The Walking Trail Making Test is more accurate than a Dual-Task Walking Test for screening the level of fall risk among community-dwelling older people

Rafael Mauti; Romain Tisserand; Anaïck Perrochon; Melanie Gallot; Arnaud Peronin; Thomas Gilbert; Patrick Fargier; Pascal Chabaud

doi:10.1371/journal.pone.0348875

Abstract

Background

Preventing falls is a major issue for older people. Aging leads to a decline in executive functions and functional capacities, increasing the risk of falling. Most cognitive-motor assessments are additional Dual-Tasks (DTs), open to prioritization strategies, leading to heterogeneous results in the screening of the level of fall risk. The Walking Trail Making Test (W-TMT) is an incorporated cognitive-motor task sensitive to detecting cognitive impairments. A high level of fall risk is generally associated with higher cognitive impairments. The objective of this study is to evaluate the detection performance of the most complex condition of the W-TMT (WTMT-B) in the screening of people at a high level of fall risk, and to compare it to an additional DT, the 6-Meter Walking Test in the DT condition (6MWT-DT).

Methodology

101 community-dwelling older people participated in the study. They were classified into three levels of fall risk (i.e., low, moderate, high) by the French Health Examination Center Fall Risk screening tool. They performed the three conditions of the W-TMT (N, A, and B) and the 6MWT in single-task and DT conditions. For both tasks, execution times were measured and compared between the levels of fall risk. Concordance index (c-index) analysis was performed to evaluate and compare the overall classification performance of the W-TMT-B and the 6MWT-DT for the three levels of fall risk. Receiver operating characteristic analyses (providing the Area Under the Curve, AUC) were conducted on the same variables to evaluate and compare the capacity of both tasks to detect people at a high level of fall risk.

Results

For the W-TMT, ANCOVA (corrected by spontaneous walking speed) showed significant group*condition interaction (F_4,194 = 8.27, p < .001). Post-hoc analyses showed that, only in the W-TMT-B condition, execution time was longer for people at a high level of fall risk vs moderate (M = 30.69s, SD = 15.45s vs M = 21.54s, SD = 7.17s, p = .048) and low levels (M = 20.16s, SD = 7.90s, p = .031). No differences were found for the 6MWT-DT execution times. The W-TMT-B better classified the levels of fall risk than the 6MWT-DT (c-index of respectively 0.63, 95%CI = .52−.74 vs 0.48, CI%95 = .38−.59, p = .03). The W-TMT-B also detected people at a high level of fall risk better than the 6MWT-DT (AUC of respectively 0.72, 95%CI = .53−.90 vs 0.46, 95%CI = .30−.62; p = .03) with a sensitivity of 61.54% and a specificity of 85.23%.

Conclusions

Screening the level of fall risk with the W-TMT-B condition is more accurate than with the 6MWT-DT. The detection performance of the W-TMT-B condition could be explained by a high cognitive-motor interference mobilizing cognitive flexibility in a complex walking context. In contrast to additional DT, the incorporated feature of the W-TMT-B condition prevents any task prioritization strategy. Consequently, the W-TMT-B could be a simple and low-cost tool using only the execution time to detect people at a high level of fall risk in community-dwelling older people.

Citation: Mauti R, Tisserand R, Perrochon A, Gallot M, Peronin A, Gilbert T, et al. (2026) The Walking Trail Making Test is more accurate than a Dual-Task Walking Test for screening the level of fall risk among community-dwelling older people. PLoS One 21(5): e0348875. https://doi.org/10.1371/journal.pone.0348875

Editor: Michael Francis Salvatore, University of North Texas Health Science Center, UNITED STATES OF AMERICA

Received: December 19, 2025; Accepted: April 22, 2026; Published: May 21, 2026

Copyright: © 2026 Mauti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are available at the following address: https://doi.org/10.5281/zenodo.19665098.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have read the journal’s policy and have the following competing interests: RM’s doctoral work is funded by the Ligue Auvergne Rhône Alpes de Rugby à XIII. Part of this funding comes from a grant from the Association Nationale de la Recherche et de la Technologie (CIFRE agreement No. 2021/1144). This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Abbreviations: 6MWT, 6-Meter Walking Test; AUC, Area Under the Curve; c-index, concordance index; BMI, Body Mass Index; CMI, Cognitive-Motor Interference; DT, Dual-Task; DTC, Dual-Task Cost; EF, Executive Functions; MoCA, Montreal Cognitive Assessment; ROC, Receiver Operating Characteristic; SRC-CES, Score de Risque de Chute des Centres d’Examen de Santé; ST, Single Task; TMT, Trail Making Test; W-TMT, Walking Trail Making Test

Introduction

Falls are a major health issue for the older population worldwide. One-third of people aged over 65 and half of people aged over 80 experience at least one fall each year [1]. Falls often result in costly injuries and represent a heavy burden on societies that will increase in the future [2]. The multiple and interdependent fall risk factors increase the heterogeneity of levels of fall risk in older adults [3]. To reduce the occurrence of falls, individuals at a high level of fall risk need to be reliably identified because they are more likely to benefit from fall prevention interventions [4,5]. Numerous tools exist to identify people at a high level of fall risk, and can be divided into two main categories: self-reported questionnaires and performance-based measures [6]. Considering that no consensus has been reached on a unique tool, there is still a need to develop multifactorial assessments that can reliably screen older people who are at a high level of fall risk [7].

Older adults fall mostly while walking. Consequently, many fall risk assessment tools focus on locomotion performance, particularly in continuous, unperturbed straight-line walking [8,9]. A prospective study showed that a spontaneous walking speed below the threshold of 0.7 m.s^-1 is associated with a high risk of falling [10]. However, the applicability of this threshold is limited in community-dwelling older people, because they typically have a higher spontaneous walking speed [11]. Using a complex walking task could make such assessments more relevant [12]. Complex walking involves changing direction, navigating on oriented pathways, or avoiding obstacles, which reflect everyday locomotion better than continuous, unperturbed straight-line walking [12,13]. Additionally, complexity is increased by requirement to divide attention between a walking task and a concurrent task, which particularly seems to increase fall occurrences in older people. People who struggle to maintain a conversation while walking are considered to be at a higher risk of falling than those who do not struggle [14]. Adding a cognitive task during simple walking (i.e., additional Dual-Task, DT) generally results in a slowing of walking speed [15]. This reflects the “Dual Task Cost” (DTC) (i.e., the cognitive cost of the concurrent cognitive task), and an increase in the DTC is associated with a high risk of falling [16,17].

Performance in additional DTs may depend on the prioritization strategy used by the participants (i.e., priority allocated to the locomotor or the additional cognitive task) because two distinct goals are present [18]. This prioritization strategy may result in inter-individual and intra-individual variations in performance that do not reflect the true probability of falling, thus limiting the relevance of using an additional DT to assess the level of fall risk. Moreover, studies suggest that the complexity of both the locomotor and cognitive tasks influences how relevant additional DTs are for fall risk assessment [12,19]. However, even when using an additional DT with a complex walking task, studies have obtained mixed results in predicting fall occurrences [20,21]. This may be due to the additive aspect of the DTs used, which still allows the participant to prioritize one task over the other [22]. Evidence also suggests that DT testing insufficiently detect people at a high level of fall risk with a sensitivity lower than 55.00% for different types of additional DT [23,24]. Thus, the most accurate type of DT remains to be determined, and alternative screening tools should be considered.

Incorporated cognitive-motor tasks are a promising alternative to additional DT, because the cognitive and motor tasks are interdependent. The single and shared overarching goal excludes any possibility of prioritization strategy [22]. Several incorporated cognitive-motor tasks involving complex walking have been developed based on neuropsychological assessments targeting fall risk-related factors [12]. Particularly, the Trail Walking Test, a walking task on a 5x5m square setup, has been designed based on the Trail Making Test (TMT) [25], a neuropsychological test assessing Executive Functions (EF) such as processing speed, visuospatial abilities, and cognitive flexibility [26]. As age-related decline in EF is a significant fall risk factor [27,28], this cognitive-motor task showed promising results in the screening of fall risk. A retrospective study found that the performance in the Trail Walking Test discriminated between fallers and non-fallers (based on history of fall) in community-dwelling older people [29]. A prospective study also found the Trail Walking Test more appropriate to predict falls than conventional mobility assessments (e.g., Timed Up and Go test and simple walking test) with a sensitivity of 66.10% and a specificity of 83.90% [30]. Thus, incorporated cognitive-motor tasks, designed based on the TMT test, can be considered particularly promising for predicting falls, as has been demonstrated in the case of the Trail Walking Test.

Another test derived from the TMT, the Walking Trail Making Test (W-TMT), has also been developed [31]. Compared to the Trail Walking Test, the W-TMT is quicker to set up and administer; it focuses on linear forward walking, requiring high precision in placing steps on targets while avoiding distractors, rather than on spatial navigation involving continuous changes in direction. The W-TMT consists of walking on targets following a cognitive rule in three separate conditions of increasing complexity: (i) W-TMT-N, that requires to walk on numeric targets in increasing order; (ii) W-TMT-A, the same as W-TMT-N but with distractors to avoid, and (iii) W-TMT-B, that requires to alternate between numeric and alphabetic targets (increasing and alphabetic order respectively) while avoiding distractors. Performance in the W-TMT is measured using execution time in each condition and Delta-WTMT (i.e., the cognitive cost of increased complexity between the B and A conditions). Interestingly, the execution time in the W-TMT-B condition and the Delta-WTMT were both significantly increased in people with EF-related cognitive impairments, a significant fall risk factor [31–33]. However, no study assessed whether the W-TMT performance could differentiate between different levels of fall risk in community-dwelling older people, compared to an additional DT involving simple walking.

