The integrative model of ICT effects on Adolescents’ well-being (iMEW): The synthesis of theories from developmental psychology, media and communications, and health

ABSTRACT

Information and communication technologies (ICTs) have become commonplace in adolescents’ lives, and they have grown in importance during the COVID-19 pandemic, when online communication became standard for many parts of life. This brings the need for developmental psychology to revise and update its theories for these new societal challenges. It is beneficial to look at these changes from an interdisciplinary perspective to enrich developmental psychology with knowledge from other fields. This theoretical article proposes the Integrative Model of ICT Effects on Adolescents’ Well-being (iMEW), which integrates the Problem Behaviour Theory from developmental psychology and the Differential Susceptibility to Media Effects Model from media and communications. It draws inspiration from the Ecological Systems Theory and the Health Belief Model. The new model brings a more comprehensive understanding to adolescents’ development and the effects of ICTs on their well-being. It is also helpful for the empirical research because it can serve as a roadmap for research that focuses on the effects of ICT.

KEYWORDS:

• adolescents
• well-being
• developmental psychology
• DSMM
• problem behaviour theory

Introduction

Adolescents frequently use information and communication technologies (ICTs), and the various forms of activities, ranging from online games to social media to instant messengers, have become integral parts of their lives during the last few decades (Smahel et al., 2020). This digitalization has raised concerns regarding how online behaviours influence adolescents during this developmental period. Along with facing a number of biological, cognitive, and social changes, adolescents need to achieve important developmental tasks (e.g., identity, sexuality, and intimacy) (Havighurst, 1972). Research has found that adolescents use ICTs in line with their developmental tasks (Subrahmanyam & Šmahel, 2011), providing both important benefits and potential hindrances (Kurek et al., 2017). The rapid technological changes need to be taken into account in developmental theories. This paper focuses on an interdisciplinary integration of theories from developmental psychology with fields that have longer traditions in research focused on the impact (and adoption) of ICTs: media and communication, and health. Accordingly, we integrated the following highly influential models from different fields: Differential Susceptibility to Media Effects Model (DSMM; Valkenburg & Peter, 2013) from media and communications; the Problem Behaviour Theory (PBT; Jessor, 2014) from developmental psychology; and concepts from the Health Belief Model (HBM; Champion & Skinner, 2008); and we were inspired by the Ecological Systems Theory (EST; Bronfenbrenner, 1977). This integration of theories from these different fields was missing, and it can provide a more comprehensive understanding of adolescents’ development in the context of the opportunities and challenges related to ICT usage. Therefore, in this theoretical article, we propose the new integrative model of ICT effects on adolescents’ well-being (iMEW), which will allow us to better understand the current development of adolescents. This model and theoretical framework can also be used in empirical research as a roadmap for thinking about the complex interrelationships among the variables. It can be helpful for research questions where previous studies found inconsistent findings or where researchers need to incorporate ICT usage and its effects into their hypothesized models.

In the following section, we explain adolescents’ developmental tasks and their relation to ICT usage. The next section describes the underlying theories that we used for the development of the new model. The ‘iMEW model’ section describes the model itself. The next section lists five propositions of the model and provides examples from the empirical evidence that supports the new model. In the Discussion, we introduce how the model strengthens previous theories and how it could be used in the research.

Developmental tasks and ICT use

According to Havighurst, developmental tasks arise in a certain period of the life of an individual. Successful achievement of these tasks leads to their happiness and success in later tasks, but failure leads to unhappiness and the disapproval of society (Havighurst, 1972). According to his theory, adolescents have to master the following developmental tasks: achieve new and mature relations with age-mates; achieve a masculine or feminine social role; accept one’s physique; achieve emotional independence from parents and other adults; prepare for marriage and family life; prepare for an economic career; acquire a set of values and an ethical system; and desire and achieve socially responsible behaviour. Although adolescence has changed from the time of Havighurst’s research and some tasks might be less central (e.g., preparing for marriage), most of the tasks remain relevant and manifest in digital media (Subrahmanyam & Šmahel, 2011).

The following developmental tasks were described as important in relation to adolescents’ ICT usage in previous research (Blinka & Smahel, 2009; Borca et al., 2015; Smahel et al., 2014): peer relationships and friendships (e.g., online communications with peers and friends, friendship development); parent-adolescent relationships (e.g., parental rules, differentiation from parents in online environments); sexuality and intimacy (discussions about sexuality online); identity development online in many aspects (see, Borca et al., 2015); and morality and ethics (e.g., discussing and reading about ethics).

