Psychosocial Correlates of Recreational Screen Time among Adolescents

Joanie Roussel-Ouellet (), Dominique Beaulieu, Lydi-Anne Vézina-Im, Stéphane Turcotte, Valérie Labbé and Danielle Bouchard
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Joanie Roussel-Ouellet: Département des Sciences de la Santé, Université du Québec à Rimouski (UQAR), Campus de Lévis, 1595 Boulevard Alphonse-Desjardins, Lévis, QC G6V 0A6, Canada
Dominique Beaulieu: Département des Sciences de la Santé, Université du Québec à Rimouski (UQAR), Campus de Lévis, 1595 Boulevard Alphonse-Desjardins, Lévis, QC G6V 0A6, Canada
Lydi-Anne Vézina-Im: Département des Sciences de la Santé, Université du Québec à Rimouski (UQAR), Campus de Lévis, 1595 Boulevard Alphonse-Desjardins, Lévis, QC G6V 0A6, Canada
Stéphane Turcotte: Centre de Recherche du CISSS de Chaudière-Appalaches, 143 Rue Wolfe, Lévis, QC G6V 3Z1, Canada
Valérie Labbé: CHAU-Hôtel-Dieu de Lévis, 143 Rue Wolfe, Lévis, QC G6V 3Z1, Canada
Danielle Bouchard: CHAU-Hôtel-Dieu de Lévis, 143 Rue Wolfe, Lévis, QC G6V 3Z1, Canada

IJERPH, 2022, vol. 19, issue 24, 1-15

Abstract: The study objective was to identify the psychosocial correlates of recreational screen time among adolescents. Data collection took place in four high schools from the Chaudière-Appalaches region (Quebec, Canada) from late April to mid-May 2021. A total of 258 French-speaking adolescents (69.8% between 15 and 16 years and 66.3% girls) answered an online questionnaire based on the Reasoned Action Approach. Recreational screen time was measured using the French version of a validated questionnaire. Adolescents reported a mean of 5 h and 52 min/day of recreational screen time. Recreational screen time was associated with being a boy (β = 0.33; p < 0.0001) and intention to limit recreational screen time to a maximum of 2 h/day (β = −0.15; p = 0.0001); this model explained 30% of the variance in behavior. Intention to limit recreational screen time to a maximum of 2 h/day in the next month was associated with attitude (β = 0.49; p < 0.0001), self-identity (β = 0.33; p < 0.0001), being a boy (β = −0.21; p = 0.0109), perceived behavioral control (β = 0.18; p = 0.0016), and injunctive norm (β = 0.17; p < 0.0001); this model explained 70% of the variance in intention. This study identified avenues to design public health interventions aimed at lowering recreational screen time among this population.

Keywords: screen time; concurrent screen use; adolescent; correlates; Reasoned Action Approach (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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