A Study on Technostress Emotional Exhaustion and Sleep Quality Among Future Psychologists in India an Intervention Plan

A Study on Technostress Emotional Exhaustion and Sleep Quality Among Future Psychologists in India an Intervention Plan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Study on Technostress Emotional Exhaustion and Sleep Quality Among Future Psychologists in India an Intervention Plan Dr. Sneha Saha, Nandita Agrawal, Nupur Varun, Monika Mittal, Priyadharshini S, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8411730/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background The rapid changes in academic curriculum and the increasing use of digital platforms in learning, assessment, and communication have increased the use of technology among psychology students. Although digital technologies have increased access, accessibility and efficiency, continuous connectivity has brought about technostress, which is a psychological strain linked with the excessive use of technology. Objectives This research investigated the relationship between technostress, emotional exhaustion and sleep quality among emerging Indian psychologists, and whether emotional exhaustion acts as a mediator between technostress and sleep quality. Method A Quantitative correlational research design was used. Data was collected from psychology students aged 18 to 25 years. SPSS was used to analyse the descriptive statistics and Pearson’s correlation, whereas the mediation analysis was done with the help of Jamovi. Results The findings showed that there was a positive correlation between technostress and emotional exhaustion and significant relationship between emotional exhaustion and poor sleep quality. The mediation analysis shows emotional exhaustion completely mediates the relationship between technostress and sleep quality, whereas emotional exhaustion has an indirect effect of technostress on sleep quality with no significant effect. Conclusion The findings of this study support the need to implement interventions that reduce emotional burnout in order to enhance sleep quality and personal well-being among psychology students. Therefore, this research suggests the REPEAT intervention model, which is expected to lead to digital balance, emotional regulation, and restorative sleep. Technostress Emotional Exhaustion Sleep Quality Psychology Students Intervention Figures Figure 1 Introduction Technology is now an inseparable part of both the academic and research domains in the last decade, especially among the students in the field of psychology who make excessive use of the digital platform for both their coursework and academic research, and professional communication. Although technology has increased the level of accessibility and efficiency, it has also become a major contributor to technostress, which is described as a psychological and physical stress caused by overindulgence in digital technology and the virtual world (Latorre, 2018 ). Technostress has been demonstrated to lead to emotional fatigue and sleep quality problems that, in turn are predictors of psychological well-being and academic achievement (Jiang et al., 2015). Empirical evidence also shows that technostress has a detrimental effect on academic performance among university students because attentional control, memory consolidation, and learning ability are impaired due to constant exposure to digital information and the need to be constantly connected, which results in low academic motivation and performance (Torales et al., 2022 ). The results of Indian institutions of higher education also support the existence of a strong negative correlation between technostress and academic performance, indicating that increased technological stress can severely affect the academic performance of students (Mahapatra et al., 2023). Moreover, screen time has also been associated with circadian rhythm disruption, such as a decrease in sleep onset and poor sleep quality (Jniene et al., 2019 ). According to Yunita et al. ( 2023 ) that increased usage of educational technology especially mobile phones is a cause of technostress, which disrupts circadian regulation due to cognitive load and over exposure. In line with this, Yao and Wang ( 2022 ) showed that technostress is escalated by compulsive mobile phone use and information overload which consequently leads to poor sleep quality. In contrast, Abojedi et al. ( 2023 ) concluded that the use of technology has no direct influence on sleep quality, but rather serves as a mediator of the effect, which occurs via perceived stress, showing that emotional control and time-management skills are significant in reducing sleep disturbances. Goddard ( 2011 ) also found a significant relationship between poor sleep quality and technostress. Recently, Hapsari et al. ( 2024 ) found a strong negative relationship between technostress and quality of sleep, along with screen time, device multiplicity, and socioeconomic status were identified as major contributors. All of these findings suggest that although technostress is consistently associated with sleep impairment, the intensity and direction of the association may differ based on the individual and situational moderators, highlighting the importance of further empirical studies. Emotional exhaustion is a fundamental component of burnout, a state of emotional depletion caused by long-term exposure to stressors (Maslach and Jackson, 1981). Psychology students are especially susceptible to emotional exhaustion due to rigorous academic demands, emotionally charged programs, and early introduction to clinical practice (Brand et al., 2014). Empirical research has always reported high scores of emotional exhaustion in university students, which are usually followed by a lack of motivation, poor learning, and negative mental health consequences (Ibrahim et al., 2013). Moreover, emotional exhaustion has been revealed to predispose people with sleeping disorders, which only increases cognitive and emotional stress (Elfering et al., 2018). Research studies examining the overlap between technostress and emotional exhaustion have shown that technostress exacerbates emotional fatigue through cognitive load and emotional distress. As discovered by Stoeckl and Eckhardt ( 2023 ), teacher sociability was also found to lower emotional exhaustion in the online education environment during the COVID-19 pandemic, but components of technostress, including techno-overload and techno-complexity, had a significant impact in increasing emotional exhaustion. Similarly, Buenadicha-Mateos et al. ( 2022 ) found that perceived stress and intrapersonal conflict mediated the relationship between technostress and emotional exhaustion in university students, highlighting the impact of emotional and social aspects as well as technological needs. Upadhyaya and Vrinda ( 2021 ) determined five dimensions of technostress, such as techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty, and discovered that those students were more vulnerable to technostress, who had lower levels of Information in Computer Technology (ICT), were postgraduates, and female, which underscores the necessity of targeted intervention. Wang et al. (2021) also established that technostress developers, including techno-complexity, techno-insecurity, and techno-uncertainty, led to burnout in technology-enhanced learning settings, which in turn, undermined self-regulation, learning agency, and persistence among male students. Those who are less predisposed towards technology-enhanced learning were specifically impacted. A study conducted by Li et al. ( 2020 ) identified academic pressure, financial stress, and social challenges as the main stressors leading to emotional exhaustion and burnout in university students, and the poor quality of sleep turned out to be one of the major predictors because of compromised cognitive functioning and lack of stress recovery. Jarrett et al. ( 2019 ) showed that emotional exhaustion has an indirect connection with sleep disorders, which disrupts emotion regulation, leading to perceived stress, especially in medical students. Xu et al. (2019) conducted a study on students in nursing programs and found that sleep disturbances were essentially the direct predictors of emotional exhaustion, with sleep-related stress becoming an additional predictor of depressive symptoms indicating that cognitive preoccupation with sleep increases exhaustion. According to Hu et al. ( 2020 ), the connection between disrupted sleep patterns and depressive symptoms was mediated by emotional exhaustion in shift workers, and evening-oriented sleep patterns led to increased fatigue and susceptibility to mental health issues. The overall implication of such findings is that, although sleep disturbances can be a direct contributor to emotional exhaustion, there are indirect mechanisms in which emotion regulation deficits and cognitive strain can interfere with this relationship. Although the literature on the topic of technostress, emotional exhaustion, and sleep quality continues to expand, few studies in particular have explored the relationship between these aspects among psychology students in the context of the Indian setting. Additionally, the mediating effect of emotional exhaustion on the technostress-sleep quality association has not been well studied. This dichotomy is especially critical in light of recent changes to the curriculum that encourage earlier exposure to professional training in psychology. These interrelated problems are critical in the process of protecting the well-being of students and nurturing them to become competent and resilient mental health professionals. Digital wellness, sleep hygiene, mindfulness-based interventions, and resilience training are thus urgently necessary, and longitudinal studies should be conducted in the future to establish the directionality and the dynamics of the relationship between technostress, emotional exhaustion and sleep quality. Methodology Objectives 1. To investigate the relationship between technostress and emotional exhaustion among psychology students. 2. To investigate the association between technostress and sleep quality in psychology students. 3. To investigate whether emotional exhaustion mediates the relationship between technological stress and sleep quality. Hypothesis H1: Higher levels of technostress will be associated with greater emotional exhaustion among psychology students. H2: Higher levels of technostress will be associated with lower sleep quality among psychology students. H3: The association between technostress and sleep quality will be partially mediated by emotional exhaustion, such that higher technostress will contribute to increased emotional exhaustion, which in turn will be linked to lower sleep quality. Sampling technique ● Sample Size: 200 participants (161 females, 39 males) ● Sample Age Range: 18-25 years ● Sampling Method: Purposive Sampling Inclusive and Exclusive Criteria The sample comprised of psychology students between the ages of 18 and 25 years old, pursuing or having recently graduated undergraduate or postgraduate psychology degrees, using digital devices in their academic activities and gave informed consent and could communicate in English. Measures and Tools Sleep Quality Scale (SQS)- The scale was developed by Yi et al. (2006). The instrument comprises 28 self-report questions designed to assess various aspects of sleep functioning, including daytime symptoms, sleep restoration, difficulties with sleep initiation and maintenance, waking problems, and overall satisfaction with sleep. The scale is suitable for adults aged 18 to 59, and it is administered in pencil-and-paper format. The respondents will be asked to answer each item on a four-point Likert scale, where a score of 0 corresponds to "few" and a response of "almost always" corresponds to 3. Questions that relate to post-sleep restoration and sleep satisfaction are reverse-scored before analysis. The total score ranges from 0 to 84, where higher scores indicate poorer sleep quality and more sleep-related problems. The scale exhibits strong psychometric