To investigate levels of recreational physical activity, screen time, sleep quality and mood in undergraduate medicine and nursing students.

Observational, cross-sectional study using an online survey administered during the academic term in 2024.

International Health Professions University in Bahrain.

279 undergraduate students from the school of medicine and school of nursing.

Physical activity levels (International Physical Activity Questionnaire-Short Form), recreational screen time (Sedentary Behaviour Questionnaire), sleep quality (Pittsburgh Sleep Quality Index) and mood (Brief Mood Introspection Scale) were measured and compared across groups, and associations between measures were assessed.

Participants reported high rates of not meeting physical activity recommendations (46.6%), high levels of recreational screen time (median=32 hours per week) and poor-quality sleep (63.1%). Males reported higher levels of physical activity, screen time and sleep quality. Higher sleep quality was observed for the school of medicine, the preclinical stage of study and participants living alone. Overweight and obese participants had significantly higher recreational screen time and more unpleasant and tired moods. Higher levels of screen time and lower sleep quality were associated with tired, unpleasant and negative moods, while not meeting physical activity recommendations was associated with poor sleep in addition to unpleasant, tired and negative moods.

Physical activity levels are positively associated with mood and sleep quality in young adult healthcare students. Recreational screen time is negatively associated with mood but has no relationship with sleep quality. Intervention programmes to increase physical activity are warranted for young adults in healthcare training.

Study data is available on reasonable request from the corresponding author.

Data are available upon reasonable request.

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The age between 18 and 29 years is recognised as a unique and distinct period of development of vital importance.1 Several chronic disease prevention interventions have specifically addressed young adults between 18 and 29 years as a distinct group, focusing on obesity prevention and physical activity.2 Studies of disease trajectory patterns have shown that low physical activity during young adulthood is associated with diabetes and hyperlipidaemia at midlife,3 while high physical activity is associated with lower incidence of cardiovascular disease events,4 and high television (TV) viewing at age 23 years is associated with hypertension, hyperlipidaemia and obesity at midlife.5 A high body mass index (BMI) at age 21 years has been considered a predictor of premature mortality.6

Positive correlations have been shown between physical activity and mental health within this age group.7 Worldwide, mental disorders are among the most common health problems,8 9 and the period of late adolescence and young adulthood is when many mood disorders emerge.10 11 During young adulthood, identity is explored and formed with the accompanying anxieties related to relationships, entering the workforce and formulating a personal worldview.1 Understandably, anxiety and mood disorders are highly prevalent during these years relative to older adulthood.12

Young adults living in the post-COVID-19 pandemic are exposed to environments where education, entertainment, instruction, socialising and just about every aspect of human existence are delivered to individuals via smartphones. Screen exposure has become an integral part of life, and young adults must embrace the smartphone culture to succeed in relationships, careers, education and training.13 14

Depression is common among young adults, who inevitably have more screen exposure than older adults. This period coincides with a crucial psychological development phase. Studies have suggested that excessive screen exposure may influence mental health, such as anxiety disorders,15 depression16 17 and problems with sleep.18 Associations between depression and obesity are well established.19

Screen time has been considered an indication of sedentary behaviour.17 Studies have shown that sedentary behaviour associated with screen time is linked to an increased risk for cardiovascular disease,20 21 obesity,22 23 diabetes24 and increased risk of death.25 A positive association between sedentary behaviour and obesity is well established. Mechanistically, this may be explained by the associated co-activities of snacking behaviours and other poor dietary habits linked with excessive recreational screen time and sedentary behaviours.26 27

Young adulthood is a period of occupational and educational choices where many embark on tertiary education. University, per se, has its associated stressors. These can be disproportionately higher among trainee doctors and nurses.28 29 Sleep disturbances are common among medical students,30 and their prevalence is higher than that of non-medical students and the general population.30 Recreational screen time is high among medical students, and this has been associated with poor mental health.30

Understanding the factors that influence the well-being of young adult healthcare students, particularly those in high-pressure academic programmes like medicine and nursing, is of great importance as they must be physically and mentally well to provide optimal care for others. These students often experience significant stress due to their rigorous coursework and clinical duties, which can negatively impact their physical activity levels, sleep quality and overall mood. With the increasing reliance on digital devices for both academic and recreational purposes, excessive screen time has become a growing concern, potentially exacerbating these challenges. This study reports the levels of recreational screen time (using TV, computer, cell phone and tablet), physical activity, sleep quality and mood among young adult students of medicine and nursing and any associations between these measures. By examining the relationships between these factors, this study provides valuable insights into the unique well-being needs of healthcare students, which is essential for developing targeted interventions and policies to promote healthier lifestyles and improve their overall health.

