Prevalence of Sleep Disruption and Its Association with Smartphone Addiction Among University Students in Greater Noida, Uttar Pradesh

Prevalence of Sleep Disruption and Its Association with Smartphone Addiction Among University Students in Greater Noida, Uttar Pradesh | 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 Prevalence of Sleep Disruption and Its Association with Smartphone Addiction Among University Students in Greater Noida, Uttar Pradesh Ajit Kumar Lenka, Tabitha Okunlola, Supriya Awasthi, Rajshree Chanchal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9250794/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Introduction: Smartphone use has become an integral part of daily life among young adults, with increasing concerns about its impact on sleep health. Aim: This study examined the relationship between smartphone addiction and sleep quality among university students in Greater Noida, India. Methods: A quantitative, cross-sectional design was employed, and data were collected from 222 participants aged 18–25 years through a structured online questionnaire. The Smartphone Addiction Scale-Short Version (SAS-SV) and the Pittsburgh Sleep Quality Index (PSQI) were used to assess addiction levels and sleep quality, respectively. Additional open-ended questions provided qualitative insights into usage patterns and perceived effects of smartphone use. Results: Descriptive findings showed that 55.4% of participants met the criteria for smartphone addiction and 75.2% reported poor sleep quality (PSQI > 5). Chi-square analysis revealed a significant association between smartphone addiction and poor sleep quality (χ²(1) = 15.2, p < 0.001). Correlation analysis indicated a moderate positive relationship between smartphone addiction scores and global PSQI scores (r = 0.379, p < 0.001). Regression results showed that smartphone addiction significantly predicted poorer sleep quality, explaining 14.4% of the variance (B = 0.125, p < 0.001). Thematic analysis of qualitative responses highlighted late-night use, notifications, emotional dependence, and academic impacts as key contributors to sleep disruption. Conclusion: These findings suggest that using smartphones for long hours is significantly linked to poor sleep quality in young adults. Interventions promoting healthy digital habits, sleep hygiene education, and self-regulation strategies may help reduce the adverse effects of excessive smartphone use on sleep health among university students. Smartphone addiction Sleep quality PSQI SAS-SV University students Greater Noida Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction The rapid proliferation of smartphones has transformed communication, learning, and social interaction, particularly among young adults. India has witnessed exponential growth in usage of smartphone, with users exceeding 750 million, a large proportion of whom are adolescents and university students [ 1 ]. Figure 1 illustrates the number of active smartphone users (in millions) across the top 10 countries globally, with China and India leading in user volume. Source : https://explodingtopics.com/blog/smartphone-stats accessed date on 25-05-2025. While smartphones provide accessibility and connectivity, concerns have emerged about excessive use, dependence, and their potential consequences on health, especially sleep pattern. Sleep is a fundamental biological process essential for physical restoration, cognitive function, and mental health. In young adults, poor sleep quality is associated with impaired academic performance, emotional instability, reduced productivity, and heightened risk of chronic disease [ 2 ]. The rise of digital technology has introduced new challenges to maintaining healthy sleep patterns, with smartphone use being one of the most significant disruptors [ 1 ]. Smartphone addiction is characterized by compulsive, uncontrolled, and excessive use that interferes with daily life [ 3 , 4 ]. The Smartphone Addiction Scale–Short Version (SAS-SV) is widely used to measure this construct and has shown increasing rates of addiction across university population [ 5 ]. Parallelly, the Pittsburgh Sleep Quality Index (PSQI) remains a standard tool for assessing subjective sleep quality [ 6 ]. A growing body of evidence suggests that high smartphone use is linked to reduced sleep duration, delayed sleep onset, frequent awakenings, and overall poor sleep quality [ 4 ]. Studies conducted internationally highlight consistent patterns. For example, research from South Korea and China has shown that smartphone overuse is significantly associated with insomnia, daytime dysfunction, and poor academic outcomes [ 7 , 8 , 9 ]. Similarly, studies in Western countries report that nighttime exposure to smartphones, due to blue light emission, social media engagement, and constant notifications contributes to sleep disruption [ 10 , 9 , 11 ]. In India, where smartphone penetration among youth is rapidly increasing, recent studies have also reported alarmingly high rates of problematic use and compromised sleep health among students [ 12 , 13 , 14 ]. Despite this evidence, the relationship between smartphone addiction and sleep quality remains underexplored in the Indian context, particularly in fast-growing urban areas such as Greater Noida, where students are highly dependent on digital technology for academic and social purposes. Furthermore, many existing studies rely solely on quantitative surveys, providing limited insights into the underlying behavioral and psychosocial mechanisms by which smartphones disrupt sleep. Addressing this gap, the present study investigates the association between smartphone addiction and sleep quality among young adults in Greater Noida, India. Using a cross-sectional survey, smartphone addiction was assessed with the SAS-SV and sleep quality with the PSQI. In addition to quantitative measures, open-ended questions captured students’ perceptions and experiences, allowing for a mixed-methods perspective. The objective of the study were to determine the prevalence of smartphone addiction and poor sleep quality among young adults and to examine the association between smartphone addiction and sleep quality. By integrating quantitative and qualitative findings, this study contributes to the growing literature on digital health challenges and provides evidence for targeted interventions promoting healthier smartphone use and improved sleep hygiene among university students. 2 Materials And Methods 2.1 Study Design and Setting A quantitative, cross-sectional study was conducted among university students in Greater Noida, Uttar Pradesh, India. Greater Noida was selected as a major educational hub and focused on three main universities with a diverse student population. Source:Fieldwork 2.2 Study Population and Sampling The study targeted students aged 18–25 years enrolled in undergraduate or postgraduate programs. Eligibility criteria included regular smartphone use (≥ 2 hours per day) and willingness to provide informed consent. Students with diagnosed sleep disorders, medical conditions affecting sleep, or using sleep medications were excluded. A stratified random sampling approach was applied to ensure gender balance and representation across academic disciplines. The required sample size was calculated using Cochran’s formula at a 95% confidence level, 5% margin of error, and p = 0.5, yielding 382 participants. A total of 222 valid responses were retained for final analysis after data cleaning and exclusion of incomplete or inconsistent responses (Fig. 2 ). 2.3 Data Collection Tools Data were collected using a structured, self-administered online questionnaire (KoboToolbox). The instrument comprised four sections: Sociodemographic information – including age, gender, academic level, field of study, socioeconomic status, and other background variables. Smartphone addiction – measured using the Smartphone Addiction Scale–Short Version (SAS-SV), a 10-item validated tool with gender-specific cut-offs (≥ 31 for males, ≥ 33 for females) to classify addiction risk. Sleep quality – assessed using the Pittsburgh Sleep Quality Index (PSQI), which measures seven components of sleep and provides a global score (0–21). A score > 5 indicates poor sleep quality. Qualitative items – 11 open-ended questions explored perceptions of smartphone use, late-night habits, emotional dependence, and strategies for improving sleep. 2.4 Data Analysis Data were coded and analyzed using IBM SPSS (version 25). Descriptive statistics summarized demographic variables, smartphone use patterns, and sleep quality. Inferential tests included: Chi-square test to examine the association between smartphone addiction and sleep quality, Pearson correlation to assess the relationship between SAS-SV and PSQI scores, and Linear regression to determine the predictive effect of smartphone addiction on sleep quality. Open-ended responses were analyzed thematically to identify recurring patterns and contextual factors influencing sleep. 