BACKGROUND Malnutrition is a challenge among older adults and can result in serious health consequences. However, the dietary intake monitoring needed to identify malnutrition for early intervention is affected by issues like difficulty remembering or needing a dietitian to interpret the results. OBJECTIVE To co-design a tool using automated food classification to monitor dietary intake and food preferences, as well as food-related symptoms and mood and hunger ratings, for use in care homes. METHODS An advisory group co-designed the tool features. A small or medium enterprise (SME) developed a prototype tool where the user takes a photo of a meal pre- and post-eating, it links to a database of foods, and associates meals with symptoms and ratings. A second version was tested with older adults/care staff. Feedback refined a third version and a new advisory group suggested design adaptations for care homes. Four care homes tested this and identified barriers to usability and features to enhance value. Systematic text condensation was used to describe themes across the different types of data. RESULTS Key features identified included ratings of hunger, mood, and gastrointestinal symptoms that could be associated with eating specific foods, and a traffic light system to indicate risk. Issues included staff time, Wi-Fi connectivity, recognition of pureed food, and fortification. Different models for potential use and commercialisation were identified, including peer-use between residents to support less able residents, staff-only use of the tool, care home-personalised database menus for easy photo selection, and continued measurement in highest risk residents only using the traffic light system. CONCLUSIONS The tool was deemed useful for monitoring dietary habits and associated symptoms, but further design improvements that are needed were identified. These would need to be incorporated prior to formal evaluation of the tool as an intervention in this setting. Co-design was vital to help make the tool fit for the setting and users targeted.
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Connelly et al. (2024) studied this question.
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