The Effects of an Extemporaneous Speech Dual-task on Older Adult Gait Control

The Effects of an Extemporaneous Speech Dual-task on Older Adult Gait Control | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Effects of an Extemporaneous Speech Dual-task on Older Adult Gait Control Ahmadreza Souri, Mandana Sanandaji, Shane Caswell, Oladipo Eddo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7674245/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 18 You are reading this latest preprint version Abstract Older adults modify their gait during dual-task (DT) walking to maintain stability while completing a cognitive task, but the underlying mechanics remain unclear. Many studies use unrealistic concurrent DTs involving sensory interference or discrete (non-continuous) cognitive demands. The purpose of this study was to investigate the influence of realistic DT demands on gait control using continuous extemporaneous speech that mimics real-world cognitive demands in older adults. We analyzed changes in speech performance and spatiotemporal gait parameters, propulsion, and center of pressure (CoP) displacement and velocity in 15 older adults walking at a typical pace on a 10-meter path in two single-task (seated speech, walking only) and one DT (walking while speaking continuously on a randomly assigned topic) conditions. Linear mixed-effects regression analysis revealed no changes to speech during DT compared with seated speech (all p > .17). In contrast, gait was significantly altered during DT compared with walking only, supporting the notion that realistic attentional demands interfere with gait in older adults, resulting in slower walking speed, shorter step length, and prolonged stance times (all p < .001). Participants reduced both anterior-posterior CoP displacement and velocity, along with decreased propulsive force (all p < .001), suggesting a stability-focused compensatory strategy that prioritized extemporaneous speech while controlling forward progression. The absence of changes in both medial-lateral CoP displacement, velocity, and step width (all p > .22) indicates that lateral balance mechanisms were unchanged with DT demands, potentially through effective hip control or ankle strategies. Physical sciences/Engineering Biological sciences/Neuroscience gait stability spatiotemporal kinetic gait biomechanics aging cognitive-motor Figures Figure 1 Introduction Nearly 30% of individuals aged 65 or older sustain a fall, and among them, 50% are likely to experience additional falls [1]. Given that most falls in older adults occur during walking, [2,3] it is not surprising that unstable gait patterns (e.g., slower and wider steps) are associated with a higher risk of falls [4,5]. Completing concurrent cognitive tasks while walking, known as dual-tasking (DT), is a common aspect of daily life, such as walking while carrying a cup of coffee or conversing with someone. Older adult gait is often characterized by reduced walking speed and shorter step lengths compared to younger adults, [6] but when walking with a DT, older adult gait slows even more than young adults, [7] and worse DT walking performance is linked to an increased risk of falling [8]. Older adults require increased attention to complete DT walking, supported by increased cortical activation during DT balance tasks in older than younger adults, [9] potentially due to slower decision-making and reduced efficiency in sensory integration and inhibitory processes [10]. But even for healthy young adults, completing DT walking is challenging [11]. Both younger and older adults take slower, shorter, and wider steps during DT walking compared to single-task (ST) walking, suggesting they attempt to gain mechanical stability in their gait to dedicate attentional resources to the cognitive task without compromising balance [11–15]. However, the underlying stability-related mechanisms for these changes in gait biomechanics remain unclear. Furthermore, studies often use unfamiliar and unrealistic cognitive tasks such as serial subtraction/addition and the auditory/visual Victoria Stroop test, which induce discrete, non-continuous cognitive demands that likely fail to translate into the real-world [12–15]. Testing a continuous and realistic extemporaneous speech DT that mimics the cognitive demands and implicit goals of conversational speech may better represent cognitive-motor demands during DT gait control [16]. Although gait speed is frequently reported in DT studies, walking performance can be quantified using various biomechanical outcomes including spatiotemporal indicators, (e.g., speed, step time, length and width) and kinetics (i.e., peak anterior ground reaction force at push-off (aGRF peak) and center of pressure (CoP) metrics). The CoP, the instantaneous point where the resultant vertical ground reaction force (GRF) acts on the plantar surface, is represented as a 2D projection onto the supporting surface (e.g., ground or force plate). CoP progression is depicted by a coordinate trajectory as it moves from the heel to the forefoot, [17] and illustrates the combined neuromuscular response that governs the movement of the center of mass (CoM) to maintain both forward motion and upright balance in the anterior–posterior (AP) and medial–lateral (ML) directions [18]. Compared to young adults, older adults show a more medial CoP trajectory from initial contact through mid-stance, faster CoP velocity at initial contact that slows down during mid-stance [17], and a more lateral CoP trajectory during late stance/push-off [19]. However, how CoP dynamics change during DT walking in older adults remains unknown. Understanding these changes could provide insight into age-related changes in neuromuscular control of gait that contribute to mobility deficits in this population. Neuromuscular contributions to gait control and stability can be better understood through shifts of the CoP that reflect modulation of frontal ankle moments (lateral ankle mechanism) and the push-off mechanism (antero-posterior ankle propulsion) to stabilize the center of mass (CoM) through the stance phase [20]. During the swing phase, foot placement is used to control the position of the swing leg relative to the body to maintain stability and forward progression [20]. To continuously analyze such neuromuscular mechanisms across an entire step, statistical parametric mapping (SPM) provides a powerful tool for assessing CoP shifts across the entire stance phase. By preserving the continuous nature of CoP data and analyzing the time-series, SPM allows for a more holistic evaluation of CoP modulations that contribute to stability, rather than limiting analyses to discrete events like heel strike or toe-off [21]. Existing DT studies have primarily focused on step parameters without integrating continuous CoP analysis, leaving a gap in our understanding of the stability-related locomotor strategies that underlie spatiotemporal aspects of gait in older adult DT walking. Considering the entire CoP trajectory, instead of predefined foot regions (e.g., rearfoot, midfoot, forefoot) or gait events (e.g., heel strike, toe off), ensures that critical stability-related neuromuscular adaptations, such as CoP shifts in response to DT demands, are not oversimplified. Our study leverages SPM to analyze CoP position and velocity across step time, providing a nuanced view of how cognitive-motor processes impact gait stability in older adults during DT walking. By comparing CoP displacement and velocity between DT and ST conditions, we aim to reveal the neuromuscular control and stability-related factors that contribute to increased fall risk in older adults. In particular, difficulty maintaining gait while performing a cognitive task, such as walking while talking, has been identified as a strong predictor of falls in aging [22], offering new perspectives for fall prevention. Thus, the purpose of our study was to investigate how engaging in extemporaneous speech while walking affects gait stability in older adults by examining CoP displacement and CoP velocity using a continuous metric (SPM), and contextualizing our results with spatiotemporal metrics and peak aGRF to understand the contributions of foot placement, push off, and the lateral ankle mechanism. We assessed cognitive-motor interference in older adults by measuring silent speech pauses (>150ms) during a continuous DT walking task. (1) We hypothesized that older adults would take slower, shorter, wider steps, with an increase in both step time and double stance percentage within step time to complete a DT walk. (2) We further hypothesized that participants would increase stability during the DT through an increase in ML CoP displacement and mean ML CoP velocity, a decrease in AP CoP displacement and AP CoP mean velocity during single stance, and a reduction in peak aGRF at push-off. To understand CoP dynamics across time, we used SPM to compare changes in CoP position and velocity between ST and DT conditions during the stance phase of walking. Methods 2.1. Participants Data was collected at two separate institutions by the same researcher who followed ethical guidelines and maintained approval by Ohio University (#21-F-35) and George Mason University’s Institutional Review Board (#1978962-1; continuation, #1993873-2). All participants provided written informed consent before participation in agreement with ethical guidelines compliant with the Declaration of Helsinki. Fifteen community-dwelling older adults participated in this study. Participants were recruited through flyers at senior and fitness centers and word of mouth. To be eligible, individuals had to be: (1) 65 years or older, (2) able to walk 10 meters unaided, (3) score 25 or more on the Montreal Cognitive Assessment, and (4) native English speakers. We excluded individuals who had: (1) self-reported neurological issues (e.g., stroke, Parkinson's, multiple sclerosis), (2) significant physical conditions (e.g., cardiovascular disease, severe osteoarthritis, or recent joint replacement), (3) respiratory issues that affect balance (e.g., Chronic Obstructive Pulmonary Disease), or (4) difficulty walking unassisted for 20 minutes. Additionally, (5) individuals with dementia, psychotic disorders, (6) uncorrected vision or hearing problems, or (7) difficulties with speaking and understanding English were not eligible. The recruitment process aimed for a diverse range of ethnic and cultural backgrounds, as well as a balanced representation of genders. 