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

The study investigates how digital burnout—arising from prolonged engagement with digital devices and platforms—relates to general psychological health among nursing students. Within nursing education, intensive academic requirements, clinical training, and expanded online learning increase susceptibility to technostress and burnout. Despite extensive international literature, there is a contextual gap in the UAE regarding digital burnout’s determinants and its specific psychological health impacts among nursing students. The research aims to quantify levels of digital burnout and general psychological health, examine their correlation, and identify demographic and academic predictors in a UAE nursing cohort, highlighting implications for institutional support and student well-being.

Prior work defines burnout as a psychological syndrome involving emotional exhaustion, depersonalization, and reduced efficacy (Maslach), with high prevalence reported among medical and nursing students, especially during pandemic-driven online learning. Studies show elevated digital burnout and technostress (e.g., techno-overload, invasion, uncertainty), with links to anxiety, depression, and physiological markers (elevated cortisol, reduced CoQ10). Intrinsic contributors include heavy workloads, perfectionism, and performance pressures; extrinsic contributors involve insufficient institutional support, inadequate training/resources, and limited psychosocial services. Physical health risks associated with chronic stress and sedentary online learning include hypertension, cardiovascular disease, immune compromise, obesity, and diabetes. Coping strategies (active/problem-focused coping, planning, social support) can mitigate distress. Institutional interventions—emotional support, educator training, resilience/well-being modules, regular communication, and digital wellness tools (mindfulness/CBT apps)—demonstrate benefit. A conceptual framework posits digital burnout (digital aging, deprivation, emotional exhaustion) adversely affecting general psychological health (somatic symptoms, anxiety/distress, social dysfunction, depression). There remains a need for region-specific, cross-sectional evidence in UAE nursing students focusing on digital burnout as distinct from general academic burnout.

Design and setting: Cross-sectional, correlational quantitative survey conducted February–March of the 2024–2025 academic year at Fatima College of Health Sciences, United Arab Emirates. Population and sampling: Female undergraduate nursing students (Levels 1–4), full-time; convenience sampling with invitation to all eligible students. Sample size calculated using Epi-Info v7 with 5% margin of error, 95% confidence level, power 0.80, alpha 0.05, and 5% nonresponse; final n = 140 participants. Instruments: (1) Demographics: age, student level, number of classes per semester, department. (2) Digital Burnout Scale (DBS-24): 24 items across three subscales—digital aging (12 items), digital deprivation (6 items), emotional exhaustion (6 items)—rated on 5-point Likert; total 24–120 (higher = more burnout). Reliability in study: overall α = 0.912; subscales—digital aging α = 0.944, digital deprivation α = 0.876, emotional exhaustion α = 0.901. KMO = 0.832, Bartlett p < 0.001. (3) General Health Questionnaire (GHQ-28): 28 items across somatic symptoms, anxiety/distress, social dysfunction, depression; 4-point Likert scoring 0–3, total 0–84 with categories: 0–23 normal, 24–36 mild distress, 37–59 moderate distress, 60–84 severe distress. Reliability in study: overall α = 0.871; subscales—somatic α = 0.898, anxiety/distress α = 0.913, social dysfunction α = 0.860, depression α = 0.845. KMO = 0.871, Bartlett p < 0.001. Validity and bias controls: Pilot with 14 students (10%) assessed clarity/feasibility; pilot participants included in final sample. Anonymity and confidentiality emphasized to reduce social desirability bias; validated instruments used to reduce measurement bias. Data collection: Surveys distributed via university email; data collection February–March 2025; completion time 10–15 minutes; informed consent obtained electronically; encrypted submission; original unmodified DBS-24 and GHQ-28 used. Statistical analysis: SPSS v23 used. Descriptive statistics (frequencies, means, SD, medians, percentages). Pearson correlations for associations. Multiple linear regression for predictors of digital burnout and general psychological health. Regression assumptions checked: linearity via scatterplots; Shapiro–Wilk normal residuals (digital burnout model W = 0.976, p = 0.078; psychological health model W = 0.861, p = 0.089); independence via Durbin–Watson; multicollinearity via VIF (digital burnout model 1.02–1.18; psychological health model 1.03–1.21). Significance threshold α = 0.05. Ethics: Approved by Fatima College of Health Sciences Research Ethics Committee (IRB FECE-2-24-25-R.IBRAHIM2); adherence to Declaration of Helsinki; voluntary participation and data protection ensured.

