Digital and technology-enabled approaches in dietary assessment: addressing bias, error, and feasibility in population- and community-based research

Dietary intake data are essential for understanding diet-disease relationships, informing policy, and evaluating nutrition interventions. This is particularly challenging in population-and community-based research, where varying dietary patterns, motivation to participate, and practical constraints limit the feasibility of highly controlled dietary assessment methods. Advances in digital tools, mobile technologies, and artificial intelligence are transforming how dietary data are collected, processed, and analyzed. This narrative review examines how digital and technology-enabled approaches impact key sources of bias and error across the stages of dietary assessment, including sample selection, participant reporting, food classification, and nutrient composition assignment. We also assess the feasibility of implementing these approaches in large-scale research settings, considering cost, infrastructure, respondent burden, and scalability. While many technologies have the potential to reduce specific sources of bias and measurement error, such as recall limitations, interviewer effects, and misclassification, they may simultaneously introduce new challenges, including selection bias, increased analytic complexity, and high development or implementation costs. These tradeoffs are most pronounced in large and heterogeneous study populations, where assessment methods must balance accuracy with feasibility across diverse contexts. Constraints related to connectivity, device access, and technical capacity may further limit implementation in low-resource settings. Technological innovations offer significant opportunities to improve dietary assessment, but their value depends on context-specific tradeoffs between accuracy, feasibility, and feasibility. Careful consideration of these factors is essential when selecting methods for population- and community-based nutrition research. STATEMENT OF SIGNIFICANCE: This review provides a synthesis of technological approaches to dietary data collection, processing, and analysis, evaluating their implications for bias, measurement error, and feasibility across population- and community-based research.