Carotenoids but not flavonoids are associated with improvements in spatial working memory in younger adults in a flavonoid-rich v. -poor fruit and vegetable intervention study

Findings from animal studies suggest that components of fruit and vegetables (F&V) may protect against, and even reverse, age-related decline(1,2) in aspects of cognitive functioning such as spatial working memory (SWM). Human subjects in vivo and in vitro studies indicate that anti-inflammatory, anti-oxidant and cell-signalling properties of flavonoids and carotenoids, non-nutrient components of F&V, may underpin this protective effect(3–5). The Flavonoid University of Reading Study (FLAVURS), designed to explore the dose-response relationship between dietary F&V flavonoids and CVD, enabled the investigation of such an association with SWM. FLAVURS is an 18-week parallel three-arm randomised controlled dietary intervention trial with four time points, measured at 6-weekly intervals from baseline. Low F&V consumers at risk of CVD aged 26–70 years were randomly assigned to high flavonoid (HF), low flavonoid (LF) or control group. F&V intake increased by two daily 80 g portions every 6 weeks, with either HF or LF F&V, in addition to each participant's habitual diet, while controls maintained their habitual diet. At each visit, participants completed a cognitive test battery with SWM as the primary outcome. The HF group showed significantly higher levels of urinary flavonoids than LF or controls at 12 weeks (P<0.001) as expected, but surprisingly only higher levels than LF at 18 weeks (P<0.01). The LF group showed higher levels of plasma carotenoids than the other groups at 18 weeks (P<0.001). No group differences were found for SWM overall, however, age-group sub-analyses (26–50 and 51–70 years of age) showed differences from 0 to 18 weeks for younger adults, with LF improving significantly more than the other two groups on SWM (P<0.05). As nutritional absorption is known to decrease with age, separate stepwise regressions were performed on the two age groups irrespective of dietary group, with urinary flavonoids and plasma carotenoids as predictors. For younger adults, improved SWM performance from 0 to 18 weeks was associated with higher carotenoid levels, β=0.28, t(55)=2.10, P<0.05, accounting for 7.5% of the variance, R2=0.075, F(1,54)=4.41, P=0.040. For older adults, no between-group SWM differences were found. Findings suggest that F&V-based flavonoids and carotenoids may provide benefits for cognitive function, and that carotenoids in particular may improve cognitive performance in SWM. Given that these benefits were restricted to younger adults, future work is needed to test the reliability of this finding, as well as determine the mechanisms by which age-dependent differences in F&V responsiveness occur

Cognitive tests used in chronic adult human randomised controlled trial micronutrient and phytochemical intervention studies

In recent years there has been a rapid growth of interest in exploring the relationship between nutritional therapies and the maintenance of cognitive function in adulthood. Emerging evidence reveals an increasingly complex picture with respect to the benefits of various food constituents on learning, memory and psychomotor function in adults. However, to date, there has been little consensus in human studies on the range of cognitive domains to be tested or the particular tests to be employed. To illustrate the potential difficulties that this poses, we conducted a systematic review of existing human adult randomised controlled trial (RCT) studies that have investigated the effects of 24 d to 36 months of supplementation with flavonoids and micronutrients on cognitive performance. There were thirty-nine studies employing a total of 121 different cognitive tasks that met the criteria for inclusion. Results showed that less than half of these studies reported positive effects of treatment, with some important cognitive domains either under-represented or not explored at all. Although there was some evidence of sensitivity to nutritional supplementation in a number of domains (for example, executive function, spatial working memory), interpretation is currently difficult given the prevailing 'scattergun approach' for selecting cognitive tests. Specifically, the practice means that it is often difficult to distinguish between a boundary condition for a particular nutrient and a lack of task sensitivity. We argue that for significant future progress to be made, researchers need to pay much closer attention to existing human RCT and animal data, as well as to more basic issues surrounding task sensitivity, statistical power and type I error

Capturing health and eating status through a nutritional perception screening questionnaire (NPSQ9) in a randomised internet-based personalised nutrition intervention : the Food4Me study

