Eyestrains among smartphone users while watching videos in Taipei MRT carriages: A comparison between sitting and standing postures

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Our study investigated eyestrain in 48 young participants (24 men and 24 women) while viewing films on smartphones in MRT carriages. We examined two viewing postures (sitting and standing) and two durations (15 and 30 min), measuring critical flicker fusion frequency (CFF) reduction, visual fatigue scale (VFS) scores, and viewing distance (VD). The results indicated that the main effects of the independent variables were nearly all significant, with two-way interactions (sex x posture and posture x time) significantly affecting most responses. Women exhibited notable differences between postures, with higher CFF reduction when sitting compared to standing (3.47 Hz vs. 1.90 Hz; p < 0.001) and shorter VD when sitting compared to standing (25.5 cm vs. 34.3 cm; p < 0.001). Conversely, standing led to higher VFS scores for women compared to sitting (8.94 vs. 4.60; p < 0.001). This suggests that women may be more sensitive to motion sickness, particularly when standing in a moving MRT carriage. Men showed higher CFF reduction while standing compared to women ( p < 0.01), but no significant difference between sexes while sitting. Visual fatigue indices were significantly higher after 30 min of viewing compared to 15 min, with amplified effects on VFS score and VD. These findings support the recommendation that users should take a break after 20 min of smartphone use, even in MRT carriages. Although subjective fatigue may not always be perceived, watching videos while sitting in MRT carriages leads to unexpectedly high objective visual fatigue (i.e., CFF reduction), necessitating greater caution. Health sciences/Health care Health sciences/Risk factors MRT carriages smartphone use eyestrain critical flicker fusion frequency (CFF) visual fatigue scale (VFS) viewing distance Figures Figure 1 Figure 2 Introduction Research indicates a growing global trend in spending time on daily activities and media entertainment via smartphones1-3. The widespread use of smartphones has become a defining aspect of modern life, influencing both personal and professional spheres. In Taiwan, as of 2023, the average daily smartphone usage per person is 7.23 hours. Watching videos is one of the primary uses, with smartphones being the most commonly used device due to their mobility. The average daily video viewing time per person is 1.32 hours, and this number continues to rise annually4. While smartphones have introduced numerous conveniences and advantages to modern lifestyles, they have also brought about persistent challenges. These challenges, along with efforts to mitigate them, are increasingly being recognized as significant issues. Field observations and previous studies on neck and shoulder strain have considered various factors, such as user posture (seated, standing, and walking), hand usage (one-handed or two-handed), and participant demographics (including those with and without neck/shoulder pain). These studies have highlighted the distinct impact of these factors on head or neck flexion angles5-7. In modern society, smartphones have become indispensable tools for personal audio-visual enjoyment, with mobile viewing being particularly widespread. Watching films on smartphones is especially popular among young people, sometimes leading to addictive behaviors8. Unlike tasks such as browsing or texting, watching films on smartphones demands continuous eye fixation to follow the plot, leading to extended visual focus. This unique interaction style could introduce variations into previous research findings, particularly regarding eyestrain assessment. The increased sense of eye fixation and prolonged engagement required for smartphone film viewing necessitates a reevaluation of how eyestrain is measured and understood in these contexts. Digital eyestrain can be categorized into external and internal symptoms, as described by Sheedy et al.9 Internal symptoms include blurred vision, diplopia, fatigue, and headaches, resulting from strain on the eye's refractive and binocular vision systems. In contrast, external symptoms are primarily associated with dry eye syndrome. From an ergonomic perspective, the visual load from smartphone use — particularly focusing on internal symptoms experienced during films viewing in both sitting and standing postures within a dynamic environment like a Mass Rapid Transit (MRT) carriage — merits thorough investigation. Wu et al.10 found that using smartphones in dynamic stimulation scenarios significantly increased visual fatigue compared to static scenarios. To assess this, both objective measures such as critical flicker fusion frequency (CFF) reduction and subjective measures such as the visual fatigue scale (VFS) are applicable for comprehensive evaluation. The CFF is the point at which a flickering light source appears to be steady, illustrating the interplay between the eyes and the brain11. Despite some recent studies questioning its validity as a definitive measure of visual load12,13, CFF is still widely accepted for evaluating eyestrain and visual fatigue14-16. It has been effectively used to assess visual fatigue from smartphone use, with decreases in CFF after smartphone engagement indicating heightened visual and mental fatigue11. To gauge subjective visual fatigue, researchers often use the questionnaire developed by Heuer et al.17, a well-regarded tool in visual fatigue assessments18-20. Additionally, viewing distance (VD) — the distance between the eyes and the smartphone screen — plays a crucial role in visual fatigue. Prior studies consistently show that a shorter VD increases the demands on accommodation and vergence, thereby potentially worsening eyestrain symptoms21-23. Users aware of the risks of eyestrain often neglect to maintain a proper VD while immersed in their smartphone activities. Ho et al. 24 presented an application that reminds users to keep a certain distance from the device, demonstrating its effectiveness in encouraging safe usage habits. To alleviate eyestrain, maintaining a proper VD and limiting screen time are generally recommended25-27. However, further investigation is needed to understand how various smartphone usage scenarios, such as daily routines in MRT carriages, might affect these recommendations. The MRT is one of the most common forms of public transportation in big cities. Due to its smooth and punctual nature, many people use their smartphones while taking the MRT for activities such as socializing and watching videos. Watching videos, in particular, requires continuously focusing on the screen, which may lead to visual strain. While extensive research has examined neck and shoulder strain caused by forward head and neck/head flexion during smartphone use6,7,28, eyestrain remains a critical yet underexplored issue. Predominantly investigated through epidemiological and optical studies29-31, the ergonomic implications of eyestrain during smartphone use are insufficiently addressed. Previous research has occasionally considered VD alongside head and neck flexion angles6,7,32, but the absence of direct measures for visual fatigue renders these evaluations speculative. There is a crucial need for studies that incorporate explicit indicators of visual fatigue to comprehensively assess eyestrain associated with smartphone usage. More than 40% of Taipei MRT passengers use smartphones33, with an even higher proportion reported in the Delhi Metro, India34. However, these surveys were conducted several years ago. Given the advancements and increased availability of personal video and entertainment software, the actual observed proportion of smartphone usage is likely higher today. Building on these considerations, this study explored the impact of smartphone viewing activities on visual fatigue through a comparative analysis. The study hypothesized that fixating on a smartphone screen, especially during dynamic activities like standing in a relatively unstable environment, could potentially heighten eyestrain compared to sitting. This increase in visual load might result from the higher demands on the eyes' accommodation and vergence while standing. Additionally, it was postulated that visual load would intensify with longer viewing times, as exemplified by the 30-min duration. Materials and Methods To assess the visual load when viewing videos on smartphones in MRT carriages, a group of participants (24 men and 24 women) engaged in simulated smartphone interactions for either 15 or 30 min, adopting either a sitting or standing posture. Data collection included reductions in CFF, self-reported VFS scores, and VD. These metrics were employed to evaluate the impact of mobile video viewing on eyestrain. This study received ethical approval from the Ethics Committee of National Taiwan University in Taiwan (Approval Code: NTU-REC 202312EM051), affirming its adherence to ethical standards