The purpose of this retrospective study was to evaluate the detection performance of two different cognitive-motor tasks in the screening of a high level of fall risk in community-dwelling older people: the W-TMT, as an incorporated cognitive-motor task, and a simple walking DT, as an additional cognitive-motor task. Three hypotheses were formulated: (i) execution time in the W-TMT-B, the most complex condition, will be significantly higher for people at a high level of fall risk compared to people at lower levels; (ii) execution time in the W-TMT-B condition, will detect people at a higher level of fall risk better than chance; (iii) detection performance will be significantly better for the W-TMT-B than for the 6MWT-DT, which will perform no better than chance.

Methods

All data used in this study were extracted from the 13EVAL cluster randomized controlled trial (Clinical Trials: NCT05625828). The objective of the 13EVAL study was to compare the effects of two different physical exercise programs on functional mobility and executive functions: (i) the “Silver XIII® Équilibre” program, a cognitive-motor program offered by the French Rugby-League Federation, and (ii) a control program based on walking and resistance training following the “Vivifrail®” international guidelines [34]. The pre-test and post-test assessment sessions included functional capacities, cognitive functions, and cognitive-motor abilities assessments. Data used in the present study were extracted only from the pre-test sessions.

Participants

One hundred and one community-dwelling older people (M = 75.88 years, SD = 6.23, range: 65–92; 85 women) participated in the study. Participants were recruited by the French Rugby-League Federation between September 13^th, 2022, and April 24^th, 2025. Inclusion criteria were: being 65 years of age or older, living in the community, being able to walk independently, and to follow instructions for testing. Exclusion criteria were: more than three falls in the last 12 months, musculoskeletal disorders impairing posture or gait, central or peripheral neurological disease (e.g., previous stroke, Parkinson’s disease), psychiatric disorders, probable depression (Geriatric Depression Scale score > 10) [35], neurocognitive disorder (Montreal Cognitive Assessment (MoCA) score < 18) [36], obesity (Body Mass Index (BMI) > 35), contraindication to practicing a physical activity, and involvement in another research protocol. If needed, participants were instructed to wear their common correction glasses.

Ethics

The study was approved by a national ethic committee (CPP Île de France IV – EUDRACT 2022-001517-38). All participants were informed of the nature and aim of the study by an informative note and signed a written consent form. All procedures were in accordance with the Declaration of Helsinki with ethical standards, legal requirements and international norms.

Measures

General characteristics.

The duration of education (in years), age (in years), height (in m) and weight (in kg) were self-reported by the participants. BMI ((weight*height)/2, in kg.m^-2) was calculated.

Global cognition.

The global cognition of the participants was measured using the MoCA (score range: 0–30) [36]. The MoCA includes measures of EF, language, attention, orientation, calculation, visuospatial ability, and memory. It enables the screening of early cognitive impairments in older adults (score greater than 18 and less than 26). MoCA scores were adjusted for educational level according to the recommendations of Nasreddine et al. (2005) [36].

Cognitive flexibility.

Cognitive flexibility was assessed using the paper-pencil TMT [25]. This test consists of connecting the encircled numbers and letters disposed on an A4 paper sheet using a pencil. It includes two parts: part A, with only numbers from 1 to 25, and part B, in which participants must alternate between numbers from 1 to 12 and letters from A to L. Execution time was measured in each part using a hand chronometer, and a Delta-TMT was calculated as the difference between the execution times obtained in part B and part A. Execution times were compared to the normative standard of the TMT-A and TMT-B recorded in 8,995 French older adults living in the community by Amieva et al., (2009) [37]. These norms consider the person’s gender, age, and level of education. Both the number of years of schooling and the attainment of the French Baccalaureate (Bac) were used to define the level of education. Using the provided percentiles, four normative classes were defined: deficient (below the 10th), inferior (between the 10th and 25th), average (from the 25th to the 75th), and superior (above the 75th).

Level of fall risk.

The level of fall risk was assessed using a multifactorial screening tool recommended by the French National Health Insurance Fund and the French National Authority for Health: the “Score de Risque de Chute des Centres d’Examen de Santé” (SRC-CES) or Health Examination Center Fall Risk screening tool (translation proposed by Bongue et al. 2011) [38]. This tool was validated with a prospective design in a sample of 1,759 community-dwelling older people [39]. It includes a 5-item questionnaire and one performance measure, assessing a total of six fall risk factors. A score is assigned to each of them based on the following criteria (zero point for each criterion not met): being a woman (two points); living alone (one point); using psychoactive drug (one point); suffering from osteoarthritis (one point); number of fall(s) in the past year (two points for one fall, four points for two falls, six points for three and more falls); and having balance problems, intended as “a change in the arms position during the first five seconds of a one-leg balance test” (one point). The total score ranges from 0 to 12, and the authors provided cut-offs for three levels of fall risk: low (0–3), moderate (4–6), and high (7–12) [40]. In the present study, these three levels of fall risk were used for all analyses.

The 6-meter walking test (6MWT).

Participants were instructed to walk straight at spontaneous speed in a 10-meter-long and 1-meter-wide corridor [41]. The initial 2-meter segment was for accelerating and reaching the spontaneous walking speed, while the last 2-meter segment was for decelerating and stopping (Fig 1). There were two experimental conditions: Single Task (ST), i.e., only walking at spontaneous speed, and DT, i.e., walking at spontaneous speed while performing a concurrent cognitive task. This cognitive task was a mental tracking task consisting of continuously subtracting seven starting from a random three-digit number provided by the investigator [18]. In the DT condition, instructions were given to perform the two tasks as best as possible. All participants performed two recorded trials in the ST condition and then one training trial, followed by two recorded trials in the DT condition. The execution time was measured for six meters using photocells Witty (Microgate©, Italy). For each condition, the mean of the two trials was used for analysis. The mean execution time in the ST condition was used to compute the spontaneous walking speed.

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Fig 1. The 6-Meter Walking Test (6MWT) and the Walking Trail Making Test (W-TMT) setups.

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The Walking – Trail Making Test (W-TMT).

Participants performed a version of the W-TMT inspired by Perrochon & Kemoun (2014) and adapted to fit with the 6MWT setup (Fig 1) [33]. The central measurement zone was 6-meter long and was prolonged by a 2-meter-long blank portion at the beginning and at the end. The targets were printed in the measurement zone in a “chevron-shape” disposition and measured 8 centimeters in diameter. The three experimental conditions of increasing complexity were: (i) W-TMT-N, with only numbers from 1 to 20 and where participants had to walk on the 20 numeric targets in increasing order; (ii) W-TMT-A, with the same 20 numbers as in W-TMT-N but with 20 other numeric distractors that the participants had to avoid while walking; (iii) W-TMT-B, with both numbers and letters as targets, where participants had to walk on the targets alternating between numbers and letters in increasing order for numbers and alphabetical order for letters (i.e., 1–A–2–B–3–C, etc.) while avoiding 20 other numeric and alphabetic distractors. To ensure comprehension of the instructions, participants were familiarized with each condition on a 3-meter-long and 1-meter-wide training carpet during at least one trial. During the test, an investigator stayed as close as possible to the participant to ensure safety. Participants started with the W-TMT-N condition, and the order of the W-TMT-A and W-TMT-B conditions was randomized. The execution time was measured for six meters using the same setup as in the 6MWT, and one trial was performed for each condition.

Cognitive costs.

For the 6MWT, the cognitive cost was calculated using the following equation proposed by Kelly et al. (2010): DTC=[(DT- ST)/ST)]x100 [42]. For the W-TMT, the cognitive cost was calculated using the following equation proposed by Persad et al. (2008): Delta-WTMT=[(W-TMT-B–W-TMT-A)/W-TMT-A]x100 [32].

Data analysis

Analyses were conducted using Jamovi® v2.6.17 (The Jamovi project, Sydney, Australia) and RStudio v2024.12.1 + 563 (R core team), with an alpha level of 0.05. Preliminary analyses were performed on dependent variables to check the normality of the distributions using the Kolmogorov-Smirnov test and the presence of outliers [43]. The Kolmogorov-Smirnov test was non-significant (p > .05) for all dependent variables, validating their normal distribution. Baseline between-group differences in age, weight, BMI, years of education, spontaneous walking speed, global cognition, and cognitive flexibility were compared using one independent factor “group” ANOVA. Chi-square tests were used to compare the distributions of different categorical variables across the three levels of fall risk: gender, number of previous falls, and normative performance classes on the TMT. For all ANOVA and ANCOVA, post-hoc comparisons were conducted using the Tukey correction when their results reached statistical significance. The effect sizes for all ANOVAs and ANCOVAs were reported using partial eta squared (η²_p) for global and interaction effects and using Cohen’s d for the post-hoc analysis. The following thresholds were selected for reporting effect sizes: (i) for η²_p .01−.06 small; .07−.14 medium; and >.14 large; and (ii) Cohen’s d 0.2–0.5 small; 0.6–0.8 medium; and >0.8 large [44].

Group performances in execution times and cognitive costs.

