From Stability to Complexity: A Systematic Review Protocol on Long-term Divergence Exponents in Gait Analysis

From Stability to Complexity: A Systematic Review Protocol on Long-term Divergence Exponents in Gait Analysis | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search From Stability to Complexity: A Systematic Review Protocol on Long-term Divergence Exponents in Gait Analysis View ORCID Profile Philippe Terrier doi: https://doi.org/10.1101/2024.12.02.24318001 Philippe Terrier 1 Haute-Ecole ARC Santé. University of Applied Sciences and Arts Western Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Philippe Terrier For correspondence: Philippe.terrier{at}he-arc.ch Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT Long-term divergence exponents derived from nonlinear gait analysis (maximum Lyapunov exponent method) have recently been reinterpreted as measures of gait complexity rather than stability. This shift necessitates a comprehensive review of existing literature. This systematic review protocol aims to critically examine studies using long-term divergence exponents in gait analysis. The focus will be on reconciling previous findings with current understanding, evaluating methodological approaches, and synthesizing comparable results. We will search Web of Science (including MEDLINE) for peer-reviewed articles published between 2001 and 2024 that report long-term divergence exponents calculated using Rosenstein’s algorithm in human gait studies. Two independent reviewers will screen articles and extract data on study characteristics, methodological specifications, and result interpretations. Primary outcomes will include tracking how result interpretations have evolved over time and identifying potential reinterpretations based on current knowledge. Secondary outcomes will address methodological standardization. Data synthesis will primarily be narrative. Where possible, meta-analyses will be conducted for studies with comparable methods and objectives. Given the expected exploratory nature of many included studies, a narrative assessment of methodological quality will be performed instead of a formal risk of bias evaluation. This review will consolidate understanding of long-term divergence exponents as measures of gait complexity and automaticity, establish standardized computational methods, and inform future research and clinical applications in gait analysis. 1 Introduction The maximum Lyapunov exponent method is a sophisticated mathematical approach, derived from chaos theory, for analyzing nonlinear dynamical systems and their sensitivity to initial conditions [ 2 ]. In gait analysis, this method quantifies how the locomotor system responds to small perturbations during walking by examining the rate at which initially adjacent trajectories in state space of gait dynamics diverge over time [ 3 ]. The analysis typically involves reconstructing the attractor of the gait dynamics from continuous kinematic or kinetic signals and computing logarithmic divergence curves [ 4 ]. Two distinct time scales have emerged as relevant for gait assessment: short-term divergence, computed over the range of 0 to 1 stride, which evaluates the immediate response of the gait to perturbations and is interpreted as a measure of local dynamic stability, and long-term divergence, computed over 4-10 strides, which examines the behavior of the system over multiple gait cycles [ 5 ]. While short-term divergence has shown consistent associations with aging gait, fall risk and dynamic balance control [ 6 – 8 ], some studies have revealed puzzling discrepancies, with short-term and long-term divergence often responding differently or even inversely to experimental conditions [ 9 , 10 ], suggesting that they may capture fundamentally different aspects of gait control [ 11 ]. While early studies used long-term divergence exponents to assess gait stability [ 3 , 12 ], there is increasing evidence that this measure captures distinct features of gait control than its short-term counterpart. During steady walking, stride parameters fluctuate with complex fractal patterns characterized by long-range correlations between successive strides, reflecting the elaborate organization of locomotor control (gait complexity) [ 13 ]. Several studies