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Article

Beyond the Timed Up and Go: Dual-Task Gait Assessments Improve Fall Risk Detection and Reflect Real-World Mobility in Multiple Sclerosis

by
Michael VanNostrand
1,2,*,
Myeongjin Bae
1,
Natalie Lloyd
1,
Sadegh Khodabandeloo
3 and
Susan L. Kasser
1
1
Department of Rehabilitation and Movement Science, University of Vermont, Burlington, VT 05405, USA
2
Department of Health Sciences, Wayne State University, 259 Mack ave, Detroit, MI 48201, USA
3
Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA
*
Author to whom correspondence should be addressed.
Sclerosis 2025, 3(3), 22; https://doi.org/10.3390/sclerosis3030022
Submission received: 21 April 2025 / Revised: 14 June 2025 / Accepted: 20 June 2025 / Published: 22 June 2025
(This article belongs to the Special Issue Advances in Balance and Gait Assessment, Monitoring and Rehabilitation in Multiple Sclerosis)
Versions Notes

Abstract

Background: Falls are common among individuals with multiple sclerosis (MS), yet standard clinical mobility assessments—such as the Timed Up and Go (TUG)—may not fully capture the complexities of real-world ambulation, leading to suboptimal fall identification. There is a critical need to evaluate the ecological validity of these assessments and identify alternative tests that better reflect real-world mobility and more accurately detect falls. This study examined the ecological validity of the TUG and novel dual-task clinical assessments by comparing laboratory-based gait metrics to community ambulation in individuals with MS and evaluated their ability to identify fallers. Methods: Twenty-seven individuals with MS (age 59.11 ± 10.57) completed the TUG test and three novel dual-task mobility assessments (TUG-extended, 25-foot walk and turn, and Figure 8 walk), each performed concurrently with a phonemic verbal fluency task. After lab assessments, the participants wore accelerometers for three consecutive days. Gait speed and stride regularity data was collected during both the in-lab clinical assessments and identified walking bouts in the community. The participants were stratified as fallers or non-fallers based on self-reported fall history over the previous six months. Findings: Significant differences were observed between the TUG and real-world ambulation for both gait speed (p < 0.01) and stride regularity (p = 0.04). No significant differences were found in gait metrics between real-world ambulation and both the 25-foot walk and turn and TUG-extended. Intraclass correlation coefficient analysis demonstrated good agreement between the 25-foot walk and turn and real-world ambulation for both gait speed (ICC = 0.75) and stride regularity (ICC = 0.81). When comparing the TUG to real-world ambulation, moderate agreement was observed for gait speed (ICC = 0.56) and poor agreement for stride regularity (ICC = 0.41). The 25-foot walk and turn exhibited superior predictive ability of fall status (AUC = 0.76) compared to the TUG (AUC = 0.67). Conclusions: The 25-foot walk and turn demonstrated strong ecological validity. It also exhibited superior predictive ability of fall status compared to the TUG. These findings support the 25-foot walk and turn as a promising tool for assessing mobility and fall risk in MS, warranting further study.

