Media Multitasking and Its Impact on Attention and Emotional Well-being Among University Students

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Results indicate that frequent multitaskers experience reduced attentional accuracy and increased reaction times, signifying cognitive costs linked to divided attention. However, structural equation modelling revealed no significant direct relationship between multitasking and perceived stress, suggesting that multitasking-induced attentional deficits do not necessarily translate to heightened stress. These findings challenge cognitive load theories that associate multitasking with increased stress, pointing to the potential moderating effects of individual resilience and coping mechanisms. This study highlights the complexity of multitasking’s impact on cognitive and emotional health, advocating for further research into individual factors that may influence stress responses in multitasking contexts. Psychology media multitasking attention perceived stress cognitive performance university students Figures Figure 1 Figure 2 Figure 3 Introduction Multitasking on the media is a condition that describes the habit of interacting with several forms of media simultaneously [1]. This practice has been quite common for university students due to the wide availability of smartphones, social networks, streaming services, and such forms of digital technology [2]. Although researchers were largely interested in that behaviour because it could affect attentional control, cognitive processes, and emotional health, that concern was not without controversy. Coupled with demanding coursework and continuous connectivity, this study will show how multitasking on media influences cognitive capabilities and perceived stress levels among students. In this context, the concept of media multitasking applies to the practice of switching between multiple streams of media at the same time or in rapid succession [1]. University students multitask as they jump between various social media feeds, academic content, messaging programmes, and entertainment platforms [3], [4]. Recent findings have expressed apprehension regarding the effect of media multitasking on attentional abilities [5], [6], [7], [8], considering that there was a multiplier of evidence that sustained attention and inhibitory control is usually weakened due to constant switching [9], thereby reducing the ability of a person to concentrate on something with his/her full attention [10]. Furthermore, the cognitive load in media multitasking is believed to negatively impact emotional regulation, leading to higher scores of perceived stress [5]. Cognitive load theory gives a useful framework from which one can gain an understanding of the potential negative consequences of media multitasking [1], [11]. This theory postulates that human mental capacity is inherently limited, adding that very heavy cognitive loads, such as those resulting from frequent multitasking, will hamper efficient processing [12]. Students in universities are very vulnerable because their academic environment puts them in a position to pay constant attention [13], [14], [15]. However, as a result of the profound and sometimes chronic use of digital devices increases the possibility of split attention and cognitive load [16], [17]. The results of this divided attention have mostly evidenced declines in academic achievement [18], slower reaction times [19], lower accuracy on attention-requiring tasks [20], and problems in maintaining focus [21]. Another key domain influenced by media multitasking concerns emotional well-being [22]. PSS is a subjective measure of stress that represents the degree of perceived inability to cope with and manage environmental demands [7], [23]. In frequent multitaskers, many streams of incoming media would make a person feel like they are in an uncontrollable and unpredictable environment [24]. Although multitasking can create temporary highs or illusions of better productivity, some of its permanent effects include increased anxiety and increased stress due to a lessened capacity for continuous focusing and also a notable impairment in emotional regulation [25], [26]. The link between cognitive control and emotional well-being is of greater concern with respect to media multitasking. Assessed by tools such as the Continuous Performance Test (CPT) [22], [27], [28], sustained attention is one of the salient ingredients of cognitive performance and, more importantly, a key to academic performance [29], [30], [31]. On the contrary, multitasking habits significantly deteriorate sustained attention [32]; for instance, they slow down reaction times and decrease the accuracy of tasks [33], which damages someone's ability to perform complex cognitive tasks. This decline in attention capacity can lead to further intensification of feelings of inadequateness and stress, eventually affecting emotional well-being [34]. With the increasing number of studies focussing on media multitasking and its effects, precisely how it affects attention control and stress is still a mystery. Some say that frequent multitaskers may gain consistent advantages in the division of attention-a factor that improves one's ability to process more than one input simultaneously [6]. On the other hand, some studies show that frequent switching between tasks may weaken the brain's focus on one single task, which results in cognitive fatigue and increased stress [35]. Such inconsistency in findings calls for a detailed investigation of how media multitasking influences mental and emotional effects. The given study attempts to describe the complex interrelationship between media multitasking, attention control, and emotional well-being among university students using the MMT-R Scale, CPT, and PSS tests to quantify multitasking behaviours, attention performance, and perceived stress. Better clarification of these associations will provide the basis for developing strategies that will help university students optimise their use of media while preserving cognitive health and emotional resilience. Based on this literature, we hypothesise that 1. university students who score higher on the Media Multitasking-Revised Scale will exhibit well and truly impaired attentional control, as reflected by increased reaction times and reduced accuracy on the Continuous Performance Test. 2. Higher frequencies of multitasking in media will relate to higher perceived measures of stress according to the Perceived Stress Scale (PSS), indicating that more frequent engagement in multiple sources of media contributes to higher levels of stress among university students. 3. There will be an interaction effect of media multitasking behaviour and attentional control on perceived stress; for example, reduced attentional control mediates the relationship between high multitasking frequency and increased levels of stress. Methodology Questionnaires: MMM-S The Media Multitasking Revised (MMT-R) Scale [36] was used in this study to assess participants' multitasking behaviours, mainly their frequency and intensity of engaging with multiple media sources simultaneously. The MMT-R consists of 18 items, each rated on a 1-5 Likert scale, with response options tailored based on item type: frequency items use the scale 1 (Never) to 5 (Always), while other items use 1 (Not at all) to 5 (Very much). Notably, the first item is reverse-scored to account for the avoidance of distraction. Total scores range from 18 to 90, with higher scores indicating more frequent and intense multitasking behaviours in the media. This scale allowed us to quantify participants' multitasking tendencies, supporting the analysis of multitasking's influence on attentional control and stress outcomes. CPT The Continuous Performance Test (CPT) is a widely used neuropsychological task to measure sustained attention and inhibitory control [37]. In this task, participants respond to target stimuli by pressing a key while withholding responses to non-target stimuli, testing their ability to control impulsive reactions and maintain focus over time. This study used CPT to investigate how multitasking behaviour in media relates to cognitive control. Participants completed a continuous performance test (CPT) to assess sustained attention and inhibitory control. The CPT required participants to respond to target stimuli while withholding responses to nontarget stimuli. Two key metrics were recorded: reaction time (RT), which measured the time taken to respond to target stimuli, and CPT accuracy, which reflected the proportion of correct responses. PSS The Perceived Stress Scale (PSS) is a widely used psychological instrument designed to measure how people perceive their lives as stressful. Developed by Cohen et al. (1983) [23], the PSS assesses how unpredictable, uncontrollable, and overloaded respondents find their daily lives. Focusses on the subjective experience of stress rather than objective stressors, making it a valuable tool to understand personal perceptions of stress. The PSS consists of 10 or sometimes 14 items, and respondents were asked to rate how often they experienced certain feelings or thoughts during the past month on