Behavioural patterns of university students during the COVID-19 pandemic: A cross-sectional study of the effects of active transportation, uninterrupted sitting time, and screen use on physical activity and sitting time [version 1; peer review: awaiting peer review]

Background: The closure of universities due to the coronavirus disease 2019 (COVID-19) pandemic may alter the behaviour of students. This study aimed to determine the effect of the pandemic on physical activity and sitting time in French students prior to confinement and during confinement. Methods: This was a cross-sectional study based on data collected via an online questionnaire for university students during the second confinement in France (between October and December 2020). Participants (N= 2873) completed the International Physical Activity Questionnaire, which assessed physical activity and sedentary behaviour, and contained questions about modes of transport, and perception of uninterrupted sitting time and screen time prior to confinement and during confinement. Multiple regression models assessed how active transportation, uninterrupted sitting time, and screen time studying increased or reduced during confinement. Students who adopted the most active transport time prior to confinement were the least likely to increase their screen time during confinement. Conclusions: Confinement reduced physical activity levels and increased sitting time, mainly among students who adopted active transport and accumulated longer uninterrupted sitting time. Students who combined-long periods of uninterrupted sitting time with high screen use could be a riskier profile for health. Analysis of physical activity time and sitting position should include its accumulation patterns. levels in this population, to our knowledge, only one study has shown that active transportation reduction might be associated with this decrease. 2 For this reason, in our study, an amplifying effect of active transportation time on reducing students' physical activity levels duringconfinement was hypothesised andconfirmed. Students whospent time undertaking active transportation to university and vigorous physical activity levels prior to confinement predicted a significant reduction in moderate physical activity levels during confinement. However, active transportation time does not predict changes in vigorous physical activity levels during the confinement period. Active transportation is considered a physical activity characterised by light to moderate intensity 28 ; the students in this study adopted walking to the university prior to confinement.


Introduction
The confinement caused by the coronavirus disease 2019 (COVID-19) pandemic reduced the physical activity levels of university students 1 by reducing active transportation to commute 2 and increased sitting time and screen time. 3 Adopting active transportation, which includes any human-powered forms of travel, such as walking or cycling, makes people achieve the recommended physical activity levels for health (i.e., 150 minutes/week moderate physical activity). 4 Students who adopt active transportation display higher physical activity levels than those who do not adopt it. [5][6][7] Although the threshold for physical activity to achieve health benefits is well defined, this is not the case for sitting time. 8 Despite this lack of evidence, recent studies suggest that uninterrupted sitting for periods longer than 30 minutes has a detrimental impact on health. 9 A threshold of six to eight hours per day of sitting and three to four hours of television is a health risk. 10 Students spend more than six hours per day studying while sitting down, 11 thus increasing health risks when sitting time accumulates uninterrupted. 12 Besides this sitting time, students increasingly use screens to study, 13 mainly computers. 14 While there has been a tendency to associate screens with sitting time, screen use does not always reflect that behaviour. 15 Regardless of sitting time, screen time could be an independent health risk factor. 16 Furthermore, the relationship between physical activity, sitting time and screen time is mixed. A systematic review showed no association between physical activity and sitting time. 17 Even high physical activity levels do not mitigate the adverse effects of high screen time on health. 18 Some studies have shown that students' physical activity levels are negatively associated with sitting time, 19 where high sitting time displaces total physical activity time, 20 mainly light physical activity. 21 Similarly, when people spend more than six hours sitting, they replace that time with walking and moderate to vigorous physical activity. 22 Most studies about confinement in the COVID-19 pandemic have focused on total times of changes in physical activity levels, sitting time and screen time. For this reason, the main objective of this study was to assess if active transportation time, uninterrupted sitting time, and screen time spent studying prior to confinement were factors influencing these changes ( Figure 1).

H1:
The first hypothesis was that the longer the duration in minutes of students' active transportation trips prior to confinement, the greater the reduction in their physical activity levels, excluding active transportation time during confinement.

H2:
The second hypothesis was that the greater the uninterrupted sitting time prior to confinement in students, the greater the increase in the amount of time they would spend sitting during confinement.
H3: The third hypothesis was for screen time; due to the increase in online courses during confinement, the longer the screen time studying prior to confinement in students, the greater the increase in their screen time during confinement.
H4: Finally, we hypothesised that there would be no relationship between active transportation, sitting time and screen time during confinement.