3 Ethical Considerations Ethical approval was obtained from the Institutional Ethics Committee of NIIMS (Ref: NIIMS/IEC-SC/April 2025/44). Written informed consent was obtained from all participants. Participation was voluntary, anonymity is maintained, and participants retained the right to withdraw at any stage without penalty (Appendix A). 4 Results 4.1 Socio-Demographic Characteristics of Participants A total of 222 students from universities in Greater Noida participated in the study. The majority of respondents were between the ages of 18–22 years, with a near-equal distribution of males and females. Participants represented diverse academic disciplines, including health sciences, engineering, management, and social sciences. Most students reported regular smartphone use of at least 2 hours daily, meeting the inclusion criteria. Socioeconomic background varied, with family monthly income ranging from low to high brackets, providing a representative sample of the student population (Table 1 ). Table 1 Socio-demographic characteristics of the study population Variable Category Frequency (n) Percentage (%) Age of the respondent (in years) 18–20 years 68 30.6 21–23 years 86 38.76 24–26 years 68 30.6 Gender Male 116 52.3 Female 106 47.7 Educational Level Undergraduate 154 69.4 Postgraduate 56 25.2 Ph.D. 12 5.4 Year of Study 1st Year 38 17.1 2nd Year 91 41.0 3rd Year 64 28.8 4th year and above 29 13.1 Monthly income of Family (in Rs.) 10000 rupees 3 1.4 11,000–20,000 rupees 9 4.1 21,000–30,000 rupees 20 9.0 31,000–40,000 rupees 46 20.7 41,000–50,000 rupees 62 27.9 Above 50,000 rupees 82 36.9 Source: Fieldwork 4.2 Prevalence of Smartphone Addiction The Smartphone Addiction Scale–Short Version (SAS-SV) was used to assess addiction levels. Results indicated that 55.4% of participants met the cut-off for smartphone addiction (≥ 31 for males; ≥33 for females). This prevalence aligns with prior studies conducted among Indian students. Notably, students who reported average daily smartphone use exceeding 6 hours were significantly more likely to fall into the “addicted” category (p < 0.01). Social media and entertainment were the most frequently cited primary purposes of use, although academic reliance on smartphones was also substantial. Checking the phone immediately after waking up was reported by 67% of participants, reflecting habitual and compulsive behaviors. Source: Fieldwork Classification based on SAS-SV cutoff scores — ≥31 for males and ≥ 33 for females indicates problematic use (Source: Kwon et al., 2013) Smartphone addiction was assessed using the standardized SAS-SV cutoff: scores of ≥ 31 for males and ≥ 33 for females were considered indicative of addiction. Based on this criterion, the bar graph shown in Fig. 3 shows the distribution of smartphone addiction across gender groups indicates that a slightly higher proportion of males were classified as smartphone addicts compared to females. Specifically, 68 male participants (30.6%) were identified as addicts, while 48 males (21.6%) were non-addicts. Among females, 55 participants (24.8%) were categorized as addicts, and 51 (23.0%) were non-addicts. Overall, 123 participants (55.4%) in the study were classified as smartphone addicts, while 99 (44.6%) were non-addicts 4.3 Sleep Quality Assessment Based on PSQI global score; a score > 5 indicates poor sleep quality. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), where a global score of greater than 5 indicates poor sleep quality. The bar graph shown in Fig. 4 reveal that the majority of respondents experienced poor sleep quality. Specifically, 75.2% (n = 167) of the total respondents were categorized as having poor sleep, while only 24.8% (n = 55) had good sleep quality. Among males, 76.7% reported poor sleep quality, compared to 73.6% of females. These findings underscore the pervasive impact of disrupted sleep among university students in the region. Source: Fieldwork 4.4 Association Between Smartphone Addiction and Sleep Quality A Chi-square test of independence was conducted to examine the association between smartphone addiction status and sleep quality among participants (Table 2 ). The results shows a statistically significant relationship between the two variables, χ²(1) = 15.2, p < .001. Specifically, a higher proportion of participants categorized as smartphone addicts reported poor sleep quality (85.4%) compared to non-addicts (62.6%). Conversely, good sleep quality was more prevalent among non-addicts (37.4%) than addicts (14.6%). Table 2 Association between smartphone addiction status and sleep quality status Addiction Status Poor Sleep Quality Good Sleep Quality Total Non-Addict 62 (62.6%) 37 (37.4%) 99 (100%) Addict 105 (85.4%) 18 (14.6%) 123 (100%) Total 167 (75.2%) 55 (24.8%) 222 (100%) Note : χ²(1) = 15.2, p < .001. test statistic and p-value. Source: Fieldwork, 2025 A moderate positive correlation was found, r (220) = 0.379, p < .001, when a Pearson’s correlation (Table 3 ) was conducted to further examine the relationship between smartphone addiction and sleep quality indicating that as smartphone addiction levels increased, sleep quality worsened. Table 3 Correlation between smartphone addiction and sleep quality scores Variables Pearson’s r p-value Spearman’s ρ p-value SAS_TOTAL vs PSQI Score 0.379 < .001 0.339 < .001 An independent samples t-test was conducted to compare smartphone addiction scores (SAS_TOTAL) between participants with poor and good sleep quality (Table 4 ). The analysis revealed a statistically significant difference in smartphone addiction scores between the two groups, t (77.5) = 2.82, p = .006, with a moderate effect size (Cohen’s d = 0.464). Participants with poor sleep quality (M = 32.1, SD = 9.5) had significantly higher smartphone addiction scores than those with good sleep quality (M = 27.1, SD = 12.0). Table 4 Comparison of smartphone addiction scores by sleep quality status Sleep Quality Group Frequency (N) Mean SAS_TOTAL SD POOR 167 32.1 9.5 GOOD 55 27.1 12.0 Source: Fieldwork Table 5 Predictive relationship between smartphone addiction and sleep quality Model Summary R 0.379 R² 0.144 Adjusted R² 0.140 Predictor B SE t p-value Intercept 4.248 0.6718 6.32 < .001 SAS_TOTAL 0.125 0.0206 6.08 < .001 Table 5 shows a simple linear regression analysis was conducted to examine whether smartphone addiction scores (SAS_TOTAL) significantly predicted sleep quality, as measured by the global PSQI score. The overall model was significant, F (1, 220) = 36.96, p < .001, and accounted for approximately 14.4% of the variance in sleep quality ( R² = 0.144, Adjusted R² = 0.140). The regression coefficient for smartphone addiction was also statistically significant ( B = 0.125, SE = 0.0206, t = 6.08, p < .001), indicating that higher smartphone addiction scores significantly predicted poorer sleep quality. This reinforces the positive relationship found in previous analyses and suggests that smartphone addiction is a meaningful predictor of sleep disruption in young adults. 4.5 Qualitative Insights from Open-Ended Questions Thematic analysis of open-ended responses highlighted three recurrent themes: Bedtime Procrastination - Many participants admitted delaying sleep intentionally due to extended scrolling, chatting, or streaming at night. Emotional Dependence - Several students described their smartphones as a “comfort” or “escape,” often using them to cope with stress, loneliness, or boredom. Perceived Negative Consequences - Students acknowledged fatigue, difficulty in concentrating, and declining academic performance as consequences of late-night smartphone use. These narratives provided deeper context to the quantitative findings, illustrating how psychological and behavioral factors perpetuate the cycle of smartphone overuse and poor sleep. Table 6 Thematic analysis Theme Subthemes (Codes) Definition Illustrative Quotes Frequency Sleep Disruption Late-night use, Notifications, Sleep loss Patterns of smartphone use that delay or interrupt normal sleep, including staying up late and waking due to alerts. “I stayed up till 3 a.m. watching reels.”, “Even in deep sleep, one ping and I check my phone.”, “Sometimes instead of 8h of sleep, I got 3 or 4.” 