Screen time and stress: understanding how digital burnout influences health among nursing students

Abstract

Introduction

With the growing reliance on digital platforms in education, nursing students face increasing exposure to screen time and academic pressures. Despite existing research, region-specific studies on how digital burnout predicts psychological health in UAE nursing students are limited.

Aim

The study aimed to assess the correlation between digital burnout and nursing students’ general psychological health and identify variables that predict both.

Methods

This study employed quantitative methods, utilizing correlational and descriptive approaches. The study was conducted during the 2024–2025 academic year and involved a sample of 140 nursing students. Statistical testing encompassed descriptive statistics, correlation analyses, and multiple regression analysis.

Results

The level of digital burnout was high, and general psychological health was moderate. The correlation analysis revealed a positive and significant correlation between students’ overall digital burnout scale scores and overall health scores (r = 0.71, p < 0.001). A multivariate regression study identified significant determinants of digital burnout and general psychological health among students. Younger students, those enrolled in over five classes, and nursing students exhibited elevated symptoms of digital burnout. Conversely, the academic level showed no substantial impact. Additionally, digital burnout was a major predictor of poor general psychological health, but other demographic and academic variables were not substantial.

Conclusion

This study demonstrates that digital burnout, primarily induced by academic pressures, considerably affects the mental and physical well-being of nursing students. Specific institutional strategies such as fostering digital well-being, modifying course loads, and augmenting mental health support are crucial for safeguarding student wellness and cultivating resilient future nursing professionals.

Clinical trial number

Not applicable.

Introduction

Technology is now essential in contemporary education, especially within nursing disciplines, where students must interact with electronic learning platforms, digital assessments, and virtual clinical simulations [1]. Although these tools enhance accessibility and efficiency, they also contribute to a phenomenon termed digital burnout, characterized by mental and physical exhaustion resulting from extended exposure to digital devices and online environments [2]. Nursing students frequently balance rigorous academic responsibilities with clinical training, making them particularly susceptible to the adverse impacts of digital overload [3].

Maslach first proposed and described burnout as an ongoing cycle of exhaustion, cynicism, and diminished dedication among social workers [4]. Following several empirical studies, Maslach worked with other authors to reformulate the idea. It developed a stricter and operational definition of burnout as a psychological syndrome marked by emotional exhaustion, cynicism or depersonalization, and a diminished sense of professional efficacy [5]. Research shows a significant incidence of burnout among nursing students, with prevalence rates ranging from 16.7 to 59.9% during the pandemic’s online learning phase [3]. Nursing students displayed increased levels of digital burnout, as evidenced by average scores that reflect substantial digital aging, deprivation, and exhaustion [6]. A study of Egyptian medical students and staff during the COVID-19 pandemic found that 7.6% of students experienced high levels of technostress, which was closely linked to digital burnout [7].

A study among undergraduate medical students in India revealed an academic burnout prevalence of 16.84%, with 35% experiencing high emotional exhaustion and 65% reporting low professional efficacy [8]. Another study in the United Arab Emirates indicated that over 69% of medical and health sciences students reported significant levels of burnout, characterized by emotional exhaustion, depersonalization, and diminished personal accomplishment [9]. Burnout is prevalent not only among undergraduate students but also significantly impacts graduate nursing students. A scoping literature review indicates that graduate nursing students show elevated burnout levels compared to their age-matched peers and the general population, which has considerable implications for their mental health, empathy, and professional behavior [10].

Nursing students often face significant pressure due to the stringent requirements of their academic curricula, leading to increased susceptibility to digital burnout [11, 12]. Digital burnout among nursing students is a complex phenomenon primarily influenced by intrinsic factors. These factors are deeply rooted in students’ personal and academic experiences, often intensified by the demanding nature of nursing education [7]. Nursing students face substantial academic pressures, marked by heavy workloads and high expectations for academic performance, which significantly contribute to burnout [13]. The culture of perfectionism within medical education heightens stress, as students strive for unattainable standards, resulting in chronic stress and psychological strain [14].

