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Providing dietary suggestions based on an individual's nutritional needs may contribute to the prevention of non-communicable dietary related diseases. Consumer acceptance is crucial for the success of these personalised nutrition services. The current study aims to build on previous studies by exploring whether ambivalent feelings and contextual factors could help to further explain consumers' usage intentions regarding personalised nutrition services. An online administered survey was conducted in December 2016 with a final sample of 797 participants in the Netherlands. Different models were tested and compared by means of structural equation modelling. The final model indicated that the result of weighing personalisation benefits and privacy risks (called the risk-benefit calculus) is positively related to the intention to use personalised nutrition advice, suggesting a more positive intention when more benefits than risks are perceived. Additionally, the model suggests that more ambivalent feelings are related to a lower intention to use personalised nutrition advice. Finally, we found that the more the eating context is perceived as a barrier to use personalised nutrition advice, the more ambivalent feelings are perceived. In conclusion, the current study suggests the additional value of ambivalent feelings as an affective construct, and eating context as a possible barrier in predicting consumers' intention to use personalised nutrition advice. This implies that personalised nutrition services may need to address affective concerns and consider an individual's eating context.

Objective: A high-fibre diet is associated with a lower risk for diseases. However, few adults meet the dietary fibre recommendation. Therefore, the effects and acceptance of an algorithm-generated personalised dietary advice (PDA) compared with general advice (GA) on fibre intake were investigated. Design: A 6-week, single-blind randomised controlled trial with a 3-month follow-up. Setting: PDA was based on habitual intake and provided fibre-rich alternatives using a website; GA contained brochures. Dietary intake was assessed at baseline, week 1, week 6 and 3-month follow-up. Both groups evaluated their advice at week 6. All participants had access to PDA from week 7 until 3-month follow-up. Participants: Two groups of healthy adults: PDA (n 34) and GA (n 47). For 3-month follow-up analysis, participants were re-divided into visitors (n 52) and non-visitors (n 26) of the PDA. Results: At week 6, energy intake remained stable in both groups, but fibre intake per 1000 kcal increased non-significantly in both groups (PDA = Δ0·5 ± 2·8; GA = Δ0·8 ± 3·1, P = 0·128). Importantly, a significantly higher percentage of PDA participants adhered to the recommendation compared with week 1 (PDA = 21 % increase; GA = 4 % increase, P ≤ 0·001). PDA participants evaluated the advice significantly better compared with GA participants. At 3-month follow-up, fibre intake increased compared with baseline (visitors = Δ2·2 ± 2·6, P < 0·001; non-visitors = Δ1·5 ± 1·9, P = 0·001), but was insignificantly different between groups. Visitors had a decrease and non-visitors had an increase in energy intake (visitors =Δ − 132 ± 525; non-visitors = Δ109 ± 507, P = 0·055). Conclusions: The algorithm-generated PDA was well accepted and stimulated adherence to the recommendations more than GA, indicating to be a suitable and cost-efficient method for improving dietary fibre intake in healthy adults.

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