58 Physical Effects Fatigue/tiredness, Eye strain, Headache Physical symptoms linked to prolonged smartphone use, particularly before bed. “My eyes hurt a lot after scrolling.”, “I feel very tired and sleepy the next morning.”, “I get headaches when I use my phone too much.” 44 Emotional Effects Anxiety/stress, Guilt/shame Psychological impacts of heavy smartphone use, including anxiety when disconnected and guilt over overuse. “If I don’t have my phone, I feel like I’m missing something important.”, “I feel guilty wasting so much time.”, “I get stressed when my phone is not with me.” 33 Addiction & Dependence Addiction acknowledgment Self-awareness of compulsive or dependent patterns of smartphone use. “Yes, I am addicted to my phone.”, “I can’t stop checking it.”, “I depend on it for everything.” 29 Usage Patterns / Motives Social media, Boredom/entertainment Reasons for using smartphones, often involving leisure, boredom relief, and online content. “I use it to watch YouTube till I sleep.”, “When I’m bored, I scroll TikTok.”, “Playing games helps me pass time.” 48 Academic & Daily Functioning Academic impact Negative influence of late-night use on focus, study, and day-to-day productivity. “I can’t concentrate in class after sleeping so late.”, “It affects my study time.”, “I lose focus the next day.” 17 Coping & Regulation Coping strategy Efforts to reduce use and improve sleep through self-control or habit changes. “I keep my phone aside after 11 p.m.”, “I started reading books instead.”, “I turned off notifications at night.” 25 Normalization & Habit Habituation Acceptance of late-night smartphone use as a routine part of life. “I just got habituated.” It is normal for me now.” 9 Perceived Negative Impact Time waste Awareness of time wasted due to phone overuse. “I waste hours scrolling.”, “I could have done something useful instead.” 13 Social Connection Social connection Using smartphones to communicate and maintain relationships. “I talk to friends till late at night.”, “I use it to connect with my family back home.” 15 Source: Fieldwork 5 Discussion This study examined the prevalence of smartphone addiction and its impact on sleep quality among university students in Greater Noida. The findings reveal alarmingly high levels of smartphone addiction and poor sleep quality among the students, consistent with international literature highlighting health consequences of digital overuse. The findings of the study reveal that half of the population is addicted to smart phone which affects them in different ways. The prevalence of smartphone addiction (55.4%) in this study is within the range reported in similar studies across India and abroad [ 15 , 16 , 17 ]. University students represent a particularly vulnerable group due to academic pressures, social demands, and easy access to digital technology. The high prevalence of habitual behaviors, such as phone-checking upon waking up, highlights the compulsive nature of smartphone use. The majority of students reported poor sleep quality, with over one-third experiencing short sleep duration and nearly half reporting difficulties with sleep initiation. These findings echo Hale and Guan’s [ 18 ] systematic review, which highlighted shorter sleep duration and delayed onset as common outcomes of evening screen exposure. Poor sleep among students is not merely a lifestyle inconvenience as it directly affects mental health, academic performance, and overall quality of life. Several mechanisms may account for the observed association between smartphone addiction and poor sleep, such as prolonged nighttime screen use which suppresses melatonin and therefore delaying circadian rhythms, engaging with stimulating content before bed and this heightens cognitive activity and delays sleep onset, reliance on smartphone for stress relief or social interactions which then disrupts sleep cycles. The integration of quantitative and qualitative findings in this study supports these mechanisms, particularly behavioral displacement and emotional dependence, as students explicitly described delaying sleep for phone use and relying on devices for comfort. Our findings are consistent with studies conducted in South Asia and other parts of the world. For instance, Lee et al. [ 19 ] in China and Akbari et al. [ 20 ] in Pakistan both reported significant correlations between smartphone dependence and poor PSQI scores. Similarly, Demir et al. [ 21 ] found gender variations, with females often reporting higher addiction scores and worse sleep outcomes, a trend partially reflected in the present study. The consistency of findings across diverse contexts underscores the universal nature of the problem in young adult population. However, the prevalence reported here appears higher than some global averages, suggesting cultural or contextual factors, such as high social media penetration in India, may exacerbate risks. The results have important implications for student health and academic performance. Poor sleep quality has been strongly linked to depression, anxiety, reduced productivity, and impaired cognitive performance. Given that smartphones have become integral to student life, interventions must balance digital benefits with awareness of associated risks. Potential strategies include: Incorporating digital wellness and sleep hygiene workshops into university curricula, encouraging use of screen-time monitoring, blue light filters, and wind-down modes, promoting alternatives to late-night phone use, such as reading or mindfulness practices, Universities could implement “digital detox” campaigns or provide institutional support for healthier technology use. A key strength of this study is its mixed-methods design, combining quantitative scales with qualitative exploration to provide a comprehensive understanding of smartphone use and sleep disruption. Additionally, the use of validated tools (SAS-SV and PSQI) enhances reliability and comparability with global studies. However, limitations include reliance on self-reported data, which may be subject to recall or social desirability bias. The cross-sectional design prevents causal inference, though strong associations were observed. Furthermore, the study is limited to students in Greater Noida, which may restrict generalizability beyond similar urban academic population. 6 Conclusion In conclusion, this study highlights the high prevalence of smartphone addiction and poor sleep quality among young adults in Greater Noida, with clear evidence of a negative association between the two. The findings emphasize the urgent need for interventions promoting digital wellness and sleep hygiene among university population. Addressing smartphone overuse is not merely a matter of personal choice but a public health priority with implications for mental health, academic success, and long-term well-being. Declarations Acknowledgement: This work forms part of a dissertation submitted by Okunlola Tabitha Tolulope to the Department of Public Health, School of Allied Health Sciences, Noida International University. Author contributions Okunlola Tabitha Tolulope: Conceptualization, methodology, formal analysis, writing-original draft. Ajit Kumar Lenka: Methodology, formal analysis, investigation, resources, software, supervision, writing-review & editing. Supriya Awasthi: Supervision, Overall study design and structure, Critical review. Rajeshree Chanchal: Methodology, formal analysis, validation, writing—review & editing. Ethics approval and consent to participate Ethical approval was obtained from the Institutional Ethics Committee of Noida International Institute of Medical Sciences (Ref: NIIMS/IEC-SC/April 2025/44). All procedures followed relevant guidelines. Written informed consent was obtained, and participation was voluntary, anonymous, and withdrawable at any time. Data Availability Statement The datasets generated and/or analysed during the current study are not publicly available due to ethical restrictions protecting participant privacy but are available from the corresponding author on reasonable request. Consent for publication Not applicable Informed consent Written informed consent was obtained from all participants before their inclusion in the study. Competing interests The authors declare that they have no competing interests. Funding No specific funding was received for this study. References The Society for Adolescent Health and Medicine. Young Adult Health and Well-Being: A Position Statement of the Society for Adolescent Health and Medicine. J Adolesc Health. 2017;60(6):758–9. Clement-Carbonell V, Portilla-Tamarit I, Rubio-Aparicio M, Madrid-Valero JJ. Sleep Quality, Mental and Physical Health: A Differential Relationship. Int J Environ Res Public Health. 2021;18(2):460. Loleska S, Pop-Jordanova N. 