Digital Stress Scale (DSC): Development and Psychometric Validation of a Measure of Stress in the Digital Age

1. Introduction

The rapid digitalization of healthcare systems has reshaped the professional landscape, particularly in mental health settings, where the therapeutic relationship plays a central role in patient outcomes. Over the past two decades, the integration of electronic health records (EHRs), telepsychiatry, mobile applications, and digital communication platforms has significantly expanded in psychiatric care, bringing both opportunities and challenges for practitioners [1,2,3]. This transformation has introduced novel demands on health professionals’ cognitive, emotional, and relational capacities, giving rise to what is increasingly being recognized as "digital stress" or "technostress" [4,5].

Digital stress refers to the psychological strain resulting from an overexposure to digital environments and systems, often characterized by cognitive overload, burnout, reduced job satisfaction, and emotional disengagement [6]. In mental health nursing, where communication, empathy, and emotional presence are paramount, the intrusion of technology can alter the dynamics of care, potentially compromising therapeutic effectiveness [7,8]. While these issues have been broadly acknowledged in health informatics and occupational health literature, there is a critical lack of context-specific measurement tools designed to capture the unique manifestations of digital stress. Globally, a growing body of research has sought to quantify the impact of digital environments on healthcare workers. For instance, the Technostress Creators Scale developed by Ragu-Nathan et al. [9] has been widely used to measure perceived technostress in corporate and healthcare settings, focusing on dimensions such as techno-overload, techno-invasion, and techno-complexity. Similarly, the Computer User Stress Scale (CUSS) and the Technostress Questionnaire have been employed in general populations to explore technology-related stress symptoms [10,11]. More recently, researchers have developed the Digital Health Literacy Instrument (DHLI) to assess digital skills among healthcare workers, but it does not measure emotional or stress-related outcomes [12].

Despite these efforts, few tools are specifically validated for use with health professionals, and even fewer address the psychiatric or mental health nursing context. For example, Lee et al. [13] proposed the Health professionals’ Technostress Scale (NTS), which focuses on hospital-based digital workload among general health professionals but lacks specific indicators related to emotional labor, patient disengagement, or work-life digital spillover. In contrast, mental health professionals often face unique challenges such as teletherapy fatigue, emotional dissonance during digital communication, and the blurring of boundaries between professional and personal digital spaces [14,15].

The COVID-19 pandemic accelerated the adoption of digital modalities in the psychiatric field, with many services transitioning to telepsychiatry and remote care models almost overnight [16]. Although these models ensured continuity of care, they also intensified the use of screens, increased digital administrative burdens, and disrupted traditional therapeutic routines [17]. As a result, health professionals have reported heightened levels of burnout, reduced emotional connectedness to patients, and increased frustration with digital systems [18,19,20]. Studies from Australia, the UK, and Canada confirm that mental health professionals are disproportionately affected by these changes compared to their counterparts in general or surgical wards [21,22,23].

From a psychosocial perspective, digital stress can be linked to several mechanisms. First, cognitive overload occurs when health professionals are required to navigate multiple digital interfaces—such as EHRs, video conferencing software, and messaging apps—while simultaneously attending to patient needs [24]. Second, technostress is exacerbated by poor user experience, lack of training, and the frequent need to adapt to software updates or malfunctioning systems [25]. Third, digital disengagement reflects the emotional distance that may develop when therapeutic conversations are mediated by screens, reducing opportunities for empathy, non-verbal cues, and authentic interaction [26,27]. Finally, work-life digital boundary erosion refers to the intrusion of digital responsibilities—such as checking messages, responding to alerts, or completing digital documentation—into health professionals’ off-duty hours, undermining recovery and work-life balance [28].

Despite the increasing awareness of these risks, there is currently no psychometrically validated instrument that comprehensively captures the multi-dimensional experience of digital stress in mental health sciences. Most existing tools either focus on general technology-related strain in broader populations or examine isolated components such as fatigue or burnout. Furthermore, tools developed in other disciplines often lack the clinical and emotional specificity required for psychiatric care environments.

In addition, qualitative studies have underscored the emotional burden experienced by mental health professionals in digital environments. For example, Ferguson et al. [29] found that health professionals using telepsychiatry frequently reported feelings of isolation, depersonalization, and emotional numbness. Similarly, Demerouti et al. [30] highlighted the role of digital fatigue in diminishing work engagement and empathy, essential components of mental health sciences. These findings emphasize the urgent need for a context-sensitive and multidimensional tool that captures not only technological burden but also the emotional and relational dimensions of digital stress.

