The current research on young people and technology derives its findings mainly from questionnaires in which children, young people, parents, and teachers answer questions about the use of technology and online activities. Such inquiries are not precise because they do not make it possible to objectively capture the details of what children and young people really come into contact with and how it affects them.

With contemporary children and young people spending twice as much time on mobile devices as in 2010 (source EU Kids Online 2020), it is no longer enough to have rough estimates of times, activities, and experiences that they have online. To move research forward and provide relevant information for creating a safe online environment, we need to know exactly what children, and young people in particular, are doing on their mobile devices.

More specific information

The latest research from our team found that 70% of Czech children use social networks daily, 75% of Czech children watch videos on YouTube daily, and 73% use a smartphone to communicate with family and friends. This information, unfortunately, does not include the content they view on social networks or many other specifics.

• What content are they watching on YouTube?
• How do they have fun on the internet?
• What effect does this content have on children's behavior and their well-being?

Although popular media often publicize the fact that technology has a generally negative effect on people, in reality, we are still looking for answers to these questions.

Beyond cross-sectional surveys

The mostly cross-sectional nature of internet and technology research in relation to the well-being of adolescents has so far generated inconsistent and often vague findings that do not allow for concrete implications to be made. The innovative real-time smartphone data collection process that we propose can address knowledge gaps that cross-sectional and self-report-based research cannot. It allows for more accurate measurement of technology and social media use, not only with exact and objective estimates of screen and app time, but also with an accurate assessment of active and passive use through the number of screen touches to uncover the exact content young people engage in and scroll through every day and by seeing the reasons that they turn to their phone screens (i.e. recreational, goal-oriented, procrastination). 

Comprehensive approach to well-being

The research on this topic also often uses well-being measures that assess adolescents’ average well-being across time and investigates only between-person associations of technology use with these overall well-being estimates. We aim to more comprehensively look at well-being in relation to smartphones and technology.

• With pre- and post-study questionnaires, we will be able to focus on long-term well-being (physical, psychological, social).
• With short in-app EMA (ecological momentary assessment) questionnaires during four bursts of intensive two-week data collection, we will look at well-being as a momentary affective state in the situational contexts of everyday life.

By combining this information and looking at within-person effects, we will be able to understand both the long-term and short-term effects of smartphones on young people’s well-being more precisely than before.

About the theory

The goal of the FUTURE project, and this working group as well, is to develop a comprehensive integrated evidence-based theory depicting the complex impacts of technology usage on the physical, psychological, and social well-being of adolescents from both the short- and long-term perspectives.

We looked at existing theories that we aim to integrate and enhance. As the starting point, we use the Differential Susceptibility to Media Effects Model (DSMM) (Valkenburg & Peter, 2013), which aims to explain why some individuals are more susceptible to media effects and how and why media influences them. In essence, the DSMM distinguishes three types of differential susceptibility variables to media effects (e.g. dispositional, developmental, social) that affect the individuals’ media use. The use leads to immediate media responses (e.g. cognitive, emotional, excitable) that serve as moderators between the media use and the media effect (i.e. the main outcome). The DSMM acknowledges that susceptibility variables affect media use as predictors but also stimulate or reduce media effects as moderators. Finally, media effects can also reciprocally influence differential susceptibility variables, media use, and media responses. In our project, we adapt the DSMM model to focus on well-being as the main outcome and we enrich it with detailed consideration of both the direct and indirect impact of risky and beneficial activities, accounting for the diverse psycho-social and environmental influences in adolescence. Thus, we will also utilize other selected relevant theories that can provide more insights into adolescents’ well-being and media use and which can enhance various aspects of the DSMM model.

• Valkenburg, P. M., & Peter, J. (2013). The differential susceptibility to media effects model. Journal of communication, 63(2), 221-243.

To conceptualize the differential susceptibility variables, we draw from Bronfenbrenner’s (1977) Ecological Systems Theory (EST) and Jessor’s (2014) Problem Behavior Theory (PBT). The EST provides a framework for understanding the influences on development by situating the adolescent within a system of relationships affected by multiple levels (i.e. systems) of the environment. Based on EST, we differentiate the susceptibility variables differently from DSMM and focus on the individual (i.e. microsystem), social (i.e. mesosystem), and country levels (i.e. macrosystem). The PBT provides a framework for adolescents’ problem behavior and specifies risk and protective factors that affect their development. It recognizes several domains within which we can identify these factors, including personality, perceived environment, and actual behavior. Other important factors in PBT include vulnerabilities and opportunities to engage in risks, controls against risks, and social support for positive behaviors. These factors thus appear among differential susceptibility variables, but also in media use, which can be conceptualized as either a risky or positive behavior. Finally, using the PBT allows us to overcome the dominant risk perspective because the PBT considers protective factors not only as protective for risks but also as promotive for positive development.

• Bronfenbrenner, U. (1977). Toward an experimental ecology of human development. American psychologist, 32(7), 513.

