Key result
Workplace screening program linked to a consistent drop in hypertension prevalence over five years.
Why the study?
Workplaces offer key opportunities for early hypertension diagnosis and treatment, but innovative machine learning approaches are underutilized to evaluate workplace screening programs.
Population
1, 723 samples or data values from university workforce employees
Design
Longitudinal study
Authors
Loading...
ML monitoring of workplace hypertension trends is feasible; leaves open causal impact and generalizability.
Observational (n=1,723)
No
Machine learning algorithms can feasibly and sustainably evaluate workplace health screening initiatives to monitor hypertension trends over time.
Adeleke et al. (2024) conducted an observational in Hypertension (n=1,723). Machine learning evaluation of workplace screening was evaluated on Blood pressure status (low, normal, and high). Machine learning evaluation of a 5-year university workplace screening program (N=1,723, mean age 42) demonstrated a consistent drop in hypertension prevalence from 2018 to 2022.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: