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. 2025 Mar 19;15:9527. doi: 10.1038/s41598-025-94038-6

Age-related differences in the effect of mental fatigue on obstacle crossing in virtual reality

Natsuko Wasaki 1,✉, Kazuki Hiranai 1, Akiko Takahashi 1
PMCID: PMC11923284  PMID: 40108372

Abstract

In Japan, falls are the most common type of occupational accident, with inattention being one of its primary causes. Both mental fatigue and aging contribute to inattention and decline in physical performance; however, how these factors interact to affect physical performance is not fully understood. This study compared the effects of mental fatigue on physical movements between younger (aged 25–34 years) and middle-aged adults (aged 55–64 years) and examined age-specific fall risks. A total of 34 participants rated their fatigue using the visual analog scale, performed the psychomotor vigilance task as a measure of sustained attention, and completed an obstacle-crossing task in a virtual reality environment to measure toe clearance and swing time. Results showed that mental fatigue increased fall risk across all ages, with age-specific contributing factors. For middle-aged adults, reduced balance control and lower sensitivity to fatigue heightened fall risk. Meanwhile, in younger adults, mental fatigue combined with energy-efficient strategies raised fall risk. These findings highlight unique age-related risk factors exacerbated by mental fatigue, underscoring the importance of early guidance and education before middle age.

Keywords: Mental fatigue, Aging, Physical performance, Virtual reality

Subject terms: Psychology, Human behaviour

Introduction

In Japan, falls comprise the most prevalent occupational accident requiring more than four days of leave1. A study2 analyzing the occupational accident database3 published by the Ministry of Health, Labour, and Welfare of Japan noted that fall accidents are often associated with workers’ “inattention.” For example, instances include a worker tripping over a drainage ditch due to inattention, leading to a broken toe, or stumbling while moving within a factory, injuring their hand. Evidently, it is necessary to examine how factors lead to inattention and increase fall risk in order to prevent fall accidents.

Mental fatigue is one of the factors that affect the cognitive function of attention. Mental fatigue refers to the sense of exhaustion or apathy experienced during or after prolonged cognitively demanding tasks4. It is not uncommon for individuals to experience mental fatigue (cognitive fatigue), which causes them to feel spaced out, making it difficult to concentrate and increasing the likelihood of making mistakes. Mental fatigue impairs decision-making and problem-solving abilities4,5 and causes a decline in sustained attention—the ability to continuously focus on a task4,6–8—which can lead to human errors9. Guo et al.8 induced mental fatigue in participants using a dual task, which involved tracking randomly moving and detecting randomly presented visual stimuli, for approximately one hour. As the task duration increased, subjective fatigue levels increased, tracking and detection accuracy of target stimuli decreased, and reaction times increased, indicating that mental fatigue leads to declined sustained attention and affects behavior.

Mental fatigue also negatively contributes to goal-directed attention6,10, but not stimulus-driven attention. As mental fatigue impairs goal-directed control and shifts behavior toward a more stimulus-driven state, actions become more automatic and less flexible. This increases the likelihood of being unable to respond appropriately to unexpected situations6, even in healthy young adults, suggesting that its effects may be particularly pronounced in older adults10.

Changes in sustained attention, brain activity, decision-making abilities, and motivation resulting from mental fatigue affect physical movements4,5,11–15. In a study by MacMahon et al.13, participants performed a 90-minute cognitive task aimed at inducing mental fatigue, with a 3000-meter run before and after the task to measure physical performance. They found that completion times were longer in the experimental condition, in which mental fatigue was induced, than in the control condition. Thus, mental fatigue can impair attention (e.g., reduced hazard detection) and potentially affect physical performance (e.g., decreased behavioral flexibility), which may, in turn, increase the risk of falls in real-world occupational accidents.

