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Obesity is a global health challenge marked by substantial inter-individual differences in responses to dietary and lifestyle interventions. Traditional weight loss strategies often overlook critical biological variations in genetics, metabolic profiles, and gut microbiota composition, contributing to poor adherence and variable outcomes. Our primary aim is to identify key biological and behavioral effectors relevant to precision medicine for weight control, with a particular focus on nutrition, while also discussing their current and potential integration into digital health platforms. Thus, this review aligns more closely with the identification of influential factors within precision medicine (e.g., genetic, metabolic, and microbiome factors) but also explores how these factors are currently integrated into digital health tools. We synthesize recent advances in nutrigenomics, nutritional metabolomics, and microbiome-informed nutrition, highlighting how tailored dietary strategies-such as high-protein, low-glycemic, polyphenol-enriched, and fiber-based diets-can be aligned with specific genetic variants (e.g., FTO and MC4R), metabolic phenotypes (e.g., insulin resistance), and gut microbiota profiles (e.g., abundance, SCFA production). In parallel, digital health tools-including mobile health applications, wearable devices, and AI-supported platforms-enhance self-monitoring, adherence, and dynamic feedback in real-world settings. Mechanistic pathways such as gut-brain axis regulation, microbial fermentation, gene-diet interactions, and anti-inflammatory responses are explored to explain inter-individual differences in dietary outcomes. However, challenges such as cost, accessibility, and patient motivation remain and should be addressed to ensure the effective implementation of these integrated strategies in real-world settings. Collectively, these insights underscore the pivotal role of precision nutrition as a cornerstone for personalized, scalable, and sustainable obesity interventions.
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http://dx.doi.org/10.3390/nu17162695 | DOI Listing |
BMC Med Inform Decis Mak
September 2025
Emergency Department, Helios Spital, Überlingen, Germany.
Background: The increasing amount of data routinely collected on ICUs poses a challenge for clinicians which is aggravated with data-heavy therapies like Continuous Kidney Replacement Therapy (CKRT). We developed the CKRT Supporting Software Prototype (CKRT-SSP), a clinical decision support system for use before, during and after CKRT. The aim of this user experience (UX) study was to prospectively evaluate CKRT-SSP in terms of usability, user experience, and workload in a simulated ICU setting.
View Article and Find Full Text PDFBMC Rheumatol
September 2025
Department of Environment and Biosciences, School of Business, Innovation and Sustainability, Halmstad University, Halmstad, Sweden.
J Orthop Res
September 2025
Interdisciplinary Orthopedics, Department of Orthopedics Surgery, Aalborg University Hospital, Aalborg, Denmark.
Functional recovery after total knee arthroplasty (TKA) varies widely among individuals, and traditional assessments often fail to detect subtle changes in real-world walking ability. Wearable sensors offer continuous and objective tracking of gait outside of clinical settings. In this prospective, longitudinal study, thirty-one patients undergoing unilateral TKA wore thigh-mounted accelerometers continuously from 2 weeks before surgery through 90 days postoperatively.
View Article and Find Full Text PDFNeurol Sci
September 2025
Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.
The rapid evolution of digital tools in recent years after COVID-19 pandemic has transformed diagnostic and therapeutic practice in neurology. This shift has highlighted the urgent need to integrate digital competencies into the training of future specialists. Key innovations such as telemedicine, artificial intelligence, and wearable health technologies have become central to improving healthcare delivery and accessibility.
View Article and Find Full Text PDFJ Imaging Inform Med
September 2025
Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
Large language models (LLMs) have been successfully used for data extraction from free-text radiology reports. Most current studies were conducted with LLMs accessed via an application programming interface (API). We evaluated the feasibility of using open-source LLMs, deployed on limited local hardware resources for data extraction from free-text mammography reports, using a common data element (CDE)-based structure.
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