The impact of VR environment for elderly short-form video viewers on flow and mental workload

The impact of VR environment for elderly short-form video viewers on flow and mental workload | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The impact of VR environment for elderly short-form video viewers on flow and mental workload Zhichuan Tang, Yingjia Ding, Ruoshen Tang, Lufang Zhang, Nuo Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6286033/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The development of virtual reality (VR) technology in recent years has the potential to meet the increasingly immersive entertainment needs of elderly users. This study explored the impact of the VR environment on the flow experience and mental workload of elderly users while watching short-form videos. Two experiments were conducted. Experiment 1 developed and tested a model for VR flow experience which examined the impact of VR environments on vividness, interactivity, telepresence, flow experience, mental workload, and satisfaction. Experiment 2 adopted EEG in addition to subjective questionnaires to further test some of hypotheses from Experiment 1. The results indicate that, compared to traditional 2D short-form videos, VR short-form videos offers an enhanced telepresence through increased vividness and interactivity, thereby amplifying the flow experience. But the impact of mental workload on flow varies depending on the type of content. The mental workload has little effect on flow when watching scenery content but significant negative effects when watching educational content. Humanities/Cultural and media studies Social science/Cultural and media studies Virtual reality short-form videos elderly flow experience mental workload telepresence Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction As populations age globally, there is a growing recognition of the importance of enhancing the quality of life for elderly individuals. Older adults often face difficulties participating in activities due to physiological decline (Ouwehand et al., 2007 ). Virtual Reality (VR) technology, with its multimodal interaction and immersive experience, is considered valuable for improving their quality of life. In healthcare, VR has been successfully applied to treatment, rehabilitation, and cognitive training for older adults (Coyle et al., 2015 ; Amin et al., 2017 ; Tuena et al., 2020 ). In entertainment, studies show that seniors find VR-based physical activities more engaging and enjoyable (Hsieh et al., 2018 ; Rodríguez-Almagro et al., 2024 ). VR is also shaping digital media consumption (Witmer & Singer, 1998 ; Zou & Yang, 2018 ), including short videos, which have gained a high level of popularity among the elderly (Fishwick, 2020 ; Ng & Indran, 2023 ). For example, YouTube and Facebook launched 360-degree video streams accessible via VR devices in 2016; Douyin, internationally known as TikTok, also launched a VR version on its HMD device in 2023. As VR technology continues to evolve and mature, there will be more and more elderly viewers of VR short-form videos, which prompts scholars and practitioners to explore the user experience of older people viewing short-form videos in VR. VR is primarily intended to offer media consumers an engaging and immersive experience by amplifying telepresence (Biocca & Levy, 2013 ). In academia, scholars have widely used the concept of flow experience to examine such experiences described as "the holistic sensation that people feel when they act with total involvement" (Csikszentmihalyi, 1996 ). This sensation includes feelings of enjoyment, psychological immersion, energized focus, and involvement (Csikszentmihalhi, 1997 ). Further, flow is recognized as a useful construct to apply to human-computer interactions (Csikszentmihalyi, 1990 ; Khang et al., 2013 ). Studies have proven that VR can enhance viewers' flow experience through telepresence when watching long videos(like sports or news)(Shin, 2018 ; Kim & Ko, 2019 ). Therefore, VR technology may achieve the same effect when watching short-form videos. However, some other studies have shown that the additional visual information and active interaction brought by 360° videos will increase the workload (Gold & Windscheid, 2020 ). Viewers have to put in additional thinking and actions to deal with new ways of presenting and interacting with information, especially for elderly users. Therefore, short-form videos in VR may lead to more mental workload. These uncomfortable experiences may impair the flow. For example, Hong and Chang found that cognitive load negatively correlates with flow experience (Chang et al., 2017 ; Hong et al., 2020 , 2022 ). However, they tested only with utilitarian content that aims to provide useful value (i.e., a utilitarian goal) (Batra & Ahtola, 1991 ), such as courses or training. The other type of content that provides the emotional value of enjoyment and pleasure (i.e., a hedonic goal) is hedonic content, such as music, movies, scenery, etc. Research on both types is necessary because these two types make up most of what older people watch in short-form videos (Yongai et al., 2021 ). There is a lack of studies investigating the impact of VR technology on the flow experience of elderly users watching short-form videos. This study is conducted in the context of browsing VR short-form videos to explore how factors such as telepresence and mental workload affect flow experience. For this purpose, we developed a theoretical framework based on relevant theories of telepresence, mental workload, and flow. Considering different content types, we designed a 2 (media types: VR-2D) * 2 (content types: utilitarian-hedonic) comparison experiment. In addition, the measurement of mental workload is an essential topic in literature related to it. Some scholars use subjective reports and electroencephalography (EEG), with EEG being considered an objective and direct method (Allison & Polich, 2008 ). Accordingly, this study conducted the second experiment using subjective and objective measurements to accurately observe how older users feel. Literature review and hypotheses Telepresence VR videos in this study refer to 360-degree videos played in a VR environment. This is a popular technology that creates an immersive experience by presenting a 360-degree view of the scene to the viewer (Gold & Windscheid, 2020). Watching 360-degree videos through an HMD with head-tracking and stereoscopic capabilities allows the viewers to experience full immersion in the VR environment, whereas through a 2D screen in which viewers control the viewing direction by dragging the video, viewers can only experience partial immersion (Tse et al., 2017). Immersive technologies are often measured by the "telepresence" they create, which Steuer defined as the degree of presence in the mediated environment instead of the immediate physical environment (Steuer, 1992). For example, while viewing a scenery video in VR, individuals immerse themselves in a remote location through the medium, despite physically being in their living room. The mediated environment can be a "real" environment distant in time or space or a computer-synthesized animated virtual world. Sheridan(Sheridan, 1992) proposed three technological determinants of presence: 1) the extent of sensory information (the bits of the information transmitted to sensors of the observer); 2) the control of the relation of sensors to the environment (the ability of the observer to modify the sensors including his viewpoint), and 3) the ability to modify the physical environment (the degree of motor control to change objects in the environment). Some scholars such as Steuer(Steuer, 1992), Laurel(Laurel, 1991), and Rheingold(Rheingold, 1991) theorized the first factor as vividness and the last two factors as interactivity in their research. Vividness can be summarized as a function of sensory breadth and sensory depth(Steuer, 1992). The former refers to the number of sensory dimensions presented simultaneously and the latter to the resolution within each dimension. For example, video media with audio and graphics provide more sensory breadth than broadcasting, and video with a 360-degree view provides more sensory depth than regular video. Interactivity can be categorized into three different types: user-to-user, user-to-content, and user-to-system(Mcmillan, 2006). Research on media technology mainly focuses on the interactivity of user-to-system, which is defined by Stuer as "the degree to which the users of a medium can influence the form or content of the mediated environment "(Steuer, 1992). Three factors contribute to interactivity: 1) speed, the rate at which inputs can be absorbed by the mediated environment; 2) range, the amount of change that can affect the mediated environment; and 3) mapping, the ability of a system to map its control naturally and predictably to changes in the mediated environment. For example, the telephone permits faster interaction speeds than an answering machine; short-form videos that can be paused, fast-forwarded, and liked at any time have a greater interaction range than live TV that can only be turned on and off; and HMD with head-tracking have a more natural interaction mapping for switching viewpoints compared to directional keyboards. Therefore, it is evident that VR technologies with richer sensory and interactive experiences deliver higher levels of vividness and interactivity compared to 2D media(G. Wang et al., 2018; Zou & Yang, 2018; Bogicevic et al., 2019). Various media technologies with different forms of information presentation and interaction provide different levels of presence, which is now widely accepted as a necessary and predictive element of immersive experience(J. J. Lee, 2007). Furthermore, VR technology has been demonstrated to achieve a stronger sense of telepresence through enhanced vividness and interactivity in a variety of scenarios, such as e-shopping (Coyle & Thorson, 2001), virtual tourism (Cheng & Huang, 2022), sports viewing (Kim & Ko, 2019), online learning (Kwon, 2019), etc.. Therefore, hypotheses are made: H1 ： Vividness has a positive effect on telepresence when experiencing short-form videos in VR. H2 ： Interactivity has a positive effect on telepresence when experiencing short-form videos in VR. Flow experience Csikszentmihalyi first introduced the concept of flow experience in 1975 and described it as "a feeling of immersion, energized focus, and involvement, often accompanied by positive emotions" (Csikszentmihalhi, 1997). The flow experience is considered a form of intrinsic motivation because "the experience itself is so enjoyable that people will do it even at great cost, for the sheer sake of doing it" (Csikszentmihalyi, 1990). The flow is admitted as an optimal experience that can be characterized by cognitive absorption and intrinsic enjoyment. Currently, the flow experience has been widely studied in computer-mediated communication (CMC) environments and used as a valid metric for online user experience (Khang et al., 2013; Shin, 2019). It is empirically proven that online media, especially short-form videos, provide a high level of absorption and considerable enjoyment, and these emotions can then be a powerful motivator for repeated use and the start of flow. There has been a large and growing body of literature linking presence to flow (Draper et al., 1998; Draper & Blair, 1996; Skadberg & Kimmel, 2004). Both presence and flow require focused attention to realize a "loss of character-independent self-consciousness" (Csikszentmihalyi, 1990; Draper & Blair, 1996). In addition, users are more likely to obtain intrinsic rewards like enjoyment and delight when they feel a sense of presence. Telepresence has been shown in numerous studies to be a precondition for experiencing flow (Chen, 2006; Hoffman & Novak, 1996; Zaman et al., 2010), including in VR environments(Faiola et al., 2013; Kim & Ko, 2019). Therefore, hypotheses are made: H3 ： Telepresence has a positive effect on flow experience when experiencing short-form videos in VR. Flow experience and satisfaction Satisfaction is defined as a general psychological state related to and resulting from the actual experience relative to the expected experience (Bhattacherjee, 2001). Satisfaction is regarded as the final stage of the psychological process (Giese & Cote, 2000), which is a key factor in technology acceptance and willingness to continue use (M.-R. Lee et al., 2015; Shin, 2019; Xu et al., 2023). Satisfaction has also been described as an emotion of positive (satisfaction) or negative (dissatisfaction) feelings (Shin, 2019). Thus, it has been suggested that the flow, as a positive emotional state (Zaman et al., 2010), makes sense to explain satisfaction with the online consumption experience (Calvo-Porral et al., 2017). Much of the current literature has demonstrated that user satisfaction in computer-mediated environments can be well predicted by flow experience (O’Cass & Carlson, 2010; Williams, 2014; Ozkara et al., 2016; T. Zhou & Lu, 2011; Albatati et al., 2023). For example, online gamers in a state of flow exhibit higher levels of satisfaction (Chou & Ting, 2003), and flow has shown a similar positive impact on satisfaction in VR environments (Kim & Ko, 2019). Based on flow and satisfaction literature, we propose that flow predicts user satisfaction with VR short-form videos. Therefore, hypotheses are made: H4 ： Flow experience has a positive effect on spectators' satisfaction when experiencing short-form videos in VR. Mental workload Beyond the immersive experience, VR technology also has potential negative effects. The user's cognitive system is under increasing stress as human-computer interaction (HCI) systems become more complex(Kumar & Kumar, 2016). It has been shown that 360° video with a wider field of view may lead to a higher mental workload (Gold & Windscheid, 2020). Mental workload, or cognitive load, is referred to the total amount of human mental effort or memory required to perform a task (Sweller, 1988). Mental workload can be subjectively perceived by a person while engaged in an activity (Hart & Staveland, 1988). According to the Cognitive Load Theory, the load has been categorized into three types based on its source: intrinsic cognitive load (ICL) which is related to the complexity of the task itself; extraneous cognitive load (ECL) which is the result of cognitive resources being allocated to performing task-irrelevant operations; and germane cognitive load (GCL) which is generated by the process involving procedural thought and action (Sweller, 1988). In line with the theory of Cognitive Load, mental workload is considered a multidimensional construct related to the characteristics of the task, the operator, and the environment in which the task is performed (Nino et al., 2023). Thus, media type can have an impact on mental workload from the extraneous aspect. In terms of VR videos, it has been observed that viewers in a VR environment perceive a higher cognitive load for its additional visual information and rich possibilities of interaction. Further, extraneous load easily occurs in any computer-based information presentation system for elders who mostly lack familiarity with computers. In contrast, the content of VR videos determines the intrinsic load. For example, the load of news or educational content, whose main objective is to deliver useful information (i.e., utilitarian content) (Choi & Jung, 2016), is generally higher than that of music or scenery content, whose main objective is to satisfy affective gratification over cognitive needs (i.e., hedonic content). Without examining the various content of short-form videos which bury different levels of mental workload, researchers cannot figure out the attitudes and behavior of viewers. The sum of various loads should be