Type: Research Paper | Subject: Psychology | Level: Undergraduate | Word Count: ~2,200 words | Referencing: Harvard
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For your Level 5 Research Methods module, design and report a quantitative study (2,000–2,500 words) examining the relationship between social media use and sleep quality in a sample of UK undergraduate students. Your report should follow standard scientific report conventions and include a literature review, method, results, and discussion section.
This study examined the relationship between social media use and sleep quality in a sample of UK undergraduate students. A cross-sectional survey design was used with 142 participants (M age = 20.3 years, SD = 1.8) recruited from a UK university. Participants completed the Pittsburgh Sleep Quality Index (PSQI) and a self-report measure of daily and bedtime social media use. A significant positive correlation was found between bedtime social media use and PSQI global score, r(140) = .34, p < .001, indicating that greater use of social media in the hour before sleep was associated with poorer sleep quality. Hierarchical regression showed that bedtime use, but not total daily use, remained a significant predictor of sleep quality after controlling for age and gender, β = .29, p < .001. Findings support a displacement and pre-sleep arousal explanation and have implications for student wellbeing interventions.
University life often disrupts established sleep routines, and poor sleep quality is widely reported among undergraduate populations, with consequences for academic performance, mood, and physical health (Hershner and Chervin, 2014). At the same time, social media platforms have become deeply embedded in student daily life, with many young adults checking feeds multiple times per hour, including in the period immediately before sleep. Two broad mechanisms have been proposed to explain why social media use might impair sleep. The displacement hypothesis suggests that time spent on social media simply substitutes for time that would otherwise be spent sleeping, delaying bedtime and shortening total sleep duration. A second, arousal-based account proposes that the emotional and cognitive content of social media feeds, combined with exposure to bright screen light, increases physiological and psychological arousal close to bedtime, making it harder to fall asleep even when bedtime itself is not delayed (Christensen et al., 2016).
Existing research supports a link between screen-based social media use and poorer sleep outcomes in adolescent and young adult samples. Woods and Scott (2016) found that more frequent and more emotionally invested social media use was associated with poorer sleep quality, higher anxiety and lower self-esteem among UK teenagers. Levenson et al. (2016) reported similar associations among US young adults, and a broader review by Alonzo et al. (2021) concluded that the relationship between social media use and sleep is consistently negative, although effect sizes vary considerably across studies. However, much of this literature either focuses on adolescents rather than university students or does not distinguish between total daily use and use specifically around bedtime. Given that UK undergraduates represent a population with both high social media engagement and well-documented sleep difficulties, this study aimed to examine the relationship between social media use, both overall and at bedtime, and self-reported sleep quality in a sample of UK undergraduate students. It was hypothesised that (1) greater bedtime social media use would be associated with poorer sleep quality, and (2) bedtime use would predict sleep quality independently of total daily use.
Research into digital media and sleep has developed considerably over the past two decades, moving from broad measures of screen time towards more specific measures of social media engagement. Early work by Van den Bulck (2004) examined television and computer use among secondary-school pupils and found that heavier use was associated with later self-reported bedtimes and reduced sleep duration, establishing an early evidence base for the displacement hypothesis. As smartphones and social media platforms became more widespread, subsequent studies shifted focus towards these more portable and habit-forming technologies.
Woods and Scott (2016) surveyed UK adolescents and found that both overall and night-time-specific social media use predicted poorer sleep quality, alongside higher anxiety and depression scores, and that emotional investment in social media, rather than simple duration of use, was a particularly strong predictor of poor outcomes. This suggests that how young people use social media may matter as much as how much they use it. In a US sample of young adults, Levenson et al. (2016) found that both the frequency of social media checking and the total time spent on social media platforms were independently associated with sleep disturbance, even after controlling for depression and anxiety, indicating a relationship that is not fully explained by co-occurring mental health difficulties.
