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Article

Comparison of Foot-Response Reaction Time Between Younger and Older Adults Using the Foot Psychomotor Vigilance Test

by
Yutaka Yoshida
1,* and
Kiyoko Yokoyama
2
1
Graduate School of Design & Architecture, Nagoya City University, 2-1-10, Kita Chikusa, Chikusa-ku, Nagoya 464-0083, Japan
2
Graduate School of Data Science, Nagoya City University, 1, Yamanohata, Mizuho-cho, Mizuho-ku, Nagoya 467-8501, Japan
*
Author to whom correspondence should be addressed.
J. Ageing Longev. 2026, 6(1), 17; https://doi.org/10.3390/jal6010017
Submission received: 30 November 2025 / Revised: 17 January 2026 / Accepted: 27 January 2026 / Published: 2 February 2026
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Abstract

Reaction time (RT) is a key indicator of cognitive and motor processing speed, and its age-related decline has important implications for everyday activities such as driving. However, conventional Psychomotor Vigilance Tests (PVTs) assess hand responses and do not capture lower-limb reaction characteristics relevant to pedal operations. This study aimed to compare RT characteristics between younger and older adults using the foot-response version of the PVT (Foot PVT) and to examine factors associated with RT. Sleep-related variables, physical activity level (PAL), and height were analyzed, and RT distribution characteristics were evaluated. Twenty younger adults (24 ± 3 years, range: 22–29 years) and twenty-four older adults (73 ± 5 years, range: 66–84 years) performed a 10 min Foot PVT. Mean RT was significantly slower in older adults (818 ± 105 ms) than in younger adults (700 ± 73 ms) (p < 0.001), indicating an age-related delay of approximately 120 ms. Older adults showed lower skewness and kurtosis, suggesting more homogeneous and cautious responses. In younger adults, height was negatively correlated with RT (r = −0.593, p = 0.006), and multiple regression analysis identified height as a significant predictor (adjusted R2 = 0.316). No significant predictors were found in older adults. In the combined sample, age and height jointly explained 37.2% of the variance in mean RT. These findings indicate that Foot PVT performance reflects both biomechanical characteristics and age-related declines in reaction speed, supporting its utility for assessing lower-limb reaction capabilities relevant to driving and aging.

