31 research outputs found
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Online dietary intake estimation : The food4me food frequency questionnaire
Copyright ©Hannah Forster, Rosalind Fallaize, Caroline Gallagher, Clare B O’Donovan, Clara Woolhead, Marianne C Walsh, Anna L Macready, Julie A Lovegrove, John C Mathers, Michael J Gibney, Lorraine Brennan, Eileen R Gibney. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 09.06.2014. 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.Dietary assessment methods are important tools for nutrition research. Online dietary assessment tools have the potential to become invaluable methods of assessing dietary intake because, compared with traditional methods, they have many advantages including the automatic storage of input data and the immediate generation of nutritional outputs. Objective: The aim of this study was to develop an online food frequency questionnaire (FFQ) for dietary data collection in the Food4Me study and to compare this with the validated European Prospective Investigation of Cancer (EPIC) Norfolk printed FFQ. Methods: The Food4Me FFQ used in this analysis was developed to consist of 157 food items. Standardized color photographs were incorporated in the development of the Food4Me FFQ to facilitate accurate quantification of the portion size of each food item. Participants were recruited in two centers (Dublin, Ireland and Reading, United Kingdom) and each received the online Food4Me FFQ and the printed EPIC-Norfolk FFQ in random order. Participants completed the Food4Me FFQ online and, for most food items, participants were requested to choose their usual serving size among seven possibilities from a range of portion size pictures. The level of agreement between the two methods was evaluated for both nutrient and food group intakes using the Bland and Altman method and classification into quartiles of daily intake. Correlations were calculated for nutrient and food group intakes. Results: A total of 113 participants were recruited with a mean age of 30 (SD 10) years (40.7% male, 46/113; 59.3%, 67/113 female). Cross-classification into exact plus adjacent quartiles ranged from 77% to 97% at the nutrient level and 77% to 99% at the food group level. Agreement at the nutrient level was highest for alcohol (97%) and lowest for percent energy from polyunsaturated fatty acids (77%). Crude unadjusted correlations for nutrients ranged between .43 and .86. Agreement at the food group level was highest for other fruits (eg, apples, pears, oranges) and lowest for cakes, pastries, and buns. For food groups, correlations ranged between .41 and .90. Conclusions: The results demonstrate that the online Food4Me FFQ has good agreement with the validated printed EPIC-Norfolk FFQ for assessing both nutrient and food group intakes, rendering it a useful tool for ranking individuals based on nutrient and food group intakes.Peer reviewedFinal Published versio
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Phenotypic factors influencing the variation in response of circulating cholesterol level to personalised dietary advice in the Food4Me study
Individual response to dietary interventions can be highly variable. The phenotypic characteristics of those who will respond positively to personalised dietary advice are largely unknown. The objective of this study was to compare the phenotypic profiles of differential responders to personalised dietary intervention, with a focus on total circulating cholesterol. Subjects from the Food4Me multi-centre study were classified as responders or non-responders to dietary advice on the basis of the change in cholesterol level from baseline to month 6, with lower and upper quartiles defined as responder and non-responder groups, respectively. There were no significant differences between demographic and anthropometric profiles of the groups. Furthermore, with the exception of alcohol, there was no significant difference in reported dietary intake, at baseline. However, there were marked differences in baseline fatty acid profiles. The responder group had significantly higher levels of stearic acid (18 : 0, P=0·034) and lower levels of palmitic acid (16 : 0, P=0·009). Total MUFA (P=0·016) and total PUFA (P=0·008) also differed between the groups. In a step-wise logistic regression model, age, baseline total cholesterol, glucose, five fatty acids and alcohol intakes were selected as factors that successfully discriminated responders from non-responders, with sensitivity of 82 % and specificity of 83 %. The successful delivery of personalised dietary advice may depend on our ability to identify phenotypes that are responsive. The results demonstrate the potential use of metabolic profiles in identifying response to an intervention and could play an important role in the development of precision nutrition
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Higher vegetable protein consumption, assessed by an isoenergetic macronutrient exchange model, is associated with a lower presence of overweight and obesity in the web-based Food4me European study
