Unraveling gait automaticity decline independent of cognitive decline in Parkinson’s disease: a study using the new Parkinson's Affordable Neuro-movement Detection and Analysis (PANDA) system

Unraveling gait automaticity decline independent of cognitive decline in Parkinson’s disease: a study using the new Parkinson's Affordable Neuro-movement Detection and Analysis (PANDA) system | 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 Unraveling gait automaticity decline independent of cognitive decline in Parkinson’s disease: a study using the new Parkinson's Affordable Neuro-movement Detection and Analysis (PANDA) system Matheus Silva d'Alencar, Gabriel Venas Santos, André Frazão Helene, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7466502/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background: Daily life mobility often requires walking while performing simultaneous cognitive or motor tasks, such as talking or carrying items This ability relies on automaticity, the nervous system’s capacity to coordinate movements with minimal attentional resources, which is impaired in Parkinson’s disease (PD). Increased attentional control during gait is a key strategy to mitigate this impairment. The interplay between automaticity and cognitive function affects gait performance under dual task (DT) conditions. Aging may also impair both automaticity and cognition. While several studies have examined gait deficiencies under DT in PD, the decline in gait automaticity from early to intermediate stages, independent of cognitive impairment, education, and aging, remains unclear. Objective: To investigate the decline in gait automaticity associated with disease progression, independent of cognition, age, gender, and education in people with PD. Methods: 114 individuals with PD were divided into three groups based on Hoehn and Yahr (H&Y) stages, matched for age, gender, years of schooling, and global cognitive capacity assessed by the Montreal Cognitive Assessment (MoCA). Participants completed three gait tests (Timed Up and Go; 10-meter walking test; 6-meter bidimensional gait analysis - PANDA), under DT conditions two different cognitive tasks (verbal fluency and regressive counting). The order of gait, conditions and cognitive task parameters were randomized. The primary variable for Timed Up and Go and the 10-meter walking test was the time to complete the test. For the 6-meter bidimensional gait analysis (PANDA-gait), the new Gait Performance Index (GPI) was calculated from various gait cinematic parameters. Results: Kruskal-Wallis ANOVA showed that the GPI from DT with verbal fluency differentiated the three groups (H&Y I-II, p<.03; H&Y I-III, p<.00001; H&Y II-II, p<.02). The GPI from DT with regressive counting and the other two clinical tests did not show significant differences between H&Y I-II. Conclusion: Gait automaticity progressively declines from the early stage of PD, as indicated by the GPI, independent of cognitive capacity, age, gender, and education. The GPI, based on the low-cost, brief, and friendly PANDA-gait, may serve as an alternative for early detection and monitoring of gait automaticity decline in PD in clinical settings. Health sciences/Neurology Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 1 Introduction Globally, disability and mortality due to Parkinson's disease (PD) are increasing more rapidly than for any other neurological disorder 1 . As the disease progresses, individuals face heightened difficulty in performing everyday tasks, which reduces their independence and quality of life. A primary deficit in PD is the decrease in automaticity - the ability to perform tasks without conscious attention to execution 2,3 . This deficit impairs the execution of normal, automatic, habitual actions in daily routines 3 . Consequently, improving gait automaticity has become a focus of retraining and rehabilitation efforts for people with PD 4,5 . Decreased gait automaticity in PD is linked to dopaminergic denervation 6 , alterations in brain activation 7,8 , and brain morphometry 9 . It is also associated with increased disability 10 , and reduced quality of life 11 . People with PD may recruit additional attentional resources to compensate for impaired automaticity 12,13 . The addition of a concomitant secondary task, often a cognitive task, can highlight the lack of automaticity, particularly in gait. A meta-analysis confirmed that dual task (DT) walking degrades gait performance in people with PD 14 . Several studies have demonstrated that people with mild to moderate PD stages have a reduced ability to maintain gait parameters under DT compared to healthy controls 15,16,17 . The DT paradigm also shows that gait automaticity deficits worsen with disease progression 14,18 , particularly from mild to moderate PD stages 11 . This deficit is also associate with the presence of freezing of gait 19 and more severe non-motor symptoms 20 . Decreased gait automaticity has been observed even in the early 21 and in prodromal phase of PD 22,23 . Besides disease severity, several factors impact gait parameters under DT, highlighting the multifactorial nature of automaticity deficits. Sociodemographic features, particularly age, education, and gender 10,18,24 , as well as clinical aspects, particularly cognitive function 20,25 , have been identified as confounding factors. The strong relationship between cognition and gait performance under DT suggests that reduced automaticity may indicate subtle changes and that DT paradigms may serve as markers of future cognitive decline 24,26 . Cognitive reserve likely plays a key role in the effectiveness of compensatory strategies to minimize automaticity deficits 27 . The relationship between cognitive capacity and compensatory strategies may explain the impact of the type of secondary cognitive task on DT performance. A meta-analysis comparing four categories of secondary tasks (Arithmetic, Language, Memory and Motor) found that all significantly impaired gait speed in people with PD 14 . Other studies indicate that DT performance is more affected by more complex cognitive secondary tasks 27,28 . Although gait performance under DT is considered a key feature of early PD, previous studies investigating the impact of disease progression on gait automaticity deficits included few patients in the early stages of the disease 10,18 . To our knowledge, no study has investigated the impact of disease progression from early to moderate PD stages determined by the Hoehn & Yahr (H&Y) scale, independently of age, education, and global cognitive capacity. The aim of this study was to compare the decline in gait automaticity in people with early to intermediate PD stages, matched for age, gender, education, and global cognitive capacity. Gait automaticity was assessed using a DT paradigm, which involved two distinct concomitant cognitive tasks during two standard clinical tests and a new, low-cost, bidimensional cinematic gait analysis system 30 . In the article where this system was first introduced, it was not given a name. Here, we introduce the following name for it: Parkinson’s Affordable Neuro-movement Detection and Analysis (PANDA) system. Our primary hypothesis is that disease progression, as indicated by the H&Y scale, significantly impacts gait automaticity, irrespective of age, gender, education, and cognitive capacity. A better understanding on the decline in automaticity, independent of cognitive decline and aging, may facilitate the development of new rehabilitations interventions. 2 Methods 2.1 Study design A cross-sectional study followed the STROBE Statement 31 . The stages and procedures are shown in Figure 1. 2.2 Setting All evaluations were performed in individual sessions by a physiotherapist specialized in movement disorders, in a controlled environment designed to minimize auditory and visual interference. 2.3 Participants A convenience sample of 114 people with PD was recruited for this study. Eligibility criteria included: (a) confirmed diagnosis of idiopathic PD based on the UK Parkinson's Disease Society Brain Bank diagnostic criteria 32 ; (b) disease stages I-III according to the H&Y scale 9 ; (c) age between 51 and 85 years; (d) 2-20 years of schooling; (e) current use of dopaminergic medication; (f) the ability to ambulate independently; and (g) no signs of dementia (determined by a Montreal Cognitive Assessment (MoCA) score ³ 21) or major depression (determined by a Geriatric Depression Scale score £ 6). Non-eligibility criteria included: (a) the presence of neurological disorders other than PD; (b) the presence of musculoskeletal, cardiovascular or respiratory diseases, or uncorrected visual/auditory disturbances that could interfere with gait performance. 2.4. Recruitment Participants were recruited consecutively from the contacts of the AMPARO network (www.amparo.numec.prp.usp.br) between June 2018 and June 2020 using a non-probability sampling method. To ensure group matching by H&Y stages, participants’ age, gender, schooling, and MoCA scores were controlled. Initially, eligibility criteria were verified. Subsequently, participants were informed about the study procedures and those who met the criteria were asked to provide informed consent. This study was approved by the appropriate Ethics Committee (#CAAE 67388816.2.0000.065) and conducted in accordance with the Helsinki Declaration. 