Trend Change Analysis of postural balance in Parkinson`s disease discriminates between medication state

Trend Change Analysis of postural balance in Parkinson`s disease discriminates between medication state | 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 Trend Change Analysis of postural balance in Parkinson`s disease discriminates between medication state Piotr Wodarski, Jurkojć Jurkojć, Marta Chmura, Elke Warmerdam, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3776085/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Maintaining static balance is relevant and common in everyday life and it depends on a correct intersegmental coordination. A change or reduction in postural capacity has been linked to increased risk of falls. People with Parkinson's disease (PD) experience motor symptoms affecting the maintenance of a stable posture. The aim of the study is to understand the intersegmental changes in postural sway and to apply a trend change analysis to uncover different movement strategies between PD patients and healthy adults. Methods In total, 61 healthy participants, 40 young (YO), 21 old participants (OP), and 29 PD patients (13 during medication on, PDoff; 23 during medication on, PDon) were included. Participants stood quietly for 10 seconds. Inertial sensors at the head, sternum, and lumbar region collected tri-axial accelerations. Classical postural parameters and the trend change analysis (TCA) was applied on inertial measurement unit data of the head, sternum, and pelvis between groups. Objective This study aims to explore the potential application of TCA for the assessment of postural stability using IMUs, and secondly, to employ this analysis within the context of neurological diseases, specifically Parkinson's disease. Results Comparison of sensors locations revealed significant differences between head, sternum and pelvis for almost all parameters and cohorts. When comparing PDon and PDoff, the TCA revealed differences that were not seen by any other parameter. Conclusions While all parameters could differentiate between sensor locations, no group differences could be uncovered except for the TCA that allowed to distinguish between the PD on/off. The potential of the TCA to assess disease progression, response to treatment or even the prodromal PD phase should be explored in future studies. Trial registration: The research procedure was approved by the ethical committee of the Medical Faculty of Kiel University (D438/18). The study is registered in the German Clinical Trials Register (DRKS00022998). Parkinson Disease Trend Change Index Body Balance Postural Stability balance wearable sensors neurology Figures Figure 1 Figure 2 Introduction Maintaining an upright posture, or static balance, is a fundamental aspect of human life that underscores the intricate interconnections of the vestibular, visual, and somatosensory systems within the central nervous system [ 1 ]. The significance of static balance spans across all age groups but becomes increasingly critical with aging and neurological disorders. Aging introduces a gradual decline in postural control, predisposing individuals to an elevated risk of falls and associated injuries. This decline in static balance is influenced by a multitude of age-related factors, encompassing alterations in sensory input, muscle strength, joint flexibility, and neural processing [ 2 ]. Concurrently, Parkinson's disease (PD), a neurodegenerative disorder characterized by the loss of dopaminergic neurons, for example in the substantia nigra, presents profound challenges to postural control [ 3 ]. Posture is far from a mere static alignment of body segments; it represents a dynamic process characterized by continuous adjustments to maintain stability while performing various tasks. A particularly intriguing aspect of postural control is the necessity for specific body segments to remain stable while others adapt to accommodate external demands. For instance, the head must remain relatively stable to preserve visual focus and spatial orientation [ 4 ], while the pelvis may need to make slight adjustments to accommodate changes in terrain or task requirements [ 5 ]. Unconsciously, humans stabilize their gaze and maintain awareness of their body position [ 6 ] but also stabilize their head to ensure balance [ 7 ]. For example [ 8 ] found that children with cerebral palsy exhibit greater head angle variability, suggesting a compensatory strategy and [ 5 ] observed significant head stabilization during various locomotor tasks, with the head compensating for translation and rotation. Amblard et al. [ 9 ] demonstrated the ability to voluntarily stabilize the head in space during trunk movements, even in weightlessness. Assessments of postural sway in people with mild traumatic brain injury revealed increased sway of the center of mass and less head stabilization compared with healthy controls [ 10 ]. In addition [ 11 ] showed that intersegmental coordination patterns differ e.g. between Parkinson’s disease and cerebellar ataxia. These studies collectively highlight the role of intersegmental coordination in postural control, particularly in individuals with motor impairments introducing another layer of complexity to our understanding of static balance. Inertial measurement units (IMUs) or wearable health technology can be used to measure static balance [ 12 , 13 ] and to extract meaningful postural parameters [ 14 , 15 ]. The reliability and validity of those parameters have been extensively examined [ 16 , 17 ]. A recent development in the field of postural analysis involves the trend change analysis (TCA). Specifically, it can detect a small number of quick corrections, an increased frequency of longer-duration corrections, and an elongation in the displacement between successive postural corrections. Adapted from techniques originally employed in stock exchange analyses, the TCA facilitates the quantification of postural corrections in both the anteroposterior (A/P) and mediolateral (M/L) directions. Moreover, it allows for the calculation of the number of adaptations, the time interval between successive posture corrections [ 18 ] providing insights about the body's responses to postural challenges [ 19 ]. The research presented herein aims to delve into the intricate relationship between static balance, PD, aging, and the dynamic adjustments involving intersegmental changes in postural sway. The objectives of this study are twofold: Firstly, to explore the potential application of TCA for the assessment of postural stability using IMUs, and secondly, to employ this analysis within the context of neurological diseases, specifically PD. We hypothesized that the TCA could differentiate between persons with PD (pwPD) and healthy adults and also distinguish, in pwPD, between dopaminergic on (PDon) and dopaminergic off phases (PDoff). Methods Participants In total, 61 healthy participants, 40 young (YO) [20 women (age: 29.5 years ± 8.5 years; weight: 79.5 ± 11.5 kg; height: 1.85 ± 0.08 m) and 20 men (age: 27.5 years ± 7.1 years; weight: 66.3 ± 8.5 kg; height: 1.73 ± 0.05 m)] and 21 old (OP) [11 women (age: 72.5 years ± 5.9 years; weight: 83.9 ± 13.3 kg; height: 1.81 ± 0.08 m) and 10 men (age: 70.9 years ± 6.0 years; weight: 68.9 ± 12.5 kg; height: 1.66 ± 0.06 m)] and 29 pwPD [18 women (age: 63.2 years ± 11.7 years; weight: 88.5 ± 15.3 kg; height: 1.78 ± 0.07 m) and 11 men (age: 68.0 years ± 7.3 years; weight: 69.3 ± 14.4 kg; height: 1.67 ± 0.06 m)]. All participants were either inpatients at the neurogeriatric ward of the Neurology Center at the University Hospital Schleswig-Holstein, Campus Kiel, or spouses of the patients or members of the professional team. pwPD were diagnosed according to current guidelines [ 20 ]. Thirteen pwPD participated as PDoff, 23 as PD on, and 7 as bothe PDon and PDoff. