The effect of time spent sitting and excessive gaming on the weight status, and perceived weight stigma among Taiwanese young adults

Abstract

Background

During the COVID-19 pandemic, physical inactivity and sedentary behaviors (i.e., longer sitting time and excessive gaming) increased because governments across the globe adopted stringent mitigation strategies such as social distancing and lockdowns to curb the spread of the virus. Excessive gaming was one of the coping mechanisms used to deal with the pressure associated with the pandemic. Moreover, perceived weight stigma (PWS) and weight status became more salient concerns among young adults during the COVID-19 pandemic. The current study sought to investigate the relationship between time spent sitting, excessive gaming, weight status, and PWS of Taiwanese Young adults. Additionally, weight status and PWS were examined as mediators between both sedentary behaviors.

Methods

This cross-sectional study involved 600 participants who were recruited through Taiwan universities. All participants completed a demographic questionnaire (including weight and height) and self-report measures including the International Physical Activity Questionnaire short form (IPAQ-SF), the Perceived Weight Stigma Scale (PWSS), and the Internet Gaming Disorder Scale-short form (IGDS9-SF). PROCESS model was performed to test the potential mediation roles of weight status and PWS. Moreover, we categorized participants into two groups based on the sitting-time item in the IPAQ-SF: students whose sitting time was less than 8 h daily, and those more than 8 h daily.

Results

The group that had less than 8 h had significantly higher PWS and IGDS9-SF scores than the other group. Sitting time was negatively associated with weight status, PWS, and IGDS9-SF. Additionally, we found a significantly direct effect between time spent sitting and excessive gaming. Both weight status and PWS were significant mediators in the association between time spent sitting and excessive gaming.

Conclusions: The present study demonstrated important negative correlates of excessive sedentary behaviors. Prevention efforts should focus on promoting physical activity and providing information to decrease sedentary behavior among university students.

1. Introduction

1.1. Sedentary behaviors and their impacts on health

1.1.1. The definitions of sedentary behaviors

Definitions of sedentary behavior include (1) energy expenditure ≤1.5 metabolic equivalents of tasks (METs) [1,2] and (2) behavioral inactivity (e.g., quietly standing or not talking) [1]. Although the definition of sedentary behavior is under debate [1,2], recent evidence proposed the term “stationary behavior” to indicate sedentary behavior without ambulatory movement in any posture [1,2]. The present study thus used the definition “sedentary behavior without ambulatory movement in any posture,” especially focusing on sitting posture.

1.1.2. The risk of sedentary behaviors on health

High levels of sedentary behavior (especially prolonged sitting time) increased risk for all-cause and cardiovascular disease (CVD) [3] and other chronic diseases [[4], [5], [6]] via individuals' unhealthy dietary and snacking behavior [7]. Therefore, public health advocates should focus on decreasing sitting time, together with encouraging physical activity [5]. Similarly, excessive gaming may partly account for the adverse consequences of sedentary behavior [5]. People with excessive gaming problem can experience disturbances in normal daily life such as basic routine (e.g., sleeping), socialization, and productive activities (e.g., school or work) [[8], [9], [10], [11], [12], [13], [14], [15]]. Moreover, excessive gaming has been found to relate to mental health problems [16] and psychological distress [15,17], although the causality is yet to be determined.

1.2. Lifestyle changes and psychological distress during COVID-19 pandemic

The COVID-19 pandemic has greatly disturbed lifestyles worldwide, with regular activities being interrupted [18,19]. For example, the Taiwan government implemented and encouraged action plans such as travel bans, social distancing, and quarantine to control COVID-19 spread [20]. Self-isolation at home may be associated with lower levels of physical activity and may lead to increased body weight, emotional distress (i.e., stress, anxiety, and depression), as well as increased sedentary behavior (e.g., online gaming and sitting time) [19,21]. Subsequently, young adults may have reduced their levels of health-related quality of life (HRQoL) during the COVID-19 pandemic [19,[21], [22], [23], [24], [25], [26]].

Previous research found that young people had increased their sitting time by over 28% (from 5 to 8 h daily) during home confinement [27]. Additionally, evidence reported increased gaming use (by 14.8%) during the COVID-19 pandemic as compared with before the COVID-19 pandemic [28]. Moreover, increased sedentary behavior resulting from home isolation during the COVID-19 pandemic was accompanied by reduced physical activity, which is associated with chronic health conditions [26,29].

