Impact of Smartphone usage on Sleep Quality among Nursing Students: A Descriptive Cross-Sectional Study
Assistant Professor, Saraswati Nursing Institute, Dhianpura, Roop Nagar, Punjab, India.
*Corresponding Author E-mail: shivanikaushal812@gmail.com, pt0176971@gmail.com
ABSTRACT:
Background: Due to covid-19 educational institutions were entirely closed from March 2020. There is irresolvable to stop the spread of the Covid pandemic, which affect the school children. So, virtual classes were started, instead of traditional education. Due to this virtual learning, the screen hours spending got increased. Spending long time in the screen results in eye problems among school children. Digital eye strains are the most common problem, due to long term usage of the digital devices. The common eye associated problems, due to this perpetual usage of digital devices are dry eyes, itching, foreign body sensation, blurring of vision, headache and watering of the eyes. Methods: Quasi experimental non randomized control group design was adopted for the study. Purposive sampling technique was used to select 30 participants for study group and 30 participants for control group. Pretest was done through the digital device eye symptoms rating scale and schirmer’s dry eye test. Ophthalmic exercises were given for 15 minutes once a day for seven consecutive days. Post test was done to both study group and control group on the seventh day after intervention through the same tool. Discussion: The findings have revealed that the pretest mean score on selected eye parameters such as digital device eye symptoms 39.90 and 37.03 and for dryness of eyes are 8.57 and 10.03 in study group and control group. The paired ‘t’ test value of study group and control group on digital device eye symptoms are 15.62 and 1.62 and dryness of eyes are 6.82 and 1.469 respectively which were significant at p≤0.05 and highly significant at p≤0.01, p≤0.001. Hence, the research hypothesis H1 accepted. The posttest mean score on selected eye parameters such as digital device eye symptoms are 18.47 and 38.96 and for dryness of eyes are 18 and 9.73 in study group and control group respectively. The unpaired ‘t’ test value for digital device eye symptoms is 8.50 and for dryness of eyes is 7.28 which is significant at p≤0.05 and highly significant at p≤0.01, p≤0.001. Conclusion: Based on the findings the study revealed that ophthalmic exercises are more effective in reducing selected eye parameters among virtual learners.
KEYWORDS: Smartphone addiction, Sleep quality, Nursing students, Pittsburgh Sleep Quality Index, Digital behavior.
1. INTRODUCTION:
The rapid proliferation of smartphone technology has profoundly altered communication patterns, educational practices, and healthcare delivery worldwide. Smartphones, characterized by continuous internet access and multifunctionality, have become indispensable tools for students, particularly those in health sciences education.
In nursing education, smartphones are extensively used for accessing academic materials, clinical guidelines, drug references, and online learning platforms.³ Despite their educational advantages, excessive smartphone usage has emerged as a growing public health concern. Smartphone addiction is increasingly conceptualized as a form of behavioral addiction, sharing core characteristics with substance-related disorders, including compulsive use, impaired control, tolerance, withdrawal symptoms, and disruption of daily activities.⁸ Unlike substance dependence, behavioural addiction is reinforced through immediate digital gratification, social validation, and continuous stimulation, making young adults particularly susceptible.⁷
Sleep is a vital physiological process essential for physical restoration, cognitive processing, emotional regulation, and immune function. Adequate sleep is particularly crucial for nursing students, who must maintain sustained attention, sound clinical judgment, and psychomotor efficiency during academic and clinical activities. Poor sleep quality has been associated with decreased concentration, impaired memory consolidation, mood disturbances, and increased risk of anxiety, depression, and burnout among healthcare students.⁵
Emerging evidence indicates that excessive smartphone use, especially during evening and nighttime hours, disrupts circadian rhythms and delays sleep onset. Exposure to blue light emitted from smartphone screens suppresses melatonin secretion, leading to prolonged sleep latency and reduced sleep duration.³ Furthermore, constant notifications and social media engagement contribute to psychological arousal, further impairing sleep quality.¹
Globally, smartphone usage among university students has increased dramatically, with studies reporting addiction prevalence ranging from 30% to 70%.⁹ In India, the increasing penetration of smartphones, combined with academic stress and lifestyle changes, has intensified concerns regarding digital overuse among students. Nursing students face unique challenges due to irregular schedules, clinical rotations, and academic demands, making them especially vulnerable to sleep disturbances associated with excessive smartphone use.
