Digital and Psychological Well-Being Among Technical University Students: Exploring the Impact of Digital Engagement in Higher Education

Abstract

This study investigates the relationship between digital well-being and psychological well-being among undergraduate students enrolled in technical academic programs at a Romanian university. Emphasizing the impact of academic digital technology use, digital autonomy, work–life balance, and the quality of online interactions, the research addresses the increasing digitalization in higher education and its implications for students’ mental and emotional health. A purposive sample of 208 students from Electronics, Telecommunications, Automation, Computer Science, and Engineering in Foreign Languages programs was surveyed using validated instruments, including a Digital Well-Being Questionnaire, Ryff’s Psychological Well-Being Scale, and the WHO-5 Well-Being Index. A mix of descriptive and inferential statistics was employed to comprehensively explore the associations between digital well-being and psychological outcomes. The findings highlight significant associations between digital engagement patterns and both psychological and subjective well-being, with notable differences influenced by sociodemographic factors such as gender. This study contributes to the emerging field of digital well-being in academic contexts by providing data-driven insights that can inform institutional strategies to support student health and optimize digital learning environments.

1. Introduction

The pervasive integration of digital technologies into everyday life has transformed the ways individuals learn, communicate, and engage with their environments. In the context of higher education, the COVID-19 pandemic significantly accelerated digital transformation processes, leading to a widespread reliance on digital tools for instruction, communication, and mental health support (Sharma & Sharma, 2022; Thianthai & Tamdee, 2024). These shifts have intensified scholarly interest in the concept of digital well-being—a multidimensional construct that encompasses individuals’ ability to maintain psychological, academic, and social balance while interacting within digital environments (Ochs & Riemann, 2018).

Higher education settings are particularly susceptible to the challenges and opportunities presented by digitalization. University students, often categorized as digital natives, navigate academic pressures, social transitions, and constant connectivity, which may impact their well-being in both positive and negative ways (Burr et al., 2020). Existing research highlights that not only the quantity of time spent online, but also the quality and intentionality of digital engagement play a critical role in determining students’ psychological outcomes (Fukuyama, 2018; Hayran & Anik, 2021). Recent literature emphasizes that digital well-being in higher education is influenced not only by the quantity of technology use but also by its quality and intentionality. Theoretical frameworks such as Self-Determination Theory (Ryan & Deci, 2000) and the Job Demands-Resources Model (Bakker & Demerouti, 2017) suggest that students’ autonomy, competence, and relatedness are key mediators of psychological outcomes in digital environments. Longitudinal research demonstrates that digital engagement has dynamic effects on mental health, highlighting the need for temporally sensitive measurements (Montag et al., 2019; Przybylski & Weinstein, 2019). Specific manifestations of digital engagement in higher education include participation in online lectures, collaborative tools, educational social media, and digital mental health interventions, all of which can either support or hinder well-being depending on usage patterns and institutional support.

In response, the promotion of digital well-being has emerged as a priority for researchers, educators, and policymakers, particularly in technologically intensive academic systems such as those found in STEM disciplines.

However, despite the growing relevance of the topic, digital well-being remains an evolving and somewhat contested construct. It is shaped by diverging theoretical frameworks and research traditions. Some scholars emphasize the risks of excessive or poorly managed digital use, citing issues such as technostress, digital fatigue, and constant availability (Stanica et al., 2024a; Hayran et al., 2020) while others highlight the potential of digital engagement to enhance psychological and academic functioning through purposeful, well-designed technological integration (Stanica et al., 2024b; Levin et al., 2017).

This duality of risk and opportunity underscores the need for a balanced, evidence-based perspective. It also calls for a clearer conceptual distinction between related constructs (e.g., digital autonomy, digital overload, and screen dependency), which are often used interchangeably in the literature but may operate differently in academic settings.

While Society 5.0 emphasizes human-centered innovation through AI, IoT, and data ecosystems, its principles have yet to be fully explored in educational well-being contexts (Fukuyama, 2018). This study seeks to bridge that gap by examining how Romanian university students in technical programs experience digital transformation at a psychological level.

Additionally, the paper evaluates current initiatives aimed at cultivating digital well-being among university students and faculty, offering insights for future research, institutional policies, and educational interventions.

To clarify the study’s focus early on, the research aims to explore the relationship between digital well-being and psychological well-being in higher education. It investigates how variables such as digital stress, perceived control, autonomy, work–life balance, and quality of online interaction contribute to mental health outcomes—drawing particular attention to gender differences and institutional factors.

1.1. Literature Review

The emergence of digital well-being as a distinct construct has been driven by the exponential growth of information technologies and their integration into the daily experiences of students and educators. In the post-pandemic academic landscape, digital well-being is understood not merely in terms of screen time or digital consumption, but as a multidimensional experience shaped by context, agency, and purpose (Burr et al., 2020; Montag & Walla, 2016).

Early conceptualizations often focused on negative psychological effects such as technostress, digital fatigue, or screen dependency (Ayyagari et al., 2011; Tarafdar et al., 2015). However, more recent literature adopts a balanced view, emphasizing both risks and opportunities. For instance, Ryff and Keyes’ model outlines six dimensions of psychological well-being—autonomy, environmental mastery, personal growth, positive relationships, purpose in life, and self-acceptance—that serve as a useful lens for understanding digital well-being in academic settings (Ryff & Keyes, 1995). Digital technologies can both support and undermine these dimensions, depending on usage patterns and contextual factors (Montag & Walla, 2016).