1. Introduction

Reaction time (RT) is a fundamental cognitive index that reflects the speed of a series of neural processing steps—from perceiving an external sensory stimulus to evaluating and processing it, and finally generating a motor response. A widely used method for quantitatively assessing RT is the Psychomotor Vigilance Test (PVT), which has been applied for decades [1,2]. The PVT measures a button-press response to a simple visual stimulus and is highly sensitive to fluctuations in arousal level and attentional function. RT obtained from the PVT provides a direct indication of central nervous system information-processing speed and is influenced by physiological and psychological factors such as sleep deprivation, fatigue, aging, and individual differences [3,4,5]. Therefore, quantitative assessment of RT is essential for understanding human cognitive and motor-control abilities. The PVT has been broadly applied across diverse fields, including clinical research, ergonomics, and traffic psychology [3,6]. Recent studies have optimized PVT duration and measurement precision, validated shortened protocols [7,8], and explored its applications in occupational settings such as night-shift work [9,10]. Additionally, smartphone-based PVT applications have been developed to predict sleep insufficiency and declines in alertness [11,12].
RT is particularly critical in tasks such as automobile driving, in which rapid and accurate responses to visual stimuli are required. Even slight delays may contribute directly to traffic accidents, making RT-based assessment of attention and alertness an important topic in traffic safety research [13,14,15]. At the same time, many countries—including Japan—are experiencing rapid population aging. As of 2024, individuals aged 65 years or older account for 29.3% of Japan’s population, which is the highest proportion worldwide, and this value is projected to reach 34.8% by 2040 [16]. Furthermore, as of October 2019, older adults accounted for nearly one-third of all licensed drivers in Japan [17]. Traffic accidents involving older drivers remain a major societal concern, partly due to age-related declines in cognitive and motor functions. According to the Tokyo Metropolitan Police Department, human factors such as delayed detection contribute to nearly 80% of traffic accidents in which an older driver is the primary party, with many cases involving delayed responses to signal changes or pedal misapplication errors, such as pressing the accelerator instead of the brake [18]. These incidents likely reflect age-related reductions in information-processing speed and response-control ability. Thus, simple and objective methods for evaluating individual reaction capabilities are needed to prevent such accidents.
Conventional PVTs are designed for hand responses and therefore cannot directly assess the foot responses required during driving. To address this limitation, a novel foot-response version of the PVT (Foot PVT) was developed [19]. The Foot PVT uses a three-color visual stimulus (red, yellow, blue) arranged to resemble a traffic signal, and participants respond by pressing one of three pedals. The system requires only a low-cost commercial foot pedal and a laptop computer. Participants rest their foot on the center pedal during the waiting period and press the left or right pedal depending on the stimulus color, enabling automated measurement of RT. Our previous study [19] demonstrated that older adults exhibited significantly slower RTs than younger adults and showed fewer false starts, suggesting that older adults may adopt a more cautious response strategy, prioritizing accuracy over speed. Moreover, the Foot PVT captures multiple behavioral indicators—including false starts, miss responses, and correction time—allowing a more comprehensive assessment of attentional stability and response accuracy than the conventional PVT. Therefore, this system has potential applications as a cognitive and behavioral evaluation tool for assessing driving ability and supporting older drivers. Compared with conventional hand-based PVTs, the Foot PVT directly evaluates lower-limb reaction functions relevant to pedal operations, allows assessment of reaction speed, accuracy, and response stability in a driving-related context, and requires minimal experimental setup.
RT measured by the Foot PVT may be influenced by factors beyond age. For example, sleep quality and alertness directly affect attention and response speed [10,20]. Regular physical activity level (PAL) helps maintain muscle strength and nerve conduction velocity and may contribute to more efficient motor responses [21]. Furthermore, physical characteristics such as height may influence RT through differences in neural conduction distance or biomechanical movement patterns [22]. In addition, recent driving-related studies have shown that brake pedal RT and lower-limb responses to visual stimuli are influenced by perceptual and motor factors, and that age-related delays are evident in foot-based response tasks during driving scenarios [23,24,25]. However, few studies have simultaneously examined such physiological and lifestyle factors, and research using foot-response tasks in older adults remains limited. Additionally, the shape of the RT distribution provides important information on response stability and attentional characteristics.
Based on these considerations, the present study aimed to compare RT characteristics between younger and older adults using the Foot PVT and to analyze multiple factors associated with RT. Accordingly, this study addressed the following research questions:
(i)
Do lower-limb RT and its distributional characteristics differ between younger and older adults?
(ii)
Are sleep-related factors, PAL, or physical characteristics such as height associated with RT performance?

2. Materials and Methods

2.1. Study Design

This cross-sectional observational study was conducted at the School of Design & Architecture, Nagoya City University from July 2024 to October 2025. The studies involving human subjects were reviewed and approved by the School of Design & Architecture, Nagoya City University Institutional Review Board (No. 6 Geirin-No. 1, approved 23 April 2024). All participants provided written informed consent prior to participation, and the study was conducted in accordance with the Declaration of Helsinki. This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [26].

2.2. Participants

The participants consisted of 20 younger adults (24 ± 3 years, range:22–29 years, 15 males and 5 females) and 24 older adults (73 ± 5 years, range:66–84 years, 12 males and 12 females). Before the experiment, we collected information on driving experience, visual acuity, height, and weekly PAL (types and durations of exercise). The average driving experience of the younger group was 3 ± 3 years (range: 0–9 years), while that of the older group was 48 ± 9 years (range: 32–60 years). We also confirmed that participants could correctly discriminate the three stimulus colors (red, yellow, blue) and verified that none had color vision deficiency. All participants were community-dwelling adults. The inclusion criteria were possession of a valid driver’s license and current engagement in driving in daily life. Participants with self-reported neurological, musculoskeletal, or psychiatric diseases were excluded from the study. All older participants were active drivers at the time of the experiment.
Because the Foot PVT is a recently developed task and the present study was exploratory in nature, no a priori power analysis was performed. However, in our previous study using the same task, significant age-related differences in RT were observed with a sample size of 11 participants per group [19]. The present study employed a larger sample size.