Studies demonstrate that aging reduces various cognitive functions16–21. For example, it declines processing speed, working memory capacity, inhibitory control, and long-term memory20, all of which require the cognitive function of attention. One study suggested that declines in certain cognitive functions due to aging can begin as early as in the 20–30 s18. However, it remains uncertain whether aging affects all aspects of attention22. Sustained attention does not necessarily decline with age. Studies have shown that the capacity for sustained attention may remain unchanged across age groups23 and that sustained attention improves with maturation in early adulthood and remains stable without age-related decline in older adulthood24. It does not show the same degree of decline as other attentional functions like selective attention and divided attention. However, as mentioned earlier, sustained attention is negatively impacted by mental fatigue. Older adults may be particularly susceptible to the effects of mental fatigue due to age-related changes in cognitive resources and fatigue resistance. Therefore, while aging itself may not have a significant impact on sustained attention, the interaction between aging and mental fatigue may lead to different outcomes, not only affecting sustained attention but also influencing physical performance.

Older adults tend to have reduced physical functions, such as muscle strength25, leading to an increased fall risk26,27. However, cognitive decline related to aging is also closely associated with a decline in physical function28,29. Executive function, which is a cognitive skill involved in focusing to achieve goal-directed behavior30,31, primarily comprises three functions: working memory, inhibition (e.g., filtering out irrelevant information), and cognitive flexibility (e.g., adapting to changing rules). In Atkinson et al.’s28 longitudinal study on older adults, a decline in executive function was associated with reduced walking speed. They concluded that memory, visuospatial skills, cognitive processing speed, and the ability to maintain attention by inhibiting irrelevant information—processes related to executive function—are crucial for efficient planning, initiation, and maintenance of walking. Therefore, a decline or deficiency in executive function may impact efficient and safe walking, potentially increasing fall risk30.

The primary causes of falls are slipping and tripping32, and among cases of tripping, many occur due to tripping over obstacles33. Obstacle crossing, which is one form of adaptive locomotion, has been examined across various age groups34–37. The characteristics of obstacle crossing in older adults include higher toe clearance (distance from the obstacle) compared to younger adults27, slower crossing speed when stepping over an obstacle34,35, and shorter step length and reduced heel–strike distance after crossing the obstacle34. In other words, compared to younger adults, older adults tend to land their feet closer to the obstacle, increasing the risk of contact obstacles34. Additionally, their smaller step length and narrower base of support make them more prone to losing balance, ultimately leading to a higher risk of falls35. However, the impact of mental fatigue on obstacle crossing has not been sufficiently studied. A review paper on the effects of fatigue on body movement38 has summarized research on the effects of physical fatigue on obstacle crossing. Nonetheless, the impact of mental fatigue on obstacle crossing remains to be explored.

As outlined above, there exists a substantial body of literature on the effects of both mental fatigue and aging on attentional function and physical movement. However, knowledge about how the interaction between these two factors influences obstacle crossing movement is insufficient. Therefore, this study aimed to compare the effects of mental fatigue on obstacle crossing across different age groups and to examine age-specific fall risks. This study focused on participants aged 25–34 and 55–64 years—the latter middle-aged group being classified as “older workers” in Japan. According to Wasaki and Takahashi2, occupational accidents caused by inattention are frequent in the 55–64 age group.

As both mental fatigue and aging can lead to declines in cognitive and motor functions, when both factors impact physical performance, middle-aged adults may experience greater performance decline and an increased risk of falls compared to younger adults. Specifically, the middle-aged group is expected to report a greater sense of fatigue after the mental fatigue task and engage in movements that increase the risk of obstacle contact and falls, such as decreased clearance compared to the younger group (the measurement indicators for obstacle crossing have been described in the Methods section). Considering the growing population of older workers in the current aging society, understanding the effects of mental fatigue on physical performance is critical.

