Gait Speed as a Superior Screening Indicator for Mild Cognitive Impairment Compared to Walk Ratio and Dual-Task Cost: A Cross-Sectional Study

Gait Speed as a Superior Screening Indicator for Mild Cognitive Impairment Compared to Walk Ratio and Dual-Task Cost: A Cross-Sectional Study | 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 Research Article Gait Speed as a Superior Screening Indicator for Mild Cognitive Impairment Compared to Walk Ratio and Dual-Task Cost: A Cross-Sectional Study Xiaoqin Wang, Jiani Wu, Qi Tian, Xintong Liu, Weihua Yu, Yang Lü This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5336317/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The most accurate and sensitive quantitative indicator for screening patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) had yet to be established. This study aimed to assess the comparative efficacy of gait speed, walk ratio, and dual-task cost (DTC) in detecting patients with SCD and MCI. Methods Our study involved the measurement and comparison of clinical features and gait indicators among 96 patients with MCI, 66 patients with SCD, and 50 individuals with normal cognition (NC). The correlation analysis, receiver operating characteristic curves (ROCs), and binary logistic regression analysis were utilized to investigate the relationship between gait indicators, SCD, and MCI. Results The female patients exhibited a greater susceptibility to SCD and MCI (p < 0.001). Significant differences in gait speed, walk ratio, and DTC were observed between NC and MCI group, as well as between SCD and MCI group (all p < 0.05). However, no significant differences were identified between NC and SCD group. After adjusting for gender, age, education level, Body mass index (BMI), and Mini-mental State Examination (MMSE) scores, a significant correlation was observed between gait speed and the risk of developing MCI. Importantly, the ROC curve showed that the AUC of dual speed is the highest at 0.7662 [95% CI (0.6935,0.8388)]. The AUCs of single speed, single walk ratio, dual walk ratio, and DTC were 0.7333, 0.6027, 0.6609, and 0.5907, respectively. Notably, the DTC had no predictive ability (p = 0.55). Conclusions The gait speed, walk ratio, and DTC could identify MCI but were not effective in identifying SCD. Furthermore, gait speed emerged as the most accurate and sensitive indicator for identifying individuals with MCI when compared to walk ratio and DTC. mild cognitive impairment subjective cognitive decline gait speed walk ratio and dual-task cost Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are early appearance of cognitive decline[ 1 – 3 ]that can be warning signs of virous disease, including Parkinson's disease, brain damage, and more. Early identification and treatment of SCD and MCI patients are crucial to delaying disease progression and improving patient outcomes[ 4 , 5 ]. Recent studies have shown that gait abnormalities may firstly appearance in the early stages of cognitive decline[ 6 ]. While there are various gait indicators for identifying with cognitive decline, they are rare and controversial in SCD and MCI patients. Therefore, funding a gait indicator to identify SCD and MCI is currently a hot topic of concern[ 7 , 8 ]. Gait is a behavioral characteristic of human walking that requires the cooperation of multiple systems, including the brain, nerves, bones, muscles, and joints [ 9 ]. A safe and effective gait is closely related to human health across the lifespan [ 10 ] and is a predictor of cognitive decline[ 11 , 12 ]. Currently, direct indicators such as speed, stride, swing phase, support phase, and gait variability are extensively utilized for the study, diagnosis, and monitoring of cognitive impairment. Nevertheless, gait speed is widely considered to be a more effective screening tool for cognitive decline than other indicators. Interestingly, the walk ratio and DTC are also utilized as indirect measurement indicators to study patients with cognitive decline, which has garnered significant attention from scholars[ 13 , 14 ] [ 15 ] [ 16 , 17 ]. However, the literature’s data remains insufficient and diverse on both SCD and MCI populations, and the evaluation effects of these indicators are still unclear. Hence, it is necessary to conduct a comparative analysis to assess the effectiveness of these three indicators and measurement methods in detecting SCD and MCI through gait analysis. An increasing body of evidence supports the use of dual-task gait to distinguish the progression between varying degrees of cognitive impairment [ 13 , 18 – 20 ]. Currently, the most common dual-task gait paradigm is counting from 100 to 1, which was applied in our previous research[ 21 ]. As well known, the reason for the strong association between motor control and functional/structural aspects during dual-task performance is due to the shared motion and cognition control within neural networks[ 22 – 25 ]. Impaired execution, attention, and memory are cognitive factors that are closely related to the decrease in dual-task speed [ 26 ], indicating that dual-task speed may be a more effective indicator than single-task speed [ 19 , 26 , 27 ]. However, there is limited research available on whether dual-task walk ratio is a better indicator than single-task walk ratio. The objective of this study is to investigate which indicator—gait speed, walk ratio, or DTC—demonstrates the highest accuracy and sensitivity for identifying individuals with MCI. The study focuses on analyzing the single-task speed, dual-task speed, single-task walk ratio, dual-task walk ratio, and DTC. This study aims to provide valuable insights into the screening methods for identifying SCD and MCI, facilitating early detection and intervention for cognitive decline. Methods Participant selection This was a cross-sectional study conducted from June 2021 to April 2023(clinical trial number: 20212901; time of ethics approval: 10 May 2021). Participants were recruited from patients attending the Department of Geriatrics at Chongqing Medical University, patients' family members, and community-dwelling older adults; For recruitment, please refer to our previous study [ 21 ]. General inclusion and exclusion criteria The inclusion criteria included the following: (1) age 60 years and older; (2) ability to independently walk 8 meters back and forth; and (3) consent to participate in the study. The exclusion criteria were as follows: (1) insufficient proficiency in Chinese; (2) the presence of significant known chronic brain disease (moderate to severe chronic static leukoencephalopathy and/or previous traumatic injury) and comorbid severe cardiopulmonary diseases (heart failure, myocardial infarction, or emphysema);(3) motor system dysfunction (osteoarthritis or knee/hip joint disease/multiple sclerosis/developmental disorders/Huntington disease/other rarer brain illnesses); (4) ongoing alcohol, drug abuse or neuropsychiatric drugs; (5) participants incapable of walking more than 8 meters unassisted;(6) refusal to participate. Clinical group ascertainment The diagnosis of MCI and SCD was made by experts in the Memory Clinical of The First Affiliated Hospital of Chongqing Medical University, based on a combination of neuropsychological tests, medical history, physical examination, blood tests, and brain magnetic resonance imaging (MRI) or computed tomography (CT). NC Participants were considered normal cognition controls if they fulfilled the following criteria: (1) 60 years of age or older; Mini-mental State Examination (MMSE) > 27; (2) No complaints about memory loss; (3) normal neurological exam; (4) Lawton & Brody Instrumental Activities of Daily Living (IADL) < 14 points. (5) Global Clinical Dementia Rating (CDR) equal to 0. SCD The definition of SCD was based on Jessen et al. [ 28 ] [ 29 ]. In brief: (1) no objective cognitive impairment. Global CDR = 0 (i.e., no signs of objective cognitive impairment); and MMSE > 27; (2) a "yes" answer to the question: "Do you consider yourself to have problems with memory or thinking? " . MCI The MCI Criteria were referred to the National Institute on Aging—Alzheimer’s Association (NIA-AA) [ 30 ]: (1) Global CDR 0.5 ~ 1; (2) MMSE 23-27points; (3) IADL scores > 14. Cognitive Assessment The patients underwent neuropsychological assessments through face-to-face interviews, which were conducted by a trained nurse or an experienced doctor. The scales used in our study included the Chinese version of the Mini-Mental State Examination (MMSE), the Clock-Drawing Test (CDT), Trail Making Test A (TMT-A), Trail Making Test B (TMT-B), attention assessment using the Digit Span Test backward (DSB) and forward (DSF), Basic Activities of Daily Living Scale (BADL), Instrumental Activities of Daily Living Scale (IADL), and Clinical Dementia Rating (CDR). Gait Assessment Prior to the trial, participants were presented with a standardized visual demonstration. During the trial, participants wore shoes equipped with sensor devices (GAIT Rite, version 4.5; CIR systems Inc, Dalian, China). When performing the single-task, participants walked 8 meters back and forth in a comfortable and natural manner. The dual-task required participants to walk freely while simultaneously counting from 100 to 1. To balance and minimize the effects of learning and fatigue, only one trial was conducted in each condition. Upon completion of the experiment, it was ensured that the data were transferred intact to the computer for automatic saving. The formula for the cost (percentage) of dual-task gait [ 31 , 32 ] was utilized: Statistical Analysis We conducted statistical analysis using SPSS software (version 26). Harman's single-factor analysis[ 33 ] was used to test for common method bias in the original data. Count data that followed a normal distribution were compared between groups using the χ2 test [ 34 ], with results expressed as n (%). One-way ANOVA was used for normal distribution data, while nonparametric testing was used for variables that did not follow a normal distribution. Data were presented as mean ± standard deviation (M ± SD). Post hoc multiple comparison LSD was used for pairwise comparison of gait indicators among the three groups. Spearman correlation analysis was used to explore the relationship between cognitive scales and gait indicators. Receiver operating characteristic (ROC) curve was used to evaluate the ability of gait indicators to differentiate disease, with area under curve of ROC(AUC) used to quantify accuracy and sensitivity. Binary logistic regression was used to explore the relationship between gait indicators and risk of MCI. Differences were considered significant at P < 0.05. Graphs were plotted using Gradprism10 and Origin.2021. Results Test for common method bias Harman's single-factor test was used to analyze the data, and the results showed