a scale from 0 (never) to 4 (very often). Some items are negatively worded (e.g., “In the last month, how often have you felt confident about your ability to handle your problems?”) and are reversed to account for positive perceptions of control. The total score is calculated by summing all item scores after appropriate reverse scoring. Higher scores indicate higher perceived stress without specific cutoffs, although scores are generally interpreted in relative terms (higher or lower stress compared to population norms). Statistical analysis In this study, we performed all statistical analyses and data visualisation using Python libraries, including Statsmodels and Scipy. stats models provided comprehensive statistical models, allowing us to analyse and explore variable relationships. For structural equation modelling (SEM), we used Semopy, which enabled the construction and evaluation of complex pathway models involving latent variables. Result Participants: In this experiment, we first invited 523 respondents. During preliminary examinations, which included questionnaires and tasks, we had to exclude 18 participants for showing random performance in CPT tasks, either very long response time or brief responses below 200 ms, and then another 5 participants for having provided data expressly not realistic regarding hours of media usage. Thus, in the final count, we recruited 500 participants between 18 and 40 years of age, with a mean age of 28.7 years (SD = 7.03), to give a wide representation of young to middle-aged adults. The sample was also reasonably representative in terms of gender distribution, with 53.8% of males and 46.2% of females. This allows the current study to analyse multitasking and attentional performance in diverse age and gender groups, increasing thus the generalisability of the findings. The descriptive analysis of the MMT score showed that the mean score was 52.97 (SD = 20.92), and the variance was 437.72, which shows great variability in multitasking behaviours within the sample. The 95% confidence interval of the MMT, thus ranging from 51.13 to 54.81, suggests that the population mean is 53. This width in this range shows the variation within the participants of media multitasking engagement, an important variable as a function of consideration due to its influence on outcomes related both to attention and cognition. Table 1 shows the descriptive statistics for all scales and tasks, along with variance and 95% CI. Table 1: descriptive statistics for all scales and tasks together with variance and 95% CI. Measure Mean (SD) Variance 95% CI MMT score 52.97 20.92 437.72 [51.13, 54.81] Reaction time (ms) 556.8 80.06 6409.72 [549.77, 563.83] CPT Accuracy 0.89 0.083 0.0069 [0.88, 0.90] PSS score 20.27 10.63 113.06 [19.33, 21.20] Media Usage Hours 4.39 2.31 5.32 [4.19, 4.59] The mean of the participant's reaction time was varied by 556.8 milliseconds, SD = 80.06, hence a variance of 6409.72. The great spread in reaction times is captured by a 95% confidence interval of 549.77-563.83 milliseconds, which evidences variability within the subjects' attention and, therefore, possibly within their multitasking behaviours. The CPT Accuracy—working out the consistency of the participants' attention and control—profiled an average of 0.89, with a standard deviation of 0.083 and a variance of 0.0069. The 95% confidence interval it reaches spans from a low of 0.88 to a high of 0.90, which tells us that participants are fairly accurate and consistent in performing the task. The small width of the interval reflects that, though multitasking influence may contribute to minor attentional fluctuations, most participants show a very high level of focus during the task. The PSS score has a mean of 20.27 and a standard deviation of 10.63, with a variance of 113.06. Estimates of the 95% confidence interval range from 19.33 to 21.20, suggesting data on a moderate level of stress within this sample, although levels of stress vary significantly. Considering the increased scores present among heavy multitaskers, this measure serves as an indicator of how multitasking behaviour in media might relate to stress. Finally, Media Use Hours reported an average of 4.39 hr/day, SD = 2.31, variance of 5.32, and 95% CI of 4.19 to 4.59 h. Then, considering the PSS scoring rules, we divided the participants according to their levels of stress into three groups low stress (0-13), moderate stress (14-26), and high stress level (27-40). This classification was applied by coding each participant's PSS score according to these thresholds, thus capturing our sample's perceived stress range. Most of our participants (51.0%) fall into the category of moderate stress, 24.0% are classified as low stress, and 25.0% as high stress. The gender distribution in the PSS categories was such that 51.2% of men and 48.8% of women were classified in the high-stress category, totalling 125. In the low-stress category, there were 51.7% males and 48.3% females, totalling 120. The age distribution in the moderate stress group was 56.1% male and 43.9% female, while the low-stress group had 51.9% male and 48.1% female subjects, with sample sizes of 255 and 111, respectively. The descriptive statistics for each stress category indicate that the participants in the high-stress group had an average MMT of 55.89 (SD = 21.66). In contrast, the low- and moderate-stress groups had mean scores of 49.99 (SD = 20.02) and 52.94 (SD = 20.85), respectively. Reaction times averaged 563.46 ms (SD = 83.16) in the high-stress group, compared to 548.36 ms (SD = 76.99) in the low-stress group and 557.51 ms (SD = 79.89) in the moderate-stress group. CPT Accuracy, reflecting attention consistency, showed slight variance between groups, with mean scores of 0.888 (SD = 0.085) in the high-stress group, 0.896 (SD = 0.085) in the low-stress group, and 0.888 (SD = 0.082) in the moderate-stress group. The hours of media usage followed a similar pattern, with an average daily usage of 4.62 hours (SD = 2.26) in the high-stress group, 4.35 hours (SD = 2.34) in the low-stress group, and 4.30 hours (SD = 2.31) in the moderate-stress group. Table 2 provides a detailed view of the statistical data for each task based on the PSS category. Also, Figure 1 shows a plot of the data based on the category of PSS. Table 2: descriptive statistics for tasks based on PSS categorisation. Female(%) Male(%) PSS Category MMT Mean(SD) RT Mean(SD) CPT Accuracy Mean(SD) Media Usage Mean(SD) 61 (48.8%) 64 (51.2%) high. 55.88(21.66) 563.46(83.16) 0.88(0.08) 4.6(2.25) 58(48.33%) 62 (51.77%), low 49.99(20.02) 548.35(76.99) 0.89(0.08) 4.35(2.34) 112 (43.22%) 143 (56.77%) moderate 52.94(20.84) 557.50(79.89) 0.88(0.07) 4.30(2.31) ANOVA tests assessed whether differences in MMT, reaction time, CPT accuracy, and media usage hours were statistically significant between the three stress levels. The results did not show significant differences between stress categories for any of these variables, with F statistics and p values as follows: for MMT, F(2, 497) = 2.45, p = 0.088; for reaction time, F(2, 497) = 1.11, p = 0.33; for precision of CPT, F (2, 497) = 0.45, p = 0.64; and for media usage hours, F(2, 497) = 0.80, p = 0.45. These findings indicate that while there are slight variations in multitasking scores, reaction times, accuracy, and media usage between stress categories, none of these differences reach statistical significance in this sample. To capture a complete picture, we conducted a correlation analysis. The correlation analysis reveals several significant relationships among the key variables, with significance levels denoted by asterisks (* for p < 0.05, ** for p < 0.01, *** for p < 0.001). In particular, MMT shows a strong positive correlation with reaction time (r = 0.65, p < 0.001), indicating that higher multitasking behaviour is associated with longer reaction times, which may suggest decreased attention control. Furthermore, MMT is negatively correlated with the precision of CPT (r = -0.67, p < 0.001), supporting that frequent multitaskers have a lower precision on attentional tasks. Reaction time also negatively correlates with CPT (r = -0.56, p < 0.001), implying that participants with slower reaction times tend to have lower precision, which may further reflect the cognitive costs associated with impaired attention control. Interestingly, the PSS score has weak but significant positive correlations with both MMT (r = 0.12, p < 0.01) and reaction time (r = 0.1, p < 0.05), suggesting that higher stress levels may be associated with increased multitasking tendencies and longer reaction times, respectively. The variable of the PSS Category categorises stress into low, moderate, and high levels and correlates very strongly with the PSS score (r = 0.93, p < 0.001), confirming consistency in stress categorisation. We use structural equation modelling (SEM) to examine the complex relationships between media multitasking, attentional performance, and stress levels. SEM allows for simultaneous direct and indirect effects analysis, enabling us to model both observed and latent constructs, such as Media Multitasking and Attentional Control. This approach is beneficial in our study, as it accounts