Underlying theories of the iMEW model

Our proposed model is mainly based on the DSMM and PBT, which we explain in more detail below. It was also inspired by the Health Belief Model and the Ecological Systems Theory, which we introduce only briefly.

Differential susceptibility to media effects model

The DSMM presents a media-oriented model that aims to integrate earlier theories and insights on media effects into four propositions (Valkenburg & Peter, 2013). First, media effects depend on a set of differential susceptibility variables, which stem from individual dispositions (e.g., traits, skills, cognitions), development, and social context. Second, the relationship between media use and the consequent long-term media effects (e.g., social connectedness) is mediated by short-term response states, which can be cognitive, emotional, or excitative. Third, the differential susceptibility variables have two roles in the model: they directly impact the media use and moderate its effect on the response states. And, fourth, media effects are transactional and further impact the differential susceptibility variables, media use, and the response states (Valkenburg & Peter, 2013). The DSMM provides a complex overview of the inter-relationships between individual characteristics, the choices of media, and the responses to the media. That said, due to its focus on various types of media and various users, the DSMM’s categories of variables, namely the differential susceptibility variables and media use, are broad and somewhat vague. Therefore, we aim to integrate the DSMM with other relevant models that can help tailor it more specifically for adolescents and ICTs.

Problem behaviour theory

The PBT is oriented towards risky and health-promoting behaviours (Jessor, 2014) in adolescents. According to Jessor (2014), adolescents engage in behaviours that present a risk for health, but also an opportunity to socialize with peers and show one’s maturity (e.g., underage drinking). Nowadays, ICTs present a space for such behaviours (e.g., cyberaggression). The aim of the PBT is to identify variables that influence whether and to what extent someone will engage in risky behaviours. The model recognizes several types of both risk and protective factors. The risk variables directly influence risky behaviour and include model risks (e.g., activities observed in peers and parents, such as substance use), opportunity risks (e.g., substance accessibility), and vulnerability risks (individual characteristics; e.g., sensation seeking). The protective factors prevent risky behaviour, both directly and as moderators of the risk factors. In the absence of risk, they promote desirable behaviour as predictors. The PBT recognizes model protectives (e.g., parents’ health-promoting activities), social support (e.g., proximity to peers), and control protectives (e.g., strictness of parental rules). Variables related to one’s own behaviour (e.g., attitudes towards risks or academic under/achievement) can also present either risk or protective factors (Jessor, 2014). The PBT is limited by its inability to explain exactly how risky and protective factors can impact risky behaviours. Nonetheless, the PBT presents a particularly relevant framework because it proves useful for identifying such factors and the potential differential susceptibility variables on the individual and social levels.

Health belief model

Similar to the PBT, the HBM focuses on risky behaviours and their prevention and promotion of desirable habits, albeit from a different perspective. The HBM mainly reflects on factors related to cognition, motivation, and voluntary actions to prevent risks or promote well-being. The model proposes cues and factors that motivate an individual to cease a risky, although pleasant, behaviour (e.g., unhealthy diet) or to start a desirable one (e.g., physical activity). First, in order to be motivated to act, one must recognize a risky condition and perceive it as a personal threat (i.e., feel susceptible and perceive it as severe). Second, the optimal path of action is chosen through the comparison of perceived benefits and barriers related to the action. The likelihood of action grows with higher perceived severity and higher benefits at lower costs. And, third, the actual action is also influenced by one’s self-efficacy (Champion & Skinner, 2008). The HBM is successful in explaining what determines one’s will to change a behaviour, although the model does not consider factors that account for the sustainability of the change (Schwarzer & Luszczynska, 2008). Notwithstanding, the HBM can inspire our model by pinpointing susceptibility variables at the individual level (e.g., cognitive processes) that can impact media use.

