From Screens to Serenity: Evaluating the Effect of Digital Detox on Mental and Physiological Health

From Screens to Serenity: Evaluating the Effect of Digital Detox on Mental and Physiological Health | 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 From Screens to Serenity: Evaluating the Effect of Digital Detox on Mental and Physiological Health Soufia Farrukh, Sara Reza, Sara Babar, Mazhar Faiz Alam, Mavra Imtiaz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6572563/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Dec, 2025 Read the published version in BMC Medical Education → Version 1 posted 9 You are reading this latest preprint version Abstract Background: Excessive screen time has been linked to increased stress, anxiety, cognitive fatigue, and physiological dysregulation. Prolonged digital engagement raises biochemical stress markers, including cortisol, C-reactive protein (CRP), interleukin-6 (IL-6), and oxidative stress markers such as malondialdehyde (MDA), catalase (CAT), and superoxide dismutase (SOD), which can harm mental and physical health. This study evaluates the impact of a 2-week digital detox intervention on psychological well-being, biochemical stress markers, and autonomic function in medical students. Methods: A prospective interventional study was conducted at two medical colleges in Pakistan. Participants (n=240) were randomized into three groups: digital detox with alternative activities, screen-time reduction only, and a control group with no intervention. Compliance was monitored via app-based tracking and daily logs. Pre- and post-intervention assessments included biochemical markers (cortisol, catecholamines, CRP, IL-6, MDA, CAT, SOD), physiological parameters (heart rate variability, blood pressure, pulse rate), and psychometric scores (Perceived Stress Scale, Generalized Anxiety Disorder-7). Results: The digital detox with alternative activities group showed the most significant improvements. Cortisol decreased by 32% (p<0.001), CRP by 33% (p<0.001), and IL-6 by 38% (p<0.001). Perceived stress and anxiety significantly declined (p<0.001), while heart rate variability improved, and systolic blood pressure and pulse rate decreased (p<0.01). MDA levels moderately dropped (p0.05). The screen-time reduction group showed moderate improvements, while the control group had no significant changes. Conclusion: A 2-week digital detox, especially with alternative activities, significantly reduces stress, anxiety, and biochemical stress markers while improving autonomic regulation. These findings highlight the potential of structured digital detox interventions to enhance mental and physiological well-being in medical students. Digital detox stress markers inflammatory markers oxidative stress mental health medical students Figures Figure 1 Figure 2 INTRODUCTION The increasing reliance on digital technology has transformed medical education, offering unprecedented access to online learning resources, virtual simulations, and digital communication platforms. However, excessive screen time has raised concerns regarding its potential adverse effects on mental and physiological health, particularly among medical students who are already subjected to high academic stress [ 1 , 2 ]. In Pakistan, internet penetration has reached 45.7%, with over 111.0 million active users as per 2024 data [ 3 ]. Studies indicate that prolonged digital engagement is associated with increased stress, anxiety, sleep disturbances, and cognitive overload, leading to impaired concentration, emotional exhaustion, and reduced academic performance [ 4 , 5 ]. Beyond psychological consequences, chronic exposure to digital screens can trigger physiological stress responses, primarily mediated through the hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system (ANS) dysregulation [ 6 ]. Persistent activation of the HPA axis results in elevated secretion of cortisol and catecholamines (epinephrine, norepinephrine), contributing to heightened sympathetic activity, increased heart rate, and elevated blood pressure [ 7 ]. Prolonged digital overuse is also linked to increased systemic inflammation, as evidenced by elevated levels of C-reactive protein (CRP) and interleukin-6 (IL-6), both of which have been implicated in stress-related immune dysregulation [ 8 ]. Furthermore, oxidative stress plays a crucial role in the physiological impact of chronic stress. Increased metabolic activity and prolonged sympathetic stimulation lead to excessive production of reactive oxygen species (ROS), resulting in oxidative damage. Malondialdehyde (MDA), a byproduct of lipid peroxidation, serves as a biomarker of oxidative stress, while superoxide dismutase (SOD) and catalase represent key enzymatic antioxidants responsible for neutralizing ROS. Dysregulation of these oxidative stress markers has been associated with cognitive decline, neuroinflammation, and increased susceptibility to stress-related disorders [ 8 , 9 ]. To comprehensively assess the physiological impact of digital overuse, heart rate variability (HRV), blood pressure (BP), and pulse rate are widely recognized as objective indicators of autonomic function. HRV, in particular, reflects the balance between sympathetic and parasympathetic nervous system activity, with reduced HRV being a well-established marker of chronic stress and autonomic dysfunction [ 10 ]. These physiological and biochemical parameters, when combined with validated psychometric assessments such as the Perceived Stress Scale (PSS) and Generalized Anxiety Disorder-7 (GAD-7), provide a robust framework for evaluating the multidimensional effects of excessive screen exposure [ 11 ]. Digital detox interventions, defined as structured periods of reduced screen exposure, have emerged as potential strategies to mitigate the negative impact of digital overuse on mental and physiological health [ 12 ]. However, existing research is limited to programs that rely predominantly on self-reported psychological outcomes, with minimal emphasis on biochemical and physiological validation. Furthermore, these challenges are particularly pronounced in resource-constrained settings like Pakistan, where access to mental health support systems is limited, and awareness about the impacts of digital overuse remains low. The lack of structured interventions and preventive strategies exacerbates the burden on medical students, who often have no access to digital well-being education or coping mechanisms. This study aims to address these gaps by exploring the impact of a structured digital detox intervention on the mental and physiological health of medical students in Pakistan. By combining psychological assessments with biochemical analysis of stress markers, this research provides an in-depth understanding of the advantages of limited screen time. METHODS Study Design This was a prospective interventional study designed to assess the effects of a structured 2-week digital detox program on mental health, stress-related biochemical markers, and physiological responses among medical students. A mixed-methods approach was employed, integrating quantitative psychometric evaluations, biochemical analyses, and physiological assessments with qualitative insights from participant experiences to comprehensively examine the impact of digital detox. Study Population : The study was conducted at two medical Colleges in Pakistan: Quaid-e-Azam Medical College, Bahawalpur (Public), and Shahida Islam Medical Complex, Lodhran (Private) in September 2024. Sampling Technique Participants were selected using a stratified random sampling technique to ensure equal representation across different academic years and gender groups. Sample Size : The sample size was calculated using a statistical formula for interventional studies. Assuming a medium effect size (Cohen’s d = 0.5), with 80% power and a significance level of 0.05, the required sample size was 240 participants, randomly assigned in a 1:1:1 ratio to one of three groups: Group A (Digital Detox + Alternative Activities, n = 80): Participants underwent a 2-week structured digital detox program, incorporating screen-time restrictions alongside engagement in alternative activities such as mindfulness, exercise, and social interactions. Group B (Screen-Time Reduction Only, n = 80): Participants reduced their digital screen exposure to the same extent as Group A but did not engage in any structured alternative activities. Group C (Control Group, n = 80): Participants continued their regular screen-time habits without any imposed restrictions. Inclusion Criteria The study included medical students between the ages of 18 and 25 years. Participants were required to use digital devices, such as smartphones, tablets, or laptops, for at least four hours daily for either academic or recreational purposes. Additionally, only students who were willing to participate and provided written informed consent were included in the study. Exclusion Criteria Students diagnosed with chronic physical or mental health conditions requiring regular medication were excluded from the study. Additionally, those who attended counseling for mental health issues during the study were not included. Students who refused to adhere to the digital detox protocol were also excluded. Intervention : In the intervention group, recreational screen use (like social media and gaming) was capped at one hour daily, and academic use at two hours. Non-essential screen time was banned from 9:00 AM to 5:00 PM, except for academic needs, and all screens were avoided one hour before bedtime (9:00 PM to 10:00 PM) to improve sleep and nervous system regulation. Participants engaged in alternative activities like mindfulness, exercise, journaling, and in-person socializing, supported by faculty mentors and peer groups for accountability. The screen-time reduction group followed the same screen limits but didn’t participate in alternative activities, helping assess whether screen reduction alone drove improvements. The control group maintained their usual digital habits without restrictions. Compliance was ensured through daily reminders through SMS and email, app-based tracking (e.g., RescueTime, Apple Screen Time), random checks through weekly screenshots of screen-time data, and daily logs documenting digital use, activities, and personal reflections. The framework of this digital detox program is shown in Fig. 1 . Data Collection Biochemical parameters were assessed through blood samples collected before and after the intervention period. Cortisol, catecholamines (epinephrine