Sample: n = 140; ages 18–20 (51.4%), 21–24 (48.6%); levels: L1 14.3%, L2 27.9%, L3 16.4%, L4 41.4%; courses per semester: 2–3 (24.3%), 4–5 (48.6%), >5 (27.1%); departments: Nursing 47.1%, Emergency 42.1%, Psychology 10.7%. Digital burnout: Overall mean 73.41 ± 20.88 (high). Subscales: digital aging 33.18 ± 12.30 (high), emotional exhaustion 21.54 ± 8.40 (high), digital deprivation 18.69 ± 6.74 (high). General psychological health (GHQ-28): Overall mean 38.55 ± 12.71 (moderate distress). Subdomains: anxiety/distress 11.04 ± 5.81 (moderate), somatic symptoms 10.66 ± 4.98 (moderate), social dysfunction 8.99 ± 4.21 (mild), depression 7.86 ± 5.29 (mild). Correlations: Total digital burnout positively correlated with overall psychological health (r = 0.71, p < 0.001). Subscale correlations with overall psychological health: emotional exhaustion r = 0.65 (p < 0.001), digital deprivation r = 0.77 (p < 0.001), digital aging r = 0.34 (p = 0.019). Predictors of digital burnout (multiple regression): Model significant F = 10.22, p < 0.001; R² = 0.232 (adjusted R² = 0.210). Significant predictors: age 21–24 (B = −8.91, β = −0.30, p = 0.011; lower burnout vs 18–20), classes >5 (B = 17.52, β = 0.60, p < 0.001; higher burnout), nursing department (B = 6.42, β = 0.22, p = 0.046; higher burnout). Student level 4 not significant (p = 0.197). Predictors of general psychological health (multiple regression): Model significant F = 17.91, p < 0.001; R² = 0.509 (adjusted R² = 0.491). Total digital burnout was a strong positive predictor (B = 0.43, β = 8.96, p < 0.001). Other demographic/academic variables (age, level, department, classes >5) were not significant.

Findings confirm high levels of digital burnout among nursing students, with digital aging and emotional exhaustion most prominent, aligning with global evidence of technostress in Generation Z students. Despite reliance on digital tools, lower digital deprivation suggests burnout is driven more by compulsory academic usage than by compulsive leisure use, indicating potential for relief through intentional disconnection. Moderate overall psychological distress, particularly anxiety and somatic symptoms, points to meaningful functional impacts that warrant intervention before progression to more severe social dysfunction or depression. The strong positive association between digital burnout and poorer psychological health directly addresses the research question, highlighting digital burnout as the key driver over demographic or academic variables. Institutional relevance is substantial: high course loads and nursing-specific demands were linked to higher burnout, guiding targeted strategies such as optimized course scheduling, integrated stress management, digital hygiene practices, and enhanced counseling services. Recognizing at-risk groups (students with >5 classes, nursing majors, younger cohorts) can help tailor support. These results reinforce the need to treat digital burnout as a systemic educational health issue and to implement comprehensive digital well-being policies within nursing education.

The study demonstrates significant digital burnout among UAE nursing students, predominantly in digital aging and emotional exhaustion, and its strong association with worse psychological health. Burnout appears driven by academic demands rather than compulsive technology use. Institutions should implement balanced digital practices (digital breaks, ergonomics, blended learning), adjust course loads, and expand mental health supports to protect student well-being and resilience. Future research should employ longitudinal and mixed-methods designs, include diverse academic programs, and examine additional variables (pre-existing mental health, personality, sleep, physical activity, coping) to clarify causal pathways and inform targeted interventions.

Cross-sectional design limits causal inference and captures only a single time point. Self-reported measures may be affected by mood, recall, and social desirability bias. Generalizability is constrained by the specific institutional context (female-only, high proportion of nursing and emergency students). While regression assumptions were assessed and met (linearity, normality of residuals, homoscedasticity, multicollinearity, independence), minor violations may be undetected. Unmeasured variables (pre-existing mental health conditions, personality traits, sleep quality, physical activity, coping mechanisms) could influence associations. Future studies should consider longitudinal or mixed-methods approaches with broader, more diverse samples.