Ecological systems theory

The EST recognizes that human development is impacted by several levels of contextual factors, which are described in a nested model with five layers (Bronfenbrenner, 1977). The microsystem consists of people and institutions that directly impact the person, such as family and peers. Due to the increasing presence of digital technologies, later revisions of the theory incorporated a techno-subsystem between the microsystem and the individual to account for the role of online environments on development (Johnson, 2010; Johnson & Puplampu, 2008). The mesosystem refers to the various interactions of various microsystems. This layer appears crucial because their effects are often non-additive and it is the interplay that determines their impact on the individual (see, e.g., Benson & Buehler, 2012). The exosystem impacts people in the microsystem and, indirectly, the person in the centre (e.g., a change in a parent’s workload influences the way they raise their child). The macrosystem and chronosystem overarch the model because they reflect general factors, such as geography, culture, economy, changes in time, and historical events (Bronfenbrenner, 1977). The main shortcoming of the model lies in its limited testability, except for the effect of the microsystem, which are manifested through proximal processes. Yet, the ideas from the EST can guide our approach to susceptibility variables on a social level. First, it shows the importance of the interactions between social-level variables. And, second, on the social level, we need to consider the larger societal role (i.e., country, culture) that was neglected in the DSMM and differentiate it from social variables from other layers.

The integrative model of ICT effects on adolescents’ well-being (iMEW)

In this article, we propose a new integrative model (iMEW) that aims to develop our understanding of the factors related to the effects of ICTs on adolescents’ well-being (see, Figure 1). It is based on the adaptation and integration of the above-mentioned models and theories, and it is enriched by the results of previous empirical research. We developed the model from the structure of the DSMM, which we adapted specifically to address the well-being and accomplishment of developmental tasks during adolescence. Then we integrated ideas from the PBT to account for risk and protective factors related to adolescents’ online behaviours. We incorporated cognitive processes related to healthy and preventive behaviours based on the HBM. And, lastly, we included the culture level under the susceptibility variables, which roughly corresponds to the macrosystem in the EST.

Figure 1. The conceptual Integrative Model of ICT Effects on adolescents’ Well-being (iMEW).

Note: The list of specific variables that we mention in the boxes are only examples and not exhaustive

We will first describe each section of the model, and then specify the interrelations. The first section represents the ‘susceptibility variables’, which we conceptualize as individual-, social-, and culture-level variables related to the adolescent. Note that Figure 1 includes only examples of susceptibility variables, while many other possible variables on all of the other levels are not mentioned in the figure nor in this text. The susceptibility variables should be selected in regard to a specific research question. Individual variables include demographic characteristics, traits, and mood, plus cognition, motivation, and skills. The box with developmental tasks (see, Figure 1) lies between the individual and social levels because its variables can relate to both levels. For example, identity development can rely on the individual level (questions such as ‘Who I am?’), but also on the social level (questions such as ‘Where do I belong?’). Sexuality includes physical maturation at the individual level and the development of intimate relationships at the social level. The developmental tasks include the areas mentioned in the previous section (Havighurst, 1972), such as identity, sexuality, friendship, relationships, relationships with parents, peers, and others. Social variables are concerned with relationships with family, peers, and the community. Culture variables include the country of origin, values, norms related to the adolescent’s development, media, and technology provision.

The middle box in Figure 1 shows online activities, which are described as four dimensions that are independent of each other in the iMEW. Each dimension offers a specific perspective through which online activities have been approached in prior research. We note that the selected four dimensions do not present an exhaustive description of online activities. There are many more possible dimensions, such as virtual presence, online flow, and virtual self-presence (Stavropoulos et al., 2022). Researchers should select the appropriate dimensions of the online activities in line with the focus of their research.

The dimensions in the iMEW model can be used to cluster similar activities, which is the approach that we recommend. However, an online activity targeted in research can be situated within one or more dimensions, and the selection of a dimension depends upon the goal of the study, as mentioned in the previous paragraph. The proposed dimensions are based on previous research that attempted to define the dimensions of online activities, online opportunities, and risks among children and adolescents (Livingstone et al., 2018; Smahel et al., 2014). The first dimension, online interaction and content consumption, is related to whether the online activity is active (i.e., commenting, discussing) or passive (i.e., looking at online content). This dimension was found to be useful to differentiate adolescents’ online activities in relation to developmental tasks in previous research (Smahel et al., 2014). The second dimension is the degree to which these activities are associated with online risks or opportunities in relation to well-being (Fikkers et al., 2016). While some activities are more often connected to harms than benefits (e.g., cyberbullying), or the other way around (e.g., learning from quality sources), many others can be associated with both (e.g., meeting online friends can be risky because of possible harm and privacy violations, but also beneficial because of possible friendship development). Importantly, this differentiation is not rigid and universal. It depends on adolescents’ individual characteristics (e.g., skills, motivation) and also on how exactly they approach the online activity. In the iMEW, we propose that each online activity might be differentially associated with adolescents’ well-being. Therefore, we expect researchers to identify the risky and beneficial dimensions of online activities in relation to the specific outcomes they examine.