Several studies have demonstrated a significant association between smartphone addiction and poor sleep quality among medical and nursing students. Higher smartphone usage was significantly correlated with increased PSQI scores among Indian medical students.⁴ Nnighttime smartphone use was strongly associated with poor sleep quality and elevated stress levels among medical students.⁶ Despite growing evidence, limited institution-specific data are available on smartphone-related sleep disturbances among nursing students in Punjab.
Therefore, this study was undertaken to assess smartphone usage patterns and evaluate their impact on sleep quality among nursing students. Understanding this relationship is essential for developing targeted interventions aimed at promoting digital well-being, academic performance, and overall health among future nursing professionals.
A quantitative descriptive cross-sectional research design was adopted to assess smartphone usage and sleep quality among nursing students.
The study was conducted at a selected nursing institute located in Punjab, India, offering undergraduate nursing programs.
The study population comprised undergraduate nursing students enrolled in B.Sc. Nursing and GNM programs. A total of 100 students were selected using a purposive sampling technique.
Students who were present during data collection and willing to participate were included in the study. Students who declined participation or were absent at the time of data collection were excluded.
Data were collected using three standardized tools:
A socio-demographic questionnaire The Smartphone Addiction Scale
The Pittsburgh Sleep Quality Index (PSQI)
The PSQI is a widely validated instrument used to assess subjective sleep quality and sleep disturbances over a one-month period.²
Content validity of the instruments was established through expert review. The reliability of the smartphone addiction tool demonstrated a Cronbach’s alpha coefficient of 0.94, indicating high internal consistency.
Ethical approval was obtained from the Institutional Ethics Committee. Written informed consent was secured from all participants. Confidentiality and anonymity were strictly maintained throughout the study.
Data were analysed using descriptive statistics, including frequency and percentage distributions. Associations between smartphone addiction and selected socio-demographic variables were assessed using the chi-square test. A p-value of less than 0.05 was considered statistically significant.
Among the 100 nursing students included in the study, the majority (76%) were aged between 19 and 21 years. Female students constituted 99% of the sample, reflecting the gender distribution commonly observed in nursing education in India. Regarding academic programs, 69% of participants were enrolled in B.Sc. Nursing, while 31% were pursuing GNM courses. Most students (59%) resided in hostels, and 41% were day scholars.
Table 1: Frequency And Percentage Distribution of Subjects as Per Their Socio-Demographic Variables
|
Characteristics |
Frequency (n) |
%age |
|
Age 16-18 years 19-21years 22-24 years Above 24 years |
18 76 6 0 |
18.0 76.0 6.0 0 |
|
Gender Male Female |
1 99 |
1.0 99.0 |
|
Residing at Day scholar |
41 |
41.0 |
|
Hostler Paying guest |
59 0 |
59.0 0 |
|
Education B.Sc. Nursing M.Sc. Nursing GNM Post basic |
69 0 31 0 |
69.0 0 31.0 0 |
|
Type of family Nuclear Joint Extended |
62 35 3 |
62.0 35.0 3.0 |
|
At which time you using mobile phone Morning Afternoon Evening Night |
0 3 49 48 |
0 3.0 49.0 48.0 |
|
Duration of using in a day 1-2 hours 2-4hours 4-6hours More than 6 hours |
38 48 10 4 |
38.0 48.0 10.0 4.0
|
|
Purpose of using phone in a day Entertainment Study Communication All of the above |
5 2 0 93 |
5.0 2.0 0 93.0 |
|
From how long you are using phone 1-2years 2-4years 4-6years More than 6 years |
29 33 21 16 |
29.0 33.0 21.0 16.0 |
Fig.1: Percentage distribution of students by their age in years
In terms of smartphone usage patterns, 61% of participants reported using smartphones for more than five years. Daily smartphone usage exceeding five hours was reported by 54% of students, indicating prolonged digital engagement. The primary purposes of smartphone use included social media engagement, academic activities, entertainment, and communication.
