Motivation for Physical Activity as a Key Determinant of Sedentary Behavior Among Postsecondary Students

Abstract

It is known that the transition to adulthood represents a critical period of life when acquiring healthy behaviors can influence lifestyle and health throughout adulthood. Given the importance of the consequences of a sedentary lifestyle, identifying influence factors is key to improving healthy behaviors. The objective of this study is to explore the role of postsecondary students’ motivation toward physical activity in the association with their screen time and out-of-school physical activity practice. A total of 1522 postsecondary students (90% were aged 17-20 years) recruited from 17 postsecondary institutions completed the self-reported questionnaire during course time. Multivariate linear regression was used to assess the association between motivation to move including additional predictors of behavior such as intention and tendency to self-activate and self-reported screen time and physical activity controlling for age and sex. Motivation including all 3 motivational variables (interest, utility, competence) was negatively associated with screen time, b = −0.498 (95% CI between −0.635 and −0.361) and positively associated with moderate-to-vigorous physical activity, b = 133.986, (95% CI between 102.129 and 165.843). Of the 3 motivational variables, interest had the strongest negative association with screen time, b = −0.434 (95% CI between −0.551 and −0.317), and the strongest positive association with physical activity, b = 113.671, (95% CI between 86.396 and 140.946). These findings indicate that the motivation of postsecondary students toward physical activity significantly influences their behaviors, including screen time and physical activity engagement.

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Motivation for Physical Activity as a
Key Determinant of Sedentary Behavior
Among Postsecondary Students
Rachel Surprenant, MEd1,2 , Isabelle Cabot, PhD2,3,
and Caroline Fitzpatrick, PhD2
Abstract
It is known that the transition to adulthood represents a critical period of life when acquiring healthy behaviors can influence
lifestyle and health throughout adulthood. Given the importance of the consequences of a sedentary lifestyle, identifying
influence factors is key to improving healthy behaviors. The objective of this study is to explore the role of postsecondary
students’ motivation toward physical activity in the association with their screen time and out-of-school physical activity
practice. A total of 1522 postsecondary students (90% were aged 17-20 years) recruited from 17 postsecondary institutions
completed the self-reported questionnaire during course time. Multivariate linear regression was used to assess the
association between motivation to move including additional predictors of behavior such as intention and tendency to self-
activate and self-reported screen time and physical activity controlling for age and sex. Motivation including all 3 motivational
variables (interest, utility, competence) was negatively associated with screen time, b = −0.498 (95% CI between −0.635
and −0.361) and positively associated with moderate-to-vigorous physical activity, b = 133.986, (95% CI between 102.129
and 165.843). Of the 3 motivational variables, interest had the strongest negative association with screen time, b = −0.434
(95% CI between −0.551 and −0.317), and the strongest positive association with physical activity, b = 113.671, (95% CI
between 86.396 and 140.946). These findings indicate that the motivation of postsecondary students toward physical activity
significantly influences their behaviors, including screen time and physical activity engagement.
Keywords
motivation, screen time, physical activity, postsecondary students, sedentary behavior
What is already known on this topic?
Some studies have linked adolescent screen time to physical activity, while others have found associations between
motivation to be physically active and involvement in physical activity. However, less research has examined associa-
tions between motivation for physical activity and screen time.
How does this research contribute to the field?
This study contributes by exploring the relationship between motivation toward physical activity, considering three
dimensions of motivation simultaneously, as well as intention to practice physical activity and tendency to self-activate,
and screen time and physical activity practice among postsecondary students.
What are this research’s implications toward practice?
Our findings may help in the elaboration and implementation of healthy lifestyle interventions in school settings and
suggest that teachers and practitioners direct their efforts on influencing student motivation and interest in physical
activity.