have shown that long-term divergence exponents are strongly associated with the correlation structure of stride intervals, as measured by scaling exponents that characterize stride-to-stride fluctuation complexity [ 4 , 14 – 17 ]. Studies comparing normal walking with externally cued conditions found that long-term divergence exponents decreased substantially, while short-term divergence exponents remained largely unchanged [ 15 – 17 ]. These findings replicate what is observed in studies using scaling exponents to characterize gait complexity and cued walking [ 13 ]. Further evidence showed that acceleration signals with identical shapes, but modified stride interval complexity exhibited long-term divergence exponents that varied dramatically with the type of correlation structure applied, with differences of up to 66% between anticorrelated and correlated signals [ 18 ]. The strong relationship between stride interval complexity and long-term divergence exponents, combined with its weak association with fall risk and stability measures, has led researchers to propose the term “Attractor Complexity Index” (ACI) as a more appropriate name that better reflects their true nature as a measure of gait complexity rather than stability [ 18 ]. Recent evidence suggests that gait complexity may reflect the degree of cognitive involvement in locomotor control (gait automaticity [ 19 ]), as executive function appears to orchestrate the regulation of stride-to-stride fluctuations [ 17 , 20 , 21 ]. Therefore, long-term divergence exponents hold potential for advancing clinical gait assessment by providing a more nuanced understanding of gait control, particularly in conditions where traditional measures of stability may fall short. Additionally, the ACI could facilitate the development of wearable technologies for ecological monitoring of gait complexity in real-world settings, offering valuable insights into motor-cognitive interactions during daily activities [ 17 ]. This fundamental shift in our understanding of long-term divergence exponents requires a critical re-examination of the existing literature. Many studies published prior to this conceptual evolution interpreted long-term divergence as an index of stability, potentially leading to conclusions that need to be reconsidered in light of its newly recognized role as a measure of gait complexity. While these studies may have misinterpreted their findings through the lens of stability, their data remain valuable and could provide insights when reanalyzed from a new perspective. Moreover, substantial methodological heterogeneity exists across studies, both in the measurement techniques used to capture gait dynamics (including accelerometry, motion capture, and joint angle measurements [ 22 ]) and in the algorithmic procedures employed to calculate divergence exponents (such as time delay selection, embedding dimension, and stride range definition). A systematic review would therefore serve three important purposes: first, to reconcile previous findings with our current understanding of long-term divergence as a measure of gait complexity; second, to evaluate and potentially standardize the experimental and computational methods used across studies to improve the reliability and comparability of future research; and third, to quantitatively synthesize results from comparable studies through meta-analysis when possible. 2 Methods 2.1 Study Eligibility The systematic review will include original, peer-reviewed research articles that describe studies of human locomotion, including studies of healthy participants or patients (or both) of all ages. We will consider observational, experimental, and intervention studies, with no restriction on sample size. Studies must use specifically the calculation of divergence curves based on the maximum Lyapunov exponent method using Rosenstein’s algorithm [ 3 , 4 ], with explicit reporting of long-term divergence exponents. We will consider articles published between 2001 and 2024. Studies analyzing running gait or other locomotion modes will be excluded. Similarly, pure modeling studies based on computer simulations of locomotion will not be considered. Studies that do not provide sufficient methodological information to assess the computation of long-term divergence exponents will be excluded, as will review articles, letters, editorials, and conference abstracts ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1. Inclusion and exclusion criteria. 