Perceived physical literacy, self-reported physical activity, and sports video gaming among university students: a cross-sectional study

Abstract

Sports video gaming (SVG) is a popular form of digital engagement among young adults, yet its associations with physical literacy (PL), sport motivation, and physical activity (PA) remain unclear. This study examined the relationships between SVG engagement, perceived PL, sport motivation, and self-reported PA among university students, and tested whether these associations differed by sex. A cross-sectional survey was conducted among 1797 students (850 males, 947 females). Multiple linear regression analyses examined associations between SVG engagement and perceived PL, autonomous motivation, and moderate- and vigorous-intensity PA, adjusting for age, general (non-sports) video gaming time, and sedentary behavior. SVG engagement was positively associated with perceived PL, autonomous motivation, and both moderate- and vigorous-intensity PA, independent of covariates. In contrast, general video gaming showed negative or null associations with PL-related outcomes. Although males reported higher SVG engagement, sex did not moderate the associations between SVG engagement and PL or PA. These findings support a refined, genre-specific perspective on digital gaming and highlight SVG as a form of digital engagement aligned with favorable PA-related outcomes among university students.

1. Introduction

Physical literacy (PL) is a holistic construct encompassing physical competence, confidence, motivation, and knowledge for lifelong engagement in physical activity (PA).¹ Research on PL and PA among university students demonstrates that this period is a crucial developmental stage during which young adults establish lifelong health and activity patterns.² This transition period marks a shift from structured, school-based physical education to largely self-directed lifestyle choices, a change consistently associated with declines in PA.^3,4 Evidence from transition-focused research demonstrates that the move into university is linked to a precipitous drop in PA and fitness.^5,6 At the same time, global evidence consistently shows that young adults exhibit insufficient PA levels. According to the World Health Organization,⁷ over 80% of adolescents are insufficiently active, suggesting a pattern that likely continues into young adulthood.

Video gaming has become one of the most popular forms of leisure worldwide, with particularly high engagement among adolescents and young adults. Recent global estimates indicate that approximately 2.7 billion people play video games across a wide range of age groups, underscoring the widespread popularity of gaming among younger populations.⁸ Despite its entertainment value, video gaming is typically performed in a sedentary posture, contributing significantly to screen time and being associated with adverse health outcomes.⁹ Research also suggests that screen-based sedentary behaviors, including video gaming, are negatively related to multiple domains of PL, such as physical competence, motivation and confidence, and knowledge and understanding.¹⁰

Video games span a diverse range of genres, including action, adventure, role-playing, strategy, racing, and sports.¹¹ Sports video games (SVGs) represent one of the most widely played genres among youth. SVGs simulate traditional sports and require players to control virtual athletes on-screen using game controllers in a seated position.¹² In the United States, five SVG titles ranked among the top ten best-selling video games in 2024, such as EA Sports College Football 25, NBA 2K25, and Madden NFL 25.⁸ Despite being primarily played in a sedentary context, the sport-themed content of SVGs may offer a distinct experience relative to other video game genres. As such, SVG engagement may be uniquely related to sport-related perceptions and key psychological domains of PL, with potential implications for PA behavior.

Emerging evidence nevertheless suggests that SVG engagement should not be treated as generic digital gaming. For example, Ng et al.¹³ using a nationally representative sample of Finnish adolescents aged 11–15 years, reported clear genre-specific associations between digital gaming and PA. Their findings showed that playing SVGs was positively associated with engaging in PA on four or more days per week and with sport club participation, whereas frequent engagement in first-person shooter or esports-style games was negatively associated with sport club participation. Similarly, subsequent research demonstrated that during and after the COVID-19 lockdown, SVG engagement was consistently and positively associated with meeting PA recommendations, while esports participation showed negative or null associations with daily PA.¹⁴ Together, these findings challenge displacement-based assumptions and suggest that SVG may coexist with, or even align with, physically active lifestyles.

Motivation represents a central psychological mechanism through which SVG engagement may relate to PL and PA. Self-Determination Theory (SDT) provides a well-established framework for understanding the quality of motivation underlying sport and PA participation.^15,16 According to SDT, motivation varies along a continuum from autonomous motivation, in which individuals engage in activity because it is enjoyable or personally meaningful, to controlled motivation, which reflects participation driven by internal or external pressures such as guilt or social expectations. Amotivation represents a lack of intention or perceived value toward participation.¹⁷ Extensive research has shown that autonomous motivation is consistently associated with higher PA participation, stronger persistence, and more positive physical literacy profiles, whereas controlled motivation and amotivation are linked to lower engagement and poorer activity outcomes.^5,18 These distinctions highlight the importance of examining not only the amount of motivation, but also its quality, when investigating PA-related behaviors.

Guided by the Physical Activity Relationship (PAR) approach, PA is conceptualized not as an isolated behavior but as part of a broader network of relationships involving lifestyle practices, motivations, identities, and social contexts.¹⁹ From this perspective, engagement in digital gaming is not assumed to displace PA; instead, gaming behaviors may coexist with or relate differently to specific forms and intensities of PA. The PAR framework further emphasizes the importance of examining PA by intensity domains, recognizing that vigorous PA, moderate PA, walking, and sedentary behavior may show distinct relational patterns with contextual factors. This framework provides a theoretical basis for analyzing SVG engagement in relation to domain-specific PA outcomes rather than relying solely on total PA or displacement-based assumptions.

Video game genres differ in their psychological and behavioral implications. Whereas long playtime in massive multiplayer online role-playing games (MMORPGs) has been associated with lower PA levels, frequent engagement with SVGs has been positively related to more frequent exercise and vigorous PA participation.²⁰ This pattern may reflect an underlying interest in sports among SVG players. Furthermore, playing SVGs may enhance self-efficacy and perceived competence related to sport participation, thereby promoting motivation toward real-life physical activities.²¹ A recent scoping review on SVGs indicates that participation in SVGs is positively associated with the psychological and cognitive domains of PL, highlighting their potential role in shaping individuals’ attitudes toward PA.²² Given that lifestyle behaviors established during university often track into adulthood, this context presents a strategic opportunity to examine whether SVGs can may be associated with both digital sport experiences and real-life PA engagement.