2.5 Procedures All participants were tested 40 to 120 minutes after their L-dopa dose (ON period) during a single individual session. The severity of motor symptoms was assessed using Section III of the Movement Disorder Society-sponsored Unified Parkinson's Disease Rating Scale (MDS-UPDRS III). This section comprises 18 tests scored on an ordinal scale from 0 (low severity) to 4 (high severity) and was treated as a continuous variable. The MDS-UPDRS III has demonstrated excellent factor validity, test-retest reliability (ICC ¼ = .93), high internal consistency, and responsiveness 30 . Global cognitive capacity was evaluated using the MoCA, a widely recognized tool for detecting cognitive symptoms, including mild ones. The MoCA is effective in distinguishing PD patients in different cognitive states (no cognitive impairment, mild cognitive impairment, or dementia) from healthy controls 33,34,35 , and it can detect changes even in the early stages of the disease 36 . This tool has been used in several studies to investigates DT performance 37,38,39 . Depressive symptoms were assessed using the Geriatric Depression Scale (GDS), a common self‐report rating scale for depression in PD 40 . The GDS short form includes 15 dichotomous questions (YES or NO) regarding the participant's mood, with higher scores indicating a greater likelyhood of depression. Following the clinical evaluation, participants performed three gait tests under single task (ST) and DT conditions. Visual markers indicating the start and end positions for each test were placed on the floor. The use of assistive devices was not permitted. The order of gait test, conditions and cognitive task parameters were randomized. The randomization process involved drawing pieces of paper, each specifying details about the gait test, condition, and cognitive task parameters. 1) Gait test order: 10-meter walk test (10mWT), Timed Up and Go (TUG), and PANDA-gait (see below). 2) Conditions order for each gait test: Single Task (ST), i.e., without a concomitant task, DT with the Countdown task (DTc), and DT with the Verbal Fluency task (DTv). 3) Parameters for cognitive tasks: numbers (90-100) for Countdown task, and letters (A-N-O-R-S) for Verbal Fluency task. The instruction for ST in all tests was to “walk at your usual speed”. For DTc, the instruction was to "walk at your usual speed while counting down as many numbers as possible from X". For DTv, participants were instructed to "walk at your usual speed while saying as many words as possible starting with the letter X, avoiding repetition." Task prioritization for the DT condition was not applied, as it reduces the ability to discriminate group differences 38 . 2.6 Variables 2.6.1 PANDA gait Participants were instructed to walk in a straight line for 6 meters in a flat, well-lit space isolated from excessive noise. They wore non-slip black socks with a yellow sticker placed on the lateral malleolus of the left foot. Videos were recorded with each participant walking from right to left to allow the software to visualize and read the sticker (Figure 2). Participants were instructed to start walking at the command “Go” and to stop and remain in place at the command "Stop”. This procedure, which we will call PANDA gait, was conducted under three different conditions: ST, DTc and DTv, as previously detailed. For more detailed information, refer to d’Alencar et. al 30 . The Gait Performance Index (GPI) for each condition was calculated based on five gait parameters: Nx (number of steps), RmX (average stride size), RmY (average foot elevation relative to the ground), VyNeg (foot descent speed on the vertical axis), and DuPar (duration that the foot remained at zero speed). The GPI was defined according to the following equation: (1) A higher GPI value indicates a more symmetrical gait with larger steps in both the vertical and horizontal axes and less time in double support. Conversely, a lower GPI value, indicating a more asymmetrical gait with reduced step height and length and increased time in double support, corresponds to worse gait performance. 2.6.2 Timed Up & Go Test under DT (TUG-DT) The TUG test is valuable in an outpatient setting due to its brevity, minimal equipment requirements, and ease of administration. The TUG test correlates strongly with functional mobility and gait velocity in PD patients 41,42 . Additionally, it has demonstrated high test-retest and inter-rater reliability in PD populations 43 . During the TUG test, participants were instructed to rise from a chair, walk forward at their normal speed for three meters, turn around, walk back to the chair, and sit down. The TUG test under dual-task conditions (TUG-DT) has been utilized to evaluate people with PD 44,45 . The test was conducted under three conditions: ST, DTc and DTv. The time taken to complete the test was measured using a digital stopwatch by a research assistant. 2.2.3 10-Meters Walk Test under DT (10mWT-DT) The 10-Meter Walk Test (10mWT) assesses walking speed over a short distance, expressed in meters per second. Participants were instructed to walk a defined distance, and the time was recorded 46 . This test was also performed under ST, DTc and DTv conditions, consistent with other studies on DT performance in PD 15,47 . The time taken to complete the test was measured using a digital stopwatch by a research assistant. 2.3 Analysis The normal distribution of the samples was assessed using the Kolmogorov-Shapiro test. For variables demonstrating a normal distribution, such as age and MDS-UPDRS-III scores, distribution homogeneity was tested using Levene’s test. Variables that exhibited a normal distribution, including age and years of schooling, were analyzed using One-Way ANOVA, with the groups (H&YI, H&YII, H&YIII) considered as factors. Effect sizes were calculated for each factor that reached a statistically significant level. When statistically significant differences were detected, the Tukey post-hoc test was applied for pairwise comparisons between the groups. Variables that did not exhibit a normal distribution, including MoCA scores, UPDRS-III scores, GDS scores, TUG-DT, 10mWT-DT, and GPI-DT, were analyzed using Kruskal-Wallis ANOVA (KW-ANOVA), with the groups (H&YI, H&YII, H&YIII) considered as factors. When statistically significant differences were observed, multiple comparisons of the average ranks for each pair of groups were conducted. Normal z-values were computed for each comparison, and post-hoc probabilities were corrected for the number of comparisons for a two-sided test of significance. Additionally, the Wilcoxon test was used to compare the conditions (ST, DTc and DTv) for the three gait tests for all participants. Differences were considered significant when p<0.05. Statistical analyses were performed using Statistica Version 13 (TIBCO Software Inc. USA). 3 Results As anticipated, there were no significant difference in age, gender, years of schooling, and MoCA scores among the three groups. However, significant differences were observed in L-Dopa dosage, GDS scores, UPDRS-III scores, and FoG scores, which align with the expected progression of the disease (Table 1). Table 1: Demographic and clinical characteristics of the participants according to disease stages (H&Y). Variables HY I (n=30) HY II (n=48) HY III (n=36) p-value Post-hoc Age 65.23 (7.86) # 65.45 (8.59) # 65.45 (8.59) # > 0.05 # - Schooling 11.96 (5.12) 12.10 (4.68) 13.47 (5,69) > 0.05 - Gender (male) 17 35 21 > 0.05 - LEDD 250 (100-350) 350 (250-425) 400 (300-475) .0008 I<II* / I<III / II0,05 - GDS 4 (2-5) 3 (2-5) 5 (3-6) .0264 II<III* MDS-UPDRS III 12.50 (9-17) 20.50 (16-25) 23 (18-31) .0000 I<II* / I<III* / II<III* FoG 2 (0-5) 3 (2-6.5) 6.5 (3-10.5) .0001 I < III* / II < III* TUG-ST 8.11 (7.46-9.69) 8.76 (7.58-10.25) 11.91 (9.79-14.29) .0000 I < III* / II < III* TUG-DTc 10.63 (8.7-12.94) 11.53 (9.76-14.12) 16.32 (12.40-17.90) 0.0001 I<III* / I<II* / II<III* TUG-DTv 11.16 (9.13-13.59) 8.51 (7.18-9.98) 9.42 (8-13.15) 0.0000 I<III*/ I<II* / II<III* 10mWT-ST 7.90 (7.25-9.21) 8.51 (7.18-9.98) 9.42 (8-13.15) .0202 I<III* 10mWT-DTc 9.42 (8.26-13.31) 9.85 (8.48-11.89) 12.64 (10.12-16.83) 0.0016 I<III* / II<III* 10mWT-DTv 9.43 (8,56-11.41) 10.87 (9.38-13.08) 14.90 (11.42-20.62) 0.0003 I<III* / II<III* GPI-ST 0.035 (0.023-0.052) 0.023 (0.012-0.038) 0.013 (0.008-0.028) 0.0000 I<II* / I<III* / II<III* GPI-DTc 0.023 (0.013-0.036) 0.015 (0.007-0.026) 0.008 (0.004-0.018) 0.0006 I<III* / II<III* GPI-DTv 0.018 (0.011-0.032) 0.009 (0.006-0.019) 0.004 (0.002-0.012) 0.0000 I<II* / I<III* / II<III* Legend: Mean (standard deviation) and median (interquartile range 25 th – 75 th percentile) values. P-values obtained by Kruskal-Wallis ANOVA* and one-way ANOVA # . Abbreviations: H&Y (Hoehn &Yahr stages); Schooling (Educational level in years); LEDD (Levodopa Equivalent Daily Dose); MoCA (Montreal Cognitive Assessment Total); GDI (Geriatric Depression Scale); MDS-UPDRS III (Section III – Unified Parkinson’s Disease Rating Scale); FoG (Freezing of Gait Questionnaire); TUG-ST (Timed Up and Go in single task); TUG-DTc (Timed Up and Go in dual task with countdown); TUG-DTv (Timed Up and Go in dual task with verbal fluency); 10mWT-ST (10 meters walk-test in single task); 10mWT-DTc (10 meters walk-test in dual task with countdown); 10mWT-DTv (10 meters walk-test in dual task with verbal fluency); GPI-ST (Gait Performance Index in single task); GPI-DTc (Gait Performance Index in dual task with countdown); GPI-DTv (Gait Performance Index in dual task with verbal fluency). P-values of significant between-subtype comparisons are