The demand for academic excellence correlates with mental health issues, such as anxiety and depression, which are common among nursing students [15, 16]. The transition to online learning has led to an increase in screen time and a decrease in face-to-face interactions, which has contributed to digital burnout [17, 18]. Depersonalization, characterized by a sense of detachment from oneself and others, is a significant factor contributing to burnout, frequently arising from the rigorous demands of nursing training [12]. The shift to digital platforms in nursing education has led to the emergence of technostress, characterized by elements such as techno-overload, techno-invasion, and techno-uncertainty [19]. The frequent utilization of digital tools and platforms may induce stress and anxiety, thereby exacerbating burnout in nursing students who are already coping with substantial academic demands [11].

Intrinsic factors significantly contribute to digital burnout among nursing students; however, the influence of extrinsic factors, including institutional support and the academic environment, must also be considered. Numerous nursing institutions do not provide sufficient support systems for students to navigate the transition to digital tools [20]. This encompasses inadequate training and resources for the effective utilization of digital technologies, resulting in heightened stress and burnout [20, 21]. The absence of effective psychosocial support systems, including counseling services and peer support networks, results in students lacking essential resources to manage the pressures of digital and academic demands [22, 23].

Digital burnout significantly impacts the psychological well-being of nursing students. Elevated technostress correlates with increased cortisol and reduced CoQ10 enzyme levels, suggesting chronic stress and possible long-term health implications [7]. Burnout is significantly associated with mental health issues, including depression and anxiety, as demonstrated in a study of health profession students in Qatar, which identified burnout as a strong predictor of anxiety [24, 25]. The prevalence of depressive symptoms increases during examination periods, exacerbating burnout [10, 21, 26]. The requirements of online learning intensify the psychological effects of burnout amid the COVID-19 pandemic. A study conducted with medical students in China indicated that the transition to online learning heightened stress levels and academic burnout. At the same time, resilience served as a mediating factor in alleviating these impacts [27]. A study conducted among nursing students in South Africa revealed that the pandemic intensified mental health issues, with participants indicating elevated levels of psychological distress, including depression, anxiety, and stress [28].

The physical health consequences of digital burnout in nursing students warrant attention. Elevated technostress correlates with increased cortisol levels and reduced CoQ10 enzyme levels, serving as indicators of chronic stress and potential physical health issues [7]. A study among university students indicated a positive correlation between digital burnout levels and perceived stress levels, implying that sustained digital burnout may result in physical health issues, including hypertension, cardiovascular diseases, and weakened immune function [29]. The physical health consequences of burnout are exacerbated by the sedentary characteristics of online learning, potentially resulting in adverse health outcomes such as obesity, diabetes, and cardiovascular diseases if not addressed [30].

Nursing students utilize diverse coping strategies to address the stress and burnout associated with digital fatigue. A study involving undergraduate medical students revealed that the majority employed active coping strategies, including problem-solving and seeking social support, to address their academic burnout [8]. A study involving nursing professional students indicated that positive coping strategies, including planning, problem-solving, and active coping, correlated with lower stress levels and enhanced psychological health [31].

Institutions can significantly contribute by offering emotional support, training educators to identify and manage burnout, and establishing healthy practices during online learning sessions [18]. A qualitative study among health profession students in Qatar underscored the significance of institutional support in alleviating burnout. Students expressed the necessity for psychological support programs, mentorship, and the integration of well-being and resilience modules into the curriculum [32]. A study conducted among nursing students in South Africa indicated a preference for weekly online communication from their institutions as a supportive measure during the pandemic [28]. Web-based tools and mobile applications, including mindfulness and cognitive behavioral therapy apps, have demonstrated efficacy in alleviating stress and burnout among nursing students [33]. Early intervention and the promotion of these resources can improve students’ coping mechanisms. The integration of online and in-person learning may alleviate digital burnout by offering a more balanced educational experience [34].

Despite growing acknowledgment of digital burnout as a significant concern among nursing students, comprehensive research investigating its specific relationship to general psychological health outcomes in this population remains insufficient. Previous research has predominantly focused on the psychological aspects of burnout, including stress, anxiety, and emotional exhaustion, especially in the context of the transition to online learning during the COVID-19 pandemic.