1. Introduction

The progressive neurodegeneration associated with multiple sclerosis (MS) results in a continual decline in mobility [1]. Mobility impairment is often reported to be the most common and impactful symptom experienced by people with the disease [2], and several studies have shown that loss of mobility contributes to reduced community participation and functional independence [3,4]. As walking becomes more difficult, the fall risk increases and falls become quite prevalent, with over 50% of ambulant people with MS reporting at least one fall in any 3-month period [5]. Falls in individuals with MS often lead to fear of falling, reduced physical activity and community participation, and lower quality of life [6,7,8]. This inactivity further decreases strength, endurance, and mobility, increasing the risk for future falls [9,10]. Given the trajectory of mobility decline and its functional consequences over time, identifying and mitigating fall risk are important health imperatives.
Mobility impairment in MS is influenced by multiple factors, and growing evidence suggests that performing a cognitive task while walking (dual-tasking) increases fall risk [11]. Since cognitive dysfunction contributes to mobility impairment and affects over 60% of individuals with MS [12,13,14], incorporating cognitive–motor dual-task paradigms into walking assessments is essential. Lab-based studies have shown that dual-task walking reduces stride regularity and gait speed, both of which heighten fall risk [15,16,17,18]. Notably, real-world walking is indistinguishable from lab-based dual-task walking, exhibiting comparable gait speed and stride regularity [19]. Additionally, research has found strong associations between cognitive function, gait speed, and stride regularity in real-world setting, emphasizing the need to integrate cognitive tasks into mobility assessments to reflect everyday walking challenges [19,20,21,22].
Beyond incorporating cognition, it is also essential to further evaluate mobility challenges that individuals with MS find particularly difficult. As previous research has identified that a majority of falls occur indoors and during activities of daily living [23], assessments that include movements typically required in these contexts are crucial. As such, two common everyday movements that have been demonstrated to be more challenging for individuals with MS, postural transitions [24,25] and turning [26,27], are critical to include in mobility assessments. Since stride regularity is a sensitive measure of mobility decline and fall risk [28], and previous research has shown decreased stride regularity after turning [26], the inclusion of turns in mobility assessments for fall risk detection is vital to identify those with MS who have increased mobility decline.
While in-lab findings have identified dual-task walking, postural transitions, and turning as important indicators of mobility performance and disease progression [26,29,30], these insights have not been effectively translated into clinical assessments or early fall risk prediction [31]. For example, the timed 25-foot walk (T25-FW) lacks a cognitive task, turns, and postural transitions. Similarly, while the Timed Up and Go (TUG) includes a postural transition and turn, its short distance and lack of a cognitive task limit its ability to assess cognitive–motor interference. The TUG–cognitive has been introduced as a dual-task paradigm, but previous reviews have found it inadequate for fall risk assessment due to its failure to evaluate cognitive performance and the insufficient walking distance to induce meaningful cognitive–motor interference [32]. As a result, widely used clinical assessments fail to incorporate ecologically valid mobility components that may contribute to fall susceptibility in individuals with MS.
Given the limited ecological validity of the current assessments, novel dual-task mobility measures that reflect and correlate with real-world ambulation are essential. As such, the primary goal of this study is to examine the ability of novel dual-task mobility assessments to predict fall risk in individuals with MS. Additionally, we sought to compare both the novel dual-task assessments and the routinely performed TUG to real-world community ambulation in individuals with MS. We hypothesize that the novel dual-task assessments will better differentiate fallers from non-fallers with MS, leading to improved predictive accuracy. Additionally, we hypothesize that the novel assessments will be more ecologically valid, resulting in higher agreement with real-world ambulation compared to the TUG.

2. Methods

The participants were recruited from an existing database as well as through community outreach. Interested individuals contacted the principal investigator (PI) to initiate the enrollment process. To be eligible, the participants had to have physician-diagnosed MS based on the McDonald criteria (all subtypes) [33]. They also needed to report none-to-moderate mobility impairment, defined as a score of 0–6 on the Patient-Determined Disease Steps (PDDSs) [34]. The PDDS is a validated, self-reported measure of disability that approximates the Expanded Disability Status Scale (EDSS) [35], with higher scores indicating greater mobility impairment. Participants were excluded if they had experienced an exacerbation of symptoms in the previous month. This study did not limit medication use as an inclusion or exclusion criterion. Participants were excluded from the study if they had a neurological disorder or medical diagnosis other than MS that might cause significant balance problems. Individuals who were interested in this study underwent a phone screening to determine eligibility and, if eligible, provided written informed consent prior to testing. All study components had been approved by the Institutional Review Board at the authors’ institution and informed consent was received from all participants prior to this study.