BACKGROUND: National guidelines emphasize healthy eating to promote wellbeing and prevention of non-communicable diseases. The perceived healthiness of food is determined by many factors affecting food intake. A positive perception of healthy eating has been shown to be associated with greater diet quality. Internet-based methodologies allow contact with large populations. Our present study aims to design and evaluate a short nutritional perception questionnaire, to be used as a screening tool for assessing nutritional status, and to predict an optimal level of personalisation in nutritional advice delivered via the Internet. METHODS: Data from all participants who were screened and then enrolled into the Food4Me proof-of-principle study (n = 2369) were used to determine the optimal items for inclusion in a novel screening tool, the Nutritional Perception Screening Questionnaire-9 (NPSQ9). Exploratory and confirmatory factor analyses were performed on anthropometric and biochemical data and on dietary indices acquired from participants who had completed the Food4Me dietary intervention (n = 1153). Baseline and intervention data were analysed using linear regression and linear mixed regression, respectively. RESULTS: A final model with 9 NPSQ items was validated against the dietary intervention data. NPSQ9 scores were inversely associated with BMI (β = -0.181, p < 0.001) and waist circumference (Β = -0.155, p < 0.001), and positively associated with total carotenoids (β = 0.198, p < 0.001), omega-3 fatty acid index (β = 0.155, p < 0.001), Healthy Eating Index (HEI) (β = 0.299, p < 0.001) and Mediterranean Diet Score (MDS) (β = 0. 279, p < 0.001). Findings from the longitudinal intervention study showed a greater reduction in BMI and improved dietary indices among participants with lower NPSQ9 scores. CONCLUSIONS: Healthy eating perceptions and dietary habits captured by the NPSQ9 score, based on nine questionnaire items, were associated with reduced body weight and improved diet quality. Likewise, participants with a lower score achieved greater health improvements than those with higher scores, in response to personalised advice, suggesting that NPSQ9 may be used for early evaluation of nutritional status and to tailor nutritional advice. TRIAL REGISTRATION: NCT01530139 .Peer reviewedFinal Published versio

Single-hole properties in the tt-JJ and strong-coupling models

We report numerical results for the single-hole properties in the $t$-$J$ model and the strong-coupling approximation to the Hubbard model in two dimensions. Using the hopping basis with over $10^6$ states we discuss (for an infinite system) the bandwidth, the leading Fourier coefficients in the dispersion, the band masses, and the spin-spin correlations near the hole. We compare our results with those obtained by other methods. The band minimum is found to be at ($\pi/2,\pi/2$) for the $t$-$J$ model for $0.1 \leq t/J \leq 10$, and for the strong-coupling model for $1 \leq t/J \leq 10$. The bandwidth in both models is approximately $2J$ at large $t/J$, in rough agreement with loop-expansion results but in disagreement with other results. The strong-coupling bandwidth for t/J\agt6 can be obtained from the $t$-$J$ model by treating the three-site terms in first-order perturbation theory. The dispersion along the magnetic zone face is flat, giving a large parallel/perpendicular band mass ratio.Comment: 1 RevTeX file with epsf directives to include 8 .eps figures 8 figure files encoded using uufile

Application of behavior change techniques in a personalized nutrition Electronic Health intervention study: protocol for the web-based Food4Me randomized controlled trial

Background: In order to determine the efficacy of behavior change techniques (BCT) applied in dietary and physical activity intervention studies, it is first necessary to record and describe techniques which have been used during such interventions. Published frameworks used in dietary and smoking cessation interventions undergo continuous development and most are not adapted for online delivery. The Food4Me study (N=1607) provided the opportunity to use existing frameworks to describe standardized online techniques employed in a large-scale internet-based intervention to change dietary behaviour and physical activity. Objectives: To describe techniques embedded in the Food4Me study design and explain the selection rationale. To demonstrate the use of behaviour change technique taxonomies, develop standard operating procedures for training, and identify strengths and limitations of the Food4Me framework that will inform its use in future studies. Methods: The 6-month randomized controlled trial took place simultaneously in 7 European countries, with participants receiving one of 4 levels of personalized advice (generalized, intake-based, intake+phenotype-based and intake+phenotype+gene-based). A 3-phase approach was taken: (I), existing taxonomies were reviewed and techniques were identified a priori for possible inclusion in the Food4Me study; (II) a standard operating procedure was developed to maintain consistency in the use of methods and techniques across research centers; (III) the Food4Me BCT framework was reviewed and updated post intervention. An analysis of excluded techniques was also conducted. Results: Of 46 techniques identified a priori as being applicable to Food4Me, 17 were embedded in the intervention design. Eleven were from a dietary taxonomy and 6 from a smoking cessation taxonomy. In addition, the 4-category smoking cessation framework structure was adopted for clarity of communication. Smoking cessation texts were adapted for dietary use where necessary. A posteriori, a further 9 techniques were included. Examination of excluded items highlighted the distinction between techniques considered appropriate for face-to-face vs internet-based delivery. Conclusions: The use of existing taxonomies facilitated the description and standardization of techniques used in Food4Me. We recommend that for complex studies of this nature, technique analysis should be conducted a priori to develop standardized procedures and training, and reviewed a posteriori to audit the techniques actually adopted. The present framework description makes a valuable contribution to future systematic reviews and meta-analyses which explore technique efficacy and underlying psychological constructs. This was a novel application of the behavior change taxonomies, and was the first internet-based personalized nutrition intervention to use such a framework remotely