For the 6MWT, a 2-factor mixed ANOVA was conducted on execution time. The repeated factor “condition” consisted of the experimental condition (two levels: ST, DT). The independent factor “group” consisted of the level of fall risk (three levels: low, moderate, high). For the W-TMT, a 2-factor mixed ANCOVA was conducted on execution time, using spontaneous walking speed as a covariate. The repeated factor “condition” consisted of the experimental condition (three levels: W-TMT-N, W-TMT-A, W-TMT-B). The independent factor “group” consisted of the level of fall risk (three levels: low, moderate, high). For analysis of both the DTC and the Delta-WTMT, an independent factor “group” ANOVA was conducted for comparison among the three levels of fall risk.

Concordance index (c-index) and Receiver Operating Characteristic analyses.

Execution time is a simple variable illustrating performance in the two cognitive-motor tasks. Considering that the most complex condition of each task will better detect people at a high level of fall risk, an evaluation of the detection performance of the 6MWT-DT and the W-TMT-B was performed based on the execution time and the level of fall risk.

c-index analysis. Due to the ordinal nature of the three levels of fall risk (i.e., low, moderate, and high), a c-index analysis was performed using the Hmisc R package [45]. This approach quantifies to what extent the W-TMT-B and the 6MWT-DT can classify individuals depending on their respective levels of fall risk. The c-index is the ratio between the number of concordant pairs and the total number of comparable pairs [46]. A comparable pair consists of two participants with different levels of fall risk. The pair is considered concordant if the participant with the highest level of fall risk also shows a higher execution time in the cognitive-motor task. Thus, the c-index reflects the probability that the execution time correctly classifies a randomly chosen higher risk level above a lower risk level. Interpretation of the c-index is as follows: a value of 1 indicates perfect classification (the model always classifies individuals with higher levels of fall risk above individuals with lower levels); a value of 0.5 corresponds to a model classifying randomly; and values between 0.5 and 1 reflect the extent to which the model performs better than chance. The c-index values were computed using a bootstrap resampling procedure with 5,000 iterations to provide 95% Confidence Intervals (95%CI) around the c-index estimates and to perform pairwise comparisons between the W-TMT-B and the 6MWT-DT classification performance.

Receiver Operating Characteristic (ROC) analysis. A focus was made on screening people at a high level of fall risk because of their higher need for fall prevention interventions than people at moderate and low levels. To apply a standard binary ROC analysis method, a dichotomization was made: the high level of fall risk category was compared to a group combining the low and moderate levels of fall risk category [47,48]. For each cognitive-motor task (6MWT-DT and W-TMT-B), the Area Under the Curve (AUC) was calculated. An AUC of 1 indicates perfect screening, whereas an AUC of 0.5 reflects chance-level screening. To statistically compare the detection performance of these two tasks, the AUCs were compared using DeLong’s test [49]. Global accuracy (in %), sensitivity (i.e., the true positive rate), and specificity (i.e., the true negative rate) were calculated. The optimal cutoff value for execution time was determined by Youden’s index (sensitivity+(specificity-1)) [47].

Results

General characteristics

The average spontaneous walking speed was 1.14 ± 0.19 m.s^-1; range [0.70–1.62]. The average MoCA score was 25.07 ± 3.10; range [18–30], with 50.50% (N = 51) of all participants with a MoCA score below the threshold of 26/30 suggesting a probable cognitive impairment. The average TMT part A execution time was 40.11 ± 15.11 s; range [20.80–91.00], the average TMT part B was 92.46 ± 48.10 s; range [35.70–300], and the average Delta-TMT was 52.34 ± 40.59 s; range [5.00–243.13].

Baseline differences

Regarding continuous variables, no differences were observed among the three levels of fall risk in demographic data (i.e., age, weight, and BMI), years of education, spontaneous walking speed, MoCA score, and both TMT-A and TMT-B execution times. Delta-TMT differed significantly among levels of fall risk (F_2,98 = 3.26, p = .043, η²_p = .06, small effect). Post-hoc comparisons showed that people at a high level of fall risk had a larger Delta-TMT than people at a low level of fall risk (respectively M = 73.64 s, SD = 60.59, vs M = 41.23 s, SD = 26.32 s, p = .037, d = 0.82, large effect) (Table 1).

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Table 1. Baseline differences in continuous variables among the three levels of fall risk given by the SRC-CES.

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Regarding categorical variables, gender ratios differed significantly, with proportionally more women than men in the high and moderate levels of fall risk than in the low level of fall risk (χ² = 30.76, p < .001). The distribution of non-fallers, single fallers, and recurrent fallers differed significantly among levels of fall risk (χ² = 46.29, p < .001). The high level of fall risk category is composed of only fallers, with proportionally more single fallers and recurrent fallers compared to the moderate and the low levels of fall risk (Table 2).

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Table 2. Baseline differences in categorical variables among the three levels of fall risk given by the SRC-CES.

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Execution time and cognitive cost in locomotor tasks

For the 6MWT, a significant main effect of the condition was found for the 2-factor mixed ANOVA (F_2,98 = 89.74, p < .001, η²_p = .48, large effect). Post-hoc analyses showed that participants took significantly more time to walk 6 meters in the DT condition than in the ST condition (M = 10.74 s, SD = 4.79, vs M = 5.44 s, SD = 0.98, p < .001, d = 1.17, large effect). No interaction was found between conditions and the level of fall risk (F_2,98 = 0.64, p = .53) (Fig 2A). The DTC analysis by levels of fall risk showed no difference (F_2,98 = 1.09, p = .34) (Fig 2A).

For the W-TMT, a significant interaction between conditions and the level of fall risk was found (F_4,194 = 8.27, p < .001, η²_p = .15, large effect). Post-hoc comparisons showed that, in the W-TMT-B condition only, people at a high level of fall risk had a longer execution time than people at moderate (M = 30.69 s, SD = 15.45, vs M = 21.54 s, SD = 7.17 s, p = .048, d = 1.04, large effect) and low levels of fall risk (M = 20.16 s, SD = 7.90, p = .031, d = 1.19, large effect) (Fig 2B). The Delta-WTMT analysis by level of fall risk reached statistical significance (F_2,98 = 8.74, p < .001, η²_p = .15, large effect). Post-hoc comparisons showed that people with a high level of fall risk had a greater percentage of slowing than people with moderate (M = 138.22%, SD = 85.61, vs M = 69.06%, SD = 45.42, p < .001, d = 1.24, large effect) and low levels of fall risk (M = 69.12%, SD = 56.76, p < .001, d = 1.24, large effect) (Fig 2B).

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Fig 2. Means and standard deviations of the 6MWT execution times and DTC (A), and of the W-TMT execution times and Delta-WTMT (B). (*p < .05; **p < .01; ***p < .005).

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c-index analyses

The execution time of the 6MWT-DT did not classify better than chance people depending on their respective level of fall risk (c-index = .48; IC%95 = .38−.59; p = .73). The execution time of the W-TMT-B correctly classified people according to their level of fall risk in 63.31% of the cases (c-index = .63; 95%CI = .52−.74; p = .02). Pairwise comparison between the 6MWT-DT and the W-TMT-B showed that the W-TMT-B had a significantly higher classification performance than the 6MWT-DT to classify people according to their level of fall risk based on execution time (c-index difference = .15; 95%CI = .01−.29; p = .03).

ROC analyses

Screening of people at a high level of fall risk from those at lower risk levels was not better than chance based on the execution time of the 6MWT-DT (AUC = .46, 95%CI = .30−.62, p = .61). This same screening was correct based on the execution time of the W-TMT-B (AUC = .72, 95%CI = .53−.90, p = .02) (Fig 3 and Table 3). Pairwise comparison between the two tasks showed that the detection performance of people at a high level of fall risk was significantly higher for the W-TMT-B than for the 6MWT-DT (AUC difference = .26; 95%CI = .03−.49; Z_score = 2.18, p = .03). For the W-TMT-B condition, the overall accuracy was 82.18%, with a sensitivity of 61.54% and a specificity of 85.23% (Table 3). According to the Youden index, the best cutoff value was 28.17s.

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Table 3. Results of the receiver operating characteristic analysis.

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Fig 3. Receiver operating characteristic curves for the 6MWT-DT and the W-TMT-B.

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Discussion

The purpose of this retrospective study was to evaluate the detection performance of two different cognitive-motor tasks in screening for a high level of fall risk in community-dwelling older people: the W-TMT, as an incorporated cognitive-motor task, and a simple walking DT, as an additional cognitive-motor task. Based on the c-index and AUC analyses, the main finding is that the execution time measured in the W-TMT-B condition is sensitive both to classify the levels of fall risk and to detect community-dwelling older people at a high level of fall risk from those at lower levels. Another important finding is that the detection performance was significantly higher for the W-TMT-B than for the 6MWT-DT, which performed no better than chance. This result suggests that incorporated cognitive-motor tasks are more accurate than additional cognitive-motor tasks for detecting people at a high level of fall risk among relatively healthy community-dwelling older adults.

The W-TMT-B, a tool to detect older people at a high level of fall risk

The execution time measured in the W-TMT-B condition both classified the three levels of fall risk, based on the c-index, and detected people at a high level of fall risk from those at lower levels better than chance, based on the AUC (Fig 3). Specifically, a significant AUC of 0.72, a fair sensitivity (61.54%), and a high specificity (85.23%) was found (Table 3). The results of the present study are comparable to the results from the study of Bongue et al. (2011) (AUC of 0.70, a sensitivity of 19.20%, and a specificity of 96.5% for the cut-off of seven points determining the high level of fall risk category [39]), used to validate the SRC-CES fall risk screening tool with a prospective design. The present study also corroborates results of a prospective study based on the “Trail Walking Test” (AUC of 0.78, a sensitivity of 66.10% and a specificity of 83.90% in the B condition [30]). Thus, the W-TMT-B showed comparable accuracy, based on a measure of the level of fall risk. It is important to note that, in both previously cited studies and in the present study, the sensitivity (i.e., true positive rate) is lower than the specificity (i.e., true negative rate). This difference between sensitivity and specificity could be explained by the low prevalence of the category of interest (i.e., fallers or people at a high level of fall risk).