2.1. In-Lab Testing

Demographic information, including age, gender, disease duration, MS subtype, and mobility impairment (PDDSs), was obtained. All subjects completed walking assessments wearing small wireless motion sensors (OPAL sensors, APDM Inc., Portland, OR, USA) [36]. Outcome measures included gait speed and stride regularity, as previous research had identified these metrics as measures affected in early stages of MS that could be detected with inertial sensors [37]. Gait speed and stride regularity were calculated in MATLAB (R2022a) using consistent algorithms previously validated for individuals with neurological conditions, enabling direct comparisons between real-world ambulation and laboratory-based environments [19,38]. For stride regularity, the values can range from 0 to 1, with higher values indicating increased stride regularity.
Once properly fitted, the participants were instructed to complete a series of walking assessments: TUG, TUG-extended, 25-foot walk and turn, and Figure 8 walk. All walking trials, except the TUG, were performed with a concurrent cognitive task. The participants walked at a self-selected pace and were allowed to use an assistive device if needed. The order of the four mobility assessments was randomized across the participants to control for order effects. A member of the research team closely spotted each participant during all walking trials to ensure safety, and rest breaks were provided as needed. To classify individuals as fallers or non-fallers, the participants were asked whether or not they had experienced a fall in the previous six months [39,40,41].

2.2. Walking Assessments

2.2.1. Timed Up and Go

The participants were positioned in a standard-height chair with their back against the chair’s backrest. Next, they were directed to rise from the seated position, walk a distance of 3 m to a cone, execute a turn, return to the chair, and resume a seated posture. The TUG is a clinically accessible measure of mobility that has proven to be both reliable and valid in individuals with MS [42].

2.2.2. Timed Up and Go Extended

Consistent with the research conducted by Evans et al. [43], the participants performed a modified TUG assessment, known as the TUG-extended. The participants began the assessment from a seated position in a stable and standard chair with armrests. They were instructed to stand up from their seated position, walk seven meters at a comfortable pace, make a 180-degree turn, and return to a seated position at the starting point. The TUG-extended is a reliable and sensitive measure of mobility in individuals with neurological conditions [44].

2.2.3. 25-Foot Walk and Turn

The participants completed a modified version of the 25-foot walk assessment [45]. The trial was conducted similarly to the traditional 25-foot walk, in which participants are instructed to walk a distance of 25 feet. However, in this novel assessment, the participants were instructed to walk the 25 feet, make a 180-degree turn, and return to the starting position. The 25-foot walk assessment has shown to be a valid measure of ambulatory performance in individuals with MS [46].

2.2.4. Figure 8 Walking

For this task, the participants were positioned at the midpoint of a Figure 8, with cones placed five feet away both in the anterior and posterior directions. They were instructed to walk in a Figure 8 pattern around the cones back to the starting point. The trial concluded once the participant completed one full Figure 8. Previous research has highlighted the reliability and validity of the Figure 8 walk test in individuals with MS [47].

2.3. Cognitive Outcome Measure

To assess cognitive performance while completing the novel clinical assessments, the participants of this study completed the phonemic verbal fluency task, an ecologically valid cognitive task that has shown to be sensitive at identifying deficits in processing speed and executive functions in individuals with MS [30]. The participants were instructed to name as many words as possible that begin with a given letter (i.e., F, A, and S) for the duration of the walking tasks. Letters were randomized across the three walking tasks (i.e., TUG-extended, 25-foot walk and turn, Figure 8 walk) for all participants. No task prioritization instructions were provided during dual-task trials. To standardize cognitive performance by time, the number of correct utterances was divided by the time taken to complete the trial in seconds.

2.4. Real-World Ambulation Data Collection

Upon completion of in-lab assessments, a subset of participants was fitted with a small, body-fixed inertial sensor (Axivity AX3, York, UK) taped to their lower back (between lumbar vertebrae 4 and 5). The AX3 sensor was programmed to a sampling frequency of 100 hertz and at a range of ±8 g. The recorded accelerometer data was stored locally on the sensor’s internal memory until it was extracted by a research team member. This sensor was used to capture real-world community gait speed and stride regularity for three days after in-lab testing. This time period was selected as previous research utilizing body-fixed inertial sensors identified three days as a sufficient amount of time to capture real-world ambulation data [28]. The subjects were instructed to leave the device on for the following three days and not to deviate from their usual daily activities. Upon completion of the three-day period, the sensors were collected from the participants and returned to the lab for data extraction and processing.