Action-related information trumps system information: influencing consumers’ intention to reduce food waste

In order to substantially reduce food waste at the household level, it is essential to change consumer behavior. Informing consumers about the food waste issue is a promising means of bringing about behavior change: research confirms that information can increase food waste reduction behavior. However, it has yet to be determined what kind of information is most effective and exactly how that information affects consumer food waste behavior. This study compares the effects of system vs. action-related information (i.e., knowing what impacts specific actions entail vs. knowing how specific actions can help to accomplish a goal) on behavioral intention towards food waste. That is, the study focuses on the effect of information on the role of food waste in the food system versus information of actions that can be taken to avoid it. Moreover, an adapted model of the Theory of Planned Behavior is used to assess how these information effects are mediated by consumers’ attitude, norms, and perceived behavioral control. Results from an online experiment with a between-subjects design (N = 2,248) show that action-related information significantly increases respondents’ intention to reduce food waste while system information has no significant effect. The change in behavioral intention in the action-related information group is ascribed to greater personal norm activation, more favorable attitudes towards food waste reduction, and higher perceived behavioral control of food waste behaviors. Even though system information does not significantly increase intention to reduce food waste, it results in more favorable attitudes towards food waste reduction. The findings provide insights for policy makers and NGOs on what type of information to consider when designing effective food waste reduction campaigns targeted at consumers, with action-related information supporting the opportunity for consumer behavior change

Clustering of adherence to personalised dietary recommendations and changes in healthy eating index within the Food4Me study

Objective: To characterise clusters of individuals based on adherence to dietary recommendations and to determine whether changes in Healthy Eating Index (HEI) scores in response to a personalised nutrition (PN) intervention varied between clusters. Design: Food4Me study participants were clustered according to whether their baseline dietary intakes met European dietary recommendations. Changes in HEI scores between baseline and month 6 were compared between clusters and stratified by whether individuals received generalised or PN advice. Setting: Pan-European, Internet-based, 6-month randomised controlled trial. Subjects: Adults aged 18–79 years (n1480). Results: Individuals in cluster 1 (C1) met all recommended intakes except for red meat, those in cluster 2 (C2) met two recommendations, and those in cluster 3 (C3) and cluster 4 (C4) met one recommendation each. C1 had higher intakes of white fish, beans and lentils and low-fat dairy products and lower percentage energy intake from SFA (P<0·05). C2 consumed less chips and pizza and fried foods than C3 and C4 (P<0·05). C1 were lighter, had lower BMI and waist circumference than C3 and were more physically active than C4 (P<0·05). More individuals in C4 were smokers and wanted to lose weight than in C1 (P<0·05). Individuals who received PN advice in C4 reported greater improvements in HEI compared with C3 and C1 (P<0·05). Conclusions: The cluster where the fewest recommendations were met (C4) reported greater improvements in HEI following a 6-month trial of PN whereas there was no difference between clusters for those randomised to the Control, non-personalised dietary intervention

Online dietary intake estimation : Reproducibility and validity of the Food4Me food frequency questionnaire against a 4-day weighed food record

