Abstract
Background:
The rapid increase in the use of electronic gadgets has markedly transformed the lifestyle patterns of young adults. These devices have become indispensable tools for communication, academic learning, and leisure activities. However, excessive use of electronic gadgets—particularly during evening hours and before bedtime—has been associated with disturbances in sleep patterns. Additionally, increased reliance on electronic devices (EDs) for leisure activities has contributed to reduced levels of physical activity among young adults, raising concerns about their overall health and well-being.
Objective:
This study aimed to assess the extent of ED usage and explore its relationship with sleep quality and physical activity levels among undergraduate nursing students in Manipal, Udupi, Karnataka.
Methods:
A cross-sectional descriptive study design was employed between January 2025 and June 2025 among undergraduate nursing students in Manipal, Udupi, Karnataka. A total of 380 students were randomly selected to participate in the study. Data were collected using self-administered questionnaires, including a demographic proforma, an electronic gadget usage questionnaire, the Sleep Quality Scale (SQS), and a physical activity level questionnaire.
Results:
Statistical analysis revealed that the mean age of the participants was 19.86 ± 1.4 years, and 62.9% of the students resided in hostels. Smartphones were the most commonly used devices (59.7%), followed by the use of multiple EDs (38.9%). A substantial proportion of participants (70%) reported using electronic gadgets for multiple purposes, including academics, social communication, and entertainment, and 40.3% reported spending 3–4 h per day on non-academic screen time. The majority of respondents (91.6%) demonstrated moderate levels of electronic gadget usage, while 66.6% reported poor sleep quality. With regard to physical activity, 55% of participants exhibited low activity levels. A statistically significant weak negative correlation was observed between electronic gadget usage and sleep quality (ρ = −0.213, P < 0.001), and the correlation between electronic gadget usage and physical activity was weakly negative but not statistically significant (ρ = −0.090, P = 0.080). In contrast, a significant positive correlation was found between physical activity and sleep quality (ρ = 0.195, P < 0.001). Ordinal logistic regression showed that extreme gadget usage is associated with poor sleep quality (odds ratio [OR]-5.16; P < 0.05), and the linear regression model examining the association between electronic gadget usage and physical activity was statistically significant (R² = 0.0483, P < 0.001). The results indicated that the usage of electronic gadgets was associated with poorer sleep quality and lower levels of physical activity among students.
Conclusion:
The findings of this study highlight the importance of adequate sleep and the need to limit ED use, particularly before bedtime, to promote better sleep quality. Encouraging increased physical activity and responsible ED usage is essential for maintaining optimal physical and mental well-being among nursing students and young adults.
Keywords: Electronic gadgets, physical activity, screen time, sleep quality, young adults
BACKGROUND
Electronic devices (EDs) have become an essential part of daily life due to their widespread accessibility and usefulness in supporting routine activities.[1] In India, the use of smartphones, tablets, iPads, and laptops has increased considerably among students for academic, professional, and recreational purposes. As these devices occupy a substantial portion of users’ daily time, they may influence important physiological functions such as sleep and physical activity.[2] Their use often extends into the period before bedtime, and evidence suggests that excessive nighttime screen exposure can disrupt sleep by suppressing melatonin secretion, delaying sleep onset, and reducing sleep duration, thereby increasing the risk of adverse physical and mental health outcomes.[3,4]
Following the coronavirus disease 2019 (COVID-19) pandemic in India, the transition to online education necessitated the provision of smartphones, laptops, and other electronic gadgets by parents to facilitate students’ participation in virtual classes and related academic activities.[5] Consequently, the use of EDs has increased substantially in recent years and has become an integral part of students’ daily lives. Students now rely on electronic gadgets not only for educational purposes but also for communication, entertainment, and other leisure activities.[6] Although these devices play a vital role in supporting learning and connectivity, their inappropriate or excessive use may negatively affect academic performance, occupational productivity, and overall physical and mental health. Therefore, students should be encouraged to use electronic gadgets responsibly and for their intended purposes to maximize benefits while minimizing potential adverse effects.[7]