listed in bold. *p-values < .0001 3.2 Gait performance There were statistically significant differences between ST and both DTc and DTv for the three gait tests (Table 2). Table 2: Wilcoxon matched pairs tests, showing statistically significant differences between conditions and gait tests. Pairs of Variables p-value TUG-ST x TUG-DTc < .000001* TUG-ST x TUG-DTv < .000001* TUG-DTc x TUG-DTv < .000001* 10mWT-ST x 10mWT-DTc < .000001* 10mWT-ST x 10mWT-DTv < .000001* 10mWT-DTc x 10mWT-DTv < .000001* GPI-ST x GPI-DTc < .000001* GPI-ST x GPI-DTv < .000001* GPI-DTc x GPI-DTv < .000001* P-values obtained by Wilcoxon test*. Abbreviations: TUG-ST (Timed Up and Go in single task); TUG-DTc (Timed Up and Go in dual task with countdown); TUG-DTv (Timed Up and Go in dual task with verbal fluency); 10mWT-ST (10 meters walk-test in single task); 10mWT-DTc (10 meters walk-test in dual task with countdown); 10mWT-DTv (10 meters walk-test in dual task with verbal fluency); GPI-ST (Gait Performance Index in single task); GPI-DTc (Gait Performance Index in dual task with countdown); GPI-DTv (Gait Performance Index in dual task with verbal fluency). The KW-ANOVA for the time to complete TUG-DTc and TUG-DTv revealed a statistically significant effect for group. Multiple comparison tests indicated statistically significant differences between groups H&YI and H&YIII, as well as between H&Y II and H&Y III. Similar results were observed for 10mWT-DTc, 10mWT-DTv, and GPI-DTc (Table 1). In contrast, the KW-ANOVA test for the GPI-DTv showed a statistically significant effect for group with statistically significant differences between all groups (Table 1 and Figure 3). 4 Discussion Automaticity and cognitive impairments are recognized as initial clinical manifestations of PD that deteriorate with disease progression. While numerous studies have demonstrated a correlation between these factors, the impact of disease progression on automaticity remains unclear when considering cognitive decline, aging, gender, and education. This study, to our knowledge, is the first to show a progressive decline in automaticity across different stages of PD in individuals with comparable age, gender, education, and global cognitive capacity using a user-friendly and low-cost gait analysis system (the PANDA system). Based on the presented results, which confirmed our primary hypothesis, we highlight below our key findings. Association with disease progression Automaticity is associated with disease progression independent of age and gender. Both automaticity and cognition are affected by aging, which reduces the efficiency of automatic motor control 48 . Older adults have more difficulty achieving automaticity compared to younger adults and recruit more brain areas to compensate for the increased difficulty 49 . The poorer gait performance observed under DT conditions in older adults compared to younger adults confirms the decline in automaticity with aging 50 . Gender may also impact DT gait performance 18 , with men experiencing more interference than women 51,52 . Considering that PD prevalence increases after age 60, both aging and disease processes can affect automaticity. Our results show a progressive decline in gait automaticity associated with disease evolution, as determined by H&Y staging, in people of comparable age and gender. Higher H&Y stages correlate with progressive dopaminergic loss 53 , reinforcing the validity of gait automaticity decline as a disease progression indicator. Independence from cognitive decline Automaticity is affected by disease evolution independent of global cognitive decline. Early PD stages involve dysfunction in the sensorimotor circuitsof the basal ganglia 54 , affecting output signals to connected networks involved in automatic movement control. As the disease progresses, changes in effective connectivity become more abnormal 55 . Automaticity impairment is an early sign of PD 21 and worsens with disease progression 11,18 . Although the mechanisms underlying cognitive dysfunction in PD are not fully understood, cognitive impairments are early clinical manifestations that worsen with disease progression 38 . People with PD show impairments in tasks requiring attentional resource sharing, attentional set shifting, visuospatial function, and implicit learning 54 , all of which are involved in DT. The executive region of the striatum may compensate for sensorimotor circuit dysfunction in maintaining automaticity 6 . The impairment in automatic gait control in PD is compensated by an increased reliance on goal-directed control, which demands high attentional resources 56 . In people with PD, attentional focus shifts from automatic to controlled movement patterns within the striatum 57 . Reduced effective connectivity in brain areas associated with automaticity, coupled with maintained activation in brain areas responsible for goal-direct movements during later learning stages 55 , reinforces the notion that goal-directed control is a key compensatory strategy for deficits in automatic motor control 3 . Isolating the effects of automaticity and cognitive impairments on gait performance under DT conditions is challenging due to their strong interplay 39 . The high correlation between DT performance and cognitive capacity confirms this interaction. Our results clearly indicate a progressive decline in gait automaticity from the early stages of PD, even in patients with comparable global cognitive capacity. The MoCA scores are sensitive indicators of cognitive decline over time 58 , neurometabolic alterations 59 , and abnormal cortical and subcortical functional connectivity 60 , and are widely associated with DT performance in people with PD 39 . Therefore, the progressive decrease in gait performance under DT conditions can be attributed to disruptions in automaticity associated with PD. Recent evidence indicates that reduced gait automaticity during dual-task walking is strongly related to cognitive ability 67,68 , while in Parkinson’s disease, dual-tasking decreases gait smoothness without necessarily increasing gait variability indices commonly associated with fall risk 69 . Our findings align with these results, suggesting that prefrontal overactivation reflects compensatory mechanisms under increased attentional demand, particularly in Parkinson’s disease, and may represent potential targets for interventions aimed at mobility preservation and fall prevention. Impact of education Although education is not directedly associated with cognitive capacity, it is an independent predictor of cognitive dysfunction in PD 61 and has been correlated with gait performance under DT 18 . Our results show a progressive automaticity decline associated with disease stage, regardless of education level, in a sample with a wide range of schooling years. GPI efficiency The GPI was more efficient in identifying early automaticity decline in PD than standard clinical tests. TUG and 10mWT have been used to evaluate DT gait performance 15,44,45,47 , showing differences between ST and DT gait performance in PD 21 , between people with PD and healthy controls 15 , and between mild and moderate PD. However, these tests have not reliably distinguished between mild PD stages and healthy individuals 11 . Several gait analysis systems have detected disruptions in gait automaticity, with some systems identifying differences between early to mild PD stages (H&Y I-II) and healthy individuals 21,38 , and among healthy non-manifesting carriers of a PD-associated gene mutation (G2019S) 22 . Despite their effectiveness, these systems have limited clinical applicability due to their high cost and complexity. The GPI, a new gait index based on the PANDA system used in this study, successfully detect early and progressive gait automaticity decline in PD, which standard clinical tests could not identify. Furthermore, while various cognitive concurrent tasks have been used to evaluate gait automaticity under DT conditions, the verbal fluency task - a simple test that requires no equipment - was sufficient to discriminate the progressive decline across PD stages . Conversely, regressive counting was not able to differentiate all PD stages. The complexity of concurrent tasks affects several gait parameters under DT conditions in people with PD 62,63 . DT interference occurs when two tasks compete for attentional resources, leading to deterioration in one or both tasks 64 . More complex cognitive tasks may demand higher attentional resources, reducing the efficiency of compensatory mechanisms for automaticity impairment, thus exacerbating gait alterations. Language functions, generally intact in early PD 65 , may minimize the interaction between cognitive and automaticity impairments during DT. Novel methods to measure DT effects, such as assessing bidirectional interference of motor and cognitive tasks 17 , offer complementary information but require more time to execute, which may hinder their widespread clinical use. Given that the GPI efficiently detected automaticity decline from early PD stages in a short, user-friendly, and low-cost manner, it represents a promising alternative for early detection and monitoring of DT gait performance. Implications for rehabilitation Successful community mobility requires gait automaticity, posing a complex challenge for people with PD 56 . Training under DT conditions may improve performance, but whether improvements are due to reduced automaticity impairment or better attentional