that the common method bias of this study was not significant, as the percentage of variance explained for the first common factor was only 29.758%, which is lower than the threshold of 40%. General data and clinical features The characteristics of various gait indicators in the three groups Binary logistic regression models were used to analyze the relationship between gait indicators and MCI (Fig. 3 ). After adjusting for variables such as gender, age, education level, BMI, and MMSE scores, a significant correlation was observed between gait speed and the risk of developing MCI. Interestingly, a similar relationship is not present in the walk ratio or DTC and the risk of MCI (p > 0.05). Correlation analysis between gait indicators and Clinical Cognitive Assessment Scales. Figure 4 summarizes the correlation analysis results. The DTC was not significantly associated with MMSE, CDT, DSB, DSF, BADL, and IADL (r < 0.2), but was positively correlated with TMT-A and TMT-B (r = 0.26 and 0.21). Single speed was positively correlated with MMSE and CDT (r = 0.36 and 0.24), and negatively correlated with TMT-A and TMT-B (r=-0.20 and − 0.25). Dual speed was significantly positively correlated with MMSE and CDT (r = 0.34 and 0.31), and negatively correlated with TMT-A and TMT-B (r=-0.25 and − 0.30). Single walk ratio was positively correlated with MMSE and CDT (r = 0.21 and 0.20), while dual walk ratio was significantly positively correlated with MMSE and CDT (r = 0.22 and 0.20). Ability of gait indicators to predict MCI The study utilized the ROC curve to evaluate the comparative efficacy for cognitive impairment based on five gait metrics (Fig. 5 ). The AUC was used to measure the model's accuracy. The AUC of dual speed is the highest at 0.7662 [95% CI (0.6935,0.8388)]. The AUCs of single speed, single walk ratio, dual walk ratio, and DTC were 0.7333, 0.6027, 0.6609, and 0.5907, respectively. Notably, the DTC had no predictive ability (p = 0.55). The cut-off values for single speed, dual speed, single walk ratio, and dual walk ratio were 78.50, 65.50, 45.99, and 52.70, respectively, indicating that individuals below these thresholds can be considered MCI. Discussion This study find that female patients are a high-risk group for SCD and MCI than men populations. The gait speed, walk ratio, and DTC could identify MCI but were not effective in identifying SCD. As expected, gait speed emerged as the most accurate and sensitive indicator for identifying individuals with MCI when compared to walk ratio and DTC. The diagnostic utility of DTC in the evaluation of MCI is limited. The direct measurement methods are more effective than indirect detection methods. The dual-task paradigm demonstrates greater efficacy in screening individuals with MCI The female patients are a high-risk group for SCD and MCI than men populations (Table 1), consistent with previous studies[35]. However, there are conflicting views on this topic. A meta-analysis suggests that men have greater frontal lobe executive deficits than females [36], indicating that men might be more susceptible to cognitive impairment. However, this result is based on the Parkinson's disease population. Despite mixed findings, our study find that women are more prone to cognitive impairment. The gait speed, walk ratio, and DTC could identify MCI but were not effective in identifying SCD. The speed of the MCI group is significantly lower than that of NC and SCD groups, which is consistent with our previous research[21] . Also, the walk ratio of MCI is larger than that of NC and SCD groups. The possible reason is that the walk ratio is an indicator of gait automation[37] and central gait coordination[38] to reflect abnormal gait [15]. Meanwhile, the DTC in the MCI group is significantly higher than that in the NC and SCD groups, which is a clinical indicator of cognitive-motor dysfunction[31]. Overall, our results confirm that gait indicators differ between patients with cognitive impairment and the normal cognitive population. Despite abnormal pathological changed in the brain, gait indicators of SCD patients remained normal. As is well known, individuals with SCD exhibit abnormal pathological changes in the brain, including abnormal deposition of β-amyloid and tau proteins, grey matter atrophy, white matter disruption, and defective cerebral function [39-41] .However, gait locomotion is mainly governed by the higher centers of the brain[42], which involve attention, memory, and executive functions[43, 44], and determine spatial localization through integrating visual, somatosensory, and vestibular functions[22, 25, 45]. The brain regions responsible for executing gait mismatch the SCD-related brain damage areas. Moving forward, it is crucial to prioritize the search for alternative indicators that hold significance. The diagnostic utility of DTC in the evaluation of MCI is limited. Firstly, our findings reveal that DTC shows no significant correlation with the MMSE (r<0.2), which contradicts the results reported by Venema et al.[46] . This discrepancy may be attributable to the narrower cognitive range of our population (MMSE 23-30) compared to that of Venema et al. (MMSE 7-30). Secondly, our finding diverges from the observations made by Hausdorff et al.[47]. This inconsistency could be related to the differing applications of DTC as a predictive tool for cognitive decline. The direct measurement methods are more effective than indirect detection methods. Specifically, gait speed has been found to be a better evaluation indicator than walk ratio and DTC, as indicated by the higher AUC. Furthermore, our finding shows a positive correlation between gait speed and MMSE, TMT-A, and TMT-B, which is consistent with previous research[48-50]. While there was no significant difference between walk ratio, DTC and MMSE. Additionally, the acquisition of direct indicators is efficient and straightforward, whereas the process of obtaining indirect indicators is laborious, susceptible to errors, and time-intensive. Consequently, prioritizing direct indicators in gait measurement metrics is a feasible approach. Regardless of the method employed to obtain gait indicators-whether direct or indirect-the dual-task paradigm demonstrates greater efficacy in screening individuals with MCI. In our study, we utilized the walk ratio as an indirect indicator to test this hypothesis, noting that speed serves as a direct measurement indicator and may yield different outcomes compared to indirect measurement. The results indicate that the AUC for the dual walk ratio exceeds that of the single walk ratio, thereby supporting our hypothesis. This discrepancy may arise because the indirectly measured walk ratio is derived from directly measured gait indicators. Consequently, the selection of the dual task paradigm serves as an effective method for disease screening. This study possesses several strengths. First, it is the inaugural investigation to compare gait indicators—such as speed, walk ratio, and DTC—in individuals diagnosed with SCD and MCI, representing an original contribution to the field. Second, participants were specifically restricted to those with SCD and MCI, aligning with the principles of early detection, diagnosis, and prevention of cognitive decline. Third, the stringent inclusion and exclusion criteria applied in this study effectively minimized confounding factors, resulting in highly reproducible data. Finally, this research provides valuable reference resources for clinical gait investigations and future studies focused on walk ratio and DTC. Conclusions Our study has established that gait speed is a more effective indicator for identifying MCI compared to DTC and walk ratio. This finding implies that gait speed should be prioritized as the preferred indicator for the diagnosis of MCI. The principal contribution of this study lies in providing novel references for gait indicators that may be employed in future MCI screening endeavors. Declarations Conflicts of interest All the authors declare no competing interests related to the manuscript. Ethical approval The research was approved by the Medical Ethics Committee of The First Affiliated Hospital of Chongqing Medical University (approval number: 20212901; time of ethics approval: 10 May 2021) and followed the ethical code for research with humans as stated by the Declaration of Helsinki. All participants provided written informed consent to participate in this study. Funding The study was supported by the National Key R&D Program of China (2018YFC2001700), the Chongqing Talent Plan (cstc2022ycjh-bgzxm0184), the Key Project of Technological Innovation and Application Development of Chongqing Science and Technology Bureau (CSTC2021jscx-gksbN0020), the Science Innovation Programs Led by the Academicians in Chongqing under Project (cstc2020yszx-jscxX0006), the Science and Technology Research Program Of Chongqing Municipal Education Commission (KJQN201900109); and the Intelligent Medicine Program of Chongqing Medical University (ZHYX2019008, ZHYX202110). Author Contribution Xiaoqin Wang: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing—original draft. Yang Lü: Formal analysis, Writing—review and editing, Visualization. Qi Tian: Formal analysis; validation. Jiani Wu: Methodology, Validation, Writing—review and editing, Supervision, Project administration, Funding acquisition. Xing-Tong Liu: data curation; methodology. Wei-Hua Yu: Software; project administration; supervision. All authors have read and approved the final manuscript. Acknowledgements We would like to thank all patients, their families, and the investigators who participated in this trial. All authors reviewed the manuscript and approved the final manuscript before submission. All authors reviewed the manuscript and gave their final approval for publication. Data availability The data that support the findings of this study are available on request from the author Yang Lü. The data are not publicly available due to privacy and ethical restrictions. References Petersen RC, Stevens JC, Ganguli M, Tangalos EG, Cummings JL, DeKosky ST. Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2001;56(9):1133–42. DeCarli C. Mild cognitive impairment: prevalence, prognosis, aetiology, and treatment. Lancet Neurol. 2003;2(1):15–21. An R, Gao Y, Huang X, Yang Y, Yang C, Wan Q. Predictors of progression from subjective cognitive decline to objective cognitive impairment: A systematic review and meta-analysis of longitudinal studies. Int J Nurs Stud. 2024;149:104629. Tao D, Awan-Scully R, Ash GI, Pei Z, Gu Y, Gao Y, Cole A, Baker JS. The effectiveness of dance movement interventions for older adults with mild cognitive impairment, Alzheimer's disease, and dementia: A systematic scoping review and meta-analysis. Ageing Res Rev. 2023;92:102120. Friedrich MJ. Researchers test strategies to prevent Alzheimer disease. JAMA. 2014;311(16):1596–8. Camicioli R, Howieson D, Oken B, Sexton G, Kaye J. Motor slowing precedes cognitive impairment in the oldest old. Neurology. 