©Rosalind Fallaize, Hannah Forster, Anna L Macready, Marianne C Walsh, John C Mathers, Lorraine Brennan, Eileen R Gibney, Michael J Gibney, Julie A Lovegrove. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 11.08.2014. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.Background: Advances in nutritional assessment are continuing to embrace developments in computer technology. The online Food4Me food frequency questionnaire (FFQ) was created as an electronic system for the collection of nutrient intake data. To ensure its accuracy in assessing both nutrient and food group intake, further validation against data obtained using a reliable, but independent, instrument and assessment of its reproducibility are required. Objective: The aim was to assess the reproducibility and validity of the Food4Me FFQ against a 4-day weighed food record (WFR). Methods: Reproducibility of the Food4Me FFQ was assessed using test-retest methodology by asking participants to complete the FFQ on 2 occasions 4 weeks apart. To assess the validity of the Food4Me FFQ against the 4-day WFR, half the participants were also asked to complete a 4-day WFR 1 week after the first administration of the Food4Me FFQ. Level of agreement between nutrient and food group intakes estimated by the repeated Food4Me FFQ and the Food4Me FFQ and 4-day WFR were evaluated using Bland-Altman methodology and classification into quartiles of daily intake. Crude unadjusted correlation coefficients were also calculated for nutrient and food group intakes. Results: In total, 100 people participated in the assessment of reproducibility (mean age 32, SD 12 years), and 49 of these (mean age 27, SD 8 years) also took part in the assessment of validity. Crude unadjusted correlations for repeated Food4Me FFQ ranged from .65 (vitamin D) to .90 (alcohol). The mean cross-classification into "exact agreement plus adjacent" was 92% for both nutrient and food group intakes, and Bland-Altman plots showed good agreement for energy-adjusted macronutrient intakes. Agreement between the Food4Me FFQ and 4-day WFR varied, with crude unadjusted correlations ranging from .23 (vitamin D) to .65 (protein, % total energy) for nutrient intakes and .11 (soups, sauces and miscellaneous foods) to .73 (yogurts) for food group intake. The mean cross-classification into "exact agreement plus adjacent" was 80% and 78% for nutrient and food group intake, respectively. There were no significant differences between energy intakes estimated using the Food4Me FFQ and 4-day WFR, and Bland-Altman plots showed good agreement for both energy and energy-controlled nutrient intakes. Conclusions: The results demonstrate that the online Food4Me FFQ is reproducible for assessing nutrient and food group intake and has moderate agreement with the 4-day WFR for assessing energy and energy-adjusted nutrient intakes. The Food4Me FFQ is a suitable online tool for assessing dietary intake in healthy adults.Peer reviewedFinal Published versio

A dietary feedback system for the delivery of consistent personalized dietary advice in the web-based multicenter Food4Me study

Background: Despite numerous healthy eating campaigns, the prevalence of diets high in saturated fatty acids, sugar, and salt and low in fiber, fruit, and vegetables remains high. With more people than ever accessing the Internet, Web-based dietary assessment instruments have the potential to promote healthier dietary behaviors via personalized dietary advice. Objective: The objectives of this study were to develop a dietary feedback system for the delivery of consistent personalized dietary advice in a multicenter study and to examine the impact of automating the advice system. Methods: The development of the dietary feedback system included 4 components: (1) designing a system for categorizing nutritional intakes; (2) creating a method for prioritizing 3 nutrient-related goals for subsequent targeted dietary advice; (3) constructing decision tree algorithms linking data on nutritional intake to feedback messages; and (4) developing personal feedback reports. The system was used manually by researchers to provide personalized nutrition advice based on dietary assessment to 369 participants during the Food4Me randomized controlled trial, with an automated version developed on completion of the study. Results: Saturated fatty acid, salt, and dietary fiber were most frequently selected as nutrient-related goals across the 7 centers. Average agreement between the manual and automated systems, in selecting 3 nutrient-related goals for personalized dietary advice across the centers, was highest for nutrient-related goals 1 and 2 and lower for goal 3, averaging at 92%, 87%, and 63%, respectively. Complete agreement between the 2 systems for feedback advice message selection averaged at 87% across the centers. Conclusions: The dietary feedback system was used to deliver personalized dietary advice within a multi-country study. Overall, there was good agreement between the manual and automated feedback systems, giving promise to the use of automated systems for personalizing dietary advice

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

Background: The 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. Methods: Participants 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 10 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. Results: Of 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 \ub1 0.37 vs 21.1 \ub1 0.65; P=0.005). Percentage energy (31.2 \ub1 0.59 vs 32.7 \ub1 0.59; P=0.031), percentage total fat (31.5 \ub1 0.37 vs 33.3 \ub1 0.65; P=0.021), SFA (36.0 \ub1 0.43 vs 37.8 \ub1 0.75; P=0.034) and sugars (31.7 \ub1 0.44 vs 34.7 \ub1 0.78; P&lt;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. 21Conclusions: Compared 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 24 used to target intakes of discretionary foods