This was an observational cross-sectional study at an international health professions university in Bahrain. Based on the university’s undergraduate population size of 1300, the sample size required to reach statistical significance with a confidence level of 95% and a margin of error of 5% was calculated to be 297. Inclusion criteria for participating in the study were being 18 years of age or older, being a registered undergraduate student in either the school of nursing or the school of medicine during the 2023/2024 academic year, and giving informed consent to participate in the study. A convenience sample of the student population was recruited through a variety of channels including university email, advertising posters within the university campus, in addition to student council, class representative and academic staff announcements. The survey was opened in January 2024 during the second semester of the 2023/2024 academic year and was closed in February 2024 as this time did not overlap with any scheduled examinations. The study survey was hosted online on Microsoft Office Forms, and all recorded responses were anonymous. Participants were first provided with the participant information leaflet and confirmed electronic consent before completing the survey.

In total, 295 participants consented to participate in the study and completed the survey. During data processing and validation, 16 responses were found to be invalid. They were excluded from the study due to missing or invalid data in BMI (3), IPAQ (7), SBQ screen time (0), PSQI (2) and BMIS (4) components of the survey. This left a sample of 279 eligible participants with complete, valid responses.

The study sample (n=279) consisted of 83 (29.7%) males and 196 (70.3%) females with a median age of 20 years and IQR from 19 to 21 years. The reported age range is typical for students enrolled in undergraduate medical and nursing programmes in this region. According to BMI category, 22 (7.9%) were underweight, 149 (53.4%) normal weight, 70 (25.1%) overweight and 38 (13.8%) obese. The majority of participants were enrolled in the school of medicine, 222 (79.6%), while 57 (20.4%) were from the school of nursing. All students were enrolled in programmes consisting of preclinical and clinical stages of study, of which 213 (76.3%) were classified as being in the preclinical stage of study and 66 (23.7%) in the clinical stage of study. The majority of participants, 195 (69.5%), were living with company, either roommates or family, as opposed to living alone. In total, 25 (9%) participants identified as being diagnosed with a mental or developmental disorder, and 37 (13.3%) reported a chronic disease diagnosis.

Physical activity was assessed and categorised based on IPAQ-SF responses: 80 (28.7%) had low, 117 (41.9%) had moderate and 82 (29.4%) had high levels of physical activity with a total of 149 (53.4%) meeting physical activity recommendations of at least 150 min of moderate intensity or 75 min vigorous intensity physical activity per week. The median recreational screen time was 32 hours per week, and the IQR spanned from 21 to 42 hours per week. The daily median number of hours asleep was 7 hours per day with an IQR of 5–8, and the median PSQI score was 7 (IQR 4–9). The medians and IQRs of participants’ mood scores on the four different scales of the BMIS are reported in table 1.

Normality tests confirmed that all scales (MET min per week, recreational screen time, PSQI and all parts of the BMIS) were not normally distributed. Non-parametric tests were therefore used for all comparisons. Significant differences between males and females were identified in MET min per week, hours per week of recreational screen time, PSQI score and the arousal–calm scale of the BMIS.

Males reported higher MET min per week, 2447 (IQR 1110–3990), compared with females, 1288 (IQR 466–2691). The Pearson χ² test indicated a significant association between gender and physical activity categories (χ²=3.877, p=0.049); however, the OR was not significant. Males also reported higher median recreational screen time, 35 (IQR 27–44) hours per week, compared with females, 29 (IQR 20–42) hours per week. An association was also observed when comparing gender and recreational screen time categories (χ²=8.078, p=0.004), which indicated males were more than two times as likely as females to engage in ≥4 hours of recreational screen time per day (OR=2.26, 95% CI (1.279 to 3.997)), with a small-to-medium effect size (ϕ=0.17).

Females had significantly higher median PSQI score, 7 (IQR 4–10) versus 6 (IQR 4–9), indicating they experienced lower quality sleep compared with males. However, significant gender differences in frequencies of low (PSQI >5) and high (PSQI ≤5) categories of sleep quality were not detected.

Female participants had a higher median score on the arousal–calm scale in the BMIS, 32 (IQR 30–35) versus 31 (IQR 28–43), indicating they were less calm and more aroused compared with male participants.

