Preliminary Validity of a Wireless Lighting System (WLS) for Cognitive–Motor Response Assessment,and Its Relationship with Motor Function and Activities of Daily Living in Patients with Stroke

Abstract

Background

As interest in dual-task rehabilitation for stroke patients, which integrates cognitive and motor functions, continues to grow, there is increasing recognition of the need to expand cognitive assessments to include motor components.

Objective

This study aimed to evaluate the preliminary evidence for the validity of the Wireless Lighting System (WLS) as a sensor-based cognitive–motor response assessment tool in stroke patients and to investigate its relationship with motor function and activities of daily living (ADL).

Methods

Sixty-four patients with stroke were recruited from ‘G’ rehabilitation hospital. Cognitive function was evaluated using the Korean version of the Mini-Mental State Examination (MMSE-K). Motor function and ADL were assessed using a digital handheld dynamometer, the Berg balance scale, the 10-meter walk test, and the Korean version of the modified Barthel Index, respectively. Cognitive–motor response times were measured using a WLS (Witty SEM).

Results

All WLS cognitive–motor response times were moderately negatively correlated with the MMSE-K total score (r = −0.431 to −0.469). Grip strength was only negatively related to the reaction time task (r = −0.363). However, moderate correlations with all WLS tasks were observed for balance (r = −0.512 to −0.602), gait (r = 0.399 to 0.513), and ADL (r = −0.563 to −0.650).

Conclusion

These findings provide preliminary evidence supporting the potential utility of the WLS as a sensor-based cognitive–motor response assessment tool in identifying combined cognitive and motor impairments. However, given this study's cross-sectional design and single-center setting, further longitudinal and validation studies are required to establish clinical applicability.

Keywords

activities of daily living balance cognitive function gait rehabilitation stroke

Introduction

Stroke is a leading cause of disability and death globally, impacting over 12 million individuals annually (Feigin et al., 2022). The prevalence of stroke is increasing due to population growth and aging, making it the second most common cause of acquired disabilities worldwide. Post-stroke cognitive impairment is a frequent complication, significantly elevating the risk of developing dementia following a stroke event (Delgado et al., 2022), potentially accelerating dementia onset by up to 10 years (Mijajlović et al., 2017). The risk of post-stroke dementia within one year is 50% higher in patients with stroke than in the general population (Pendlebury & Rothwell, 2019), and approximately 40% of stroke patients exhibit mild cognitive impairment (Sexton et al., 2019). Poststroke cognitive impairment is a crucial outcome measure with significant downstream consequences. More severe cognitive impairment correlates with poorer motor function, reduced ability to perform activities of daily living (ADL), and increased mortality (Zietemann et al., 2018). This underscores the importance of addressing post-stroke cognitive impairment.

The Mini-Mental State Examination (MMSE) is the most widely used tool for screening post-stroke cognitive impairment. Several studies have reported the validity of the MMSE as a screening tool for patients with stroke (Agrell & Dehlin, 2000; Appelros, 2005). In a previous study, the MMSE was moderately effective in screening for mild cognitive impairment and suitable for moderate cognitive deficits or dementia in patients with stroke one-month post-stroke (Bour et al., 2010). However, the MMSE has limited sensitivity for executive dysfunction, which is commonly observed in post-stroke populations (Nys et al., 2005b; Pendlebury et al., 2010). Despite this limitation, the MMSE-K was selected for the present study because of its widespread clinical use, ease of administration, and suitability as a general cognitive screening tool in rehabilitation settings (Khaw et al., 2021). Therefore, in the present study, this tool was used as a pragmatic reference measure, rather than as a comprehensive assessment of executive function.

A growing body of research has demonstrated an association between cognitive and motor function among older adults with dementia (Kang et al., 2022; Park et al., 2020) and individuals with stroke. Einstad et al. (2021) investigated the association between motor and cognitive functions after stroke and found that motor performance was correlated with memory, executive function, and overall cognition. Balance, gait dysfunction, and ADL are critical, measurable, and early indicators of cognitive impairment in patients with stroke (Lee et al., 2021; Ursin et al., 2015). Ursin et al. (2015) also found a significant relationship between cognitive impairment, balance, and gait function in patients one-year post-stroke. Lee et al. (2021) demonstrated the impact of cognitive impairment post-stroke on ADL, noting that a considerable number of cognitively impaired patients tended to be more dependent. Although some evidence has been derived from studies on older adults with dementia, these findings are consistent with those observed in stroke populations, as both groups exhibit similar patterns of impairment associated with cognitive decline, such as reduced executive function and dual-task performance (Kang et al., 2022; Montero-Odasso, Muir, et al., 2012; Montero-Odasso, Verghese, et al., 2012; Park et al., 2020; Plummer et al., 2013).