controlled away from exceeding the cognitive capacity of the information processor and eventually impairing their performance. The concept of mental workload or cognitive load has been suggested to predict the performance of the activity (Schrader & Bastiaens, 2012) as well as the user's mental state (Dybvik et al., 2021), such as flow experience. The negative correlation between ICL and flow has also been confirmed by Hong in his VR program (Hong et al., 2020, 2022). Therefore, the following hypothesis is made: H5 ： Cognitive workload has a negative effect on flow experience when experiencing 360 short-form videos in VR. Measurements of mental workload are also a vital issue for CLT researchers which can be generally categorized as subjective (like rating scales) or objective (like physiological parameters) (Brunken et al., 2003). With the increasing demand for accuracy, electroencephalography (EEG) has been adopted by a large number of scholars to measure mental workload (Antonenko & Niederhauser, 2010; Y. Zhou et al., 2017; Örün & Akbulut, 2019). Also, there have been numerous studies employing EEG in VR environments and proven to still be effective (Khedher et al., 2019; Strayer et al., 2017; Tugtekin & Odabasi, 2022). Therefore, subjective self-reports and EEG are both included in this study to measure mental workload. Empirical studies Fig. 1 shows the theoretical model of this study in which the links between the constructs were broadly discussed in previous sections. To investigate the effects of VR on the mental workload and flow of elderly short-video users, two experiments have been conducted in this study, a questionnaire survey and an EEG analysis, in both of which media type (VR-2D) and content type (hedonic-utilitarian) were manipulated. Table 1 shows the design of the two experiments. Table 1. Empirical studies of the current research. Experiments Data collection method Subject grouping method Experiment 1 Questionnaire survey 2 * 2 between-subjects study Experiment 2 EEG brainwave measurement, Questionnaire survey 2 * 2 within-subjects study Experiment 1 examined the flow model to test all hypotheses through SEM using the data from a questionnaire survey for elderly users, where a 2 (media type: VR and 2D) * 2 (content type: hedonic and utilitarian) between-subjects experimental design was used. Experiment 2 added EEG data to assess mental workload and further test some of the hypotheses related to mental workload using the same materials and subjective measures in experiment 1. Given the feasibility of the EEG equipment and environment, a within-subjects study design, where the same subjects tested all conditions, was adopted to eliminate possible errors due to differences in subjects, equipment measurements, etc. (Wu et al., 2022). Experiment 2 also examined the connection of self-reported mental workload with EEG signals. Experiment 1 Design and participants The subjects were native Chinese speakers aged 50 years and older (mean age = 54.3, SD = 2.36). All participants were voluntary and could stop the experiment at any time. Upon completion of the experiment, all subjects were given gifts such as fruits. The study has been approved by the ethics committee of Zhejiang University of Technology and was conducted following the ethical standards outlined in the 1964 Declaration of Helsinki. The participants all signed written informed consent before the study. Based on the power analysis calculated by G*Power for the present experimental design, the required sample size was 126 cases (effect size = 0.30, alpha level =0.05, efficacy =0.80, number of groups = 4). Therefore, a valid sample of 127 cases (71 females, 56 males) was finally collected for this experiment, of which 34 were in scenery content & 2D condition, 31 in scenery content & VR condition, 32 in knowledge content & 2D condition, and 30 in knowledge content & VR condition. Their VR experience (e.g., “whether have ever used VR”) and content preferences (5-point Likert scales, e.g., “I'm interested in scenery content”, range “does not apply at all” to “fully applies”) were also collected. There were only 11 (18.9%) who had ever used VR and their interest in scenery content (M = 2.63, SD = 0.74) and knowledge content (M = 2.87, SD = 0.82) ranged on average in the middle part of the scales. Materials Materials for the study were selected from 360-degree videos on Douyin or YouTube platforms, all of which were 50-70 s and can be displayed in VR or 2D screen. Short-form videos of scenery content showed famous places in the world, such as the Venice City, the Great Wall, etc.. While educational videos showed simple general knowledge, such as the dangers of smoking, and the introduction of artificial satellite, etc.. Before the experiment, a pilot study among 5 elderly people was conducted to investigate their perceived ICL across 20 short-form videos on a 2D screen. The perceived ICL was measured using a 5-point Likert question (“Please indicate the extent to which you see the teams below as rivals”, range “does not apply at all” to “fully applies”). Finally, 5 short-form videos of scenery content (M = 1.32, SD = 0.27) and another 5 of educational content (M = 2.96, SD = 0.34) were included. No hierarchical knowledge included between the educational videos. The study manipulated the media by presenting the stimulus through a 2D screen or a VR device. Participants in the VR condition viewed the short-form videos using a Pico4 VR headset, which weighed about 295 grams and provided 4320*2160 resolution in a 105-degree field of view. In the 2D condition, participants viewed the short-form videos on a phone screen that was a 6.1-inch handheld display device weighing approximately 194 grams with a resolution of 1792*828. Regarding the format of the playing, in the 2D condition, short-form VR videos were played on the Douyin platform. In the VR condition, short-form VR videos were played in a player that emulated Douyin and was designed and built in Unity. During the experiment, participants were told that they could only interact with panorama browsing, switching between short-form videos, and liking the video. In the 2D screen condition, participants rotated the phone screen to browse the panorama, slid up and down the screen to switch between short-form videos, and clicked the "like" button on the right side of the screen. In the VR condition, the same interaction buttons were presented on the lower right side of the view as on the 2D screen. And the participants rotated their heads to view, flicked the joystick up and down to switch, and liked the video by aiming at icons with the handle ray and then pressing the button. The interaction icons were identical, all white with 90% opacity, and the icon for Like would turn red after being clicked. Measures We used a Likert scale fixed from 1 (strongly disagree) to 5 (strongly agree) to measure vividness, interactivity, telepresence, flow, mental workload, and satisfaction. The questionnaire was adapted from previous studies and translated into Chinese. Specifically, we used three items from Kim and Ko's study (Kim & Ko, 2019) to measure vividness, and three items from Park and Yoo's study (Park & Yoo, 2020) to measure interactivity. Telepresence was assessed using three items from Kim's research (Kim & Ko, 2019). After conducting a comprehensive review of the literature, we interpreted the flow experience of viewers browsing short-form videos as a mental state marked by perceived enjoyment, perceived control, attentional focus, and temporal distortion, using four items from Chang and Pearce's study (Chang, 2013; Pearce et al., 2005). The items measuring mental workload were adapted from Chang's study and Antonenko and Niederhauser's study (Antonenko & Niederhauser, 2010; Chang et al., 2017). Satisfaction was measured using three items from previous studies by McKinney (McKinney et al., 2002) and Kim (Kim & Ko, 2019). The final scale of this experiment consisted of twenty question items. Prior to conducting the formal experiment, we pretested the measurement items among 24 university students and refined the final questionnaire. Structural equation modeling was first developed using AMOS 28.0 to test the measurement model. According to the criteria proposed by Hu and Bentler (Hu & Bentler, 1999), the goodness-of-fit indices of all the tests of the structural model were satisfactory :χ2/df = 1.817 , RMR = 0.045, AGFI = 0.837, CFI = 0.924, and RMSEA = 0.053, which indicated that the proposed structural model fit the data well . A validated factor analysis (CFA) was then used to validate the six-factor model consisting of vividness, interactivity, telepresence, mental workload, flow, and satisfaction. We accessed internal consistency, reliability, convergent validity, and discriminant validity. The CFA results presented in Table 2 indicate that all factor loadings were statistically significant. The Cronbach alpha ranged from 0.693 to 0.838, which exceeds the reliability threshold of 0.6 suggested by Fornell and Larcker (Fornell & Larcker, 1981). Additionally, the construct's mean-variance extracted (AVE) values ranged from 0.539 to 0.714, which is greater than the threshold of 0.5, and the coefficient of reliability (CR) ranged from 0.806 to 0.880, which is greater than the threshold of 0.7. These results indicate acceptable measurement reliability (Bagozzi & Yi, 1989). In addition, in order to test the discriminative power of the constructs, the square root of the average extracted variance (AVE) for each construct must be greater than the correlation between the constructs (Fornell & Larcker, 1981). The correlation matrix in Table 3 indicates that the root of AVE on the squared diagonal is greater than the corresponding off-diagonal inter-construct correlation and reaches significance (p < 0.05). Thus, the discriminant validity of all factors is supported. Table 2. Factor loadings and indicators of internal consistency and reliability. Constructs Items Lambda Cronbach's Alpha AVE CR Vividness Viv1 0.901 0.755 0.628 0.832 Viv2 0.823 Viv3 0.628 Interactivity Int1 0.916 0.844 0.714 0.880 Int2 0.757 Int3 0.933 Telepresence Tel1 0.883 0.758 0.584 0.806 Tel2 0.713 Tel3 0.682 Flow Experience FE1 0.739 0.765 0.563 0.836 FE2 0.880 FE3 0.791 FE4 0.774 Mental Workload MW1 0.905 0.777 0.539 0.821 MW2 0.656 MW3 0.640 WM4 0.705 Satisfaction Sat1 0.843 0.783 0.603 0.819 Sat2 0.738 Sat3 0.844 Table 3. Means, standard deviations (SD), and correlations Vivid Inter Tele MW Flow Sat Vivid 0.792 Inter 0.476 0.845 Tele 0.352 0.338 0.764 MW 0.021 0.259 0.018 0.750 Flow 0.229 0.226 0.637 -0.272 0.734 Sat 0.397 0.361 0.528 -0.135 0.559 0.777 Mean 4.050 4.040 3.630 2.900 3.820 3.730 SD 0.650 0.590 0.730 0.900 0.480 0.610 Procedure This experiment was divided into three phases. Participants were assigned at random to one condition in the initial phase and informed that their involvement in the experiment was entirely voluntary. Then they were given a brief overview of the entire procedure, signed a consent form upon understanding, and completed demographic questions. In the second phase, participants took part in the experiment one at a time, viewing groups of short-form videos of scenery or educational content types via a phone or a VR device. During the last phase, the participants were tasked with ranking all the dependent variables. After completing the experiment, they were given fruit as a thank-you gift. The average time for each participant to complete the experiment was 30-35 minutes. Results Results of hypothesis testing The hypotheses were tested by constructing structural equation modeling through AMOS. The results of testing the hypotheses showed that all hypotheses were supported when all subjects were considered together. Specifically, both vividness (β=0.375, p<0.001) and interactivity (β=0.411, p<0.05) contributed significantly to the sense of presence, proving H1 and H2. It was also found that the telepresence was positively correlated with the experience of flow (β=0.787, p<0.001), whereas the mental workload had a negative effect on the experience of flow (β=-0.097 , p<0.05), proving H3 and H4 respectively. In addition, flow experience had a significant effect on satisfaction (β=0.886, p<0.001) which demonstrated H5. Results of group differences The samples were divided into two groups based on different content types, and the model was tested against the grouped samples. Both the scenery content group (n = 65, χ2/df = 2.344, RMR = 0.057) and the educational content group (n = 65, χ2/df = 2.176, RMR = 0.053) turned out fit well for the model, in both of which H1-H5 were supported. But there were still some significant differences. In the model with scenery content, mental workload did not have a statistically significant effect on the experience of heart flow (β = 0.042, p > 0.05); whereas in the model with educational content, brain load had a significant negative effect on the experience of heart flow (β = -0.111, p < 0.05). Thus, hypothesis 6 was supported. In addition, the extent to which the flow contributed to satisfaction decreased in the model with educational content compared to scenery content. Experiment 2 In experiment 1, we tested the hypotheses with the method of structural equation modeling, and found that mental workload played different roles in different content types of short-form videos. According to the CLT, the content types lead to different intrinsic loads, while the media types will have an effect on extrinsic loads. In order to further distinguish the effects of media types and content types on elderly short-form video viewers, we added EEG measurements to the subjective questionnaire. Design and participants Another 12 participants (6 males and 6 females) were invited to participate in Experiment 2. The subjects were also Chinese residents aged over 50 years (M=53.8; SD=1.72 ). All participants ensured that they had no previous exposure to VR equipment. Due to the demand for EEG analysis, only right-handed seniors without known brain problems (such as epilepsy or stroke) were invited to participate. The study has been approved by the ethics committee of Zhejiang University of Technology and was conducted following the required ethical standards. All participants were voluntary and signed a written informed consent form before the experiment. They could stop the experiment at any time and received thank gifts after the experiment. Considering the feasibility of the EEG equipment and environment, this experiment used a within-subjects study design, in which the same participant tested all 2 (media-type: 2D-VR)*2 (content-type: scenery-educational) conditions to minimize randomized outcomes. This method was adopted to eliminate possible errors due to differences in subjects, equipment measurements, etc. (Wu et al., 2022). In this case, subjects were randomized into experiments with different order of conditions and took a 5-minute break after each condition experiment to exclude the effects of cumulative fatigue or novelty from sequential media type and content type. The final results proved no significant difference. Materials The display platform and equipment for Experiment 2 were the same as Experiment 1, but the stimulus was adapted to the within-subjects experiment as follows: for each participant was required to experience all 4 conditions, two sets of scenery materials and two sets of educational materials were required. Experiment 2 contained 12 short-form videos of 50-70s duration, divided into four groups. Every short-form video can be displayed in VR or on 2D screen. Measures In this experiment, objective measurements of EEG were added to the subjective measurements of Experiment 1. In general, the reliability of EEG signal analysis depends greatly on the extent to which the acquired data are contaminated by artifacts (Govindan