resource management remains unclear 66 . The study’s findings contribute to understanding gait automaticity decline associate with PD progression, independent of cognitive decline and aging, and can inform the development of more effective rehabilitation strategies. Strengths and limitations The study’s strengths include the inclusion of participants across three PD stages with comparable age, education, and global cognitive capacity, and the randomization of tasks, conditions, and parameters to avoid order bias. However, as a cross-sectional study, the results were based on a single evaluation. Longitudinal studies are needed to confirm the effect of disease progression on gait automaticity. Additionally, the results were based on behavioral measures; further studies should include brain activation monitoring to enhance understanding of gait automaticity impairment in PD. Declarations Conflicts of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions Matheus Silva d'Alencar: Research Project (Organization; Execution); Statistical Analysis (Design); Manuscript (Writing of the first draft) Gabriel Venas Santos: Research Project (Execution) André Frazão Helene: Manuscript (Review and Critique); Statistical Analysis (Review and Critique) Antonio Carlos Roque: Manuscript (Review and Critique); Statistical Analysis (Review and Critique) José Garcia Vivas Miranda: Statistical Analysis (Review and Critique); Manuscript (Writing of the first draft; Review and Critique) Maria Elisa Pimentel Piemonte: Research Project (Conception; Organization); Statistical Analysis (Design; Execution; Review and Critique); Manuscript (Writing of the first draft) Funding – This article was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES, Brazil (Grant number: 88882.377008/2019-01). – This article was produced as part of the activities of FAPESP Research, Innovation and Dissemination Center for Neuromathematics (Grant number: #2013/07699-0, São Paulo Research Foundation). – This article is supported by FAPESP grant No. 2025/02885-7 – This article was support in part by the National Council of Technological and Scientific Development, CNPq, Brazil (Grants: 307828/2018-2 and 303359/2022-6). 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3","display":"","copyAsset":false,"role":"figure","size":11611,"visible":true,"origin":"","legend":"\u003cp\u003eKW-ANOVA, demonstrating statically significant differences in the GPI-DTv scores between three groups.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7466502/v1/e695943404e2af53aaaca8dd.png"},{"id":101792522,"identity":"5b72f46f-ba6b-4fd8-b027-47dea116b11d","added_by":"auto","created_at":"2026-02-03 16:12:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":756270,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7466502/v1/83a361ec-0e1c-446f-a9c7-2dff46038945.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unraveling gait automaticity decline independent of cognitive decline in Parkinson’s disease: a study using the new Parkinson's Affordable Neuro-movement Detection and Analysis (PANDA) system","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGlobally, disability and mortality due to Parkinson's disease (PD) are increasing more rapidly than for any other neurological disorder\u003csup\u003e1\u003c/sup\u003e. As the disease progresses, individuals face heightened difficulty in performing everyday tasks, which reduces their independence and quality of life.\u003c/p\u003e\n\u003cp\u003eA primary deficit in PD is the decrease in automaticity\u0026nbsp;-\u0026nbsp;the ability to perform tasks without conscious attention to execution\u003csup\u003e2,3\u003c/sup\u003e. This deficit impairs the execution of normal, automatic, habitual actions in daily routines\u003csup\u003e3\u003c/sup\u003e. Consequently, improving gait automaticity has become a focus of retraining and rehabilitation efforts for people with PD\u003csup\u003e4,5\u003c/sup\u003e. Decreased gait automaticity in PD is linked to dopaminergic denervation\u003csup\u003e6\u003c/sup\u003e, alterations in brain activation\u003csup\u003e7,8\u003c/sup\u003e, and brain morphometry\u003csup\u003e9\u003c/sup\u003e. It is also associated with increased disability\u003csup\u003e10\u003c/sup\u003e, and reduced quality of life\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePeople with PD may recruit additional attentional resources to compensate for impaired automaticity\u003csup\u003e12,13\u003c/sup\u003e. The addition of a concomitant secondary task, often a cognitive task, can highlight the lack of automaticity, particularly in gait. A meta-analysis confirmed that dual task (DT) walking degrades gait performance in people with PD\u003csup\u003e14\u003c/sup\u003e. Several studies have demonstrated that people with mild to moderate PD stages have a reduced ability to maintain gait parameters under DT compared to healthy controls\u003csup\u003e15,16,17\u003c/sup\u003e. The DT paradigm also shows that gait automaticity deficits worsen with disease progression\u003csup\u003e14,18\u003c/sup\u003e, particularly from mild to moderate PD stages\u003csup\u003e11\u003c/sup\u003e. This deficit is also associate with the presence of freezing of gait\u003csup\u003e19\u003c/sup\u003e and more severe non-motor symptoms\u003csup\u003e20\u003c/sup\u003e. Decreased gait automaticity has been observed even in the early\u003csup\u003e21\u003c/sup\u003e and in prodromal phase of PD\u003csup\u003e22,23\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBesides disease severity, several factors impact gait parameters under DT, highlighting the multifactorial nature of automaticity deficits. Sociodemographic features, particularly age, education, and gender\u003csup\u003e10,18,24\u003c/sup\u003e, as well as clinical aspects, particularly cognitive function\u003csup\u003e20,25\u003c/sup\u003e, have been identified as confounding factors.\u003c/p\u003e\n\u003cp\u003eThe strong relationship between cognition and gait performance under DT suggests that reduced automaticity may indicate subtle changes and that DT paradigms may serve as markers of future cognitive decline\u003csup\u003e24,26\u003c/sup\u003e. Cognitive reserve likely plays a key role in the effectiveness of compensatory strategies to minimize automaticity deficits\u003csup\u003e27\u003c/sup\u003e. The relationship between cognitive capacity and compensatory strategies may explain the impact of the type of secondary cognitive task on DT performance. A meta-analysis comparing four categories of secondary tasks (Arithmetic, Language, Memory and Motor) found that all significantly impaired gait speed in people with PD\u003csup\u003e14\u003c/sup\u003e. Other studies indicate that DT performance is more affected by more complex cognitive secondary tasks\u003csup\u003e27,28\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAlthough gait performance under DT is considered a key feature of early PD, previous studies investigating the impact of disease progression on gait automaticity deficits included few patients in the early stages of the disease\u003csup\u003e10,18\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo our knowledge, no study has investigated the impact of disease progression from early to moderate PD stages determined by the Hoehn \u0026amp; Yahr (H\u0026amp;Y) scale, independently of age, education, and global cognitive capacity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe aim of this study was to compare the decline in gait automaticity in people with early to intermediate PD stages, matched for age, gender, education, and global cognitive capacity. Gait automaticity was assessed using a DT paradigm, which involved two distinct concomitant cognitive tasks during two standard clinical tests and a new, low-cost, \u0026nbsp;bidimensional cinematic gait analysis system\u003csup\u003e30\u003c/sup\u003e. In the article where this system was first introduced, it was not given a name. Here, we introduce the following name for it: Parkinson’s Affordable Neuro-movement Detection and Analysis (PANDA) system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur primary hypothesis is that disease progression, as indicated by the H\u0026amp;Y scale, significantly impacts gait automaticity, irrespective of age, gender, education, and cognitive capacity. A better understanding on the decline in automaticity, independent of cognitive decline and aging, may facilitate the development of new rehabilitations interventions.