The objective was to evaluate differences in macronutrient intake and to investigate the possible association between consumption of vegetable protein and the risk of overweight/obesity, within the Food4Me randomised, online intervention. Differences in macronutrient consumption among the participating countries grouped by EU Regions (Western Europe, British Isles, Eastern Europe and Southern Europe) were assessed. Relation of protein intake, within isoenergetic exchange patterns, from vegetable or animal sources with risk of overweight/obesity was assessed through the multivariate nutrient density model and a multivariate-adjusted logistic regression. A total of 2413 subjects who completed the Food4Me screening were included, with self-reported data on age, weight, height, physical activity and dietary intake. As success rates on reducing overweight/obesity are very low, form a public health perspective, the elaboration of policies for increasing intakes of vegetable protein and reducing animal protein and sugars, may be a method of combating overweight/obesity at a population level
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Characteristics of European adults who dropped out from the Food4Me Internet-based personalised nutrition intervention
Objective To characterise participants who dropped out of the Food4Me Proof-of-Principle study.
Design The Food4Me study was an Internet-based, 6-month, four-arm, randomised controlled trial. The control group received generalised dietary and lifestyle recommendations, whereas participants randomised to three different levels of personalised nutrition (PN) received advice based on dietary, phenotypic and/or genotypic data, respectively (with either more or less frequent feedback).
Setting Seven recruitment sites: UK, Ireland, The Netherlands, Germany, Spain, Poland and Greece.
Subjects Adults aged 18–79 years (n 1607).
Results A total of 337 (21 %) participants dropped out during the intervention. At baseline, dropouts had higher BMI (0·5 kg/m2; P<0·001). Attrition did not differ significantly between individuals receiving generalised dietary guidelines (Control) and those randomised to PN. Participants were more likely to drop out (OR; 95 % CI) if they received more frequent feedback (1·81; 1·36, 2·41; P<0·001), were female (1·38; 1·06, 1·78; P=0·015), less than 45 years old (2·57; 1·95, 3·39; P<0·001) and obese (2·25; 1·47, 3·43; P<0·001). Attrition was more likely in participants who reported an interest in losing weight (1·53; 1·19, 1·97; P<0·001) or skipping meals (1·75; 1·16, 2·65; P=0·008), and less likely if participants claimed to eat healthily frequently (0·62; 0·45, 0·86; P=0·003).
Conclusions Attrition did not differ between participants receiving generalised or PN advice but more frequent feedback was related to attrition for those randomised to PN interventions. Better strategies are required to minimise dropouts among younger and obese individuals participating in PN interventions and more frequent feedback may be an unnecessary burden
Association between diet-quality scores, adiposity, total cholesterol and markers of nutritional status in European adults: findings from the Food4Me study
Diet-quality scores (DQS), which are developed across the globe, are used to define adherence to specific eating patterns and have been associated with risk of coronary heart disease and type-II diabetes. We explored the association between five diet-quality scores (Healthy Eating Index, HEI; Alternate Healthy Eating Index, AHEI; MedDietScore, MDS; PREDIMED Mediterranean Diet Score, P-MDS; Dutch Healthy Diet-Index, DHDI) and markers of metabolic health (anthropometry, objective physical activity levels (PAL), and dried blood spot total cholesterol (TC), total carotenoids, and omega-3 index) in the Food4Me cohort, using regression analysis. Dietary intake was assessed using a validated Food Frequency Questionnaire. Participants (n = 1480) were adults recruited from seven European Union (EU) countries. Overall, women had higher HEI and AHEI than men (p < 0.05), and scores varied significantly between countries. For all DQS, higher scores were associated with lower body mass index, lower waist-to-height ratio and waist circumference, and higher total carotenoids and omega-3-index (p trends < 0.05). Higher HEI, AHEI, DHDI, and P-MDS scores were associated with increased daily PAL, moderate and vigorous activity, and reduced sedentary behaviour (p trend < 0.05). We observed no association between DQS and TC. To conclude, higher DQS, which reflect better dietary patterns, were associated with markers of better nutritional status and metabolic health
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Effect of personalized nutrition on health-related behaviour change: evidence from the Food4me European randomized controlled trial
Background: Optimal nutritional choices are linked with better health, but many current interventions to improve diet have limited effect. We tested the hypothesis that providing personalized nutrition (PN) advice based on information on individual diet and lifestyle,
phenotype and/or genotype would promote larger, more appropriate, and sustained changes in dietary behaviour.