2.3. Foot PVT Task and Experimental Protocol

RT was measured using the Foot PVT application developed by the authors. Detailed specifications of this system are provided in our previous report [19]. On the home screen, participants entered the output file name and test duration. After pressing the Enter key, three white-outlined circles were displayed in the center of a black background. When the test began, one of the circles was randomly filled with color. The three circles were arranged from left to right as blue, yellow, and red (Figure 1).
The foot pedal device (USB Foot Switch, S23-P, Shenzhen Winsun Technology Co., Ltd., Shenzhen, Guangdong, China) consisted of three pedals (Figure 1). Before each trial, participants kept their right foot on the center pedal. When a blue stimulus appeared, they pressed the right pedal; when a red or yellow stimulus appeared, they pressed the left pedal (Figure 1). If the correct pedal was pressed, the screen displayed the RT along with the word “True,” after which the display returned to black for the next trial. If an incorrect pedal was pressed, the message “The opposite one more time” appeared, and the screen did not advance until the correct pedal was pressed. In the case of a false start, the message “False start” was displayed, and the participant returned their foot to the center pedal to wait for the next stimulus. RT was defined as the time from stimulus onset to the pedal press. The inter-stimulus interval varied randomly between 2 and 10 s.
The Foot PVT was administered between 11:00 and 14:00. Participants first completed a 5 min practice session, followed by a 10 min main test. All pedal operations were performed barefoot using the right foot. Participants were instructed to “press the pedal as quickly and accurately as possible.” The Foot PVT was conducted on a 15.6-inch laptop PC placed approximately 1.5 m in front of the participant. The chair height was individually adjusted to ensure that each participant could comfortably operate the pedals. The laptop was equipped with Windows 10 Pro (64-bit), an Intel® Core™ i5-6300U processor (2.40 GHz), and Intel® HD Graphics 520. To avoid the transient decrease in alertness associated with the post-lunch dip, all measurements were conducted before lunch, and participants were instructed to take their meal only after completing the Foot PVT session.

2.4. Sleep Assessment

To evaluate sleep conditions on the night prior to the experiment, the OSA Sleep Inventory MA version was administered [27,28]. Upon awakening on the test day, participants completed the questionnaire, which consists of 16 items such as “fatigue remains,” “I can concentrate,” and “I slept well.” Each item was rated on a four-point digital scale. Based on the responses, five standardized factor scores were calculated: sleepiness on rising (Sleep1), initiation and maintenance of sleep (Sleep2), dreaming (Sleep3), recovery from fatigue (Sleep4), and sleep duration (Sleep5). According to the scoring system, each factor score is standardized with a mean of 50.

2.5. Recorded Data

For each pedal press, the output file recorded the following five items: (a) stimulus color (Color), (b) correctness of the response (Evaluation), (c) interval until stimulus onset (Interval), (d) RT, and (e) elapsed time.
These data allowed calculation of correction time following an incorrect response. An example of the output format is shown in Table 1. In the Evaluation column, “T” denotes a correct response (True), and “F” denotes an incorrect response (False). In the example, a false start occurred on the third trial, yielding an RT of −3623 ms, indicating that the pedal was pressed 3623 ms earlier than the expected stimulus onset. In the fourth trial, an incorrect pedal was pressed in response to a red stimulus, followed by a correction press (4_Retry). The correction time for this error was recorded as the RT of 4_Retry (1770 ms). When an incorrect pedal is pressed, the display does not advance until the correct pedal is pressed, resulting in an Interval value of 0 ms for the retry trial.

2.6. Statistical Analysis

2.6.1. Overview and Primary Analysis

The primary statistical objective of this study was to compare mean RT between younger and older adults. Secondary analyses were conducted to examine age-related differences in the distributional characteristics of RT and to explore factors associated with RT performance.

2.6.2. Descriptive Statistics and Normality Assessment

Descriptive statistics were calculated for all variables. Continuous variables with normal distributions are presented as mean ± standard deviation, whereas variables that did not meet normality assumptions are summarized using median values.
Normality of RT measures (mean, standard deviation, median, skewness, and kurtosis), sleep-related factors, PAL [29], and height was assessed using the Shapiro–Wilk test.

2.6.3. Group Comparisons

For variables that satisfied normality and homogeneity of variance, intergroup comparisons between younger and older adults were performed using Student’s independent t-tests. When homogeneity of variance was violated, Welch’s t-test was applied. For variables that did not meet normality assumptions, the nonparametric Mann–Whitney U test was used.
Group comparisons were conducted for mean RT (primary outcome) and for secondary RT measures, including RT standard deviation, median, skewness, and kurtosis.

2.6.4. Correlation Analysis

Pearson’s correlation coefficients were calculated to evaluate associations between mean RT and explanatory variables, including sleep-related factors, height, and PAL. Correlation analyses were performed separately for younger adults, older adults, and the combined sample.

