Assessing the Quality of Digital Experience: Implications for Digital Well-being and Immersion in Six European Countries

Assessing the Quality of Digital Experience: Implications for Digital Well-being and Immersion in Six European Countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing the Quality of Digital Experience: Implications for Digital Well-being and Immersion in Six European Countries Nuria Codina, Maria Marentes-Castillo, Rafael Valenzuela, Ruth Odgen, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9269714/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Digital technologies (DT) have become deeply embedded in everyday life, evolving into a key determinant of contemporary quality of life. However, the impact and the experiences of using DT are not homogeneous across age groups and cultural contexts. This study examines how age moderates the relationship between the Quality of Digital Experience (QDE) comprising the three dimensions of social connectedness, well-being, and time/efficiency and Immersion in Digital Life (IDL) across six European countries. Drawing on data from 5,270 adults, moderation analyses show that all QDE dimensions significantly predict IDL, identifying social connectedness as the strongest contributor to digital well-being. Age moderated these relationships differently across countries: younger adults generally showed stronger associations between QDE factors and IDL (particularly regarding the well-being dimension), whereas in contexts like Czechia and Poland, older adults demonstrated higher levels of digital immersion. These findings underscore the importance of integrating a life-course perspective and cultural context to understand subjective digital well-being, highlighting the need for applied policies and interventions that promote high-quality digital inclusion across the lifespan. Digital experience immersion in digital life age moderation social connectedness subjective digital well-being Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Digital technologies (DT) have profoundly reshaped numerous dimensions of contemporary quality of life, influencing leisure, social interaction, access to services, health, and overall well-being (Suárez Álvarez & Vicente, 2024). But these impacts vary across age groups and cultural contexts, creating distinct experiences, opportunities, and challenges (Motorga, 2023; Van Herwegen, 2024). Understanding these diverse uses and experiences is essential for designing interventions and policies that reduce digital divides and promote well-being. Research and action in this regard is urgent, as the pervasive integration of digital technologies into everyday contexts has rendered them an indispensable component of current societal functioning (Salehan & Negahban, 2013). Nowadays, the capacity to facilitate social connectivity or enabling individuals to initiate, sustain, and deepen meaningful relationships that enhance social support is recognized as a salient feature of digital technologies that directly influences quality of life and mental health (Chan, 2015). This expanded potential for interpersonal connection has prompted significant interest in its implications for subjective well-being (Hu et al., 2025; Utz & Breuer, 2017). Across the lifespan, these dynamics shift; for instance, while multimodal connectedness and strong-tie communication are essential for the well-being of older adults (Chan, 2015), internet use typically declines with age, especially among individuals aged 75 and older (Klímová et al., 2018). These patterns may reflect evolving motivations for social interaction, where maintaining strong ties becomes central to social well-being in later life. Although fewer studies have explored these differences across the lifespan (Bell et al., 2015; Beyens et al., 2020), recent findings suggest that social media use mediates well-being, particularly regarding loneliness and eudaimonic well-being, but its decline with age is often associated with poorer life satisfaction outcomes (Wu et al., 2023). Beyond age-related patterns, recent research has emphasized the importance of understanding the subjective quality of digital engagement, highlighting how personal experiences shape digital inclusion and overall quality of life. For instance, the perception of digital belongingness has been shown to directly enhance social well-being, while internet skills, as a form of digital potential, contribute indirectly (Büchi et al., 2018). Illustrating this in an applied context, evidence from Czechia shows that an increasing number of older individuals are adopting digital tools—such as care pads in retirement homes that facilitate continuous interaction and foster a sense of connectedness (European Union Agency for Fundamental Rights [FRA], 2023; Hedvicakova & Svobodova, 2017; Šimonová et al., 2017). These tools not only underpin access to social support (Hu et al., 2025; Utz & Breuer, 2017) and online guidance (van Ingen & Matzat, 2018), but also reflect broader societal efforts to promote active aging and reduce the generational digital divide (Faverio, 2022). Altogether, these developments underscore the need to explore how individuals subjectively experience and integrate digital technologies into their everyday lives, recognizing the personal and contextual meanings that define subjective digital well-being. That is, recent research increasingly emphasizes the importance of examining the quality of digital engagement, reflecting a growing recognition that the implications of DT extend far beyond quantifying time spent on devices or identifying patterns of problematic internet use (Ancis, 2020; Cernohorská et al., 2025; Codina et al., 2025; Huang, 2010; Marchant & O’Donohoe, 2019; Salehan & Negahban, 2013; Walsh et al., 2024). In response to this complexity, two emerging conceptual frameworks have been proposed: Quality of Digital Experience (QDE) and Immersion in Digital Life (IDL) (Witowska et al., 2025a, 2025b). QDE refers to individuals’ subjective experiences with digital technologies, particularly in relation to perceived well-being, social connectedness, and time efficiency. IDL, by contrast, captures the extent to which various life domains such as communication, leisure, and interpersonal relationships, are mediated through digital platforms. Together, these frameworks offer a more comprehensive understanding of digital engagement and its integration into the very fabric of daily quality of life. This study aims to gain a deeper understanding of the dynamics of Quality of Digital Experience (QDE) as well as its relationship with Immersion in Digital Life (IDL), which is critical for designing inclusive digital environments that promote healthy digital practices and a subjective experience of well-being across diverse age groups. Investigating these factors provides essential applied insights for enhancing overall life satisfaction in the digital age. Specifically, the present study investigates how IDL differs across age groups and how age moderates the relationship between QDE and IDL across six European countries. By examining these interactions, we provide a more comprehensive view of digital engagement and its direct implications for human flourishing and well-being in diverse sociocultural contexts. The present study Based on the previous literature, this study has established two objectives: a) Analyse the differences between five age groups (from 18 to +60 years old) in terms of their level of Immersion in Digital Life in six European countries (Czechia, Germany, Poland, Spain, Switzerland, United Kingdom). b) Examine whether and to what extent, age moderates the relationships between dimensions of the Quality of Digital Experience and the Immersion in Digital Life. In order to achieve this objective, we tested the following moderation model: the three dimensions of Quality of Digital Experience (QDE; time and efficiency, well-being, and social connectedness) as the independent variables, Immersion in Digital Life (IDL) as the outcome, and age as the moderator. Therefore, three models were tested, considering the combination of each Quality of Digital Experience dimension (time and efficiency, well-being, and social connectedness). For each model, sex (man or woman) was introduced as control variable ( see Figure 1 ). [Insert Figure 1 here] Methods Participants As shown in table 1, participants were 5,270 adults from six European countries. For the purposes of statistical analyses, the 16 participants who classified their sex as “other” were excluded. In addition, the participants were divided into 5 age groups: 18-29; 30-39; 40-49; 50-59; +60 years old (see Table 1). [Insert Table 1 here] Instruments The Quality of Digital Experience Scale (QDES; Witowska et al., 2025a; 2025b). This scale designed and validated for the languages of participating countries (Witowska et al., 2025b; Czechia, Germany, Poland, Spain, Switzerland, United Kingdom) were considered, which assessed lived experience of digital technology use in terms of well-being (5 items; e.g., I am able to improve my mood using digital technology), social connectedness (12 items; e.g., Using digital technology helps me to deepen relationships with others), and time and efficiency (9 items; e.g., Using digital technology makes it