et al., 2016). Artifacts are unwanted signals, and artifacts can be attributed to contamination from non-physiological sources (e.g., power line noise, changes in electrode impedance, etc.) or physiological sources (e.g., potentials induced by blinks, head movements, and body movement), etc. (Fatourechi et al., 2007). This requires[13] subjects to be still for accurate acquisition of brain waves. However, in some special circumstances such as gaming, the user's movement cannot be restricted (Berta et al., 2013). As in gaming (Nijholt et al., 2009), when the user is viewing short-form videos in VR, blinking, head and body movements are unavoidable and closely associated with their state. The presence of artifacts in these situations can be considered as additional information of EEG (Berta et al., 2013). Therefore, this experiment still attempts to use EEG to measure mental workload during users' behavior of browsing short-form videos. In addition to the EEG measurements, Experiment 2 used the same demographic questionnaires and subjective scales as Experiment 1. This experiment used the event-related desynchronization/synchronization percentage (ERD%/RES%) in the alpha and theta rhythms as a valid measure of mental workload. The increased mental workload results in higher ERD% for alpha rhythm as well as higher ERS% for theta rhythm. To make the comparison more intuitive, we used the negative ERD% in replace of ERS% for theta rhythm. The computational formula is ERD % = ( A-R )/ R × 100, where A denotes the band power of interest interval, and R denotes the band power of the baseline or reference interval (Pfurtscheller & Lopes da Silva, 1999). A 30-s segment of the baseline interval was collected before a subject started browsing short-form videos. By manually marking the switching time of short-form videos on the EEG recordings as a reference, other 30-s segments of the test interval were obtained for the short-form video going to display. ERD% values of alpha and theta brain wave rhythms were computed for each short-form video in each condition of each subject. The average ERD% values were then calculated for the two brain wave rhythms in each of the four conditions. Specifically, the final ERD% value of each experimental condition was the average for all subjects in that condition, where the average for three short-form videos for each subject was first calculated. Procedure Prepare phase Each subject scheduled an individual time to participate in the experiment conducted in a quiet laboratory to prevent distraction. The laboratory setup provided consistent environmental conditions for all subjects. When the subject was comfortably seated at a table in a height-adjustable chair, a researcher would give an introduction to the experiment and equipment and inform the voluntary nature of their involvement. After that, subjects were asked to sign a consent form and complete demographic questions. The researcher then connected the EEG equipment, attaching disposable vinyl electrodes to the appropriate recording locations on the subject's skull. Electrode impedance is limited to less than 10 kΩ. The subjects were reminded to minimize unnecessary movements during the experimental task. Treatment phase After placing all electrodes and turning on the EEG equipment, the subject closed her eyes on command and sat in a relaxed state until an appropriate and stable brainwave state was observed. A 30-second sample of baseline brainwaves was recorded before the subject was instructed to open her eyes and begin browsing short-form videos of one condition. At the end of the set of short-form videos, the subject was asked to complete a subjective assessment questionnaire and then changed to the next condition. The subject would close his eyes and relax for at least three minutes until the brainwaves returned to baseline. Then the baseline brainwave sample would be recorded again. The subject was then instructed to start the next task. Had finished viewing the four short-form video groups, the participants were offered a fruit gift and allowed to leave. Data Analysis Regarding subjective measures, the scores for each item in the questionnaire were tallied in SPSS where the differences between each pair of task conditions for each item were examined using ANOVA. Regarding the EEG data, data preprocessing and analysis were performed using MATLAB and the EEGLAB toolbox. For preprocessing, EEG data were downsampled to 500 Hz and filtered using a bandpass of 1-60 Hz. To address motion artifacts, the EEG data were then processed by ICA to identify and remove independent components corresponding to noise. Among these, blinks and noisy channels with potentials exceeding ±100 μV were manually rejected. The average rejection rate for all participants was 2.2%. The EEG data were then averaged and re-referenced .To calculate the mental workload, the EEG data of each test and baseline condition were analyzed in the frequency domain using the FFT (500 ms sliding window) and calculated the band power values (absolute values) for the alpha and theta frequency ranges (theta: 4-7 Hz, alpha: 10-13 Hz). The percentage change relative to the baseline in the power of the alpha and theta bands was calculated for each short-form video according to the ERD% formula by Pfurtscheller (Pfurtscheller & Lopes da Silva, 1999). The analyzed data was averaged for each participant in each condition. A two-way repeated measures ANOVA was conducted in order to test the effects of the cross-media type and content type conditions on mental workload. Results Effects of media and content types on mental workload This experiment explored the effects of media types (2D and VR) and media contents (scenery content and educational content) on the subjectively and objectively measured mental workload of elderly viewers. And the potential interactions between these two types were also included. Specifically, the dependent variables included three measures of mental workload: self-reported mental workload, ERD% values of theta (4-7 Hz), and alpha (10-13 Hz) brain wave rhythms. Table 4 describes the descriptive statistics of the dependent variables. Table 4: Descriptive statistics for measures of mental workload Media Type Content Type Subjective ratings θ-ERD% α-ERD% M SD M SD M SD 2D Scenery 12.92 1.51 -13.57 5.92 44.09 4.06 Educational 15.17 1.03 -21.64 5.02 47.83 7.19 VR Scenery 14.25 2.09 -14.68 6.20 45.43 5.16 Educational 14.58 1.62 -19.66 5.03 46.39 5.39 The results of the two-way repeated measures ANOVA (RM-ANOVA) for mental workload showed that the main effect of media type was not significant (p > 0.326). In contrast, the main effect of content type was significant (p 0.408). The interaction effect between media type and content type was significant only for θ-ERD% (F(1,11) = 5.291, p = 0.042, η² = 0.325), also indicating a relatively large effect size (η² = 0.325). However, the interaction effects for α-ERD% and subjective ratings were not significant (p > 0.114). This suggests that the effect of content on θ-band activity differs between media types. A follow-up simple effects analysis for the interaction effect on θ-ERD% revealed that the difference between media types was significant only for the educational content condition (Δ = -1.985, p = 0.044), whereas no significant difference was found in the scenery content condition (p = 0.621). Specifically, in the educational content condition, VR media (M = -19.65; SD = 1.45) exhibited significantly higher θ-ERD% than 2D media (M = -21.64; SD = 1.45). Table 5. Two-way RM-ANOVA on three measures of mental workload Source F p η p 2 Media Type θ-ERD% 0.687 0.425 0.059 α-ERD% 0.015 0.906 0.001 Subjective ratings 1.057 0.326 0.088 Content Type θ-ERD% 15.194 0.002 0.580 α-ERD% 7.566 0.019 0.408 Subjective ratings 8.729 0.013 0.442 Media * Content θ-ERD% 5.287 0.042 0.325 α-ERD% 2.590 0.136 0.191 Subjective ratings 2.940 0.114 0.211 Relationship between objective and subjective mental workload Experiments 2 were conducted to objectively address mental workload through processing the theta waves of EEG data from the frontal lobe (i.e., Fz) and the alpha waves from the occipital lobe (i.e., Pz), where we examined correlations between subjective and objective results. When valid EEG data from all subjects were considered together, subjective scores showed a significantly negative correlation with ERD% values of theta waves at Fz (r=-0.330, p=0.022) and a nonsignificant correlation with ERD% values of alpha waves at Pz (r=0.038, p=0.795). Discussion This study aimed to investigate through subjective and objective experiments the effects of VR on the flow and mental workload of elderly viewers when browsing short-form videos, in both of which media type (2D-VR) and content type (hedonic-utilitarian) had been controlled. In Experiment 1, a flow model of six factors (vividness, interactivity, telepresence, mental workload, flow, and satisfaction) was developed to explore the influences on the flow experience during browsing short-form videos in VR environments and whether it was consistent between different content. Experiment 2 used EEG to measure mental workload, which further demonstrated the effects of media type and content type on mental workload and eventually impact flow. Also, the EEG results showed some correlation with the subjective measures. Overall, the results of both experiments highlight that VR facilitates a flow experience for older viewers browsing short-form videos, only though with some content it can carry some mental workload. In the following, the main results will be discussed in further detail. Flow in VR short-form video browsing As Experiment 1 has shown, in the context of short-form video consumption, VR technology provides a higher level of vividness and interactivity than traditional 2D media, through which VR significantly enhances the telepresence and further facilitates the flow experience. This finding supports the theory that vividness and interactivity are determinants of telepresence (Steuer, 1992 ), as well as the related literature on the ability of VR to provide enhanced telepresence. In addition, the results indicated that telepresence is an important antecedent of the flow, regardless of whether the type of short-form video content is hedonic or utilitarian. This result supports the findings of Shin et al. (Chen, 2006 ; Novak et al., 2003 ; Shin, 2018 ; Kim & Ko, 2019 ). This may also allow for the inference that VR technology in media consumption is relatively acceptable for older age groups with a large degree of similarity to traditional technologies such as 2D screens. The model also pointed out that the flow greatly increased short-form video viewers' satisfaction regardless of the medium. This result is in line with previous research on the beneficial effects of flow on satisfaction in different contexts. Therefore, we can draw the conclusion that, in VR short-form videos, the flow experience is a powerful predictor of user experience satisfaction. Effects of media and content types of short-form videos We tested all the hypotheses respectively in scenery content and educational content, in which H1-H5 were supported. However, the hypothesis about the effect of mental workload on flow varied depending on the type of content. More specifically, when watching hedonic content, the mental workload perceived by viewers had almost no effect on flow. When watching utilitarian content, such as educational videos, the mental workload had an obstructive effect on flow. In Experiment 2, the group of educational content resulted in higher alpha ERD% and lower theta ERD% than that of scenery content for the same media type. However, the difference in EEG results between the VR media group and the 2D media group was not significant. Combined with EEG-based measures, it can be concluded that the mental workload perceived by elderly users mainly comes from the intrinsic load of the educational content. In the literature on cognitive theory, cognitive load has been categorized into intrinsic, extraneous, and germane loads (Paas & Van Merriënboer, 1994 ; Sweller, 1988 ), with intrinsic load playing a decisive role. And the intrinsic load is determined by the inherent complexity of the content. In contrast, the extrinsic load imposed by VR proved to be virtually unaffected. This result is consistent with Gold's findings (Gold & Windscheid, 2020 ). It can be argued that VR short-form video is more suitable for displaying hedonic content for older adults which takes up fewer cognitive resources giving way to handling media. When providing utilitarian content, it is noted that the complexity of content has to be controlled away from exceeding the cognitive capacity of the elderly. On the other hand, in response to the difference in the degree of the flow devoting to satisfaction between content types (β scenery content > β educational content ), we suggest that when users browse utilitarian content, their satisfaction with the experience may also derive from the fulfillment they feel after absorbing useful information. Subjective and objective measurements of mental workload In this study, we used both subjective and objective methods to investigate the mental workload during browsing short-form VR videos. The subjective and objective results all showed that the mental workload showed significant differences between content types (scenery-educational content), while the differences between media types (2D-VR) were not significant. This suggests that the media type brings limited additional mental workload to the viewer relative to the content type. We can conclude that it is feasible to measure mental workload with the ERD/ERS% values of theta and alpha EEG waves (Y. Zhou et al., 2017 ; Örün & Akbulut, 2019 ; Y. Wang, 2020 ). On the other hand, the correlation of subjective and objective results was also tested and only a significant correlation between ERD% of frontal theta waves and rating scales was reported in Experiment 2. This result could potentially be attributed to the alpha waves being more sensitive to the changes of workload than the retrospective reports (Scharinger, 2023 ), especially for the elderly who may have difficulty understanding questions or expressing subjective experiences. Still, considering the individual variability prevalent in current EEG experiments, we suggest a combination of subjective and objective measurements as an appropriate method for studying the mental workload. Implications Theoretical implications This study contributes to the impact of VR on user experience, especially of the elderly groups. Although a large number of studies have demonstrated that users are more likely to experience flow experience in VR environments, the process of flow for older users had not received sufficient attention. In addition, VR, as an emerging media presentation form for short-form videos, has yet to be studied for the new experiences it brings. This study reveals how VR can influence the user experience over traditional media forms by focusing on the impact of its enhanced immersive experience and additional load on the flow for older users. This study further compared the differences in user experience when viewing short-form videos of different content types in a VR environment. Previous studies have demonstrated that employing VR media to present learning content to provide useful information (i.e., utilitarian-targeted content) facilitates a more focused absorption of knowledge. But it had not been experimentally demonstrated whether it performs the same effect on the absorption of older users who suffer from degraded cognitive abilities. Using short-form videos of educational content as an example, this study demonstrated that the load imposed by watching utilitarian content in VR had a negative effect on the flow for older