Association Between Screen Time and Sleep: An Online Survey

The work was a class-based methods project and was not IRB-approved for external dissemination. The following page serves as an example of a student paper.

Aaqib F. Azeez, January 2026

How much does screen time affect our sleep quality?

Abstract: The relationship between electronic devices and poor sleep quality has raised concern among young adults. In this study, the associations between weekday screen time and sleep latency, sleep duration, and sleep quality were examined through a sample of undergraduate college students. 40 students from a large public university in the United States completed an online Qualtrics survey, where they were asked about their typical weekday and weeknight screen use through questions derived from a Screen-Time Questionnaire and the Brief-Pittsburgh Sleep Quality Index, a sleep quality measurement scale. Pearson correlations were conducted on 29 of the responses to test the relationships between total weekday screen time and sleep duration, total weekday screen time and sleep quality, and between total weeknight screen time and sleep latency. The results indicate that total weekday screen time and poor sleep quality had a moderate positive association, meaning that the longer the screen time, the worse one's sleep quality was. Associations between sleep latency and sleep duration were not significant. These findings suggest that greater weekday screen exposure may be associated with the quality of one's sleep, versus the duration or latency of their sleep. Limitations to our research include non-probability sampling (convenience sampling) and self-reported measurements of screen time. Future research should obtain representative samples and employ objective measures to track sleep quality, latency, and duration.

Keywords: screen time, sleep quality, sleep latency, sleep duration, college students

Educational level: this is a tertiary (university) resource.

Subject classification: this is a psychology resource.

Type classification: this is a paper resource.

Completion status: this resource has reached a high level of completion.

Introduction

Smartphones have evolved into an essential part of daily life, naturally raising concerns about their impact on sleep quality. Poor sleep quality is associated with a multitude of health risks, including metabolic syndromes. Because young adults are in a crucial period of development, it is worth investigating the impact of excessive screen time. Weekday patterns for young adults are crucial to study since they differ in comparison to weekend sleeping patterns. Research has shown that higher differences between weekday and weekend sleep duration have been linked with higher stress, lower life satisfaction, and higher chances of failing a study exam.

Various studies have negatively linked smartphone usage to sleep quality, including increased sleep latency, shorter sleep duration, daytime drowsiness, and insomnia. Regarding the effect of timing in screen use, Sumter et al. (2024) reported a negative association between screen use within an hour of bedtime and sleep time. Other studies assess overall screen time, while our study will measure screen time and its association with sleep quality on a weekday basis. Our goal is to measure weekday screen time and observe its association with sleep quality. We have also eliminated weekends, as people tend to observe drastically different sleep behaviors on weekends compared to weekdays.

Smartphones have evolved into an essential part of daily life, naturally raising concerns about their impact on sleep quality

We used past insights and gaps to guide us in conducting this study, where participants will self-report their measures through an online survey. We evaluated the relationship between the predictor, screen time use, and three criterion variables: the time it takes for an individual to fall asleep (sleep latency), the total length of the individual's sleep (sleep duration), and the overall quality of the individual's sleep (sleep quality). Sleep latency was compared to screen time use during a typical weeknight, while sleep duration and sleep quality were compared to screen time use during a typical weekday.

Blue light, which is emitted from phone screens, disrupts melatonin production (a hormone that regulates sleep). Therefore, we hypothesized that screen time use on a typical weeknight will be positively correlated with sleep latency, screen time use on a typical weekday will be negatively correlated with sleep duration, and screen time use on a typical weekday will be negatively correlated with sleep quality. Participants in the study were undergraduate students in a psychology class who completed an online Qualtrics survey.

Methods

Participants were recruited for the study through convenience sampling from an upper-division psychology course at a public university in the United States. All students in the course were eligible to participate and were individually emailed a link to the survey via their student email to complete. Participation was voluntary, no incentives were given to participants for completing the survey, and partial responses were accounted for. An exclusion criterion was not present for this research. Surveys that were not fully completed were kept for descriptive analysis, while listwise deletion was used preceding inferential analysis.  