With a growing focus on dual-task rehabilitation, which integrates cognitive and motor functions, expanding cognitive assessments to include motor functions may provide valuable insights for future research. Dual-task performance assessments are clinically important as they simulate real-life conditions for ADL performance and can better predict potential risks, such as falls (Kearney et al., 2013; Montero-Odasso, Muir, et al., 2012; Montero-Odasso, Verghese, et al., 2012). Ecological testing that links cognitive and motor aspects can more realistically evaluate cognitive impact on daily tasks. In ADL, individuals often face cognitively demanding physical tasks, such as food preparation, navigating obstacles, and quick reactions (Béraud-Peigné et al., 2023).

Recently, computer-based Wireless Lighting System (WLS), such as BlazePod, Fitlight, and Witty SEM, have emerged as tools for cognitive–motor response assessment. These systems prompt participants to respond swiftly and accurately to visual stimuli presented through multiple LED modules. WLS enhance computerized testing by providing precise response latency in milliseconds and improving test sensitivity. This technology-based approach enables the objective evaluation of subtle changes in cognitive–motor responses, reduces subjective evaluator errors, and yields measurable outcomes, such as reaction time and accuracy (Béraud-Peigné et al., 2023). Béraud-Peigné et al. (2023) illustrated the effectiveness of the WLS in evaluating cognitive function in older adults. This tool can evaluate a broad spectrum of cognitive functions, minimizing floor and ceiling effects, to match a patient's performance level. Although the utility of WLS has been demonstrated in healthy older adults, application in patients with stroke remains unexplored. Therefore, the present study aimed to examine the preliminary validity of WLS (Witty SEM)-based cognitive–motor response assessment in patients with stroke, using the MMSE-K as a reference measure, and to subsequently explore its associations with motor function and ADL.

Material and Methods

Participants

This cross-sectional study enrolled 64 patients with stroke admitted to ‘G’ Rehabilitation Hospital between April and September 2025. The sample size was determined using G*Power software (version 3.1.9.7; Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany). A two-tailed test for correlation analysis was conducted with a significance level (α) of 0.05, an effect size (r) of 0.40, and a statistical power (1−β) of 0.80. The minimum sample size required was 43. Initially, 70 individuals were recruited, but six were excluded due to discharge, refusal of evaluation, or deteriorating health conditions. Ultimately, 64 participants were included, comprising stroke patients who had experienced ischemic infarction or cerebral hemorrhage within the prior 12 months. Exclusion criteria included: (1) recurrent stroke, (2) psychiatric disorders such as depression or schizophrenia, (3) musculoskeletal surgery within the past 6 months, (4) visual field deficits, and (5) severe communication difficulties. Table 1 provides detailed information on the general and clinical characteristics of participants. All participants were briefed on the study objectives and procedures, and signed a consent form before participation. The experimental procedures were approved and monitored by the Institutional Review Board of the KWU (No. 1041465-202504-HR-001-03). This study was registered with the Clinical Research Information Service (CRiS) of the Republic of Korea, which is listed on the World Health Organization International Clinical Trials Registry Platform (No. KCT0010603).

Table 1.

Demographic and Clinical Characteristics of Participants.

Characteristics	Data (N = 64)
Age (years)	72.52 ± 6.36^a
Height (cm)	163.11 ± 8.11
Weight (kg)	62.00 ± 10.89
Female/male, n (%)	35 (54.7)/29 (45.3)
Stroke duration (months)	6.05 ± 3.32
Stroke type, n (%)
Ischemic	46(71.9)
Hemorrhagic	18(28.1)
More-affected side, n (%)
Right, n (%)	34 (53.1)
Left, n (%)	26 (40.6)
Both, n (%)	4(6.3)
Modified Ashworth Scale (score)
More-affected side	0.75 ± 1.01
Less-affected side	0
Manual Function Test (score)
More-affected side	14.29 ± 9.43
Less-affected side	28.12 ± 2.39

Data are presented as the mean ± standard deviation^a. n: number of subjects.