The blue light emitted from digital screens has been shown to disrupt sleep patterns, thereby affecting physical health and general wellbeing.[8,9] Due to a lack of awareness, excessive gadget use can have negative psychological, physiological, and social consequences, exacerbated by unrestricted internet access, social media exposure, and constant messaging opportunities.[7] Smartphone addiction and poor sleep quality have become prevalent health concerns, particularly among nursing students.[10] Also, this will impact academic performance, clinical performance,[11] and most likely tend to engage in risky behavior. Such overuse may also impair academic performance and increase the likelihood of engaging in risky behaviors, posing potential longterm consequences for students.[12]
Physiologically, sleep plays a vital role in maintaining and enhancing both physical and mental health. Numerous factors have been shown to influence sleep and its regulatory pathways.[13] Behavioral patterns and sleep hygiene practices significantly affect sleep regulation and can disrupt normal sleepregulating mechanisms.[14] Overuse of EDs has been associated with several adverse health outcomes, including headaches, eye strain, persistent neck and back pain, stress, anxiety, and disturbed sleep patterns.[15]
Excessive screen time can also negatively affect physical activity levels, as prolonged engagement with electronic gadgets reduces opportunities for exercise, thereby promoting a sedentary lifestyle and increasing the risk of related health problems among children and adolescents.[16] While EDs offer convenience and entertainment, excessive dependence may contribute to reduced physical activity, increasing the risk of obesity, cardiovascular disease, and other lifestylerelated conditions.[17] Prolonged use of EDs, particularly in improper postures, may result in musculoskeletal discomfort, including neck, shoulder, and back pain. Additionally, extended screen exposure can cause eye strain, dryness, and visual discomfort, potentially leading to Computer Vision Syndrome.[18] Several studies have reported that excessive gadget use is associated with increased levels of anxiety, depression, and attentionrelated problems.[19]
Studies conducted in various countries have demonstrated that excessive use of electronic gadgets, especially smartphones, is associated with reduced physical activity, body pain, neck pain, and sleep deprivation. Research from the United States and Korea has reported adverse effects of excessive ED use on students’ physical health and physical activity levels.[20] Exposure to light emitted from smartphones, computers, and tablets—characterized by blueenriched, shortwavelength light—resembles exposure to morning sunlight and can significantly disrupt circadian rhythms.[21] Therefore, ED use before bedtime should be minimized. The increasing duration of ED use, particularly during evening hours, warrants considerable attention. The present study aimed to assess the extent of ED usage and explore its relationship with sleep quality and physical activity levels among undergraduate nursing students.
METHODS AND MATERIALS
Study design and setting
A cross-sectional study was conducted among undergraduate nursing students from January 2025 to June 2025. Participants were from Manipal College of Nursing and Manipal School of Nursing, MAHE, Manipal, Udupi, Karnataka. The study population included students enrolled in the first to fourth year of the BSc Nursing program, first and second year of the Post Basic BSc (PBBSc) Nursing program, and first to third year of the Diploma in Nursing program.
Study participants
Undergraduate nursing students aged between 18 and 24 years were eligible to participate in the study. Students aged 25 years or older, those with major health conditions or disorders (such as diagnosed sleep disorders or physical disabilities), and students who did not own or use EDs were excluded. The study was reviewed and approved by the Institutional Ethics Committee (IEC2:62/2025). Permission was obtained from the Dean and Principals of the respective nursing institutions. Participation was voluntary, and informed written consent was obtained from all respondents prior to data collection.
Sample size
Assuming a prevalence of sleep disturbance of 50% with a margin of error of 5%, the estimated sample size was calculated to be 380 participants. The sample size estimation was performed using PASS 2023 software (NCSS, LLC).[22] The study sample was drawn from undergraduate nursing students aged 18–25 years enrolled at Manipal College of Nursing and Manipal School of Nursing, MAHE, Manipal.
Sampling technique and sampling plan
A stratified random sampling technique with proportionate allocation was employed. Participants were selected through a stratified random sampling method with proportionate allocation, considering the year of study as the strata. Approximately 700 students were enrolled across the nursing programs after eligibility screening. About 20 students were excluded for not meeting eligibility criteria, and a few refused to participate in the study. The study population included students enrolled in the first to fourth year of the BSc Nursing program, first and second year of the PBBSc Nursing program, and first to third year of the Diploma in Nursing program [Supplementary Figure 1 (97.3KB, tif) ].