Original Research Article
1254032INQXXX10.1177/00469580241254032INQUIRYSurprenant et al
research-article2024
Introduction
The transition to adulthood is generally accompanied by a
sharp drop in physical activity and fitness levels among stu-
dents.1-7 For example, according to one study, 37.2% of
postsecondary students (n = 1886) engaged in less than
10 min of weekly physical activity outside of school.8 At the
same time, the amount of time allocated to recreational
screen use among young adults is considerable, averaging
4.7 h per day.9 Consequently, it is unsurprising that a

2 INQUIRY
significant number of postsecondary students do not meet
24-Hour movement guidelines for adults.10 More specifi-
cally, 38.9% fail to achieve the recommended 150 min per
week of moderate-to-vigorous physical activity, 63.8%
exceed the maximum of 3 h per day of recreational screen
time, and 43.7% exceed the recommended limit of 8 h of sed-
entary time per day.9
Screen time and physical activity behaviors adopted dur-
ing the transition to adulthood may become important pre-
dictors of long-term health, due to the predisposition to carry
such behavioral patterns into adulthood.11 Both are also inde-
pendently associated with health status among youth.12-14 In
addition, a sedentary lifestyle, including high levels of screen
time and low levels of physical activity, is considered one of
the world’s leading causes of mortality,15 and is linked to car-
diovascular disease,16,17 type 2 diabetes,18 and certain types
of cancers.19,20 Sedentary behaviors are also associated with
higher risks of anxiety,21 depression,22 and indicators of psy-
chological distress such as low self-esteem, feelings of lone-
liness, and high levels of stress.23 In addition, screen media
use in particular is associated with increased levels of anxi-
ety24 and depression25 in youth.
From a public health perspective, it is essential to identify
the modifiable factors or determinants of sedentary behav-
iors in postsecondary students in order to improve interven-
tions and health promotion efforts. Youth motivation is an
essential determinant of their eventual engagement in a
behavior.26,27 Motivation helps trigger engagement in behav-
ior, which translates into participation in a task.28
Consequently, the concept of engagement is closely linked to
that of motivation, since motivation precedes engage-
ment.29,30 We understand motivation as “a process in which
goal-directed activity is instigated and sustained” (p. 5).31
Various models of motivation (including Expectation-Value,
Self-determination and Motivational dynamics models) from
different approaches agree that motivation includes specific
dimensions of interest (eg,: I enjoy being physically active),
utility (eg,: Physical activity practice is useful for me), and
perceived competence (eg,: When I am physically active, I
feel competent).30,32-36
In socio-cognitive approach theories, interest consists of
emotions and cognitions and is divided into 2 types of inter-
est: situational and personal. Situational interest is mainly
emotional, temporary and dependent on the environment,
while personal interest content emotions and cognitions, is
stable and inherent to the individual. In the process of
developing an interest, situational interest precedes the
deployment of personal interest.37 Interest is similar of intrin-
sic motivation in self-determination theory.38 For its part, the
utility attributed to an activity is defined as the person’s
assessment of the compatibility between this activity and the
person’s goal pursuit.34,39 It’s similar to extrinsic motivation
in self-determination theory.40 Competence refers to an indi-
vidual’s perception of their ability to achieve an activity
properly.30 In self-determination theory, it represents 1 of the
3 fundamental psychological needs.41
Motivation for physical activity in turn influences predic-
tors of behavior as the intention to engage in practicing phys-
ical activity and the tendency to self-activate. Indeed,
intention is considered closer to behavior than motivation
because it includes the planning stage of the intended behav-
ior.42 Conceptually, intention is between motivation and the
concrete engagement in the planned behavior.43 The ten-
dency to self-activate, reflecting a personal propensity to
engage in what has been planned, is closely and positively
linked to motivation and this has been examined in young
adults.44 For example, as illustrated in Figure 1, a person
might be motivated by an activity (eg, cycling), and plan a
time to engage in it (intention). However, just before the
planned activity (cycling), an alternative motivational activ-
ity might arise (eg, playing a video game with friends), creat-
ing a motivational conflict45,46 that threatens the execution of
the planned behavior. If the person possesses a tendency to
self-activate (meaning a predisposition to carry out what is
planed, as defined earlier), he or she would be more likely to
enact the planed behavior. Although tendency to self-activate
has been linked to motivation to move, less is known about
its potential role as a determinant of screen time in young
adulthood.