In the present study, the prevalence of the high level of fall risk category was only 12.90% (Table 2). Yet, all older people at a high level of fall risk were fallers. Among them 61.54% were single fallers and 38.46% were recurrent fallers (Table 2). This suggests that participants detected at a high level of fall risk by the W-TMT-B need to be oriented towards clinicians to provide a more precise assessment of fall risk factors and fall prevention interventions [5]. The high specificity indicates that the W-TMT-B performance is suitable for identifying people at low and moderate levels of fall risk. Consequently, people classified within these two categories could be oriented towards primary preventive care interventions. It is crucial to point out that the best detection performance was found in the W-TMT most complex condition (W-TMT-B), which suggests this condition could be used to better detect older people at a high level of fall risk.

The W-TMT-B condition involves cognitive flexibility, an independent fall risk factor

People at a high level of fall risk had the longest execution time during the W-TMT-B condition (Fig 2B). This could be explained by early EF alteration in this population. Indeed, Persad et al. (2008) showed that older people with early EF impairment had a significantly longer execution time in the W-TMT-B condition compared to healthy older people, and this result was replicated with all versions of the task [32,33,50,51]. Moreover, falls are common in cognitively impaired older people and particularly in those with EF impairment [28,52–54]. EF are also known to be highly involved in quick adaptation to changes, especially when a fast and adequate reaction to postural disturbances is required to avoid falling [55–57]. Thus, elongation of the execution time in the W-TMT-B condition might reflect early alteration of EF in community-dwelling older people and help to classify these people at a high level of fall risk. The Delta-WTMT focuses on the cognitive functions specifically mobilized during the W-TMT-B condition, when compared to the W-TMT-A condition. The particularity of the W-TMT-B condition is the alternating sequence of numbers and letters, as in the original paper-pencil TMT part B [25]. Since the TMT part B condition is associated with cognitive flexibility, so should the W-TMT-B condition [26]. Consequently, the Delta-WTMT scores might reflect the participants’ efficiency in cognitive flexibility. As the participants at a high level of fall risk also showed a significantly higher paper-pencil Delta-TMT than participants at a low level (Table 1), participants at a high level should have the greatest cognitive flexibility impairments. Mobilization of cognitive flexibility during the W-TMT-B condition can be further explained by the goal-oriented locomotor processes that take place during the execution of the task. The complex walking path induced by the placement of targets in a non-linear “chevron-shaped” pattern reproduces challenging terrain conditions such as an obstacle avoidance situation [31]. These tasks mobilize cognitive flexibility to adapt the goal-oriented locomotor processes continuously [58]. Consequently, performing the W-TMT seems to mobilize cognitive flexibility because it implies a complex conscious control, including evaluation of the spatial environment and efficient motor planning [8].

Screening with the W-TMT-B is more accurate than with the 6MWT-DT

A novelty of the present study is that both diagnostic accuracy analyses showed that the W-TMT-B was significantly more performant than the 6MWT-DT to (i) classify people according to the three levels of fall risk, based on the c-index, and (ii) detect people at a high level of fall risk from the two other levels, based on the AUC (Fig 3 and Table 3). These results offers new insights to cope with the conclusions of different reviews suggesting that additional DT tasks showed mixed results in the prediction of falls [23,24,59,60]. Cognitive-Motor Interference (CMI) taking place in a DT situation requires that both task must be performed simultaneously [18].

In an additional DT, the prioritization strategy offers a solution to reduce the level of CMI by giving priority to one task over the other. In the DT condition of the 6MWT, participants were instructed to perform both tasks as well as possible, leaving the possibility to prioritize one objective over the other [22]. Consequently, the performance in this condition depends on factors that could orient task prioritization. Participants may have prioritized based on their own estimation of the complexity of the cognitive task. In this study, a mental-tracking arithmetic task was used, and results showed a larger increase of execution time in the DT condition compared to previous studies with similar tasks and populations [18]. Considering the population tested in the present study, this finding confirms that the mental-tracking task was of sufficiently high cognitive complexity for the DT. The motor task consisted of straight walking at spontaneous speed, which primarily relies on functional capacities [61,62]. Given that most participants had a high spontaneous walking speed, the complexity of the locomotor task was probably too low [10,62,63]. Consequently, the combination of a low complexity in the motor task with a high complexity in the cognitive task may have led participants to prioritize the cognitive task. The review conducted by Yogev-Seligmann et al. (2012) about additional DTs has reported that community-dwelling older people, as well as young adults, often adopt a “posture-second” strategy to focus more on the cognitive task due to a high confidence in their postural control [64].

Another possibility is that participants may have prioritized depending on their level of cognitive reserve [65]. Half of the participants had a MoCA score below 26, which is the threshold to detect early cognitive impairments [36]. Considering that people with poor cognitive performance may be the ones with a higher level of fall risk, they could have been detected by a longer execution time. However, people with probable cognitive impairment may have prioritized the locomotor task due to their reduced ability to overcome the high cognitive complexity of the DT condition, resulting in a short execution time. This could explain why no differences were found in 6MWT-DT between people at high level of fall risk compared to lower levels (Fig 2A).

In contrast, no task prioritization is possible during the W-TMT-B because the cognitive and motor tasks are interdependent and share a single overarching goal. Unlike the additional DT paradigm, CMI takes place in an incorporated DT paradigm as long as the participant is performing the task [66]. Moreover, by incorporating a complex cognitive rule into a complex walking paradigm, it possibly mobilizes shared neural networks for processing both the cognitive and motor dimensions of the task increasing the level of CMI [66–68].

Limitations and perspectives

Since this is an ancillary study, the results were obtained using a retrospective design based on the pre-test outcomes of the 13EVAL study. As a result, the predictive value of the assessment needs to be determined. Moreover, the 13EVAL study excluded individuals who had experienced more than three falls to ensure their safety during physical activity interventions. Recurrent fallers with more than three falls could have been classified in the high level of fall risk category. As a result, it could have restricted the number of participants in this category. In addition, the sample consisted of 81.17% (N = 85) women, which limits the generalization of results to the men population. More research using a prospective design with a follow-up and reliability analyses is needed to enhance the clinical value of this assessment and determine whether the W-TMT-B can become an effective fall prediction tool.

Conclusions

The W-TMT-B execution time efficiently detected people at a high level of fall risk compared to lower levels in community-dwelling older people. The detection performance of the W-TMT-B condition could be explained by a high cognitive-motor interference induced by the involvement of cognitive flexibility in a complex walking context. In contrast, the poor accuracy of the DT assessment during the 6MWT could be explained by the low complexity of the walking task proposed in an additional context that left open access to task prioritization strategies. Together, the findings of this study support that incorporated cognitive-motor tasks proposed in complex walking contexts, such as the W-TMT, could be more appropriate than additional DTs based on simple walking to investigate the level of fall risk in community-dwelling older people. Consequently, with a simple variable (i.e., the execution time) and a cut-off score of 28.17s, the W-TMT-B condition could be a low-cost and operational tool easily administered by physical activity instructors to: (i) propose primary preventive care interventions to older adults at low and moderate levels of fall risk; and (ii) refer people at high level of fall risk for in-depth clinical investigations.

Supporting information

S1 Results. Normative classes for the Trail Making Test.

https://doi.org/10.1371/journal.pone.0348875.s001

(DOCX)

Acknowledgments

The authors would like to express their sincere gratitude to the Ligue Auvergne Rhône Alpes de Rugby à XIII for its financial and material support, as well as for its invaluable assistance in recruiting participants for this study. We would also like to thank its president, Mr. Jacques CAVEZZAN, for his enthusiasm and unconditional support throughout Rafael MAUTI’s doctoral project.