2.6.5. Multiple Regression Analysis

Multiple regression analysis was performed with mean RT as the dependent variable and age group, height, the five sleep-related factors, and PAL as explanatory variables. A bidirectional stepwise selection procedure was applied to identify significant predictors, given the exploratory nature of the study and the absence of strong a priori hypotheses regarding variable inclusion. In linear regression analysis, the assumption of normality pertains to the distribution of model residuals rather than to the distributions of the dependent or independent variables themselves.
Multicollinearity among explanatory variables was evaluated using the condition number. PAL was calculated as total weekly metabolic equivalent of task hours (METs·h/week), obtained by multiplying activity-specific METs values—based on established literature—by the weekly duration (hours) of each activity [30,31].

2.6.6. Additional Analyses

Because the frequency of incorrect responses (misses, false starts, and correction time) was low, these variables were summarized descriptively and were not subjected to inferential statistical testing.

2.6.7. Software and Significance Level

All statistical analyses were performed using Python (version 3.12.7; statsmodels and SciPy packages). The significance level was set at p < 0.05 for two-tailed tests.

3. Results

Table 2 presents the percentile distribution of PAL in the younger and older groups. The median PAL was 0 METs·h/week in the younger group and 15 METs·h/week in the older group, indicating that half of the younger participants had no regular exercise habits. In contrast, many older adults engaged in regular physical activity; overall, 8 younger and 21 older participants reported habitual weekly exercise. The interquartile range was wider in the older group (5.3–19.0 METs·h/week) than in the younger group (0–6.0 METs·h/week), and several older adults showed very high activity levels (up to 98 METs·h/week). These findings suggest that the older group demonstrated both greater variability and generally higher PAL. Among younger adults, activity typically consisted of short-duration, high-intensity exercise such as strength training or running performed 1–3 times per week, and walking for commuting was not included in PAL. In contrast, many older adults regularly engaged in light-to-moderate aerobic activities such as walking, early-morning radio calisthenics [32], swimming, or golf; among them, 14 participants reported continuous weekly walking. Thus, the older group exhibited a higher frequency of habitual exercise and a predominance of sustained aerobic activity.
Table 3 summarizes the means ± standard deviations and group comparisons for the five sleep-related factors, height, and PAL. None of the sleep-related variables differed significantly between younger and older adults (p = 0.102–0.930). In contrast, height was significantly greater in the younger group (168 ± 9 cm) than in the older group (162 ± 9 cm; p = 0.039), and PAL was significantly higher in the older group (4.9 ± 7.6 vs. 19.6 ± 23.2 METs·h/week; p = 0.002). The median PAL was 0 METs·h/week in younger adults and 15 METs·h/week in older adults, consistent with the finding that half of the younger group had no exercise habit. The older group showed a wide PAL range, with maximum values reaching 98 METs·h/week, creating a highly right-skewed distribution in which the lower bound of the mean ± standard deviation (SD) fell below zero.
Table 4 shows the basic RT statistics, highlighting age-related differences in the distributional characteristics of RT, particularly skewness and kurtosis. Both RT mean and RT median were significantly slower in the older group (RT mean: 700 ± 73 vs. 818 ± 105 ms, p < 0.001; RT median: 675 ± 72 vs. 796 ± 96 ms, p < 0.001). In contrast, RT SD did not differ significantly between groups (p = 0.284). RT skewness was significantly higher in younger adults (1.93 ± 0.99 vs. 1.41 ± 0.69, p = 0.047), and kurtosis was also significantly higher (7.20 ± 4.66 vs. 4.13 ± 3.24, p = 0.014). These findings indicate that the older group exhibited a flatter RT distribution with fewer extreme delayed responses. Correlation analyses showed no significant associations between RT mean and either skewness or kurtosis in either age group (r = 0.054–0.252, p > 0.28).
Table 5 presents the Pearson correlation coefficients between mean RT and each explanatory variable. In younger adults, greater height and higher Sleep1 scores were moderately associated with shorter mean RT (height: r = −0.593, p = 0.006; Sleep1: r = −0.557, p = 0.011). In the older group, however, no variables were significantly associated with mean RT. When all participants were pooled, greater height was associated with shorter mean RT (r = −0.478, p = 0.001). In contrast, higher PAL was associated with longer mean RT in the combined sample (r = 0.325, p = 0.032), but the direction of this association differed by age group (a negative trend in younger adults and a positive trend in older adults). Because many younger adults had PAL = 0, the between-group difference likely inflated the overall correlation; therefore, mean RT cannot be considered consistently associated with PAL. Significant correlations between mean RT and selected explanatory variables are illustrated in Figure 2.
Multiple regression analysis revealed that height was the only significant predictor of mean RT in younger adults, whereas no significant predictors were identified in older adults; in the combined sample, both height and age group were significant predictors of mean RT. Table 6 shows the results of the multiple regression analysis with RT mean as the dependent variable. Stepwise selection identified height as the only significant predictor in the younger group (B = −4.57, p = 0.006; adjusted R2 = 0.316; F = 13.75, p = 0.006), whereas no significant predictors were identified in the older group. In the combined model, height and age group were selected as significant explanatory variables (adjusted R2 = 0.372; F = 13.75, p < 0.001). Height had a negative regression coefficient (B = −3.89, p = 0.010), while age group had a positive coefficient (B = 95.12, p = 0.001). These findings indicate that greater height was associated with faster reactions, whereas higher age was associated with slower reactions. Overall, Foot PVT performance appears to reflect a combination of physical characteristics and age-related effects.
Table 7 summarizes the frequency of misses and false starts. Most participants completed the Foot PVT without any misses or false starts. The mean correction time was similar between groups (Younger: 666 ± 347 ms; Older: 714 ± 225 ms). Because these events were infrequent, no inferential statistical analyses were performed.