properties, including high internal consistency (α = .92) and acceptable test-retest reliability (r = .81). Emotional Exhaustion Scale (EES)- Originally developed by Maslach and Jackson (1981) and later modified to fit the academic level by Ramos et al (2005). The scale consists of 10 questions that investigate emotional exhaustion and burnout related to academic requirements over the last 12 months. The respondents review each item on a five-point Likert scale, with 1 indicating 'rarely' and 5 indicating 'always'. The range of scores, which are total, is between 10 and 50, with high scores indicating more emotional exhaustion. The presence of established cut-offs categorizes scores as follows: 1019 as low, 2029 as moderate, 3039 as high, and 4050 as very high levels of emotional exhaustion. The scale is highly internally consistent (α = 0.893) and exhibits acceptable levels of item homogeneity, as indicated by a mean inter-item correlation of 0.33. Technostress Scale- The Technostress Scale was used to measure technostress (Tarafdar et al., 2007). The instrument comprises 23 items assessed using a five-point Likert scale and evaluated across five dimensions of technostress: techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty. Techno-overload refers to the pressure of working faster and longer due to the use of technology, while techno-invasion relates to the level of technology's intrusion into personal time and boundaries. The techno-complexity shows a response of inadequacy due to the perceived lack of technological abilities. In academic institutions, techno-insecurity refers to anxiety in terms of academic performance compared to students who are better technologically fitted. Techno-uncertainty is associated with the pressure created by the constant technological changes and updates. The reliability analysis revealed satisfactory internal consistency in all subscales, with Cronbach's alpha values exceeding .70. Procedure The study was conducted online, with an invitation sent through university portals and social media groups targeting Psychology students. Upon clicking the study link, participants were presented with a detailed informed consent form that explained the study's purpose, procedures, and their rights as participants. Once consent was provided, they proceeded to complete the questionnaire via a Google Form. The form was designed to gather data on technostress, perceived emotional exhaustion, and sleep quality, and included a manipulation check to ensure the accuracy of responses. Throughout the process, every effort was made to ensure confidentiality, no identifying information was collected, and all data was stored securely. Contact information was provided for any further questions or concerns. Result In this section, the statistical results of the research is provided, which was analysed with the assistance of SPSS for descriptive and pearson’s correlation and Jamovi for mediation analysis. The distribution of the data regarding technostress, sleep quality, and emotional exhaustion was reviewed with the help of descriptive statistics. The correlation analysis by Pearson was used to evaluate the relationships of all three variables and the subdomains of technostress, sleep quality, and emotional exhaustion. Lastly, mediation was carried out to investigate the possibility of emotional exhaustion mediating the relationship between technostress and sleep quality. Table 1 Descriptive Statistics for the Study Variables Technostress Sleep Quality Emotional Exhaustion N 200 200 200 Mean 2.87 44.25 33.25 Median 2.95 44.50 33.50 Standard Deviation 0.607 10.19 8.04 Shapiro-Wilk W 0.991 0.991 0.987 Skewness -.246 -.105 -.029 A sample population of 200 participants was used to compute descriptive statistics of technostress, sleep quality, and emotional exhaustion. According to Table 1, the mean technostress score is M = 2.87 (SD = 0.61), which means the perceived technostress is moderate. The scores of emotional exhaustion were also moderate (M = 33.25, SD = 8.04), whereas the scores of sleep quality were of an average level (M = 44.25, SD = 10.19). The distribution of all variables was reasonably symmetric (the mean and median were close to each other). The Shapiro-Wilk test was used to determine the normality of the data. The findings showed that there was no significant non-compliance with the assumption of normality when it comes to technostress (W = 0.991), sleep quality (W = 0.991), and emotional exhaustion (W = 0.987). The skewness values of all variables was within acceptable range. Hence, parametric analyses were considered suitable for further correlation and mediation analyses. Table 2 Correlation Between Technostress, Sleep Quality, and Emotional Exhaustion Technostress Sleep Quality Emotional Exhaustion Technostress Pearson's r — .345** .459** Sig. (2-tailed) — — — N 200 200 200 Sleep Quality Pearson's r .345** — .552** Sig. (2-tailed) .000 — .000 N 200 200 200 Emotional Exhaustion Pearson's r .459** .552** — Sig. (2-tailed) .000 .000 — N 200 200 200 **. The correlation is significant at the p < .01 level (2-tailed). The correlation analysis performed by Pearson was aimed at investigating the correlation between technostress and sleep quality with emotional exhaustion. Technostress was significantly and positively related to emotional exhaustion (r =.46, p <.01), as indicated in Table 2. Another positive correlation, which was also significant, between Technostress and Sleep quality (r =.35, p <.01). Also, emotional exhaustion had a high positive relationship with sleep quality (r =.55, p =.01). These findings suggest that increased technostress level is linked to increased emotional exhaustion and that emotional exhaustion is highly correlated with sleep quality even among the students of psychology. Table 3 Correlation Between Sub-domains of Technostress, Sleep Quality, and Emotional Exhaustion Variable SQ EE TO TI TC TIS TU SQ 1 200 .552** .000 200 .308** .000 200 .279** .000 200 .245** .000 200 .394** .000 200 .060 .401 200 EE .552** .000 200 1 200 .424** .000 200 .490** .000 200 .324** .000 200 .429** .000 200 .041 .563 200 TO .308** .000 200 .424** .000 200 1 200 .505** .000 200 .293** .000 200 .446** .000 200 .330** .000 200 TI .279** .000 200 .490** .000 200 .505** .000 200 1 200 .345** .000 200 .486** .000 200 .170* .016 200 TC .245** .000 200 .324** .000 200 .293** .000 200 .345** .000 200 1 200 .529** .000 200 .230** .001 200 TIS .394** .000 200 .429** .000 200 .446** .000 200 .486** .000 200 .230** .001 200 1 200 .286** .000 200 TU .060 .401 200 .041 .563 200 .330** .000 200 .170* .016 200 .230** .001 200 .286** .000 200 1 200 **. The correlation is significant at the p < .01 level (2-tailed). * . The correlation is significant at the .05 level (two-tailed). Additional testing was done on the correlation between the sub-domains of technostress and sleep quality and emotional exhaustion. Techno-overload (r = .42, p < .01), techno-invasion (r = .49, p < .01), techno-complexity (r =.32, p <.01) and techno-insecurity (r =.43, p <.01) had a significant and positive correlation with emotional exhaustion as shown in Table 3. The quality of sleep was also significantly positively correlated with some of the technostress dimensions, specifically techno-insecurity (r =.39, p < .01) and techno-overload (r =.31, p <.01). Techno-uncertainty had less significant and mainly insignificant links to the sleep quality and emotional exhaustion. In general, the trend of correlations indicates that higher levels of digital workload, permanuity of connectivity, perceived complexity, and insecurity regarding technology are related to higher levels of emotional exhaustion and a distorted sleep quality. An analysis carried out was a mediation to determine the mediating effect of emotional exhaustion between technostress and sleep quality. The mediation model is based on the hypothesis and is shown in Figure 1 and path coefficients are given in Table 4. Table 4 Mediation Model between technostress, emotional exhaustion and sleep quality Pathway Effect B SE 95% CI [LL, UL] β z p Indirect Effect TOTAL TSS → Total EE → TOTAL SQ 3.84 0.73 [2.41, 5.27] 0.23 5.26 < .001 Component Effects TOTAL TSS → TOTAL EE 6.08 0.83 (4.45, 7.71) 0.46 0.46 7.31 Direct Effect Total EE → TOTAL SQ 0.63 0.08 (0.47,0.80) 0.50 7.57 < .001 TOTAL TSS → TOTAL SQ 1.96 1.11 [−0.21, 4.12] 0.12 1.77 .077 Total Effect TOTAL TSS → TOTAL SQ 5.80 1.12 [3.61, 7.99] 0.35 5.19 < .001 The emotional exhaustion indirect effect of technostress on sleep quality was statistically significant (indirect effect = 3.84, SE = 0.73, 95 percent confidence interval [2.41, 5.27], p <.001). The results showed that technostress was a significant predictor of emotional exhaustion (B = 6.08, SE = 0.83, p < .001) as well as sleep quality being significantly predicted by emotional exhaustion (B = 0.63, SE = 0.08, p <.001). The direct relationship between technostress and the quality of sleep was not found to be significant (B = 1.96, SE = 1.11, p =.077), though the overall impact was significant (B = 5.80, SE = 1.12, p <.001). These results show that emotional exhaustion is the complete mediator of the relationship between technostress and sleep quality. Discussion The current study examined the correlation between technostress, emotional exhaustion and sleep quality among emerging psychologists in the Indian context. The results have shown a relationship between emotional exhaustion and poor sleep quality, as well as higher levels of technostress. Emotional exhaustion emerged as a crucial mediating variable in the relationship between technostress and sleep quality, suggesting that the emotional load associated with the stressor of technology has a more significant impact on sleep disruption than the direct effect of technostress. These results are indicative of how long-term digital demands can lead to mental stress and impaired restorative functioning. The results were in line with the first hypothesis, which states that emotional exhaustion among psychology students would be related to an increased level of technostress. Such a correlation highlights how cumulative exposure to techno-overload, techno-complexity, and techno-invasion are some of the causes of emotional depletion. Such findings align with prior studies by Stoeckl and Eckhardt ( 2023 ) and Buenadicha-Mateos et al. ( 2022 ), who have indicated that high levels of technological requirements in educational settings are among the factors contributing to emotional fatigue, especially under the condition of cognitive load and prolonged emotional stress. In psychology students, technostress can additionally deplete the emotional resources already exposed to emotionally intensive academic content and early clinical experiences, therefore, further predisposing them to burnout. The second hypothesis that a greater level of technostress would be correlated with poor sleep quality was also accepted. This finding aligns with previous studies that have demonstrated the overuse of technologies, information overload, and constant digital connection disrupt circadian rhythms and sleep mechanisms. Similarly, research by Yunita et al. ( 2023 ) and Yao and Wang ( 2022 ) also found that involuntary gadget use and cognitive overstimulation contribute to the development of sleep disorders. The current results suggest that technostress disrupts cognitive and emotional disengagement before sleep, thus leading to poor sleep quality. The third hypothesis was about the mediating role of emotional exhaustion between technostress and sleep quality. The findings showed that the mediation effect was significant, which proves that the relationship between technostress and sleep quality is mediated by emotional exhaustion. This observation confirms and builds on the study by Abojedi et al. ( 2023 ), who postulated that the indirect effects of technostress on sleep are mediated by stress-related processes. In contrast, Goddard ( 2011 ) proposed different factors as the mechanisms behind technological stress and sleep-related consequences. The current research gives evidence that emotional exhaustion serves as a key psychological mediator