Assessment using the Smartphone Addiction Scale revealed a high prevalence of problematic smartphone use. A majority of students (59%) demonstrated severe smartphone addiction, while 38% exhibited moderate addiction. A small proportion (3%) fell into the very severe addiction category. Notably, none of the participants were classified under the mild or non-addicted category, indicating pervasive smartphone dependency within the study population.These findings suggest that excessive smartphone use is a common behavioral pattern among nursing students, potentially predisposing them to adverse health outcomes.
Table: 2 Smart-Phone Addition Scale
|
S. No |
Items |
Never |
Rare |
At intervals |
Always |
|
1 |
Missing planned work due to smart phone use due to smart-phone usage. |
35(35.0%) |
50(50.0%) |
13(13.0%) |
2(2.0%) |
|
2 |
Having a hard time concentrating in class while doing assignments or while working due to smart phone use. |
35(35.0%) |
36(36.0%) |
20(20.0%) |
9(9.0%) |
|
3 |
Experiencing light headache or blurred vision due to excessive smart phone use. |
20(20.0%) |
51(51.0%) |
22(22.0%) |
6(6.0%) |
|
4 |
Feeling pain in the wrists or at the back of the neck while using a smart phone. |
35(35.0%) |
36(36.0%) |
24(24.0%) |
5(5.0%) |
|
5 |
Feeling tired and lack of adequate sleep due to excessive smart phone usage. |
34(34%) |
37(37.0%) |
14(14.0%) |
14(14.0%) |
|
6 |
Feeling calm and cozy while using a smart phone. |
16(16.0%) |
33(33.0%) |
31(31.0%) |
18(18.0%) |
|
7 |
Feeling pleasant or excited while using a smart phone. |
6(6.0%) |
30(30.0%) |
30(30.0%) |
34(34.0%) |
|
8 |
Feeling confident while using a smart phone. |
17(17.0%) |
28(28.0%) |
23(23.0%) |
32(32.0%) |
|
9 |
Being able to get that rid off stress with a smart phone. |
19(19.0%) |
29(29.0%) |
36(36.0%) |
16(16.0%) |
|
10 |
There is nothing more fun to do than using my smart phone. |
39(39.0%) |
29(29.0%) |
19(19.0%) |
12(12.0%) |
|
11 |
My life would be empty without my smart phone. |
46(46.0%) |
16(16.0%) |
15(15.0%) |
22(22.0%) |
|
12 |
Feeling most liberal while using a smart phone. |
21(21.0%) |
42(42.0%) |
18(18.0%) |
16(16.0%) |
|
13 |
Using a smart phone is the most fun thing to do. |
20(20.0%) |
28(28.0%) |
34(34.0%) |
14(14.0%) |
|
14 |
Won’t be able to stand not having a smart phone. |
47(47.0%) |
21(21.0%) |
8(8.0%) |
23(23.0%) |
|
15 |
Feeling impatience and fretful when I am not holding my smart phone. |
42(42.0%) |
30(30.0%) |
21(21.0%) |
7(7.0%) |
|
16 |
Having my smart phone in my mind even when I am not using it. |
43(43.0%) |
35(35.0%) |
12(12.0%) |
9(9.0%) |
|
17 |
I will never give up using my smart phone even when my daily life is already greatly affected by it. |
32(32.0%) |
41(41.0%) |
16(16.0%) |
10(10.0%) |
|
18 |
Getting irritated when bothered while using my smart phone. |
19(19.0%) |
46(46.0%) |
22(22.0%) |
12(12.0%) |
|
19 |
Bringing my smart phone the toilet even when I am in hurry to get there. |
75(75.0%) |
12(12.0%) |
6(6.0%) |
7(7.0%) |
|
20 |
Feeling great meeting more people via smart phone use. |
26(26.0%) |
34(34.0%) |
20(20.0%) |
20(20.0%) |
|
21 |
Feeling that my relationships with my smart phone buddies are more intimate than my relationship with my real-life friends. |