4. Discussion

This study investigated age-related differences in lower-limb RT using the Foot PVT and examined distributional characteristics and behavioral response patterns associated with aging. The overall findings demonstrated that older adults exhibited slower RTs but more stable and conservative response patterns compared with younger adults.
First, both RT mean and RT median were significantly slower in the older group, clearly confirming age-related declines in reaction speed even in the Foot PVT. This result is consistent with previous findings from finger-based PVT studies [33,34,35], reaffirming that aging slows neural processing from visual stimulus detection to motor execution.
Second, although RT SD did not differ significantly between groups, the distributional characteristics showed notable differences. The older group exhibited significantly lower skewness and kurtosis, indicating a flatter distribution with fewer extreme delays and greater response consistency. While aging generally slows neural processing, older adults may adopt a cautious response strategy that prioritizes avoiding excessively delayed responses.
Third, the older group showed fewer false starts, reflecting conservative behavior that suppresses premature responses. Such patterns align with prior research showing that older adults compensate for declines in cognitive and sensory function through safety-oriented driving behaviors, such as reducing speed and increasing following distance [36,37,38].
One motivation for including height as an explanatory variable was to examine whether neural conduction distance—from visual perception to lower-limb motor execution—could influence RT. Based on this rationale, taller individuals with longer central-to-peripheral pathways would theoretically be expected to show slightly longer RTs [39,40]. However, the present empirical findings did not support this hypothesis; instead, in the younger group, greater height was associated with shorter RTs.
This unexpected association suggests that factors other than neural conduction distance may contribute to lower-limb RT performance. The Foot PVT requires lateral foot movements involving ankle inversion/eversion and hip abduction/adduction, which resemble the rotational foot motions used during accelerator–brake transitions in driving. Previous biomechanical studies indicate that pedal switching relies primarily on lateral foot rotation rather than whole-leg movement and is accompanied by increased leg muscle activation [41]. It is therefore possible that taller individuals, who may differ in lower-limb geometry, muscle leverage, or movement strategy, can initiate lateral foot movements more efficiently, resulting in shorter RTs. However, these biomechanical interpretations are provisional and remain speculative, as direct measurements of muscle strength, joint range of motion, and lower-limb morphology were not obtained in the present study. Future studies incorporating such measurements are required to clarify the mechanisms underlying the observed height–RT relationship.
None of the five sleep-related factors differed significantly between groups, and their associations with RT were limited. In younger adults, Sleep1 showed a moderate negative correlation with RT, suggesting that subjective sleepiness or insufficient sleep impairs attention, consistent with prior research [2,42]. In contrast, no sleep factor correlated with RT in the older group. Age-related changes in sleep architecture (reduced deep sleep and increased awakenings) and larger circadian fluctuations in alertness may obscure such associations [43,44]. Older adults also show dissociations between subjective sleep ratings and physiological sleep indices [45], making single-time subjective assessments insufficient for capturing effects on attention. To minimize confounding from sleep inertia and the post-lunch dip, all measurements were conducted between 11:00 and 14:00. This interval has been shown in large-scale PVT studies to yield the most stable RT performance [46], supporting the validity of our protocol.
The participant sample exhibited a pronounced imbalance in PAL. Younger adults had a median PAL of 0 METs·h/week, indicating a largely inactive group, whereas older adults displayed substantially higher PAL, with many engaging in regular walking, swimming, radio exercises, or golf. Higher PAL is known to support cognitive and attentional functioning in older adults [47,48], suggesting that the observed RT difference of approximately 120 ms may underestimate the contrast that would be observed between younger adults and sedentary older adults. Conversely, physical inactivity has been associated with reduced attentional performance and increased response variability in younger adults [49,50]. In the present study, the large proportion of inactive individuals in the younger group may therefore have contributed to the greater variability observed in their RT distribution, rather than indicating any clinical cognitive impairment. Taken together, the older adults included in this study may represent a relatively healthier-than-average and more physically active subgroup, and the observed age-related differences in reaction time may be underestimated compared with those expected in more sedentary older populations.