easy to complete tasks in my life). The scale explores to what extent digital technology use is experienced to have a positive impact on these three areas of life. The 26 items) are responded to on a 5-point Likert-type scale where 1 is strongly disagree and 5 strongly agree. Only in the case of Czechia, the scale contains 22 items (see Witowska et al., 2025b). Immersion in Digital Life Scale (IDLS; Witowska et al., 2025a; 2025b). This scale also was elaborated and validated for the languages whose countries (Witowska et al., 2025b; Czechia, Germany, Poland, Spain, Switzerland, United Kingdom) were considered. The scale is composed of 5 items (e.g., To what extent are the activities you do for relaxation digital?) intended to measure the extent of digital technology use in different areas of life (i.e., social relationships, communication with family, free time activities, and time management). The response to this scale is through a visual analogue scale with the two anchors “not at all digital” (score of 0) to “completely digital” (score of 100). Statistical analyses Descriptive and normality analyses were performed for the scales using SPSS version 30 statistical software. Data distribution was considered normal when the skewness and kurtosis indices ranged between -1 and +1. Pearson’s correlations were assessed between the study variables. A univariate analysis of variance (ANOVA) was conducted to determine whether the IDL differed across age groups, as a first step to the main objective. To determine whether and to what extent the associations between QDE and IDL were moderated by age (see Fig. 1), we used the macro-PROCESS version 4.2 selecting model 1 for simple moderation and controlling sex reported by participants. Three models were tested considering the combination of each dimension of QDE (time and efficiency, well-being, and social connectedness) for each country. To further interpret the interaction effects that showed to be statistically significant, we computed simple slopes for high and low values of the moderator (i.e., one SD above and one below the sample’s age mean) and plotted the corresponding regression lines separately. Procedure All participants gave written informed consent prior to the collection of the research data. Ethical approval was obtained from Liverpool John Moores University Research Ethics Committee (UREC; minimal risk registration number: 23/PSY/071). This study complies with the recommendations of the Declaration of Helsinki (World Medical Association, 2025). Results Descriptive and correlations analyses The results of the above correlations showed that there is a moderate to strong relationship between the dimensions of QDES and IDLS (see Table 2). [Insert Table 2 here] ANOVA analyses by age group As a first step to the main objective, a univariate analysis of variance (ANOVA) was conducted to determine whether the IDL differed across age groups created based on the age distribution of participants. After verifying that the assumptions of normality and homogeneity of variances required for the analysis of variance (ANOVA) were met, post hoc comparisons between groups were conducted using Tukey’s test, in order to identify statistically significant differences in the means pairwise (see Table 3). [Insert Table 3 here] Results of ANOVA analyses revealed statistically significant differences between the age groups for the Immersion in Digital Life for all countries. Post hoc results for each country are presented below and can be observed in the Figure 2: [Insert Figure 2 here] 1. Czechia Post hoc through Tukey test showed statistically significant differences between the more than 60 years old group ( M = 6,23; SD = 3,07) and the 18-29 group ( M = 5,57; SD = 1,74). Also, the +60-age group showed statistically significant differences with the 30-39 group ( M = 5,31; SD = 2,09), the 40-49 group ( M = 4,90; SD = 2,15) and the 50-59 group ( M = 4,66; SD = 1,71) groups. At the same time the 50-59 and 40-49 groups showed statistically significant differences with the 30-39 group, and this last group showed statistically significant differences with the 18-29 and +60-age groups. 2. Germany Post hoc through Tukey test showed statistically significant differences between the 30-39 group ( M = 6,5; SD = 2,19), the 18-29 group ( M = 5,28; SD = 1,41), and the 40-49 group ( M = 5,25; SD = 1,74). At the same time, with the 50-59 group ( M = 4,51; SD = 2.07) and the +60-age group ( M = 3,37; SD = 2,02). On the other hand, the 40-49 group showed no statistically significant differences with the 18-29 group. 3. Poland Post hoc through Tukey test showed statistically significant differences between the 30-39 ( M = 5,53; SD = 1,86) and the 18-29 groups ( M = 5,74; SD = 1,45) with the 40-49 group ( M = 4,94; SD = 2,05), the 50-59 group ( M = 4,65; SD = 2.15) and the +60-age group ( M = 4,92; SD = 2,29). On the other hand, the 30-39 and 18-29 groups showed no statistically significant differences between them. Also, there were no statistically significant differences between the 50-59, 40-49, and +60age groups. 4. Spain Post hoc through Tukey test showed no statistically significant differences between the 30-39 group ( M = 5,19; SD = 1,63) and the 18-29 group ( M = 5,17; SD = 1,63), but these two age groups showed statistically significant differences with the 40-49 group ( M = 4,41; SD = 1,63), the 50-59 group ( M = 4,18; SD = 1,63) and the +60-age group ( M = 4,08; SD = 1,63). Between these last three-age groups there were no statistically significant differences for the IDL. 5. Switzerland Post hoc through Tukey test showed statistically significant differences between the 30-39 group ( M = 6,18; SD = 1,75) and the 18-20 group ( M = 5,62; SD = 1,63) with the 40-49 ( M = 4,92; SD = 2,02) group; at the same time the 30-39 and 18-20 groups showed statistically significant differences with the 50-59 ( M = 4,11; SD = 2,02) group and the +60-age group ( M = 3,95; SD = 2,08). There were no statistically significant differences between the 30-39 group and the 18-29 group, and neither between the 50-59 group and the +60-age group. 6. United Kingdom Post hoc through Tukey test showed statistically significant differences between the 18-29 group ( M = 6,09; SD = 1,48), and the following groups: 30-39 group ( M = 5,79; SD = 1,78), and the 40-49 ( M = 5,60; SD = 1,88) group, with the 50-59 ( M = 4,71; SD = 1,88) group and the +60-age group ( M = 4,09; SD = 1,96). On the other hand, there were no statistically significant differences between the 18-29, 30-39, and 40-49 groups. Moderation effect Country-specific moderation effects are illustrated below, with complete moderation values provided in the Supplementary Material. In Czechia, age moderated the effects of each dimension of QDE, social connectedness ( R 2 = ,47; p = .01), well-being ( R 2 = ,40; p = .01), and time and efficiency ( R 2 = ,25; p = .01) on IDL variations. In these relationships, it can be observed that as age increases IDL (steeper slope) will be greater, even significantly exceeding the relationship between the QDE and IDL in the younger adult group. While sex was included as a covariate in all models, it was statistically significant only in the well-being and time and efficiency models (see Figure 3). [Insert Figure 3 here] In the case of Germany, the effect of age is seen in social connectedness ( R 2 = ,42; p = .01) and well-being ( R 2 = ,38; p = .01), with a similar dynamic in both cases: younger individuals exhibit greater QDE, which positively predicts IDL. The association persists in older individuals, though QDE levels are lower.”. Sex was included as a covariate in all models but did not reach statistical significance in this sample (see Figure 4). [Insert Figure 4 here] Results for Poland show that the increase in IDL related to the increase in the three studied dimensions of QDE is stronger among older people: social connectedness ( R 2 = ,31; p = .01), well-being ( R 2 = .29; p = .01), and time and efficiency ( R 2 = ,23; p = .01). As age increases, immersion in digital life levels also increases to almost the same level as in younger groups, although the levels in QDE are smaller than among the younger ones. Sex was included as a covariate in all models but did not reach statistical significance in this sample (see Figure 5). [Insert Figure 5 here] In Spain, the relationships between QDE and IDL moderated by age also follow a similar pattern, where initially, at older ages, the effect of well-being ( R 2 = ,32; p = .01) and time and efficiency ( R 2 = ,26; p = .01) significantly increase positively the level of IDL compared to younger adults. While sex was included as a covariate in all models, it was statistically significant only in the time and efficiency model (see Figure 6). [Insert Figure 6 here] Furthermore, in the case of Switzerland, age only moderates the relationship between time and efficiency (R 2 = ,29; p = .01) and IDL, where again the slope increases in older ages compared with younger people. While sex was included as a covariate in all models, it was statistically significant only in the time and efficiency model (see Figure 7). [Insert Figure 7 here] Finally, for UK, age only moderates the relationship between well-being ( R 2 = ,36; p = .01) and IDL, where the slope also increases