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Kiel University (D438/18) and all participants provided written informed consent before participation. Participants were excluded when their fall risk was determined to be too high (> 2 falls in the previous week), corrected visual acuity was below 60%, they scored ≤ 5 points in the Montreal Cognitive Assessment (MoCA) test [ 21 ], had current or past chronic substance abuse (except nicotine), and were not able to perform at least one of the walking tasks [ 22 ]. Protocol Participants were recorded while standing still for 10 seconds using an IMU motion capture system (Noraxon USA Inc., myoMOTION, Scottsdale, AZ, USA). The participants were asked to stand with their feet together, side-by-side and fix their gaze on a point on a white wall. Three IMUs were attached to the body (pelvic, sternum and head) using elastic bands with a special housing for the IMU to clip into (see Fig. 1 ). The IMU data were collected at 200 Hz in the commercial software package (Noraxon MR3.16) (see [ 22 , 23 ]). The research procedure was approved by the ethical committee of the Medical Faculty of Kiel University (D438/18). The study is registered in the German Clinical Trials Register (DRKS00022998). Sensor Data Processing The IMU data was processed by custom written scripts using MATLAB (MathWorks, Nantick, MA) based on [ 24 ]. The parameters provided information about the sway jerkiness (JERK) (cm2/s5), the sway area (SURFACE) (cm2), path (PATH) (cm), mean velocity (MV) (cm/s), range of acceleration (RANGE) (cm/s2) and root mean square of the acceleration (RMS) (cm/s2). In addition, the TCA was applied. Acceleration signals were filtered with a low-pass filter (7 Hz low-pass Butterworth filter). The method is based on a Moving Average Convergence Divergence (MACD) indicator calculation algorithm and evaluates the relationships of exponential moving averages (EMAs) for the recorded signal [ 18 ]. Calculations can be performed for any time-varying signal. In the case of the tests used, recorded acceleration signals were used, the S signal is the acceleration signal. In the first step of calculations, for the signal S, the MACD line was determined as the difference between two EMAs (Eq. 2) with lengths of 12 and 26 samples according to Eq. 1. \(MACD=EM{A}_{S,12}- EM{A}_{S, 26}\) , Eq. 1 Where EMA S , 12 - faster exponential moving average for signal S, EMA S,26 - slower exponential moving average for signal S \(EMA=\frac{{p}_{0}+ \left(1-\alpha \right){p}_{1}+{\left(1-\alpha \right)}^{2}{p}_{2}+\dots + {\left(1-\alpha \right)}^{N}{p}_{N}}{1+\left(1-\alpha \right)+{\left(1-\alpha \right)}^{2}+\dots +{\left(1-\alpha \right)}^{N}}\) , Eq. 2 Where, p 0 – ultimate value, p 1 – penultimate value, p N – value preceding N periods, N = number of periods, α = a smoothing coefficient equal to 2/(N + 1). In the next step, the signal line is calculated as an EMA with a length of 9 samples from the MACD line signal in accordance with Eq. 3. \(Signal line =EM{A}_{MACD line, 9}\) Eq. 3 The intersection of the MACD line and the Signal line determines the trend change points in the S signal. The number of intersections determines the TCI (trend changes index). In the next step, the time intervals between successive points of trend changes in the S signal were calculated. In this way, the MACD_dT array was determined, the average value of which is the value of the TCI_dT. As a consequence, the displacement between subsequent trend change points were calculated and the results constitute the MACD_dS array. The average value of the array is the value of the TCI_dS (Fig. 2 ). In this study, the displacement of the signal is the difference in the acceleration values between successive points of trend change on the acceleration signal. To summarize, TCI determines the number of trend changes in the assumed research period, TCI_dT defines the average time between detected trend changes, and TCI_dS determines the average value of the acceleration change between subsequent trend changes. Indices were determined for each of the three directions of measurement, and then the resultant values were determined i.e. for TCI as the sum of the number of trend changes detected in each direction of the measured accelerations (in the X, Y and Z axes), and for TCI_dT, TCI_dS as the square root of the sum of squares of the values calculated in each direction. Statistical analysis The analyses were performed using Matlab R2022a and JASP (Version 0.16.1 JASP Team (2022)) was used for all statistical analyses. In the first step of the analyses, comparisons of the indicators determined for the PDon and PDoff groups were made. The normality of data distribution was tested using histograms and the Shapiro–Wilk test. Based on the sample size and the non-normally distributed values, variables were compared across the four subgroups using the Kruskal–Wallis test including the Dunn post hoc in case of a significant main effect. Results Sensor parameters differentiate between sensor positions but not groups in controls and PDon No significant differences were found between cohorts, but the sensor position differed for all cohorts and all parameters except TCI and TCI_dT for PDon (Table 1 ). Table 1 Sensor parameters to differentiate between groups and sensor positions in controls and PDon Parameters Group level Sensor position YO post hoc p < 0.05 OP post hoc p < 0.05 PDon post hoc p < 0.05 JERK n.s. H(2) = 60.29, p < 0.001 head vs. sternum and pelvis head vs. sternum and pelvis head vs. sternum and pelvis MV n.s. H(2) = 70.87, p < 0.001 head vs. sternum and pelvis head vs. sternum and pelvis head vs. sternum and pelvis PATH n.s. H(2) = 70.87, p < 0.001 head vs. sternum and pelvis head vs. sternum and pelvis head vs. sternum and pelvis RMS n.s. H(2) = 73.18, p < 0.001 head vs. sternum and pelvis head vs. sternum and pelvis head vs. sternum and pelvis SURFACE n.s. H(2) = 69.59, p < 0.001 head vs. sternum and pelvis head vs. pelvis head vs. sternum and pelvis RANGE n.s. H(2) = 54.82, p < 0.001 head vs. sternum and pelvis head vs. pelvis head vs. sternum and pelvis TCI n.s. H(2) = 44.27, p < 0.001 head vs. sternum and pelvis head vs. sternum and pelvis TCI_dT n.s. H(2) = 57.37, p < 0.001 head vs. sternum and pelvis head vs. sternum and pelvis TCI_dS n.s. H(2) = 79,63, p < 0.001 head vs. sternum and pelvis head vs. sternum and pelvis head vs. sternum and pelvis TCI_dV n.s. H(2) = 58.94, p < 0.001 head vs. sternum and pelvis head vs. sternum and pelvis head vs. sternum and pelvis The median values of each of the variables of interest measured in the study groups, along with the sample size are provided in supplementary data S1. TCA but no other parameters differentiate between PDon and PDoff JERK (H(2) = 12.63, p = 0.002), MV (H(2) = 11.11, p = 0.004, PATH (H(2) = 11.11, p = 0.004, RMS (H(2) = 13.09, p = 0.001, SURFACE (H(2) = 17.12, p < 0.001, RANGE (H(2) = 11.59, p = 0.003, TCI_dV (H(2) = 7.59, p = 0.022, and TCI_dS (H(2) = 9.13, p = 0.01 differed between the sensor positions. However, none of these parameters differentiated between cohorts. Vice-versa, TCI (H(2) = 13.40, p < 0.001 and TCI_dT (H(2) = 13.21, p < 0.001 differentiated between PDon and PDoff but not between the sensor positions (Table 2 ). Table 2 Sensor parameters to differentiate between groups and sensor positions in PDon and PDoff Parameters Group level Sensor position JERK n.s. H(2) = 12.63, p = 0.002 MV n.s. H(2) = 11.11, p = 0.004 PATH n.s. H(2) = 11.11, p = 0.004 RMS n.s. H(2) = 13.09, p = 0.001 SURFACE n.s. H(2) = 17.12, p < 0.001 RANGE n.s. H(2) = 11.59, p = 0.003 TCI H(2) = 13.40, p < 0.001 n.s. TCI_dT H(2) = 13.21, p < 0.001 n.s. TCI_dS n.s. H(2) = 9.13, p = 0.01 TCI_dV n.s. H(2) = 7.59, p = 0.022 Discussion This study investigated static postural sway performance of healthy young and old controls and pwPD in a static postural task using three different sensor locations. The aim of the study was to analyze the postural sway performance and the dynamic adjustments involving intersegmental changes in postural sway, to evaluate whether the parameters could uncover distinct postural sway behavior between the different cohorts. Our results confirmed that both, classical postural parameters and TCA, could uncover sway differences between the segments but only the TCA could differentiate between PDon and PDoff. The results of the current study show no group differences between the healthy adults and pwPD, confirming results from a previous study investigating static sway with increasing task difficulty [ 25 ]. This is of interest as PD is known for its altered postural reflexes with a disruption of the precisely coordinated execution of agonist and antagonist muscles (associated with bradykinesia and rigidity), which results in difficulty to maintain static postural stability [ 26 – 28 ] due to a reduction of the area of sway [ 29 ]. While pwPD have shown larger values for sway acceleration, jerk and sway velocity during postural balance compared to age-matched healthy controls [ 30 , 31 ] they also show an increased jerkiness during the performance of cognitive task [ 32 ], suggesting an interaction of cognitive functions, including multisensory integration, with static balance mechanisms. Our results highlight larger motions from the head compared to the sternum and the pelvis. The results convey with previous findings [ 10 ] basing their findings upon the biomechanical principal of a double-inverted pendulum. The double-inverted pendulum allows to be controlled by the ankles, the hip or both, while assuming a rigid head-on-trunk coupling. Almost all parameters were able to distinguish between sensor position indicating the complex relationship between the dynamic intersegmental adjustments and postural sway. The results suggest that for a relative simple and short balance tasks pwPD can perform control-like, which could be related to the location of the pathology within the central nervous system and its extensive compensation possibilities [ 33 ] and by using alternative pathways or even networks [ 34 ]. There is some evidence that dopaminergic medication can improve static sway [ 35 , 36 ]. However, there are not many IMU-based studies available that can show these differences. One reason may be that the parameters currently assessed for this performance are not covering disease-relevant changes. Here we introduced TCA in the analysis of static sway in PDon and PDoff, and could in fact detect significant differences only with this approach (but not with the conventional parameters). We found a higher number of TCIs and smaller TCI_dT values in PDoff compared to PDon. This is coherent with previous results obtained for COP measurements showing an increase in TCIs and reduction of TCI_dT in pwPD compared to healthy individuals [ 37 ]. In our view, this perspective also aligns with a pathomechanistic standpoint. Previous research, as indicated by Bizid (please cite Bizid 2009 here), suggests that low frequencies are predominantly associated with visuo-vestibular regulation, while high frequencies are associated with proprioceptive regulation. Additionally, it is well-established that visual perception and integration are strongly dopamine-dependent [ 38 ]. Therefore, we hypothesize that the results observed through TCA most likely reflect visual deficits resulting from a dopaminergic deficit. This is particularly evident, given that lower leg proprioceptive performance does not appear to be influenced by dopaminergic treatment [ 39 ]. Limitations The study faces a few limitations. First, pwPD measured in both medication states was relatively low, potentially limiting the generalizability of findings and the ability to capture the full spectrum of balance-related issues in PD. Another constraint lies in the brief 10-second measurement duration, which may not provide a comprehensive representation of individuals' balance control capabilities, particularly in dynamic real-world scenarios. Additionally, the use of a side-by-side stance as a measure may pose limitations as it may not be challenging enough to detect subtle differences between cohorts or uncover changes in postural control based on intersegmental coordination. These limitations emphasize the need for cautious interpretation of results and highlight areas for future research to address these constraints and provide a more nuanced understanding of balance control in Parkinson's disease and other relevant populations. Nevertheless, considering these limitations, it is all the more remarkable that the TCA parameters were so effective in distinguishing between PDon and PDoff. Clinical implication: This study investigated static sway in healthy individuals and pwPD using three sensor locations. Results show that traditional postural parameters effectively distinguish between segments. However, and even more relevant, the introduction of TCA proves instrumental in detecting significant differences between PD on and off medication, showcasing its potential in assessing disease-relevant changes not captured by conventional parameters. Declarations Funding: The publication is supported by the Rector's habilitation grant implemented under the Excellence Initiative - Research University program. Silesian University of Technology, grant number: 07/030/SDU/10-07-01 Authors' contributions: The authors declare that all authors were fully involved in the study and the preparation of the manuscript and that the material within has not been and will not be submitted for publication elsewhere. Availability of data and materials: supplementary data S1: www.biomechanik.pl/extraMaterials/subMatTable.pdf Acknowledgements: We thank all study participants for their support and engagement. Author details: PW, JJ, MCh: Silesian University of Technology, Faculty of Biomedical Engineering, Department of Biomechatronics, Gliwice, Poland EW: Division of Surgery, Saarland University, 66421 Homburg, Germany KC: Skyfi Sp. z o.o., Gliwice, Poland RR, MAH, WM, CH: Department of Neurology, Kiel University, 24105 Kiel, Germany Ethics approval and consent to participate: The research procedure was approved by the ethical committee of the Medical Faculty of Kiel University (D438/18). The study is registered in the German Clinical Trials Register (DRKS00022998). Consent for publication: All authors express their full consent to publication of the material Competing interests: The authors declare no conflict of interest. References Winter DADA. Human balance and posture control during standing and walking. Gait & Posture. 1995;3:193–214. Chen X, Qu X. Age-Related Differences in the Relationships Between Lower-Limb Joint Proprioception and Postural Balance. Hum Factors. 2019;61:702–11. 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Supplementary Files subMat.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Mar, 2024 Reviews received at journal 22 Mar, 2024 Reviewers agreed at journal 15 Mar, 2024 Reviews received at journal 01 Mar, 2024 Reviewers agreed at journal 05 Feb, 2024 Reviewers agreed at journal 30 Dec, 2023 Reviewers agreed at journal 29 Dec, 2023 Reviewers invited by journal 29 Dec, 2023 Editor assigned by journal 21 Dec, 2023 Submission checks completed at journal 21 Dec, 2023 First submitted to journal 19 Dec, 2023 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. 