2.2. Procedure Older adults walked overground in their comfortable footwear at typical speeds for 10 trials on a 10-meter path, back and forth across floor-embedded force plates (Bertec, Columbus, OH) recording forces and moments at a frequency of 1000Hz. Position data were recorded using motion capture technology at a frequency of 100Hz, with 39 retroreflective markers placed on bony landmarks, following the Plug-in-Gait full-body model (Vicon Motion Systems, Oxford, UK). For the ST, participants were instructed to ‘walk at their typical speed’ and their starting position was adjusted so that their feet would strike the force plates without overtly asking them to target their steps. Some participants performed up to 15 trials due to repositioning. For the DT, participants were first given a list of 26 topics and asked to select those they felt comfortable discussing for one minute. Next, they were instructed to speak continuously on randomly assigned topics, such as their first job, favorite sport, or pets. First, participants completed a seated single-task speech trial, where they spoke about a randomly selected topic while seated for one minute as the baseline to analyze speech performance. Then, participants were assigned another random topic and instructed to speak while continuously walking across the same 10-meter path as ST walking, but without stopping, ensuring uninterrupted speech throughout. Participants were reminded that the content of their speech was not important, but to try to keep talking the entire time. Instructions provided before each trial were: “Walk at a typical pace while speaking about the assigned topic,” continuing for one minute until instructed to stop. Speech was recorded by a wireless microphone (Moveo, Los Angeles, CA) connected to an iPhone. 2.3. Data analysis Retroreflective marker trajectory data were smoothed using the Woltring filter with a mean square error smoothing parameter set to 10 and GRF and CoP data exported from Vicon Nexus software (v2.15.0, Vicon Motion Systems Ltd., Oxford, UK) were low-pass filtered with a 4th order Butterworth filter at cut-off frequencies of 10Hz. CoP data were not downsampled for the CoP variables used in the linear mixed-effects regression (LMER) analysis but were downsampled to 101 time-normalized points for the SPM analysis. Trajectory data from markers at the left and right posterior superior iliac spine (PSIS), heel (calcaneus), and toe (head of the second metatarsal) were used to calculate key gait events. The center of left and right PSIS markers was defined as a virtual sacrum marker. Heel strike events were defined as the instant of maximum horizontal distance between the heel marker and the virtual sacrum marker, and toe-off events were defined as the instant of minimum horizontal distance between the toe marker and the virtual sacrum marker [23]. Determined gait events were used to calculate spatiotemporal gait parameters including step length, step velocity, step width, aGRF peak, step time, and double stance percentage during step time (Table 1). Additionally, CoP displacement and CoP mean velocity in the AP and ML directions for the single stance phase were calculated. Before the SPM analysis, the CoP trajectories were first translated to set the origin at the heel strike of the analyzed foot. According to established procedures for SPM analyses, Principal Component Analysis was applied to align the CoP trajectories with the principal axes, where the first principal component represented the AP direction, and the second represented the medio-lateral direction [24]. The CoP trajectories for the left foot were mirrored along the medio-lateral axis to ensure directional symmetry with the right foot [25]. The trajectories were normalized to the participant’s foot length, calculated as the Euclidean distance between the heel and the second metatarsal marker, and expressed as a percentage of foot length to allow for inter-subject comparisons, with values exceeding 100% when the CoP progresses beyond the second metatarsal The CoP trajectories were temporally normalized to 100% of the stance phase of gait to standardize the temporal resolution. The average of these re-registered CoP values across trials was calculated for each participant for the final SPM analysis. CoP velocity across time was calculated through central difference method [26]. All variables were determined using custom MATLAB (R2022a, Mathworks Inc., Natick, MA) code with the assistance of ChatGPT, available upon reasonable request. The number and duration of silent pauses in speech are commonly used as indicators of the cognitive demands associated with different extemporaneous speaking conditions [27,28]; pauses in speech during DT walking with unprepared monologues reflects underlying cognitive-motor interference with speech planning [11]. Two trained research assistants used the open-source software PRAAT (version 6.2.14) to detect and quantify the number and average length of silent pauses, defined as those lasting longer than 150 milliseconds, during the extemporaneous speech task [16]. Following established protocols, research assistants manually annotated the onset and offset of silent pauses by analyzing spectrograms and waveform displays [11,28]. Custom MATLAB scripts from a previously published study were used to calculate pause metrics for each trial, including average of pause durations, total number of pauses, and cumulative pause time [16]. Table 1 Definitions of calculated gait parameters Gait Parameter Definition Step length The horizontal distance between the heel strike of one foot and the heel strike of the opposite foot. Step velocity The speed at which a single step is taken, calculated by dividing the step length by the single stance time. Step width The lateral distance between the heel strike of one foot and the heel strike of the opposite foot. Peak anterior ground reaction force at push-off (aGRF peak) The maximum propulsive force generated in the anterior direction during the push-off phase of gait. Step time The duration between the initial contact of one foot and the initial contact of the opposite foot during walking. Double stance percentage The portion of the step time during which both feet are in contact with the ground, expressed as a percentage of the step time. CoP displacement The total distance the CoP moves in the AP and ML directions during the single stance, calculated as the difference between CoP at the contralateral foot’s heel strike and its preceding toe-off. CoP mean velocity The average speed of CoP movement during single stance, obtained by dividing CoP displacement in both ML and AP directions by the duration of single stance. 2.4. Statistical Analysis To test our hypotheses, gait parameters, CoP measures, and speech-related variables were contrast-coded for comparison between ST (reference condition) and DT conditions using LMER models implemented with the ‘fitlme’ function in MATLAB. These models included a random effect for participant and nested random effects for participant within condition. For LMER results, we reported the unstandardized regression coefficient ( B ), standard error ( SE ), t-statistic ( t ), and p-value ( p ). Model assumptions, including normality of residuals, linearity, and homoscedasticity, were evaluated through visual inspection of histograms, Q-Q plots, and residuals versus fitted value plots. Residuals appeared approximately normally distributed with no substantial violations of linearity or constant variance, supporting the validity of the model. Given the use of 13 separate LMER models (10 gait and 3 speech variables), a Bonferroni correction was applied to control for the increased risk of Type I error due to multiple comparisons. The corrected alpha level was set at α = .0038 (.05 / 13). Finally, a 1D-SPM paired t-test was conducted in MATLAB using open-source SPM1d code (vM.0.4.5, www.spm1D.org) to offer a more detailed CoP comparison between ST and DT conditions across stance time. Normality assumptions were assessed for each direction of CoP measures. AP CoP data passed the normality test ( p > .05) and were analyzed using the parametric paired t-test. However, ML CoP data did not meet normality assumptions ( p < .05); therefore, a nonparametric permutation-based paired t-test was employed for those comparisons. SPM analysis reduces the risk of Type I errors commonly associated with testing multiple variables while maintaining the spatiotemporal integrity of continuous data [19]. As a result, SPM provides an interpretation of CoP modulation throughout the stance phase. Statistical significance was set at α = .05 for SPM analysis. Results Due to a recording error, gender and age data were not captured for one participant, resulting in 14 participants with reported gender (8 females, 5 males, 1 unreported) and age (mean ± SD: 71 ± 4 years). Height and mass data were captured for all 15 participants (mean ± SD: height = 1.69 ± .09 m, mass = 70.9 ± 13.7 kg). One participant, although they passed the cognitive inclusion criteria, revealed they were unable to speak fluently about a randomly assigned topic during the DT walking condition and was excluded from the speech analysis and SPM analysis. The speech recording of one participant was excluded from the speech analysis due to poor audio quality caused by excessive background noise. However, due to a small sample size their ST gait metrics were included in the LMER model, which allows for missing data. Participants completed 10 ST walking trials with starts and stops, and an average of nine DT continuous passes along the walkway resulting in a total of 297 steps across ST ( n = 167) and DT ( n = 130) conditions. LMER showed a significant decrease from ST to DT in AP CoP displacement ( p < .001) and AP CoP mean velocity ( p < .001, Table 2). There were no significant differences between conditions in ML CoP displacement ( p = .602) or ML CoP mean velocity ( p = .223). Step length ( p < .001), step velocity ( p < .001), step time ( p < .001), and aGRF peak ( p < .001) decreased while double stance percentage ( p .17), total number of pauses ( p > .63), and cumulative pause time ( p > .24) between DT and seated single-task speech conditions (Table 3). SPM waveform analysis found the AP and ML CoP position did not differ between DT and ST walking at any point during stance ( p > .05). However, AP CoP velocity significantly decreased between ≈ 37–58% of the stance phase ( t = -4.12, p < .001) and significantly increased between ≈ 81–82.5% ( t = 4.12, p = .008) and