A literature review was conducted that examines how
motivation toward physical activity can influence both
screen time and the practice of physical activity. Some stud-
ies have linked adolescent screen time to moderate-to-vigor-
ous physical activity.47-49 Others have found associations
between motivation for physical activity, and engagement in
physical activity in adolescents.50,51 To date, less research has
examined associations between motivation for physical
activity and screen time. This research gap contributes to the
pertinence of this study. The objective of this study is to
examine how motivation for physical activity contributes to
screen time and physical activity practice among young
adults. More specifically we examine how motivation and its
1Cégep de Saint-Hyacinthe, Saint-Hyacinthe, QC, Canada
2Université de Sherbrooke, Sherbrooke, QC, Canada
3Cégep Édouard-Montpetit, Longueuil, QC, Canada
Received 18 January 2024; revised 23 April 2024; revised manuscript accepted 24 April 2024
Corresponding Author:
Rachel Surprenant, Cégep de Saint-Hyacinthe, 3000 Av. Boullé, Saint-Hyacinthe, QC J2S 1H9, Canada.
Email: rsurprenant@cegepsth.qc.ca

Surprenant et al 3
dimensions (interest, utility, competence) and additional pre-
dictors of behavior (intention and tendency to self-activate)
are associated with screen time and physical activity. We
hypothesize that young adult’s motivation toward physical
activity will be associated with lower screen time and greater
physical activity levels.
Methods
Participants
In the present study, we use a community-based convenience
sample of 1706 participants between the ages of 17 and 42
recruited from 17 colleges in the province of Quebec, Canada
called “collèges d’enseignement général et professionnel”
(CEGEP). CEGEPs are publicly funded postsecondary edu-
cational institutions, offering 2-year pre-university programs
and 3-year vocational programs. A cohort of 815 students
was recruited during the Fall 2021 semester and a second
cohort of 891 students was recruited during the Winter 2022
semester. Participants provided informed consent and com-
pleted a questionnaire during their physical education class.
This study received approval from the ethics review boards
of all participating institutions, and all participants signed a
written informed consent before completing the survey.
Students with missing data on continuous or control vari-
ables were excluded from the study (10.8% of the baseline
sample), resulting in a final sample of 1522 students. In total,
there were 923 females (60.6%) and 595 males (39.1%)
participants.
Measures
Predictors
Motivation. We created a variable to measure motivation for
physical activity, comprising 14 items from 3 scales, each
targeting a dimension of motivation; 6 items measure inter-
est in physical activity; 4 items measure the utility of physi-
cal activity; and 4 items measure competence in physical
activity practice. Participants rated each item on a Likert
scale ranging from 1 (strongly disagree) to 5 (strongly
agree). We used the mean of each scale (interest,
usefulness, competence) to represent this variable ranged
from 1 to 5.
Dimensions of Motivation
Interest. This variable derives from items developed and
validated in the studies by Cabot et al,27,52 and was subse-
quently adapted for a study focusing specifically on interest
in physical activity.53 This variable contains 6 items (λ = 0.90)
and represents the two dimensions of personal interest (cog-
nitive and affective). The cognitive dimension enables the
participant to express interest in learning about physical
activity (eg,: I enjoy learning about physical activity even
outside the school context); the affective dimension enables
emotional expression of interest in physical activity (eg,: I
always want to be physically active).
Utility. This variable comprises 4 items (λ = 0.85) derived
from the utility attributed to the practice of physical activity
scale: It’s important for me to engage in regular physical
activity practice; I find it worthwhile to engage in regular
physical activity practice; Regular physical activity practice
is useful for me; and Regular physical activity practice brings
me benefits in life.54 Studies of Hulleman and Harackie-
wicz34 justified a slight adjustment of the items by reformu-
lating them in a more personal way. For example, the item
“Regular physical activity practice is useful”54 became
“Regular physical activity practice is useful for me.”53
Competence. This variable represents the participants’ per-
ceived competence toward the practice of physical activity54
which is composed of 4 Likert-type items (λ = 0.84): I am
good at physical activity; When I do physical activity, I am
among the best; When I do physical activity, I feel compe-
tent; and I know many things about physical activity.
Intention. This variable reflects the intention to meet physi-
cal activity guidelines and was measured using the following
question: “The World Health Organization recommends at
least 150 min of moderate-intensity physical activity or at
least 75 min of vigorous-intensity physical activity each
week. Over the next 3 months, do you intend to follow these
Figure 1. Relationship between motivation, intention, tendency to self-activate and behavior.