and guidelines. All testing processes were carried out in accordance with the relevant guidelines and regulations of the 2013 World Medical Association Declaration of Helsinki. Informed consent was obtained from all participants and attested for publication of the identifying information/images in an online open-access publication. Participants In this study, we recruited 24 individuals, equally divided by sex, with no history of musculoskeletal disorders or visual impairments. The selection criteria required a minimum of one year of daily smartphone usage for at least 3 hours, based on prior research. Among male participants, the average (± standard deviation) age, height, and body mass were 21.2 (± 1.3) years, 173.6 (± 4.6) cm, and 69.8 (± 9.8) kg, respectively. Female participants had an average age, height, and body mass of 21.7 (± 1.1) years, 160.2 (± 3.5) cm, and 53.8 (± 8.2) kg, as detailed in Table 1 . Notably, the male participants were significantly taller and heavier than the female participants ( p < 0.01). However, baseline CFFs were consistent across both sexes, averaging 38.8 Hz for males and 39.1 Hz for females. This CFF range aligned with reported typical values for adults, which fall between 35 and 40 Hz11. Prior to data collection, participants received a thorough explanation of the testing protocol and provided informed consent using a consent form. Table 1 Basic information of male and female participants in the study Items Males (n = 24) Females (n = 24) Differences p Age (years) 21.2 (1.3) 0.3 21.7 (1.1) -0.5 0.544 Stature (cm) 173.6 (4.6) 160.2 (3.5) 13.4 < 0.001 Body mass (kg) 69.8 (9.8) 53.8 (8.2) 16.0 < 0.001 Critical fusion frequency (Hz) 38.8 (2.6) 39.1 (1.7) -0.3 0.862 Note: Data (mean, with standard deviation in parentheses) were examined between sexes by independent t test. The CFF measurement The CFF is a well-recognized metric used to gauge eyestrain, where a reduction in CFF value corresponds to increased eye fatigue35,36. We measured CFF using a Handy Flicker device (Handy Flicker HF-II, Neitz, Tokyo, Japan). The ascending and descending thresholds were meticulously recorded and then averaged to establish the pre- and post-task CFF for each measurement. This process of measuring CFF was repeated twice, and the resulting values were averaged for precision. During each test, the flicker frequency was systematically increased from a minimum threshold of 20 Hz until participants consistently perceived the presented light stimuli as stable. This threshold, as perceived by participants, provided insights into the critical frequency, indicating the highest frequency at which participants no longer perceived flickering. Following this, the frequency was gradually decreased until participants indicated that they once again perceived the presented light stimuli as flickering or vibrating, following the methodology outlined by Gautam and Vinay11. Subjective visual fatigue rating In this study, the VFS score was used to assess the eyestrain after viewing the films in a requested testing condition17. The VFS questionnaire comprises six items: ( 1 ) It is hard for me to see. ( 2 ) I have a strange feeling around my eyes. ( 3 ) My eyes feel tired. ( 4 ) I feel numb. ( 5 ) I feel dizzy looking at the screen. ( 6 ) I have a headache. Participants rated these questions on a 10-point scale, where 1 indicated "not at all" and 10 indicated "extremely serious." The scores for the six items were then averaged to provide an overview of the severity of experienced visual fatigue. In our analysis, we considered the change in the VFS resulting from the undertaken film viewing activity. Specifically, the fatigue score recorded at the beginning of each testing session was regarded as the baseline against which subsequent measurements were compared. The VD measurements Since this study was conducted in actual MRT carriages, it lacked the controlled environment of a laboratory. To mitigate this, we pre-selected a fixed position in the carriage for conducting the sitting and standing experiments. During the pilot test, we positioned a camera 2 m away from the vertical sagittal plane of the participant. We then used the camera's controlled parameters to establish the dimensional ratio on the participant's sagittal plane. After calibration, this ratio was employed as the basis for measuring the VD during the experiments, estimating the actual VD from the size obtained on the image. To derive VD, we meticulously captured symmetrical sagittal images and utilized CorelDRAW (Corel Co., Graphics Suite, 2023) for precise digital markings. The experimenters identified the participant's eyeball (E) and the midpoint of the phone's length (M) on the digital images to calculate VD. In the pilot test, the discrepancy between actual and estimated VD was a mere 0.6 cm, showcasing an acceptable accuracy that underscored the quality of our estimations. Experimental design and procedure This study collected data on CFF reduction, VFS scores, and VD measurements across four distinct smartphone-viewing trials. These trials combined two viewing postures (sitting and standing) and two viewing durations (15 min and 30 min) for 48 young participants. The 30-min viewing duration aligns with Leung et al.1, who asked participants to watch a movie on a smartphone while walking on a treadmill or sitting in a chair, though they did not measure intermediate times. In Taipei, the average one-way commuting time is 32.7 min, totaling about 1 hr daily37. In each trial, CFF and VFS assessments were conducted at the start and end of the viewing session. Participants watched four films in four different combinations of posture and duration, each performed in separate sessions to avoid accumulating fatigue. These combinations were arranged in a randomized sequence. Participants used their own smartphones with the assigned movies pre-loaded before the experiments. To avoid distorting the testing situation compared to the real world, participants were allowed to carry their usual commuting backpacks. However, backpacks that were too large or heavy, potentially altering body posture, were excluded from the study. Each trial lasted 15 or 30 min, with VD data recorded during the final 1-min interval at 15-s intervals. These values were averaged for analysis. To minimize errors and participant fatigue, a minimum 10-min resting period was imposed between trials. The Taipei MRT carriage typically maintains a temperature of 24°C, with an average speed of 35 km/hr and a maximum speed of 80 km/hr. The average illumination in the carriage at 100 cm above the floor is over 250 lx, with a minimum of 200 lx38. Statistical analysis The collected data from the study underwent thorough analysis using SPSS 23.0 statistical software (IBM Corp., Armonk, NY, USA), with a significance level of 0.05 for all tests. The primary objective was to examine the impacts of participant sex (men and women), viewing posture (sitting and standing) and duration (15 min and 30 min) on the measured variables (CFF reduction, VFS, and VD) through a three-way ANOVA. In the analysis, participant sex was designated as a between-subject factor, while posture and duration were considered within-subject factors. Post-hoc comparisons were performed using independent t-tests to uncover significant differences between groups. To assess the practical significance of any identified independent variables, power values were calculated following Cohen's established guidelines39. An effect size of ≥ 0.2 signifies a small effect, ≥ 0.5 represents a medium effect, and ≥ 0.8 indicates a large effect. Prior to conducting the analyses, the Kolmogorov-Smirnov test was utilized to evaluate the alignment of numerical variables with the normal distribution. Additionally, Levene's test was employed to examine the equality of variances, ensuring the robustness of the analytical framework. Results The results of the Kolmogorov-Smirnov test indicated that the collected data, both for the entire group and subgroups, followed a normal distribution ( p > 0.05). Similarly, Levene's test demonstrated homogeneity in the data ( p > 0.05). These findings confirmed that the data met the assumptions necessary for subsequent ANOVAs. Table 2 presents the findings of the three-way ANOVA, showing the effects of participant sex, viewing posture, and viewing durations as independent variables. The analysis indicated a significant influence for almost all responses studied. However, due to the significant two-way interaction effects between sex and posture, as well as posture and time, the main effects of the independent variables require further cross-analyses for confirmation. Table 2 Three-way ANOVA results for all responses Variables Responses SS DF MS F p Power Sex (S) CFF reduction 2.08 1 2.08 1.19 0.276 0.192 Visual fatigue score 145.26 1 145.26 12.14 < 0.01 0.934 Viewing distance 3400.33 1 3400.33 193.30 < 0.001 1.000 Posture (P) CFF reduction 35.88 1 35.88 20.53 < 0.001 0.995 Visual Fatigue score 382.51 1 382.51 31.98 < 0.001 