decreased between ≈ 88–92% ( t = -4.12, p = .037). ML CoP velocity significantly decreased in DT compared to ST between ≈ 17–20% of the stance phase ( t = -3.94, p = .006). CoP position, velocity, and their SPM results in both AP and ML directions are shown in Figure 1. Table 2 . Comparisons of CoP and stepping outcome measures between ST and DT Variable ST (Mean ± SD) DT (Mean ± SD) B SE t p AP CoP displacement (mm) 117 ± 13 104 ± 14 -12.20 2.31 -5.27 < .001 AP CoP mean velocity (mm/s) 322 ± 46 271 ± 53 -44.46 6.35 -7.00 < .001 ML CoP displacement (mm) 12 ± 9 13 ± 9 -0.37 0.70 -0.52 .602 ML CoP mean velocity (mm/s) 32 ± 23 34 ± 22 -2.36 1.93 -1.22 .223 Step length (m) 0.69 ± 0.06 0.64 ± 0.04 -0.05 0.01 -5.80 < .001 Step velocity (m/s) 1.35 ± 0.16 1.14 ± 0.19 -0.19 0.03 -7.14 < .001 Step width (m) 0.09 ± 0.03 0.10 ± 0.04 -0.00 0.00 -0.21 .834 Step time (s) 0.52 ±.06 0.58 ± 4.08 0.04 0.01 5.13 < .001 Double stance percentage (%) 28.81 ± 4.27 32.20 ± 4.17 2.10 0.33 6.34 < .001 Peak aGRF (N) 141.8 ± 35.1 114.9 ± 22.2 -24.54 4.94 -4.97 < .001 Notes : ST = single task, DT = dual task, SD = standard deviation, AP = anterior–posterior, ML = medial–lateral, CoP = center of pressure, aGRF peak = peak anterior ground reaction force at push-off, mm = millimeter, m = meter, s = second, N = newtons Table 3 . Comparisons of speech-related performance variables between ST and DT Variable ST (Mean ± SD) DT (Mean ± SD) B SE t p Average pause duration (s) 0.58 ± .14 0.66 ± .16 0.08 0.06 1.41 < .17 Total number of pauses 42.77 ± 18.39 46.08 ± 17.50 3.31 6.76 0.49 < .63 Cumulative pause time (s) 24.38 ± 11.25 28.93 ± 8.66 4.55 3.78 1.20 < .24 Notes : ST = single task, DT = dual task, SD = standard deviation, s = second. Discussion This study aimed to determine how cognitive–motor interference in a real-world dual-task scenario affects gait dynamics in older adults. We examined the effects of walking while performing extemporaneous speech on spatiotemporal gait parameters, aGRF peak, CoP displacement, and CoP velocity in older adults. Our findings partially support our first hypothesis. We predicted that older adults would demonstrate slower step velocity, shorter and wider steps, as well as prolonged step time and increased double stance percentage during the DT condition. Consistent with this prediction, older adults exhibited reduced step velocity, shorter step length, longer step time, and a greater percentage of double stance under the DT condition, aligning with previous research [ 14 , 15 , 29 , 30 ]. However, contrary to our hypothesis, step width did not significantly change under the dual-task condition. The observed gait modifications suggest walking while speaking extemporaneously requires additional attentional resources for older adults, leading to adjustments aimed at maintaining stability. Older adults maintained extemporaneous speech production by altering gait control in the antero-posterior direction. Capacity-sharing theory posits performing two tasks simultaneously can lead to interference if they rely on the same cognitive resources [ 31 ]; the brain has limited cognitive resources and, when both tasks compete for these resources, performance on one or both tasks may decline [ 32 ]. The cognitive processes required for extemporaneous speech (i.e., executive function, language retrieval, semantic memory) may significantly overlap with those engaged during gait, causing cognitive-motor interference [ 33 ]. Supporting this, functional neuroimaging indicates working memory tasks and gait activate overlapping brain regions, particularly the supplementary motor area and frontoparietal regions [ 34 , 35 ]. When both walking and extemporaneous speech are performed simultaneously, shared neural resources may become allocated towards the cognitive task, reducing the capacity available for gait control in older adults [ 15 ], leading to the observed changes to gait control in the antero-posterior direction. According to the speech performance results, the lack of significant differences in average pause duration, total number of pauses, and cumulative pause time between the DT and seated single-task speech conditions provides evidence that older adults prioritize speech performance over gait [ 36 ]. Our hypothesis that older adults would take wider steps during the DT was not substantiated as there was no significant difference between step widths during a DT. The effect of DT on step width has been reported inconsistently, with reports of no significant change [ 37 ] and increases in step width under DT [ 14 , 38 ]. The lack of a significant difference in step width between DT and ST in this study may be due to differences in the types of cognitive tasks and walking surfaces used by researchers. For example, a wider step width during a Visual Stroop DT may result from using a wrap-around screen placed in front of participants on a treadmill, requiring them to look straight ahead [ 14 ]. A visually based DT may cause sensory interference or lead participants to prioritize stabilizing the visual platform, limiting their ability to adjust foot placement and maintain a normal step width. When comparing three types of verbal dual-tasks (i.e., letter fluency, category fluency, serial subtraction) older adults increase step width [ 38 ]. However, walking on a treadmill differs from overground walking, as the moving belt provides continuous propulsion [ 39 ], potentially altering gait mechanics which could lead to compensatory adjustments in step width. Additionally, people are generally more familiar with overground walking, which may allow for more energy efficient locomotor control even under DT conditions, whereas less familiarity with treadmill walking may require step width adjustments for stability. Our prediction that participants would increase stability during DT walking was partially substantiated by reduced AP CoP displacement and mean velocity during single stance, and lower peak aGRF at push-off. However, contrary to our expectations, there were no significant changes in ML CoP displacement or mean velocity, which is in line with the lack of change in step width during the DT walking. Previous older adult DT gait research using a verbal fluency task measured AP center of force (CoF), defined as the smoothed trajectory of the resultant force distribution during the stance phase of gait, and reported a significant decrease in CoF mean velocity during DT walking [ 40 ], which aligns with our findings. Our results suggest that in the AP direction, the foot placement strategy (i.e., reducing step length) is used to adjust how far and how fast the body moves forward, consequently reducing AP CoP velocity. During DT walking, increased step time and decreased step length suggest a strategy to limit forward progression of the CoM. As a result, CoM in AP direction might remain closer to the base of support for a longer period, reducing walking speed. In agreement with our hypotheses, decreases in peak aGRF at push-off during DT walking compared to ST walking align with the reduction in step velocity, AP CoP displacement and mean velocity during single stance [ 41 ], suggesting participants reduced reliance on the push-off mechanism for gait velocity and enhancing stability. SPM analysis also revealed a reduction in AP CoP velocity during the mid-stance phase (≈ 37–58% of the stance phase, the beginning of single stance), consistent with the interpretation that AP CoP velocity during the single stance phase positively affects gait speed in older adults [ 42 ]. Our findings suggest older adults adopt a compensatory strategy limiting push-off force to avoid destabilizing forward shifts when attentional resources are divided, prioritizing keeping the CoM away from the anterior boundary of the base of support, supported by reductions in AP CoP displacement and velocity. Modulating push-off minimizes the need for rapid AP adjustments in case of a perturbation, effectively slowing forward momentum. Additionally, increased double stance percentage allows for prolonged stabilization with both feet on the ground, further reducing reliance on forward push-off for AP stability and indicating a cautious, stability-focused strategy under DT conditions. SPM analysis in the AP direction revealed increased AP CoP velocity at 81–82.5% of the stance phase in DT gait, corresponding to the late terminal stance phase when the heel lifts, suggesting a delayed compensatory push-off during the DT. Possibly, our sample of older adults were attempting to regain lost propulsion due to deceleration earlier in the step [ 43 ]. Conversely, a slower AP CoP velocity at 88–92% of the gait cycle, occurring during pre-swing as the stance foot rapidly unloads in preparation for toe-off, may reflect a conservative gait strategy aimed at avoiding instability by slowing down the CoM during the transition from double stance to single stance on the opposite limb. Similar findings in people with Parkinson’s disease support that cognitive-motor interference during DT gait may be most exaggerated during the weight shift and the transition from single to double support [ 30 ]. The absence of changes in ML CoP displacement and velocity suggests that the lateral ankle mechanism similarly manages ML balance under both ST and DT conditions. Lateral ankle moments contribute to ML CoP control alongside foot placement strategies [ 20 , 44 ]. During steady-state walking, a negative correlation between foot placement errors and subsequent ML CoP displacement occurs during the single-stance phase, indicating a step placed too far medially is compensated by a lateral CoP shift, while an overly lateral step is compensated with a medial CoP shift [ 44 ]. Older adults increased their double stance percentage during DT walking, promoting uniform foot placement through consistent step width. Hence, the lateral ankle mechanism effectively regulates ML stability demands during a DT by ensuring consistent ML CoP displacement and velocity. During DT walking, SPM analysis revealed older adults briefly decreased ML CoP velocity at 17–20% of the early single stance phase. This reduction may reflect a deliberate stiffening strategy in the frontal plane to minimize excessive lateral sway as only one foot maintains contact with the ground. Older adults exhibit a larger margin of stability and greater hip moments in the frontal plane compared to younger adults, possibly as a strategy to prevent lateral balance loss [ 45 ]. Thus, another possible explanation for the non-significant ML results is