4 INQUIRY
recommendations?” Participants rated this question as fol-
lows: (1) yes; (2) yes, maybe; (3) no, probably not; (4) no.
Responses were reverse coded so that higher scores reflect
higher levels of intention to practice physical activity.
Tendency to self-activate. The tendency to be active relates
specifically to an individual’s ability to maintain control over
planned behavior until it is realized. This variable was
inspired by the notion of behavioral control and was devel-
oped from validated items.44 The scale comprises 4 items
(λ = 0.85) on a 5-point Likert scale (1-5) from strongly dis-
agree to strongly agree: When I want to do physical activity,
I do it; I am able to put myself in action to influence my
physical condition; When I plan to practice physical activity,
I really do it; I am able to go beyond my desire to be active:
I really am).
Dependent Variables
Screen time. Screen time was assessed using the following
question: How many hours a day do you usually spend on
screen during your free time (outside school or work)? To
ensure the participant excluded screen time devoted to school
or work obligations, an additional question specifically tar-
geted work-based screen time.
Physical activity. Participants reported physical activity prac-
tice during a typical week over the last 3 months before the
beginning of the semester. Specifically, they indicated the
duration (minutes/week), nature (eg, swimming, jogging,
playing soccer), and intensity (eg, low, moderate, vigorous)
of physical activity (Table 1,55). The responses were then
used to estimate a single variable reflecting total weekly
minutes of moderate and vigorous-intensity physical activ-
ity. The number of minutes spent in vigorous-intensity activ-
ity was multiplied by 2 and then added to the number of
minutes spent in moderate-intensity activity to reflect the
WHO’s recommendation in which the duration of vigorous-
intensity activity is equivalent to twice the duration of mod-
erate-intensity activity. The World Health Organization56
recommends a minimum of 150 min of moderate-intensity
endurance activity or a minimum of 75 min of vigorous-
intensity endurance activity per week for health benefits.
Covariates. Participants reported age (in years) and sex, as
either male or female.
Data Analysis
We estimate a series of multiple linear regression with 95%
confidence intervals to examine the contribution of young
adult’s motivation for physical activity to their screen time
and physical activity practice, adjusting for age and sex. The
intention to engage in physical activity practice and the ten-
dency to self-activate were also included as additional
predictors in the model examining the associations between
motivation and screen time and physical practice. To limit
the impact of extreme values, values outside ± 3 standard
deviation thresholds were considered outliers and removed
from the analyses.57 All analyses were conducted using IBM
SPSS Statistics for Windows, Version 28.0 (IBM Corp.,
Armonk, NY, USA).58
Results
Descriptive statistics (n, % or mean, SD) are presented in
Table 1 for the total sample. A total of 1522 participants
(89.2% of the baseline sample) provided complete data and
were used for the analysis. Our sample was predominantly
female (60.6% females). Participants were aged 17 to
42 years (M = 19.09, SD = 2.18) but 90.0% were between age
17 and 20 years. Participants in our sample spent on average
3.87 h per day for recreational screen time and 48.2% of the
sample exceeded daily recommendations of 3 h or less per
day. With regard to physical activity, participants were active
an average of 381 min per week and 43.8% of the sample did
not meet physical activity guidelines.
Regression Results
Table 2 presents the results of the multivariate analyses used
to model the association between motivation toward physical
activity, including intention or tendency to self-activate and
screen time and physical activity. Significant associations
were observed between all motivational variables (interest,
utility, competence) and screen time. Of all three dimensions
Table 1. Descriptive Characteristics.
Total sample
(n = 1522)
Sex, n (%)
Male 595 (39.10)
Female 923 (60.60)
Other/Preferred not to answer 4 (0.30)
Age, years, n (%)
17-18 704 (46.20)
19-20 667 (43.80)
≥21 151 (10.00)
Motivation, (mean, SD)
Motivation (include interest, utility,
competence)
3.57 (0.86)
Interest 3.35 (1.00)
Utility 4.03 (0.89)
Competence 3.33 (0.95)
Intention, (mean, SD) 3.28 (0.77)
Tendency to self-activate, (mean, SD) 3.53 (0.99)
Screen time (hours/day, mean, SD) 3.87 (2.35)
Physical activity (minutes/week, mean, SD) 380.74 (512.27)
Note. SD = standard deviation.