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Okunlola","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tabitha","middleName":"","lastName":"Okunlola","suffix":""},{"id":635729252,"identity":"112bd479-4d8a-464f-b7eb-92c43463a6d1","order_by":2,"name":"Supriya Awasthi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Supriya","middleName":"","lastName":"Awasthi","suffix":""},{"id":635729254,"identity":"adaf3993-8efe-495c-bcc0-9688895c2d6f","order_by":3,"name":"Rajshree Chanchal","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rajshree","middleName":"","lastName":"Chanchal","suffix":""}],"badges":[],"createdAt":"2026-03-28 07:53:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9250794/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9250794/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108956444,"identity":"c92e49f2-f296-447a-b6c7-080ace8a2384","added_by":"auto","created_at":"2026-05-11 08:13:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46482,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal distribution of active smartphone users by country\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource : https://explodingtopics.com/blog/smartphone-stats\u003c/em\u003e accessed date on 25-05-2025.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9250794/v1/23164491f47fb9406028a728.png"},{"id":108956395,"identity":"4975a73b-ca1a-4518-bee4-d420d8de8798","added_by":"auto","created_at":"2026-05-11 08:13:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31395,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of the study areas and study population\u003c/p\u003e\n\u003cp\u003eSource:Fieldwork\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9250794/v1/186f9410a197997e8aec2b4e.png"},{"id":108956558,"identity":"41c2e5a9-7974-484c-a0cb-57820083d2db","added_by":"auto","created_at":"2026-05-11 08:14:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28862,"visible":true,"origin":"","legend":"\u003cp\u003eSmartphone addiction status of respondents\u003c/p\u003e\n\u003cp\u003eSource: Fieldwork\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9250794/v1/bf87ca7ccd5dedbec35c9ade.png"},{"id":108956342,"identity":"338484cc-7bba-40b0-8b18-c7201d7967a1","added_by":"auto","created_at":"2026-05-11 08:12:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47495,"visible":true,"origin":"","legend":"\u003cp\u003eSleep quality status of respondents\u003c/p\u003e\n\u003cp\u003eSource: Fieldwork\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9250794/v1/f0213362fba8c8210aeae76a.png"},{"id":108977622,"identity":"88d3e7e3-8bda-4170-a3a5-21d8d8be91f3","added_by":"auto","created_at":"2026-05-11 11:32:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":427525,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9250794/v1/9a3037e4-1e06-4e53-85fe-ca44168fb466.pdf"},{"id":108956403,"identity":"f36413e0-cb2c-4b19-8ad6-6f6e3c7062b0","added_by":"auto","created_at":"2026-05-11 08:13:11","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":225947,"visible":true,"origin":"","legend":"","description":"","filename":"SmartphoneDATAFILE.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9250794/v1/bc7204872aecbdfd4fe6fad9.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence of Sleep Disruption and Its Association with Smartphone Addiction Among University Students in Greater Noida, Uttar Pradesh","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe rapid proliferation of smartphones has transformed communication, learning, and social interaction, particularly among young adults. India has witnessed exponential growth in usage of smartphone, with users exceeding 750\u0026nbsp;million, a large proportion of whom are adolescents and university students [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the number of active smartphone users (in millions) across the top 10 countries globally, with China and India leading in user volume.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSource\u003c/em\u003e : \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://explodingtopics.com/blog/smartphone-stats\u003c/span\u003e\u003cspan address=\"https://explodingtopics.com/blog/smartphone-stats\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e accessed date on 25-05-2025.\u003c/p\u003e \u003cp\u003eWhile smartphones provide accessibility and connectivity, concerns have emerged about excessive use, dependence, and their potential consequences on health, especially sleep pattern. Sleep is a fundamental biological process essential for physical restoration, cognitive function, and mental health. In young adults, poor sleep quality is associated with impaired academic performance, emotional instability, reduced productivity, and heightened risk of chronic disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The rise of digital technology has introduced new challenges to maintaining healthy sleep patterns, with smartphone use being one of the most significant disruptors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSmartphone addiction is characterized by compulsive, uncontrolled, and excessive use that interferes with daily life [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The Smartphone Addiction Scale\u0026ndash;Short Version (SAS-SV) is widely used to measure this construct and has shown increasing rates of addiction across university population [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Parallelly, the Pittsburgh Sleep Quality Index (PSQI) remains a standard tool for assessing subjective sleep quality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A growing body of evidence suggests that high smartphone use is linked to reduced sleep duration, delayed sleep onset, frequent awakenings, and overall poor sleep quality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies conducted internationally highlight consistent patterns. For example, research from South Korea and China has shown that smartphone overuse is significantly associated with insomnia, daytime dysfunction, and poor academic outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, studies in Western countries report that nighttime exposure to smartphones, due to blue light emission, social media engagement, and constant notifications contributes to sleep disruption [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In India, where smartphone penetration among youth is rapidly increasing, recent studies have also reported alarmingly high rates of problematic use and compromised sleep health among students [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite this evidence, the relationship between smartphone addiction and sleep quality remains underexplored in the Indian context, particularly in fast-growing urban areas such as Greater Noida, where students are highly dependent on digital technology for academic and social purposes. Furthermore, many existing studies rely solely on quantitative surveys, providing limited insights into the underlying behavioral and psychosocial mechanisms by which smartphones disrupt sleep.\u003c/p\u003e \u003cp\u003eAddressing this gap, the present study investigates the association between smartphone addiction and sleep quality among young adults in Greater Noida, India. Using a cross-sectional survey, smartphone addiction was assessed with the SAS-SV and sleep quality with the PSQI. In addition to quantitative measures, open-ended questions captured students\u0026rsquo; perceptions and experiences, allowing for a mixed-methods perspective. The objective of the study were to determine the prevalence of smartphone addiction and poor sleep quality among young adults and to examine the association between smartphone addiction and sleep quality. By integrating quantitative and qualitative findings, this study contributes to the growing literature on digital health challenges and provides evidence for targeted interventions promoting healthier smartphone use and improved sleep hygiene among university students.\u003c/p\u003e"},{"header":"2 Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Setting\u003c/h2\u003e \u003cp\u003eA quantitative, cross-sectional study was conducted among university students in Greater Noida, Uttar Pradesh, India. Greater Noida was selected as a major educational hub and focused on three main universities with a diverse student population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource:Fieldwork\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Population and Sampling\u003c/h2\u003e \u003cp\u003eThe study targeted students aged 18\u0026ndash;25 years enrolled in undergraduate or postgraduate programs. Eligibility criteria included regular smartphone use (\u0026ge;\u0026thinsp;2 hours per day) and willingness to provide informed consent. Students with diagnosed sleep disorders, medical conditions affecting sleep, or using sleep medications were excluded. A stratified random sampling approach was applied to ensure gender balance and representation across academic disciplines.