2.5. Real-World Ambulation Data Processing

Unfiltered 3-axis accelerometer data collected on AX3 sensors was exported and analyzed using the MATLAB (R2022a). Outcome variables of interest include both gait speed and stride regularity, and were calculated with algorithms previously used in persons with MS [21]. These algorithms were designed for optimal use with lower back sensors, and previous research has identified a high correlation between these algorithms and gait metrics calculated via the GaitRite instrumented walkway [48]. Consistent with previous literature that examined real-world ambulation in populations with neurological disorders, community data was only included for analysis if the walking trial included thirty or more seconds of continuous walking [19,21,28,49]. This criterion ensured that the collected data was representative of an individual’s typical gait pattern.

3. Analysis

To assess the utility of the mobility assessments, the participants were divided into two groups based on whether or not they had experienced a fall in the previous six months [39,40,41]. To compare the gait and cognition variables between fallers (≥1 fall) and non-fallers (0 fall) for the clinical assessments, an independent sample T-test was conducted, and Cohen’s d was included as a measure of effect and interpreted as small (d ≤ 0.20), moderate (d = 0.21–0.79), or large (d ≥ 0.80) [50]. A logistic regression model was utilized to evaluate the ability of gait speed and stride regularity to predict fall status for each clinical assessment. Additionally, in the three novel assessments, the number of correct utterances per second on the verbal fluency task was incorporated into each model. The area under the receiver operating curve (AUC) was computed from each logistic regression result, and model fit was assessed using the Hosmer–Lemeshow goodness-of-fit test.
To assess whether there was a significant difference between real-world gait metrics and gait metrics from the clinical assessments, a paired sample T-test was used with a p < 0.05 to signal significance. Effect sizes (Cohen’s d) were calculated between testing environments. To examine the strength of correlation between in-lab walking and real-world ambulation, a single measurement, two-way mixed effect, absolute agreement intraclass correlation coefficient (ICC) model with all in-lab mobility metrics was compared to at-home walking measures. Specifically, gait speed and stride regularity measures derived from in-lab clinical assessments were compared to the corresponding metrics obtained from AX3 sensors worn on the lower back for three consecutive days in the community. The ICC values were interpreted as showing poor reliability (below 0.5), moderate (0.5–0.75) good (0.75–0.90), and excellent (>0.90) [51]. All data analyses were performed in SPSS version 27.0 (SPSS Inc., Chicago, IL, USA).

4. Results

A summary of descriptive statistics is presented in Table 1 below. A convenience sample of 27 participants, 14 fallers and 13 non-fallers was included in this study. No differences were seen between groups for age (p = 0.63), years post diagnosis (p = 0.32), gender (p = 0.24), or MS subtype (p = 0.26). Though no differences were seen between group on the PDDSs (p = 0.07), a large effect size was observed (d = −0.74).

4.1. Fall Risk Results

A summary of the clinical assessment outcomes for each group is presented in Table 2 below. The results indicate no significant differences and moderate effect sizes between the groups in terms of gait speed on the TUG (p = 0.39, d = 0.34), TUG-extended (p = 0.39, d = 0.34), 25-foot walk and turn (p = 0.30, d = 0.41), and Figure 8 walk (p = 0.47, d = 0.28). Additionally, no significant differences were found for the TUG (p = 0.18, d = 0.54), TUG-extended (p = 0.13, d = 0.61), 25-foot walk and turn (p = 0.06, d = 0.77), and Figure 8 walk (p = 0.19, d = 0.52) in terms of stride regularity, although the 25-foot walk and turn demonstrated a moderate to large effect size, suggesting a potentially meaningful difference despite the lack of statistical significance.
Regarding verbal fluency task performance, a significant difference and large effect size was observed between the groups during the 25-foot walk and turn task (p = 0.02, d = 0.94), with the fallers group scoring lower on average. However, no significant differences were noted between the groups for the verbal fluency task during the TUG-extended (p = 0.97, d = 0.01) and the Figure 8 walk (p = 0.58, d = 0.22).
The contributions of all variables in each regression are presented in Table 3. The TUG correctly identified 64.30% of fallers and 61.50% of non-fallers, yielding an overall predictive value of 63.00%. The model fit was deemed acceptable (Hosmer–Lemeshow test, p = 0.49), and the Nagelkerke R2 was 0.10. The AUC statistic for this model was 0.67 (95% CI, 0.46–0.88). The TUG-extended assessment exhibited an overall predictive value of 51.90% (sensitivity 46.20%, specificity 57.10%). The goodness-of-fit results indicated that the data did not fit the model well (p = 0.03). The AUC of the model was 0.63 (95% CI, 0.42–0.85), and the Nagelkerke R2 was 0.13. The 25-foot walk and turn model demonstrated an acceptable fit (p = 0.06) and a Nagelkerke R2 of 0.32. Overall, the 25-foot walk and turn correctly identified 71.40% of fallers and had a specificity of 69.20%, resulting in a 70.40% predictive value. The AUC for this model was 0.76 (95% CI, 0.56–0.96). Lastly, the Figure 8 walk model exhibited an acceptable fit (p = 0.51) and had an overall predictive value of 63.00% (sensitivity 53.80%, specificity 71.40%). This resulted in an AUC of 0.67 (95% CI, 0.45–0.88) and a Nagelkerke R2 of 0.10.

