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Effects on nutrient intake of a family-based intervention to promote increased consumption of low-fat starchy foods through education, cooking skills and personalised goal setting: the Family Food and Health Project

Published online by Cambridge University Press:  12 October 2011

Peter J. Curtis
Affiliation:
Institute for Ageing and Health, The Human Nutrition Research Centre, William Leech Building, Newcastle University, Newcastle upon TyneNE2 4HH, UK Norwich Medical School, University of East Anglia, Norwich, NorfolkNR4 7TJ, UK
Ashley J. Adamson
Affiliation:
Institute for Ageing and Health, The Human Nutrition Research Centre, William Leech Building, Newcastle University, Newcastle upon TyneNE2 4HH, UK
John C. Mathers*
Affiliation:
Institute for Ageing and Health, The Human Nutrition Research Centre, William Leech Building, Newcastle University, Newcastle upon TyneNE2 4HH, UK
*
*Corresponding author: Professor J. C. Mathers, fax +44 191 222 8943, email john.mathers@newcastle.ac.uk
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Abstract

Reducing the prevalence of fat-rich, energy-dense diets is a public health priority. The present parallel-designed randomised study compared three interventions aimed to increase intakes of low-fat starchy foods and to reduce fat intakes among 589 individuals from 169 families in the Family Food and Health Project (FFHP). Intervention A was education only, intervention B provided ‘cook and eat’ sessions only, whereas intervention C included personalised goal setting, ‘cook and eat’ and education. Diet was assessed at baseline (T0) and at 3 months (T1), 6 months (T2) and 18 months (T3) post-intervention. Retention rates were 75 % at T1, 63 % at T2 and 40 % at T3. ANCOVA (baseline intake as covariate) was assessed between intervention differences at T1, T2 and T3. At T1, individuals in intervention C consumed less fat (P = 0·02) and more total carbohydrate (P = 0·001), starch (P = 0·04) vitamin C (P = 0·002) and NSP (P = 0·01) than those in intervention A. Whereas similar dietary intakes were reported across interventions at T2, participants in intervention C had less energy-dense diets that contained more NSP and vitamin C at T3 than intervention A (P < 0·0001, P = 0·002 and P = 0·01, respectively). Across all intervention groups, the more socially deprived participants in the FFHP (n 119) consumed less fat (P = 0·01) and more total carbohydrate (P = 0·02) at T2 than the least socially deprived (n 240). These data demonstrate the importance of personalised goal setting to translate knowledge and practical cooking skills into healthier food choices, suggesting that low-fat starchy food-focused interventions may be effective in reducing fat intake.

Type
Full Papers
Copyright
Copyright © The Authors 2011

It is widely recognised that dietary patterns that are typified by energy-dense, fat-rich foods (often described as a ‘Western diet’) are associated with an increased prevalence of obesity(Reference Rolls1, Reference Johnson, Mander and Jones2), diabetes(Reference Wang, Luben and Khaw3, Reference Erber, Hopping and Grandinetti4), CVD(5) and cancers at several sites(6), and effective strategies to improve such dietary patterns are a public health priority(5). The present UK dietary target to reduce fat intake to no more than 35 % of energy derived from food (%FE)(7) was established in 1984(8) and this target has been confirmed in a number of subsequent UK government health initiatives(7, 9). However, food purchasing data from the UK National Food Survey(10) and the Family Food 2008 report (based on the 2008 Expenditure and Food Survey data, UK) suggest that habitual intakes of fat in the UK are resistant to change and, if anything, have been increasing in recent years(11). Although these indices of food purchasing should be interpreted with caution, it seems that reaching the dietary target for fat intake remains difficult for some UK consumers.

For more than a decade(12), it has been suggested that low-fat starchy foods such as bread, cereals, rice, pasta, potatoes, legumes and pulses may be a suitable replacement for energy-dense foods of low nutrient quality. These starchy foods are often described as ‘complex carbohydrate-rich’ foods and some are ‘whole-grain-rich’ foods and recommendations to eat more of these food groups continue to be promoted(1315). Despite this, evidence suggests that consumers have mixed views about the role of such foods in their diets(Reference Stubenitsky and Mela16). Compared with the recommendations, present intakes of these foods are sub-optimal and, for example, it has been reported recently that only 55 % of French subjects meet specific targets for starchy food intake(Reference Touvier, Mejean and Kesse-Guyot17).

To date, relatively few studies have assessed explicitly the effectiveness of interventions aimed at increasing intake of low-fat starchy foods as a route to reduced intake of dietary fat. Of those that have, studies have either been restricted to obese/pre-obese individuals and those at increased risk of CVD(Reference Saris, Astrup and Prentice18Reference Drummond, Kirk and Jackson20) or have focused on a particular type of low-fat starchy food, e.g. breakfast cereals(Reference Kirk, Burkill and Cursiter21Reference Rosado, del R Arellano and Montemayor23). Few studies have used the family as a setting for low-fat starchy food/complex carbohydrate dietary interventions(Reference Paineau, Beaufils and Boulier24). Although the available evidence is limited, it has been shown that fat intake can be reduced as a consequence of increasing intake of low-fat starchy foods/complex carbohydrates(Reference Kirk, Burkill and Cursiter21), and that the adoption of such diets can result in significant improvements in body mass(Reference Saris, Astrup and Prentice18, Reference Poppitt, Keogh and Prentice19, Reference Paineau, Beaufils and Boulier24, Reference Neumark-Sztainer, Hannan and Story25) and biochemical markers of CVD risk(Reference Albertson, Affenito and Bauserman22, Reference Rosado, del R Arellano and Montemayor23).

In meta-analyses and systematic reviews of previous intervention strategies, specific intervention components such as family involvement, cooking skills and food-related activity, peer-support and tailored goal setting have been shown to facilitate dietary change(Reference Pignone, Ammerman and Fernandez26Reference Perez-Escamilla, Hromi-Fiedler and Vega-Lopez29). In particular, there is evidence that family involvement is conducive to positive changes in diet and other health constructs including body weight management(Reference Paineau, Beaufils and Boulier24, Reference Cullen, Lara Smalling and Thompson30Reference Burgess-Champoux, Larson and Neumark-Sztainer33). In addition, confidence in the ability to prepare and cook healthy food is correlated positively with purchasing of healthy foods (e.g. vegetables) in families(Reference Winkler and Turrell34), consumption of vegetables in adults(Reference Zehle, Smith and Chey35) and meeting dietary targets (e.g. for fat and whole-grain intake) in young adults(Reference Larson, Perry and Story36). Interventions that have taught food preparation and cooking skills have resulted in increased intakes of healthy foods(Reference Wrieden, Anderson and Longbottom37Reference Brown and Hermann39).

The Family Food and Health Project (FFHP) was designed to determine which combinations of key facilitators of dietary change (i.e. information based food-related activities, cooking skills or personalised goal setting) would affect the translation of a message to increase the intake of low-fat starchy foods into improvement in dietary behaviours and, in particular, in reductions in fat intake. To this end, we conducted a randomised, parallel-group design intervention study in which we compared the efficacy of three dietary intervention strategies that differed in the amount of education and cooking skill provision and the use of a personalised goal setting framework. The FFHP also examined the sustainability of changes in dietary behaviours.

Experimental methods

Study participants

The data presented in the present paper relate to the individuals in the families that participated in the FFHP. Between 1998 and 2000, 169 families with a mean dietary intake at screening, who did not meet the targets of the study ( ≤ 29 %FE from starch and/or ≥ 35 %FE from fat) and who were in the middle three quintiles (quintiles 2–4) of relative socioeconomic deprivation, based on unemployment, car ownership, owner occupation and overcrowding in given postcode areas (Townsend Deprivation Index(Reference Townsend, Phillimore and Beattie40)) were recruited to one of three interventions promoting increased consumption of low-fat starchy foods (Fig. 1).