Measures
The following instruments were used for data collection: a demographic proforma, an electronic gadgets usage questionnaire, a physical activity level questionnaire, and the Sleep Quality Scale (SQS). All self-developed questionnaires were validated, demonstrated satisfactory reliability, and were pilot tested before final administration.
-
Demographic Proforma
A demographic proforma was used to collect details related to participants’ age, gender, course of study, year of study, place of residence, domicile, and average hours of sleep per night.
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Electronic Gadgets Usage Questionnaire
A researcher-developed questionnaire was used to assess electronic gadget usage. Participants were asked to report the type of devices used, purpose of use, duration of ownership, average daily usage hours, number of calls made, number of messages sent, and related factors. The tool was validated by five subject experts, and content validity was established based on expert agreement and feedback. Minor modifications were incorporated following expert suggestions. The scale-level content validity index (S-CVI) was 1. Reliability was assessed using the test–retest method, yielding a correlation coefficient (r) of 0.92. Based on total scores, gadget usage was categorized into extreme, moderate, and low usage levels.
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Sleep Quality Scale (SQS)
The SQS, a standardized instrument developed by Chol Shin, was used to assess sleep quality across various populations. The scale comprises 28 items measuring six domains: daytime symptoms, restoration after sleep, problems initiating and maintaining sleep, difficulty in waking, and sleep satisfaction. Responses are recorded on a four-point Likert scale (0 = “few,” 1 = “sometimes,” 2 = “often,” and 3 = “almost always”). Scores for items under the domains of restoration after sleep and sleep satisfaction are reverse-coded prior to summation. Total scores range from 0 to 84, with higher scores indicating poorer sleep quality.[23]
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Physical Activity Level Questionnaire
The physical activity questionnaire consisted of both structured questions and Likert-scale items. Twelve questions assessed the duration and type of physical activity performed during the previous seven days. Additionally, nine Likert-scale items evaluated outcomes related to physical activity. The tool demonstrated excellent content validity (S-CVI = 0.98) and adequate internal consistency reliability, with a Cronbach’s alpha coefficient of 0.77. Physical activity levels were categorized based on total scores as high (21–25), moderate (15–20), low (10–14), and very low (below 10).
Ethical considerations
The study was conducted in accordance with established ethical principles. Ethical approval was obtained from the Institutional Ethics Committee of Manipal Academy of Higher Education (MAHE) (Approval No: IEC2-62-2025). The confidentiality and anonymity of all participants were ensured throughout the study, and participation was entirely voluntary.
Data collection procedure
After obtaining permission from the respective program coordinators, eligible participants were approached in their classrooms and provided with detailed information regarding the purpose of the study and the voluntary nature of participation. Written informed consent was obtained from all participants prior to data collection, and confidentiality of the information provided was assured. Self-administered questionnaires were distributed and collected in a classroom setting under the supervision of the researcher. Baseline demographic data were collected using a demographic proforma, followed by assessment of electronic gadget usage, sleep quality, and physical activity levels using standardized and validated instruments. To minimize missing data, all completed questionnaires were reviewed for completeness immediately after collection. Participants were requested to complete any unanswered items at the time of data collection. As a result, the final dataset contained no missing values, and no additional methods for handling missing data were required.
Data analysis
Data were analyzed using JAMOVI statistical software, version 2.5.6 (The jamovi project, Sydney, Australia). Descriptive statistics were used to summarize sample characteristics and patterns of ED usage. Inferential statistics were applied to examine the relationships between electronic gadget usage, sleep quality, and physical activity levels.
RESULTS
Table 1 presents the demographic characteristics of the study participants (N = 380). The mean age of the participants was 19.86 ± 1.4 years. The majority of students were female (72.1%), enrolled in the BSc Nursing program (67.4%), and residing in hostels (62.9%). Most participants belonged to nuclear families (86%) and originated from urban (47.4%) or rural (36.2%) areas. Approximately 75% of students reported sleeping for 5–7 h per night, while 6% reported sleeping for fewer than 5 h [Table 1].
Table 1.