Despite the growing acknowledgment of digital burnout in international literature, especially due to the surge in remote education during and after the COVID-19 epidemic, there is an urgent necessity for region-specific research. Initial definitions of burnout (e.g., Maslach, 1976) predominantly concentrated on professional environments [5]; however, contemporary research and scoping reviews have broadened the focus to encompass healthcare and nursing students, emphasizing the multifaceted origins of burnout, which include both intrinsic and extrinsic stressors [10, 25]. Despite all of this, research focused on digital burnout separate from general academic or clinical burnout remains comparatively underdeveloped.

Few studies have explored the psychological and physical health effects of digital burnout in the UAE, a culturally and technologically unique country. UAE research has focused on academic stress and digital involvement rather than how digital burnout, a multifaceted phenomenon, influences psychological health effects. Importantly, no cross-sectional study has examined UAE nursing students’ digital burnout determinants and psychological health. Thus, this study addresses a methodological and contextual gap in the literature, enriching our understanding of digital burnout in nursing education.

The conceptual framework, Fig. 1, illustrates the dynamic interplay between digital-related stressors and general psychological health. At the core of the model is digital burnout, which emerges as a result of three primary contributing factors: digital aging, digital deprivation, and emotional exhaustion. These elements reflect the toll of prolonged digital exposure, lack of access or control over digital tools, and the emotional fatigue stemming from continuous digital engagement. Digital burnout, in turn, negatively affects general psychological health, which is depicted as a multifaceted construct comprising somatic symptoms, anxiety/distress, social dysfunction, and depression. The framework suggests that managing the precursors to digital burnout is essential for preserving mental and physical health in increasingly digital environments. This model provides a valuable basis for understanding how digital experiences can significantly shape well-being and highlights the need for interventions that target both technological and psychological domains.

Fig. 1.

Conceptual framework

Materials and methods

Research design, setting, and sample

In the academic year 2024–2025, a cross-sectional, correlational quantitative survey was conducted from February to March. Undergraduate scholars from the nursing discipline participated in the study at the Fatima College of Health Sciences in the United Arab Emirates. The researchers utilized convenience sampling to recruit participants for this study. The institution’s student ensemble consists solely of females, enabling researchers to select a sample using convenience sampling. All full-time nursing undergraduates progressing through Levels 1 to 4 of the college are invited to participate, provided they understand the research purpose and are willing to contribute. The Epi-Info version 7 application efficiently calculates sample size using a 5% margin of error at a 95% confidence level, with a power of 0.80 and a significance level of 0.05. Additionally, a nonresponse rate of 5% was incorporated into the calculation. Ultimately, the final sample size expanded to include 140 students who were present and willing to participate.

Study instruments

To achieve the aim of the study, three instruments were used:

Demographic data

The demographic information included age, student level, number of classes per semester, and department.

Digital burnout scale (DBS-24)

Erten and Ozdemir created the DBS original version as a key instrument for evaluating burnout from using digital devices [35]. Comprising 24 questions, the scale is split into three sub-domains to assess digital burnout: digital aging, digital deprivation, and emotional exhaustion. Variables on the digital aging subscale include 12 items evaluating the imbalance between real and virtual life due to excessive digital platform usage. Digital deprivation includes 6 statements measuring the physical and psychological discomfort experienced when away from digital devices. Finally, emotional exhaustion included 6 items assessing emotional fatigue resulting from digital device usage.

Every item received a five-point Likert scale rating ranging from “strongly disagree” to “entirely agree.” The researchers calculated the average score for every component as well as the total scale score, that is, the average of the three dimensions. The total score runs from 24 to 120; high scores indicate significant degrees of digital burnout. With an α = 0.95, the Korean DBS version showed strong internal consistency, concept validity, and criterion validity [36]. The overall Cronbach’s alpha coefficient α in the study was 0.912, and Cronbach’s alpha for the three subdomains was as follows: digital aging (12 items; α = 0.944), digital deprivation (6 items; α = 0.876), and emotional exhaustion (6 items; α = 0.901). The Kaiser–Meyer–Olkin (KMO) value is equal to 0.832. The Bartlett test p-value is less than 0.001, indicating solid interitem correlations [37].