between technological stress and sleep disruption. It implies that the emotionally demanding aspect of long-term digital demands is more powerful in disrupting sleep compared to exposure to technology. The reported relationship between emotional exhaustion and sleep quality also supports the existing literature, which suggests a close connection between fatigue associated with burnout and sleep disruptions. The studies by Li et al. ( 2020 ) and Jarrett et al. ( 2019 ) demonstrated that sleep deprivation is a cause of emotional fatigue affecting cognitive recovery and the ability to regulate emotions. Within the framework of technostress, the increased digital demands can extend physiological and psychological stimulation and rumination, contributing to the further process of exhaustion and impaired sleep quality. Combined, these results imply that the relationship between technostress and emotional exhaustion is reinforced by impaired sleep, which in turn leads to an increase in the chances of psychological distress among students. Implications The results highlight the significance of coping with technostress in maintaining emotional and sleep health, particularly among psychology students undergoing training and awaiting employment in the field of mental health care. The unmanaged digital stress and emotional burnout can not only affect personal well-being but also decrease professional competence, empathy, and clinical effectiveness. Higher education institutions must therefore focus more on integrating digital wellness programs, emotional regulation training programs and resilience-building programs into their academic programs. Clinical educators and supervisors must be vigilant in observing the emotional health of students, encourage healthy sleeping habits, and promote self-care measures to help students achieve high academic success and professional growth. At the policy level, the regulatory organizations may introduce policies regarding the use of academic technology, flexible learning, and increased access to mental health services to minimize cognitive overload and burnout risk. The results also bring the Cognitive Load Theory into the plan and stress that too much online activity can result in emotionally and physiologically significant pressure when the cognitive resources are constantly targeted. Proposed Intervention Plan (REPEAT Model): R – Regulate Digital Use By implementing digital hygiene and screen-time limitations, we can promote mindful and planned technology engagement (Ramadhan et al., 2024 ). E – Educate (Psychoeducation & Skills) Develop awareness about technology-induced stress, about one’s capacity to handle emotions, and how to cope in situations to boost self-regulation and digital well-being (Bondanini et al., 2020 ). P – Promote Restful Sleep Techniques of Cognitive Behavioral Therapy for Insomnia (CBT-I) can be implemented to promote restful sleep, sleep hygiene, and decrease fatigue (Donaldson et al., 2022 ). E – Enhance Resilience To increase strength and management abilities, one can incorporate mindfulness-based and emotional regulation practices (Dawson et al., 2022 ). A – Adjust Workload & Boundaries Set boundaries between academic, digital and personal life to prevent burnout (Bes et al., 2023 ). T – Track, Evaluate & Iterate Tracking can help maintain consistency in monitoring technostress, and sleep indicators can aid in achieving long-term improvement (Ramadhan et al., 2024 ). Limitations and Future Directions The study has several limitations. The study of Indian psychology students limits the externalisation of the results to other cultures and professional settings. Emotional exhaustion and sleep quality may have been influenced by other unmeasured factors, which include physical health, lifestyle, caffeine intake, and prior psychological conditions. Additionally, a potential bias in the selection process may have occurred due to the use of online data collection only, as it excluded individuals with limited access to digital media. Future studies should employ longitudinal and experimental studies to demonstrate the directional and temporal correlation between technostress, emotional exhaustion and sleep quality. It would be more externally valid to increase the sample to include students of other disciplines, working professionals, and other socioeconomic groups. Use of objective measurements, including digital use tracking and sleep monitors, can help decrease the level of self-reporting bias and provide more accurate information. The importance of structured intervention programs, such as digital wellness training, mindfulness-based interventions, and emotional regulation workshops, will also help in assessing the applied importance of this study. Conclusion This study examines the complex interconnection between the sleep quality of future Indian psychologists, emotional exhaustion, and technostress. The findings indicate that the negative impact of technostress on sleep disturbances is enhanced by emotional exhaustion, where the former acts as a strong mediating factor. Such results underscore the urgency of competencies in emotional self-regulation and greater digital wellness acuity, particularly in students whose future careers are in the field of mental health. In the modern digital environment, alleviating digital stress is an essential requirement for protecting psychological well-being, academic success, and professional effectiveness. This study significantly increases the body of evidence on how technology-induced stress affects emotional well-being and sleep. Declarations Clinical Trial Registration Clinical Trial number: not applicable Human Ethics and Consent to Participate Ethical approval for the study was obtained from the Institutional Ethics Committee of CHRIST (Deemed to be) University, Delhi NCR, India. All procedures involving human participants were conducted in accordance with institutional ethical guidelines. Informed consent was obtained from all participants prior to participation in the study. Consent to Publish All authors have approved the manuscript and consent to its publication. Competing Interests The authors declare that they have no financial or non-financial competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution S.S. had the idea of the study, was in-charge of the research design, and provided overall supervision. The literature review was conducted by N.A. and N.V., who collected the data and processed it, wrote the original manuscript. M.M. and P.S. helped in gathering and initial analysis of data.M.G. helped to make the methodology finer, revise in a critical manner and strengthen theoretical foundation. edited and approved by all the authors. The final version of the manuscript was reviewed by all authors. Data Availability The datasets generated and/or analyzed in the current study are not publicly available due to ethical restrictions and the need to protect the confidentiality and privacy of the participants. References Abojedi, A., Alsheikh Ali, A. S., & Basmaji, J. (2023). Assessing the impact of technology use, social engagement, emotional regulation, and sleep quality among undergraduate students in Jordan: Examining the mediating effect of perceived and academic stress. Health Psychology Research, 11. https://doi.org/10.52965/001c.73348 Alotaibi, A. D., Alosaimi, F. M., Alajlan, A. A., & Bin Abdulrahman, K. A. (2020). The relationship between sleep quality, stress, and academic performance among medical students. Journal of Family & Community Medicine, 27 (1), 23–28. https://doi.org/10.4103/jfcm.JFCM_132_19 Arbabisarjou, A., Mehdi, H. S., Sharif, M. R., Alizadeh, K. H., Yarmohammadzadeh, P., & Feyzollahi, Z. (2016). The relationship between sleep quality and social intimacy, and academic burn-out in students of medical sciences. Global Journal of Health Science, 8 (5), 231–238. https://doi.org/10.5539/gjhs.v8n5p231 Bes, I., Koutsimani, P., Montgomery, A., & Georganta, K. (2023). Organizational interventions and occupational burnout: A meta-analysis. Frontiers in Psychology, 14, 1134567. https://doi.org/10.3389/fpsyg.2023.1134567 Bondanini, G., Giorgi, G., Ariza-Montes, A., Vega-Muñoz, A., & Andreucci-Annunziata, P. (2020). Technostress: The dark side of technology in the workplace. International Journal of Environmental Research and Public Health, 17 (21), 8013. https://doi.org/10.3390/ijerph17218013 Brod, C. (1984). Technostress: The Human Cost of the Computer Revolution. Addison Wesley. Buenadicha-Mateos, F., González-Suárez, S. M., & Sánchez-Fernández, M. D. (2022). Emotional exhaustion caused by technostress among university students. Education and Information Technologies, 27 (6), 8443–8461. https://doi.org/10.1007/s10639-022-11086-4 Dawson, D., McMillan, J. M., & Thomas, M. (2022). A scalable cognitive behavioral therapy for insomnia program improves sleep outcomes: A systematic review. Frontiers in Sleep, 1, 1002437. https://doi.org/10.3389/frsle.2022.1002437 Donaldson, R., Smith, L., & McGowan, C. (2022). Digital cognitive behavioral therapy for insomnia and its impact on mental health: A meta-analysis. Frontiers in Digital Health, 4, 856790. https://doi.org/10.3389/fdgth.2022.856790 Goddard, M. S. (2011). Sleep quality, technostress, and maladaptive use of technology: Predictors of depression among college students. [Doctoral dissertation, University of Memphis]. University of Memphis Digital Commons. https://digitalcommons.memphis.edu/etd/350 Hapsari, E. A., Rohmatullayaly, E. N., & Widayati, K. A. (2024). Technostress and sleep quality among university students in Indonesia: A cross-sectional study. Asian Journal of Social Health and Behavior, 7 (4), 197–202. https://doi.org/10.4103/shb.shb_177_24 Hu, Y., Niu, Z., Dai, L., Maguire, R., Zong, Z., Hu, Y., & Wang, D. (2020). The relationship between sleep pattern and depression in Chinese shift workers: A mediating role of emotional exhaustion. Australian Journal of Psychology, 72 (1), 68–81. Isabel, M., & Rodrigo, Ó. (2022). Analysis of the emotional exhaustion derived from techno-stress in the next generation of qualified employees. Frontiers in Psychology, 13, 792606. https://doi.org/10.3389/fpsyg.2022.792606 Jarrett, N. L., Yamane, D. E., Gildner, D. J., & Pickett, S. M. (2019). The indirect effect of sleep quality on emotional exhaustion through emotion regulation difficulties and perceived stress in a sample of U.S. medical students. Sleep, 42 (Supplement_1), A87–A87. https://doi.org/10.1093/sleep/zsz067.211 Jniene, A., Errguig, L., El Hangouche, A. J., Rkain, H., Aboudrar, S., El Ftouh, M., & Dakka, T. (2019). Perception of sleep disturbances due to technology use and caffeine consumption among Moroccan medical students. BioMed Research International, 2019, 1–8. https://doi.org/10.1155/2019/2347968 Kassim, E. S., Ahmad, S. F. S., Bahari, A. H., Fadzli, F. N. M., & Adzmi, N. S. H. M. (2021). The effect of technostress on emotional exhaustion and coping strategies. International Journal of Academic Research in Business and Social Sciences, 11 (5), 544–559. Latorre, F. (2018). Technostress: Definition, symptoms, and risk factors of technology-induced stress. Retrieved from https://doi.org/10.1007/s00420-018-1352-1 Li, C., Zhang, Y., Randhawa, A. K., & Madigan, D. J. (2020). Emotional exhaustion and sleep problems in university students: Does mental toughness matter? Personality and Individual Differences, 163, 110046. https://doi.org/10.1016/j.paid.2020.110046 Lin, Y., Mutz, J., Clough, P. J., & Papageorgiou, K. A. (2017). Mental toughness and individual differences in learning, educational and work performance, psychological well-being, and personality: A systematic review. Frontiers in Psychology, 8, 1345. https://doi.org/10.3389/fpsyg.2017.01345 Memon, A. R., Gupta, C. C., Crowther, M. E., Ferguson, S. A., Tuckwell, G. A., & Vincent, G. E. (2021). Sleep and mental health in university students: A systematic review and meta-analysis. Sleep Medicine, 82, 62–69. https://doi.org/10.1016/j.sleep.2021.03.002 Owens, J. A., Drobnich, D., Baylor, A., & Lewin, D. (2017). School start time change: An in-depth examination of school districts in the United States. Journal of School Health, 87 (7), 515–522. https://doi.org/10.1111/josh.12526 Ramadhan, R. N., Putra, A. R., & Sari, D. R. (2024). Impacts of digital social media detox on mental health and well-being. Frontiers in Psychology, 15, 1365841. https://doi.org/10.3389/fpsyg.2024.1365841 Stoeckl, F., & Eckhardt, A. (2023). Sociability and technostress in online classes: The effects on students’ emotional exhaustion during the COVID-19 pandemic. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12 (2), 257–285. https://doi.org/10.1207/s15516709cog1202_4 Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24 (1), 301–328. https://doi.org/10.2753/MIS0742-1222240109 Torales, J., O’Higgins, M., Castaldelli-Maia, J. M., & Ventriglio, A. (2022). Technostress and its impact on mental health: A growing concern in the digital age. Asian Journal of Psychiatry, 65, 102867. https://doi.org/10.1016/j.ajp.2022.102867 Upadhyaya, P., & Vrinda. (2021). Impact of technostress on academic productivity of university students. Education and Information Technologies, 26 (2), 1647–1664. Van Laethem, M., Van Vianen, A. E. M., & Derks, D. (2017). Daily fluctuations in smartphone use, work-related stress, and sleep: A dynamic perspective. Journal of Occupational Health Psychology, 22 (4), 429–440. https://doi.org/10.1037/ocp0000049 Vallone, F., Bellagamba, F., & Tanzini, M. (2023). The effects of technostress on academic performance and psychological well-being among university students. Computers in Human Behavior, 139, 107613. https://doi.org/10.1016/j.chb.2023.107613 Wang, X., Li, Z., Ouyang, Z., & Xu, Y. (2020). The Achilles heel of technology: How does technostress affect university students’ wellbeing and technology-enhanced learning? International Journal of Environmental Research and Public Health, 18 (23), 12322. https://doi.org/10.3390/ijerph182312322 Yao, N., & Wang, Q. (2022). Technostress from smartphone use and its impact on university students’ sleep quality and academic performance. Asia-Pacific Education Researcher, 32 (3), 317–326. https://doi.org/10.1007/s40299-022-00654-5 Yeomans, M., & Oyanedel, J. (2021). Psychometric Properties of the Emotional Exhaustion Scale (ECE) in Chilean Higher Education Students. European Journal of Investigation in Health, Psychology and Education, 12 (1), 50–60. https://doi.org/10.3390/ejihpe12010005 Yi, H., Shin, K., & Shin, C. (2006). Development of the sleep quality scale. Journal of Sleep Research, 15 (3), 309–316. Yunita, S., Susilawati, S., Riniawati, R., & Fajriah, Y. N. (2023). Exploring college students’ technostress phenomenon in using ed-tech. Journal of Research in Instructional, 3 (2), 242–257. https://doi.org/10.30862/jri.v3i2.280 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers agreed at journal 01 Mar, 2026 Reviews received at journal 18 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers invited by journal 18 Jan, 2026 Editor assigned by journal 30 Dec, 2025 Submission checks completed at journal 30 Dec, 2025 First submitted to journal 30 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8411730","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576486786,"identity":"d17e5c1b-24de-4c25-bd23-2e95dc1d7125","order_by":0,"name":"Dr. Sneha Saha","email":"","orcid":"","institution":"CHRIST (Deemed to be) University, Delhi NCR","correspondingAuthor":false,"prefix":"Dr.","firstName":"Sneha","middleName":"","lastName":"Saha","suffix":""},{"id":576486787,"identity":"bcb5046c-2d3e-4f45-a10f-fb8b109a0d02","order_by":1,"name":"Nandita Agrawal","email":"data:image/png;base64,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","orcid":"","institution":"CHRIST (Deemed to be) University, Delhi NCR","correspondingAuthor":true,"prefix":"","firstName":"Nandita","middleName":"","lastName":"Agrawal","suffix":""},{"id":576486788,"identity":"7702247b-f09c-4574-a08b-8dbc08912e96","order_by":2,"name":"Nupur Varun","email":"","orcid":"","institution":"CHRIST (Deemed to be) University, Delhi NCR","correspondingAuthor":false,"prefix":"","firstName":"Nupur","middleName":"","lastName":"Varun","suffix":""},{"id":576486789,"identity":"8d60fd62-004a-47e3-9b87-1472b5c0d166","order_by":3,"name":"Monika Mittal","email":"","orcid":"","institution":"CHRIST (Deemed to be) University, Delhi NCR","correspondingAuthor":false,"prefix":"","firstName":"Monika","middleName":"","lastName":"Mittal","suffix":""},{"id":576486791,"identity":"28de18fa-7b0e-4b6c-b75e-d348270d8760","order_by":4,"name":"Priyadharshini S","email":"","orcid":"","institution":"CHRIST (Deemed to be) University, Delhi NCR","correspondingAuthor":false,"prefix":"","firstName":"Priyadharshini","middleName":"","lastName":"S","suffix":""},{"id":576486792,"identity":"e0d0fee5-1a74-4ef5-ad01-138a453c17e0","order_by":5,"name":"Dr. Mohit Gothwal","email":"","orcid":"","institution":"CHRIST (Deemed to be) University, Delhi NCR","correspondingAuthor":false,"prefix":"Dr.","firstName":"Mohit","middleName":"","lastName":"Gothwal","suffix":""}],"badges":[],"createdAt":"2025-12-20 11:08:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8411730/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8411730/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100857955,"identity":"0fcdc0a6-dc2e-44fc-8c01-9c5389b0c8e7","added_by":"auto","created_at":"2026-01-22 07:23:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2200628,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscript1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8411730/v1/2d791dadca055b5b86e03686.docx"},{"id":101942676,"identity":"cff1cbce-4bc8-4989-a51d-a5f16ba30778","added_by":"auto","created_at":"2026-02-05 09:32:31","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7951,"visible":true,"origin":"","legend":"","description":"","filename":"e33a48c513354923b666b080eb2b8588.json","url":"https://assets-eu.researchsquare.com/files/rs-8411730/v1/1f9e46eee6e7f1c0d57c443f.json"},{"id":100858453,"identity":"e11378aa-5a4d-4dc3-a459-2f62e5865365","added_by":"auto","created_at":"2026-01-22 07:24:18","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112541,"visible":true,"origin":"","legend":"","description":"","filename":"e33a48c513354923b666b080eb2b85881enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8411730/v1/b48fa2637927e429c5fd5c79.xml"},{"id":100821848,"identity":"d4639e31-08ee-41b6-961e-6f1bfcf45991","added_by":"auto","created_at":"2026-01-21 18:03:31","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4966,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8411730/v1/16b4185adb7c48eb66f05a01.png"},{"id":100949801,"identity":"bc314acb-5ed2-4fa7-85f8-b73cad67f4f7","added_by":"auto","created_at":"2026-01-23 07:05:47","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":107771,"visible":true,"origin":"","legend":"","description":"","filename":"e33a48c513354923b666b080eb2b85881structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8411730/v1/da799f127d463d6306678014.xml"},{"id":100821854,"identity":"c71da2f4-b71e-437e-9258-7cdff63ce2ab","added_by":"auto","created_at":"2026-01-21 18:03:31","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121902,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8411730/v1/111d33e6d87c321cefdc718d.html"},{"id":100821851,"identity":"9ed8f004-b8c5-4b11-bc90-ef8768a58430","added_by":"auto","created_at":"2026-01-21 18:03:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMediation model showing Emotional Exhaustion as a mediator between Technostress and Sleep Quality\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e TSS = Total Technostress Score; EE = Emotional Exhaustion; SQ = Sleep Quality.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8411730/v1/e31cc6b36a979f6e511435d3.png"},{"id":101943953,"identity":"a01f1cd4-a925-457a-b285-63d3c9e33f83","added_by":"auto","created_at":"2026-02-05 09:46:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":850280,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8411730/v1/7e0c9494-ec75-4f8d-a611-f65afb8b09ca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Study on Technostress Emotional Exhaustion and Sleep Quality Among Future Psychologists in India an Intervention Plan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTechnology is now an inseparable part of both the academic and research domains in the last decade, especially among the students in the field of psychology who make excessive use of the digital platform for both their coursework and academic research, and professional communication. Although technology has increased the level of accessibility and efficiency, it has also become a major contributor to technostress, which is described as a psychological and physical stress caused by overindulgence in digital technology and the virtual world (Latorre, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Technostress has been demonstrated to lead to emotional fatigue and sleep quality problems that, in turn are predictors of psychological well-being and academic achievement (Jiang et al., 2015). Empirical evidence also shows that technostress has a detrimental effect on academic performance among university students because attentional control, memory consolidation, and learning ability are impaired due to constant exposure to digital information and the need to be constantly connected, which results in low academic motivation and performance (Torales et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The results of Indian institutions of higher education also support the existence of a strong negative correlation between technostress and academic performance, indicating that increased technological stress can severely affect the academic performance of students (Mahapatra et al., 2023). Moreover, screen time has also been associated with circadian rhythm disruption, such as a decrease in sleep onset and poor sleep quality (Jniene et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to Yunita et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) that increased usage of educational technology especially mobile phones is a cause of technostress, which disrupts circadian regulation due to cognitive load and over exposure. In line with this, Yao and Wang (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) showed that technostress is escalated by compulsive mobile phone use and information overload which consequently leads to poor sleep quality. In contrast, Abojedi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) concluded that the use of technology has no direct influence on sleep quality, but rather serves as a mediator of the effect, which occurs via perceived stress, showing that emotional control and time-management skills are significant in reducing sleep disturbances. Goddard (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) also found a significant relationship between poor sleep quality and technostress. Recently, Hapsari et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found a strong negative relationship between technostress and quality of sleep, along with screen time, device multiplicity, and socioeconomic status were identified as major contributors. All of these findings suggest that although technostress is consistently associated with sleep impairment, the intensity and direction of the association may differ based on the individual and situational moderators, highlighting the importance of further empirical studies.