Recent contributions suggest that digital well-being is not static but dynamic, fluctuating according to the intensity, purpose, and context of technology use (Vanden Abeele, 2021). In higher education, this dynamic character is reflected in students’ diverse experiences with online learning platforms, collaborative technologies, and digital assessments. Theoretical frameworks such as Self-Determination Theory (Ryan & Deci, 2000) highlight the role of digital autonomy and competence in promoting positive outcomes, whereas the Job Demands-Resources Model (Bakker & Demerouti, 2017) emphasizes how digital overload or constant connectivity can generate stress and exhaustion. Integrating these perspectives allows for a nuanced understanding of how digital engagement supports or undermines psychological well-being in university settings.

Integrating these theoretical frameworks allows not only for identifying the risks and benefits of digital engagement but also for comparing how institutional resources and individual self-regulation strategies can moderate the effects on psychological well-being. Such integration highlights that digital well-being is best understood as the result of dynamic interactions between individual agency and structural factors in higher education.

Additionally, recent research has emphasized the need to consider individual agency in digital interactions, suggesting that well-being outcomes are significantly mediated by students’ capacity to make informed, reflective choices about how and why they engage with technology.

At a broader societal level, the transition from Society 4.0 to Society 5.0—driven by AI, IoT, and big data—promotes a human-centered technological integration aimed at enhancing individual quality of life (Fukuyama, 2018). In education, this paradigm shift calls for balancing innovation with emotional and psychological needs, advocating for the development of digital literacies that promote resilience, critical reflection, and mental health (Hainagiu, 2012; Fontenelle-Tereshchuk, 2025; Asselin et al., 2024).

The complexity of digital well-being is further highlighted by the subjective and contextual nature of digital engagement. Dienlin and Johannes (2020) argue that passive versus active use and social versus non-social interactions significantly mediate the psychological outcomes of technology use. Passive scrolling, for instance, may correlate with negative mental health outcomes, while purposeful, social, or creative digital engagement may enhance autonomy and life satisfaction.

Furthermore, this distinction aligns with the dual-process models in psychology, which differentiate between automatic, habitual technology use and deliberate, goal-directed digital engagement. Understanding this spectrum is essential for designing effective digital well-being interventions.

Moreover, a landmark cross-disciplinary study emphasizes the importance of methodological innovation in digital well-being research (Davis et al., 2024). They advocate for multidimensional assessment tools, inclusion of non-Western contexts, and educational interventions rooted in real-world applications. Their neo-ecological framework integrates environmental, social, and individual layers to account for the dynamic interplay between digital experiences and psychological outcomes (Navarro & Tudge, 2023).

Several studies also underscore the relational dimension of digital well-being. Excessive use of social media can exacerbate feelings of social comparison, envy, anxiety and fear of missing out (FOMO), particularly during emotionally vulnerable periods such as academic transitions (Hayran & Anik, 2021; Hayran et al., 2020). Conversely, technology-mediated support systems—such as digital mental health interventions—offer scalable, accessible means of promoting psychological well-being (Pankow et al., 2024; Becker & Torous, 2019; Gao et al., 2020). These interventions, when embedded within academic institutions, have shown positive effects on students’ stress management, emotional regulation, and perceived academic competence. A growing body of literature also points to the significance of digital hygiene—intentional practices such as screen-free routines, app usage monitoring, and digital detox strategies—as a moderating factor in promoting sustainable digital well-being.

Additionally, life satisfaction represents a central metric in assessing digital well-being, especially when evaluating the subjective experiences of university staff and students. The Satisfaction with Life Scale developed by Diener et al. (1985) complements psychological measures by offering a validated, global assessment of well-being. This tool has proven effective in educational research for capturing the broader implications of digital engagement on individual life quality.

Taken together, these findings suggest that digital well-being is best conceptualized not as a static trait, but as a dynamic outcome, shaped by individual behavior, emotional regulation capacities, institutional structures, and sociocultural context. This complexity necessitates a multidimensional research agenda that bridges psychological theory, educational practice, and technological design.

1.2. Empirical Studies on Digital Well-Being in Higher Education

1.2.1. International Perspectives

A growing body of international scholarship has expanded the empirical basis for understanding digital well-being.

The synthesis by Davis et al. (2024) remains one of the most comprehensive efforts to date, consolidating perspectives from 38 researchers across 12 countries. Key recommendations include the development of culturally adaptable metrics, integration of qualitative methodologies, and co-design of interventions with students and educators.

Further, Fontenelle-Tereshchuk (2025) explores the ambivalent role of digital platforms among first-year university students. While digital tools facilitate academic engagement and social interaction, they may also amplify stress and performance anxiety. This finding aligns with the meta-review by Asselin et al. (2024), which notes that the quality and intent of digital use—rather than the amount of screen time—are critical determinants of student well-being.

Another foundational study by Dienlin and Johannes (2020) critiques alarmist narratives around screen time, calling instead for nuanced approaches that recognize adolescents’ digital agency. Their findings reveal that meaningful engagement with digital content, particularly in social or educational contexts, can yield neutral or even positive effects on well-being. Building on this perspective, Becker and Torous (2019) demonstrate that digital mental health tools offer scalable and effective support for students, reducing stress and improving access to psychological care. Extending these insights, Gao et al. (2020) highlight that digital platforms, although linked to risks such as information overload, can also foster resilience and social connection during disruptive events such as the COVID-19 pandemic.

These international studies collectively challenge deterministic views of digital technology as inherently harmful, underscoring instead the importance of context, intent, and digital literacy in shaping well-being outcomes.