Sample characteristics of the study participants, n=380
| Sample characteristics | Frequency (f) | Percentage (%) |
|---|---|---|
| Age | Mean±SD 19.86±SD 1.4 | |
| Gender | n | % |
| Male | 106 | 27.9 |
| Female | 274 | 72.1 |
| Course | ||
| BScN | 256 | 67.4 |
| PBBScN | 22 | 5.8 |
| GNM | 102 | 26.8 |
| Type of family | ||
| Nuclear | 327 | 86 |
| Joint | 53 | 14 |
| Staying at | ||
| Hostel | 239 | 62.9 |
| Day scholar | 141 | 37.1 |
| Domicile | ||
| Urban | 180 | 47.4 |
| Rural | 138 | 36.2 |
| Semi-urban | 62 | 16.3 |
| No. of hours sleeping/day | ||
| Less than 5 h | 22 | 5.8 |
| 5-7 h | 286 | 75.3 |
| More than 7 h | 72 | 18.9 |
SD: Standard deviation, BScN: Bachelor of Science in Nursing, PBBScN: Post Basic Bachelor of Science in Nursing, GNM: General Nursing and Midwifery
Table 2 illustrates the frequency and percentage distribution of students based on electronic gadget usage. Smartphones were the most commonly used devices (59.7%), followed by the use of multiple devices (38.9%). Nearly 70% of participants reported using electronic gadgets for multiple purposes, including academic work, social communication, and entertainment. Approximately 33% of students reported using gadgets for 3–4 years, and 40.3% spent an average of 3–4 h per day on non-academic screen time. Regarding self-regulation strategies, 29.2% emphasized maintaining sleep and physical health, while 22.9% reported setting screen-time limits [Table 2].
Table 2.
Frequency and percentage distribution of the students on electronic gadgets usage, n=380
| Gadget’s usage | Frequency | Percentage |
|---|---|---|
| Frequently used gadgets* | ||
| Smartphone | 227 | 59.7 |
| Smartphone and Laptop | 2 | 5 |
| Smartphone and iPad | 3 | 8 |
| Multiple | 148 | 38.9 |
| Purpose of the gadgets used* | ||
| For Study | 44 | 11.6 |
| To make Calls | 7 | 1.8 |
| To Messaging | 5 | 1.3 |
| Social media activities | 58 | 15.3 |
| Multiple purpose | 266 | 70 |
| Duration of electronic gadgets usage | ||
| Less than 1 year | 15 | 3.9 |
| 1–2 years | 43 | 11.3 |
| 3–4 years | 128 | 33.7 |
| 5–6 years | 101 | 26.6 |
| More than 6 years | 93 | 24.5 |
| Spend time daily on devices (for non-study purposes). | ||
| Less than 1 h | 28 | 7.4 |
| 1–2 h | 87 | 22.9 |
| 3–4 h | 153 | 40.3 |
| 5–6 h | 73 | 19.2 |
| More than 6 h | 39 | 10.3 |
| Proactive measures taken to manage screen time* | ||
| Setting screen time limits | 87 | 22.9 |
| App usage/restricting usage | 34 | 8.9 |
| Engaging in offline hobbies | 80 | 21.1 |
| Giving importance to sleep and physical health | 111 | 29.2 |
| None | 68 | 17.9 |
“*” multiple responses
As shown in Supplementary Table 1, students residing in hostels reported higher durations of electronic gadget usage compared to day scholars. Hostelites were more likely to spend 5–6 h (21.3%) and more than 6 h (11.3%) per day on electronic gadgets for non-academic purposes than day scholars (15.6% and 8.5%, respectively). These findings suggest that students living away from their parents tend to spend more time on electronic gadgets, whereas parental supervision among day scholars may help regulate gadget use beyond academic requirements.
Supplementary Table 1.
Duration of the electronic gadgets used by the Hostelites and Day scholars, n=380
| Duration | Hostelites f (%) | Day scholars f (%) | P |
|---|---|---|---|
| Less than 1 h | 12 (5) | 16 (11.3) | 0.0001 |
| 1–2 h | 40 (16.7) | 47 (33.3) | |
| 3–4 h | 109 (45.6) | 44 (28.8) | |
| 5–6 h | 51 (21.3) | 22 (15.6) | |
| More than 6 h | 27 (11.3) | 12 (8.5) |
Domicile and purpose of gadget use
Students from urban, rural, and semi-urban backgrounds predominantly reported multipurpose gadget use. Comparatively, urban students reported higher usage for social media (18.3%) and academic purposes (15.6%) than students from other domicile categories.