Fig. 1 Flow chart of family recruitment to the Family Food and Health Project and the subsequent involvement of individuals at the 3, 6 and 18 months post-intervention dietary assessments (T 1, T 2 and T 3, respectively). Intervention A, education only; intervention B, cook and eat only; intervention C, education+cook and eat+personalised goal setting; %FE, percentage of energy derived from food, excluding alcohol.

At the time of the study, the mean UK adult %FE derived from starch and fat was approximately 24 and 40 %, respectively, with a target to reduce %FE from fat to 35 %. On this basis, an increase in %FE from starch to 29 % was judged to be an important change. In the absence of data on variance of change in %FE from starch or fat following intervention, based on available data on inter-person variation in %FE from fat(Reference Adamson, Foster and Butler41), fifty individuals would be required to detect a change of 3·5 % units in energy from starch (α = 0·05, power 90 %). We anticipated that this would provide the power to detect a reciprocal change in %FE from fat.

Families were defined as groups of cohabiting persons containing any number of adults and at least one person under the age of 16 years. Local Health Authority National Health Service (NHS) registers were used to identify 2977 prospective families (addresses identified to have a child under the age of 16 years) who were sent a study information pack by the Local Health Authority, which was labelled ‘for the attention of the parent/guardian’ of the named person under 16 years of age residing in the household. The information pack included a participant information sheet, a contact sheet for families to register an interest in the trial and a stamped addressed envelope (addressed to the research team). Recruitment took place in six phases, and was restricted to targeted areas of Newcastle upon Tyne, UK, which had local community venues that were suitable for hosting the interventions. Families received no financial incentive to participate in the study. A total of 475 (16 % response) of households mailed responded positively (4 % gave a negative response), and of these 335 families were contacted to discuss the study further. Of these, 232 had a baseline dietary assessment, thirty-seven families met the dietary target of ≥ 29 %FE from starch and ≤ 35 %FE from fat and were excluded, twenty-six families were lost to follow-up before randomisation to treatment and the remaining 169 proceeded to the intervention (Fig. 1). Families with individual prescribed high-fat diets (n 2 children) were excluded; a total of 589 individuals participated per protocol in the study (Table 1). Baseline data were collected at T 0, with outcome data collected at 3 months (T 1), 6 months (T 2) and 18 months (T 3) after the end of the intervention. The dietary data from individuals reporting less than 3 d at any given time point (T 0, T 1, T 2 or T 3), were excluded from analysis at that data assessment point (at T 0, T 1, T 2 and T 3, the number of individuals with incomplete dietary records was 1, 11, 7 and 3, respectively). Collection of outcome data at T 3 included families recruited in phases 2–6 only, as funding for this additional work was secured after the due date for the T 3 assessment for those recruited in phase 1. The present study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects were approved by the Newcastle and North Tyneside Joint Ethics Committee and the Health Authorities Research Committee. Written informed consent was obtained from all subjects.

Table 1 Baseline non-dietary characteristics of the 169 families and the 589 individuals randomised to three intervention groups in the Family Food and Health Project and the retention rates of individuals at 3, 6 and 18 months post-intervention (T 1, T 2 and T 3, respectively)

(Mean values and standard deviations where variability of the mean is reported)*

Intervention A, education; intervention B, cook and eat; intervention C, education+cook and eat+personalised goal setting; T 0, baseline assessment; SES, socio-economic status; GL, general linear.

* SES differed significantly between interventions at baseline: P = 0·04.

Post-hoc Tukey test showed that SES differed between interventions B and C (P = 0·03).

The interventions

Participating families were randomised by enumeration district (ED; a group of approximately 150 adjacent postal addresses). Briefly, participating families were first grouped by ED, and then three groups were formed that (1) consisted of contiguous ED and (ii) contained approximately equal numbers of families. The three groups were then randomly allocated to the three intervention strategies (interventions A–C). Allocation by ED was chosen to minimise potential ‘contamination’ arising from close neighbours being allocated to different interventions. All three interventions aimed to promote increased intake of low-fat starchy foods by both broadening the range of low-fat starchy foods consumed and increasing the portion sizes of these foods. At the beginning of the study (T 0), each family received a selection of low-fat starchy foods to trial at home and a recipe file with low-fat starchy meal/snack ideas.

The interventions were led by state-registered dietitians and were delivered using three strategies that differed in intensity (i.e. the amount of contact time with dietitians) and scope (i.e. the range of behavioural strategies adopted in the intervention). The three interventions included (1) a one-off ‘education-based’ intervention (intervention A); (2) a ‘cook and eat’ cooking skills-based intervention (intervention B); and (3) an ‘education+cook and eat+personalised goal setting’ intervention (intervention C).

Intervention A was centred on a health fayre, which was held in a local community centre and to which all members of the family were invited. Intervention A adopted an attitude-, awareness- and knowledge-centred approach (delivered during a 2 h session) that aimed to increase awareness of ‘low-fat starchy foods’, identified target portion sizes for men, women and children and emphasised on the potential health benefits of consuming more of these foods. To appeal to all family members, the health fayre included food tasting, food-related games and food challenges.

Intervention B (based on the ‘cook and eat’ approach) was skills-centred and was delivered in community centres. Families allocated to intervention B were offered four ‘cook and eat’ sessions each lasting for 2 h (over 6 weeks), which focused on the meal planning, food preparation and cooking skills required to incorporate more low-fat starchy foods into familiar meals and snacks. In addition, families were encouraged to try less familiar low-fat starchy foods during the ‘cook and eat’ sessions. Up to ten families were facilitated in each session and the cooking ingredients were provided at no cost. All family members were encouraged to participate in the food preparation and cooking, and each family was encouraged to prepare a different meal. A crèche facility was provided at the venue to accommodate those children unable to participate in the cooking sessions. At the end of each session, the foods prepared by all families were shared and families were encouraged to discuss their cooking experience and their preferences of the meals prepared. This approach was adopted to encourage group support from other families as well as from the study dietitians. In addition, a 2 h ‘children-only’ cooking session was also provided as part of intervention B; this was offered to all children aged at least 8 years.

Families allocated to intervention C received both the health fayre and ‘cook and eat’ sessions offered to participants in interventions A and B, respectively, and, in addition, were offered personalised goal setting. The latter was delivered by the study dietitians and was based on stages of change models(Reference Prochaska, DiClemente, Miller and Heather42). Briefly, the process consisted of negotiation and agreement of three personalised dietary goals (consistent with the project aim of increasing low-fat starchy foods) at T 0 followed by monitoring, evaluation of progress and negotiation of further dietary goals, as original goals were achieved at two subsequent personalised face-to-face meetings with a research dietitian.

Dietary intake data

An in-house devised 3 d estimated food diary was used to report the dietary intakes of individuals at four time points, namely, T 0, T 1, T 2 and T 3 (Reference Fletcher, Rugg-Gunn and Matthews43). Dietary reporting days ran sequentially and included two weekdays and one weekend day. For each food item consumed, a full description was recorded (time of consumption, brand name, relevant packaging information (e.g. weight and nutrient composition)) and the participants were asked to include an approximation of the quantity of each food consumed in household measures, e.g. slice, teaspoon or cup. Leftovers were excluded from the reporting process. The amount of food consumed was quantified at the interview principally by using the photographic atlas of food portion sizes(Reference Nelson, Atkinson and Meyer44) and from weights identified on product packaging. Where this approach was not applicable, a standard typical portion weight(45) was assigned. Energy and nutrient intakes were calculated using the UK food composition tables that were available at the time of the study. These were the supplements to the McCance and Widdowson standard UK food tables (Royal Society of Chemistry 1988–96(Reference Holland, Unwin and Buss46Reference Chan, Brown and Church54)) and the fifth edition(Reference Holland, Welch and Unwin55). Where necessary, additional food composition data were obtained from Ministry of Agriculture, Fisheries and Food surveillance information sheets(Reference Brookes5663) and manufacturers' data. We are aware of the limitations of the dietary reporting approach used in the present study and the potential benefit of having biomarkers as independent indices of food intake(Reference Adamson and Mathers64, Reference Penn, Boeing and Boushey65). However, there are no validated biomarkers suitable for measuring %FE from fat or intakes of starch (the primary foci of the present study).

