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There is increasing interest in modelling longitudinal dietary data and classifying individuals into subgroups (latent classes) who follow similar trajectories over time. These trajectories could identify population groups and time points amenable to dietary interventions. This paper aimed to provide a comparison and overview of two latent class methods: group-based trajectory modelling (GBTM) and growth mixture modelling (GMM). Data from 2963 mother–child dyads from the longitudinal Southampton Women’s Survey were analysed. Continuous diet quality indices (DQI) were derived using principal component analysis from interviewer-administered FFQ collected in mothers pre-pregnancy, at 11- and 34-week gestation, and in offspring at 6 and 12 months and 3, 6–7 and 8–9 years. A forward modelling approach from 1 to 6 classes was used to identify the optimal number of DQI latent classes. Models were assessed using the Akaike and Bayesian information criteria, probability of class assignment, ratio of the odds of correct classification, group membership and entropy. Both methods suggested that five classes were optimal, with a strong correlation (Spearman’s = 0·98) between class assignment for the two methods. The dietary trajectories were categorised as stable with horizontal lines and were defined as poor (GMM = 4 % and GBTM = 5 %), poor-medium (23 %, 23 %), medium (39 %, 39 %), medium-better (27 %, 28 %) and best (7 %, 6 %). Both GBTM and GMM are suitable for identifying dietary trajectories. GBTM is recommended as it is computationally less intensive, but results could be confirmed using GMM. The stability of the diet quality trajectories from pre-pregnancy underlines the importance of promotion of dietary improvements from preconception onwards.
Systematic reviews and meta-analyses suggest that behaviour change interventions have modest effect sizes, struggle to demonstrate effect in the long term and that there is high heterogeneity between studies. Such interventions take huge effort to design and run for relatively small returns in terms of changes to behaviour.
So why do behaviour change interventions not work and how can we make them more effective? This article offers some ideas about what may underpin the failure of behaviour change interventions. We propose three main reasons that may explain why our current methods of conducting behaviour change interventions struggle to achieve the changes we expect: 1) our current model for testing the efficacy or effectiveness of interventions tends to a mean effect size. This ignores individual differences in response to interventions; 2) our interventions tend to assume that everyone values health in the way we do as health professionals; and 3) the great majority of our interventions focus on addressing cognitions as mechanisms of change. We appeal to people’s logic and rationality rather than recognising that much of what we do and how we behave, including our health behaviours, is governed as much by how we feel and how engaged we are emotionally as it is with what we plan and intend to do.
Drawing on our team’s experience of developing multiple interventions to promote and support health behaviour change with a variety of populations in different global contexts, this article explores strategies with potential to address these issues.
Arachidonic acid (ARA) and DHA, supplied primarily from the mother, are required for early development of the central nervous system. Thus, variations in maternal ARA or DHA status may modify neurocognitive development. We investigated the relationship between maternal ARA and DHA status in early (11·7 weeks) or late (34·5 weeks) pregnancy on neurocognitive function at the age of 4 years or 6–7 years in 724 mother–child pairs from the Southampton Women’s Survey cohort. Plasma phosphatidylcholine fatty acid composition was measured in early and late pregnancy. ARA concentration in early pregnancy predicted 13 % of the variation in ARA concentration in late pregnancy (β=0·36, P<0·001). DHA concentration in early pregnancy predicted 21 % of the variation in DHA concentration in late pregnancy (β=0·46, P<0·001). Children’s cognitive function at the age of 4 years was assessed by the Wechsler Preschool and Primary Scale of Intelligence and at the age of 6–7 years by the Wechsler Abbreviated Scale of Intelligence. Executive function at the age of 6–7 years was assessed using elements of the Cambridge Neuropsychological Test Automated Battery. Neither DHA nor ARA concentrations in early or late pregnancy were associated significantly with neurocognitive function in children at the age of 4 years or the age of 6–7 years. These findings suggest that ARA and DHA status during pregnancy in the range found in this cohort are unlikely to have major influences on neurocognitive function in healthy children.
Low birth weight has been inconsistently associated with risk of
developing affective disorders, including major depressive disorder
(MDD). To date, studies investigating possible associations between birth
weight and bipolar disorder (BD), or personality traits known to
predispose to affective disorders such as neuroticism, have not been
conducted in large cohorts.
To assess whether very low birth weight (<1500 g) and low birth weight
(1500–2490 g) were associated with higher neuroticism scores assessed in
middle age, and lifetime history of either MDD or BD. We controlled for
possible confounding factors.
Retrospective cohort study using baseline data on the 83 545 UK Biobank
participants with detailed mental health and birth weight data. Main
outcomes were prevalent MDD and BD, and neuroticism assessed using the
Eysenck Personality Inventory Neuroticism scale - Revised (EPIN-R)
Referent to normal birth weight, very low/low birth weight were
associated with higher neuroticism scores, increased MDD and BD. The
associations between birth weight category and MDD were partially
mediated by higher neuroticism.
These findings suggest that intrauterine programming may play a role in
lifetime vulnerability to affective disorders.
To test the hypothesis that maternal psychological profiles relate to children’s quality of diet.