Participants

In total, 40 participants (N = 40) submitted the survey. 36 participants reported their gender, with seven of the participants identifying themselves as male (17.5%), 27 of the participants identifying as female (67.5%), and two of the participants identifying as other (5.0%). Four of the participants did not report their gender identification (10%). 36 participants reported their grade level, with one of the participants self-identifying as a sophomore (2.5%), four of the participants self-identifying as juniors (10.0%), 27 of the participants self-identifying as seniors (67.5%), and four of the participants self-identifying as being in their 5^th+ year (10.0%). Four of the participants did not report their grade level (10.0%). In terms of enrollment status in college, 24 of the participants were enrolled full-time (60.0%), while 11 of the participants were enrolled part-time (27.5%). Five of the participants did not report their enrollment status (12.5%). Lastly, for weekly working hours, four of the participants worked zero hours (10.0%), three of the participants worked 0-10 hours (7.5%), seven of the participants worked 11-20 hours (17.5%), seven of the participants worked 21-30 hours (17.5%), and 14 of the participants worked over 31 hours (35.0%). Five of the participants did not report their weekly working hours (12.5%). 29 participants reported their age, with the mean age being 27.10 (SD = 7.504, range = 18-53).

Measures

To measure screen-time, we derived questions from an 18-item Screen-Time Questionnaire. In our study, we only incorporated questionnaires relating to the average weekday screen use total and the average weeknight screen use total. An example question from this questionnaire measuring average weekday screen use total is “thinking of an average weekday (from when you wake up until you go to sleep), how much time do you spend using each of the following types of screen as the primary activity?”, where the participant has to answer with the number of hours and the number of minutes using a television, TV-connected device, laptop, smartphone, and/or tablet. The Screen-Time Questionnaire has been found to have good relative reliability, with an intraclass correlation coefficient score between .68 and .89 for all questions (.60+ is good relative reliability, while .75+ is excellent relative reliability). The Questionnaire has good convergent validity, as the Questionnaire was found to be positively and significantly correlated with the Conners Parent Rating Scale.

To measure the dependent variables (sleep quality, sleep latency, and sleep duration within the last month), we derived questions from the brief version of the Pittsburgh Sleep Quality Index (B-PSQI; Sancho-Domingo et al., 2021). In the B-PSQI, questions such as “during the past month, how long has it usually taken you to fall asleep each night?” were used to assess sleep latency. Questions, such as “during the past month, how many hours of actual sleep did you get at night?” are used to measure sleep duration. The overall score calculated from the B-PSQI (out of 15) was used to determine the B-PSQI score, which is positively associated with poorer sleep quality. 16 questions of the B-PSQI (one was removed due to redundancy as it was found to measure the same entity as another question, while two other questions were combined into one item measuring sleep efficiency) are reported to have good internal reliability, with a polychoric ordinal alpha of .81. The B-PSQI also has a high convergent validity, yielding r = .895 with the PSQI.

Procedures

The emailed link took participants onto a Qualtrics survey, where they reviewed the purpose of the study, the researchers behind the study, the description of the research study, the incentives for completing the study, a notice of up-to-date information regarding the participant’s participation, a notice of confidentiality, the withdrawal privilege, the compensation provided for illness and injury, and the agreement to the voluntary consent. On the second page, participants were asked for their age, gender, class rank, enrollment status, and typical working hours per week. On the third page, the participants were then asked about their sleeping habits within the last month, derived from the B-PSQI. On the fourth and final page, the participants were then asked about their screen-time usage to calculate the average weekday and weeknight screen use total. The survey was made accessible on October 12, 2025, and interested students had until October 16, 2025, to complete it. All participants were given the same survey. Responses to the dependent variable were measured in hours and minutes when applicable or four-point Likert-type options (very good, fairly good, fairly bad, very bad). Responses to the independent variable were measured in hours and minutes for screen time on a weekday or weeknight for a variety of electronic devices (television, TV-connected devices, laptop or computer, smartphone, tablet) as either a primary (actively engaged) or background activity (being played in the background as background noise/secondary priority). The overall process took no more than roughly 15 minutes to complete. The experimenters did not manipulate any variables, as this was a survey. The research was conducted as part of a course-based research project and, therefore, was not in need of explicit institutional review board approval.  

Results

The assumptions, including normality, outliers, and linearity, were tested and were found to be met. Additionally, no duplicates were found in the final data set. Therefore, no violations were found for the independence of observations.