for the potential mediating role of attention control in the relationship between multitasking behaviour and stress. By capturing these interrelated pathways, SEM provides a nuanced understanding of how multitasking might influence cognitive and emotional outcomes. The SEM analysis explored the relationship between media multitasking and attention control, its effects on stress, and its associations with specific observed variables. Multitasking was specified as a latent variable defined by the observed indicators of MMT and usage hours of the media, while attention control was represented by reaction time and accuracy of the CPT. Pathways from media multitasking to attention control and from latent variables to PSS score were evaluated, along with further exploratory pathways to reaction time, CPT accuracy, and additional observed measures. The analysis revealed several relationships within the model. First, the pathway from multitasking in the media to attention control exhibited a significant positive effect (β = 0.88, p < .001), suggesting that increased multitasking participation predicts better attention control, operationalised here as improved reaction time and precision in continuous performance tasks. This significant positive association underscores a potential adaptive aspect of media multitasking in attentional processes. Similarly, a significant negative association was found between attention control and CPT Accuracy (β = -0.24, p < .001), indicating that increased attention control correlates with lower CPT precision scores, which can suggest a trade-off effect in the attentional allocation of resources during multitasking contexts. Regarding the pathways that involve stress outcomes, media multitasking did not show a statistically significant direct effect on PSS Score (β = 4.02, p = .187), indicating that, in this model, multitasking behaviour did not directly predict stress levels. On the contrary, attention control demonstrated a nonsignificant negative path to PSS Score (β = -2.19, p = .549), suggesting that there is no direct link between attention control and stress. Interestingly, the model included a path from reaction time to PSS score. However, this relationship was also non-significant (β = 0.0004, p = .966), as was the path from CPT accuracy to PSS Score (β = 13.62, p = .200). These findings indicate that while attentional capacities may be enhanced through multitasking, this does not translate into significant stress reductions, aligning with existing literature that suggests that attentional benefits of multitasking may not necessarily mitigate stress. In addition, exploratory paths were examined for their association with media use hours and attentional outcomes. Media use hours did not significantly predict CPT Accuracy (β = -0.01, p = .109), indicating that variations in media engagement do not correspond to significant changes in performance accuracy. However, reaction time was significantly associated with Media Multitasking (β = 50.74, p < .001), highlighting that multitasking is strongly related to slower reaction times. This finding aligns with previous studies that emphasise the potential attentional cost of frequent multitasking behaviours. The general model explained 56.5% of the variance in MMT, suggesting a moderate to strong predictive capacity of the model’s specified latent variables and pathways for multitasking behaviours. However, the model did not significantly explain stress outcomes, indicating that additional variables outside of multitasking and attention control are likely contributors to stress, as measured by the PSS score. Discussion This study addresses the implications of media multitasking on attention and emotional well-being, focussing on the university student population. Our findings suggest several shade relationships between multitasking behaviour, cognitive control, and perceived stress. Using subjective measures (questionnaires) and objective performance metrics (CPT) provides a comprehensive understanding of the mental and emotional impact of multitasking. Although descriptive statistics indicated some variability in multitasking tendencies and stress, statistical analyses did not reveal significant differences between stress categories regarding multitasking on media, attention performance, or media usage. This lack of substantial effect implies that the relationship between media multitasking and stress is complex and may not be directly observed in attention metrics alone. Our study used the MMT-R scale to assess multitasking behaviour in media, which is aligned with some other studies, such as [ 32 ], [ 36 ], which revealed notable variability within our sample, reflecting how students engage with simultaneous media sources. Correlation analysis indicated significant associations between media multitasking scores, longer reaction times, and reduced CPT accuracy, suggesting that higher levels of multitasking are linked with lower attention control, this is align with results by [ 9 ]. This aligns with existing literature, which often indicates that high-frequency multitaskers may struggle to focus on a given task due to the divided attention inherent in multitasking [ 8 ], [ 28 ], [ 38 ]. The SEM findings further supported these relationships, showing that media multitasking was positively associated with attention control, measured by reaction time and accuracy, although this did not affect perceived stress. This suggests a paradox where frequent multitasking might develop attentional skills that enhance some cognitive functions but simultaneously impair focus and sustained attention [ 5 ], [ 39 ], [ 40 ], particularly in more complex or sustained contexts. Interestingly, our results also highlight that, despite the cognitive demands of multitasking in media, no direct relationship was found between multitasking and perceived stress levels, as measured by PSS. This finding diverges from previous studies that suggested that multitasking could contribute to higher stress levels due to increased cognitive load. The absence of a significant relationship between multitasking and stress in our data could be influenced by individual coping strategies or varying personal stress thresholds [ 41 ]. The gender distribution within stress categories also did not indicate significant differences in stress levels or multitasking behaviours, suggesting that both men and women might experience similar effects of multitasking on the media on attention control and emotional well-being. The implications of these findings for university students are noteworthy. Given the high prevalence of media multitasking in academic settings [ 3 ], understanding its impacts on cognitive performance and stress is critical. Frequent multitasking is correlated with increased reaction times and decreased task accuracy, suggesting that students who multitask may face challenges in maintaining focus during sustained activities such as studying or attending lectures. In addition, the absence of a significant link to perceived stress raises questions about how students perceive and manage their stress levels and multitasking habits. An interpretation could be that students may not directly associate their multitasking behaviour with stress despite the possible cognitive toll it takes. From an educational perspective, interventions to improve attention control and reduce unnecessary multitasking could be beneficial. Encouraging students to engage in mindfulness practices or focus exercises might help mitigate the cognitive costs associated with high levels of multitasking [ 42 ]. The absence of a direct link between multitasking and stress in this study also underscores the need for more research to explore other potential mediators or moderators, such as personality traits, resilience, or lifestyle factors, which could influence the relationship between multitasking, cognitive performance, and emotional outcomes. Conclusions In summary, this research offers important insights into the intricate connections between media multitasking, attention regulation, and perceived stress in university students. The results indicate that while multitasking may affect cognitive performance, it does not necessarily lead to higher stress levels. Future studies should investigate these relationships more deeply, considering other elements such as coping strategies, personality characteristics, and lifestyle influences. Strategies aimed at improving attentional regulation and minimising unnecessary multitasking could be particularly useful in academic settings, helping students optimise their cognitive and emotional health. Limitations The results of our study should be assessed with certain limitations in mind. The main constraint is the dependence on self-reported information on multitasking behaviours and stress levels. Self-reported data can be influenced by biases such as social desirability or recall bias, causing participants to understate or exaggerate their multitasking and stress experiences. These potential inaccuracies may impact the extent to which our findings can be generalised. A further limitation lies in the composition of the sample. Most of the participants in our