Only one scale was significantly different between the school of medicine and the school of nursing. Participants enrolled in the school of nursing had a higher median PSQI score, 8 (IQR 6–11) versus 7 (IQR 4–9). Similar results were seen for groups based on stage of study. Preclinical participants differed from clinical participants in PSQI score, having a median of 8 (IQR 6–10) compared with the median for preclinical participants of 6 (IQR 4–9). Participants in their clinical stages were found to be two times as likely to have low quality sleep compared with preclinical participants (χ²=5.964, p=0.015), (OR=2.15, 95% CI (1.155 to 4.032)), ϕ=0.15. The observed sleep quality associations across stages of study and schools are deemed to be linked. This may be due to the relative proportions of participants in the different stages in our sample from each school. The proportion of clinical students in the school of nursing sample was biased towards the clinical stage (86%), while the school of medicine was heavily biased towards the preclinical stage (93%).

Participants’ living situation was observed to show differences in both the physical activity and sleep domains. Participants living alone had higher levels of MET min per week, 1760 (IQR 900–3813) and better sleep quality, 6 (IQR 4–9), compared with those who lived with company, respectively 1386 (IQR 456–2820) and 7 (IQR 5–10). Participants living with company were two times as likely to report poor quality sleep compared with those living alone (χ²=5.398, p=0.020), (OR=1.85, 95% CI (1.098 to 3.115)), with a small-to-medium effect size (ϕ=0.14). This observed association may also be linked to the stage of study association with sleep, as 96.5% of those living alone were in the preclinical stage of study, compared with 67.5% of participants who were living with company who were in the preclinical stage.

Participants who declared a diagnosis of a mental/developmental disorder or a chronic disease both showed significant differences in three of the four BMIS scales (pleasant–unpleasant, positive–tired, negative–relaxed) compared with those who did not report such a diagnosis. Similar patterns were seen in group differences for either diagnosis, typified with a diagnosis being associated with lower scores for both the pleasant–unpleasant scale and positive–tired scale and higher scores observed in the negative–relaxed scale of the BMIS. Each observed difference suggests that those who reported a diagnosis had less positive or more negative mood states compared with those without a diagnosis. Additionally, participants with a chronic disease diagnosis reported a significantly higher median PSQI score, 9 (IQR 7–12), compared with those without a chronic disease, 7 (IQR 4–9). Participants with a chronic disease diagnosis were more than three times as likely to have reported low quality sleep compared with those who did not (χ²=7.850, p=0.005), (OR=3.46, 95% CI (1.390 to 8.597)), with a small-to-medium effect size (ϕ=0.17).

Underweight/normal weight and overweight/obese groups showed significant differences in recreational screen time, pleasant–unpleasant scale and positive–tired scale. The underweight/normal weight group reported significantly less screen time, 28 (IQR 20–37) hours per week, compared with the overweight/obese group, 37 (25 – 45) hours per week. Underweight/normal weight participants were two times as likely to report <4 hours per day of recreational screen time compared with overweight and obese participants (χ²=4.137, p=0.042), (OR=1.69, 95% CI (1.017 to 2.817)), ϕ=0.12. Higher median scores on the pleasant–unpleasant and positive–tired scales of the BMIS were detected for the underweight/normal weight group, table 2, compared with the overweight/obese group.

Participants who did not meet physical activity recommendations had significantly different median PSQI scores, 7 (IQR 5–10) compared with those who did meet recommendations, 6 (IQR 4–9), indicating participants with lower levels of physical activity experienced lower sleep quality; however, no association was observed between achieving physical activity recommendations and sleep quality categories. Participants who achieved physical activity recommendations also showed significantly higher median scores on the pleasant–unpleasant and positive–tired scales. Relatively higher scores on the negative–relaxed scale were reported by participants who did not achieve recommendations.

Participants who reported less than 4 hours per day of recreational screen time showed significant differences in the pleasant–unpleasant, positive–tired and negative–relaxed scales of the BMIS compared with those who had more than 4 hours per day. The group with lower levels of recreational screen time had higher scores in both the pleasant–unpleasant and positive–tired scales and lower scores in the negative–relaxed scale, table 2.

Differences in the median scores on three of the four BMIS scales (pleasant–unpleasant, positive–tired, negative–relaxed) were detected between low-quality and high-quality sleep groups. The observed trend paralleled those of achieving physical activity recommendations and recreational screen time levels, with higher scores in both the pleasant–unpleasant and positive–tired scales and lower scores in the negative–relaxed scale.