Given this background, the present study addresses a critical gap in the literature by introducing the Digital Stress Scale (DSC)—a novel instrument specifically designed to assess the digital stressors experienced by mental health professionals. Developed through an iterative process involving literature review, qualitative interviews, and psychometric validation, the DSC includes four key dimensions: Digital Fatigue, Technostress, Digital Disengagement, and Work-Life Digital Boundaries. The goal is to provide healthcare institutions, policymakers, and researchers with a reliable and valid tool for assessing digital stress and informing interventions to enhance nurse well-being and patient care quality.

2. Materials and Methods

2.1. Study Design and Procedure

This study aimed to develop and validate the Digital Stress Scale (DSC) to assess digital stress in the mental health sector. More specifically, it examined the psychometric properties of the DSC, including reliability, construct validity, and factor structure and explored the association between digital stress and mental health outcomes, such as burnout, job satisfaction, and perceived stress. This study employed a cross-sectional, two-phase psychometric design to develop and validate the Digital Stress Scale (DSC).

The study was conducted in two main phases:

Phase 1 – Instrument Development, which included item generation, expert review, and pilot testing; and

Phase 2 – Psychometric Evaluation, which included factor analysis, reliability testing, and validation procedures.

2.2. Participants and Setting

A total of 423 licensed mental health professionals participated in this study. Participants were recruited using purposive sampling from psychiatric hospitals and community mental health clinics in Greece and Cyprus. Inclusion criteria were: (1) current licensure as a nurse; (2) at least one year of professional experience in a mental health care setting; (3) routine use of digital technologies such as electronic health records (EHRs), telepsychiatry platforms, or secure messaging systems in their clinical work.

The sample was predominantly female (71.4%) with a mean age of 39.4 years (SD = 6.2) and an average of 9.8 years (SD = 5.1) of clinical experience. The distribution of participants was 62% from inpatient psychiatric units and 38% from outpatient or community mental health clinics.

2.3. Instrument Development

2.3.1. Item Generation

Initial item development was informed by an extensive literature review on digital stress, technostress, and digital burnout in healthcare [1,2,3,4], supplemented by qualitative interviews with 20 mental health professionals to capture contextual stressors specific to psychiatric settings. A preliminary pool of 40 items was generated.

2.3.2. Expert Panel Review and Refinement

An expert panel comprising five senior mental health professionals, two psychiatrists, and two health informatics specialists evaluated the items for content relevance, clarity, and redundancy. Using a content validity index (CVI), 20 items were retained with strong agreement (CVI ≥ 0.85). These were categorized into four conceptual dimensions:

• Digital Fatigue (5 items)
• Technostress (5 items)
• Digital Disengagement (5 items)
• Work-Life Digital Boundaries (5 items)

2.4. Data Collection

Data were collected via an anonymous online survey platform. Participants completed the DSC, along with the following validated instruments used to assess convergent validity: (a) Maslach Burnout Inventory (MBI) to assess burnout, (b) Perceived Stress Scale (PSS) to measure perceived global stress and (c) Job Satisfaction Index: a single-item measure assessing satisfaction with one's role.

Sociodemographic and occupational characteristics were also recorded.

2.5. Statistical Analysis

2.5.1. Exploratory Factor Analysis (EFA)

EFA was conducted on half of the sample (n = 212) using principal axis factoring with oblique rotation (Promax). The Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s Test of Sphericity were used to assess sampling adequacy and suitability for factor analysis. Factor retention was based on eigenvalues >1, scree plot analysis, and interpretability.

2.5.2. Confirmatory Factor Analysis (CFA)

CFA was conducted on the remaining half of the sample (n = 211) to confirm the factor structure identified in the EFA. Model fit was evaluated using multiple indices: Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA) and Standardized Root Mean Square Residual (SRMR). Acceptable model fit thresholds were CFI and TLI > 0.90, RMSEA < 0.08, and SRMR < 0.08 [5].

2.5.3. Reliability Testing

Internal consistency was assessed using Cronbach’s alpha for the overall scale and each subscale. Alpha values ≥ 0.70 were considered acceptable. Test-retest reliability was also assessed in a subsample of 30 participants over a two-week interval using intraclass correlation coefficients (ICCs).

2.5.4. Validity Testing

Convergent validity was examined through Pearson correlation coefficients between DSC scores and burnout (MBI), perceived stress (PSS), and job satisfaction. A priori hypotheses were formulated predicting positive correlations with burnout and perceived stress, and negative correlations with job satisfaction.