study were university students, which restricts the applicability of the results to other groups. University students might exhibit different multitasking behaviours and stress experiences that do not reflect the larger adult population. Consequently, future research should seek to involve more varied demographic groups, encompassing people of different age brackets, educational levels, and work environments. Furthermore, the cross-sectional nature of our study restricts our ability to draw causal conclusions about the connections between media multitasking, attention control, and stress. Although we found correlations among these variables, we are unable to definitively establish the direction of the causality. Longitudinal research would be advantageous in gaining a deeper understanding of how multitasking behaviours influence cognitive and emotional well-being over time. Another possible limitation is the type of attention control measures utilised. The Continuous Performance Test (CPT) offers a practical yet limited evaluation of attention and inhibitory control. Future research could benefit from including a wider variety of cognitive tasks to evaluate different aspects of attention and executive function, providing a more complete understanding of how multitasking influences cognitive processes. Ultimately, the lack of a significant direct link between multitasking and perceived stress might also be affected by other unmeasured factors, such as personality traits, coping mechanisms, or personal variations in stress tolerance. These elements could serve as mediators or moderators in the connection between multitasking and stress, and their absence in our study suggests an avenue for future research. Declarations All participant has electronically read and signed the consent of participating in this study. This study has been approved by the Ethical Committee of the Psychological Committee Ethics at Bogazici University, with approval ID 2024-12073, and is conducted in accordance with the principles outlined in the Declaration of Helsinki. 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Differ. , vol. 111, pp. 272–280, 2017, doi: 10.1016/J.PAID.2017.02.014. M. Shin and E. Kemps, “Media multitasking as an avoidance coping strategy against emotionally negative stimuli,” Anxiety Stress Coping , vol. 33, pp. 440–451, 2020, doi: 10.1080/10615806.2020.1745194. E. D. de Bruin, J. E. van der Zwan, and S. Bögels, “A RCT comparing daily mindfulness meditations, biofeedback exercises, and daily physical exercise on attention control, executive functioning, mindful awareness, self-compassion, and worrying in stressed young adults,” Mindfulness , vol. 7, pp. 1182–1192, 2016, doi: 10.1007/s12671-016-0561-5. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-6702175","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458972717,"identity":"7c6f274a-1737-4e35-82c9-603334a98f0f","order_by":0,"name":"Saarif Ekrem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBAC+x4ILQehCojQYsADoY2hXAMGBjYitSQ2kKDl7MNHNxjq0vvbew9/5jH4I28u38D44WMObi32vO3GxjkMh3NnnDmXJs1jYGC4s42BWXLmNjy28LOxSecwHMjdIJFjxgzUwrjhGAMbMy9+Ley/c4AOM5DIMQY6zMCesBbeNjbmHAbmBKAWA5DDEglr4TnGLJ1jcNhwxpkzZpJzDIyTNxxLbMbrF/ueNMbPORV18vztPcYf3lTI2W44fPjgh494tEDtQuExNhBSPwpGwSgYBaOAAAAAsoBEPUzw19oAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Saarif","middleName":"","lastName":"Ekrem","suffix":""},{"id":458972718,"identity":"c0cb5836-9709-4af5-a894-d38b400b1789","order_by":1,"name":"Jamal Bagci","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jamal","middleName":"","lastName":"Bagci","suffix":""},{"id":458972719,"identity":"d5256c5a-8d9e-45db-a876-41e9f3fe05e6","order_by":2,"name":"Elvan Karamoglu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Elvan","middleName":"","lastName":"Karamoglu","suffix":""}],"badges":[],"createdAt":"2025-05-19 22:30:46","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6702175/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6702175/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83428837,"identity":"6befe170-bd6b-4211-9e66-a8a4af5d8809","added_by":"auto","created_at":"2025-05-26 06:10:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1375144,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive statistics of MMT, reaction time, accuracy of CPT, and media use hours by Perceived Stress Scale (PSS) category (low, moderate, high). The bars represent the mean values for each PSS category, and the error bars show one standard deviation.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6702175/v1/89abd6f7bf741d815051f9bf.png"},{"id":83428288,"identity":"2c03b0a2-090e-46a0-adb5-8e01cdf4a600","added_by":"auto","created_at":"2025-05-26 06:02:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1235565,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix that displays relationships between key variables, with significance levels marked. Positive and negative correlations are represented by colour intensity, and significance is indicated by asterisks (* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001). Notable correlations include those between MMT and reaction time and CPT accuracy, indicating potential cognitive impacts of media multitasking.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6702175/v1/7198de513799e8cbb9db900e.png"},{"id":83428290,"identity":"a82a71b6-6b84-4e51-aad3-cba694f6c278","added_by":"auto","created_at":"2025-05-26 06:02:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":457991,"visible":true,"origin":"","legend":"\u003cp\u003ePath diagram illustrating the relationships between latent and observed variables within the structural equation model. Latent variables, represented by ellipses (Media Multitasking and Attentional Control), and observed variables, represented by rectangles (MMT-R Score, Media Usage Hours, Reaction Time, CPT Accuracy, and PSS Score), demonstrate the hypothesized pathways. Solid lines indicate statistically significant relationships (p \u0026lt; 0.05), with the path coefficient (β) and the p-value annotated along each path. The dashed lines represent nonsignificant relationships (NS), where p \u0026lt;0.05, suggesting that there is no statistically meaningful association within the context of this model. The key findings include significant positive associations between multimedia multitasking and Attentional Control (β = 0.88, p \u0026lt; 0.001) and between reaction time and Media Multitasking (β = 50.74, p \u0026lt; 0.001), highlighting areas of solid influence in the model.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6702175/v1/7fe1a052cc2848304c268660.png"},{"id":83428976,"identity":"2c19809a-a68e-42e4-aced-215702bed835","added_by":"auto","created_at":"2025-05-26 06:19:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2452108,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6702175/v1/ecc9d7e0-3295-461e-ad8d-eccb76571e94.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMedia Multitasking and Its Impact on Attention and Emotional Well-being Among University Students\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultitasking on the media is a condition that describes the habit of interacting with several forms of media simultaneously\u0026nbsp;[1]. This practice has been quite common for university students due to the wide availability of smartphones, social networks, streaming services, and such forms of digital technology\u0026nbsp;[2]. Although researchers were largely interested in that behaviour because it could affect attentional control, cognitive processes, and emotional health, that concern was not without controversy. Coupled with demanding coursework and continuous connectivity, this study will show how multitasking on media influences cognitive capabilities and perceived stress levels among students. In this context, the concept of media multitasking applies to the practice of switching between multiple streams of media at the same time or in rapid succession\u0026nbsp;[1]. University students multitask as they jump between various social media feeds, academic content, messaging programmes, and entertainment platforms\u0026nbsp;[3], [4].\u003c/p\u003e\n\u003cp\u003eRecent findings have expressed apprehension regarding the effect of media multitasking on attentional abilities\u0026nbsp;[5], [6], [7], [8], considering that there was a multiplier of evidence that sustained attention and inhibitory control is usually weakened due to constant switching\u0026nbsp;[9], thereby reducing the ability of a person to concentrate on something with his/her full attention\u0026nbsp;[10]. Furthermore, the cognitive load in media multitasking is believed to negatively impact emotional regulation, leading to higher scores of perceived stress\u0026nbsp;[5]. Cognitive load theory gives a useful framework from which one can gain an understanding of the potential negative consequences of media multitasking\u0026nbsp;[1], [11]. This theory postulates that human mental capacity is inherently limited, adding that very heavy cognitive loads, such as those resulting from frequent multitasking, will hamper efficient processing\u0026nbsp;[12]. Students in universities are very vulnerable because their academic environment puts them in a position to pay constant attention\u0026nbsp;[13], [14], [15]. However, as a result of the profound and sometimes chronic use of digital devices increases the possibility of split attention and cognitive load\u0026nbsp;[16], [17]. The results of this divided attention have mostly evidenced declines in academic achievement\u0026nbsp;[18], slower reaction times\u0026nbsp;[19], lower accuracy on attention-requiring tasks\u0026nbsp;[20], and problems in maintaining focus\u0026nbsp;[21].