Impact of an AI-enabled UV Hand Hygiene Intervention in Primary Schools: a Community Intervention Study

Overview

Background Hand hygiene refers to the practice of keeping hands clean to prevent the spread of disease. Hand hygiene workshops are an important tool to educate students about the importance of keeping their hands clean and healthy. Objectives The primary objective of this project is to evaluate the effectiveness of an educational intervention previously used in other settings that incorporates a fluorescence-based device to quantify the quality of hand hygiene in real time, together with a training workshop for students, with the aim of improving health promotion and prevention in the short and medium term (1, 3, and 6 months). Methods This is a sub-study of the Sentinel Schools Project. It is a controlled before-after study with school level allocation using a single matched pair (1 intervention school; 1 control school) and repeated measures conducted in schools belonging to the Sentinel Schools network within the Camp de Tarragona health region. The study population comprises students enrolled in primary education at these schools. The project field team carried out a pre-intervention assessment of handwashing quality in both study arms using a device that uses fluorescence to detect adequately washed areas of the hand. In the intervention arm, an educational training session on hand hygiene was delivered, followed by post-intervention assessments at short and medium term in both study arms. To examine the acceptability of the intervention, an ad hoc questionnaire was administered to the teachers who supervise the participating classes and also focal groups and enterprises were made to access the acceptability of the intervention. Statistical Analysis Frequencies and percentages will be calculated for qualitative variables; for quantitative variables, the mean and standard deviation, as well as minimum and maximum values, will be reported. Ninety-five percent confidence intervals will be presented for means and for proportions. Bivariate analyses for quantitative variables will be performed, estimating Pearson correlation coefficients and/or linear regression.