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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., (©Iris M de Hoogh, Machiel J Reinders, Esmée L Doets, Femke P M Hoevenaars, Jan L Top. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.03.2023.)

Background: Various multifaceted factors need to be addressed to improve the health and quality of life of people with type 2 diabetes (T2D). Therefore, we developed a web-based decision support tool that comprises a more holistic diagnosis (including 4 domains: body, thinking and feeling, behavior, and environment) and personalized advice. This 360° diagnostic tool enables people with T2D and health care professionals at the general practice to obtain an overview of the most important T2D-related issues and, subsequently, determine the most suitable intervention for the person with T2D., Objective: This study aimed to describe the systematic and iterative development and evaluation of the web-based 360° diagnostic tool., Methods: We defined the requirements for the web-based 360° diagnostic tool based on previously developed tools, a literature review, and inputs from a multidisciplinary team of experts. As part of the conceptualization, we defined 3 requirements: diagnostics; feedback; and advice, consultation, and follow-up. Next, we developed and designed the content for each of these requirements. We evaluated the diagnostic part of the tool (ie, measurement instruments and visualization) with a qualitative design, in a usability study with a think-aloud strategy and interview questions, among 8 people with T2D at a Dutch general practice., Results: For each of the 4 domains, specific parameters and underlying elements were selected, and measurement instruments (including clinical data and questionnaires) were chosen. Cutoff values were defined to identify high-, middle-, and low-ranking scores, and decision rules were developed and implemented using R scripts and algorithms. A traffic light color visual design was created (profile wheel) to provide an overview of the scores per domain. We mapped the interventions that could be added to the tool and developed a protocol designed as a card deck with motivational interview steps. Furthermore, the usability study showed that people with T2D perceived the tool as easy to use, useful, easy to understand, and insightful., Conclusions: Preliminary evaluation of the 360° diagnostic tool by experts, health care professionals, and people with T2D showed that the tool was considered relevant, clear, and practical. The iterative process provided insights into the areas of improvement, which were implemented. The strengths, shortcomings, future use, and challenges are also discussed., (©Zeena Harakeh, Iris M de Hoogh, Hilde van Keulen, Gino Kalkman, Eugene van Someren, Pepijn van Empelen, Wilma Otten. Originally published in JMIR Formative Research (https://formative.jmir.org), 07.03.2023.)

Digital health technologies may support the management and prevention of disease through personalized lifestyle interventions. Wearables and smartphones are increasingly used to continuously monitor health and disease in everyday life, targeting health maintenance. Here, we aim to demonstrate the potential of wearables and smartphones to (1) detect eating moments and (2) predict and explain individual glucose levels in healthy individuals, ultimately supporting health self-management. Twenty-four individuals collected continuous data from interstitial glucose monitoring, food logging, activity, and sleep tracking over 14 days. We demonstrated the use of continuous glucose monitoring and activity tracking in detecting eating moments with a prediction model showing an accuracy of 92.3% (87.2-96%) and 76.8% (74.3-81.2%) in the training and test datasets, respectively. Additionally, we showed the prediction of glucose peaks from food logging, activity tracking, and sleep monitoring with an overall mean absolute error of 0.32 (+/-0.04) mmol/L for the training data and 0.62 (+/-0.15) mmol/L for the test data. With Shapley additive explanations, the personal lifestyle elements important for predicting individual glucose peaks were identified, providing a basis for personalized lifestyle advice. Pending further validation of these digital biomarkers, they show promise in supporting the prevention and management of type 2 diabetes through personalized lifestyle recommendations.

Background: Type 2 diabetes (T2D) is a lifestyle-related disease whose prevalence increases with age. Diabetes self-management through mobile health (mHealth) apps enables patients with T2D to improve their health. According to the Technology Acceptance Model (TAM), technology acceptance (ie, intended use) is necessary to ensure mHealth can be implemented successfully. Therefore, the specific acceptance requirements of patients with T2D should be considered., Objective: This cross-sectional study aims to examine the extent to which different TAM predictors are associated with the acceptance of a diabetes app including an electronic coach (eCoach; Iris app) among patients with T2D., Methods: Using a web-based survey, data on 92 patients with T2D (mean age 62.76 years, SD 8.29 years) were collected. Acceptance of the Iris app with the TAM predictors (ie, perceived usefulness, perceived ease of use, social influence, perceived self-efficacy, perceived security, prior usage experience, perceived health, and propensity of data/information sharing) was assessed. Further, control variables (ie, gender, age, education, ethnicity, household, BMI, amount of years with diabetes, diabetes-related complaints, and medication use) were assessed., Results: Multiple linear regression analyses showed that acceptance of the Iris app was positively associated with perceived usefulness (β=.57, P<.001), social influence (subjective norm; β=.20, P=.004), and willingness to share data (β=.25, P<.001). In addition, acceptance regarding the Iris app was higher among patients with T2D with overweight (β=.23, P=.01) or obese BMI (β=.21, P=.01). The model explained 75.8% of the variance in the acceptance of the Iris app by patients with T2D. In addition, perceived usefulness of the Iris app was positively related to perceived ease of use (β=.32, P<.001), subjective norm (β=.26, P=.004), perceived control (β=.19, P=.03), willingness to share data (β=.20, P=.01) regarding the Iris app, and perceived security regarding general use of apps/smartphone/internet (β=.15, P=.04). The model explained 58.2% of the variance in patients' perceived usefulness about the Iris app., Conclusions: Among patients with T2D, the belief that the use of the Iris app is helpful/beneficial, the willingness to share their Iris app data, and others' approval of using this app can stimulate the acceptance of this app. In addition, the belief that the use of (health) apps is reliable and secure, the belief that the use of the Iris app is easy to use, a higher perceived capability and personal control with using this app, the willingness to share their Iris app data, and others' approval of using this app can stimulate the perceived usefulness of such an app. These TAM predictors explained a high variance in acceptance and perceived usefulness of the Iris app. Implications for practice are addressed., (©Zeena Harakeh, Hilde Van Keulen, Koen Hogenelst, Wilma Otten, Iris M De Hoogh, Pepijn Van Empelen. Originally published in JMIR Formative Research (https://formative.jmir.org), 16.08.2022.)