that older adults may rely on maintaining consistent hip or knee joint moments between DT and ST conditions, rather than ankle moments, for ML stability. Additionally, older adults exhibit stronger associations between hip moments and the margin of stability in the sagittal plane than younger adults, suggesting increased reliance on hip control rather than ankle mechanisms for balance [ 45 ]. Perhaps due to less musculature and fewer joint degrees of freedom in the frontal plane (ML direction) compared to the sagittal plane (AP direction) for generating corrective torques, older adults rely more on AP adjustments to maintain stability during DT walking. Significance and Implications The observed changes in older adult gait mechanics during DT walking highlight the importance of cognitive-motor interference in maintaining balance during a realistic DT. Interventions designed to improve DT walking, such as cognitive training or balance exercises that simulate real-life challenges like conversing while walking, may prove beneficial in reducing fall-risk. Moreover, our findings emphasize the need to consider cognitive load when assessing fall-risk in older adults, as even routine tasks like extemporaneous speaking can alter gait stability. Limitations and Future Suggestions The study's sample size of 15 older adults limits the generalizability of the findings. A larger participant pool would provide more robust and reliable insights into how DT impacts gait stability. Another limitation of this study is that it does not account for potential differences in gait stability during DT between male and female older adults. Without examining sex-specific responses to cognitive tasks, the findings may not fully capture variations in gait stability, limiting the ability to tailor interventions effectively for both groups. A further limitation of this study is the lack of analysis on hip and ankle joint moments, as well as the relationship between CoP and CoM during DT walking. Future research should explore these factors to provide a more comprehensive understanding of gait stability in older adults. Future studies should also investigate how different types of cognitive tasks during DT walking impact gait stability, as different tasks may elicit distinct neuromuscular responses. Finally, comparisons of gait stability between young and older adults during DT walking would provide a better understand of age-related changes in DT performance and fall-risks. Conclusion In conclusion, our study demonstrates that DT walking with an extemporaneous speech task significantly alters gait mechanics in older adults, particularly in AP direction. To maintain the DT, older adults decreased AP CoP displacement, AP CoP velocity, and peak aGRF at push-off. Our participants walked slower and reduced step length while increasing step time and double stance percentage. Our findings contribute to a deeper understanding of the stability-related mechanisms involved in walking during realistic cognitive-motor tasks for older adults. Interventions aimed at improving gait stability during everyday tasks in older adults should focus on enhancing antero-posterior gait control and incorporating realistic dual-tasks. Declarations Data availability Access to the datasets used or analyzed in this research is available upon reasonable request from the corresponding author. Funding Declaration This project received no funding. Acknowledgements The authors would like to thank all participants for their voluntary involvement in this study. We also extend our appreciation to the students who assisted with data collection and speech data processing. We thank Dustin Grooms for his support transferring this project. Author contributions AS: Conceptualization; Methodology; Data curation; Formal analysis; Writing—original draft & review & editing. MS: Data curation, Formal analysis; Writing—original draft. SC: Writing—review & editing. OE: Writing—review & editing. ACS: Methodology; Writing—review & editing. TR: Conceptualization; Methodology; Writing— review & editing; Supervision. Competing interests The authors declare no competing interests. References Ruchinskas, R. Clinical prediction of falls in the elderly. Am. J. Phys. Med. Rehabil. 82, 273–278 (2003). Rubenstein, L. Z., Josephson, K. R. & Robbins, A. S. Falls in the nursing home. Ann. Intern. Med. 121, 442–451 (1994). Berg, W. P., Alessio, H. 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H., Daffertshofer, A. & Bruijn, S. M. Ankle muscles drive mediolateral center of pressure control to ensure stable steady state gait. Sci. Rep. 11, 21481 (2021). Siragy, T., Russo, Y. & Horsak, B. Mediolateral margin of stability highlights motor strategies for maintaining dynamic balance in older adults. PloS One 19, e0313034 (2024). Additional Declarations No competing interests reported. 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12:27:34","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129061,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7674245/v1/d3e035a42382cb0009da460f.html"},{"id":96249878,"identity":"9516e9c0-93a2-4074-a335-a4dd815970d1","added_by":"auto","created_at":"2025-11-19 07:36:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":304440,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of CoP position and velocity during stance time in ST and DT conditions using Statistical Parametric Mapping (SPM). Shaded areas show SD (dark gray = ST, light gray = DT), and lines represent means (solid = ST, dashed = DT). Significant differences detected by SPM t-tests are marked with horizontal black bars. (a) AP CoP position (% foot length). (b) ML CoP position (% foot length). (c) AP CoP velocity (mm/s). (d) ML CoP velocity (mm/s).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7674245/v1/4312aac53c53c7f17003ee00.png"},{"id":96257171,"identity":"9a6c9769-2bc6-4a9f-8b22-2f97e85eb404","added_by":"auto","created_at":"2025-11-19 07:51:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":918752,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7674245/v1/0695ae27-8c74-4eb1-a2e2-608cf4f929cb.pdf"},{"id":96180509,"identity":"6f592c2e-324e-412b-81b5-674d1a3bec2f","added_by":"auto","created_at":"2025-11-18 12:27:39","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":117275388,"visible":true,"origin":"","legend":"","description":"","filename":"data.zip","url":"https://assets-eu.researchsquare.com/files/rs-7674245/v1/03aa728cb336a47880483dfa.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Effects of an Extemporaneous Speech Dual-task on Older Adult Gait Control\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNearly 30% of individuals aged 65 or older sustain a fall, and among them, 50% are likely to experience additional falls\u0026nbsp;[1]. Given that most falls in older adults occur during walking,\u0026nbsp;[2,3]\u0026nbsp;it is not surprising that unstable gait patterns (e.g., slower and wider steps) are associated with a higher risk of falls\u0026nbsp;[4,5]. Completing concurrent cognitive tasks while walking, known as dual-tasking (DT), is a common aspect of daily life, such as walking while carrying a cup of coffee or conversing with someone. Older adult gait is often characterized by reduced walking speed and shorter step lengths compared to younger adults,\u0026nbsp;[6]\u0026nbsp;but when walking with a DT, older adult gait slows even more than young adults,\u0026nbsp;[7]\u0026nbsp;and worse DT walking performance is linked to an increased risk of falling\u0026nbsp;[8]. Older adults require increased attention to complete DT walking, supported by increased cortical activation during DT balance tasks in older than younger adults,\u0026nbsp;[9]\u0026nbsp;potentially due to slower decision-making and reduced efficiency in sensory integration and inhibitory processes\u0026nbsp;[10]. But even for healthy young adults, completing DT walking is challenging\u0026nbsp;[11].\u003c/p\u003e\n\u003cp\u003eBoth younger and older adults take slower, shorter, and wider steps during DT walking compared to single-task (ST) walking, suggesting they attempt to gain mechanical stability in their gait to dedicate attentional resources to the cognitive task without compromising balance\u0026nbsp;[11\u0026ndash;15]. However, the underlying stability-related mechanisms for these changes in gait biomechanics remain unclear. Furthermore, studies often use unfamiliar and unrealistic cognitive tasks such as serial subtraction/addition and the auditory/visual Victoria Stroop test, which induce discrete, non-continuous cognitive demands that likely fail to translate into the real-world\u0026nbsp;[12\u0026ndash;15]. Testing a continuous and realistic extemporaneous speech DT that mimics the cognitive demands and implicit goals of conversational speech may better represent cognitive-motor demands during DT gait control\u0026nbsp;[16].\u003c/p\u003e\n\u003cp\u003eAlthough gait speed is frequently reported in DT studies, walking performance can be quantified using various biomechanical outcomes including spatiotemporal indicators, (e.g., speed, step time, length and width) and kinetics (i.e., peak anterior ground reaction force at push-off (aGRF peak) and center of pressure (CoP) metrics). The CoP, the instantaneous point where the resultant vertical ground reaction force (GRF) acts on the plantar surface, is represented as a 2D projection onto the supporting surface (e.g., ground or force plate). CoP progression is depicted by a coordinate trajectory as it moves from the heel to the forefoot,\u0026nbsp;[17]\u0026nbsp;and illustrates the combined neuromuscular response that governs the movement of the center of mass (CoM) to maintain both forward motion and upright balance in the anterior\u0026ndash;posterior (AP) and medial\u0026ndash;lateral (ML) directions\u0026nbsp;[18]. Compared to young adults, older adults show a more medial CoP trajectory from initial contact through mid-stance, faster CoP velocity at initial contact that slows down during mid-stance\u0026nbsp;[17], and a more lateral CoP trajectory during late stance/push-off\u0026nbsp;[19]. However, how CoP dynamics change during DT walking in older adults remains unknown. Understanding these changes could provide insight into\u0026nbsp;age-related changes in neuromuscular control of gait that contribute to mobility deficits in this population.