General health questionnaire (GHQ-28)

A 28-GHQ is a widely used self-administered screening tool developed by Dr. David Goldberg to detect current psychological distress and potential psychiatric disorders in the general population as well as in primary care settings [37, 38]. It is designed to identify individuals who are experiencing mental health difficulties, particularly symptoms of anxiety, depression, and social dysfunction, but who may not yet have received a formal diagnosis. The GHQ is not a diagnostic tool but serves as an effective means of identifying individuals who may benefit from further psychological assessment or support.

Every item received a four-point Likert scale rating ranging from “not at all” to “much more than usual.” Responses are scored on a scale from 0 to 3, leading to a total possible score between 0 and 84. Interpretation of Likert scores generally categorizes 0–23 as normal, 24–36 as mild distress, 37–59 as moderate distress, and 60–84 as severe distress, with higher scores indicating more severe symptoms within that domain.

The overall Cronbach’s alpha coefficient α in the study was 0.871, and Cronbach’s alpha for the four subdomains was as follows: somatic symptoms (7 items; α = 0.898), anxiety and distress (7 items; α = 0.913), social dysfunction (7 items; α = 0.860), and depression (7 items; α = 0.845), indicating that the questionnaire consistently measures what it is supposed to measure. The reliability of the GHQ-28 was found to be very high in different contexts [39, 40], the Kaiser–Meyer–Olkin (KMO) value is equal to 0.871. The Bartlett test p-value is less than 0.001, indicating solid interitem correlations.

Validity & reliability

A pilot study was conducted with 14 students (10% of the overall sample size) to evaluate the clarity, applicability, relevance, and feasibility of the survey tools, as well as to estimate the time required for data collection. The pilot research results were not modified, and the patients who participated in the pilot were included in the primary study population. This quantitative study employed procedural and statistical controls to prevent bias. Random sampling decreased selection bias, while verified and pre-tested measuring instruments ensured reliability and reduced measurement bias. To decrease social desirability biases, anonymity and confidentiality were highlighted.

Data collection

The research team distributed surveys through an email system hosted on the university server to maintain participant anonymity. The data collection period spanned from February to March 2025, allowing sufficient time for students to complete the survey. Participants typically spent between 10 and 15 min on the survey. All data collection processes adhered to ethical standards, including informed consent, data confidentiality, and participant safety. The original, unmodified versions of the DBS-24 and GHQ-28 were employed in this study to ensure consistency with validated psychometric properties. At the beginning of the survey, the study goals were clearly explained, and participants were informed about their rights, including voluntary participation and data protection. Students indicated their consent by selecting the appropriate response, and the surveys were submitted via an encrypted electronic system. Contact information for the research team was also provided.

Statistical analysis

The current research data were processed using the Statistical Package for Social Sciences (SPSS) version 23. Descriptive data were quantified using numerical values, including numbers, percentages, minimum and maximum values, averages, and standard deviations. Before applying the appropriate statistical test, the normality of distribution for continuous and dependent variables was assessed. Before conducting the multiple regression analyses presented in Table 3 (predicting digital burnout) and Table 4 (predicting general psychological health), key assumptions of linear regression were examined to ensure the validity of the results. Linearity was confirmed through scatterplots showing appropriate relationships between the independent and dependent variables. The Shapiro-Wilk test indicated that the residuals were normally distributed for both models W = 0.976, p = 0.078 for the digital burnout model, and W = 0.861, p = 0.089 for the psychological health model supporting the use of parametric tests. Independence of errors was confirmed using the Durbin-Watson statistic, which showed no significant autocorrelation in either model. Multicollinearity was also assessed, and all predictor variables in both models had Variance Inflation Factor (VIF) values below the accepted threshold of 10. Specifically, for the digital burnout model, VIF values ranged from 1.02 to 1.18, and for the psychological health model, they ranged from 1.03 to 1.21, indicating no multicollinearity concerns. These diagnostics confirmed that the assumptions for multiple regression were adequately met, supporting the robustness and reliability of the findings reported in both models.

Table 3.

Multiple regression analysis of factors influencing total digital burnout (n = 140)

Predictor	B	Beta	SE	T	p
Intercept	67.55	2.30	2.83	23.86	< 0.001
Age = 21–24	-8.91	-0.30	3.45	4.58	0.011
Dept = Nursing	6.42	0.22	3.18	3.01	0.046
Classes = Above 5	17.52	0.60	4.57	8.76	< 0.001
Student Level = 4	5.80	0.20	4.47	1.29	0.197
R² = 0.232, Adjusted R² = 0.210, F = 10.22, p < 0.001.