Methods
This was a cross-sectional study. Students from 16 faculties enrolled (N=6705) in the university sports service (SUAPS) received an invitation via the institutional e-mails to a health webinar for French students (Webex ® ) (November 10, 2020) on physical activity and confinement. A link to an online questionnaire was then sent to each student via the institutional e-mail addresses, except for sports sciences students. Due to the type of training, these students were the only ones who partially maintained physical activities during the confinement, which would bias the data. The questionnaire was a voluntary survey, and it was available on the Drag n Survey ® platform between November 10th and 18th, 2020. The survey used in this study is available as Extended data. 23 There was a separate informed consent page where students agreed to participate by checking a checkbox. There were non-monetary incentives to participate. Participants could obtain the final results of the study. A total of 2986 students visited the website and agreed to participate in the study and only 2873 completed the screening questionnaire. Each questionnaire included a completeness check with forced response items (i.e., highlight mandatory items). In order to prevent a single user from filling in the same questionnaire multiple times, one response per IP address was possible (the flowchart is shown in Figure 2).

Ethics
The National Commission for Data Protection and Liberties (CNIL-France) approved the study (2221060v0-CNIL). The final decision was determined on 8 th February 2021. The posteriori approval of our study is because it was not possible to know that the second wave of COVID-19 would occur in France. The protocol was submitted one week before the second Covid wave in France and initially this study was only supposed to investigate the effects of the first wave of COVID-19. We then adapted the protocol to examine the condition of confinement in the second wave of the pandemic.
The statement followed the Declaration of Helsinki and the French methodological reference MR-01. Students who visited the informed consent page and agreed to participate were considered to have provided informed consent. If participants did not want to participate in the survey, they could turn off the electronic questionnaire and drop out.

Measures
Demographics. Information was collected, including sex and age in years (open-ended question).
Physical activity. Participants answered questions about how much time and how many days they spent doing moderate and vigorous physical activities during the previous seven days: First, "prior to the confinement" and then "during the confinement". The survey was based on the International Physical Activity Questionnaire (IPAQ) short form. 24 Transportation type for commuting. Participants were asked what type of transport they habitually used to commute to university (open-ended question). Following which, the participants answered the following question: "On a typical day (prior to confinement), how many minutes do you spend carrying out active transportation?". A definition of active transportation was then proposed to the participants in the following statement: "Active transportation is defined as any form of human-powered transportation (e.g., walking and cycling)".
Sitting time. The IPAQ short form included the following question: "In the past seven days, how much time did you spend sitting during a weekday? (i.e., hours and minutes per day)". For this study, this question was asked prior to confinement and during confinement. Screen time. Students indicated average minutes per day of screen-time usage prior to and during confinement. The question posed with regards to their time prior to the confinement was: "Normally (prior to confinement), how many hours on average per day do you spend in front of a screen (computer, tablet, smartphone, TV…)?". The question posed with regards to their time during the confinement was: "How many hours on average per day (during confinement) do you spend in front of a screen (computer, tablet, smartphone, TV)?".
Screen time studying. Students reported their average daily hours using a computer (i.e., desktop, laptop, tablet) or smartphone to study. The question posed was, "Normally (prior to confinement), how many hours on average per day do you spend in front of a screen studying (computer, tablet, smartphone)?". The question posed with regards to their time during the confinement was: "Normally (during confinement), how many hours on average per day do you spend in front of a screen studying (computer, tablet, smartphone)?".