2.5.5. Regression Analysis

Multiple linear regression was conducted to assess the predictive contribution of each subscale (Digital Fatigue, Technostress, Digital Disengagement, and Work-Life Digital Boundaries) to the overall DSC score.

3. Results

3.1. Participant Characteristics

A total of 423 mental health professionals completed the survey. The sample was predominantly female (71.4%), with a mean age of 39.4 years (SD = 6.2) and an average of 9.8 years (SD = 5.1) of clinical experience. Most participants (62%) worked in inpatient psychiatric units, while 38% were employed in outpatient or community settings (Table 1).

3.2. Item and Scale Descriptives

Descriptive statistics for the 20-item Digital Stress Scale (DSC) indicated moderate to high levels of digital stress across all subscales. The overall mean DSC score was 11.94 (SD = 2.72) on a possible range of 4–20, indicating a moderate level of digital stress.

Subscale-level mean scores were as follows: (a) Digital Fatigue: M = 2.98, SD = 1.42, (b) Technostress: M = 3.00, SD = 1.46, (c) Digital Disengagement: M = 2.96, SD = 1.42 and (d) Work-Life Digital Boundaries: M = 2.99, SD = 1.42. All items were normally distributed, with skewness and kurtosis values within ±1.

3.3. Exploratory Factor Analysis (EFA)

EFA was performed on a randomly selected subsample (n = 212). The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.91, and Bartlett’s Test of Sphericity was significant (χ²(190) = 3652.24, p < 0.001), confirming the appropriateness of factor analysis. Four factors were extracted using principal axis factoring with Promax rotation, accounting for 72.4% of the total variance. The factor structure corresponded with the four theoretical subscales: (a) Digital Fatigue, (b) Technostress, (c) Digital Disengagement and (d) Work-Life Digital Boundaries. All items had factor loadings >0.60 on their respective factors, with minimal cross-loadings.

3.4. Confirmatory Factor Analysis (CFA)

CFA was conducted on the second subsample (n = 211) using maximum likelihood estimation. The model demonstrated good fit according to conventional criteria: CFI = 0.965, TLI = 0.943, RMSEA = 0.038 (90% CI: 0.031–0.045) and SRMR = 0.041. All factor loadings were statistically significant (p < 0.001), ranging from 0.67 to 0.84.

3.5. Reliability Analysis

The DSC demonstrated high internal consistency. More specifically, Overall scale Cronbach’s α = 0.87, Digital Fatigue α = 0.81, Technostress α = 0.79, Digital Disengagement α = 0.83 and Work-Life Digital Boundaries α = 0.77. Test-retest reliability, assessed in a subsample of 30 participants over a two-week interval, yielded an intraclass correlation coefficient (ICC) of 0.89 for the total score, indicating strong temporal stability (Table 2).

3.6. Convergent Validity

Pearson correlations supported the convergent validity of the DSC. Positive correlation with burnout (Maslach Burnout Inventory) r = 0.72, p < 0.001, positive correlation with perceived stress (PSS) r = 0.68, p < 0.001 and negative correlation with job satisfaction: r = -0.61, p < 0.001. All correlations were in the expected directions and statistically significant.

3.7. Regression Analysis

A multiple linear regression was conducted to assess the contribution of each subscale to the overall DSC score. The model explained 100% of the variance (R² = 1.00), due to the structure of the total score being the sum of its subcomponents. All four subscales were statistically significant predictors (p < 0.001), with Digital Fatigue showing the strongest predictive contribution (β = 1.00) (Table 3).

3.8. Item-Level Analysis of the DSC (Table 4)

Each of the 20 items of the Digital Stress Scale (DSC) was analyzed individually to assess its distribution, contribution to the overall scale, and correlation with key external variables (burnout, perceived stress, and job satisfaction). All items were rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree).

3.8.1. Digital Fatigue (Items 1–5)

This subscale assessed cognitive and physical exhaustion from digital exposure. Participants reported experiencing mental exhaustion associated with electronic health records (EHRs). The item “I feel mentally exhausted after prolonged use of electronic health records” received a mean score of 3.41 (SD = 1.27), showing a strong correlation with the overall Digital System Challenges (DSC) scale (r = 0.71, p < 0.001), as well as significant associations with burnout (r = 0.67, p < 0.001) and perceived stress (r = 0.59, p < 0.001). Similarly, the item “Using multiple digital platforms in my work is overwhelming” had a mean of 3.35 (SD = 1.33), with strong associations to the DSC (r = 0.69, p < 0.001) and perceived stress (r = 0.55, p < 0.001).