Total energy intake (%TE; including energy derived from alcohol) and food energy intake (excluding energy derived from alcohol) were calculated and the intakes of macronutrients were expressed as a proportion of food energy (%FE). The present format was chosen to aid comparisons between individuals who differed in age and sex (both characteristics known to influence absolute dietary intakes). As indicators of dietary quality, the intakes of NSP and selected micronutrients (vitamin C, Fe and Ca) were calculated and intakes expressed in g (for NSP) or mg (for micronutrients) per MJ of food energy consumed. Dietary energy density was calculated and expressed as kJ/g of food. Milk and milk-based drinks were included in food for this purpose, as milk was a constituent part of commonly consumed meals, e.g. breakfast, in mashed vegetables and as a component of dairy-based sauces. Milk-based beverages included in the analysis included milk, milkshakes and hot chocolate. Beverages excluded from the analysis include all alcohol-based beverages, fruit juices, soft drinks, water, tea and coffee. The dietary data reported at T 0, T 1, T 2 and T 3 include data only for those individuals who completed the 3 d dietary record per protocol; partial dietary records (i.e. 1 or 2 d records) were excluded.

Statistical analyses

Differences in the dietary and non-dietary characteristics of the three intervention groups at T 0 were determined using one-way ANOVA with post hoc Tukey's multiple comparison testing. The principal analysis of intervention effect on subsequent nutrient intake (at T 1, T 2 and T 3) was undertaken using a univariate general linear ANCOVA model (GLM), with T 0 values used as a covariate. Post hoc multiple pairwise comparisons of the GLM data were performed using the Bonferroni test. The data presented (Tables 2 and 3) for T 1, T 2 and T 3 intake are the estimated marginal mean values and standard errors derived from the primary GLM of intervention effect (GLM1; fixed factor, intervention group; covariates, socioeconomic status (SES), age, sex, phase of recruitment, parental participation and T 0 dietary intake). In Tables 2 and 3, T 0 intake refers to the mean and standard error of the mean derived from baseline ANOVA analysis.

Table 2 Individual intakes of energy (MJ), alcohol (as percentage of energy derived from food and alcohol (%TE)) and macronutrients (as percentage of energy derived from food, excluding alcohol (%FE)) across all three intervention groups at baseline (T 0), and the intakes of individuals grouped by intervention allocation at T 0 and 3, 6 and 18 months post-intervention (T 1, T 2 and T 3, respectively)

(Mean values with their standard errors at T 0 and adjusted mean values with their standard errors at T 1, T 2, and T 3)

Intervention A, education only; intervention B, cook and eat only; intervention C, education+cook and eat+personalised goal setting.

Mean values were significantly different between intervention (univariate general linear ANCOVA models): * P < 0·05, ** P < 0·01, *** P < 0·0001.

Mean values were significantly different for intervention A v. intervention B (P = 0·002; post-hoc Bonferroni test, pairwise comparisons).

Mean values were significantly different for intervention A v. intervention C (P = 0·04; post-hoc Bonferroni test, pairwise comparisons).

§ Mean values were significantly different for intervention A v. intervention B (P < 0·0001; post-hoc Bonferroni test, pairwise comparisons).

Mean values were significantly different for intervention A v. intervention C (P < 0·001; post-hoc Bonferroni test, pairwise comparisons).

Mean values were significantly different for intervention A v. intervention C (P = 0·02; post-hoc Bonferroni test, pairwise comparisons).

†† Mean values were significantly different for intervention A v. intervention C (P = 0·001; post-hoc Bonferroni test, pairwise comparisons).

‡‡ Mean values were significantly different for intervention A v. intervention B (P = 0·02; post-hoc Bonferroni test, pairwise comparisons).

§§ Mean values were significantly different for intervention A v. intervention B (P = 0·004; post-hoc Bonferroni test, pairwise comparisons).

Table 3 Individual intakes of energy density (kJ/g), NSP (g/MJ) and micronutrients (mg/MJ) across all three intervention groups at baseline (T 0), and intakes at T 0 and 3, 6 and 18 months post-intervention (T 1, T 2 and T 3, respectively) of individuals by intervention allocation

(Mean values with their standard errors at T 0 and adjusted mean values with their adjusted standard errors at T 1, T 2, and T 3)

Intervention A, education only; intervention B, cook and eat only; intervention C, education+cook and eat+personalised goal setting.

Mean values were significantly different between intervention (univariate general linear ANCOVA models): * P < 0·05, ** P < 0·01, *** P < 0·0001.

Mean values were significantly different for intervention B v. intervention A (P = 0·02; post-hoc Bonferroni test, pairwise comparisons).

Mean values were significantly different for intervention C v. intervention A (P = 0·012; post-hoc Bonferroni test, pairwise comparisons).

§ Mean values were significantly different for intervention C v. intervention B (P < 0·0001; post-hoc Bonferroni test, pairwise comparisons).

Mean values were significantly different for intervention C v. intervention A (P = 0·002; post-hoc Bonferroni test, pairwise comparisons).

Mean values were significantly different for interventions B and C v. intervention A (P < 0·0001; post-hoc Bonferroni test, pairwise comparisons).

†† Mean values were significantly different for intervention C v. intervention A (P = 0·01; post-hoc Bonferroni test, pairwise comparisons).

‡‡ Mean values were significantly different for intervention B v. intervention A (P = 0·004; post-hoc Bonferroni test, pairwise comparisons).

§§ Mean values were significantly different for intervention C v. intervention B (P = 0·003; post-hoc Bonferroni test, pairwise comparisons).

∥∥ Mean values were significantly different for intervention B v. intervention C (P < 0·001; post-hoc Bonferroni test, pairwise comparisons).

¶¶ Mean values were significantly different for intervention C v. intervention A (P = 0·002; post-hoc Bonferroni test, pairwise comparisons).

††† Mean values were significantly different for intervention B v. intervention C (P = 0·001; post-hoc Bonferroni test, pairwise comparisons).

Two further GLM models were conducted as secondary analyses. GLM2 (fixed factor, SES; covariates, intervention group, age, sex, phase of recruitment, parental participation and T 0 dietary intake) was conducted to establish whether the significant difference in SES, observed between intervention groups at baseline, influenced intervention effectiveness. Of note, in GLM2, quintile 3 (3%) the seventeen subjects in SES were omitted from the analysis due to underrepresentation in the model (Table 1). GLM3 (fixed factor, parental participation; covariates, intervention group, SES, age, phase of recruitment and T 0 dietary intake) was conducted to identify whether the level of familial involvement per se, across the three dietary interventions, could have influenced the degree of dietary change. For GLM3, individuals were categorised according to the number of parents/guardians who participated in the project (i.e. either one or two parents). Parents/guardians were considered to be ‘participating’ if they consented to dietary assessment. The individuals assigned to the grouping of one ‘participating’ parent included those with one parent residing in the household and families where only one of the resident parents was willing to participate in the study. Data were analysed using SPSS 16.0 for Windows statistical analysis software (SPSS Inc., Chicago, IL, USA) and probability levels ≤ 0·05 were considered significant.