References

1. WHO. Global report on falls prevention in older age. World Health Organization; 2007. Available from: https://www.who.int/publications/i/item/9789241563536
2. Heinrich S, Rapp K, Rissmann U, Becker C, König H-H. Cost of falls in old age: a systematic review. Osteoporos Int. 2010;21(6):891–902. pmid:19924496
- View Article
- PubMed/NCBI
- Google Scholar
3. Li Y, Hou L, Zhao H, Xie R, Yi Y, Ding X. Risk factors for falls among community-dwelling older adults: a systematic review and meta-analysis. Front Med (Lausanne). 2023;9:1019094. pmid:36687461
- View Article
- PubMed/NCBI
- Google Scholar
4. Montero-Odasso M, van der Velde N, Martin FC, Petrovic M, Tan MP, Ryg J, et al. World guidelines for falls prevention and management for older adults: a global initiative. Age Ageing. 2022;51(9):afac205. pmid:36178003
- View Article
- PubMed/NCBI
- Google Scholar
5. National Institute for Health and Care Excellence (NICE). Falls: assessment and prevention in older people and people 50 and over at higher risk: evidence review E - method of assessment. London: NICE; 2025. Available from: http://www.ncbi.nlm.nih.gov/books/NBK616102/
6. Park S-H. Tools for assessing fall risk in the elderly: a systematic review and meta-analysis. Aging Clin Exp Res. 2018;30(1):1–16. pmid:28374345
- View Article
- PubMed/NCBI
- Google Scholar
7. Mancini M, Horak FB. The relevance of clinical balance assessment tools to differentiate balance deficits. Eur J Phys Rehabil Med. 2010;46(2):239–48. pmid:20485226
- View Article
- PubMed/NCBI
- Google Scholar
8. Zhang W, Low L-F, Schwenk M, Mills N, Gwynn JD, Clemson L. Review of gait, cognition, and fall risks with implications for fall prevention in older adults with dementia. Dement Geriatr Cogn Disord. 2019;48(1–2):17–29. pmid:31743907
- View Article
- PubMed/NCBI
- Google Scholar
9. Berg WP, Alessio HM, Mills EM, Tong C. Circumstances and consequences of falls in independent community-dwelling older adults. Age Ageing. 1997;26(4):261–8. pmid:9271288
- View Article
- PubMed/NCBI
- Google Scholar
10. Montero-Odasso M, Schapira M, Soriano ER, Varela M, Kaplan R, Camera LA, et al. Gait velocity as a single predictor of adverse events in healthy seniors aged 75 years and older. J Gerontol A Biol Sci Med Sci. 2005;60(10):1304–9. pmid:16282564
- View Article
- PubMed/NCBI
- Google Scholar
11. Goldspink DF. Ageing and activity: their effects on the functional reserve capacities of the heart and vascular smooth and skeletal muscles. Ergonomics. 2005;48(11–14):1334–51. pmid:16338704
- View Article
- PubMed/NCBI
- Google Scholar
12. Grosboillot N, Gallou-Guyot M, Lamontagne A, Bonnyaud C, Perrot A, Allali G, et al. Towards a comprehensive framework for complex walking tasks: characterization, behavioral adaptations, and clinical implications in ageing and neurological populations. Ageing Res Rev. 2024;101:102458. pmid:39153599
- View Article
- PubMed/NCBI
- Google Scholar
13. Shumway-Cook A, Guralnik JM, Phillips CL, Coppin AK, Ciol MA, Bandinelli S, et al. Age-associated declines in complex walking task performance: the Walking InCHIANTI toolkit. J Am Geriatr Soc. 2007;55(1):58–65. pmid:17233686
- View Article
- PubMed/NCBI
- Google Scholar
14. Ludin-Olsson I, Nyberg L, Gustafson Y. Stops walking when talking as a predictor of falls in elderly people. Lancet. 1997 Mar 1;349(9052):617.
- View Article
- Google Scholar
15. Smith E, Cusack T, Blake C. The effect of a dual task on gait speed in community dwelling older adults: a systematic review and meta-analysis. Gait Posture. 2016;44:250–8. pmid:27004667
- View Article
- PubMed/NCBI
- Google Scholar
16. Li F, Harmer P. Prevalence of falls, physical performance, and dual-task cost while walking in older adults at high risk of falling with and without cognitive impairment. Clin Interv Aging. 2020;15:945–52. pmid:32606636
- View Article
- PubMed/NCBI
- Google Scholar
17. Commandeur D, Klimstra MD, MacDonald S, Inouye K, Cox M, Chan D, et al. Difference scores between single-task and dual-task gait measures are better than clinical measures for detection of fall-risk in community-dwelling older adults. Gait Posture. 2018;66:155–9. pmid:30195218
- View Article
- PubMed/NCBI
- Google Scholar
18. Al-Yahya E, Dawes H, Smith L, Dennis A, Howells K, Cockburn J. Cognitive motor interference while walking: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2011;35(3):715–28. pmid:20833198
- View Article
- PubMed/NCBI
- Google Scholar
19. Muhaidat J, Kerr A, Evans JJ, Skelton DA. The test-retest reliability of gait-related dual task performance in community-dwelling fallers and non-fallers. Gait Posture. 2013;38(1):43–50. pmid:23146196
- View Article
- PubMed/NCBI
- Google Scholar
20. Tong Y, Rong J, Tian X, Wang Y, Chen Z, Adams R, et al. Use of dual-task timed-up-and-go tests for predicting falls in physically active, community-dwelling older adults-a prospective study. J Aging Phys Act. 2023;31(6):948–55. pmid:37263592
- View Article
- PubMed/NCBI
- Google Scholar
21. Nualyong T, Siriphorn A. Accuracy of the figure of 8 walk test with and without dual-task to predict falls in older adults. J Bodyw Mov Ther. 2022;30:69–75. pmid:35500981
- View Article
- PubMed/NCBI
- Google Scholar
22. Herold F, Hamacher D, Schega L, Müller NG. Thinking while moving or moving while thinking - concepts of motor-cognitive training for cognitive performance enhancement. Front Aging Neurosci. 2018;10:228. pmid:30127732
- View Article
- PubMed/NCBI
- Google Scholar
23. Beck Jepsen D, Robinson K, Ogliari G, Montero-Odasso M, Kamkar N, Ryg J, et al. Predicting falls in older adults: an umbrella review of instruments assessing gait, balance, and functional mobility. BMC Geriatr. 2022;22(1):615. pmid:35879666
- View Article
- PubMed/NCBI
- Google Scholar
24. Zijlstra A, Ufkes T, Skelton DA, Lundin-Olsson L, Zijlstra W. Do dual tasks have an added value over single tasks for balance assessment in fall prevention programs? A mini-review. Gerontology. 2008;54(1):40–9. pmid:18460873
- View Article
- PubMed/NCBI
- Google Scholar
25. Reitan RM. Validity of the trail making test as an indicator of organic brain damage. PMS. 1958;8(7):271.
- View Article
- Google Scholar
26. Kortte KB, Horner MD, Windham WK. The trail making test, part B: cognitive flexibility or ability to maintain set? Appl Neuropsychol. 2002;9(2):106–9. pmid:12214820
- View Article
- PubMed/NCBI
- Google Scholar
27. Muir SW, Gopaul K, Montero Odasso MM. The role of cognitive impairment in fall risk among older adults: a systematic review and meta-analysis. Age Ageing. 2012;41(3):299–308. pmid:22374645
- View Article
- PubMed/NCBI
- Google Scholar
28. Mirelman A, Herman T, Brozgol M, Dorfman M, Sprecher E, Schweiger A, et al. Executive function and falls in older adults: new findings from a five-year prospective study link fall risk to cognition. PLoS One. 2012;7(6):e40297. pmid:22768271
- View Article
- PubMed/NCBI
- Google Scholar
29. Schott N. Trail Walking Test zur Erfassung der motorisch-kognitiven Interferenz bei älteren Erwachsenen: entwicklung und Überprüfung der psychometrischen Eigenschaften des Verfahrens. Z Gerontol Geriatr. 2015;48:722–33.
- View Article
- Google Scholar
30. Yamada M, Ichihashi N. Predicting the probability of falls in community-dwelling elderly individuals using the trail-walking test. Environ Health Prev Med. 2010;15(6):386–91. pmid:21432571
- View Article
- PubMed/NCBI
- Google Scholar
31. Alexander NB, Ashton-Miller JA, Giordani B, Guire K, Schultz AB. Age differences in timed accurate stepping with increasing cognitive and visual demand: a walking trail making test. J Gerontol A Biol Sci Med Sci. 2005;60(12):1558–62. pmid:16424288
- View Article
- PubMed/NCBI
- Google Scholar
32. Persad CC, Jones JL, Ashton-Miller JA, Alexander NB, Giordani B. Executive function and gait in older adults with cognitive impairment. J Gerontol A Biol Sci Med Sci. 2008;63(12):1350–5. pmid:19126848
- View Article
- PubMed/NCBI
- Google Scholar
33. Perrochon A, Kemoun G. The Walking Trail-Making Test is an early detection tool for mild cognitive impairment. Clin Interv Aging. 2014;9:111–9. pmid:24426778
- View Article
- PubMed/NCBI
- Google Scholar
34. Izquierdo M, Rodriguez-Mañas L, Sinclair AJ. What is new in exercise regimes for frail older people — how does the Erasmus Vivifrail Project take us forward? J Nutr Health Aging. 2016;20(7):736–7.
- View Article
- Google Scholar
35. Yesavage JA, Sheikh JI. Geriatric Depression Scale (GDS): recent evidence and development of a shorter version. Clin Gerontol. 1986;5:165–73.
- View Article
- Google Scholar
36. Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695–9. pmid:15817019
- View Article
- PubMed/NCBI
- Google Scholar
37. Amieva H, Le Goff M, Stoykova R, Lafont S, Ritchie K, Tzourio C, et al. Trail Making Test A et B (version sans correction des erreurs) : normes en population chez des sujets âgés, issues de l’étude des trois Cités. Rev Neuropsychol. 2009;1:210.
- View Article
- Google Scholar
38. Haute Autorité de Santé. Référentiel concernant l’évaluation du risque de chutes chez le sujet âgé autonome et sa prévention. HAS; 2012. Available from: https://www.has-sante.fr/jcms/c_1525705/fr/avis-de-la-has-concernant-l-evaluation-du-risque-de-chutes-chez-le-sujet-age-autonome-et-sa-prevention
39. Bongue B, Dupré C, Beauchet O, Rossat A, Fantino B, Colvez A. A screening tool with five risk factors was developed for fall-risk prediction in community-dwelling elderly. J Clin Epidemiol. 2011;64(10):1152–60. pmid:21463927
- View Article
- PubMed/NCBI
- Google Scholar
40. Cetaf. Score de risque de chute. Available from: https://www.cetaf.fr/score-de-risque-de-chute/. Accessed 2025 October 12.
41. Smith R. Validation and reliability of the elderly mobility scale. Physiotherapy. 1994;80(11):744–7.
- View Article
- Google Scholar
42. Kelly VE, Janke AA, Shumway-Cook A. Effects of instructed focus and task difficulty on concurrent walking and cognitive task performance in healthy young adults. Exp Brain Res. 2010;207(1–2):65–73. pmid:20931180
- View Article
- PubMed/NCBI
- Google Scholar
43. Osborne JW. Best practices in data cleaning: a complete guide to everything you need to do before and after collecting your data. Los Angeles, Calif.: SAGE; 2013.
44. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. New York: Routledge; 1988. https://doi.org/10.4324/9780203771587
45. Harrell FE. Hmisc: harrell miscellaneous. Available from: https://cran.r-project.org/web/packages/Hmisc/index.html
46. Van Calster B, Van Belle V, Vergouwe Y, Steyerberg EW. Discrimination ability of prediction models for ordinal outcomes: relationships between existing measures and a new measure. Biom J. 2012;54(5):674–85. pmid:22711459
- View Article
- PubMed/NCBI
- Google Scholar
47. Fawcett T. An introduction to ROC analysis. Pattern Recogn Lett. 2006;27(8):861–74.
- View Article
- Google Scholar
48. Hand DJ, Till RJ. A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn. 2001;45:171–86.
- View Article
- Google Scholar
49. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45. pmid:3203132
- View Article
- PubMed/NCBI
- Google Scholar
50. Klotzbier TJ, Schott N. Cognitive-motor interference during walking in older adults with probable mild cognitive impairment. Front Aging Neurosci. 2017;9:350. pmid:29321738
- View Article
- PubMed/NCBI
- Google Scholar
51. Osuka Y, Kim H, Watanabe Y, Taniguchi Y, Kojima N, Seino S, et al. A stepping trail making test as an indicator of cognitive impairment in older adults. J Clin Med. 2020;9(9):2835. pmid:32887235
- View Article
- PubMed/NCBI
- Google Scholar
52. Kearney FC, Harwood RH, Gladman JRF, Lincoln N, Masud T. The relationship between executive function and falls and gait abnormalities in older adults: a systematic review. Dement Geriatr Cogn Disord. 2013;36(1–2):20–35. pmid:23712088
- View Article
- PubMed/NCBI
- Google Scholar
53. Herman T, Mirelman A, Giladi N, Schweiger A, Hausdorff JM. Executive control deficits as a prodrome to falls in healthy older adults: a prospective study linking thinking, walking, and falling. J Gerontol A Biol Sci Med Sci. 2010;65(10):1086–92. pmid:20484336
- View Article
- PubMed/NCBI
- Google Scholar
54. Delbaere K, Kochan NA, Close JCT, Menant JC, Sturnieks DL, Brodaty H, et al. Mild cognitive impairment as a predictor of falls in community-dwelling older people. Am J Geriatr Psychiatry. 2012;20(10):845–53. pmid:23011051
- View Article
- PubMed/NCBI
- Google Scholar
55. Diamond A. Executive functions. Annu Rev Psychol. 2013;64:135–68.
- View Article
- Google Scholar
56. Springer S, Giladi N, Peretz C, Yogev G, Simon ES, Hausdorff JM. Dual-tasking effects on gait variability: the role of aging, falls, and executive function. Mov Disord. 2006;21(7):950–7. pmid:16541455
- View Article
- PubMed/NCBI
- Google Scholar
57. Rogers MW, Mille M-L. Timing paradox of stepping and falls in ageing: not so quick and quick(er) on the trigger. J Physiol. 2016;594(16):4537–47. pmid:26915664
- View Article
- PubMed/NCBI
- Google Scholar
58. Persad CC, Giordani B, Chen HC, Ashton-Miller JA, Alexander NB, Wilson CS, et al. Neuropsychological predictors of complex obstacle avoidance in healthy older adults. J Gerontol B Psychol Sci Soc Sci. 1995;50(5):P272-7. pmid:7656077
- View Article
- PubMed/NCBI
- Google Scholar
59. Muir-Hunter SW, Wittwer JE. Dual-task testing to predict falls in community-dwelling older adults: a systematic review. Physiotherapy. 2016;102(1):29–40. pmid:26390824
- View Article
- PubMed/NCBI
- Google Scholar
60. Bayot M, Dujardin K, Dissaux L, Tard C, Defebvre L, Bonnet CT, et al. Can dual-task paradigms predict falls better than single task? - a systematic literature review. Neurophysiol Clin. 2020;50(6):401–40. pmid:33176988
- View Article
- PubMed/NCBI
- Google Scholar
61. Bohannon RW, Williams Andrews A. Normal walking speed: a descriptive meta-analysis. Physiotherapy. 2011;97(3):182–9. pmid:21820535
- View Article
- PubMed/NCBI
- Google Scholar
62. Houles M, Abellan Van Kan G, Rolland Y, Andrieu S, Anthony P, Bauer J. La vitesse de marche comme critère de fragilité chez la personne âgée vivant au domicile. Cah Annee Gerontol. 2010;2:13–23.
- View Article
- Google Scholar
63. De Souto Barreto P, Ferrandez AM, Saliba-Serre B. Questionnaire d’activité physique pour les personnes âgées (QAPPA): validation d’un nouvel instrument de mesure en langue française. Sci Sports. 2011;26: 11–8.
- View Article
- Google Scholar
64. Yogev-Seligmann G, Hausdorff JM, Giladi N. Do we always prioritize balance when walking? Towards an integrated model of task prioritization. Mov Disord. 2012;27(6):765–70. pmid:22419512
- View Article
- PubMed/NCBI
- Google Scholar
65. Stern Y. Cognitive reserve. Neuropsychologia. 2009;47(10):2015–28. pmid:19467352
- View Article
- PubMed/NCBI
- Google Scholar
66. Beurskens R, Bock O. Age-related deficits of dual-task walking: a review. Neural Plast. 2012;2012:131608. pmid:22848845
- View Article
- PubMed/NCBI
- Google Scholar
67. Mirelman A, Shema S, Maidan I, Hausdorff JM. Gait. Handb Clin Neurol. 2018;159:119–34. pmid:30482309
- View Article
- PubMed/NCBI
- Google Scholar
68. Woollacott M, Shumway-Cook A. Attention and the control of posture and gait: a review of an emerging area of research. Gait Posture. 2002;16(1):1–14. pmid:12127181
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. WHO. Global report on falls prevention in older age. World Health Organization; 2007. Available from: https://www.who.int/publications/i/item/9789241563536