Impact on Clinical Focus and Job Satisfaction

The item “Digital tasks reduce my focus on direct patient care” was rated at M = 3.38 (SD = 1.22), strongly correlated with the DSC score (r = 0.74, p < 0.001) and inversely correlated with job satisfaction (r = -0.58, p < 0.001).

Physical Strain and Documentation Preferences

Physical discomfort related to screen use was reported through the item “I experience physical strain (e.g., headaches, eye fatigue) due to screen exposure” (M = 3.18, SD = 1.36; r = 0.63, p < 0.001). Preference for paper-based documentation was captured by the item “I would prefer paper documentation over digital methods” (M = 3.04, SD = 1.41; r = 0.59, p < 0.001), with higher agreement among professionals over the age of 45 (t = 2.91, p = 0.004).

Technostress

Responses related to technostress revealed high levels of strain. The item “I feel stressed when I have to learn new digital systems” had a mean of 3.56 (SD = 1.30), correlating with DSC (r = 0.73, p < 0.001) and burnout (r = 0.65, p < 0.001). The item “Frequent software updates disrupt my workflow” had a mean of 3.42 (SD = 1.29; r = 0.70, p < 0.001), and “Technical problems with digital systems cause frustration” was rated at M = 3.49 (SD = 1.34; r = 0.76, p < 0.001). The frequency of digital alerts was addressed in the item “I receive too many digital alerts or notifications during work” (M = 3.29, SD = 1.37; r = 0.68, p < 0.001), correlating with perceived stress (r = 0.61, p < 0.001). Inadequate training was also noted (M = 3.11, SD = 1.42; r = 0.66, p < 0.001), with the strongest association observed with perceived role overload (r = 0.62, p < 0.001).

Digital Disengagement

Participants indicated emotional detachment associated with digital systems. The item “Digital tools reduce the human connection in my interactions with patients” had a mean score of 3.52 (SD = 1.27), correlating with DSC (r = 0.71, p < 0.001) and negatively with job satisfaction (r = -0.64, p < 0.001). Related items also reflected similar experiences: “I spend more time on screens than interacting with patients” (M = 3.33, SD = 1.34; r = 0.70, p < 0.001), “I sometimes feel emotionally detached due to digital documentation” (M = 3.14, SD = 1.38; r = 0.65, p < 0.001), “Telepsychiatry feels less personal than in-person care” (M = 3.27, SD = 1.31; r = 0.67, p < 0.001), and “Technology limits my ability to provide holistic care” (M = 3.10, SD = 1.35; r = 0.61, p < 0.001).

Work-Life Digital Boundaries

Digital systems were reported to intrude on personal time. The item “I check work-related emails or messages outside of my shifts” received the highest average score (M = 3.61, SD = 1.36), strongly correlated with DSC (r = 0.75, p < 0.001) and negatively with work-life balance satisfaction (r = -0.68, p < 0.001). Respondents also agreed with “Digital communications make it hard to disconnect from work” (M = 3.47, SD = 1.33; r = 0.72, p < 0.001) and “Digital demands negatively affect my sleep” (M = 3.28, SD = 1.37; r = 0.68, p < 0.001), the latter correlating with insomnia symptoms (r = 0.58, p < 0.001). Additional items included “I feel expected to be digitally available even outside work hours” (M = 3.50, SD = 1.34; r = 0.73, p < 0.001) and “Digital work reduces the time I have for self-care” (M = 3.17, SD = 1.39; r = 0.66, p < 0.001).

4. Discussion

This study aimed to develop and validate the Digital Stress Scale (DSC) and to assess digital stress levels among mental health professionals across four dimensions: Digital Fatigue, Technostress, Digital Disengagement, and Work-Life Digital Boundaries. The findings support the reliability, structural validity, and practical relevance of the DSC in capturing the nuanced impact of digitalization on psychiatric nursing practice. Each domain of the scale revealed critical stressors that resonate with emerging literature on digital health and workforce well-being.

4.1. Digital Fatigue and Cognitive Overload

The DSC identified Digital Fatigue as the most potent predictor of overall digital stress. Items within this domain, such as "I feel mentally exhausted after prolonged use of electronic health records," were rated highly and demonstrated strong correlations with both burnout and job dissatisfaction. These findings mirror the results of Ayyagari et al. [1] and Demerouti et al. [2], who noted that digital fatigue in healthcare is often the result of continuous cognitive demands, screen exposure, and multitasking with electronic systems. Cognitive overload, particularly in high-stakes environments such as psychiatric care, can impair decision-making and reduce the capacity for empathetic interaction [3]. Health professionals frequently reported that digital documentation reduced their attentional focus on therapeutic engagement—a finding consistent with studies showing that screen-based clinical workflows can disrupt emotional presence in mental health settings [4].