2.2 Information Sources and Search Strategy We will search for relevant articles using the Clarivate Web of Science search engine to retrieve references from both MEDLINE and the Web of Science Core Collection databases. The search strategy will be developed using an iterative process, starting with initial keywords based on the research question. We will analyze the text words contained in the titles and abstracts of the retrieved articles, as well as the index terms used to describe these articles, to refine our search terms. If necessary, we will conduct additional searches using newly identified keywords to ensure comprehensive coverage. For example, our preliminary search string could be TS = ((gait* OR walk*) AND (stability OR dynamic* OR nonlinear OR “non-linear”) AND (lyapunov OR “local divergence” OR (divergent exponent*)) AND (child* OR adult* OR people OR subject* OR person* OR participant*)). The complete Web of Science search strategy, including all search terms and Boolean operators, will be provided to ensure reproducibility. While we will include articles in both English and French as these are the languages spoken by the investigators, we anticipate that most, if not all, relevant articles will be in English. The search will include articles published between 2001 and 2024, as 2001 marks the beginning of the application of maximum Lyapunov exponent methods to gait analysis. 2.3 Study Selection Process Two research assistants will independently screen the retrieved articles in two phases. In the first phase, they will review titles and abstracts using broad inclusion criteria, as the specific use of long-term divergence exponents is often not explicitly mentioned in abstracts. This initial screening will likely include studies using various nonlinear methods for gait analysis, including short-term divergence exponents, Wolf’s algorithm [ 23 ], and other stability measures. From an estimated initial pool of 200-400 articles, studies that potentially involve nonlinear gait analysis will proceed to the second phase, where their full texts will be thoroughly reviewed to specifically identify those using long-term divergence exponents based on Rosenstein’s algorithm. Additionally, the reference lists of included articles will be hand-searched to identify potentially relevant studies that may have been missed by the electronic database search ( Fig.1 ). Disagreements between the two reviewers will be resolved through discussion, with the principal investigator (PT) acting as arbitrator if consensus cannot be reached. Based on previous systematic reviews in this field [ 6 , 22 ], we estimate that 20-40 articles will meet all inclusion criteria for the outcome analysis. Download figure Open in new tab Figure 1. Flow-chart of the selection process. 2.4 Data Collection Process and Data Items The investigators will chart the data from the included studies using standardized extraction tables ( Table 2 – 4 ). They will collect three main categories of information. First, study characteristics will include the publication year, authors, sample size, participant demographics (age, gender, health status), and research context (methodological study, clinical evaluation, or intervention evaluation). Second, methodological specifications will detail the measurement methods (primary measurement system: accelerometer, goniometer, motion capture; signal type: velocity, acceleration, angle, position; sampling rate; measurement duration and/or number of strides; walking condition: overground or treadmill), signal processing procedures (filtering and resampling methods), and the specific parameters used to compute long-term divergence exponents (time delay, embedding dimension, stride range). Third, we will extract information about the aim of each study, study conclusion, and interpretation of long-term divergence exponent results. View this table: View inline View popup Download powerpoint Table 2. Data extraction template for study characteristics and population distribution. View this table: View inline View popup Download powerpoint Table 3. Data extraction template for methodological and technical specifications View this table: View inline View popup Download powerpoint Table 4. Data extraction template for study aims and long-term divergence exponent findings. 