People’s experiences with weight stigma have increased during COVID-19 pandemic [30]. Moreover, weight stigma may lead to psychological distress (e.g., stress, depression, anxiety) as well as maladaptive eating behaviors [31]. Even while social distancing, youth may have experienced perceived weight stigma (PWS), feeling stigmatized by others due to weight, and may have increased their greater use of screen time during the COVID-19 pandemic [[30], [31], [32]]. However, it is unclear how sedentary behaviors (i.e., prolonged sitting time and excessive gaming) are associated with weight status and weight stigma.

1.2.1. Sedentary behaviors and weight status during the COVID-19 pandemic

Sedentary behavior may be an important and independent cause of weight gain among youth [33]. People have shown significantly higher prevalence of sedentary behavior (i.e., longer sitting time) and unhealthy eating behaviors that lead to weight gain during the COVID-19 pandemic [34,35]. A Chinese study revealed that the average BMI significantly increased among young adults during lockdown in the COVID-19 pandemic (22.1 kg/m²) when compared to before lockdown (21.8 kg/m²), with the prevalence of overweight increasing from 21.4% to 24.6% [36].

1.2.2. The relationship between sedentary behaviors, PWS and psychological distress

Increased sitting time during quarantine may have resulted from the limitations imposed on many activities [37]. Restrictions on entertainment may lead to lower work capability and elevated sedentary behavior [37]. Moreover, prolonged sitting time could have an impact on HRQoL and mental health [25,38]. However, prolonged sitting time was positively associated with mental health and negatively associated with depression during COVID-19 pandemic [39]. Additionally, some studies highlighted that excessive gaming was associated with stress from the pandemic, and gaming could be as a coping mechanism used to reduce pressure [28,40]. However, excessive gaming may be associated with negative consequences in youth: psychological distress (depression, anxiety, and stress), sleep disorders, and impaired HRQoL [41,42].

1.2.3. Excessive gaming during the COVID-19 pandemic

The definition of excessive gaming used in the present study followed the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) criteria [43]. That is, the following criteria were used to evaluate the level of excessive gaming: (1) preoccupation, (2) withdrawal, (3) persisted using, (4) lacking self-control, (5) losing interests, (6) negative consequences, (7) gaming for emotional regulation, (8) deception, and (9) impaired relationship or work [43]. Youth have demonstrated increased screen time (i.e., online gaming) during the COVID-19 pandemic [40,44,45]. Moreover, negative impacts of excessive gaming on mental health and academic performance during the COVID-19 pandemic have been documented [[46], [47], [48], [49], [50], [51], [52], [53], [54]].

1.3. The present study

More research is needed to clarify the relationship between both important sedentary behaviors (i.e., longer sitting time, and excessive gaming) and to understand how time spent sitting may be associated with prolonged gaming. Additionally, weight status and PWS might be mediators between time spent sitting and excessive gaming. To address this knowledge gap, we investigated the relationship between time spent sitting, excessive gaming, weight status, and PWS. Moreover, we examined whether weight status and PWS were potential mediators. Our study hypothesized that 1) Time spent sitting (more than 8 h a day) would be significantly associated with weight status, PWS, and excessive gaming; 2) Weight status and PWS would significantly mediate the relationship between time spent sitting and excessive gaming. Fig. 1 additionally illustrates the hypothetical mediation model for time spent sitting, weight status, PWS, and excessive gaming.

Fig. 1.

Hypothetical mediation model for time spent sitting, weight status, perceived weight stigma (PWS), and excessive gaming.

2. Methods

2.1. Participants

This cross-sectional study involved 600 participants recruited through online convenience sampling from university students in Taiwan between August 2 and September 3, 2021. The participants were recruited by online questionnaire survey via Google Forms and could access the online survey via a hyperlink and a QR code to log onto Google Forms. Research assistants distributed the online survey link, which was hosted on the university website and Facebook. The study’s information, purpose, and informed consent were contained on the first page of the online questionnaires. Individuals who provided their informed consent to participate could continue answering all scales. All participants completed a self-reported questionnaire via online survey (i.e., Google Forms). The questionnaires included demographics questionnaire assessing age, gender, self-reported weight and height, self-reported any condition or disease (e.g., cold, mental illness) together with different standardized questionnaires (please see Measures section for details). The individuals were eligible to participate if they were 1) age ≥20 years; 2) could understand and read the Chinese language; 3) enrolled at universities in Taiwan.

The study proposal's approval was obtained by Chi Mei Medical Center's Human Subjects Ethics Review Board (11007-006) before data collection. Participants were informed of the study's purpose, inclusion criteria, and other detailed information. The mean age of the respondents was 22.81 ± 3.75 years. Moreover, there were more female participants (65%) than male participants (35%).