Surprenant et al 5
for motivation to move, interest had the strongest negative
association with screen time, b = −0.434 (95% CI between
−0.551 and −0.317). Each unit of utility attributed to physi-
cal activity made significant negative contributions to screen
time b = −0.360 (95% CI between −0.492 and −0.227).
Finally, competence in physical activity practice scale was
also associated with significant decreases in screen time
(hours), b = −0.426 (95% CI between −0.552 and −0.300).
Model Motivation presents unstandardized regression coef-
ficients for motivation including all 3 motivational variables
(interest, utility, competence), and adjusted for age and sex.
In this Model, we observed the strongest negative associa-
tion with screen time, b = −0.498 (95% CI between −0.635
and −0.361). In Model Intention, the intention to practice
physical activity was included in the Model Motivation.
Significant associations were observed with screen time,
b = −0.384 (95% CI between −0.551 and −0.218) after con-
trolling for covariates. Instead of intention, the tendency to
self-activate was included in the last model which made
smaller contributions to screen time, b = −0.326 (95% CI
between −0.561 and −0.091).
Significant associations were also observed between all
motivational variables and physical activity practice. The
dimension of interest had the strongest positive association
with physical activity, b = 113.671, (95% CI between 86.396
and 140.946). Each unit of utility attributed to physical activ-
ity made significant positive contributions to physical activ-
ity, b = 103.421 (95% CI between 72.717 and 134.125).
Competence in physical activity practice was also associated
with significant increases in physical activity (minutes),
b = 112.137, (95% CI between 82.827 and 141.447). Model
Motivation made the strongest positive association with
physical activity, b = 133.986, (95% CI between 102.129 and
165.843). In Model Intention, being motivated by physical
activity was associated with an increase of b = 117.923 (95%
CI between 79.310 and 156.536) in the physical activity
practice scores after controlling for covariates. Finally,
Model Tendency to self-activate made smaller contributions
to physical activity, b = 95.543 (95% CI between 40.497 and
150.589).
Discussion
As far as we know, the present study is the first to describe
the relationship between motivation toward physical activ-
ity, screen time and physical activity practice among post-
secondary students. In addition, this is the first study to
simultaneously consider these dimensions of motivation as
well as intention to practice physical activity and tendency
to self-activate. After adjusting for age and sex, we found
that motivation for physical activity was the strongest pre-
dictor of youth screen time and moderate-to-vigorous physi-
cal activity. Being interested in and feeling competent in
performing physical activity were the two dimensions of
motivation most strongly linked to physical activity involve-
ment and time spent using screens in our sample of young
adults. Neither intention to engage in physical activity nor
tendency to self-activate were related to our outcomes. This
is consistent with another study that demonstrates the key
role of intrinsic motivation, which is conceptually linked to
interest in physical activity, in enhancing levels of physical
activity.59
Our study extends previous work on adolescents by sug-
gesting that similar motivational processes may be involved
in the adoption of lifestyle habits.51 The present result is also
in line with a recent study of motivation for physical activity
and sedentary behaviors among secondary students which
revealed that intrinsic motivation was negatively linked with
sedentary behaviors and positively associated with physical
activity engagement.60 Furthermore, our findings align with
another study showing positive associations between healthy
behaviors (eg, physical activity and healthy weight control
behaviors) and autonomous motivation.61
Some limitations should be considered. First, our study
used a cross-sectional design that does not allow us to infer
the directionality in the observed associations. For instance,
Table 2. Unstandardized regression coefficients and 95% confidence intervals (CIs) for screen time and physical activity according to
motivation for physical activity (n = 1522).