1998;50(5):1496–8. Li X, Zhang ZJ. Neuropsychological and neuroimaging characteristics of amnestic mild cognitive impairment subtypes: a selective overview. CNS Neurosci Ther. 2015;21(10):776–83. Panza F, Solfrizzi V, Barulli MR, Santamato A, Seripa D, Pilotto A, Logroscino G. Cognitive Frailty: A Systematic Review of Epidemiological and Neurobiological Evidence of an Age-Related Clinical Condition. Rejuvenation Res. 2015;18(5):389–412. Lord S, Galna B, Verghese J, Coleman S, Burn D, Rochester L. Independent domains of gait in older adults and associated motor and nonmotor attributes: validation of a factor analysis approach. J Gerontol Biol Sci Med Sci. 2013;68(7):820–7. Ni L, Lv W, Sun D, Sun Y, Sun Y, Xu X, Chang M, Han X, Tao S, Hu X, et al. Pathological Gait Signatures of Post-stroke Dementia With Toe-Off and Heel-to-Ground Angles Discriminate From Alzheimer's Disease. Front Aging Neurosci. 2021;13:766884. Holtzer R, Epstein N, Mahoney JR, Izzetoglu M, Blumen HM. Neuroimaging of mobility in aging: a targeted review. J Gerontol Biol Sci Med Sci. 2014;69(11):1375–88. Hirvensalo M, Rantanen T, Heikkinen E. Mobility difficulties and physical activity as predictors of mortality and loss of independence in the community-living older population. J Am Geriatr Soc. 2000;48(5):493–8. Montero-Odasso M, Oteng-Amoako A, Speechley M, Gopaul K, Beauchet O, Annweiler C, Muir-Hunter SW. The motor signature of mild cognitive impairment: results from the gait and brain study. J Gerontol Biol Sci Med Sci. 2014;69(11):1415–21. Pieruccini-Faria F, Sarquis-Adamson Y, Anton-Rodrigo I, Noguerón-García A, Bray NW, Camicioli R, Muir-Hunter SW, Speechley M, McIlroy B, Montero-Odasso M. Mapping Associations Between Gait Decline and Fall Risk in Mild Cognitive Impairment. J Am Geriatr Soc. 2020;68(3):576–84. Knapstad MK, Naterstad I, Bogen B. The association between cognitive impairment, gait speed, and Walk ratio. Front Aging Neurosci. 2023;15:1092990. Bahureksa L, Najafi B, Saleh A, Sabbagh M, Coon D, Mohler MJ, Schwenk M. The Impact of Mild Cognitive Impairment on Gait and Balance: A Systematic Review and Meta-Analysis of Studies Using Instrumented Assessment. Gerontology. 2017;63(1):67–83. Fuentes-Abolafio IJ, Stubbs B, Pérez-Belmonte LM, Bernal-López MR, Gómez-Huelgas R, Cuesta-Vargas A. Functional objective parameters which may discriminate patients with mild cognitive impairment from cognitively healthy individuals: a systematic review and meta-analysis using an instrumented kinematic assessment. Age Ageing. 2021;50(2):380–93. Laske C, Sohrabi HR, Frost SM, López-de-Ipiña K, Garrard P, Buscema M, Dauwels J, Soekadar SR, Mueller S, Linnemann C, et al. Innovative diagnostic tools for early detection of Alzheimer's disease. Alzheimers Dement. 2015;11(5):561–78. MacAulay RK, Wagner MT, Szeles D, Milano NJ. Improving Sensitivity to Detect Mild Cognitive Impairment: Cognitive Load Dual-Task Gait Speed Assessment. J Int Neuropsychol Soc. 2017;23(6):493–501. Latorre Román P, Muñoz Jiménez M, Salas Sánchez J, Consuegra González P, Del Moreno R, Herrador Sánchez JA, López Ivanco MDA, Linares Jiménez C. Navas Morales JF, Párraga Montilla JA: Complex Gait Is Related to Cognitive Functioning in Older People: A Cross-Sectional Study Providing an Innovative Test. Gerontology. 2020;66(4):401–8. Wang X, Yu W, Huang L, Yan M, Zhang W, Song J, Liu X, Yu W, Lü Y. Gait Indicators Contribute to Screening Cognitive Impairment: A Single- and Dual-Task Gait Study. Brain Sci 2023, 13(1). Scherder E, Eggermont L, Swaab D, van Heuvelen M, Kamsma Y, de Greef M, van Wijck R, Mulder T. Gait in ageing and associated dementias; its relationship with cognition. Neurosci Biobehav Rev. 2007;31(4):485–97. Montero-Odasso M, Bergman H, Phillips NA, Wong CH, Sourial N, Chertkow H. Dual-tasking and gait in people with mild cognitive impairment. The effect of working memory. BMC Geriatr. 2009;9:41. Rosso AL, Studenski SA, Chen WG, Aizenstein HJ, Alexander NB, Bennett DA, Black SE, Camicioli R, Carlson MC, Ferrucci L, et al. Aging, the central nervous system, and mobility. J Gerontol Biol Sci Med Sci. 2013;68(11):1379–86. Rosso AL, Verghese J, Metti AL, Boudreau RM, Aizenstein HJ, Kritchevsky S, Harris T, Yaffe K, Satterfield S, Studenski S, et al. Slowing gait and risk for cognitive impairment: The hippocampus as a shared neural substrate. Neurology. 2017;89(4):336–42. Camicioli R, Howieson D, Lehman S, Kaye J. Talking while walking: the effect of a dual task in aging and Alzheimer's disease. Neurology. 1997;48(4):955–8. Bürki CN, Bridenbaugh SA, Reinhardt J, Stippich C, Kressig RW, Blatow M. Imaging gait analysis: An fMRI dual task study. Brain Behav. 2017;7(8):e00724. Jessen F, Amariglio RE, van Boxtel M, Breteler M, Ceccaldi M, Chételat G, Dubois B, Dufouil C, Ellis KA, van der Flier WM, et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimers Dement. 2014;10(6):844–52. Morris JC, Ernesto C, Schafer K, Coats M, Leon S, Sano M, Thal LJ, Woodbury P. Clinical dementia rating training and reliability in multicenter studies: the Alzheimer's Disease Cooperative Study experience. Neurology. 1997;48(6):1508–10. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, et al. The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7(3):270–9. Montero-Odasso M, Muir SW, Speechley M. Dual-task complexity affects gait in people with mild cognitive impairment: the interplay between gait variability, dual tasking, and risk of falls. Arch Phys Med Rehabil. 2012;93(2):293–9. Montero-Odasso MM, Sarquis-Adamson Y, Speechley M, Borrie MJ, Hachinski VC, Wells J, Riccio PM, Schapira M, Sejdic E, Camicioli RM, et al. Association of Dual-Task Gait With Incident Dementia in Mild Cognitive Impairment: Results From the Gait and Brain Study. JAMA Neurol. 2017;74(7):857–65. Yang Y, Gu K, Meng C, Li J, Lu Q, Zhou X, Yan D, Li D, Pei C, Lu Y, et al. Relationship between sleep and serum inflammatory factors in patients with major depressive disorder. Psychiatry Res. 2023;329:115528. McHugh ML. The chi-square test of independence. Biochem Med (Zagreb). 2013;23(2):143–9. Larrieu S, Letenneur L, Orgogozo JM, Fabrigoule C, Amieva H, Le Carret N, Barberger-Gateau P, Dartigues JF. Incidence and outcome of mild cognitive impairment in a population-based prospective cohort. Neurology. 2002;59(10):1594–9. Curtis AF, Masellis M, Camicioli R, Davidson H, Tierney MC. Cognitive profile of non-demented Parkinson's disease: Meta-analysis of domain and sex-specific deficits. Parkinsonism Relat Disord. 2019;60:32–42. Bogen B, Moe-Nilssen R, Ranhoff AH, Aaslund MK. The walk ratio: Investigation of invariance across walking conditions and gender in community-dwelling older people. Gait Posture. 2018;61:479–82. Egerton T, Danoudis M, Huxham F, Iansek R. Central gait control mechanisms and the stride length - cadence relationship. Gait Posture. 2011;34(2):178–82. Wang X, Huang W, Su L, Xing Y, Jessen F, Sun Y, Shu N, Han Y. Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease. Mol Neurodegener. 2020;15(1):55. Rabin LA, Smart CM, Amariglio RE. Subjective Cognitive Decline in Preclinical Alzheimer's Disease. Annu Rev Clin Psychol. 2017;13:369–96. Sun Y, Yang FC, Lin CP, Han Y. Biochemical and neuroimaging studies in subjective cognitive decline: progress and perspectives. CNS Neurosci Ther. 2015;21(10):768–75. Christensen LO, Petersen N, Morita H, Nielsen J. Corticospinal function during human walking. Ann N Y Acad Sci. 1998;860:546–9. Al-Yahya E, Dawes H, Smith L, Dennis A, Howells K, Cockburn J. Cognitive motor interference while walking: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2011;35(3):715–28. Sheridan PL, Solomont J, Kowall N, Hausdorff JM. Influence of executive function on locomotor function: divided attention increases gait variability in Alzheimer's disease. J Am Geriatr Soc. 2003;51(11):1633–7. Montero-Odasso M, Verghese J, Beauchet O, Hausdorff JM. Gait and cognition: a complementary approach to understanding brain function and the risk of falling. J Am Geriatr Soc. 2012;60(11):2127–36. Venema DM, Bartels E, Siu KC. Tasks matter: a cross-sectional study of the relationship of cognition and dual-task performance in older adults. J Geriatr Phys Ther. 2013;36(3):115–22. Hausdorff JM, Schweiger A, Herman T, Yogev-Seligmann G, Giladi N. Dual-task decrements in gait: contributing factors among healthy older adults. J Gerontol Biol Sci Med Sci. 2008;63(12):1335–43. Sakurai R, Ishii K, Yasunaga M, Takeuchi R, Murayama Y, Sakuma N, Sakata M, Oda K, Ishibashi K, Ishiwata K, et al. The neural substrate of gait and executive function relationship in elderly women: A PET study. Geriatr Gerontol Int. 2017;17(11):1873–80. Tuena C, Maestri S, Serino S, Pedroli E, Stramba-Badiale M, Riva G. Prognostic relevance of gait-related cognitive functions for dementia conversion in amnestic mild cognitive impairment. BMC Geriatr. 2023;23(1):462. Tian Q, Simonsick EM, Resnick SM, Shardell MD, Ferrucci L, Studenski SA. Lap time variation and executive function in older adults: the Baltimore Longitudinal Study of Aging. Age Ageing. 2015;44(5):796–800. Tables Table 1 Comparison of general data and scale features among the three groups. Characteristics Full Sample (n=212) NC (n=50) SCD (n=66) MCI (n=96) χ²/ANOVA/Kruskal-Wallis M±SD M±SD M±SD M±SD F p Sex <0.001 abc Male, n 76 18 23 35 Female, n 137 32 44 61 Age (y) 71.31±7.274 71.04±7.428 72.60±6.571 70.56±7.604 1.599 0.205 c Education (y) 10.22±4.256 10.22±4.256 10.61±3.823 9.54±4.621 2.377 0.095 BMI (kg/m 2 ) 22.96±3.193 22.96±3.193 23.21±2.863 23.21±2.863 0.621 0.538 Height (cm) 158.86±7.897 158.86±7.897 159.02±8.227 158.33±7.864 0.645 0.526 Weight (Kg) 58.15±10.224 59.05±10.358 58.88±9.674 57.16±10.586 0.819 0.442 MMSE 27.22±2.646 29.70±0.544 28.84±1.009 24.79±1.930 252.789 <0.001 abc CDT 12.85±3.435 14.72±0.73 13.76±2.386 11.23±4.128 55.524 <0.001 abc TMT-A(s) 68.66±34.281 50.32±16.171 55.02±20.963 87.78±38.827 52.773 <0.001 bc TMT-B(s) 137.60±84.390 73.96±29.707 106.08±64.267 193.03±81.530 92.668 <0.001 abc DSB 5.54±2.394 7.44±2.120 5.79±2.222 4.39±1.943 53.806 <0.001 abc DSF 8.14±1.413 9.14±0.808 8.42±1.157 7.42±1.441 58.008 <0.001 abc BADL 6.59±1.397 6.68±1.558 6.21±0.755 6.8±1.600 8.734 0.013 c IADL 9.44±1.864 8.46±1.199 8.82±1.252 10.38±2.069 62.944 <0.001 bc Data presented as mean ± standard deviation (M±SD) for continuous variables and percentage for dichotomous variables. a − c The independent samples t-test further revealed the source of Kruskal-Wallis’s difference (a. NC vs. SCD. b. NC vs. MCI. c. SCD vs. MCI.) (p<0.05, significant difference between the two groups).Abbreviations: NC, normal cognition; SCD, subjective cognitive decline; MCI, mild cognitive impairment; BMI, Body mass index (kg/mg 2 ); MMSE, Mini-Mental State Examination; CDT, Clock-Drawing Test; TMT-A, Trail Making Test A; TMT-B, Trail Making Test B; DSB, Digit Span Test backward; DSF, Digit Span Test forward; BADL, Basic Activities of Life Scale; IADL, Instrumental Activities of Daily Living; Table 2 Comparison of gait indicators among the three groups. Characteristics Full Sample (n=212) NC (n=50) SCD (n=66) MCI (n=96) ANOVA Post-hoc statistics M±SD M±SD M±SD M±SD F p (I)Groups (J)Grooups p DTC (%) 15.70±11.121 13.13±9.779 13.97±9.121 18.28±12.556 6.296 0.031 * Control SCD 0.579 MCI 0.018 * SCD MCI 0.048 * Single-task Speed (cm/s) 82.00±15.933 87.83±12.046 88.77±14.502 75.54±15.293 20.732 <0.001 *** Control SCD 0.656 MCI <0.001 *** SCD MCI <0.001 *** Walk ratio (%) 50.49±8.833 52.46±8.186 51.92±8.397 48.43±9.012 5.527 0.005 ** Control SCD 0.683 MCI 0.005 ** SCD MCI 0.009 ** Dual-task Speed (cm/s) 69.74±16.511 76.00±15.144 76.23±14.059 61.80±15.489 23.127 <0.001 *** Control SCD 0.951 MCI <0.001 *** SCD MCI <0.001 *** Walk ratio (%) 56.31±14.091 58.73±12.824 59.89±14.123 52.47±13.870 6.535 0.002 ** Control SCD 0.657 MCI 0.012 * SCD MCI <0.001 *** Data presented as mean ± standard deviation (M±SD) for continuous variable. Abbreviations: NC, normal cognition; SCD, subjective cognitive decline; MCI, mild cognitive impairment; DTC, dual task cost. * p < 0.05; ** p < 0.01, *** p < 0.001 represents statistical significance. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5336317","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372193912,"identity":"3a4eda5a-6d8c-44e5-ae8c-0d4e6592f7a9","order_by":0,"name":"Xiaoqin Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqin","middleName":"","lastName":"Wang","suffix":""},{"id":372193913,"identity":"3eb8be8c-bd0d-495f-9c0d-a36e5a1966f6","order_by":1,"name":"Jiani Wu","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiani","middleName":"","lastName":"Wu","suffix":""},{"id":372193914,"identity":"422964e5-b02d-4bc1-9a23-bda41afa9ca1","order_by":2,"name":"Qi Tian","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Tian","suffix":""},{"id":372193915,"identity":"c547ca7c-0004-4782-a4b8-404d42209172","order_by":3,"name":"Xintong Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xintong","middleName":"","lastName":"Liu","suffix":""},{"id":372193916,"identity":"0c32d03d-1b86-480d-828c-e7181ca4d862","order_by":4,"name":"Weihua Yu","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weihua","middleName":"","lastName":"Yu","suffix":""},{"id":372193917,"identity":"46b16604-4f00-4534-876c-0b107ae7319b","order_by":5,"name":"Yang Lü","email":"data:image/png;base64,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","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Lü","suffix":""}],"badges":[],"createdAt":"2024-10-26 08:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5336317/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5336317/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69913880,"identity":"1b53eb76-ca24-4d5e-887e-cda4f01e9413","added_by":"auto","created_at":"2024-11-26 14:15:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55217,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between groups of Speed, Dual speed, Walk ratio, Dual-walk ratio and DTC across different genders. A. composite pie chart. Abbreviations: M-NC, man normal cognitive; M-SCD, man subjective cognitive decline; M-MCI, man mild cognitive impairment. B-D: differences among single speed, dual speed, single walk ratio, dual walk ratio, and DTC by ages in female and male groups. E-F: differences among single speed, dual speed, single walking ratio, dual walking ratio, and DTC by MMSE scores in female and male groups. Abbreviations: MMSE, Mini-Mental State Examination.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5336317/v1/3cb125e4f2568a7f22af59e1.png"},{"id":69912856,"identity":"2911bad2-bc43-4133-b088-e50f53bd6911","added_by":"auto","created_at":"2024-11-26 14:07:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103596,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between groups of Speed, Dual speed, Walk ratio, Dual-walk ratio and DTC. \u003csup\u003e#\u003c/sup\u003e p \u0026lt; 0.05; \u003csup\u003e##\u003c/sup\u003e p \u0026lt; 0.01, \u003csup\u003e###\u003c/sup\u003e p \u0026lt; 0.001 represents statistical significance and ns represents statistical no significance.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5336317/v1/f75e0e7597034f0fc38fd76e.png"},{"id":69914885,"identity":"6272da5b-7d6a-47ea-b341-cdd7f0e8a836","added_by":"auto","created_at":"2024-11-26 14:23:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22102,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate and multivariate logistic regression models evaluate the associations between gait indicators and the risk of MCI. Abbreviations: OR, odds ratio; DTC, dual-task cost. Model1: adjusting for gender, age. Model2: adjusting for gender, age, education, BMI, and MMSE. * p \u0026lt; 0.05; ** p \u0026lt; 0.01, *** p \u0026lt; 0.001 represents statistical significance.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5336317/v1/49c489d45c5dfb21c768282a.png"},{"id":69912861,"identity":"41fe09f5-8830-4d86-b7b3-ea48eaa5af7b","added_by":"auto","created_at":"2024-11-26 14:07:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":91369,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between cognitive scales and gait indicators by spearman correlation analysis. The color is darker, and the circle is larger, representing a stronger correlation; Otherwise, the correlation is smaller. Abbreviations: DTC, dual task cost.MMSE, Mini-Mental State Examination; CDT, Clock-Drawing Test; TMT-A, Trail Making Test A; TMT-B, Trail Making Test B; DSB, Digit Span Test backward; DSF, Digit Span Test forward; BADL, Basic Activities of Life Scale;IADL, Instrumental Activities of Daily Living.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5336317/v1/e51f41dd8eba72ccce9a3cfa.png"},{"id":69912858,"identity":"587662f1-8b37-4504-8828-251605d917a6","added_by":"auto","created_at":"2024-11-26 14:07:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46565,"visible":true,"origin":"","legend":"\u003cp\u003eGait indicators classifiers for normal cognitive and MCI. The AUCs show the independent accuracy of gait indicators classifier disease diagnosis. Abbreviations: AUC: Area Under Curve of ROC; CI, confidence interval. DTC, dual task cost. The red represents the biggest AUC.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5336317/v1/d0e98b2adf797d1ce8394d42.png"},{"id":69916917,"identity":"0c179361-7c94-4901-a359-14fd2259b3d4","added_by":"auto","created_at":"2024-11-26 14:39:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1200765,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5336317/v1/601a61c3-f73e-4140-9850-943c69131648.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gait Speed as a Superior Screening Indicator for Mild Cognitive Impairment Compared to Walk Ratio and Dual-Task Cost: A Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSubjective cognitive decline (SCD) and mild cognitive impairment (MCI) are early appearance of cognitive decline[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]that can be warning signs of virous disease, including Parkinson's disease, brain damage, and more. Early identification and treatment of SCD and MCI patients are crucial to delaying disease progression and improving patient outcomes[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recent studies have shown that gait abnormalities may firstly appearance in the early stages of cognitive decline[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. While there are various gait indicators for identifying with cognitive decline, they are rare and controversial in SCD and MCI patients. Therefore, funding a gait indicator to identify SCD and MCI is currently a hot topic of concern[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGait is a behavioral characteristic of human walking that requires the cooperation of multiple systems, including the brain, nerves, bones, muscles, and joints [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A safe and effective gait is closely related to human health across the lifespan [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and is a predictor of cognitive decline[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Currently, direct indicators such as speed, stride, swing phase, support phase, and gait variability are extensively utilized for the study, diagnosis, and monitoring of cognitive impairment. Nevertheless, gait speed is widely considered to be a more effective screening tool for cognitive decline than other indicators. Interestingly, the walk ratio and DTC are also utilized as indirect measurement indicators to study patients with cognitive decline, which has garnered significant attention from scholars[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the literature\u0026rsquo;s data remains insufficient and diverse on both SCD and MCI populations, and the evaluation effects of these indicators are still unclear. Hence, it is necessary to conduct a comparative analysis to assess the effectiveness of these three indicators and measurement methods in detecting SCD and MCI through gait analysis.\u003c/p\u003e \u003cp\u003eAn increasing body of evidence supports the use of dual-task gait to distinguish the progression between varying degrees of cognitive impairment [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Currently, the most common dual-task gait paradigm is counting from 100 to 1, which was applied in our previous research[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As well known, the reason for the strong association between motor control and functional/structural aspects during dual-task performance is due to the shared motion and cognition control within neural networks[\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Impaired execution, attention, and memory are cognitive factors that are closely related to the decrease in dual-task speed [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], indicating that dual-task speed may be a more effective indicator than single-task speed [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, there is limited research available on whether dual-task walk ratio is a better indicator than single-task walk ratio.\u003c/p\u003e \u003cp\u003eThe objective of this study is to investigate which indicator\u0026mdash;gait speed, walk ratio, or DTC\u0026mdash;demonstrates the highest accuracy and sensitivity for identifying individuals with MCI. The study focuses on analyzing the single-task speed, dual-task speed, single-task walk ratio, dual-task walk ratio, and DTC. This study aims to provide valuable insights into the screening methods for identifying SCD and MCI, facilitating early detection and intervention for cognitive decline.