A type 2 diabetes mellitus (T2DM) subtyping method that determines the T2DM phenotype based on an extended oral glucose tolerance test is proposed. It assigns participants to one of seven subtypes according to their β-cell function and the presence of hepatic and/or muscle insulin resistance. The effectiveness of this subtyping approach and subsequent personalized lifestyle treatment in ameliorating T2DM was assessed in a primary care setting. Sixty participants, newly diagnosed with (pre)diabetes type 2 and not taking diabetes medication, completed the intervention. Retrospectively collected data of 60 people with T2DM from usual care were used as controls. Bodyweight (p < 0.01) and HbA1c (p < 0.01) were significantly reduced after 13 weeks in the intervention group, but not in the usual care group. The intervention group achieved 75.0% diabetes remission after 13 weeks (fasting glucose ≤ 6.9 mmol/L and HbA1c < 6.5% (48 mmol/mol)); for the usual care group, this was 22.0%. Lasting (two years) remission was especially achieved in subgroups with isolated hepatic insulin resistance. Our study shows that a personalized diagnosis and lifestyle intervention for T2DM in a primary care setting may be more effective in improving T2DM-related parameters than usual care, with long-term effects seen especially in subgroups with hepatic insulin resistance.

Although lifestyle interventions can lead to diabetes remission, it is unclear to what extent type 2 diabetes (T2D) remission alters or improves the underlying pathophysiology of the disease. Here, we assess the effects of a lifestyle intervention on T2D reversal or remission and the effects on the underlying pathology. In a Dutch primary care setting, 15 adults with an average T2D duration of 13.4 years who were (pharmacologically) treated for T2D received a diabetes subtyping ("diabetyping") lifestyle intervention (DLI) for six months, aiming for T2D remission. T2D subtype was determined based on an OGTT. Insulin and sulphonylurea (SU) derivative treatment could be terminated for all participants. Body weight, waist/hip ratio, triglyceride levels, HbA1c, fasting, and 2h glucose were significantly improved after three and six months of intervention. Remission and reversal were achieved in two and three participants, respectively. Indices of insulin resistance and beta cell capacity improved, but never reached healthy values, resulting in unchanged T2D subtypes. Our study implies that achieving diabetes remission in individuals with a longer T2D duration is possible, but underlying pathology is only minimally affected, possibly due to an impaired beta cell function. Thus, even when T2D remission is achieved, patients need to continue adhering to lifestyle therapy.

Personalized nutrition may be more effective in changing lifestyle behaviors compared to population-based guidelines. This single-arm exploratory study evaluated the impact of a 10-week personalized systems nutrition (PSN) program on lifestyle behavior and health outcomes. Healthy men and women ( n = 82) completed the trial. Individuals were grouped into seven diet types, for which phenotypic, genotypic and behavioral data were used to generate personalized recommendations. Behavior change guidance was also provided. The intervention reduced the intake of calories (-256.2 kcal; p < 0.0001), carbohydrates (-22.1 g; p < 0.0039), sugar (-13.0 g; p < 0.0001), total fat (-17.3 g; p < 0.0001), saturated fat (-5.9 g; p = 0.0003) and PUFA (-2.5 g; p = 0.0065). Additionally, BMI (-0.6 kg/m 2 ; p < 0.0001), body fat (-1.2%; p = 0.0192) and hip circumference (-5.8 cm; p < 0.0001) were decreased after the intervention. In the subgroup with the lowest phenotypic flexibility, a measure of the body's ability to adapt to environmental stressors, LDL (-0.44 mmol/L; p = 0.002) and total cholesterol (-0.49 mmol/L; p < 0.0001) were reduced after the intervention. This study shows that a PSN program in a workforce improves lifestyle habits and reduces body weight, BMI and other health-related outcomes. Health improvement was most pronounced in the compromised phenotypic flexibility subgroup, which indicates that a PSN program may be effective in targeting behavior change in health-compromised target groups.

