Office workers are exposed to a range of occupational health risks, including prolonged sedentary behaviour, postural load, elevated heart rate, and noise, yet objective and continuous monitoring of these risk factors in workplace settings remains uncommon. This study aimed to co-design occupational health visualisations based on smartphone and smartwatch data, through a multi-stakeholder group of office workers and occupational health professionals. A generative co-design framework was applied, comprising a pre-design phase with a field study and questionnaire, a structured multi-stakeholder workshop, and a follow-up evaluation session. Thematic analysis of the workshop transcript yielded 17 occupational health themes, which were subsequently assessed for technical feasibility relative to the available sensing platform. Of the 27 discrete visualisation elements proposed across both groups, the majority were classified as directly addressable using smartphone and smartwatch sensor data. Visualisations covering physical activity, heart rate, environmental noise exposure, and postural load were implemented in Python using real-world data collected from office workers. The follow-up session provided qualitative confirmation that the developed visualisations were interpretable and aligned with the stakeholder expectations. The generative co-design framework proved well-suited to the occupational health visualisation context, enabling structured translation of stakeholder requirements into technically feasible and interpretable visualisation outputs.

Office workers are exposed to a range of occupational health risks, including prolonged sedentary behaviour, postural load, elevated heart rate, and noise, yet objective and continuous monitoring of these risk factors in workplace settings remains uncommon. This study aimed to co-design occupational health visualizations based on smartphone and smartwatch data, through a multi-stakeholder group of office workers and occupational health professionals. A generative co-design framework was applied, comprising a pre-design phase with a field study and questionnaire, a structured multi-stakeholder workshop, and a follow-up evaluation session. Thematic analysis of the workshop transcript yielded 17 occupational health themes, which were subsequently assessed for technical feasibility relative to the available sensing platform. Of the 27 discrete visualization elements proposed across both groups, the majority were classified as directly addressable using smartphone and smartwatch sensor data. Visualizations covering physical activity and sedentary behaviour, heart rate, environmental noise exposure, and postural load were implemented in Python using real-world data collected from office workers. The follow-up session confirmed the interpretability and clarity of the developed visualizations. The generative co-design framework proved well-suited to the occupational health visualization context, enabling structured translation of stakeholder requirements into technically feasible and interpretable visualization outputs.

Background and Objective: According to the literature, poor sleep quality in the police work has an impact on health, performance and safety of these professionals. The study sought to evaluate the sleep quality and the influence of shift work in a sample of municipal police officers. Method: Exploratory, cross-sectional study, carried out through an online questionnaire, in a convenience sample of (n=50) municipal police officers. Sleep quality was assessed using the PSQI index, by applying the Pittsburgh Sleep Quality Index questionnaire. Results: The mean PSQI index was 8,6 ± 3,9, and 78,6% of the respondents had poor sleep quality (PSQI > 5). The number of hours slept was on average 5,2 ± 1,1 hours, below the recommended level, and a significant percentage of respondents (16,7%) took sleep medication at least once a week. The police officers who work shifts had a mean PSQI index tending to be higher than that of colleagues who have a stable daytime schedule, but the difference was not significant (p-value > 0,05). Conclusions: Given the observed poor sleep quality in the sample and the possible relation with shift work, action measures are proposed, in order to promote health and safety of municipal police officers.