[ref2] 2. Heinrich S, Rapp K, Rissmann U, Becker C, König H-H. Cost of falls in old age: a systematic review. Osteoporos Int. 2010;21(6):891–902. pmid:19924496
View Article
PubMed/NCBI
Google Scholar

[3] View Article

[4] PubMed/NCBI

[5] Google Scholar

[ref3] 3. Li Y, Hou L, Zhao H, Xie R, Yi Y, Ding X. Risk factors for falls among community-dwelling older adults: a systematic review and meta-analysis. Front Med (Lausanne). 2023;9:1019094. pmid:36687461
View Article
PubMed/NCBI
Google Scholar

[7] View Article

[8] PubMed/NCBI

[9] Google Scholar

[ref4] 4. Montero-Odasso M, van der Velde N, Martin FC, Petrovic M, Tan MP, Ryg J, et al. World guidelines for falls prevention and management for older adults: a global initiative. Age Ageing. 2022;51(9):afac205. pmid:36178003
View Article
PubMed/NCBI
Google Scholar

[11] View Article

[12] PubMed/NCBI

[13] Google Scholar

[ref5] 5. National Institute for Health and Care Excellence (NICE). Falls: assessment and prevention in older people and people 50 and over at higher risk: evidence review E - method of assessment. London: NICE; 2025. Available from: http://www.ncbi.nlm.nih.gov/books/NBK616102/

[ref6] 6. Park S-H. Tools for assessing fall risk in the elderly: a systematic review and meta-analysis. Aging Clin Exp Res. 2018;30(1):1–16. pmid:28374345
View Article
PubMed/NCBI
Google Scholar

[16] View Article

[17] PubMed/NCBI

[18] Google Scholar

[ref7] 7. Mancini M, Horak FB. The relevance of clinical balance assessment tools to differentiate balance deficits. Eur J Phys Rehabil Med. 2010;46(2):239–48. pmid:20485226
View Article
PubMed/NCBI
Google Scholar

[20] View Article

[21] PubMed/NCBI

[22] Google Scholar

[ref8] 8. Zhang W, Low L-F, Schwenk M, Mills N, Gwynn JD, Clemson L. Review of gait, cognition, and fall risks with implications for fall prevention in older adults with dementia. Dement Geriatr Cogn Disord. 2019;48(1–2):17–29. pmid:31743907
View Article
PubMed/NCBI
Google Scholar

[24] View Article

[25] PubMed/NCBI

[26] Google Scholar

[ref9] 9. Berg WP, Alessio HM, Mills EM, Tong C. Circumstances and consequences of falls in independent community-dwelling older adults. Age Ageing. 1997;26(4):261–8. pmid:9271288
View Article
PubMed/NCBI
Google Scholar