During the time of data collection, Taiwan’s Central Epidemic Command Center (CECC) announced that this period was level-two epidemic control restrictions [55,56]. The Taiwan government encouraged people to practice social distancing, masking at all times including indoors and outdoors, except when eating and drinking. However, university teaching was conducted on site while following epidemic prevention measures [55,56].

2.2. Measures

2.2.1. Participants' characteristics: assessed using self-report questions

The participants' age was asked using one item “What is your current age in years?”; their height was asked using “What is your current height in cm?”; their weight was asked using “What is your current weight in kg?”. Afterward, weight status based on body mass index (BMI) was calculated using self-reported height and weight: weight in kg divided by squared height in m². Moreover, participants' physical activity level was calculated using the IPAQ-SF (without the sitting time item).

2.2.2. Sitting time: assessed using International Physical Activity Questionnaire-Short Form (IPAQ-SF) final item

IPAQ-SF is a seven-items instrument assessing physical activity levels (including vigorous, moderate, and time spent walking) and sedentary behaviors (i.e., time spent sitting) during the last 7 days [57]. In this study, we examined time spent sitting by using the final (the 7th) item from the IPAQ-SF. Participants were asked to report the hours they spent during a week including sitting at a table, reading, or lying down during watching television in work, home, or during their free time [58]. The question is “During the last 7 days, how much time did you spend sitting on a weekday?” Participants reported the time spent sitting as hours and/or minutes per day or don’t know/not sure. Evidence has indicated that the time spent sitting item has moderate to excellent test-retest reliability (coefficients above 0.70) in both the English version and the Chinese version (coefficients above 0.97) [57,59]. However, to the best our knowledge, there is no suggested cut-off to indicate levels of sitting time in the IPAQ-SF. According to the evidence [[60], [61], [62]], time spent sitting greater than 8 h a day can be associated with increased risk of mortality, decreased mental health, and quality of life. Therefore, we categorized the time spent sitting into two groups: less than 8 h a day and more than 8 h a day.

2.2.3. Internet gaming disorder: assessed using Internet Gaming Disorder Scale-Short Form (IGDS9-SF) summed score

The IGDS9-SF is a nine-item instrument assessing the severity of Internet gaming disorder (IGD) according to nine IGD criteria proposed by the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) [63,64]. The IGDS9-SF asked the participants using a five-point Likert type scale (1: Never; 2: Rarely; 3: Sometimes; 4: Often; and 5: Very often) about their online and/or offline gaming activities over a previous year. Total scores are calculated by summing all 9 items of IGDS9-SF, and higher scores indicate greater risk of developing IGD [65]. A sample item of IGDS9-SF is “Do you systematically fail when trying to control or cease your gaming activity? ” The internal consistency of IGDS9-SF was satisfactory in both the English version (Cronbach's α = 0.91) [65] and Chinese version (Cronbach's α = 0.91) [64]. Additionally, the internal consistency of IGDS9-SF was acceptable in the present study (Cronbach's α = 0.95).

2.2.4. Perceived weight stigma: assessed using Perceived Weight Stigma Scale (PWSS) summed score

The PWSS is a ten-item instrument assessing the severity of PWS via dichotomous scale (0: No; 1:Yes). Total scores are calculated by summing all 10 items of PWSS, and higher scores indicate greater perceived weight stigma [66]. The sample item of PWSS is “People behave as if you are inferior because of your weight status”. The internal consistency of PWSS was satisfactory in the Chinese version (Cronbach's α = 0.84) [67]. Additionally, the internal consistency of the PWSS was acceptable in the present study (Cronbach's α = 0.87).

2.3. Statistical analysis

All data analyses were performed using SPSS version 26 (IBM Corp., Armonk, NY). We first used descriptive statistics to summarize participants' demographics. Independent t-test was then used to compare differences in age, body mass index (BMI, kg/m²), physical activity (calculated MET minutes/week using IPAQ-SF), PWSS (total) score, and IGDS9-SF (total) score between the two groups according to their time spent sitting: sitting less than 8 h a day versus sitting more than 8 h a day. Pearson correlation was used to analyze the association between time spent sitting, BMI, PWSS (total) score, and IGDS9-SF (total) score.