and norepinephrine), and IL-6 were measured using electrochemiluminescence immunoassay (Cobas e411, Roche Diagnostics) to evaluate the endocrine stress response. C-reactive protein (CRP) was analyzed using the Cobas c303 automated analyzer to assess systemic inflammation. To quantify oxidative stress, MDA levels were measured via the thiobarbituric acid reactive substances (TBARS) assay at 532 nm, while SOD activity was assessed through the NBT reduction assay at 560 nm. CAT activity was determined by the H₂O₂ decomposition assay at 240 nm, providing insights into antioxidant enzyme function during stress. Physiological measurements included heart rate variability (HRV), blood pressure (BP), and pulse rate. Heart rate variability (HRV) was continuously monitored using a wearable ECG-based device (Polar H10) to assess autonomic nervous system regulation. HRV was measured using the standard deviation of normal-to-normal intervals (SDNN), which reflects overall autonomic variability; higher SDNN values indicate better vagal tone and reduced sympathetic stress response. BP was measured daily using an automated digital sphygmomanometer, and the resting pulse rate was manually recorded via radial artery palpation, averaging three consecutive measurements to ensure accuracy. Psychometric assessments were conducted using the Perceived Stress Scale (PSS) and General Anxiety Disorder Scale (GAD-7), administered before and after the stress period, to evaluate changes in stress perception and anxiety levels. Additionally, qualitative data were collected through post-intervention focus group discussions, which explored subjective experiences, challenges, benefits, and behavioral changes during the digital detox program. Data Analysis : Descriptive statistics (mean ± SD) were used to summarize demographic and clinical data. Normality of continuous variables was assessed using the Shapiro-Wilk test. The test revealed non-normal distribution for all key biochemical and psychometric variables: cortisol (W = 0.935, p < 0.001), CRP (W = 0.927, p < 0.001), IL-6 (W = 0.942, p < 0.001), MDA (W = 0.911, p < 0.001), SOD (W = 0.956, p = 0.003), catalase (W = 0.948, p = 0.005), PSS (W = 0.962, p = 0.004), and GAD-7 (W = 0.951, p = 0.006). Based on these findings, non-parametric tests were applied throughout the analysis. Within-group comparisons were conducted using the Wilcoxon signed-rank test, while between-group comparisons across the three study arms were performed using the Kruskal-Wallis test. For multiple comparisons, Bonferroni correction was applied where appropriate to control for type I error. Correlation analyses were carried out using Spearman’s rank correlation due to non-normal distributions, and effect sizes were estimated using the non-parametric equivalent of Cohen’s d (r = Z/√N). Values of r = 0.1, 0.3, and 0.5 were interpreted as small, medium, and large effect sizes, respectively. Thematic analysis was conducted following Braun and Clarke’s six-step framework. Two researchers independently coded the transcripts using NVivo, followed by peer debriefing to refine theme development. Discrepancies in coding were resolved through discussion, and coding consistency was assessed using Cohen’s kappa (κ = 0.82), indicating strong inter-rater reliability. Data saturation was reached by the fourth focus group when no new themes emerged. All statistical analyses were performed using SPSS version 28, with significance set at p < 0.05. Ethical Considerations Ethical approval was granted by the Institutional Review Boards of Quaid-e-Azam Medical College and Shahida Islam Medical College. Participation was voluntary, with no academic penalties for refusal or withdrawal. Faculty involved in the intervention had no academic authority over participants to avoid bias. Participants received clear verbal and written explanations of the study’s purpose, procedures, and confidentiality measures. Written informed consent was obtained, emphasizing their right to withdraw anytime without penalty. Recruitment was conducted through open calls and independent facilitators to prevent undue influence. All data were anonymized and securely stored, with no identifiable details shared in reports or publications. The complete flow of participants from enrollment through allocation, follow-up, and analysis is shown in Fig. 2 . RESULTS A total of 240 medical students participated in the study, with an equal distribution across the three study groups. The mean age of participants was 22.1 years (SD = 2.4 years), with a female predominance (58%, n = 139) and male representation (42%, n = 101). Participants were enrolled across five academic years, ensuring balanced representation. The mean daily screen time across all participants at baseline was 6.7 hours (SD = 1.5 hours). Baseline demographics of study participants are summarized in Table 1 . Table 1 Baseline Demographics of Study Participants Variable Total (n = 240) Digital Detox + Alternative Activities (n = 80) Screen-Time Reduction Only (n = 80) Control Group (n = 80) Age (years) 22.1 (2.4) 22.3 (2.5) 22.0 (2.3) 21.9 (2.4) Female (n, %) 139 (58%) 46 (57%) 47 (59%) 46 (58%) Male (n, %) 101 (42%) 34 (43%) 33 (41%) 34 (42%) Academic Year 1 (n, %) 48 (20%) 15 (19%) 17 (21%) 16 (20%) Academic Year 2 (n, %) 53 (22%) 18 (23%) 17 (22%) 18 (21%) Academic Year 3 (n, %) 43 (18%) 14 (18%) 15 (19%) 14 (17%) Academic Year 4 (n, %) 48 (20%) 16 (20%) 15 (19%) 17 (21%) Academic Year 5 (n, %) 48 (20%) 17 (20%) 16 (19%) 15 (21%) Screen Time (hours) 6.7 (1.5) 6.6 (1.4) 6.7 (1.5) 6.8 (1.6) Physiological Measurements : The average systolic blood pressure (SBP) for all participants was 121.5 mmHg (SD = 10.2), and the average diastolic blood pressure (DBP) was 77.3 mmHg (SD = 7.9). Participants in the digital detox + alternative activities group showed a highly significant reduction in SBP to 115.6 mmHg (SD = 8.9; p < 0.001; r = 0.48) and in DBP to 73.9 mmHg (SD = 6.3; p < 0.001; r = 0.42) post-intervention, compared to the screen-time reduction-only group (SBP: 119.2 mmHg, SD = 9.4; DBP: 75.8 mmHg, SD = 7.1). The control group showed no significant changes. Heart rate variability (HRV), measured by SDNN, significantly improved in the digital detox + alternative activities group, with an average of 50.1 ms (SD = 8.4), compared to 44.2 ms (SD = 9.0) in the screen-time reduction group and 39.5 ms (SD = 7.7) in the control group (p < 0.001; r = 0.62). Pulse rate was also significantly reduced in the digital detox + alternative activities group to 71.8 bpm (SD = 6.8), in contrast to 76.5 bpm (SD = 7.1) in the screen-time reduction group and 80.2 bpm (SD = 7.4) in the control group (p < 0.001; r = 0.58). Biochemical Analysis Salivary cortisol, CRP, and IL-6 levels showed statistically significant reductions in the digital detox + alternative activities group (p < 0.001) with large effect sizes (r = 0.70, 0.61, and 0.59, respectively), while the screen-time reduction group showed moderate improvements and the control group remained unchanged. Catecholamines and MDA also improved significantly in the intervention groups (p < 0.05, r = 0.49 and 0.41, respectively). Catalase levels showed a mild increase only in the detox group (p = 0.041; r = 0.28), and SOD changes were not significant (p = 0.187). A summary of biochemical marker changes across groups is presented in Table 2 . Table 2 Changes in Biochemical Markers Pre- and Post-Intervention Variable Digital Detox + Activities (Pre → Post) Screen-Time Reduction Only (Pre → Post) Control Group (Pre → Post) p-value Morning Cortisol (nmol/L) 15.7 (4.4) → 12.8 (3.9) 15.9 (4.5) → 14.2 (4.2) 15.1 (4.8) → 15.0 (4.7) < 0.001 24h Urinary Epinephrine (µg/day) 35.2 (9.4) → 30.1 (8.1) 34.7 (9.3) → 33.9 (9.0) 34.5 (9.5) → 34.4 (9.6) < 0.05 24h Urinary Norepinephrine (µg/day) 110.5 (24.6) → 102.8 (22.8) 109.9 (22.6) → 104.4 (22.1) 110.3 (23.8) → 109.8 (22.8) < 0.05 MDA (nmol/mL) 2.9 (0.9) → 2.1 (0.5) 2.8 (0.8) → 2.6 (0.6) 2.9 (0.8) → 2.9 (0.7) < 0.01 SOD (U/mg protein) 50.7 (12.6) → 51.1 (11.5) 50.3 (12.4) → 50.3 (10.9) 50.1 (12.3) → 49.8 (9.8) 0.187 Catalase (U/mg protein) 30.3 (7.8) → 32.3 (8.0) 30.5 (7.6) → 31.2 (7.1) 30.2 (7.7) → 30.0 (7.3) 0.041 CRP (mg/L) 2.9 (0.8) → 1.7 (0.6) 2.8 (0.9) → 2.3 (0.7) 2.7 (0.9) → 2.7 (0.8) < 0.001 IL-6 (pg/mL) 4.3 (1.2) → 2.6 (1.0) 4.1 (1.3) → 2.8 (1.1) 4.2 (1.3) → 4.2 (1.2) < 0.001 * Data are presented as Mean (SD). Shapiro-Wilk test indicated non-normal distribution for all biochemical variables (p < 0.01). Between-group comparisons were performed using the Kruskal-Wallis test with Bonferroni correction. Within-group changes were assessed using the Wilcoxon signed-rank test. Psychometric Assessments Perceived Stress Scale (PSS) and GAD-7 scores significantly decreased in the digital detox + alternative activities group (p < 0.001) with large effect sizes (r = 0.65 and 0.61, respectively). The screen-time reduction group showed moderate improvement, while the control group exhibited no meaningful change. Details of pre- and post-intervention psychometric scores across all groups are summarized in Table 3 . Table 3 Changes in Psychometric Scores Pre- and Post-Intervention Variable Digital Detox + Activities (Pre → Post) Screen-Time Reduction Only (Pre → Post) Control Group (Pre → Post) p-value PSS Score 26.2 (4.2) → 17.8 (3.5) 26.0 (4.3) → 21.6 (4.2) 25.9 (4.5) → 25.7 (4.5) < 0.001 GAD-7 Score 12.4 (3.7) → 7.1 (3.0) 12.2 (3.6) → 10.3 (3.4) 12.1 (3.7) → 12.0 (3.7) < 0.001 * Data are presented as Mean (SD). PSS and GAD-7 scores were non-normally distributed based on the Shapiro-Wilk test (p < 0.01). Kruskal-Wallis test with Bonferroni correction was used for between-group comparisons; Wilcoxon signed-rank test was used for within-group comparisons. Digital usage Data from the app tracking software revealed significant differences in screen time between the intervention and control groups. Participants in the intervention group lowered their daily screen usage by 58% from 7.3 hours to 3.1 hours. The control group reported minimal changes as they reduced their screen time from 7.2 to 6.8 hours only. This reduction aligns with the observed improvements in mental health and stress markers, reinforcing the efficacy of the digital detox program. Tracking data confirmed adherence to screen time limits in the intervention group. Correlation Analysis Correlation analysis revealed significant associations between device usage patterns, mental health scores, and biochemical stress markers, as shown in Table 4 . Spearman’s rank correlation revealed significant associations between baseline screen time and