Methods

Participants

The current experiment involved 34 participants: 17 aged 25–34 years (eight men, nine women; Mage = 29.3, SD = 3.04, Mheight = 162.17 cm, SD = 8.26, Mweight = 56.48 kg, SD = 8.38) and 17 aged 55–64 years (nine men, eight women; Mage = 59.2, SD = 3.05, Mheight = 166.58 cm, SD = 8.36, Mweight = 60.10 kg, SD = 9.53). All participants had normal or corrected-to-normal visual acuity and no physical pain. The sample size was calculated using G*Power 3.1.9.739,40. For a two-way analysis of variance (ANOVA) between two groups, an effect size of 0.25 was assumed, based on Cohen’s41 guidelines. The significant level and power were set at 5% and 80%, respectively.

The study was conducted in accordance with the protocols approved by the Ethics Committee of the National Institute of Occupational Safety and Health, Japan for experimental research involving human participants and in compliance with the Declaration of Helsinki. Written informed consent was obtained from all participants before enrollment in the study. Participants were instructed to refrain from consuming alcohol or caffeine in the evening before the experiment and to maintain their usual sleep duration.

Apparatus

Fatigue was measured using an application42,43 via iPhone13 Pro (iOS:17.0.2, refresh rate: 120 Hz). The virtual reality (VR) environment was displayed through a head-mounted display (HMD: VIVE Pro Eye, HTC Corp.) at a resolution of 1440 × 1600 pixels (one eye) and with a refresh rate of 90 Hz, controlled by a personal computer (iiyama STYLE-15FR105-i7-TASX, iiyama Corp., CPU: Intel Corei7-1087H, RAM:32.0 GB, GPU: NVIDIA GeForce RTX 3070 8GB GDDR6). Real-time 3D development platform Unity (version 2021.3.21f1, Unity Technologies) with C# was used to create and present the VR environment, control the experimental sequences, and collect the data. Motion tracking devices (VIVE Tracker 3.0, HTC Corp.) were placed bilaterally on top of the foot to measure foot clearance and swing time.

Procedure

The experiment was conducted between approximately 9:30 and 10:00 am, with one person per day, for 34 days (the duration of each experiment was approximately 1–1.5 h). Figure 1 illustrates the experimental flow. Both before and after the mental workload task, the participants reported their feelings of fatigue using the visual analog scale (VAS) and performed the psychomotor vigilance task (PVT) to indicate fatigue and the obstacle-crossing task in the VR space. To prioritize safety by eliminating the risk of collisions with physical obstacles and to enhance experimental control, the obstacle-crossing task was performed in a VR environment. Mental fatigue was experimentally generated using the Uchida–Kraepelin performance test (UK-test), which is a mental workload task.

Fig. 1.

Fig. 1

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Experimental flow.

Visual analog scale (VAS) and psychomotor vigilance task (PVT)

To evaluate mental fatigue, participants were asked to report their subjective fatigue using VAS and perform PVT both before and after the mental workload task. VAS and PVT are effective methods for assessing mental fatigue15,44,45. In VAS, the participants were asked “Please indicate your current level of fatigue by tapping on the bar, using the sensations represented on both ends of the line as a reference” (in Japanese), with response options ranging from 0 (“Feeling great; no fatigue at all”) to 100 (“So exhausted that I can’t do anything”).

PVT is mainly used to measure sustained attention46, which is a characteristic of mental fatigue7,8,47, by recording response times (RT) or the number of lapses46. Following previous studies45,48, a 3-minute PVT was conducted in this experiment, with interstimulus intervals varying randomly from 1000 to 4000 milliseconds and RTs of ≥ 355 milliseconds defined as lapses. The participants were required to press the touchscreen on the smartphone (iPhone13 Pro) as soon as possible when a number was presented. After each trial, RT was displayed as feedback for 300 milliseconds.

Obstacle-crossing task in a VR environment

The participants wore the HMD and attached motion tracker to the top of their left and right feet, respectively (Fig. 2A and B). They were then instructed to stand on the start line on the floor in the VR environment (Fig. 2C). In each trial, they started walking at their preferred speed at the experimenter’s cue and crossed three obstacles of different heights (large: 0.4 m, medium: 0.2 m, and small: 0.1 m in Unity unit), which were placed at equal intervals (1 m in Unity unit). Participants were informed that they were free to choose the right or left leg first when crossing the obstacles. After crossing the obstacles and reaching the “goal,” they returned to the original position and the experimenter began the next trial. Each participant completed six trials for three heights with three possible locations. A practice trial was conducted before starting the experiment.