Statistical analysis
We calculated the differences (i.e., delta Δ) in physical activity, sitting and screen time between the period prior to confinement and the period during confinement, using Microsoft Excel version 16.48 (RRID:SCR_016137). These differences were the dependent variables for four multiple regression models. Sex and age were included in the regression models as control variables to assess the impact of confounding variables. A graphic method of generalized additive models (GAM) 25 examined subgroups and interactions. The graphs are available as Extended data. 26 All missing data were removed.
The models were used to prove our fourth hypothesis. The first regression model was for moderate physical activity and the second regression model was for vigorous physical activity. Both models were used to test the first hypothesis, which stated that the longer time a student adopted active transportation, the greater the reduction in physical activity minutes during confinement. The third model tested the second hypothesis that those who spent longer sitting for an uninterrupted time prior to confinement, the greater the increase in sitting time during confinement. The fourth model tested the third hypothesis, which suggested that the higher time students spent in front of a screen for study, the greater the increase in screen time during confinement. All regression analyses were performed with RStudio (2021.09.0) (RRID:SCR_000432) and the package lme4 (RRID:SCR_015654). 27 Results Table 1 presents the characteristics of 2873 students. 32 Overall, the sample was balanced between men (46%) and women (54%). They were aged between 18 and 29 years old (M=19.93, SD=1.76) and they were, according to IPAQ classification, inactive prior to confinement (60%). Students adopted walking (70%) as a mode of active commuting, 17% participated in cycling, 8% were passive commuters (e.g., car, bus, underground), 3% combined walking and cycling, and 2% adopted any combination of active and passive transport (e.g., bus, tramway and walking or tramway and cycling). The average duration of active transportation, prior to confinement, was approximately 30 minutes per trip. Moreover, the students spent approximately eight hours sitting down per day and with an average uninterrupted sitting time of almost 100 minutes. Students spent more than 10 hours per day using screens, of which approximately five hours were for studying and five hours outside of study hours. Analyses of the GAM models did not show the presence of subgroups within the data or interactions between variables. 26 Hypothesis Four multiple regression models assessed the extent to which active transportation, uninterrupted sitting time, and screen time studying before confinement exacerbated or attenuated the effects of confinement on total physical activity time and sitting time. Multiple regression models showed that when there was a negative effect in changes, it opposes the effect of confinement, if there is a positive change effect, it increases the impact of confinement.
H1: The first hypothesis was that the longer the duration in minutes of students' active transportation trips prior to confinement, the greater the reduction in their physical activity time during confinement.    To evaluate our first hypothesis, we conducted two multiple regression analyses (Table 2), one for moderate physical activity (Model 1) and one for vigorous physical activity (Model 2). The results of the first model indicated that five predictors explained 47% of the variance in moderate physical activity time (R 2 adjusted =0.46, F(9,2863)=283.1, p<0.001). Older students (β age =-0.73, p<0.001), as well as those who accumulated more moderate physical activity (β=-0.71, p<0.001) and sitting time prior to confinement (β=-0.01, p<0.001), had significantly less reduction in their moderate physical activity levels during confinement. It was found that active transportation time predicted a larger reduction in moderate physical activity (β=0. 16, p<0.001), as did vigorous physical activity prior to confinement (β=0.07, p<0.001).
The second model showed that three predictors explained 64% of the variance in vigorous physical activity (R 2 adjusted = 0.64, F(9,2863)=580, p<0.001). Students who engaged in vigorous physical activity (β=-0.95, p<0.001) for more minutes prior to confinement showed significantly less reduction in vigorous physical activity during confinement. Conversely, students who presented higher amounts of moderate physical activity prior to confinement (β=0.05, p<0.01) showed a significantly greater reduction in their levels of vigorous physical activity.

H2:
The second hypothesis was that the greater the uninterrupted sitting time prior to confinement in students, the greater the increase in the amount of time they would spend sitting during confinement.
H3: The third hypothesis was for screen time; due to the increase in online courses during confinement, the longer the screen time studying prior to confinement in students, the greater the increase in their screen time during confinement.
H4: Finally, we hypothesised that there would be no relationship between active commuting, sitting time and screen time during confinement.
Multiple regression results on changes in moderate physical activity showed that the reduction in moderate physical activity levels was less significant for students who spent more time sitting prior to confinement (β=-0.01, p<0.001). Model 2 showed no relationship between vigorous physical activity and either sitting or screen time. Model 3 showed no relationship between physical activity behaviour and sitting time or screen time. Finally, there was a negative relationship between the increase of screen time during confinement and active transportation minutes prior to confinement (Model 4). Students who adopted active transportation showed a less significant increase in screen time during confinement (β=-0.52, p=0.001).