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipant selection\u003c/h2\u003e \u003cp\u003e This was a cross-sectional study conducted from June 2021 to April 2023(clinical trial number: 20212901; time of ethics approval: 10 May 2021). Participants were recruited from patients attending the Department of Geriatrics at Chongqing Medical University, patients' family members, and community-dwelling older adults; For recruitment, please refer to our previous study [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGeneral inclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eThe inclusion criteria included the following: (1) age 60 years and older; (2) ability to independently walk 8 meters back and forth; and (3) consent to participate in the study.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were as follows: (1) insufficient proficiency in Chinese; (2) the presence of significant known chronic brain disease (moderate to severe chronic static leukoencephalopathy and/or previous traumatic injury) and comorbid severe cardiopulmonary diseases (heart failure, myocardial infarction, or emphysema);(3) motor system dysfunction (osteoarthritis or knee/hip joint disease/multiple sclerosis/developmental disorders/Huntington disease/other rarer brain illnesses); (4) ongoing alcohol, drug abuse or neuropsychiatric drugs; (5) participants incapable of walking more than 8 meters unassisted;(6) refusal to participate.\u003c/p\u003e\n\u003ch3\u003eClinical group ascertainment\u003c/h3\u003e\n\u003cp\u003eThe diagnosis of MCI and SCD was made by experts in the Memory Clinical of The First Affiliated Hospital of Chongqing Medical University, based on a combination of neuropsychological tests, medical history, physical examination, blood tests, and brain magnetic resonance imaging (MRI) or computed tomography (CT).\u003c/p\u003e\n\u003ch3\u003eNC\u003c/h3\u003e\n\u003cp\u003eParticipants were considered normal cognition controls if they fulfilled the following criteria: (1) 60 years of age or older; Mini-mental State Examination (MMSE)\u0026thinsp;\u0026gt;\u0026thinsp;27; (2) No complaints about memory loss; (3) normal neurological exam; (4) Lawton \u0026amp; Brody Instrumental Activities of Daily Living (IADL)\u0026thinsp;\u0026lt;\u0026thinsp;14 points. (5) Global Clinical Dementia Rating (CDR) equal to 0.\u003c/p\u003e\n\u003ch3\u003eSCD\u003c/h3\u003e\n\u003cp\u003eThe definition of SCD was based on Jessen et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In brief: (1) no objective cognitive impairment. Global CDR\u0026thinsp;=\u0026thinsp;0 (i.e., no signs of objective cognitive impairment); and MMSE\u0026thinsp;\u0026gt;\u0026thinsp;27; (2) a \"yes\" answer to the question: \"Do you consider yourself to have problems with memory or thinking? \" .\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMCI\u003c/h2\u003e \u003cp\u003eThe MCI Criteria were referred to the National Institute on Aging\u0026mdash;Alzheimer\u0026rsquo;s Association (NIA-AA) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]: (1) Global CDR 0.5\u0026thinsp;~\u0026thinsp;1; (2) MMSE 23-27points; (3) IADL scores\u0026thinsp;\u0026gt;\u0026thinsp;14.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCognitive Assessment\u003c/h3\u003e\n\u003cp\u003eThe patients underwent neuropsychological assessments through face-to-face interviews, which were conducted by a trained nurse or an experienced doctor. The scales used in our study included the Chinese version of the Mini-Mental State Examination (MMSE), the Clock-Drawing Test (CDT), Trail Making Test A (TMT-A), Trail Making Test B (TMT-B), attention assessment using the Digit Span Test backward (DSB) and forward (DSF), Basic Activities of Daily Living Scale (BADL), Instrumental Activities of Daily Living Scale (IADL), and Clinical Dementia Rating (CDR).\u003c/p\u003e\n\u003ch3\u003eGait Assessment\u003c/h3\u003e\n\u003cp\u003ePrior to the trial, participants were presented with a standardized visual demonstration. During the trial, participants wore shoes equipped with sensor devices (GAIT Rite, version 4.5; CIR systems Inc, Dalian, China). When performing the single-task, participants walked 8 meters back and forth in a comfortable and natural manner. The dual-task required participants to walk freely while simultaneously counting from 100 to 1. To balance and minimize the effects of learning and fatigue, only one trial was conducted in each condition. Upon completion of the experiment, it was ensured that the data were transferred intact to the computer for automatic saving. The formula for the cost (percentage) of dual-task gait [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] was utilized:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1731339120.png\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe conducted statistical analysis using SPSS software (version 26). Harman's single-factor analysis[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] was used to test for common method bias in the original data. Count data that followed a normal distribution were compared between groups using the χ2 test [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], with results expressed as n (%). One-way ANOVA was used for normal distribution data, while nonparametric testing was used for variables that did not follow a normal distribution. Data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). Post hoc multiple comparison LSD was used for pairwise comparison of gait indicators among the three groups. Spearman correlation analysis was used to explore the relationship between cognitive scales and gait indicators. Receiver operating characteristic (ROC) curve was used to evaluate the ability of gait indicators to differentiate disease, with area under curve of ROC(AUC) used to quantify accuracy and sensitivity. Binary logistic regression was used to explore the relationship between gait indicators and risk of MCI. Differences were considered significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Graphs were plotted using Gradprism10 and Origin.2021.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTest for common method bias\u003c/h2\u003e \u003cp\u003eHarman's single-factor test was used to analyze the data, and the results showed that the common method bias of this study was not significant, as the percentage of variance explained for the first common factor was only 29.758%, which is lower than the threshold of 40%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGeneral data and clinical features\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe characteristics of various gait indicators in the three groups\u003c/h2\u003e \u003cp\u003eBinary logistic regression models were used to analyze the relationship between gait indicators and MCI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After adjusting for variables such as gender, age, education level, BMI, and MMSE scores, a significant correlation was observed between gait speed and the risk of developing MCI. Interestingly, a similar relationship is not present in the walk ratio or DTC and the risk of MCI (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e \u003cb\u003eCorrelation analysis between gait indicators and Clinical Cognitive Assessment Scales.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the correlation analysis results. The DTC was not significantly associated with MMSE, CDT, DSB, DSF, BADL, and IADL (r\u0026thinsp;\u0026lt;\u0026thinsp;0.2), but was positively correlated with TMT-A and TMT-B (r\u0026thinsp;=\u0026thinsp;0.26 and 0.21). Single speed was positively correlated with MMSE and CDT (r\u0026thinsp;=\u0026thinsp;0.36 and 0.24), and negatively correlated with TMT-A and TMT-B (r=-0.20 and \u0026minus;\u0026thinsp;0.25). Dual speed was significantly positively correlated with MMSE and CDT (r\u0026thinsp;=\u0026thinsp;0.34 and 0.31), and negatively correlated with TMT-A and TMT-B (r=-0.25 and \u0026minus;\u0026thinsp;0.30). Single walk ratio was positively correlated with MMSE and CDT (r\u0026thinsp;=\u0026thinsp;0.21 and 0.20), while dual walk ratio was significantly positively correlated with MMSE and CDT (r\u0026thinsp;=\u0026thinsp;0.22 and 0.20).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAbility of gait indicators to predict MCI\u003c/h2\u003e \u003cp\u003eThe study utilized the ROC curve to evaluate the comparative efficacy for cognitive impairment based on five gait metrics (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The AUC was used to measure the model's accuracy. The AUC of dual speed is the highest at 0.7662 [95% CI (0.6935,0.8388)]. The AUCs of single speed, single walk ratio, dual walk ratio, and DTC were 0.7333, 0.6027, 0.6609, and 0.5907, respectively. Notably, the DTC had no predictive ability (p\u0026thinsp;=\u0026thinsp;0.55). The cut-off values for single speed, dual speed, single walk ratio, and dual walk ratio were 78.50, 65.50, 45.99, and 52.70, respectively, indicating that individuals below these thresholds can be considered MCI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study find that female patients are a high-risk group for SCD and MCI than men populations. The\u0026nbsp;gait speed, walk ratio, and DTC could identify MCI but were not effective in identifying SCD. As expected, gait speed emerged as the most accurate and sensitive indicator for identifying individuals with MCI when compared to walk ratio and DTC. The diagnostic utility of DTC in the evaluation of MCI is limited.\u0026nbsp;The direct measurement methods are more effective than indirect detection methods. The dual-task paradigm demonstrates greater efficacy in screening individuals with MCI\u003c/p\u003e\n\u003cp\u003eThe female patients are a high-risk group for SCD and MCI than men populations (Table 1), consistent with previous studies[35]. However, there are conflicting views on this topic. A meta-analysis suggests that men have greater frontal lobe executive deficits than females\u0026nbsp;[36], indicating that men might be more susceptible to cognitive impairment. However, this result is based on the Parkinson\u0026apos;s disease population. Despite mixed findings, our study find that women are more prone to cognitive impairment.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;gait speed, walk ratio, and DTC could identify MCI but were not effective in identifying SCD. The speed of the MCI group is significantly lower than that of NC and SCD groups, which is consistent with our previous research[21]\u0026nbsp;. Also, the walk ratio of MCI is larger than that of NC and SCD groups. The possible reason is that the walk ratio is an indicator of gait automation[37]\u0026nbsp;and central gait coordination[38]\u0026nbsp;to reflect abnormal gait\u0026nbsp;[15]. Meanwhile, the DTC in the MCI group is significantly higher than that in the NC and SCD groups, which is a clinical indicator of cognitive-motor dysfunction[31]. Overall, our results confirm that gait indicators differ between patients with cognitive impairment and the normal cognitive population.