\u003c/p\u003e\n\u003cp\u003eNeuromuscular contributions to gait control and stability can be better understood through shifts of the CoP that reflect modulation of frontal ankle moments (lateral ankle mechanism) and the push-off mechanism (antero-posterior ankle propulsion) to stabilize the center of mass (CoM) through the stance phase [20]. During the swing phase, foot placement is used to control the position of the swing leg relative to the body to maintain stability and forward progression\u0026nbsp;[20].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eTo continuously analyze such neuromuscular mechanisms across an entire step, statistical parametric mapping (SPM) provides a powerful tool for assessing CoP shifts across the entire stance phase. By preserving the continuous nature of CoP data and analyzing the time-series, SPM allows for a more holistic evaluation of CoP modulations that contribute to stability, rather than limiting analyses to discrete events like heel strike or toe-off\u0026nbsp;[21].\u003c/p\u003e\n\u003cp\u003eExisting DT studies have primarily focused on step parameters without integrating continuous CoP analysis, leaving a gap in our understanding of the stability-related locomotor strategies that underlie spatiotemporal aspects of gait in older adult DT walking. Considering the entire CoP trajectory, instead of predefined foot regions (e.g., rearfoot, midfoot, forefoot) or gait events (e.g., heel strike, toe off), ensures that critical stability-related neuromuscular adaptations, such as CoP shifts in response to DT demands, are not oversimplified. Our study leverages SPM to analyze CoP position and velocity across step time, providing a nuanced view of how cognitive-motor processes impact gait stability in older adults during DT walking. By comparing CoP displacement and velocity between DT and ST conditions, we aim to reveal the neuromuscular control and stability-related factors that contribute to increased fall risk in older adults. In particular, difficulty maintaining gait while performing a cognitive task, such as walking while talking, has been identified as a strong predictor of falls in aging\u0026nbsp;[22], offering new perspectives for fall prevention.\u003c/p\u003e\n\u003cp\u003eThus, the purpose of our study was to investigate how engaging in extemporaneous speech while walking affects gait stability in older adults by examining CoP displacement and CoP velocity using a continuous metric (SPM), and contextualizing our results with spatiotemporal metrics and peak aGRF to understand the contributions of foot placement, push off, and the lateral ankle mechanism. We assessed cognitive-motor interference in older adults by measuring silent speech pauses (\u0026gt;150ms) during a continuous DT walking task.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e(1) We hypothesized that older adults would take slower, shorter, wider steps, with an increase in both step time and double stance percentage within step time to complete a DT walk. (2) We further hypothesized that participants would increase stability during the DT through an increase in ML CoP displacement and mean ML CoP velocity, a decrease in AP CoP displacement and AP CoP mean velocity during single stance, and a reduction in peak aGRF at push-off. To understand CoP dynamics across time, we used SPM to compare changes in CoP position and velocity between ST and DT conditions during the stance phase of walking.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData was collected at two separate institutions by the same researcher who followed ethical guidelines and maintained approval by Ohio University (#21-F-35) and George Mason University\u0026rsquo;s Institutional Review Board (#1978962-1; continuation, #1993873-2). All participants provided written informed consent before participation in agreement with ethical guidelines compliant with the Declaration of Helsinki. Fifteen community-dwelling older adults participated in this study. Participants were recruited through flyers at senior and fitness centers and word of mouth. To be eligible, individuals had to be: (1) 65 years or older, (2) able to walk 10 meters unaided, (3) score 25 or more on the Montreal Cognitive Assessment, and (4) native English speakers. We excluded individuals who had: (1) self-reported neurological issues (e.g., stroke, Parkinson\u0026apos;s, multiple sclerosis), (2) significant physical conditions (e.g., cardiovascular disease, severe osteoarthritis, or recent joint replacement), (3) respiratory issues that affect balance (e.g., Chronic Obstructive Pulmonary Disease), or (4) difficulty walking unassisted for 20 minutes. Additionally, (5) individuals with dementia, psychotic disorders, (6) uncorrected vision or hearing problems, or (7) difficulties with speaking and understanding English were not eligible. The recruitment process aimed for a diverse range of ethnic and cultural backgrounds, as well as a balanced representation of genders.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOlder adults walked overground in their comfortable footwear at typical speeds for 10 trials on a 10-meter path, back and forth across floor-embedded force plates (Bertec, Columbus, OH) recording forces and moments at a frequency of 1000Hz. Position data were recorded using motion capture technology at a frequency of 100Hz, with 39 retroreflective markers placed on bony landmarks, following the Plug-in-Gait full-body model (Vicon Motion Systems, Oxford, UK). For the ST, participants were instructed to \u0026lsquo;walk at their typical speed\u0026rsquo; and their starting position was adjusted so that their feet would strike the force plates without overtly asking them to target their steps. Some participants performed up to 15 trials due to repositioning. For the DT, participants were first given a list of 26 topics and asked to select those they felt comfortable discussing for one minute. Next, they were instructed to speak continuously on randomly assigned topics, such as their first job, favorite sport, or pets. First, participants completed a seated single-task speech trial, where they spoke about a randomly selected topic while seated for one minute as the baseline to analyze speech performance. Then, participants were assigned another random topic and instructed to speak while continuously walking across the same 10-meter path as ST walking, but without stopping, ensuring uninterrupted speech throughout. Participants were reminded that the content of their speech was not important, but to try to keep talking the entire time. Instructions provided before each trial were: \u0026ldquo;Walk at a typical pace while speaking about the assigned topic,\u0026rdquo; continuing for one minute until instructed to stop. Speech was recorded by a wireless microphone (Moveo, Los Angeles, CA) connected to an iPhone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRetroreflective marker trajectory data were smoothed using the Woltring filter with a mean square error smoothing parameter set to 10 and GRF and CoP data exported from Vicon Nexus software (v2.15.0, Vicon Motion Systems Ltd., Oxford, UK) were low-pass filtered with a 4th order Butterworth filter at cut-off frequencies of 10Hz. CoP data were not downsampled for the CoP variables used in the linear mixed-effects regression (LMER) analysis but were downsampled to 101 time-normalized points for the SPM analysis. Trajectory data from markers at the left and right posterior superior iliac spine (PSIS), heel (calcaneus), and toe (head of the second metatarsal) were used to calculate key gait events. The center of left and right PSIS markers was defined as a virtual sacrum marker. Heel strike events were defined as the instant of maximum horizontal distance between the heel marker and the virtual sacrum marker, and toe-off events were defined as the instant of minimum horizontal distance between the toe marker and the virtual sacrum marker\u0026nbsp;[23]. Determined gait events were used to calculate spatiotemporal gait parameters including step length, step velocity, step width, aGRF peak, step time, and double stance percentage during step time (Table 1). Additionally, CoP displacement and CoP mean velocity in the AP and ML directions for the single stance phase were calculated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBefore the SPM analysis, the CoP trajectories were first translated to set the origin at the heel strike of the analyzed foot. According to established procedures for SPM analyses, Principal Component Analysis was applied to align the CoP trajectories with the principal axes, where the first principal component represented the AP direction, and the second represented the medio-lateral direction [24]. The CoP trajectories for the left foot were mirrored along the medio-lateral axis to ensure directional symmetry with the right foot [25]. The trajectories were normalized to the participant\u0026rsquo;s foot length, calculated as the Euclidean distance between the heel and the second metatarsal marker, and expressed as a percentage of foot length to allow for inter-subject comparisons, with values exceeding 100% when the CoP progresses beyond the second metatarsal The CoP trajectories were temporally normalized to 100% of the stance phase of gait to standardize the temporal resolution. The average of these re-registered CoP values across trials was calculated for each participant for the final SPM analysis. CoP velocity across time was calculated through central difference method [26]. All variables were determined using custom MATLAB (R2022a, Mathworks Inc., Natick, MA) code with the assistance of ChatGPT, available upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe number and duration of silent pauses in speech are commonly used as indicators of the cognitive demands associated with different extemporaneous speaking conditions [27,28]; pauses in speech during DT walking with unprepared monologues reflects underlying cognitive-motor interference with speech planning [11]. Two trained research assistants used the open-source software PRAAT (version 6.2.14) to detect and quantify the number and average length of silent pauses, defined as those lasting longer than 150 milliseconds, during the extemporaneous speech task [16]. Following established protocols, research assistants manually annotated the onset and offset of silent pauses by analyzing spectrograms and waveform displays [11,28]. Custom MATLAB scripts from a previously published study were used to calculate pause metrics for each trial, including average of pause durations, total number of pauses, and cumulative pause time [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Definitions of calculated gait parameters\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 125px;\"\u003e\n \u003cp\u003eGait Parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 476px;\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eStep length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 476px;\"\u003e\n \u003cp\u003eThe horizontal distance between the heel strike of one foot and the heel strike of the opposite foot.