Several common measurement gaps have been identified, including the inability to record the setting, increased burden on participants, and excessive time or financial requirements for proper administration. Proxy reporting by various caregivers, biased reporting, and variations in human milk composition are potential sources of error, the impact of which remains unknown.
The development of artificial intelligence (AI)-based tools for data processing and analysis shows promise. However, it is crucial to consider individual, social, and systemic biases when constructing the algorithms underlying AI technologies. Novel digital technologies that passively and actively collect information are being developed. Yet, further refinement is necessary to ensure their adaptability for dietary assessment purposes.
To overcome these limitations, the researchers suggest employing bite-counting technology as a potential solution. By quantifying the number of bites taken during