Empirical studies underscore that digital well-being in higher education is not determined by screen time alone, but by the quality, purpose, and context of digital engagement. While digital tools can support learning and connection, they may also increase stress if used uncritically. Student agency, institutional support, and culturally sensitive interventions are key to promoting positive outcomes. In this context, institutional efforts must go beyond reactive mental health services to include proactive design of digital environments that align with students’ values and cognitive–emotional needs. Overall, digital well-being emerges as a dynamic construct shaped by individual behavior and broader educational environments.

However, most existing research, including the present study, relies on cross-sectional designs, which limits the possibility of establishing causal relationships. Recent methodological advances in digital well-being research emphasize longitudinal experiments and temporally sensitive measurements (Montag et al., 2019; Przybylski & Weinstein, 2019), suggesting the need for future studies to adopt more sophisticated designs capable of capturing the dynamic nature of digital engagement and psychological well-being. Despite this methodological limitation, the current study addresses other gaps by focusing on a non-Western, technical university population and by integrating multiple theoretical perspectives, thereby complementing existing longitudinal evidence with culturally specific and multidimensional insights.

1.2.2. Romanian Research and Institutional Interventions

In Romania, recent empirical efforts have sought to contextualize digital well-being within local educational systems. A notable initiative is the ERASMUS+ DIGIWELL project (Cazan et al., 2024), which focuses on mitigating technostress among university students and faculty. The project identifies five core stressors: information overload, digital complexity, constant availability, insecurity, and pressure for rapid adaptation. The proposed interventions include:

• Mentoring programs that build digital competencies and emotional resilience;
• Self-assessment tools to monitor and enhance digital behavior;
• Reflective online courses to cultivate mindful, responsible technology use.

These strategies are intended to be scalable and institutionally embedded, promoting sustainable well-being in increasingly digital academic environments.

Additionally, Grosseck et al. (2023) conducted a national survey of 60 Romanian university teachers to evaluate their use of digital assessment tools. The study revealed significant disparities in digital competency levels and corresponding pedagogical practices. Teachers with higher digital confidence were more likely to implement innovative, student-centered assessment methods. However, barriers such as insufficient training and lack of institutional support were also noted—factors that indirectly affect digital well-being by increasing workload and stress.

Another comprehensive study involving 1813 university teachers from both public and private universities analyzed how effective technology use and personal factors explain workplace well-being (Truța et al., 2023). Results showed that technology self-efficacy, core self-evaluations, performance expectancy, facilitating conditions, actual technology use, and technostress significantly predict well-being at work. Notably, technostress and technology use mediated these relationships. This research also validated the Use of the Technology Scale as a reliable tool to assess the academic work context regarding ICT use. The findings provide valuable insights for human resource management strategies in higher education, emphasizing the importance of supporting digital literacy and reducing technostress to enhance faculty well-being.

These findings highlight the importance of institutional readiness and digital infrastructure in shaping the psychological experience of both students and staff in higher education. They also support the notion that digital well-being should be viewed through a systems lens, integrating pedagogical, emotional, and technical dimensions.

Together, these studies show that digital well-being in academic contexts must address not only students’ experiences but also those of faculty members. Institutional policies should therefore integrate digital literacy development, emotional support, and pedagogical innovation to ensure holistic well-being across the entire academic community.

Unique Contribution of This Study

This study advances the field of digital well-being research by focusing on Romanian students enrolled in technical higher education programs—a population highly exposed to digital transformation but often underrepresented in the literature. Unlike many prior studies that address either risks (e.g., technostress) or opportunities (e.g., digital autonomy), this research examines multiple factors simultaneously, including digital autonomy, work–life balance, and the quality of online interactions, and explores their distinct impact on students’ psychological well-being. By integrating Self-Determination Theory, the Job Demands–Resources Model, and Ryff’s multidimensional framework of psychological well-being, the study provides an empirically grounded and theoretically comprehensive perspective on digital engagement in academic life. Moreover, it incorporates culturally specific data from Romania—a non-Western context seldom explored in this domain—and investigates gender differences in digital well-being, offering nuanced insights with practical implications for tailored interventions. Collectively, these contributions address critical gaps in existing research and provide evidence-based recommendations for enhancing digital and psychological well-being in technical academic environments.

1.3. Study Aim and Hypotheses

The primary aim of this study is to explore the relationship between digital well-being and psychological well-being among university students enrolled in technical academic programs, focusing on the influence of academic digital technology use, digital autonomy, work–life balance, and the quality of online interactions. Digital well-being is conceptualized here as the ability to maintain a healthy and balanced engagement with digital technologies, characterized by a sense of control, reduced digital stress, and positive online experiences. Psychological well-being is understood following Ryff’s multidimensional framework, encompassing autonomy, personal growth, and self-acceptance.

In light of increasing digitalization in higher education—especially within technologically intensive disciplines—this research seeks to identify how students’ digital engagement contributes to their overall mental and emotional health. The study also investigates the role of sociodemographic factors, primarily gender, in shaping perceptions of digital well-being and its psychological outcomes, acknowledging that diverse student groups may experience digital environments differently.

The study’s design is particularly timely given the post-pandemic acceleration of digital transformation, which continues to redefine academic life and well-being metrics within technical universities.