Discussion
The main objective of this study was to determine whether the adoption of active transportation, uninterrupted sitting time, and screen study time predicted changes in physical activity, sitting time and screen time in students during confinement.
While studies on confinement and its effects on university students showed that it reduced physical activity levels in this population, to our knowledge, only one study has shown that active transportation reduction might be associated with this decrease. 2 For this reason, in our study, an amplifying effect of active transportation time on reducing students' physical activity levels during confinement was hypothesised and confirmed. Students who spent time undertaking active transportation to university and vigorous physical activity levels prior to confinement predicted a significant reduction in moderate physical activity levels during confinement. However, active transportation time does not predict changes in vigorous physical activity levels during the confinement period. Active transportation is considered a physical activity characterised by light to moderate intensity 28 ; the students in this study adopted walking to the university prior to confinement.
Moreover, active transportation could be part of students' daily routines, given that the city the study took place in has high active transportation rates (Walk Score ® city of Villeurbanne: 89 errands can be accomplished on foot and very bikeable: 70-89). 31 However, the suspension of campus lectures reduced active commuting, decreasing overall moderate physical activity levels. This factor explains why active transportation time did not predict a greater reduction in vigorous physical activity levels. In addition, students were inactive and spent no more than 30 minutes per day in physical activity, coupled with reduced movement perimeter during confinement (i.e., 5 km around the house).
One might have expected that moderate and vigorous physical activity levels before confinement would explain changes in the respective levels during confinement. However, a crossover effect was found between physical activity levels. On the one hand, the more time spent in vigorous physical activity, the greater the reduction in moderate physical activity time and vice-versa. This result could be because physical activity levels are part of a continuum 29 in which if more time is spent in vigorous intensities, more time is also spent in moderate activities. For students who spent more time in moderate physical activity prior to confinement, their practice during confinement was limited to that intensity, and vigorous physical activity was reduced more significantly.
Our second hypothesis was confirmed; the longer the uninterrupted time spent in a seated position before confinement, the more the students increased their time in that position during confinement. Furthermore, this increased time was exacerbated by the screen's use during confinement. Uninterrupted sitting time has been identified as a health risk factor in adults. 9 This behaviour pattern is particularly marked in university students who accumulate this time during their lectures. 30 Consequently, the point is the accumulation pattern of sitting time (i.e., how this time accumulates). For example, two students might accumulate six hours in total, but one will do so in three periods of two hours each and another in six periods of one hour.
Regarding the positive association between sitting time and screen use, it is likely that the online courses during the pandemic period have increased the association between screen use and sitting in university students, increasing the use of screens to study. 14 Similarly, screen use in students could be interpreted as a proxy for time spent sitting.
The third hypothesis postulated that the more time students used screens prior to confinement, the more they would increase this time during confinement. The results did not confirm this hypothesis; on the contrary, the more time spent using screens prior to confinement, the smaller the increase in this behaviour. One possible explanation could be that high time spent using screens did not undergo significant modifications since screen-based devices have become pervasive in students' lives. 14 Likewise, the time students spent using screens could be independent of the confinement factor. However, screen time would not be independent of the uninterrupted time spent sitting since the longer the prolonged periods of this behaviour, the greater the increase in screen time. Thus, it could be postulated that students defined as prolongers of sitting time 21 would be more prone to prolonged use of screens and vice-versa.
The last hypothesis postulated that there would be no relationship between active transportation times, sitting, and screen time was partially confirmed. The relationship between moderate physical activity and students' sitting time showed that low levels of physical activity could coexist with high levels of sitting time in students. This inactive-sedentary profile should be the most alarming health risk factor. Inactivity itself results in low levels of physical and mental health, but if it is associated with high levels of sitting time, there is a potentially higher risk of suffering from metabolic diseases or exacerbating risk for mortality. 20 Another element highlighted is the "protective" effect of time spent in active transportation. This study highlight that the more time students adopted active transportation, the less they increased screen use in confinement. This phenomenon could reflect a negative association between the two behaviours and that somehow, screen time would be displaced by active transportation time. 22 Similarly, students who adopt active transportation are generally more active people 5 and spend less time using screens.
The present study is not free of limitations. Behaviours were measured via questionnaires, leading to the overestimation of physical activity and underestimation of sitting time. This phenomenon is associated with social desirability. The students who could answer this questionnaire had access to the internet and were enrolled in the health seminars offered during the pandemic.

Conclusions
Our study confirms that active transportation is the primary source of physical activity in the daily lives of university students. Similarly, the time young people spend studying contributes to the accumulation of high levels of sitting time, which the use of screens could exacerbate. Finally, the ubiquity of screens and their association with sedentary behaviours could induce prolonged sitting positions, generating patterns of behaviour that are riskier for students' health. Therefore, the study of online courses and e-learning on physical activity levels and sitting time remains undetermined.