50(50.0%) |
20(20.0%) |
4(4.0%) |
24(24.0%) |
|
22 |
Not being able to use my smart phone would be as painful as loosing a friend. |
47(47.0%) |
26(26.0%) |
15(15.0%) |
12(12.0%) |
|
23 |
Constantly checking my smart phone so as not to miss conversation between other people on twitter or Facebook. |
39(39.0%) |
31(31.0%) |
20(20.0%) |
10(10.0%) |
|
24 |
Checking SNS (social networking service) sites like twitter or Facebook right after waking up. |
43(43.0%) |
26(26.0%) |
10(10.0%) |
21(21.0%) |
|
25 |
Preferring talking with my smart phone buddies to hanging out with my real-life friends or with the other members of my family. |
45(45.0%) |
26(26.0%) |
14(14.0%) |
14(14.0%) |
|
26 |
Preferring searching from my smart phone to asking other people. |
24(24.0%) |
32(32.0%) |
18(18.0%) |
26(26.0%) |
|
27 |
My fully charged battery does not last for one whole day. |
28(28.0%) |
44(44.0%) |
12(12.0%) |
15(15.0%) |
|
28 |
Using my smart phone longer than I had intended. |
36(36.0%) |
36(36.0%) |
12(12.0%) |
15(15.0%) |
|
29 |
Feeling the urge to use my smart phone again right after I stopped using it . |
22(22.0%) |
46(46.0%) |
15(15.0%) |
16(16.0%) |
|
30 |
Having tried time and again to shorten my smart phone use time, but failing all the time. |
30(30.0%) |
41(41.0%) |
12(12.0%) |
17(17.0%) |
|
31 |
Always Thinking I should shorten my smart phone use time. |
20(20.0%) |
23(23.0%) |
16(16.0%) |
39(39.0%) |
|
32 |
The people around tell me I use my smart phone too much. |
40(40.0%) |
30(30.0%) |
10(10.0%) |
20(20.0%) |
Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI). The results demonstrated that 86% of students experienced poor sleep quality, as indicated by PSQI scores greater than 5. The remaining 14% of participants reported average sleep quality. None of the students achieved PSQI scores indicative of good sleep quality.
Key components contributing to poor sleep quality included delayed sleep onset, reduced sleep duration, frequent nighttime awakenings, and daytime dysfunction. These findings highlight a substantial burden of sleep disturbances among nursing students.
|
S. No. |
Physical domain |
|
|
|
|
|
1. |
During the past month, what time have you usually gone to bed at night? |
88(88.0%) |
8(8.0%) |
0(0.0%) |
4(4.0%) |
|
2. |
During the past month, how long (in minutes) has it usually taken you to fall asleep each night? |
53(53.0%) |
36(36.0%) |
3(3.0%) |
8(8.0%) |
|
3. |
During the past month, what time have you usually gotten up in the morning? |
37(37.0%) |
46(46.0%) |
0(0.0%) |
17(17.0%) |
|
4. |
During the past month, how many hours of actual sleep did you get at night? (This may be different then the number of hours you spent in bed.) |
77(77.0%) |
18(18.0%) |
0(0.0%) |
5(5.0%) |
|
5. |
During the past month, how often have you had trouble sleeping because you.... |
Not during the past month |
Less than once a week |
Once or twice a week |
Three or more times a week |
|
a. |
Cannot get to sleep within 30 minutes |
46(46.0%) |
28(28.0%) |
15(15.0%) |
11(11.0%) |
|
b. |
Wake up in the middle of the night or early morning |
30(30.0%) |
24(24.0%) |
23(23.0%) |
23(23.0%) |
|
c. |
Have to get up to use the bathroom |