\u003c/p\u003e\n\u003cp\u003eDespite abnormal pathological changed in the brain, gait indicators of SCD patients remained normal. As is well known, individuals with SCD exhibit abnormal pathological changes in the brain, including abnormal deposition of \u0026beta;-amyloid and tau proteins, grey matter atrophy, white matter disruption, and defective cerebral function\u0026nbsp;[39-41]\u0026nbsp;.However, gait locomotion is mainly governed by the higher centers of the brain[42], which involve attention, memory, and executive functions[43, 44], and determine spatial localization through integrating visual, somatosensory, and vestibular functions[22, 25, 45]. The brain regions responsible for executing gait mismatch the SCD-related brain damage areas. Moving forward, it is crucial to prioritize the search for alternative indicators that hold significance.\u003c/p\u003e\n\u003cp\u003eThe diagnostic utility of DTC in the evaluation of MCI is limited.\u0026nbsp;Firstly, our findings reveal that\u0026nbsp;DTC shows no significant correlation with the MMSE (r\u0026lt;0.2), which contradicts the results reported by Venema et al.[46]\u0026nbsp;. This discrepancy may be attributable to the narrower cognitive range of our population (MMSE 23-30) compared to that of Venema et al. (MMSE 7-30).\u0026nbsp;Secondly, our finding diverges from the observations made by Hausdorff et al.[47]. This inconsistency could be related to the differing applications of DTC as a predictive tool for cognitive decline.\u003c/p\u003e\n\u003cp\u003eThe direct measurement methods are more effective than indirect detection methods. Specifically, gait speed has been found to be a better evaluation indicator than walk ratio and DTC, as indicated by the higher AUC. Furthermore, our finding shows a positive correlation between gait speed and MMSE, TMT-A, and TMT-B, which is consistent with previous research[48-50]. While there was no significant difference between walk ratio, DTC and MMSE. Additionally, the acquisition of direct indicators is efficient and straightforward, whereas the process of obtaining indirect indicators is laborious, susceptible to errors, and time-intensive. Consequently, prioritizing direct indicators in gait measurement metrics is a feasible approach.\u003c/p\u003e\n\u003cp\u003eRegardless of the method employed to obtain gait indicators-whether direct or indirect-the dual-task paradigm demonstrates greater efficacy in screening individuals with MCI. In our study, we utilized the walk ratio as an indirect indicator to test this hypothesis, noting that speed serves as a direct measurement indicator and may yield different outcomes compared to indirect measurement. The results indicate that the AUC for the dual walk ratio exceeds that of the single walk ratio, thereby supporting our hypothesis. This discrepancy may arise because the indirectly measured walk ratio is derived from directly measured gait indicators. Consequently, the selection of the dual task paradigm serves as an effective method for disease screening.\u003c/p\u003e\n\u003cp\u003eThis study possesses several strengths. First, it is the inaugural investigation to compare gait indicators\u0026mdash;such as speed, walk ratio, and DTC\u0026mdash;in individuals diagnosed with SCD and MCI, representing an original contribution to the field. Second, participants were specifically restricted to those with SCD and MCI, aligning with the principles of early detection, diagnosis, and prevention of cognitive decline. Third, the stringent inclusion and exclusion criteria applied in this study effectively minimized confounding factors, resulting in highly reproducible data. Finally, this research provides valuable reference resources for clinical gait investigations and future studies focused on walk ratio and DTC.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study has established that gait speed is a more effective indicator for identifying MCI compared to DTC and walk ratio. This finding implies that gait speed should be prioritized as the preferred indicator for the diagnosis of MCI. The principal contribution of this study lies in providing novel references for gait indicators that may be employed in future MCI screening endeavors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConflicts of interest\u003c/strong\u003e \u003cp\u003eAll the authors declare no competing interests related to the manuscript.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e The research was approved by the Medical Ethics Committee of The First Affiliated Hospital of Chongqing Medical University (approval number: 20212901; time of ethics approval: 10 May 2021) and followed the ethical code for research with humans as stated by the Declaration of Helsinki. All participants provided written informed consent to participate in this study.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was supported by the National Key R\u0026amp;D Program of China (2018YFC2001700), the Chongqing Talent Plan (cstc2022ycjh-bgzxm0184), the Key Project of Technological Innovation and Application Development of Chongqing Science and Technology Bureau (CSTC2021jscx-gksbN0020), the Science Innovation Programs Led by the Academicians in Chongqing under Project (cstc2020yszx-jscxX0006), the Science and Technology Research Program Of Chongqing Municipal Education Commission (KJQN201900109); and the Intelligent Medicine Program of Chongqing Medical University (ZHYX2019008, ZHYX202110).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXiaoqin Wang: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing\u0026mdash;original draft. Yang L\u0026uuml;: Formal analysis, Writing\u0026mdash;review and editing, Visualization. Qi Tian: Formal analysis; validation. Jiani Wu: Methodology, Validation, Writing\u0026mdash;review and editing, Supervision, Project administration, Funding acquisition. Xing-Tong Liu: data curation; methodology. Wei-Hua Yu: Software; project administration; supervision. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe would like to thank all patients, their families, and the investigators who participated in this trial. All authors reviewed the manuscript and approved the final manuscript before submission. All authors reviewed the manuscript and gave their final approval for publication.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are available on request from the author Yang L\u0026uuml;. The data are not publicly available due to privacy and ethical restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePetersen RC, Stevens JC, Ganguli M, Tangalos EG, Cummings JL, DeKosky ST. Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2001;56(9):1133\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeCarli C. Mild cognitive impairment: prevalence, prognosis, aetiology, and treatment. Lancet Neurol. 2003;2(1):15\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn R, Gao Y, Huang X, Yang Y, Yang C, Wan Q. Predictors of progression from subjective cognitive decline to objective cognitive impairment: A systematic review and meta-analysis of longitudinal studies. Int J Nurs Stud. 2024;149:104629.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao D, Awan-Scully R, Ash GI, Pei Z, Gu Y, Gao Y, Cole A, Baker JS. The effectiveness of dance movement interventions for older adults with mild cognitive impairment, Alzheimer's disease, and dementia: A systematic scoping review and meta-analysis. Ageing Res Rev. 2023;92:102120.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedrich MJ. Researchers test strategies to prevent Alzheimer disease. JAMA. 2014;311(16):1596\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamicioli R, Howieson D, Oken B, Sexton G, Kaye J. Motor slowing precedes cognitive impairment in the oldest old. Neurology. 1998;50(5):1496\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Zhang ZJ. Neuropsychological and neuroimaging characteristics of amnestic mild cognitive impairment subtypes: a selective overview. CNS Neurosci Ther. 2015;21(10):776\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanza F, Solfrizzi V, Barulli MR, Santamato A, Seripa D, Pilotto A, Logroscino G. Cognitive Frailty: A Systematic Review of Epidemiological and Neurobiological Evidence of an Age-Related Clinical Condition. Rejuvenation Res. 2015;18(5):389\u0026ndash;412.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLord S, Galna B, Verghese J, Coleman S, Burn D, Rochester L. Independent domains of gait in older adults and associated motor and nonmotor attributes: validation of a factor analysis approach. J Gerontol Biol Sci Med Sci. 2013;68(7):820\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNi L, Lv W, Sun D, Sun Y, Sun Y, Xu X, Chang M, Han X, Tao S, Hu X, et al. Pathological Gait Signatures of Post-stroke Dementia With Toe-Off and Heel-to-Ground Angles Discriminate From Alzheimer's Disease. Front Aging Neurosci. 2021;13:766884.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoltzer R, Epstein N, Mahoney JR, Izzetoglu M, Blumen HM. Neuroimaging of mobility in aging: a targeted review. J Gerontol Biol Sci Med Sci. 2014;69(11):1375\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirvensalo M, Rantanen T, Heikkinen E. Mobility difficulties and physical activity as predictors of mortality and loss of independence in the community-living older population. J Am Geriatr Soc. 2000;48(5):493\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontero-Odasso M, Oteng-Amoako A, Speechley M, Gopaul K, Beauchet O, Annweiler C, Muir-Hunter SW. The motor signature of mild cognitive impairment: results from the gait and brain study. J Gerontol Biol Sci Med Sci. 2014;69(11):1415\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePieruccini-Faria F, Sarquis-Adamson Y, Anton-Rodrigo I, Noguer\u0026oacute;n-Garc\u0026iacute;a A, Bray NW, Camicioli R, Muir-Hunter SW, Speechley M, McIlroy B, Montero-Odasso M. Mapping Associations Between Gait Decline and Fall Risk in Mild Cognitive Impairment. J Am Geriatr Soc. 2020;68(3):576\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnapstad MK, Naterstad I, Bogen B. The association between cognitive impairment, gait speed, and Walk ratio. Front Aging Neurosci. 2023;15:1092990.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahureksa L, Najafi B, Saleh A, Sabbagh M, Coon D, Mohler MJ, Schwenk M. The Impact of Mild Cognitive Impairment on Gait and Balance: A Systematic Review and Meta-Analysis of Studies Using Instrumented Assessment. Gerontology. 