Locomotion of Parkinsonian Patient: Are There Relations Between the Long Range Autocorrelations and the Neurological Impairments, Walking Abilities and the ... View Homepage


Ontology type: schema:MedicalStudy     


Clinical Trial Info

YEARS

2014-

ABSTRACT

Parkinson's disease (PD) is one of the most common neurodegenerative disorders. The parkinsonian gait is characterized by reducted stride length and gait speed, postural disorders (with a high risk of falling) and a modification of stride duration variability. This variability can be assessed by its magnitude (SD and CV) and its temporal organization (long-range autocorrelations). Healthy human gait presents with an interdependency between consecutive cycles that can span over hundreds of strides (long-range autocorrelations). Numerous observations plead for a relation between long-range autocorrelations and functional abilities of the system. Complementary to drugs, rehabilitation becomes an important way to treat PD. The aim of our study is to assess by a controlled, randomized, single blinded clinical study, the effect of physical exercise on stride duration variability, neurological impairments and walking abilities of parkinsonian patients. Physical exercise program will include 30 sessions spread over 15 weeks following the guidelines. Long-range correlations analysis, including the study of Hurst and α exponents, will be performed on a minimum of 512 consecutive cycles. Finally, the functional assessment of the parkinsonian patient will be done according to International Classification of Functioning Disability and Health (ICF). Detailed Description BACKGROUND One of the most common features of human movement is its variability across multiple repetition of the same rhythmic task (1). In humans, many periodic signals, such as gait, heartbeat, respiratory and neuronal activities are characterized by their temporal complexity, fluctuating in a complex manner over time. Although fluctuations between cycles could appear to vary randomly, without apparent correlations between cycles, healthy systems possess the memory of preceding values of the series displaying a complex temporal structure. In order to assess variability in physiological time series, several mathematical methods can be used. On one hand, classical mathematical methods, usually applied on shorter time series (tens of data points), quantify the fluctuation magnitude in a set of values independently of their order in the distribution, by computing the standard deviation (SD) and the coefficient of variation (CV). On the other hand, more complex mathematical methods, applied on longer time series (≥512 cycles), can be used to assess the fluctuation dynamics over time (3). These latter methods have demonstrated that variability of numerous physiological signals (cardiac and respiratory rhythm or locomotor activities e.g.) exhibit long-range autocorrelations, whereby the statistical inter-dependency between cycles spans of a very large number of cycles (14). This temporal organization of variability is thus an intrinsic property within numerous biological systems. Moreover, it could provide insight into the neurophysiological organization and into the regulation of these systems (32). Recent studies claimed that these fluctuations, included in an optimal range, would represent the underlying physiologic capability to make flexible adaptations to everyday stresses placed on the human body (32). Therefore, the presence of such temporal dynamics is thought to be a critical marker of health and their breakdown as an index of pathological condition (18, 25, 32). In human heart rate for instance, deviations from an optimum of variability in either the direction of randomness (atrial fibrillation e.g.) or the over-regularity (congestive heart failure e.g.) indicate the loss of the adaptive capabilities of the system (9, 32). Alongside, some central nervous system diseases influence the variability, especially, of gait. Indeed, neurodegenerative disorders such as Parkinson and Huntington diseases are characterized, among others, by a modification of walking variability (observed by a breakdown of long-range autocorrelations) and a high risk of falling. Although the origin of long-range autocorrelation remains unknown, their breakdown in such diseases suggests a central control mechanism (8, 11, 13, 16, 17, 36). RESEARCH PROJECT Affecting about 1% of the population over the age of 60, Parkinson's disease (PD) is one of the most common neurodegenerative disorders. PD is progressive in nature, and so patients face increased difficulties with activities of daily living and various aspects of mobility such as gait, transfers, balance, and posture. Ultimately, this leads to decreased independence, inactivity, and social isolation, resulting in reduced quality of life. Consequently, the improvement of locomotion is one of the most important aims of the management of PD. The management of PD has traditionally centered on drug therapy, with levodopa viewed as the "gold standard" treatment. However, even with optimal medical management, parkinsonian patients experience deterioration in body function, daily activities and participation. For this reason, support has been increasing for the inclusion of rehabilitation therapies as an adjuvant to pharmacological and neurosurgical treatment. Indeed, regular physical activity slows down the progression and decrease the fall risk. Moreover, exercise has demonstrated its effectiveness for both preservation of functional abilities and prevention