Fig. 2.

Fig. 2

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A head-mounted display and motion trackers were used as experimental equipment (A and B). Participants stood at a virtual starting line and walked to a virtual finish line, crossing over theobstacles in a virtual environment (C).

Measurement of behavioral performance

In the obstacle-crossing task, we measured foot clearance (Fig. 3A) and swing time (Fig. 3B) while the participants crossed the obstacles. A passage detection area was set up on the obstacles (Fig. 3C), and the event of entry and exit of the motion trackers were recorded. The clearance was determined by recording the y-coordinate when the tracker entered the collision detection area. Swing time was calculated as the time between the tracker’s entry to and exit from the collision detection area, indicating the duration of passing over the obstacles. Before the experiment began, the trackers were calibrated. With this setting, the position of the trackers on the floor was set as 0 cm of height on the Y-axis.

Fig. 3.

Fig. 3

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Definitions of foot clearance (A), swing time (B), and passage detection area on the obstaclesare shown in red for convenience (the actual color is transparent and invisible) (C).

Mental workload task

The UK-test was used to induce mental fatigue. It is a performance test that is widely used to assess character and mental stress by measuring test-takers’ performance speed and accuracy49,50. Several studies have used it to induce mental fatigue in experimental settings50–52. The participants performed the UK-test by following the audio instructions immediately after the obstacle-crossing task. They were required to perform single-digit addition for 30 min, divided into two 15-min sessions with a 5-minute interval.

Data analysis

Two participants—one from each age group—for whom measurements could not be obtained successfully due to equipment failure were excluded from the analysis. The final analysis included 32 participants, comprising 16 younger and 16 middle-aged adults.

Statistical analyses were performed on values from the VAS, PVT (including RT and the number of lapses), and foot clearance and swing time for the leading and trailing foot. Foot clearance was calculated by subtracting the height of the instep (the y-coordinate at the participants’ initial position) and the height of the obstacles. The mean value for each parameter was calculated across participants. A repeated-measures two-way mixed-design ANOVA with one between-subject factor (age) and one within-subject factor (mental fatigue) was conducted for all the parameters. The significance level was set at 0.05 for all analyses. A simple main effects test was conducted if the interaction was significant, and Holm’s method was used for multiple comparisons. Generalized eta squared (ηG2) values were used to estimate the effect sizes, with ηG2 of 0.02 defined as small; 0.13 as medium, and 0.26 as large53.

Results

As shown in Fig. 4A, both younger and middle-aged adults reported increased subjective fatigue after the mental workload task, with the increase being particularly higher in younger adults. The ANOVA revealed a significant main effect of mental fatigue (F (1, 30) = 31.36, p < .001, ηG2 = 0.20), but not of age (F (1, 30) = 2.79, p = .10). The interaction between mental fatigue and age was significant (F (1, 30) = 7.60, p = .01, ηG2 = 0.05), and the simple main effect of age was significant in post mental workload task. The multiple comparisons showed that younger adults reported significantly higher subjective fatigue than middle-aged adults after the metal workload task (t (60) = 2.81, p = .007, d = 1.39).

Fig. 4.

Fig. 4

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Visual analog scale for fatigue (A), reaction time (B), and the number of lapses (C) inpsychomotor vigilance task performance before (pre) and after (post) the mental workload task. The colors of the bars indicate the age group (light gray: younger adults, dark gray: middle-aged adults). Error bars represent 95% confidence intervals.

