. This work represents an important landmark in the field of automatically delivered web-based personalized dietary interventions.Trial RegistrationClinicalTrials.gov NCT03250858; https://clinicaltrials.gov/ct2/show/NCT03250858

Can Personalized Nutrition Improve People’s Diets?

Each person differs in physical characteristics such as eye color, but also in likes and dislikes. These differences are due to our genes and our environments, including what we eat. What we eat affects our health, and each of us has individual nutritional needs. This is the basis for the idea of personalized nutrition. In our research study, called the Food4Me Study, we tested whether personalized nutrition advice helped over 1,600 people to eat healthier diets. We collected information about each person, including what they ate, and we collected samples of saliva to examine their genes. We gave each person either the usual advice about healthy eating (such as “eat more vegetables”) or advice that was personalized based on the individual’s characteristics. After 6 months, we discovered that people who received personalized nutrition advice improved their diets more than people who received the typical healthy eating advice.

Dairy matrix: is the whole greater than the sum of the parts?

Dairy foods are a heterogeneous group of products that vary in physical state and structure; profile and amounts of essential nutrients, bioactive ingredients, and other constituents; the extent of alteration of these constituents by processing, whether they are fermented or aged; and addition of constituents during manufacture. The complexity of the dairy matrix is associated with a heterogeneous impact on health outcomes from increased, decreased, or neutral effects for specific dairy products and specific health outcomes. Researchers must become more nuanced and systematic in their study of the role of dairy products in health to develop meaningful dietary recommendations. This review of the evidence for the dairy matrix and health points out the dearth of randomized controlled trials and of mechanistic insights. The variable effects of dairy-product consumption on health suggest possibilities for personalized nutrition advice.

Does personalised nutrition advice based on apolipoprotein E and methylenetetrahydrofolate reductase genotype affect dietary behaviour?

Background: Dietary intake is linked to numerous modifiable risk factors of cardiovascular disease. Current dietary recommendations in the UK to reduce the risk of cardiovascular disease are not being met. A genotype-based personalised approach to dietary recommendations may motivate individuals to make positive changes in their dietary behaviour. Aim: To determine the effect of a personalised nutrition intervention, based on apolipoprotein E (ApoE, rs7412; rs429358) and methylenetetrahydrofolate reductase (MTHFR, rs1801133) genotype, on reported dietary intake of saturated fat and folate in participants informed of a risk genotype compared to those informed of non-risk genotype. Methods: Baseline data (n = 99) were collected to determine genotype (non-risk vs risk), dietary intake and cardiovascular risk (Q-Risk®2 cardiovascular risk calculator). Participants were provided with personalised nutrition advice via email based on their ApoE and MTHFR genotype and reported intake of folate and saturated fat. After 10 days, dietary intake data were reported for a second time. Results: Personalised nutrition advice led to favourable dietary changes, irrespective of genotype, in participants who were not meeting dietary recommendations at baseline for saturated fat (p < 0.001) and folate (p = 0.002). Only participants who were informed of a risk ApoE genotype met saturated fat recommendations following personalised nutrition advice. Conclusion: Incorporation of genotype-based personalised nutrition advice in a diet behaviour intervention may elicit favourable changes in dietary behaviour in participants informed of a risk genotype. Participants informed of a non-risk genotype also respond to personalised nutrition advice favourably but to a lesser extent.