positively in older ages compared with younger people. Although sex was included as a covariate in all models, it was statistically significant only in the well-being model (see Figure 8). [Insert Figure 8 here] Discussion This study examined digital engagement across six European countries, highlighting how age moderates the relationship between the Quality of Digital Experience (QDE) and Immersion in Digital Life (IDL). The findings contribute to a growing body of literature that seeks to understand digital engagement not merely in terms of access or frequency, but through the lens of experiential quality and contextual variation. Such insights are critical for establishing intervention criteria and social policies aimed at enhancing the subjective well-being and quality of life of citizens in a digitized society. Regarding the first objective, the results challenge the assumption of a linear generational digital divide. We found high levels of IDL among adults over 60 years old in Czechia and Poland. This could reflect successful national efforts to promote digital inclusion, such as the integration of care technologies in retirement homes or initiatives aimed at active aging (Digital Poland, 2025; European Union Agency for Fundamental Rights [FRA], 2023; Hedvicakova & Svobodova, 2017). These results align with recent evidence suggesting that digital inclusion and social participation are fundamental drivers of subjective well-being in older populations (Hu et al., 2025). In contrast, Spain and the United Kingdom followed a more traditional linear trend, where immersion is highest among younger "digital natives" and declines with age (Mertala et al., 2024). These variations underscore that digital immersion is not a universal phenomenon but is shaped by national strategies, cultural attitudes toward aging, and available infrastructure (Motorga, 2023; Ross & Maynard, 2021). Concerning the second objective, our results reveal that the social connectedness dimension of QDE is the most consistent predictor of IDL across all ages. This highlights that digital interactions are central to how individuals perceive their immersion in digital environments (Witowska et al., 2025a). This finding resonates with Chan’s (2015) assertion that multimodal connectedness and strong-tie communication are essential for maintaining social capital and emotional well-being. In relation to the well-being dimension of QDE, our study underscores that its relationship with IDL tends to be stronger among older adults. This suggests that for older populations, technology is increasingly viewed as a tool for life satisfaction rather than just a functional utility. This perspective is supported by Vanden Abeele’s (2021) dynamic model of digital well-being, which emphasizes the balance between connectivity and disconnectivity. As shown by Suárez Álvarez and Vicente (2024), the impact of internet use on life satisfaction is complex; it is the quality of the experience, rather than mere time spent online, that determines whether technology fosters healthy digital practices or leads to stress. Consequently, digital inclusion should be understood as a holistic process that empowers users to enhance their autonomy and social connectedness, especially in later life stages (Klímová et al., 2018; Tomczyk & Kielar, 2025). Finally, regarding the time and efficiency dimension, the results revealed it to be the least consistent predictor of IDL. This suggests that while functional benefits are important, they do not necessarily foster deep digital engagement unless accompanied by social or emotional value. This aligns with recent critiques advocating for more nuanced measures of digital experience over simple screen-time metrics (Witowska et al., 2025b). Among younger users, the high levels of immersion reflect a fuzzy use of technology where boundaries between work, leisure, and identity construction are blurred, reflecting contemporary views of the self as a fluid phenomenon in digital spaces (Codina, 2007; Codina et al., 2025; Ross & Maynard, 2021). In sum, this study advances the understanding of digital immersion by integrating experiential quality, age, and cultural context. It calls for differentiated approaches to digital inclusion that prioritize subjective digital well-being. Future research should incorporate longitudinal trajectories and additional variables like education or digital literacy to further explore how the Fourth Industrial Revolution reconfigures our experience of time, leisure, and belonging. Conclusion This study provides strong evidence that the Quality of Digital Experience (QDE), particularly regarding social connectedness and well-being, is a fundamental determinant of Immersion in Digital Life (IDL). Our findings reveal that this relationship is not uniform across age groups or national contexts, identifying age as a significant moderator. Specifically, older individuals generally showed stronger associations between QDE and IDL, especially concerning the well-being dimension. Notably, in countries such as Czechia and Poland, older adults demonstrated high levels of digital immersion, suggesting that age-related engagement is shaped by broader sociocultural factors and proactive policies that promote active aging through technology. Among the three QDE dimensions, social connectedness consistently emerged as the most influential predictor of IDL. This finding underscores the central role of meaningful digital interactions in fostering technological engagement and supports the notion that digital platforms can enhance social capital and psychosocial flourishing across the lifespan provided the experience is perceived as positive and supportive. These insights have direct practical implications for the design of age-sensitive digital policies. Whether in education, workplace training, or lifelong learning, understanding how different age groups and cultural contexts influence engagement is essential for strategies aimed at enhancing digital well-being, reducing exclusion, and promoting healthy digital habits. Crucially, our study indicates that digital immersion is not inherently beneficial or detrimental; its impact on subjective quality of life depends on the quality and purpose of the engagement. As digital technologies continue to permeate all aspects of daily life, future research should adopt longitudinal approaches to track how these experiences evolve. We propose that subsequent studies incorporate broader sociodemographic variables such as educational level, employment status (e.g., active workers vs. retirees), professional device use, and digital literacy. Including these factors will provide a more comprehensive view of how digital immersion is shaped by life circumstances, ultimately informing more effective interventions to promote human flourishing in the digital age. Declarations Funding The author(s) declare financial support was received for the research and/or publication of this article. The CHANSE Project TIMe experience in Europe’s Digital age (TIMED) under CHANSE ERA-NET Co-fund programme, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement no 101004509. References Ancis, J. R. (2020). 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PLOS ONE, 19 (10), e0306910. https://doi.org/10.1371/journal.pone.0306910 Witowska, J., Ogden, R., Schoetensack, C., Goncikowska, K., Wittmann, M., Černohorská, V., Codina, N., Martin-Soelch, C., Fernández Boente, M., Giner, G., Pestana, J. V., & Papastamatelou, J. (2025a). New measurements of digital technology use: The Immersion in Digital Life and Quality of Digital Experience scales. Frontiers in Psychiatry, 16 , 1595536. https://doi.org/10.3389/fpsyt.2025.1595536 Witowska, J., Schoetensack, C., Ogden, R. S., Goncikowska, K., Chappuis, S., Fernández Boente, M., Klegr, T., Papastamatelou, J., Valenzuela, R., Černohorská, V., Codina, N., Martin Soelch, C., Wittmann, M., Pestana, J. V., Giner, G., & Meteier, Q. (2025b). Validation of The Immersion in Digital Life and Quality of Digital Experience Scales in German, French, Spanish, Polish and Czech. Frontiers in Psychiatry, 16 , 1645260. https://doi.org/10.3389/FPSYT.2025.1645260 World Medical Association. (2025). Declaration of Helsinki: Ethical principles for medical research involving human participants. JAMA, 333 (1), 71–74. https://doi.org/10.1001/JAMA.2024.21972 Wu, J., Lin, X. Y., Lin, T., & Almeida, D. (2023). Well-being in middle-aged adults: The mediating role of social media use. Innovation in Aging, 7 (Supplement_1), 850. https://doi.org/10.1093/geroni/igad104.2740 Tables Table 1 Data distribution and descriptive information about study participants Country n Men Women Other Age M ( SD ) Czechia 968 445 520 3 18-66 45,23 (13,33) Germany 822 414 405 3 18-71 47,67 (13,12) Poland 1208 603 604 1 18-65 42,28 (12,94) Spain 795 381 412 2 18-65 46,43 (17,77) Switzerland 647 307 337 3 18-82 45,29 (14,96) United Kingdom 830 424 402 4 18-73 45,26 (13,36) Total 5,270 2,574 2,680 16 Table 2 Descriptive and correlations analyses of study variables for each country Variable M SD Skewness Kurtosis 1 2 3 Czechia 1.Time and efficiency (QDES) 3,39 0,78 -0,27 0,20 2. Well-being (QDES) 3,26 0,78 -0,25 0,06 .52** 3.Social connectedness (QDES) 3,70 0,73 -0,21 -0,27 .45** .70** 4. Immersion in DL (IDLS) 5,20 