Figure 4B and C show the RT and the number of lapses of the PVT, respectively. Middle-aged adults exhibited longer RTs (Fig. 4B) and more lapses than younger adults both before and after the mental workload task. The ANOVA revealed that mental fatigue did not affect RT (F (1, 29) = 2.80, p = .10) or the number of lapses (F (1, 29) = 1.32, p = .25) in PVT, whereas age significantly affected both (F (1, 30) = 13.39, p = .001, ηG2 = 0.27 for RT; F (1, 29) = 7.46, p = .01, ηG2 = 0.17 for the number of lapses). The interaction between mental fatigue and age was not significant (F (1, 29) = 0.38, p = .54 for RT; F (1, 29) = 0.23, p = .63 for the number of lapses).

Figure 5 shows foot clearance for leading foot (Fig. 5A) and trailing foot (Fig. 5B). After the mental workload task, a consistent decrease in leading foot clearance was observed in both younger and middle-aged groups. However, the extent of this change did not vary by age, with a similar trend in both groups. The ANOVA revealed a significant main effect of mental fatigue (F (1, 30) = 11.64, p = .002, ηG2 = 0.03), but not of age (F (1, 30) = 0.18, p = .67). The interaction between mental fatigue and age was not significant (F (1, 30) = 0.79, p = .38).

Fig. 5.

Fig. 5

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Foot clearance for leading foot (A) and trailing foot (B) before (pre) and after (post) themental workload task. The colors of the bars indicate the age group (light gray: younger adults, darkgray: middle-aged adults). Error bars represent 95% confidence intervals.

The trailing foot clearance (Fig. 5B) decreased after the mental workload task. However, both before and after the task, middle-aged adults exhibited greater clearance compared to younger adults. The ANOVA revealed a significant main effect of mental fatigue (F (1, 30) = 24.08, p < .001, ηG2 = 0.06) and age (F (1, 30) = 7.49, p = .01, ηG2 = 0.18). The interaction between mental fatigue and age was not significant (F (1, 30) = 0.10, p = .74).

A comparison of foot clearance across six trials revealed no significant differences in any condition, either before or after the mental workload task. Before the mental workload task, neither the leading foot nor the trailing foot showed significant changes across trials, age effects, or interactions between trial and age (Leading foot: main effect of trial (F (5, 150) = 0.87, p = .45), age (F (1, 30) = 2.70, p = .11). The interaction between trial and age was as follows: (F (5, 150) = 0.63, p = .59). Trailing foot: main effect of trial (F (5, 150) = 0.36, p = .85), age (F (1, 30) = 2.15, p = .15). The interaction between trial and age was as follows: (F (5, 150) = 0.87, p = .49). Similarly, after the mental workload task, no significant differences were observed for either the leading or trailing foot across trials, age effects, or their interactions (Leading foot: main effect of trial (F (1, 145) = 0.56, p = .68), age (F (1, 29) = 3.92, p = .057). The interaction between trial and age was as follows: (F (5, 145) = 1.74, p = .14). Trailing foot: main effect of trial (F (1, 145) = 1.27, p = .28), age (F (1, 29) = 1.84, p = .18). The interaction between trial and age was as follows: (F (1, 145) = 0.98, p = .42).

Swing time of the leading foot (Fig. 6A) was not affected by mental fatigue but was lesser in middle-aged adults. The ANOVA showed no significant main effect of age (F (1, 30) = 3.90, p = .057), nonetheless, the effect size was approximately medium (ηG2 = 0.10). The effects of mental fatigue (F (1, 30) = 0.03, p = .85) and interaction between age and mental fatigue (F (1, 30) = 0.07, p = .79) were not significant.

Fig. 6.

Fig. 6

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Swing time for leading foot (A) and trailing foot (B) before (pre) and after (post) the mentalworkload task. The colors of the bars indicate the age group (light gray: younger adults, dark gray:middle-aged adults). Error bars represent 95% confidence intervals.

Swing time of the trailing foot (Fig. 6B) showed no significant differences in the main effects of mental fatigue (F (1, 30) = 1.99, p = .16), age (F (1, 30) = 2.17, p = .15), or the interaction between mental fatigue and age (F (1, 30) = 0.06, p = .79).