Results

Baseline data

Following screening, 169 families (including 589 individuals of all ages) were randomised to one of three dietary interventions; 63 % of families (n 106 families, containing 62 % of individuals in the study) were from the more affluent SES quintile (SES2) compared with 34 % of families (n 58 families, containing 35 % of individuals in the study) from the less affluent SES quintile (SES4). At T 0, the intervention groups were similar (P>0·05) with respect to mean child age per family, the number of children per family and the number of children aged under 5 or 16 years and over (Table 1). However, assessment of socioeconomic deprivation showed that despite randomisation to intervention treatment, the intervention groups differed at baseline (ANOVA, P = 0·04). Tukey's post hoc testing showed that a greater proportion of the most socially deprived individuals were randomised to intervention C (accounting for 51 % of the ‘education+cook and eat+personalised goal setting’ group) compared with intervention B (accounting for 22 % of the ‘cook and eat’ group; P = 0·03). Intakes of energy (MJ), alcohol (%TE) and macronutrients (as a %FE) were similar at baseline (Table 2), but individuals in intervention B consumed more vitamin C than those in interventions C and A (‘education only’ group; P < 0·001 and P = 0·02, respectively) and had a less energy-dense diet (kJ/g) than individuals in intervention C (P = 0·003; Table 3).

Primary analyses of effects of the interventions on dietary outcomes

Our primary analysis aimed to test the hypothesis that the nature (and intensity) of family-based interventions, aimed at increasing intake of low-fat starchy foods, differed in their effectiveness in eliciting change in dietary behaviour. To assess intervention efficacy, estimates of dietary intake were collected at T 1, T 2 and T 3; a total of 444 individuals completed T 1, 369 completed T 2 and 198 completed T 3 (Table 1).

Starch, fat and total carbohydrate intake

After adjusting the data for covariates in GLM1, dietary intakes at T 1 differed by intervention group for fat (P = 0·02), carbohydrate (P < 0·01) and starch (P = 0·03; all expressed as a %FE). In each case, Bonferroni analysis showed that individuals randomised to intervention C consumed less fat (P = 0·02) and more total carbohydrate (P = 0·001) and starch (P = 0·04) than those in intervention A (Table 2). Although the between-intervention difference in fat was not sustained at subsequent data collections (T 2 and T 3), there were significant inter-intervention differences in starch intake at T 2 (P = 0·02) and carbohydrate intake at T 3 (P = 0·04). Those individuals in intervention A consumed more starch than individuals in intervention B at T 2 (Bonferroni test; P = 0·04), whereas at T 3, the individuals in intervention B had higher intakes of total carbohydrate (P = 0·04) than those in intervention A (Table 2).

Micronutrient and energy intake (food energy, total energy and energy density)

After adjusting the data for covariates in GLM1, the intakes of vitamin C (mg/MJ) and NSP (g/MJ) at T 1 differed between intervention groups (both P < 0·01), with intervention C consuming more vitamin C than intervention A (P = 0·002) and also more NSP than both interventions A and B (P = 0·01 and P < 0·0001, respectively; Table 3). At T 1, total energy and food energy intakes differed between intervention groups (P < 0·01 and P < 0·0001, respectively). In each instance, individuals in intervention A reported lower intakes of total energy and food energy than those in interventions B and C (total energy, P = 0·002 and P = 0·04; food energy, P < 0·001 and P < 0·001 for intervention A v. interventions B and C, respectively). At T 2, there were no between-intervention differences for micronutrients, energy intakes (total energy and food energy) or energy density.

At T 3, there were significant between-intervention differences for intakes of NSP (g/MJ; P < 0·01), vitamin C (P = 0·04) and Ca (P = 0·001; expressed in mg/MJ) and for energy density of food consumed (g/kJ; P < 0·0001). Post hoc testing showed that individuals in intervention C consumed more NSP (P = 0·002) and vitamin C (P = 0·01) than those in intervention A, and that the diets of those in interventions containing a ‘cook and eat’ component (interventions B and C) were less energy dense than those for individuals in intervention A (both P < 0·0001). In addition, at T 3, participants in intervention B consumed more Ca than those in both interventions A and C (P = 0·004 and P = 0·001, respectively).

Secondary analyses of factors potentially influencing intervention effects

Outcomes from GLM2 demonstrated that individuals in the most socially deprived group in the present study (SES quintile 4; n 119 at T 2) consumed less fat (P = 0·01; adjusted mean values and their standard error; 34·8 (se 0·51) %FE compared with 36·3 (se 0·35) %FE for SES2 (Fig. 2(a)), more carbohydrate (P = 0·02; adjusted mean values and standard error; 50·3 (se 0·50) %FE compared with 48·9 (se 0·35) %FE for SES2 (Fig. 2(b)), and less food energy (P = 0·01; adjusted mean values and standard error; 7·32 (se 0·17) MJ compared with 7·85 (se 0·11) MJ for SES2) and total energy intake (P = 0·05; adjusted mean values and standard error; 7·69 (se 0·18) MJ compared with 8·11 (se 0·13) MJ for SES2) than those in the least socially deprived group in the present study (SES quintile 2; n 240 at T 2). At T 1, those in the least deprived quintile also consumed more food energy (P = 0·04). Using GLM3, we observed that individuals within families with two parents/guardians participating in the FFHP consumed more carbohydrate at T 2 (P = 0·01) than did individuals within families with one participating parent/guardian. At both T 1 and T 3, individuals from families with two participating parents/guardians had higher sucrose intakes (P = 0·03 at T 1, P = 0·05 at T 3).

Fig. 2 Mean dietary intakes of percentage of food energy (%FE) from (a) fat and (b) total carbohydrate at baseline (T 0), and at 3 and 6 months post-intervention (T 1 and T 2, respectively) by individuals in the Family Food and Health Project, grouped by quintile of relative socioeconomic status (SES) assessed using the Townsend Deprivation Index. SES2 refers to the least socially deprived participants () and SES4 refers to the most socially deprived participants (). Values are adjusted means, with their standard errors represented by vertical bars. * Mean value of fat intake (%FE) for the SES4 group was significantly lower than that for the SES2 group at T 2 (P = 0·01; univariate general linear ANCOVA model (model 2)). † Mean value of the SES4 group for the intake of %FE from carbohydrate was significantly higher than that for the SES2 group (P = 0·02; univariate general linear ANCOVA model (model 2)).

Retention of individuals within each intervention group

Compared with T 0, individual retention rates in the study (estimated as those who completed the corresponding dietary assessments) were 75 % at T 1, 63 % at T 2 and 40 % at T 3 (T 3 data are given as a percentage of those individuals recruited in phases 2–6). At T 1 and T 2, participant retention was in the order: intervention A>intervention B>intervention C (Table 1). As described earlier, T 3 data were obtained only from families recruited in phases 2–6. There were less marked differences in retention rates between intervention groups at T 3.

Discussion

The primary objective of the present family-based intervention was to establish whether a focus on increasing the intake of low-fat starchy foods could lower the intake of dietary fat towards the UK dietary target of not more than 35 %FE(7). We tested the relative efficacy of three intervention strategies that differed in the provision of education, teaching of cooking skills and the use of a personalised goal setting framework. Several of the elements of these interventions, namely, family component, food-related activity and goal setting(Reference Pignone, Ammerman and Fernandez26, Reference Ammerman, Lindquist and Lohr27, Reference Pearson, Biddle and Gorely66), have been shown previously to improve dietary behaviours. The present study is one of the first to test the hypothesis that a family-focused, low-fat starchy food intervention will improve the dietary behaviours of (relatively) high-fat consumers.