Andreassen et al. (2012) developed the Facebook Addiction Scale, providing later researchers with a validated tool for capturing compulsive or problematic patterns of use rather than simple duration, a distinction that has proved useful in explaining why some heavy users show few sleep difficulties while some moderate users show many. Christensen et al. (2016) used objective smartphone tracking data, rather than self-report, and found that later and more variable smartphone use was associated with poorer sleep, lending support to arousal-based explanations that implicate the timing of use rather than only its volume. Twenge and Campbell (2018) extended this line of enquiry to a very large adolescent dataset and found that screen time, including social media, was associated with lower psychological wellbeing, with the association strongest among the heaviest users, consistent with a threshold pattern rather than a simple linear one.
A systematic review by Alonzo et al. (2021) synthesised this literature and concluded that social media use is reliably, if modestly, associated with poorer sleep quality and mental health outcomes in young people, though the review also highlighted several limitations of the existing evidence base: most studies are cross-sectional, rely on self-report measures of both social media use and sleep, and rarely distinguish clearly between total use and use specifically around bedtime. Scott and Woodhead (2019) attempted to address some of these gaps in a UK sample, reporting that bedtime-specific use was a stronger predictor of poor sleep than total daily use, but their sample was drawn from sixth-form rather than university students. The present study extends this UK-based work to an undergraduate population, using a validated measure of sleep quality (Buysse et al., 1989) alongside separate measures of total and bedtime social media use, in order to clarify which aspect of use is most strongly related to sleep outcomes in this group.
Design. This study used a cross-sectional, correlational survey design. The predictor variables were total daily social media use and bedtime social media use (both self-reported, in minutes); the outcome variable was global sleep quality as measured by the Pittsburgh Sleep Quality Index (PSQI).
Participants. A total of 142 undergraduate students (104 female, 34 male, 4 non-binary; M age = 20.3 years, SD = 1.8, range 18–25) were recruited from a UK university via the psychology participant pool and university social media pages, using an opportunity sampling method. Participants were required to own a smartphone and to use at least one social media platform regularly. All participants provided informed consent prior to taking part, and the study was approved by the university’s departmental ethics committee.
Materials. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989), a 19-item self-report measure yielding a global score from 0 to 21, with higher scores indicating poorer sleep quality; scores above 5 are conventionally taken to indicate clinically significant sleep difficulty. Social media use was measured with two items adapted from Andreassen et al. (2012), asking participants to estimate their total daily social media use in minutes, and separately their social media use in the 60 minutes before their usual bedtime. A brief demographic questionnaire collected age and gender.
Procedure. The survey was hosted online and took approximately 10 minutes to complete. Participants were provided with an information sheet, gave consent electronically, and completed the measures in a fixed order (demographics, social media use, PSQI). On completion, participants were shown a debrief screen containing signposting to university wellbeing and sleep support services.
Analysis. Data were screened for outliers and missing values prior to analysis. Pearson correlation coefficients were calculated between social media use variables and PSQI global score. A three-step hierarchical multiple regression was then conducted, entering age and gender at step one, total daily use at step two, and bedtime use at step three, to establish whether bedtime use predicted sleep quality independently of total use.
Descriptive statistics for the main study variables are shown in Table 1. On average, participants reported moderate levels of daily social media use and a mean PSQI global score above the clinical cut-off of 5, indicating that poor sleep quality was common in this sample.
| Variable | N | Mean | SD | Range |
|---|---|---|---|---|
| Daily social media use (minutes) | 142 | 187.4 | 62.3 | 45–380 |
| Bedtime social media use (minutes, last hour before sleep) | 142 | 42.6 | 24.8 | 0–120 |
| PSQI global score | 142 | 7.8 | 3.1 | 2–16 |
Table 1. Descriptive statistics for social media use and sleep quality (N = 142).
