Empowering citizens as frontline screeners: a framework with blended learning for cardiovascular risk assessment
Year: 2025
Type: article
Study design: Clinical Trial
Abstract: Population-wide screening for cardiovascular risk factors (CVRF) is challenging, given the shortage of healthcare professionals. Laypeople may aid in CVRF screening. We therefore investigated a blended training programme to conduct a basic CVRF assessment, including point-of-care testing (PoC). We conducted a pre-experimental study with one-group pretest–posttest design with repeated measures between September and December 2024. Fourteen laypeople completed a training on measuring body mass index (BMI), blood pressure and PoC testing of blood glucose and lipids. Each tester conducted screenings on five different individuals, using a mobile application for guidance. We evaluated their practical skills and benchmarked their performance to experts. Feasibility was 100% for BMI and blood pressure assessment. From the first to the fifth screening, feasibility for blood glucose rose from 92.9 to 100% and for lipids from 78.6 to 92.9%. Average test time dropped from 20.7 to 16.9 min (P = 0.011), approaching the 16.6 min of the experts (P = 0.49). Practical skills improved from 88.8% at the first to 97.2% at the fifth screening (P = 0.004). Agreement with experts was excellent for BMI (mean relative bias: − 0.5%) and blood pressure (e.g. systolic: +2.9%), reasonable for blood glucose (− 0.1%), total cholesterol (+ 1.3%) and HDL cholesterol (+ 9.7%), but poor for triglycerides (− 16.8%). Self-rated confidence and skilfulness increased by the final session. In conclusion, laypeople reliably assessed CVRF with blended training, intuitive equipment and a mobile screening assistant. Within five sessions, agreement with experts was acceptable for all measurements but PoC-derived triglycerides. Future studies should assess the real-world scalability of citizen-conducted screenings.
Source: Scientific Reports
Authors: Roxane Brouwers, Evangelos Ntalianis, Ella Wils, Everton Santana, Tatiana Kuznetsova, Nicholas Cauwenberghs
Institution: KU Leuven
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: Impetus, G1180579369, G2360019775, Integrative personalized computer modeling for better cardiovascular health assessment, G2779407361, G3969155478, G4009809703, Machine learning platform for better cardiovascular health assessment and risk stratification, G4324373187, G4718095871, G7046444663, G7199402522, G7724036520
OpenAlex ID: W4417134638