Mobile application with neural networks to promote the occupational health of academic staff Aplicación móvil con redes neuronales para fomentar la salud laboral del personal académico

Abstract

University faculty often face intense work environments that frequently lead to emotional exhaustion, musculoskeletal problems, and prolonged stress, impacting their personal well-being and the quality of their teaching. At the El Carmen campus of the Eloy Alfaro Lay University of Manabí, there is a lack of digital platforms to detect individualized threats and mechanisms for ongoing monitoring of faculty well-being. This research aimed to create a mobile app supported by neural networks to monitor and promote occupational hygiene among academic staff. To achieve this, inductive and deductive approaches were used, including a comprehensive census of all 100 educators using Likert scale questionnaires, supplemented by semi-structured dialogues with five program leaders. Technical development was carried out using Kotlin and Jetpack Compose, within the SCRUM framework and in two-week cycles. The findings showed that 58% of the group does not manage anxiety well, 42% have limited knowledge of hazard avoidance, and 56% follow static routines. The tool comprises nine detailed forms that analyze aspects of workplace health, based on Firebase and featuring a dashboard with dynamic graphics. Ultimately, the tool achieved a 96% integration rate, a user satisfaction score of 4.2 out of 5.0, 94% agreement with professional assessments, and a 99.83% reduction in the number of incidents during the analysis period compared to traditional techniques, establishing itself as a robust resource for tailored prevention strategies