\u003c/p\u003e \u003cp\u003eThe required sample size was calculated using Cochran\u0026rsquo;s formula at a 95% confidence level, 5% margin of error, and p\u0026thinsp;=\u0026thinsp;0.5, yielding 382 participants. A total of 222 valid responses were retained for final analysis after data cleaning and exclusion of incomplete or inconsistent responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Collection Tools\u003c/h2\u003e \u003cp\u003eData were collected using a structured, self-administered online questionnaire (KoboToolbox). The instrument comprised four sections:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSociodemographic information\u003c/b\u003e \u0026ndash; including age, gender, academic level, field of study, socioeconomic status, and other background variables.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSmartphone addiction\u003c/b\u003e \u0026ndash; measured using the Smartphone Addiction Scale\u0026ndash;Short Version (SAS-SV), a 10-item validated tool with gender-specific cut-offs (\u0026ge;\u0026thinsp;31 for males, \u0026ge;\u0026thinsp;33 for females) to classify addiction risk.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSleep quality\u003c/b\u003e \u0026ndash; assessed using the Pittsburgh Sleep Quality Index (PSQI), which measures seven components of sleep and provides a global score (0\u0026ndash;21). A score\u0026thinsp;\u0026gt;\u0026thinsp;5 indicates poor sleep quality.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eQualitative items\u003c/b\u003e \u0026ndash; 11 open-ended questions explored perceptions of smartphone use, late-night habits, emotional dependence, and strategies for improving sleep.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Analysis\u003c/h2\u003e \u003cp\u003eData were coded and analyzed using IBM SPSS (version 25). Descriptive statistics summarized demographic variables, smartphone use patterns, and sleep quality. Inferential tests included: Chi-square test to examine the association between smartphone addiction and sleep quality, Pearson correlation to assess the relationship between SAS-SV and PSQI scores, and Linear regression to determine the predictive effect of smartphone addiction on sleep quality.\u003c/p\u003e \u003cp\u003eOpen-ended responses were analyzed thematically to identify recurring patterns and contextual factors influencing sleep.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Ethical Considerations","content":"\u003cp\u003eEthical approval was obtained from the Institutional Ethics Committee of NIIMS (Ref: NIIMS/IEC-SC/April 2025/44). Written informed consent was obtained from all participants. Participation was voluntary, anonymity is maintained, and participants retained the right to withdraw at any stage without penalty (Appendix A).\u003c/p\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Socio-Demographic Characteristics of Participants\u003c/h2\u003e \u003cp\u003eA total of 222 students from universities in Greater Noida participated in the study. The majority of respondents were between the ages of 18\u0026ndash;22 years, with a near-equal distribution of males and females. Participants represented diverse academic disciplines, including health sciences, engineering, management, and social sciences. Most students reported regular smartphone use of at least 2 hours daily, meeting the inclusion criteria. Socioeconomic background varied, with family monthly income ranging from low to high brackets, providing a representative sample of the student population (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\u003eSocio-demographic characteristics of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"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 \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\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge of the respondent (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;23 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u0026ndash;26 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducational Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUndergraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eYear of Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2nd Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3rd Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4th year and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMonthly income of Family (in Rs.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10000 rupees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,000\u0026ndash;20,000 rupees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,000\u0026ndash;30,000 rupees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31,000\u0026ndash;40,000 rupees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41,000\u0026ndash;50,000 rupees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 50,000 rupees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Fieldwork\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Prevalence of Smartphone Addiction\u003c/h2\u003e \u003cp\u003eThe Smartphone Addiction Scale\u0026ndash;Short Version (SAS-SV) was used to assess addiction levels. Results indicated that 55.4% of participants met the cut-off for smartphone addiction (\u0026ge;\u0026thinsp;31 for males; \u0026ge;33 for females). This prevalence aligns with prior studies conducted among Indian students.\u003c/p\u003e \u003cp\u003eNotably, students who reported average daily smartphone use exceeding 6 hours were significantly more likely to fall into the \u0026ldquo;addicted\u0026rdquo; category (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Social media and entertainment were the most frequently cited primary purposes of use, although academic reliance on smartphones was also substantial. Checking the phone immediately after waking up was reported by 67% of participants, reflecting habitual and compulsive behaviors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: Fieldwork\u003c/p\u003e \u003cp\u003e \u003cb\u003eClassification based on SAS-SV cutoff scores \u0026mdash; \u0026ge;31 for males and \u0026ge;\u0026thinsp;33 for females indicates problematic use (Source: Kwon et al., 2013)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSmartphone addiction was assessed using the standardized SAS-SV cutoff: scores of \u0026ge;\u0026thinsp;31 for males and \u0026ge;\u0026thinsp;33 for females were considered indicative of addiction. Based on this criterion, the bar graph shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the distribution of smartphone addiction across gender groups indicates that a slightly higher proportion of males were classified as smartphone addicts compared to females. Specifically, 68 male participants (30.6%) were identified as addicts, while 48 males (21.6%) were non-addicts. Among females, 55 participants (24.8%) were categorized as addicts, and 51 (23.0%) were non-addicts. Overall, 123 participants (55.4%) in the study were classified as smartphone addicts, while 99 (44.6%) were non-addicts\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Sleep Quality Assessment\u003c/h2\u003e \u003cp\u003e \u003cb\u003eBased on PSQI global score; a score\u0026thinsp;\u0026gt;\u0026thinsp;5 indicates poor sleep quality.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), where a global score of greater than 5 indicates poor sleep quality. The bar graph shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveal that the majority of respondents experienced poor sleep quality. Specifically, 75.2% (n\u0026thinsp;=\u0026thinsp;167) of the total respondents were categorized as having poor sleep, while only 24.8% (n\u0026thinsp;=\u0026thinsp;55) had good sleep quality. Among males, 76.7% reported poor sleep quality, compared to 73.6% of females. These findings underscore the pervasive impact of disrupted sleep among university students in the region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: Fieldwork\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Association Between Smartphone Addiction and Sleep Quality\u003c/h2\u003e \u003cp\u003eA Chi-square test of independence was conducted to examine the association between smartphone addiction status and sleep quality among participants (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results shows a statistically significant relationship between the two variables, χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;15.2, p \u0026lt; .001. Specifically, a higher proportion of participants categorized as smartphone addicts reported poor sleep quality (85.4%) compared to non-addicts (62.6%). Conversely, good sleep quality was more prevalent among non-addicts (37.4%) than addicts (14.6%).