Predictors of Cognitive Fuction in School Children: Screen Time, Nutritional Status and Pneumonia

Authors

(1) Program Studi Sarjana Kedokteran, Departemen Ilmu Kesehatan Masyaarakat, Fakultas Kedokteran Universitas Pembangunan Nasional Veteran Jakarta, Jakarta, Indonesia, ID
(2) Program Studi Sarjana Kedokteran, Departemen Etik Medikolegal, Fakultas Kedokteran Universitas Pembangunan Nasional Veteran Jakarta, Jakarta, Indonesia, ID
(3) Program Studi Sarjana Kedokteran, Departemen Ilmu Biologi, Fakultas Kedokteran Universitas Pembangunan Nasional Veteran Jakarta, Jakarta, Indonesia, ID
(4) Program Studi Sarjana Kedokteran, Fakultas Kedokteran Universitas Pembangunan Nasional Veteran Jakarta, Jakarta, Indonesia, ID
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How to Cite

Kristanti, M., Ramdhani, I., Putri Wahyuni, Y., Isra Rizqie, F., Khairunnisa, K., Devinta Putri Syafira, B., Fathiya Muthmainnah, N., Varisiya, D., & Anjani, A. (2026). Predictors of Cognitive Fuction in School Children: Screen Time, Nutritional Status and Pneumonia . Jurnal Ilmiah Kesehatan (JIKA), 8(2), 306-317. https://doi.org/10.36590/jika.v8i2.1990

Abstract

Cognitive development and intelligence of children are shaped by multifactorial influences, including genetic predisposition, environmental conditions, nutritional status, cognitive stimulation, educational exposure, and overall health status. The average Intelligence Quotient (IQ) of the Indonesian population is 89.96, which is categorized within the average range at the global level. This study aims to identify significant risk factors associated with lower cognitive function in children and to determine the optimal risk score threshold. A cross-sectional analytical study was conducted involving 80 school-aged children selected through simple random sampling. IQ was measured using the CFIT administered by child psychologists, while nutritional status was assessed BMI-for-age (BMI/U) criteria. This research we used logistic regression modeling to identify significant determinants, followed by ROCcurve analysis, Youden Index calculation, and risk score estimation—derived from the ratio of each significant predictor to the smallest significant coefficient. The result significant association is screen time (PR=5,23; 95% CI:1,49–18,29), history of pneumonia (PR=3,92; 95% CI:1,13–13,68), and poor nutritional status (PR=4,03; 95% CI:1,34–12,11). The predictive model showed an Area Under the Curve (AUC) of 0.794, with a Youden Index threshold of 2 (0,630–0,882), indicating the cut-off point for increased risk of lower intelligence in children. Screen time, a history of pneumonia, and poor nutritional status were identified as key predictors of lower cognitive function, with a risk score of  ≥ 2 as the threshold. Parental control over digital exposure and the reinforcement of preventive health behaviors are essential components in improving children’s cognitive outcomes.

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