stress-related markers. Positive correlations were observed between screen time and cortisol (r = + 0.72, p < 0.001), CRP (r = + 0.65, p < 0.001), and PSS scores (r = + 0.68, p < 0.001), while post-intervention screen time showed inverse correlations with these outcomes, supporting the observed improvements. Table 4 Correlation Analysis Between Screen Time, Mental Health Scores, and Biochemical Stress Markers Variable Baseline Screen Time (r) Post-Intervention Screen Time (r) p-value Significance Perceived Stress Scale (PSS) + 0.68 -0.61 < 0.001 Highly Significant General Anxiety Disorder (GAD-7) + 0.63 -0.58 < 0.001 Highly Significant Cortisol + 0.72 -0.64 < 0.001 Highly Significant Catecholamines + 0.50 -0.41 0.01 Moderate Significance C-reactive Protein (CRP) + 0.65 -0.57 < 0.001 Highly Significant Interleukin-6 (IL-6) + 0.60 -0.52 < 0.01 Highly Significant Malondialdehyde (MDA) + 0.52 -0.44 < 0.01 Moderate Significance Superoxide Dismutase (SOD) -0.10 + 0.12 0.42 Not Significant Catalase -0.18 + 0.21 0.08 Slight Significance Heart Rate Variability (HRV) -0.58 + 0.62 < 0.001 Highly Significant Systolic Blood Pressure (SBP) + 0.53 -0.50 < 0.001 Highly Significant Diastolic Blood Pressure (DBP) + 0.49 -0.46 < 0.001 Highly Significant Pulse Rate + 0.56 -0.51 < 0.001 Highly Significant * Spearman’s rank correlation coefficients are reported due to non-normality of all variables as confirmed by Shapiro-Wilk test (p < 0.01). Qualitative Insights The post-program group meetings showed that the participants in the treatment group experienced both improved personal habits and mental well-being. Our thematic analysis produced three main themes along with their supporting subthemes that represent what participants learned from their digital detox experience. The findings show participants gained better attention skills with improved sleep and felt less anxious as confirmed by what these individuals shared about their experiences. Table 5 outlines the qualitative themes and subthemes derived from participant experiences following the intervention. Table 5 Themes and Subthemes identified Main Theme Subthemes Illustrative Quote Unplugging the Habit - Realizing excessive screen time - Emotional discomfort during restriction - Confronting dependency “I didn’t realize how hooked I was until I had to actually stop using my phone. I felt agitated for the first few days.” (Participant 43, Year 3) “My screen time was shocking. I had no idea it was over 9 hours.” (Participant 58, Year 2) Mental Reset - Improved clarity and focus - Emotional stability - Reduced anxiety and overstimulation “By the fourth day, I felt mentally lighter... like a fog had lifted.” (Participant 112, Year 4) “I wasn’t as anxious as I used to be. It’s like my brain stopped buzzing.” (Participant 165, Year 4) Regaining Academic Control - Fewer distractions during study - Improved concentration span - Better academic scheduling “I was able to study for 2 hours straight without checking my phone. That’s a big deal for me.” (Participant 89, Year 3) “I finally finished a topic without having to re-read it five times.” (Participant 133, Year 1) Reclaiming Rest - Increased awareness of bedtime use - Earlier sleep initiation - Enhanced sleep quality “I started sleeping at 10:30 pm without scrolling for hours. I woke up fresher.” (Participant 176, Year 2) “I had dreams again—like real ones. Probably because I wasn’t watching anything before bed.” (Participant 121, Year 3) Reconnecting Offline - Playing board games and group discussions - Rebuilding face-to-face communication - Improved family time “We played Ludo after dinner for the first time in years. It made our evenings feel more real.” (Participant 203, Year 4) “Talking to my sister instead of texting her from the next room—felt weird at first, but good.” (Participant 97, Year 2) DISCUSSION The findings of this study indicate that a 2-week digital detox significantly reduced psychological stress and anxiety, accompanied by notable decreases in physiological stress markers. The most pronounced biochemical changes were reductions in cortisol, CRP, IL-6, catecholamines, and MDA, with minor changes in catalase and no significant alterations in SOD. HRV improved, while BP and pulse rate decreased, reinforcing the role of autonomic regulation in stress mitigation. However, the magnitude of improvement varied among markers, likely reflecting differences in physiological adaptation to stress recovery. The substantial reduction in cortisol aligns with previous studies demonstrating that behavioral stress-reduction interventions rapidly modulate HPA axis activity [ 13 , 14 ]. Similarly, the decrease in CRP and IL-6 corroborates prior findings suggesting that digital overstimulation contributes to chronic low-grade inflammation, which can be reversed by limiting screen exposure [ 8 , 15 , 16 ]. The moderate decrease in catecholamines is consistent with literature indicating that sympathetic nervous system adaptations occur more gradually compared to cortisol-driven responses [ 17 – 19 ]. However, some studies report minimal autonomic changes following behavioral interventions, suggesting that additional strategies, such as structured relaxation techniques, may be required for full autonomic normalization [ 20 ]. The observed decline in MDA suggests a reduction in oxidative stress following digital detox, supporting existing evidence that stress-induced ROS generation contributes to lipid peroxidation [ 8 , 9 , 19 ]. While catalase increased slightly, the lack of significant change in SOD suggests that endogenous antioxidant defenses may require longer adaptation periods. These findings extend prior research indicating that short-term digital detox primarily mitigates oxidative damage rather than enhancing antioxidant enzyme activity [ 9 , 19 ]. In contrast, some studies depict a significant improvement in SOD levels after stress relief [ 21 ]. Consistent with previous studies, the intervention led to improvements in perceived stress and anxiety, reinforcing the link between biochemical and psychological well-being [ 11 , 12 ]. Similar to our study, a review states that participants engaging in alternative activities alongside digital detox exhibited the most substantial benefits, aligning with evidence that behavioral engagement enhances stress resilience beyond mere screen-time reduction [ 22 , 23 ]. However, improvements in sleep quality were modest despite nighttime screen restriction, previous literature has shown a marked improvement in sleep quality [ 24 ]. This suggests that additional lifestyle factors, such as caffeine intake and sleep hygiene, influence sleep regulation [ 25 ]. The qualitative results confirmed the statistical decreases in stress and anxiety. The participants experienced better mental clarity and increased awareness about their screen usage after the detox, which matched the positive changes in PSS and HRV results. The agreement between these different data points confirms that behavioral modifications led to better physiological results. Future research should aim to build on these findings by exploring several important areas. First, investigating the long-term sustainability of digital detox effects through extended follow-ups would provide insights into the durability of its benefits. Second, incorporating wearable technology could offer more precise and objective tracking of participant compliance and behavior. Third, future studies should include more diverse populations and institutional settings to enhance the generalizability of results. Additionally, it is important to explore potential gender differences in stress responses to digital detox, as this could inform more personalized interventions. Lastly, evaluating the impact of structured sleep interventions in combination with digital detox strategies may uncover synergistic effects that further promote mental well-being and academic performance. Strengths and Limitations A major strength of this study is that it uses an objective approach in conjunction with the proposed model to validate the effects of digital detox by employing psychological assessments, biochemical stress markers, and physiological monitoring (HRV, BP, pulse rate). The use of three study groups (detox + alternative activities, detox only, and control) also helps to disentangle whether behavioral engagement is important in stress reduction over and above screen-time restriction. However, some limitations should be noted. Whether these effects are sustainable in the long run is unknown because follow-up assessments were not done. It is not known whether these benefits last for more than 15 days. Self-reports and app-based logging were used to track compliance, which was strong, but could have been affected by reporting biases; wearable technology would have increased accuracy. Since this study was carried out in two medical colleges in Pakistan, the findings may not be generalizable to other populations. Investigation into a wider population and context would be useful to establish the generalizability of the findings. Lastly, the possibility of a placebo effect cannot be excluded; participants may have believed in the curative properties of the treatment, which may have affected their stress and anxiety scores. While physiological markers minimized bias, future studies should incorporate sham interventions or blinding to control for expectancy effects. CONCLUSION This study provides strong evidence that a 2-week structured digital detox intervention significantly reduces stress, anxiety, and physiological stress markers, particularly when combined with alternative activities. The observed biochemical improvements in oxidative stress, inflammation, and autonomic balance highlight the physiological benefits of reducing digital overstimulation. While digital detox effectively mitigates acute stress, future research should explore its long-term sustainability and broader applicability across diverse populations. Declarations Ethics approval and consent to participate: The study was approved by the Institutional Review Boards of Quaid-e-Azam Medical College, Bahawalpur, and Shahida Islam Medical College, Lodhran. All participants provided written informed consent. Participation was voluntary, and withdrawal was allowed at any stage without penalty. Consent for publication: Not applicable. Availability of data and materials: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions: S.F. conceptualized the study and provided overall supervision. S.R. performed data collection, analysis, and figure/table preparation. S.B. conducted the literature review and contributed to referencing and psychometric assessment. M.F.A. proofread the manuscript and gave final approval. M.I. supported data collection and statistical analysis. All authors reviewed and approved the final manuscript. Acknowledgements: The authors acknowledge the use of AI-based tools for language enhancement. All research content, analysis, and interpretation were developed and verified by the authors. 