4.2. Comparing Lab-Based Assessments to Real-World Ambulation

Table 4 below provides the results of both the paired sample T-test and ICC analyses. Statistically significant differences were observed in both gait speed and stride regularity between real-world ambulation and the traditional TUG. On average, the participants exhibited a significantly faster gait speed during the TUG than their real-world gait speed (p < 0.01, d = 0.70) and demonstrated significantly more stride regularity (p = 0.04, d = 0.48).
When examining the novel dual-task clinical assessments in relation to real-world ambulation, significant differences were observed in gait speed for the Figure 8 walk test (p = 0.03, d = 0.53), indicating slower gait speeds during the Figure 8 walk. However, no differences were found between real-world ambulation and the TUG-extended (p = 0.44, d = 0.18) or the 25-foot walk and turn (p = 0.37, d = 0.21) in terms of gait speed. Additionally, no differences in stride regularity were noted between real-world ambulation and the TUG-extended (p = 0.52, d = 0.15), 25-foot walk and turn (p = 0.22, d = 0.29), and Figure 8 walk (p = 0.53, d = 0.15).
The ICC results comparing real-world ambulation with the TUG demonstrated moderate reliability for gait speed (ICC = 0.56) and poor reliability for stride regularity (ICC = 0.41). Comparing the TUG-extended to real-world ambulation, moderate reliability was observed for both gait speed (ICC = 0.66) and stride regularity (ICC = 0.63), respectively. Similar findings were seen in the comparison between the Figure 8 walk and real-world ambulation, as the ICC analysis yielded moderate reliability for both gait speed (ICC = 0.61) and stride regularity (ICC = 0.74). Finally, when comparing the 25-foot walk and turn with real-world ambulation, the results indicated good reliability for both gait speed (ICC = 0.75) and stride regularity (ICC = 0.81).