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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-3776085","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":262207672,"identity":"d1bda566-9521-4605-8671-c9fcf40db18d","order_by":0,"name":"Piotr Wodarski","email":"","orcid":"","institution":"Silesian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Piotr","middleName":"","lastName":"Wodarski","suffix":""},{"id":262207673,"identity":"6ac72a4e-5711-411a-9964-0b41b0c63871","order_by":1,"name":"Jurkojć Jurkojć","email":"","orcid":"","institution":"Silesian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jurkojć","middleName":"","lastName":"Jurkojć","suffix":""},{"id":262207674,"identity":"7c98f5ab-dffd-4b9e-a034-abd6f44e8548","order_by":2,"name":"Marta Chmura","email":"","orcid":"","institution":"Silesian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Chmura","suffix":""},{"id":262207675,"identity":"fc3887c9-17cb-476b-b7d9-1898d2931e00","order_by":3,"name":"Elke Warmerdam","email":"","orcid":"","institution":"Saarland University","correspondingAuthor":false,"prefix":"","firstName":"Elke","middleName":"","lastName":"Warmerdam","suffix":""},{"id":262207676,"identity":"8ed6f4f4-3706-4f50-990c-558d8d66cbab","order_by":4,"name":"Robbin Romijnders","email":"","orcid":"","institution":"Kiel University","correspondingAuthor":false,"prefix":"","firstName":"Robbin","middleName":"","lastName":"Romijnders","suffix":""},{"id":262207677,"identity":"84076b97-18f5-4772-a133-0b90dde0c4bc","order_by":5,"name":"Markus A. Hobert","email":"","orcid":"","institution":"Kiel University","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"A.","lastName":"Hobert","suffix":""},{"id":262207678,"identity":"40db06e2-abc7-4c2e-90c1-1578a8444daf","order_by":6,"name":"Walter Maetzler","email":"","orcid":"","institution":"Kiel University","correspondingAuthor":false,"prefix":"","firstName":"Walter","middleName":"","lastName":"Maetzler","suffix":""},{"id":262207679,"identity":"2db545db-01fe-488e-8a19-2ed9e4259ebc","order_by":7,"name":"Krzysztof Cygoń","email":"","orcid":"","institution":"Skyfi Sp. z o.o.","correspondingAuthor":false,"prefix":"","firstName":"Krzysztof","middleName":"","lastName":"Cygoń","suffix":""},{"id":262207680,"identity":"1f090e0c-0575-47c7-a77c-d6f658ab17cd","order_by":8,"name":"Clint Hansen","email":"data:image/png;base64,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","orcid":"","institution":"Kiel University","correspondingAuthor":true,"prefix":"","firstName":"Clint","middleName":"","lastName":"Hansen","suffix":""}],"badges":[],"createdAt":"2023-12-19 09:29:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3776085/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3776085/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49146266,"identity":"85f69620-289a-4a5a-aff3-d2f66c651ba3","added_by":"auto","created_at":"2024-01-03 20:14:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135530,"visible":true,"origin":"","legend":"\u003cp\u003ePlacement of the inertial measurement units on the head, sternum and pelvis.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3776085/v1/19862dad02b452e5c79672f0.jpg"},{"id":49146268,"identity":"0f8c32b7-e588-45e4-bb38-d90576e63b81","added_by":"auto","created_at":"2024-01-03 20:14:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127149,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical explanation of the Trend Change Index (TCI), the delta time between successive TCIs (MACD_dT) as well as the delta space between successive TCIs (MACD_dS) in an acceleration signal from a sensor on the pelvis with an observation phase of about 3 seconds. Seven trend changes (indicated by the seven red dots) are shown. All determined MACD_dTs were used to calculate TCI_dT and all MACD_dSs to calculate TCI_dS according to the procedure described in the text.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3776085/v1/608569d231bdbf6a4d068286.jpg"},{"id":49146493,"identity":"9b05affa-4892-4b60-8f17-42dc7bdfc619","added_by":"auto","created_at":"2024-01-03 20:22:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":493303,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3776085/v1/7480cff7-ee56-4680-be57-2484ae0e31dc.pdf"},{"id":49146267,"identity":"b8df7ba5-c79b-44be-8c35-97ca67541398","added_by":"auto","created_at":"2024-01-03 20:14:51","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":85080,"visible":true,"origin":"","legend":"","description":"","filename":"subMat.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3776085/v1/291f58e72d68e5fa7da4c4f7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trend Change Analysis of postural balance in Parkinson`s disease discriminates between medication state","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaintaining an upright posture, or static balance, is a fundamental aspect of human life that underscores the intricate interconnections of the vestibular, visual, and somatosensory systems within the central nervous system [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The significance of static balance spans across all age groups but becomes increasingly critical with aging and neurological disorders. Aging introduces a gradual decline in postural control, predisposing individuals to an elevated risk of falls and associated injuries. This decline in static balance is influenced by a multitude of age-related factors, encompassing alterations in sensory input, muscle strength, joint flexibility, and neural processing [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Concurrently, Parkinson's disease (PD), a neurodegenerative disorder characterized by the loss of dopaminergic neurons, for example in the substantia nigra, presents profound challenges to postural control [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePosture is far from a mere static alignment of body segments; it represents a dynamic process characterized by continuous adjustments to maintain stability while performing various tasks. A particularly intriguing aspect of postural control is the necessity for specific body segments to remain stable while others adapt to accommodate external demands. For instance, the head must remain relatively stable to preserve visual focus and spatial orientation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], while the pelvis may need to make slight adjustments to accommodate changes in terrain or task requirements [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnconsciously, humans stabilize their gaze and maintain awareness of their body position [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] but also stabilize their head to ensure balance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For example [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] found that children with cerebral palsy exhibit greater head angle variability, suggesting a compensatory strategy and [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] observed significant head stabilization during various locomotor tasks, with the head compensating for translation and rotation. Amblard et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] demonstrated the ability to voluntarily stabilize the head in space during trunk movements, even in weightlessness. Assessments of postural sway in people with mild traumatic brain injury revealed increased sway of the center of mass and less head stabilization compared with healthy controls [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] showed that intersegmental coordination patterns differ e.g. between Parkinson\u0026rsquo;s disease and cerebellar ataxia. These studies collectively highlight the role of intersegmental coordination in postural control, particularly in individuals with motor impairments introducing another layer of complexity to our understanding of static balance.