1.000 Viewing distance 1150.52 1 1150.52 65.40 < 0.001 1.000 Time (T) CFF reduction 66.51 1 66.51 38.05 < 0.001 1.000 Visual fatigue score 772.01 1 772.01 64.54 < 0.001 1.000 Viewing distance 468.75 1 468.75 26.65 < 0.001 0.999 S×P CFF reduction 24.08 1 24.08 13.78 < 0.001 0.958 Visual fatigue score 109.51 1 109.51 9.16 < 0.01 0.853 Viewing distance 841.69 1 841.69 47.85 < 0.001 1.000 S×T CFF reduction 1.33 1 1.33 0.76 0.384 0.140 Visual fatigue score 13.55 1 13.55 1.13 0.289 0.185 Viewing distance 2.08 1 2.08 0.12 0.731 0.064 P×T CFF reduction 1.51 1 1.51 0.86 0.355 0.152 Visual fatigue score 64.17 1 64.17 5.37 < 0.05 0.635 Viewing distance 82.69 1 82.69 4.70 < 0.05 0.578 S×P×T CFF reduction 6.75 1 6.75 3.86 0.051 0.489 Visual fatigue score 3.26 1 3.26 0.27 0.603 0.081 Viewing distance 9.19 1 9.19 0.52 0.471 0.111 Note: CFF, critical flicker fusion frequency. Figure 1 compares three responses for two postures across genders. In general, men exhibited lower subjective fatigue scores and longer VD than women, with nonsignificant posture effects on the responses (all p > 0.05). Conversely, posture effects were significant for women (all p < 0.001). Women showed greater reductions in CFF and shorter VDs when sitting, but experienced lower subjective fatigue levels when standing. These opposing results warrant further discussion. Figure 2 illustrates the interaction between posture and time. As viewing time increased from 15 to 30 min, fatigue levels rose and VD decreased. Additionally, the differences in VFS score and VD between postures became more pronounced with the extension to 30 min. Discussion The growing trend of watching videos on smartphones in MRT carriages necessitates an understanding of how various postures and viewing durations affect eyestrain, critical for user comfort and well-being. Unlike browsing or texting, video watching presents unique challenges due to its continuous narrative, making prior research outcomes potentially less applicable. This insight led us to investigate a crucial yet often overlooked aspect of modern digital entertainment: the risk of eyestrain during smartphone video viewing in different postures and between sexes on Taipei's MRT. Our findings unexpectedly revealed that the visual load generated by watching videos while standing may not be higher than that caused by sitting in MRT carriages. The impact of viewing posture on objective eyestrain differs between sexes, with prolonged viewing exacerbating the strain. While watching videos in MRT carriages, women reported greater eyestrain while standing ( p < 0.001), whereas objective measurements (CFF reduction) showed increased strain when sitting ( p < 0.001). Interestingly, this posture-related eyestrain was significant only among female participants, indicating distinct viewing behaviors between sexes. VD emerged as a key factor, suggesting that eyestrain in MRT carriages is predominantly influenced by how long videos are watched. Furthermore, our study identified a potentially dangerous oversight: the assumption that sitting is visually less demanding might lead users to underestimate the strain on their eyes. Compared to the objective measure of CFF reduction, the subjective VFS measure did not consistently reflect the heightened eyestrain reported by female participants. This discrepancy points to an unrecognized visual load. These findings underscore an essential message: to mitigate eyestrain, especially during extended viewing sessions, it may be necessary to reconsider the common practice of watching videos on smartphones in MRT carriages. Our study found that in moving MRT carriages, objective visual eyestrain, measured by the CFF reduction, was significantly greater when watching videos in a sitting position compared to a standing position ( p < 0.001, Table 2 ). Contrary to common assumptions, this phenomenon was observed only in female participants (Fig. 1 ). This may be attributed to the shorter VD for females when sitting and viewing videos on smartphones. A shorter VD, especially on mobile devices, demands higher vergence and accommodation responses, leading to tension in the extraocular, ciliary, and pupillary muscles24,40. This tension is a primary factor in digital eyestrain21-23. Near vision tasks are unnatural for the eyes, which have evolved to focus on distant objects where the eye muscles are more relaxed41. The difference in VD between the two viewing postures could be related to the specific environment of MRT carriages. When sitting, the relatively stable body posture allows for closer focus on the phone screen. Conversely, when standing, participants had to hold the ring handle with one hand and their smartphones with the other, resulting in a less balanced body position and difficulty in bringing the smartphone closer to the eyes. This standing posture required more effort from muscles like the trapezius and biceps brachii to maintain, leading to a trade-off between viewing clarity and hand fatigue, and consequently a larger VD than when sitting42. Additionally, standing required participants to pay attention to the movements of passengers around them, preventing continuous focus on the smartphones and thereby reducing the load on the eye muscles and alleviating fatigue43,44. As shown in Fig. 1 , sex differences in VD can be partly attributed to variations in average body size, specifically the relatively shorter forearm length of females, leading to a shorter VD compared to males45. Additionally, the viewing posture influenced VD, with females exhibiting a shorter VD when sitting than when standing. Before the advent of PCs, smartphones, and tablets, the ideal reading distance was determined by the Harmon method, which involves making a fist, holding it to one's cheek, and measuring the distance from the elbow to the eyes. For adults, this Harmon distance is typically 36-41 cm24. Engaging in near-vision tasks within this distance, such as reading and writing, can cause eyestrain or headaches46, and this distance is also recommended for smartphone use24. In Taiwan, the average difference in forearm length between sexes among youths is approximately 3.5 cm47, which aligns with the sex difference in VD observed in the sitting position (Fig. 1 ). However, the relatively shorter VD observed in women in our study may also be attributed to postural differences between sexes. Women may tend to hold their phones closer to their eyes, resulting in a shorter VD and higher visual load (Fig. 1 ). Korakakis et al.48 found that women consistently adopt more upright postures than men in standing scenarios, which may also explain the significant difference in VD between the two viewing postures for female participants. We noted that females exhibited opposite trends in visual fatigue between the two smartphone viewing postures. Objective CFF reductions indicated less fatigue in a sitting position, whereas subjective VFS scores indicated higher fatigue. Regarding the general usage of visual devices, smartphones are among the most frequently used for dynamic visual content in moving vehicles, such as MRT carriages49-51. In general, visual fatigue, which arises from the discrepancy between accommodation and convergence52, causes eye strain, focus difficulty, headaches53, and motion sickness, known as visually induced motion sickness (VIMS)54,55. In our study, the higher subjective fatigue levels observed in standing positions may reflect more severe VIMS compared to sitting. Furthermore, females have been reported to be more susceptible to VIMS and experience greater discomfort than males56-58, which may explain why males exhibited fewer VIMS symptoms compared to females. Although recent studies have not definitively confirmed CFF as a valid measure for all usage situations12-13, CFF continues to be widely recognized as a reliable tool for assessing eyestrain and visual fatigue14-16. Additionally, CFF has recently found utility in evaluating visual fatigue resulting from smartphone use. In our analyses, when the smartphone viewing time was extended from 15 min to 30 min, both subjective and objective visual fatigue indicators increased significantly in both viewing postures, while VD decreased. Additionally, we observed that both VFS and VD differences further expanded (Fig. 2 ). The 20-20-20 rule recommends people to take their eyes off screens every 20 min and look at something 20 feet away for at least 20 s27,59. This rule suggests that 20 min may be the time limit for a visual fixation task, indicating the need for breaks to relieve visual load. The results of our study on watching videos in MRT carriages are consistent with the 20-20-20 rule, supporting its recommendation for reducing visual fatigue. This study has several limitations. Firstly, we recruited 48 young men and women as participants, limiting the generalizability of our findings to other demographic groups, such as children and the elderly, even though young individuals comprise the largest segment of smartphone users in Taipei MRT carriages. Secondly, the