Metabolic and Cardiovascular Impact of Ketotic Extreme Carbohydrate Restriction

Human trials of ketogenic diets (KDs) have shown promise in weight and glucose management in subjects with type 2 diabetes (T2D), prompting organizations, including Diabetes Canada, to condone their use through recent position statements. However, the effects of intervention with strict dietary regimes on the progression of cardiovascular disease and glucoregulatory processes, like the incretin effect, have not been fully elucidated. The incretins, glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), potentiate insulin secretion postprandially to control glycemia but are rapidly inactivated by dipeptidyl peptidase-4 (DPP4) cleavage. In this study, we used atherosclerosis-susceptible mice with diet-induced obesity and dysglycemia to investigate the effect of intervention with extreme carbohydrate restriction. PCSK9 overexpressing mice were fed a high fat, high cholesterol diet (HFD) for 10 weeks, followed by a 12-week continuation or intervention with a KD or chow diet. Mice fed a KD diet had elevated free fatty acids and ketone bodies in the fed state and lower circulating DPP4. Elevated GIP and hyperinsulinemia were also reversed. KD mice had increased insulin secretion in response to 10 mM glucose supplemented with GLP-1 and GIP than HFD mice during ex-vivo perifusion analysis. Additionally, liver triglycerides, cholesterol, and aortic sinus atherosclerosis significantly improved with ketogenic and chow interventions. Nanostring analysis of the aorta demonstrated differentially expressed inflammatory genes and pathways in KD versus HFD feeding. Understanding how dietary regimens drive the metabolic milieu and surrounding pathologies will advance personalized nutrition advice for glycemic control.

Personalized Nutrition Advice: Preferred Ways of Receiving Information Related to Psychological Characteristics.

The primary goal of this study is to be able to discern specific types of consumers in terms of their psychosocial characteristics who may need different ways of receiving dietary advice. Knowing these types will enable a better fit of advice to consumers’ psychosocial characteristics, hereby stimulating healthy eating as the probability of compliance to the advice can potentially increase. The study draws upon several psychological theories to distinguish unique underlying factors that can subsequently be used to personalize nutrition information for consumers. A number of general psychological scales (self-regulation, action and coping self-efficacy, social comparison, intrinsic motivation, health info processing, need for cognition and for affect, and regulatory focus) are filled out by 988 respondents, including their preferences for receiving personalized forms of nutrition advice. The set of joint items from various psychological constructs is analyzed using a Principal Component Analysis to find underlying psychological characteristics. The PCA produces four components (explaining 51% of variation), that could be interpreted as ‘intrinsic interest and capabilities for healthy eating,’ ‘perceived difficulty to eat healthily,’ ‘self-worth insecurity,’ and ‘seeking positive challenges,’ respectively. By means of a Logistic Regression these components are able to predict preferences for different forms of receiving nutrition advice. This first component shows that a mind set for maintaining a healthy diet goes together with an interest in receiving an advice on what do to and on how that will affect one’s health. The second component predicts a preference for a fixed moment to receive information/advice. This may be a strategy of those that perceive difficulties to eat healthily, to help them control their healthy food intake. The insecurity that the third component models seems to lead to a wish for receiving specific advice about their health situation at fixed moments in time. The fourth component is a small component, therefore its prediction of a wish for an advice focussing on prevention of negative consequences is probably not a strong result. The study does point out that there appear different psychosocial types of consumers, that may benefit by being addressed according to their preference for receiving nutrition advice on specific moments, of a specific level of detail or pointing at the type of consequences the advice has. A better fit of the advice to the psychosocial characteristics of the recipient, captured in the identified components in the current study, may lead to an increase in compliance, although that will have to be further investigated in subsequent work.

Nutrition knowledge, food choices and diet quality of genotyped and non-genotyped individuals during the COVID-19 pandemic.

Background: Severe obesity (body mass index ≥ 40 kg/m2) and non-communicable diseases, both influenced by diet, have been associated with COVID-19. Genotype-based personalised nutrition advice may improve nutrition knowledge and enhance behaviour change towards better diet quality compared with conventional recommendations. Aim: To investigate the nutrition knowledge, food choices and diet quality in genotyped and non-genotyped individuals during the COVID-19 pandemic. Methods: One hundred and twenty-three healthy UK adults were recruited using convenience sampling through social networks. The online questionnaire consisted of the General Nutrition Knowledge Questionnaire, the Food Choices Questionnaire, and the EPIC-Norfolk Food Frequency Questionnaire (FFQ). FFQ was used to calculate participant diet quality with the Diet Quality Index-International and socio-demographic and anthropometric data. Results: Median general nutrition knowledge, diet variety and diet balance scores were higher in genotyped compared with non-genotyped individuals (71.0 ± 11.0 vs. 61.0 ± 15.0, p = <.001, 18.00 ± 5.00 vs. 15.00 ± 5.00, p = .007 and 2.00 ± 4.00 vs. 0.00 ± 2.00, p = .025, respectively). Pooled sample multiple regression showed that health motive positively influenced while familiarity motive negatively influenced diet quality index scores (β = .428, t = 4.822, p = <.001 and β =-.356, t =-4.021, p = .001, respectively). Conclusions: Nutrition knowledge and diet quality indices of balance and variety were higher among genotyped compared with non-genotyped individuals; overall diet quality was similar between groups. This may be due to pandemic-specific factors, such as altered motives of food choice and availability.