adults. This result emphasizes the possible impact of the intrinsic load of the activities (especially with utilitarian goals) when studying older adults' user experience in VR environments. Practical implications Providing immersive experiences for older users in VR environments is challenging. This study offers some help by helping to understand how older users are immersed in VR environments and providing helpful evidence and design recommendations for designing the user experience in VR for older users. The study came up with a flow model on browsing short-form videos in VR environments aimed to understand what factors can facilitate or inhibit the flow experience. In order to promote the flow for older users in VR environments, there are three ways for VR designers: 1) increasing vividness, such as increasing the level of detail and richness of the images, and also enriching the experience from the sound; 2) increasing interactivity, such as making human-computer interactions in VR closer to natural interactions, so that the user forgets wearing a device; and 3) reducing the mental workload that may be perceived by the user, such as designing reasonable layout and clear navigation which can prevent users from getting lost in VR. In the comparison of different media types and different content types from this study, important design recommendations on balancing the media and content are made for VR developers. Specifically, when providing hedonic content, the load brought by VR is relatively limited. That means designers can focus more on immersing older users through enhanced vividness and interactivity. In contrast, when providing utilitarian content, designers need to be more attentive to avoiding additional load. Conclusions We conclude from the study that VR has the role of promoting flow experience, but it may also lead to additional mental workload. The results of the two experiments confirm that short-form VR videos, compared to traditional 2D short-form videos, offer enhanced telepresence through increased vividness and interactivity, thereby amplifying the flow experience. While the negative effects of mental workload on flow only work when watching educational content. And VR can only bring about a limited increase in mental workload. Despite some limitations, the current study provides new insights into the flow experience in VR environments, which not only makes a theoretical contribution to consumer behavior and digital media literature but also offers practical recommendations for media providers and VR developers. Declarations Ethical approval In accordance with ethical standards and guidelines for research involving human subjects, all necessary ethics approvals have been obtained for the conduct of this study. The research protocol was reviewed and approved by Zhejiang University of Technology’s Research Ethics Committee (IRB Number: 2023D003). Funding This work was supported by the National Social Science Fund of China (No. 22CTQ016). Author Contribution The authors confirm their contribution to the paper as follows: Conceptualization: [Zhichuan Tang], [Yingjia Ding]; Methodology: [Yingjia Ding], [Ruoshen Tang], [Nuo Chen]; Formal analysis and investigation: [Yingjia Ding]; Writing - original draft preparation: [Yingjia Ding], [Ruoshen Tang], [Nuo Chen]; Writing review and editing: [Zhichuan Tang], [Yingjia Ding]; Funding acquisition: [Zhichuan Tang]. Supervision: [Lufang Zhang]. 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Frontiers in Human Neuroscience , 14 . https://doi.org/10.3389/fnhum.2020.00093 Tugtekin U, Odabasi HF (2022) Do Interactive Learning Environments Have an Effect on Learning Outcomes, Cognitive Load and Metacognitive Judgments? Educ Inform Technol 27(5):7019–7058. https://doi.org/10.1007/s10639-022-10912-0 Wang G, Gu W, Suh A (2018) The Effects of 360-Degree VR Videos on Audience Engagement: Evidence from the New York Times. In F. F.-H. Nah & B. S. Xiao (Eds.), HCI in Business, Government, and Organizations (pp. 217–235). Springer International Publishing. https://doi.org/10.1007/978-3-319-91716-0_17 Wang Y (2020) Humor and camera view on mobile short-form video apps influence user experience and technology-adoption intent, an example of TikTok (DouYin). Comput Hum Behav 110:106373. https://doi.org/10.1016/j.chb.2020.106373 Williams KD (2014) The effects of dissociation, game controllers, and 3D versus 2D on presence and enjoyment. Comput Hum Behav 38:142–150. https://doi.org/10.1016/j.chb.2014.05.040 Witmer BG, Singer MJ (1998) Measuring Presence in Virtual Environments: A Presence Questionnaire. Presence: Teleoperators Virtual Environ 7(3):225–240. https://doi.org/10.1162/105474698565686 Wu S-F, Kao C-H, Lu Y-L, Lien C-J (2022) A Method Detecting Student’s Flow Construct during School Tests through Electroencephalograms (EEGs): Factors of Cognitive Load, Self-Efficacy, Difficulty, and Performance. Appl Sci 12(23) Article 23. https://doi.org/10.3390/app122312248 Xu W, Liang H-N, Yu K, Wen S, Baghaei N, Tu H (2023) Acceptance of Virtual Reality Exergames Among Chinese Older Adults. Int J Human–Computer Interact 39(5):1134–1148. https://doi.org/10.1080/10447318.2022.2098559 Yongai J, Wenli L, Menghan Z, Donghui W, Wenbo H (2021) Short Video APP Use and the Life of Mid-age and Older Adults: An Exploratory Study Based on a Social Survey. Popul Res 45(3):31 Zaman M, Anandarajan M, Dai Q (2010) Experiencing flow with instant messaging and its facilitating role on creative behaviors. Comput Hum Behav 26(5):1009–1018. https://doi.org/10.1016/j.chb.2010.03.001 Zhou T, Lu Y (2011) Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Comput Hum Behav 27(2):883–889. https://doi.org/10.1016/j.chb.2010.11.013 Zhou Y, Xu T, Cai Y, Wu X, Dong B (2017) Monitoring Cognitive Workload in Online Videos Learning Through an EEG-Based Brain-Computer Interface. In P. Zaphiris & A. Ioannou (Eds.), Learning and Collaboration Technologies. Novel Learning Ecosystems (pp. 64–73). Springer International Publishing. https://doi.org/10.1007/978-3-319-58509-3_7 Zou W, Yang F (2018) Measuring Quality of Experience of Novel 360-Degree Streaming Video During Stalling. In B. Li, L. Shu, & D. Zeng (Eds.), Communications and Networking (pp. 417–424). Springer International Publishing. https://doi.org/10.1007/978-3-319-78130-3_43 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6286033","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":456282973,"identity":"ae8acc3c-6c27-45f7-a93a-d526e002c91e","order_by":0,"name":"Zhichuan Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYNCCCgZmMM1DlGo2EHEGqIWNJC2MbVAGUVrk5zc/e/h1Xh27wf0Gxgdv2xjkzQlpYWxjMzeW3cbGbHCMgdlwbhuD4c4GAlqY2RjMpCW38YC0sEnztjEkGBwg5BU29m/SknMkQFrYfxOlhYeNx0zyY4MB2BZmorRIsOWUSTMcS2CWPJbYLDnnnIThBkJa5JuPb5P8UVOXzHf48MEPb8ps5AnaAgLMwOhIBgZeA8hWItQDAeMPBgY74pSOglEwCkbBiAQAt60z6cGUL30AAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Zhichuan","middleName":"","lastName":"Tang","suffix":""},{"id":456282974,"identity":"83a10a7d-6f78-46fa-b26d-024030e96717","order_by":1,"name":"Yingjia Ding","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yingjia","middleName":"","lastName":"Ding","suffix":""},{"id":456282977,"identity":"a2162e80-81d4-437d-b8a5-ed3a93d72e6a","order_by":2,"name":"Ruoshen Tang","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ruoshen","middleName":"","lastName":"Tang","suffix":""},{"id":456282978,"identity":"08dd8ee3-ba1a-4503-90a2-033c99c7b0d5","order_by":3,"name":"Lufang Zhang","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Lufang","middleName":"","lastName":"Zhang","suffix":""},{"id":456282980,"identity":"ed5f0b87-d125-4699-94a0-d81b7f433c5e","order_by":4,"name":"Nuo Chen","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Nuo","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-03-23 02:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6286033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6286033/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82926741,"identity":"43693e90-f75c-4b98-a9ec-21071825996c","added_by":"auto","created_at":"2025-05-16 20:02:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32325,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical model.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6286033/v1/fa027152880d4aabe448a223.jpg"},{"id":82926742,"identity":"7fa100eb-d028-42b2-b208-b0ead1cec942","added_by":"auto","created_at":"2025-05-16 20:02:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52263,"visible":true,"origin":"","legend":"\u003cp\u003eScreenshots of short-form videos on VR player system\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6286033/v1/5d95a5649daeefa5eea15d49.jpg"},{"id":82927268,"identity":"c380ef3f-5ecc-4461-aab0-71e954e4ac76","added_by":"auto","created_at":"2025-05-16 20:18:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88421,"visible":true,"origin":"","legend":"\u003cp\u003eScreenshots of short-form videos on 2D Douyin platform\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6286033/v1/cea8c3f7531e01f033f0a7c0.jpg"},{"id":82927270,"identity":"533cb410-58b5-402a-8eb9-bb5a39a64dec","added_by":"auto","created_at":"2025-05-16 20:18:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":48063,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the model. Note: \u003csup\u003e∗\u003c/sup\u003e=p \u0026lt; .05, \u003csup\u003e∗∗\u003c/sup\u003e=p \u0026lt; .01, \u003csup\u003e∗∗∗\u003c/sup\u003e=p \u0026lt; .001\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6286033/v1/f4d6b7ced25478f7d2bf44b3.jpg"},{"id":82926750,"identity":"c64c128d-c576-49f1-8031-33c748f3c6f9","added_by":"auto","created_at":"2025-05-16 20:02:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":143271,"visible":true,"origin":"","legend":"\u003cp\u003eGroup differences in the models. Note: \u003csup\u003e∗\u003c/sup\u003e=p \u0026lt; .05, \u003csup\u003e∗∗\u003c/sup\u003e=p \u0026lt; .01, \u003csup\u003e∗∗∗\u003c/sup\u003e=p \u0026lt; .001\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6286033/v1/63134a095c765d7c5698f047.jpg"},{"id":82927132,"identity":"430f31c9-96b5-4da4-a005-3b1c9b4f6444","added_by":"auto","created_at":"2025-05-16 20:10:42","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":117753,"visible":true,"origin":"","legend":"\u003cp\u003ePhotos of the experimental phase\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6286033/v1/848fa0749a4b9b5ceaccbf43.jpg"},{"id":108139030,"identity":"a1e4dbbe-9788-41cd-8890-fa475241d344","added_by":"auto","created_at":"2026-04-29 18:25:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":981058,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6286033/v1/f4c257d4-5e40-4afd-8e20-ecd5cbe40dfb.pdf"},{"id":82926744,"identity":"53644feb-81f0-4038-9418-bca00853fcf8","added_by":"auto","created_at":"2025-05-16 20:02:42","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21101,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6286033/v1/ac7b28534a519bff1d9b45fd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of VR environment for elderly short-form video viewers on flow and mental workload","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs populations age globally, there is a growing recognition of the importance of enhancing the quality of life for elderly individuals. Older adults often face difficulties participating in activities due to physiological decline (Ouwehand et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Virtual Reality (VR) technology, with its multimodal interaction and immersive experience, is considered valuable for improving their quality of life. In healthcare, VR has been successfully applied to treatment, rehabilitation, and cognitive training for older adults (Coyle et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Amin et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tuena et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In entertainment, studies show that seniors find VR-based physical activities more engaging and enjoyable (Hsieh et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rodr\u0026iacute;guez-Almagro et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). VR is also shaping digital media consumption (Witmer \u0026amp; Singer, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Zou \u0026amp; Yang, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), including short videos, which have gained a high level of popularity among the elderly (Fishwick, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ng \u0026amp; Indran, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, YouTube and Facebook launched 360-degree video streams accessible via VR devices in 2016; Douyin, internationally known as TikTok, also launched a VR version on its HMD device in 2023. As VR technology continues to evolve and mature, there will be more and more elderly viewers of VR short-form videos, which prompts scholars and practitioners to explore the user experience of older people viewing short-form videos in VR.\u003c/p\u003e \u003cp\u003eVR is primarily intended to offer media consumers an engaging and immersive experience by amplifying telepresence (Biocca \u0026amp; Levy, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In academia, scholars have widely used the concept of flow experience to examine such experiences described as \"the holistic sensation that people feel when they act with total involvement\" (Csikszentmihalyi, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). This sensation includes feelings of enjoyment, psychological immersion, energized focus, and involvement (Csikszentmihalhi, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Further, flow is recognized as a useful construct to apply to human-computer interactions (Csikszentmihalyi, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Khang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Studies have proven that VR can enhance viewers' flow experience through telepresence when watching long videos(like sports or news)(Shin, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kim \u0026amp; Ko, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, VR technology may achieve the same effect when watching short-form videos.\u003c/p\u003e \u003cp\u003eHowever, some other studies have shown that the additional visual information and active interaction brought by 360\u0026deg; videos will increase the workload (Gold \u0026amp; Windscheid, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Viewers have to put in additional thinking and actions to deal with new ways of presenting and interacting with information, especially for elderly users. Therefore, short-form videos in VR may lead to more mental workload. These uncomfortable experiences may impair the flow. For example, Hong and Chang found that cognitive load negatively correlates with flow experience (Chang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hong et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, they tested only with utilitarian content that aims to provide useful value (i.e., a utilitarian goal) (Batra \u0026amp; Ahtola, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), such as courses or training. The other type of content that provides the emotional value of enjoyment and pleasure (i.e., a hedonic goal) is hedonic content, such as music, movies, scenery, etc. Research on both types is necessary because these two types make up most of what older people watch in short-form videos (Yongai et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is a lack of studies investigating the impact of VR technology on the flow experience of elderly users watching short-form videos. This study is conducted in the context of browsing VR short-form videos to explore how factors such as telepresence and mental workload affect flow experience. For this purpose, we developed a theoretical framework based on relevant theories of telepresence, mental workload, and flow. Considering different content types, we designed a 2 (media types: VR-2D) * 2 (content types: utilitarian-hedonic) comparison experiment. In addition, the measurement of mental workload is an essential topic in literature related to it. Some scholars use subjective reports and electroencephalography (EEG), with EEG being considered an objective and direct method (Allison \u0026amp; Polich, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Accordingly, this study conducted the second experiment using subjective and objective measurements to accurately observe how older users feel.