Social interaction represents another important dimension of gaming. According to the Entertainment Software Association, 72% of players engage in gaming with others, and a majority believe video games facilitate social connection and diversity.²³ These social experiences may further influence behaviors and self-perceptions related to PA engagement.

Sex differences in gaming behavior are well documented. Males consistently report higher gaming frequency than females.^24,25 Additionally, females are less likely to play SVGs and generally show lower interest in sports-themed games.²⁶ Previous research has also found that the beneficial effects of SVG on PA may be stronger in males than females.²⁷ These findings highlight the need to examine sex-specific patterns in both gaming behaviors and their associations with PL and PA. Within the PAR framework, sex can be conceptualized as a moderating factor that shapes how gaming behaviors relate to PA and related psychosocial constructs. Examining SVG × sex interaction effects therefore allows for a more nuanced understanding of whether the relationships between SVG engagement, PL, and PA differ between male and female students, rather than assuming uniform associations across sexes.

Although PL can be assessed using objective, performance-based measures,²⁸ such assessments require standardized testing environments, trained assessors, specialized equipment, and substantial time, making them difficult to implement in large-scale questionnaire-based studies.²⁹ In contrast, perceived PL, defined as individuals' self-evaluations of their motivation, confidence, knowledge, and perceived physical competence, offers a practical and theoretically grounded approach for survey research.^1,28 While perceived PL is related to, but not interchangeable with, objective assessments of PL, it is particularly relevant for understanding motivational and cognitive processes that support engagement in PA. Given the self-reported nature of the present study and its large university sample, perceived PL was therefore used as an appropriate construct to examine students’ PL-related beliefs and their potential associations with PA and gaming behaviors.

In addition to PL, the present study assessed PA using self-reported measures rather than objective monitoring. Although device-based assessments provide precise estimates of PA,³⁰ they are often impractical in large-scale, cross-sectional research due to financial, logistical, and participant burden constraints. Given the cross-sectional design and large sample size, objective PA assessment was not feasible and therefore not employed. Self-reported PA measures are widely used in epidemiological studies, demonstrating acceptable reliability and moderate correlations with objective PA indicators among young adults.³¹

Despite the growing popularity of SVGs and their potential influence on PL and PA, empirical research in this area remains limited and unevenly distributed across contexts. A recent scoping review on SVGs and PL indicates that existing studies have been conducted predominantly in European and North American settings, with little research explicitly examining these relationships in Asian contexts.²² This gap is noteworthy, as gaming cultures, sport participation patterns, and academic pressures in Asian societies may differ substantially from those in Western countries. Furthermore, sex-specific patterns in SVG engagement are not well understood, particularly with respect to whether the effects of SVGs on PL and PA differ between males and females. Existing evidence suggests notable sex differences in gaming behaviors, yet these have seldom been examined in relation to PL or PA outcomes. These gaps highlight the need for research that clarifies the associations among SVG behaviors, PL, and PA, and considers the potential moderating role of sex using multivariable analytical approach.

Accordingly, the present study examined the associations between SVG engagement, perceived PL, sport motivation, and self-reported PA among university students using regression-based analyses. Four hypotheses were proposed. First, greater SVG engagement would be positively associated with perceived PL (H1). Second, greater SVG engagement would be positively associated with autonomous motivation toward sport participation (H2). Third, greater SVG engagement would be positively associated with higher levels of self-reported PA, particularly moderate- and vigorous-intensity PA (H3). Finally, based on the PAR framework and prior evidence of sex differences in gaming behavior and sport engagement, sex was hypothesized to moderate the associations between SVG engagement and perceived PL (H4) as well as PA outcomes (H5), such that stronger positive associations would be observed among male students than female students.

2. Methods

2.1. Study design and participants

This cross-sectional study was conducted during the second semester at The Chinese University of Hong Kong (CUHK), with data collection taking place between January and February 2025.

All Year 1 undergraduate students at CUHK are required to enroll in physical education (PE) courses as part of the university curriculum. Additionally, senior-year students may choose to enroll in elective PE courses (e.g., taekwondo, hip-hop dance) for extra credit based on personal interest. Participants were recruited from both required and elective PE classes. The online questionnaire was administered during scheduled PE lessons by the respective lecturers. To reduce potential bias or perceived coercion related to lecturers administering the questionnaire during scheduled PE lessons, several safeguards were implemented. Students were clearly informed that participation was voluntary and would not affect their grades or course standing. The questionnaire was completed anonymously via a secure online platform, and lecturers could not view individual responses or identify who participated. Only the research team, who had no teaching responsibilities for the students, had access to the anonymized data. These procedures helped protect participant autonomy and minimize the influence of the lecturer's role.

Data were collected using an online questionnaire distributed via Qualtrics. A QR code linking to the questionnaire was displayed in the teaching area, allowing students to access and complete the survey using their mobile devices. Participation was voluntary, and electronic informed consent was obtained prior to data collection. All students enrolled in PE courses were eligible to participate. In addition, they were free to refuse answering the questionnaire and could withdraw at any time. Students under the age of 18 were excluded to ensure compliance with ethical standards for research involving adult participants. No other exclusion criteria were applied.

Ethical approval for this study was obtained from the Survey and Behavioural Research Ethics Committee of CUHK (Ref. No.: SBRE-24-0047). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 2024 revision of the Declaration of Helsinki (https://www.wma.net/policies-post/wma-declaration-of-helsinki/).

