Determination of physical activity, sedentary time and mental wellbeing in 1st year postgraduate research students at the University of Glasgow: a mixed methods approach
Bibliographic record
Abstract
University is a time associated with several life changes and represents a key time for development and maintenance of healthy lifestyle behaviours (Trost et al., 2002). Current research shows university students are highly inactive, sedentary and have poor mental health (El Ansari et al., 2011; Moulin and Irwin, 2017; Hunt and Eisenberg, 2010). However, most of this research has been conducted on undergraduate student samples. Therefore, there is a lack of research on physical activity (PA), sedentary time (ST) and mental wellbeing (MWB) in postgraduate students, particularly postgraduate research (PGR) student samples who have distinct needs and challenges. A systematic review on graduate students’ PA, ST, mental health (MH)/MWB, perceptions of PA, exercise and sport, perceived barriers to PA, benefits of PA and motivations for PA was conducted. From 187 papers identified, 25 studies were included for review. Currently, there is a distinct lack of research for PA, ST and MWB in United Kingdom (UK) graduate students. Therefore, the aims of this study were to examine PA, ST and MWB in UK PGR students, the relationship between these variables and to understand underlying perceptions of PA, exercise and sport; perceived barriers to PA; perceived benefits of PA and motivations for PA in UK PGR students. This study utilised a mixed methods approach. First year PGR students (n=100) registered at the University of Glasgow (UofG) completed an online questionnaire which assessed demographic characteristics, subjectively assessed PA and ST (International Physical Activity Questionnaire (IPAQ)) and MWB (Warwick Edinburgh Mental Wellbeing Scale (WEMWBS)). A sub-cohort (n=20) had PA and ST objectively assessed over 7 days via a hip-worn ActiGraph GT3X accelerometer. From this sub-cohort, PGR students (n=6) volunteered to participate in qualitative focus groups to assess 1st year PGR students’ perceptions of PA, exercise and sport; perceived barriers to PA; perceived benefits of PA and motivations for PA. First year PGR students self-reported being physically active (median moderate vigorous physical activity (MVPA) 195 ± 225 minutes/week, 62.9% meeting PA guidelines (PAG)) yet highly sedentary (median 8 ± 4 hours/day sitting time). In terms of MWB, PGR students reported moderate levels of MWB (48 ± 13 WEMWBS score). The randomised sub-cohort of PGR students who had PA and ST objectively assessed were also physically active yet sedentary (highly active: ActiGraph GT3X estimated MVPA 554.3 ± 187.1 minutes/week, 100% meeting PAG; highly sedentary: 9.6 ± 1.4 hours/day sedentary). No significant correlations between PA, ST and MWB or PA, ST and PGR study time were found. However, a significant weak inverse correlation was found between MWB and PGR study time (rs= -0.242, p=0.015). Qualitative research identified PGR students’ perceptions of PA, sports and exercise. The top perceived barriers to PA were a lack of information and/or awareness of opportunities, undergraduate focused PA opportunities, PGR studies and barriers specific to UofG Sport gym facilities. Whilst the most common perceived benefits of PA were the positive impact of PA on mental and physical health and PGR students’ study/work life. Balancing out unhealthy eating habits, health benefits, PGR study/work benefits and image were identified as the most commonly cited motivations for PA. This PGR student cohort (n=100) were active (met PAG), had a high ST with no significant associations between PA, ST and MWB. On the basis of existing literature, it is likely that despite being physically active, PGR students’ high ST is associated with an increased risk of negative physical and mental health outcomes and non-communicable diseases (Biswas et al., 2015; Owen et al., 2010; Owen, 2012; Atkin et al., 2012; Gibson et al., 2017). Therefore, there should be an integrated approach across the UofG to address this high ST and consider ways to reduce ST. This could be through identification of determinants of ST in the PGR population, changes to the built environment and dissemination of information on the benefits of MVPA, risks of ST, relevant opportunities and strategies to increase PA and decrease ST. An interesting finding was the weak yet significant inverse correlation between MWB and PGR study time with steps needing to be taken to address this.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".