2.5 Outcomes and Prioritization The primary outcomes will focus on findings of the included studies, specifically: how results were interpreted, the evolution of this interpretation over time, and how findings might be reinterpreted in light of current understanding of long-term divergence as a gait complexity and automaticity measure. We will particularly examine relationships between long-term divergence, other gait measures, and experimental conditions that may support its role as a gait complexity index. Secondary outcomes will address methodological standardization, including measurement specifications, signal processing procedures, and computation parameters. 2.6 Data Synthesis The synthesis will primarily be narrative, focusing on the interpretation of long-term divergence exponents and methodological considerations. Additionally, if we identify at least four studies with comparable methods and objectives (e.g., comparing young vs. older adults, or examining specific clinical conditions), we will conduct meta-analyses of effect sizes. For these quantitative analyses, we will calculate standardized mean differences and their 95% confidence intervals. Study heterogeneity will be assessed using I 2 statistics, and pooled effect sizes will be computed using a fixed-effect model. The results of these meta-analyses will be presented using forest plots. 2.7 Risk of Bias Assessment Given the exploratory nature of many studies using maximum Lyapunov exponents in gait analysis, where the primary aim was often to investigate the potential of the method rather than to test specific hypotheses, a formal risk of bias assessment using standardized tools would not be appropriate. Instead, we will conduct a narrative assessment focusing on three key aspects of methodological quality. First, we will examine analytical rigor, including the completeness of parameter reporting and the appropriateness of computational methods. Second, we will assess the thoroughness of the reporting of results, particularly with respect to the presentation of descriptive statistics and measures of variability. Third, we will discuss sample size considerations, recognizing that many exploratory studies may have used convenience samples rather than formal power calculations. This narrative approach to quality assessment is consistent with the developmental nature of the field, where methodological standardization is still evolving. The results of this assessment will inform recommendations for improving the quality of future research using long-term divergence analysis. 3 Dissemination Plans This protocol has been published as a preprint on medRxiv. The completed systematic review will be submitted for publication in a peer-reviewed journal focusing on biomechanics, gait analysis, or research methodology. The results will be presented at relevant scientific conferences in the field of biomechanics, exercise sciences, or motor control. The results will be disseminated to both the clinical and research communities to promote standardization of long-term divergence analysis methods and proper interpretation of this measure. 4 Discussion and Conclusion Nonlinear analysis of human gait provides unique insights into locomotor control that complement traditional biomechanical approaches [ 6 , 24 ]. While short-term divergence provides information about dynamic stability and fall risks [ 5 , 7 , 8 , 11 , 22 ], long-term divergence (now called the Attractor Complexity Index) is likely associated with gait automaticity and cognitive involvement in locomotor control [ 15 , 18 ]. This knowledge is particularly valuable for assessing the cognitive demands of walking, as reduced gai t automaticity—characterized by greater reliance on attentional resources—has been linked to pathological gait patterns and may serve as a potential biomarker for diagnosing motor-cognitive impairments, monitoring disease progression, and tailoring rehabilitation strategies in clinical populations [ 19 , 25 – 27 ]. This systematic review aims to consolidate our