\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\u003eAssociation between smartphone addiction status and sleep quality status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAddiction Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor Sleep Quality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood Sleep Quality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Addict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62 (62.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (37.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAddict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105 (85.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e167 (75.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55 (24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e222 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote\u003c/b\u003e: χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;15.2, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. test statistic and p-value. Source: Fieldwork, 2025\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA moderate positive correlation was found, \u003cem\u003er\u003c/em\u003e(220)\u0026thinsp;=\u0026thinsp;0.379, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, when a Pearson\u0026rsquo;s correlation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was conducted to further examine the relationship between smartphone addiction and sleep quality indicating that as smartphone addiction levels increased, sleep quality worsened.\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\u003eCorrelation between smartphone addiction and sleep quality scores\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson\u0026rsquo;s r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpearman\u0026rsquo;s ρ\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\u003eSAS_TOTAL vs PSQI Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.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\u003eAn independent samples t-test was conducted to compare smartphone addiction scores (SAS_TOTAL) between participants with poor and good sleep quality (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The analysis revealed a statistically significant difference in smartphone addiction scores between the two groups, \u003cem\u003et\u003c/em\u003e(77.5)\u0026thinsp;=\u0026thinsp;2.82, \u003cem\u003ep\u003c/em\u003e = .006, with a moderate effect size (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.464). Participants with poor sleep quality (M\u0026thinsp;=\u0026thinsp;32.1, SD\u0026thinsp;=\u0026thinsp;9.5) had significantly higher smartphone addiction scores than those with good sleep quality (M\u0026thinsp;=\u0026thinsp;27.1, SD\u0026thinsp;=\u0026thinsp;12.0).\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\u003eComparison of smartphone addiction scores by sleep quality status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep Quality Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean SAS_TOTAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Fieldwork\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003ePredictive relationship between smartphone addiction and sleep quality\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\u003eModel Summary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAS_TOTAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows a simple linear regression analysis was conducted to examine whether smartphone addiction scores (SAS_TOTAL) significantly predicted sleep quality, as measured by the global PSQI score. The overall model was significant, \u003cem\u003eF\u003c/em\u003e(1, 220)\u0026thinsp;=\u0026thinsp;36.96, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, and accounted for approximately 14.4% of the variance in sleep quality (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.144, Adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.140).\u003c/p\u003e \u003cp\u003eThe regression coefficient for smartphone addiction was also statistically significant (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.125, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0206, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.08, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), indicating that higher smartphone addiction scores significantly predicted poorer sleep quality. This reinforces the positive relationship found in previous analyses and suggests that smartphone addiction is a meaningful predictor of sleep disruption in young adults.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Qualitative Insights from Open-Ended Questions\u003c/h2\u003e \u003cp\u003eThematic analysis of open-ended responses highlighted three recurrent themes:\u003c/p\u003e \u003cp\u003e \u003cem\u003eBedtime Procrastination\u003c/em\u003e- Many participants admitted delaying sleep intentionally due to extended scrolling, chatting, or streaming at night. \u003cem\u003eEmotional Dependence\u003c/em\u003e\u003cb\u003e-\u003c/b\u003e Several students described their smartphones as a \u0026ldquo;comfort\u0026rdquo; or \u0026ldquo;escape,\u0026rdquo; often using them to cope with stress, loneliness, or boredom. \u003cem\u003ePerceived Negative Consequences\u003c/em\u003e- Students acknowledged fatigue, difficulty in concentrating, and declining academic performance as consequences of late-night smartphone use. These narratives provided deeper context to the quantitative findings, illustrating how psychological and behavioral factors perpetuate the cycle of smartphone overuse and poor sleep.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThematic analysis\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubthemes (Codes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIllustrative Quotes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep Disruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLate-night use, Notifications, Sleep loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatterns of smartphone use that delay or interrupt normal sleep, including staying up late and waking due to alerts.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ldquo;I stayed up till 3 a.m. watching reels.\u0026rdquo;, \u0026ldquo;Even in deep sleep, one ping and I check my phone.\u0026rdquo;, \u0026ldquo;Sometimes instead of 8h of sleep, I got 3 or 4.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFatigue/tiredness, Eye strain, Headache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysical symptoms linked to prolonged smartphone use, particularly before bed.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ldquo;My eyes hurt a lot after scrolling.\u0026rdquo;, \u0026ldquo;I feel very tired and sleepy the next morning.\u0026rdquo;, \u0026ldquo;I get headaches when I use my phone too much.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotional Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety/stress, Guilt/shame\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePsychological impacts of heavy smartphone use, including anxiety when disconnected and guilt over overuse.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ldquo;If I don\u0026rsquo;t have my phone, I feel like I\u0026rsquo;m missing something important.\u0026rdquo;, \u0026ldquo;I feel guilty wasting so much time.\u0026rdquo;, \u0026ldquo;I get stressed when my phone is not with me.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAddiction \u0026amp; Dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAddiction acknowledgment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-awareness of compulsive or dependent patterns of smartphone use.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ldquo;Yes, I am addicted to my phone.\u0026rdquo;, \u0026ldquo;I can\u0026rsquo;t stop checking it.\u0026rdquo;, \u0026ldquo;I depend on it for everything.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsage Patterns / Motives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial media, Boredom/entertainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReasons for using smartphones, often involving leisure, boredom relief, and online content.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ldquo;I use it to watch YouTube till I sleep.\u0026rdquo;, \u0026ldquo;When I\u0026rsquo;m bored, I scroll TikTok.\u0026rdquo;, \u0026ldquo;Playing games helps me pass time.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic \u0026amp; Daily Functioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic impact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative influence of late-night use on focus, study, and day-to-day productivity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ldquo;I can\u0026rsquo;t concentrate in class after sleeping so late.\u0026rdquo;, \u0026ldquo;It affects my study time.\u0026rdquo;, \u0026ldquo;I lose focus the next day.