Multiple regression analysis showed that height was the only significant predictor of RT in the younger group, explaining 31.6% of the variance. This finding suggests that individual differences in RT were strongly influenced by biomechanical factors. In contrast, no significant predictors emerged in the older group, implying that RT is shaped by multifactorial age-related changes, including neuromuscular decline and increased cautiousness. In the combined model including both age groups, height and age were selected as significant predictors, jointly explaining 37.2% of the variance. Height contributed to faster RTs, whereas age contributed to slower RTs. Overall, Foot PVT performance appears to be primarily determined by two stable individual characteristics—body size and aging—consistent with prior work demonstrating that PVT performance reflects multiple stable individual traits [46].
The RT distribution in the younger group was characterized by high skewness (1.93 ± 0.99) and high kurtosis (7.20 ± 4.66), indicating a heavy-tailed distribution with occasional extremely delayed responses. This pattern suggests greater susceptibility to transient attentional lapses despite generally faster responses. PVT research has emphasized that mean RT alone is insufficient to characterize attentional performance and that delayed responses and tail heaviness provide important insights [51,52], consistent with the present findings. In contrast, the older group exhibited lower skewness (1.41 ± 0.69) and lower kurtosis (4.13 ± 3.24), indicating fewer extreme delays and more homogeneous responses. This pattern suggests that older adults may prioritize response stability and avoid extremely delayed reactions, consistent with a cautious response strategy.
Several limitations should be acknowledged. First, PAL differed markedly between the two groups, with many highly active older adults; therefore, the present RT characteristics may not generalize to less active older populations. Second, sex was not included as an explanatory factor because the sex distribution was unbalanced in the younger group, and further stratification by sex would have resulted in insufficient sample sizes for reliable statistical analysis. Third, daily driving duration was not quantitatively assessed or controlled in this study and may represent an additional confounding factor, as driving exposure could influence lower-limb reaction performance. Fourth, biomechanical variables such as leg length, joint mobility, and muscle strength were not directly measured, leaving interpretations regarding the height–RT relationship inferential. Fifth, the Foot PVT relies on lateral foot movement, a specific motor modality; generalization to other RT tasks requires further investigation. Finally, sleep factors were assessed only using subjective questionnaires; incorporating objective sleep measurements may facilitate more precise evaluation of their relationship with attentional performance.

5. Conclusions

This study demonstrated that lower-limb RT measured using the Foot PVT differs between younger and older adults, with older adults exhibiting delayed responses accompanied by increased response stability. These findings indicate that aging affects not only reaction speed but also response regulation strategies during foot-operated tasks.
From a clinical and practical perspective, the Foot PVT may serve as a useful tool for evaluating lower-limb reaction capabilities relevant to driving and daily motor activities in older adults. Assessing foot-response characteristics may help clinicians and researchers better understand age-related changes in motor control that are not captured by hand-based RT tests. In addition, the Foot PVT may complement existing hand-based reaction time assessments by providing foot-specific information relevant to screening, monitoring, or training lower-limb response functions in driving-related and aging research contexts.
Future studies should incorporate direct measurements of biomechanical factors such as muscle strength, joint mobility, and lower-limb morphology, as well as objective sleep assessments, to clarify their contributions to reaction performance. Longitudinal studies and investigations in clinical populations will further support the application of the Foot PVT in aging and traffic-safety research.