R²: Coefficient of determination

B: Unstandardized Coefficients

Beta: Standardized Coefficients

t: t-test of significance

SE: standard error

*: Statistically significant at p ≤ 0.05

Table 4.

Multiple regression analysis of factors influencing total general psychological health (n = 140)

Predictor	B	Beta	SE	T	p
Intercept	8.37	38.55	3.24	3.58	0.011
Age = 21–24	1.41	0.70	1.76	0.80	0.423
Student Level = 4	-1.26	-1.410	0.96	-1.31	0.192
Dept = Nursing	-1.23	-0.616	1.60	-0.76	0.444
Classes = Above 5	2.82	1.25	2.25	1.25	0.212
Total digital burnout	0.43	8.96	0.040	10.75	< 0.001
R² = 0.509, adjusted R2 = 0.491, F = 17.91, p < 0.001.

R²: Coefficient of determination

B: Unstandardized Coefficients

Beta: Standardized Coefficients

t: t-test of significance

SE: standard error

*: Statistically significant at p ≤ 0.05

Descriptive statistics quantified demographic data by frequency, means, standard deviations, medians, and percentages. We used Pearson’s correlation coefficient to assess the strength of relationships between variables in our correlational analysis. A multiple linear regression analysis was conducted to identify key predictors of digital burnout, considering both demographic variables. The statistical analyses were performed with a significance level of α = 0.05.

Ethical considerations

The Fatima College of Health Sciences Research Ethics Committee approved the project [IRB approval number: FECE-2-24-25-R.IBRAHIM2]. Each participant learned about their data protection rights. Since the poll did not capture personal data, all data is safe. All responses will be safely stored for research. An advanced data protection system meets ethical requirements, making study participation voluntary. The research met the newest Declaration of Helsinki 2024 standards [41].

Results

A total of 140 students participated in the study. Key demographic characteristics included a nearly even distribution across age groups and academic levels, with a notable majority of students enrolled in 4–5 courses per semester and primarily from the nursing and emergency departments. Detailed demographic data are presented in Table 1.

Table 1.

Distribution of studied students by demographic characteristics (n = 140)

Category	n	%
Age
18–20	72	51.4%
21–24	68	48.6%
Student Level
First level	20	14.3%
Second level	39	27.9%
Third level	23	16.4%
Fourth level	58	41.4%
Number of classes/terms
2–3	34	24.3%
4–5	68	48.6%
Above 5	38	27.1%
Department
Psychology	15	10.7%
Nursing	66	47.1%
Emergency	59	42.1%

According to Table 2, the overall digital burnout level was high, with a mean score of 73.41 ± 20.88. In terms of individual subscales, the digital aging subscale had the highest mean score (33.18 ± 12.30), followed by the emotional exhaustion subscale (21.54 ± 8.40), while the digital deprivation subscale had the lowest (18.69 ± 6.74). In addition, the overall general psychological health was moderate, with a mean score of 38.55 ± 12.71. Regarding the subdomains, the distress/anxiety and somatic symptoms subscales showed moderate distress with mean scores of 11.04 ± 5.81 and 10.66 ± 4.98, respectively. Whereas social dysfunction and depression were in mild distress with mean scores of 8.99 ± 4.21 and 7.86 ± 5.29, respectively.

Table 2.

Mean scores of domains of digital burnout and general psychological health (n = 140)

Variables	M	SD	Min	Max	Interpretation
Emotional Exhaustion	21.54	8.40	6.00	30.00	High
Digital Deprivation	18.69	6.74	6.00	30.00	High
Digital Aging	33.18	12.30	12.00	60.00	High
The overall digital burnout	73.41	20.88	24.00	120.00	High
Somatic Symptoms	10.66	4.98	0.00	22.00	Moderate distress
Distress/Anxiety	11.04	5.81	0.00	21.00	Moderate distress
Social Dysfunction	8.99	4.21	0.00	21.00	Mild distress
Depression	7.86	5.29	0.00	19.00	Mild distress
Overall general psychological health	38.55	12.71	8.57	73.07	Moderate distress

M: Means

SD: Standard Deviation

In terms of the correlation analysis in Fig. 2, a strong, positive, and significant correlation was noted between overall general psychological health and overall digital burnout, r = 0.71, p = 0.001. The previously mentioned correlation was detected with all subscales of digital burnout, which were emotional exhaustion r = 0.65, p = 0.001, digital deprivation r = 0.77, p < 0.001, and digital aging r = 0.34, p = 0.019 (Fig. 2).