46(46.0%) |
21(21.0%) |
16(16.0%) |
17(17.0%) |
|
d. |
Cannot breathe comfortably |
84(84.0%) |
7(7.0%) |
7(7.0%) |
2(2..0%) |
|
e. |
Cough or snore loudly |
84(84.0%) |
8(8.0%) |
8(8.0%) |
0(0.0%) |
|
f. |
Feel to cold |
62(62.0%) |
20(20.0%) |
9(9.0%) |
9(9.0%) |
|
g. |
Feel to hot |
53(53.0%) |
19(19.0%) |
18(18.0%) |
10(10.0%) |
|
h. |
Have bad dreams |
41(41.0%) |
21(21.0%) |
24(24.0%) |
14(14.0%) |
|
i. |
Have pain |
61(61.0%) |
20(20.0%) |
7(7.0%) |
12(12.0%) |
|
j. |
Other reason(s), please describe: |
89(89.0%) |
8(8.0%) |
1(1.0%) |
2(2.0%) |
|
6. |
During the past month, how often have you taken medicine to help you sleep (prescribed or “over the counter”)? |
90(90.0%) |
2(2.0%) |
7(7.0%) |
1(1.0%) |
|
7. |
During the past month, how often had you trouble staying awake while driving, eating meals, or engaging in social activity? |
72(72.0%) |
21(21.0%) |
6(6.0%) |
1(1.0%) |
|
|
|
No problem at all |
Only a very slight problem |
Somewhat of a problem |
Avery big problem |
|
8. |
During the past month, how much of a problem has it been for you to keep up enough enthusiasm to get things to done? |
59(59.0%) |
25(25.0%) |
13(13.0%) |
3(3.0%) |
|
|
|
Very good |
Fairly good |
Fairly bad |
Very bad |
|
9. |
During the past month, how would you rate your sleep quality overall? |
53(53.0%) |
33(33.0%) |
11(11.0%) |
3(3.0%) |
|
|
|
No bed partner or room-mate |
Partner/roommate in other room |
Partner in same room but not in same bed |
Partner in same bed |
|
10. |
Do you have a bed partner, ask him/her how often in the past month you have had: |
41(41.0%) |
5(5.0%) |
41(41.0%) |
13(13.0%) |
|
a. |
Loud snoring |
81(81.0%) |
7(7.0%) |
10(10.0%) |
2(2.0%) |
|
b. |
Long pauses between breathes while asleep |
85(85.0%) |
9(9.0%) |
4(4.0%) |
2(2.0%) |
|
c. |
Legs twitching or jerking while you sleep |
66(66.0%) |
15(15.0%) |
6(6.0%) |
13(13.0%) |
|
d. |
Episodes of disorientation or confusion during sleep |
71(71.0%) |
11(11.0%) |
13(13.0%) |
5(5.0%) |
|
e. |
Other restlessness while you sleep, please describe: |
80(80.0%) |
9(9.0%) |
5(5.0%) |
6(6.0%) |
3.4 Association Between Smartphone Addiction and Selected Variables:
Chi-square analysis revealed statistically significant associations between smartphone addiction and selected smartphone usage variables. A significant association was found between smartphone addiction and duration of daily smartphone use (χ² = 173.004, p = 0.010), indicating that students who used smartphones for longer durations were more likely to exhibit severe addiction.
Additionally, a statistically significant association was observed between smartphone addiction and purpose of smartphone usage (χ² = 115.233, p = 0.027). Students who used smartphones for multiple purposes, particularly social media and entertainment, demonstrated higher levels of addiction.
No statistically significant association was found between smartphone addiction and duration of smartphone ownership (p = 0.209), suggesting that the intensity of current usage, rather than years of ownership, plays a more critical role in addiction development.
Table 4. Association between Demographic Variable and Smart-phone Addition among nursing students.