Author Contributions

Conceptualization, Y.Y.; methodology, Y.Y.; software, Y.Y.; validation, Y.Y., K.Y.; formal analysis, Y.Y.; investigation, Y.Y.; resources, K.Y.; data curation, Y.Y.; writing—original draft preparation, Y.Y.; writing—review and editing, Y.Y.; visualization, Y.Y.; supervision, K.Y.; project administration, K.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The studies involving human subjects were reviewed and approved by School of Design & Architecture, Nagoya City University Institutional Review Board (No. 6 Geirin-No. 1, approved 23 April 2024). All participants provided written informed consent prior to participation, and the study was conducted in accordance with the Declaration of Helsinki.

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study.

Data Availability Statement

The RT datasets analyzed in this study are not publicly available due to privacy considerations. However, the software used for RT measurement and analysis is publicly available and can be downloaded as described in Reference [19]. Additional information may be obtained from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
Foot PVTfoot-response version of the PVT
METsmetabolic equivalent of task
PALphysical activity level
PVTpsychomotor vigilance test
RTreaction time
SDstandard deviation
STROBEstrengthening the reporting of observational studies in epidemiology

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Jal 06 00017 g001
Figure 1. Test screens used in the Foot PVT (upper panels) and the foot pedal device used in the experiment (lower panel). The foot pedal is a three-pedal type switch (size: 365 mm (W) × 143 mm (D) × 41 mm (H); weight: 382 g; switch type: photoelectric; interface: USB; cable length: 2 m; rating: 5 V/60 mA; service life: >2 million operations).
Figure 1. Test screens used in the Foot PVT (upper panels) and the foot pedal device used in the experiment (lower panel). The foot pedal is a three-pedal type switch (size: 365 mm (W) × 143 mm (D) × 41 mm (H); weight: 382 g; switch type: photoelectric; interface: USB; cable length: 2 m; rating: 5 V/60 mA; service life: >2 million operations).
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Figure 2. Scatter plots showing significant correlations with RT mean. Upper: Sleep1 vs. RT in the younger group; Middle: Height vs. RT in the younger group; Lower: Height vs. RT in all participants. Dotted lines indicate linear regression fits; regression equations and determination coefficients (R2) are shown in each panel. A higher Sleep1 score indicates less sleepiness upon waking.
Figure 2. Scatter plots showing significant correlations with RT mean. Upper: Sleep1 vs. RT in the younger group; Middle: Height vs. RT in the younger group; Lower: Height vs. RT in all participants. Dotted lines indicate linear regression fits; regression equations and determination coefficients (R2) are shown in each panel. A higher Sleep1 score indicates less sleepiness upon waking.
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Table 1. Example of recorded data.
Table 1. Example of recorded data.
Event No.ColorEvaluationInterval [ms]RT [ms]Elapsed Time [ms]
1YellowT644511087562
2BlueT636961114,566
3False startF9227−362320,170
4RedF7427110528,723
4_Retry RedT0177030,493
5YellowT506290936,472
Abbreviations: T, true response; F, false response; RT, reaction time; ms, milliseconds.
Table 2. Percentile distribution of PAL in younger and older adults. The PAL distribution was highly right-skewed; younger adults included many inactive participants, whereas older adults showed wide variability.
Table 2. Percentile distribution of PAL in younger and older adults. The PAL distribution was highly right-skewed; younger adults included many inactive participants, whereas older adults showed wide variability.
Statistic (METs·h/Week)Younger (n = 20)Older (n = 24)Comment
Minimum00Both groups included participants with no regular exercise.
25% tile05.3One quarter of younger adults did not exercise at all.
Median015.0Half of the younger adults had 0 METs·h/week, whereas older adults averaged 15.
75% tile6.019.0Upper quartile of older adults exercised regularly.
Maximum20.598.0A few older adults were extremely active (outlier level).