2.2. Measures

2.2.1. Demographic information

Participants were asked to provide basic demographic and background information, including age, sex, and ethnicity. Academic information was also collected, including participants’ academic department and faculty, year of study, to describe the disciplinary background of the sample. Participants also reported their sport participation background, including the types of sports they regularly participated in and the number of years of involvement in those sports. These data were collected for descriptive purposes. Demographic variables were used to describe the sample and were considered as potential covariates in subsequent analyses.

2.2.2. Video gaming habits

Participants reported their video gaming behaviors using a combination of open-ended and fixed-choice items assessing gaming time, frequency, session duration, gaming history, devices used, and game genres. Numeric responses were treated as continuous variables for analysis.

Sample items included:

• •“How much time per week do you play video games (in hours)?”
• •“How many days per week do you play video games?”
• •“How long have you been playing video games (in years)?”

Participants who reported no video game participation during the reference period were classified as non-gamers. Those who reported regular video gaming activity were further categorized based on the types of video games they played. Participants who played video games but did not report participation in sports-themed video games were classified as non-sports video game (non-SVG) gamers, whereas those who reported playing one or more sports-themed video games were classified as SVG players.

For SVG players, additional open-ended numeric questions assessed SVG-specific duration and frequency. Mean values were calculated by averaging participants’ self-reported numeric responses.

General (non-sports) video gaming time was calculated by subtracting SVG gaming time from total weekly video gaming time and was used to represent time spent playing non-sports video games. This variable was included as a covariate in subsequent regression analyses to account for overall gaming exposure.

2.2.3. Self-reported PA level

Self-reported PA levels were assessed using the International Physical Activity Questionnaire – Short Form (IPAQ-SF).³¹ The IPAQ-SF asks seven questions, and evaluates the frequency and duration of vigorous, moderate, and walking activities, as well as time spent sitting, over the past seven days. Referring to prior population-based studies on PA issues,^32,33 the data were collected in minutes per week for each of the PA intensities. Then minutes were calculated for metabolic equivalent (MET) values by using the MET-minutes computation formulation recommendations.³⁴

The IPAQ-SF is suitable for use with individuals ages 15 years and older. Its reliability and validity have been supported by a large-scale international study conducted by Craig et al.,³¹ which involved participants from 12 countries. The study reported acceptable test-retest reliability, with correlations typically exceeding 0.80, and moderate criterion validity, with correlations ranging from 0.30 to 0.50 when compared to accelerometer data.

2.2.4. Perceived PL

Perceived PL was measured using the 9-item Perceived Physical Literacy Instrument (PPLI), which includes three subscales: knowledge and understanding, self-expression and communication with others, and sense of self and self-confidence.³⁵ Responses were recorded on a 5-point Likert scale. The instrument has demonstrated satisfactory construct validity (RMSEA = 0.08; CFI = 0.94; SRMR = 0.04) and convergent validity (CR = 0.72-0.78; AVE = 0.43-0.54) in adolescent populations.³⁵

2.2.5. Social engagement in PA

To assess the social dimension of PL, four items from the Adolescent Physical Literacy Questionnaire (APLQ) were employed. The APLQ is a validated 25-item instrument designed to measure various domains of PL among adolescents.³⁶ It was selected because it is the only available validated tool that explicitly includes the social domain, which was central to this study's objectives. Four items specifically targeting social engagement in physical activities were selected. Participants responded to each item using a 5-point Likert scale ranging from 1 = “strongly disagree” to 5 = “strongly agree”. The selected items included:

• •“I do sports and physical activity with family or friends.”
• •“I encourage others to do sports and physical activity with me.”
• •“I participate in group sports outside of school time.”
• •“I have made new friendships in sport and physical activity.”

2.2.6. Sport motivation

Sport motivation was assessed using the Sport Motivation Scale-6 (SMS-6), a validated instrument based on Self-Determination Theory.³⁷ The SMS-6 consists of 24 items measuring six types of motivational regulation toward sport participation: intrinsic motivation, integrated regulation, identified regulation, introjected regulation, external regulation, and amotivation. Participants responded to each item using a 7- point Likert-type scale, with higher scores indicating stronger endorsement of the corresponding motivational regulation.

Each motivational regulation was measured using four items. In the present study, the items were grouped according to the original scale structure as follows:

intrinsic motivation (Items 1, 6, 14, 18), integrated regulation (Items 2, 9, 13, 21), identified regulation (Items 3, 7, 8, 20), introjected regulation (Items 10, 11, 16, 24), external regulation (Items 4, 15, 19, 23), and amotivation (Items 5, 12, 17, 22).

Consistent with self-determination theory, motivation was analyzed using theoretically meaningful composite scores. Autonomous motivation was calculated as the mean of intrinsic motivation, integrated regulation, and identified regulation subscale scores. Controlled motivation was calculated as the mean of introjected and external regulation subscale scores. Amotivation was analyzed as a separate construct. Higher scores reflect higher levels of the respective motivational regulation.

2.3. Sample size and power analysis

An a priori power analysis was conducted using G∗Power 3.1 to determine the minimum sample size required for the regression-based analyses employed in this study. Power calculations were based on detecting small effect sizes, consistent with conventional benchmarks for behavioral and social science research.³⁸

For multiple linear regression models, a small effect size was specified (f² = .02), with a two-tailed significance level of α = .05 and statistical power (1 − β) set at .80. Assuming up to six predictors in the regression models (including SVG engagement, sex, interaction terms, and covariates), the required minimum sample size was N = 485.

The final analytic sample of 1797 participants substantially exceeded this requirement, providing sufficient power to detect small main effects and interaction effects in the regression analyses.

2.4. Statistical analyses

Descriptive statistics were computed to summarize participant characteristics, video gaming behaviors, PL measures, motivational variables, PA levels, and sitting time. Continuous variables are presented as means and standard deviations (SD) when the assumption of normality was met. In accordance with IPAQ-SF reporting guidelines, variables derived from the IPAQ-SF, including vigorous-intensity PA, moderate-intensity PA, walking, and sitting time, are summarized using medians and interquartile ranges (IQRs), as these indicators are typically right-skewed in population-based samples. This approach provides a more accurate representation of central tendency and variability for non-normally distributed activity data.