2,18 0,12 -0,37 .42** .59** .67** Germany 1.Time and efficiency (QDES) 3,33 0,79 -0,43 0,21 2. Well-being (QDES) 3,13 0,87 -0,39 -0,22 .59** 3.Social connectedness (QDES) 3,72 0,70 -0,70 1,05 .51** .76** 4. Immersion in DL (IDLS) 4,90 2,12 -0.14 -0,70 .23** .53** .59** Poland 1.Time and efficiency (QDES) 3,82 0,71 -0,70 1,10 2. Well-being (QDES) 3,47 0,79 -0,51 0,28 .64** 3.Social connectedness (QDES) 3,15 0,86 -0,19 -0,27 .57** .77** 4. Immersion in DL (IDLS) 5,17 1,99 -0,16 -0,20 .41** .50** .52** Spain 1.Time and efficiency (QDES) 3,74 0,74 -0,75 0,90 2. Well-being (QDES) 3,24 0,83 -0,33 0,12 .61** 3.Social connectedness (QDES) 3,21 0,81 -0,48 0,28 .58** .72** 4. Immersion in DL (IDLS) 4,51 2,00 0,10 -0,45 .46** .53** .55** Switzerland 1.Time and efficiency (QDES) 3,66 0,78 -0,95 1,41 2. Well-being (QDES) 3,15 0,84 -0,20 -0,24 .56** 3.Social connectedness (QDES) 2,94 0,87 -0,17 -0,26 .52** .76** 4. Immersion in DL (IDLS) 5,01 2,10 -0,16 -0,62 .41** ,61** .64** United Kingdom 1.Time and efficiency (QDES) 3.86 0,64 -0,78 1,53 2. Well-being (QDES) 3,48 0,81 -0,47 0,09 .63** 3.Social connectedness (QDES) 3,32 0,85 -0,50 -0,02 .59** .73** 4. Immersion in DL (IDLS) 5,22 1,95 -0,28 0,09 .46** .52** .57** Note . Range of dimensions of QDES: 1-5; Range of IDLS: 1-10; ** p < .01 Table 3 ANOVA analysis by age group for each country Country F df p 2 Czechia 14,66 4 0,01 0,06 Germany 56,89 4 0,01 0,22 Poland 13,55 4 0,01 0,04 Spain 10,28 4 0,01 0,05 Switzerland 14,66 4 0,01 0,06 United Kingdom 31,39 4 0,01 0,13 Additional Declarations No competing interests reported. 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2","display":"","copyAsset":false,"role":"figure","size":44481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of IDL scores by age group and country\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9269714/v1/92a59b5a85a14eba3c67438a.png"},{"id":107624297,"identity":"23d9ea43-77b9-4f76-94e2-8f239ba4e342","added_by":"auto","created_at":"2026-04-23 10:13:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":306692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModeration plot for Czechia. “Interpolation slope (units): ±13.33”\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9269714/v1/0de63dc46fd2d76ae926295c.png"},{"id":107624298,"identity":"ed7ecbbe-89ca-422c-ba28-ca5515359ada","added_by":"auto","created_at":"2026-04-23 10:13:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":219980,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModeration plot corresponding for Germany. “Interpolation slope (units): ±13.12”\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9269714/v1/da1318c9f259e2ebfe00f664.png"},{"id":107707479,"identity":"f76b96da-28c0-4238-aea6-a51edec95d18","added_by":"auto","created_at":"2026-04-24 09:20:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":327048,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModeration plot corresponding for Poland. “Interpolation slope (units): ±12.94”\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9269714/v1/7c2e2b4d5f8a8c48884412b2.png"},{"id":107624291,"identity":"35b45b4c-216a-4165-ac5f-3894d72cdf5b","added_by":"auto","created_at":"2026-04-23 10:13:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":218559,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModeration plot corresponding for Spain. “Interpolation slope (units): ±12.77”\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9269714/v1/5a0e5a43acf0df54e10b06a9.png"},{"id":107624294,"identity":"75729023-2458-4618-b136-4a0c88ffd504","added_by":"auto","created_at":"2026-04-23 10:13:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":103832,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModeration plot corresponding for Switzerland. “Interpolation slope (units): ±14.93”\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9269714/v1/22d8e06252d9bfc7df6e14d2.png"},{"id":107624293,"identity":"41fff39c-cbdb-41b7-876a-5e4839b0658a","added_by":"auto","created_at":"2026-04-23 10:13:39","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":118453,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModeration plot corresponding for United Kingdom. “Interpolation slope (units): ±13.30”\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9269714/v1/94c96ec0116f556a8caaca48.png"},{"id":109296384,"identity":"d2c1af4f-12e4-4bbc-919e-4b50ed38a095","added_by":"auto","created_at":"2026-05-15 08:46:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1774925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9269714/v1/f0c3bd36-bd28-48b9-a7e4-29d361dde003.pdf"},{"id":107624289,"identity":"8a09e790-8c53-475b-9bc1-e4cb21785101","added_by":"auto","created_at":"2026-04-23 10:13:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25810,"visible":true,"origin":"","legend":"","description":"","filename":"20260408Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9269714/v1/7fba294b18d2e910ba90232a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Quality of Digital Experience: Implications for Digital Well-being and Immersion in Six European Countries","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDigital technologies (DT) have profoundly reshaped numerous dimensions of contemporary quality of life, influencing leisure, social interaction, access to services, health, and overall well-being (Su\u0026aacute;rez \u0026Aacute;lvarez \u0026amp; Vicente, 2024). But these impacts vary across age groups and cultural contexts, creating distinct experiences, opportunities, and challenges (Motorga, 2023; Van Herwegen, 2024). Understanding these diverse uses and experiences is essential for designing interventions and policies that reduce digital divides and promote well-being. Research and action in this regard is urgent, as the pervasive integration of digital technologies into everyday contexts has rendered them an indispensable component of current societal functioning (Salehan \u0026amp; Negahban, 2013).\u003c/p\u003e\n\n\u003cp\u003eNowadays, the capacity to facilitate social connectivity or enabling individuals to initiate, sustain, and deepen meaningful relationships that enhance social support is recognized as a salient feature of digital technologies that directly influences quality of life and mental health (Chan, 2015). This expanded potential for interpersonal connection has prompted significant interest in its implications for subjective well-being (Hu et al., 2025; Utz \u0026amp; Breuer, 2017). Across the lifespan, these dynamics shift; for instance, while multimodal connectedness and strong-tie communication are essential for the well-being of older adults (Chan, 2015), internet use typically declines with age, especially among individuals aged 75 and older (Kl\u0026iacute;mov\u0026aacute; et al., 2018). These patterns may reflect evolving motivations for social interaction, where maintaining strong ties becomes central to social well-being in later life. Although fewer studies have explored these differences across the lifespan (Bell et al., 2015; Beyens et al., 2020), recent findings suggest that social media use mediates well-being, particularly regarding loneliness and eudaimonic well-being, but its decline with age is often associated with poorer life satisfaction outcomes (Wu et al., 2023).\u003c/p\u003e\n\n\u003cp\u003eBeyond age-related patterns, recent research has emphasized the importance of understanding the subjective quality of digital engagement, highlighting how personal experiences shape digital inclusion and overall quality of life. For instance, the perception of digital belongingness has been shown to directly enhance social well-being, while internet skills, as a form of digital potential, contribute indirectly (B\u0026uuml;chi et al., 2018). Illustrating this in an applied context, evidence from Czechia shows that an increasing number of older individuals are adopting digital tools\u0026mdash;such as care pads in retirement homes that facilitate continuous interaction and foster a sense of connectedness (European Union Agency for Fundamental Rights [FRA], 2023; Hedvicakova \u0026amp; Svobodova, 2017; \u0026Scaron;imonov\u0026aacute; et al., 2017). These tools not only underpin access to social support (Hu et al., 2025; Utz \u0026amp; Breuer, 2017) and online guidance (van Ingen \u0026amp; Matzat, 2018), but also reflect broader societal efforts to promote active aging and reduce the generational digital divide (Faverio, 2022). Altogether, these developments underscore the need to explore how individuals subjectively experience and integrate digital technologies into their everyday lives, recognizing the personal and contextual meanings that define subjective digital well-being.\u003c/p\u003e\n\n\u003cp\u003eThat is, recent research increasingly emphasizes the importance of examining the quality of digital engagement, reflecting a growing recognition that the implications of DT extend far beyond quantifying time spent on devices or identifying patterns of problematic internet use (Ancis, 2020; Cernohorsk\u0026aacute; et al., 2025; Codina et al., 2025; Huang, 2010; Marchant \u0026amp; O\u0026rsquo;Donohoe, 2019; Salehan \u0026amp; Negahban, 2013; Walsh et al., 2024). In response to this complexity, two emerging conceptual frameworks have been proposed: Quality of Digital Experience (QDE) and Immersion in Digital Life (IDL) (Witowska et al., 2025a, 2025b). QDE refers to individuals\u0026rsquo; subjective experiences with digital technologies, particularly in relation to perceived well-being, social connectedness, and time efficiency. IDL, by contrast, captures the extent to which various life domains such as communication, leisure, and interpersonal relationships, are mediated through digital platforms. Together, these frameworks offer a more comprehensive understanding of digital engagement and its integration into the very fabric of daily quality of life.