Abeltino Alessio, Riente Alessia, Bianchetti Giada, Serantoni Cassandra, De Spirito Marco, Capezzone Stefano, Esposito Rosita, Maulucci Giuseppe
Department of Neuroscience, Metabolic Intelligence Lab, Università Cattolica del Sacro Cuore, Rome, Italy.
Complex operational unit of Physics for life science, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.
Nutr Rev. 2025 Feb 1;83(2):e574-e601. doi: 10.1093/nutrit/nuae035.
The objective of this review was to critically examine existing digital applications, tailored for use by citizens and professionals, to provide diet monitoring, diet planning, and precision nutrition. We sought to identify the strengths and weaknesses of such digital applications, while exploring their potential contributions to enhancing public health, and discussed potential developmental pathways. Nutrition is a critical aspect of maintaining good health, with an unhealthy diet being one of the primary risk factors for chronic diseases, such as obesity, diabetes, and cardiovascular disease. Tracking and monitoring one's diet has been shown to help improve health and weight management. However, this task can be complex and time-consuming, often leading to frustration and a lack of adherence to dietary recommendations. Digital applications for diet monitoring, diet generation, and precision nutrition offer the promise of better health outcomes. Data on current nutrition-based digital tools was collected from pertinent literature and software providers. These digital tools have been designed for particular user groups: citizens, nutritionists, and physicians and researchers employing genetics and epigenetics tools. The applications were evaluated in terms of their key functionalities, strengths, and limitations. The analysis primarily concentrated on artificial intelligence algorithms and devices intended to streamline the collection and organization of nutrition data. Furthermore, an exploration was conducted of potential future advancements in this field. Digital applications designed for the use of citizens allow diet self-monitoring, and they can be an effective tool for weight and diabetes management, while digital precision nutrition solutions for professionals can provide scalability, personalized recommendations for patients, and a means of providing ongoing diet support. The limitations in using these digital applications include data accuracy, accessibility, and affordability, and further research and development are required. The integration of artificial intelligence, machine learning, and blockchain technology holds promise for improving the performance, security, and privacy of digital precision nutrition interventions. Multidisciplinarity is crucial for evidence-based and accessible solutions. Digital applications for diet monitoring and precision nutrition have the potential to revolutionize nutrition and health. These tools can make it easier for individuals to control their diets, help nutritionists provide better care, and enable physicians to offer personalized treatment.
本综述的目的是严格审查现有的数字应用程序，这些应用程序是为公民和专业人员量身定制的，用于提供饮食监测、饮食规划和精准营养。我们试图确定此类数字应用程序的优势和劣势，同时探索它们对促进公众健康的潜在贡献，并讨论潜在的发展途径。营养是保持健康的关键因素，不健康的饮食是肥胖、糖尿病和心血管疾病等慢性病的主要风险因素之一。已证明跟踪和监测个人饮食有助于改善健康状况和体重管理。然而，这项任务可能复杂且耗时，常常导致挫败感以及对饮食建议的依从性不足。用于饮食监测、饮食生成和精准营养的数字应用程序有望带来更好的健康结果。从相关文献和软件提供商处收集了有关当前基于营养的数字工具的数据。这些数字工具是为特定用户群体设计的：公民、营养师以及使用遗传学和表观遗传学工具的医生和研究人员。根据其关键功能、优势和局限性对这些应用程序进行了评估。分析主要集中在旨在简化营养数据收集和整理的人工智能算法和设备上。此外，还对该领域未来可能的进展进行了探索。为公民设计的数字应用程序允许进行饮食自我监测，它们可以成为体重和糖尿病管理的有效工具，而针对专业人员的数字精准营养解决方案可以提供可扩展性、为患者提供个性化建议以及提供持续饮食支持的手段。使用这些数字应用程序的局限性包括数据准确性、可及性和可承受性，需要进一步的研究和开发。人工智能、机器学习和区块链技术的整合有望改善数字精准营养干预措施的性能、安全性和隐私性。多学科性对于基于证据且易于获取的解决方案至关重要。用于饮食监测和精准营养的数字应用程序有可能彻底改变营养和健康领域。这些工具可以使个人更容易控制饮食，帮助营养师提供更好的护理，并使医生能够提供个性化治疗。