• Jessor, R. (2014). Problem behavior theory: A half-century of research on adolescent behavior and development. In R. M. Lerner, A. C. Petersen, R. K. Silbereisen, & J. Brooks-Gunn (Eds.), The developmental science of adolescence: History through autobiography (p. 239–256). Psychology Press.

The positive view of technology usage for well-being improvement is also accentuated in the Health Belief Model (Ahadzadeh et al., 2015), which is widely used to explain health behavior change and how people use technology for change if they are aware of a threat from a specific health condition or behavior and perceive benefits from the considered actions. This model will be especially useful for the examination of the effects on physical well-being.

The conceptual model of the project

Based on the aforementioned theoretical inputs, we adapted the DSMM and developed our base conceptual model for the effects of online activities on adolescents’ well-being. The model is described below (Figure 1).

• Ahadzadeh, A. S., Sharif, S. P., Ong, F. S., & Khong, K. W. (2015). Integrating health belief model and technology acceptance model: an investigation of health-related internet use. Journal of medical Internet research, 17(2), e45.

Well-being

In our model, long-term well-being, which is conceptualized as physical, psychological, and social, corresponds to the media effect in DSMM. The same dimensions are also present in the short-term perspective (i.e. media response in DSMM), acknowledging that online activities can also lead to immediate and temporary changes in an adolescent’s well-being. We will thus understand both the short-term and long-term effects of technology on adolescents’ well-being as outcomes. The strength of this project is its comprehensive focus on all dimensions of well-being. However, it is not possible to focus on all of the possible indicators of well-being. We selected the following indicators to be captured in our research.

The indicators of physical well-being will be adolescents’ sleeping and physical in/activity (including sedentary behavior) habits. Both indicators can be affected in the short term (e.g. disrupted sleep after engaging in online gaming or higher physical activity after using mHealth applications) and in the long term (e.g. general sleeping and physical in/activity patterns over a longer period of time).

The indicators of psychological well-being in our project will be adolescents’ emotional state (e.g. distress, anxiety, happiness), self-esteem, and life satisfaction. The short-term aspects in this well-being dimension correspond to temporary emotional changes as a result of technology usage (e.g. being upset by comments on a social networking profile, feeling happy while communicating with a friend), and the long-term aspects correspond to more permanent and stable emotional levels (e.g. increased anxiety after facing repeated cyber aggression, enhanced self-esteem as a function of participation in online communities).

As for the indicators of adolescents’ social well-being, we will examine the short- and long-term aspects of relationship quality (with peers, family, school, community) including perceived social support and social skills. Short-term effects include increased perceived support, and long-term effects include increased connectedness among family members.

Differential susceptibility variables

Based on the EST, we categorize the susceptibility variables differently from the DSMM and specifically focus on those that can constitute risk or protective factors in line with the PBT. The selection of specific variables is also based on our prior research on children’s and adolescents’ risky activities online (see e.g. Smahel & Wright, 2014; Smahel et al., 2014). In our conceptual model, we differentiate:

• individual-level variables with factors such as age, gender, developmental stage, and skills
• social-level variables with factors such as peers, family, school, and community, and we examine and account for the inequalities reflected in media use and well-being by focusing on the role of socioeconomic status (SES)
• country-level variables such as culture, media, values, technology provision, and welfare.

Online activities

• Smahel, D. & Wright, M. F.(eds) (2014). Meaning of online problematic situations for children. Results of qualitative cross-cultural investigation in nine European countries. London: EU Kids Online, London School of Economics and Political Science

• Smahel, D., Wright, M. F., & Cernikova, M. (2014). Classification of online problematic situations in the context of youths’ development. Communications, 39(3), 233-260.

Media use in our model is understood to be the extent and content of adolescents' online activities. Based on previous research (Castellacci & Tveito, 2018, Livingstone et al., 2011), we will focus predominantly on a set of activities that have shown to have a positive or negative impact on well-being or that raise substantial concern about their effect on well-being. As a basic differentiation, we distinguish between interaction activities (e.g. communication through technology with friends, family, and unknown people, participating in online groups) and consumption activities (e.g. viewing online content). In the project, we will also focus on the general pattern of technology usage (e.g. time and frequency, excessive use, usage of diverse devices). It is crucial to note that, although we will measure specific activities, our investigation will lead to the detection of more general patterns and the recognition of media affordances, such as interactivity, and textual or visual character. That is, we will focus on the meaning of the activity regardless of the specific device, platform, or application used. The focus on affordances rather than on how exactly they are achieved will allow us to provide insights and generate models that will be applicable in the future with technologies not yet developed.

• Castellacci, F., & Tveito, V. (2018). Internet use and well-being: A survey and a theoretical framework. Research policy, 47(1), 308-325.

• Livingstone, S., Haddon, L., Görzig, A., and Ólafsson, K. (2011). Risks and safety on the internet: The perspective of European children. Full Findings. LSE, London: EU Kids Online.