Measurement of Digital Stress in Primary School Students and the Relationship between These Dimensions and Aspects of Mental Health

• In Event: Poster Session 6
  In Poster Session: Measurement of Digital Stress in Primary School Students and the Relationship between These Dimensions and Aspects of Mental Health

Proposal

In recent years, children and adolescents have faced significant changes in their lives, particularly due to the integration of new technologies since the early 2000s – Prensky (2001) described this generation as "Digital Natives." Interactive media are now deeply embedded in daily life, playing a key role in the socialization of young people. Digital networks, smartphones, and games rank among the most popular leisure activities, alongside family, school, and peers (Feierabend et al., 2023; Suter et al., 2023). Many adolescents start their day by checking social networks and end it with a final post, showing the influence of interactive media on their lives (Kross et al., 2021; Külling et al., 2022). Despite research, knowledge gaps remain, especially regarding childhood, problematic usage patterns (Kliesener et al., 2022), and the impact on mental health (Haidt, 2024; Twenge et al., 2022). This study aims to address these gaps on the relationship between media use and mental health, focusing on two questions: (1) What are the psychometric characteristics of scales measuring digital stress and mental health among primary students? (2) Can dimensions of digital stress explain the mental health of primary students? To answer these research questions, a survey was conducted in the fall of 2023, involving 181 students from 3rd to 6th grade across three schools in the Canton of Bern (M=10.2 years; SD=1.43). The digital stress scale was measured using 13 items, capturing three characteristics of digital stress on a four-point rating scale: (I) Excessive Screen Time (3 items), (II) Constant Availability and Information Overload (5 items), and (III) Social Media Stress (5 items). Reliability ranged from acceptable to good (α=.66–.77). The theoretically postulated three-factor model showed good fit (χ²=78.1; df=51; p=.00; CFI=.95; TLI=.94; RMSEA=.05; SRMR=.05), with factor loadings between λ=.55 and .76. Latent inter-factor correlations were relatively high but distinguishable (r=.58–.80). For health variables, the German-language validation of the DASS-Y (Szabo & Lovibond, 2022) was used to assess mental health through three factors: Depression (7 items, α=.92), Anxiety (7 items, α=.90), and Stress (7 items, α=.90). Confirmatory factor analysis confirmed the three-factor structure (χ²=114.7; df=65; p=.38; CFI=.95; TLI=.94; RMSEA=.08; SRMR=.05), with satisfactory factor loadings (λ=.60–.87). Inter-factor correlations were high but distinguishable (r=.65–.84). Multiple regression models were used to analyze the predictive relationships with the DASS-Y. The findings aligned with expectations, providing additional evidence for the predictive validity of the measurement instruments. Specifically, the Depression scale could be predicted by social media use (β = .26, p<.05), with a significant effect observed only among girls (girls: β=.45, p<.01; boys: β=.15, p>.05). Furthermore, the Stress construct scale was particularly predicted by the subscales of digital stress (excessive screen time: β=.23, p<.01; constant availability & information overload: β=.38, p<.001). Conversely, for the Anxiety dimension, Social Media Stress emerged as a predictive factor (β=.40, p<.001). The explained variances in predicting mental health ranged between adj. R²=.28–.41.

Authors

• Arvid Nagel, University of Teacher Education NMS Bern

• Felix Kruse, St. Gallen University of Teacher Education