Cross-sectional study. Mothers provided information on their health-related psychological factors and aspects of their child’s mealtime environment. Children’s diet quality was assessed using an FFQ from which weekly intakes of foods and a diet Z-score were calculated. A high score described children with a better quality diet. Cluster analysis was performed to assess grouping of mothers based on psychological factors. Mealtime characteristics, describing how often children ate while sitting at a table or in front of the television, their frequency of takeaway food consumption, maternal covert control and food security, and children’s quality of diet were examined, according to mothers’ cluster membership.
Mother–child pairs (n 324) in the Southampton Initiative for Health. Children were aged 2–5 years.
Two main clusters were identified. Mothers in cluster 1 had significantly higher scores for all psychological factors than mothers in cluster 2 (all P<0·001). Clusters were termed ‘more resilient’ and ‘less resilient’, respectively. Children of mothers in the less resilient cluster ate meals sitting at a table less often (P=0·03) and watched more television (P=0·01). These children had significantly poorer-quality diets (β=−0·61, 95 % CI −0·82, −0·40, P≤0·001). This association was attenuated, but remained significant after controlling for confounding factors that included maternal education and home/mealtime characteristics (P=0·006).
The study suggests that mothers should be offered psychological support as part of interventions to improve children’s quality of diet.
To determine adherence to nutritional guidelines by pregnant women in New Zealand and maternal characteristics associated with adherence.
A cohort of the pregnant women enrolled into New Zealand’s new birth cohort study, Growing Up in New Zealand.
Women residing within a North Island region of New Zealand, where one-third of the national population lives.
Pregnant women (n 5664) were interviewed during 2009–2010. An FFQ was administered during the face-to-face interview.
The recommended daily number of servings of vegetables and fruit (≥6) were met by 25 % of the women; of breads and cereals (≥6) by 26 %; of milk and milk products (≥3) by 58 %; and of lean meat, meat alternatives and eggs (≥2) by 21 %. One in four women did not meet the recommendations for any food group. Only 3 % met all four food group recommendations. Although adherence to recommendation for the vegetables/fruit group did not vary by ethnicity (P=0·38), it did vary for the breads/cereals, milk/milk products and meat/eggs groups (all P<0·001). Adherence to recommendations for the vegetables/fruit group was higher among older women (P=0·001); for the breads/cereals group was higher for women with previous children (P<0·001) and from lower-income households (P<0·001); and for the meat/eggs group was higher for women with previous children (P=0·003) and from lower-income households (P=0·004).
Most pregnant women in New Zealand do not adhere to nutritional guidelines in pregnancy, with only 3 % meeting the recommendations for all four food groups. Adherence varies more so with ethnicity than with other sociodemographic characteristics.
To evaluate the use of an administered eighty-item FFQ to assess nutrient intake and diet quality in 3-year-old children.
Frequency of consumption and portion size of the foods listed on the FFQ during the 3 months preceding the interview were reported by the child's main caregiver; after the interview a 2 d prospective food diary (FD) was completed on behalf of the child. Nutrient intakes from the FFQ and FD were estimated using UK food composition data. Diet quality was assessed from the FFQ and FD according to the child's scores for a principal component analysis-defined dietary pattern (‘prudent’ pattern), characterised by high consumption of fruit, vegetables, water and wholemeal cereals.
Children (n 892) aged 3 years in the Southampton Women's Survey.
Intakes of all nutrients assessed by the FFQ were higher than FD estimates, but there was reasonable agreement in terms of ranking of children (range of Spearman rank correlations for energy-adjusted nutrient intakes, rs = 0·41 to 0·59). Prudent diet scores estimated from the FFQ and FD were highly correlated (r = 0·72). Some family and child characteristics appeared to influence the ability of the FFQ to rank children, most notably the number of child's meals eaten away from home.
The FFQ provides useful information to allow ranking of children at this age with respect to nutrient intake and quality of diet, but may overestimate absolute intakes. Dietary studies of young children need to consider family and child characteristics that may impact on reporting error associated with an FFQ.
It is recognised that eating habits established in early childhood may track into adult life. Developing effective interventions to promote healthier patterns of eating throughout the life course requires a greater understanding of the diets of young children and the factors that influence early dietary patterns. In a longitudinal UK cohort study, we assessed the diets of 1640 children at age 3 years using an interviewer-administered FFQ and examined the influence of maternal and family factors on the quality of the children's diets. To describe dietary quality, we used a principal components analysis-defined pattern of foods that is consistent with healthy eating recommendations. This was termed a ‘prudent’ diet pattern and was characterised by high intakes of fruit, vegetables and wholemeal bread, but by low intakes of white bread, confectionery, chips and roast potatoes. The key influence on the quality of the children's diets was the quality of their mother's diets; alone it accounted for almost a third of the variance in child's dietary quality. Mothers who had better-quality diets, which complied with dietary recommendations, were more likely to have children with comparable diets. This relationship remained strong even after adjustment for all other factors considered, including maternal educational attainment, BMI and smoking, and the child's birth order and the time spent watching television. Our data provide strong evidence of shared family patterns of diet and suggest that interventions to improve the quality of young women's diets could be effective in improving the quality of their children's diets.