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eStep velocity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 476px;\"\u003e\n \u003cp\u003eThe speed at which a single step is taken, calculated by dividing the step length by the single stance time.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eStep width\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 476px;\"\u003e\n \u003cp\u003eThe lateral distance between the heel strike of one foot and the heel strike of the opposite foot.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003ePeak anterior ground reaction force at push-off (aGRF peak)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 476px;\"\u003e\n \u003cp\u003e\u0026nbsp;The maximum propulsive force generated in the anterior direction during the push-off phase of gait.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eStep time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 476px;\"\u003e\n \u003cp\u003eThe duration between the initial contact of one foot and the initial contact of the opposite foot during walking.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eDouble stance percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 476px;\"\u003e\n \u003cp\u003eThe portion of the step time during which both feet are in contact with the ground, expressed as a percentage of the step time.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eCoP displacement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 476px;\"\u003e\n \u003cp\u003eThe total distance the CoP moves in the AP and ML directions during the single stance, calculated as the difference between CoP at the contralateral foot\u0026rsquo;s heel strike and its preceding toe-off.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eCoP mean velocity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 476px;\"\u003e\n \u003cp\u003eThe average speed of CoP movement during single stance, obtained by dividing CoP displacement in both ML and AP directions by the duration of single stance.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test our hypotheses, gait parameters, CoP measures, and speech-related variables were contrast-coded for comparison between ST (reference condition) and DT conditions using LMER models implemented with the \u0026lsquo;fitlme\u0026rsquo; function in MATLAB. These models included a random effect for participant and nested random effects for participant within condition. For LMER results, we reported the unstandardized regression coefficient (\u003cem\u003eB\u003c/em\u003e), standard error (\u003cem\u003eSE\u003c/em\u003e), t-statistic (\u003cem\u003et\u003c/em\u003e), and p-value (\u003cem\u003ep\u003c/em\u003e). Model assumptions, including normality of residuals, linearity, and homoscedasticity, were evaluated through visual inspection of histograms, Q-Q plots, and residuals versus fitted value plots. Residuals appeared approximately normally distributed with no substantial violations of linearity or constant variance, supporting the validity of the model. Given the use of 13 separate LMER models (10 gait and 3 speech variables), a Bonferroni correction was applied to control for the increased risk of Type I error due to multiple comparisons. The corrected alpha level was set at \u003cem\u003e\u0026alpha;\u003c/em\u003e = .0038 (.05 / 13). Finally, a 1D-SPM paired t-test was conducted in MATLAB using open-source SPM1d code (vM.0.4.5, www.spm1D.org) to offer a more detailed CoP comparison between ST and DT conditions across stance time. Normality assumptions were assessed for each direction of CoP measures. AP CoP data passed the normality test (\u003cem\u003ep\u003c/em\u003e \u0026gt; .05) and were analyzed using the parametric paired t-test. However, ML CoP data did not meet normality assumptions (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05); therefore, a nonparametric permutation-based paired t-test was employed for those comparisons. SPM analysis reduces the risk of Type I errors commonly associated with testing multiple variables while maintaining the spatiotemporal integrity of continuous data\u0026nbsp;[19]. As a result, SPM provides an interpretation of CoP modulation throughout the stance phase. Statistical significance was set at \u003cem\u003e\u0026alpha;\u003c/em\u003e = .05 for SPM analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDue to a recording error, gender and age data were not captured for one participant, resulting in 14 participants with reported gender (8 females, 5 males, 1 unreported) and age (mean \u0026plusmn; SD: 71 \u0026plusmn; 4 years). Height and mass data were captured for all 15 participants (mean \u0026plusmn; SD: height = 1.69 \u0026plusmn; .09 m, mass = 70.9 \u0026plusmn; 13.7 kg). One participant, although they passed the cognitive inclusion criteria, revealed they were unable to speak fluently about a randomly assigned topic during the DT walking condition and was excluded from the speech analysis and SPM analysis. The speech recording of one participant was excluded from the speech analysis due to poor audio quality caused by excessive background noise. However, due to a small sample size their ST gait metrics were included in the LMER model, which allows for missing data. Participants completed 10 ST walking trials with starts and stops, and an average of nine DT continuous passes along the walkway resulting in a total of 297 steps across ST (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 167) and DT (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 130) conditions. LMER showed a significant decrease from ST to DT in AP CoP displacement (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and AP CoP mean velocity (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001, Table 2). There were no significant differences between conditions in ML CoP displacement (\u003cem\u003ep\u003c/em\u003e = .602) or ML CoP mean velocity (\u003cem\u003ep\u003c/em\u003e = .223). Step length (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001), step velocity (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001), step time (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and aGRF peak (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001) decreased while double stance percentage (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001) increased from ST to DT walking. Finally, step width did not change significantly (\u003cem\u003ep\u003c/em\u003e = .15). There were no significant differences in average of pause durations (\u003cem\u003ep\u003c/em\u003e \u0026gt; .17), total number of pauses (\u003cem\u003ep\u003c/em\u003e \u0026gt; .63), and cumulative pause time (\u003cem\u003ep\u003c/em\u003e \u0026gt; .24) between DT and seated single-task speech conditions (Table 3).\u003c/p\u003e\n\u003cp\u003eSPM waveform analysis found the AP and ML CoP position did not differ between DT and ST walking at any point during stance (\u003cem\u003ep\u003c/em\u003e \u0026gt; .05). However, AP CoP velocity significantly decreased between \u0026asymp; 37\u0026ndash;58% of the stance phase (\u003cem\u003et\u0026nbsp;\u003c/em\u003e= -4.12, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and significantly increased between \u0026asymp; 81\u0026ndash;82.5% (\u003cem\u003et\u003c/em\u003e = 4.12, \u003cem\u003ep\u003c/em\u003e = .008) and decreased between \u0026asymp; 88\u0026ndash;92% (\u003cem\u003et\u003c/em\u003e = -4.12, \u003cem\u003ep\u003c/em\u003e = .037). ML CoP velocity significantly decreased in DT compared to ST between \u0026asymp; 17\u0026ndash;20% of the stance phase (\u003cem\u003et\u003c/em\u003e = -3.94, \u003cem\u003ep\u003c/em\u003e = .006). CoP position, velocity, and their SPM results in both AP and ML directions are shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Comparisons of CoP and stepping outcome measures between ST and DT\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eST\u003c/p\u003e\n \u003cp\u003e(Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eDT\u003c/p\u003e\n \u003cp\u003e(Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eAP CoP displacement (mm) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e117 \u0026plusmn; 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e104 \u0026plusmn; 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-12.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eAP CoP mean velocity (mm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e322 \u0026plusmn; 46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e271 \u0026plusmn; 53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-44.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eML CoP displacement (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e12 \u0026plusmn; 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e13 \u0026plusmn; 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eML CoP mean velocity (mm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e32 \u0026plusmn; 23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e34 \u0026plusmn; 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eStep length (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.69 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.64 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-5.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eStep velocity (m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.35 \u0026plusmn; 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.14 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eStep width (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.09 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.10 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eStep time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.52 \u0026plusmn;.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.58 \u0026plusmn; 4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eDouble stance percentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e28.81 \u0026plusmn; 4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e32.20 \u0026plusmn; 4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePeak aGRF (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e141.8 \u0026plusmn; 35.