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoping \u0026amp; Regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoping strategy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEfforts to reduce use and improve sleep through self-control or habit changes.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ldquo;I keep my phone aside after 11 p.m.\u0026rdquo;, \u0026ldquo;I started reading books instead.\u0026rdquo;, \u0026ldquo;I turned off notifications at night.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormalization \u0026amp; Habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHabituation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcceptance of late-night smartphone use as a routine part of life.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ldquo;I just got habituated.\u0026rdquo; It is normal for me now.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Negative Impact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime waste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAwareness of time wasted due to phone overuse.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ldquo;I waste hours scrolling.\u0026rdquo;, \u0026ldquo;I could have done something useful instead.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Connection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial connection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsing smartphones to communicate and maintain relationships.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ldquo;I talk to friends till late at night.\u0026rdquo;, \u0026ldquo;I use it to connect with my family back home.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Fieldwork\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eThis study examined the prevalence of smartphone addiction and its impact on sleep quality among university students in Greater Noida. The findings reveal alarmingly high levels of smartphone addiction and poor sleep quality among the students, consistent with international literature highlighting health consequences of digital overuse.\u003c/p\u003e \u003cp\u003eThe findings of the study reveal that half of the population is addicted to smart phone which affects them in different ways. The prevalence of smartphone addiction (55.4%) in this study is within the range reported in similar studies across India and abroad [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. University students represent a particularly vulnerable group due to academic pressures, social demands, and easy access to digital technology. The high prevalence of habitual behaviors, such as phone-checking upon waking up, highlights the compulsive nature of smartphone use.\u003c/p\u003e \u003cp\u003eThe majority of students reported poor sleep quality, with over one-third experiencing short sleep duration and nearly half reporting difficulties with sleep initiation. These findings echo Hale and Guan\u0026rsquo;s [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] systematic review, which highlighted shorter sleep duration and delayed onset as common outcomes of evening screen exposure. Poor sleep among students is not merely a lifestyle inconvenience as it directly affects mental health, academic performance, and overall quality of life. Several mechanisms may account for the observed association between smartphone addiction and poor sleep, such as prolonged nighttime screen use which suppresses melatonin and therefore delaying circadian rhythms, engaging with stimulating content before bed and this heightens cognitive activity and delays sleep onset, reliance on smartphone for stress relief or social interactions which then disrupts sleep cycles.\u003c/p\u003e \u003cp\u003eThe integration of quantitative and qualitative findings in this study supports these mechanisms, particularly behavioral displacement and emotional dependence, as students explicitly described delaying sleep for phone use and relying on devices for comfort.\u003c/p\u003e \u003cp\u003eOur findings are consistent with studies conducted in South Asia and other parts of the world. For instance, Lee et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] in China and Akbari et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] in Pakistan both reported significant correlations between smartphone dependence and poor PSQI scores. Similarly, Demir et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] found gender variations, with females often reporting higher addiction scores and worse sleep outcomes, a trend partially reflected in the present study.\u003c/p\u003e \u003cp\u003eThe consistency of findings across diverse contexts underscores the universal nature of the problem in young adult population. However, the prevalence reported here appears higher than some global averages, suggesting cultural or contextual factors, such as high social media penetration in India, may exacerbate risks. The results have important implications for student health and academic performance. Poor sleep quality has been strongly linked to depression, anxiety, reduced productivity, and impaired cognitive performance. Given that smartphones have become integral to student life, interventions must balance digital benefits with awareness of associated risks.\u003c/p\u003e \u003cp\u003ePotential strategies include: Incorporating digital wellness and sleep hygiene workshops into university curricula, encouraging use of screen-time monitoring, blue light filters, and wind-down modes, promoting alternatives to late-night phone use, such as reading or mindfulness practices, Universities could implement \u0026ldquo;digital detox\u0026rdquo; campaigns or provide institutional support for healthier technology use. A key strength of this study is its mixed-methods design, combining quantitative scales with qualitative exploration to provide a comprehensive understanding of smartphone use and sleep disruption. Additionally, the use of validated tools (SAS-SV and PSQI) enhances reliability and comparability with global studies.\u003c/p\u003e \u003cp\u003eHowever, limitations include reliance on self-reported data, which may be subject to recall or social desirability bias. The cross-sectional design prevents causal inference, though strong associations were observed. Furthermore, the study is limited to students in Greater Noida, which may restrict generalizability beyond similar urban academic population.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eIn conclusion, this study highlights the high prevalence of smartphone addiction and poor sleep quality among young adults in Greater Noida, with clear evidence of a negative association between the two. The findings emphasize the urgent need for interventions promoting digital wellness and sleep hygiene among university population. Addressing smartphone overuse is not merely a matter of personal choice but a public health priority with implications for mental health, academic success, and long-term well-being.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work forms part of a dissertation submitted by Okunlola Tabitha Tolulope to the Department of Public Health, School of Allied Health Sciences, Noida International University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOkunlola Tabitha Tolulope: Conceptualization, methodology, formal analysis, writing-original draft. Ajit Kumar Lenka: Methodology, formal analysis, investigation, resources, software, supervision, writing-review \u0026amp; editing. Supriya Awasthi: Supervision, Overall study design and structure, Critical review. Rajeshree Chanchal: Methodology, formal analysis, validation, writing\u0026mdash;review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Institutional Ethics Committee of Noida International Institute of Medical Sciences (Ref: NIIMS/IEC-SC/April 2025/44). All procedures followed relevant guidelines. Written informed consent was obtained, and participation was voluntary, anonymous, and withdrawable at any time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to ethical restrictions protecting participant privacy but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants before their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo specific funding was received for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThe Society for Adolescent Health and Medicine. Young Adult Health and Well-Being: A Position Statement of the Society for Adolescent Health and Medicine. J Adolesc Health. 2017;60(6):758\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClement-Carbonell V, Portilla-Tamarit I, Rubio-Aparicio M, Madrid-Valero JJ. Sleep Quality, Mental and Physical Health: A Differential Relationship. Int J Environ Res Public Health. 2021;18(2):460.