Analysis of the dietary intakes of the 589 individuals randomised to intervention revealed that our most intensive intervention, which provided knowledge, taught practical cooking skills and utilised a goal setting strategy (intervention C), was more effective in lowering fat intake in the short term (T 1) than the education alone treatment (intervention A). This is consistent with reports that intensive interventions are more effective in inducing beneficial dietary changes than brief interventions(Reference Pignone, Ammerman and Fernandez26). As anticipated, the increase in carbohydrate intake in the ‘education+cook and eat+personalised goal setting’ group (intervention C) was predominantly in the form of starch. Improvements in the macronutrient balance of the diet were matched by significant increases in vitamin C and NSP concentration relative to the other intervention strategies, which suggests that this intervention was most effective in improving the overall nutritional quality of the diet.

The present observations suggest that a personalised intervention approach that set and re-evaluated individual dietary goals helped to translate theory (education) and practical cooking skills (cook and eat) into changes in dietary behaviour, as has been reported by others for a range of diet and health settings(Reference Shilts, Horowitz and Townsend67) including reducing fat intake(Reference Hoy, Winters and Chlebowski68) and improving the dietary behaviours of low-income groups(Reference Shilts, Horowitz and Townsend69).

Despite the greater relative effectiveness of intervention C in the short term (T 1), all three interventions had intakes at 6 and 18 months which were statistically similar for the key macronutrients that were targeted in the study (i.e. fat, total carbohydrate and starch), with intakes across all intervention groups being lower in fat and higher in carbohydrate and starch compared with baseline. An explanation for the lack of sustained superiority in effectiveness from the personalised goal setting approach (intervention C) at 6 and 18 months may be attributable, in part, to the present trial design, which did not incorporate further skill provision, goal setting or reinforcement of the dietary message after the interventions were completed. Recent systematic review data show that periodic prompts to sustain behaviour change can be efficacious(Reference Fry and Neff70).

Of interest was the finding that, at T 3, those exposed to interventions B and C consumed diets that were significantly lower in energy density than those exposed to intervention A. The common characteristic of both interventions B and C was the inclusion of a family-based cooking skills element that encouraged participants to base meals and snacks on low-fat starchy foods. A reduction in energy density in the longer term is of particular consequence, as the diets of adults and children that are less energy-dense correlate with greater nutrient quality(Reference Ledikwe, Blanck and Khan71Reference Schroder, Vila and Marrugat75) and, conversely, more energy-dense diets are associated with a higher risk of a cluster of conditions including obesity(Reference Rolls1, Reference Johnson, Mander and Jones2), diabetes(Reference Wang, Luben and Khaw3) and CVD(5).

Although not designed to investigate socioeconomic differences in response to the interventions, the FFHP recruited participants by ED (as a surrogate for socioeconomic circumstances) and families were randomised to interventions within ED. We observed that individuals residing in areas of greater relative social deprivation exhibited more favourable dietary changes in response to the dietary intervention (averaged across all three interventions) than those residing in more relatively affluent areas, and that this difference was significant for measurements made T 2. The mean reported reductions in fat and increases in carbohydrate at the 3- and 6-month assessments (see Fig. 2) were greatest for the least affluent participants in the present study (SES4) and met the UK dietary recommendations(7, 8) at both time points. The present data are in contrast with the findings of a recent systematic review of the socioeconomic determinants of dietary interventions(Reference Oldroyd, Burns and Lucas76), which has shown that positive changes in the intakes of fruits, vegetables and fat are associated with greater affluence. In addition, a recent review of forty-two school-based studies showed limited intervention effectiveness in children from lower socioeconomic groups(Reference Van Cauwenberghe, Maes and Spittaels77).

The widening gap in health provision and dietary behaviours associated with social inequalities(Reference Wrieden, Connaghan and Morrison78Reference Deshmukh-Taskar, Nicklas and Yang80) underscores the importance of developing interventions that are effective among the more disadvantaged. The present data are the first to demonstrate that an intervention focused on increasing the intake of low-fat starchy foods may be particularly effective in lower-income households in producing sustained reductions in fat intakes. These findings require confirmation in further intervention studies, targeted to households of increased deprivation and designed specifically to test the efficacy of a low-fat starchy food intervention.

We designed a family-based intervention because of the evidence that such interventions produce favourable dietary changes(Reference Ammerman, Lindquist and Lohr27, Reference Pearson, Biddle and Gorely66), that shared family meals are positively associated with greater dietary quality(Reference Larson, Neumark-Sztainer and Hannan81, Reference Pearson, Biddle and Gorely82) and that childhood food choices ‘track’ into life-long eating habits(Reference Larson, Neumark-Sztainer and Hannan81, Reference Lake, Adamson and Craigie83). Additional parental/guardian involvement has been shown to enhance the effectiveness of breakfast eating interventions(Reference Pearson, Biddle and Gorely82).

When considering the importance of the present findings, a number of characteristics of the study design should be noted. We undertook interventions in families because of the potential beneficial changes with respect to shared food shopping, food preparation and eating behaviours. However, a consequence of this approach is that the dietary data from individuals within these families, and to a lesser extent the intervention groups, will exhibit some degree of inter-dependency. Similarly, the present strategy of grouping families by ED in an attempt to minimise ‘contamination’ of the intervention message between neighbours may have resulted in subjects in neighbouring families not being fully independent. As such, the extrapolation of the present findings to other settings should be approached with caution. Further, fewer subjects dropped-out from intervention A than from the more time-demanding, and arguably more successful, interventions C and B. As a result of this disparity in retention rates, it could be speculated that interventions C and B may have had greater representation of more highly motivated subjects, and that the present per protocol assessment may therefore favour the more demanding interventions. As aforementioned, families in phase 1 of recruitment were not able to participate in T 3; this, along with attrition of phases 2–6 families at the 18-month assessment, means that the T 3 results should be interpreted with some caution. Finally, across the intervention groups, there was a pattern of lower reported mean energy intake (although, less pronounced in intervention C) at T 1, T 2 and T 3 (compared with baseline) and it is possible that this may indicate higher levels of dietary underreporting as the study progressed.

Conclusions

In the present family-based study, we have shown that the most intensive low-fat starchy food intervention, which included education, cooking skills and goal setting components, was the most effective in reducing fat intake and improving diet quality in both the short and longer term. Irrespective of intervention approach, the secondary analysis of the present study suggested that individuals residing in households with greater socioeconomic deprivation responded more favourably to the intervention in the medium term than those from more affluent areas. Further studies, which are explicitly designed to account for differences in SES, are warranted to confirm whether this positive approach to dietary change (encouraging greater intake of low-fat starchy foods) may be useful in addressing social inequalities in dietary intake.

Acknowledgements

The present study was commissioned by the Food Standards Agency (project code, N09001). A. J. A. and J. C. M. designed the study and obtained funding. P. J. C. conducted the research and analysed the data. P. J. C., A. J. A. and J. C. M. wrote the paper. All authors read and approved the final manuscript. J. C. M. had primary responsibility for final content. The authors acknowledge the contributions made by the study dietitians: Jane Loughridge, Clare Beard and Nigel Denby; the study's qualitative researcher: Anne Spendiff; the quantitative data collector for 18-month assessments: Una Tohill; the support team at the dietary intervention sessions: Julie Hooper, Emma Foster, Emma Northover and Sara Wynn; and are grateful for quality assurance assessment of the dietary interventions performed by Moira Hill and for the clerical assistance of Pauline Winship. In addition, they also acknowledge the input of Simon Raybould for power calculations and geographical mapping, Leslie Carlin for project management assistance and the study's steering committee; Andrew Rugg-Gunn, Martin White, Chris Seal, Neil Niven, Nigel Unwin, Rosie Stacey and John Matthews. Finally, the authors thank the participants of the FFHP, Newcastle upon Tyne, who gave their time to ensure the success of the project. The authors declare no conflict of interest.

