Children, Vol. 12, Pages 744: Predictors of Static Postural Loading in Primary-School-Aged Children: Comparing Elastic Net and Multiple Regression Methods

Children doi: 10.3390/children12060744

Authors:
Mohammad Ali Mohseni Bandpei
Reza Osqueizadeh
Hamidreza Goudrazi
Nahid Rahmani
Abbas Ebadi

Background/Objectives: Adverse effects of a sedentary lifestyle on an individual’s overall health are inevitable. With reference to primary-school-aged children, the establishment of effective postural hygiene is critical as it not only promotes optimal musculoskeletal development but also significantly influences their long-term well-being and productivity. This study aimed to develop and internally validate a regularized regression model to predict static postural loading (SPL) in primary school children. Methods: The outcome and predictors of SPL were shortlisted through a systematic review of the literature and expert panels. Data were derived from 258 primary school children. We developed regularized elastic net (EN) and used multiple linear regression (MLR) as a reference. Both models were fitted through five-fold cross-validation with 10 iterations. The grid search technique was used to find the optimal combination of hyperparameters α and λ for the EN. We conducted a permutation importance analysis to obtain and compare predictor rankings for each model. Results: Both models presented a good and comparable fit, with the EN marginally outperforming the MLR in error metrics. Postural risk, sedentary behavior, task duration, and BMI were the most important predictors of SPL in primary school children. Conclusions: The proof of a direct impact of a sedentary lifestyle on children’s overall health is both credible and alarming. Hence, proper identification and management of contributing factors to static postural loading in this age group is critical. In various clinical settings, where the objective is to develop a model that accurately forecasts the outcome, advanced regularized regression methods have evidently shown great performance.

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Mohammad Ali Mohseni Bandpei www.mdpi.com