\u003c/p\u003e \u003cp\u003eInertial measurement units (IMUs) or wearable health technology can be used to measure static balance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and to extract meaningful postural parameters [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The reliability and validity of those parameters have been extensively examined [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A recent development in the field of postural analysis involves the trend change analysis (TCA). Specifically, it can detect a small number of quick corrections, an increased frequency of longer-duration corrections, and an elongation in the displacement between successive postural corrections. Adapted from techniques originally employed in stock exchange analyses, the TCA facilitates the quantification of postural corrections in both the anteroposterior (A/P) and mediolateral (M/L) directions. Moreover, it allows for the calculation of the number of adaptations, the time interval between successive posture corrections [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] providing insights about the body's responses to postural challenges [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe research presented herein aims to delve into the intricate relationship between static balance, PD, aging, and the dynamic adjustments involving intersegmental changes in postural sway. The objectives of this study are twofold: Firstly, to explore the potential application of TCA for the assessment of postural stability using IMUs, and secondly, to employ this analysis within the context of neurological diseases, specifically PD. We hypothesized that the TCA could differentiate between persons with PD (pwPD) and healthy adults and also distinguish, in pwPD, between dopaminergic on (PDon) and dopaminergic off phases (PDoff).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eIn total, 61 healthy participants, 40 young (YO) [20 women (age: 29.5 years\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5 years; weight: 79.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5 kg; height: 1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 m) and 20 men (age: 27.5 years\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1 years; weight: 66.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5 kg; height: 1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 m)] and 21 old (OP) [11 women (age: 72.5 years\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 years; weight: 83.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3 kg; height: 1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 m) and 10 men (age: 70.9 years\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0 years; weight: 68.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5 kg; height: 1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 m)] and 29 pwPD [18 women (age: 63.2 years\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7 years; weight: 88.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3 kg; height: 1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 m) and 11 men (age: 68.0 years\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3 years; weight: 69.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4 kg; height: 1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 m)].\u003c/p\u003e \u003cp\u003eAll participants were either inpatients at the neurogeriatric ward of the Neurology Center at the University Hospital Schleswig-Holstein, Campus Kiel, or spouses of the patients or members of the professional team. pwPD were diagnosed according to current guidelines [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Thirteen pwPD participated as PDoff, 23 as PD on, and 7 as bothe PDon and PDoff.\u003c/p\u003e \u003cp\u003e The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Kiel University (D438/18) and all participants provided written informed consent before participation. Participants were excluded when their fall risk was determined to be too high (\u0026gt;\u0026thinsp;2 falls in the previous week), corrected visual acuity was below 60%, they scored\u0026thinsp;\u0026le;\u0026thinsp;5 points in the Montreal Cognitive Assessment (MoCA) test [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], had current or past chronic substance abuse (except nicotine), and were not able to perform at least one of the walking tasks [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eProtocol\u003c/h2\u003e \u003cp\u003eParticipants were recorded while standing still for 10 seconds using an IMU motion capture system (Noraxon USA Inc., myoMOTION, Scottsdale, AZ, USA). The participants were asked to stand with their feet together, side-by-side and fix their gaze on a point on a white wall. Three IMUs were attached to the body (pelvic, sternum and head) using elastic bands with a special housing for the IMU to clip into (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The IMU data were collected at 200 Hz in the commercial software package (Noraxon MR3.16) (see [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]). The research procedure was approved by the ethical committee of the Medical Faculty of Kiel University (D438/18). The study is registered in the German Clinical Trials Register (DRKS00022998).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSensor Data Processing\u003c/h2\u003e \u003cp\u003eThe IMU data was processed by custom written scripts using MATLAB (MathWorks, Nantick, MA) based on [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The parameters provided information about the sway jerkiness (JERK) (cm2/s5), the sway area (SURFACE) (cm2), path (PATH) (cm), mean velocity (MV) (cm/s), range of acceleration (RANGE) (cm/s2) and root mean square of the acceleration (RMS) (cm/s2).\u003c/p\u003e \u003cp\u003eIn addition, the TCA was applied. Acceleration signals were filtered with a low-pass filter (7 Hz low-pass Butterworth filter). The method is based on a Moving Average Convergence Divergence (MACD) indicator calculation algorithm and evaluates the relationships of exponential moving averages (EMAs) for the recorded signal [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Calculations can be performed for any time-varying signal. In the case of the tests used, recorded acceleration signals were used, the S signal is the acceleration signal.\u003c/p\u003e \u003cp\u003eIn the first step of calculations, for the signal S, the MACD line was determined as the difference between two EMAs (Eq.\u0026nbsp;2) with lengths of 12 and 26 samples according to Eq.\u0026nbsp;1.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(MACD=EM{A}_{S,12}- EM{A}_{S, 26}\\)\u003c/span\u003e \u003c/span\u003e, Eq.\u0026nbsp;1\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere EMA\u003csub\u003eS\u003c/sub\u003e,\u003csub\u003e12\u003c/sub\u003e - faster exponential moving average for signal S,\u003c/p\u003e \u003cp\u003eEMA\u003csub\u003eS,26\u003c/sub\u003e - slower exponential moving average for signal S\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(EMA=\\frac{{p}_{0}+ \\left(1-\\alpha \\right){p}_{1}+{\\left(1-\\alpha \\right)}^{2}{p}_{2}+\\dots + {\\left(1-\\alpha \\right)}^{N}{p}_{N}}{1+\\left(1-\\alpha \\right)+{\\left(1-\\alpha \\right)}^{2}+\\dots +{\\left(1-\\alpha \\right)}^{N}}\\)\u003c/span\u003e \u003c/span\u003e, Eq.