\u003c/p\u003e\n\u003cp\u003eAnother key domain influenced by media multitasking concerns emotional well-being\u0026nbsp;[22]. PSS is a subjective measure of stress that represents the degree of perceived inability to cope with and manage environmental demands\u0026nbsp;[7], [23]. In frequent multitaskers, many streams of incoming media would make a person feel like they are in an uncontrollable and unpredictable environment\u0026nbsp;[24]. Although multitasking can create temporary highs or illusions of better productivity, some of its permanent effects include increased anxiety and increased stress due to a lessened capacity for continuous focusing and also a notable impairment in emotional regulation\u0026nbsp;[25], [26]. The link between cognitive control and emotional well-being is of greater concern with respect to media multitasking. Assessed by tools such as the Continuous Performance Test (CPT)\u0026nbsp;[22], [27], [28], sustained attention is one of the salient ingredients of cognitive performance and, more importantly, a key to academic performance\u0026nbsp;[29], [30], [31]. On the contrary, multitasking habits significantly deteriorate sustained attention\u0026nbsp;[32]; for instance, they slow down reaction times and decrease the accuracy of tasks\u0026nbsp;[33], which damages someone\u0026apos;s ability to perform complex cognitive tasks. This decline in attention capacity can lead to further intensification of feelings of inadequateness and stress, eventually affecting emotional well-being\u0026nbsp;[34].\u003c/p\u003e\n\u003cp\u003eWith the increasing number of studies focussing on media multitasking and its effects, precisely how it affects attention control and stress is still a mystery. Some say that frequent multitaskers may gain consistent advantages in the division of attention-a factor that improves one\u0026apos;s ability to process more than one input simultaneously\u0026nbsp;[6]. On the other hand, some studies show that frequent switching between tasks may weaken the brain\u0026apos;s focus on one single task, which results in cognitive fatigue and increased stress\u0026nbsp;[35]. Such inconsistency in findings calls for a detailed investigation of how media multitasking influences mental and emotional effects. The given study attempts to describe the complex interrelationship between media multitasking, attention control, and emotional well-being among university students using the MMT-R Scale, CPT, and PSS tests to quantify multitasking behaviours, attention performance, and perceived stress. Better clarification of these associations will provide the basis for developing strategies that will help university students optimise their use of media while preserving cognitive health and emotional resilience.\u003c/p\u003e\n\u003cp\u003eBased on this literature, we hypothesise that\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. university students who score higher on the Media Multitasking-Revised Scale will exhibit well and truly impaired attentional control, as reflected by increased reaction times and reduced accuracy on the Continuous Performance Test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. Higher frequencies of multitasking in media will relate to higher perceived measures of stress according to the Perceived Stress Scale (PSS), indicating that more frequent engagement in multiple sources of media contributes to higher levels of stress among university students.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. There will be an interaction effect of media multitasking behaviour and attentional control on perceived stress; for example, reduced attentional control mediates the relationship between high multitasking frequency and increased levels of stress.\u003c/p\u003e"},{"header":"Methodology","content":"\u003ch2\u003eQuestionnaires:\u003c/h2\u003e\n\u003ch3\u003eMMM-S\u003c/h3\u003e\n\u003cp\u003eThe Media Multitasking Revised (MMT-R) Scale\u0026nbsp;[36]\u0026nbsp;was used in this study to assess participants\u0026apos; multitasking behaviours, mainly their frequency and intensity of engaging with multiple media sources simultaneously. The MMT-R consists of 18 items, each rated on a 1-5 Likert scale, with response options tailored based on item type: frequency items use the scale 1 (Never) to 5 (Always), while other items use 1 (Not at all) to 5 (Very much). Notably, the first item is reverse-scored to account for the avoidance of distraction. Total scores range from 18 to 90, with higher scores indicating more frequent and intense multitasking behaviours in the media. This scale allowed us to quantify participants\u0026apos; multitasking tendencies, supporting the analysis of multitasking\u0026apos;s influence on attentional control and stress outcomes.\u003c/p\u003e\n\u003ch3\u003eCPT\u003c/h3\u003e\n\u003cp\u003eThe Continuous Performance Test (CPT) is a widely used neuropsychological task to measure sustained attention and inhibitory control\u0026nbsp;[37]. In this task, participants respond to target stimuli by pressing a key while withholding responses to non-target stimuli, testing their ability to control impulsive reactions and maintain focus over time. This study used CPT to investigate how multitasking behaviour in media relates to cognitive control. Participants completed a continuous performance test (CPT) to assess sustained attention and inhibitory control. The CPT required participants to respond to target stimuli while withholding responses to nontarget stimuli. Two key metrics were recorded: reaction time (RT), which measured the time taken to respond to target stimuli, and CPT accuracy, which reflected the proportion of correct responses.\u003c/p\u003e\n\u003ch3\u003ePSS\u003c/h3\u003e\n\u003cp\u003eThe Perceived Stress Scale (PSS) is a widely used psychological instrument designed to measure how people perceive their lives as stressful. Developed by Cohen et al. (1983)\u0026nbsp;[23], the PSS assesses how unpredictable, uncontrollable, and overloaded respondents find their daily lives. Focusses on the subjective experience of stress rather than objective stressors, making it a valuable tool to understand personal perceptions of stress. The PSS consists of 10 or sometimes 14 items, and respondents were asked to rate how often they experienced certain feelings or thoughts during the past month on a scale from 0 (never) to 4 (very often). Some items are negatively worded (e.g., \u0026ldquo;In the last month, how often have you felt confident about your ability to handle your problems?\u0026rdquo;) and are reversed to account for positive perceptions of control. The total score is calculated by summing all item scores after appropriate reverse scoring. Higher scores indicate higher perceived stress without specific cutoffs, although scores are generally interpreted in relative terms (higher or lower stress compared to population norms).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we performed all statistical analyses and data visualisation using Python libraries, including Statsmodels and Scipy. stats models provided comprehensive statistical models, allowing us to analyse and explore variable relationships. For structural equation modelling (SEM), we used Semopy, which enabled the construction and evaluation of complex pathway models involving latent variables.\u0026nbsp;\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003eParticipants:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this experiment, we first invited 523 respondents. During preliminary examinations, which included questionnaires and tasks, we had to exclude 18 participants for showing random performance in CPT tasks, either very long response time or brief responses below 200 ms, and then another 5 participants for having provided data expressly not realistic regarding hours of media usage. Thus, in the final count, we recruited 500 participants between 18 and 40 years of age, with a mean age of 28.7 years (SD = 7.03), to give a wide representation of young to middle-aged adults. The sample was also reasonably representative in terms of gender distribution, with 53.8% of males and 46.2% of females. This allows the current study to analyse multitasking and attentional performance in diverse age and gender groups, increasing thus the generalisability of the findings.\u003c/p\u003e\n\u003cp\u003eThe descriptive analysis of the MMT score showed that the mean score was 52.97 (SD = 20.92), and the variance was 437.72, which shows great variability in multitasking behaviours within the sample. The 95% confidence interval of the MMT, thus ranging from 51.13 to 54.81, suggests that the population mean is 53. This width in this range shows the variation within the participants of media multitasking engagement, an important variable as a function of consideration due to its influence on outcomes related both to attention and cognition. Table 1 shows the descriptive statistics for all scales and tasks, along with variance and 95% CI.