Statistical evaluation of the effectiveness of an automated physical condition monitoring system for higher education students based on cloud scripts
DOI:
https://doi.org/10.32782/2077-6594/2026.3/01Keywords:
digital interventions, body mass index (BMI), Google Apps Script, physical condition monitoring, higher educationAbstract
Purpose is to develop, scientifically substantiate, and statistically verify the effectiveness of an automated system for personalized monitoring and correction of higher education students’ physical condition based on cloud low-code technologies integrated into the Google Workspace ecosystem. Materials and methods. The experimental study was conducted at the Communal Institution of Higher Education “Kremenchuk Humanitarian and Technological Academy” within the framework of international scientific collaboration with the National University of Mar del Plata (Argentina). A total of 120 higher education students were randomly assigned to an experimental group (EG, n = 60) and a control group (CG, n = 60). The monitoring system was developed using Google Workspace and Google Apps Script. Statistical analysis was performed using jamovi 2.6.44 software with Pearson’s chi-square test, the Wilcoxon signed-rank test, and Spearman’s rank correlation coefficient. Results. The personalized automated intervention significantly improved students’ physical condition. A statistically significant reduction in BMI was observed in the experimental group (Wilcoxon W = 4856, p < .001, rB = 0.813), whereas no meaningful changes were found in the control group. A strong positive correlation was identified between students’ digital engagement and BMI reduction (rs = 0.744, p < .001), confirming the effectiveness of systematic personalized digital support. Conclusions. The developed cloud-based monitoring system proved to be an effective, scalable, and cost-efficient tool for supporting students’ health-preserving behavior. The integration of Google Apps Script into the institutional digital environment enables continuous monitoring, automated personalized feedback, and optimization of teachers’ workload without requiring additional software or server infrastructure.
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