\u0026nbsp;2\u003c/p\u003e \u003cp\u003eWhere, p\u003csub\u003e0\u003c/sub\u003e \u0026ndash; ultimate value, p\u003csub\u003e1\u003c/sub\u003e \u0026ndash; penultimate value, p\u003csub\u003eN\u003c/sub\u003e \u0026ndash; value preceding N periods, N\u0026thinsp;=\u0026thinsp;number of periods, α\u0026thinsp;=\u0026thinsp;a smoothing coefficient equal to 2/(N\u0026thinsp;+\u0026thinsp;1).\u003c/p\u003e \u003cp\u003eIn the next step, the signal line is calculated as an EMA with a length of 9 samples from the MACD line signal in accordance with Eq.\u0026nbsp;3.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(Signal line =EM{A}_{MACD line, 9}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;3\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe intersection of the MACD line and the Signal line determines the trend change points in the S signal. The number of intersections determines the TCI (trend changes index).\u003c/p\u003e \u003cp\u003eIn the next step, the time intervals between successive points of trend changes in the S signal were calculated. In this way, the MACD_dT array was determined, the average value of which is the value of the TCI_dT. As a consequence, the displacement between subsequent trend change points were calculated and the results constitute the MACD_dS array. The average value of the array is the value of the TCI_dS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In this study, the displacement of the signal is the difference in the acceleration values between successive points of trend change on the acceleration signal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo summarize, TCI determines the number of trend changes in the assumed research period, TCI_dT defines the average time between detected trend changes, and TCI_dS determines the average value of the acceleration change between subsequent trend changes. Indices were determined for each of the three directions of measurement, and then the resultant values were determined i.e. for TCI as the sum of the number of trend changes detected in each direction of the measured accelerations (in the X, Y and Z axes), and for TCI_dT, TCI_dS as the square root of the sum of squares of the values calculated in each direction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe analyses were performed using Matlab R2022a and JASP (Version 0.16.1 JASP Team (2022)) was used for all statistical analyses. In the first step of the analyses, comparisons of the indicators determined for the PDon and PDoff groups were made.\u003c/p\u003e \u003cp\u003eThe normality of data distribution was tested using histograms and the Shapiro\u0026ndash;Wilk test. Based on the sample size and the non-normally distributed values, variables were compared across the four subgroups using the Kruskal\u0026ndash;Wallis test including the Dunn post hoc in case of a significant main effect.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSensor parameters differentiate between sensor positions but not groups in controls and PDon\u003c/h2\u003e \u003cp\u003eNo significant differences were found between cohorts, but the sensor position differed for all cohorts and all parameters except TCI and TCI_dT for PDon (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensor parameters to differentiate between groups and sensor positions in controls and PDon\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensor\u003c/p\u003e \u003cp\u003eposition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYO post hoc\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOP post hoc\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePDon post hoc\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJERK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;60.29,\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;70.87,\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePATH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;70.87,\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;73.18,\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSURFACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;69.59,\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehead vs. pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRANGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;54.82,\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehead vs. pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;44.27,\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCI_dT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;57.37,\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCI_dS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;79,63,\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCI_dV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;58.94,\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ehead vs. sternum and pelvis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe median values of each of the variables of interest measured in the study groups, along with the sample size are provided in supplementary data S1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTCA but no other parameters differentiate between PDon and PDoff\u003c/h2\u003e \u003cp\u003eJERK (H(2)\u0026thinsp;=\u0026thinsp;12.63, p\u0026thinsp;=\u0026thinsp;0.002), MV (H(2)\u0026thinsp;=\u0026thinsp;11.11, p\u0026thinsp;=\u0026thinsp;0.004, PATH (H(2)\u0026thinsp;=\u0026thinsp;11.11, p\u0026thinsp;=\u0026thinsp;0.004, RMS (H(2)\u0026thinsp;=\u0026thinsp;13.09, p\u0026thinsp;=\u0026thinsp;0.001, SURFACE (H(2)\u0026thinsp;=\u0026thinsp;17.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, RANGE (H(2)\u0026thinsp;=\u0026thinsp;11.59, p\u0026thinsp;=\u0026thinsp;0.003, TCI_dV (H(2)\u0026thinsp;=\u0026thinsp;7.59, p\u0026thinsp;=\u0026thinsp;0.022, and TCI_dS (H(2)\u0026thinsp;=\u0026thinsp;9.13, p\u0026thinsp;=\u0026thinsp;0.01 differed between the sensor positions. However, none of these parameters differentiated between cohorts. Vice-versa, TCI (H(2)\u0026thinsp;=\u0026thinsp;13.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and TCI_dT (H(2)\u0026thinsp;=\u0026thinsp;13.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 differentiated between PDon and PDoff but not between the sensor positions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensor parameters to differentiate between groups and sensor positions in PDon and PDoff\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensor position\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJERK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;12.63, p\u0026thinsp;=\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;11.11, p\u0026thinsp;=\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePATH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;11.11, p\u0026thinsp;=\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;13.09, p\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSURFACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;17.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRANGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;11.59, p\u0026thinsp;=\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;13.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCI_dT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;13.