In summary, both younger and middle-aged adults reported increased subjective fatigue after the mental workload task. However, perceptions of mental fatigue varied with age, with younger adults scoring higher in subjective fatigue than middle-aged adults after the mental workload task. The effect of age was observed only for RT and number of lapses in the PVT. Mental fatigue and age partially influenced obstacle-crossing indicators. Foot clearance was affected by fatigue, whereas foot clearance of the trailing foot was affected by age.

Discussion

This study investigated the effects of mental fatigue on physical performance across two different age groups and examined fall risks associated with each age group. While mental fatigue impacted obstacle-crossing movements, age-related differences in behavioral characteristics also played a substantial role.

After the mental workload task, both age groups showed an increase in subjective fatigue levels, with younger adults showing a greater increase compared to middle-aged adults. Previous research has shown that older adults may demonstrate a tolerance to monotonous tasks54 and that younger adults tend to report higher subjective mental fatigue compared to older adults55. The results of the present study are consistent with these previous findings. However, there is also evidence suggesting that sensitivity to mental fatigue peaks in middle age54. Therefore, the lower subjective mental fatigue ratings observed in the middle-aged group in this study should be interpreted cautiously, considering the physical performance results as well. It is possible that middle-aged adults may have reduced sensitivity to mental fatigue. In contrast, only the effect of age was observed in PVT. This indicates that this study could not confirm whether sustained attention was diminished by the mental fatigue task based on PVT. In the present study, the obstacle-crossing task was conducted immediately after the mental fatigue task, while PVT was conducted at the end. The experimental procedure of the study may have contributed to the lack of observed effects of mental fatigue on PVT results.

Notably, the mental workload task caused a significant reduction in foot clearance, which was more pronounced in the trailing foot than in the leading foot. Unlike the leading foot, the trailing foot cannot rely on online visual information while walking. The addition of mental fatigue to the reliance on offline, memory-based visual information likely led to a reduction in sustained attention directed toward the trailing foot, decreasing its clearance. The result that mental fatigue similarly affected physical performance in middle-aged and younger adults suggests the possibility that middle-aged adults may have reduced sensitivity to mental fatigue on the VAS. Additionally, a study investigating the relationship between mental fatigue and aging using electroencephalogram56 suggested that while both younger and older groups were negatively affected by mental fatigue, the underlying mechanisms differed: younger adults experienced a decline in motivation due to the monotony of the task, whereas older adults were affected by increased consumption of cognitive resources. While inattention caused by mental fatigue is certainly a factor contributing to the decline in physical performance, changes in brain activity and motivation, which reduce task processing efficiency, are also considered contributing factors14,57.

Regarding the effect of age, middle-aged adults exhibited greater clearance for the trailing foot compared to younger adults. This result was contrary to our hypothesis that clearance will be reduced in the middle-aged group. Previous research suggests that older adults tend to adopt a higher step to maintain a safety margin27,58. Our results indicate that middle-aged adults maintained a larger safety margin even when they were affected by mental fatigue. However, the participants conducted the mental workload task for approximately 30 min. This is a relatively short duration compared to actual working hours, where the mental strain is higher. Middle-aged adults may experience heightened mental strain but be unaware of it under prolonged mental fatigue, potentially increasing the risk of contact obstacles.

The middle-aged adults tended to cross the obstacles with the leading foot faster. Since slower walking speeds contribute to upper body stability27,35, this result suggests a potential decline in balance control. Under such conditions, the risk of losing balance and the likelihood of falling may increase. Considering the results of foot clearance and swing time, mental fatigue reduced foot clearance in both age groups. However, middle-aged adults maintained a safety margin with the trailing foot despite fatigue. In contrast, for the leading foot, no significant difference of foot clearance was observed in both age groups, with middle-aged adults showing a tendency for faster swing time. Upon stumbling with the leading leg, they might be unable to recover their body balance, leading to a higher risk of falling59.