PA outcomes were calculated using the official IPAQ-SF scoring protocol. To minimize the influence of over-reporting commonly associated with self-reported PA, a truncation rule was applied whereby daily minutes of walking, moderate-intensity, and vigorous-intensity activity were capped at 180 min, and total weekly PA was limited to a maximum of 960 min prior to the calculation of MET-minutes per week. Consistent with IPAQ-SF scoring guidelines, responses of “don't know/not sure” for either frequency or duration were treated as missing data for both variables within that activity category to ensure conservative and standardized scoring. Categorical variables are presented as frequencies and percentages.

Sex differences in continuous variables were examined using independent samples t-tests for normally distributed data. For IPAQ-SF MET values and sitting time, the Kruskal–Wallis test was used because these variables exhibited non-normal distributions and skewness, which violate the assumptions of parametric tests.

Internal consistency of multi-item scales (PPLI, APLQ-social, SMS-6) was evaluated using Cronbach's alpha, with values ≥ .70 considered acceptable. Pearson's product-moment correlation coefficients were calculated to examine associations between video gaming behaviors (time, frequency, duration), PL measures, motivation, PA level, and sitting time, stratified by sex. Correlation strength was interpreted as small (r = .10 - .29), medium (r = .30 - .49), or large (r ≥ .50) and were used to provide descriptive context for subsequent regression analyses.

To examine descriptive differences in PL, motivation, and PA across gaming groups (non-gamers, non-SVG gamers, SVG players), one-way analyses were conducted using appropriate parametric or non-parametric procedures based on distributional assumptions. These group comparisons were included for contextual purposes and were not used for primary hypothesis testing.

Primary hypotheses were tested using multiple linear regression analyses. Separate regression models were specified for perceived PL, autonomous motivation, and PA outcomes. For PA analyses, vigorous- and moderate-intensity PA variables were log-transformed prior to analysis due to skewed distributions. Regression models adjusted for age, general (non-sports) video gaming time, and, where appropriate, sitting time. Moderation by sex was examined using hierarchical regression analyses, with interaction terms created by multiplying SVG engagement by sex.

All analyses were performed using IBM SPSS Statistics v.27 (IBM Corp., Armonk, N.Y., USA), with statistical significance set at p < .05 (two-tailed).

3. Results

3.1. Participant characteristics

Participant characteristics stratified by sex are presented in Table 1. A total of 1797 university students were included in the analysis, comprising 850 males and 947 females. The mean age of the sample was 18.44 years (SD = 1.42), with no meaningful age difference between males and females. The majority of participants were Year 1 students enrolled in required physical education courses. Specifically, 1748 participants (97.3%) were Year 1 students taking compulsory PE courses, while the remaining 49 participants (2.7%) were non–Year 1 students enrolled in elective PE courses.

Table 1.

Participant characteristics and video gaming behaviours stratified by sex.

	Total		Males		Females
	N	Value	N	Value	N	Value
Age, mean (SD)	1797	18.44 (1.42)	850	18.42 (1.40)	947	18.46 (1.44)
Video gaming time, hrs/wk, mean (SD)	1797	5.29 (8.52)	850	8.33 (10.46)	947	2.57 (4.90)
Video gaming frequency, days/wk, mean (SD)	1797	3.16 (2.81)	850	4.33 (2.59)	947	2.11 (2.59)
Video gaming duration, mins/session, mean (SD)	1797	41.11 (51.01)	850	59.17 (57.87)	947	24.91 (37.13)
Video gaming habit
Non-gamers, n (%)	1797	599 (33.33)	850	138 (16.24)	947	461 (48.68)
Non-SVG gamers, n (%)	1797	943 (52.48)	850	500 (58.82)	947	443 (46.78)
SVG players, n (%)	1797	255 (14.19)	850	212 (24.94)	947	43 (4.54)
PPLI total score, mean (SD)	1797	3.42 (0.61)	850	3.49 (0.63)	947	3.35 (0.58)
APLQ-social total score, mean (SD)	1797	3.34 (0.85)	850	3.49 (0.84)	947	3.21 (0.83)
Sport Motivation (SMS-6), mean (SD)
Autonomous Motivation	1797	4.23 (1.18)	850	4.40 (1.16)	947	4.07 (1.17)
Controlled Motivation	1797	3.61 (1.26)	850	3.87 (1.23)	947	3.38 (1.20)
Amotivation	1797	3.56 (1.27)	850	3.70 (1.31)	947	3.43 (1.21)
Physical Activity Level (IPAQ-SF),
Vigorous PA, MET-min/wk, median (IQR)	1405	0 (0-480)	638	0 (0-960)	767	0 (0-0)
Moderate PA, MET-min/wk, median (IQR)	1297	0 (0-240)	589	80 (0-480)	708	0 (0-160)
Walking, MET-min/wk, median (IQR)	1045	693 (495-1386)	526	693 (495-1386)	519	693 (462-1386)
Sitting, min/day, median (IQR)	726	300 (190-410)	367	300 (190-410)	359	300 (190-410)

Note.

SMS-6 = Sport Motivation Scale-6; IPAQ-SF = International Physical Activity Questionnaire–Short Form.

PPLI = Perceived Physical Literacy Instrument; APLQ = Active Physical Literacy Questionnaire.

SVG = sports video game; SD = standard deviation; IQR = interquartile range.