To evaluate the relative validity of an FFQ for assessing nutrient intakes in 12-month-old infants.
Design and setting
The FFQ was developed to assess the diets of infants born to women in the Southampton Women’s Survey (SWS), a population-based survey of young women and their offspring. The energy and nutrient intakes obtained from an interviewer-administered FFQ were compared with those obtained from 4d weighed diaries (WD).
Subjects and methods
A sub-sample of fifty infants (aged 1 year) from the SWS had their diets assessed by both methods. The FFQ recorded the frequencies and amounts of foods and drinks consumed by the infants over the previous 28 d; milk consumption was recorded separately. The WD recorded the weights of all foods and drinks consumed by the infants on 4 d following the FFQ completion.
The Spearman rank correlation coefficients for intakes of energy, macronutrients and eighteen micronutrients, determined by the two methods, ranged from r = 0·25 to 0·66. Bland–Altman statistics showed that mean differences between methods were in the range +5 % to +60 % except for vitamin D (+106 %). Differences in micronutrient intake were partly explained by changes in patterns of milk consumption between the two assessments.
Although there were differences in absolute energy and nutrient intakes between methods, there was reasonable agreement in the ranking of intakes. The FFQ is a useful tool for assessing energy and nutrient intakes of healthy infants aged around 12 months.
The impact of variations in current infant feeding practice on bone mineral accrual is not known. We examined the associations between duration of breast-feeding and compliance with infant dietary guidelines and later bone size and density at age 4 years. At total of 599 (318 boys) mother–child pairs were recruited from the Southampton Women's Survey. Duration of breast-feeding was recorded and infant diet was assessed at 6 and 12 months using FFQ. At 6 and 12 months the most important dietary pattern, defined by principal component analysis, was characterised by high consumption of vegetables, fruits and home-prepared foods. As this was consistent with infant feeding recommendations, it was denoted the ‘infant guidelines’ pattern. At age 4 years, children underwent assessment of whole-body bone size and density using a Hologic Discovery dual-energy X-ray absorptiometry instrument. Correlation methods were used to explore the relationships between infant dietary variables and bone mineral. There was no association between duration of breast-feeding in the first year of life and 4-year bone size or density. ‘Infant guidelines’ pattern scores at 6 and 12 months were also unrelated to bone mass at age 4 years. We observed wide variations in current infant feeding practice, but these variations were not associated with differences in childhood bone mass at age 4 years.
Anthropometric indices of adiposity include BMI, waist circumference and waist:height ratio. In the recruitment phase of a prospective cohort study carried out between 1998 and 2002 we studied a population sample of 11 786 white Caucasian non-pregnant women in Southampton, UK aged 20–34 years, and explored the extent to which proposed cut-off points for the three indices identified the same or different women and how these indices related to adiposity. Height, weight and waist circumference were measured and fat mass was estimated from skinfold thicknesses; fat mass index was calculated as fat mass/height1·65. Of the subjects, 4869 (42 %) women were overweight (BMI ≥ 25 kg/m2) and 1849 (16 %) were obese (BMI ≥ 30 kg/m2). A total of 890 (8 %) subjects were not overweight but had a waist circumference ≥ 80 cm and 748 (6 %) subjects were overweight but had a waist circumference < 80 cm (6 %). Of the women, 50 % had a BMI ≥ 25 kg/m2 or a waist circumference ≥ 80 cm or a waist:height ratio ≥ 0·5. Of the variation in fat mass index, 85 % was explained by BMI, 76 % by waist circumference and 75 % by waist:height ratio. Our findings demonstrate that many women are differentially classified depending on which index of adiposity is used. As each index captures different aspects of size in terms of adiposity, there is the need to determine how the three indices relate to function and how they can be of use in defining risk of ill health in women.
There is growing interest in the use of dietary patterns as measures of exposure in studies of diet–disease relationships. However, relatively little is known about the impact of the type of dietary assessment method on the patterns observed. Using FFQ and food diary data collected from 585 women in early pregnancy we used principal component analysis to define dietary patterns. The first pattern was very similar in both datasets and was termed the ‘prudent’ diet. The second pattern, whilst comparable for the FFQ and food diaries, showed greater variation in coefficients than the prudent pattern; it was termed the ‘Western’ diet. Differences between the FFQ and diary scores were calculated for each woman for both the prudent and Western diet patterns. Of the differences in the prudent diet score, 95 % lay within ± 1·58 sd of the mean, and 95 % of the differences in the Western diet scores lay within ± 2·22 sd of the mean. Pearson's correlation coefficients were 0·67 (P < 0·001) for the prudent diet score and 0·35 (P < 0·001) for the Western diet score. The agreement between the FFQ and diary scores was lowest amongst respondents who were younger, had lower educational attainment and whose diaries were coded as ‘poor, probably incomplete’, although these effects were small. The first two dietary patterns identified in this cohort of pregnant women appear to be defined similarly by both FFQ and diary data, suggesting that FFQ data provide useful information on dietary patterns.
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