\u003c/p\u003e"},{"header":"Literature review and hypotheses","content":"\u003ch2\u003eTelepresence\u003c/h2\u003e\n\u003cp\u003eVR videos in this study refer to 360-degree videos played in a VR environment. This is a popular technology that creates an immersive experience by presenting a 360-degree view of the scene to the viewer (Gold \u0026amp; Windscheid, 2020). Watching 360-degree videos through an HMD with head-tracking and stereoscopic capabilities allows the viewers to experience full immersion in the VR environment, whereas through a 2D screen in which viewers control the viewing direction by dragging the video, viewers can only experience partial immersion (Tse et al., 2017).\u003c/p\u003e\n\u003cp\u003eImmersive technologies are often measured by the \u0026quot;telepresence\u0026quot; they create, which Steuer defined as the degree of presence in the mediated environment instead of the immediate physical environment (Steuer, 1992). For example, while viewing a scenery video in VR, individuals immerse themselves in a remote location through the medium, despite physically being in their living room. The mediated environment can be a \u0026quot;real\u0026quot; environment distant in time or space or a computer-synthesized animated virtual world.\u003c/p\u003e\n\u003cp\u003eSheridan(Sheridan, 1992) proposed three technological determinants of presence: 1) the extent of sensory information (the bits of the information transmitted to sensors of the observer); 2) the control of the relation of sensors to the environment (the ability of the observer to modify the sensors including his viewpoint), and 3) the ability to modify the physical environment (the degree of motor control to change objects in the environment). Some scholars such as Steuer(Steuer, 1992), Laurel(Laurel, 1991), and Rheingold(Rheingold, 1991) theorized the first factor as vividness and the last two factors as interactivity in their research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVividness can be summarized as a function of sensory breadth and sensory depth(Steuer, 1992). The former refers to the number of sensory dimensions presented simultaneously and the latter to the resolution within each dimension. For example, video media with audio and graphics provide more sensory breadth than broadcasting, and video with a 360-degree view provides more sensory depth than regular video. Interactivity can be categorized into three different types: user-to-user, user-to-content, and user-to-system(Mcmillan, 2006). Research on media technology mainly focuses on the interactivity of user-to-system, which is defined by Stuer as \u0026quot;the degree to which the users of a medium can influence the form or content of the mediated environment \u0026quot;(Steuer, 1992). Three factors contribute to interactivity: 1) speed, the rate at which inputs can be absorbed by the mediated environment; 2) range, the amount of change that can affect the mediated environment; and 3) mapping, the ability of a system to map its control naturally and predictably to changes in the mediated environment. For example, the telephone permits faster interaction speeds than an answering machine; short-form videos that can be paused, fast-forwarded, and liked at any time have a greater interaction range than live TV that can only be turned on and off; and HMD with head-tracking have a more natural interaction mapping for switching viewpoints compared to directional keyboards. Therefore, it is evident that VR technologies with richer sensory and interactive experiences deliver higher levels of vividness and interactivity compared to 2D media(G. Wang et al., 2018; Zou \u0026amp; Yang, 2018; Bogicevic et al., 2019).\u003c/p\u003e\n\u003cp\u003eVarious media technologies with different forms of information presentation and interaction provide different levels of presence, which is now widely accepted as a necessary and predictive element of immersive experience(J. J. Lee, 2007). Furthermore, VR technology has been demonstrated to achieve a stronger sense of telepresence through enhanced vividness and interactivity in a variety of scenarios, such as e-shopping (Coyle \u0026amp; Thorson, 2001), virtual tourism (Cheng \u0026amp; Huang, 2022), sports viewing (Kim \u0026amp; Ko, 2019), online learning (Kwon, 2019), etc.. Therefore, hypotheses are made:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Vividness has a positive effect on telepresence when experiencing short-form videos in VR.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Interactivity has a positive effect on telepresence when experiencing short-form videos in VR.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eFlow experience\u003c/h2\u003e\n\u003cp\u003eCsikszentmihalyi first introduced the concept of flow experience in 1975 and described it as \u0026quot;a feeling of immersion, energized focus, and involvement, often accompanied by positive emotions\u0026quot; (Csikszentmihalhi, 1997). The flow experience is considered a form of intrinsic motivation because \u0026quot;the experience itself is so enjoyable that people will do it even at great cost, for the sheer sake of doing it\u0026quot; (Csikszentmihalyi, 1990). The flow is admitted as an optimal experience that can be characterized by cognitive absorption and intrinsic enjoyment. Currently, the flow experience has been widely studied in computer-mediated communication (CMC) environments and used as a valid metric for online user experience (Khang et al., 2013; Shin, 2019). It is empirically proven that online media, especially short-form videos, provide a high level of absorption and considerable enjoyment, and these emotions can then be a powerful motivator for repeated use and the start of flow.\u003c/p\u003e\n\u003cp\u003eThere has been a large and growing body of literature linking presence to flow (Draper et al., 1998; Draper \u0026amp; Blair, 1996; Skadberg \u0026amp; Kimmel, 2004). Both presence and flow require focused attention to realize a \u0026quot;loss of character-independent self-consciousness\u0026quot; (Csikszentmihalyi, 1990; Draper \u0026amp; Blair, 1996). In addition, users are more likely to obtain intrinsic rewards like enjoyment and delight \u0026nbsp;when they feel a sense of presence. Telepresence has been shown in numerous studies to be a precondition for experiencing flow (Chen, 2006; Hoffman \u0026amp; Novak, 1996; Zaman et al., 2010), including in VR environments(Faiola et al., 2013; Kim \u0026amp; Ko, 2019). Therefore, hypotheses are made:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003cstrong\u003eTelepresence has a positive effect on flow experience when experiencing short-form videos in VR.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eFlow experience and satisfaction\u003c/h2\u003e\n\u003cp\u003eSatisfaction is defined as a general psychological state related to and resulting from the actual experience relative to the expected experience (Bhattacherjee, 2001). Satisfaction is regarded as the final stage of the psychological process (Giese \u0026amp; Cote, 2000), which is a key factor in technology acceptance and willingness to continue use (M.-R. Lee et al., 2015; Shin, 2019; Xu et al., 2023). Satisfaction has also been described as an emotion of positive (satisfaction) or negative (dissatisfaction) feelings (Shin, 2019). Thus, it has been suggested that the flow, as a positive emotional state (Zaman et al., 2010), makes sense to explain satisfaction with the online consumption experience (Calvo-Porral et al., 2017).\u003c/p\u003e\n\u003cp\u003eMuch of the current literature has demonstrated that user satisfaction in computer-mediated environments can be well predicted by flow experience (O\u0026rsquo;Cass \u0026amp; Carlson, 2010; Williams, 2014; Ozkara et al., 2016; T. Zhou \u0026amp; Lu, 2011; Albatati et al., 2023). For example, online gamers in a state of flow exhibit higher levels of satisfaction (Chou \u0026amp; Ting, 2003), and flow has shown a similar positive impact on satisfaction in VR environments (Kim \u0026amp; Ko, 2019). Based on flow and satisfaction literature, we propose that flow predicts user satisfaction with VR short-form videos. Therefore, hypotheses are made:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003cstrong\u003eFlow experience has a positive effect on spectators\u0026apos; satisfaction when experiencing short-form videos in VR.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eMental workload\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eBeyond the immersive experience, VR technology also has potential negative effects. \u0026nbsp;The user\u0026apos;s cognitive system is under increasing stress as human-computer interaction (HCI) systems become more complex(Kumar \u0026amp; Kumar, 2016). It has been shown that 360\u0026deg; video with a wider field of view may lead to a higher mental workload (Gold \u0026amp; Windscheid, 2020). Mental workload, or cognitive load, is referred to the total amount of human mental effort or memory required to perform a task (Sweller, 1988). Mental workload can be subjectively perceived by a person while engaged in an activity (Hart \u0026amp; Staveland, 1988).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the Cognitive Load Theory, the load has been categorized into three types based on its source: intrinsic cognitive load (ICL) which is related to the complexity of the task itself; extraneous cognitive load (ECL) which is the result of cognitive resources being allocated to performing task-irrelevant operations; and germane cognitive load (GCL) which is generated by the process involving procedural thought and action (Sweller, 1988).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn line with the theory of Cognitive Load, mental workload is considered a multidimensional construct related to the characteristics of the task, the operator, and the environment in which the task is performed (Nino et al., 2023). Thus, media type can have an impact on mental workload from the extraneous aspect. In terms of VR videos, it has been observed that viewers in a VR environment perceive a higher cognitive load for its additional visual information and rich possibilities of interaction. Further, extraneous load easily occurs in any computer-based information presentation system for elders who mostly lack familiarity with computers.\u003c/p\u003e\n\u003cp\u003eIn contrast, the content of VR videos determines the intrinsic load. For example, the load of news or educational content, whose main objective is to deliver useful information (i.e., utilitarian content) (Choi \u0026amp; Jung, 2016), is generally higher than that of music or scenery content, whose main objective is to satisfy affective gratification over cognitive needs (i.e., hedonic content). Without examining the various content of short-form videos which bury different levels of mental workload, researchers cannot figure out the attitudes and behavior of viewers. The sum of various loads should be controlled away from exceeding the cognitive capacity of the information processor and eventually impairing their performance. The concept of mental workload or cognitive load has been suggested to predict the performance of the activity (Schrader \u0026amp; Bastiaens, 2012) as well as the user\u0026apos;s mental state (Dybvik et al., 2021), such as flow experience. The negative correlation between ICL and flow has also been confirmed by Hong in his VR program (Hong et al., 2020, 2022). Therefore, the following hypothesis is made:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH5\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cognitive workload has a negative effect on flow experience when experiencing 360 short-form videos in VR.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeasurements of mental workload are also a vital issue for CLT researchers which can be generally categorized as subjective (like rating scales) or objective (like physiological parameters) (Brunken et al., 2003). With the increasing demand for accuracy, electroencephalography (EEG) has been adopted by a large number of scholars to measure mental workload (Antonenko \u0026amp; Niederhauser, 2010; Y. Zhou et al., 2017; \u0026Ouml;r\u0026uuml;n \u0026amp; Akbulut, 2019). Also, there have been numerous studies employing EEG in VR environments and proven to still be effective (Khedher et al., 2019; Strayer et al., 2017; Tugtekin \u0026amp; Odabasi, 2022). Therefore, subjective self-reports and EEG are both included in this study to measure mental workload.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEmpirical studies\u003c/h2\u003e\n\u003cp\u003eFig. 1 shows the theoretical model of this study in which the links between the constructs were broadly discussed in previous sections.\u003c/p\u003e\n\u003cp\u003eTo investigate the effects of VR on the mental workload and flow of elderly short-video users, two experiments have been conducted in this study, a questionnaire survey and an EEG analysis, in both of which media type (VR-2D) and content type (hedonic-utilitarian) were manipulated. Table 1 shows the design of the two experiments.\u003c/p\u003e\n\u003cp\u003eTable 1. Empirical studies of the current research.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperiments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 202px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData collection method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubject grouping method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperiment 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 202px;\"\u003e\n \u003cp\u003eQuestionnaire survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e2 * 2 \u0026nbsp; \u0026nbsp; between-subjects study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperiment 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 202px;\"\u003e\n \u003cp\u003eEEG brainwave measurement,\u003c/p\u003e\n \u003cp\u003eQuestionnaire survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e2 * 2 within-subjects study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExperiment 1 examined the flow model to test all hypotheses through SEM using the data from a questionnaire survey for elderly users, where a 2 (media type: VR and 2D) * 2 (content type: hedonic and utilitarian) between-subjects experimental design was used.\u003c/p\u003e\n\u003cp\u003eExperiment 2 added EEG data to assess mental workload and further test some of the hypotheses related to mental workload using the same materials and subjective measures in \u0026nbsp;experiment 1. Given the feasibility of the EEG equipment and environment, a within-subjects study design, where the same subjects tested all conditions, was adopted to eliminate possible errors due to differences in subjects, equipment measurements, etc. (Wu et al., 2022). Experiment 2 also examined the connection of self-reported mental workload with EEG signals.