2017;63(1):67\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuentes-Abolafio IJ, Stubbs B, P\u0026eacute;rez-Belmonte LM, Bernal-L\u0026oacute;pez MR, G\u0026oacute;mez-Huelgas R, Cuesta-Vargas A. Functional objective parameters which may discriminate patients with mild cognitive impairment from cognitively healthy individuals: a systematic review and meta-analysis using an instrumented kinematic assessment. Age Ageing. 2021;50(2):380\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaske C, Sohrabi HR, Frost SM, L\u0026oacute;pez-de-Ipi\u0026ntilde;a K, Garrard P, Buscema M, Dauwels J, Soekadar SR, Mueller S, Linnemann C, et al. Innovative diagnostic tools for early detection of Alzheimer's disease. Alzheimers Dement. 2015;11(5):561\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacAulay RK, Wagner MT, Szeles D, Milano NJ. Improving Sensitivity to Detect Mild Cognitive Impairment: Cognitive Load Dual-Task Gait Speed Assessment. J Int Neuropsychol Soc. 2017;23(6):493\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLatorre Rom\u0026aacute;n P, Mu\u0026ntilde;oz Jim\u0026eacute;nez M, Salas S\u0026aacute;nchez J, Consuegra Gonz\u0026aacute;lez P, Del Moreno R, Herrador S\u0026aacute;nchez JA, L\u0026oacute;pez Ivanco MDA, Linares Jim\u0026eacute;nez C. Navas Morales JF, P\u0026aacute;rraga Montilla JA: Complex Gait Is Related to Cognitive Functioning in Older People: A Cross-Sectional Study Providing an Innovative Test. Gerontology. 2020;66(4):401\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Yu W, Huang L, Yan M, Zhang W, Song J, Liu X, Yu W, L\u0026uuml; Y. Gait Indicators Contribute to Screening Cognitive Impairment: A Single- and Dual-Task Gait Study. Brain Sci 2023, 13(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScherder E, Eggermont L, Swaab D, van Heuvelen M, Kamsma Y, de Greef M, van Wijck R, Mulder T. Gait in ageing and associated dementias; its relationship with cognition. Neurosci Biobehav Rev. 2007;31(4):485\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontero-Odasso M, Bergman H, Phillips NA, Wong CH, Sourial N, Chertkow H. Dual-tasking and gait in people with mild cognitive impairment. The effect of working memory. BMC Geriatr. 2009;9:41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosso AL, Studenski SA, Chen WG, Aizenstein HJ, Alexander NB, Bennett DA, Black SE, Camicioli R, Carlson MC, Ferrucci L, et al. Aging, the central nervous system, and mobility. J Gerontol Biol Sci Med Sci. 2013;68(11):1379\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosso AL, Verghese J, Metti AL, Boudreau RM, Aizenstein HJ, Kritchevsky S, Harris T, Yaffe K, Satterfield S, Studenski S, et al. Slowing gait and risk for cognitive impairment: The hippocampus as a shared neural substrate. Neurology. 2017;89(4):336\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamicioli R, Howieson D, Lehman S, Kaye J. Talking while walking: the effect of a dual task in aging and Alzheimer's disease. Neurology. 1997;48(4):955\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026uuml;rki CN, Bridenbaugh SA, Reinhardt J, Stippich C, Kressig RW, Blatow M. Imaging gait analysis: An fMRI dual task study. Brain Behav. 2017;7(8):e00724.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJessen F, Amariglio RE, van Boxtel M, Breteler M, Ceccaldi M, Ch\u0026eacute;telat G, Dubois B, Dufouil C, Ellis KA, van der Flier WM, et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimers Dement. 2014;10(6):844\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris JC, Ernesto C, Schafer K, Coats M, Leon S, Sano M, Thal LJ, Woodbury P. Clinical dementia rating training and reliability in multicenter studies: the Alzheimer's Disease Cooperative Study experience. Neurology. 1997;48(6):1508\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, et al. The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7(3):270\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontero-Odasso M, Muir SW, Speechley M. Dual-task complexity affects gait in people with mild cognitive impairment: the interplay between gait variability, dual tasking, and risk of falls. Arch Phys Med Rehabil. 2012;93(2):293\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontero-Odasso MM, Sarquis-Adamson Y, Speechley M, Borrie MJ, Hachinski VC, Wells J, Riccio PM, Schapira M, Sejdic E, Camicioli RM, et al. Association of Dual-Task Gait With Incident Dementia in Mild Cognitive Impairment: Results From the Gait and Brain Study. JAMA Neurol. 2017;74(7):857\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Gu K, Meng C, Li J, Lu Q, Zhou X, Yan D, Li D, Pei C, Lu Y, et al. Relationship between sleep and serum inflammatory factors in patients with major depressive disorder. Psychiatry Res. 2023;329:115528.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcHugh ML. The chi-square test of independence. Biochem Med (Zagreb). 2013;23(2):143\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarrieu S, Letenneur L, Orgogozo JM, Fabrigoule C, Amieva H, Le Carret N, Barberger-Gateau P, Dartigues JF. Incidence and outcome of mild cognitive impairment in a population-based prospective cohort. Neurology. 2002;59(10):1594\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCurtis AF, Masellis M, Camicioli R, Davidson H, Tierney MC. Cognitive profile of non-demented Parkinson's disease: Meta-analysis of domain and sex-specific deficits. Parkinsonism Relat Disord. 2019;60:32\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBogen B, Moe-Nilssen R, Ranhoff AH, Aaslund MK. The walk ratio: Investigation of invariance across walking conditions and gender in community-dwelling older people. Gait Posture. 2018;61:479\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgerton T, Danoudis M, Huxham F, Iansek R. Central gait control mechanisms and the stride length - cadence relationship. Gait Posture. 2011;34(2):178\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Huang W, Su L, Xing Y, Jessen F, Sun Y, Shu N, Han Y. Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease. Mol Neurodegener. 2020;15(1):55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabin LA, Smart CM, Amariglio RE. Subjective Cognitive Decline in Preclinical Alzheimer's Disease. Annu Rev Clin Psychol. 2017;13:369\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y, Yang FC, Lin CP, Han Y. Biochemical and neuroimaging studies in subjective cognitive decline: progress and perspectives. CNS Neurosci Ther. 2015;21(10):768\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristensen LO, Petersen N, Morita H, Nielsen J. Corticospinal function during human walking. Ann N Y Acad Sci. 1998;860:546\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Yahya E, Dawes H, Smith L, Dennis A, Howells K, Cockburn J. Cognitive motor interference while walking: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2011;35(3):715\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheridan PL, Solomont J, Kowall N, Hausdorff JM. Influence of executive function on locomotor function: divided attention increases gait variability in Alzheimer's disease. J Am Geriatr Soc. 2003;51(11):1633\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontero-Odasso M, Verghese J, Beauchet O, Hausdorff JM. Gait and cognition: a complementary approach to understanding brain function and the risk of falling. J Am Geriatr Soc. 2012;60(11):2127\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenema DM, Bartels E, Siu KC. Tasks matter: a cross-sectional study of the relationship of cognition and dual-task performance in older adults. J Geriatr Phys Ther. 2013;36(3):115\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHausdorff JM, Schweiger A, Herman T, Yogev-Seligmann G, Giladi N. Dual-task decrements in gait: contributing factors among healthy older adults. J Gerontol Biol Sci Med Sci. 2008;63(12):1335\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakurai R, Ishii K, Yasunaga M, Takeuchi R, Murayama Y, Sakuma N, Sakata M, Oda K, Ishibashi K, Ishiwata K, et al. The neural substrate of gait and executive function relationship in elderly women: A PET study. Geriatr Gerontol Int. 2017;17(11):1873\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuena C, Maestri S, Serino S, Pedroli E, Stramba-Badiale M, Riva G. Prognostic relevance of gait-related cognitive functions for dementia conversion in amnestic mild cognitive impairment. BMC Geriatr. 2023;23(1):462.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian Q, Simonsick EM, Resnick SM, Shardell MD, Ferrucci L, Studenski SA. Lap time variation and executive function in older adults: the Baltimore Longitudinal Study of Aging. Age Ageing. 2015;44(5):796\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 643px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1 Comparison of general data and scale features among the three groups.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull Sample (n=212)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=66)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=96)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026chi;\u0026sup2;/ANOVA/Kruskal-Wallis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eMale, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eFemale, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eAge (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e71.31\u0026plusmn;7.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e71.04\u0026plusmn;7.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e72.60\u0026plusmn;6.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e70.56\u0026plusmn;7.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.205\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eEducation (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e10.22\u0026plusmn;4.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e10.22\u0026plusmn;4.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e10.61\u0026plusmn;3.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e9.54\u0026plusmn;4.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e22.96\u0026plusmn;3.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e22.96\u0026plusmn;3.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e23.21\u0026plusmn;2.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e23.21\u0026plusmn;2.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e158.86\u0026plusmn;7.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e158.86\u0026plusmn;7.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e159.02\u0026plusmn;8.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e158.33\u0026plusmn;7.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eWeight (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e58.15\u0026plusmn;10.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e59.05\u0026plusmn;10.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e58.88\u0026plusmn;9.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e57.16\u0026plusmn;10.