Clear sex differences in video gaming behaviours were observed. Males reported higher weekly video gaming time, greater gaming frequency, and longer gaming duration per session than females. Nearly half of female students were classified as non-gamers (48.68%), compared with 16.24% of males, whereas SVG players accounted for 24.94% of males but only 4.54% of females. Males also reported slightly higher levels of perceived PL, social PL, and sport motivation. PA levels were generally low across the sample, particularly for vigorous activity, although males reported higher moderate-to-vigorous physical activity than females. Walking activity and sitting time were comparable between sexes.

3.2. Correlations between gaming behaviors, PL, motivation, and PA

Bivariate correlations between video gaming behaviors, PL, motivation, PA, and sitting time are presented in Table 2 and are reported to provide descriptive context for the subsequent regression analyses.

Table 2.

Bivariate correlations between video gaming behaviours, perceived PL, motivation, PA, and sitting time, stratified by sex.

Male
	PPLI	APLQ - social	Autonomous Motivation	Controlled Motivation	Amotivation	Vigorous PA	Moderate PA	Walking	Sitting Time
	(n = 850)	(n = 850)	(n = 850)	(n = 850)	(n = 850)	(n = 638)	(n = 589)	(n = 526)	(n = 367)
Video gaming time	−.20∗∗	−.17∗∗	−.14∗∗	−.15∗∗	−.06	−.04	−.08	0	.28∗∗
Video gaming frequency	−.16∗∗	−.07∗	−.07∗	−.07∗	−.03	−.04	−.12	0	0.1
Video gaming duration	−.11∗∗	−.09∗∗	−.06	−.07∗	−.02	.04	−.01	.13∗∗	.22∗∗
SVG gaming time	.15∗∗	.15∗∗	.17∗∗	.18∗∗	.18∗∗	.05	−.02	−.08	−0.01
SVG gaming frequency	.17∗∗	.29∗∗	.24∗∗	.27∗∗	.06	.15∗∗	.09∗	−.04	−.13∗
SVG gaming duration	.12∗∗	.21∗∗	.18∗∗	.19∗∗	.07	.14∗∗	.03	−.05	0.06

Female
	PPLI	APLQ - social	Autonomous Motivation	Controlled Motivation	Amotivation	Vigorous PA	Moderate PA	Walking	Sitting Time
	(n = 947)	(n = 947)	(n = 947)	(n = 947)	(n = 947)	(n = 767)	(n = 708)	(n = 519)	(n = 359)
Video gaming time	−.14∗∗	−.11∗∗	−.14∗∗	−.09∗∗	.01	−.04	−.02	.01	.17∗∗
Video gaming frequency	−.17∗∗	−.16∗∗	−.16∗∗	−.14∗∗	.02	−.05	−.07	0	.16∗∗
Video gaming duration	−.12∗∗	−0.05	−.10∗∗	−.09∗∗	−.01	0	−.04	.03	.13∗
SVG gaming time	.08∗	0.06	.05	.04	.02	.07∗	0	−.02	−0.05
SVG gaming frequency	.10∗∗	.08∗	.11∗∗	.13∗∗	.05	.03	.02	−.04	−0.02
SVG gaming duration	.09∗∗	.09∗∗	.07∗	.08∗	.05	.06	.01	.02	−0.08

∗∗. Correlation is significant at the 0.01 level (2-tailed).

∗. Correlation is significant at the 0.05 level (2-tailed).

Across both males and females, general video gaming behaviors (time, frequency, and duration) were generally associated with lower perceived PL, lower autonomous motivation, and greater sitting time, with associations tending to be stronger among males. In contrast, SVG engagement demonstrated a more favorable pattern, showing positive correlations with perceived PL, autonomous motivation, and vigorous PA. SVG behaviors showed little or no association with sitting time. Across sexes, controlled motivation and amotivation exhibited weaker and less consistent associations with gaming behaviors compared with autonomous motivation.

3.3. Descriptive group differences across gaming categories

Descriptive differences in PL, sport motivation, and PA across non-gamers, non-SVG gamers, and SVG players are presented in Table 3 SVG players consistently reported higher levels of perceived PL, autonomous motivation, and moderate-to-vigorous PA than both non-gamers and non-SVG gamers. Walking activity showed minimal variation across gaming groups. These unadjusted group comparisons are provided to illustrate overall patterns and to contextualize the regression analyses reported later in this study.

Table 3.

Descriptive differences in physical literacy, sport motivation and PA across gaming groups.

	Non-gamers		non-SVG gamers		SVG players		Test statistic	p	Effect size
	n	Value	n	Value	n	Value
PPLI total score, M(SD)	599	3.45 (0.59)	943	3.32 (0.59)	255	3.71 (0.62)	F(2,1794) = 44.85	<.001	η2 = .048
APLQ-social total score, M(SD)	599	3.31 (0.86)	943	3.20 (0.82)	255	3.89 (0.71)	Welch F(2, 723.11) = 89.21	<.001	ηp2 = .198
Sport Motivation (SMS-6), M(SD)
Autonomous Motivation	599	4.23 (1.19)	943	4.05 (1.15)	255	4.90 (.99)	Welch F(2, 726.64) = 69.81	<.001	ηp2 = .161
Controlled Motivation	599	3.57 (1.22)	943	3.42 (1.23)	255	4.45 (1.11)	F(2, 1794) = 73.43	<.001	η2 = .076
Amotivation	599	3.46 (1.28)	943	3.54 (1.21)	255	3.87 (1.38)	F(2, 1794) = 9.48	<.001	η2 = .010
IPAQ-SF, MET-min/wk, Mdn(IQR)
Vigorous PA	482	0 (0-290)	755	0 (0-240)	168	480 (0-1890)	H(2) = 79.02	<.001	η2H = .055
Moderate PA	444	0 (0-240)	699	0 (0-240)	154	160 (0-600)	H(2) = 27.59	<.001	η2H = .020
Walking	340	693 (495-1386)	571	693 (462-1386)	134	693 (495-1386)	H(2) = 0.55	= .758	η2H < .001
Total PA	258	1386 (693–2548)	439	1386 (693–2475)	97	2213 (1182–3884)	H(2) = 25.56	<.001	η2H = .030
Sitting time, min/day, Mdn(IQR)	235	300 (210–390)	406	300 (180–420)	85	240 (120–360)	K(2) = 9.82	.007	η2H = .011

Note.