durations of smartphone viewing (15 and 30 min) used in our study do not reflect actual daily usage patterns, posing challenges in extrapolating our results to real-world scenarios. Moreover, the degree of smartphone usage addiction across different sexes was not rigorously controlled, and the selection of the videos used in this study might carry inherent limitations, warranting further exploration. During video viewing in MRT carriages, fluctuating environmental factors (including lighting and crowding of passengers around the users) could influence visual fatigue, emphasizing the need to include these factors in future research. Finally, it must be noted that this study was conducted in Taipei MRT carriages. The design and environment of MRT carriages in different regions and countries vary, which may also cause differences in study results. Conclusions This study explored the assessment of visual fatigue caused by watching videos on smartphones in Taipei MRT carriages. The independent variables included participant sex, viewing posture, and time duration. The unique environment of MRT carriages may negatively impact users' behavior while looking at their smartphones. Overall, the study found that objective visual fatigue was higher in the sitting position than in the standing position, contrary to expectations. Additionally, the objective and subjective visual fatigue levels of women in different postures were exactly opposite, likely due to the interaction between shorter VD and VIMS. The study also showed that viewing videos for 30 min caused higher visual strain than viewing for 15 min, suggesting that the 20-20-20 rule for visual activity may also apply in MRT carriages. Declarations Competing interests Authors have no financial or non-financial interests to disclose. Funding This research was partially supported by the National Science and Technology Council (NSTC), Taiwan (grant number 113-2221-E-131-029-MY3), and the APC was also funded by NSTC. Author Contribution Y-L.C provided the conceptualization. Y-L.C and H-T.N designed the investigation. K-H.C, P-C.H, and C-T.H performed the experiment, supervised data acquisition, and analyzed data. H-T.N wrote the original draft. Y-L.C reviewed and edited the final manuscript. All authors have read and agreed to the published version of the manuscript. Acknowledgments The authors would like to thank all participants for their contributions to the experiment. Data Availability The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request. Ethics declaration This research was approved by the Ethics Committee of National Taiwan University, Taiwan (protocol code NTU-REC 202312EM051) and was conducted according to the guidelines of the Declaration of Helsinki. Other ethical criteria included written consent to participate in the study and withdraw from the study whenever participants were willing. References Leung, T.W., Chan, C.T., Lam, C.H., Tong, Y.K. & Kee, C.S. 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Korakakis, V., O’Sullivan, K., Whiteley, R., O’Sullivan, P.B., Korakaki, A., Kotsifaki, A., et al. Notions of “optimal” posture are loaded with meaning. Perceptions of sitting posture among asymptomatic members of the community. Musculoskelet. Sci. Pract. 51, 102310 (2021). Cha, Y.H., Golding, J.F., Keshavarz, B., Furman, J., Kim, J.S., Lopez-Escamez, J.A. et al. Motion sickness diagnostic criteria: Consensus document of the classification committee of the Bárány society. J. Vestib. Res. 31, 327–344 (2021). Xie, W., He, D. & Wu, G. Inducers of motion sickness in vehicles: A systematic review of experimental evidence and meta-analysis. Transp. Res. F: Traffic Psychol. Behav. 99, 167–188 (2023). Keshavarz, B., Murovec, B., Mohanathas, N. & Golding, J.F. The visually induced motion sickness susceptibility questionnaire (VIMSSQ): estimating individual susceptibility to motion sickness-like symptoms when using visual devices. Hum. Factors 65, 107–124 (2023). 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The role of lifestyle factors, biological sex, and racial identity for (visually induced) motion sickness susceptibility: Insights from an online survey. Displays 82, 102666 (2024). Talens-Estarelles, C., Cerviño, A., García-Lázaro, S., Fogelton, A., Sheppard, A. & Wolffsohn, J.S. The effects of breaks on digital eye strain, dry eye and binocular vision: Testing the 20-20-20 rule. Cont. Lens Anterior Eye 46, 101744 (2023). Additional Declarations No competing interests reported. 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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-4829016","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":344837304,"identity":"33040788-7d73-43f5-ae9c-01c5ea1104bf","order_by":0,"name":"Yi-Lang Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACdgY2IGnDwMDM3AATM8CvhRmsJQ3IYARrkYBoSSCo5TAQE6vF4DDzswcfd5yP5m9nbGC6UXO4joG9eZsE44/DeLSwmRvOPHM7d8ZhxgbmnGOHJRh4jpVJMCTg08LDJs3bdju3AaQFSEowSOSYAbXcxq/lb9u53PlwLfJviNDC2HYgdwPCFh78WiQPs5lJ9rYl524Eajmccyxdso0nrdgiIe0/Ti18x5ufSfxss8udd/7wwcc5Ndb8/OyHN974YJOGU4vCASQOmA2KJrwxKd+AR3IUjIJRMApGARgAAFngUVoik7FWAAAAAElFTkSuQmCC","orcid":"","institution":"Ming Chi University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Yi-Lang","middleName":"","lastName":"Chen","suffix":""},{"id":344837305,"identity":"8a57f61e-e51e-40b5-8309-9defa2322aef","order_by":1,"name":"Kuo-Hao Chu","email":"","orcid":"","institution":"Ming Chi University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Kuo-Hao","middleName":"","lastName":"Chu","suffix":""},{"id":344837306,"identity":"d1993ebf-a666-480e-8a8a-3dedbfc4fe44","order_by":2,"name":"Po-Chun Huang","email":"","orcid":"","institution":"Ming Chi University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Po-Chun","middleName":"","lastName":"Huang","suffix":""},{"id":344837307,"identity":"6be71dcc-6a26-490f-afe5-84b083347b7f","order_by":3,"name":"Chieh-Ting Ho","email":"","orcid":"","institution":"Ming Chi University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Chieh-Ting","middleName":"","lastName":"Ho","suffix":""},{"id":344837308,"identity":"35e8df0e-5f7b-4a20-9005-db95eddaf759","order_by":4,"name":"Hong-Tam Nguyen","email":"","orcid":"","institution":"Ming Chi University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Hong-Tam","middleName":"","lastName":"Nguyen","suffix":""}],"badges":[],"createdAt":"2024-07-30 13:05:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4829016/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4829016/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-76334-9","type":"published","date":"2024-10-25T15:57:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64238589,"identity":"ca4145b4-1161-4de1-9a0d-26118ff95ea7","added_by":"auto","created_at":"2024-09-10 17:07:29","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":432889,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction effects of sex and posture on the three investigated responses.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4829016/v1/5cbb9efd4b676fae3019e8ab.jpeg"},{"id":64238588,"identity":"98486366-facd-432d-9b3d-7c89e1d1cc2b","added_by":"auto","created_at":"2024-09-10 17:07:28","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":480940,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction effects of posture and viewing time on the three investigated responses.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4829016/v1/a74111c717f4f623f32b082b.jpeg"},{"id":67681853,"identity":"249af9dd-1eec-4d86-8ad8-0aa92879cdd8","added_by":"auto","created_at":"2024-10-28 16:10:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1412069,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4829016/v1/fd3ca534-39f1-4a24-8a60-72d9bbf8f800.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Eyestrains among smartphone users while watching videos in Taipei MRT carriages: A comparison between sitting and standing postures","fulltext":[{"header":"Introduction","content":"\u003cp\u003eResearch indicates a growing global trend in spending time on daily activities and media entertainment via smartphones1-3. The widespread use of smartphones has become a defining aspect of modern life, influencing both personal and professional spheres. In Taiwan, as of 2023, the average daily smartphone usage per person is 7.23 hours. Watching videos is one of the primary uses, with smartphones being the most commonly used device due to their mobility. The average daily video viewing time per person is 1.32 hours, and this number continues to rise annually4. While smartphones have introduced numerous conveniences and advantages to modern lifestyles, they have also brought about persistent challenges. These challenges, along with efforts to mitigate them, are increasingly being recognized as significant issues.