\u003c/p\u003e\n\u003cp\u003eTable 1: descriptive statistics for all scales and tasks together with variance and 95% CI.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"517\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.1412%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3791%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3791%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7331%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.3675%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.1412%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMT score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3791%;\"\u003e\n \u003cp\u003e52.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3791%;\"\u003e\n \u003cp\u003e20.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7331%;\"\u003e\n \u003cp\u003e437.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.3675%;\"\u003e\n \u003cp\u003e[51.13, 54.81]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.1412%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReaction time (ms)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3791%;\"\u003e\n \u003cp\u003e556.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3791%;\"\u003e\n \u003cp\u003e80.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7331%;\"\u003e\n \u003cp\u003e6409.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.3675%;\"\u003e\n \u003cp\u003e[549.77, 563.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.1412%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCPT Accuracy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3791%;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3791%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7331%;\"\u003e\n \u003cp\u003e0.0069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.3675%;\"\u003e\n \u003cp\u003e[0.88, 0.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.1412%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSS score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3791%;\"\u003e\n \u003cp\u003e20.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3791%;\"\u003e\n \u003cp\u003e10.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7331%;\"\u003e\n \u003cp\u003e113.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.3675%;\"\u003e\n \u003cp\u003e[19.33, 21.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.1412%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedia Usage Hours\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3791%;\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3791%;\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7331%;\"\u003e\n \u003cp\u003e5.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.3675%;\"\u003e\n \u003cp\u003e[4.19, 4.59]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe mean of the participant\u0026apos;s reaction time was varied by 556.8 milliseconds, SD = 80.06, hence a variance of 6409.72. The great spread in reaction times is captured by a 95% confidence interval of 549.77-563.83 milliseconds, which evidences variability within the subjects\u0026apos; attention and, therefore, possibly within their multitasking behaviours.\u003c/p\u003e\n\u003cp\u003eThe CPT Accuracy\u0026mdash;working out the consistency of the participants\u0026apos; attention and control\u0026mdash;profiled an average of 0.89, with a standard deviation of 0.083 and a variance of 0.0069. The 95% confidence interval it reaches spans from a low of 0.88 to a high of 0.90, which tells us that participants are fairly accurate and consistent in performing the task. The small width of the interval reflects that, though multitasking influence may contribute to minor attentional fluctuations, most participants show a very high level of focus during the task.\u003c/p\u003e\n\u003cp\u003eThe PSS score has a mean of 20.27 and a standard deviation of 10.63, with a variance of 113.06. Estimates of the 95% confidence interval range from 19.33 to 21.20, suggesting data on a moderate level of stress within this sample, although levels of stress vary significantly. Considering the increased scores present among heavy multitaskers, this measure serves as an indicator of how multitasking behaviour in media might relate to stress. Finally, Media Use Hours reported an average of 4.39 hr/day, SD = 2.31, variance of 5.32, and 95% CI of 4.19 to 4.59 h.\u003c/p\u003e\n\u003cp\u003eThen, considering the PSS scoring rules, we divided the participants according to their levels of stress into three groups low stress (0-13), moderate stress (14-26), and high stress level (27-40). This classification was applied by coding each participant\u0026apos;s PSS score according to these thresholds, thus capturing our sample\u0026apos;s perceived stress range. Most of our participants (51.0%) fall into the category of moderate stress, 24.0% are classified as low stress, and 25.0% as high stress.\u003c/p\u003e\n\u003cp\u003eThe gender distribution in the PSS categories was such that 51.2% of men and 48.8% of women were classified in the high-stress category, totalling 125. In the low-stress category, there were 51.7% males and 48.3% females, totalling 120. The age distribution in the moderate stress group was 56.1% male and 43.9% female, while the low-stress group had 51.9% male and 48.1% female subjects, with sample sizes of 255 and 111, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe descriptive statistics for each stress category indicate that the participants in the high-stress group had an average MMT of 55.89 (SD = 21.66). In contrast, the low- and moderate-stress groups had mean scores of 49.99 (SD = 20.02) and 52.94 (SD = 20.85), respectively. Reaction times averaged 563.46 ms (SD = 83.16) in the high-stress group, compared to 548.36 ms (SD = 76.99) in the low-stress group and 557.51 ms (SD = 79.89) in the moderate-stress group.\u003c/p\u003e\n\u003cp\u003eCPT Accuracy, reflecting attention consistency, showed slight variance between groups, with mean scores of 0.888 (SD = 0.085) in the high-stress group, 0.896 (SD = 0.085) in the low-stress group, and 0.888 (SD = 0.082) in the moderate-stress group. The hours of media usage followed a similar pattern, with an average daily usage of 4.62 hours (SD = 2.26) in the high-stress group, 4.35 hours (SD = 2.34) in the low-stress group, and 4.30 hours (SD = 2.31) in the moderate-stress group. Table 2 provides a detailed view of the statistical data for each task based on the\u0026nbsp;PSS category. Also, Figure 1 shows a plot of the data based on the\u0026nbsp;category of PSS.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 2: descriptive statistics for tasks based on PSS categorisation.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6127%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9718%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSS Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4366%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMT Mean(SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRT Mean(SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7887%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCPT Accuracy Mean(SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1408%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedia Usage Mean(SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e61 (48.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6127%;\"\u003e\n \u003cp\u003e64 (51.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9718%;\"\u003e\n \u003cp\u003ehigh.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4366%;\"\u003e\n \u003cp\u003e55.88(21.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9648%;\"\u003e\n \u003cp\u003e563.46(83.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7887%;\"\u003e\n \u003cp\u003e0.88(0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1408%;\"\u003e\n \u003cp\u003e4.6(2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e58(48.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6127%;\"\u003e\n \u003cp\u003e62 (51.77%),\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9718%;\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4366%;\"\u003e\n \u003cp\u003e49.99(20.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9648%;\"\u003e\n \u003cp\u003e548.35(76.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7887%;\"\u003e\n \u003cp\u003e0.89(0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1408%;\"\u003e\n \u003cp\u003e4.35(2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e112 (43.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6127%;\"\u003e\n \u003cp\u003e143 (56.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9718%;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4366%;\"\u003e\n \u003cp\u003e52.94(20.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9648%;\"\u003e\n \u003cp\u003e557.50(79.