\u003c/p\u003e\n\n\u003cp\u003eThis study aims to gain a deeper understanding of the dynamics of Quality of Digital Experience (QDE) as well as its relationship with Immersion in Digital Life (IDL), which is critical for designing inclusive digital environments that promote healthy digital practices and a subjective experience of well-being across diverse age groups. Investigating these factors provides essential applied insights for enhancing overall life satisfaction in the digital age. Specifically, the present study investigates how IDL differs across age groups and how age moderates the relationship between QDE and IDL across six European countries. By examining these interactions, we provide a more comprehensive view of digital engagement and its direct implications for human flourishing and well-being in diverse sociocultural contexts.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eThe present study\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003eBased on the previous literature, this study has established two objectives:\u003c/p\u003e\n\n\u003cp\u003ea) Analyse the differences between five age groups (from 18 to +60 years old) in terms of their level of Immersion in Digital Life in six European countries (Czechia, Germany, Poland, Spain, Switzerland, United Kingdom). \u003c/p\u003e\n\n\u003cp\u003eb) Examine whether and to what extent, age moderates the relationships between dimensions of the Quality of Digital Experience and the Immersion in Digital Life. In order to achieve this objective, we tested the following moderation model: the three dimensions of Quality of Digital Experience (QDE; time and efficiency, well-being, and social connectedness) as the independent variables, Immersion in Digital Life (IDL) as the outcome, and age as the moderator. Therefore, three models were tested, considering the combination of each Quality of Digital Experience dimension (time and efficiency, well-being, and social connectedness). For each model, sex (man or woman) was introduced as control variable (\u003cem\u003esee Figure 1\u003c/em\u003e).\u003c/p\u003e\n\n\u003cp\u003e[Insert Figure 1 here]\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003eAs shown in table 1, participants were 5,270 adults from six European countries. For the purposes of statistical analyses, the 16 participants who classified their sex as \u0026ldquo;other\u0026rdquo; were excluded. In addition, the participants were divided into 5 age groups: 18-29; 30-39; 40-49; 50-59; +60 years old (see Table 1).\u003c/p\u003e\n\n\u003cp\u003e[Insert Table 1 here]\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eInstruments\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eThe Quality of Digital Experience Scale\u003c/em\u003e (QDES; Witowska et al., 2025a; 2025b). This scale designed and validated for the languages of participating countries (Witowska et al., 2025b; Czechia, Germany, Poland, Spain, Switzerland, United Kingdom) were considered, which assessed lived experience of digital technology use in terms of well-being (5 items; e.g., I am able to improve my mood using digital technology), social connectedness (12 items; e.g., Using digital technology helps me to deepen relationships with others), and time and efficiency (9 items; e.g., Using digital technology makes it easy to complete tasks in my life). The scale explores to what extent digital technology use is experienced to have a positive impact on these three areas of life. The 26 items) are responded to on a 5-point Likert-type scale where 1 is strongly disagree and 5 strongly agree. Only in the case of Czechia, the scale contains 22 items (see Witowska et al., 2025b). \u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eImmersion in Digital Life Scale\u003c/em\u003e (IDLS; Witowska et al., 2025a; 2025b). This scale also was elaborated and validated for the languages whose countries (Witowska et al., 2025b; Czechia, Germany, Poland, Spain, Switzerland, United Kingdom) were considered. The scale is composed of 5 items (e.g., To what extent are the activities you do for relaxation digital?) intended to measure the extent of digital technology use in different areas of life (i.e., social relationships, communication with family, free time activities, and time management). The response to this scale is through a visual analogue scale with the two anchors \u0026ldquo;not at all digital\u0026rdquo; (score of 0) to \u0026ldquo;completely digital\u0026rdquo; (score of 100).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003eDescriptive and normality analyses were performed for the scales using SPSS version 30 statistical software. Data distribution was considered normal when the skewness and kurtosis indices ranged between -1 and +1. Pearson\u0026rsquo;s correlations were assessed between the study variables. A univariate analysis of variance (ANOVA) was conducted to determine whether the IDL differed across age groups, as a first step to the main objective. \u003c/p\u003e\n\n\u003cp\u003eTo determine whether and to what extent the associations between QDE and IDL were moderated by age (see Fig. 1), we used the macro-PROCESS version 4.2 selecting model 1 for simple moderation and controlling sex reported by participants. Three models were tested considering the combination of each dimension of QDE (time and efficiency, well-being, and social connectedness) for each country. To further interpret the interaction effects that showed to be statistically significant, we computed simple slopes for high and low values of the moderator (i.e., one SD above and one below the sample\u0026rsquo;s age mean) and plotted the corresponding regression lines separately. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eProcedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants gave written informed consent prior to the collection of the research data. Ethical approval was obtained from Liverpool John Moores University Research Ethics Committee (UREC; minimal risk registration number: 23/PSY/071). This study complies with the recommendations of the Declaration of Helsinki (World Medical Association, 2025).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive and correlations analyses\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003eThe results of the above correlations showed that there is a moderate to strong relationship between the dimensions of QDES and IDLS (see Table 2).\u003c/p\u003e\n\n\u003cp\u003e[Insert Table 2 here]\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eANOVA analyses by age group\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003eAs a first step to the main objective, a univariate analysis of variance (ANOVA) was conducted to determine whether the IDL differed across age groups created based on the age distribution of participants. After verifying that the assumptions of normality and homogeneity of variances required for the analysis of variance (ANOVA) were met, \u003cem\u003epost hoc\u003c/em\u003e comparisons between groups were conducted using Tukey\u0026rsquo;s test, in order to identify statistically significant differences in the means pairwise (see Table 3).\u003c/p\u003e\n\n\u003cp\u003e[Insert Table 3 here]\u003c/p\u003e\n\n\u003cp\u003eResults of ANOVA analyses revealed statistically significant differences between the age groups for the Immersion in Digital Life for all countries. \u003cem\u003ePost hoc\u003c/em\u003e results for each country are presented below and can be observed in the Figure 2:\u003c/p\u003e\n\n\u003cp\u003e[Insert Figure 2 here]\u003c/p\u003e\n\n\u003cp\u003e1. Czechia\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePost hoc\u003c/em\u003e through Tukey test showed statistically significant differences between the more than 60 years old group (\u003cem\u003eM\u003c/em\u003e = 6,23; \u003cem\u003eSD\u003c/em\u003e = 3,07) and the 18-29 group (\u003cem\u003eM\u003c/em\u003e = 5,57; \u003cem\u003eSD\u003c/em\u003e = 1,74). Also, the +60-age group showed statistically significant differences with the 30-39 group (\u003cem\u003eM\u003c/em\u003e = 5,31; \u003cem\u003eSD\u003c/em\u003e = 2,09), the 40-49 group (\u003cem\u003eM\u003c/em\u003e = 4,90; \u003cem\u003eSD\u003c/em\u003e = 2,15) and the 50-59 group (\u003cem\u003eM\u003c/em\u003e = 4,66; \u003cem\u003eSD\u003c/em\u003e = 1,71) groups. At the same time the 50-59 and 40-49 groups showed statistically significant differences with the 30-39 group, and this last group showed statistically significant differences with the 18-29 and +60-age groups.