\u003c/p\u003e \u003cp\u003eField observations and previous studies on neck and shoulder strain have considered various factors, such as user posture (seated, standing, and walking), hand usage (one-handed or two-handed), and participant demographics (including those with and without neck/shoulder pain). These studies have highlighted the distinct impact of these factors on head or neck flexion angles5-7. In modern society, smartphones have become indispensable tools for personal audio-visual enjoyment, with mobile viewing being particularly widespread. Watching films on smartphones is especially popular among young people, sometimes leading to addictive behaviors8. Unlike tasks such as browsing or texting, watching films on smartphones demands continuous eye fixation to follow the plot, leading to extended visual focus. This unique interaction style could introduce variations into previous research findings, particularly regarding eyestrain assessment. The increased sense of eye fixation and prolonged engagement required for smartphone film viewing necessitates a reevaluation of how eyestrain is measured and understood in these contexts.\u003c/p\u003e \u003cp\u003eDigital eyestrain can be categorized into external and internal symptoms, as described by Sheedy et al.9 Internal symptoms include blurred vision, diplopia, fatigue, and headaches, resulting from strain on the eye's refractive and binocular vision systems. In contrast, external symptoms are primarily associated with dry eye syndrome. From an ergonomic perspective, the visual load from smartphone use \u0026mdash; particularly focusing on internal symptoms experienced during films viewing in both sitting and standing postures within a dynamic environment like a Mass Rapid Transit (MRT) carriage \u0026mdash; merits thorough investigation. Wu et al.10 found that using smartphones in dynamic stimulation scenarios significantly increased visual fatigue compared to static scenarios. To assess this, both objective measures such as critical flicker fusion frequency (CFF) reduction and subjective measures such as the visual fatigue scale (VFS) are applicable for comprehensive evaluation.\u003c/p\u003e \u003cp\u003eThe CFF is the point at which a flickering light source appears to be steady, illustrating the interplay between the eyes and the brain11. Despite some recent studies questioning its validity as a definitive measure of visual load12,13, CFF is still widely accepted for evaluating eyestrain and visual fatigue14-16. It has been effectively used to assess visual fatigue from smartphone use, with decreases in CFF after smartphone engagement indicating heightened visual and mental fatigue11.\u003c/p\u003e \u003cp\u003eTo gauge subjective visual fatigue, researchers often use the questionnaire developed by Heuer et al.17, a well-regarded tool in visual fatigue assessments18-20. Additionally, viewing distance (VD) \u0026mdash; the distance between the eyes and the smartphone screen \u0026mdash; plays a crucial role in visual fatigue. Prior studies consistently show that a shorter VD increases the demands on accommodation and vergence, thereby potentially worsening eyestrain symptoms21-23. Users aware of the risks of eyestrain often neglect to maintain a proper VD while immersed in their smartphone activities. Ho et al. 24 presented an application that reminds users to keep a certain distance from the device, demonstrating its effectiveness in encouraging safe usage habits. To alleviate eyestrain, maintaining a proper VD and limiting screen time are generally recommended25-27. However, further investigation is needed to understand how various smartphone usage scenarios, such as daily routines in MRT carriages, might affect these recommendations.\u003c/p\u003e \u003cp\u003eThe MRT is one of the most common forms of public transportation in big cities. Due to its smooth and punctual nature, many people use their smartphones while taking the MRT for activities such as socializing and watching videos. Watching videos, in particular, requires continuously focusing on the screen, which may lead to visual strain. While extensive research has examined neck and shoulder strain caused by forward head and neck/head flexion during smartphone use6,7,28, eyestrain remains a critical yet underexplored issue. Predominantly investigated through epidemiological and optical studies29-31, the ergonomic implications of eyestrain during smartphone use are insufficiently addressed. Previous research has occasionally considered VD alongside head and neck flexion angles6,7,32, but the absence of direct measures for visual fatigue renders these evaluations speculative. There is a crucial need for studies that incorporate explicit indicators of visual fatigue to comprehensively assess eyestrain associated with smartphone usage.\u003c/p\u003e \u003cp\u003eMore than 40% of Taipei MRT passengers use smartphones33, with an even higher proportion reported in the Delhi Metro, India34. However, these surveys were conducted several years ago. Given the advancements and increased availability of personal video and entertainment software, the actual observed proportion of smartphone usage is likely higher today. Building on these considerations, this study explored the impact of smartphone viewing activities on visual fatigue through a comparative analysis. The study hypothesized that fixating on a smartphone screen, especially during dynamic activities like standing in a relatively unstable environment, could potentially heighten eyestrain compared to sitting. This increase in visual load might result from the higher demands on the eyes' accommodation and vergence while standing. Additionally, it was postulated that visual load would intensify with longer viewing times, as exemplified by the 30-min duration.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eTo assess the visual load when viewing videos on smartphones in MRT carriages, a group of participants (24 men and 24 women) engaged in simulated smartphone interactions for either 15 or 30 min, adopting either a sitting or standing posture. Data collection included reductions in CFF, self-reported VFS scores, and VD. These metrics were employed to evaluate the impact of mobile video viewing on eyestrain. This study received ethical approval from the Ethics Committee of National Taiwan University in Taiwan (Approval Code: NTU-REC 202312EM051), affirming its adherence to ethical standards and guidelines. All testing processes were carried out in accordance with the relevant guidelines and regulations of the 2013 World Medical Association Declaration of Helsinki. Informed consent was obtained from all participants and attested for publication of the identifying information/images in an online open-access publication.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eIn this study, we recruited 24 individuals, equally divided by sex, with no history of musculoskeletal disorders or visual impairments. The selection criteria required a minimum of one year of daily smartphone usage for at least 3 hours, based on prior research. Among male participants, the average (\u0026plusmn;\u0026thinsp;standard deviation) age, height, and body mass were 21.2 (\u0026plusmn;\u0026thinsp;1.3) years, 173.6 (\u0026plusmn;\u0026thinsp;4.6) cm, and 69.8 (\u0026plusmn;\u0026thinsp;9.8) kg, respectively. Female participants had an average age, height, and body mass of 21.7 (\u0026plusmn;\u0026thinsp;1.1) years, 160.2 (\u0026plusmn;\u0026thinsp;3.5) cm, and 53.8 (\u0026plusmn;\u0026thinsp;8.2) kg, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Notably, the male participants were significantly taller and heavier than the female participants (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, baseline CFFs were consistent across both sexes, averaging 38.8 Hz for males and 39.1 Hz for females. This CFF range aligned with reported typical values for adults, which fall between 35 and 40 Hz11. Prior to data collection, participants received a thorough explanation of the testing protocol and provided informed consent using a consent form.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic information of male and female participants in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMales (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemales (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDifferences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.2 (1.3)\u003c/p\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.7 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStature (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173.6 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160.2 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.8 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.8 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical fusion frequency (Hz)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.8 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Data (mean, with standard deviation in parentheses) were examined between sexes by independent t test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eThe CFF measurement\u003c/h2\u003e \u003cp\u003eThe CFF is a well-recognized metric used to gauge eyestrain, where a reduction in CFF value corresponds to increased eye fatigue35,36. We measured CFF using a Handy Flicker device (Handy Flicker HF-II, Neitz, Tokyo, Japan). The ascending and descending thresholds were meticulously recorded and then averaged to establish the pre- and post-task CFF for each measurement. This process of measuring CFF was repeated twice, and the resulting values were averaged for precision.