Building on the existing literature that links digital technology use with both benefits and risks for mental health (Hayran & Anik, 2021; Levin et al., 2017), the following hypotheses were formulated:

General Hypothesis:

Higher levels of digital well-being—including low digital stress, strong digital autonomy, balanced digital engagement, and positive online interactions—are significantly associated with higher psychological well-being (as measured by Ryff’s six dimensions) among undergraduate students in technical academic programs.

Specific Hypotheses:

H1. 

Frequent use of digital technologies for academic purposes is positively associated with increased levels of digital stress and fatigue, which in turn negatively affect students’ psychological well-being.

H2. 

Students reporting higher psychological well-being (in dimensions such as autonomy and environmental mastery) will also report higher digital well-being, reflecting reduced digital overload and better coping mechanisms.

H3. 

Digital autonomy (such as ability to self-regulate online behavior), a stable work–life balance, and high-quality online academic interactions are positively associated with psychological well-being, particularly in the domains of purpose in life and personal growth.

H4. 

Significant gender differences will be observed in digital well-being profiles (such as digital stress, autonomy, coping strategies), with females expected to report higher digital fatigue and different coping styles, which may also influence psychological well-being scores.

These hypotheses reflect an integrated view of digital well-being that moves beyond screen time metrics, incorporating emotional, behavioral, and social dimensions critical to psychological functioning in digital academic spaces.

This study aims to contribute to the growing body of research on digital well-being in higher education, providing data-driven insights to inform institutional policies and support mechanisms tailored to technical academic ecosystems.

2. Materials and Methods

2.1. Sample

The study sample consisted of 208 undergraduate students enrolled in Years 1 through 4, purposefully selected from three academic programs within a Romanian technical university (National University of Technology and Science Politehnica Bucharest): Electronics, Telecommunications and Information Technology (63%), Automation and Computer Science (35%), and Engineering in Foreign Languages (1%). The sample was nearly gender-balanced, with 51% female and 49% male participants. Most students were in their advanced years of study, with 91% enrolled in Year 3, 2% in Year 4, 6% in Year 1, and 1% in Year 2. Data were collected between February and May 2025.

These programs were intentionally chosen due to their strong curricular emphasis on the daily integration of digital tools and technologies—both in academic tasks and personal routines. A non-probabilistic purposive sampling method was employed to ensure the selection of participants who regularly use digital devices in both academic and personal contexts, thereby enhancing the relevance and applicability of the findings. The prioritization of students in advanced academic years was intended to leverage their foundational disciplinary knowledge and prolonged exposure to various digital platforms, learning management systems, and AI-powered tools, thus capturing more nuanced insights into the relationship between digital well-being and academic engagement across levels of experience. Although purposive sampling targets relevant participants, it may introduce homogeneity bias. Future multi-site sampling could enhance generalizability across different institutional contexts.

This study complied with internationally accepted ethical standards, including the principles outlined in the Declaration of Helsinki (World Medical Association, 1975/2013; revised 2013). The study protocol was reviewed and approved by the Scientific Research Ethics Committee of the National University of Science and Technology Politehnica Bucharest (Approval No. 100 and date of 5 February 2025). The questionnaire was administered online via Google Forms, and participants were informed about the purpose of the study, the voluntary nature of participation, and their right to withdraw at any time. No personally identifiable information was collected. Participation was entirely anonymous, and data were analyzed in aggregate form.

2.2. Data Collection and Research Instruments

Data were collected using three self-administered questionnaires distributed via Google Forms, a platform selected for its user-friendly interface and accessibility, particularly suited to digital-native student populations (Stover et al., 2016). The instruments included:

A Digital Well-Being Questionnaire developed to assess students’ perceptions of their technology usage in academic and personal life. Items focused on patterns of digital technology usage, perceived benefits, stressors, and strategies for self-regulation. This instrument was designed in accordance with frameworks proposed by Vanden Abeele (2021) and Burr et al. (2020), emphasizing multidimensional aspects of digital well-being such as control, agency, and overload. The composite questionnaire consisted of thirteen items, including ten multiple-choice questions designed to capture the frequency and context of digital technology use, two Likert-scale items assessing the perceived impact of digital engagement on psychological well-being and the balance between academic and personal life, and one open-ended question exploring the Effects of Technology on Students’ Physical and Mental Health. This mixed-format design allowed for both categorical and ordinal data analysis, enhancing the methodological rigor. This mixed-format design supports robust data triangulation and enhances the reliability of inferences related to psychological well-being in digitally mediated educational settings (Creswell & Plano Clark, 2018).

Ryff’s Psychological Well-Being Scale (Short Version): Psychological well-being was assessed using the validated 44-item version of Ryff’s scale (Ryff & Keyes, 1995; Ryff, 1989), which evaluates six core dimensions: autonomy, environmental mastery, personal growth, positive relations, purpose in life, and self-acceptance. This version has been psychometrically validated in Romanian contexts by Kállay and Rus (2014), supporting its cross-cultural applicability and internal consistency. While Ryff’s scale has a long-standing empirical foundation, it is nearly 30 years old, and its Romanian validation is approaching 10 years. Considering the evolving conceptualization of psychological well-being, especially in post-pandemic contexts, future studies could benefit from integrating updated or complementary instruments to capture contemporary dimensions of well-being. Nevertheless, the use of this instrument allows for meaningful comparisons with international benchmarks and supports the study’s empirical rigor.

World Health Organization Well-Being Index (WHO-5): The WHO-5 is a widely used, concise tool for assessing subjective psychological well-being. Originally developed in the 1990s by Per Bech at the Psychiatric Centre North Zealand (Denmark), the WHO-5 has been translated and validated in multiple cultural settings (Topp et al., 2015). In 2024, copyright was officially transferred to the World Health Organization to facilitate broader dissemination as an open-access global resource.