References

1 Rolls, BJ (2010) Plenary Lecture 1: Dietary strategies for the prevention and treatment of obesity. Proc Nutr Soc 69, 7079.CrossRefGoogle ScholarPubMed
2 Johnson, L, Mander, AP, Jones, LR, et al. (2008) A prospective analysis of dietary energy density at age 5 and 7 years and fatness at 9 years among UK children. Int J Obes (Lond) 32, 586593.CrossRefGoogle ScholarPubMed
3 Wang, J, Luben, R, Khaw, KT, et al. (2008) Dietary energy density predicts the risk of incident type 2 diabetes: the European Prospective Investigation of Cancer (EPIC)–Norfolk Study. Diabetes Care 31, 21202125.CrossRefGoogle ScholarPubMed
4 Erber, E, Hopping, BN, Grandinetti, A, et al. (2010) Dietary patterns and risk for diabetes: the multiethnic cohort. Diabetes Care 33, 532538.CrossRefGoogle ScholarPubMed
5 World Health Organization (2003) Diet, Nutrition and the Prevention of Chronic Diseases. Joint WHO/FAO Expert Consultation. WHO Technical Report Series no. 916. Geneva: WHO.Google Scholar
6 World Cancer Research Fund/American Institute for Cancer Research (2007) Food, Nutrition, Physical Activity, and the Prevention of Cancer: A Global Perspective. Washington, DC: WCRF/AICR.Google Scholar
7 Department of Health (2005) Choosing a Better Diet: A Food and Health Action Plan. London: DOH Publications (Gateway reference 4618).Google Scholar
8 Department of Health and Social Security (1984) Report on Health and Social Subjects. Vol. 28. Diet and Cardiovascular Disease: Committee on Medical Aspects of Food Policy: Report of the Panel on Diet in Relation to Cardiovascular Disease. London: The Stationery Office.Google Scholar
9 Department of Health (1992) The Health of the Nation: A Strategy for Health in England. London: The Stationery Office.Google Scholar
10 Department for Environment, Food and Rural Affairs (2004) National Food Survey, 2000. London: Her Majesty's Stationery Office (series no. 4512).Google Scholar
11 Department for Environment, Food and Rural Affairs (2010) Family Food. A Report on the 2008 Family Food Module of the Living Costs and Food Survey. National Statistics Publication by DEFRA. London: The Stationery Office. http://www.defra.gov.uk/statistics/files/defra-stats-food-family-annual-2008.pdf.Google Scholar
12 Department of Health (1994) Nutritional Aspects of Cardiovascular Disease. Report of the Cardiovascular Review Group of the Committee on Medical Aspects of Food Policy. London: The Stationery Office.Google Scholar
13 Food Standards Agency, Wales (2003) Food and Well Being. Reducing Inequalities through a Nutrition Strategy for Wales. Cardiff, Wales: Food Standards Agency. http://www.food.gov.uk/multimedia/pdfs/foodandwellbeing.pdf.Google Scholar
14 The Scottish Government (2008) Healthy Eating in Schools. A Guide to Implementing the Nutritional Requirements for Food and Drink in Schools (Scotland) Regulations 2008. Edinburgh: The Scottish Government. http://www.scotland.gov.uk/Resource/Doc/238187/0065394.pdf.Google Scholar
15 British Dietetic Association (BDA) (2004) Obesity; BDA response to the House of Commons Health Committee Third Report of Session 2003–04, Volume 1. Media Statement 27 May 2004.Google Scholar
16 Stubenitsky, K & Mela, DJ (2000) UK consumer perceptions of starchy foods. Br J Nutr 83, 277285.CrossRefGoogle ScholarPubMed
17 Touvier, M, Mejean, C, Kesse-Guyot, E, et al. (2010) Variations in compliance with starchy food recommendations and consumption of types of starchy foods according to sociodemographic and socioeconomic characteristics. Br J Nutr 103, 14851492.CrossRefGoogle ScholarPubMed
18 Saris, WH, Astrup, A, Prentice, AM, et al. (2000) Randomized controlled trial of changes in dietary carbohydrate/fat ratio and simple vs complex carbohydrates on body weight and blood lipids: the CARMEN study. The Carbohydrate Ratio Management in European National diets. Int J Obes Relat Metab Disord 24, 13101318.Google Scholar
19 Poppitt, SD, Keogh, GF, Prentice, AM, et al. (2002) Long-term effects of ad libitum low-fat, high-carbohydrate diets on body weight and serum lipids in overweight subjects with metabolic syndrome. Am J Clin Nutr 75, 1120.CrossRefGoogle ScholarPubMed
20 Drummond, S, Kirk, T, Jackson, J, et al. (2003) Effectiveness of dietary advice given by community dietitians to men with elevated blood cholesterol in a clinical setting: a pilot study. J Hum Nutr Diet 16, 8183.CrossRefGoogle Scholar
21 Kirk, TR, Burkill, S & Cursiter, M (1997) Dietary fat reduction achieved by increasing consumption of a starchy food – an intervention study. Eur J Clin Nutr 51, 455461.CrossRefGoogle ScholarPubMed
22 Albertson, AM, Affenito, SG, Bauserman, R, et al. (2009) The relationship of ready-to-eat cereal consumption to nutrient intake, blood lipids, and body mass index of children as they age through adolescence. J Am Diet Assoc 109, 15571565.CrossRefGoogle ScholarPubMed
23 Rosado, JL, del R Arellano, M, Montemayor, K, et al. (2008) An increase of cereal intake as an approach to weight reduction in children is effective only when accompanied by nutrition education: a randomized controlled trial. Nutr J 7, 28.CrossRefGoogle ScholarPubMed
24 Paineau, DL, Beaufils, F, Boulier, A, et al. (2008) Family dietary coaching to improve nutritional intakes and body weight control: a randomized controlled trial. Arch Pediatr Adolesc Med 162, 3443.CrossRefGoogle ScholarPubMed
25 Neumark-Sztainer, D, Hannan, PJ, Story, M, et al. (2003) Family meal patterns: associations with sociodemographic characteristics and improved dietary intake among adolescents. J Am Diet Assoc 103, 317322.CrossRefGoogle ScholarPubMed
26 Pignone, MP, Ammerman, A, Fernandez, L, et al. (2003) Counseling to promote a healthy diet in adults: a summary of the evidence for the U.S. Preventive Services Task Force. Am J Prev Med 24, 7592.CrossRefGoogle ScholarPubMed
27 Ammerman, AS, Lindquist, CH, Lohr, KN, et al. (2002) The efficacy of behavioral interventions to modify dietary fat and fruit and vegetable intake: a review of the evidence. Prev Med 35, 2541.CrossRefGoogle ScholarPubMed
28 Pomerleau, J, Lock, K, Knai, C, et al. (2005) Interventions designed to increase adult fruit and vegetable intake can be effective: a systematic review of the literature. J Nutr 135, 24862495.CrossRefGoogle ScholarPubMed
29 Perez-Escamilla, R, Hromi-Fiedler, A, Vega-Lopez, S, et al. (2008) Impact of peer nutrition education on dietary behaviors and health outcomes among Latinos: a systematic literature review. J Nutr Educ Behav 40, 208225.CrossRefGoogle ScholarPubMed
30 Cullen, KW, Lara Smalling, A, Thompson, D, et al. (2009) Creating healthful home food environments: results of a study with participants in the expanded food and nutrition education program. J Nutr Educ Behav 41, 380388.CrossRefGoogle ScholarPubMed
31 Oude Luttikhuis, H, Baur, L, Jansen, H, et al. (2009) Interventions for treating obesity in children. Cochrane Database of Systematic Reviews issue 1, CD001872. http://www.mrw.interscience.wiley.com/cochrane/clsysrev/articles/CD001872/frame.html.CrossRefGoogle ScholarPubMed
32 American Dietetic Association (ADA) (2006) Position of the American Dietetic Association: individual-, family-, school-, and community-based interventions for pediatric overweight. J Am Diet Assoc 106, 925945.CrossRefGoogle Scholar
33 Burgess-Champoux, TL, Larson, N, Neumark-Sztainer, D, et al. (2009) Are family meal patterns associated with overall diet quality during the transition from early to middle adolescence? J Nutr Educ Behav 41, 7986.CrossRefGoogle ScholarPubMed
34 Winkler, E & Turrell, G (2009) Confidence to cook vegetables and the buying habits of Australian households. J Am Diet Assoc 109, 17591768.CrossRefGoogle ScholarPubMed
35 Zehle, K, Smith, BJ, Chey, T, et al. (2008) Psychosocial factors related to diet among women with recent gestational diabetes: opportunities for intervention. Diabetes Educ 34, 807814.CrossRefGoogle ScholarPubMed
36 Larson, NI, Perry, CL, Story, M, et al. (2006) Food preparation by young adults is associated with better diet quality. J Am Diet Assoc 106, 20012007.CrossRefGoogle ScholarPubMed
37 Wrieden, WL, Anderson, AS, Longbottom, PJ, et al. (2007) The impact of a community-based food skills intervention on cooking confidence, food preparation methods and dietary choices – an exploratory trial. Public Health Nutr 10, 203211.CrossRefGoogle ScholarPubMed
38 Fulkerson, JA, Rydell, S, Kubik, MY, et al. (2010) Healthy Home Offerings via the Mealtime Environment (HOME): feasibility, acceptability, and outcomes of a pilot study. Obesity (Silver Spring) 18, S69S74.CrossRefGoogle ScholarPubMed
39 Brown, BJ & Hermann, JR (2005) Cooking classes increase fruit and vegetable intake and food safety behaviors in youth and adults. J Nutr Educ Behav 37, 104105.CrossRefGoogle Scholar
40 Townsend, P, Phillimore, P & Beattie, A (1998) Health and Deprivation: Inequality and the North. London, New York: Croom Helm.Google Scholar
41 Adamson, AJ, Foster, E, Butler, TJ, et al. (2001) Non-diabetic relatives of type 2 diabetic families: dietary intake contributes to the increased risk of diabetes. Diabet Med 18, 984990.CrossRefGoogle Scholar