\u003c/p\u003e \u003cp\u003eDuring each test, the flicker frequency was systematically increased from a minimum threshold of 20 Hz until participants consistently perceived the presented light stimuli as stable. This threshold, as perceived by participants, provided insights into the critical frequency, indicating the highest frequency at which participants no longer perceived flickering. Following this, the frequency was gradually decreased until participants indicated that they once again perceived the presented light stimuli as flickering or vibrating, following the methodology outlined by Gautam and Vinay11.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSubjective visual fatigue rating\u003c/h2\u003e \u003cp\u003eIn this study, the VFS score was used to assess the eyestrain after viewing the films in a requested testing condition17. The VFS questionnaire comprises six items: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) It is hard for me to see. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) I have a strange feeling around my eyes. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) My eyes feel tired. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) I feel numb. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) I feel dizzy looking at the screen. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) I have a headache. Participants rated these questions on a 10-point scale, where 1 indicated \"not at all\" and 10 indicated \"extremely serious.\" The scores for the six items were then averaged to provide an overview of the severity of experienced visual fatigue. In our analysis, we considered the change in the VFS resulting from the undertaken film viewing activity. Specifically, the fatigue score recorded at the beginning of each testing session was regarded as the baseline against which subsequent measurements were compared.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eThe VD measurements\u003c/h2\u003e \u003cp\u003eSince this study was conducted in actual MRT carriages, it lacked the controlled environment of a laboratory. To mitigate this, we pre-selected a fixed position in the carriage for conducting the sitting and standing experiments. During the pilot test, we positioned a camera 2 m away from the vertical sagittal plane of the participant. We then used the camera's controlled parameters to establish the dimensional ratio on the participant's sagittal plane. After calibration, this ratio was employed as the basis for measuring the VD during the experiments, estimating the actual VD from the size obtained on the image. To derive VD, we meticulously captured symmetrical sagittal images and utilized CorelDRAW (Corel Co., Graphics Suite, 2023) for precise digital markings. The experimenters identified the participant's eyeball (E) and the midpoint of the phone's length (M) on the digital images to calculate VD. In the pilot test, the discrepancy between actual and estimated VD was a mere 0.6 cm, showcasing an acceptable accuracy that underscored the quality of our estimations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eExperimental design and procedure\u003c/h2\u003e \u003cp\u003eThis study collected data on CFF reduction, VFS scores, and VD measurements across four distinct smartphone-viewing trials. These trials combined two viewing postures (sitting and standing) and two viewing durations (15 min and 30 min) for 48 young participants. The 30-min viewing duration aligns with Leung et al.1, who asked participants to watch a movie on a smartphone while walking on a treadmill or sitting in a chair, though they did not measure intermediate times. In Taipei, the average one-way commuting time is 32.7 min, totaling about 1 hr daily37. In each trial, CFF and VFS assessments were conducted at the start and end of the viewing session. Participants watched four films in four different combinations of posture and duration, each performed in separate sessions to avoid accumulating fatigue. These combinations were arranged in a randomized sequence. Participants used their own smartphones with the assigned movies pre-loaded before the experiments. To avoid distorting the testing situation compared to the real world, participants were allowed to carry their usual commuting backpacks. However, backpacks that were too large or heavy, potentially altering body posture, were excluded from the study. Each trial lasted 15 or 30 min, with VD data recorded during the final 1-min interval at 15-s intervals. These values were averaged for analysis. To minimize errors and participant fatigue, a minimum 10-min resting period was imposed between trials. The Taipei MRT carriage typically maintains a temperature of 24\u0026deg;C, with an average speed of 35 km/hr and a maximum speed of 80 km/hr. The average illumination in the carriage at 100 cm above the floor is over 250 lx, with a minimum of 200 lx38.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe collected data from the study underwent thorough analysis using SPSS 23.0 statistical software (IBM Corp., Armonk, NY, USA), with a significance level of 0.05 for all tests. The primary objective was to examine the impacts of participant sex (men and women), viewing posture (sitting and standing) and duration (15 min and 30 min) on the measured variables (CFF reduction, VFS, and VD) through a three-way ANOVA. In the analysis, participant sex was designated as a between-subject factor, while posture and duration were considered within-subject factors. Post-hoc comparisons were performed using independent t-tests to uncover significant differences between groups. To assess the practical significance of any identified independent variables, power values were calculated following Cohen's established guidelines39. An effect size of \u0026ge;\u0026thinsp;0.2 signifies a small effect, \u0026ge; 0.5 represents a medium effect, and \u0026ge;\u0026thinsp;0.8 indicates a large effect. Prior to conducting the analyses, the Kolmogorov-Smirnov test was utilized to evaluate the alignment of numerical variables with the normal distribution. Additionally, Levene's test was employed to examine the equality of variances, ensuring the robustness of the analytical framework.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe results of the Kolmogorov-Smirnov test indicated that the collected data, both for the entire group and subgroups, followed a normal distribution (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similarly, Levene's test demonstrated homogeneity in the data (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These findings confirmed that the data met the assumptions necessary for subsequent ANOVAs.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the findings of the three-way ANOVA, showing the effects of participant sex, viewing posture, and viewing durations as independent variables. The analysis indicated a significant influence for almost all responses studied. However, due to the significant two-way interaction effects between sex and posture, as well as posture and time, the main effects of the independent variables require further cross-analyses for confirmation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThree-way ANOVA results for all responses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePower\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSex (S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFF reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisual fatigue score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e145.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViewing distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3400.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3400.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e193.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePosture (P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFF reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisual Fatigue score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e382.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e382.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViewing distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1150.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1150.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTime (T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFF reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisual fatigue score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e772.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e772.