Personalised nutrition advice reduces intake of discretionary foods and beverages: findings from the Food4Me randomised controlled trial

BackgroundThe effect of personalised nutrition advice on discretionary foods intake is unknown. To date, two national classifications for discretionary foods have been derived. This study examined changes in intake of discretionary foods and beverages following a personalised nutrition intervention using these two classifications.MethodsParticipants were recruited into a 6-month RCT across seven European countries (Food4Me) and were randomised to receive generalised dietary advice (control) or one of three levels of personalised nutrition advice (based on diet [L1], phenotype [L2] and genotype [L3]). Dietary intake was derived from an FFQ. An analysis of covariance was used to determine intervention effects at month 6 between personalised nutrition (overall and by levels) and control on i) percentage energy from discretionary items and ii) percentage contribution of total fat, SFA, total sugars and salt to discretionary intake, defined by Food Standards Scotland (FSS) and Australian Dietary Guidelines (ADG) classifications.ResultsOf the 1607 adults at baseline, n = 1270 (57% female) completed the intervention. Percentage sugars from FSS discretionary items was lower in personalised nutrition vs control (19.0 ± 0.37 vs 21.1 ± 0.65; P = 0.005). Percentage energy (31.2 ± 0.59 vs 32.7 ± 0.59; P = 0.031), percentage total fat (31.5 ± 0.37 vs 33.3 ± 0.65; P = 0.021), SFA (36.0 ± 0.43 vs 37.8 ± 0.75; P = 0.034) and sugars (31.7 ± 0.44 vs 34.7 ± 0.78; P < 0.001) from ADG discretionary items were lower in personalised nutrition vs control. There were greater reductions in ADG percentage energy and percentage total fat, SFA and salt for those randomised to L3 vs L2.ConclusionsCompared with generalised dietary advice, personalised nutrition advice achieved greater reductions in discretionary foods intake when the classification included all foods high in fat, added sugars and salt. Future personalised nutrition approaches may be used to target intake of discretionary foods.Trial registrationClinicaltrials.gov NCT01530139. Registered 9 February 2012.

Personalized Nutrition Advice Reduces Intake of Discretionary Foods and Beverages: Findings From the Food4Me Randomized Controlled Trial