42 Prochaska, JO & DiClemente, CC (1986) Toward a comprehensive model of change. In Addictive Behaviors: Processes of Change, pp. 327 [Miller, WR and Heather, N, editors]. New York, NY: Plenum Press.CrossRefGoogle Scholar
43 Fletcher, ES, Rugg-Gunn, AJ, Matthews, JN, et al. (2004) Changes over 20 years in macronutrient intake and body mass index in 11- to 12-year-old adolescents living in Northumberland. Br J Nutr 92, 321333.CrossRefGoogle ScholarPubMed
44 Nelson, M, Atkinson, M & Meyer, J (1997) A Photographic Atlas of Food Portion Sizes. London: Ministry of Agriculture, Fisheries and Food (MAFF) Publications.Google Scholar
45 Ministry of Agriculture. Fisheries and Food (MAFF) (1994) Food Portion Sizes: Second Edition. London: Her Majesty's Stationery Office.Google Scholar
46 Holland, B, Unwin, ID & Buss, DH (1988) Cereals and Cereal Products: The Third Supplement to McCance and Widdowson's The Composition of Foods, 4th ed. London: Royal Society of Chemistry and Ministry of Agriculture, Fisheries and Food.Google Scholar
47 Holland, B, Unwin, ID & Buss, DH (1989) Milk Products and Eggs: The Fourth Supplement to McCance and Widdowson's The Composition of Foods, 4th ed. London: Royal Society of Chemistry and Ministry of Agriculture, Fisheries and Food.Google Scholar
48 Holland, B, Unwin, ID & Buss, DH (1991) Vegetables, Herbs and Spices: The Fifth Supplement to McCance and Widdowson's The Composition of Foods, 4th ed. London: Royal Society of Chemistry and Ministry of Agriculture, Fisheries and Food.CrossRefGoogle Scholar
49 Holland, B, Unwin, ID & Buss, DH (1992) Fruit and Nuts: The First Supplement to McCance and Widdowson's The Composition of Foods, 5th ed. London: Royal Society of Chemistry and Ministry of Agriculture, Fisheries and Food.CrossRefGoogle Scholar
50 Holland, B, Welch, AA & Buss, DH (1992) Vegetable Dishes: The Second Supplement to McCance and Widdowson's The Composition of Foods, 5th ed. London: Royal Society of Chemistry and Ministry of Agriculture, Fisheries and Food.Google Scholar
51 Holland, B, Brown, J & Buss, DH (1993) Fish and Fish Products: The Third supplement to McCance and Widdowson's The Composition of Foods, 5th ed. London: Royal Society of Chemistry and Ministry of Agriculture, Fisheries and Food.CrossRefGoogle Scholar
52 Chan, W, Brown, J & Buss, DH (1994) Miscellaneous Foods: The Fourth Supplement to McCance and Widdowson's The Composition of Foods, 5th ed. London: Royal Society of Chemistry and Ministry of Agriculture, Fisheries and Food.CrossRefGoogle Scholar
53 Chan, W, Brown, J, Lee, SM, et al. (1995) Meat, Poultry and Game: The Fifth Supplement to McCance and Widdowson's The Composition of Foods, 5th ed. London: Royal Society of Chemistry and Ministry of Agriculture, Fisheries and Food.CrossRefGoogle Scholar
54 Chan, W, Brown, J, Church, SM, et al. (1995) Meat Products and Dishes: The Sixth Supplement to McCance and Widdowson's The Composition of Foods, 5th ed. London: Royal Society of Chemistry and Ministry of Agriculture, Fisheries and Food.Google Scholar
55 Holland, B, Welch, AA, Unwin, ID, et al. (1991) McCance and Widdowson's The Composition of Food, 5th ed. London, Cambridge: Royal Society of Chemistry.Google Scholar
56 Brookes, A (1995) Nutrient Analysis of Pizza, Part 1 – Analytical Results. Ministry of Agriculture, Fisheries and Food (MAFF), in Report Series No. B915 by RHM Technology, MAFF Project No. 2B023.Google Scholar
57 Brookes, A (1995) Nutrient Analysis of Selected Foods. Part One – Analytical Results. Ministry of Agriculture, Fisheries and Food (MAFF), in Report Series No. B913 by RHM Technology, MAFF Project No. 2B021.Google Scholar
58 Ministry of Agriculture, Fisheries and Food (MAFF) (1995) Nutrient Analysis of Foods Commonly Consumed by School Children, Nutritional Survey, 1995. Part 2 – Analytical Report, MAFF Project No. AN 1036.Google Scholar
59 Aspland and James Ltd (1997) Nutrient Analysis of Ethnic Takeaway Foods 1997. Part 2 – Analytical Report, MAFF Project No. AN 1053.Google Scholar
60 Ministry of Agriculture, Fisheries and Food (MAFF) (1999) Nutrient Analysis of Other Milks and Creams. MAFF Surveillance Sheet, number 178. http://archive.food.gov.uk/maff/archive/food/infsheet/1999/no178/178milk.htm.Google Scholar
61 Ministry of Agriculture, Fisheries and Food (MAFF) (2000) Nutrient Analysis of Bread and Morning Goods. MAFF Surveillance Sheet, number 194. http://archive.food.gov.uk/maff/archive/food/infsheet/2000/no194/194bred.htm.Google Scholar
62 Ministry of Agriculture, Fisheries and Food (MAFF) (2000) Nutrient Analysis of Cheese. MAFF Surveillance Sheet, Number 196. http://archive.food.gov.uk/maff/archive/food/infsheet/2000/no196/196chee.htm.Google Scholar
63 Ministry of Agriculture, Fisheries and Food (MAFF) (2000) Nutrient Analysis of Ice creams and Desserts. MAFF Surveillance Sheet, Number 195. http://archive.food.gov.uk/maff/archive/food/infsheet/2000/no195/195dess.htm.Google Scholar
64 Adamson, AJ & Mathers, JC (2004) Effecting dietary change. Proc Nutr Soc 63, 537547.CrossRefGoogle ScholarPubMed
65 Penn, L, Boeing, H, Boushey, CJ, et al. (2010) Assessment of dietary intake: NuGO symposium report. Genes Nutr 5, 205213.CrossRefGoogle ScholarPubMed
66 Pearson, N, Biddle, SJ & Gorely, T (2009) Family correlates of fruit and vegetable consumption in children and adolescents: a systematic review. Public Health Nutr 12, 267283.CrossRefGoogle ScholarPubMed
67 Shilts, MK, Horowitz, M & Townsend, MS (2004) Goal setting as a strategy for dietary and physical activity behavior change: a review of the literature. Am J Health Promot 19, 8193.CrossRefGoogle ScholarPubMed
68 Hoy, MK, Winters, BL, Chlebowski, RT, et al. (2009) Implementing a low-fat eating plan in the Women's Intervention Nutrition Study. J Am Diet Assoc 109, 688696.CrossRefGoogle ScholarPubMed
69 Shilts, MK, Horowitz, M & Townsend, MS (2009) Guided goal setting: effectiveness in a dietary and physical activity intervention with low-income adolescents. Int J Adolesc Med Health 21, 111122.CrossRefGoogle Scholar
70 Fry, JP & Neff, RA (2009) Periodic prompts and reminders in health promotion and health behavior interventions: systematic review. J Med Internet Res 11, e16.CrossRefGoogle ScholarPubMed
71 Ledikwe, JH, Blanck, HM, Khan, LK, et al. (2006) Low-energy-density diets are associated with high diet quality in adults in the United States. J Am Diet Assoc 106, 11721180.CrossRefGoogle ScholarPubMed
72 Schroder, H, Covas, M, Elosua, R, et al. (2008) Diet quality and lifestyle associated with free selected low-energy density diets in a representative Spanish population. Eur J Clin Nutr 62, 11941200.CrossRefGoogle Scholar
73 Drewnowski, A, Monsivais, P, Maillot, M, et al. (2007) Low-energy-density diets are associated with higher diet quality and higher diet costs in French adults. J Am Diet Assoc 107, 10281032.CrossRefGoogle ScholarPubMed
74 Patterson, E, Warnberg, J, Poortvliet, E, et al. (2010) Dietary energy density as a marker of dietary quality in Swedish children and adolescents: the European Youth Heart Study. Eur J Clin Nutr 64, 356363.CrossRefGoogle ScholarPubMed
75 Schroder, H, Vila, J, Marrugat, J, et al. (2008) Low energy density diets are associated with favorable nutrient intake profile and adequacy in free-living elderly men and women. J Nutr 138, 14761481.CrossRefGoogle ScholarPubMed
76 Oldroyd, J, Burns, C, Lucas, P, et al. (2008) The effectiveness of nutrition interventions on dietary outcomes by relative social disadvantage: a systematic review. J Epidemiol Community Health 62, 573579.CrossRefGoogle ScholarPubMed
77 Van Cauwenberghe, E, Maes, L, Spittaels, H, et al. (2010) Effectiveness of school-based interventions in Europe to promote healthy nutrition in children and adolescents: systematic review of published and ‘grey’ literature. Br J Nutr 103, 781797.CrossRefGoogle ScholarPubMed
78 Wrieden, WL, Connaghan, J, Morrison, C, et al. (2004) Secular and socio-economic trends in compliance with dietary targets in the north Glasgow MONICA population surveys 1986–1995: did social gradients widen? Public Health Nutr 7, 835842.CrossRefGoogle ScholarPubMed
79 Friel, S, Newell, J & Kelleher, C (2005) Who eats four or more servings of fruit and vegetables per day? Multivariate classification tree analysis of data from the 1998 Survey of Lifestyle, Attitudes and Nutrition in the Republic of Ireland. Public Health Nutr 8, 159169.CrossRefGoogle ScholarPubMed
80 Deshmukh-Taskar, P, Nicklas, TA, Yang, SJ, et al. (2007) Does food group consumption vary by differences in socioeconomic, demographic, and lifestyle factors in young adults? The Bogalusa Heart Study. J Am Diet Assoc 107, 223234.CrossRefGoogle ScholarPubMed
81 Larson, NI, Neumark-Sztainer, D, Hannan, PJ, et al. (2007) Family meals during adolescence are associated with higher diet quality and healthful meal patterns during young adulthood. J Am Diet Assoc 107, 15021510.CrossRefGoogle ScholarPubMed
82 Pearson, N, Biddle, SJ & Gorely, T (2009) Family correlates of breakfast consumption among children and adolescents. A systematic review. Appetite 52, 17.CrossRefGoogle ScholarPubMed
83 Lake, AA, Adamson, AJ, Craigie, AM, et al. (2009) Tracking of dietary intake and factors associated with dietary change from early adolescence to adulthood: The ASH30 study. Obes Facts 2, 157165.CrossRefGoogle ScholarPubMed
Figure 0

