Health-Holland

Nutrition is very important to keep blood sugar levels balanced. If blood sugar levels are too high, it can lead to diseases such as cardiovascular disease and type 2 diabetes (T2DM). Therefore, adjusting what one eats, also called a diet or nutritional intervention, can help prevent these diseases. However, not everyone responds the same to a diet. In about 30% of people, a diet does not work as hoped. This can be due to various reasons, such as a person's metabolism, genetic predisposition, the composition of the food one eats, or the bacteria in the intestines. Everyday things like sleep, stress, and movement also play a role. The investigators used a computer model to classify people with overweight and obesity into groups based on these factors. The investigators call such a group a 'Metabolic Phenotype', or in short 'Metabotype'. Based on the Metabotype, a personalised diet was developed (personalised nutrition intervention) that may better suit each person's unique situation.
The investigators hypothesize that a precision nutrition intervention, tailored to Metabotypes identified through unsupervised clustering (using the aforementioned computer model) of predefined, accurate features related to cardiometabolic health-specifically, tissue-specific glucose and lipid metabolism and detailed body composition-will enhance blood glucose homeostasis, reduce cardiometabolic risk, and improve adherence to the intervention and mental well-being, compared to population-based dietary guidelines. The present project will contribute to targeted and efficient precision-based dietary strategies for individuals at increased risk of T2DM.

The objective of this study, Vital@Work reintegration program, is to support sick-listed workers with stress-related complaints in their return to work based on a personalized program. Therefore, the aim of this study is test whether participants supported by a tailored eHealth program, and if needed, additionally supported by a structured and stepwise Participatory Approach (PA) involving the sick-listed worker, their direct supervisor and a neutral party, show a faster and sustainable return to work as compared to participants in the control condition. This program is investigated in four different organizations, which differ in sector, size (small and large organizations), type of organization (private or public) and type of work.

The development of a machine learning algorithm that predicts American Society of Anesthesiologist-Physical Status (ASA-PS) based on preoperative variables would not only improve clinical decision-making in patient risk stratification but also offer a more reliable tool for administrative and regulatory uses. Therefore, the development of such a machine learning tool presents a significant opportunity to advance both the science and practice of perioperative care. Incorporating medication use into the algorithm could further enhance its predictive power, as it is closely linked to systemic disease. This addition could help refine the ASA-PS classification, making it an even more valuable tool in the clinical setting.