of complications (cardiovascular, osteoporosis,…). Until now, few studies have included the analysis of variability in the functional assessment of patients presenting a neurological disease, such as PD. Yet, walking disorders and falls represent not only an important cost for the society but also a sizeable individual risk of morbi/mortality. An appropriate rehabilitation program should allow for reduction at once the risks and costs resulting from these disorders. The investigator hypothesize that the analysis of walking variability could be useful as clinical tool in the assessment of fall risk and as assessment tool of the therapeutic effectiveness (medication and/or physical exercise) in PD. Therefore, the aims of this study are (1) to assess the influence of physical exercise on human walking variability and (2) to study its potential correlations with walking abilities and neurological impairments of parkinsonian patients. Patients The investigators will recruit 50 patients with idiopathic Parkinson's disease from the department of Neurology of Cliniques universitaires Saint-Luc (Brussels, Belgium) The study is approved by the ethics committee. All patients will give informed written consent to the study. Eligibility criteria are: diagnosis idiopathic Parkinson (according to the Brain Bank criteria of the United Kingdom Parkinson's Disease Society), disease severity (according to modified Hoehn & Yahr stages I to IV), absence of dementia (Minimal Mini Mental State Examination score of 24 or higher), stable drug usage in the last 4 weeks and adequate vision and hearing, achieved using corrective lenses and/or hearing aid if required. Patients will be excluded if they have severe co-morbidity, other neurological problems, acute medical problems (e.g. MI, diabetes) and joint problems affecting mobility, and unpredictable "Off"-periods (score >2, MDS-UPDRS item 4.5). Procedure The present study is a controlled, randomized, single blinded clinical study with a crossover design. The control group will not change its usual physical activity whereas the intervention group will benefit from the physical exercise program. This latter will include 30 sessions of circuit-group training of 60 min (twice a week) spread over 15 weeks. Then, the two groups will be crossed. According to the recent guidelines, the program will include a specific work on balance, posture, gait, fitness, dual tasks and stretching. All sessions will be performed at an adequate intensity (i.e. 60-80% of predicted maximal heart rate). At least 512 cycles will be recorded (at a high sampling rate (512 Hz)) on a treadmill at a self-selected comfortable speed using a unidimensional accelerometer taped on the right lateral malleolus. Functional assessment based on the 3 domains of the International Classification of Functioning, Disability and Health (ICF) Patients will be assessed before intervention (T0) and at 15 (T1), 30 (T2), 45 (T3) and 60 weeks (T4) among the 3 ICF domains: Impairments will assessed by MDS-UPDRS, an instrumented gait analysis (kinematic, kinetic, electromyographic and energetic) (18), the 6 minute walk distance, the 10 meter walk test, the ABC-Scale and the BESTest (including the Functional Reach Test, the Push & Release and the Get Up & Go test). Activities, participation and quality of life will be evaluated the Impact on Participation and Autonomy Questionnaire (IPAQ) and a fall diary. Walking variability analysis Revolution time variability will be appreciated by classical and complex mathematical methods. Classical mathematical methods (standard deviation, coefficient of variation) allow for evaluating the fluctuation magnitude, while complex mathematical methods (long-range autocorrelations) assess the dynamics of fluctuations over time (3). The presence of long-range autocorrelations will be evaluated using the integrated approach proposed by Rangarajan and Ding and validated by Crevecoeur et al. in the context of physiological time series. These methods are described in greater details elsewhere. Briefly, the Hurst exponent (H) will be calculated using the rescaled range analysis and the α exponent will be evaluated using the power spectral density of the time series. For each time series, both methods will be applied to sequences of 512 consecutive gait strides. In theory, the exponents H and α are asymptotically related by the relation H. Hence, the integrated approach consists of separately computing H and α, and verifying that these two parameters are consistent through the equation d=H-(1+α)/2=0. A value of d ≤ 0.10 is considered acceptable since the asymptotic parameters are evaluated on finite time series. In summary, the following three conditions must be satisfied to conclude for the presence of long-range autocorrelations : H > 0.5; α is significantly different from 0 and lower than 1; and d ≤ 0.10 When inconsistencies appear between H and α, the investigators will use the randomly shuffled surrogate data test to reject the null hypothesis that the series under investigation has no temporal structure (i.e. uncorrelated random process). PERSPECTIVES By studying the influence of physical exercise on human walking variability and its potential correlations with walking abilities and neurological impairments of parkinsonian patients, the investigators hope to demonstrate that the analysis of walking variability could be use as a clinical tool in the assessment of fall risk and as an assessment tool of the therapeutic effectiveness (medication and/or physical exercise) in PD. More... »