As previously discussed, mental fatigue may increase fall risk during obstacle-crossing across all ages, though the contributing factors may differ by age. In younger adults, the combination of mental fatigue and energy-efficient, low-impact strategies may potentially increasing fall risk. In middle-aged adults, reduced balance control and sensitivity to mental fatigue may elevate fall risk. Each age group faces unique fall risk factors that may be exacerbated by mental fatigue.

This study has some limitations. The responses on the VAS may include not only mental but also physical fatigue. Participants were instructed to obtain sufficient sleep the night before the experiment; however, their actual physical fatigue levels were not precisely monitored. Additionally, for some participants, the use of an HMD and visual stimuli in the VR environment could have imposed mental strain, leading to accumulated fatigue. In this way, the unique experimental environment, which differs from real-world conditions, may have contributed to both physical and mental fatigue through factors beyond the UK-test. Therefore, in order to clearly distinguish between mental and physical fatigue, it is necessary to conduct evaluations using multiple questionnaires in combination with detailed observations of physiological indicators.

Additionally, the 30-minute mental fatigue task was shorter than typical working hours, increasing the possibility of obtaining different results for longer mental workload tasks. For instance, in actual workplace settings, prolonged attention for several hours is often required, and tasks may involve complex decision-making. Under such conditions, the progression of fatigue may differ from that induced in this experiment. Therefore, to investigate the effects of prolonged mental fatigue on walking and attention in a real work environment, designing experiments that simulate more realistic work demands will be necessary.

Furthermore, differences in familiarity with VR environments across age groups may have influenced the results. Participants with less VR experience may have faced greater cognitive load and adaptation challenges, which may have affected fatigue levels and walking performance. Future research should control VR experiences or implement an acclimation phase to minimize these effects.

Finally, this study did not examine individual cognitive traits or physical characteristics of participants (e.g., muscle strength, balance ability) in detail, which may have limited the ability to fully assess their effects on walking and fatigue. For example, participants with lower baseline balance ability might have been more affected by walking in a VR environment or by mental fatigue. Future research should consider participants’ fundamental motor and cognitive abilities to analyze in more detail how these factors influence mental fatigue and its effects on walking in VR environments.

Conclusion

This study investigated the effects of mental fatigue on physical movements and age-specific fall risks by comparing younger (aged 25–34 years) and middle-aged adults (aged 55–64 years). Our study demonstrated that mental fatigue increased fall risk across all age groups; however, the underlying mechanisms differed by age. In middle-aged adults, impaired balance control and reduced sensitivity to fatigue may have contributed to a higher fall risk. However, in younger adults, a combination of mental fatigue and energy-efficient movement strategies was suggested to increase fall risk. These findings highlight that mental fatigue exacerbates age-specific risk factors for falls, emphasizing the importance of appropriate guidance and education at an early stage before reaching middle age. Future research should aim to clearly distinguish between the effects of mental and physical fatigue; incorporate experimental designs that include prolonged mental workload; and consider participants’ motor abilities and cognitive characteristics to develop age-specific intervention strategies to improve balance control, sustain attention, and enhance fatigue management in occupational settings. In Japan, the number of workers aged 60 and above60, as well as their occupational accidents, has been steadily increasing. This study found distinct tendencies in this age group compared to the younger group. While numerous studies have focused on older adults, there are relatively few studies specifically examining the middle-aged population. Future research must place greater emphasis on the middle-aged group to clarify the relationship between cognition and physical movements. Further, mental fatigue and aging are not the only factors that lead to inattention. It is necessary to investigate various situations that result in inattention in order to further examine the relationship between inattention and fall risk.

Acknowledgements

The authors would like to thank T. Kubo and Y. Nishimura for providing the fatigue checker application for measuring mental fatigue. This work was supported by JSPS KAKENHI Grant Number JP23K19000.

Author contributions

All authors planned and designed the experiment. N.W. conducted the experiment and analyzed the data. All authors discussed the results, wrote the manuscript, and approved the final manuscript.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request and with the permission of National Institute of Occupational Safety and Health, Japan (JNIOSH).

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request and with the permission of National Institute of Occupational Safety and Health, Japan (JNIOSH).

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