Additionally, we used the Hayes' Model 4 in the PROCESS macro (via SPSS) with 5000 bootstrapping resamples to examine mediation models [68]. Mediation models were constructed using weight status and PWSS score as mediators; time spent sitting as independent variable; and IGDS9-SF score as dependent variable. The mediation effect and the 95% confidence intervals (CIs) were performed to describe the significance of the association. Moreover, we used the time spent sitting as a continuous variable to make it more statistically sound in the mediation analysis.

3. Results

Table 1 presents participants' descriptive data (N = 600). The average BMI was 22.00 kg/m² (SD = 3.72). Most of the participants were without any condition or disease (94%) during the survey period. Moreover, 76% of participants were sitting less than 8 h a day and 24% of participants were sitting more than 8 h a day.

Table 1.

Participants' demographic information.

	Mean (SD)	N (%)
Age (year)	22.81 (3.75)	600
Gender
Female	–	391 (65.2)
Male	–	209 (34.8)
BMI (kg/m2)	22.00 (3.72)
< 18.5	17.23 (0.94)	87 (14.5)
18.5–24.0	21.01 (1.55)	361 (60.2)
> 24.0	27.03 (2.79)	152 (25.3)
Any condition or diseases
Yes	–	36 (6)
No	–	564 (94)
Time spent sitting
< 8 h	–	457 (76.2)
> 8 h	–	143 (23.8)
PWSS	1.32 (2.27)	–
IGDS9-SF	18.80 (8.27)	–

BMI Body Mass Index.

PWSS Perceived Weight Stigma Scale.

IGDS9-SF Internet Gaming Disorder Scale-Short Form.

Table 2 shows the differences between the two sitting-time groups (i.e., sitting less than 8 h a day and sitting more than 8 h a day). We found that the participants with sitting less than 8 h a day had significantly higher BMI (22.29 ± 3.71) than the other group (21.02 ± 3.63; p < .001). Participants with sitting less than 8 h a day had significantly higher PWSS score (1.49 ± 2.38) and IGDS9-SF score (19.63 ± 7.74) than the other group (PWSS score: 0.80 ± 1.77; IGDS9-SF score: 16.15 ± 7.74; p < .001 and p < .001, respectively). Physical activity in the group sitting less than 8 h a day was higher, but the difference was not statistically significant (1022.84 ± 1933.46 MET minutes per week vs. 841.76 ± 1143.74 MET minutes per week; p = .170).

Table 2.

Difference between groups sitting <8 h/day and sitting >8 h/day.

	Mean (SD)		t	p -value
	<8 h	>8 h
Age	22.94 (4.04)	22.42 (2.62)	1.79	0.075
BMI (kg/m2)	22.29 (3.71)	21.02 (3.63)	3.60	<.001∗
Physical activity (MET mins/week)	1022.84 (1933.46)	841.76 (1143.74)	1.38	0.170
PWSS	1.49 (2.38)	0.80 (1.77)	3.68	<.001∗
IGDS9-SF	19.63 (8.27)	16.15 (7.74)	4.47	<.001∗

BMI: Body Mass Index.

PWSS: Perceived Weight Stigma Scale.

IGDS9-SF: Internet Gaming Disorder Scale-Short Form.

^∗p < .05.

Table 3 shows the correlation between time spent sitting, weight status, PWSS score, and IGDS9-SF score. Time spent sitting was significantly and negatively associated with weight status (r = 0.15, p < .001), PWSS score (r = 0.13, p = .002), and IGDS9-SF score (r = 0.18, p < .001). Weight status was significantly and positively associated with PWSS score (r = 0.18, p < .001), and IGDS9-SF score (r = 0.20, p < .001). Moreover, PWSS score was significantly and positively associated with IGDS9-SF score (r = 0.31, p < .001).

Table 3.

Correlations between time spent sitting, weight status, PWSS, and IGDS9-SF.

	Time spent sitting	Weight status	PWSS	IGDS9-SF
Time spent sitting	–	–	–	–
Weight status	−0.15∗∗	–	–	–
PWSS	−0.13∗∗	0.18∗∗	–	–
IGDS9-SF	−0.18∗∗	0.20∗∗	0.31∗∗	–

PWSS: Perceived Weight Stigma Scale.

IGDS9-SF: Internet Gaming Disorder Scale-Short Form.

^∗∗p < .01.

Fig. 2 shows the results of the proposed mediation model. The mediation model results indicated that time spent sitting had significantly direct effects to IGDS9-SF score (coefficient = −2.43, t = −3.24, p = .013). Additionally, weight status with PWS were significant mediators of the relationship between time spent sitting and IGDS9-SF (coefficient = −0.12, t = −0.25, p = .041).