Methods: Adults from seven European countries were recruited to an internet-delivered intervention (Food4Me) and randomized to: (i) conventional dietary advice (control) or to PN advice based on: (ii) individual baseline diet; (iii) individual baseline diet plus
phenotype (anthropometry and blood biomarkers); or (iv) individual baseline diet plus phenotype plus genotype (five diet-responsive genetic variants). Outcomes were
dietary intake, anthropometry and blood biomarkers measured at baseline and after 3 and 6 months’ intervention.
Results: At baseline, mean age of participants was 39.8 years (range 18–79), 59% of participants were female and mean body mass index (BMI) was 25.5 kg/m2. From the
enrolled participants, 1269 completed the study. Following a 6-month intervention, participants randomized to PN consumed less red meat [-5.48 g, (95% confidence interval:-
10.8,-0.09), P=0.046], salt [-0.65 g, (-1.1,-0.25), P=0.002] and saturated fat [-1.14 % of energy, (-1.6,-0.67), P<0.0001], increased folate [29.6 mg, (0.21,59.0), P=0.048] intake and had higher Healthy Eating Index scores [1.27, (0.30, 2.25), P=0.010) than those randomized to the control arm. There was no evidence that including phenotypic and phenotypic plus genotypic information enhanced the effectiveness of the PN advice.
Conclusions: Among European adults, PN advice via internet-delivered intervention produced larger and more appropriate changes in dietary behaviour than a conventional approach
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Profile of European adults interested in internet-based personalized nutrition: The Food4Me Study
Purpose
Personalised intervention may have greater potential for reducing the global burden of non-communicable diseases and for promoting better health and wellbeing across the life-span than the conventional “one size fits all” approach. However, the characteristics of individuals interested in personalised nutrition (PN) are unclear. Therefore, the aim of this study was to describe the characteristics of European adults interested in taking part in an internet-based PN study.
Methods
Individuals from seven European countries (UK, Ireland, Germany, the Netherlands, Spain, Greece and Poland) were invited to participate in the study via the Food4Me website (http://www.food4me.org). Two screening questionnaires were used to collect data on socio-demographic, anthropometric and health characteristics as well as dietary intakes.
Results
A total of 5662 individuals expressed an interest in the study (mean age 40 ± 12.7; range 15-87 years). Of these 64.6% were female and 96.9% were Caucasian. Overall, 12.9% were smokers and 46.8% reported the presence of a clinically diagnosed disease. Furthermore, 46.9% were overweight or obese and 34.9% were sedentary during leisure time. Assessment of dietary intakes showed that 54.3% of individuals reported consuming at least 5 portions of fruit and vegetables per day, 45.9% consumed more than 3 servings of wholegrains and 37.2% limited their salt intake to less than 5.75g per day.
Conclusions
Our data indicate that individuals volunteering to participate in an internet-based PN study are broadly representative of the European adult population, most of whom had adequate nutrient intakes but who could benefit from improved dietary choices and greater physical activity. Future use of internet-based PN approaches is thus relevant to a wide target audience
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Frequent nutritional feedback, personalized advice, and behavioral changes: findings from the European Food4Me internet-based RCT
Introduction: This study tested the hypothesis that providing personalized nutritional advice and feedback more frequently would promote larger, more appropriate, and sustained changes in dietary behavior as well as greater reduction in adiposity.