\u003c/p\u003e\n\n\u003cp\u003e2. Germany\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePost hoc\u003c/em\u003e through Tukey test showed statistically significant differences between the 30-39 group (\u003cem\u003eM\u003c/em\u003e = 6,5; \u003cem\u003eSD\u003c/em\u003e = 2,19), the 18-29 group (\u003cem\u003eM\u003c/em\u003e = 5,28; \u003cem\u003eSD\u003c/em\u003e = 1,41), and the 40-49 group (\u003cem\u003eM\u003c/em\u003e = 5,25; \u003cem\u003eSD\u003c/em\u003e = 1,74). At the same time, with the 50-59 group (\u003cem\u003eM\u003c/em\u003e = 4,51; \u003cem\u003eSD\u003c/em\u003e = 2.07) and the +60-age group (\u003cem\u003eM\u003c/em\u003e = 3,37; \u003cem\u003eSD\u003c/em\u003e = 2,02). On the other hand, the 40-49 group showed no statistically significant differences with the 18-29 group.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e3. Poland\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePost hoc\u003c/em\u003e through Tukey test showed statistically significant differences between the 30-39 (\u003cem\u003eM\u003c/em\u003e = 5,53; \u003cem\u003eSD\u003c/em\u003e = 1,86) and the 18-29 groups (\u003cem\u003eM\u003c/em\u003e = 5,74; \u003cem\u003eSD\u003c/em\u003e = 1,45) with the 40-49 group (\u003cem\u003eM\u003c/em\u003e = 4,94; \u003cem\u003eSD\u003c/em\u003e = 2,05), the 50-59 group (\u003cem\u003eM\u003c/em\u003e = 4,65; \u003cem\u003eSD\u003c/em\u003e = 2.15) and the +60-age group (\u003cem\u003eM\u003c/em\u003e = 4,92; \u003cem\u003eSD\u003c/em\u003e = 2,29). On the other hand, the 30-39 and 18-29 groups showed no statistically significant differences between them. Also, there were no statistically significant differences between the 50-59, 40-49, and +60age groups.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e4. Spain\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePost hoc\u003c/em\u003e through Tukey test showed no statistically significant differences between the 30-39 group (\u003cem\u003eM\u003c/em\u003e = 5,19; \u003cem\u003eSD\u003c/em\u003e = 1,63) and the 18-29 group (\u003cem\u003eM\u003c/em\u003e = 5,17;\u003cem\u003e SD\u003c/em\u003e = 1,63), but these two age groups showed statistically significant differences with the 40-49 group (\u003cem\u003eM\u003c/em\u003e = 4,41;\u003cem\u003e SD\u003c/em\u003e = 1,63), the 50-59 group (\u003cem\u003eM\u003c/em\u003e = 4,18;\u003cem\u003e SD\u003c/em\u003e = 1,63) and the +60-age group (\u003cem\u003eM\u003c/em\u003e = 4,08;\u003cem\u003e SD\u003c/em\u003e = 1,63). Between these last three-age groups there were no statistically significant differences for the IDL.\u003c/p\u003e\n\n\u003cp\u003e5. Switzerland\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePost hoc\u003c/em\u003e through Tukey test showed statistically significant differences between the 30-39 group (\u003cem\u003eM\u003c/em\u003e = 6,18; \u003cem\u003eSD\u003c/em\u003e = 1,75) and the 18-20 group (\u003cem\u003eM\u003c/em\u003e = 5,62; \u003cem\u003eSD\u003c/em\u003e = 1,63) with the 40-49 (\u003cem\u003eM\u003c/em\u003e = 4,92; \u003cem\u003eSD\u003c/em\u003e = 2,02) group; at the same time the 30-39 and 18-20 groups showed statistically significant differences with the 50-59 (\u003cem\u003eM\u003c/em\u003e = 4,11; \u003cem\u003eSD\u003c/em\u003e = 2,02) group and the +60-age group (\u003cem\u003eM\u003c/em\u003e = 3,95; \u003cem\u003eSD\u003c/em\u003e = 2,08). There were no statistically significant differences between the 30-39 group and the 18-29 group, and neither between the 50-59 group and the +60-age group. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e6. United Kingdom \u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePost hoc\u003c/em\u003e through Tukey test showed statistically significant differences between the 18-29 group (\u003cem\u003eM\u003c/em\u003e = 6,09; \u003cem\u003eSD\u003c/em\u003e = 1,48), and the following groups: 30-39 group (\u003cem\u003eM\u003c/em\u003e = 5,79; \u003cem\u003eSD\u003c/em\u003e = 1,78), and the 40-49 (\u003cem\u003eM\u003c/em\u003e = 5,60; \u003cem\u003eSD\u003c/em\u003e = 1,88) group, with the 50-59 (\u003cem\u003eM\u003c/em\u003e = 4,71; \u003cem\u003eSD\u003c/em\u003e = 1,88) group and the +60-age group (\u003cem\u003eM\u003c/em\u003e = 4,09; \u003cem\u003eSD\u003c/em\u003e = 1,96). On the other hand, there were no statistically significant differences between the 18-29, 30-39, and 40-49 groups. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eModeration effect\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003eCountry-specific moderation effects are illustrated below, with complete moderation values provided in the Supplementary Material.\u003c/p\u003e\n\n\u003cp\u003eIn Czechia, age moderated the effects of each dimension of QDE, social connectedness (\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = ,47; \u003cem\u003ep\u003c/em\u003e = .01), well-being (\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = ,40;\u003cem\u003e p\u003c/em\u003e = .01), and time and efficiency (\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = ,25;\u003cem\u003e p\u003c/em\u003e = .01) on IDL variations. In these relationships, it can be observed that as age increases IDL (steeper slope) will be greater, even significantly exceeding the relationship between the QDE and IDL in the younger adult group. While sex was included as a covariate in all models, it was statistically significant only in the well-being and time and efficiency models (see Figure 3).\u003c/p\u003e\n\n\u003cp\u003e[Insert Figure 3 here]\u003c/p\u003e\n\n\u003cp\u003eIn the case of Germany, the effect of age is seen in social connectedness (\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = ,42;\u003cem\u003e p\u003c/em\u003e = .01) and well-being (\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = ,38;\u003cem\u003e p\u003c/em\u003e = .01), with a similar dynamic in both cases: younger individuals exhibit greater QDE, which positively predicts IDL. The association persists in older individuals, though QDE levels are lower.\u0026rdquo;. Sex was included as a covariate in all models but did not reach statistical significance in this sample (see Figure 4).\u003c/p\u003e\n\n\u003cp\u003e[Insert Figure 4 here]\u003c/p\u003e\n\u003cp\u003eResults for Poland show that the increase in IDL related to the increase in the three studied dimensions of QDE is stronger among older people: social connectedness (\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = ,31;\u003cem\u003e p\u003c/em\u003e = .01), well-being (\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = .29;\u003cem\u003e p\u003c/em\u003e = .01), and time and efficiency (\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = ,23;\u003cem\u003e p\u003c/em\u003e = .01). As age increases, immersion in digital life levels also increases to almost the same level as in younger groups, although the levels in QDE are smaller than among the younger ones. Sex was included as a covariate in all models but did not reach statistical significance in this sample (see Figure 5).\u003c/p\u003e\n\n\u003cp\u003e[Insert Figure 5 here]\u003c/p\u003e\n\n\u003cp\u003eIn Spain, the relationships between QDE and IDL moderated by age also follow a similar pattern, where initially, at older ages, the effect of well-being (\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = ,32;\u003cem\u003e p\u003c/em\u003e = .01) and time and efficiency (\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = ,26;\u003cem\u003e p\u003c/em\u003e = .01) significantly increase positively the level of IDL compared to younger adults. While sex was included as a covariate in all models, it was statistically significant only in the time and efficiency model (see Figure 6).\u003c/p\u003e\n\n\u003cp\u003e[Insert Figure 6 here]\u003c/p\u003e\n\n\u003cp\u003eFurthermore, in the case of Switzerland, age only moderates the relationship between time and efficiency (R\u003csup\u003e2\u003c/sup\u003e = ,29;\u003cem\u003e p\u003c/em\u003e = .01) and IDL, where again the slope increases in older ages compared with younger people. While sex was included as a covariate in all models, it was statistically significant only in the time and efficiency model (see Figure 7).\u003c/p\u003e\n\n\u003cp\u003e[Insert Figure 7 here]\u003c/p\u003e\n\n\n\u003cp\u003eFinally, for UK, age only moderates the relationship between well-being (\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = ,36;\u003cem\u003e p\u003c/em\u003e = .01) and IDL, where the slope also increases positively in older ages compared with younger people. Although sex was included as a covariate in all models, it was statistically significant only in the well-being model (see Figure 8).\u003c/p\u003e\n\n\u003cp\u003e[Insert Figure 8 here]\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined digital engagement across six European countries, highlighting how age moderates the relationship between the Quality of Digital Experience (QDE) and Immersion in Digital Life (IDL). The findings contribute to a growing body of literature that seeks to understand digital engagement not merely in terms of access or frequency, but through the lens of experiential quality and contextual variation. Such insights are critical for establishing intervention criteria and social policies aimed at enhancing the subjective well-being and quality of life of citizens in a digitized society.