understanding of long-term divergence as a measure of gait complexity and automaticity and to establish standardized computational methods. The results will inform both research methodology and clinical applications, potentially improving the assessment of gait disorders using wearable technology in unsupervised (free-living) conditions [ 6 , 17 , 24 , 28 ]. Standardization of measurement and computational methods would facilitate the implementation of these analyses in both research and clinical settings, leading to more reliable and comparable assessments of gait complexity across populations and conditions. Data Availability No data were collected or analyzed for this protocol. The completed systematic review will include a comprehensive data sharing plan. All data extracted from included studies during the review process will be made available. 5 Declarations 5.1 Funding The ACIDS (attractor complexity index document search) study is founded by the “fonds recherche et impulsion” (research and impulse fund) granted by the “commission scientifique du domaine santé” (scientific commission of the health faculty) at HES - SO, University of Applied Sciences and Arts Western Switzerland. The funder had no role in the development of this protocol, including the design of the study, the writing of the manuscript, or the decision to publish the protocol. The funder will have no role in the collection, analysis, or interpretation of data in the subsequent systematic review. 5.2 Competing interests The lead author (PT) has published extensively in the field of nonlinear gait analysis over the past 15 years and anticipates that several of his own papers may be included in the systematic review. To mitigate potential bias, two investigators who are early-career researchers without specialization in nonlinear gait analysis will independently conduct the study selection process. This approach aims to ensure greater neutrality in article inclusion. The author declares no other competing interests. 5.3 Ethics approval and consent to participate Ethics approval was not required for this systematic review as it involves the analysis of previously published data and does not involve human participants or animal subjects. 5.4 Authors’ contributions PT conceived the study, obtained funding, designed the protocol, and wrote the manuscript. The author read and approved the final version of the manuscript. 5.5 Availability of data and materials No data were collected or analyzed for this protocol. The completed systematic review will include a comprehensive data sharing plan. All data extracted from included studies during the review process will be made available. 5.7 Registration This protocol was not registered in PROSPERO or other systematic review registries due to the exploratory nature of many studies expected to be included and the limited scope for formal risk of bias assessment. Given these constraints, we opted to publis h this protocol as a preprint on medRxiv to ensure transparency and allow for peer feedback prior to conducting the full review. Any future amendments to this protocol will be documented and reported in the final systematic review publication. 5.6 Acknowledgements The author would like to express sincere gratitude to Marco Pedrotti for his support throughout the ACIDS project. Special thanks are extended to colleagues at the institution for their assistance. References [1]. L. Shamseer , D. Moher , M. Clarke , D. Ghersi , A. Liberati , M. Petticrew , P. Shekelle , L.A. Stewart , Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation , BMJ 349 ( 2015 ) g7647 . doi: 10.1136/bmj.g7647 . OpenUrl Abstract / FREE Full Text [2]. ↵ J.B. Dingwell , Lyapunov Exponents , in: Wiley Encyclopedia of Biomedical Engineering , John Wiley & Sons, Ltd , 2006 . doi: 10.1002/9780471740360.ebs0702 . OpenUrl CrossRef [3]. ↵ J.B. Dingwell , J.P. Cusumano , P.R. Cavanagh , D. Sternad , Local dynamic stability versus kinematic variability of continuous overground and treadmill walking , Journal of Biomechanical Engineering-Transactions of the Asme 123 ( 2001 ) 27 – 32 . doi: 10.1115/1.1336798 . OpenUrl CrossRef PubMed Web of Science [4]. ↵ P. Terrier , O. Dériaz , Non-linear dynamics of human locomotion: effects of rhythmic auditory cueing on local dynamic stability , Front Physiol 4 ( 2013 ) 230 . doi: 10.3389/fphys.2013.00230 . OpenUrl CrossRef PubMed [5]. ↵ J.L.