Abstract
Obesity and overweight remain major global public health challenges. In this paper, we examine current applications of digital nutrition technologies in weight management using the scoping review framework of Arksey and O'Malley. Visual recognition technologies have evolved from early-stage food image recognition into more advanced systems, although the vast diversity of food types continues to pose challenges for accuracy and generalizability. The combination of wearable devices and non-wearable sensors has increased the diversity of collected data and improved user comfort. Data integration and analysis tools, such as digital virtual twins, support personalized interventions by integrating multidimensional data. In addition, emerging applications have demonstrated encouraging clinical outcomes. However, existing studies have limitations, such as small sample sizes, high levels of information bias, and low evidence quality. Barriers, such as limited accessibility and high initial costs, further constrain the scalability of digital nutrition tools. Future studies should focus on expanding sample sizes, improving population diversity, and developing effective assessment methods and adjustments for specific target populations. Privacy and data security concerns must also be addressed to ensure safe implementation. Overall, digital nutrition technologies offer a promising approach to weight management, but continuous efforts in research and cost reduction are needed to fully realize their potential in weight management and to promote health for all.