Different digital activities are part of our daily lives and new positive practices have emerged alongside (Madden et al., 2013). This has also sparked concerns among researchers and educators regarding adolescents development and well-being. Children grow up immersed in digital media and a big part of their time is spent using socio-digital technologies (Rheingold, 2012). This may have both positive and negative consequences (Przybylski & Weinstein, 2016).  

As it appears that the role of digital technology in everyday life is deepening, researchers have become more interested in investigating the associations between digital technology use and well-being (eg. Bartel, Gradisar & Williamson, 2015; Bruni, Sette, Fontanesi, Baiocca, Laghi & Baumgartner, 2015). As digital activities blend with our daily activities, fatigue has simultaneously reported to be increased (Raniti ym., 2017), which gives more reasons to study this phenomenon.

In this study socio-digital participation (SDP) refers to activities, where the key focus is in online-interaction with others in different networked communities including gaming (see Hakkarainen, Hietajärvi, Alho, Lonka & Salmela-Aro, 2015). Varying digital activities form different underlying SDP orientations (e.g. social media consumption, gaming, interest-driven knowledge creation), which gives reason to suspect that the effects of socio-digital participation on well-being and sleep may vary accordingly. Thus, screen time recommendations based on time may turn out to be too simplistic. Even though use of digital technology may be dominated by passive consuming, it can also involve meaningful activities that promote learning. Instead of just studying the amount of digital technology use research should focus on the quality of use by looking at different orientations regarding participation in the digital world and how these are related to other spheres of life. 

Based on the displacement hypothesis (Neuman, 1988) the effects of socio-digital participation are claimed to be negative because digital activities supplant other alternate activities (eg. physical exercise, sleeping, socializing with peers and family). The Goldilocks Hypothesis instead state that moderate levels of digital technology use are not harmful (Etchells, Gage, Rutherford, & Munafò, 2016; Przybylski, 2014) and may even be beneficial in our digitally connected society. Overuse, in turn, may displace alternate activities (Valkenburg & Peter, 2009), whereas “too little” use may deprive adolescents from important social online communities.  Nevertheless, both of these hypotheses rely on time. Some studies also show that gaming and social online interaction may have some positive effects on well-being when emotional investment is taken into consideration (e.g. Cleland Woods & Scott, 2016; Ryan, R., Rigby, C. & Przybylski, 2006). This is why we suggest that the context and quality of the digital activities might be as equally important as the time spent in the digital world.

Therefore, the aim of this study is to explore the relationships between various socio-digital activities (including social media and gaming), sleeping habits and well-being among early adolescents. More specifically, the aim is to examine how different socio-digital activities, self-reported sleep quality, amount of sleep and bedtime, school burnout and general life satisfaction are associated. In addition, the aim is to explore if the sleep factors mediate the association between socio-digital participation and well-being.

The data (N=696) for this study were acquired from the Mind the Gap -research data (funded by Academy of Finland), collected in Helsinki in spring 2013. The participants were 6th graders (11-12 years) from 33 different primary schools. Researchers and teachers collected identical paper questionnaires during an ordinary school day. Participants evaluated their quality of sleep during the past six months, hours they slept and the time they usually went to bed on school days. Well-being was studied through School Burnout Inventory (see Salmela-Aro, Kiuru, Leskinen & Nurmi, 2009) and Satisfaction with Life Scale (see Diener, Emmons & Larsen, 1985). Socio-digital participation (SDP) was measured by using an inventory developed by Hakkarainen, Hietajärvi, Alho, Lonka & Salmela-Aro (2015). Preliminary analyses were done using IBM SPSS Statistics (24.0) and correlational network analyses will be done using JASP 8.5. We will examine correlational dynamics between socio-digital participation, sleeping habits and well-being using regularized partial correlation network analysis, which allows us to infern interactions, multicollinearity and predictive mediation between the variables and also the potential causal pathways (Epskamp & Fried, 2017). Analysis will be done separately for boys and girls, because socio-digital practices have been shown to be quite gendered in Finland.