Abbreviations: PAL, physical activity level; METs, metabolic equivalent of task; h, hours.
Table 3. Group comparisons of sleep-related factors, PAL, and height.
Table 3. Group comparisons of sleep-related factors, PAL, and height.
FactorYounger (n = 20, Mean ± SD)Older (n = 24, Mean ± SD)Testp-Value
Sleep147.5 ± 8.649.1 ± 9.0S0.532
Sleep247.1 ± 7.845.8 ± 10.4S0.664
Sleep350.6 ± 10.149.7 ± 9.9M0.930
Sleep446.3 ± 9.650.8 ± 8.5S0.102
Sleep547.0 ± 7.651.2 ± 11.8M0.194
Height (cm)168 ± 9162 ± 9S0.039
PAL (METs·h/week)4.9 ± 7.619.6 ± 23.2M0.002
Statistical tests are indicated as follows: S = Student’s independent t-test (parametric, equal vari ances); M = Mann–Whitney U test (nonparametric).
Table 4. Descriptive statistics and group comparisons of RT.
Table 4. Descriptive statistics and group comparisons of RT.
IndexYounger (n = 20, Mean ± SD)Older (n = 24, Mean ± SD)Testp-Value
RT mean (ms)700 ± 73
(666–734)		818 ± 105
(773–863)		S<0.001
RT median (ms)675 ± 72
(641–709)		796 ± 96
(754–837)		S<0.001
RT SD (ms)122 ± 25139 ± 53M0.283
Skewness1.93 ± 0.991.41 ± 0.69S0.047
Kurtosis7.20 ± 4.664.13 ± 3.24S0.014
r (RT mean vs. Skewness)0.0540.109-0.822 (Older: 0.611)
r (RT mean vs. Kurtosis)0.252−0.135-0.283 (Older: 0.528)
Values in parentheses for RT mean and RT median represent the 95% confidence intervals. Statistical tests are indicated as follows: S = Student’s independent t-test (parametric, equal variances); M = Mann–Whitney U test (nonparametric).
Table 5. Pearson’s correlation coefficients between RT mean and explanatory variables.
Table 5. Pearson’s correlation coefficients between RT mean and explanatory variables.
FactorYounger (n = 20)Older (n = 24)All (n = 44)
rp-Valuerp-Valuerp-Value
Sleep1−0.5570.011−0.2610.216−0.2480.104
Sleep2−0.2230.346−0.1840.391−0.2000.194
Sleep30.3030.1940.0930.6640.1150.457
Sleep4−0.3630.115−0.1380.523−0.0410.791
Sleep5−0.3880.091−0.2670.206−0.1270.411
Height−0.5930.006−0.2790.188−0.4780.001
PAL (METs·h/week)−0.2690.2520.2310.2770.3250.032
Table 6. Results of multiple regression analysis with RT mean as the dependent variable.
Table 6. Results of multiple regression analysis with RT mean as the dependent variable.
ModelPredictorBSEtpAdjusted R2F (p)
(i) Younger Constant1467.18245.675.970.0000.31613.75
(0.006)
Height (cm)−4.571.46−3.130.006
(ii) Older Constant818.1521.8137.520.0000—
(iii) CombinedConstant1258.94255.714.920.0000.37213.75
(0.001)
Height (cm)−3.891.45−2.690.010
Age group95.1227.663.440.001
Age group (1 = younger, 2 = older), Combined model (younger + older).
Table 7. Frequency of misses and false starts in the Foot PVT.
Table 7. Frequency of misses and false starts in the Foot PVT.
CategoryYoung
(n = 20)		Older
(n = 24)		CategoryYoung
(n = 20)		Older
(n = 24)		Correction Time (ms)
Miss countCount of participantsFalse start countCount of participantsMean ± SD
0 times11160 times1019Young: 666 ± 347
Older: 714 ± 225
1 time461 time22
2 times412 times53
8 times113–9 times30
Numbers indicate the count of participants per category in each age group. Each participant pressed the pedals approximately 90 times during the 10 min Foot PVT session.
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Yoshida, Y.; Yokoyama, K. Comparison of Foot-Response Reaction Time Between Younger and Older Adults Using the Foot Psychomotor Vigilance Test. J. Ageing Longev. 2026, 6, 17. https://doi.org/10.3390/jal6010017

AMA Style

Yoshida Y, Yokoyama K. Comparison of Foot-Response Reaction Time Between Younger and Older Adults Using the Foot Psychomotor Vigilance Test. Journal of Ageing and Longevity. 2026; 6(1):17. https://doi.org/10.3390/jal6010017

Chicago/Turabian Style

Yoshida, Yutaka, and Kiyoko Yokoyama. 2026. "Comparison of Foot-Response Reaction Time Between Younger and Older Adults Using the Foot Psychomotor Vigilance Test" Journal of Ageing and Longevity 6, no. 1: 17. https://doi.org/10.3390/jal6010017

APA Style

Yoshida, Y., & Yokoyama, K. (2026). Comparison of Foot-Response Reaction Time Between Younger and Older Adults Using the Foot Psychomotor Vigilance Test. Journal of Ageing and Longevity, 6(1), 17. https://doi.org/10.3390/jal6010017

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