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003e2.1 \u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A cross-sectional study followed the STROBE Statement\u003csup\u003e31\u003c/sup\u003e. The stages and procedures are shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e2.2 \u003cstrong\u003eSetting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll evaluations were performed in individual sessions by a physiotherapist specialized in movement disorders, in a controlled environment designed to minimize auditory and visual interference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.3 \u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA convenience sample of 114 people with PD was recruited for this study. Eligibility criteria included: (a) confirmed diagnosis of idiopathic PD based on the UK Parkinson\u0026apos;s Disease Society Brain Bank diagnostic criteria\u003csup\u003e32\u003c/sup\u003e; (b) disease stages I-III according to the H\u0026amp;Y scale\u003csup\u003e9\u003c/sup\u003e; (c) age between 51 and 85 years; (d) 2-20 years of schooling; (e) current use of dopaminergic medication; (f) the ability to ambulate independently; and (g) no signs of dementia (determined by a Montreal Cognitive Assessment (MoCA) score \u0026sup3; 21) or major depression (determined by a Geriatric Depression Scale score \u0026pound; 6). Non-eligibility criteria included: (a) the presence of neurological disorders other than PD; (b) the presence of musculoskeletal, cardiovascular or respiratory diseases, or uncorrected visual/auditory disturbances that could interfere with gait performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Recruitment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were recruited consecutively from the contacts of the AMPARO network (www.amparo.numec.prp.usp.br) between June 2018 and June 2020 using a non-probability sampling method. To ensure group matching by H\u0026amp;Y stages, participants\u0026rsquo; age, gender, schooling, and MoCA scores were controlled. Initially, eligibility criteria were verified. Subsequently, participants were informed about the study procedures and those who met the criteria were asked to provide informed consent. This study was approved by the appropriate Ethics Committee (#CAAE 67388816.2.0000.065) and conducted in accordance with the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were tested 40 to 120 minutes after their L-dopa dose (ON period) during a single individual session. The severity of motor symptoms was assessed using Section III of the Movement Disorder Society-sponsored Unified Parkinson\u0026apos;s Disease Rating Scale (MDS-UPDRS III). This section comprises 18 tests scored on an ordinal scale from 0 (low severity) to 4 (high severity) and was treated as a continuous variable. The MDS-UPDRS III has demonstrated excellent factor validity, test-retest reliability (ICC \u0026frac14; = .93), high internal consistency, and responsiveness\u003csup\u003e30\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGlobal cognitive capacity was evaluated using the MoCA, a widely recognized tool for detecting cognitive symptoms, including mild ones. The MoCA is effective in distinguishing PD patients in different cognitive states (no cognitive impairment, mild cognitive impairment, or dementia) from healthy controls\u003csup\u003e33,34,35\u003c/sup\u003e, and it can detect changes even in the early stages of the disease\u003csup\u003e36\u003c/sup\u003e. This tool has been used in several studies to investigates DT performance\u003csup\u003e37,38,39\u003c/sup\u003e. Depressive symptoms were assessed using the Geriatric Depression Scale (GDS), a common self‐report rating scale for depression in PD\u003csup\u003e40\u003c/sup\u003e. The GDS short form includes 15 dichotomous questions (YES or NO) regarding the participant\u0026apos;s mood, with higher scores indicating a greater likelyhood of depression.\u003c/p\u003e\n\u003cp\u003eFollowing the clinical evaluation, participants performed three gait tests under single task (ST) and DT conditions. Visual markers indicating the start and end positions for each test were placed on the floor. The use of assistive devices was not permitted.\u003c/p\u003e\n\u003cp\u003eThe order of gait test, conditions and cognitive task parameters were randomized. The randomization process involved drawing pieces of paper, each specifying details about the gait test, condition, and cognitive task parameters.\u003c/p\u003e\n\u003cp\u003e1) Gait test order: 10-meter walk test (10mWT), Timed Up and Go (TUG), and PANDA-gait (see below).\u003c/p\u003e\n\u003cp\u003e2) Conditions order for each gait test: Single Task (ST), i.e., without a concomitant task, DT with the Countdown task (DTc), and DT with the Verbal Fluency task (DTv).\u003c/p\u003e\n\u003cp\u003e3) Parameters for cognitive tasks: numbers (90-100) for Countdown task, and letters (A-N-O-R-S) for Verbal Fluency task.\u003c/p\u003e\n\u003cp\u003eThe instruction for ST in all tests was to \u0026ldquo;walk at your usual speed\u0026rdquo;. For DTc, the instruction was to \u0026quot;walk at your usual speed while counting down as many numbers as possible from X\u0026quot;. For DTv, participants were instructed to \u0026quot;walk at your usual speed while saying as many words as possible starting with the letter X, avoiding repetition.\u0026quot;\u003c/p\u003e\n\u003cp\u003eTask prioritization for the DT condition was not applied, as it reduces the ability to discriminate group differences\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.6 \u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e2.6.1 PANDA gait\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants were instructed to walk in a straight line for 6 meters in a flat, well-lit space isolated from excessive noise. They wore non-slip black socks with a yellow sticker placed on the lateral malleolus of the left foot. Videos were recorded with each participant walking from right to left to allow the software to visualize and read the sticker (Figure 2). Participants were instructed to start walking at the command \u0026ldquo;Go\u0026rdquo; and to stop and remain in place at the command \u0026quot;Stop\u0026rdquo;. This procedure, which we will call PANDA gait, was conducted under three different conditions: ST, DTc and DTv, as previously detailed. For more detailed information, refer to d\u0026rsquo;Alencar et. al\u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe Gait Performance Index (GPI) for each condition was calculated based on five gait parameters: Nx (number of steps), RmX (average stride size), RmY (average foot elevation relative to the ground), VyNeg (foot descent speed on the vertical axis), and DuPar (duration that the foot remained at zero speed). The GPI was defined according to the following equation:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"image\" style=\"text-align: start; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255); font-size: medium; font-family: \u0026quot;\u0026quot;;\"\u003e(1)\u003c/p\u003e\n\u003cp\u003eA higher GPI value indicates a more symmetrical gait with larger steps in both the vertical and horizontal axes and less time in double support. Conversely, a lower GPI value, indicating a more asymmetrical gait with reduced step height and length and increased time in double support, corresponds to worse gait performance.\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003c/p\u003e\n\u003cp\u003e2.6.2 Timed Up \u0026amp; Go Test under DT (TUG-DT)\u003c/p\u003e\n\u003cp\u003eThe TUG test is valuable in an outpatient setting due to its brevity, minimal equipment requirements, and ease of administration. The TUG test correlates strongly with functional mobility and gait velocity in PD patients\u003csup\u003e41,42\u003c/sup\u003e. Additionally, it has demonstrated high test-retest and inter-rater reliability in PD populations\u003csup\u003e43\u003c/sup\u003e. During the TUG test, participants were instructed to rise from a chair, walk forward at their normal speed for three meters, turn around, walk back to the chair, and sit down. The TUG test under dual-task conditions (TUG-DT) has been utilized to evaluate people with PD\u003csup\u003e44,45\u003c/sup\u003e. The test was conducted under three conditions: ST, DTc and DTv. The time taken to complete the test was measured using a digital stopwatch by a research assistant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2.3 10-Meters Walk Test under DT (10mWT-DT)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 10-Meter Walk Test (10mWT) assesses walking speed over a short distance, expressed in meters per second. Participants were instructed to walk a defined distance, and the time was recorded\u003csup\u003e46\u003c/sup\u003e. This test was also performed under ST, DTc and DTv conditions, consistent with other studies on DT performance in PD\u003csup\u003e15,47\u003c/sup\u003e. The time taken to complete the test was measured using a digital stopwatch by a research assistant.\u003c/p\u003e\n\u003cp\u003e2.3 Analysis\u003c/p\u003e\n\u003cp\u003eThe normal distribution of the samples was assessed using the Kolmogorov-Shapiro test. For variables demonstrating a normal distribution, such as age and MDS-UPDRS-III scores, distribution homogeneity was tested using Levene\u0026rsquo;s test.\u003c/p\u003e\n\u003cp\u003eVariables that exhibited a normal distribution, including age and years of schooling, were analyzed using One-Way ANOVA, with the groups (H\u0026amp;YI, H\u0026amp;YII, H\u0026amp;YIII) considered as factors. Effect sizes were calculated for each factor that reached a statistically significant level. When statistically significant differences were detected, the Tukey post-hoc test was applied for pairwise comparisons between the groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVariables that did not exhibit a normal distribution, including MoCA scores, UPDRS-III scores, GDS scores, TUG-DT, 10mWT-DT, and GPI-DT, were analyzed using Kruskal-Wallis ANOVA (KW-ANOVA), with the groups (H\u0026amp;YI, H\u0026amp;YII, H\u0026amp;YIII) considered as factors. When statistically significant differences were observed, multiple comparisons of the average ranks for each pair of groups were conducted. Normal z-values were computed for each comparison, and post-hoc probabilities were corrected for the number of comparisons for a two-sided test of significance.