Fig. 2.

Scatter plots between domains of burnout and total general psychological health (n = 140)

A multiple regression analysis was conducted to investigate the predictors of overall digital burnout in students. The model was found to be statistically significant, F = 10.22, p < 0.001, and accounted for roughly 23.2% of the variance in digital burnout scores (R² = 0.232). Students aged 21–24 exhibited markedly low digital burnout scores (B = -8.91, β = -0.30, p = 0.011). Students enrolled in more than five classes per semester exhibited markedly elevated burnout levels (B = 17.52, β = 0.60, p < 0.001). Affiliation with the nursing department correlated with increased burnout (B = 6.42, β = 0.22, p = 0.046). The impact of being in level 4 was not statistically significant (p = 0.197) (Table 3).

A multiple regression analysis was deployed to identify the predictors of overall general psychological health. They showed statistical significance, F = 17.91, p < 0.001, and accounted for roughly 50.9% of the variation in general psychological health scores (R² = 0.509). Total digital burnout was identified as a strong positive predictor (B = 0.43, β = 8.96, p < 0.001) (Table 4).

Discussion

The present study sought to investigate, among nursing students, the levels and determinants of digital burnout as well as their relationship with general psychological health. The results showed a high overall level of digital burnout; the most noticeable subscale is digital aging, followed by emotional exhaustion. However, the digital deprivation subscale had the lowest.

Recent results indicate that nursing students (e.g., nursing undergraduates) report above-average digital burnout levels​ [42]. This echoes the results of a study conducted among 194 nursing students who experienced significant digital burnout, characterized by high scores in digital aging, emotional exhaustion, and digital deprivation [43]. These findings highlight that many future nursing professionals are feeling significantly drained by their digital engagements, albeit without strong signs of digital overdependence. A high digital aging score means students often cannot strike a healthy balance between online activities and real-life needs​. Many likely feel that they are constantly occupied with online lectures, educational apps, or academic communications. This perpetual connectivity can lead to classic signs of technostress, such as attention deficits, persistent fatigue, and feeling unable to “unplug. This justification is backed by the results of a study of the Digital Burnout Scale in Korea that found that Generation Z university students similarly experienced significant digital burnout, reinforcing that this is a generational issue, not isolated to one country [36]. In that context, digital aging was highlighted as a key characteristic, again emphasizing how overuse of digital tech leads to an imbalanced life and fatigue​. Our nursing students’ high digital aging scores echo this global trend.

Similarly, the elevated emotional exhaustion scores suggest that students are psychologically drained by digital workloads. Emotional exhaustion in this context may manifest as feelings of apathy towards studies, irritability with peers or instructors, and difficulty concentrating due to sheer mental fatigue​. In a study among 328 regular students from the FIS-UNCP, it was provided in the context that both emotional exhaustion and a lack of academic efficacy showed a positive relationship with technological anxiety [44].

In contrast, digital deprivation, being the least pronounced subscale, is an intriguing finding. It suggests that, despite heavy reliance on technology for their studies, many nursing students do not exhibit severe withdrawal symptoms when offline. In other words, they are not as “addicted” to technology for its own sake; their burnout stems more from obligatory digital use (for coursework, lectures, etc.) rather than compulsive use of social media or the internet in leisure. This could be a silver lining; it indicates a level of self-awareness and desire for balance. They feel exhausted by tech, yet are not entirely dependent on it for comfort or identity. The low level of digital deprivation suggests students may cope by unplugging when possible. Many likely jump at opportunities to socialize face-to-face, exercise, or rest without devices and engage in activities that alleviate the pressure of being continually online. This pattern (high exhaustion but low attachment) paints a picture of students who recognize the toll of digital overload and, at least to some extent, want to disconnect, even if academic obligations limit their ability to do so fully.