Measurements

Cognitive Function Measure

The Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment in clinical and research settings. We used the Korean version of the MMSE (MMSE-K) endorsed by the National Institute of Neurological Communicative Disease and Stroke, Alzheimer's Disease, and Related Disorders Association. This evaluation tool comprises six sub-items: time and place orientation, memory registration and recall, attention and calculation, and language/visual-spatial construction. The scores were adjusted for participants’ educational levels and ranged from 0 to 30. Higher scores indicate better cognitive function, whereas lower scores indicate poorer function. The MMSE-K categorizes a score of 24 or above as “definitely normal,” 20–23 as “suspected dementia,” and 19 or below as “definite dementia.” The MMSE-K demonstrates strong or excellent test-retest reliability (r = 0.76–0.90), inter-rater reliability (intraclass correlation coefficient, ICC = 0.94–0.99), and concurrent validity (r = 0.83–0.92) in evaluating cognitive function in mild cognitive impairment, Alzheimer's disease, and healthy adults (Baek et al., 2016).

Motor Function and ADL Measures

Motor functions such as grip strength, balance, and gait were assessed using a digital handheld dynamometer (DHD), Berg balance scale (BBS), and the 10-meter walk test (10-MWT). Grip strength was initially evaluated as an indirect measure of overall muscle strength employing a DHD (Jamar Hydraulic Hand Dynamometer; Patterson Medical, Warrenville, IL, USA). The participants were seated upright with standardized upper limb positioning, including shoulder adduction, elbow flexion at 90°, and neutral forearm and wrist positions (Roberts et al., 2011). Two maximal isometric contractions were conducted for each hand and the highest recorded value was used for analysis, with a minimum of 30 s of rest between trials (Huang et al., 2022). This study demonstrated high test–retest reliability (ICC = 0.86 for both hands, p < 0.001). Subsequently, balance ability was evaluated using the BBS, a widely employed standardized tool for post-stroke patients. The BBS comprises the following 14 functional tasks: sitting-to-standing, standing unsupported, standing-to-sitting, transfers, turning 360°, standing with eyes closed, tandem standing, and single-leg standing. Each task is scored on a 5-point scale (0–4), yielding a maximum total score of 56. Higher scores indicate better balance ability. The BBS has demonstrated excellent inter-rater reliability (ICC = 0.95) and high concurrent validity (r = 0.71) in individuals post-stroke (Chou et al., 2006; Mao et al., 2002). Gait ability was assessed using the 10-MWT. Participants walked along a straight walkway at a self-selected or fast pace without running, and the middle segment (10 m) of the walkway was timed to exclude the acceleration and deceleration phases. Walking speed was quantified in meters per second (m/s). The 10-MWT has shown high test–retest reliability (ICC = 0.83) and robust concurrent validity (r = 0.80–0.95) in individuals post-stroke (Cheng et al., 2020). Functional independence in ADL was assessed using the Korean version of the modified Barthel Index (MBI-K), which has been culturally adapted to reflect Korean lifestyle characteristics. This assessment comprises 10 items, including feeding, personal hygiene, bathing, dressing, toilet use, chair/bed transfers, ambulation, stair climbing, and bowel/bladder control. Each item is rated on a scale of 0 to 10, summing up to 100 points. Higher scores indicate greater independence in basic ADL. The MBI-K has demonstrated excellent reliability and validity, with a Cronbach's α of 0.994 and acceptable discriminative index values (0.78–0.91) (Choi et al., 2012).