The third dimension within online activities is inspired by the Health Belief Model (Champion & Skinner, 2008) and it takes into account the degree to which adolescents’ online behaviours are (un)safe. It is guided by cautious versus not-reflected behaviours, such as what preventive measures the adolescents use to prevent possible online risks (Vandoninck et al., 2014). Such measures can impact adolescents’ experiences with regards to the use of ICTs and their subsequent well-being and attainment of developmental tasks. Lastly, the fourth dimension of online activities prioritizes the developmental perspective, specifically the role of activities as a way to the fulfilment of the developmental tasks (Subrahmanyam & Šmahel, 2011), such as reading about and discussing certain topics, like spirituality or lifestyles, as part of identity exploration. The several possible categories and activities of ICT usage in relation to developmental tasks are described by Borca et al. (2015).

The last section includes the effects on short- and long-term well-being. For well-being, we used the definition suggested by the World Health Organization (WHO, 2001), which recognizes three key dimensions: (a) physical well-being, which consists of health perception, the absence of disease, and physical functionality (Minkkinen, 2013); (b) psychological well-being, which includes the presence of positive affect and the absence of negative affect; and (c) social well-being, which covers the quality of relationships with others, and includes social belonging, social acceptance, and social integration. In the iMEW model, well-being states are outputs and developmental tasks are predictors. However, there is also reciprocal relationships back from well-being to developmental tasks and online activities. For instance, low social acceptance (belonging to the social well-being) can impact the developmental tasks at the social level of susceptibility variables.

The effects of online activities on well-being can be short-term (e.g., feeling happy) and long-term (e.g., happiness), which roughly corresponds to the response states and media effects in the DSMM. The same online activity can have a different, or even opposite, impact in the short- and long-term perspectives and it is therefore important to differentiate between the effects. It is possible that an activity, such as excessive game playing, might make adolescents happy at the present moment, increasing short-term psychological well-being and helping to fulfill the developmental task of relationship formation and maintenance, but the activity can also have negative outputs in the long-term perspective.

Propositions of iMEW: Empirical evidence of the model

We introduce five propositions of the iMEW model that are helpful for understanding the model and its usage. We also describe the empirical evidence for these propositions.

1. Causality in the iMEW model: The DSMM proposes causal relationships between variables, which have successfully been tested (Landrum et al., 2019). Following the DSMM’s propositions, the iMEW also aims to depict causal relationships. Hence, susceptibility variables have an impact on adolescents’ online activities, and those activities affect short- and long-term well-being tasks. The iMEW newly differentiates between the effects on short- and long-term well-being, which seems to be an important difference for the empirical research.

2. Beneficial and detrimental impact of ICT: A salient question in developmental psychology, media and communications, and health is whether and when ICT usage is beneficial and when it is detrimental. The PBT and HBM show that the outcome of ICT usage depends on a list of factors. In the iMEW, we combine the variables outlined by both models.

First, one’s ICT use is impacted by one’s voluntary decisions, which are based on the awareness of the risks and benefits. As shown in the HBM, in order to act, one needs to perceive the risk as severe, feel self-efficacious to act, and know that the preventive behavior presents minimum barriers and maximizes the benefits (Champion & Skinner, 2008). For example, people who acknowledge the risks related to using public Wi-Fi tend to access it less often or adjust their online behavior when connected to it (Maimon et al., 2020), leading to more or less cautious behavior.

Second, one’s ICT use and its outcome is also impacted by individual characteristics, such as traits, attitudes, and opportunities, as well as by social factors, as outlined by the PBT (Jessor, 2014). Social factors can impact ICT use by supporting or discouraging certain types of online activities. For example, peer approval can mitigate online risky behaviors, such as mobile addiction (Chang et al., 2019).

