Using communication technologies to deliver public health agendas in National Health Service food and drink automated vending

This research responded to a National Health Service (NHS) wide problem. The problem is how to create healthier automated food and drink vending services. The research’s’ interpretation of this central research problem is embedded in the Facilities Management (FM) perspective. Vending retail products do not support government healthy lifestyle policies and initiatives. FMs have to change this through catering contracts. However, there is little guidance on how to design, evidence and operationalise improvement. The research tested vending point of sale designs over a year, trying to reduce the sale of unhealthy products. Secondly, it developed a novel application of a nutritional profile to enable the service design process and evidence change. Thirdly, the research baselined service level information through survey n=1,292. Night shift staff were a key stakeholder as it was thought that vending was their only retail catering and the impact was unknown. Regression modelling and multivariate analysis was used in the survey and design tests. Linear regression was used to understand the impact of vending point of sale design on sales. Logistic regression was used to test service level perceptions in the survey. The statistical methods used were flexible. The survey design and analysis is widely applicable to evaluate many services. The research found that in combination, changing product ranges, adding nutritional labels, and moving water to eye level significantly reduces unhealthy sales. However real change requires healthier vending products. The nutritional profile adapted is highly suitable to standardise service and evaluate how healthy vending products really are. The survey was a novel and statistically robust addition to FM service evaluation. It proved staff perception of poor catering, inadequate breaks, innutritious food and need for staff food education. Vending was central. Finally, making meaningful service improvements and setting thresholds in the statistical models confidently required in depth first-hand knowledge