\u003c/p\u003e\n\u003cp\u003eAdditionally, the Wilcoxon test was used to compare the conditions (ST, DTc and DTv) for the three gait tests for all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDifferences were considered significant when p\u0026lt;0.05. Statistical analyses were performed using Statistica Version 13 (TIBCO Software Inc. USA).\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003eAs anticipated, there were no significant difference in age, gender, years of schooling, and MoCA scores among the three groups. However, significant differences were observed in L-Dopa dosage, GDS scores, UPDRS-III scores, and FoG scores, which align with the expected progression of the disease (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Demographic and clinical characteristics of the participants according to disease stages (H\u0026amp;Y).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"708\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHY I (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHY II (n=48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHY III (n=36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePost-hoc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.23 (7.86)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.45 (8.59)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.45 (8.59)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026gt; 0.05\u003csup\u003e#\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSchooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.96 (5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.10 (4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.47 (5,69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026gt; 0.05\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGender (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026gt; 0.05\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLEDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e250 (100-350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e350 (250-425)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e400 (300-475)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e.0008\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eI\u0026lt;II* / I\u0026lt;III / II\u0026lt;III*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMoCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25,50 (24-27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26 (23-27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24 (22-26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026gt;0,05\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (2-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (2-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (3-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e.0264\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eII\u0026lt;III*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMDS-UPDRS III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.50 (9-17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.50 (16-25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23 (18-31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e.0000\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eI\u0026lt;II* / I\u0026lt;III* / II\u0026lt;III*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFoG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (0-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (2-6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.5 (3-10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e.0001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eI \u0026lt; III* / II \u0026lt; III*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTUG-ST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.11 (7.46-9.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.76 (7.58-10.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.91 (9.79-14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e.0000\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eI \u0026lt; III* / II \u0026lt; III*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTUG-DTc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.63 (8.7-12.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.53 (9.76-14.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.32 (12.40-17.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e0.0001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eI\u0026lt;III* / I\u0026lt;II* / II\u0026lt;III*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTUG-DTv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.16 (9.13-13.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.51 (7.18-9.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.42 (8-13.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e0.0000\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eI\u0026lt;III*/ I\u0026lt;II* / II\u0026lt;III*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e10mWT-ST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.90 (7.25-9.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.51 (7.18-9.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.42 (8-13.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e.0202\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eI\u0026lt;III*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e10mWT-DTc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.42 (8.26-13.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.85 (8.48-11.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.64 (10.12-16.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e0.0016\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eI\u0026lt;III* / II\u0026lt;III*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e10mWT-DTv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.43 (8,56-11.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.87 (9.38-13.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.90 (11.42-20.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e0.0003\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eI\u0026lt;III* / II\u0026lt;III*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGPI-ST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.035 (0.023-0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.023 (0.012-0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.013 (0.008-0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e0.0000\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eI\u0026lt;II* / I\u0026lt;III* / II\u0026lt;III*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGPI-DTc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.023 (0.013-0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.015 (0.007-0.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008 (0.004-0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e0.0006\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eI\u0026lt;III* / II\u0026lt;III*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGPI-DTv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.018 (0.011-0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009 (0.006-0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004 (0.002-0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e0.0000\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eI\u0026lt;II* / I\u0026lt;III* / II\u0026lt;III*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: Mean (standard deviation) and median (interquartile range 25\u003csup\u003eth\u003c/sup\u003e \u0026ndash; 75\u003csup\u003eth\u003c/sup\u003e percentile) values. P-values obtained by Kruskal-Wallis ANOVA* and one-way ANOVA\u003csup\u003e#\u003c/sup\u003e. Abbreviations: H\u0026amp;Y (Hoehn \u0026amp;Yahr stages); Schooling (Educational level in years); LEDD (Levodopa Equivalent Daily Dose); MoCA (Montreal Cognitive Assessment Total); GDI (Geriatric Depression Scale); MDS-UPDRS III (Section III \u0026ndash; Unified Parkinson\u0026rsquo;s Disease Rating Scale); FoG (Freezing of Gait Questionnaire); TUG-ST (Timed Up and Go in single task); TUG-DTc (Timed Up and Go in dual task with countdown); TUG-DTv (Timed Up and Go in dual task with verbal fluency); 10mWT-ST (10 meters walk-test in single task); 10mWT-DTc (10 meters walk-test in dual task with countdown); 10mWT-DTv (10 meters walk-test in dual task with verbal fluency); GPI-ST (Gait Performance Index in single task); GPI-DTc (Gait Performance Index in dual task with countdown); GPI-DTv (Gait Performance Index in dual task with verbal fluency). P-values of significant between-subtype comparisons are listed in bold. *p-values \u0026lt; .0001\u003c/p\u003e\n\u003cp\u003e3.2 Gait performance\u003c/p\u003e\n\u003cp\u003eThere were statistically significant differences between ST and both DTc and DTv for the three gait tests (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2: Wilcoxon matched pairs tests, showing statistically significant differences between conditions and gait tests.