-S. Su , J.B. Dingwell , Dynamic stability of passive dynamic walking on an irregular surface , J Biomech Eng 129 ( 2007 ) 802 – 810 . doi: 10.1115/1.2800760 . OpenUrl CrossRef PubMed [6]. ↵ A. Amirpourabasi , S.E. Lamb , J.Y. Chow , G.K.R. Williams , Nonlinear Dynamic Measures of Walking in Healthy Older Adults: A Systematic Scoping Review , SENSORS 22 ( 2022 ). doi: 10.3390/s22124408 . OpenUrl CrossRef [7]. ↵ S.M. Bruijn , O.G. Meijer , P.J. Beek , J.H. van Dieën , Assessing the stability of human locomotion: a review of current measures , J R Soc Interface 10 ( 2013 ) 20120999 . doi: 10.1098/rsif.2012.0999 . OpenUrl CrossRef PubMed [8]. ↵ M.J.P. Toebes , M.J.M. Hoozemans , R. Furrer , J. Dekker , J.H. van Dieen , Local dynamic stability and variability of gait are associated with fall history in elderly subjects , Gait & Posture 36 ( 2012 ) 527 – 531 . doi: 10.1016/j.gaitpost.2012.05.016 . OpenUrl CrossRef PubMed Web of Science [9]. ↵ H.R.F. Yakhdani , H.A. Bafghi , O.G. Meijer , S.M. Bruijn , N. van den Dikkenberg , A.B. Stibbe , B.J. van Royen , J.H. van Dieen , Stability and variability of knee kinematics during gait in knee osteoarthritis before and after replacement surgery , Clinical Biomechanics 25 ( 2010 ) 230 – 236 . doi: 10.1016/j.clinbiomech.2009.12.003 . OpenUrl CrossRef PubMed [10]. ↵ K.S. van Schooten , L.H. Sloot , S.M. Bruijn , H. Kingma , O.G. Meijer , M. Pijnappels , J.H. van Dieën , Sensitivity of trunk variability and stability measures to balance impairments induced by galvanic vestibular stimulation during gait , Gait Posture 33 ( 2011 ) 656 – 660 . doi: 10.1016/j.gaitpost.2011.02.017 . OpenUrl CrossRef PubMed Web of Science [11]. ↵ S.M. Bruijn , D.J.J. Bregman , O.G. Meijer , P.J. Beek , J.H. van Dieën , Maximum Lyapunov exponents as predictors of global gait stability: a modelling approach , Med Eng Phys 34 ( 2012 ) 428 – 436 . doi: 10.1016/j.medengphy.2011.07.024 . OpenUrl CrossRef PubMed [12]. ↵ J.B. Dingwell , J.P. Cusumano , D. Sternad , P.R. Cavanagh , Slower speeds in patients with diabetic neuropathy lead to improved local dynamic stability of continuous overground walking , J Biomech 33 ( 2000 ) 1269 – 1277 . OpenUrl CrossRef PubMed Web of Science [13]. ↵ P. Terrier , Fractal Fluctuations in Human Walking: Comparison Between Auditory and Visually Guided Stepping , Ann Biomed Eng 44 ( 2016 ) 2785 – 2793 . doi: 10.1007/s10439-016-1573-y . OpenUrl CrossRef PubMed [14]. ↵ E. Sejdic , Y. Fu , A. Pak , J.A. Fairley , T. Chau , The Effects of Rhythmic Sensory Cues on the Temporal Dynamics of Human Gait , Plos One 7 ( 2012 ) e43104 . doi: 10.1371/journal.pone.0043104 . OpenUrl CrossRef PubMed [15]. ↵ P. Terrier , Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues , PeerJ 7 ( 2019 ) e7417 . doi: 10.7717/peerj.7417 . OpenUrl CrossRef PubMed [16]. Piergiovanni , S , Terrier , P , Effects of metronome walking on long-term attractor divergence and correlation structure of gait: a validation study in older people , Scientific Reports 14 ( 2024 ) 15784 . doi: 10.1038/s41598-024-65662-5 . OpenUrl CrossRef PubMed [17]. ↵ S. Piergiovanni , P. Terrier , Validity of Linear and Nonlinear Measures of Gait Variability to Characterize Aging Gait with a Single Lower Back Accelerometer , Sensors 24 ( 2024 ) 7427 . doi: 10.3390/s24237427 . OpenUrl CrossRef [18]. ↵ P. Terrier , F. Reynard , Maximum Lyapunov exponent revisited: Long-term attractor divergence of gait dynamics is highly sensitive to the noise structure of stride intervals , Gait Posture 66 ( 2018 ) 236 – 241 . doi: 10.1016/j.gaitpost.2018.08.010 . OpenUrl CrossRef PubMed [19]. ↵ D.J. Clark , Automaticity of walking: functional significance, mechanisms, measurement and rehabilitation strategies , Frontiers in Human Neuroscience 9 ( 2015 ) 246 . doi: 10.3389/fnhum.2015.00246 . OpenUrl CrossRef [20]. ↵ L.M. Decker , F. Cignetti , N. Stergiou , Executive function orchestrates regulation of task-relevant gait fluctuations , Gait Posture 38 ( 2013 ) 537 – 540 . doi: 10.1016/j.gaitpost.2012.12.018 . OpenUrl CrossRef PubMed [21]. ↵ M. Roerdink , C.P. de Jonge , L.M. Smid , A. Daffertshofer , Tightening Up the Control of Treadmill Walking: Effects of Maneuverability Range and Acoustic Pacing on Stride-to-Stride Fluctuations , Front. Physiol . 