\u003c/p\u003e\n\n\u003cp\u003eRegarding the first objective, the results challenge the assumption of a linear generational digital divide. We found high levels of IDL among adults over 60 years old in Czechia and Poland. This could reflect successful national efforts to promote digital inclusion, such as the integration of care technologies in retirement homes or initiatives aimed at active aging (Digital Poland, 2025; European Union Agency for Fundamental Rights [FRA], 2023; Hedvicakova \u0026amp; Svobodova, 2017). These results align with recent evidence suggesting that digital inclusion and social participation are fundamental drivers of subjective well-being in older populations (Hu et al., 2025). In contrast, Spain and the United Kingdom followed a more traditional linear trend, where immersion is highest among younger \u0026quot;digital natives\u0026quot; and declines with age (Mertala et al., 2024). These variations underscore that digital immersion is not a universal phenomenon but is shaped by national strategies, cultural attitudes toward aging, and available infrastructure (Motorga, 2023; Ross \u0026amp; Maynard, 2021).\u003c/p\u003e\n\n\u003cp\u003eConcerning the second objective, our results reveal that the social connectedness dimension of QDE is the most consistent predictor of IDL across all ages. This highlights that digital interactions are central to how individuals perceive their immersion in digital environments (Witowska et al., 2025a). This finding resonates with Chan\u0026rsquo;s (2015) assertion that multimodal connectedness and strong-tie communication are essential for maintaining social capital and emotional well-being.\u003c/p\u003e\n\n\u003cp\u003eIn relation to the well-being dimension of QDE, our study underscores that its relationship with IDL tends to be stronger among older adults. This suggests that for older populations, technology is increasingly viewed as a tool for life satisfaction rather than just a functional utility. This perspective is supported by Vanden Abeele\u0026rsquo;s (2021) dynamic model of digital well-being, which emphasizes the balance between connectivity and disconnectivity. As shown by Su\u0026aacute;rez \u0026Aacute;lvarez and Vicente (2024), the impact of internet use on life satisfaction is complex; it is the quality of the experience, rather than mere time spent online, that determines whether technology fosters healthy digital practices or leads to stress. Consequently, digital inclusion should be understood as a holistic process that empowers users to enhance their autonomy and social connectedness, especially in later life stages (Kl\u0026iacute;mov\u0026aacute; et al., 2018; Tomczyk \u0026amp; Kielar, 2025).\u003c/p\u003e\n\n\u003cp\u003eFinally, regarding the time and efficiency dimension, the results revealed it to be the least consistent predictor of IDL. This suggests that while functional benefits are important, they do not necessarily foster deep digital engagement unless accompanied by social or emotional value. This aligns with recent critiques advocating for more nuanced measures of digital experience over simple screen-time metrics (Witowska et al., 2025b). Among younger users, the high levels of immersion reflect a fuzzy use of technology where boundaries between work, leisure, and identity construction are blurred, reflecting contemporary views of the self as a fluid phenomenon in digital spaces (Codina, 2007; Codina et al., 2025; Ross \u0026amp; Maynard, 2021).\u003c/p\u003e\n\n\u003cp\u003eIn sum, this study advances the understanding of digital immersion by integrating experiential quality, age, and cultural context. It calls for differentiated approaches to digital inclusion that prioritize subjective digital well-being. Future research should incorporate longitudinal trajectories and additional variables like education or digital literacy to further explore how the Fourth Industrial Revolution reconfigures our experience of time, leisure, and belonging.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides strong evidence that the Quality of Digital Experience (QDE), particularly regarding social connectedness and well-being, is a fundamental determinant of Immersion in Digital Life (IDL). Our findings reveal that this relationship is not uniform across age groups or national contexts, identifying age as a significant moderator. Specifically, older individuals generally showed stronger associations between QDE and IDL, especially concerning the well-being dimension. Notably, in countries such as Czechia and Poland, older adults demonstrated high levels of digital immersion, suggesting that age-related engagement is shaped by broader sociocultural factors and proactive policies that promote active aging through technology.\u003c/p\u003e\n\n\u003cp\u003eAmong the three QDE dimensions, social connectedness consistently emerged as the most influential predictor of IDL. This finding underscores the central role of meaningful digital interactions in fostering technological engagement and supports the notion that digital platforms can enhance social capital and psychosocial flourishing across the lifespan provided the experience is perceived as positive and supportive.\u003c/p\u003e\n\n\u003cp\u003eThese insights have direct practical implications for the design of age-sensitive digital policies. Whether in education, workplace training, or lifelong learning, understanding how different age groups and cultural contexts influence engagement is essential for strategies aimed at enhancing digital well-being, reducing exclusion, and promoting healthy digital habits. Crucially, our study indicates that digital immersion is not inherently beneficial or detrimental; its impact on subjective quality of life depends on the quality and purpose of the engagement.\u003c/p\u003e\n\n\u003cp\u003eAs digital technologies continue to permeate all aspects of daily life, future research should adopt longitudinal approaches to track how these experiences evolve. We propose that subsequent studies incorporate broader sociodemographic variables such as educational level, employment status (e.g., active workers vs. retirees), professional device use, and digital literacy. Including these factors will provide a more comprehensive view of how digital immersion is shaped by life circumstances, ultimately informing more effective interventions to promote human flourishing in the digital age.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare financial support was received for the research and/or publication of this article. The CHANSE Project TIMe experience in Europe\u0026rsquo;s Digital age (TIMED)\u0026nbsp; \nunder CHANSE ERA-NET Co-fund programme, which has received funding from the European Union\u0026rsquo;s Horizon 2020 Research and Innovation Programme, under Grant Agreement no 101004509.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAncis, J. R. (2020). The age of cyberpsychology: An overview. \u003cem\u003eTechnology, Mind, and Behavior, 1\u003c/em\u003e(1). https://doi.org/10.1037/tmb0000009\u003c/li\u003e\n\u003cli\u003eBell, V., Bishop, D. V. M., \u0026amp; Przybylski, A. K. (2015). The debate over digital technology and young people. \u003cem\u003eBMJ, 351\u003c/em\u003e, h3064. https://doi.org/10.1136/bmj.h3064\u003c/li\u003e\n\u003cli\u003eBeyens, I., Pouwels, J. L., van Driel, I. I., Keijsers, L., \u0026amp; Valkenburg, P. M. (2020). The effect of social media on well-being differs from adolescent to adolescent. \u003cem\u003eScientific Reports, 10\u003c/em\u003e(1), 10763. https://doi.org/10.1038/s41598-020-67727-7\u003c/li\u003e\n\u003cli\u003eB\u0026uuml;chi, M., Festic, N., \u0026amp; Latzer, M. (2018). 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IBE \u0026mdash; Science of Learning Portal. https://solportal.ibe-unesco.org/articles/the-impact-of-digital-technology-on-cognitive-processes-and-learning-outcomes-in-early-childhood-evidence-from-neuroscience/\u003c/li\u003e\n\u003cli\u003evan Ingen, E., \u0026amp; Matzat, U. (2018). Inequality in mobilizing online help after a negative life event: The role of education, digital skills, and capital-enhancing internet use. \u003cem\u003eInformation, Communication \u0026amp; Society, 21\u003c/em\u003e(4), 481\u0026ndash;498. https://doi.org/10.1080/1369118X.2017.1293708\u003c/li\u003e\n\u003cli\u003eVanden Abeele, M. M. P. (2021). Digital wellbeing as a dynamic construct. \u003cem\u003eCommunication Theory, 31\u003c/em\u003e(4), 932\u0026ndash;955. https://doi.org/10.1093/ct/qtaa024\u003c/li\u003e\n\u003cli\u003eWalsh, L. C., Regan, A., Okabe-Miyamoto, K., \u0026amp; Lyubomirsky, S. (2024). Does putting down your smartphone make you happier? 