Abstract Objectives This study aimed to examine changes in intake of discretionary foods and beverages following a personalized nutrition intervention using two national classifications for discretionary foods. Methods Participants were recruited into a 6-month RCT across seven European countries (Food4Me) and were randomized to receive generalized dietary advice (Control) or one of three levels of personalized nutrition advice (based on dietary, phenotypic and genotypic information). Dietary intake from a FFQ was used to determine change between baseline and month 6 in (i) % energy, % contribution to total fat, SFA, total sugars and salt and (ii) contribution (%) made by sweets and snacks to intake of total fat, SFA, sugars and salt from discretionary foods and beverages, defined by Food Standards Scotland (FSS) and the Australian Dietary Guidelines (ADG). Results A total of 1270 adults (40.9 (SD 13.0) years; 57% female) completed the intervention. At month 6, percentage sugars from FSS discretionary items was lower in personalized nutrition vs control (19.0 ± 0.37 vs 21.1 ± 0.65; P = 0.005). Percentage energy (31.2 ± 0.59 vs 32.7 ± 0.59; P = 0.031), % total fat (31.5 ± 0.37 vs 33.3 ± 0.65; P = 0.021), SFA (36.0 ± 0.43 vs 37.8 ± 0.75; P = 0.034) and sugars (31.7 ± 0.44 vs 34.7 ± 0.78; P &amp;lt; 0.001) from ADG discretionary items were lower in personalized nutrition vs control. The % contribution of sugars from sweets and snacks was lower in personalized nutrition vs control (19.1 ± 0.36 vs 21.5 ± 0.63; P &amp;lt; 0.001). At 3 months, effects were consistent for ADG discretionary items, while there was no significant differences in personalized nutrition vs control for FSS discretionary items. Conclusions Compared with generalized dietary advice, personalized nutrition advice achieved greater reductions in intake of discretionary foods and beverages when the classification included all foods high in fat, added sugars and salt. Future personalized nutrition strategies may be used to target intake of discretionary foods and beverages. Funding Sources European Commission Food, Agriculture, Fisheries and Biotechnology Theme of the Seventh Framework Programme for Research and Technological Development [265494]. KML is supported by a NHMRC Emerging Leadership Fellowship (APP1173803).

Does Personalized Nutrition Advice Improve Dietary Intake in Healthy Adults? A Systematic Review of Randomized Controlled Trials

Personalized nutrition (PN) behavior-change interventions are being used increasingly in attempts to improve dietary intake; however, the impact of PN advice on improvements in dietary intake has not been reviewed systematically. The aim of this systematic review was to evaluate the effect of PN advice on changes in dietary intake compared with generalized advice in healthy adults. Three databases (EMBASE, PubMed, and CINAHL) were searched between 2009 and 2020 for randomized controlled trials (RCTs) that tested the effect of PN and tailored advice based on diet, phenotype, or genetic information. The Evidence Analysis Library Quality Criteria checklist was used to conduct a risk-of-bias assessment. Information on intervention design and changes in nutrients, foods, and dietary patterns was extracted from the 11 studies meeting the inclusion criteria. Studies were conducted in the United States, Canada, or Europe; reported outcomes on 57 to 1488 participants; and varied in follow-up duration from 1 to 12 mo. Five studies incorporated behavior-change techniques. The risk of bias for included studies was low. Overall, the available evidence suggests that dietary intake is improved to a greater extent in participants randomly assigned to receive PN advice compared with generalized dietary advice. Additional well-designed PN RCTs are needed that incorporate behavior-change techniques, a broader range of dietary outcomes, and comparisons between personalization based on dietary, biological, and/or lifestyle information.

PeRsOnalised nutriTion for hEalthy livINg: The PROTEIN project

AbstractPersonalised nutrition is a novel public health strategy aiming to promote positive diet and lifestyle changes. Tailored dietary and physical activity advice may be more appropriate than a generalised ‘one‐size‐fits‐all’ approach as it is more biologically relevant to the individual. Information and computing technology, smartphones and mobile applications have become an integral part of modern life and thereby present the opportunity for novel methods to encourage individuals to lead a healthier lifestyle. This article introduces the European Union‐funded PROTEIN project (PeRsOnalised nutriTion for hEalthy livINg) consortium and introduces the associated work packages. The primary objective of the PROTEIN project is to produce a novel adaptable mobile application suite based on sound nutrition and physical activity advice from experts in their field, accessible to all population groups, with differing health outcomes, whose behaviour can be tracked with a variety of sensors and health hazard perception. The mobile application ‘ecosystem’ that will be developed by the consortium includes a platform, mobile suite, cloud services, artificial intelligence advisor, game suite, modelling of expert’s knowledge, users’ behaviour data collection, data analysis and a dashboard for healthcare professionals. It is proposed that users will find the provision of personalised nutrition advice and real‐time data capture through a smartphone application useful, and importantly, will be encouraged by this to make positive health behaviour changes.

Co-designing personalised nutrition advice with adults aged 65+ years: a user study of the eNutri web app

An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.