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViewing distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e468.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e468.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eS\u0026times;P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFF reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisual fatigue score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e109.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViewing distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e841.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e841.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eS\u0026times;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFF reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisual fatigue score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViewing distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP\u0026times;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFF reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisual fatigue score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViewing distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eS\u0026times;P\u0026times;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFF reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisual fatigue score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViewing distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: CFF, critical flicker fusion frequency.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e compares three responses for two postures across genders. In general, men exhibited lower subjective fatigue scores and longer VD than women, with nonsignificant posture effects on the responses (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Conversely, posture effects were significant for women (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women showed greater reductions in CFF and shorter VDs when sitting, but experienced lower subjective fatigue levels when standing. These opposing results warrant further discussion. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the interaction between posture and time. As viewing time increased from 15 to 30 min, fatigue levels rose and VD decreased. Additionally, the differences in VFS score and VD between postures became more pronounced with the extension to 30 min.\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThe growing trend of watching videos on smartphones in MRT carriages necessitates an understanding of how various postures and viewing durations affect eyestrain, critical for user comfort and well-being. Unlike browsing or texting, video watching presents unique challenges due to its continuous narrative, making prior research outcomes potentially less applicable. This insight led us to investigate a crucial yet often overlooked aspect of modern digital entertainment: the risk of eyestrain during smartphone video viewing in different postures and between sexes on Taipei's MRT. Our findings unexpectedly revealed that the visual load generated by watching videos while standing may not be higher than that caused by sitting in MRT carriages. The impact of viewing posture on objective eyestrain differs between sexes, with prolonged viewing exacerbating the strain.\u003c/p\u003e \u003cp\u003eWhile watching videos in MRT carriages, women reported greater eyestrain while standing (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas objective measurements (CFF reduction) showed increased strain when sitting (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Interestingly, this posture-related eyestrain was significant only among female participants, indicating distinct viewing behaviors between sexes. VD emerged as a key factor, suggesting that eyestrain in MRT carriages is predominantly influenced by how long videos are watched. Furthermore, our study identified a potentially dangerous oversight: the assumption that sitting is visually less demanding might lead users to underestimate the strain on their eyes. Compared to the objective measure of CFF reduction, the subjective VFS measure did not consistently reflect the heightened eyestrain reported by female participants. This discrepancy points to an unrecognized visual load. These findings underscore an essential message: to mitigate eyestrain, especially during extended viewing sessions, it may be necessary to reconsider the common practice of watching videos on smartphones in MRT carriages.\u003c/p\u003e \u003cp\u003eOur study found that in moving MRT carriages, objective visual eyestrain, measured by the CFF reduction, was significantly greater when watching videos in a sitting position compared to a standing position (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Contrary to common assumptions, this phenomenon was observed only in female participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This may be attributed to the shorter VD for females when sitting and viewing videos on smartphones. A shorter VD, especially on mobile devices, demands higher vergence and accommodation responses, leading to tension in the extraocular, ciliary, and pupillary muscles24,40. This tension is a primary factor in digital eyestrain21-23. Near vision tasks are unnatural for the eyes, which have evolved to focus on distant objects where the eye muscles are more relaxed41.\u003c/p\u003e \u003cp\u003eThe difference in VD between the two viewing postures could be related to the specific environment of MRT carriages. When sitting, the relatively stable body posture allows for closer focus on the phone screen. Conversely, when standing, participants had to hold the ring handle with one hand and their smartphones with the other, resulting in a less balanced body position and difficulty in bringing the smartphone closer to the eyes. This standing posture required more effort from muscles like the trapezius and biceps brachii to maintain, leading to a trade-off between viewing clarity and hand fatigue, and consequently a larger VD than when sitting42. Additionally, standing required participants to pay attention to the movements of passengers around them, preventing continuous focus on the smartphones and thereby reducing the load on the eye muscles and alleviating fatigue43,44.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, sex differences in VD can be partly attributed to variations in average body size, specifically the relatively shorter forearm length of females, leading to a shorter VD compared to males45. Additionally, the viewing posture influenced VD, with females exhibiting a shorter VD when sitting than when standing. Before the advent of PCs, smartphones, and tablets, the ideal reading distance was determined by the Harmon method, which involves making a fist, holding it to one's cheek, and measuring the distance from the elbow to the eyes. For adults, this Harmon distance is typically 36-41 cm24. Engaging in near-vision tasks within this distance, such as reading and writing, can cause eyestrain or headaches46, and this distance is also recommended for smartphone use24. In Taiwan, the average difference in forearm length between sexes among youths is approximately 3.5 cm47, which aligns with the sex difference in VD observed in the sitting position (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, the relatively shorter VD observed in women in our study may also be attributed to postural differences between sexes. Women may tend to hold their phones closer to their eyes, resulting in a shorter VD and higher visual load (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Korakakis et al.48 found that women consistently adopt more upright postures than men in standing scenarios, which may also explain the significant difference in VD between the two viewing postures for female participants.\u003c/p\u003e \u003cp\u003eWe noted that females exhibited opposite trends in visual fatigue between the two smartphone viewing postures. Objective CFF reductions indicated less fatigue in a sitting position, whereas subjective VFS scores indicated higher fatigue. Regarding the general usage of visual devices, smartphones are among the most frequently used for dynamic visual content in moving vehicles, such as MRT carriages49-51. In general, visual fatigue, which arises from the discrepancy between accommodation and convergence52, causes eye strain, focus difficulty, headaches53, and motion sickness, known as visually induced motion sickness (VIMS)54,55. In our study, the higher subjective fatigue levels observed in standing positions may reflect more severe VIMS compared to sitting. Furthermore, females have been reported to be more susceptible to VIMS and experience greater discomfort than males56-58, which may explain why males exhibited fewer VIMS symptoms compared to females. Although recent studies have not definitively confirmed CFF as a valid measure for all usage situations12-13, CFF continues to be widely recognized as a reliable tool for assessing eyestrain and visual fatigue14-16. Additionally, CFF has recently found utility in evaluating visual fatigue resulting from smartphone use.