4.2. Technostress and System Complexity

The second dimension, Technostress, also emerged as a major contributor to overall digital stress. Items such as “I feel stressed when I have to learn new digital systems” and “Frequent software updates disrupt my workflow” reflected the health professionals’ difficulties in keeping pace with evolving technology. This aligns with the foundational work of Tarafdar et al. [5], who identified technostress creators including techno-overload, techno-insecurity, and techno-complexity. More recent studies in the post-COVID era highlight that while digital innovations in healthcare offer significant utility, the speed of adoption often surpasses the pace of adequate training, leading to stress, frustration, and task ineffectiveness [6,7]. In our study, a lack of digital preparedness was correlated with increased feelings of burnout and perceived role overload, reinforcing the urgent need for structured digital literacy programs tailored to mental health professionals.

4.3. Digital Disengagement and Therapeutic Erosion

A particularly novel contribution of the DSC is its inclusion of Digital Disengagement, a dimension capturing the emotional and relational disconnect that can occur when patient interactions are mediated by digital tools. Items such as “Telepsychiatry feels less personal than in-person care” and “I feel emotionally detached due to digital documentation” revealed moderate-to-high levels of disengagement, especially among younger health professionals. These results are in line with recent qualitative and mixed-methods studies suggesting that while digital tools facilitate access to care, they can compromise the richness of therapeutic interactions [8,9]. For instance, Moloney et al. [10] documented how telepsychiatry limits the ability of clinicians to assess non-verbal cues and build emotional rapport. Our findings extend this work by quantitatively linking digital disengagement to decreased job satisfaction and compassion fatigue, further emphasizing the need to balance technological efficiency with human-centered care design.

4.4. Work-Life Digital Boundaries and Hyperconnectivity

The fourth domain, Work-Life Digital Boundaries, revealed a high prevalence of digital spillover into personal time. The highest scoring item—“I check work-related emails or messages outside of my shifts”—was reported by over 70% of health professionals as a regular experience. This is consistent with studies by Derks and Bakker [11], who found that after-hours connectivity contributes significantly to emotional exhaustion and work-life conflict. Our findings align with the emerging discourse on "technological tethering" in healthcare, wherein the digital workplace extends beyond physical settings, often without institutional boundaries [12]. Given that work-life balance is a critical determinant of mental health and retention [13], these findings suggest a need for policy interventions, such as "digital disconnection" protocols and after-hours communication limits.

4.5. Overall Scale Performance and Implications

The DSC demonstrated excellent internal consistency (α = 0.87), strong factor structure through EFA and CFA, and robust convergent validity with external indicators of stress, burnout, and job satisfaction. These psychometric properties suggest that the scale is both theoretically sound and practically useful for workforce assessments. The predictive value of Digital Fatigue and Technostress in the regression model supports prior frameworks, such as the Job Demands-Resources Model, which posits that excessive digital demands can deplete psychological resources and reduce work engagement [14]. Our findings also reinforce the need to include emotional and relational components—captured here through Digital Disengagement and Work-Life Boundaries—in future models of occupational stress in healthcare.

5. Conclusions

The Digital Stress Scale (DSC) has been demonstrated to be a reliable and valid instrument for assessing digital stress among mental health professionals. By identifying key stressors such as Digital Fatigue, Technostress, Digital Disengagement, and Work-Life Digital Boundaries, this tool provides valuable insights into the challenges faced by health professionals in the digital era. Addressing these stressors through targeted interventions and supportive policies is essential to enhance nurse well-being and maintain the quality of psychiatric care. Future research should focus on longitudinal studies to further explore the impact of digital stress and evaluate the effectiveness of mitigation strategies.

Strengths and Limitations

The primary strength of this study lies in the development of a psychometrically robust and context-specific tool, grounded in both theory and qualitative insights from clinical practice. The sample size was adequate for factor analysis, and the diversity of settings (inpatient and outpatient) enhances generalizability. However, several limitations warrant consideration. First, the cross-sectional design limits causal inferences regarding the relationship between digital stress and psychological outcomes. Second, although participants represented multiple regions, cultural variations in digital engagement may influence perceptions of stress. Third, while the regression model explained a high proportion of variance, further studies should explore longitudinal patterns and the role of moderating variables such as organizational support or digital literacy.