Research Proposal Sample: Screen Time and Anxiety Among University Students

Model Answer

Introduction and Background

Concerns about the mental health of university students have intensified over the past decade, with anxiety consistently identified as one of the most commonly reported psychological difficulties among this population (Twenge and Campbell, 2018). Over the same period, smartphone ownership and daily screen time have risen sharply, with university students among the heaviest users of social media, messaging and streaming platforms. A growing body of research has examined whether these two trends are related, with mixed findings: some studies report a small but consistent association between heavy screen use and poorer psychological wellbeing (Twenge and Campbell, 2018; Vahedi and Saiphoo, 2018), while others suggest the relationship is weaker than commonly assumed once confounding variables such as sleep quality and offline social support are taken into account (Orben and Przybylski, 2019).

Much of the existing evidence, however, has focused on adolescents rather than the university population specifically, and relatively few UK-based studies have examined how screen time relates to anxiety among students who are simultaneously balancing academic pressure, financial concerns and social transition into independent adult life. This proposal addresses that gap by outlining a study designed to examine the relationship between daily screen time and self-reported anxiety symptoms among UK undergraduate students, with particular attention to the type of screen activity involved rather than total screen duration alone.

The transition to university is itself a period of heightened vulnerability to anxiety, involving new academic demands, unfamiliar social environments and, for many students, distance from established support networks for the first time. Understanding whether, and how, screen use interacts with this already stressful period is therefore of direct practical relevance to university wellbeing services, which are increasingly asked to design digital wellbeing initiatives without a strong UK-specific evidence base to draw upon.

Research Aim, Objectives and Questions

Aim. To investigate the relationship between daily screen time and self-reported anxiety symptoms among UK university students.

Objectives:

• To measure daily screen time and distinguish between social media use, messaging and other screen activities.
• To assess self-reported anxiety symptoms using a validated psychometric measure.
• To examine whether the type of screen activity, rather than total screen time alone, predicts anxiety symptoms.
• To explore whether sleep quality mediates any observed relationship between screen time and anxiety.

Research Questions:

1. Is there a significant association between daily screen time and anxiety symptoms among UK university students?
2. Does the type of screen activity (social media, messaging, streaming) differentially predict anxiety symptoms?
3. Does sleep quality mediate the relationship between screen time and anxiety symptoms?

Brief Literature Context

Two theoretical positions dominate the literature on screen time and wellbeing. The displacement hypothesis suggests that time spent on screens displaces activities protective of mental health, such as sleep, exercise and face-to-face social contact, thereby increasing anxiety indirectly (Twenge and Campbell, 2018). By contrast, the technology-specific hypothesis argues that the effect depends heavily on how screens are used: passive scrolling on social media is associated with poorer wellbeing, whereas screen use for active communication with existing friends shows a weaker or negligible association (Odgers and Jensen, 2020).

Orben and Przybylski (2019), using large-scale secondary datasets, found that the association between digital technology use and adolescent wellbeing was statistically significant but extremely small in effect size, comparable in magnitude to that of eating potatoes, and argued that policy debate and media coverage have substantially overstated the risk relative to the evidence. Elhai et al. (2017) offer a complementary account through the lens of problematic smartphone use, proposing that anxiety and depression may be better predictors of compulsive checking behaviour than checking behaviour is a predictor of anxiety, raising the possibility of reverse or bidirectional causality that cross-sectional designs cannot resolve on their own. Vahedi and Saiphoo’s (2018) meta-analysis found a small but consistent positive correlation between smartphone use and both stress and anxiety across the studies reviewed, though the authors cautioned that most included studies could not establish the direction of causality. This proposal is designed with these methodological limitations explicitly in mind.

A further consideration is that most existing studies measure screen time as a single aggregate figure, which may obscure important differences between activity types. Social media browsing, for instance, has been linked more consistently to social comparison and upward comparison effects that plausibly relate to anxiety, whereas messaging close friends or family may serve a protective, socially connecting function even when it occupies a similar amount of time on screen. This proposal treats activity type as a central variable rather than a secondary consideration, distinguishing it from much of the prior literature reviewed above.

Methodology

Design. The study will adopt a cross-sectional, quantitative correlational design, appropriate for examining associations between screen time, screen activity type, sleep quality and anxiety symptoms within a single data collection window. A correlational design suits an undergraduate dissertation-scale project where establishing causality through experimental manipulation is not feasible, although the proposal explicitly acknowledges that this design cannot determine the direction of any relationship found between variables.