As shown in Table 3, the majority of participants (91.6%; Mean ± standard deviation (SD): 26.9 ± 4.04) demonstrated moderate levels of electronic gadget usage. Poor sleep quality was reported by 66.6% of students (Mean ± SD: 37.1 ± 8.24). In terms of physical activity, 55% of participants exhibited low activity levels (Mean ± SD: 41.9 ± 10.7), while only 3.7% reported high physical activity levels. These findings indicate that prolonged electronic gadget usage may contribute to poorer sleep quality and lower physical activity levels among students.
Table 3.
Level of electronic gadgets usage, sleep quality, and physical activity, n=380
| Variables | f (%) | Mean±SD |
|---|---|---|
| Electronic gadgets use | ||
| Extreme use | 12 (3.2) | 26.9±4.04 |
| Moderate use | 348 (91.6) | |
| Mild use | 20 (5.3) | |
| Sleep Quality | ||
| Good | 110 (28.9) | |
| Poor | 253 (66.6) | 37-1±8.24 |
| Very poor | 17 (4.5) | |
| Physical activity level | ||
| High activity | 14 (3.7) | |
| Moderate | 112 (29.5) | 41.9±10.7 |
| Low | 209 (55) | |
| Very low | 45 (11.8) |
SD: Standard deviation
Electronic gadget usage, sleep quality, and physical activity across programs
A comparison of electronic gadget usage, sleep quality, and physical activity across different nursing programs was carried out. PBBSc Nursing students demonstrated comparatively lower electronic gadget usage, better sleep quality, and higher physical activity levels. No statistically significant association was observed between electronic gadget usage and course enrolled (P = 0.072), or between course enrolled and sleep quality (P = 0.323). However, a statistically significant association was found between course enrolled and physical activity level (P = 0.024), with PBBSc Nursing students showing relatively higher activity levels.
As presented in Table 4, a statistically significant weak negative correlation was observed between electronic gadget usage and sleep quality (ρ = −0.213, P < 0.001), suggesting that increased gadget use may be linked to a decline in sleep quality. A statistically significant positive correlation was found between physical activity and sleep quality (ρ = 0.195, P < 0.001). This suggests that students who engaged in higher levels of physical activity tended to report better sleep quality. While the relationship was weak, its statistical significance indicates that physical activity may contribute positively to sleep quality. In contrast, the correlation between electronic gadget usage and physical activity was weakly negative but not statistically significant (ρ = −0.090, P = 0.080). Although the negative coefficient suggests a tendency for higher gadget usage to be associated with lower physical activity levels, the relationship was not strong enough to reach statistical significance. Therefore, no significant association between electronic gadget usage and physical activity was identified in the present study.
Table 4.
Correlation between Electronic gadgets usage and Sleep quality and Physical activity, n=380
| Variables | Spearman rho (ρ) | P |
|---|---|---|
| Electronic gadgets usage and Sleep quality | -0.213 | <0.001 |
| Physical activity and Sleep quality | 0.195 | <0.001 |
| Electronic gadgets usage and Physical activity | -0. 090 | 0.080 |
After checking for other independent/confounding variables, we found that age as significant variable in predicting physical activity along with electronic gadgets usage. Sleep quality does not obey normality; therefore, sleep quality is considered a categorical variable, and ordinal logistic regression was used to predict electronic gadgets as a predictor. We also tested all confounding variables; none of them was significant.
Table 5: Ordinal logistic regression of electronic gadgets usage and sleep quality
Table 5.
Ordinal logistic regression of electronic gadgets usage and sleep quality, n=380
| Threshold | Estimate | SE | Z | McFadden R² | OR | P |
|---|---|---|---|---|---|---|
| Poor sleep–good sleep | 0.666 | 0.671 | 0.993 | 0.00985 | 1.95 | 0.321 |
| Very poor sleep–good sleep | 1.641 | 0.796 | 2.061 | 5.16 | 0.039 |
SE: Standard error; OR: Odds Ratio
Sleep quality was analyzed as an ordinal outcome. The odds of being in poor sleep compared to good sleep are about 1.95 times higher, but this result is not statistically significant (P > 0.05). The odds of being in very poor sleep compared to good sleep are about 5.16 times higher, and this result is statistically significant (P < 0.05). It is indicated that higher levels of electronic gadget usage are associated with poorer sleep quality, but this association is statistically significant only at the highest level of usage [Table 5]. The model fit indices (Deviance = 578, AIC =586) further indicate limited predictive ability.