Screen time B
(95% CI)
Physical activity B
(95% CI)
Motivational variables
Interest −0.434 (−0.551, −0.317)** 113.671 (86.396, 140.946)**
Utility −0.360 (−0.492, −0.227)** 103.421 (72.717, 134.125)**
Competence −0.426 (−0.552, −0.300)** 112.137 (82.827, 141.447)**
Model motivation −0.498 (−0.635, −0.361)** 133.986 (102.129, 165.843)**
Model intention −0.384 (−0.551, −0.218)** 117.923 (79.310, 156.536)**
Model tendency to self-activate −0.326 (−0.561, −0.091)* 95.543 (40.497, 150.589)**
Note. Screen time variable is measured in hours per day. Physical activity variable is measured in minutes per week. Motivation includes interest, utility,
competence, and is adjusted for age and sex. Model Intention is Motivation plus adjustment for intention. Model Tendency to self-activate is Motivation
plus adjustment for Tendency to self-activate.
*P < .01. **P < .001.

6 INQUIRY
it may be case that accumulating lower levels of physical
activity and spending more time in front of screens are con-
tributing to decrease motivation in youth. Experimental or
longitudinal studies could examine changes in the relation-
ship over time and clarify the direction of the association. In
addition, we used self-reported measures of motivation,
screen time, and physical activity involvement. This could
have resulted in shared measurement error or social desir-
ability bias. Indeed, self-reported measures, particularly
reports of moderate-to-vigorous physical activity, can lead to
overestimations.62 As such, future studies using objective
measures, such as accelerometers are warranted. Another
limitation is the use of a convenience sample which is poten-
tially limiting the generalizability of the findings. Finally, we
had limited socioeconomic variables for the participants. For
instance, the socioeconomic status or ethnicity of the stu-
dents may moderate the observed associations.
The main strength of this study is our ability to simultane-
ously consider the impact of multiple dimensions of motiva-
tion on screen time and physical activity in young adults. To
date, most research has focused on the role of the sociodemo-
graphic characteristics of individuals in the adoption of life-
style choices.49,63 Though important, these determinants are
likely to be more difficult to leverage through interventions.
Our study is one of the first to examine how psychological
motivation for physical activity contributes to the adoption
of healthy behaviors. Furthermore, our study provides a
detailed account of these associations by considering mul-
tiple dimensions of motivation (ie, interest, utility, compe-
tence, intention, and tendency to self-activate).
The present study may help the elaboration and imple-
mentation of healthy lifestyle interventions in the postsec-
ondary school setting. First, our results indicate the
importance of psychological interventions aimed at encour-
aging students to adopt or maintain a healthy, active lifestyle
which are consistent with recent work showing that peda-
gogical practices focusing on enjoyment and perceived ben-
efits of physical activity influence students to get moving.64,65
Feeling pleasure is one of the important factors that makes
people do more physical activity.66 Our findings suggest that
teachers and practitioners should work specifically to influ-
ence motivation and improve interest in physical activity by
setting up activities that bring pleasure and positive emo-
tions. Moreover, focusing on specific motivational behav-
iors to provide necessary support could improve healthy
behaviors.67
Conclusion
Motivation to move plays an important role in screen time
and the practice of physical activity outside of the school
context among postsecondary students. Future studies should
use longitudinal designs and objective measures of physical
activity to better understand the association between stu-
dents’ motivation, screen time and physical activity practice.
Our results support the development of interventions to tar-
get the motivation to move in physical education classes to
reduce sedentary behavior among students.
Acknowledgments
The authors thank all CEGEP teachers and students who made this
research possible. Shannon Bell, research colleague, is also thanked
for providing assistance in the linguistic revision.
Author Contributions
RS carried out the cross-sectional study, reviewed the current litera-
ture on the subject, performed the statistical analysis, and drafted
the manuscript. IC carried out the cross-sectional study, reviewed
the current literature on the subject, and drafted the manuscript. CF
edited the manuscript and provided critical feedback.
Availability of Data and Materials
Not applicable.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
research has been funded by the Ministère de l’Enseignement
supérieur under the Program d’aide à la recherche sur l’enseignement
et l’apprentissage [11664]. In addition, the preparation of this arti-
cle has benefited from a financial contribution from the Ministère
de l’Enseignement supérieur under the Program d’aide à la diffu-
sion des résultats de recherche.
ORCID iD
Rachel Surprenant https://orcid.org/0009-0001-2343-1271
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