|
Social demographic variables |
Options |
chi2 |
df |
p-value |
Level of significance |
|
Age |
a) 16-18yrs b) 19-21yrs c) 22-24yrs d) Above 24yrs |
68.375 |
88 |
0.940 |
Significant |
|
Gender |
a) Male b) Female |
100.000 |
44 |
0.000 |
Significant |
|
Residing at |
Day-scholar Hostler Paying guest |
43.985 |
44 |
0.472 |
Significant |
|
Education |
a) BSc. nursing b) MSc. nursing c) GNM d) Post basic |
46.860 |
44 |
0.356 |
Significant |
|
Type of family |
a) Nuclear b) Joint c) Extended |
78.640 |
88 |
0.752 |
Significant |
|
At which time you using mobile phone |
a) Moring b) Afternoon c) Evening d) Night |
85.799 |
88 |
0.547 |
Significant |
|
Duration of using in a day |
a) 1-2hrs b) 2-4hrs c) 4-6hrs d) More than 6hrs |
173.004 |
132 |
0.010 |
Significant |
|
Purpose of using phone in a day |
a) Entertainment b) Study c) Communication d) All of the above |
115.233 |
88 |
0.027 |
Significant |
|
From how long you are using phone |
a) 1-2hrs b) 2-4hrs c) 4-6hrs d) More than 6hrs |
190.942 |
176 |
0.209 |
Not significant |
The present study identified a high prevalence of smartphone addiction and poor sleep quality among nursing students, underscoring a significant behavioral and public health concern. The finding that nearly two-thirds of participants exhibited severe smartphone addiction is consistent with previous studies conducted among healthcare students, which report addiction prevalence ranging from 48% to 72%.7
The observed prevalence of poor sleep quality (86%) exceeds that reported in several international studies, where rates ranged from 55% to 75% among medical and nursing students.1 This disparity may be attributed to increased smartphone penetration, academic stress, and lifestyle changes among Indian nursing students.
The significant association between smartphone addiction and duration of daily smartphone use aligns with findings reported, who demonstrated that prolonged daily usage is a strong predictor of behavioral addiction.8 Extended screen time, particularly during evening hours, disrupts circadian rhythms and delays sleep onset through suppression of melatonin secretion.3
Furthermore, the significant association between smartphone addiction and purpose of usage highlights the role of social media and entertainment applications in reinforcing compulsive smartphone behavior. Continuous engagement with social networking platforms has been shown to increase psychological arousal, fear of missing out, and sleep disturbances.6
The absence of a significant association between smartphone addiction and years of smartphone ownership suggests that addiction is more closely linked to usage intensity and behavioral patterns rather than duration of exposure. This finding reinforces the importance of addressing current digital habits rather than focusing solely on years of device ownership.
Collectively, these findings emphasize the need for targeted interventions aimed at promoting responsible smartphone use and improving sleep hygiene among nursing students.
The findings of this study have important implications for nursing practice, education, research, and administration, particularly in addressing excessive smartphone use and its adverse impact on sleep quality among nursing students.
Nurses should incorporate assessment of smartphone usage patterns and sleep behaviors into routine health evaluations. Early identification of problematic digital use and sleep disturbances can facilitate timely counseling on sleep hygiene, stress management, and responsible smartphone use, thereby reducing the risk of long-term physical and psychological consequences.
Nursing curricula should integrate content on digital well-being and sleep health to enhance students’ awareness of the impact of excessive smartphone use on cognitive performance and clinical competence. Educators should promote balanced technology use and model appropriate digital behavior in academic and clinical settings.
Further research employing longitudinal and interventional designs is warranted to establish causal relationships and evaluate the effectiveness of targeted interventions. Multicenter studies and mixed-method approaches may strengthen the evidence base and inform context-specific nursing strategies.
Nursing administrators should implement institutional policies that encourage responsible smartphone use and support student well-being. Access to wellness programs, counseling services, and clear guidelines on digital device use may contribute to improved academic performance, reduced burnout, and enhanced professional readiness.
The study concludes that excessive smartphone usage is highly prevalent among nursing students and is significantly associated with poor sleep quality. Prolonged daily smartphone use and multi-purpose digital engagement contribute substantially to sleep disturbances. Addressing smartphone addiction through structured educational interventions, behavioral counseling, and institutional support is essential to promote healthy sleep patterns and optimize academic and clinical performance among nursing students.
All the authors contribute to the work.
No conflict of interest.
We sincerely thank our faculty members of Saraswati Nursing Institute , and the subject’s cooperation despite
their busy schedules. We would like to thank God almighty and our parents for being the guiding stars in our lives.
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Received on 03.06.2026 Revised on 30.06.2026 Accepted on 24.07.2026 Published on 10.08.2026 Available online from August 14, 2026 Int. J. of Advances in Nursing Management. 2026;14(3):174-180. DOI: 10.52711/2454-2652.2026.00037 ©A and V Publications All right reserved
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