2.3. Analysis

Data were analyzed using a combination of descriptive and inferential statistical methods to thoroughly investigate the relationships between digital well-being and psychological outcomes. Descriptive statistics included frequency distributions and percentage breakdowns to outline patterns of digital technology use and general perceptions among participants.

Inferential analyses primarily employed the Two-Sample t-Test Assuming Unequal Variances to assess differences in well-being levels across key user groups. Specifically, group comparisons were conducted based on gender (male vs. female), academic year (early vs. advanced years), and levels of digital technology usage (high vs. low users). Key statistical outputs reported included means, variances, degrees of freedom, t-values, and both one-tailed and two-tailed p-values. Prior to conducting these tests, assumptions of normality and homogeneity of variances were examined using the Shapiro–Wilk test and Levene’s test, respectively, to ensure the appropriateness of parametric analyses.

The analyses were conducted primarily using Microsoft Excel, based on standard statistical procedures and formulas informed by SPSS guidelines and best practices. This approach ensured accessibility and transparency while maintaining methodological rigor.

The analytical process was carried out on two complementary levels:

• Vertical Analysis: Examined item-level and individual response trends to identify nuanced participant insights regarding digital well-being dimensions and psychological well-being indicators.
• Horizontal Analysis: Explored aggregated data patterns across variables and participant groups to reveal overarching themes and generalizable trends within the dataset.
• In addition to the quantitative analyses, qualitative responses to the open-ended item on Effects of Technology on Students’ Physical and Mental Health were examined, analyzed using Braun and Clarke’s (2006) six-phase thematic analysis framework, which involved: (1) familiarization with the data through repeated readings, (2) generation of initial codes, (3) searching for recurring patterns, (4) reviewing and refining themes, (5) defining and naming emergent themes, and (6) producing a final analytic narrative supported by illustrative quotes. Frequencies within each thematic category were quantified to connect numerical patterns with underlying qualitative insights, highlighting both physical and psychological impacts of technology use, as well as coping strategies and perceptions of control. This combined approach ensures a richer, more nuanced understanding of students’ digital well-being experiences while maintaining methodological rigor.

Subsequent inferential statistics, including Pearson correlation analyses and multiple regression models, were used to explore the relationships between continuous variables such as digital autonomy, work–life balance, quality of online interactions, and psychological well-being scores.

To further enhance methodological rigor, future research could integrate objective digital usage metrics (e.g., device logs), which would help reduce self-report bias and allow for a more precise exploration of causal pathways between technology use and well-being.

This comprehensive, multi-level approach aligns with best practices in educational psychology and digital health research (Creswell & Plano Clark, 2018; Ryff, 1989). Particular attention was given to internal consistency and contextual sensitivity, given the rapidly evolving digital learning environments and their complex psychological effects (Hofmann et al., 2017). The multi-level analytic strategy thus provides robust insights into how academic digital technology engagement impacts the mental and emotional well-being of students enrolled in technology-intensive programs.

3. Results

3.1. Digital Well-Being and Psychological Well-Being

A strong and statistically significant positive correlation was observed between digital well-being and psychological well-being (r = 0.83, p < 0.001, N = 208), suggesting that students who manage their digital lives effectively tend to report better psychological health outcomes (Montag & Walla, 2016; Asselin et al., 2024). A Welch’s independent samples t-test confirmed a highly significant difference in score distributions (t(53) = −47.75, p < 0.001), reinforcing the robustness of this relationship (

Table 1. Digital well-being and psychological well-being.

	Digital Well-Being	Psychological Well-Being
Digital well-being *	1
Psychological well-being	0.83	1

* Pearson correlation is significant at the 0.000001 level (2-tailed), p < 0.001.

).

Descriptive data showed that 39% of participants expressed moderate concern (Likert score = 3) about the negative effects of digital technology, potentially indicating emerging signs of technostress (Tarafdar et al., 2015). Meanwhile, 39.8% reported engaging daily in offline restorative activities such as cooking, resting, or socializing—behaviors associated with digital self-regulation and reduced burnout risk (Reinecke et al., 2017).

Screen time exhibited a very strong positive correlation with perceived stress (r = 0.95, p < 0.001, N = 208), indicating that longer daily screen exposure is closely linked to elevated stress levels. This aligns with the prior literature on digital fatigue and psychological strain (Becker & Torous, 2019; Hofmann et al., 2017).

Usage patterns revealed that 37.4% of participants spent 2–4 h daily on screens, while 32.5% reported 4–6 h. Participants experiencing moderate stress (Likert score = 3) reported increased screen time alongside symptoms such as concentration difficulties, sleep disruptions, eye strain, and headaches.

When broken down by use purpose, academic activities predominated at moderate durations (37% for 2–4 h/day), while recreational use declined sharply with higher screen time (dropping from 48% at 2–4 h to 12% beyond 6 h), suggesting either screen fatigue or academic prioritization.

Regarding physical symptoms, 36% of participants reported eye strain and posture discomfort, while 20% cited fatigue and headaches. Importantly, 79% reported taking frequent screen breaks—potentially reflecting adaptive behaviors to mitigate physiological strain (Thomée et al., 2011), as we have seen in Figure 1.