[28] View Article

[29] PubMed/NCBI

[30] Google Scholar

[ref10] 10. Montero-Odasso M, Schapira M, Soriano ER, Varela M, Kaplan R, Camera LA, et al. Gait velocity as a single predictor of adverse events in healthy seniors aged 75 years and older. J Gerontol A Biol Sci Med Sci. 2005;60(10):1304–9. pmid:16282564
View Article
PubMed/NCBI
Google Scholar

[32] View Article

[33] PubMed/NCBI

[34] Google Scholar

[ref11] 11. Goldspink DF. Ageing and activity: their effects on the functional reserve capacities of the heart and vascular smooth and skeletal muscles. Ergonomics. 2005;48(11–14):1334–51. pmid:16338704
View Article
PubMed/NCBI
Google Scholar

[36] View Article

[37] PubMed/NCBI

[38] Google Scholar

[ref12] 12. Grosboillot N, Gallou-Guyot M, Lamontagne A, Bonnyaud C, Perrot A, Allali G, et al. Towards a comprehensive framework for complex walking tasks: characterization, behavioral adaptations, and clinical implications in ageing and neurological populations. Ageing Res Rev. 2024;101:102458. pmid:39153599
View Article
PubMed/NCBI
Google Scholar

[40] View Article

[41] PubMed/NCBI

[42] Google Scholar

[ref13] 13. Shumway-Cook A, Guralnik JM, Phillips CL, Coppin AK, Ciol MA, Bandinelli S, et al. Age-associated declines in complex walking task performance: the Walking InCHIANTI toolkit. J Am Geriatr Soc. 2007;55(1):58–65. pmid:17233686
View Article
PubMed/NCBI
Google Scholar

[44] View Article

[45] PubMed/NCBI

[46] Google Scholar

[ref14] 14. Ludin-Olsson I, Nyberg L, Gustafson Y. Stops walking when talking as a predictor of falls in elderly people. Lancet. 1997 Mar 1;349(9052):617.
View Article
Google Scholar

[48] View Article

[49] Google Scholar

[ref15] 15. Smith E, Cusack T, Blake C. The effect of a dual task on gait speed in community dwelling older adults: a systematic review and meta-analysis. Gait Posture. 2016;44:250–8. pmid:27004667
View Article
PubMed/NCBI
Google Scholar

[51] View Article

[52] PubMed/NCBI

[53] Google Scholar

[ref16] 16. Li F, Harmer P. Prevalence of falls, physical performance, and dual-task cost while walking in older adults at high risk of falling with and without cognitive impairment. Clin Interv Aging. 2020;15:945–52. pmid:32606636
View Article
PubMed/NCBI
Google Scholar

[55] View Article

[56] PubMed/NCBI

[57] Google Scholar

[ref17] 17. Commandeur D, Klimstra MD, MacDonald S, Inouye K, Cox M, Chan D, et al. Difference scores between single-task and dual-task gait measures are better than clinical measures for detection of fall-risk in community-dwelling older adults. Gait Posture. 2018;66:155–9. pmid:30195218
View Article
PubMed/NCBI
Google Scholar

[59] View Article

[60] PubMed/NCBI

[61] Google Scholar

[ref18] 18. Al-Yahya E, Dawes H, Smith L, Dennis A, Howells K, Cockburn J. Cognitive motor interference while walking: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2011;35(3):715–28. pmid:20833198
View Article
PubMed/NCBI
Google Scholar

[63] View Article

[64] PubMed/NCBI

[65] Google Scholar

[ref19] 19. Muhaidat J, Kerr A, Evans JJ, Skelton DA. The test-retest reliability of gait-related dual task performance in community-dwelling fallers and non-fallers. Gait Posture. 2013;38(1):43–50. pmid:23146196
View Article
PubMed/NCBI
Google Scholar

[67] View Article

[68] PubMed/NCBI

[69] Google Scholar

[ref20] 20. Tong Y, Rong J, Tian X, Wang Y, Chen Z, Adams R, et al. Use of dual-task timed-up-and-go tests for predicting falls in physically active, community-dwelling older adults-a prospective study. J Aging Phys Act. 2023;31(6):948–55. pmid:37263592
View Article
PubMed/NCBI
Google Scholar

[71] View Article

[72] PubMed/NCBI

[73] Google Scholar

[ref21] 21. Nualyong T, Siriphorn A. Accuracy of the figure of 8 walk test with and without dual-task to predict falls in older adults. J Bodyw Mov Ther. 2022;30:69–75. pmid:35500981
View Article
PubMed/NCBI
Google Scholar

[75] View Article

[76] PubMed/NCBI

[77] Google Scholar

[ref22] 22. Herold F, Hamacher D, Schega L, Müller NG. Thinking while moving or moving while thinking - concepts of motor-cognitive training for cognitive performance enhancement. Front Aging Neurosci. 2018;10:228. pmid:30127732
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref23] 23. Beck Jepsen D, Robinson K, Ogliari G, Montero-Odasso M, Kamkar N, Ryg J, et al. Predicting falls in older adults: an umbrella review of instruments assessing gait, balance, and functional mobility. BMC Geriatr. 2022;22(1):615. pmid:35879666
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref24] 24. Zijlstra A, Ufkes T, Skelton DA, Lundin-Olsson L, Zijlstra W. Do dual tasks have an added value over single tasks for balance assessment in fall prevention programs? A mini-review. Gerontology. 2008;54(1):40–9. pmid:18460873
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref25] 25. Reitan RM. Validity of the trail making test as an indicator of organic brain damage. PMS. 1958;8(7):271.
View Article
Google Scholar

[91] View Article

[92] Google Scholar

[ref26] 26. Kortte KB, Horner MD, Windham WK. The trail making test, part B: cognitive flexibility or ability to maintain set? Appl Neuropsychol. 2002;9(2):106–9. pmid:12214820
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref27] 27. Muir SW, Gopaul K, Montero Odasso MM. The role of cognitive impairment in fall risk among older adults: a systematic review and meta-analysis. Age Ageing. 2012;41(3):299–308. pmid:22374645
View Article
PubMed/NCBI
Google Scholar

[98] View Article

[99] PubMed/NCBI

[100] Google Scholar

[ref28] 28. Mirelman A, Herman T, Brozgol M, Dorfman M, Sprecher E, Schweiger A, et al. Executive function and falls in older adults: new findings from a five-year prospective study link fall risk to cognition. PLoS One. 2012;7(6):e40297. pmid:22768271
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref29] 29. Schott N. Trail Walking Test zur Erfassung der motorisch-kognitiven Interferenz bei älteren Erwachsenen: entwicklung und Überprüfung der psychometrischen Eigenschaften des Verfahrens. Z Gerontol Geriatr. 2015;48:722–33.
View Article
Google Scholar

[106] View Article

[107] Google Scholar

[ref30] 30. Yamada M, Ichihashi N. Predicting the probability of falls in community-dwelling elderly individuals using the trail-walking test. Environ Health Prev Med. 2010;15(6):386–91. pmid:21432571
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref31] 31. Alexander NB, Ashton-Miller JA, Giordani B, Guire K, Schultz AB. Age differences in timed accurate stepping with increasing cognitive and visual demand: a walking trail making test. J Gerontol A Biol Sci Med Sci. 2005;60(12):1558–62. pmid:16424288
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref32] 32. Persad CC, Jones JL, Ashton-Miller JA, Alexander NB, Giordani B. Executive function and gait in older adults with cognitive impairment. J Gerontol A Biol Sci Med Sci. 2008;63(12):1350–5. pmid:19126848
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref33] 33. Perrochon A, Kemoun G. The Walking Trail-Making Test is an early detection tool for mild cognitive impairment. Clin Interv Aging. 2014;9:111–9. pmid:24426778
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref34] 34. Izquierdo M, Rodriguez-Mañas L, Sinclair AJ. What is new in exercise regimes for frail older people — how does the Erasmus Vivifrail Project take us forward? J Nutr Health Aging. 2016;20(7):736–7.
View Article
Google Scholar

[125] View Article

[126] Google Scholar

[ref35] 35. Yesavage JA, Sheikh JI. Geriatric Depression Scale (GDS): recent evidence and development of a shorter version. Clin Gerontol. 1986;5:165–73.
View Article
Google Scholar

[128] View Article

[129] Google Scholar

[ref36] 36. Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695–9. pmid:15817019
View Article
PubMed/NCBI
Google Scholar

[131] View Article

[132] PubMed/NCBI

[133] Google Scholar

[ref37] 37. Amieva H, Le Goff M, Stoykova R, Lafont S, Ritchie K, Tzourio C, et al. Trail Making Test A et B (version sans correction des erreurs) : normes en population chez des sujets âgés, issues de l’étude des trois Cités. Rev Neuropsychol. 2009;1:210.
View Article
Google Scholar

[135] View Article

[136] Google Scholar

[ref38] 38. Haute Autorité de Santé. Référentiel concernant l’évaluation du risque de chutes chez le sujet âgé autonome et sa prévention. HAS; 2012. Available from: https://www.has-sante.fr/jcms/c_1525705/fr/avis-de-la-has-concernant-l-evaluation-du-risque-de-chutes-chez-le-sujet-age-autonome-et-sa-prevention

[ref39] 39. Bongue B, Dupré C, Beauchet O, Rossat A, Fantino B, Colvez A. A screening tool with five risk factors was developed for fall-risk prediction in community-dwelling elderly. J Clin Epidemiol. 2011;64(10):1152–60. pmid:21463927
View Article
PubMed/NCBI
Google Scholar

[139] View Article

[140] PubMed/NCBI

[141] Google Scholar

[ref40] 40. Cetaf. Score de risque de chute. Available from: https://www.cetaf.fr/score-de-risque-de-chute/. Accessed 2025 October 12.