Out of 40 submitted surveys, 13 surveys were only partially completed. Therefore, listwise deletion was utilized for optimal Pearson correlation analysis, and the final sample was subsequently 29. Pearson correlations were used to test the relationship between sleep quality and total weekday screen time, sleep duration and total weekday screen time, and sleep latency and total weeknight screen time. Total weekday screen time was positively associated with poorer sleep quality, r(27) = .37, p = .05, 95% CI [.01, .65], indicating that the higher one's screen use was, the worse one's sleep quality was. Total weekday screen time was negatively associated with sleep duration, r(27) = –.28, p = .20, 95% CI [–.56, .13], though this relationship was not statistically significant. Total weeknight screen time was positively associated with sleep latency, r(27) = .21, p = .27, 95% CI [–.17, .54], and this relationship was also not statistically significant.

This indicates that as total weekday screen time increased, the sleep quality score on the B-PSQI increased, thereby showing worse sleep quality.

Discussion

We hypothesized in this study that screen time on a typical weeknight will be positively associated with sleep latency, screen time on a typical weekday will be negatively associated with sleep duration, and screen time on a typical weekday will be positively associated with poor sleep quality. The relationship between screen time on a typical weekday and poor sleep quality was statistically significant. However, the relationship between screen time on a typical weeknight and sleep latency and the relationship between screen time on a typical weekday and sleep duration were not statistically significant. The findings show that poor sleep quality was positively associated with screen time, but the association with how long it took the students to fall asleep and sleep duration was weak and therefore, not statistically significant. The significant finding correlates with previous research that affirms that the longer one's screen time is, the worse one's sleep quality tends to be. Blue light emitted from phones has been suggested in past literature to be associated with reduced melatonin production, which may relate to an association with sleep quality. An association between sleep latency and screen time may not have been found due to the absence of blue light emission due to different screen modes (such as Night mode on the iPhone, for example), lack of engagement with emotional content, or not using the phone before bed. Sleep duration may not have shown an association, as participants may not have used their electronic devices right before bed, which previous research has found to have an association with shorter sleep duration. Overall, our results suggest that our hypothesis on the positive relationship between screen time on a typical weekday and poorer sleep quality was supported, but our hypotheses on the positive relationship between screen time on a typical weeknight and sleep latency and the negative relationship between screen time on a typical weekday and sleep duration were unsupported by our findings. Research into the relationship between screen time and an individual's sleep quality is vital in today's age due to the popular usage of electronic devices and previous research reporting its negative association with sleep quality. Both Arshad et al. (2021) and Chen et al. (2024) found that increased screen time was associated with poorer sleep quality, furthering the negative association between blue light and melatonin production and contributing to a scientific consensus on the topic. Findings from our study could be practiced individually by students in their pursuit of academic success, such as reducing overall phone dependency to improve sleep quality.

Objective measures of sleep time can be recorded on devices such as Fitbits (pictured here).

Despite the significant finding, this study faced some limitations that should be considered. Firstly, participants self-reported the duration of their primary and secondary activities using electronic devices, which may be inaccurate, as people may not accurately track the time they use their electronic devices, especially if one of the electronic devices serves as a secondary activity. Secondly, the survey was completed by participants who decided to open the link and complete the survey, which threatens external validity as the survey suffers from self-selection bias. Lastly, the researcher recruited participants from her psychology class, which is a type of non-probability sampling known as convenience sampling, which threatens external validity.

Future research should incorporate objective measures of screen time and sleep duration. For instance, future researchers can have participants install an app on their electronic devices that tracks screen time, and they can have participants wear a Fitbit watch that tracks the participants' sleep duration. Additionally, measuring other variables that may have an association with sleep quality, such as exercise and daily stress, would be useful since these variables could have a more noteworthy association with sleep quality than the intended IV (screen time). Lastly, a probability sampling method should be employed to get a sample that is representative of the population.

Insufficient sleep has plagued 60% of young adults in the United States, and with the rapid rise of electronic device usage, it is pivotal to explore the relationship between the two. By conducting this study and contributing to a growing amount of evidence that associates poor sleep quality with increased screen time, these findings may lead to the formulation of evidence-based strategies that can help address this rising issue.

Acknowledgement

Ashley Doane of Old Dominion University is credited with the study design and collecting the data for the research.

See also

• Azeez, A. F. (2026, January 8). Association Between Screen Time and Sleep: An Online Survey. Retrieved from osf.io/preprints/psyarxiv/5s2hd_v1- Course-based instructional example (non-IRB, educational use only).
• Sleep quality (en.wikipedia)
• Screen time (en.wikipedia)
• Screen time and mental health (en.wikipedia)