Quantitatively, students enrolled in 5 courses in the semester, especially from the nursing department, were considered predictors for high digital burnout levels. This could be explained because extensive screen time is a key stressor. Multiple hours of online lectures, electronic health record training modules, virtual simulations, and studying via digital materials each day​ could explain why being enrolled in nursing studies is a predictor. When compared to students in other fields, nursing students may face additional stressors that elevate their burnout. The rigorous nature of medical and nursing education means baseline stress is high; layering excessive digital engagement on top of that can produce a perfect storm of burnout. Some studies have indeed found that nursing students have higher burnout rates than their peers [45].

Students’ general psychological health was deemed to be moderate; considerable anxiety and somatic symptoms showed moderate distress, whereas social dysfunction and depression were in mild distress. The results of the study were in line with many research results. For instance, in Malaysia, 43.5% of medical students reported anxiety, with first-year students being more susceptible [46]. In Indonesia, 43.7% of medical students experienced symptoms of anxiety, with mild anxiety being the most common [47]. In a Malaysian study, 54.4% of students reported experiencing somatic symptoms during the pandemic. They suggested that the transition to online learning and the lack of physical interaction may have contributed to these symptoms, as students struggled to adapt to new learning environments [48]. Social dysfunction and depression were reported at mild levels. In the same line with our results, in Qatar, 37.7% of students had mild depression symptoms, with a smaller percentage experiencing moderate to severe symptoms [49]. In India, 68.3% of nursing students reported depression, but the severity varied, with only 23.5% experiencing extremely severe depression [50].

From the GHQ-28 perspective, moderate overall distress and anxiety reflect that students have, in general, noticed changes or disturbances in their normal functioning or mood that are more than trivial. Goldberg’s GHQ conceptualization emphasizes the “inability to carry out normal functions” and the “appearance of new and distressing phenomena as key signs of deteriorating mental health​. Thus, a moderate GHQ-28 score implies that many students are struggling to some extent with maintaining their usual well-being and daily performance. This level of distress is significant: while it may not correspond to acute clinical psychiatric illness for everyone, it is far from a “healthy” score. In practical terms, moderate overall GHQ-28 distress among nursing students should prompt attention to their mental well-being, as it suggests heightened stress and possible need for supportive interventions before issues worsen.

In contrast to the above domains, the GHQ-28 scores for social dysfunction and depression in the students were only in the mild distress range. A mild level of social dysfunction means that most students only occasionally experience difficulties in their daily social or role performance. Mild impairment suggests that, overall, students are largely managing to carry out their normal functions, with perhaps some inefficiency or stress, but not a dramatic loss of social or occupational functioning. The students might have occasional low moods or demotivation.

The contrast between mild social dysfunction/depression and moderate anxiety/somatic symptoms may also suggest a stage or intensity difference: high stress is manifesting early as anxiety and physical symptoms but has not progressed to breakdown in social functioning or severe depression for most students. This could be due to the relatively young age and high adaptability of students or the availability of peer support in the collegiate environment that keeps them socially engaged despite stress. It may also reflect that the time frame of evaluation was such that anxiety peaked (perhaps during a challenging period in training), but depressive symptoms did not have time to deepen. From an intervention standpoint, this is a window of opportunity; maintaining social connectedness and providing resources for mood support can ensure that mild depression symptoms do not worsen.

Digital burnout and general psychological health showed a strong and positive correlation, meaning that higher burnout levels are linked to worse health results. On the other hand, digital burnout most clearly predicted overall health; other demographic and academic variables had no appreciable influence. The factors contributing to this correlation include the intensity of academic demands, the integration of digital technologies in learning, and individual coping mechanisms. These elements collectively impact the mental and physical health of nursing students, leading to burnout and associated health issues. A study among medical students is a significant issue affecting medical students and residents, characterized by emotional exhaustion, depersonalization, and a sense of reduced personal accomplishment [45].

Conclusion

This study identified significant digital burnout among nursing students, particularly in digital aging and emotional exhaustion, primarily attributed to academic pressures rather than compulsive technology usage. Although students exhibited only minimal indications of social dysfunction and depression, moderate anxiety and somatic symptoms suggest increasing mental distress. The significant correlation between digital burnout and poor psychological health underscores the necessity for immediate support. Educational institutions must confront this issue by implementing balanced digital practices and mental health services to safeguard student well-being and ensure the resilience of the future nursing workforce.

