The role of Technology and machine learning to monitor diet in older adults; learning lessons to develop a new prototype

Citation

Connelly J & Whittaker AC (2026) The role of Technology and machine learning to monitor diet in older adults; learning lessons to develop a new prototype. Proceedings of the Nutrition Society. https://doi.org/10.1017/s0029665125102097

Abstract
Positive food consumption remains one of the most common challenges among older adults in the UK with at least 10% in community settings and up to 45% in care homes affected by malnutrition. It is strongly associated with frailty, functional and health decline. Tracking and understanding the impact of diet is not easy. There are problems with monitoring diet and malnutrition screening such as difficulty remembering, lack of time, or needing a dietician to interpret the results. Computerised tailored education may be a positive solution to these issues. Due to the rise in smartphone ownership the use of technology to monitor diet is becoming more popular. This review paper will aim to look at the issues with current methods of dietary monitoring particularly in older adults, it will present the benefits and barriers of using to monitor food intake. It will discuss how a photo food monitoring app was developed to address the current issues with technology and how it was tested with older adults living in community and care settings. The prototype was co-developed and incorporated automated food classification to monitor dietary intake and food preferences and tested with older adults. The prototype was usable to both older adults and care workers and feedback on how to improve its use was collected. Key design improvements to make it quicker and more accurate were suggested for future testing in this population. With adaptions this prototype could be beneficial to older adults living in both community and care settings.

Keywords
Digital technology; Machine learning; Dietary monitoring; Older adults

Journal
Proceedings of the Nutrition Society

• The role of Technology and machine learning to monitor diet in older adults; learning lessons to develop a new prototype.