Fig. 1 Flow chart of family recruitment to the Family Food and Health Project and the subsequent involvement of individuals at the 3, 6 and 18 months post-intervention dietary assessments (T1, T2 and T3, respectively). Intervention A, education only; intervention B, cook and eat only; intervention C, education+cook and eat+personalised goal setting; %FE, percentage of energy derived from food, excluding alcohol.

Figure 1

Table 1 Baseline non-dietary characteristics of the 169 families and the 589 individuals randomised to three intervention groups in the Family Food and Health Project and the retention rates of individuals at 3, 6 and 18 months post-intervention (T1, T2 and T3, respectively)(Mean values and standard deviations where variability of the mean is reported)*

Figure 2

Table 2 Individual intakes of energy (MJ), alcohol (as percentage of energy derived from food and alcohol (%TE)) and macronutrients (as percentage of energy derived from food, excluding alcohol (%FE)) across all three intervention groups at baseline (T0), and the intakes of individuals grouped by intervention allocation at T0 and 3, 6 and 18 months post-intervention (T1, T2 and T3, respectively)(Mean values with their standard errors at T0 and adjusted mean values with their standard errors at T1, T2, and T3)

Figure 3

Table 3 Individual intakes of energy density (kJ/g), NSP (g/MJ) and micronutrients (mg/MJ) across all three intervention groups at baseline (T0), and intakes at T0 and 3, 6 and 18 months post-intervention (T1, T2 and T3, respectively) of individuals by intervention allocation(Mean values with their standard errors at T0 and adjusted mean values with their adjusted standard errors at T1, T2, and T3)

Figure 4

Fig. 2 Mean dietary intakes of percentage of food energy (%FE) from (a) fat and (b) total carbohydrate at baseline (T0), and at 3 and 6 months post-intervention (T1 and T2, respectively) by individuals in the Family Food and Health Project, grouped by quintile of relative socioeconomic status (SES) assessed using the Townsend Deprivation Index. SES2 refers to the least socially deprived participants () and SES4 refers to the most socially deprived participants (). Values are adjusted means, with their standard errors represented by vertical bars. * Mean value of fat intake (%FE) for the SES4 group was significantly lower than that for the SES2 group at T2 (P = 0·01; univariate general linear ANCOVA model (model 2)). † Mean value of the SES4 group for the intake of %FE from carbohydrate was significantly higher than that for the SES2 group (P = 0·02; univariate general linear ANCOVA model (model 2)).