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e114.9 \u0026plusmn; 22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-24.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eNotes\u003c/em\u003e: ST = single task, DT = dual task, SD = standard deviation, AP = anterior\u0026ndash;posterior, ML = medial\u0026ndash;lateral, CoP = center of pressure, aGRF peak = peak anterior ground reaction force at push-off, mm = millimeter, m = meter, s = second, N = newtons\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 3\u003c/strong\u003e. Comparisons of speech-related performance variables between ST and DT\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eST (Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eDT (Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eAverage pause duration (s) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.58 \u0026plusmn; .14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.66 \u0026plusmn; .16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt; .17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eTotal number of pauses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e42.77 \u0026plusmn; 18.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e46.08 \u0026plusmn; 17.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt; .63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eCumulative pause time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e24.38 \u0026plusmn; 11.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e28.93 \u0026plusmn; 8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt; .24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eNotes\u003c/em\u003e: ST = single task, DT = dual task, SD = standard deviation, s = second.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to determine how cognitive\u0026ndash;motor interference in a real-world dual-task scenario affects gait dynamics in older adults. We examined the effects of walking while performing extemporaneous speech on spatiotemporal gait parameters, aGRF peak, CoP displacement, and CoP velocity in older adults. Our findings partially support our first hypothesis. We predicted that older adults would demonstrate slower step velocity, shorter and wider steps, as well as prolonged step time and increased double stance percentage during the DT condition. Consistent with this prediction, older adults exhibited reduced step velocity, shorter step length, longer step time, and a greater percentage of double stance under the DT condition, aligning with previous research [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, contrary to our hypothesis, step width did not significantly change under the dual-task condition. The observed gait modifications suggest walking while speaking extemporaneously requires additional attentional resources for older adults, leading to adjustments aimed at maintaining stability.\u003c/p\u003e\u003cp\u003eOlder adults maintained extemporaneous speech production by altering gait control in the antero-posterior direction. Capacity-sharing theory posits performing two tasks simultaneously can lead to interference if they rely on the same cognitive resources [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; the brain has limited cognitive resources and, when both tasks compete for these resources, performance on one or both tasks may decline [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The cognitive processes required for extemporaneous speech (i.e., executive function, language retrieval, semantic memory) may significantly overlap with those engaged during gait, causing cognitive-motor interference [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Supporting this, functional neuroimaging indicates working memory tasks and gait activate overlapping brain regions, particularly the supplementary motor area and frontoparietal regions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. When both walking and extemporaneous speech are performed simultaneously, shared neural resources may become allocated towards the cognitive task, reducing the capacity available for gait control in older adults [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], leading to the observed changes to gait control in the antero-posterior direction. According to the speech performance results, the lack of significant differences in average pause duration, total number of pauses, and cumulative pause time between the DT and seated single-task speech conditions provides evidence that older adults prioritize speech performance over gait [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur hypothesis that older adults would take wider steps during the DT was not substantiated as there was no significant difference between step widths during a DT. The effect of DT on step width has been reported inconsistently, with reports of no significant change [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and increases in step width under DT [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The lack of a significant difference in step width between DT and ST in this study may be due to differences in the types of cognitive tasks and walking surfaces used by researchers. For example, a wider step width during a Visual Stroop DT may result from using a wrap-around screen placed in front of participants on a treadmill, requiring them to look straight ahead [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A visually based DT may cause sensory interference or lead participants to prioritize stabilizing the visual platform, limiting their ability to adjust foot placement and maintain a normal step width. When comparing three types of verbal dual-tasks (i.e., letter fluency, category fluency, serial subtraction) older adults increase step width [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, walking on a treadmill differs from overground walking, as the moving belt provides continuous propulsion [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], potentially altering gait mechanics which could lead to compensatory adjustments in step width. Additionally, people are generally more familiar with overground walking, which may allow for more energy efficient locomotor control even under DT conditions, whereas less familiarity with treadmill walking may require step width adjustments for stability.\u003c/p\u003e\u003cp\u003eOur prediction that participants would increase stability during DT walking was partially substantiated by reduced AP CoP displacement and mean velocity during single stance, and lower peak aGRF at push-off. However, contrary to our expectations, there were no significant changes in ML CoP displacement or mean velocity, which is in line with the lack of change in step width during the DT walking. Previous older adult DT gait research using a verbal fluency task measured AP center of force (CoF), defined as the smoothed trajectory of the resultant force distribution during the stance phase of gait, and reported a significant decrease in CoF mean velocity during DT walking [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], which aligns with our findings. Our results suggest that in the AP direction, the foot placement strategy (i.e., reducing step length) is used to adjust how far and how fast the body moves forward, consequently reducing AP CoP velocity. During DT walking, increased step time and decreased step length suggest a strategy to limit forward progression of the CoM. As a result, CoM in AP direction might remain closer to the base of support for a longer period, reducing walking speed.\u003c/p\u003e\u003cp\u003eIn agreement with our hypotheses, decreases in peak aGRF at push-off during DT walking compared to ST walking align with the reduction in step velocity, AP CoP displacement and mean velocity during single stance [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], suggesting participants reduced reliance on the push-off mechanism for gait velocity and enhancing stability. SPM analysis also revealed a reduction in AP CoP velocity during the mid-stance phase (\u0026asymp;\u0026thinsp;37\u0026ndash;58% of the stance phase, the beginning of single stance), consistent with the interpretation that AP CoP velocity during the single stance phase positively affects gait speed in older adults [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Our findings suggest older adults adopt a compensatory strategy limiting push-off force to avoid destabilizing forward shifts when attentional resources are divided, prioritizing keeping the CoM away from the anterior boundary of the base of support, supported by reductions in AP CoP displacement and velocity. Modulating push-off minimizes the need for rapid AP adjustments in case of a perturbation, effectively slowing forward momentum. Additionally, increased double stance percentage allows for prolonged stabilization with both feet on the ground, further reducing reliance on forward push-off for AP stability and indicating a cautious, stability-focused strategy under DT conditions.\u003c/p\u003e\u003cp\u003eSPM analysis in the AP direction revealed increased AP CoP velocity at 81\u0026ndash;82.5% of the stance phase in DT gait, corresponding to the late terminal stance phase when the heel lifts, suggesting a delayed compensatory push-off during the DT. Possibly, our sample of older adults were attempting to regain lost propulsion due to deceleration earlier in the step [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Conversely, a slower AP CoP velocity at 88\u0026ndash;92% of the gait cycle, occurring during pre-swing as the stance foot rapidly unloads in preparation for toe-off, may reflect a conservative gait strategy aimed at avoiding instability by slowing down the CoM during the transition from double stance to single stance on the opposite limb. Similar findings in people with Parkinson\u0026rsquo;s disease support that cognitive-motor interference during DT gait may be most exaggerated during the weight shift and the transition from single to double support [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe absence of changes in ML CoP displacement and velocity suggests that the lateral ankle mechanism similarly manages ML balance under both ST and DT conditions. Lateral ankle moments contribute to ML CoP control alongside foot placement strategies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. During steady-state walking, a negative correlation between foot placement errors and subsequent ML CoP displacement occurs during the single-stance phase, indicating a step placed too far medially is compensated by a lateral CoP shift, while an overly lateral step is compensated with a medial CoP shift [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Older adults increased their double stance percentage during DT walking, promoting uniform foot placement through consistent step width. Hence, the lateral ankle mechanism effectively regulates ML stability demands during a DT by ensuring consistent ML CoP displacement and velocity.