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e27.22\u0026plusmn;2.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e29.70\u0026plusmn;0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e28.84\u0026plusmn;1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e24.79\u0026plusmn;1.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e252.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e12.85\u0026plusmn;3.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e14.72\u0026plusmn;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e13.76\u0026plusmn;2.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e11.23\u0026plusmn;4.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e55.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTMT-A(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e68.66\u0026plusmn;34.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e50.32\u0026plusmn;16.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e55.02\u0026plusmn;20.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e87.78\u0026plusmn;38.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e52.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTMT-B(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e137.60\u0026plusmn;84.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e73.96\u0026plusmn;29.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e106.08\u0026plusmn;64.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e193.03\u0026plusmn;81.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e92.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e5.54\u0026plusmn;2.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e7.44\u0026plusmn;2.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e5.79\u0026plusmn;2.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e4.39\u0026plusmn;1.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e53.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8.14\u0026plusmn;1.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e9.14\u0026plusmn;0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8.42\u0026plusmn;1.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e7.42\u0026plusmn;1.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e58.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eBADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e6.59\u0026plusmn;1.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e6.68\u0026plusmn;1.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e6.21\u0026plusmn;0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e6.8\u0026plusmn;1.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e8.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.013\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eIADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e9.44\u0026plusmn;1.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e8.46\u0026plusmn;1.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8.82\u0026plusmn;1.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e10.38\u0026plusmn;2.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e62.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData presented as mean \u0026plusmn; standard deviation (M\u0026plusmn;SD) for continuous variables and percentage for dichotomous variables. \u003csup\u003ea\u003c/sup\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003csup\u003ec\u003c/sup\u003eThe independent samples t-test further revealed the source of Kruskal-Wallis\u0026rsquo;s difference (a. NC vs. SCD. b. NC vs. MCI. c. SCD vs. MCI.) (p\u0026lt;0.05, significant difference between the two groups).Abbreviations: NC, normal cognition; SCD,\u0026nbsp;subjective cognitive decline; MCI, mild cognitive impairment; BMI,\u0026nbsp;Body mass index (kg/mg\u003csup\u003e2\u003c/sup\u003e); \u0026nbsp;MMSE, Mini-Mental State Examination; CDT, Clock-Drawing Test; TMT-A, Trail Making Test A; TMT-B, Trail Making Test B; DSB, Digit Span Test backward; DSF, Digit Span Test forward; BADL, Basic Activities of Life Scale; IADL, Instrumental Activities of Daily Living;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"693\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 693px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eComparison of gait indicators among the three groups.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull Sample (n=212)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=66)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=96)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eANOVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-hoc statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(I)Groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(J)Grooups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDTC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e15.70\u0026plusmn;11.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e13.13\u0026plusmn;9.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e13.97\u0026plusmn;9.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e18.28\u0026plusmn;12.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e6.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.031\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eSCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.018\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.048\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSingle-task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSpeed (cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e82.00\u0026plusmn;15.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e87.83\u0026plusmn;12.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e88.77\u0026plusmn;14.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e75.54\u0026plusmn;15.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e20.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eSCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eWalk ratio (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e50.49\u0026plusmn;8.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e52.46\u0026plusmn;8.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e51.92\u0026plusmn;8.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e48.43\u0026plusmn;9.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e5.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.005\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eSCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.005\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDual-task\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSpeed (cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e69.74\u0026plusmn;16.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e76.00\u0026plusmn;15.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e76.23\u0026plusmn;14.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e61.80\u0026plusmn;15.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e23.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eSCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eWalk ratio (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e56.31\u0026plusmn;14.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e58.73\u0026plusmn;12.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e59.89\u0026plusmn;14.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e52.47\u0026plusmn;13.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e6.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eSCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.012\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData presented as mean \u0026plusmn; standard deviation (M\u0026plusmn;SD) for continuous variable. Abbreviations: NC, normal cognition; SCD, subjective cognitive decline; MCI, mild cognitive impairment; DTC, dual task cost. * p \u0026lt; 0.05; ** p \u0026lt; 0.01, *** p \u0026lt; 0.001 represents statistical significance.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"mild cognitive impairment, subjective cognitive decline, gait speed, walk ratio, and dual-task cost","lastPublishedDoi":"10.21203/rs.3.rs-5336317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5336317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe most accurate and sensitive quantitative indicator for screening patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) had yet to be established. This study aimed to assess the comparative efficacy of gait speed, walk ratio, and dual-task cost (DTC) in detecting patients with SCD and MCI.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOur study involved the measurement and comparison of clinical features and gait indicators among 96 patients with MCI, 66 patients with SCD, and 50 individuals with normal cognition (NC). The correlation analysis, receiver operating characteristic curves (ROCs), and binary logistic regression analysis were utilized to investigate the relationship between gait indicators, SCD, and MCI.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe female patients exhibited a greater susceptibility to SCD and MCI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Significant differences in gait speed, walk ratio, and DTC were observed between NC and MCI group, as well as between SCD and MCI group (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no significant differences were identified between NC and SCD group. After adjusting for gender, age, education level, Body mass index (BMI), and Mini-mental State Examination (MMSE) scores, a significant correlation was observed between gait speed and the risk of developing MCI. Importantly, the ROC curve showed that the AUC of dual speed is the highest at 0.7662 [95% CI (0.6935,0.8388)]. The AUCs of single speed, single walk ratio, dual walk ratio, and DTC were 0.7333, 0.6027, 0.6609, and 0.5907, respectively. Notably, the DTC had no predictive ability (p\u0026thinsp;=\u0026thinsp;0.55).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe gait speed, walk ratio, and DTC could identify MCI but were not effective in identifying SCD. Furthermore, gait speed emerged as the most accurate and sensitive indicator for identifying individuals with MCI when compared to walk ratio and DTC.\u003c/p\u003e","manuscriptTitle":"Gait Speed as a Superior Screening Indicator for Mild Cognitive Impairment Compared to Walk Ratio and Dual-Task Cost: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-26 14:07:49","doi":"10.21203/rs.3.rs-5336317/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4388d8d7-4fba-45cd-97ab-052d7af3442c","owner":[],"postedDate":"November 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-26T14:07:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-26 14:07:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5336317","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5336317","identity":"rs-5336317","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}