\u003c/p\u003e \u003cp\u003eEmotional exhaustion is a fundamental component of burnout, a state of emotional depletion caused by long-term exposure to stressors (Maslach and Jackson, 1981). Psychology students are especially susceptible to emotional exhaustion due to rigorous academic demands, emotionally charged programs, and early introduction to clinical practice (Brand et al., 2014). Empirical research has always reported high scores of emotional exhaustion in university students, which are usually followed by a lack of motivation, poor learning, and negative mental health consequences (Ibrahim et al., 2013). Moreover, emotional exhaustion has been revealed to predispose people with sleeping disorders, which only increases cognitive and emotional stress (Elfering et al., 2018). Research studies examining the overlap between technostress and emotional exhaustion have shown that technostress exacerbates emotional fatigue through cognitive load and emotional distress. As discovered by Stoeckl and Eckhardt (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), teacher sociability was also found to lower emotional exhaustion in the online education environment during the COVID-19 pandemic, but components of technostress, including techno-overload and techno-complexity, had a significant impact in increasing emotional exhaustion. Similarly, Buenadicha-Mateos et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that perceived stress and intrapersonal conflict mediated the relationship between technostress and emotional exhaustion in university students, highlighting the impact of emotional and social aspects as well as technological needs. Upadhyaya and Vrinda (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) determined five dimensions of technostress, such as techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty, and discovered that those students were more vulnerable to technostress, who had lower levels of Information in Computer Technology (ICT), were postgraduates, and female, which underscores the necessity of targeted intervention. Wang et al. (2021) also established that technostress developers, including techno-complexity, techno-insecurity, and techno-uncertainty, led to burnout in technology-enhanced learning settings, which in turn, undermined self-regulation, learning agency, and persistence among male students. Those who are less predisposed towards technology-enhanced learning were specifically impacted.\u003c/p\u003e \u003cp\u003eA study conducted by Li et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) identified academic pressure, financial stress, and social challenges as the main stressors leading to emotional exhaustion and burnout in university students, and the poor quality of sleep turned out to be one of the major predictors because of compromised cognitive functioning and lack of stress recovery. Jarrett et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) showed that emotional exhaustion has an indirect connection with sleep disorders, which disrupts emotion regulation, leading to perceived stress, especially in medical students. Xu et al. (2019) conducted a study on students in nursing programs and found that sleep disturbances were essentially the direct predictors of emotional exhaustion, with sleep-related stress becoming an additional predictor of depressive symptoms indicating that cognitive preoccupation with sleep increases exhaustion. According to Hu et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the connection between disrupted sleep patterns and depressive symptoms was mediated by emotional exhaustion in shift workers, and evening-oriented sleep patterns led to increased fatigue and susceptibility to mental health issues. The overall implication of such findings is that, although sleep disturbances can be a direct contributor to emotional exhaustion, there are indirect mechanisms in which emotion regulation deficits and cognitive strain can interfere with this relationship.\u003c/p\u003e \u003cp\u003eAlthough the literature on the topic of technostress, emotional exhaustion, and sleep quality continues to expand, few studies in particular have explored the relationship between these aspects among psychology students in the context of the Indian setting. Additionally, the mediating effect of emotional exhaustion on the technostress-sleep quality association has not been well studied. This dichotomy is especially critical in light of recent changes to the curriculum that encourage earlier exposure to professional training in psychology. These interrelated problems are critical in the process of protecting the well-being of students and nurturing them to become competent and resilient mental health professionals. Digital wellness, sleep hygiene, mindfulness-based interventions, and resilience training are thus urgently necessary, and longitudinal studies should be conducted in the future to establish the directionality and the dynamics of the relationship between technostress, emotional exhaustion and sleep quality.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. To investigate the relationship between technostress and emotional exhaustion among\u003c/p\u003e\n\u003cp\u003epsychology students.\u003c/p\u003e\n\u003cp\u003e2. To investigate the association between technostress and sleep quality in psychology\u003c/p\u003e\n\u003cp\u003estudents.\u003c/p\u003e\n\u003cp\u003e3. To investigate whether emotional exhaustion mediates the relationship between\u003c/p\u003e\n\u003cp\u003etechnological stress and sleep quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH1: Higher levels of technostress will be associated with greater emotional exhaustion among\u003c/p\u003e\n\u003cp\u003epsychology students.\u003c/p\u003e\n\u003cp\u003eH2: Higher levels of technostress will be associated with lower sleep quality among psychology students.\u003c/p\u003e\n\u003cp\u003eH3: The association between technostress and sleep quality will be partially mediated by\u003c/p\u003e\n\u003cp\u003eemotional exhaustion, such that higher technostress will contribute to increased emotional\u003c/p\u003e\n\u003cp\u003eexhaustion, which in turn will be linked to lower sleep quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling technique\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e● Sample Size: 200 participants (161 females, 39 males)\u003c/p\u003e\n\u003cp\u003e● Sample Age Range: 18-25 years\u003c/p\u003e\n\u003cp\u003e● Sampling Method: Purposive Sampling\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusive and Exclusive Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample comprised of psychology students between the ages of 18 and 25 years old, pursuing or having recently graduated undergraduate or postgraduate psychology degrees, using digital devices in their academic activities and gave informed consent and could communicate in English.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures and Tools\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eSleep Quality Scale (SQS)-\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe scale was developed by Yi et al. (2006). The instrument comprises 28 self-report questions designed to assess various aspects of sleep functioning, including daytime symptoms, sleep restoration, difficulties with sleep initiation and maintenance, waking problems, and overall satisfaction with sleep. The scale is suitable for adults aged 18 to 59, and it is administered in pencil-and-paper format. The respondents will be asked to answer each item on a four-point Likert scale, where a score of 0 corresponds to \u0026quot;few\u0026quot; and a response of \u0026quot;almost always\u0026quot; corresponds to 3. Questions that relate to post-sleep restoration and sleep satisfaction are reverse-scored before analysis. The total score ranges from 0 to 84, where higher scores indicate poorer sleep quality and more sleep-related problems. The scale exhibits strong psychometric properties, including high internal consistency (\u0026alpha; = .92) and acceptable test-retest reliability (r = .81).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eEmotional Exhaustion Scale (EES)-\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eOriginally developed by Maslach and Jackson (1981) and later modified to fit the academic level by Ramos et al (2005). The scale consists of 10 questions that investigate emotional exhaustion and burnout related to academic requirements over the last 12 months. The respondents review each item on a five-point Likert scale, with 1 indicating \u0026apos;rarely\u0026apos; and 5 indicating \u0026apos;always\u0026apos;. The range of scores, which are total, is between 10 and 50, with high scores indicating more emotional exhaustion. The presence of established cut-offs categorizes scores as follows: 1019 as low, 2029 as moderate, 3039 as high, and 4050 as very high levels of emotional exhaustion. The scale is highly internally consistent (\u0026alpha; = 0.893) and exhibits acceptable levels of item homogeneity, as indicated by a mean inter-item correlation of 0.33.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eTechnostress Scale-\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe Technostress Scale was used to measure technostress (Tarafdar et al., 2007). The instrument comprises 23 items assessed using a five-point Likert scale and evaluated across five dimensions of technostress: techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty. Techno-overload refers to the pressure of working faster and longer due to the use of technology, while techno-invasion relates to the level of technology\u0026apos;s intrusion into personal time and boundaries. The techno-complexity shows a response of inadequacy due to the perceived lack of technological abilities. In academic institutions, techno-insecurity refers to anxiety in terms of academic performance compared to students who are better technologically fitted. Techno-uncertainty is associated with the pressure created by the constant technological changes and updates. The reliability analysis revealed satisfactory internal consistency in all subscales, with Cronbach\u0026apos;s alpha values exceeding .70.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted online, with an invitation sent through university portals and social media groups targeting Psychology students. Upon clicking the study link, participants were presented with a detailed informed consent form that explained the study\u0026apos;s purpose, procedures, and their rights as participants. Once consent was provided, they proceeded to complete the questionnaire via a Google Form. The form was designed to gather data on technostress, perceived emotional exhaustion, and sleep quality, and included a manipulation check to ensure the accuracy of responses. Throughout the process, every effort was made to ensure confidentiality, no identifying information was collected, and all data was stored securely. Contact information was provided for any further questions or concerns.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003eIn this section, the statistical results of the research is provided, which was analysed with the assistance of SPSS for descriptive and pearson\u0026rsquo;s correlation and Jamovi for mediation analysis. The distribution of the data regarding technostress, sleep quality, and emotional exhaustion was reviewed with the help of descriptive statistics. The correlation analysis by Pearson was used to evaluate the relationships of all three variables and the subdomains of technostress, sleep quality, and emotional exhaustion. Lastly, mediation was carried out to investigate the possibility of emotional exhaustion mediating the relationship between technostress and sleep quality.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eDescriptive Statistics for the Study Variables\u003c/em\u003e\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 233px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eTechnostress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eSleep Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eEmotional Exhaustion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 233px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 233px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e44.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e33.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 233px;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e44.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e33.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 233px;\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e10.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e8.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 233px;\"\u003e\n \u003cp\u003eShapiro-Wilk W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 233px;\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e-.