10 ( 2019 ). doi: 10.3389/fphys.2019.00257 . OpenUrl CrossRef PubMed [22]. ↵ S. Mehdizadeh , The largest Lyapunov exponent of gait in young and elderly individuals: A systematic review , Gait & Posture 60 ( 2018 ) 241 – 250 . doi: 10.1016/j.gaitpost.2017.12.016 . OpenUrl CrossRef PubMed [23]. ↵ C.O. Moraiti , N. Stergiou , H.S. Vasiliadis , E. Motsis , A. Georgoulis , Anterior cruciate ligament reconstruction results in alterations in gait variability , Gait & Posture 32 ( 2010 ) 169 – 175 . doi: 10.1016/j.gaitpost.2010.04.008 . OpenUrl CrossRef PubMed Web of Science [24]. ↵ D. Kobsar , J.M. Charlton , C.T.F. Tse , J.-F. Esculier , A. Graffos , N.M. Krowchuk , D. Thatcher , M.A. Hunt , Validity and reliability of wearable inertial sensors in healthy adult walking: a systematic review and meta-analysis , J Neuroeng Rehabil 17 ( 2020 ) 62 . doi: 10.1186/s12984-020-00685-3 . OpenUrl CrossRef PubMed [25]. ↵ S.A. Bridenbaugh , R.W. Kressig , Motor cognitive dual tasking: early detection of gait impairment, fall risk and cognitive decline , Z Gerontol Geriatr 48 ( 2015 ) 15 – 21 . doi: 10.1007/s00391-014-0845-0 . OpenUrl CrossRef [26]. G. Yogev-Seligmann , J.M. Hausdorff , N. Giladi , The role of executive function and attention in gait , Mov Disord 23 ( 2008 ) 329 – 342 ; quiz 472. doi: 10.1002/mds.21720 . OpenUrl CrossRef PubMed Web of Science [27]. ↵ T. Warlop , C. Detrembleur , G. Stoquart , T. Lejeune , A. Jeanjean , Gait Complexity and Regularity Are Differently Modulated by Treadmill Walking in Parkinson’s Disease and Healthy Population , Front Physiol 9 ( 2018 ) 68 . doi: 10.3389/fphys.2018.00068 . OpenUrl CrossRef PubMed [28]. ↵ E. Warmerdam , J.M. Hausdorff , A. Atrsaei , Y. Zhou , A. Mirelman , K. Aminian , A.J. Espay , C. Hansen , L.J.W. Evers , A. Keller , C. Lamoth , A. Pilotto , L. Rochester , G. Schmidt , B.R. Bloem , W. Maetzler , Long-term unsupervised mobility assessment in movement disorders , The Lancet Neurology 0 ( 2020 ). doi: 10.1016/S1474-4422(19)30397-7 . OpenUrl CrossRef PubMed [29]. © 2024 The Author. This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0) . View the discussion thread. Back to top Previous Next Posted December 03, 2024. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following From Stability to Complexity: A Systematic Review Protocol on Long-term Divergence Exponents in Gait Analysis Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share From Stability to Complexity: A Systematic Review Protocol on Long-term Divergence Exponents in Gait Analysis Philippe Terrier medRxiv 2024.12.02.24318001; doi: https://doi.org/10.1101/2024.12.02.24318001 Share This Article: Copy Citation Tools From Stability to Complexity: A Systematic Review Protocol on Long-term Divergence Exponents in Gait Analysis Philippe Terrier medRxiv 2024.12.02.24318001; doi: https://doi.org/10.1101/2024.12.02.24318001 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neurology Subject Areas All Articles Addiction Medicine (573) Allergy and Immunology (865) Anesthesia (304) Cardiovascular Medicine (4457) Dentistry and Oral Medicine (445) Dermatology (383) Emergency Medicine (610) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1517) Epidemiology (15244) Forensic Medicine (30) Gastroenterology (1132) Genetic and Genomic Medicine (6620) Geriatric Medicine (669) Health Economics (1002) Health Informatics (4557) Health Policy (1372) Health Systems and Quality Improvement (1615) Hematology (543) HIV/AIDS (1272) Infectious Diseases (except HIV/AIDS) (15936) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (670) Neurology (6635) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1148) Occupational and Environmental Health (957) Oncology (3348) Ophthalmology (980) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1696) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5463) Public and Global Health (9257) Radiology and Imaging (2210) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1198) Rheumatology (598) Sexual and Reproductive Health (716) Sports Medicine (532) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a034bb441ce44ebc',t:'MTc4MDA0OTY1OQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();