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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.)

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.)