3.2. Digital Well-Being and Perceived Control

A moderate positive correlation was found between digital well-being and perceived control (r = 0.60, p < 0.001, N = 208), suggesting that higher digital autonomy—defined as the ability to regulate one’s technology use—is associated with greater psychological self-efficacy.

A Welch’s t-test revealed a significant group difference (t(5) = −17.20, p < 0.001). The mean scores were M = 20.6 (SD = 25.47) for digital well-being and M = 788.33 (SD = 107.51) for perceived control. These values were derived from instruments with different scoring ranges; thus, while they are not directly comparable, their association remains robust. This supports findings that link digital self-regulation with reduced psychological distress and enhanced life satisfaction (Hawi & Samaha, 2017; Meier et al., 2016)

3.3. Digital Fatigue and Psychological Vitality

An inverse correlation was observed between digital fatigue and psychological vitality (r = −0.23, p < 0.001, N = 208), indicating that higher levels of digital exhaustion correspond with diminished psychological energy, as we have seen in

Table 2. Digital fatigue and psychological vitality.

	Digital Fatigue	Psychological Vitality
Digital well-being *	1
Psychological well-being	−0.23	1

* Pearson correlation is significant at the 0.000001 level (2-tailed), p < 0.001.

:

A Welch’s t-test confirmed a significant group difference (t(27) = −23.52, p < 0.001), with mean scores of M = 80.29 (SD = 88.67) for fatigue and M = 867.79 (SD = 95.99) for vitality. These results reinforce prior evidence linking digital overload with impaired emotional functioning and support frameworks such as Self-Determination Theory and Ego Depletion models (Reinecke et al., 2017; Faelens et al., 2021; Ryan & Deci, 2000; Baumeister et al., 1998).

Perceived work–life balance demonstrated a very strong positive correlation with general psychological well-being (r = 0.92, p < 0.001, N = 208). A Welch’s t-test revealed a significant difference in group means (t(42) = −52.34, p < 0.001), with M = 6.98 (SD = 12.25) for balance and M = 889.45 (SD = 108.75) for well-being.

Notably, 43% of participants indicated that digital tools facilitated academic access, and 16% highlighted improved time management—factors that may contribute to enhanced balance and well-being (Derks & Bakker, 2014).

3.4. Gender and Perceived Digital Stress

A negligible but statistically significant negative correlation was observed between gender and perceived digital stress (r = −0.02, p < 0.001, N = 208). Despite statistical significance, the effect size is trivial, indicating no meaningful predictive relationship (Figure 2)

A paired-samples t-test also found a significant difference (t(205) = −22.93, p < 0.001), with gender-coded scores (M = 1.49, SD = 0.50) and digital stress ratings (M = 3.25, SD = 0.97), yet this too has limited real-world impact.

No significant differences were found in perceptions of academic–life balance across academic years. A Welch’s t-test yielded t(14) = −1.15, p = 0.271, and Pearson’s r = 0.11, p > 0.05, (N = 208)—indicating a weak and non-significant relationship.

However, descriptive trends showed that fewer third-year students (42.2%) valued online access compared to first-years (66.7%), and fewer considered technology distracting (5.3% vs. 8.3%)—possibly reflecting digital adaptation or maturity (Funke et al., 2025).

A moderate positive correlation was found between academic digital engagement and positive social relationships (r = 0.42, p < 0.001, N = 208), suggesting that active academic tech use may foster stronger peer connections.

A Welch’s t-test confirmed this association (t(7) = −29.18, p < 0.001), with mean values of M = 24.97 (academic tech use) and M = 935.43 (social relationship score), despite differing measurement scales.

These findings align with evidence that digital learning platforms can enhance social presence and collaboration (Al Lily et al., 2020; Barrot et al., 2021; Xie et al., 2020; Garrison & Vaughan, 2008).

3.5. Digital Well-Being, Life Balance, and Subjective Well-Being

The average WHO-5 score was 54, indicating slightly above-average subjective well-being in the sample. Thematic analysis of 208 qualitative responses revealed that 43.2% of participants cited improved access to resources, and 9.2% reported increased flexibility as key digital benefits—findings consistent with Aristovnik et al. (2020).

Effects of Technology on Students’ Physical and Mental Health—A Thematic Analysis

A total of 197 valid student responses were analyzed using Braun and Clarke’s (2006) six-phase thematic analysis framework. From these responses, distinct codes were generated, reflecting the multifaceted nature of participants’ answers, as many included multiple perspectives within a single response. To complement the quantitative findings on digital well-being, stress, and screen time, descriptive statistics were employed to quantify the frequency of responses within each thematic category. This approach allows for a richer understanding of students’ experiences, linking numerical patterns with the underlying qualitative insights that highlight both the physical and psychological impacts of technology use, as well as coping strategies and perceptions of control.

Physical impact (Frequency, n = 144): Participants reported musculoskeletal discomfort, eye strain, sleep disturbances, and fatigue. Example responses included: “Physically, my eyes feel tired and I get headaches after prolonged use” and “After 3–4 h at the screen, I experience fatigue and sleep problems.”

Psychological impact (positive: Frequency, n = 58, negative: Frequency, n = 79): Responses ranged from relaxation, entertainment, and social connection to anxiety, stress, concentration difficulties, and feelings of guilt. For instance, some noted: “Technology helps me relax through a movie or staying in touch with friends”, while others reported: “Mentally, I experience lack of motivation and difficulty concentrating.”

Dependence and loss of control (Frequency, n = 74): Participants described compulsive checking of devices, time loss, and emotional attachment. Example: “I feel like I’m losing part of my day, part of my life.”