5. Discussion

The limited ecological validity of current clinical assessments highlights the challenges associated with understanding balance and mobility impairments in individuals with MS. Current clinical mobility assessments such as TUG often fail to incorporate a secondary cognitive task, thereby limiting their ability to reproduce the complexity of real-life scenarios in which dual-tasking is common. Given the critical role of dual-tasking in the daily lives of individuals with MS [52], incorporating it into lab-based assessments is essential. Thus, this study investigated the utility of three novel dual-task mobility assessments and found that the dual-task 25-foot walk and turn test better predicted fall risks and demonstrated greater correlation with real-world gait characteristics than the traditional TUG in individuals with MS. Such findings highlight the importance of considering ecological significance in mobility tests for better evaluating fall risk in individuals with MS.
The findings of this study highlight the importance of evaluating factors beyond mobility alone when assessing fall risk in individuals with MS. Although no statistically significant group differences were found for gait speed or stride regularity across clinical assessments, the fallers generally walked more slowly and exhibited reduced stride regularity. These trends, coupled with moderate effect sizes, suggest that the absence of significant differences may be attributed to the limited sample size. Notably, the 25-foot walk and turn assessment yielded larger effect sizes for both gait speed (d = 0.41) and stride regularity (d = 0.77) compared to the TUG (gait speed d = 0.34, stride regularity d = 0.54), underscoring its potential utility for differentiating fall risk. Notably, cognition did differ between fallers and non-fallers in the 25-foot walk and turn task, and it was the variable that made the most substantial contribution to this model. These findings are consistent with previous studies and underscore the importance of incorporating and quantifying cognitive aspects in mobility assessments for fall risk evaluation [53]. The findings from this study suggest that, when faced with simultaneous cognitive and motor tasks, individuals at a higher risk for falls may adopt a “posture-first” strategy. In this strategy, they prioritize maintaining balance and may sacrifice cognitive performance as a protective mechanism against falling [54]. This prioritization approach might explain why, under dual-task conditions, no significant differences were observed between groups in mobility measures, while significant differences were seen for cognition.
While the 25-foot walk and turn assessment showed increased sensitivity (71.40%) compared to the TUG (64.30%), particularly due to the cognitive outcome variable, these findings were inconsistent with the results obtained from the other mobility assessments conducted in this study, namely, the Figure 8 walk (53.80%) and the TUG-extended (46.20%). While these seemingly contradictory results warrant further investigation, they may likely be best explained by considering the attentional demands of each task. The 25-foot walk and turn involves fewer turns and/or postural transitions compared to the Figure 8 walk and the TUG-extended. Previous research has highlighted that both turning and postural transitions are increasingly challenging and require additional resources for individuals with MS [24,26,27,55,56]. Therefore, in the 25-foot walk and turn assessment, non-fallers with MS may have been able to allocate more cognitive resources to prioritize and execute the cognitive aspects of the task effectively, resulting in improved sensitivity in identifying fall risk. On the other hand, in the Figure 8 walk and TUG-extended assessments, the increased demands of turning and postural transitions might have led individuals in both faller and non-faller groups to prioritize their mobility over cognition. Consequently, this prioritization may have resulted in similar outcomes for both groups in these assessments. Future studies with larger sample sizes are warranted to further explore group differences in specific components of these tasks—such as turn velocity and sit-to-stand performance—which have previously been shown to distinguish fallers from non-fallers with MS [24,25,57].
The results obtained from the TUG in our study align with previous investigations, highlighting its limited effectiveness in identifying individuals with MS at risk of falls. We observed an AUC of 0.67, which is consistent with previous research [31,58,59,60]. To further explore this, we compared gait speed and stride regularity from the TUG with real-world mobility metrics. Individuals with MS walked faster and with more regular strides during the TUG than in daily life, suggesting that the TUG may not capture the complexity of real-world walking. These findings are consistent with previous studies that identified single-task walking in lab-based environments does not accurately reflect real-world ambulation [33]. The tendency for improved performance in controlled environments—particularly when free of cognitive demands—raises concerns about the ecological validity of the TUG. Given its limited real-world applicability and suboptimal predictive power [32,58,61,62], future work should focus on fall risk assessments that incorporate cognitive and environmental challenges reflective of everyday mobility.
Given the limited ecological validity of the TUG, this study examined the gait metrics of novel dual-task clinical assessments—the TUG-extended, 25-foot walk and turn, and Figure 8 walk—by comparing them to real-world ambulation. All three showed stronger correlations to real-world gait speed and stride regularity than the traditional TUG. However, key differences emerged. The Figure 8 walk, while moderately correlated with real-world mobility, showed significantly slower gait speeds, likely due to the increased turning demands, which are known to challenge individuals with MS [26,27,56]. In contrast, the TUG-extended and 25-foot walk and turn did not differ significantly from real-world walking, suggesting greater ecological validity when paired with a phonemic verbal fluency task. ICC analyses revealed that while both assessments showed moderate reliability, the 25-foot walk and turn demonstrated stronger associations with real-world gait speed (ICC = 0.75) and stride regularity (ICC = 0.81), compared to the TUG-extended (ICC = 0.66 and 0.63, respectively). These differences may be attributed to the TUG-extended’s inclusion of postural transitions and turns, introducing variability not typically seen in sustained community walking. The strong alignment of the 25-foot walk and turn with real-world metrics, along with its increased fall detection seen in this study, highlights its potential for mobility and fall risk assessments, warranting further research.
While this study had important strengths, its limitations should be acknowledged. Retrospective falls were utilized as the primary outcome, which may introduce recall bias, as identified in previous research [63]. Additionally, the small sample size of individuals with mild-to-moderate ambulation disability limits the generalizability of the findings to the broader MS community. Although this study aimed to explore novel assessments for potential use in clinical settings, the gait metrics were derived from inertial sensors, which may not be feasible for widespread clinic-based fall risk assessments. Future work should identify gait speed and stride regularity metrics that are practical for routine clinical use. Lastly, the multiple comparisons conducted in this study raise the potential for Type I error, and future research should consider appropriate statistical corrections and validation in larger cohorts to strengthen the robustness of the findings.