Supplementary Table 2: Linear regression analysis of electronic gadget usage and physical activity
The linear regression model examining the association between electronic gadget usage and physical activity was statistically significant (R² = 0.0483, P < 0.001). Electronic gadget usage showed a significant negative coefficient (β = –0.449, SE = 0.102, standardized β = –0.220, t = –4.38, P < 0.001), indicating a small effect size. It is interpreted that although increased electronic gadget usage was associated with lower physical activity levels, electronic gadget usage alone is not a major determinant of physical activity among nursing students, and other factors likely play a substantially larger role [Supplementary Table 2].
Supplementary Table 2.
Linear regression analysis of electronic gadget usage and physical activity, n=380
| Predictor | β Unstandardized | SE | β Standardized | t | R2 | P |
|---|---|---|---|---|---|---|
| Intercept | 49.192 | 2.792 | - | 17.62 | 0.0483 | <0.001 |
| Electronic Gadget Usage | -0.449 | 0.102 | -0.220 | -4.38 | <0.001 |
SE: Standard error
DISCUSSION
The present study examined electronic gadget usage and its association with sleep quality and physical activity levels among undergraduate nursing students in Udupi Taluk, Karnataka. The findings revealed high levels of ED usage, with smartphones being the most commonly used devices (59.7%), followed by the use of multiple devices (38.9%). A substantial proportion of students (70%) reported using devices primarily for communication and social media rather than academic purposes. These findings suggest that ED use is pervasive, with students spending considerable time engaged in non-academic activities.
The majority of students (91.6%) reported moderate gadget use, with most spending 3–6 h per day on non-academic screen activities, and 10.3% exceeding 6 h daily. Hysing et al.[24] reported that nearly all young adults used EDs during the hour preceding sleep, while studies at Al-Azhar University, Cairo, found that 96.5% of students used smartphones at bedtime, with social media use often exceeding academic use.[25] Similarly, studies from India indicate increased gadget use during weekends, primarily for social media engagement rather than educational purposes.[26]
Although 91.6% of participants were categorized as moderate electronic gadget users, a substantial proportion (66.6%) reported poor sleep quality, including 4.5% who experienced very poor sleep. Similarly, more than half of the participants (55%) reported low physical activity levels, with 11.8% demonstrating very low activity, indicating a predominantly sedentary lifestyle. This apparent discrepancy suggests that sleep quality and physical activity are influenced by multiple interacting factors beyond overall gadget use alone. Sleep quality is a multifactorial phenomenon affected by academic workload, psychological stress, anxiety, lifestyle habits, caffeine consumption, environmental conditions, and individual sleep hygiene practices. Furthermore, the classification of moderate gadget use based on total daily duration may not adequately capture bedtime-specific behaviors such as nighttime screen exposure, prolonged social media engagement, and frequent device checking before sleep, which are known to delay sleep onset and increase cognitive and emotional arousal. These findings are consistent with epidemiological evidence indicating that students commonly experience delayed bedtimes and reduced sleep duration, averaging approximately 6.5 h on weekdays, contributing to chronic sleep deprivation, sedentary behavior, and impaired academic performance.[27] Therefore, the high prevalence of poor sleep quality and low physical activity observed in this study likely reflects the combined influence of lifestyle, behavioral, and psychosocial factors, with electronic gadget use acting as one contributing factor rather than the sole determinant.