In public health initiatives, generic nutrition advice (GNA) from national guidelines has a limited effect on food-intake improvement. Personalized nutrition advice (PNA) may enable dietary behavior change. A monocentric, randomized, parallel, controlled clinical trial was performed in males ( n = 55) and females ( n = 100) aged 25 to 70 years. Participants were allocated to control, GNA or PNA groups. The PNA group consisted of automatically generated dietary advice based on personal metabolic health parameters, dietary intake, anthropometric and hemodynamic measures, gender and age. Participants who received PNA ( n = 51) improved their nutritional intake status for fruits P ( p < 0.0001), whole grains ( p = 0.008), unsalted nuts ( p < 0.0001), fish ( p = 0.0003), sugar-sweetened beverages ( p = 0.005), added salt ( p = 0.003) and less unhealthy choices ( p = 0.002), whereas no improvements were observed in the control and GNA group. PNA participants were encouraged to set a goal for one or multiple food categories. Goal-setting led to greater improvement of food categories within the PNA group including; unsalted nuts ( p < 0.0001), fruits ( p = 0.0001), whole grains ( p = 0.005), fish ( p = 0.0001), dairy ( p = 0.007), vegetables ( p = 0.01) and unhealthy choices ( p = 0.02). In a healthy population, participants receiving PNA changed their food-intake behavior more favorably than participants receiving GNA or no advice. When personal goals were set, nutritional behavior was more prone to change.

The aim of this explorative study is to evaluate whether personalized compared to generic lifestyle advice improves wellbeing in a senior population. We conducted a nine-week single-blind randomized controlled trial including 59 participants (age 67.7 ± 4.8 years) from Wageningen and its surrounding areas in the Netherlands. Three times during the intervention period, participants received either personalized advice (PA), or generic advice (GA) to improve lifestyle behavior. Personalization was based on metabolic health measures and dietary intake resulting in an advice that highlighted food groups and physical activity types for which behavior change was most urgent. Before and after the intervention period self-perceived health was evaluated as parameter of wellbeing using a self-perceived health score (single-item) and two questionnaires (Vita-16 and Short Form-12). Additionally, anthropometry and physical functioning (short physical performance battery, SPPB) were assessed. Overall scores for self-perceived health did not change over time in any group. Resilience and motivation (Vita-16) slightly improved only in the PA group, whilst mental health (SF-12) and energy (Vita-16) showed slight improvement only in the GA group. SPPB scores improved over time in both the PA and GA group. PA participants also showed a reduction in body fat percentage and hip circumference, whereas these parameters increased in the GA group Our findings suggest that although no clear effects on wellbeing were found, still, at least on the short term, personalized advice may evoke health benefits in a population of seniors as compared to generic advice., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

From a biological view, most of the processes involved in insulin resistance, which drives the pathobiology of type 2 diabetes, are reversible. This theoretically makes the disease reversible and curable by changing dietary habits and physical activity, particularly when adopted early in the disease process. Yet, this is not fully implemented and exploited in health care due to numerous obstacles. This article reviews the state of the art in all areas involved in a diabetes cure-focused therapy and discusses the scientific and technological advancements that need to be integrated into a systems approach sustainable lifestyle-based healthcare system and economy. The implementation of lifestyle as cure necessitates personalized and sustained lifestyle adaptations, which can only be established by a systems approach, including all relevant aspects (personalized diagnosis and diet, physical activity and stress management, self-empowerment, motivation, participation and health literacy, all facilitated by blended care and ehealth). Introduction of such a systems approach in type 2 diabetes therapy not only requires a concerted action of many stakeholders but also a change in healthcare economy, with new winners and losers. A "call for action" is put forward to actually initiate this transition. The solution provided for type 2 diabetes is translatable to other lifestyle-related disorders.

Personalized nutrition is fast becoming a reality due to a number of technological, scientific, and societal developments that complement and extend current public health nutrition recommendations. Personalized nutrition tailors dietary recommendations to specific biological requirements on the basis of a person's health status and goals. The biology underpinning these recommendations is complex, and thus any recommendations must account for multiple biological processes and subprocesses occurring in various tissues and must be formed with an appreciation for how these processes interact with dietary nutrients and environmental factors. Therefore, a systems biology-based approach that considers the most relevant interacting biological mechanisms is necessary to formulate the best recommendations to help people meet their wellness goals. Here, the concept of "systems flexibility" is introduced to personalized nutrition biology. Systems flexibility allows the real-time evaluation of metabolism and other processes that maintain homeostasis following an environmental challenge, thereby enabling the formulation of personalized recommendations. Examples in the area of macro- and micronutrients are reviewed. Genetic variations and performance goals are integrated into this systems approach to provide a strategy for a balanced evaluation and an introduction to personalized nutrition. Finally, modeling approaches that combine personalized diagnosis and nutritional intervention into practice are reviewed., (© The Author(s) 2017. Published by Oxford University Press on behalf of the International Life Sciences Institute.)