Cognitive–Motor Response Measure

The WLS (Witty SEM, Microgate, Italy) comprised six sets of 5 × 7 cm semaphores equipped with LED matrices capable of displaying various colors and shapes. As illustrated in Figure 1, six witty semaphores were positioned, three at the top and three at the bottom. The testing commenced with a wireless control panel operated via radio transmission. The Witty Manager software enables researchers to develop new tests, and record and store the collected results. In this study, three tasks were formulated across three phases. The presentation of visual stimuli was randomized across trials to minimize any anticipatory responses. Notably, no separate pre-experimental practice session was conducted; however, for each test, a trial comprising five example stimuli was conducted to familiarize the participants with the task. During the test, participants stood in front of a centrally located semaphore at arm's length. Seated on a chair with their feet stationary, the participants used their less-affected hand to swiftly extinguish the light, returning their hand to the center of their hip between repetitions. Witty semaphores incorporate motor sensors that require a close proximity for extinguishment. Only the response time was recorded. If a light was touched incorrectly, the motor sensor did not register the response, and no separate errors were recorded. The test was performed without any interruption. No assistive devices were used during testing, and all assessments were conducted under the examiner's supervision to ensure participant safety. In a previous reliability study, Horička et al. (2024) exhibited excellent reliability of the Defensive Reactive Agility Test using the Witty SEM, with a statistically significant ICC value (0.91) for test time.

Figure 1.

Six semaphore settings used in the WLS.

Experimental Task

The WLS cognitive–motor response tasks comprised three phases (Phase 1: reaction time task, Phase 2: simple inhibition task, and Phase 3: complex inhibition task), and motor response times were measured for each phase (Figure 2). Each phase consisted of 20 trials, with each trial occurring 1,500 ms after the previous response. The reaction time was measured in the first phase (Figure 2A, reaction time task). The reaction time in response to simple visual stimuli was measured and utilized as an indicator of cognitive–motor response time. The participants were instructed to turn off a randomly lit red semaphore as quickly as possible while the other semaphore remained off. In the second stage (Figure 2B, simple inhibition task), we measured the reaction time for simple inhibition and evaluated its ability to suppress unnecessary responses. Participants were instructed to turn off a semaphore marked with a green letter ‘O’ as quickly as possible, while the other semaphores were displayed in blue, red, and green colors associated with the letter (e.g., A, B, C, D, E, F, G, or H). The third phase (Figure 2C, complex inhibition task) measured the reaction time for complex inhibition, which required higher levels of selective attention and cognitive inhibition than the simple inhibition task. Participants were tasked with turning off a semaphore marked with the green letter ‘O’. Other semaphores are marked with a green color associated with different letters (e.g., A, B, C, D, E, F, G, or H). The total time taken to complete each phase was recorded in seconds (sec) (Béraud-Peigné et al., 2023), including the reaction time (Phase 1), reaction time with simple inhibition (Phase 2), and reaction time with complex inhibition (Phase 3) during the WLS cognitive–motor response tasks. Each phase was performed once, with a 5-min resting time between each phase.

Figure 2.

WLS cognitive–motor response tasks: Phase 1 (reaction time task), Phase 2 (simple inhibition task), and Phase 3 (complex inhibition task).

Data Analysis

Descriptive statistics, including means and standard deviations, summarized participant characteristics, and data normality, were assessed using the Shapiro–Wilk test. As data were normally distributed, Pearson's product–moment correlation coefficients (r) with 95% confidence intervals (CIs) were used to examine the relationships between cognitive function (MMSE-K) and cognitive–motor response times to validate the WLS in patients with stroke as well as among cognitive–motor response ability (WLS), motor function (DHD, BBS, and 10-MWT), and ADL (MBI-K). Additionally, partial correlation analyses, controlling for age, sex, stroke duration, and lesion type, were conducted. Correlation strength was categorized as strong (r = 0.70–1.00), moderate (r = 0.30–0.69), weak (r = 0.10–0.29), or negligible (r < 0.10). All statistical analyses were performed using SPSS for Windows (version 21.0; IBM SPSS Statistics, Armonk, NY, USA). Statistical significance was set at 0.05, and Bonferroni correction was applied to control for Type I errors due to multiple comparisons.

Results

Pearson Correlations Between MMSE-K Total Score and Cognitive–Motor Response Times of the WLS

Higher MMSE-K scores were associated with shorter cognitive–motor response times (reaction time, reaction time with simple inhibition, and reaction time with complex inhibition) across the three WLS cognitive–motor response tasks, showing moderate negative correlations (r = −0.431 to −0.469). Additionally, significant positive associations were observed between the WLS cognitive–motor response tasks, with the reaction time task showing moderate to strong correlations with both the simple inhibition (r = 0.766) and complex inhibition (r = 0.685) tasks. A strong correlation was observed between the two inhibition tasks (r = 0.822) (Table 2).