6. Conclusions

Enhancing the ecological validity of mobility assessments is essential for capturing the real-world challenges faced by individuals with MS during everyday walking. Among three novel clinical assessments, the dual-task 25-foot walk and turn demonstrated strong alignment with real-world gait characteristics and showed greater fall risk detection compared to the traditional TUG. Furthermore, although not statistically significant, the effect-size calculations indicated that the dual-task 25-foot walk and turn seems to better differentiate fallers and non-fallers compared to the traditional TUG. This study highlights the importance of incorporating cognitively demanding, ecologically relevant dual-task paradigms into clinical assessments. However, future research recruiting larger samples and tracking prospective falls is warranted to further strengthen our findings.

Author Contributions

Conceptualization, M.V.; Methodology, M.V. and S.L.K.; Formal analysis, M.V.; Investigation, M.V.; Resources, S.L.K.; Data curation, M.V., M.B., N.L. and S.K.; Writing—original draft, M.V.; Writing—review & editing, M.B. and S.L.K.; Supervision, S.L.K. 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 study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board at the University of Vermont (#STUDY00002180 Approved: 28 September 2022).

Informed Consent Statement

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

Data Availability Statement

Data supporting the conclusions of this article will be made available by the authors upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Descriptive statistics between fallers and non-fallers with MS.
Table 1. Descriptive statistics between fallers and non-fallers with MS.
VariablesFallers (N = 14)Non-Fallers (N = 13)p-ValueCohen’s d
Age (years ± SD)60.07 ± 11.3158.08 ± 10.060.63−0.18
Years post diagnosis (years ± SD)24.00 ± 15.4019.23 ± 7.670.320.38
PDDSs (median [range])2 (0–6)1 (0–6)0.07−0.74
Gender (% female)92.8676.920.24--
MS subtype8 RR, 5 SP, 1 PP11 RR, 2 SP0.26--
PDDS: Patient-Determined Disease Steps; RR: relapse-remitting; SP: secondary progressive; PP: primary progressive.
Table 2. Mobility assessment outcomes for both fallers and non-fallers.
Table 2. Mobility assessment outcomes for both fallers and non-fallers.
TestMetricsFallers (N = 14)Non-Fallers (N = 13)p-ValueCohen’s d
TUGGait speed (m/s)1.04 ± 0.321.14 ± 0.270.390.34
Stride regularity (arbitrary units)0.57 ± 0.140.65 ± 0.160.180.54
TUG-extendedGait speed (m/s)0.87 ± 0.290.96 ± 0.270.390.34
Stride regularity (arbitrary units)0.46 ± 0.190.57 ± 0.170.130.61
Verbal fluency (utterances per second)0.32 ± 0.170.32 ± 0.140.970.01
25-foot walk and turnGait speed (m/s)0.83 ± 0.300.95 ± 0.240.300.41
Stride regularity (arbitrary units)0.44 ± 0.200.59 ± 0.180.060.77
Verbal fluency (utterances per second)0.27 ± 0.120.37 ± 0.080.02 *0.94
Figure 8 walkGait speed (m/s)0.79 ± 0.270.87 ± 0.250.470.28
Stride regularity (arbitrary units)0.44 ± 0.200.59 ± 0.180.190.52
Verbal fluency (utterances per second)0.33 ± 0.150.35 ± 0.100.580.22