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7887%;\"\u003e\n \u003cp\u003e0.88(0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1408%;\"\u003e\n \u003cp\u003e4.30(2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eANOVA tests assessed whether differences in MMT, reaction time, CPT accuracy, and media usage hours were statistically significant between the three stress levels. The results did not show significant differences between stress categories for any of these variables, with F statistics and p values as follows: for MMT, F(2, 497) = 2.45, p = 0.088; for reaction time, F(2, 497) = 1.11, p = 0.33; for precision of CPT, F (2, 497) = 0.45, p = 0.64; and for media usage hours, F(2, 497) = 0.80, p = 0.45. These findings indicate that while there are slight variations in multitasking scores, reaction times, accuracy, and media usage between stress categories, none of these differences reach statistical significance in this sample.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo capture a complete picture, we conducted a correlation analysis. The correlation analysis reveals several significant relationships among the key variables, with significance levels denoted by asterisks (* for p \u0026lt; 0.05, ** for p \u0026lt; 0.01, *** for p \u0026lt; 0.001). In particular, MMT shows a strong positive correlation with reaction time (r = 0.65, p \u0026lt; 0.001), indicating that higher multitasking behaviour is associated with longer reaction times, which may suggest decreased attention control. Furthermore, MMT is negatively correlated with the precision of CPT (r = -0.67, p \u0026lt; 0.001), supporting that frequent multitaskers have a lower precision on attentional tasks.\u003c/p\u003e\n\u003cp\u003eReaction time also negatively correlates with CPT (r = -0.56, p \u0026lt; 0.001), implying that participants with slower reaction times tend to have lower precision, which may further reflect the cognitive costs associated with impaired attention control. Interestingly, the PSS score has weak but significant positive correlations with both MMT (r = 0.12, p \u0026lt; 0.01) and reaction time (r = 0.1, p \u0026lt; 0.05), suggesting that higher stress levels may be associated with increased multitasking tendencies and longer reaction times, respectively.\u003c/p\u003e\n\u003cp\u003eThe variable of the PSS Category categorises stress into low, moderate, and high levels and correlates very strongly with the PSS score (r = 0.93, p \u0026lt; 0.001), confirming consistency in stress categorisation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe use structural equation modelling (SEM) to examine the complex relationships between media multitasking, attentional performance, and stress levels. SEM allows for simultaneous direct and indirect effects analysis, enabling us to model both observed and latent constructs, such as Media Multitasking and Attentional Control. This approach is beneficial in our study, as it accounts for the potential mediating role of attention control in the relationship between multitasking behaviour and stress. By capturing these interrelated pathways, SEM provides a nuanced understanding of how multitasking might influence cognitive and emotional outcomes.\u003c/p\u003e\n\u003cp\u003eThe SEM analysis explored the relationship between media multitasking and attention control, its effects on stress, and its associations with specific observed variables. Multitasking was specified as a latent variable defined by the observed indicators of MMT and usage hours of the media, while attention control was represented by reaction time and accuracy of the CPT. Pathways from media multitasking to attention control and from latent variables to PSS score were evaluated, along with further exploratory pathways to reaction time, CPT accuracy, and additional observed measures.\u003c/p\u003e\n\u003cp\u003eThe analysis revealed several relationships within the model. First, the pathway from multitasking in the media to attention control exhibited a significant positive effect (\u0026beta; = 0.88, p \u0026lt; .001), suggesting that increased multitasking participation predicts better attention control, operationalised here as improved reaction time and precision in continuous performance tasks. This significant positive association underscores a potential adaptive aspect of media multitasking in attentional processes. Similarly, a significant negative association was found between attention control and CPT Accuracy (\u0026beta; = -0.24, p \u0026lt; .001), indicating that increased attention control correlates with lower CPT precision scores, which can suggest a trade-off effect in \u003cspan dir=\"RTL\"\u003ethe\u003c/span\u003e attentional allocation of resources during multitasking contexts.\u003c/p\u003e\n\u003cp\u003eRegarding the pathways that involve stress outcomes, media multitasking did not show a statistically significant direct effect on PSS Score (\u0026beta; = 4.02, p = .187), indicating that, in this model, multitasking behaviour did not directly predict stress levels. On the contrary, attention control demonstrated a nonsignificant negative path to PSS Score (\u0026beta; = -2.19, p = .549), suggesting that there is no direct link between attention control and stress. Interestingly, the model included a path from reaction time to PSS score. However, this relationship was also non-significant (\u0026beta; = 0.0004, p = .966), as was the path from CPT accuracy to PSS Score (\u0026beta; = 13.62, p = .200). These findings indicate that while attentional capacities may be enhanced through multitasking, this does not translate into significant stress reductions, aligning with existing literature that suggests that attentional benefits of multitasking may not necessarily mitigate stress.\u003c/p\u003e\n\u003cp\u003eIn addition, exploratory paths were examined for their association with media use hours and attentional outcomes. Media use hours did not significantly predict CPT Accuracy (\u0026beta; = -0.01, p = .109), indicating that variations in media engagement do not correspond to significant changes in performance accuracy. However, reaction time was significantly associated with Media Multitasking (\u0026beta; = 50.74, p \u0026lt; .001), highlighting that multitasking is strongly related to slower reaction times. This finding aligns with previous studies that emphasise the potential attentional cost of frequent multitasking behaviours.\u003c/p\u003e\n\u003cp\u003eThe general model explained 56.5% of the variance in MMT, suggesting a moderate to strong predictive capacity of the model\u0026rsquo;s specified latent variables and pathways for multitasking behaviours. However, the model did not significantly explain stress outcomes, indicating that additional variables outside of multitasking and attention control are likely contributors to stress, as measured by the PSS score.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study addresses the implications of media multitasking on attention and emotional well-being, focussing on the university student population. Our findings suggest several shade relationships between multitasking behaviour, cognitive control, and perceived stress. Using subjective measures (questionnaires) and objective performance metrics (CPT) provides a comprehensive understanding of the mental and emotional impact of multitasking. Although descriptive statistics indicated some variability in multitasking tendencies and stress, statistical analyses did not reveal significant differences between stress categories regarding multitasking on media, attention performance, or media usage. This lack of substantial effect implies that the relationship between media multitasking and stress is complex and may not be directly observed in attention metrics alone.\u003c/p\u003e \u003cp\u003eOur study used the MMT-R scale to assess multitasking behaviour in media, which is aligned with some other studies, such as [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which revealed notable variability within our sample, reflecting how students engage with simultaneous media sources. Correlation analysis indicated significant associations between media multitasking scores, longer reaction times, and reduced CPT accuracy, suggesting that higher levels of multitasking are linked with lower attention control, this is align with results by [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This aligns with existing literature, which often indicates that high-frequency multitaskers may struggle to focus on a given task due to the divided attention inherent in multitasking [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The SEM findings further supported these relationships, showing that media multitasking was positively associated with attention control, measured by reaction time and accuracy, although this did not affect perceived stress. This suggests a paradox where frequent multitasking might develop attentional skills that enhance some cognitive functions but simultaneously impair focus and sustained attention [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], particularly in more complex or sustained contexts.