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e-.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e-.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA sample population of 200 participants was used to compute descriptive statistics of technostress, sleep quality, and emotional exhaustion. According to Table 1, the mean technostress score is M = 2.87 (SD = 0.61), which means the perceived technostress is moderate. The scores of emotional exhaustion were also moderate (M = 33.25, SD = 8.04), whereas the scores of sleep quality were of an average level (M = 44.25, SD = 10.19). The distribution of all variables was reasonably symmetric (the mean and median were close to each other).\u003c/p\u003e\n\u003cp\u003eThe Shapiro-Wilk test was used to determine the normality of the data. The findings showed that there was no significant non-compliance with the assumption of normality when it comes to technostress (W = 0.991), sleep quality (W = 0.991), and emotional exhaustion (W = 0.987). The skewness values of all variables was within acceptable range. Hence, parametric analyses were considered suitable for further correlation and mediation analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u003cem\u003eCorrelation Between Technostress, Sleep Quality, and Emotional Exhaustion\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"569\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eTechnostress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eSleep Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eEmotional Exhaustion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eTechnostress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003ePearson\u0026apos;s r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.345**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e.459**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e200\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSleep Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003ePearson\u0026apos;s r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.345**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e.552**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e200\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eEmotional Exhaustion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003ePearson\u0026apos;s r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.459**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.552**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e**. The correlation is significant at the p \u0026lt; .01 level (2-tailed).\u003c/p\u003e\n\u003cp\u003eThe correlation analysis performed by Pearson was aimed at investigating the correlation between technostress and sleep quality with emotional exhaustion. Technostress was significantly and positively related to emotional exhaustion (r =.46, p \u0026lt;.01), as indicated in Table 2. Another positive correlation, which was also significant, between Technostress and Sleep quality (r =.35, p \u0026lt;.01). Also, emotional exhaustion had a high positive relationship with sleep quality (r =.55, p =.01).\u003c/p\u003e\n\u003cp\u003eThese findings suggest that increased technostress level is linked to increased emotional exhaustion and that emotional exhaustion is highly correlated with sleep quality even among the students of psychology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eCorrelation Between Sub-domains of Technostress, Sleep Quality, and Emotional Exhaustion\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"537\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eEE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eTO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eTIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eTU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.552**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.308**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.279**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.245**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.394**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.060\u003c/p\u003e\n \u003cp\u003e.401\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eEE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.552**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.424**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.490**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.324**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.429**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.041\u003c/p\u003e\n \u003cp\u003e.563\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eTO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.308**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.424**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.505**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.293**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.446**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.330**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.279**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.490**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.505**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.345**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.486**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.170*\u003c/p\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.245**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.324**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.293**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.345**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.529**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.230**\u003c/p\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eTIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.394**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.429**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.446**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.486**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.230**\u003c/p\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.286**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eTU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.060\u003c/p\u003e\n \u003cp\u003e.401\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.041\u003c/p\u003e\n \u003cp\u003e.563\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.330**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.170*\u003c/p\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.230**\u003c/p\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.286**\u003c/p\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e**. The correlation is significant at the p \u0026lt; .01 level (2-tailed).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e. The correlation is significant at the .05 level (two-tailed).\u003c/p\u003e\n\u003cp\u003eAdditional testing was done on the correlation between the sub-domains of technostress and sleep quality and emotional exhaustion. Techno-overload (r = .42, p \u0026lt; .01), techno-invasion (r = .49, p \u0026lt; .01), techno-complexity (r =.32, p \u0026lt;.01) and techno-insecurity (r =.43, p \u0026lt;.01) had a significant and positive correlation with emotional exhaustion as shown in Table 3. The quality of sleep was also significantly positively correlated with some of the technostress dimensions, specifically techno-insecurity (r =.39, p \u0026lt; .01) and techno-overload (r =.31, p \u0026lt;.01).\u003c/p\u003e\n\u003cp\u003eTechno-uncertainty had less significant and mainly insignificant links to the sleep quality and emotional exhaustion. In general, the trend of correlations indicates that higher levels of digital workload, permanuity of connectivity, perceived complexity, and insecurity regarding technology are related to higher levels of emotional exhaustion and a distorted sleep quality.\u003c/p\u003e\n\u003cp\u003eAn analysis carried out was a mediation to determine the mediating effect of emotional exhaustion between technostress and sleep quality. The mediation model is based on the hypothesis and is shown in Figure 1 and path coefficients are given in Table 4.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003e\u003cem\u003eMediation Model between technostress, emotional exhaustion and sleep quality\u003c/em\u003e\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"632\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003ePathway\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e95% CI [LL, UL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eIndirect Effect\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eTOTAL TSS \u0026rarr; Total EE \u0026rarr; TOTAL SQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; [2.41, \u0026nbsp; \u0026nbsp;5.27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;5.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eComponent Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eTOTAL TSS \u0026rarr; TOTAL EE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e(4.45,\u003c/p\u003e\n \u003cp\u003e7.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDirect Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTotal EE \u0026rarr; TOTAL SQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e(0.47,0.80)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;7.57\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eTOTAL TSS \u0026rarr; TOTAL SQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e[\u0026minus;0.21, 4.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eTotal Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eTOTAL TSS \u0026rarr; TOTAL SQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e5.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e[3.61, 7.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e5.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe emotional exhaustion indirect effect of technostress on sleep quality was statistically significant (indirect effect = 3.84, SE = 0.73, 95 percent confidence interval [2.41, 5.27], p \u0026lt;.001). The results showed that technostress was a significant predictor of emotional exhaustion (B = 6.08, SE = 0.83, p \u0026lt; .001) as well as sleep quality being significantly predicted by emotional exhaustion (B = 0.63, SE = 0.08, p \u0026lt;.001).\u003c/p\u003e\n\u003cp\u003eThe direct relationship between technostress and the quality of sleep was not found to be significant (B = 1.96, SE = 1.11, p =.077), though the overall impact was significant (B = 5.80, SE = 1.12, p \u0026lt;.001). These results show that emotional exhaustion is the complete mediator of the relationship between technostress and sleep quality.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study examined the correlation between technostress, emotional exhaustion and sleep quality among emerging psychologists in the Indian context. The results have shown a relationship between emotional exhaustion and poor sleep quality, as well as higher levels of technostress. Emotional exhaustion emerged as a crucial mediating variable in the relationship between technostress and sleep quality, suggesting that the emotional load associated with the stressor of technology has a more significant impact on sleep disruption than the direct effect of technostress. These results are indicative of how long-term digital demands can lead to mental stress and impaired restorative functioning.\u003c/p\u003e \u003cp\u003eThe results were in line with the first hypothesis, which states that emotional exhaustion among psychology students would be related to an increased level of technostress. Such a correlation highlights how cumulative exposure to techno-overload, techno-complexity, and techno-invasion are some of the causes of emotional depletion. Such findings align with prior studies by Stoeckl and Eckhardt (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Buenadicha-Mateos et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who have indicated that high levels of technological requirements in educational settings are among the factors contributing to emotional fatigue, especially under the condition of cognitive load and prolonged emotional stress. In psychology students, technostress can additionally deplete the emotional resources already exposed to emotionally intensive academic content and early clinical experiences, therefore, further predisposing them to burnout.