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Blood pressure and heart rate variability to assess autonomic response to an acute bout of high-intensity interval exercise in healthy young adults. Physiol Rep. 2024;12:e16142. 10.14814/phy2.16142 . Jauhar AA, Ashraf S, Mubashir A, Sharif M, Farooq K, Gardezi AA. Assessing the effect of digital detoxification on psychological burden among adults in Pakistan. Bull Bus Econ. 2024;14(1):24–9. 10.61506/01.00574 . Alanzi TM, Arif W, Aqeeli R, et al. Examining the impact of digital detox interventions on anxiety and depression levels among young adults. Cureus. 2024;16(12):e75625. 10.7759/cureus.75625 . Knezevic E, Nenic K, Milanovic V, Knezevic NN. The role of cortisol in chronic stress, neurodegenerative diseases, and psychological disorders. Cells. 2023;12(23):2726. 10.3390/cells12232726 . PMID: 38067154; PMCID: PMC10706127. Rogerson O, Wilding S, Prudenzi A, O’Connor DB. Effectiveness of stress management interventions to change cortisol levels: A systematic review and meta-analysis. Psychoneuroendocrinology. 2024;159:106415. 10.1016/j.psyneuen.2023.106415 . Li Y, Yue Y, Chen S, Jiang W, Xu Z, Chen G, et al. Combined serum IL-6, C-reactive protein, and cortisol may distinguish patients with anhedonia in major depressive disorder. Front Mol Neurosci. 2022;15:935031. 10.3389/fnmol.2022.935031 . PMID: 36090246; PMCID: PMC9449462. Amer SAAM, Fouad AM, El-Samahy M, Hashem AA, Saati AA, Sarhan AA, et al. Mental stress, anxiety, and depressive symptoms and interleukin-6 levels among healthcare workers during the COVID-19 pandemic. J Prim Care Community Health. 2021 Jan-Dec;12:21501327211027432. 10.1177/21501327211027432 . PMID: 34166137; PMCID: PMC8239961. Ross JA, Van Bockstaele EJ. The role of catecholamines in modulating responses to stress: Sex-specific patterns, implications, and therapeutic potential for post-traumatic stress disorder and opiate withdrawal. Eur J Neurosci. 2020;52(1):2429–65. 10.1111/ejn.14714 . PMID: 32125035; PMCID: PMC8351794. Dutheil F, Fournier A, Perrier C, et al. Impact of 24 h shifts on urinary catecholamine in emergency physicians: A cross-over randomized trial. Sci Rep. 2024;14:7329. 10.1038/s41598-024-58070-2 . Shaikh SN, Uqaili AA, Shah T, Memon A, Shaikh F, Dars S. Biochemical and physiological predictors of stress-induced hypertension among medical students: A cross-sectional study. J Peoples Univ Med Health Sci Women. 2024;14(2):92–100. 10.46536/jpumhs/2024/14.02.522 . Bentley TGK, D'Andrea-Penna G, Rakic M, Arce N, LaFaille M, Berman R, et al. Breathing practices for stress and anxiety reduction: Conceptual framework of implementation guidelines based on a systematic review of the published literature. Brain Sci. 2023;13(12):1612. 10.3390/brainsci13121612 . PMID: 38137060; PMCID: PMC10741869. Yilgor A, Demir C. Determination of oxidative stress level and some antioxidant activities in refractory epilepsy patients. Sci Rep. 2024;14:6688. 10.1038/s41598-024-57224-6 . Anandpara G, Kharadi A, Vidja P, Chauhan Y, Mahajan S, Patel J. A comprehensive review on digital detox: A newer health and wellness trend in the current era. Cureus. 2024;16(4):e58719. 10.7759/cureus.58719 . PMID: 38779255; PMCID: PMC11109987. Chen TL, Chang SC, Hsieh HF, Huang CY, Chuang JH, Wang HH. Effects of mindfulness-based stress reduction on sleep quality and mental health for insomnia patients: A meta-analysis. J Psychosom Res. 2020;135:110144. 10.1016/j.jpsychores.2020.110144 . Wakui N, Togawa C, Ichikawa K, Matsuoka R, Watanabe M, Okami A, et al. Relieving psychological stress and improving sleep quality by bergamot essential oil use before bedtime and upon awakening: A randomized crossover trial. Complement Ther Med. 2023;77:102976. 10.1016/j.ctim.2023.102976 . Sejbuk M, Mirończuk-Chodakowska I, Witkowska AM. Sleep quality: A narrative review on nutrition, stimulants, and physical activity as important factors. Nutrients. 2022;14(9):1912. 10.3390/nu14091912 . PMID: 35565879; PMCID: PMC9103473. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Dec, 2025 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 13 Aug, 2025 Reviews received at journal 23 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviews received at journal 10 Jun, 2025 Reviewers agreed at journal 30 May, 2025 Reviewers invited by journal 30 May, 2025 Editor assigned by journal 14 May, 2025 Submission checks completed at journal 14 May, 2025 First submitted to journal 01 May, 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. 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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-6572563","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464175436,"identity":"ecd8a5f8-6f04-4a98-bed0-cbe99f5bbe19","order_by":0,"name":"Soufia Farrukh","email":"","orcid":"","institution":"Quaid-e-Azam Medical College","correspondingAuthor":false,"prefix":"","firstName":"Soufia","middleName":"","lastName":"Farrukh","suffix":""},{"id":464175438,"identity":"4f6a82d0-cb70-430e-aada-548b20ad7010","order_by":1,"name":"Sara Reza","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDADfijN2MDAwEycFskGkrUYHCBWi3z72YcfGMq2yRsfT3664QeDjeyGA7yHDfAafibdWILh3G3DbWeemd3sYUgz3nCALzkBrxaGNAYJxrbbCWY3EsxuMzAcTtxwgMf4AF6H9T9j/gHSYjwj/RtQy3/CWhhupLGBbTGQyAHZcgCsBb/Dbjxjs0gA+mXGmTdlN3sMko1nHuZLxut9+f405hsfym7L87enb7vxo8JOtu9472EJvA4DgQQ2MAmyFIiZeQhqAAK4FjAgSssoGAWjYBSMIAAAH0lMsVvzF6sAAAAASUVORK5CYII=","orcid":"","institution":"Quaid-e-Azam Medical College","correspondingAuthor":true,"prefix":"","firstName":"Sara","middleName":"","lastName":"Reza","suffix":""},{"id":464175442,"identity":"19111c34-2e38-4303-b049-c7fcff8bd553","order_by":2,"name":"Sara Babar","email":"","orcid":"","institution":"Quaid-e-Azam Medical College","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Babar","suffix":""},{"id":464175445,"identity":"12ecf730-e482-44f4-bfdf-9ced6c0a1bc8","order_by":3,"name":"Mazhar Faiz Alam","email":"","orcid":"","institution":"Quaid-e-Azam Medical College","correspondingAuthor":false,"prefix":"","firstName":"Mazhar","middleName":"Faiz","lastName":"Alam","suffix":""},{"id":464175449,"identity":"f7520198-667b-4aad-aade-9ddb5f3a9c91","order_by":4,"name":"Mavra Imtiaz","email":"","orcid":"","institution":"Shahida Islam Medical Complex","correspondingAuthor":false,"prefix":"","firstName":"Mavra","middleName":"","lastName":"Imtiaz","suffix":""}],"badges":[],"createdAt":"2025-05-01 14:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6572563/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6572563/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12909-025-08267-4","type":"published","date":"2025-12-29T15:58:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83895065,"identity":"0c64a349-5dbf-4e84-b256-441528fa99c6","added_by":"auto","created_at":"2025-06-04 08:40:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41715,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFramework of Digital Detox Program\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6572563/v1/0a1afdd193b772a123202440.png"},{"id":83895063,"identity":"2347b93e-b63c-4495-b212-2d9487259dc6","added_by":"auto","created_at":"2025-06-04 08:40:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63814,"visible":true,"origin":"","legend":"\u003cp\u003eCONSORT-style flow diagram showing participant recruitment, allocation, follow-up, and inclusion in final analysis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6572563/v1/b60b5016ffc3272cc0eb1b15.png"},{"id":99546096,"identity":"2ea52425-d7d9-40c2-b5b2-6c177507f89f","added_by":"auto","created_at":"2026-01-05 16:10:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1205473,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6572563/v1/24daf2a9-807d-43a2-ad9b-60c84b68b8fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Screens to Serenity: Evaluating the Effect of Digital Detox on Mental and Physiological Health","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe increasing reliance on digital technology has transformed medical education, offering unprecedented access to online learning resources, virtual simulations, and digital communication platforms. However, excessive screen time has raised concerns regarding its potential adverse effects on mental and physiological health, particularly among medical students who are already subjected to high academic stress [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Pakistan, internet penetration has reached 45.7%, with over 111.0\u0026nbsp;million active users as per 2024 data [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Studies indicate that prolonged digital engagement is associated with increased stress, anxiety, sleep disturbances, and cognitive overload, leading to impaired concentration, emotional exhaustion, and reduced academic performance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond psychological consequences, chronic exposure to digital screens can trigger physiological stress responses, primarily mediated through the hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system (ANS) dysregulation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Persistent activation of the HPA axis results in elevated secretion of cortisol and catecholamines (epinephrine, norepinephrine), contributing to heightened sympathetic activity, increased heart rate, and elevated blood pressure [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Prolonged digital overuse is also linked to increased systemic inflammation, as evidenced by elevated levels of C-reactive protein (CRP) and interleukin-6 (IL-6), both of which have been implicated in stress-related immune dysregulation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, oxidative stress plays a crucial role in the physiological impact of chronic stress. Increased metabolic activity and prolonged sympathetic stimulation lead to excessive production of reactive oxygen species (ROS), resulting in oxidative damage. Malondialdehyde (MDA), a byproduct of lipid peroxidation, serves as a biomarker of oxidative stress, while superoxide dismutase (SOD) and catalase represent key enzymatic antioxidants