Table 2.

Pearson Correlations Between MMSE-K Total Score and Cognitive–Motor Response Times of the WLS.

Variables	Mean ± SD	MMSE-K	Reaction Time Task	Simple Inhibition Task	Complex Inhibition Task
MMSE-K (score)	22.53 ± 6.01	1	-	-	-
Reaction time task (sec)	51.30 ± 3.42	−0.431*	1	-	-
Simple inhibition task (sec)	74.59 ± 26.26	−0.469*	0.766*	1	-
Complex inhibition task (sec)	74.39 ± 17.94	−0.437*	0.685*	0.822*	1

SD: Standard deviation; MMSE-K: Korean version of the Mini-Mental State Examination. Correlation coefficients (r) are presented with 95% confidence intervals. *p < 0.008 after Bonferroni correction.

Pearson Correlations Between MMSE-K sub-Item Scores and Cognitive–Motor Response Times of the WLS

Among the different WLS cognitive–motor response tasks, the reaction time task was significantly and negatively associated with the memory recall and language/visual–spatial construction (r = −0.405 to −0.470) MMSE-K sub-items. Further, the simple inhibition task demonstrated moderate negative correlations with memory recall and language/visual–spatial construction (r = −0.436 to −0.535). Similarly, the complex inhibition task was significantly negatively associated with memory recall and language/visual–spatial construction (r = −0.401 to −0.427) (Table 3).

Table 3.

Pearson Correlations Between MMSE-K Sub-Item Scores and Cognitive–Motor Response Times of the WLS.

MMSE-K sub-Items (score)	Reaction Time Task (sec)	Simple Inhibition Task (sec)	Complex Inhibition Task (sec)
Time orientation	−0.281	−0.230	−0.348
Place orientation	−0.270	−0.376	−0.288
Memory registration	−0.131	−0.008	−0.067
Attention and calculation	−0.116	−0.192	−0.155
Memory recall	−0.405*	−0.535*	−0.427*
Language/visual-spatial construction	−0.470*	−0.436*	−0.401*

MMSE-K: Korean version of Mini-Mental State Examination. Correlation coefficients (r) are presented with 95% confidence intervals. *p < 0.003 after Bonferroni correction.

Pearson Correlations of WLS Cognitive–Motor Response Times with Motor Function and ADL

Grip strength (DHD) exhibited a negative association solely with the reaction time task (r = −0.363), while no significant correlations were found for the inhibition tasks among the different WLS cognitive–motor response tasks. Conversely, balance (BBS, r = −0.512 to −0.602) and gait (10-MWT, r = 0.399 to 0.513) abilities both demonstrated consistent moderate correlations across all WLS cognitive–motor response tasks, with negative and positive correlations identified for balance and gait, respectively. Moreover, ADL showed moderate negative associations with all WLS cognitive–motor response tasks (r = −0.563 to −0.650) (Table 4).

Table 4.

Pearson Correlations of WLS Cognitive–Motor Response Times with Motor Function and ADL.

Variables	Reaction Time Task	Simple Inhibition Task	Complex Inhibition Task
DHD (kg)	−0.363*	−0.222	−0.224
BBS (score)	−0.512*	−0.602*	−0.564*
10-MWT (m/s)	0.483*	0.513*	0.399*
MBI-K (score)	−0.563*	−0.645*	−0.650*

DHD: Digital handheld dynamometer, BBS: Berg Balance Scale, 10-MWT: 10-meter walk test, MBI-K: Korean version of the Modified Barthel Index. Correlation coefficients (r) are presented with 95% confidence intervals. *p < 0.004 after Bonferroni correction.

Partial Correlations of WLS Cognitive–Motor Response Times with Motor Function and ADL

In partial correlation analyses controlling for age, sex, stroke duration, and lesion type, the WLS cognitive–motor response times were found to be significantly associated with the MMSE-K total score for reaction time (r = −0.397) and simple inhibition (r = −0.418) tasks, but not for the complex inhibition task. Balance (r = −0.504 to −0.597), gait (r = 0.462 to 0.549), and ADL (r = −0.557 to −0.636) all showed consistent moderate associations across all WLS cognitive–motor response tasks (all p < .001). However, grip strength was not significantly associated with any of the WLS cognitive–motor response tasks (Table 5).