\u003c/p\u003e"},{"header":"Experiment 1","content":"\u003ch2\u003eDesign and participants\u003c/h2\u003e\n\u003cp\u003eThe subjects were native Chinese speakers aged 50 years and older (mean age = 54.3, SD = 2.36). All participants were voluntary and could stop the experiment at any time. Upon completion of the experiment, all subjects were given gifts such as fruits. The study has been approved by the ethics committee of Zhejiang University of Technology and was conducted following the ethical standards outlined in the 1964 Declaration of Helsinki. The participants all signed written informed consent before the study. Based on the power analysis calculated by G*Power for the present experimental design, the required sample size was 126 cases (effect size = 0.30, alpha level =0.05, efficacy =0.80, number of groups = 4). Therefore, a valid sample of 127 cases (71 females, 56 males) was finally collected for this experiment, of which 34 were in scenery content \u0026amp; 2D condition, 31 in scenery content \u0026amp; VR condition, 32 in knowledge content \u0026amp; 2D condition, and 30 in knowledge content \u0026amp; VR condition. Their VR experience (e.g., \u0026ldquo;whether have ever used VR\u0026rdquo;) and content preferences (5-point Likert scales, e.g., \u0026ldquo;I\u0026apos;m interested in scenery content\u0026rdquo;, range \u0026ldquo;does not apply at all\u0026rdquo; to \u0026ldquo;fully applies\u0026rdquo;) were also collected. There were only 11 (18.9%) who had ever used VR and their interest in scenery content (M\u0026nbsp;=\u0026nbsp;2.63, SD\u0026nbsp;=\u0026nbsp;0.74) and knowledge content (M\u0026nbsp;=\u0026nbsp;2.87, SD\u0026nbsp;=\u0026nbsp;0.82) ranged on average in the middle part of the scales.\u003c/p\u003e\n\u003ch2\u003eMaterials\u003c/h2\u003e\n\u003cp\u003eMaterials for the study were selected from 360-degree videos on Douyin or YouTube platforms, all of which were 50-70 s and can be displayed in VR or 2D screen. Short-form videos of scenery content showed famous places in the world, such as the Venice City, the Great Wall, etc.. While educational videos showed simple general knowledge, such as the dangers of smoking, and the introduction of artificial satellite, etc..\u003c/p\u003e\n\u003cp\u003eBefore the experiment, a pilot study among 5 elderly people was conducted to investigate their perceived ICL across 20 short-form videos on a 2D screen. The perceived ICL was measured using a 5-point Likert question (\u0026ldquo;Please indicate the extent to which you see the teams below as rivals\u0026rdquo;, range \u0026ldquo;does not apply at all\u0026rdquo; to \u0026ldquo;fully applies\u0026rdquo;). Finally, 5 short-form videos of scenery content (M\u0026nbsp;=\u0026nbsp;1.32, SD\u0026nbsp;=\u0026nbsp;0.27) and another 5 of educational content (M\u0026nbsp;=\u0026nbsp;2.96, SD\u0026nbsp;=\u0026nbsp;0.34) were included.\u0026nbsp;No hierarchical knowledge included between the educational videos.\u003c/p\u003e\n\u003cp\u003eThe study manipulated the media by presenting the stimulus through a 2D screen or a VR device. Participants in the VR condition viewed the short-form videos using a Pico4 VR headset, which weighed about 295 grams and provided 4320*2160 resolution in a 105-degree field of view. In the 2D condition, participants viewed the short-form videos on a phone screen that was a 6.1-inch handheld display device weighing approximately 194 grams with a resolution of 1792*828. Regarding the format of the playing, in the 2D condition, short-form VR videos were played on the Douyin platform. In the VR condition, short-form VR videos were played in a player that emulated Douyin and was designed and built in Unity. During the experiment, participants were told that they could only interact with panorama browsing, switching between short-form videos, and liking the video. In the 2D screen condition, participants rotated the phone screen to browse the panorama, slid up and down the screen to switch between short-form videos, and clicked the \u0026quot;like\u0026quot; button on the right side of the screen. In the VR condition, the same interaction buttons were presented on the lower right side of the view as on the 2D screen. And the participants rotated their heads to view, flicked the joystick up and down to switch, and liked the video by aiming at icons with the handle ray and then pressing the button. The interaction icons were identical, all white with 90% opacity, and the icon for Like would turn red after being clicked.\u003c/p\u003e\n\u003cp\u003eMeasures\u003c/p\u003e\n\u003cp\u003eWe used a Likert scale fixed from 1 (strongly disagree) to 5 (strongly agree) to measure vividness, interactivity, telepresence, flow, mental workload, and satisfaction. The questionnaire was adapted from previous studies and translated into Chinese. Specifically, we used three items from Kim and Ko\u0026apos;s study (Kim \u0026amp; Ko, 2019) to measure vividness, and three items from Park and Yoo\u0026apos;s study (Park \u0026amp; Yoo, 2020) to measure interactivity. Telepresence was assessed using three items from Kim\u0026apos;s research (Kim \u0026amp; Ko, 2019). After conducting a comprehensive review of the literature, we interpreted the flow experience of viewers browsing short-form videos as a mental state marked by perceived enjoyment, perceived control, attentional focus, and temporal distortion, using four items from Chang and Pearce\u0026apos;s study (Chang, 2013; Pearce et al., 2005). The items measuring mental workload were adapted from Chang\u0026apos;s study and Antonenko and Niederhauser\u0026apos;s study (Antonenko \u0026amp; Niederhauser, 2010; Chang et al., 2017). Satisfaction was measured using three items from previous studies by McKinney (McKinney et al., 2002) and Kim (Kim \u0026amp; Ko, 2019). The final scale of this experiment consisted of twenty question items.\u003c/p\u003e\n\u003cp\u003ePrior to conducting the formal experiment, we pretested the measurement items among 24 university students and refined the final questionnaire. Structural equation modeling was first developed using AMOS 28.0 to test the measurement model. According to the criteria proposed by Hu and Bentler \u0026nbsp;(Hu \u0026amp; Bentler, 1999), the goodness-of-fit indices of all the tests of the structural model were satisfactory :\u0026chi;2/df = 1.817 , RMR = 0.045, AGFI = 0.837, CFI = 0.924, and RMSEA = 0.053, which indicated that the proposed structural model fit the data well .\u003c/p\u003e\n\u003cp\u003eA validated factor analysis (CFA) was then used to validate the six-factor model consisting of vividness, interactivity, telepresence, mental workload, flow, and satisfaction. We accessed internal consistency, reliability, convergent validity, and discriminant validity. The CFA results presented in Table 2 indicate that all factor loadings were statistically significant. The Cronbach alpha ranged from 0.693 to 0.838, which exceeds the reliability threshold of 0.6 suggested by Fornell and Larcker (Fornell \u0026amp; Larcker, 1981). Additionally, the construct\u0026apos;s mean-variance extracted (AVE) values ranged from 0.539 to 0.714, which is greater than the threshold of 0.5, and the coefficient of reliability (CR) ranged from 0.806 to 0.880, which is greater than the threshold of 0.7. These results indicate acceptable measurement reliability (Bagozzi \u0026amp; Yi, 1989).\u003c/p\u003e\n\u003cp\u003eIn addition, in order to test the discriminative power of the constructs, the square root of the average extracted variance (AVE) for each construct must be greater than the correlation between the constructs (Fornell \u0026amp; Larcker, 1981). The correlation matrix in Table 3 indicates that the root of AVE on the squared diagonal is greater than the corresponding off-diagonal inter-construct correlation and reaches significance (p \u0026lt; 0.05). Thus, the discriminant validity of all factors is supported.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Factor loadings and indicators of internal consistency and reliability.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"480\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstructs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLambda\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCronbach\u0026apos;s Alpha\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAVE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eVividness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eViv1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.901\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.755\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.628\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.832\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eViv2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.823\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eViv3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.628\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eInteractivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eInt1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.916\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.844\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.714\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.880\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eInt2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.757\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eInt3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.933\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eTelepresence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eTel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.883\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.758\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.584\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.806\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eTel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.713\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eTel3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.682\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eFlow Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.739\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.765\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.563\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.836\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.880\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFE3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.791\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.774\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eMental Workload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.905\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.777\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.539\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.821\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.656\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMW3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.640\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eWM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.705\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eSatisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSat1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.843\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.783\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.603\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.819\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSat2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.738\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSat3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.844\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 3. Means, standard deviations (SD), and correlations\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"476\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eVivid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eInter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eTele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eMW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eFlow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eSat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eVivid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.792\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eInter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.476\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.845\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eTele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.352\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.338\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.764\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eMW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.259\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.018\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.750\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eFlow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.229\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.226\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.637\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.272\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.734\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eSat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.397\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.361\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.528\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.135\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.559\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.777\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e4.050\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e4.040\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.630\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2.900\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.820\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.730\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.650\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.590\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.730\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.900\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.480\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.610\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003eProcedure\u003c/h2\u003e\n\u003cp\u003eThis experiment was divided into three phases. Participants were assigned at random to one condition in the initial phase and informed that their involvement in the experiment was entirely voluntary. Then they were given a brief overview of the entire procedure, signed a consent form upon understanding, and completed demographic questions. In the second phase, participants took part in the experiment one at a time, viewing groups of short-form videos of scenery or educational content types via a phone or a VR device. During the last phase, the participants were tasked with ranking all the dependent variables. After completing the experiment, they were given fruit as a thank-you gift. The average time for each participant to complete the experiment was 30-35 minutes.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eResults\u003c/h2\u003e\n\u003ch3\u003eResults of hypothesis testing\u003c/h3\u003e\n\u003cp\u003eThe hypotheses were tested by constructing structural equation modeling through AMOS. The results of testing the hypotheses showed that all hypotheses were supported when all subjects were considered together. Specifically, both vividness (\u0026beta;=0.375, p\u0026lt;0.001) and interactivity (\u0026beta;=0.411, p\u0026lt;0.05) contributed significantly to the sense of presence, proving H1 and H2. It was also found that the telepresence was positively correlated with the experience of flow (\u0026beta;=0.787, p\u0026lt;0.001), whereas the mental workload had a negative effect on the experience of flow (\u0026beta;=-0.097 , p\u0026lt;0.05), proving H3 and H4 respectively. In addition, flow experience had a significant effect on satisfaction (\u0026beta;=0.886, p\u0026lt;0.001) which demonstrated H5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults of group differences\u003c/p\u003e\n\u003cp\u003eThe samples were divided into two groups based on different content types, and the model was tested against the grouped samples. Both the scenery content group (n = 65, \u0026chi;2/df = 2.344, RMR = 0.057) and the educational content group (n = 65, \u0026chi;2/df = 2.176, RMR = 0.053) turned out fit well for the model, in both of which H1-H5 were supported.