\u003c/p\u003e\u003cp\u003eDuring DT walking, SPM analysis revealed older adults briefly decreased ML CoP velocity at 17\u0026ndash;20% of the early single stance phase. This reduction may reflect a deliberate stiffening strategy in the frontal plane to minimize excessive lateral sway as only one foot maintains contact with the ground. Older adults exhibit a larger margin of stability and greater hip moments in the frontal plane compared to younger adults, possibly as a strategy to prevent lateral balance loss [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Thus, another possible explanation for the non-significant ML results is that older adults may rely on maintaining consistent hip or knee joint moments between DT and ST conditions, rather than ankle moments, for ML stability. Additionally, older adults exhibit stronger associations between hip moments and the margin of stability in the sagittal plane than younger adults, suggesting increased reliance on hip control rather than ankle mechanisms for balance [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Perhaps due to less musculature and fewer joint degrees of freedom in the frontal plane (ML direction) compared to the sagittal plane (AP direction) for generating corrective torques, older adults rely more on AP adjustments to maintain stability during DT walking.\u003c/p\u003e\n\u003ch3\u003eSignificance and Implications\u003c/h3\u003e\n\u003cp\u003eThe observed changes in older adult gait mechanics during DT walking highlight the importance of cognitive-motor interference in maintaining balance during a realistic DT. Interventions designed to improve DT walking, such as cognitive training or balance exercises that simulate real-life challenges like conversing while walking, may prove beneficial in reducing fall-risk. Moreover, our findings emphasize the need to consider cognitive load when assessing fall-risk in older adults, as even routine tasks like extemporaneous speaking can alter gait stability.\u003c/p\u003e\n\u003ch3\u003eLimitations and Future Suggestions\u003c/h3\u003e\n\u003cp\u003eThe study's sample size of 15 older adults limits the generalizability of the findings. A larger participant pool would provide more robust and reliable insights into how DT impacts gait stability. Another limitation of this study is that it does not account for potential differences in gait stability during DT between male and female older adults. Without examining sex-specific responses to cognitive tasks, the findings may not fully capture variations in gait stability, limiting the ability to tailor interventions effectively for both groups.\u003c/p\u003e\u003cp\u003eA further limitation of this study is the lack of analysis on hip and ankle joint moments, as well as the relationship between CoP and CoM during DT walking. Future research should explore these factors to provide a more comprehensive understanding of gait stability in older adults. Future studies should also investigate how different types of cognitive tasks during DT walking impact gait stability, as different tasks may elicit distinct neuromuscular responses. Finally, comparisons of gait stability between young and older adults during DT walking would provide a better understand of age-related changes in DT performance and fall-risks.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study demonstrates that DT walking with an extemporaneous speech task significantly alters gait mechanics in older adults, particularly in AP direction. To maintain the DT, older adults decreased AP CoP displacement, AP CoP velocity, and peak aGRF at push-off. Our participants walked slower and reduced step length while increasing step time and double stance percentage. Our findings contribute to a deeper understanding of the stability-related mechanisms involved in walking during realistic cognitive-motor tasks for older adults. Interventions aimed at improving gait stability during everyday tasks in older adults should focus on enhancing antero-posterior gait control and incorporating realistic dual-tasks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccess to the datasets used or analyzed in this research is available upon reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project received no funding.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003cbr\u003e\u003c/strong\u003eThe authors would like to thank all participants for their voluntary involvement in this study. We also extend our appreciation to the students who assisted with data collection and speech data processing. We thank Dustin Grooms for his support transferring this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAS: Conceptualization; Methodology; Data curation; Formal analysis; Writing\u0026mdash;original draft \u0026amp; review \u0026amp; editing. MS: Data curation, Formal analysis; Writing\u0026mdash;original draft. SC: Writing\u0026mdash;review \u0026amp; editing. OE: Writing\u0026mdash;review \u0026amp; editing. ACS: Methodology; Writing\u0026mdash;review \u0026amp; editing. TR: Conceptualization; Methodology; Writing\u0026mdash; review \u0026amp; editing; Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRuchinskas, R. Clinical prediction of falls in the elderly. \u003cem\u003eAm. J. Phys. Med. 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Mediolateral margin of stability highlights motor strategies for maintaining dynamic balance in older adults. \u003cem\u003ePloS One\u003c/em\u003e \u003cstrong\u003e19,\u003c/strong\u003e e0313034 (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gait stability, spatiotemporal, kinetic, gait biomechanics, aging, cognitive-motor","lastPublishedDoi":"10.21203/rs.3.rs-7674245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7674245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOlder adults modify their gait during dual-task (DT) walking to maintain stability while completing a cognitive task, but the underlying mechanics remain unclear. Many studies use unrealistic concurrent DTs involving sensory interference or discrete (non-continuous) cognitive demands. The purpose of this study was to investigate the influence of realistic DT demands on gait control using continuous extemporaneous speech that mimics real-world cognitive demands in older adults. We analyzed changes in speech performance and spatiotemporal gait parameters, propulsion, and center of pressure (CoP) displacement and velocity in 15 older adults walking at a typical pace on a 10-meter path in two single-task (seated speech, walking only) and one DT (walking while speaking continuously on a randomly assigned topic) conditions. Linear mixed-effects regression analysis revealed no changes to speech during DT compared with seated speech (all \u003cem\u003ep\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;.17). In contrast, gait was significantly altered during DT compared with walking only, supporting the notion that realistic attentional demands interfere with gait in older adults, resulting in slower walking speed, shorter step length, and prolonged stance times (all \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001). Participants reduced both anterior-posterior CoP displacement and velocity, along with decreased propulsive force (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting a stability-focused compensatory strategy that prioritized extemporaneous speech while controlling forward progression. The absence of changes in both medial-lateral CoP displacement, velocity, and step width (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.22) indicates that lateral balance mechanisms were unchanged with DT demands, potentially through effective hip control or ankle strategies.\u003c/p\u003e","manuscriptTitle":"The Effects of an Extemporaneous Speech Dual-task on Older Adult Gait Control","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 12:27:29","doi":"10.21203/rs.3.rs-7674245/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-04T11:53:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-26T11:44:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-25T14:15:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-19T00:40:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T03:20:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74841331580252915283559765145325300146","date":"2026-01-06T02:23:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245630727458504694822387084618229745074","date":"2026-01-05T11:40:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88136739303821816234905255956770188692","date":"2026-01-04T19:19:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107239712920380040075701841954905642786","date":"2026-01-04T00:35:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T17:17:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118501936010360339931288678860985619365","date":"2025-12-23T16:49:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297421100420835272314228463868898038150","date":"2025-12-18T12:10:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46970728956672589627278116544656218573","date":"2025-11-10T12:11:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-06T15:39:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-03T06:46:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-29T19:13:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-25T22:16:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-25T22:13:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a3cdfa44-a3aa-4b07-a455-6c29c51e2935","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58177475,"name":"Physical sciences/Engineering"},{"id":58177476,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-05-18T08:25:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 12:27:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7674245","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7674245","identity":"rs-7674245","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}