Table 5.

Partial Correlations Between WLS and Clinical Variables.

Variables	Reaction Time Task	Simple Inhibition Task	Complex Inhibition Task
MMSE-K (score)	−0.397*	−0.418*	−0.365
DHD (kg)	−0.349	−0.225	−0.275
BBS (score)	−0.504*	−0.597*	−0.572*
10-MWT (m/s)	0.496*	0.549*	0.462*
MBI-K (score)	−0.557*	−0.629*	−0.636*

MMSE-K: Korean version of the Mini-Mental State Examination, DHD: Digital handheld dynamometer, BBS: Berg balance scale, 10-MWT: 10-meter walk test, MBI-K: Korean version of the modified Barthel index. Partial correlation coefficients (r), controlling for age, sex, stroke duration, and lesion type, are presented with 95% confidence intervals. *p < 0.003 after Bonferroni correction.

Discussion

This study aimed to evaluate the preliminary evidence for the validity of the WLS (Witty SEM) as a sensor-based cognitive–motor response assessment tool in stroke patients by comparing it with the MMSE-K and exploring its correlation with motor function and ADL. The main findings indicated that cognitive–motor response time measures (reaction time, reaction time with simple inhibition, and reaction time with complex inhibition) obtained from the WLS were all significantly correlated with scores on a standardized cognitive assessment (MMSE-K), motor function, and ADL. These results indicate that the WLS has significant potential as an assessment tool for examining the integration of cognitive and motor functions among patients with stroke.

The results of the WLS validity study provide preliminary evidence of a significant negative correlation between the MMSE-K total score and all WLS cognitive–motor response times. This indicates that higher cognitive function is associated with faster cognitive–motor response times. In addition, several MMSE-K sub-items, such as memory recall and language/visual-spatial construction, displayed significant correlations with all of the assessed reaction times in the WLS cognitive–motor response tasks. These results may reflect the cognitive demands of the task, which involves memory recall and visuospatial processing, as the participants were required to identify predefined shapes or colors and consequently execute appropriate motor responses. This study is partially consistent with the findings of Han et al. (2024), who reported that the MMSE-K total and sub-item scores, such as orientation, showed significant correlations with computer-based cognitive test results. However, some differences were observed in the pattern of associations. Firstly, the lack of a significant correlation observed in some MMSE-K sub-items may be attributed to differences in tool characteristics. While the MMSE-K screens overall cognitive status, the WLS evaluates more intricate cognitive–motor processing, including response selection and inhibitory control. Our findings indicate that the two tools share a common cognitive foundation and support the preliminary validity of the WLS as an instrument capable of assessing functional cognitive–motor performance characteristics that are difficult to detect using conventional cognitive tests alone.

In an analysis of correlations with motor function (e.g., grip strength, balance, and gait ability), grip strength exhibited a weak negative correlation with the reaction time task, suggesting that individuals with higher grip strength tended to respond more quickly to stimuli. This finding aligns with prior research, indicating that grip strength may be associated with fundamental information processing speed beyond mere muscle strength indicators (Firth et al., 2018; McGrath et al., 2019). However, no significant correlations were found in the simple and complex inhibition tasks, indicating that higher-order cognitive–motor control abilities, such as inhibitory control and attention shifting, may not be directly linked to grip strength (Sternäng et al., 2016). Balance ability demonstrated significant negative correlations with all the WLS cognitive–motor response tasks, implying that superior cognitive–motor response ability is linked to enhanced balance performance. Balance control is closely tied to cognitive aspects, such as attention allocation, response selection, and motor planning, rather than solely to posture maintenance (Woollacott & Shumway-Cook, 2002). Notably, moderate correlations between single and complex inhibition tasks underscored the significance of executive function and inhibitory control in maintaining balance in complex environments. Gait ability also exhibited significant positive correlations with all WLS cognitive–motor response tasks, affirming the intimate connection between cognitive–motor processing and gait performance. Gait necessitates sustained attentional control and environmental assessment, consistent with previous studies indicating that cognitive decline results in decreased gait speed and mobility (Ble et al., 2005).