\u003c/p\u003e \u003cp\u003eThe second hypothesis that a greater level of technostress would be correlated with poor sleep quality was also accepted. This finding aligns with previous studies that have demonstrated the overuse of technologies, information overload, and constant digital connection disrupt circadian rhythms and sleep mechanisms. Similarly, research by Yunita et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Yao and Wang (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also found that involuntary gadget use and cognitive overstimulation contribute to the development of sleep disorders. The current results suggest that technostress disrupts cognitive and emotional disengagement before sleep, thus leading to poor sleep quality.\u003c/p\u003e \u003cp\u003eThe third hypothesis was about the mediating role of emotional exhaustion between technostress and sleep quality. The findings showed that the mediation effect was significant, which proves that the relationship between technostress and sleep quality is mediated by emotional exhaustion. This observation confirms and builds on the study by Abojedi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who postulated that the indirect effects of technostress on sleep are mediated by stress-related processes. In contrast, Goddard (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) proposed different factors as the mechanisms behind technological stress and sleep-related consequences. The current research gives evidence that emotional exhaustion serves as a key psychological mediator between technological stress and sleep disruption. It implies that the emotionally demanding aspect of long-term digital demands is more powerful in disrupting sleep compared to exposure to technology.\u003c/p\u003e \u003cp\u003eThe reported relationship between emotional exhaustion and sleep quality also supports the existing literature, which suggests a close connection between fatigue associated with burnout and sleep disruptions. The studies by Li et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Jarrett et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrated that sleep deprivation is a cause of emotional fatigue affecting cognitive recovery and the ability to regulate emotions. Within the framework of technostress, the increased digital demands can extend physiological and psychological stimulation and rumination, contributing to the further process of exhaustion and impaired sleep quality. Combined, these results imply that the relationship between technostress and emotional exhaustion is reinforced by impaired sleep, which in turn leads to an increase in the chances of psychological distress among students.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eThe results highlight the significance of coping with technostress in maintaining emotional and sleep health, particularly among psychology students undergoing training and awaiting employment in the field of mental health care. The unmanaged digital stress and emotional burnout can not only affect personal well-being but also decrease professional competence, empathy, and clinical effectiveness. Higher education institutions must therefore focus more on integrating digital wellness programs, emotional regulation training programs and resilience-building programs into their academic programs. Clinical educators and supervisors must be vigilant in observing the emotional health of students, encourage healthy sleeping habits, and promote self-care measures to help students achieve high academic success and professional growth. At the policy level, the regulatory organizations may introduce policies regarding the use of academic technology, flexible learning, and increased access to mental health services to minimize cognitive overload and burnout risk. The results also bring the Cognitive Load Theory into the plan and stress that too much online activity can result in emotionally and physiologically significant pressure when the cognitive resources are constantly targeted.\u003c/p\u003e \u003cp\u003eProposed Intervention Plan (REPEAT Model):\u003c/p\u003e \u003cp\u003eR \u0026ndash; Regulate Digital Use\u003c/p\u003e \u003cp\u003eBy implementing digital hygiene and screen-time limitations, we can promote mindful and planned technology engagement (Ramadhan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eE \u0026ndash; Educate (Psychoeducation \u0026amp; Skills)\u003c/p\u003e \u003cp\u003eDevelop awareness about technology-induced stress, about one\u0026rsquo;s capacity to handle emotions, and how to cope in situations to boost self-regulation and digital well-being (Bondanini et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eP \u0026ndash; Promote Restful Sleep\u003c/p\u003e \u003cp\u003eTechniques of Cognitive Behavioral Therapy for Insomnia (CBT-I) can be implemented to promote restful sleep, sleep hygiene, and decrease fatigue (Donaldson et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eE \u0026ndash; Enhance Resilience\u003c/p\u003e \u003cp\u003eTo increase strength and management abilities, one can incorporate mindfulness-based and emotional regulation practices (Dawson et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA \u0026ndash; Adjust Workload \u0026amp; Boundaries\u003c/p\u003e \u003cp\u003eSet boundaries between academic, digital and personal life to prevent burnout (Bes et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eT \u0026ndash; Track, Evaluate \u0026amp; Iterate\u003c/p\u003e \u003cp\u003eTracking can help maintain consistency in monitoring technostress, and sleep indicators can aid in achieving long-term improvement (Ramadhan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eThe study has several limitations. The study of Indian psychology students limits the externalisation of the results to other cultures and professional settings. Emotional exhaustion and sleep quality may have been influenced by other unmeasured factors, which include physical health, lifestyle, caffeine intake, and prior psychological conditions. Additionally, a potential bias in the selection process may have occurred due to the use of online data collection only, as it excluded individuals with limited access to digital media.\u003c/p\u003e \u003cp\u003eFuture studies should employ longitudinal and experimental studies to demonstrate the directional and temporal correlation between technostress, emotional exhaustion and sleep quality. It would be more externally valid to increase the sample to include students of other disciplines, working professionals, and other socioeconomic groups. Use of objective measurements, including digital use tracking and sleep monitors, can help decrease the level of self-reporting bias and provide more accurate information. The importance of structured intervention programs, such as digital wellness training, mindfulness-based interventions, and emotional regulation workshops, will also help in assessing the applied importance of this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examines the complex interconnection between the sleep quality of future Indian psychologists, emotional exhaustion, and technostress. The findings indicate that the negative impact of technostress on sleep disturbances is enhanced by emotional exhaustion, where the former acts as a strong mediating factor. Such results underscore the urgency of competencies in emotional self-regulation and greater digital wellness acuity, particularly in students whose future careers are in the field of mental health. In the modern digital environment, alleviating digital stress is an essential requirement for protecting psychological well-being, academic success, and professional effectiveness. This study significantly increases the body of evidence on how technology-induced stress affects emotional well-being and sleep.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eClinical Trial Registration\u003c/h2\u003e\n\u003cp\u003eClinical Trial number: not applicable\u003c/p\u003e\n\u003ch2\u003eHuman Ethics and Consent to Participate\u003c/h2\u003e\n\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003efor the study was obtained from the Institutional Ethics Committee of CHRIST (Deemed to be) University, Delhi NCR, India. All procedures involving human participants were conducted in accordance with institutional ethical guidelines. Informed consent was obtained from all participants prior to participation in the study.\u003c/p\u003e\n\u003ch2\u003eConsent to Publish\u003c/h2\u003e\n\u003cp\u003eAll authors have approved the manuscript and consent to its publication.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no financial or non-financial competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.S. had the idea of the study, was in-charge of the research design, and provided overall supervision. The literature review was conducted by N.A. and N.V., who collected the data and processed it, wrote the original manuscript. M.M. and P.S. helped in gathering and initial analysis of data.M.G. helped to make the methodology finer, revise in a critical manner and strengthen theoretical foundation. edited and approved by all the authors. The final version of the manuscript was reviewed by all authors.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analyzed in the current study are not publicly available due to ethical restrictions and the need to protect the confidentiality and privacy of the participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbojedi, A., Alsheikh Ali, A. S., \u0026amp; Basmaji, J. (2023). Assessing the impact of technology use, social engagement, emotional regulation, and sleep quality among undergraduate students in Jordan: Examining the mediating effect of perceived and academic stress. \u003cem\u003eHealth Psychology Research, 11.\u003c/em\u003e https://doi.org/10.52965/001c.73348\u003c/li\u003e\n\u003cli\u003eAlotaibi, A. D., Alosaimi, F. M., Alajlan, A. A., \u0026amp; Bin Abdulrahman, K. A. (2020). The relationship between sleep quality, stress, and academic performance among medical students. \u003cem\u003eJournal of Family \u0026amp; Community Medicine, 27\u003c/em\u003e(1), 23\u0026ndash;28. https://doi.org/10.4103/jfcm.JFCM_132_19\u003c/li\u003e\n\u003cli\u003eArbabisarjou, A., Mehdi, H. S., Sharif, M. R., Alizadeh, K. H., Yarmohammadzadeh, P., \u0026amp; Feyzollahi, Z. (2016). The relationship between sleep quality and social intimacy, and academic burn-out in students of medical sciences. \u003cem\u003eGlobal Journal of Health Science, 8\u003c/em\u003e(5), 231\u0026ndash;238. https://doi.org/10.5539/gjhs.v8n5p231\u003c/li\u003e\n\u003cli\u003eBes, I., Koutsimani, P., Montgomery, A., \u0026amp; Georganta, K. (2023). Organizational interventions and occupational burnout: A meta-analysis. \u003cem\u003eFrontiers in Psychology, 14,\u003c/em\u003e 1134567. https://doi.org/10.3389/fpsyg.2023.1134567\u003c/li\u003e\n\u003cli\u003eBondanini, G., Giorgi, G., Ariza-Montes, A., Vega-Mu\u0026ntilde;oz, A., \u0026amp; Andreucci-Annunziata, P. (2020). Technostress: The dark side of technology in the workplace. \u003cem\u003eInternational Journal of Environmental Research and Public Health, 17\u003c/em\u003e(21), 8013. https://doi.org/10.3390/ijerph17218013\u003c/li\u003e\n\u003cli\u003eBrod, C. (1984). \u003cem\u003eTechnostress: The Human Cost of the Computer Revolution.\u003c/em\u003e Addison Wesley.