Fig. 2.

The mediation model for time spent sitting, weight status, perceived weight stigma (PWS), and excessive gaming.

4. Discussion

In the present study, time spent sitting was significantly and negatively associated with weight status, PWS, and excessive gaming, which did not support our first hypothesis. We found that time spent sitting had significant direct effects on excessive gaming. Weight status and PWS were significant mediators of the relationship between time spent sitting and excessive gaming, and this finding supported our second hypothesis. Furthermore, weight status was significantly and positively associated with PWS and excessive gaming. PWS was significantly and positively associated with excessive gaming.

Our finding that time spent sitting was negatively associated with excessive gaming contradicts prior evidence, which indicated that screen time activities are positively related to sitting time [69,70], It is possible that most participants were able to adjust their lifestyle during COVID-19 by increasing physical activity. Evidence has shown that physical activity increased over the course of the COVID-19 pandemic [71]. Moreover, a study in Taiwan found that Taiwanese individuals were able to continue their exercise behavior during the COVID-19 pandemic [72]. In addition, evidence suggested that individuals with high levels of physical activity before the pandemic were likely to continue exercising during the pandemic. Similarly, people with less physical activity before the pandemic were likely to increase their exercise time during the pandemic [72]. The researchers concluded that Taiwanese individuals preferred to maintain their physical activity before and during COVID-19 pandemics [72]. Therefore, individual in the current study might have adapted their lifestyles during the COVID-19 pandemic. Additionally, our result was similar to a study which demonstrated increased gaming during the pandemic [28]. Increased gaming behavior was associated with reducing stress, and individuals used gaming as a coping mechanism against stress [19,28]. Accordingly, while participants might have changed their lifestyles by increasing exercise behavior, they may have also increased their gaming use due to restricted socialization and interaction.

Interestingly, our findings indicated that time spent sitting was negatively associated with weight status and excessive gaming. According to previous research, social distancing and home isolation during the pandemic may be associated with weight gain [73]. Moreover, individuals with overweight might engage in excessive gaming to a greater degree than those without overweight [74]. Individuals with overweight may have used gaming as a protective factor against psychological distress (i.e., stress, anxiety, depression) to reduce the negative consequences of social distancing and home confinement [75]. We speculate that our participants might have had concerns about weight and appearance that may have led to distorted self-perceived weight status. Literature has demonstrated that the COVID-19 pandemic may have affected self-perceptions of weight status more than it has affected actual weight, which changed only minimally [75]. Thus, people with sitting time of less than 8 h might misperceive their weight status and, at the same time, may engage in excessive gaming in order to cope with potential psychological distress from pandemic-related restrictions. Nevertheless, our results contrast with prior studies indicating that overweight persons exhibited longer sitting time [73,76]. Therefore, individual’s weight perception could be the significant factor between longer sitting time and weight status, in addition to influencing excessive gaming.

Additionally, our results have shown that PWS mediated the relationship between time spent sitting and excessive gaming. Individuals have reported weight stigma via social media during the pandemic [30]. Social media users may have publicized their images and then been subject to weight stigma on social media (e.g., weight-related teasing or unpleasant comments) [30]. It is possible that people select gaming as a coping strategy because, unlike other forms of media, gaming platforms do not contain any distressing information related to the COVID-19 pandemic [74]. Gaming also may involve less weight stigma than other media. As suggested earlier, people might have spent less time sitting as an effort to modify their lifestyle and living patterns during the COVID-19 pandemic. Moreover, PWS could be the mediator that may drive individuals to increase their gaming use to reduce their psychological distress.

Moreover, the COVID-19 pandemic could have impacted females and males differently (e.g., in terms of media use) [77]. Males demonstrated greater media use (i.e., online gaming) than females. In our findings, the total sample might have been less affected by excessive gaming partly owing to the high ratio of females (65%) to males (35%). Further, it is important to consider the different behaviors across genders during the COVID-19 pandemic [77]. Accordingly, we encourage further studies to consider the gender and other demographic factors when investigating specific health behaviors and health promotion during the COVID-19 pandemic.

However, our findings indicated small to moderate correlations between sedentary behavior, gaming, and weight status. This observational study could not manipulate or control all potential confounders. Moreover, the COVID-19 regulations in Taiwan were not as strong as in some other countries (i.e., no extreme regulations have been implemented to prevent Taiwanese people from physical activity). Accordingly, this might explain why the correlations found were significant at the small to moderate level. Although the effect sizes of the correlations between sedentary behavior, gaming, and weight status were not large, the findings still provide some insightful information. That is, gaming time and sedentary behavior might still contribute to some weight-related problems.