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Validation of The Immersion in Digital Life and Quality of Digital Experience Scales in German, French, Spanish, Polish and Czech. \u003cem\u003eFrontiers in Psychiatry, 16\u003c/em\u003e, 1645260. https://doi.org/10.3389/FPSYT.2025.1645260\u003c/li\u003e\n\u003cli\u003eWorld Medical Association. (2025). Declaration of Helsinki: Ethical principles for medical research involving human participants. \u003cem\u003eJAMA, 333\u003c/em\u003e(1), 71\u0026ndash;74. https://doi.org/10.1001/JAMA.2024.21972\u003c/li\u003e\n\u003cli\u003eWu, J., Lin, X. Y., Lin, T., \u0026amp; Almeida, D. (2023). Well-being in middle-aged adults: The mediating role of social media use. \u003cem\u003eInnovation in Aging, 7\u003c/em\u003e(Supplement_1), 850. https://doi.org/10.1093/geroni/igad104.2740\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData distribution\u0026nbsp;\u003c/em\u003e\u003cem\u003eand descriptive information about study participants\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCzechia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18-66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45,23 (13,33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18-71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47,67 (13,12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18-65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42,28 (12,94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18-65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46,43 (17,77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSwitzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18-82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45,29 (14,96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18-73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45,26 (13,36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5,270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDescriptive and correlations analyses of study variables for each country\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"560\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eVariable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCzechia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e1.Time and efficiency (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e2. Well-being (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e3.Social connectedness (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0,27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.45**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.70**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e4. Immersion in DL (IDLS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0,37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.42**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.67**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGermany\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e1.Time and efficiency (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e2. Well-being (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e3.Social connectedness (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.76**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e4. Immersion in DL (IDLS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4,90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.23**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.53**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e1.Time and efficiency (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1,10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e2. Well-being (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0,28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.64**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e3.Social connectedness (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0,27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.57**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.77**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e4. Immersion in DL (IDLS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1,99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.41**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.50**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e1.Time and efficiency (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0,90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e2. Well-being (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.61**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e3.Social connectedness (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0,28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.58**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.72**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e4. Immersion in DL (IDLS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4,51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0,10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0,45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.46**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.53**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.55**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSwitzerland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e1.Time and efficiency (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1,41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e2. Well-being (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.56**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e3.Social connectedness (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2,94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0,26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.76**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e4. Immersion in DL (IDLS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2,10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0,62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.41**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e,61**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.64**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnited Kingdom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e1.Time and efficiency (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e2. Well-being (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.63**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e3.Social connectedness (QDES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0,85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.73**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e4. Immersion in DL (IDLS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1,95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0,28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.46**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.57**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Range of dimensions of QDES: 1-5; Range of IDLS: 1-10; ** \u003cem\u003ep\u003c/em\u003e \u0026lt; .01\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eANOVA analysis by age group for each country\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eCzechia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e14,66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e56,89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e13,55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0,04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e10,28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eSwitzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e14,66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e31,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital experience, immersion in digital life, age moderation, social connectedness, subjective digital well-being","lastPublishedDoi":"10.21203/rs.3.rs-9269714/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9269714/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Digital technologies (DT) have become deeply embedded in everyday life, evolving into a key determinant of contemporary quality of life. However, the impact and the experiences of using DT are not homogeneous across age groups and cultural contexts. This study examines how age moderates the relationship between the Quality of Digital Experience (QDE) comprising the three dimensions of social connectedness, well-being, and time/efficiency and Immersion in Digital Life (IDL) across six European countries. Drawing on data from 5,270 adults, moderation analyses show that all QDE dimensions significantly predict IDL, identifying social connectedness as the strongest contributor to digital well-being. Age moderated these relationships differently across countries: younger adults generally showed stronger associations between QDE factors and IDL (particularly regarding the well-being dimension), whereas in contexts like Czechia and Poland, older adults demonstrated higher levels of digital immersion. These findings underscore the importance of integrating a life-course perspective and cultural context to understand subjective digital well-being, highlighting the need for applied policies and interventions that promote high-quality digital inclusion across the lifespan.","manuscriptTitle":"Assessing the Quality of Digital Experience: Implications for Digital Well-being and Immersion in Six European Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 10:13:34","doi":"10.21203/rs.3.rs-9269714/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"30c7c76d-3f3c-4d9f-85ce-91fe47e10bae","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-15T02:27:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T16:30:05+00:00","index":25,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T15:27:30+00:00","index":24,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T02:40:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 10:13:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9269714","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9269714","identity":"rs-9269714","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}