Our study identified an association between high physical energy expenditure, male gender and living alone. Low physical activity levels were associated with poor sleep quality scores, tired and unpleasant mood. There was no association between physical activity levels and recreational screen time exposure. There were no differences in physical activity levels between nursing and medical students or preclinical or clinical students. Previous studies have also shown male students to have higher physical activity levels as compared with female students.7 39 40 We reported high physical activity among participants who lived alone, which reflected the findings of a study of American students in private universities, which found higher physical activity levels for students who lived without a parent or guardian.41 Our findings of an association between physical activity and sleep quality are consistent with a meta-analysis showing that higher levels of physical activity are associated with higher quality sleep in college students42 and older adults.43 Positive correlations have been found between physical activity and mental health,7 which reflects our findings concerning low physical activity with tired and unpleasant mood. More specifically, a study of university students in Ireland reported that students who met physical activity recommendations were happier than those who did not.44 Blake et al had previously reported significant differences in physical activity levels between medicine and nursing students. They revealed several barriers to achieving recommended physical activity levels that nursing students were confronted with.45 While our study found a slightly higher proportion (46.6%) of total participants did not meet physical activity recommendations as compared with 42.9% reported by Blake et al, no difference in physical activity levels between nursing and medicine groups of students was detected in our study.45 While our study reports physical activity, the perceived motives were not investigated. A study conducted on medical field students across several institutions and countries in the Western Balkans reported students’ perceived barriers and motivation for physical activity; important motivations include feeling better, reduction of stress and aesthetics, while faculty obligations was the notable barrier reported.46 These results are aligned with a multinational systematic review, which demonstrated that physical activity is associated with improved quality of life and reduced burnout among medical students.47

High recreational screen time was associated with being overweight, male gender, tiredness, unpleasantness and negative mood. There was no association between screen time and feeling calm or aroused. Similarly, there was no association between screen time exposure and the academic course of study (nursing or medicine, preclinical or clinical). A study designed to describe changes in recreational screen time behaviour patterns among young American adults during the COVID-19 pandemic reported a weekly average of 25.9±11.9 hours during 2018 and 28.5±11.6 hours in 2020.48 Our study reported average hours higher than those surveyed during the pandemic period. An association between screen time exposure and obesity is well established in the literature,49 50 which was reflected in our data. With regards to gender differences in screen time, a study from Serbia showed female medical students reported higher scores of social media addiction as compared with males, but with no differences in general smartphone addiction between the genders.51 Our study did not specifically examine social media usage; however, based on screen time alone, our results seem incongruent with these findings, as we found significantly more screen time exposure among male students. Another study reported more screen time exposure for male than female respondents, which was our finding.52 Generally, studies suggest that total recreational screen time hours per week are higher for males than females and that males exhibit a tendency towards gaming, while females tend towards social media.53 The same Serbian survey found smartphone addiction to be associated with poor sleep quality, anxiety and depression. The authors found that more than 4 hours of daily screen exposure was associated with increased levels of depression.51 However, there is a disparity in the literature regarding screen time exposure as related to mood states and young adults’ well-being; this was reflected in our observations across the four domains of mood studied. A Norwegian study of students showed a strong inverse correlation between screen time and sleep quality;52 however, we found no discernible relationship between screen time and sleep.

Poor sleep quality was associated with female gender, living with company, studying nursing and being in the clinical years of the course. Gender differences in sleep quality have been previously reported in young adults.54 Medical students have been reported to average 6.5 hours of sleep per night, which is below the recommended 7–9 hours.55 Much has been made about the high prevalence of sleep problems among medical students.55–57 However, our study suggests that nursing students’ sleep quality is a more significant issue. A study from two Mediterranean countries shows this difference in sleep quality between nursing and medical students.58 Longitudinal studies have shown that this issue of poor sleep quality follows student nurses into their professional careers long after graduation,59 with further declines in sleep quality for the first 3 years of clinical practice.59

Given that these health behaviour issues can persist from university into professional practice, the implementation of strategies and interventions to mitigate such behaviours is of great importance. A meta-analysis of randomised controlled trials of interventions to increase physical activity in university students found that a wide range of approaches, including online theory-based programmes, behavioural monitoring, gamification, activity trackers and credited course interventions, were effective in increasing total physical activity.60 A recent paper described an effective intervention to enhance nursing students’ well-being through an 8-week digital detox programme and reported significant reductions in scores on the Social Media Addiction Scale. The programme incorporated digital hygiene education, self-reflection exercises, goal setting, screen time management strategies, mental wellness modules, physical activity promotion and hobby-based alternatives.61 These results highlight the potential of structured digital detox initiatives to support healthier technology use, improve mood and promote overall well-being in healthcare students.

Data are available upon reasonable request.