4.1. Study implications

The present study found that time spent sitting was associated with excessive gaming use. Moreover, weight status and PWS could act as mediators between the relationship of time spent sitting and excessive gaming. Although people may have changed their lifestyle patterns and behavior as a way to adjust to the activity restriction, sedentary behavior (i.e., time spent sitting and excessive gaming) has also risen during the COVID-19 pandemic. Sedentary behavior could lead to poor physical health outcomes, emotional distress, or behavioral problems [26,29]. Therefore, we should be concerned about the use of sedentary activities as coping strategies (i.e., excessive gaming) which could negatively influence the health of university students. Previous evidence suggested that the COVID-19 pandemic could impact and shift individuals' behavior, and people might increase their sedentary behaviors accompanied by increased physical activity [71]. Therefore, the present results continued investigating the adaptation of people’s behavior and provided in-depth analysis of behavior change and related external factors (e.g., gender, environment) after the COVID-19 pandemic.

Moreover, recent evidence indicated that the general population could have significantly impaired health behaviors, with similar results in migraine patients during the COVID-19 pandemic [78]. The general population experienced decreased levels of physical activity and health behavior changes (e.g., dietary patterns and sleep) during the COVID-19 pandemic [78]. Furthermore, worse migraine symptoms were related to emotional distress (e.g., stress and anxiety), and prevention measures the spread of COVID-19 (e.g., wearing masks and quarantine) [78]. Thus, it would be interesting to examine additional health domains potentially impaired by the COVID-19 pandemic.

Finally, we found the real effect of weight status and PWS as mediators of the relationship between longer sitting time and excessive gaming. Therefore, we further encourage healthcare providers to consider educating young adults about the issue of weight stigma. In addition, it is imperative that young adults become aware of their own biases related to weight, which could lead to negative effects on physical health, psychological distress, and lower self-esteem [79]. Moreover, healthcare providers should carefully manage and provide treatment to reduce vulnerability to weight stigma among young people.

4.2. Strengths and limitations

The main strengths of this present study are that we had large number of participants (n = 600) and demonstrated the relationship between longer sitting time, excessive gaming, weight status, and PWS. Moreover, mediation analysis was performed to analyze how longer sitting time could be associated with excessive gaming, and how weight status and PWS could serve as mediators between longer sitting time and prolonged gaming.

As for the study limitations, first, data including demographics and all questionnaires were self-reported. The participants might have responded based on recall-related bias or their social environment. The self-reported questionnaires could also present an important bias in the findings. Second, this present study was unable to investigate potential confounders including smoking, drinking, medical history, and psychiatric symptomatology, potential factors which might have had an impact on the results. Third, the present study was conducted during the period of level-two epidemic control restrictions in Taiwan. Hence, the present results might not be representative of other periods of the pandemic. Lastly, some respondents might use different devices for gaming (e.g., computer, tablet, or smartphone); device type was not measured and could not be compared in this study.

5. Conclusion

Time spent sitting was significantly and negatively associated with excessive gaming. Moreover, weight status and PWS were found to significantly mediate the relationship between time spent sitting and excessive gaming. The study highlighted the need to pay attention to the impact of the COVID-19 pandemic among university students. Providing information to reduce sedentary behavior is an important concern for health professionals, family, and university. Moreover, we should monitor the various factors that might impact young adults while they changed their lifestyles and behaviors during the COVID-19 pandemic, in order to encourage their future health and wellbeing.

Author contribution statement

Chung-Ying Lin; Ruckwongpatr Kamolthip: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Yung-Ning Yang: Conceived and designed the experiments; Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Janet D. Latner; Kerry S. O’Brien; Chien-Ching Lin; Amir H. Pakpour: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.

Yen-Ling Chang: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.

Funding statement

This work was supported by Ministry of Science and Technology, Taiwan [MOST 110-2410-H-006-115, MOST 111-2410-H-006-100], the Higher Education Sprout Project, Ministry of Education to the Headquarters of University Advancement at National Cheng Kung University (NCKU), Taiwan, and the 2021 Southeast and South Asia and Taiwan Universities Joint Research Scheme (NCKU 31) . Yung-Ning Yang was supported by the Ministry of Science and Technology, Taiwan [MOST111-2314-B-214-007] and E-Da Hospital, Taiwan [EDAHI111002].

Data availability statement

Data will be made available on request.

Declaration of interest’s statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following is the Supplementary data to this article.