In a correlation analysis with ADL, the MBI-K showed a significant negative correlation with all WLS cognitive–motor response tasks, implying a potential relationship between reaction speed, inhibitory control, and independent ADL performance. However, the most pronounced correlation was observed in the complex inhibition task, indicating a potential association between cognitive and motor processes relevant to ADL, including situational assessment, attentional shifting, and inhibition of superfluous movements (Donovan et al., 2008; Lee et al., 2021; Skidmore et al., 2010).

Overall, the present study demonstrated that WLS cognitive–motor response times were significantly associated with both cognitive and motor function and ADL measures in individuals with stroke. Notably, these associations remained evident even after controlling for any potential confounding variables (e.g., age, sex, stroke duration, and lesion type) and applying Bonferroni correction for multiple comparisons. Overall, the findings of these analyses indicate that WLS cognitive–motor response times are associated with functional outcomes, particularly balance, gait, and ADL. These results should be interpreted with caution, but may indicate that WLS performance to some extent reflects integrated cognitive–motor processes relevant to functional abilities. Additionally, the relationship with global cognitive function, as measured by the MMSE, appeared to be comparatively weaker, which may be attributable to the limited sensitivity of the MMSE in capturing executive function and inhibitory control required in WLS tasks (Nys et al., 2005a). These findings indicate that WLS may complement conventional cognitive assessments by reflecting more functionally relevant cognitive–motor integration. In contrast, grip strength showed slightly different results before/after controlling for confounding variables including age, sex, stroke duration, and lesion type. In the present study, grip strength was found to have no significant association with the WLS cognitive-motor reaction time after controlling for confounding variables, indicating that grip strength may not be directly linked to cognitive–motor response time when potential confounding variables are considered. This study indicates that this discrepancy may be partly explained by the limitation of assessing muscle strength only in the upper extremities, which may not fully capture the overall muscle strength relevant to cognitive-motor performance.

Overall, the results of this study indicate that the WLS has potential as an assessment tool for examining the integration of cognitive and motor functions in patients with stroke. Cognitive–motor response time measures derived from the WLS correlated not only with standardized cognitive assessment scores but also with motor function and ADL outcomes. These findings indicate that cognitive–motor response performance may be related to the functional abilities commonly affected after stroke. Therefore, sensor-based cognitive–motor response assessment tools such as the WLS may offer an approach for simultaneously capturing both cognitive and motor aspects by providing both objective and quantitative measures of response time and inhibitory control.

Despite these findings, this study has several limitations. Firstly, the cross-sectional design precludes drawing any causal inferences between cognitive and motor responses and functional recovery. Therefore, longitudinal studies are required to examine the changes over time and the effects of the interventions. Additionally, because this study was conducted in a single-center setting, the generalizability and clinical applicability of the findings may be limited, therefore warranting further multicenter validation. Second, the WLS task was assessed only once, which limited the evaluation of test–retest reliability. Third, grip strength was used as a proxy for overall muscle strength, meaning that lower extremity strength was not directly assessed. Fourth, the limited characterization of stroke severity and the absence of accuracy or error data from the Witty SEM task may have affected the interpretation of the findings. Finally, although MMSE was used as a standardized measure of cognitive function, it may have a limited sensitivity in capturing executive function and inhibitory control. Therefore, further longitudinal and validation studies incorporating diverse populations and additional standardized cognitive assessments are required to comprehensively evaluate the cognitive–motor aspects of WLS performance and establish its clinical applicability. These limitations should be considered when interpreting the strengths of the validity-related findings.

Conclusion

This study evaluated preliminary evidence of the validity of the WLS as a sensor-based cognitive–motor response assessment tool in stroke patients and examined its relationship with motor function and ADL. The results of this study showed that cognitive–motor response time measures derived from the WLS correlated not only with standardized cognitive assessment scores but also with motor function and ADL. Overall, these results provide preliminary evidence supporting the potential validity of the WLS in assessing cognitive–motor response performance in stroke patients, and suggest that cognitive–motor response measures may be associated with post-stroke functional limitations. As a sensor-based cognitive–motor assessment tool, WLS has the potential to provide objective and quantitative measures for assessing the integration of cognitive and motor functions in stroke patients. However, these findings should be interpreted with caution, and further research is required to establish the clinical applicability of WLS in rehabilitation settings.

Footnotes

ORCID iDs

Yun-Ho Kim

Nam-Gi Lee

Seul-Gi Seo

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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