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePairs of Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTUG-ST x TUG-DTc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt; .000001*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTUG-ST x TUG-DTv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt; .000001*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTUG-DTc x TUG-DTv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt; .000001*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10mWT-ST x 10mWT-DTc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt; .000001*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10mWT-ST x 10mWT-DTv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt; .000001*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10mWT-DTc x 10mWT-DTv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt; .000001*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGPI-ST x GPI-DTc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt; .000001*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGPI-ST x GPI-DTv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt; .000001*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGPI-DTc x GPI-DTv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt; .000001*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eP-values obtained by Wilcoxon test*. Abbreviations: TUG-ST (Timed Up and Go in single task); TUG-DTc (Timed Up and Go in dual task with countdown); TUG-DTv (Timed Up and Go in dual task with verbal fluency); 10mWT-ST (10 meters walk-test in single task); 10mWT-DTc (10 meters walk-test in dual task with countdown); 10mWT-DTv (10 meters walk-test in dual task with verbal fluency); GPI-ST (Gait Performance Index in single task); GPI-DTc (Gait Performance Index in dual task with countdown); GPI-DTv (Gait Performance Index in dual task with verbal fluency).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe KW-ANOVA for the time to complete TUG-DTc and TUG-DTv revealed a statistically significant effect for group. Multiple comparison tests indicated statistically significant differences between groups H\u0026amp;YI and H\u0026amp;YIII, as well as between H\u0026amp;Y II and H\u0026amp;Y III. Similar results were observed for 10mWT-DTc, 10mWT-DTv, and GPI-DTc (Table 1).\u003c/p\u003e\n\u003cp\u003eIn contrast, the KW-ANOVA test for the GPI-DTv showed a statistically significant effect for group with statistically significant differences between all groups (Table 1 and Figure 3).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eAutomaticity and cognitive impairments are recognized as initial clinical manifestations of PD that deteriorate with disease progression. While numerous studies have demonstrated a correlation between these factors, the impact of disease progression on automaticity remains unclear when considering cognitive decline, aging, gender, and education. This study, to our knowledge, is the first to show a progressive decline in automaticity across different stages of PD in individuals with comparable age, gender, education, and global cognitive capacity using a user-friendly and low-cost gait analysis system (the PANDA system).\u003c/p\u003e\n\u003cp\u003eBased on the presented results, which confirmed our primary hypothesis, we highlight below our key findings.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAssociation with disease progression\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAutomaticity is associated with disease progression independent of age and gender. Both automaticity and cognition are affected by aging, which reduces the efficiency of automatic motor control\u003csup\u003e48\u003c/sup\u003e. Older adults have more difficulty achieving automaticity compared to younger adults and recruit more brain areas to compensate for the increased difficulty\u003csup\u003e49\u003c/sup\u003e. The poorer gait performance observed under DT conditions in older adults compared to younger adults confirms the decline in automaticity with aging\u003csup\u003e50\u003c/sup\u003e. Gender may also impact DT gait performance\u003csup\u003e18\u003c/sup\u003e, with men experiencing more interference than women\u003csup\u003e51,52\u003c/sup\u003e. Considering that PD prevalence increases after age 60, both aging and disease processes can affect automaticity. Our results show a progressive decline in gait automaticity associated with disease evolution, as determined by H\u0026amp;Y staging, in people of comparable age and gender. Higher H\u0026amp;Y stages correlate with progressive dopaminergic loss\u003csup\u003e53\u003c/sup\u003e, reinforcing the validity of gait automaticity decline as a disease progression indicator.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIndependence from cognitive decline\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAutomaticity is affected by disease evolution independent of global cognitive decline. Early PD stages involve dysfunction in the sensorimotor circuitsof the basal ganglia\u003csup\u003e54\u003c/sup\u003e, affecting output signals to connected networks involved in automatic movement control. As the disease progresses, changes in effective connectivity become more abnormal\u003csup\u003e55\u003c/sup\u003e. Automaticity impairment is an early sign of PD\u003csup\u003e21\u003c/sup\u003e and worsens with disease progression\u003csup\u003e11,18\u003c/sup\u003e. Although the mechanisms underlying cognitive dysfunction in PD are not fully understood, cognitive impairments are early clinical manifestations that worsen with disease progression\u003csup\u003e38\u003c/sup\u003e. People with PD show impairments in tasks requiring attentional resource sharing, attentional set shifting, visuospatial function, and implicit learning\u003csup\u003e54\u003c/sup\u003e, all of which are involved in DT. The executive region of the striatum may compensate for sensorimotor circuit dysfunction in maintaining automaticity\u003csup\u003e6\u003c/sup\u003e. The impairment in automatic gait control in PD is compensated by an increased reliance on goal-directed control, which demands high attentional resources\u003csup\u003e56\u003c/sup\u003e. In people with PD, attentional focus shifts from automatic to controlled movement patterns within the striatum\u003csup\u003e57\u003c/sup\u003e. Reduced effective connectivity in brain areas associated with automaticity, coupled with maintained activation in brain areas responsible for goal-direct movements during later learning stages\u003csup\u003e55\u003c/sup\u003e, reinforces the notion that goal-directed control is a key compensatory strategy for deficits in automatic motor control\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIsolating the effects of automaticity and cognitive impairments on gait performance under DT conditions is challenging due to their strong interplay\u003csup\u003e39\u003c/sup\u003e. The high correlation between DT performance and cognitive capacity confirms this interaction. Our results clearly indicate a progressive decline in gait automaticity from the early stages of PD, even in patients with comparable global cognitive capacity. The MoCA scores are sensitive indicators of cognitive decline over time\u003csup\u003e58\u003c/sup\u003e,\u0026nbsp;neurometabolic alterations\u003csup\u003e59\u003c/sup\u003e, and abnormal cortical and subcortical functional connectivity\u003csup\u003e60\u003c/sup\u003e, and are widely associated with DT performance in people with PD\u003csup\u003e39\u003c/sup\u003e. Therefore, the progressive decrease in gait performance under DT conditions can be attributed to disruptions in automaticity associated with PD.\u003c/p\u003e\n\u003cp\u003eRecent evidence indicates that reduced gait automaticity during dual-task walking is strongly related to cognitive ability\u003csup\u003e67,68\u003c/sup\u003e, while in Parkinson\u0026rsquo;s disease, dual-tasking decreases gait smoothness without necessarily increasing gait variability indices commonly associated with fall risk\u003csup\u003e69\u003c/sup\u003e. Our findings align with these results, suggesting that prefrontal overactivation reflects compensatory mechanisms under increased attentional demand, particularly in Parkinson\u0026rsquo;s disease, and may represent potential targets for interventions aimed at mobility preservation and fall prevention. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImpact of education\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAlthough education is not directedly associated with cognitive capacity, it is an independent predictor of cognitive dysfunction in PD\u003csup\u003e61\u003c/sup\u003e and has been correlated with gait performance under DT\u003csup\u003e18\u003c/sup\u003e. Our results show a progressive automaticity decline associated with disease stage, regardless of education level, in a sample with a wide range of schooling years.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGPI efficiency\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe GPI was more efficient in identifying early automaticity decline in PD than standard clinical tests. TUG and 10mWT have been used to evaluate DT gait performance\u003csup\u003e15,44,45,47\u003c/sup\u003e, showing differences between ST and DT gait performance in PD\u003csup\u003e21\u003c/sup\u003e, between people with PD and healthy controls\u003csup\u003e15\u003c/sup\u003e, and between mild and moderate PD. However, these tests have not reliably distinguished between mild PD stages and healthy individuals\u003csup\u003e11\u003c/sup\u003e. Several gait analysis systems have detected disruptions in gait automaticity, with some systems identifying differences between early to mild PD stages (H\u0026amp;Y I-II) and healthy individuals\u003csup\u003e21,38\u003c/sup\u003e, and among healthy non-manifesting carriers of a PD-associated gene mutation (G2019S)\u003csup\u003e22\u003c/sup\u003e. Despite their effectiveness, these systems have limited clinical applicability due to their high cost and complexity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe GPI, a new gait index based on the PANDA system used in this study, successfully detect early and progressive gait automaticity decline in PD, which standard clinical tests could not identify. Furthermore, while various cognitive concurrent tasks have been used to evaluate gait automaticity under DT conditions, the verbal fluency task - a simple test that requires no equipment - was sufficient to discriminate the progressive decline across PD stages . Conversely, regressive counting was not able to differentiate all PD stages.