PPLI = Perceived Physical Literacy Instrument; APLQ = Active Physical Literacy Questionnaire; SMS-6 = Sport Motivation Scale; SVG = Sports Video Game; IPAQ-SF = International Physical Activity Questionnaire–Short Form.

M = mean; SD = standard deviation; Mdn = median; IQR = interquartile range.

η² = eta squared; ηp² = partial eta squared; η²H = effect size for Kruskal-Wallis test.

Sport motivation was assessed using the SMS-6, which measures six types of motivational regulation based on Self-Determination Theory. In the present study, motivation was analyzed using three constructs: autonomous motivation (intrinsic, integrated, and identified regulation), controlled motivation (introjected and external regulation), and amotivation. Higher scores indicate stronger endorsement of the respective motivational regulation.

Physical activity was assessed using the IPAQ-SF. Vigorous physical activity, moderate physical activity, and walking are reported as MET-minutes per week, while sitting time is reported as minutes per day. Due to skewed distributions, IPAQ-SF variables are presented as medians and interquartile ranges.

3.4. Regression analyses of associations between SVG engagement, PL, motivation, and PA

H1

Association Between SVG Engagement and Perceived PL

To test H1, a multiple linear regression analysis was conducted with perceived PL (PPLI) as the dependent variable. SVG engagement was entered as the primary predictor, with age and general (non-sports) video gaming time included as covariates. The overall model was statistically significant and explained 6.7% of the variance in perceived physical literacy (R² = .067).

After adjustment, SVG engagement was positively associated with perceived PL. Participants who engaged in SVG reported significantly higher PPLI scores compared with non-SVG gamers and non-gamers (B = 0.324, 95% CI [0.246, 0.403], β = .186, p < .001). Age showed a small but significant positive association with PPLI (B = 0.021, β = .048, p = .035), whereas general video gaming time was negatively associated with perceived PL (B = −0.010, β = −.159, p < .001).

Overall, these findings support H1, indicating that SVG engagement is independently associated with higher perceived PL, albeit with a modest proportion of variance explained by the model.

H2

Association Between SVG Engagement and Autonomous Motivation

To test H2, a multiple linear regression analysis was conducted with autonomous motivation as the dependent variable, adjusting for age and general video gaming time.

The regression model explained a modest but meaningful proportion of variance in autonomous motivation (R² = .065). SVG engagement was strongly and positively associated with autonomous motivation, with SVG players reporting substantially higher autonomous motivation than non-SVG gamers and non-gamers (B = 0.752, 95% CI [0.601, 0.903], β = .223, p < .001). Age was not significantly associated with autonomous motivation, whereas general video gaming time was negatively associated (B = −0.014, β = −.107, p < .001). These results support H2 (see Table 4).

H3

Association Between SVG Engagement and PA

Table 4.

Multiple linear regression models predicting perceived physical literacy and autonomous motivation.

Predictor	PPLI B (95% CI)	β	p	Autonomous motivation B (95% CI)	β	p
SVG player (0 = No, 1 = Yes)	0.324 (0.246, 0.403)	.186	<.001	0.752 (0.601, 0.903)	.223	<.001
Age	0.021 (0.001, 0.040)	.048	.035	−0.008 (−0.045, 0.029)	−.010	.670
General (non-SVG) gaming time	−0.010 (−0.013, −0.007)	−.159	<.001	−0.014 (−0.019, −0.008)	−.107	<.001
Sex (0 = Female, 1 = Male)	−0.149 (−0.211, −0.087)	−.122	<.001	—	—	—
SVG × Sex	0.087 (−0.114, 0.288)	.022	.396	—	—	—
R2	.067			.065

Note. PPLI = perceived physical literacy. Autonomous motivation represents the mean of intrinsic motivation, integrated regulation, and identified regulation. Sex and the SVG × sex interaction were included only in moderation models. Coefficients are from final adjusted models.

To test H3, multiple linear regression analyses were conducted for vigorous- and moderate-intensity PA, with PA variables log-transformed prior to analysis. All models adjusted for age, general video gaming time, and sitting time.

For vigorous PA, the regression model explained 5.7% of the variance (R² = .057). SVG engagement was positively associated with vigorous PA, with SVG players reporting higher levels of vigorous activity than non-SVG gamers and non-gamers (B = 0.994, 95% CI [0.643, 1.346], β = .217, p < .001). Sitting time showed a marginal negative association, whereas age and general video gaming time were not significant predictors.

For moderate PA, the regression model explained 4.8% of the variance (R² = .048). SVG engagement was again positively associated with moderate PA (B = 0.825, 95% CI [0.492, 1.158], β = .194, p < .001). Sitting time was negatively associated with moderate PA (β = −.085, p = .038), while age and general video gaming time were not significant.

Together, these findings support H3, indicating that SVG engagement is independently associated with higher levels of moderate- and vigorous-intensity PA (see Table 5).

H4

Sex as a Moderator of the SVG–PL Association

Table 5.

Multiple linear regression models predicting moderate- and vigorous-intensity physical activity.

Predictor	Vigorous PA B (95% CI)	β	p	Moderate PA B (95% CI)	β	p
SVG player (0 = No, 1 = Yes)	0.994 (0.643, 1.346)	.217	<.001	0.825 (0.492, 1.158)	.194	<.001
Age	0.044 (−0.021, 0.109)	.052	.186	−0.028 (−0.087, 0.031)	−.037	.354
General (non-SVG) gaming time	0.003 (−0.010, 0.015)	.018	.659	−0.001 (−0.012, 0.010)	−.006	.876
Sitting time (min/day)	−0.001 (−0.001, 0.000)	−.076	.056	−0.001 (−0.001, 0.000)	−.085	.038
SVG × Sex	p > .05			p > .05
R2	.057			.048

Note. Physical activity outcomes were log10-transformed prior to analysis due to skewed distributions. All models adjusted for age, general (non-sports) video gaming time, and sitting time. SVG × sex interaction terms were tested but were not statistically significant.