Pearson correlations showed that bedtime social media use was significantly and positively associated with PSQI global score, r(140) = .34, p < .001, such that greater use of social media in the hour before sleep was associated with poorer sleep quality. Total daily social media use showed a smaller positive correlation with PSQI score, r(140) = .19, p = .024.
| Predictor | B | SE | β | p |
|---|---|---|---|---|
| Age | -0.09 | 0.14 | -.05 | .518 |
| Gender (female = 1) | 0.62 | 0.55 | .09 | .261 |
| Total daily social media use (mins) | 0.004 | 0.004 | .07 | .342 |
| Bedtime social media use (mins) | 0.041 | 0.009 | .29 | <.001 |
Table 2. Final step of hierarchical regression predicting PSQI global score, R² = .216, adjusted R² = .194, F(4, 137) = 9.43, p < .001.
The final regression model, shown in Table 2, was statistically significant, F(4, 137) = 9.43, p < .001, explaining 21.6 per cent of the variance in PSQI global score. Bedtime social media use remained a significant, independent predictor of poorer sleep quality after controlling for age, gender and total daily use, while total daily use itself did not reach significance once bedtime use was included in the model.
Figure 1. Mean PSQI global score (higher = poorer sleep) by bedtime social media use tertile (N = 142).
Figure 1 illustrates the pattern underlying these regression results, showing that mean PSQI scores rose steadily across low, moderate and high bedtime social media use groups, with the high-use group’s mean score falling within the clinically poor sleep range.
This study examined the relationship between social media use and sleep quality in a sample of UK undergraduate students, distinguishing between total daily use and use in the period immediately before sleep. As predicted, bedtime social media use was significantly associated with poorer sleep quality, and this relationship remained significant when total daily use, age and gender were controlled for. Total daily use showed only a weak association with sleep quality that did not survive control for other variables. These findings are broadly consistent with previous research (Woods and Scott, 2016; Levenson et al., 2016; Scott and Woodhead, 2019) and extend this literature to a UK undergraduate population.
The pattern of results supports an arousal-based rather than a pure displacement explanation of the social media-sleep relationship. If displacement alone were responsible, both total use and bedtime use would be expected to show similarly strong associations with sleep quality, since more total use would be expected simply to delay bedtime overall. Instead, it was specifically bedtime use that predicted poorer sleep, consistent with the proposal that the cognitive and emotional stimulation of social media content, together with exposure to bright screens, increases physiological arousal at a time when the body should be preparing for sleep (Christensen et al., 2016). This is also consistent with Andreassen et al.’s (2012) framing of problematic social media use as a pattern of engagement rather than a simple function of time spent, and with Twenge and Campbell’s (2018) suggestion that the context and timing of screen use may matter more than overall exposure.
These findings have practical implications for university wellbeing provision. Rather than encouraging students to reduce social media use in general, interventions may be more effective if they specifically target use in the pre-sleep period, for example through ‘digital sunset’ guidance or app-based screen-time limits activated in the hour before a student’s usual bedtime.
Several limitations should be noted. The cross-sectional design means that causal direction cannot be established; it is possible that poor sleepers use social media more at bedtime because they are already awake, rather than social media use causing poor sleep. All measures relied on self-report, which may be subject to recall bias, particularly for estimates of minutes spent on social media. The sample was also drawn from a single UK university using opportunity sampling, which limits generalisability, and was predominantly female, which may affect the representativeness of the findings. Future research should employ longitudinal or experimental designs, ideally combined with objective measures of both social media use and sleep, such as smartphone tracking data and actigraphy, to establish the direction and mechanism of the relationship identified here.
This study found that social media use in the hour before bed, rather than total daily use, was the stronger predictor of poor sleep quality among a sample of UK undergraduate students, even after controlling for age and gender. These findings suggest that the timing of social media use may be more important than its overall volume in shaping sleep outcomes in this population, and they point towards a specific, actionable target for student wellbeing interventions: reducing social media engagement in the period immediately before sleep, rather than attempting to reduce overall use. Given the cross-sectional nature of the design, these conclusions should be treated as preliminary, and future longitudinal and experimental research is needed to confirm the direction of the relationship and to test whether bedtime-focused interventions can produce measurable improvements in student sleep quality.
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