\u003c/p\u003e \u003cp\u003eInterestingly, our results also highlight that, despite the cognitive demands of multitasking in media, no direct relationship was found between multitasking and perceived stress levels, as measured by PSS. This finding diverges from previous studies that suggested that multitasking could contribute to higher stress levels due to increased cognitive load. The absence of a significant relationship between multitasking and stress in our data could be influenced by individual coping strategies or varying personal stress thresholds [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The gender distribution within stress categories also did not indicate significant differences in stress levels or multitasking behaviours, suggesting that both men and women might experience similar effects of multitasking on the media on attention control and emotional well-being.\u003c/p\u003e \u003cp\u003eThe implications of these findings for university students are noteworthy. Given the high prevalence of media multitasking in academic settings [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], understanding its impacts on cognitive performance and stress is critical. Frequent multitasking is correlated with increased reaction times and decreased task accuracy, suggesting that students who multitask may face challenges in maintaining focus during sustained activities such as studying or attending lectures. In addition, the absence of a significant link to perceived stress raises questions about how students perceive and manage their stress levels and multitasking habits. An interpretation could be that students may not directly associate their multitasking behaviour with stress despite the possible cognitive toll it takes.\u003c/p\u003e \u003cp\u003eFrom an educational perspective, interventions to improve attention control and reduce unnecessary multitasking could be beneficial. Encouraging students to engage in mindfulness practices or focus exercises might help mitigate the cognitive costs associated with high levels of multitasking [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The absence of a direct link between multitasking and stress in this study also underscores the need for more research to explore other potential mediators or moderators, such as personality traits, resilience, or lifestyle factors, which could influence the relationship between multitasking, cognitive performance, and emotional outcomes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this research offers important insights into the intricate connections between media multitasking, attention regulation, and perceived stress in university students. The results indicate that while multitasking may affect cognitive performance, it does not necessarily lead to higher stress levels. Future studies should investigate these relationships more deeply, considering other elements such as coping strategies, personality characteristics, and lifestyle influences. Strategies aimed at improving attentional regulation and minimising unnecessary multitasking could be particularly useful in academic settings, helping students optimise their cognitive and emotional health.\u003c/p\u003e "},{"header":"Limitations","content":"\u003cp\u003eThe results of our study should be assessed with certain limitations in mind. The main constraint is the dependence on self-reported information on multitasking behaviours and stress levels. Self-reported data can be influenced by biases such as social desirability or recall bias, causing participants to understate or exaggerate their multitasking and stress experiences. These potential inaccuracies may impact the extent to which our findings can be generalised.\u003c/p\u003e\u003cp\u003eA further limitation lies in the composition of the sample. Most of the participants in our study were university students, which restricts the applicability of the results to other groups. University students might exhibit different multitasking behaviours and stress experiences that do not reflect the larger adult population. Consequently, future research should seek to involve more varied demographic groups, encompassing people of different age brackets, educational levels, and work environments.\u003c/p\u003e\u003cp\u003eFurthermore, the cross-sectional nature of our study restricts our ability to draw causal conclusions about the connections between media multitasking, attention control, and stress. Although we found correlations among these variables, we are unable to definitively establish the direction of the causality. Longitudinal research would be advantageous in gaining a deeper understanding of how multitasking behaviours influence cognitive and emotional well-being over time. Another possible limitation is the type of attention control measures utilised. The Continuous Performance Test (CPT) offers a practical yet limited evaluation of attention and inhibitory control. Future research could benefit from including a wider variety of cognitive tasks to evaluate different aspects of attention and executive function, providing a more complete understanding of how multitasking influences cognitive processes.\u003c/p\u003e\u003cp\u003eUltimately, the lack of a significant direct link between multitasking and perceived stress might also be affected by other unmeasured factors, such as personality traits, coping mechanisms, or personal variations in stress tolerance. These elements could serve as mediators or moderators in the connection between multitasking and stress, and their absence in our study suggests an avenue for future research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eAll participant has electronically read and signed the consent of participating in this study. This study has been approved by the Ethical Committee of the Psychological Committee Ethics at Bogazici University, with approval ID 2024-12073, and is conducted in accordance with the principles outlined in the Declaration of Helsinki.\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eS.-H. Jeong and Y. Hwang, \u0026ldquo;Media multitasking effects on cognitive vs. attitudinal outcomes: a meta-analysis,\u0026rdquo; \u003cem\u003eHum. Commun. Res.\u003c/em\u003e, vol. 42, pp. 599\u0026ndash;618, 2016, doi: 10.1111/HCRE.12089.\u003c/li\u003e\n\u003cli\u003eA. C. Karpinski, P. Kirschner, I. Ozer, J. A. Mellott, and P. Ochwo, \u0026ldquo;An exploration of social networking site use, multitasking, and academic performance among United States and European university students,\u0026rdquo; \u003cem\u003eComput. Hum. Behav.\u003c/em\u003e, vol. 29, pp. 1182\u0026ndash;1192, 2013, doi: 10.1016/j.chb.2012.10.011.\u003c/li\u003e\n\u003cli\u003eS. 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B\u0026ouml;gels, \u0026ldquo;A RCT comparing daily mindfulness meditations, biofeedback exercises, and daily physical exercise on attention control, executive functioning, mindful awareness, self-compassion, and worrying in stressed young adults,\u0026rdquo; \u003cem\u003eMindfulness\u003c/em\u003e, vol. 7, pp. 1182\u0026ndash;1192, 2016, doi: 10.1007/s12671-016-0561-5.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Boğaziçi University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"media multitasking, attention, perceived stress, cognitive performance, university students","lastPublishedDoi":"10.21203/rs.3.rs-6702175/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6702175/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the effects of media multitasking on attention regulation and perceived stress in university students using the Media Multitasking-Revised (MMT-R) scale, Continuous Performance Test (CPT), and Perceived Stress Scale (PSS); we assessed multitasking behaviours, attentional performance, and stress levels among 500 students. Results indicate that frequent multitaskers experience reduced attentional accuracy and increased reaction times, signifying cognitive costs linked to divided attention. However, structural equation modelling revealed no significant direct relationship between multitasking and perceived stress, suggesting that multitasking-induced attentional deficits do not necessarily translate to heightened stress. These findings challenge cognitive load theories that associate multitasking with increased stress, pointing to the potential moderating effects of individual resilience and coping mechanisms. This study highlights the complexity of multitasking\u0026rsquo;s impact on cognitive and emotional health, advocating for further research into individual factors that may influence stress responses in multitasking contexts.\u003c/p\u003e","manuscriptTitle":"Media Multitasking and Its Impact on Attention and Emotional Well-being Among University Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-26 06:02:51","doi":"10.21203/rs.3.rs-6702175/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8741847-68df-4c2c-ad85-5c94a29df5a5","owner":[],"postedDate":"May 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48758281,"name":"Psychology"}],"tags":[],"updatedAt":"2025-05-26T06:02:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-26 06:02:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6702175","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6702175","identity":"rs-6702175","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}