To test H4, hierarchical multiple regression analyses examined whether sex moderated the association between SVG engagement and perceived PL. Age and general video gaming time were entered as covariates, followed by SVG engagement and sex as main effects, and the SVG × sex interaction term.

SVG engagement remained a significant positive predictor of PPLI across models. Sex was also a significant main effect, indicating overall differences in PPLI levels between males and females. However, the SVG × sex interaction was not statistically significant (p = .396), indicating that the association between SVG engagement and perceived PL did not differ by sex. Thus, H4 was not supported.

H5

Sex as a Moderator of the SVG–PA Association

Hierarchical regression analyses were also conducted to test whether sex moderated the association between SVG engagement and PA outcomes. For both vigorous and moderate physical activity, the SVG × sex interaction term was not statistically significant (p > .05).

These findings indicate that the positive association between SVG engagement and PA did not differ by sex. Accordingly, H5 was not supported, suggesting that SVG engagement is associated with higher PA levels in a similar manner for both male and female students.

3.4.1. Summary of findings

Across regression analyses, SVG engagement was consistently and positively associated with perceived PL, autonomous motivation, and moderate-to-vigorous PA, independent of age, general video gaming behavior, and sedentary time. While sex differences in overall levels of gaming behavior and PL were observed, sex did not moderate the associations between SVG engagement and PL or PA outcomes. Collectively, these findings suggest that SVG gaming is linked to more favorable PL and PA profiles among university students, with associations that operate similarly across sexes.

4. Discussion

The present study examined the associations between SVG engagement, perceived PL, sport motivation, and self-reported PA among university students using regression-based analyses. By distinguishing SVG engagement from general video gaming behavior and by examining PA intensity domains, the study provides a nuanced understanding of how sports-themed digital gaming relates to PL-related outcomes during a critical developmental period.

4.1. SVG engagement and perceived PL

Consistent with the first hypothesis, SVG engagement was positively associated with perceived PL after adjusting for age and general (non-sports) video gaming time. This finding suggests that engagement with sports-themed video games is linked to more favorable self-perceptions of competence, confidence, motivation, and knowledge related to PA. The association remained robust when overall gaming exposure was controlled, while general video gaming time showed a negative relationship with perceived PL. This contrast underscores the importance of differentiating SVG from non-sports gaming rather than treating all screen-based activities as behaviorally equivalent.

From a theoretical perspective, this finding aligns with both SDT and the PAR framework. SVGs simulate structured sport environments, exposing players to sport rules, tactics, and identities that may enhance familiarity and perceived competence in sport contexts. These experiences may contribute to higher perceived PL even though SVG participation itself is sedentary. Rather than displacing PA, SVG engagement appears to operate within a broader network of relationships that supports positive PL-related beliefs.

4.2. SVG engagement and autonomous motivation

Strong support was also found for the second hypothesis. SVG engagement was positively associated with autonomous motivation toward sport participation, independent of age and general video gaming behavior. This finding is particularly salient given that autonomous motivation is consistently identified as a key determinant of sustained PA engagement and long-term adherence.

This result further supports an SDT-based interpretation of SVG engagement. SVGs often incorporate features such as progressive challenges, immediate feedback, choice, and goal-directed play, which may satisfy psychological needs for competence and autonomy. In contrast, general video gaming time was negatively associated with autonomous motivation, suggesting that non-sports gaming may lack sport-relevant motivational affordances. Together, these findings highlight SVG engagement as a psychologically meaningful form of digital sport participation rather than a passive leisure activity.

4.3. SVG engagement and PA intensity

The third hypothesis was fully supported. SVG engagement was positively associated with both moderate- and vigorous-intensity PA after adjusting for age, general video gaming time, and sitting time. Notably, the associations were observed for higher-intensity PA rather than lower-intensity activities, such as walking, which showed minimal variation in descriptive analyses. This pattern suggests that SVG engagement may be more closely aligned with sport-oriented and performance-based forms of PA rather than incidental or lifestyle activity.

From a PAR perspective, these findings indicate that SVG engagement may coexist with, or reflect, a broader orientation toward structured sport participation. Students who engage in SVGs may already be more interested in competitive or vigorous PA, or SVG engagement may reinforce confidence and interest in such activities by familiarizing players with sport demands and identities. The associations with PA remained significant after accounting for sedentary behavior, indicating that SVG engagement is not simply a proxy for reduced sitting time but is specifically linked to higher-intensity PA participation.

4.4. Sex differences and moderation effects

Although descriptive analyses revealed clear sex differences in gaming behavior, with males reporting higher levels of SVG engagement, sex did not moderate the associations between SVG engagement and perceived PL or PA outcomes. In other words, once SVG engagement occurred, its associations with PL, motivation, and PA were similar for male and female students.

These findings refine assumptions regarding sex-specific benefits of SVG engagement. Previous studies have suggested stronger SVG–PA associations among males, likely reflecting higher participation rates and greater identification with sport culture. The present results indicate that the underlying mechanisms linking SVG engagement to PL-related outcomes may operate similarly across sexes, and that observed sex differences are more likely attributable to differences in exposure and uptake rather than differential effects. This interpretation suggests that SVG-based strategies have the potential to benefit both male and female students, provided that barriers to engagement are addressed.