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoleska S, Pop-Jordanova N. Is Smartphone Addiction in the Younger Population a. Public Health Problem? PRILOZI. 2021;42(3):29\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzcan B, Acimis NM. Sleep Quality in Pamukkale University Students and its relationship with smartphone addiction. Pakistan J Med Sci. 2021;37(1):206\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193\u0026ndash;213.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwon M, Kim DJ, Cho H, Yang S. The Smartphone Addiction Scale: Development and Validation of a Short Version for Adolescents. Choi DS, editor. PLoS ONE. 2013;8(12):e83558.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElhai JD, Yang H, McKay D, Asmundson GJG. COVID-19 anxiety symptoms associated with problematic smartphone use severity in Chinese adults. J Affect Disord. 2020;274.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian J, Zhao J, Xu J, Li Q, Sun T, Zhao C et al. Mobile Phone Addiction and Academic Procrastination Negatively Impact Academic Achievement Among Chinese Medical Students. Front Psychol. 2021;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQanash S, Al-Husayni F, Falata H, Halawani O, Jahra E, Murshed B, et al. Effect of Electronic Device Addiction on Sleep Quality and Academic Performance Among Health Care Students: Cross-sectional Study. JMIR Med Educ. 2021;7(4):e25662.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRatan ZA, Parrish AM, Alotaibi MS, Hosseinzadeh H. Prevalence of Smartphone Addiction and Its Association with Sociodemographic, Physical and Mental Well-Being: A Cross-Sectional Study among the Young Adults of Bangladesh. Int J Environ Res Public Health. 2022;19(24):16583.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrubaker JR, Beverly EA, Burnout P, Stress. Sleep Quality, and Smartphone Use: A Survey of Osteopathic Medical Students. J Am Osteopath Assoc. 2020;120(1):6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandrasekaran V, Kumar V, Brahadeeswari H. Prevalence of smartphone addiction and its effects on sleep quality: A cross-sectional study among medical students. Industrial Psychiatry J. 2019;28(1):82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManna N, Banerjee S, Banerjee A, Chakraborty A, Das D. Smartphone Addiction, Daytime Sleepiness and Depression among Undergraduate Medical Students: A Cross-sectional Study in a Medical College of Kolkata, India. Siriraj Med J. 2023;75(11):800\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoel A, Moinuddin A, Tiwari R, Sethi Y, Suhail M, Mohan A, et al. Effect of Smartphone Use on Sleep in Undergraduate Medical Students: A Cross-Sectional Study. Healthcare. 2023;11(21):2891\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnekha MA. Study of Smartphone Addiction and Quality of Sleep Among Young Adults. 2023;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Manglano J, L\u0026oacute;pez-Madrigal C, S\u0026aacute;daba-Chalezquer C, Serrano C, Lopez-Fernandez O. Difficulties in Establishing Truth Conditions in the Assessment of Addictive Smartphone Use in Young Adults. Int J Environ Res Public Health. 2021;19(1):358.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao Y, Liu Z, Huang L, Liu H, Tian H, Wu J et al. The impact of smartphone dependence on college students\u0026rsquo; sleep quality: the chain-mediated role of negative emotions and health-promoting behaviors. Front Public Health. 2024;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHale L, Guan S. Screen time and sleep among school-aged children and adolescents: A systematic literature review. Sleep Med Rev. 2015;21(21):50\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee YK, Blebea JS, Janssen F, Domoff SE. The Impact of Smartphone and Social Media Use on Adolescent Sleep Quality and Mental Health during the COVID-19 Pandemic. Hum Behav Emerg Technol. 2023;2023(1):1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkbari R, Esmaeil Zarei, Dormohammadi A, Gholami A. Influence of unsafe and excessive use of mobile phone on the sleep quality. Sci J Kurdistan Univ Med Sci. 2016;21(5):81\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemir Y, S\u0026uuml;mer M. Effects of smartphone overuse on headache, sleep and quality of life in migraine patients. Neurosciences. 2019;24(2):115\u0026ndash;21.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Smartphone addiction, Sleep quality, PSQI, SAS-SV, University students, Greater Noida","lastPublishedDoi":"10.21203/rs.3.rs-9250794/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9250794/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Smartphone use has become an integral part of daily life among young adults, with increasing concerns about its impact on sleep health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim:\u003c/strong\u003e This study examined the relationship between smartphone addiction and sleep quality among university students in Greater Noida, India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A quantitative, cross-sectional design was employed, and data were collected from 222 participants aged 18–25 years through a structured online questionnaire. The Smartphone Addiction Scale-Short Version (SAS-SV) and the Pittsburgh Sleep Quality Index (PSQI) were used to assess addiction levels and sleep quality, respectively. Additional open-ended questions provided qualitative insights into usage patterns and perceived effects of smartphone use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Descriptive findings showed that 55.4% of participants met the criteria for smartphone addiction and 75.2% reported poor sleep quality (PSQI \u0026gt; 5). Chi-square analysis revealed a significant association between smartphone addiction and poor sleep quality (χ²(1) = 15.2, p \u0026lt; 0.001). Correlation analysis indicated a moderate positive relationship between smartphone addiction scores and global PSQI scores (r = 0.379, p \u0026lt; 0.001). Regression results showed that smartphone addiction significantly predicted poorer sleep quality, explaining 14.4% of the variance (B = 0.125, p \u0026lt; 0.001). Thematic analysis of qualitative responses highlighted late-night use, notifications, emotional dependence, and academic impacts as key contributors to sleep disruption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThese findings suggest that using smartphones for long hours is significantly linked to poor sleep quality in young adults. Interventions promoting healthy digital habits, sleep hygiene education, and self-regulation strategies may help reduce the adverse effects of excessive smartphone use on sleep health among university students.\u003c/p\u003e","manuscriptTitle":"Prevalence of Sleep Disruption and Its Association with Smartphone Addiction Among University Students in Greater Noida, Uttar Pradesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 08:09:31","doi":"10.21203/rs.3.rs-9250794/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-06T19:23:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109887864579464864944179354257319319715","date":"2026-05-05T17:48:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52806969916813521192195067857117908584","date":"2026-05-02T06:57:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303584622506038136888909679608379918016","date":"2026-05-01T06:50:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163666007651911301856038745102337201365","date":"2026-04-29T17:59:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-29T05:17:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-11T04:38:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T12:20:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-04-09T10:01:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1dcac085-4117-4e63-9f8b-49149ef4b0b3","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-06T19:23:17+00:00","index":61,"fulltext":""},{"type":"reviewerAgreed","content":"109887864579464864944179354257319319715","date":"2026-05-05T17:48:41+00:00","index":60,"fulltext":""},{"type":"reviewerAgreed","content":"52806969916813521192195067857117908584","date":"2026-05-02T06:57:29+00:00","index":56,"fulltext":""},{"type":"reviewerAgreed","content":"303584622506038136888909679608379918016","date":"2026-05-01T06:50:12+00:00","index":39,"fulltext":""},{"type":"reviewerAgreed","content":"163666007651911301856038745102337201365","date":"2026-04-29T17:59:43+00:00","index":38,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T08:09:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 08:09:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9250794","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9250794","identity":"rs-9250794","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}