Sample and Participants. Participants will be approximately 150 undergraduate students recruited from a single UK university via departmental research participation schemes and social media advertisements, using a convenience sampling approach appropriate to the project’s scale and timeframe. Inclusion criteria will require participants to be aged 18 or over and currently enrolled on an undergraduate programme; students already receiving treatment for a diagnosed anxiety disorder (American Psychiatric Association, 2013) will be excluded to reduce confounding from clinical-level anxiety unrelated to everyday screen use.

Data Collection. Screen time will be measured using participants’ own smartphone screen-time reports, a built-in feature of both iOS and Android devices, disaggregated into social media, messaging and other categories via a self-report screenshot upload. Anxiety symptoms will be measured using the Generalised Anxiety Disorder 7-item scale, or GAD-7 (Spitzer et al., 2006), a widely validated seven-item self-report measure suitable for non-clinical student samples. Sleep quality will be assessed using the Pittsburgh Sleep Quality Index, or PSQI (Buysse et al., 1989). All measures will be administered together through a single online survey.

Analysis Approach. Data will be analysed using IBM SPSS Statistics (Field, 2018). Bivariate correlation will test the overall association between total screen time and GAD-7 scores, followed by multiple regression to examine whether screen activity type predicts anxiety scores after controlling for age and gender. A mediation analysis will test whether sleep quality (PSQI score) mediates the relationship between screen time and anxiety, using a bootstrapped indirect-effect approach appropriate for a modest sample size of this kind.

Ethics. The study will be submitted for approval to the university’s departmental research ethics committee prior to recruitment, and will follow the British Psychological Society’s Code of Human Research Ethics (British Psychological Society, 2021). Participants will provide informed consent, be informed of their right to withdraw at any point without penalty, and be provided with signposting to university counselling services given the sensitive nature of anxiety-related self-report. All data will be anonymised at the point of collection and stored securely in line with UK GDPR requirements.

Limitations. The proposed design carries acknowledged limitations. Self-reported screen-time data, even when drawn from device logs rather than pure recall, can be inaccurate if participants share devices or use a secondary phone; the cross-sectional design also means any relationship identified between screen time and anxiety cannot be interpreted as causal. These limitations are considered proportionate to the scope of an undergraduate project and are noted transparently rather than treated as fatal to the study’s value.

Timeline

The proposed project will run over six months, from ethical approval through to write-up, as summarised in Table 1 below.

Expected Contribution

This study will contribute UK-specific evidence to a literature currently dominated by US and adolescent samples, addressing a population, undergraduate students, whose combination of academic pressure, financial independence and social transition may make the relationship between screen time and anxiety distinct from that observed in younger cohorts. By distinguishing between types of screen activity rather than treating screen time as a single undifferentiated construct, the study responds directly to calls in the literature (Odgers and Jensen, 2020) for more nuanced measurement approaches.

Testing sleep quality as a mediator additionally offers a plausible mechanistic explanation that could inform targeted university wellbeing interventions, for example encouraging reduced pre-sleep social media use rather than blanket restrictions on screen time as a whole. Findings would be of practical value to university counselling and student support services seeking evidence-based guidance for digital wellbeing initiatives aimed at the undergraduate population.

References

American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders. 5th edn. Washington, DC: American Psychiatric Publishing.

British Psychological Society (2021) Code of Human Research Ethics. Leicester: BPS.

Buysse, D.J., Reynolds, C.F., Monk, T.H., Berman, S.R. and Kupfer, D.J. (1989) ‘The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research’, Psychiatry Research, 28(2), pp. 193–213.

Elhai, J.D., Dvorak, R.D., Levine, J.C. and Hall, B.J. (2017) ‘Problematic smartphone use: a conceptual overview and systematic review of relations with anxiety and depression psychopathology’, Journal of Affective Disorders, 207, pp. 251–259.

Field, A. (2018) Discovering Statistics Using IBM SPSS Statistics. 5th edn. London: Sage.

Odgers, C.L. and Jensen, M.R. (2020) ‘Annual research review: adolescent mental health in the digital age – facts, fears, and future directions’, Journal of Child Psychology and Psychiatry, 61(3), pp. 336–348.

Orben, A. and Przybylski, A.K. (2019) ‘The association between adolescent well-being and digital technology use’, Nature Human Behaviour, 3, pp. 173–182.

Spitzer, R.L., Kroenke, K., Williams, J.B.W. and Löwe, B. (2006) ‘A brief measure for assessing generalized anxiety disorder: the GAD-7’, Archives of Internal Medicine, 166(10), pp. 1092–1097.

Twenge, J.M. and Campbell, W.K. (2018) ‘Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study’, Preventive Medicine Reports, 12, pp. 271–283.

Vahedi, Z. and Saiphoo, A. (2018) ‘The association between smartphone use, stress, and anxiety: a meta-analytic review’, Stress and Health, 34(3), pp. 347–358.