Third, to determine whether a behavior is beneficial or detrimental and what factors impact it, one needs to consider the broader cultural context. The role of the risk and protective factors that underlie the adolescents’ online risky behaviors proposed by the PBT seem to be stable across different cross-cultural contexts (Vazsonyi et al., 2010). This proposition was confirmed by previous empirical findings (Mikuška et al., 2020). However, for other factors, culture can make a difference. For example, the role of the social support of friends in exposure to harmful online content was different in Finland than in Czechia and Spain in a recent study (Kvardova et al., 2021). While the role of country-level differences in online activities and ICT-related variables are often smaller than individual differences when the research focuses on culturally similar countries (Helsper et al., 2013), their roles should not be neglected. Finally, recent research identifies other individual susceptibility variables that predict the preference for online activities. For example, older adolescents are more likely to communicate with unknown people as an online activity to fulfill their developmental needs (Mýlek et al., 2020).

• (3) Clusters of online risky and protective activities: Based on research that used Jessor’s PBT in the context of adolescent’s online behaviours (Gámez-Guadix et al., 2016), it seems that similar online activities tend to cluster. We newly use this as a proposition to show that the same susceptibility variable can impact the cluster of online activities in similar ways. For example, adolescents’ emotional problems were shown to predict several online risks, including sexting, cyberbullying, excessive internet use, and exposure to sexual materials (Gassó et al., 2019). On the other hand, positive family relationships were confirmed to be a protective factor against various online risks (Kvardova et al., 2021).

• (4) Role of moderators and mediators: In the iMEW, some susceptibility variables can act as moderators or mediators. While the DSMM understands dispositional variables as both predictors of online activities and moderators between online activities and media responses, the research shows that not all variables play this dual role. Some only work either as a predictor or a moderator. For example, in Bonus et al. (2018) prior Pokémon exposure and social pressure predicted playing Pokémon Go, but it did not moderate its effect on response states, while the opposite was found for social anxiety. Furthermore, the research shows that, apart from the moderation between online activities and the well-being proposed by the DSMM, there can also be moderators between the susceptibility variables and online activities, and between short- and long-term well-being. We newly propose these relations in the iMEW model. For instance, a recent study showed that the effect of emotional problems on the exposure to harmful online content was moderated by the quality of the family environment (Kvardova et al., 2021), which fits with the protective role of social support theorized by the PBT.

• (5) Well-being and its reciprocal effects: Lastly, we suggest that the well-being variables can also have reciprocal effects on online activities and susceptibility variables. For example, some negative experiences of ICT usage might impact the well-being of adolescents negatively and, as a consequence, they might adapt their online activities, such as meeting fewer people online or installing software that prevents access to content with sexual material.

Future research should further test these propositions. Most of them are grounded in empirical research, but some were inadequately tested (i.e., reciprocal effects).

Discussion

We introduced an integrative model that allows for a better understanding of the effects of ICTs on adolescents’ well-being. The iMEW draws from developmental psychology, media and communications, and health to allow for a more comprehensive understanding of the risks and opportunities for adolescents’ development within the ongoing societal transformation, as characterized by fast technological innovations.

The iMEW revised the DSMM and enriches it in several aspects. First, we elaborated on the types of susceptibility variables in the DSMM (Valkenburg & Peter, 2013). The DSMM suggests dispositional, developmental, and social factors as the susceptibility variables. In the iMEW, for the dimension of individual factors, we described the factors that can be labelled as dispositional in a more structured way. We also suggested the integration of developmental tasks into individual-, social-, and culture-level factors under susceptibility variables. This approach allowed us to show that different developmental tasks are related to different levels of variables – the systems in the terms of the EST (Bronfenbrenner, 1977).

In comparison to the DSMM, we also developed more structure in the section related to media use by pointing out several important dimensions of online activities that may have an impact on the outcomes of ICT use. Furthermore, based on up-to-date research (Bonus et al., 2018; Landrum et al., 2019), we suggested adjustments to the Proposition 3 of the DSMM, which states that dispositional variables act as predictors for online activities and moderators for the association between online activities and media-response states. We recommended that the proposed moderation in the Proposition 3 of the DSMM could also be extended to the relationships between susceptibility variables and online activities (Kvardova et al., 2021) and between the short- and long-term effects on well-being (Landrum et al., 2019).