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCI_dS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;9.13, p\u0026thinsp;=\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCI_dV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH(2)\u0026thinsp;=\u0026thinsp;7.59, p\u0026thinsp;=\u0026thinsp;0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated static postural sway performance of healthy young and old controls and pwPD in a static postural task using three different sensor locations. The aim of the study was to analyze the postural sway performance and the dynamic adjustments involving intersegmental changes in postural sway, to evaluate whether the parameters could uncover distinct postural sway behavior between the different cohorts. Our results confirmed that both, classical postural parameters and TCA, could uncover sway differences between the segments but only the TCA could differentiate between PDon and PDoff.\u003c/p\u003e \u003cp\u003eThe results of the current study show no group differences between the healthy adults and pwPD, confirming results from a previous study investigating static sway with increasing task difficulty [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This is of interest as PD is known for its altered postural reflexes with a disruption of the precisely coordinated execution of agonist and antagonist muscles (associated with bradykinesia and rigidity), which results in difficulty to maintain static postural stability [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] due to a reduction of the area of sway [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile pwPD have shown larger values for sway acceleration, jerk and sway velocity during postural balance compared to age-matched healthy controls [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] they also show an increased jerkiness during the performance of cognitive task [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], suggesting an interaction of cognitive functions, including multisensory integration, with static balance mechanisms. Our results highlight larger motions from the head compared to the sternum and the pelvis. The results convey with previous findings [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] basing their findings upon the biomechanical principal of a double-inverted pendulum. The double-inverted pendulum allows to be controlled by the ankles, the hip or both, while assuming a rigid head-on-trunk coupling. Almost all parameters were able to distinguish between sensor position indicating the complex relationship between the dynamic intersegmental adjustments and postural sway. The results suggest that for a relative simple and short balance tasks pwPD can perform control-like, which could be related to the location of the pathology within the central nervous system and its extensive compensation possibilities [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and by using alternative pathways or even networks [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is some evidence that dopaminergic medication can improve static sway [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, there are not many IMU-based studies available that can show these differences. One reason may be that the parameters currently assessed for this performance are not covering disease-relevant changes. Here we introduced TCA in the analysis of static sway in PDon and PDoff, and could in fact detect significant differences only with this approach (but not with the conventional parameters). We found a higher number of TCIs and smaller TCI_dT values in PDoff compared to PDon. This is coherent with previous results obtained for COP measurements showing an increase in TCIs and reduction of TCI_dT in pwPD compared to healthy individuals [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In our view, this perspective also aligns with a pathomechanistic standpoint. Previous research, as indicated by Bizid (please cite Bizid 2009 here), suggests that low frequencies are predominantly associated with visuo-vestibular regulation, while high frequencies are associated with proprioceptive regulation. Additionally, it is well-established that visual perception and integration are strongly dopamine-dependent [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, we hypothesize that the results observed through TCA most likely reflect visual deficits resulting from a dopaminergic deficit. This is particularly evident, given that lower leg proprioceptive performance does not appear to be influenced by dopaminergic treatment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe study faces a few limitations. First, pwPD measured in both medication states was relatively low, potentially limiting the generalizability of findings and the ability to capture the full spectrum of balance-related issues in PD. Another constraint lies in the brief 10-second measurement duration, which may not provide a comprehensive representation of individuals' balance control capabilities, particularly in dynamic real-world scenarios. Additionally, the use of a side-by-side stance as a measure may pose limitations as it may not be challenging enough to detect subtle differences between cohorts or uncover changes in postural control based on intersegmental coordination. These limitations emphasize the need for cautious interpretation of results and highlight areas for future research to address these constraints and provide a more nuanced understanding of balance control in Parkinson's disease and other relevant populations. Nevertheless, considering these limitations, it is all the more remarkable that the TCA parameters were so effective in distinguishing between PDon and PDoff.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical implication:\u003c/h2\u003e \u003cp\u003eThis study investigated static sway in healthy individuals and pwPD using three sensor locations. Results show that traditional postural parameters effectively distinguish between segments. However, and even more relevant, the introduction of TCA proves instrumental in detecting significant differences between PD on and off medication, showcasing its potential in assessing disease-relevant changes not captured by conventional parameters.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe publication is supported by the Rector\u0026apos;s habilitation grant implemented under the Excellence Initiative - Research University program. Silesian University of Technology, grant number: 07/030/SDU/10-07-01\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e The authors declare that all authors were fully involved in the study and the preparation of the manuscript and that the material within has not been and will not be submitted for publication elsewhere.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e supplementary data S1: www.biomechanik.pl/extraMaterials/subMatTable.pdf\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe thank all study participants for their support and engagement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePW, JJ, MCh: Silesian University of Technology, Faculty of Biomedical Engineering, Department of Biomechatronics, Gliwice, Poland\u003c/p\u003e\n\u003cp\u003eEW:\u0026nbsp;Division of Surgery, Saarland University, 66421 Homburg, Germany\u003c/p\u003e\n\u003cp\u003eKC: Skyfi Sp. z o.o., Gliwice, Poland\u003c/p\u003e\n\u003cp\u003eRR, MAH, WM, CH: Department of Neurology, Kiel University, 24105 Kiel, Germany\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe research procedure was approved by the ethical committee of the Medical Faculty of Kiel University (D438/18). The study is registered in the German Clinical Trials Register (DRKS00022998).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll authors express their full consent to publication of the material\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWinter DADA. Human balance and posture control during standing and walking. Gait \u0026amp; Posture. 