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Please use this identifier to cite or link to this item:
http://hdl.handle.net/10174/40026
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| Title: | Determinants of Screen time in children aged 6-7 years: A multinomial logistic regression approach |
| Authors: | Figueira, Bruno Gonçalves, Bruno Silva, Rodrigo Almeida, Gabriela |
| Issue Date: | Oct-2025 |
| Abstract: | Screen time is an emerging determinant of health behaviours in childhood. Because screen exposure arises from intersecting individual, behavioural, and contextual influences, it benefits from multidimensional analytic approaches. Machine learning and predictive modelling can help to identify how these factors jointly affect daily screen exposure. This study aimed to identify predictors of screen time categories in children aged 6–7 years. A sample of 370 children (51.9% girls) was assessed. Screen time was classified into three categories: <1h/day (49.4%), 1–2.9h/day (43.8%), and ≥3h/day (6.7%). Independent variables included age, sex, BMI, waist-to-height ratio, motor competence (measured through Motor Competence Assessment (MCA) instrument), cardiorespiratory fitness (measured by 20 m shuttle run test), moderate-to-vigorous physical activity (MVPA), sedentary time, and sleep parameters (assessed with accelerometery). Predictors were analysed using multinomial logistic regression. The model was statistically significant (χ²(20) = 33.65, p= 029; Nagelkerke R² = .286). Sedentary behaviour was a significant predictor of screen time: each additional unit was associated with a 20% greater likelihood of belonging to the 1–2.9h/day group compared to <1h/day (p= .013; OR = 1.20, 95% CI: 1.04–1.38). Sex also differentiated groups: girls were less likely than boys to belong to the 1–2.9h/day group (p=.001; OR=0.22, 95% CI: 0.08–0.55). No significant predictors were identified for the ≥3h/day group, likely due to its small size (n = 5). Other variables (BMI, MCA, fitness, MVPA, sleep) were not significant. Sedentary behaviour and sex were the main predictors. These findings underscore the value of multidimensional, Machine learning enabled approaches for characterising screen time risk profiles, and they highlight the importance of reducing sedentary patterns and addressing gender differences in early childhood screen use. |
| URI: | http://hdl.handle.net/10174/40026 |
| Type: | lecture |
| Appears in Collections: | CHRC - Comunicações - Em Congressos Científicos Internacionais
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