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe complexity of concurrent tasks affects several gait parameters under DT conditions in people with PD\u003csup\u003e62,63\u003c/sup\u003e. DT interference occurs when two tasks compete for attentional resources, leading to deterioration in one or both tasks\u003csup\u003e64\u003c/sup\u003e. More complex cognitive tasks may demand higher attentional resources, reducing the efficiency of compensatory mechanisms for automaticity impairment, thus exacerbating gait alterations. Language functions, generally intact in early PD\u003csup\u003e65\u003c/sup\u003e, may minimize the interaction between cognitive and automaticity impairments during DT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNovel methods to measure DT effects, such as assessing bidirectional interference of motor and cognitive tasks\u003csup\u003e17\u003c/sup\u003e, offer complementary information but \u0026nbsp;require more time to execute, which may hinder their widespread clinical use. Given that the GPI efficiently detected automaticity decline from early PD stages in a short, user-friendly, and low-cost manner, it represents a promising alternative for early detection and monitoring of DT gait performance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImplications for rehabilitation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSuccessful community mobility requires gait automaticity, posing a complex challenge for people with PD\u003csup\u003e56\u003c/sup\u003e. Training under DT conditions may improve performance, but whether improvements are due to reduced automaticity impairment or better attentional resource management remains unclear\u003csup\u003e66\u003c/sup\u003e. The study\u0026rsquo;s findings contribute to understanding gait automaticity decline associate with PD progression, independent of cognitive decline and aging, and can inform the development of more effective rehabilitation strategies.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStrengths and limitations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study\u0026rsquo;s strengths include the inclusion of participants across three PD stages with comparable age, education, and global cognitive capacity, and the randomization of tasks, conditions, and parameters to avoid order bias. However, as a cross-sectional study, the results were based on a single evaluation. Longitudinal studies are needed to confirm the effect of disease progression on gait automaticity. Additionally, the results were based on behavioral measures; further studies should include brain activation monitoring to enhance understanding of gait automaticity impairment in PD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConflicts of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eMatheus Silva d'Alencar: Research Project (Organization; Execution); Statistical Analysis (Design); Manuscript (Writing of the first draft)\u003c/p\u003e\n\u003cp\u003eGabriel Venas Santos: Research Project (Execution)\u003c/p\u003e\n\u003cp\u003eAndré Frazão Helene: Manuscript (Review and Critique); Statistical Analysis (Review and Critique)\u003c/p\u003e\n\u003cp\u003eAntonio Carlos Roque: Manuscript (Review and Critique); Statistical Analysis (Review and Critique)\u003c/p\u003e\n\u003cp\u003eJosé Garcia Vivas Miranda: Statistical Analysis (Review and Critique); Manuscript (Writing of the first draft; Review and Critique)\u003c/p\u003e\n\u003cp\u003eMaria Elisa Pimentel Piemonte: Research Project (Conception; Organization); Statistical Analysis (Design; Execution; Review and Critique); Manuscript (Writing of the first draft)\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003e– This article was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES, Brazil (Grant number: 88882.377008/2019-01).\u003c/p\u003e\n\u003cp\u003e– This article was produced as part of the activities of FAPESP Research, Innovation and Dissemination Center for Neuromathematics (Grant number: #2013/07699-0, São Paulo Research Foundation).\u003c/p\u003e\n\u003cp\u003e– \u0026nbsp;This article is supported by FAPESP grant No. 2025/02885-7\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e– This article was support in part by the National Council of Technological and Scientific Development, CNPq, Brazil (Grants: 307828/2018-2 and 303359/2022-6).\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors thank Associação Brasil Parkinson for their willingness to provide the data collection environment.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGBD 2016 Neurology Collaborators. 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In Parkinson\u0026rsquo;s disease, dual-tasking reduces gait smoothness, but does not increase the gait variability indices usually associated with fall risk. \u003cem\u003eGait Posture\u003c/em\u003e. 2025;113:175-182.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7466502/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7466502/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Daily life mobility often requires walking while performing simultaneous cognitive or motor tasks, such as talking or carrying items This ability relies on automaticity, the nervous system’s capacity to coordinate movements with minimal attentional resources, which is impaired in Parkinson’s disease (PD). Increased attentional control during gait is a key strategy to mitigate this impairment. The interplay between automaticity and cognitive function affects gait performance under dual task (DT) conditions. Aging may also impair both automaticity and cognition. While several studies have examined gait deficiencies under DT in PD, the decline in gait automaticity from early to intermediate stages, independent of cognitive impairment, education, and aging, remains unclear.\u003c/p\u003e\n\u003cp\u003eObjective: To investigate the decline in gait automaticity associated with disease progression, independent of cognition, age, gender, and education in people with PD.\u003c/p\u003e\n\u003cp\u003eMethods: 114 individuals with PD were divided into three groups based on Hoehn and Yahr (H\u0026amp;Y) stages, matched for age, gender, years of schooling, and global cognitive capacity assessed by the Montreal Cognitive Assessment (MoCA). Participants completed three gait tests (Timed Up and Go; 10-meter walking test; 6-meter bidimensional gait analysis - PANDA), under DT conditions two different cognitive tasks (verbal fluency and regressive counting). The order of gait, conditions and cognitive task parameters were randomized. The primary variable for Timed Up and Go and the 10-meter walking test was the time to complete the test. For the 6-meter bidimensional gait analysis (PANDA-gait), the new Gait Performance Index (GPI) was calculated from various gait cinematic parameters.\u003c/p\u003e\n\u003cp\u003eResults: Kruskal-Wallis ANOVA showed that the GPI from DT with verbal fluency differentiated the three groups (H\u0026amp;Y I-II, p\u0026lt;.03; H\u0026amp;Y I-III, p\u0026lt;.00001; H\u0026amp;Y II-II, p\u0026lt;.02). The GPI from DT with regressive counting and the other two clinical tests did not show significant differences between H\u0026amp;Y I-II.\u003c/p\u003e\n\u003cp\u003eConclusion: Gait automaticity progressively declines from the early stage of PD, as indicated by the GPI, independent of cognitive capacity, age, gender, and education. The GPI, based on the low-cost, brief, and friendly PANDA-gait, may serve as an alternative for early detection and monitoring of gait automaticity decline in PD in clinical settings.\u003c/p\u003e","manuscriptTitle":"Unraveling gait automaticity decline independent of cognitive decline in Parkinson’s disease: a study using the new Parkinson's Affordable Neuro-movement Detection and Analysis (PANDA) system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 16:10:50","doi":"10.21203/rs.3.rs-7466502/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"167292931712308538943157425936914615114","date":"2026-05-17T01:09:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-02T06:53:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-06T13:18:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-24T19:35:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-23T23:14:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-23T23:11:17+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":"10204a09-9523-4607-8d7d-6e6d25c707a5","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"167292931712308538943157425936914615114","date":"2026-05-17T01:09:28+00:00","index":119,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62189972,"name":"Health sciences/Neurology"},{"id":62189973,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-02-03T16:10:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 16:10:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7466502","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7466502","identity":"rs-7466502","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}