\u003c/p\u003e\n\u003cp\u003eBut there were still some significant differences. In the model with scenery content, mental workload did not have a statistically significant effect on the experience of heart flow (\u0026beta; = 0.042, p \u0026gt; 0.05); whereas in the model with educational content, brain load had a significant negative effect on the experience of heart flow (\u0026beta; = -0.111, p \u0026lt; 0.05). Thus, hypothesis 6 was supported. In addition, the extent to which the flow contributed to satisfaction decreased in the model with educational content compared to scenery content.\u0026nbsp;\u003c/p\u003e"},{"header":"Experiment 2","content":"\u003cp\u003eIn \u0026nbsp;experiment 1, we tested the hypotheses with the method of structural equation modeling, and found that mental workload played different roles in different content types of short-form videos. According to the CLT, the content types lead to different intrinsic loads, while the media types will have an effect on extrinsic loads. In order to further distinguish the effects of media types and content types on elderly short-form video viewers, we added EEG measurements to the subjective questionnaire.\u003c/p\u003e\n\u003ch2\u003eDesign and participants\u003c/h2\u003e\n\u003cp\u003eAnother 12 participants (6 males and 6 females) were invited to participate in Experiment 2. The subjects were also Chinese residents aged over 50 years (M=53.8; SD=1.72 ). All participants ensured that they had no previous exposure to VR equipment. Due to the demand for EEG analysis, only right-handed seniors without known brain problems (such as epilepsy or stroke) were invited to participate. The study has been approved by the ethics committee of Zhejiang University of Technology and was conducted following the required ethical standards. All participants were voluntary and signed a written informed consent form before the experiment. They could stop the experiment at any time and received thank gifts after the experiment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsidering the feasibility of the EEG equipment and environment, this experiment used a within-subjects study design, in which the same participant tested all 2 (media-type: 2D-VR)*2 (content-type: scenery-educational) conditions to minimize randomized outcomes. This method was adopted to eliminate possible errors due to differences in subjects, equipment measurements, etc. (Wu et al., 2022). In this case, subjects were randomized into experiments with different order of conditions and took a 5-minute break after each condition experiment to exclude the effects of cumulative fatigue or novelty from sequential media type and content type. The final results proved no significant difference.\u003c/p\u003e\n\u003ch2\u003eMaterials\u003c/h2\u003e\n\u003cp\u003eThe display platform and equipment for Experiment 2 were the same as Experiment 1, but the stimulus was adapted to the within-subjects experiment as follows: for each participant was required to experience all 4 conditions, two sets of scenery materials and two sets of educational materials were required. Experiment 2 contained 12 short-form videos of 50-70s duration, divided into four groups. Every short-form video can be displayed in VR or on 2D screen.\u003c/p\u003e\n\u003ch2\u003eMeasures\u003c/h2\u003e\n\u003cp\u003eIn this experiment, objective measurements of EEG were added to the subjective measurements of Experiment 1. In general, the reliability of EEG signal analysis depends greatly on the extent to which the acquired data are contaminated by artifacts (Govindan et al., 2016). Artifacts are unwanted signals, and artifacts can be attributed to contamination from non-physiological sources (e.g., power line noise, changes in electrode impedance, etc.) or physiological sources (e.g., potentials induced by blinks, head movements, and body movement), etc. (Fatourechi et al., 2007). This requires[13] subjects to be still for accurate acquisition of brain waves.\u003c/p\u003e\n\u003cp\u003eHowever, in some special circumstances such as gaming, the user\u0026apos;s movement cannot be restricted \u0026nbsp;(Berta et al., 2013). As in gaming \u0026nbsp;(Nijholt et al., 2009), when the user is viewing short-form videos in VR, blinking, head and body movements are unavoidable and closely associated with their state. The presence of artifacts in these situations can be considered as additional information of EEG \u0026nbsp;(Berta et al., 2013). Therefore, this experiment still attempts to use EEG to measure mental workload during users\u0026apos; behavior of browsing short-form videos. In addition to the EEG measurements, Experiment 2 used the same demographic questionnaires and subjective scales as Experiment 1.\u003c/p\u003e\n\u003cp\u003eThis experiment used the event-related desynchronization/synchronization percentage \u0026nbsp;(ERD%/RES%) in the alpha and theta rhythms as a valid measure of mental workload. The increased mental workload results in higher ERD% for alpha rhythm as well as higher ERS% for theta rhythm. To make the comparison more intuitive, we used the negative ERD% in replace of ERS% for theta rhythm. The computational formula is \u003cem\u003eERD\u003c/em\u003e % = ( \u003cem\u003eA-R\u003c/em\u003e )/ \u003cem\u003eR\u003c/em\u003e \u0026times; 100, where \u003cem\u003eA\u003c/em\u003e denotes the band power of interest interval, and \u003cem\u003eR\u003c/em\u003e denotes the band power of the baseline or reference interval (Pfurtscheller \u0026amp; Lopes da Silva, 1999).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA 30-s segment of the baseline interval was collected before a subject started browsing short-form videos. By manually marking the switching time of short-form videos on the EEG recordings as a reference, other 30-s segments of the test interval were obtained for the short-form video going to display. ERD% values of alpha and theta brain wave rhythms were computed for each short-form video in each condition of each subject. The average ERD% values were then calculated for the two brain wave rhythms in each of the four conditions. Specifically, the final ERD% value of each experimental condition was the average for all subjects in that condition, where the average for three short-form videos for each subject was first calculated.\u003c/p\u003e\n\u003ch2\u003eProcedure\u003c/h2\u003e\n\u003ch3\u003ePrepare phase\u003c/h3\u003e\n\u003cp\u003eEach subject scheduled an individual time to participate in the experiment conducted in a quiet laboratory to prevent distraction. The laboratory setup provided consistent environmental conditions for all subjects. When the subject was comfortably seated at a table in a height-adjustable chair, a researcher would give an introduction to the experiment and equipment and inform the voluntary nature of their involvement. After that, subjects were asked to sign a consent form and complete demographic questions. The researcher then connected the EEG equipment, attaching disposable vinyl electrodes to the appropriate recording locations on the subject\u0026apos;s skull. Electrode impedance is limited to less than 10 k\u0026Omega;. The subjects were reminded to minimize unnecessary movements during the experimental task.\u003c/p\u003e\n\u003ch3\u003eTreatment phase\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003e\u0026nbsp;After placing all electrodes and turning on the EEG equipment, the subject closed her eyes on command and sat in a relaxed state until an appropriate and stable brainwave state was observed. A 30-second sample of baseline brainwaves was recorded before the subject was instructed to open her eyes and begin browsing short-form videos of one condition. At the end of the set of short-form videos, the subject was asked to complete a subjective assessment questionnaire and then changed to the next condition. The subject would close his eyes and relax for at least three minutes until the brainwaves returned to baseline. Then the baseline brainwave sample would be recorded again. The subject was then instructed to start the next task. Had finished viewing the four short-form video groups, the participants were offered a fruit gift and allowed to leave.\u003c/p\u003e\n\u003cp\u003eData Analysis\u003c/p\u003e\n\u003cp\u003eRegarding subjective measures, the scores for each item in the questionnaire were tallied in SPSS where the differences between each pair of task conditions for each item were examined using ANOVA. Regarding the EEG data, data preprocessing and analysis were performed using MATLAB and the EEGLAB toolbox. For preprocessing, EEG data were downsampled to 500 Hz and filtered using a bandpass of 1-60 Hz. To address motion artifacts, the EEG data were then processed by ICA to identify and remove independent components corresponding to noise. Among these, blinks and noisy channels with potentials exceeding \u0026plusmn;100 \u0026mu;V were manually rejected. The average rejection rate for all participants was 2.2%. The EEG data were then averaged and re-referenced .To calculate the mental workload, the EEG data of each test and baseline condition were analyzed in the frequency domain using the FFT (500 ms sliding window) and calculated the band power values (absolute values) for the alpha and theta frequency ranges (theta: 4-7 Hz, alpha: 10-13 Hz). The percentage change relative to the baseline in the power of the alpha and theta bands was calculated for each short-form video according to the ERD% formula by Pfurtscheller (Pfurtscheller \u0026amp; Lopes da Silva, 1999). The analyzed data was averaged for each participant in each condition. A two-way repeated measures ANOVA was conducted in order to test the effects of the cross-media type and content type conditions on mental workload.\u003c/p\u003e\n\u003ch2\u003eResults\u003c/h2\u003e\n\u003ch3\u003eEffects of media and content types on mental workload\u003c/h3\u003e\n\u003cp\u003eThis experiment explored the effects of media types (2D and VR) and media contents (scenery content and educational content) on the subjectively and objectively measured mental workload of elderly viewers. And the potential interactions between these two types were also included. Specifically, the dependent variables included three measures of mental workload: self-reported mental workload, ERD% values of theta (4-7 Hz), and alpha (10-13 Hz) brain wave rhythms. Table 4 describes the descriptive statistics of the dependent variables.\u003c/p\u003e\n\u003cp\u003eTable 4: Descriptive statistics for measures of mental workload\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"482\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedia\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContent\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubjective ratings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026theta;-ERD%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026alpha;-ERD%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eScenery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e12.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-13.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e44.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eEducational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e15.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-21.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e47.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003eVR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eScenery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e14.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-14.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e45.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eEducational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e14.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-19.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e46.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e5.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe results of the two-way repeated measures ANOVA (RM-ANOVA) for mental workload showed that the main effect of media type was not significant (p \u0026gt; 0.326). In contrast, the main effect of content type was significant (p \u0026lt; 0.019) with a large effect size (\u0026eta;\u0026sup2; \u0026gt; 0.408). The interaction effect between media type and content type was significant only for \u0026theta;-ERD% (F(1,11) = 5.291, p = 0.042, \u0026eta;\u0026sup2; = 0.325), also indicating a relatively large effect size (\u0026eta;\u0026sup2; = 0.325). However, the interaction effects for \u0026alpha;-ERD% and subjective ratings were not significant (p \u0026gt; 0.114). This suggests that the effect of content on \u0026theta;-band activity differs between media types. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA follow-up simple effects analysis for the interaction effect on \u0026theta;-ERD% revealed that the difference between media types was significant only for the educational content condition (\u0026Delta; = -1.985, p = 0.044), whereas no significant difference was found in the scenery content condition (p = 0.621). Specifically, in the educational content condition, VR media (M = -19.65; SD = 1.45) exhibited significantly higher \u0026theta;-ERD% than 2D media (M = -21.64; SD = 1.45).\u003c/p\u003e\n\u003cp\u003eTable 5. Two-way RM-ANOVA on three measures of mental workload\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026eta;\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 80px;\"\u003e\n \u003cp\u003eMedia Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026theta;-ERD%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.687\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.425\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.059\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026alpha;-ERD%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.015\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.906\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eSubjective ratings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.057\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.326\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.088\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 80px;\"\u003e\n \u003cp\u003eContent Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026theta;-ERD%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e15.194\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.580\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026alpha;-ERD%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e7.566\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.408\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eSubjective ratings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e8.729\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.442\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eMedia * Content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026theta;-ERD%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e5.287\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.325\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026alpha;-ERD%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2.590\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.136\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.191\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eSubjective ratings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2.940\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.114\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.211\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eRelationship between objective and subjective mental workload\u003c/h3\u003e\n\u003cp\u003eExperiments 2 were conducted to objectively address mental workload through processing the theta waves of EEG data from the frontal lobe (i.e., Fz) and the alpha waves from the occipital lobe (i.e., Pz), where we examined correlations between subjective and objective results. When valid EEG data from all subjects were considered together, subjective scores showed a significantly negative correlation with ERD% values of theta waves at Fz (r=-0.330, p=0.022) and a nonsignificant correlation with ERD% values of alpha waves at Pz (r=0.038, p=0.795).