4.5. Main contribution

This study makes several important contributions to the literature on SVGs and PL. First, it provides robust evidence that SVG engagement is positively associated with perceived PL, autonomous motivation, and moderate-to-vigorous PA among university students, independent of general video gaming behavior and other confounders. By distinguishing SVG from non-sport gaming, the findings highlight the importance of a genre-specific perspective, demonstrating that not all screen-based activities relate to PL and PA in the same way.

Second, the use of regression-based analyses and the examination of PA intensity domains offer a more nuanced understanding of these relationships, particularly the link between SVG engagement and higher-intensity, sport-oriented PA. The findings further show that although males report higher levels of SVG engagement, the associations between SVG and PL-related outcomes are consistent across sexes, suggesting similar underlying mechanisms.

Third, informed by Self-Determination Theory and the Physical Activity Relationship framework, this study provides a theoretically grounded explanation of how SVG engagement may support psychological and behavioral dimensions of physical engagement.

From a practical perspective, the findings suggest that SVGs can serve as psychologically supportive contexts that reinforce sport-related confidence, motivation, and engagement, particularly during the transition to university when structured PA often declines. Rather than viewing video gaming solely as detrimental, SVGs may be considered complementary tools in physical education and health promotion, for example as pre-lesson resources or reflective activities that connect digital sport experiences with real-world participation. However, SVGs should not replace physical activity but may act as gateways that support psychological readiness and sport identification.

4.6. Limitations

A number of limitations should be considered when interpreting these findings. First, the cross-sectional design precludes any conclusions about directionality or causality.³⁹ Associations between SVG, perceived PL, and PA may reflect reverse causation (e.g., students with higher sport motivation/PA being more drawn to SVG) or shared underlying factors rather than effects of SVG participation.

Second, although participants were recruited from physical education courses, the vast majority of the sample consisted of Year-1 students enrolled in required PE (1748 of 1797; 97.3%), with only a small proportion drawn from elective PE courses (49 students; 2.7%); nevertheless, elective participation may reflect higher baseline sport interest and perceived PL and could have modestly influenced the observed associations.

Third, key variables were measured via self-report, which may have introduced recall error and social desirability bias. Self-reported PA instruments can differ in their results from device-based estimates. For example, evidence comparing self-report to accelerometry indicates that participants often overreport vigorous activity and underreport sedentary time, which can distort observed associations with PL and gaming behaviors.^40,41 In addition, a notable proportion of participants selected “don't know” or “not sure” when reporting the frequency or duration of moderate- and vigorous-intensity PA, resulting in missing data for these domains. Such responses may reflect difficulties in recalling activity intensity and duration, particularly for episodic or irregular activities, and may have reduced statistical power or the precision of estimates for higher-intensity PA. Furthermore, self-reported video gaming/digital media is frequently misestimated relative to logged behaviors, and gaming time is also prone to reporting error, which may contribute to misclassification of gaming exposure and attenuation (or inflation) of associations.^42,43

Lastly, the study assessed perceived PL using the PPLI, initially developed and validated for PE teachers.³⁵ While the PPLI has been used to assess PL among a similar demographic,⁴⁴ the instrument may be missing key constructs that assess PL together in one scale (i.e., physical, psychological, social, and cognitive domains of PL). In PL research, it has been regarded important to assess PL in a way that captures the construct as an integrated whole, so the domains are not treated as isolated parts.⁴⁵ Nonetheless, there were additional measures that did assess the remaining missing constructs such as the APLQ.

4.7. Future directions

Future research should employ longitudinal and experimental designs to establish causal relationships between SVG engagement, PL, and PA. While the present findings demonstrate consistent associations, controlled intervention studies are needed to determine whether structured SVG exposure can actively enhance PL components, autonomous motivation, and sustained engagement in sport and PA.

Given the multidimensional nature of PL, future studies should incorporate more comprehensive assessments of cognitive and physical competence domains, including sport rules, tactical understanding, and motor skill proficiency. This is particularly important given the limited evidence linking SVG engagement to physical competence outcomes and the increasing interest in SVGs as educational tools within physical education contexts.

Future investigations should also examine sport-specific SVG interventions that more closely mirror real-world sport settings and assess whether skills developed through SVG play transfer to actual sport performance. In addition, given observed sex differences in SVG participation, studies should explore strategies to enhance engagement among female students through inclusive game design and pedagogical approaches.

Finally, the use of objective measures such as accelerometry and motor skill testing would complement self-reported data and help clarify mechanisms linking SVG engagement with physical competence and PA behavior.

5. Conclusion

This study examined the associations between SVG engagement, perceived PL, sport motivation, and self-reported PA among university students using regression-based analyses. The findings indicate that SVG engagement is positively associated with perceived PL, autonomous motivation, and moderate-to-vigorous intensity PA, independent of age, general (non-sports) video gaming time, and sedentary behavior, whereas general video gaming showed negative or null associations with PL-related outcomes. Although males reported higher levels of SVG engagement, sex did not moderate the associations between SVG engagement and PL or PA outcomes, suggesting that once engagement occurs, the psychological and behavioral correlates operate similarly for both male and female students. Overall, these findings support a refined, genre-specific and relational perspective on digital gaming and highlight sports video gaming as a distinct form of digital engagement meaningfully associated with favorable psychological and physical activity profiles during the university transition period.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Author contributions

Wai Keung Ho: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Writing – original draft, Visualization.

Jaime Barratt: Data curation, formal analysis, Writing – review & editing.

Siu Ming Choi: Methodology, Writing – review & editing.

Raymond Kim Wai Sum: Supervision, Methodology, Writing – review & editing.

Declaration of generative AI and AI-assisted technologies in the manuscript preparation process

During the preparation of this work the author(s) used Microsoft Copilot in order to assist with language refinement and improve clarity. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

Funding

This research received no external funding.

Conflict of interest

Raymond K.W. Sum is a member of the Editorial Board of the Journal of Exercise Science and Fitness. He was not involved in the journal’s peer review process or decisions related to this manuscript. The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.