\u003c/li\u003e\n\u003cli\u003eBuenadicha-Mateos, F., Gonz\u0026aacute;lez-Su\u0026aacute;rez, S. M., \u0026amp; S\u0026aacute;nchez-Fern\u0026aacute;ndez, M. D. (2022). Emotional exhaustion caused by technostress among university students. \u003cem\u003eEducation and Information Technologies, 27\u003c/em\u003e(6), 8443\u0026ndash;8461. https://doi.org/10.1007/s10639-022-11086-4\u003c/li\u003e\n\u003cli\u003eDawson, D., McMillan, J. M., \u0026amp; Thomas, M. (2022). A scalable cognitive behavioral therapy for insomnia program improves sleep outcomes: A systematic review. \u003cem\u003eFrontiers in Sleep, 1,\u003c/em\u003e 1002437. https://doi.org/10.3389/frsle.2022.1002437\u003c/li\u003e\n\u003cli\u003eDonaldson, R., Smith, L., \u0026amp; McGowan, C. (2022). Digital cognitive behavioral therapy for insomnia and its impact on mental health: A meta-analysis. \u003cem\u003eFrontiers in Digital Health, 4,\u003c/em\u003e 856790. https://doi.org/10.3389/fdgth.2022.856790\u003c/li\u003e\n\u003cli\u003eGoddard, M. S. (2011). \u003cem\u003eSleep quality, technostress, and maladaptive use of technology: Predictors of depression among college students.\u003c/em\u003e [Doctoral dissertation, University of Memphis]. University of Memphis Digital Commons. https://digitalcommons.memphis.edu/etd/350\u003c/li\u003e\n\u003cli\u003eHapsari, E. A., Rohmatullayaly, E. N., \u0026amp; Widayati, K. A. (2024). Technostress and sleep quality among university students in Indonesia: A cross-sectional study. \u003cem\u003eAsian Journal of Social Health and Behavior, 7\u003c/em\u003e(4), 197\u0026ndash;202. https://doi.org/10.4103/shb.shb_177_24\u003c/li\u003e\n\u003cli\u003eHu, Y., Niu, Z., Dai, L., Maguire, R., Zong, Z., Hu, Y., \u0026amp; Wang, D. (2020). The relationship between sleep pattern and depression in Chinese shift workers: A mediating role of emotional exhaustion. \u003cem\u003eAustralian Journal of Psychology, 72\u003c/em\u003e(1), 68\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eIsabel, M., \u0026amp; Rodrigo, \u0026Oacute;. (2022). Analysis of the emotional exhaustion derived from techno-stress in the next generation of qualified employees. \u003cem\u003eFrontiers in Psychology, 13,\u003c/em\u003e 792606. https://doi.org/10.3389/fpsyg.2022.792606\u003c/li\u003e\n\u003cli\u003eJarrett, N. L., Yamane, D. E., Gildner, D. J., \u0026amp; Pickett, S. M. (2019). The indirect effect of sleep quality on emotional exhaustion through emotion regulation difficulties and perceived stress in a sample of U.S. medical students. \u003cem\u003eSleep, 42\u003c/em\u003e(Supplement_1), A87\u0026ndash;A87. https://doi.org/10.1093/sleep/zsz067.211\u003c/li\u003e\n\u003cli\u003eJniene, A., Errguig, L., El Hangouche, A. J., Rkain, H., Aboudrar, S., El Ftouh, M., \u0026amp; Dakka, T. (2019). Perception of sleep disturbances due to technology use and caffeine consumption among Moroccan medical students. \u003cem\u003eBioMed Research International, 2019,\u003c/em\u003e 1\u0026ndash;8. https://doi.org/10.1155/2019/2347968\u003c/li\u003e\n\u003cli\u003eKassim, E. S., Ahmad, S. F. S., Bahari, A. H., Fadzli, F. N. M., \u0026amp; Adzmi, N. S. H. M. (2021). The effect of technostress on emotional exhaustion and coping strategies. \u003cem\u003eInternational Journal of Academic Research in Business and Social Sciences, 11\u003c/em\u003e(5), 544\u0026ndash;559.\u003c/li\u003e\n\u003cli\u003eLatorre, F. (2018). Technostress: Definition, symptoms, and risk factors of technology-induced stress. Retrieved from https://doi.org/10.1007/s00420-018-1352-1\u003c/li\u003e\n\u003cli\u003eLi, C., Zhang, Y., Randhawa, A. K., \u0026amp; Madigan, D. J. (2020). Emotional exhaustion and sleep problems in university students: Does mental toughness matter? \u003cem\u003ePersonality and Individual Differences, 163,\u003c/em\u003e 110046. https://doi.org/10.1016/j.paid.2020.110046\u003c/li\u003e\n\u003cli\u003eLin, Y., Mutz, J., Clough, P. J., \u0026amp; Papageorgiou, K. A. (2017). Mental toughness and individual differences in learning, educational and work performance, psychological well-being, and personality: A systematic review. \u003cem\u003eFrontiers in Psychology, 8,\u003c/em\u003e 1345. https://doi.org/10.3389/fpsyg.2017.01345\u003c/li\u003e\n\u003cli\u003eMemon, A. R., Gupta, C. C., Crowther, M. E., Ferguson, S. A., Tuckwell, G. A., \u0026amp; Vincent, G. E. (2021). Sleep and mental health in university students: A systematic review and meta-analysis. \u003cem\u003eSleep Medicine, 82,\u003c/em\u003e 62\u0026ndash;69. https://doi.org/10.1016/j.sleep.2021.03.002\u003c/li\u003e\n\u003cli\u003eOwens, J. A., Drobnich, D., Baylor, A., \u0026amp; Lewin, D. (2017). School start time change: An in-depth examination of school districts in the United States. \u003cem\u003eJournal of School Health, 87\u003c/em\u003e(7), 515\u0026ndash;522. https://doi.org/10.1111/josh.12526\u003c/li\u003e\n\u003cli\u003eRamadhan, R. N., Putra, A. R., \u0026amp; Sari, D. R. (2024). Impacts of digital social media detox on mental health and well-being. \u003cem\u003eFrontiers in Psychology, 15,\u003c/em\u003e 1365841. https://doi.org/10.3389/fpsyg.2024.1365841\u003c/li\u003e\n\u003cli\u003eStoeckl, F., \u0026amp; Eckhardt, A. (2023). Sociability and technostress in online classes: The effects on students\u0026rsquo; emotional exhaustion during the COVID-19 pandemic.\u003c/li\u003e\n\u003cli\u003eSweller, J. (1988). Cognitive load during problem solving: Effects on learning. \u003cem\u003eCognitive Science, 12\u003c/em\u003e(2), 257\u0026ndash;285. https://doi.org/10.1207/s15516709cog1202_4\u003c/li\u003e\n\u003cli\u003eTarafdar, M., Tu, Q., Ragu-Nathan, B. S., \u0026amp; Ragu-Nathan, T. (2007). The impact of technostress on role stress and productivity. \u003cem\u003eJournal of Management Information Systems, 24\u003c/em\u003e(1), 301\u0026ndash;328. https://doi.org/10.2753/MIS0742-1222240109\u003c/li\u003e\n\u003cli\u003eTorales, J., O\u0026rsquo;Higgins, M., Castaldelli-Maia, J. M., \u0026amp; Ventriglio, A. (2022). Technostress and its impact on mental health: A growing concern in the digital age. \u003cem\u003eAsian Journal of Psychiatry, 65,\u003c/em\u003e 102867. https://doi.org/10.1016/j.ajp.2022.102867\u003c/li\u003e\n\u003cli\u003eUpadhyaya, P., \u0026amp; Vrinda. (2021). Impact of technostress on academic productivity of university students. \u003cem\u003eEducation and Information Technologies, 26\u003c/em\u003e(2), 1647\u0026ndash;1664.\u003c/li\u003e\n\u003cli\u003eVan Laethem, M., Van Vianen, A. E. M., \u0026amp; Derks, D. (2017). Daily fluctuations in smartphone use, work-related stress, and sleep: A dynamic perspective. \u003cem\u003eJournal of Occupational Health Psychology, 22\u003c/em\u003e(4), 429\u0026ndash;440. https://doi.org/10.1037/ocp0000049\u003c/li\u003e\n\u003cli\u003eVallone, F., Bellagamba, F., \u0026amp; Tanzini, M. (2023). The effects of technostress on academic performance and psychological well-being among university students. \u003cem\u003eComputers in Human Behavior, 139,\u003c/em\u003e 107613. https://doi.org/10.1016/j.chb.2023.107613\u003c/li\u003e\n\u003cli\u003eWang, X., Li, Z., Ouyang, Z., \u0026amp; Xu, Y. (2020). The Achilles heel of technology: How does technostress affect university students\u0026rsquo; wellbeing and technology-enhanced learning? \u003cem\u003eInternational Journal of Environmental Research and Public Health, 18\u003c/em\u003e(23), 12322. https://doi.org/10.3390/ijerph182312322\u003c/li\u003e\n\u003cli\u003eYao, N., \u0026amp; Wang, Q. (2022). Technostress from smartphone use and its impact on university students\u0026rsquo; sleep quality and academic performance. \u003cem\u003eAsia-Pacific Education Researcher, 32\u003c/em\u003e(3), 317\u0026ndash;326. https://doi.org/10.1007/s40299-022-00654-5\u003c/li\u003e\n\u003cli\u003eYeomans, M., \u0026amp; Oyanedel, J. (2021). Psychometric Properties of the Emotional Exhaustion Scale (ECE) in Chilean Higher Education Students. \u003cem\u003eEuropean Journal of Investigation in Health, Psychology and Education, 12\u003c/em\u003e(1), 50\u0026ndash;60. https://doi.org/10.3390/ejihpe12010005\u003c/li\u003e\n\u003cli\u003eYi, H., Shin, K., \u0026amp; Shin, C. (2006). Development of the sleep quality scale. \u003cem\u003eJournal of Sleep Research, 15\u003c/em\u003e(3), 309\u0026ndash;316.\u003c/li\u003e\n\u003cli\u003eYunita, S., Susilawati, S., Riniawati, R., \u0026amp; Fajriah, Y. N. (2023). Exploring college students\u0026rsquo; technostress phenomenon in using ed-tech. \u003cem\u003eJournal of Research in Instructional, 3\u003c/em\u003e(2), 242\u0026ndash;257. https://doi.org/10.30862/jri.v3i2.280\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dimh","sideBox":"Learn more about [Discover Mental Health](https://www.springer.com/44192)","snPcode":"","submissionUrl":"","title":"Discover Mental Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Technostress, Emotional Exhaustion, Sleep Quality, Psychology Students, Intervention","lastPublishedDoi":"10.21203/rs.3.rs-8411730/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8411730/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe rapid changes in academic curriculum and the increasing use of digital platforms in learning, assessment, and communication have increased the use of technology among psychology students. Although digital technologies have increased access, accessibility and efficiency, continuous connectivity has brought about technostress, which is a psychological strain linked with the excessive use of technology.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis research investigated the relationship between technostress, emotional exhaustion and sleep quality among emerging Indian psychologists, and whether emotional exhaustion acts as a mediator between technostress and sleep quality.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eA Quantitative correlational research design was used. Data was collected from psychology students aged 18 to 25 years. SPSS was used to analyse the descriptive statistics and Pearson\u0026rsquo;s correlation, whereas the mediation analysis was done with the help of Jamovi.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe findings showed that there was a positive correlation between technostress and emotional exhaustion and significant relationship between emotional exhaustion and poor sleep quality. The mediation analysis shows emotional exhaustion completely mediates the relationship between technostress and sleep quality, whereas emotional exhaustion has an indirect effect of technostress on sleep quality with no significant effect.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings of this study support the need to implement interventions that reduce emotional burnout in order to enhance sleep quality and personal well-being among psychology students. Therefore, this research suggests the REPEAT intervention model, which is expected to lead to digital balance, emotional regulation, and restorative sleep.\u003c/p\u003e","manuscriptTitle":"A Study on Technostress Emotional Exhaustion and Sleep Quality Among Future Psychologists in India an Intervention Plan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 18:03:26","doi":"10.21203/rs.3.rs-8411730/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-22T21:03:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T13:51:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265173673731208477755779457722894628192","date":"2026-03-03T12:15:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326892479998238790635180728243274848484","date":"2026-03-01T16:23:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T09:44:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83002132865025236831341758397066674445","date":"2026-02-11T07:06:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-19T03:32:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-31T03:31:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-30T15:35:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Mental Health","date":"2025-12-30T15:25:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dimh","sideBox":"Learn more about [Discover Mental Health](https://www.springer.com/44192)","snPcode":"","submissionUrl":"","title":"Discover Mental Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5dfd363f-d95a-4e4f-bfa7-d253feaa604c","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-21T18:03:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 18:03:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8411730","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8411730","identity":"rs-8411730","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}