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to investigate through subjective and objective experiments the effects of VR on the flow and mental workload of elderly viewers when browsing short-form videos, in both of which media type (2D-VR) and content type (hedonic-utilitarian) had been controlled. In Experiment 1, a flow model of six factors (vividness, interactivity, telepresence, mental workload, flow, and satisfaction) was developed to explore the influences on the flow experience during browsing short-form videos in VR environments and whether it was consistent between different content. Experiment 2 used EEG to measure mental workload, which further demonstrated the effects of media type and content type on mental workload and eventually impact flow. Also, the EEG results showed some correlation with the subjective measures. Overall, the results of both experiments highlight that VR facilitates a flow experience for older viewers browsing short-form videos, only though with some content it can carry some mental workload. In the following, the main results will be discussed in further detail.\u003c/p\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eFlow in VR short-form video browsing\u003c/h2\u003e \u003cp\u003eAs Experiment 1 has shown, in the context of short-form video consumption, VR technology provides a higher level of vividness and interactivity than traditional 2D media, through which VR significantly enhances the telepresence and further facilitates the flow experience. This finding supports the theory that vividness and interactivity are determinants of telepresence (Steuer, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), as well as the related literature on the ability of VR to provide enhanced telepresence. In addition, the results indicated that telepresence is an important antecedent of the flow, regardless of whether the type of short-form video content is hedonic or utilitarian. This result supports the findings of Shin et al. (Chen, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Novak et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Shin, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kim \u0026amp; Ko, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This may also allow for the inference that VR technology in media consumption is relatively acceptable for older age groups with a large degree of similarity to traditional technologies such as 2D screens.\u003c/p\u003e \u003cp\u003eThe model also pointed out that the flow greatly increased short-form video viewers' satisfaction regardless of the medium. This result is in line with previous research on the beneficial effects of flow on satisfaction in different contexts. Therefore, we can draw the conclusion that, in VR short-form videos, the flow experience is a powerful predictor of user experience satisfaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eEffects of media and content types of short-form videos\u003c/h2\u003e \u003cp\u003eWe tested all the hypotheses respectively in scenery content and educational content, in which H1-H5 were supported. However, the hypothesis about the effect of mental workload on flow varied depending on the type of content. More specifically, when watching hedonic content, the mental workload perceived by viewers had almost no effect on flow. When watching utilitarian content, such as educational videos, the mental workload had an obstructive effect on flow. In Experiment 2, the group of educational content resulted in higher alpha ERD% and lower theta ERD% than that of scenery content for the same media type. However, the difference in EEG results between the VR media group and the 2D media group was not significant. Combined with EEG-based measures, it can be concluded that the mental workload perceived by elderly users mainly comes from the intrinsic load of the educational content. In the literature on cognitive theory, cognitive load has been categorized into intrinsic, extraneous, and germane loads (Paas \u0026amp; Van Merri\u0026euml;nboer, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Sweller, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), with intrinsic load playing a decisive role. And the intrinsic load is determined by the inherent complexity of the content. In contrast, the extrinsic load imposed by VR proved to be virtually unaffected. This result is consistent with Gold's findings (Gold \u0026amp; Windscheid, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It can be argued that VR short-form video is more suitable for displaying hedonic content for older adults which takes up fewer cognitive resources giving way to handling media. When providing utilitarian content, it is noted that the complexity of content has to be controlled away from exceeding the cognitive capacity of the elderly.\u003c/p\u003e \u003cp\u003eOn the other hand, in response to the difference in the degree of the flow devoting to satisfaction between content types (β\u003csub\u003escenery content\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;β\u003csub\u003eeducational content\u003c/sub\u003e), we suggest that when users browse utilitarian content, their satisfaction with the experience may also derive from the fulfillment they feel after absorbing useful information.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSubjective and objective measurements of mental workload\u003c/h3\u003e\n\u003cp\u003eIn this study, we used both subjective and objective methods to investigate the mental workload during browsing short-form VR videos. The subjective and objective results all showed that the mental workload showed significant differences between content types (scenery-educational content), while the differences between media types (2D-VR) were not significant. This suggests that the media type brings limited additional mental workload to the viewer relative to the content type. We can conclude that it is feasible to measure mental workload with the ERD/ERS% values of theta and alpha EEG waves (Y. Zhou et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; \u0026Ouml;r\u0026uuml;n \u0026amp; Akbulut, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Y. Wang, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, the correlation of subjective and objective results was also tested and only a significant correlation between ERD% of frontal theta waves and rating scales was reported in Experiment 2. This result could potentially be attributed to the alpha waves being more sensitive to the changes of workload than the retrospective reports (Scharinger, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), especially for the elderly who may have difficulty understanding questions or expressing subjective experiences. Still, considering the individual variability prevalent in current EEG experiments, we suggest a combination of subjective and objective measurements as an appropriate method for studying the mental workload.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003eTheoretical implications\u003c/h2\u003e \u003cp\u003eThis study contributes to the impact of VR on user experience, especially of the elderly groups. Although a large number of studies have demonstrated that users are more likely to experience flow experience in VR environments, the process of flow for older users had not received sufficient attention. In addition, VR, as an emerging media presentation form for short-form videos, has yet to be studied for the new experiences it brings. This study reveals how VR can influence the user experience over traditional media forms by focusing on the impact of its enhanced immersive experience and additional load on the flow for older users.\u003c/p\u003e \u003cp\u003eThis study further compared the differences in user experience when viewing short-form videos of different content types in a VR environment. Previous studies have demonstrated that employing VR media to present learning content to provide useful information (i.e., utilitarian-targeted content) facilitates a more focused absorption of knowledge. But it had not been experimentally demonstrated whether it performs the same effect on the absorption of older users who suffer from degraded cognitive abilities. Using short-form videos of educational content as an example, this study demonstrated that the load imposed by watching utilitarian content in VR had a negative effect on the flow for older adults. This result emphasizes the possible impact of the intrinsic load of the activities (especially with utilitarian goals) when studying older adults' user experience in VR environments.\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section4\"\u003e \u003ch2\u003ePractical implications\u003c/h2\u003e \u003cp\u003eProviding immersive experiences for older users in VR environments is challenging. This study offers some help by helping to understand how older users are immersed in VR environments and providing helpful evidence and design recommendations for designing the user experience in VR for older users.\u003c/p\u003e \u003cp\u003eThe study came up with a flow model on browsing short-form videos in VR environments aimed to understand what factors can facilitate or inhibit the flow experience. In order to promote the flow for older users in VR environments, there are three ways for VR designers: 1) increasing vividness, such as increasing the level of detail and richness of the images, and also enriching the experience from the sound; 2) increasing interactivity, such as making human-computer interactions in VR closer to natural interactions, so that the user forgets wearing a device; and 3) reducing the mental workload that may be perceived by the user, such as designing reasonable layout and clear navigation which can prevent users from getting lost in VR.\u003c/p\u003e \u003cp\u003eIn the comparison of different media types and different content types from this study, important design recommendations on balancing the media and content are made for VR developers. Specifically, when providing hedonic content, the load brought by VR is relatively limited. That means designers can focus more on immersing older users through enhanced vividness and interactivity. In contrast, when providing utilitarian content, designers need to be more attentive to avoiding additional load.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe conclude from the study that VR has the role of promoting flow experience, but it may also lead to additional mental workload. The results of the two experiments confirm that short-form VR videos, compared to traditional 2D short-form videos, offer enhanced telepresence through increased vividness and interactivity, thereby amplifying the flow experience. While the negative effects of mental workload on flow only work when watching educational content. And VR can only bring about a limited increase in mental workload. Despite some limitations, the current study provides new insights into the flow experience in VR environments, which not only makes a theoretical contribution to consumer behavior and digital media literature but also offers practical recommendations for media providers and VR developers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval\u003c/h2\u003e \u003cp\u003e In accordance with ethical standards and guidelines for research involving human subjects, all necessary ethics approvals have been obtained for the conduct of this study. The research protocol was reviewed and approved by Zhejiang University of Technology\u0026rsquo;s Research Ethics Committee (IRB Number: 2023D003).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Social Science Fund of China (No. 22CTQ016).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe authors confirm their contribution to the paper as follows: Conceptualization: [Zhichuan Tang], [Yingjia Ding]; Methodology: [Yingjia Ding], [Ruoshen Tang], [Nuo Chen]; Formal analysis and investigation: [Yingjia Ding]; Writing - original draft preparation: [Yingjia Ding], [Ruoshen Tang], [Nuo Chen]; Writing review and editing: [Zhichuan Tang], [Yingjia Ding]; Funding acquisition: [Zhichuan Tang]. Supervision: [Lufang Zhang].\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are not publicly available due to ongoing works but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlbatati B, Liu F, Wang S, Yu M (2023) Emotions and online gaming experiences: An examination of MMORPG gamers from India and the United States. 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Springer International Publishing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-78130-3_43\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-78130-3_43\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Virtual reality, short-form videos, elderly, flow experience, mental workload, telepresence","lastPublishedDoi":"10.21203/rs.3.rs-6286033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6286033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe development of virtual reality (VR) technology in recent years has the potential to meet the increasingly immersive entertainment needs of elderly users. This study explored the impact of the VR environment on the flow experience and mental workload of elderly users while watching short-form videos. Two experiments were conducted. Experiment 1 developed and tested a model for VR flow experience which examined the impact of VR environments on vividness, interactivity, telepresence, flow experience, mental workload, and satisfaction. Experiment 2 adopted EEG in addition to subjective questionnaires to further test some of hypotheses from Experiment 1. The results indicate that, compared to traditional 2D short-form videos, VR short-form videos offers an enhanced telepresence through increased vividness and interactivity, thereby amplifying the flow experience. But the impact of mental workload on flow varies depending on the type of content. The mental workload has little effect on flow when watching scenery content but significant negative effects when watching educational content.\u003c/p\u003e","manuscriptTitle":"The impact of VR environment for elderly short-form video viewers on flow and mental workload","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 20:02:38","doi":"10.21203/rs.3.rs-6286033/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"734f5338-4742-4295-b2bd-213350219152","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48499274,"name":"Humanities/Cultural and media studies"},{"id":48499275,"name":"Social science/Cultural and media studies"}],"tags":[],"updatedAt":"2026-04-29T18:24:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 20:02:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6286033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6286033","identity":"rs-6286033","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}