responsible for neutralizing ROS. Dysregulation of these oxidative stress markers has been associated with cognitive decline, neuroinflammation, and increased susceptibility to stress-related disorders [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo comprehensively assess the physiological impact of digital overuse, heart rate variability (HRV), blood pressure (BP), and pulse rate are widely recognized as objective indicators of autonomic function. HRV, in particular, reflects the balance between sympathetic and parasympathetic nervous system activity, with reduced HRV being a well-established marker of chronic stress and autonomic dysfunction [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These physiological and biochemical parameters, when combined with validated psychometric assessments such as the Perceived Stress Scale (PSS) and Generalized Anxiety Disorder-7 (GAD-7), provide a robust framework for evaluating the multidimensional effects of excessive screen exposure [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDigital detox interventions, defined as structured periods of reduced screen exposure, have emerged as potential strategies to mitigate the negative impact of digital overuse on mental and physiological health [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, existing research is limited to programs that rely predominantly on self-reported psychological outcomes, with minimal emphasis on biochemical and physiological validation. Furthermore, these challenges are particularly pronounced in resource-constrained settings like Pakistan, where access to mental health support systems is limited, and awareness about the impacts of digital overuse remains low. The lack of structured interventions and preventive strategies exacerbates the burden on medical students, who often have no access to digital well-being education or coping mechanisms.\u003c/p\u003e \u003cp\u003eThis study aims to address these gaps by exploring the impact of a structured digital detox intervention on the mental and physiological health of medical students in Pakistan. By combining psychological assessments with biochemical analysis of stress markers, this research provides an in-depth understanding of the advantages of limited screen time.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e \u003cstrong\u003eStudy Design\u003c/strong\u003e \u003cp\u003eThis was a prospective interventional study designed to assess the effects of a structured 2-week digital detox program on mental health, stress-related biochemical markers, and physiological responses among medical students. A mixed-methods approach was employed, integrating quantitative psychometric evaluations, biochemical analyses, and physiological assessments with qualitative insights from participant experiences to comprehensively examine the impact of digital detox.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy Population\u003c/b\u003e: The study was conducted at two medical Colleges in Pakistan: Quaid-e-Azam Medical College, Bahawalpur (Public), and Shahida Islam Medical Complex, Lodhran (Private) in September 2024.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSampling Technique\u003c/strong\u003e \u003cp\u003eParticipants were selected using a stratified random sampling technique to ensure equal representation across different academic years and gender groups.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSample Size\u003c/b\u003e: The sample size was calculated using a statistical formula for interventional studies. Assuming a medium effect size (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.5), with 80% power and a significance level of 0.05, the required sample size was 240 participants, randomly assigned in a 1:1:1 ratio to one of three groups:\u003c/p\u003e \u003cp\u003e \u003cb\u003eGroup A\u003c/b\u003e (Digital Detox\u0026thinsp;+\u0026thinsp;Alternative Activities, n\u0026thinsp;=\u0026thinsp;80): Participants underwent a 2-week structured digital detox program, incorporating screen-time restrictions alongside engagement in alternative activities such as mindfulness, exercise, and social interactions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGroup B\u003c/b\u003e (Screen-Time Reduction Only, n\u0026thinsp;=\u0026thinsp;80): Participants reduced their digital screen exposure to the same extent as Group A but did not engage in any structured alternative activities.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGroup C\u003c/b\u003e (Control Group, n\u0026thinsp;=\u0026thinsp;80): Participants continued their regular screen-time habits without any imposed restrictions.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInclusion Criteria\u003c/strong\u003e \u003cp\u003eThe study included medical students between the ages of 18 and 25 years. Participants were required to use digital devices, such as smartphones, tablets, or laptops, for at least four hours daily for either academic or recreational purposes. Additionally, only students who were willing to participate and provided written informed consent were included in the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExclusion Criteria\u003c/strong\u003e \u003cp\u003eStudents diagnosed with chronic physical or mental health conditions requiring regular medication were excluded from the study. Additionally, those who attended counseling for mental health issues during the study were not included. Students who refused to adhere to the digital detox protocol were also excluded.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003eIntervention\u003c/b\u003e: In the intervention group, recreational screen use (like social media and gaming) was capped at one hour daily, and academic use at two hours. Non-essential screen time was banned from 9:00 AM to 5:00 PM, except for academic needs, and all screens were avoided one hour before bedtime (9:00 PM to 10:00 PM) to improve sleep and nervous system regulation. Participants engaged in alternative activities like mindfulness, exercise, journaling, and in-person socializing, supported by faculty mentors and peer groups for accountability.\u003c/p\u003e \u003cp\u003eThe screen-time reduction group followed the same screen limits but didn\u0026rsquo;t participate in alternative activities, helping assess whether screen reduction alone drove improvements. The control group maintained their usual digital habits without restrictions.\u003c/p\u003e \u003cp\u003eCompliance was ensured through daily reminders through SMS and email, app-based tracking (e.g., RescueTime, Apple Screen Time), random checks through weekly screenshots of screen-time data, and daily logs documenting digital use, activities, and personal reflections. The framework of this digital detox program is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData Collection\u003c/strong\u003e \u003cp\u003eBiochemical parameters were assessed through blood samples collected before and after the intervention period. Cortisol, catecholamines (epinephrine and norepinephrine), and IL-6 were measured using electrochemiluminescence immunoassay (Cobas e411, Roche Diagnostics) to evaluate the endocrine stress response. C-reactive protein (CRP) was analyzed using the Cobas c303 automated analyzer to assess systemic inflammation. To quantify oxidative stress, MDA levels were measured via the thiobarbituric acid reactive substances (TBARS) assay at 532 nm, while SOD activity was assessed through the NBT reduction assay at 560 nm. CAT activity was determined by the H₂O₂ decomposition assay at 240 nm, providing insights into antioxidant enzyme function during stress. Physiological measurements included heart rate variability (HRV), blood pressure (BP), and pulse rate. Heart rate variability (HRV) was continuously monitored using a wearable ECG-based device (Polar H10) to assess autonomic nervous system regulation. HRV was measured using the standard deviation of normal-to-normal intervals (SDNN), which reflects overall autonomic variability; higher SDNN values indicate better vagal tone and reduced sympathetic stress response. BP was measured daily using an automated digital sphygmomanometer, and the resting pulse rate was manually recorded via radial artery palpation, averaging three consecutive measurements to ensure accuracy. Psychometric assessments were conducted using the Perceived Stress Scale (PSS) and General Anxiety Disorder Scale (GAD-7), administered before and after the stress period, to evaluate changes in stress perception and anxiety levels. Additionally, qualitative data were collected through post-intervention focus group discussions, which explored subjective experiences, challenges, benefits, and behavioral changes during the digital detox program.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003eData Analysis\u003c/b\u003e: Descriptive statistics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) were used to summarize demographic and clinical data. Normality of continuous variables was assessed using the Shapiro-Wilk test. The test revealed non-normal distribution for all key biochemical and psychometric variables: cortisol (W\u0026thinsp;=\u0026thinsp;0.935, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CRP (W\u0026thinsp;=\u0026thinsp;0.927, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), IL-6 (W\u0026thinsp;=\u0026thinsp;0.942, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MDA (W\u0026thinsp;=\u0026thinsp;0.911, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SOD (W\u0026thinsp;=\u0026thinsp;0.956, p\u0026thinsp;=\u0026thinsp;0.003), catalase (W\u0026thinsp;=\u0026thinsp;0.948, p\u0026thinsp;=\u0026thinsp;0.005), PSS (W\u0026thinsp;=\u0026thinsp;0.962, p\u0026thinsp;=\u0026thinsp;0.004), and GAD-7 (W\u0026thinsp;=\u0026thinsp;0.951, p\u0026thinsp;=\u0026thinsp;0.006). Based on these findings, non-parametric tests were applied throughout the analysis. Within-group comparisons were conducted using the Wilcoxon signed-rank test, while between-group comparisons across the three study arms were performed using the Kruskal-Wallis test. For multiple comparisons, Bonferroni correction was applied where appropriate to control for type I error. Correlation analyses were carried out using Spearman\u0026rsquo;s rank correlation due to non-normal distributions, and effect sizes were estimated using the non-parametric equivalent of Cohen\u0026rsquo;s d (r\u0026thinsp;=\u0026thinsp;Z/\u0026radic;N). Values of r\u0026thinsp;=\u0026thinsp;0.1, 0.3, and 0.5 were interpreted as small, medium, and large effect sizes, respectively. Thematic analysis was conducted following Braun and Clarke\u0026rsquo;s six-step framework. Two researchers independently coded the transcripts using NVivo, followed by peer debriefing to refine theme development. Discrepancies in coding were resolved through discussion, and coding consistency was assessed using Cohen\u0026rsquo;s kappa (κ\u0026thinsp;=\u0026thinsp;0.82), indicating strong inter-rater reliability. Data saturation was reached by the fourth focus group when no new themes emerged. All statistical analyses were performed using SPSS version 28, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical Considerations\u003c/strong\u003e \u003cp\u003e Ethical approval was granted by the Institutional Review Boards of Quaid-e-Azam Medical College and Shahida Islam Medical College. Participation was voluntary, with no academic penalties for refusal or withdrawal. Faculty involved in the intervention had no academic authority over participants to avoid bias. Participants received clear verbal and written explanations of the study\u0026rsquo;s purpose, procedures, and confidentiality measures. Written informed consent was obtained, emphasizing their right to withdraw anytime without penalty. Recruitment was conducted through open calls and independent facilitators to prevent undue influence. All data were anonymized and securely stored, with no identifiable details shared in reports or publications.