Social comparison and self-image (Frequency, n = 48): Participants highlighted feelings of inadequacy and reduced self-confidence due to social media comparisons. Example: “Social media makes me compare myself and lowers my self-esteem”.

Coping strategies and awareness (Frequency, n = 43): Participants described setting limits, alternating with offline activities, and moderating technology use consciously. Example: “It is necessary to disconnect sometimes to appreciate other things, like taking a walk in the park”.

These themes demonstrate that while digital technology offers functional and social benefits, it also carries potential risks for physical health, mental well-being, and emotional regulation.

Strong positive correlations were also observed between academic–personal life balance and WHO-5 sub-items, including emotional affect (e.g., “I felt cheerful,” r = 0.63) and physical energy (e.g., “I felt rested,” r = 0.58), reinforcing prior work on emotional regulation via digital engagement (Elhai et al., 2017) (Figure 3):

Across all analyses, digital autonomy, screen time, and work–life balance emerged as the most robust predictors of psychological well-being. Strong positive correlations were found between digital well-being and psychological health (r = 0.83), as well as between work–life balance and overall mental well-being (r = 0.92), highlighting the protective effects of self-regulation and balance. Conversely, excessive screen time showed a near-perfect correlation with elevated stress levels (r = 0.95), underscoring its role as a critical risk factor. Together, these findings suggest that fostering digital autonomy and maintaining balanced digital habits are essential for promoting psychological resilience in academic populations.

Given the number of statistical comparisons performed, the risk of inflated Type I error cannot be entirely excluded. As this study was exploratory in nature, no formal correction (e.g., Bonferroni adjustment) was applied. However, future confirmatory research should incorporate appropriate adjustments to control for multiple testing and strengthen the reliability of findings.

4. Discussion

This study investigated the complex interplay between digital well-being and psychological well-being among students in technical higher education settings, addressing the overarching hypothesis that digital well-being significantly influences psychological health. The results largely confirm this general hypothesis and provide nuanced insights into the specific factors shaping this relationship.

These findings collectively suggest that digital well-being should be reconceptualized not as a singular outcome but as a dynamic interplay between digital autonomy, fatigue management, social connectedness, and psychological resilience. This aligns with recent conceptualizations of digital well-being as a context-dependent construct (Vanden Abeele, 2021), highlighting that outcomes may shift according to patterns of digital engagement, contextual demands, and temporal variations.

Confirmation of general hypothesis and H2. The strong positive correlation found between digital well-being and psychological well-being (r = 0.83, p < 0.001) confirms the general hypothesis, supporting previous research that effective management of digital life corresponds with better mental health outcomes (Montag & Walla, 2016; Asselin et al., 2024). This finding also validates H2, indicating that students with higher psychological well-being concurrently report higher digital well-being. The robustness of this association, as evidenced by highly significant t-test results, underscores the importance of digital self-regulation in supporting students’ psychological resilience, echoing prior work on digital literacy and mental health (Hawi & Samaha, 2017; Reinecke et al., 2017).

As this study is cross-sectional, causal inference cannot be drawn. The observed relationships should be interpreted as correlational rather than directional. Future research should adopt longitudinal and experimental designs to explore predictive pathways and capture the evolving nature of digital well-being over time. Integrating objective digital metrics (e.g., device logs or app usage data) alongside self-reports would enhance validity, uncover potential causal pathways, and provide a more comprehensive understanding of temporal dynamics (Przybylski & Weinstein, 2019; Montag et al., 2019).

4.1. Relationship Between Digital Technology Use and Digital Stress (H1)

Regarding H1, the data demonstrate a strong, statistically significant positive correlation between screen time and perceived stress (r = 0.95, p < 0.001), consistent with the literature linking prolonged screen exposure to increased psychological strain and technostress (Becker & Torous, 2019; Hofmann et al., 2017). The nuanced pattern of screen usage—where academic screen time remained relatively stable while recreational use declined with longer exposure—suggests a prioritization effect, possibly driven by academic demands and screen fatigue. Given the exceptionally high correlation, it is worth considering the possibility of self-report bias or conceptual overlap between variables such as “screen time” and “stress symptoms”. Future studies should use objective usage metrics to validate these findings and clarify causal sequences between digital exposure and stress responses (Thomée et al., 2011; Faelens et al., 2021).

Gender differences in perceptions of digital well-being (H4). Contrary to H4, our results indicate negligible gender differences in perceptions of digital stress and digital well-being (r = −0.02, p < 0.001 with trivial effect size), aligning with prior research suggesting minimal gender-based disparity in technostress experiences (Vieira & Carlotto, 2024). Although statistically significant differences emerged in paired t-tests, these are likely attributable to large sample size rather than meaningful gender effects. Both male and female students reported similar digital stressors and adaptive behaviors, with only minor variations such as females reporting slightly higher break needs. This suggests that generalized digital well-being interventions may not require gender-specific tailoring but should focus on universal strategies for managing digital overload. The observed homogeneity across genders further reinforces the idea that interventions designed to improve digital self-regulation, fatigue management, and psychological vitality can be broadly applied within student populations.