Study design: A 6-month RCT (Food4Me) was conducted in seven European countries between 2012 and 2013.
Setting/participants: A total of 1,125 participants were randomized to Lower- (n=562) or Higher- (n=563) Frequency Feedback groups. Participants in the Lower-Frequency group received personalized nutritional advice at baseline and at Months 3 and 6 of the intervention, whereas the Higher-Frequency group received personalized nutritional advice at baseline and at Months 1, 2, 3 and 6.
Main outcome measures: The primary outcomes were change in dietary intake (at food and nutrient levels) and obesity-related traits (body weight, BMI, and waist circumference). Participants completed an online food frequency questionnaire to estimate usual dietary intake at baseline and at Months 3 and 6 of the intervention. Overall diet quality was evaluated using the 2010 Healthy Eating Index. Obesity-related traits were self-measured and reported by participant via the Internet. Statistical analyses were performed during the first quarter of 2018.
Results: At 3 months, participants in the Lower- and Higher-Frequency Feedback groups showed improvements in Healthy Eating Index score; this improvement was larger in the Higher-Frequency group than the Lower-Frequency group (=1.84, 95% CI=0.79, 2.89, p=0.0001). Similarly, there were greater improvements for the Higher- versus Lower-Frequency group for body weight (= –0.73 kg, 95% CI= –1.07, –0.38, p<0.0001), BMI (= –0.24 kg, 95% CI= –0.36, –0.13, p<0.0001), and waist circumference (= –1.20 cm, 95% CI= –2.36, –0.04, p=0.039). However, only body weight and BMI remained significant at 6 months.
Conclusions: At 3 months, higher-frequency feedback produced larger improvements in overall diet quality as well as in body weight and waist circumference compared with lower-frequency feedback. However, only body weight and BMI remained significant at 6 months.
Trial registration: Clinicaltrials.gov, NCT01530139
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Correlates of overall and central obesity in adults from seven European countries: findings from the Food4Me Study
To identify predictors of obesity in adults and investigate to what extent these predictors are independent of other major confounding factors. Data collected at baseline from 1441 participants from the Food4Me study conducted in seven European countries were included in this study. A food frequency questionnaire was used to measure dietary intake. Accelerometers were used to assess physical activity levels (PA), whereas participants self-reported their body weight, height and waist circumference via the internet. The main factors associated (p < 0.05) with higher BMI per 1-SD increase in the exposure were age (β:1.11 kg/m2), intakes of processed meat (β:1.04 kg/m2), red meat (β:1.02 kg/m2), saturated fat (β:0.84 kg/m2), monounsaturated fat (β:0.80 kg/m2), protein (β:0.74 kg/m2), total energy intake (β:0.50 kg/m2), olive oil (β:0.36 kg/m2), sugar sweetened carbonated drinks (β:0.33 kg/m2) and sedentary time (β:0.73 kg/m2). In contrast, the main factors associated with lower BMI per 1-SD increase in the exposure were PA (β:-1.36 kg/m2), intakes of wholegrains (β:-1.05 kg/m2), fibre (β:-1.02 kg/m2), fruits and vegetables (β:-0.52 kg/m2), nuts (β:-0.52 kg/m2), polyunsaturated fat (β:-0.50 kg/m2), Healthy Eating Index (β:-0.42 kg/m2), Mediterranean diet score (β:-0.40 kg/m2), oily fish (β:-0.31 kg/m2), dairy (β:-0.31 kg/m2) and fruit juice (β:-0.25 kg/m2). These findings are important for public health and suggest that promotion of increased PA, reducing sedentary behaviours and improving the overall quality of dietary patterns are important strategies for addressing the existing obesity epidemic and associated disease burden
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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 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 ± 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.
Conclusions: 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 used to target intakes of discretionary foods