Sex and age differences in attitudes and intention to adopt personalised nutrition in a UK sample

Aim There has been an increase in the development of technologies that can deliver personalised dietary advice. Devising healthy, sustainable dietary plans will mean taking into consideration extrinsic factors such as individual social circumstances. The aim of this study was to identify societal groups more or less receptive to and likely to engage with personalised nutrition initiatives. Sample and methods Volunteers were recruited via a social research agency from within the UK. The resultant sample (N = 1061) was 49% female, aged 18-65 years. Results MANOVA (Tukey HSD applied) indicated that females and younger people (aged 18-29 years) had more favourable attitudes and were more likely to intend to adopt personalised nutrition. There were no differences in attitude toward or intention to adopt personalised nutrition between different education levels, income brackets or occupational groups. Conclusion These results imply that females and younger people may be most likely to adopt personalised nutrition in the future. Initiatives to promote healthy eating should target males and older people.

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

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

MetaCliniq: a metabolomics tool for the development of a personalised nutrition service within the Stance4Health project

MetaCliniq: a metabolomics tool for the development of a personalised nutrition service within the Stance4Health project The overall objective of Stance4Health is to develop a complete smart personalised nutrition service based on the use of mobile technologies as well as tailored food production that will optimise the gut microbiota activity and long-term consumer engagement. The project will focus on the adult population (lean and overweight people) and children (lean, obese coeliac disease or food allergy).

Self-efficacy, habit strength, health locus of control and response to the personalised nutrition Food4Me intervention study

PurposeRandomised controlled trials identify causal links between variables but not why an outcome has occurred. This analysis sought to determine how psychological factors assessed at baseline influenced response to personalised nutrition.Design/methodology/approachWeb-based, randomised, controlled trial (RCT) was conducted across seven European countries. Volunteers, both male and female, aged over 18 years were randomised to either a non-personalised (control) or a personalised (treatment) dietary advice condition. Linear mixed model analysis with fixed effects was used to compare associations between internal and external health locus of control (HLoC), nutrition self-efficacy (NS-E) and self-report habit index (S-RHI) at baseline (N = 1444), with healthy eating index (HEI) and Mediterranean diet index (MDI) scores between conditions post-intervention (N = 763).FindingsAn increase in MDI scores was observed between baseline and six months in the treatment group which was associated with higher NS-E (p < 0.001), S-RHI (p < 0.001) and external HLoC (p < 0.001). Increase in HEI between baseline and six months in the treatment group was associated with higher NS-E (p < 0.001) and external HLoC (p = 0.009). Interaction between time and condition indicated increased HEI scores (p < 0.001), which were associated with higher S-RHI scores in the treatment than control group (p = 0.032). Internal HLoC had no effect on MDI or HEI.Originality/valuePsychological factors associated with behaviour change need consideration when tailoring dietary advice. Those with weaker habit strength will require communication focussed upon establishing dietary habits and support in integrating advised changes into daily routine. Information on habit strength can also be used to inform how progress towards dietary goals is monitored and fed back to the individual. Those with stronger habit strength are more likely to benefit from personalised nutrition.

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 intake of discretionary foods. Trial registration Clinicaltrials.gov NCT01530139. Registered 9 February 2012.