[ref41] 41. Smith R. Validation and reliability of the elderly mobility scale. Physiotherapy. 1994;80(11):744–7.
View Article
Google Scholar

[144] View Article

[145] Google Scholar

[ref42] 42. Kelly VE, Janke AA, Shumway-Cook A. Effects of instructed focus and task difficulty on concurrent walking and cognitive task performance in healthy young adults. Exp Brain Res. 2010;207(1–2):65–73. pmid:20931180
View Article
PubMed/NCBI
Google Scholar

[147] View Article

[148] PubMed/NCBI

[149] Google Scholar

[ref43] 43. Osborne JW. Best practices in data cleaning: a complete guide to everything you need to do before and after collecting your data. Los Angeles, Calif.: SAGE; 2013.

[ref44] 44. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. New York: Routledge; 1988. https://doi.org/10.4324/9780203771587

[ref45] 45. Harrell FE. Hmisc: harrell miscellaneous. Available from: https://cran.r-project.org/web/packages/Hmisc/index.html

[ref46] 46. Van Calster B, Van Belle V, Vergouwe Y, Steyerberg EW. Discrimination ability of prediction models for ordinal outcomes: relationships between existing measures and a new measure. Biom J. 2012;54(5):674–85. pmid:22711459
View Article
PubMed/NCBI
Google Scholar

[154] View Article

[155] PubMed/NCBI

[156] Google Scholar

[ref47] 47. Fawcett T. An introduction to ROC analysis. Pattern Recogn Lett. 2006;27(8):861–74.
View Article
Google Scholar

[158] View Article

[159] Google Scholar

[ref48] 48. Hand DJ, Till RJ. A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn. 2001;45:171–86.
View Article
Google Scholar

[161] View Article

[162] Google Scholar

[ref49] 49. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45. pmid:3203132
View Article
PubMed/NCBI
Google Scholar

[164] View Article

[165] PubMed/NCBI

[166] Google Scholar

[ref50] 50. Klotzbier TJ, Schott N. Cognitive-motor interference during walking in older adults with probable mild cognitive impairment. Front Aging Neurosci. 2017;9:350. pmid:29321738
View Article
PubMed/NCBI
Google Scholar

[168] View Article

[169] PubMed/NCBI

[170] Google Scholar

[ref51] 51. Osuka Y, Kim H, Watanabe Y, Taniguchi Y, Kojima N, Seino S, et al. A stepping trail making test as an indicator of cognitive impairment in older adults. J Clin Med. 2020;9(9):2835. pmid:32887235
View Article
PubMed/NCBI
Google Scholar

[172] View Article

[173] PubMed/NCBI

[174] Google Scholar

[ref52] 52. Kearney FC, Harwood RH, Gladman JRF, Lincoln N, Masud T. The relationship between executive function and falls and gait abnormalities in older adults: a systematic review. Dement Geriatr Cogn Disord. 2013;36(1–2):20–35. pmid:23712088
View Article
PubMed/NCBI
Google Scholar

[176] View Article

[177] PubMed/NCBI

[178] Google Scholar

[ref53] 53. Herman T, Mirelman A, Giladi N, Schweiger A, Hausdorff JM. Executive control deficits as a prodrome to falls in healthy older adults: a prospective study linking thinking, walking, and falling. J Gerontol A Biol Sci Med Sci. 2010;65(10):1086–92. pmid:20484336
View Article
PubMed/NCBI
Google Scholar

[180] View Article

[181] PubMed/NCBI

[182] Google Scholar

[ref54] 54. Delbaere K, Kochan NA, Close JCT, Menant JC, Sturnieks DL, Brodaty H, et al. Mild cognitive impairment as a predictor of falls in community-dwelling older people. Am J Geriatr Psychiatry. 2012;20(10):845–53. pmid:23011051
View Article
PubMed/NCBI
Google Scholar

[184] View Article

[185] PubMed/NCBI

[186] Google Scholar

[ref55] 55. Diamond A. Executive functions. Annu Rev Psychol. 2013;64:135–68.
View Article
Google Scholar

[188] View Article

[189] Google Scholar

[ref56] 56. Springer S, Giladi N, Peretz C, Yogev G, Simon ES, Hausdorff JM. Dual-tasking effects on gait variability: the role of aging, falls, and executive function. Mov Disord. 2006;21(7):950–7. pmid:16541455
View Article
PubMed/NCBI
Google Scholar

[191] View Article

[192] PubMed/NCBI

[193] Google Scholar

[ref57] 57. Rogers MW, Mille M-L. Timing paradox of stepping and falls in ageing: not so quick and quick(er) on the trigger. J Physiol. 2016;594(16):4537–47. pmid:26915664
View Article
PubMed/NCBI
Google Scholar

[195] View Article

[196] PubMed/NCBI

[197] Google Scholar

[ref58] 58. Persad CC, Giordani B, Chen HC, Ashton-Miller JA, Alexander NB, Wilson CS, et al. Neuropsychological predictors of complex obstacle avoidance in healthy older adults. J Gerontol B Psychol Sci Soc Sci. 1995;50(5):P272-7. pmid:7656077
View Article
PubMed/NCBI
Google Scholar

[199] View Article

[200] PubMed/NCBI

[201] Google Scholar

[ref59] 59. Muir-Hunter SW, Wittwer JE. Dual-task testing to predict falls in community-dwelling older adults: a systematic review. Physiotherapy. 2016;102(1):29–40. pmid:26390824
View Article
PubMed/NCBI
Google Scholar

[203] View Article

[204] PubMed/NCBI

[205] Google Scholar

[ref60] 60. Bayot M, Dujardin K, Dissaux L, Tard C, Defebvre L, Bonnet CT, et al. Can dual-task paradigms predict falls better than single task? - a systematic literature review. Neurophysiol Clin. 2020;50(6):401–40. pmid:33176988
View Article
PubMed/NCBI
Google Scholar

[207] View Article

[208] PubMed/NCBI

[209] Google Scholar

[ref61] 61. Bohannon RW, Williams Andrews A. Normal walking speed: a descriptive meta-analysis. Physiotherapy. 2011;97(3):182–9. pmid:21820535
View Article
PubMed/NCBI
Google Scholar

[211] View Article

[212] PubMed/NCBI

[213] Google Scholar

[ref62] 62. Houles M, Abellan Van Kan G, Rolland Y, Andrieu S, Anthony P, Bauer J. La vitesse de marche comme critère de fragilité chez la personne âgée vivant au domicile. Cah Annee Gerontol. 2010;2:13–23.
View Article
Google Scholar

[215] View Article

[216] Google Scholar

[ref63] 63. De Souto Barreto P, Ferrandez AM, Saliba-Serre B. Questionnaire d’activité physique pour les personnes âgées (QAPPA): validation d’un nouvel instrument de mesure en langue française. Sci Sports. 2011;26: 11–8.
View Article
Google Scholar

[218] View Article

[219] Google Scholar

[ref64] 64. Yogev-Seligmann G, Hausdorff JM, Giladi N. Do we always prioritize balance when walking? Towards an integrated model of task prioritization. Mov Disord. 2012;27(6):765–70. pmid:22419512
View Article
PubMed/NCBI
Google Scholar

[221] View Article

[222] PubMed/NCBI

[223] Google Scholar

[ref65] 65. Stern Y. Cognitive reserve. Neuropsychologia. 2009;47(10):2015–28. pmid:19467352
View Article
PubMed/NCBI
Google Scholar

[225] View Article

[226] PubMed/NCBI

[227] Google Scholar

[ref66] 66. Beurskens R, Bock O. Age-related deficits of dual-task walking: a review. Neural Plast. 2012;2012:131608. pmid:22848845
View Article
PubMed/NCBI
Google Scholar

[229] View Article

[230] PubMed/NCBI

[231] Google Scholar

[ref67] 67. Mirelman A, Shema S, Maidan I, Hausdorff JM. Gait. Handb Clin Neurol. 2018;159:119–34. pmid:30482309
View Article
PubMed/NCBI
Google Scholar

[233] View Article

[234] PubMed/NCBI

[235] Google Scholar

[ref68] 68. Woollacott M, Shumway-Cook A. Attention and the control of posture and gait: a review of an emerging area of research. Gait Posture. 2002;16(1):1–14. pmid:12127181
View Article
PubMed/NCBI
Google Scholar

[237] View Article

[238] PubMed/NCBI

[239] Google Scholar

Figures

Abstract

Background

Methodology

Results

Conclusions

Introduction

Methods

Participants

Ethics

Measures

General characteristics.

Global cognition.

Cognitive flexibility.

Level of fall risk.

The 6-meter walking test (6MWT).

The Walking – Trail Making Test (W-TMT).

Cognitive costs.

Data analysis

Group performances in execution times and cognitive costs.

Concordance index (c-index) and Receiver Operating Characteristic analyses.

Results

General characteristics

Baseline differences

Execution time and cognitive cost in locomotor tasks

c-index analyses

ROC analyses

Discussion

The W-TMT-B, a tool to detect older people at a high level of fall risk

The W-TMT-B condition involves cognitive flexibility, an independent fall risk factor

Screening with the W-TMT-B is more accurate than with the 6MWT-DT

Limitations and perspectives

Conclusions

Supporting information

S1 Results. Normative classes for the Trail Making Test.

Acknowledgments

References