Implications

The findings underscore an urgent necessity for academic institutions to reassess the integration of digital interaction into student life. The significant correlation between digital burnout and students’ psychological health highlights the potential dangers of excessive screen time, especially amid stringent academic requirements. To alleviate these concerns, organizations ought to establish policies that foster digital well-being. This may involve including digital wellness instruction in orientation programs, providing more flexible course loads, and promoting regular digital breaks. By cultivating healthy digital habits from the outset, institutions can enhance students’ academic achievement as well as their long-term physical and psychological health.

Furthermore, the recognition of at-risk student populations, specifically individuals aged 21–24, those undertaking extensive course loads, and nursing students, necessitates focused interventions. Nursing programs might integrate stress management and digital detox measures into their curricula, while academic advisors should actively oversee students managing five or more courses. The little predictive value of academic level and department, except nursing, indicates that the effects of digital burnout on health are more related to the severity of burnout than to the identity of the students. Consequently, tackling digital burnout should be regarded as a systemic health concern. Programs such as mental health support services, curriculum modifications, and improved digital literacy training are crucial for student achievement and for equipping future nursing professionals to handle technology-induced stress in their employment.

Limitations

This study’s cross-sectional design presents several limitations. Most notably, it restricts the ability to establish causal relationships, meaning it is unclear whether digital burnout leads to poorer health outcomes or vice versa. The data also capture only a single point in time, which may not reflect fluctuations in student stress and health throughout the academic year. Reliance on self-reported measures introduces potential bias due to mood, recall inaccuracies, or social desirability. Additionally, the findings may not be generalizable beyond the surveyed population, which had a high proportion of nursing and emergency department students. Although standard regression assumptions, including linearity, normality of residuals, homoscedasticity, multicollinearity, and independence, were assessed and met, minor violations may have been unnoticed and might potentially affect the accuracy of the results. Future research should consider longitudinal or mixed-methods approaches with more diverse academic samples to deepen understanding of digital burnout and its effects.

In addition, other potentially influential variables such as pre-existing mental health conditions, personality traits, sleep quality, physical activity, and coping mechanisms were not measured in this study. These unmeasured factors may have contributed to the observed associations and should be considered in future research to provide a more comprehensive understanding of digital burnout and psychological health among students.

Author contributions

Conceptualization: Rasha Kadri Ibrahim, Malak Khaled, Meznah Almansoori, Maryam Almazrouei, Aseel Ashraf. Methodology: Malak Khaled, Meznah Almansoori, Maryam Almazrouei, Shorok Hamed Alahmedi and Aseel Ashraf. Analysis: Abdelaziz Hendy, Rasha Kadri Ibrahim. Data Curation: Malak Khaled, Meznah Almansoori, Maryam Almazrouei, Aseel Ashraf. Writing original draft: Rasha Kadri Ibrahim, Abdelaziz Hendy. Review and Edit: Abdelaziz Hendy, Rasha Kadri Ibrahim, Shorok Hamed Alahmedi. Visualization: Shorok Hamed Alahmedi, Abdelaziz Hendy, Rasha Kadri Ibrahim, Supervision: Rasha Kadri Ibrahim, Abdelaziz Hendy.

Funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R720), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Data availability

The data are provided within the manuscript.

Declarations

Ethics approval and consent to participate

The Research Ethics Committee of Fatima College of Health Sciences will provide ethical approval for the study [IRB approval number: FECE-2-24-25-R. IBRAHIM2]. The study adhered to the principles of the Declaration of Helsinki. The questionnaires were shared with the study participants, clearly explaining the study’s purpose and the items included in the questionnaire. Written informed consent was obtained from all participants. Each participant will receive information about their rights and the terms of data protection. All information collected will be protected, as the survey does not gather personal data. All answers will be securely maintained for research purposes only. A comprehensive system for data protection is in place to fulfill ethical requirements, ensuring that participation in the study is entirely voluntary.

Consent to participate

Written informed consent was obtained from all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.