Since girls use social media more intensively than boys, we assumed that their active online-interaction (eg. chatting with friends, consuming social media) would correlate with sleep variables. Boys tend to play more digital games, so we assumed that especially gaming among them would be negatively correlated with sleep related variables. Our preliminary analysis showed that, in the male sample, active social media consuming and entertainment (eg. watching videos) correlated with later bedtime. Against our hypothesis gaming did not correlate with sleep variables in the male sample. The poor sleep quality of boys correlated with school burnout and negatively with life satisfaction, but the amount of sleep and bedtime did not correlate with well-being among boys. In the female sample, we discovered that social media consuming and entertainment driven SDP negatively correlated with all three sleep factors (sleep quality, quantity and late bedtime), which, in turn correlated with school burnout and lower life satisfaction. Based on the preliminary analysis we hypothesize that correlations between different socio-digital participation activities and well-being may be mediated by sleep.

Bartel, K., Gradisar, M. & Williamson, P. (2015). Protective and risk factors for adolescent sleep: A meta–analytic review. Sleep Medicine Reviews, (21), 72–85. Bruni, O. Sette, S., Fontanesi, L., Baiocco, R., Laghi, F. & Baumgartner, E. (2015). Technology Use and Sleep Quality in Preadolescence and Adolescence. Journal of Clinical Sleep Medicine, 11(12), 1433–1441. Cleland Woods, H. & Scott, H. (2016). #Sleepyteens: Social media use in adolescence in associated with poor sleep quality, anxiety, depression and low self-esteem. Journal of Adolescence, 51 (51), 41-49. Diener, E., Emmons, R., Larsen, R. & Griffin, S. (1985). The Satisfaction with Life Scale. Journal of Personality Assessment, 49(1), 71–75. Epskamp, S., and Fried, E.I. (2016). A Tutorial on Regularized Partial Correlation Networks, arXiv:1607.01367 Etchells, P. J., Gage, S. H., Rutherford, A. D., & Munafò, M. R. (2016). Prospective investigation of video game use in children and subsequent conduct disorder and depression using data from the Avon Longitudinal Study of Parents and Children. PLoS ONE, 11(1). Hakkarainen, K., Hietajärvi, L., Alho, K., Lonka, K. & Salmela-Aro, K. (2015). Sociodigital Revolution: Digital Natives vs Digital Immigrants. International Encyclopedia of the Social & Behavioral Sciences, 22 (6), 918-923. Neuman, S. B. (1988). The displacement effect: Assessing the relation between television viewing and reading performance. Reading Research Quarterly, 23, 414–440. Raniti, M., Allen, N., Schwartz, O., Waloszek, J., Byrne, M., … & Trinder, J. (2017). Sleep Duration and Sleep Quality: Associations With Depressive Symptoms Across Adolescence. Behavioral Sleep Medicine, 15 (3), 198-215 Salmela-Aro, K., Kiuru, N., Leskinen, E. & Nurmi, J. (2009). School Burnout Inventory (SBI). Reliability and Validity. European Journal of Psychological Assessment, 25 (1), 48-57. Madden, M., Lenhart, A., Cortesi, S., Gasser, U., Duggan, M., Smith, A. & Beaton, M. (2013). Teens, Social Media, and Privacy. Pew Internet and American Life Project Report. Rheingold, H. (2012). Net smart: How to thrive online. Cambridge, Massachusetts: The MIT press. Ryan, R., Rigby, S. & Przybylski, A. (2006). The Motivational Pull of Video Games: A Self-Determination Theory Approach. Motivation and Emotion, 30, 347–363. Przybylski, A. K. (2014). Electronic gaming and psychosocial adjustment. Pediatrics, 134, e716–e722. Przybylski, A. & Weinstein, N. (2016). A Large-Scale Test of the Goldilocks Hypothesis: Quantifying the Relations Between Digital-Screen Use and the Mental Well-Being of Adolescents. Psychological Science, 28 (2), 204–215. Valkenburg, P. M., & Peter, J. (2009). Social consequences of the Internet for adolescents: A decade of research. Current Directions in Psychological Science, 18, 1–5.