4.2. Digital Fatigue, Self-Regulation, and Psychological Resilience

The inverse relationship between digital fatigue and psychological vitality (r = −0.23, p < 0.001) highlights that excessive digital use can deplete cognitive and emotional resources, consistent with Self-Determination Theory and the Ego Depletion Framework (Ryan & Deci, 2000; Baumeister et al., 1998). The existing literature reinforces the importance of self-regulation and agency in digital contexts; for example, Hawi and Samaha (2017) found that individuals with higher digital self-control experience less psychological distress and greater life satisfaction. Burr et al. (2020) emphasized user agency—the ability to make intentional decisions about digital behavior—as a central component of well-being in digital environments. Similarly, Meier et al. (2016) showed that a strong sense of control over smartphone use correlates with lower technostress and improved subjective well-being. These findings reinforce the importance of managing digital fatigue while promoting autonomy, self-regulation, and constructive digital engagement to preserve psychological vitality.

Academic progression and perceptions of digital technology. The absence of significant differences by year of study in perceived technology impact suggests a relative stability in digital autonomy and competence across academic progression. However, subtle trends indicating decreasing perception of technology as a distraction and diminishing novelty of online resources imply growing digital maturity and adaptive regulation among students (Funke et al., 2025). This trajectory is consistent with research suggesting that repeated exposure fosters digital resilience, which may explain the gradual stabilization of digital well-being across academic stages. Future research could examine whether early interventions enhance digital resilience and academic focus longitudinally.

Overall, these results support reconceptualizing digital well-being as a multifaceted construct encompassing both positive (autonomy, self-regulation, constructive engagement) and negative (fatigue, technostress) dimensions. Interventions should move beyond avoidance-based strategies to promote tools that empower students, such as usage monitoring, do-not-disturb modes, and digital detox practices, fostering resilience and balanced, intentional engagement with technology.

Ultimately, this study advances a contextualized and theoretically informed understanding of digital well-being in higher education while identifying directions for future research to explore its complexity, dynamic nature, and causal pathways.

5. Conclusions

This study confirms the general hypothesis that the level of digital well-being significantly influences the psychological state of students in technical higher education fields. The results highlight a clear connection between the use of digital technology for academic purposes and manifestations of digital stress or fatigue (H1), supporting findings by Ayyagari et al. (2011) and Tarafdar et al. (2015) regarding the negative effects of technostress on performance and mental health. Thus, intensive technology use without adequate coping mechanisms may lead to overload and mental exhaustion, a point also emphasized by Thianthai and Tamdee (2024).

In line with hypothesis H2, students with higher levels of psychological well-being reported correspondingly higher levels of digital well-being, suggesting an interdependence between these constructs. This is consistent with the model proposed by Ryff and Keyes (1995) and observations by Dienlin and Johannes (2020) on the positive impact of digital well-being on mental health. These findings also highlight the dynamic nature of digital well-being, which may fluctuate according to usage patterns, contextual demands, and temporal changes, underlining the importance of interventions focused on promoting digital balance, particularly in technology-intensive academic environments.

Hypothesis H3 is validated through positive correlations found between digital autonomy, work–life balance, and the quality of online interactions with overall psychological well-being. These results align with research by Greenhaus and Allen (2011) and Kossek et al. (2014), emphasizing the importance of work–life balance for mental health, as well as with Burr et al.’s (2020) findings on the ethical and digital dimensions of well-being. Additionally, the quality of online interactions, often overlooked, proved to be a critical factor, supporting Asselin et al.’s (2024) observations. It should be noted, however, that students’ perceptions of work–life balance are heterogeneous, which may influence the strength of its association with psychological well-being. Future research should further explore individual differences in this domain to refine intervention strategies.

Regarding gender differences (H4), although statistically significant results were detected, the observed effects were negligible, suggesting that practical disparities in perceptions of digital well-being are minimal. These findings indicate that male and female students experience digital stress and related challenges in broadly similar ways (Vieira & Carlotto, 2024). Nevertheless, descriptive trends hint at subtle variations, which aligns with prior studies suggesting that gendered experiences of digital technology may exist but are often context-dependent. Thus, while gender-sensitive strategies may still be valuable, their design should be cautious and informed by effect sizes that reach thresholds of practical significance.

It is important to acknowledge that the cross-sectional design of this study limits the ability to draw causal inferences. To better understand the directionality and temporal dynamics of digital well-being, future research should adopt longitudinal and experimental designs that combine subjective self-reports with objective digital metrics (e.g., device logs or app usage data). Such multi-method approaches would enhance the validity of findings, uncover potential causal pathways, and more fully capture the evolving nature of digital well-being in higher education.

In conclusion, the study highlights the need for a holistic framework integrating digital well-being components in technical academic settings by promoting digital autonomy, work–life balance, and quality social interactions. Implementing such measures can contribute not only to reducing the negative effects of digital technology but also to enhancing students’ overall psychological health, thus supporting adaptation to the modern digital society as described in the Society 5.0 concept (Fukuyama, 2018). These conclusions align with international trends in digital well-being research (Davis et al., 2024) and provide a solid foundation for developing effective educational policies and psychosocial interventions tailored to the specific needs of students in technical higher education.

Overall, these findings reinforce that digital well-being is not merely the absence of negative effects (e.g., digital fatigue or technostress), but also involves positive dimensions such as autonomy, self-regulation, and constructive digital engagement. Therefore, interventions should move beyond restrictive or avoidance-based strategies and instead promote tools that empower students, such as usage monitoring, do-not-disturb modes, and digital detox practices—to foster resilience and balanced, intentional engagement with technology.

Finally, digital well-being should be viewed as a dynamic, context-dependent construct, shaped by individual behavior, emotional regulation capacities, and institutional structures. Future research should continue to prioritize longitudinal and multi-method approaches to fully capture its evolving character and inform sustainable interventions in higher education.