The study identified that higher levels of electronic gadget usage are associated with poorer sleep quality. Previous studies have reported similar associations, indicating that heavy device use is linked to sleep deficiency, reduced daytime alertness, poor academic performance, and increased depressive symptoms among children.[28] Kalal et al. and Haddaouy et al. found that smartphone addiction has become prevalent among nursing students, and it affects their sleep quality, academic performance, and clinical performance.[10,11] Prolonged device use in improper postures has also been associated with musculoskeletal risks,[29] while exposure to blue light emitted from screens has been shown to impair concentration and disrupt circadian rhythms.[30] Studies have further reported strong associations between media consumption at bedtime and reduced sleep quality and duration among medical and nursing students.[31]
Physical activity is a crucial determinant of students’ physical, psychological, and social well-being. Regular engagement in leisure-time physical activity has been shown to reduce school burnout and enhance academic performance. However, prolonged periods spent using smartphones and electronic gadgets promote sedentary behavior. In the present study, more than half of the students reported low levels of physical activity, reinforcing concerns regarding inactivity among nursing students.
The findings demonstrated a negative correlation between electronic gadget usage and physical activity, alongside a positive correlation between physical activity and sleep quality. Linear regression analysis revealed that electronic gadget usage was a significant negative predictor of physical activity levels. These results are consistent with previous research indicating that increased screen time reduces participation in physical activities such as walking, cycling, and sports, ultimately leading to sedentary lifestyles.[26] Prolonged gadget use, combined with reduced physical activity, is also associated with unhealthy dietary patterns, increased caloric intake, and a rising prevalence of obesity among school students.[32] Additionally, excessive screen exposure has been linked to negative body composition, increased cardio-metabolic risk, impaired fitness levels, behavioral issues, and reduced self-esteem.[33] Overall, extended screen time adversely affects sleep, physical activity, cognitive development, and general health, posing significant long-term risks to students’ well-being.[34]
Limitations and strengths of the study
The cross-sectional design of this study limits the ability to establish causal relationships between electronic gadget usage, sleep quality, and physical activity levels. Although statistical associations were identified, causality cannot be inferred. Several potential confounding factors influencing prolonged gadget use and its impact on sleep and physical activity were not assessed. Additionally, data on gadget usage, sleep quality, and physical activity were self-reported, which may be subject to recall bias, particularly given the young age of the participants. Information on other relevant outcomes, such as academic performance, psychological effects, and environmental influences, was not collected. Construct validity and criterion validity against established instruments such as International Physical Activity Questionnaire- Short Form (IPAQ-SF)or Global Physical Activity Questionnaire (GPAQ) were not assessed. Due to the limited number of male participants, gender-stratified analyses were not performed. Future studies with more balanced gender representation are warranted to explore potential gender-specific differences and effect modification.
Despite these limitations, the study has notable strengths. The use of a randomly selected sample and a priori sample size calculation enhanced the methodological rigor of the study by reducing selection bias and ensuring adequate statistical power to detect meaningful associations.
Importantly, the study contributes to the existing literature by highlighting the mitigating role of physical activity in reducing the adverse effects of electronic gadget usage on sleep quality. These findings support the need for targeted interventions promoting physical activity among nursing students to improve overall health and quality of life.
CONCLUSION
The present study demonstrates that ED usage is highly prevalent among nursing students, particularly for non-academic purposes such as social networking, leisure, and entertainment. Excessive use of electronic gadgets was found to negatively impact sleep quality and physical activity levels. Prolonged screen time, especially before bedtime, contributes to delayed sleep onset, shortened sleep duration, daytime fatigue, missed classes, poor academic performance, and reduced motivation for physical activity.
The increasing ownership and reliance on electronic gadgets such as smartphones and laptops have coincided with a marked decline in physical activity. Traditional activities such as sports, cycling, swimming, gym workouts, and walking are being replaced by sedentary behaviors, increasing the risk of behavioral and lifestyle-related health problems among young adults. Promoting healthy lifestyle practices—including limiting screen time before bedtime, encouraging regular physical activity, and incorporating stress-management strategies such as yoga and meditation—is essential for improving sleep quality, enhancing physical well-being, and supporting the overall health of nursing students.
Ethics approval
Ethical approval was granted by the Institutional Ethics Committee of MAHE (Approval No: IEC2-62-2025). Confidentiality and anonymity of the participants were maintained, and participation was voluntary.
Conflicts of interest
There are no conflicts of interest.
SUPPLEMENTARY MATERIALS
Consolidated standards of reporting trials diagram of sampling technique
Funding Statement
Nil.
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Supplementary Materials
Consolidated standards of reporting trials diagram of sampling technique