Design Issues in Personalized Nutrition Advice Systems.

The current health status of the general public can substantially benefit from a healthy diet. Using a personalized approach to initiate healthy dietary behavior seems to be a promising strategy, as individuals differ in terms of health status, subsequent dietary needs, and their desired behavior change support. However, providing personalized advice to a wide audience over a long period is very labor-intensive. This bottleneck can possibly be overcome by digitalizing the process of creating and providing personalized advice. An increasing number of personalized advice systems for different purposes is becoming available in the market, ranging from systems providing advice about just a single parameter to very complex systems that include many variables characterizing each individual situation. Scientific background is often lacking in these systems. In designing a personalized nutrition advice system, many design questions need to be answered, ranging from the required input parameters and accurate measurement methods (sense), type of modeling techniques to be used (reason), and modality in which the personalized advice is provided (act). We have addressed these topics in this viewpoint paper, and we have demonstrated the feasibility of setting up an infrastructure for providing personalized dietary advice based on the experience of 2 practical applications in a real-life setting.

A high-fibre personalised dietary advice given via a web tool reduces constipation complaints in adults.

Constipation can greatly impact the quality of life (QoL), which can be relieved by dietary fibres; however, preserving a higher fibre intake remains a challenge. We investigated the effects of a personalised dietary advice (PDA) on fibre intake and mild constipation complaints. A total number of twenty-five adults with mild constipation complaints were included in a 4-week observation period followed by a 4-week personalised intervention. The PDA provided high-fibre alternatives via a web tool. In weeks 1, 4 and 8, dietary intake, constipation complaints and QoL were assessed. Furthermore, participants collected a faecal sample at weeks 1, 4 and 8 to determine microbiota diversity and composition, and short-chain fatty acids (SCFA). Participants completed questions daily for 8 weeks regarding abdominal complaints, stool frequency and stool consistency. Fibre intake in week 8 was significantly higher compared to week 1 (Δ = 5·7 ± 6·7 g, P < 0·001) and week 4 (Δ = 5·2 ± 6·4 g, P < 0·001). Constipation severity and QoL significantly improved at week 8 compared to the observation period (P < 0·001). A higher fibre intake significantly reduced constipation severity (ß = -0·031 (-0·05; -0·01), P = 0·001) and the QoL (ß = -0·022 (-0·04; -0·01), P = 0·009). Stool consistency (P = 0·040) and abdominal pain (P = 0·030) improved significantly during the intervention period (P = 0·040), but stool frequency did not. Average microbial alpha diversity and composition and SCFA concentrations did not change over time, but indicated individual-specific dynamics. Several SCFAs were associated with constipation complaints. To conclude, a PDA effectively increased fibre intake and subsequently reduced constipation complaints, indicating that guided dietary adjustments are important and feasible in the treatment of mild constipation complaints.

Increasing dietary fibre intake in healthy adults using personalised dietary advice compared with general advice: a single-blind randomised controlled trial.

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.