\u003c/p\u003e \u003cp\u003eIn our analyses, when the smartphone viewing time was extended from 15 min to 30 min, both subjective and objective visual fatigue indicators increased significantly in both viewing postures, while VD decreased. Additionally, we observed that both VFS and VD differences further expanded (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The 20-20-20 rule recommends people to take their eyes off screens every 20 min and look at something 20 feet away for at least 20 s27,59. This rule suggests that 20 min may be the time limit for a visual fixation task, indicating the need for breaks to relieve visual load. The results of our study on watching videos in MRT carriages are consistent with the 20-20-20 rule, supporting its recommendation for reducing visual fatigue.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Firstly, we recruited 48 young men and women as participants, limiting the generalizability of our findings to other demographic groups, such as children and the elderly, even though young individuals comprise the largest segment of smartphone users in Taipei MRT carriages. Secondly, the durations of smartphone viewing (15 and 30 min) used in our study do not reflect actual daily usage patterns, posing challenges in extrapolating our results to real-world scenarios. Moreover, the degree of smartphone usage addiction across different sexes was not rigorously controlled, and the selection of the videos used in this study might carry inherent limitations, warranting further exploration. During video viewing in MRT carriages, fluctuating environmental factors (including lighting and crowding of passengers around the users) could influence visual fatigue, emphasizing the need to include these factors in future research. Finally, it must be noted that this study was conducted in Taipei MRT carriages. The design and environment of MRT carriages in different regions and countries vary, which may also cause differences in study results.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study explored the assessment of visual fatigue caused by watching videos on smartphones in Taipei MRT carriages. The independent variables included participant sex, viewing posture, and time duration. The unique environment of MRT carriages may negatively impact users' behavior while looking at their smartphones. Overall, the study found that objective visual fatigue was higher in the sitting position than in the standing position, contrary to expectations. Additionally, the objective and subjective visual fatigue levels of women in different postures were exactly opposite, likely due to the interaction between shorter VD and VIMS. The study also showed that viewing videos for 30 min caused higher visual strain than viewing for 15 min, suggesting that the 20-20-20 rule for visual activity may also apply in MRT carriages.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eAuthors have no financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was partially supported by the National Science and Technology Council (NSTC), Taiwan (grant number 113-2221-E-131-029-MY3), and the APC was also funded by NSTC.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY-L.C provided the conceptualization. Y-L.C and H-T.N designed the investigation. K-H.C, P-C.H, and C-T.H performed the experiment, supervised data acquisition, and analyzed data. H-T.N wrote the original draft. Y-L.C reviewed and edited the final manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors would like to thank all participants for their contributions to the experiment.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthics declaration\u003c/h2\u003e \u003cp\u003e This research was approved by the Ethics Committee of National Taiwan University, Taiwan (protocol code NTU-REC 202312EM051) and was conducted according to the guidelines of the Declaration of Helsinki. Other ethical criteria included written consent to participate in the study and withdraw from the study whenever participants were willing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLeung, T.W., Chan, C.T., Lam, C.H., Tong, Y.K. \u0026amp; Kee, C.S. Changes in corneal astigmatism and near heterophoria after smartphone use while walking and sitting. PloS One 15, e0243072 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMushroor, S., Haque, S. \u0026amp; Riyadh, A.A. The impact of smart phones and mobile devices on human health and life. Int. J. Community Med. Public Health 7, 9\u0026ndash;15 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePriya, D.B. \u0026amp; Subramaniyam, M. Fatigue due to smartphone use? Investigating research trends and methods for analysing fatigue caused by extensive smartphone usage: A review. 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Lens Anterior Eye 46, 101744 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MRT carriages, smartphone use, eyestrain, critical flicker fusion frequency (CFF), visual fatigue scale (VFS), viewing distance","lastPublishedDoi":"10.21203/rs.3.rs-4829016/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4829016/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn Taipei's MRT carriages, many passengers watch videos on their smartphones during their daily commutes, but the impact of carriage movement on visual load remains understudied. Our study investigated eyestrain in 48 young participants (24 men and 24 women) while viewing films on smartphones in MRT carriages. We examined two viewing postures (sitting and standing) and two durations (15 and 30 min), measuring critical flicker fusion frequency (CFF) reduction, visual fatigue scale (VFS) scores, and viewing distance (VD). The results indicated that the main effects of the independent variables were nearly all significant, with two-way interactions (sex x posture and posture x time) significantly affecting most responses. Women exhibited notable differences between postures, with higher CFF reduction when sitting compared to standing (3.47 Hz vs. 1.90 Hz; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and shorter VD when sitting compared to standing (25.5 cm vs. 34.3 cm; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, standing led to higher VFS scores for women compared to sitting (8.94 vs. 4.60; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that women may be more sensitive to motion sickness, particularly when standing in a moving MRT carriage. Men showed higher CFF reduction while standing compared to women (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but no significant difference between sexes while sitting. Visual fatigue indices were significantly higher after 30 min of viewing compared to 15 min, with amplified effects on VFS score and VD. These findings support the recommendation that users should take a break after 20 min of smartphone use, even in MRT carriages. Although subjective fatigue may not always be perceived, watching videos while sitting in MRT carriages leads to unexpectedly high objective visual fatigue (i.e., CFF reduction), necessitating greater caution.\u003c/p\u003e","manuscriptTitle":"Eyestrains among smartphone users while watching videos in Taipei MRT carriages: A comparison between sitting and standing postures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-10 17:07:24","doi":"10.21203/rs.3.rs-4829016/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-09T03:58:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-08T11:21:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-23T23:38:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41399932589694830903696211115444558233","date":"2024-08-15T15:49:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90592962476963170684605844921209749336","date":"2024-08-13T22:35:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-08T10:58:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-08T10:55:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-05T19:08:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-05T19:02:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-30T13:03:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a345a1d4-1628-4e35-90b9-783de12649c3","owner":[],"postedDate":"September 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36545834,"name":"Health sciences/Health care"},{"id":36545835,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-10-28T16:02:02+00:00","versionOfRecord":{"articleIdentity":"rs-4829016","link":"https://doi.org/10.1038/s41598-024-76334-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-25 15:57:32","publishedOnDateReadable":"October 25th, 2024"},"versionCreatedAt":"2024-09-10 17:07:24","video":"","vorDoi":"10.1038/s41598-024-76334-9","vorDoiUrl":"https://doi.org/10.1038/s41598-024-76334-9","workflowStages":[]},"version":"v1","identity":"rs-4829016","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4829016","identity":"rs-4829016","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}