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe complete flow of participants from enrollment through allocation, follow-up, and analysis is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 240 medical students participated in the study, with an equal distribution across the three study groups. The mean age of participants was 22.1 years (SD\u0026thinsp;=\u0026thinsp;2.4 years), with a female predominance (58%, n\u0026thinsp;=\u0026thinsp;139) and male representation (42%, n\u0026thinsp;=\u0026thinsp;101). Participants were enrolled across five academic years, ensuring balanced representation. The mean daily screen time across all participants at baseline was 6.7 hours (SD\u0026thinsp;=\u0026thinsp;1.5 hours). Baseline demographics of study participants are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Demographics of Study Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;240)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital Detox\u0026thinsp;+\u0026thinsp;Alternative Activities (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScreen-Time Reduction Only (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControl Group (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.1 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.3 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.0 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.9 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (58%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Year 1 (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Year 2 (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Year 3 (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Year 4 (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Year 5 (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScreen Time (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.8 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePhysiological Measurements\u003c/b\u003e: The average systolic blood pressure (SBP) for all participants was 121.5 mmHg (SD\u0026thinsp;=\u0026thinsp;10.2), and the average diastolic blood pressure (DBP) was 77.3 mmHg (SD\u0026thinsp;=\u0026thinsp;7.9). Participants in the digital detox\u0026thinsp;+\u0026thinsp;alternative activities group showed a highly significant reduction in SBP to 115.6 mmHg (SD\u0026thinsp;=\u0026thinsp;8.9; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.48) and in DBP to 73.9 mmHg (SD\u0026thinsp;=\u0026thinsp;6.3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.42) post-intervention, compared to the screen-time reduction-only group (SBP: 119.2 mmHg, SD\u0026thinsp;=\u0026thinsp;9.4; DBP: 75.8 mmHg, SD\u0026thinsp;=\u0026thinsp;7.1). The control group showed no significant changes. Heart rate variability (HRV), measured by SDNN, significantly improved in the digital detox\u0026thinsp;+\u0026thinsp;alternative activities group, with an average of 50.1 ms (SD\u0026thinsp;=\u0026thinsp;8.4), compared to 44.2 ms (SD\u0026thinsp;=\u0026thinsp;9.0) in the screen-time reduction group and 39.5 ms (SD\u0026thinsp;=\u0026thinsp;7.7) in the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.62). Pulse rate was also significantly reduced in the digital detox\u0026thinsp;+\u0026thinsp;alternative activities group to 71.8 bpm (SD\u0026thinsp;=\u0026thinsp;6.8), in contrast to 76.5 bpm (SD\u0026thinsp;=\u0026thinsp;7.1) in the screen-time reduction group and 80.2 bpm (SD\u0026thinsp;=\u0026thinsp;7.4) in the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.58).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBiochemical Analysis\u003c/strong\u003e \u003cp\u003eSalivary cortisol, CRP, and IL-6 levels showed statistically significant reductions in the digital detox\u0026thinsp;+\u0026thinsp;alternative activities group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with large effect sizes (r\u0026thinsp;=\u0026thinsp;0.70, 0.61, and 0.59, respectively), while the screen-time reduction group showed moderate improvements and the control group remained unchanged. Catecholamines and MDA also improved significantly in the intervention groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, r\u0026thinsp;=\u0026thinsp;0.49 and 0.41, respectively). Catalase levels showed a mild increase only in the detox group (p\u0026thinsp;=\u0026thinsp;0.041; r\u0026thinsp;=\u0026thinsp;0.28), and SOD changes were not significant (p\u0026thinsp;=\u0026thinsp;0.187). A summary of biochemical marker changes across groups is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in Biochemical Markers Pre- and Post-Intervention\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital Detox\u0026thinsp;+\u0026thinsp;Activities (Pre \u0026rarr; Post)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScreen-Time Reduction Only (Pre \u0026rarr; Post)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl Group (Pre \u0026rarr; Post)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMorning Cortisol (nmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.7 (4.4) \u0026rarr; 12.8 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.9 (4.5) \u0026rarr; 14.2 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.1 (4.8) \u0026rarr; 15.0 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e24h Urinary Epinephrine (\u0026micro;g/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.2 (9.4) \u0026rarr; 30.1 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.7 (9.3) \u0026rarr; 33.9 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.5 (9.5) \u0026rarr; 34.4 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e24h Urinary Norepinephrine (\u0026micro;g/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110.5 (24.6) \u0026rarr; 102.8 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109.9 (22.6) \u0026rarr; 104.4 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110.3 (23.8) \u0026rarr; 109.8 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMDA (nmol/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.9 (0.9) \u0026rarr; 2.1 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8 (0.8) \u0026rarr; 2.6 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.9 (0.8) \u0026rarr; 2.9 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOD (U/mg protein)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.7 (12.6) \u0026rarr; 51.1 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.3 (12.4) \u0026rarr; 50.3 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.1 (12.3) \u0026rarr; 49.8 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCatalase (U/mg protein)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.3 (7.8) \u0026rarr; 32.3 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.5 (7.6) \u0026rarr; 31.2 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.2 (7.7) \u0026rarr; 30.0 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP (mg/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.9 (0.8) \u0026rarr; 1.7 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8 (0.9) \u0026rarr; 2.3 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.7 (0.9) \u0026rarr; 2.7 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-6 (pg/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.3 (1.2) \u0026rarr; 2.6 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.1 (1.3) \u0026rarr; 2.8 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.2 (1.3) \u0026rarr; 4.2 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*\u003cem\u003eData are presented as Mean (SD). Shapiro-Wilk test indicated non-normal distribution for all biochemical variables (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Between-group comparisons were performed using the Kruskal-Wallis test with Bonferroni correction. Within-group changes were assessed using the Wilcoxon signed-rank test.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003ePsychometric Assessments\u003c/h3\u003e\n\u003cp\u003ePerceived Stress Scale (PSS) and GAD-7 scores significantly decreased in the digital detox\u0026thinsp;+\u0026thinsp;alternative activities group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with large effect sizes (r\u0026thinsp;=\u0026thinsp;0.65 and 0.61, respectively). The screen-time reduction group showed moderate improvement, while the control group exhibited no meaningful change. Details of pre- and post-intervention psychometric scores across all groups are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in Psychometric Scores Pre- and Post-Intervention\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital Detox\u0026thinsp;+\u0026thinsp;Activities (Pre \u0026rarr; Post)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScreen-Time Reduction Only (Pre \u0026rarr; Post)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl Group (Pre \u0026rarr; Post)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePSS Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.2 (4.2) \u0026rarr; 17.8 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.0 (4.3) \u0026rarr; 21.6 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.9 (4.5) \u0026rarr; 25.7 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGAD-7 Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.4 (3.7) \u0026rarr; 7.1 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.2 (3.6) \u0026rarr; 10.3 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.1 (3.7) \u0026rarr; 12.0 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e*\u003c/b\u003e \u003cem\u003eData are presented as Mean (SD). PSS and GAD-7 scores were non-normally distributed based on the Shapiro-Wilk test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Kruskal-Wallis test with Bonferroni correction was used for between-group comparisons; Wilcoxon signed-rank test was used for within-group comparisons.