TUG: Timed Up and Go; TUG-extended: Timed Up and Go extended; * denotes significant difference (p < 0.05).
Table 3. Logistic regression analysis results for clinical mobility assessments.
Table 3. Logistic regression analysis results for clinical mobility assessments.
AssessmentR2Goodness of FitAUCAUC 95% CISensitivitySpecificityPredictive Value
TUG0.100.490.670.46–0.8864.30%61.50%63.00%
TUG-extended0.130.030.630.42–0.8546.20%57.10%51.90%
25-foot walk and turn0.320.060.760.56–0.9671.40%69.20%70.40%
Figure 8 walk0.100.510.670.45–0.8853.80%71.40%63.00%
AUC: area under the curve; CI: confidence interval TUG: Timed Up and Go.
Table 4. Accelerometer-derived mobility outcome metrics for both real-world ambulation and in-lab clinical assessments and their associated p-values, effect sizes, and reliability statistics with 95% confidence intervals.
Table 4. Accelerometer-derived mobility outcome metrics for both real-world ambulation and in-lab clinical assessments and their associated p-values, effect sizes, and reliability statistics with 95% confidence intervals.
MetricsTestMean ± SDp-ValueCohen’s dICCICC
95% CI
Gait speed (m/s)Real-world ambulation0.98 ± 0.29--------
TUG1.14 ± 0.29<0.01 *0.690.560.12–0.81
TUG-extended0.93 ± 0.290.440.180.660.32–0.85
25-foot walk and turn0.93 ± 0.300.370.210.750.48–0.89
Figure 8 walk0.85 ± 0.270.03 *0.530.610.23–0.83
Stride regularity (arbitrary units)Real-world ambulation0.51 ± 0.21--------
TUG0.61 ± 0.170.04 *0.480.410.01–0.70
TUG-extended0.53 ± 0.200.520.150.630.41–0.89
25-foot walk and turn0.54 ± 0.210.220.290.810.59–0.92
Figure 8 walk0.49 ± 0.160.530.150.740.46–0.89
* Denotes significant difference (p < 0.05) from real-world ambulation, ICC values in relation to real-world ambulation, TUG: Timed Up and Go, TUG-extended: Timed Up and Go extended.
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VanNostrand, M.; Bae, M.; Lloyd, N.; Khodabandeloo, S.; Kasser, S.L. Beyond the Timed Up and Go: Dual-Task Gait Assessments Improve Fall Risk Detection and Reflect Real-World Mobility in Multiple Sclerosis. Sclerosis 2025, 3, 22. https://doi.org/10.3390/sclerosis3030022

AMA Style

VanNostrand M, Bae M, Lloyd N, Khodabandeloo S, Kasser SL. Beyond the Timed Up and Go: Dual-Task Gait Assessments Improve Fall Risk Detection and Reflect Real-World Mobility in Multiple Sclerosis. Sclerosis. 2025; 3(3):22. https://doi.org/10.3390/sclerosis3030022

Chicago/Turabian Style

VanNostrand, Michael, Myeongjin Bae, Natalie Lloyd, Sadegh Khodabandeloo, and Susan L. Kasser. 2025. "Beyond the Timed Up and Go: Dual-Task Gait Assessments Improve Fall Risk Detection and Reflect Real-World Mobility in Multiple Sclerosis" Sclerosis 3, no. 3: 22. https://doi.org/10.3390/sclerosis3030022

APA Style

VanNostrand, M., Bae, M., Lloyd, N., Khodabandeloo, S., & Kasser, S. L. (2025). Beyond the Timed Up and Go: Dual-Task Gait Assessments Improve Fall Risk Detection and Reflect Real-World Mobility in Multiple Sclerosis. Sclerosis, 3(3), 22. https://doi.org/10.3390/sclerosis3030022

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