The iMEW expands the focus of the PBT (Jessor, 2014) from the role of risk and protective factors in risky adolescent behaviours – identified here as online behaviours – to how such risks and benefits are related to the well-being and developmental tasks of adolescents. Although such an interpretation serves as the basic premise of the theory, it is not currently included in the model of the PBT. Furthermore, based on previous research in this area (Gámez-Guadix et al., 2016), the iMEW newly proposes that risks and opportunities are clustered similarly in the online environment as in the offline environment, consistent with the PBT (Jessor, 2014). This proposition has practical implications in research, by showing that similar online risks might have similar impacts on well-being.

The iMEW contributed to the HBM by integrating it into a broader context. While the HBM is valued, its inability to account for factors other than individual, voluntary, and cognitive decisions in preventive behaviours has been criticized before (Champion & Skinner, 2008). We bridged this gap by combining the model with the individual risk and protective factors stated in the PBT, such as traits, emotions, and skills (Jessor, 2014), and with social factors.

The iMEW offers a framework to examine the effects of ICTs on adolescents’ well-being. This framework involves both ICT-related and ICT-unrelated variables, focuses on the micro- and macro-level contexts, and it is specifically tailored to examine well-being in the context of developmental tasks during adolescence. While allowing for the examination of susceptibility variables at individual, social, and culture levels, it also addresses developmental tasks at each level of the susceptibility variables and recognizes the multifaceted nature and intertwined associations of developmental tasks. Thus, researchers in the field of developmental psychology can use this framework as a roadmap for thinking about the complex interrelationships among the variables. This may be specifically helpful for research questions, where previous studies show inconsistent findings as a result of omitting the third variables (i.e., moderators, mediators) or where researchers need to incorporate ICT usage into their hypothesized models. However, we need to stress that our model should be used carefully: the position and role of each variable need to be supported by theoretical argumentation, it must be relevant for each specific research problem, and it must be assessed by robust methodology that allows for the testing of causal relationships, such as in longitudinal designs.

Limitations and future research

Although the iMEW is built on prior theory and research findings to explain the effects of ICTs on adolescents’ well-being, it has some limitations that warrant explanation. We adapted and integrated theories from the fields of developmental psychology, media and communications, and health. Although these theories provided a comprehensive overview and an interdisciplinary perspective, not all of the possible perspectives were incorporated. For instance, future research could include psychophysiology or neuroscience-related constructs that may have an impact on the use of, and preference for, certain ICTs (Bolls et al., 2019). Further efforts to expand the current model are possible and desirable.

Another limitation is related to the conceptualization of online activities in the iMEW. The model describes online activities in four dimensions and intends to provide a structure to identify clusters of online activities. We based the conceptualization of the dimensions on the available research on ICTs, yet it is still quite narrow and not exhaustive. Future research could improve this part of the model into a more nuanced and detailed shape.

The current knowledge about the possible role of susceptibility variables as moderators and mediators is scattered. Some authors support the path proposed by the DSMM (Houston et al., 2018), find other moderations in the model (Landrum et al., 2019), or propose a more complex moderated mediation (Fikkers et al., 2016). Furthermore, it seems that various susceptibility variables can play different roles in different cultural contexts (Kvardova et al., 2021). Future research could uncover the substance of mediation or moderation effects between online activities and well-being. Future research could also focus on the reciprocal effects going from well-being back to susceptibility variables and online activities (e.g., Houston et al., 2018).

Another possible limitation concerns the empirical support for the relationships between the variables in our model. The relevant theories integrated in the iMEW are mainly based on studies using cross-sectional designs that provide limited evidence on the causal impact of ICTs on well-being. Yet, the iMEW (as well as the DSMM) is a model that aims to describe causal relationships. Experimental, longitudinal, ecological momentary assessment (EMA), and other designs that can inform us about causality are needed to test the proposed relationships.

Conclusions

The fast spreading of ICTs brings new societal challenges and we need to understand adolescents’ development in this changed environment. Therefore, we presented a new integrative model that helps to explain the effects of ICTs on adolescents’ well-being. The iMEW enriches developmental psychology from an interdisciplinary perspective and integrates knowledge from media and communications, and health. Therefore, it allows for a more comprehensive understanding of the opportunities and challenges related to ICTs in today’s society.

Acknowledgments

Authors acknowledge the support of the Czech Grant Agency (19-27828X)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Additional information

Funding

This work was supported by the Czech Science Foundation [19-27828X].