1995;3:193\u0026ndash;214. \u003c/li\u003e\n\u003cli\u003eChen X, Qu X. Age-Related Differences in the Relationships Between Lower-Limb Joint Proprioception and Postural Balance. Hum Factors. 2019;61:702\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eBalestrino R, Schapira AHV. Parkinson disease. Euro J of Neurology. 2020;27:27\u0026ndash;42. \u003c/li\u003e\n\u003cli\u003eGuitton D, Kearney RE, Wereley N, Peterson BW. 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CNS Drugs. 2013;27:97\u0026ndash;112. \u003c/li\u003e\n\u003cli\u003ePalakurthi B, Burugupally SP. Postural Instability in Parkinson\u0026rsquo;s Disease: A Review. Brain Sciences. 2019;9:239. \u003c/li\u003e\n\u003cli\u003eScholz E, Diener HC, Noth J, Friedemann H, Dichgans J, Bacher M. Medium and long latency EMG responses in leg muscles: Parkinson\u0026rsquo;s disease. Journal of Neurology, Neurosurgery \u0026amp; Psychiatry. 1987;50:66\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eHorak FB, Dimitrova D, Nutt JG. Direction-specific postural instability in subjects with Parkinson\u0026rsquo;s disease. Experimental Neurology. 2005;193:504\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eAdkin AL, Bloem BR, Allum JHJ. Trunk sway measurements during stance and gait tasks in Parkinson\u0026rsquo;s disease. Gait \u0026amp; Posture. 2005;22:240\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eMancini M, Horak FB, Zampieri C, Carlson-Kuhta P, Nutt JG, Chiari L. Trunk accelerometry reveals postural instability in untreated Parkinson\u0026rsquo;s disease. Parkinsonism \u0026amp; Related Disorders. 2011;17:557\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eChen T, Fan Y, Zhuang X, Feng D, Chen Y, Chan P, et al. Postural sway in patients with early Parkinson\u0026rsquo;s disease performing cognitive tasks while standing. Neurological Research. 2018;40:491\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eNackaerts E, Michely J, Heremans E, Swinnen SP, Smits-Engelsman BCM, Vandenberghe W, et al. Training for Micrographia Alters Neural Connectivity in Parkinson\u0026rsquo;s Disease. Front Neurosci. 2018;12:3. \u003c/li\u003e\n\u003cli\u003eDebaere F, Wenderoth N, Sunaert S, Van Hecke P, Swinnen SP. Internal vs external generation of movements: differential neural pathways involved in bimanual coordination performed in the presence or absence of augmented visual feedback. NeuroImage. 2003;19:764\u0026ndash;76. \u003c/li\u003e\n\u003cli\u003eBeuter A, Hern\u0026aacute;ndez R, Rigal R, Modolo J, Blanchet PJ. Postural Sway and Effect of Levodopa in Early Parkinson\u0026rsquo;s Disease. Can j neurol sci. 2008;35:65\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eMenant JC, Latt MD, Menz HB, Fung VS, Lord SR. Postural sway approaches center of mass stability limits in Parkinson\u0026rsquo;s disease. Movement Disorders. 2011;26:637\u0026ndash;43. \u003c/li\u003e\n\u003cli\u003eWodarski P, Jurkojć J, Michalska J, Kamieniarz A, Juras G, Gzik M. Balance assessment in selected stages of Parkinson\u0026rsquo;s disease using trend change analysis. J NeuroEngineering Rehabil. 2023;20:99. \u003c/li\u003e\n\u003cli\u003eNieto-Escamez F, Obrero-Gait\u0026aacute;n E, Cort\u0026eacute;s-P\u0026eacute;rez I. Visual Dysfunction in Parkinson\u0026rsquo;s Disease. Brain Sciences. 2023;13:1173. \u003c/li\u003e\n\u003cli\u003eValkovič P, Krafczyk S, B\u0026ouml;tzel K. Postural reactions to soleus muscle vibration in Parkinson\u0026rsquo;s disease: Scaling deteriorates as disease progresses. Neuroscience Letters. 2006;401:92\u0026ndash;6. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Parkinson Disease, Trend Change Index, Body Balance, Postural Stability, balance, wearable sensors, neurology","lastPublishedDoi":"10.21203/rs.3.rs-3776085/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3776085/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMaintaining static balance is relevant and common in everyday life and it depends on a correct intersegmental coordination. A change or reduction in postural capacity has been linked to increased risk of falls. People with Parkinson's disease (PD) experience motor symptoms affecting the maintenance of a stable posture. The aim of the study is to understand the intersegmental changes in postural sway and to apply a trend change analysis to uncover different movement strategies between PD patients and healthy adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn total, 61 healthy participants, 40 young (YO), 21 old participants (OP), and 29 PD patients (13 during medication on, PDoff; 23 during medication on, PDon) were included. Participants stood quietly for 10 seconds. Inertial sensors at the head, sternum, and lumbar region collected tri-axial accelerations. Classical postural parameters and the trend change analysis (TCA) was applied on inertial measurement unit data of the head, sternum, and pelvis between groups.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to explore the potential application of TCA for the assessment of postural stability using IMUs, and secondly, to employ this analysis within the context of neurological diseases, specifically Parkinson's disease.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eComparison of sensors locations revealed significant differences between head, sternum and pelvis for almost all parameters and cohorts. When comparing PDon and PDoff, the TCA revealed differences that were not seen by any other parameter.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWhile all parameters could differentiate between sensor locations, no group differences could be uncovered except for the TCA that allowed to distinguish between the PD on/off. The potential of the TCA to assess disease progression, response to treatment or even the prodromal PD phase should be explored in future studies.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003e The research procedure was approved by the ethical committee of the Medical Faculty of Kiel University (D438/18). The study is registered in the German Clinical Trials Register (DRKS00022998).\u003c/p\u003e","manuscriptTitle":"Trend Change Analysis of postural balance in Parkinson`s disease discriminates between medication state","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 20:14:46","doi":"10.21203/rs.3.rs-3776085/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-26T21:56:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-22T10:13:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"074c82ee-4082-4838-8092-d8b3072de195","date":"2024-03-15T07:47:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-01T14:30:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6c70c378-6eef-4ea0-a740-031338dd1656","date":"2024-02-05T08:28:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37f0c11a-9385-4e8a-a583-54806bf1dcb2","date":"2023-12-30T13:49:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27141839-93e0-4336-a90a-67deaa55b244","date":"2023-12-29T18:11:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-12-29T12:16:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-21T07:50:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-12-21T07:50:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of NeuroEngineering and Rehabilitation","date":"2023-12-19T09:21:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd19293e-d95a-4fff-9291-683b56b4fcb1","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-20T23:53:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-03 20:14:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3776085","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3776085","identity":"rs-3776085","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}