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDigital usage\u003c/strong\u003e \u003cp\u003eData from the app tracking software revealed significant differences in screen time between the intervention and control groups. Participants in the intervention group lowered their daily screen usage by 58% from 7.3 hours to 3.1 hours. The control group reported minimal changes as they reduced their screen time from 7.2 to 6.8 hours only. This reduction aligns with the observed improvements in mental health and stress markers, reinforcing the efficacy of the digital detox program. Tracking data confirmed adherence to screen time limits in the intervention group.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCorrelation Analysis\u003c/strong\u003e \u003cp\u003eCorrelation analysis revealed significant associations between device usage patterns, mental health scores, and biochemical stress markers, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Spearman\u0026rsquo;s rank correlation revealed significant associations between baseline screen time and stress-related markers. Positive correlations were observed between screen time and cortisol (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CRP (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and PSS scores (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while post-intervention screen time showed inverse correlations with these outcomes, supporting the observed improvements.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Analysis Between Screen Time, Mental Health Scores, and Biochemical Stress Markers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline Screen Time (r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-Intervention Screen Time (r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Stress Scale (PSS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighly Significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeneral Anxiety Disorder (GAD-7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighly Significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCortisol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighly Significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCatecholamines\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate Significance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC-reactive Protein (CRP)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighly Significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInterleukin-6 (IL-6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighly Significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMalondialdehyde (MDA)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate Significance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSuperoxide Dismutase (SOD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCatalase\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSlight Significance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeart Rate Variability (HRV)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighly Significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystolic Blood Pressure (SBP)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighly Significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiastolic Blood Pressure (DBP)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighly Significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePulse Rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighly Significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*\u003cem\u003eSpearman\u0026rsquo;s rank correlation coefficients are reported due to non-normality of all variables as confirmed by Shapiro-Wilk test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eQualitative Insights\u003c/h3\u003e\n\u003cp\u003e The post-program group meetings showed that the participants in the treatment group experienced both improved personal habits and mental well-being. Our thematic analysis produced three main themes along with their supporting subthemes that represent what participants learned from their digital detox experience. The findings show participants gained better attention skills with improved sleep and felt less anxious as confirmed by what these individuals shared about their experiences. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e outlines the qualitative themes and subthemes derived from participant experiences following the intervention.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThemes and Subthemes identified\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain Theme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubthemes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIllustrative Quote\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnplugging the Habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Realizing excessive screen time\u003c/p\u003e \u003cp\u003e- Emotional discomfort during restriction\u003c/p\u003e \u003cp\u003e- Confronting dependency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;I didn\u0026rsquo;t realize how hooked I was until I had to actually stop using my phone. I felt agitated for the first few days.\u0026rdquo; (Participant 43, Year 3)\u003c/p\u003e \u003cp\u003e\u0026ldquo;My screen time was shocking. I had no idea it was over 9 hours.\u0026rdquo; (Participant 58, Year 2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental Reset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Improved clarity and focus\u003c/p\u003e \u003cp\u003e- Emotional stability\u003c/p\u003e \u003cp\u003e- Reduced anxiety and overstimulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;By the fourth day, I felt mentally lighter... like a fog had lifted.\u0026rdquo; (Participant 112, Year 4)\u003c/p\u003e \u003cp\u003e\u0026ldquo;I wasn\u0026rsquo;t as anxious as I used to be. It\u0026rsquo;s like my brain stopped buzzing.\u0026rdquo; (Participant 165, Year 4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegaining Academic Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Fewer distractions during study\u003c/p\u003e \u003cp\u003e- Improved concentration span\u003c/p\u003e \u003cp\u003e- Better academic scheduling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;I was able to study for 2 hours straight without checking my phone. That\u0026rsquo;s a big deal for me.\u0026rdquo; (Participant 89, Year 3)\u003c/p\u003e \u003cp\u003e\u0026ldquo;I finally finished a topic without having to re-read it five times.\u0026rdquo; (Participant 133, Year 1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReclaiming Rest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Increased awareness of bedtime use\u003c/p\u003e \u003cp\u003e- Earlier sleep initiation\u003c/p\u003e \u003cp\u003e- Enhanced sleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;I started sleeping at 10:30 pm without scrolling for hours. I woke up fresher.\u0026rdquo; (Participant 176, Year 2)\u003c/p\u003e \u003cp\u003e\u0026ldquo;I had dreams again\u0026mdash;like real ones. Probably because I wasn\u0026rsquo;t watching anything before bed.\u0026rdquo; (Participant 121, Year 3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReconnecting Offline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Playing board games and group discussions\u003c/p\u003e \u003cp\u003e- Rebuilding face-to-face communication\u003c/p\u003e \u003cp\u003e- Improved family time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;We played Ludo after dinner for the first time in years. It made our evenings feel more real.\u0026rdquo; (Participant 203, Year 4)\u003c/p\u003e \u003cp\u003e\u0026ldquo;Talking to my sister instead of texting her from the next room\u0026mdash;felt weird at first, but good.\u0026rdquo; (Participant 97, Year 2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe findings of this study indicate that a 2-week digital detox significantly reduced psychological stress and anxiety, accompanied by notable decreases in physiological stress markers. The most pronounced biochemical changes were reductions in cortisol, CRP, IL-6, catecholamines, and MDA, with minor changes in catalase and no significant alterations in SOD. HRV improved, while BP and pulse rate decreased, reinforcing the role of autonomic regulation in stress mitigation. However, the magnitude of improvement varied among markers, likely reflecting differences in physiological adaptation to stress recovery.\u003c/p\u003e \u003cp\u003eThe substantial reduction in cortisol aligns with previous studies demonstrating that behavioral stress-reduction interventions rapidly modulate HPA axis activity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, the decrease in CRP and IL-6 corroborates prior findings suggesting that digital overstimulation contributes to chronic low-grade inflammation, which can be reversed by limiting screen exposure [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The moderate decrease in catecholamines is consistent with literature indicating that sympathetic nervous system adaptations occur more gradually compared to cortisol-driven responses [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, some studies report minimal autonomic changes following behavioral interventions, suggesting that additional strategies, such as structured relaxation techniques, may be required for full autonomic normalization [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe observed decline in MDA suggests a reduction in oxidative stress following digital detox, supporting existing evidence that stress-induced ROS generation contributes to lipid peroxidation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. While catalase increased slightly, the lack of significant change in SOD suggests that endogenous antioxidant defenses may require longer adaptation periods. These findings extend prior research indicating that short-term digital detox primarily mitigates oxidative damage rather than enhancing antioxidant enzyme activity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In contrast, some studies depict a significant improvement in SOD levels after stress relief [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsistent with previous studies, the intervention led to improvements in perceived stress and anxiety, reinforcing the link between biochemical and psychological well-being [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Similar to our study, a review states that participants engaging in alternative activities alongside digital detox exhibited the most substantial benefits, aligning with evidence that behavioral engagement enhances stress resilience beyond mere screen-time reduction [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, improvements in sleep quality were modest despite nighttime screen restriction, previous literature has shown a marked improvement in sleep quality [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This suggests that additional lifestyle factors, such as caffeine intake and sleep hygiene, influence sleep regulation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The qualitative results confirmed the statistical decreases in stress and anxiety. The participants experienced better mental clarity and increased awareness about their screen usage after the detox, which matched the positive changes in PSS and HRV results. The agreement between these different data points confirms that behavioral modifications led to better physiological results.