Background: Accurate measurement of food and nutrient intake is crucial for nutrition research, dietary surveillance, and disease management, but traditional methods such as 24-hour dietary recalls, food diaries, and food frequency questionnaires are often prone to recall error and social desirability bias, limiting their reliability. With the advancement of artificial intelligence (AI), there is potential to overcome these limitations through automated, objective, and scalable dietary assessment techniques. However, the effectiveness and challenges of AI applications in this domain remain inadequately explored. Objective: This study aimed to conduct a scoping review to synthesize existing literature on the efficacy, accuracy, and challenges of using AI tools in assessing food and nutrient intakes, offering insights into their current advantages and areas of improvement. Methods: This review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A comprehensive literature search was conducted in 4 databases—PubMed, Web of Science, Cochrane Library, and EBSCO—covering publications from the databases’ inception to June 30, 2023. Studies were included if they used modern AI approaches to assess food and nutrient intakes in human subjects. Results: The 25 included studies, published between 2010 and 2023, involved sample sizes ranging from 10 to 38,415 participants. These studies used a variety of input data types, including food images (n=10), sound and jaw motion data from wearable devices (n=9), and text data (n=4), with 2 studies combining multiple input types. AI models applied included deep learning (eg, convolutional neural networks), machine learning (eg, support vector machines), and hybrid approaches. Applications were categorized into dietary intake assessment, food detection, nutrient estimation, and food intake prediction. Food detection accuracies ranged from 74% to 99.85%, and nutrient estimation errors varied between 10% and 15%. For instance, the RGB-D (Red, Green, Blue-Depth) fusion network achieved a mean absolute error of 15% in calorie estimation, and a sound-based classification model reached up to 94% accuracy in detecting food intake based on jaw motion and chewing patterns. In addition, AI-based systems provided real-time monitoring capabilities, improving the precision of dietary assessments and demonstrating the potential to reduce recall bias typically associated with traditional self-report methods. Conclusions: While AI demonstrated significant advantages in improving accuracy, reducing labor, and enabling real-time monitoring, challenges remain in adapting to diverse food types, ensuring algorithmic fairness, and addressing data privacy concerns. The findings suggest that AI has transformative potential for dietary assessment at both individual and population levels, supporting precision nutrition and chronic disease management. Future research should focus on enhancing the robustness of AI models across diverse dietary contexts and integrating biological sensors for a holistic dietary assessment approach.

Background: Mild cognitive impairment (MCI) represents a prodromal stage of dementia, characterized by cognitive decline exceeding that expected with normal aging. Exercise interventions have emerged as a promising approach to counter functional decline and enhance cognitive function in the elderly MCI population. However, the optimal exercise modalities and dosage (dose-response relationship) are understudied.Objective: It aims to determine the most effective exercise modality for MCI patients by optimizing the dose-response relationship to ensure sufficient intensity to induce positive neurological adaptations.Methods: A systematic search of electronic databases, including PubMed, Embase, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials was conducted from inception to April 15, 2024. Studies evaluating the efficacy of exercise interventions in MCI participants were included. Primary outcomes of interest are global cognition and executive function. Random-effects models will be utilized for both pairwise and network meta-analysis.Results: Following the application of specific inclusion and exclusion criteria, a total of 42 articles, encompassing 2832 participants, were chosen for inclusion in a network meta-analysis. The findings revealed that multi-component exercise demonstrated superior efficacy in mitigating the deterioration of global cognition, as evidenced by standard mean differences (SMDs) of 1.09 (95% CI: 0.68 to 1.51) compared to passive controls. Additionally, multi-component exercise exhibited a significant impact on executive function, with SMDs of 2.50 (95% CI: 0.88 to 4.12) when contrasted with passive controls. Our research has demonstrated that sessions lasting 30 minutes, occurring 3-4 times per week, with interventions lasting 12-24 weeks and an intensity of 60-85% of maximum heart rate, yield higher effect sizes in improving global cognition. However, sessions lasting 30-61 minutes, with interventions lasting 25 weeks or longer, show greater effectiveness in enhancing executive function.Conclusion: A network meta-analysis identified multi-component exercise as the most effective intervention for improving global cognitive and executive function in patients with mild cognitive impairment. Notably, moderate-intensity exercise performed at least three times weekly appears beneficial, with evidence suggesting shorter sessions and higher frequencies may optimize cognitive outcomes.Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO, identifier CRD42024534922.

Background: As a primary source of added sugars in the US diet, sugar-sweetened beverage (SSB) consumption is presumed to contribute to obesity prevalence and poor oral health. We systematically synthesized and quantified evidence from US-based natural experiments concerning the impact of SSB taxes on beverage prices, sales, purchases, and consumption.Methods: A keyword and reference search was performed in PubMed, Web of Science, Cochrane Library, Scopus, and EconLit from the inception of an electronic bibliographic database to Oct 31, 2022. Meta-analysis was conducted to estimate the pooled effect of soda taxes on SSB consumption, prices, passthrough rate, and purchases.Results: Twenty-six natural experiments, all adopting a difference-in-differences approach, were included. Studies assessed soda taxes in Berkeley, Oakland, and San Francisco in California, Philadelphia in Pennsylvania, Boulder in Colorado, Seattle in Washington, and Cook County in Illinois. Tax rates ranged from 1 to 2 ¢/oz. The imposition of the soda tax was associated with a 1.06 ¢/oz. (95% confidence interval [CI] = 0.90, 1.22) increase in SSB prices and a 27.3% (95% CI = 19.3, 35.4%) decrease in SSB purchases. The soda tax passthrough rate was 79.7% (95% CI = 65.8, 93.6%). A 1 ¢/oz. increase in soda tax rate was associated with increased prices of SSBs by 0.84 ¢/oz (95% CI = 0.33, 1.35).Conclusion: Soda taxes could be effective policy leverage to nudge people toward purchasing and consuming fewer SSBs. Future research should examine evidence-based classifications of SSBs, targeted use of revenues generated by taxes to reduce health and income disparities, and the feasibility of redesigning the soda tax to improve efficiency.

Background: Nuts are nutrient-dense foods that contribute to healthier eating. Food image datasets enable artificial intelligence (AI) powered diet-tracking apps to help people monitor daily eating patterns. Aim: This study aimed to create an image dataset of commonly consumed nut types and use it to build an AI computer vision model to automate nut type classification tasks. Methods: iPhone 11 was used to take photos of 11 nut types—almond, brazil nut, cashew, chestnut, hazelnut, macadamia, peanut, pecan, pine nut, pistachio, and walnut. The dataset contains 2200 images, 200 per nut type. The dataset was randomly split into the training (60% or 1320 images), validation (20% or 440 images), and test sets (20% or 440 images). A neural network model was constructed and trained using transfer learning and other computer vision techniques—data augmentation, mixup, normalization, label smoothing, and learning rate optimization. Results: The trained neural network model correctly predicted 338 out of 440 images (40 per nut type) in the validation set, achieving 99.55% accuracy. Moreover, the model classified the 440 images in the test set with 100% accuracy. Conclusion: This study built a nut image dataset and used it to train a neural network model to classify images by nut type. The model achieved near-perfect accuracy on the validation and test sets, demonstrating the feasibility of automating nut type classification using smartphone photos. Being made open-source, the dataset and model can assist the development of diet-tracking apps that facilitate users’ adoption and adherence to a healthy diet.