\u003c/p\u003e \u003cp\u003eFuture research should aim to build on these findings by exploring several important areas. First, investigating the long-term sustainability of digital detox effects through extended follow-ups would provide insights into the durability of its benefits. Second, incorporating wearable technology could offer more precise and objective tracking of participant compliance and behavior. Third, future studies should include more diverse populations and institutional settings to enhance the generalizability of results. Additionally, it is important to explore potential gender differences in stress responses to digital detox, as this could inform more personalized interventions. Lastly, evaluating the impact of structured sleep interventions in combination with digital detox strategies may uncover synergistic effects that further promote mental well-being and academic performance.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e \u003cp\u003eA major strength of this study is that it uses an objective approach in conjunction with the proposed model to validate the effects of digital detox by employing psychological assessments, biochemical stress markers, and physiological monitoring (HRV, BP, pulse rate). The use of three study groups (detox\u0026thinsp;+\u0026thinsp;alternative activities, detox only, and control) also helps to disentangle whether behavioral engagement is important in stress reduction over and above screen-time restriction. However, some limitations should be noted. Whether these effects are sustainable in the long run is unknown because follow-up assessments were not done. It is not known whether these benefits last for more than 15 days. Self-reports and app-based logging were used to track compliance, which was strong, but could have been affected by reporting biases; wearable technology would have increased accuracy. Since this study was carried out in two medical colleges in Pakistan, the findings may not be generalizable to other populations. Investigation into a wider population and context would be useful to establish the generalizability of the findings. Lastly, the possibility of a placebo effect cannot be excluded; participants may have believed in the curative properties of the treatment, which may have affected their stress and anxiety scores. While physiological markers minimized bias, future studies should incorporate sham interventions or blinding to control for expectancy effects.\u003c/p\u003e \u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study provides strong evidence that a 2-week structured digital detox intervention significantly reduces stress, anxiety, and physiological stress markers, particularly when combined with alternative activities. The observed biochemical improvements in oxidative stress, inflammation, and autonomic balance highlight the physiological benefits of reducing digital overstimulation. While digital detox effectively mitigates acute stress, future research should explore its long-term sustainability and broader applicability across diverse populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe study was approved by the Institutional Review Boards of Quaid-e-Azam Medical College, Bahawalpur, and Shahida Islam Medical College, Lodhran. All participants provided written informed consent. Participation was voluntary, and withdrawal was allowed at any stage without penalty.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eS.F. conceptualized the study and provided overall supervision. S.R. performed data collection, analysis, and figure/table preparation. S.B. conducted the literature review and contributed to referencing and psychometric assessment. M.F.A. proofread the manuscript and gave final approval. M.I. supported data collection and statistical analysis.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAll authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors acknowledge the use of AI-based tools for language enhancement. All research content, analysis, and interpretation were developed and verified by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u0026nbsp;\u003c/strong\u003eClinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJeyapalan T, Blair E. The factors causing stress in medical students and their impact on academic outcomes: A narrative qualitative systematic review. Int J Med Stud. 2024 Apr-Jun;12(2):195\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhatake P, Reddipogu H, Salgar A. Stress among medical students and its impact on academic performance. Biomedicine. 2022;42(3):620\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.51248/.v42i3.1212\u003c/span\u003e\u003cspan address=\"10.51248/.v42i3.1212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDataReportal. Digital 2024: Pakistan [Internet]. 2024 [cited 22 Nov 2024]. 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PMID: 35565879; PMCID: PMC9103473.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital detox, stress markers, inflammatory markers, oxidative stress, mental health, medical students","lastPublishedDoi":"10.21203/rs.3.rs-6572563/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6572563/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eExcessive screen time has been linked to increased stress, anxiety, cognitive fatigue, and physiological dysregulation. Prolonged digital engagement raises biochemical stress markers, including cortisol, C-reactive protein (CRP), interleukin-6 (IL-6), and oxidative stress markers such as malondialdehyde (MDA), catalase (CAT), and superoxide dismutase (SOD), which can harm mental and physical health. This study evaluates the impact of a 2-week digital detox intervention on psychological well-being, biochemical stress markers, and autonomic function in medical students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA prospective interventional study was conducted at two medical colleges in Pakistan. Participants (n=240) were randomized into three groups: digital detox with alternative activities, screen-time reduction only, and a control group with no intervention. Compliance was monitored via app-based tracking and daily logs. Pre- and post-intervention assessments included biochemical markers (cortisol, catecholamines, CRP, IL-6, MDA, CAT, SOD), physiological parameters (heart rate variability, blood pressure, pulse rate), and psychometric scores (Perceived Stress Scale, Generalized Anxiety Disorder-7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe digital detox with alternative activities group showed the most significant improvements. Cortisol decreased by 32% (p\u0026lt;0.001), CRP by 33% (p\u0026lt;0.001), and IL-6 by 38% (p\u0026lt;0.001). Perceived stress and anxiety significantly declined (p\u0026lt;0.001), while heart rate variability improved, and systolic blood pressure and pulse rate decreased (p\u0026lt;0.01). MDA levels moderately dropped (p\u0026lt;0.01), but SOD remained unchanged (p\u0026gt;0.05). The screen-time reduction group showed moderate improvements, while the control group had no significant changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eA 2-week digital detox, especially with alternative activities, significantly reduces stress, anxiety, and biochemical stress markers while improving autonomic regulation. These findings highlight the potential of structured digital detox interventions to enhance mental and physiological well-being in medical students.\u003c/p\u003e","manuscriptTitle":"From Screens to Serenity: Evaluating the Effect of Digital Detox on Mental and Physiological Health","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-04 08:40:21","doi":"10.21203/rs.3.rs-6572563/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-13T04:06:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-23T07:46:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197003123430297710317578815580315995098","date":"2025-07-13T14:49:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-10T20:32:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256450560609521501793427626442788641395","date":"2025-05-30T12:31:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-30T11:47:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-14T09:26:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-14T09:25:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-05-01T14:23:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"626177aa-0007-45e7-961e-c529c1533d13","owner":[],"postedDate":"June 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:08:22+00:00","versionOfRecord":{"articleIdentity":"rs-6572563","link":"https://doi.org/10.1186/s12909-025-08267-4","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2025-12-29 15:58:13","publishedOnDateReadable":"December 29th, 2025"},"versionCreatedAt":"2025-06-04 08:40:21","video":"","vorDoi":"10.1186/s12909-025-08267-4","vorDoiUrl":"https://doi.org/10.1186/s12909-025-08267-4","workflowStages":[]},"version":"v1","identity":"rs-6572563","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6572563","identity":"rs-6572563","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}