URL

https://clinicaltrials.gov/show/NCT02419768

Related SciGraph Publications

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/3053", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }
    ], 
    "description": "Parkinson's disease (PD) is one of the most common neurodegenerative disorders. The parkinsonian gait is characterized by reducted stride length and gait speed, postural disorders (with a high risk of falling) and a modification of stride duration variability. This variability can be assessed by its magnitude (SD and CV) and its temporal organization (long-range autocorrelations). Healthy human gait presents with an interdependency between consecutive cycles that can span over hundreds of strides (long-range autocorrelations). Numerous observations plead for a relation between long-range autocorrelations and functional abilities of the system. Complementary to drugs, rehabilitation becomes an important way to treat PD. The aim of our study is to assess by a controlled, randomized, single blinded clinical study, the effect of physical exercise on stride duration variability, neurological impairments and walking abilities of parkinsonian patients. Physical exercise program will include 30 sessions spread over 15 weeks following the guidelines. Long-range correlations analysis, including the study of Hurst and \u03b1 exponents, will be performed on a minimum of 512 consecutive cycles. Finally, the functional assessment of the parkinsonian patient will be done according to International Classification of Functioning Disability and Health (ICF).\n\nDetailed Description\nBACKGROUND One of the most common features of human movement is its variability across multiple repetition of the same rhythmic task (1). In humans, many periodic signals, such as gait, heartbeat, respiratory and neuronal activities are characterized by their temporal complexity, fluctuating in a complex manner over time. Although fluctuations between cycles could appear to vary randomly, without apparent correlations between cycles, healthy systems possess the memory of preceding values of the series displaying a complex temporal structure. In order to assess variability in physiological time series, several mathematical methods can be used. On one hand, classical mathematical methods, usually applied on shorter time series (tens of data points), quantify the fluctuation magnitude in a set of values independently of their order in the distribution, by computing the standard deviation (SD) and the coefficient of variation (CV). On the other hand, more complex mathematical methods, applied on longer time series (\u2265512 cycles), can be used to assess the fluctuation dynamics over time (3). These latter methods have demonstrated that variability of numerous physiological signals (cardiac and respiratory rhythm or locomotor activities e.g.) exhibit long-range autocorrelations, whereby the statistical inter-dependency between cycles spans of a very large number of cycles (14). This temporal organization of variability is thus an intrinsic property within numerous biological systems. Moreover, it could provide insight into the neurophysiological organization and into the regulation of these systems (32). Recent studies claimed that these fluctuations, included in an optimal range, would represent the underlying physiologic capability to make flexible adaptations to everyday stresses placed on the human body (32). Therefore, the presence of such temporal dynamics is thought to be a critical marker of health and their breakdown as an index of pathological condition (18, 25, 32). In human heart rate for instance, deviations from an optimum of variability in either the direction of randomness (atrial fibrillation e.g.) or the over-regularity (congestive heart failure e.g.) indicate the loss of the adaptive capabilities of the system (9, 32). Alongside, some central nervous system diseases influence the variability, especially, of gait. Indeed, neurodegenerative disorders such as Parkinson and Huntington diseases are characterized, among others, by a modification of walking variability (observed by a breakdown of long-range autocorrelations) and a high risk of falling. Although the origin of long-range autocorrelation remains unknown, their breakdown in such diseases suggests a central control mechanism (8, 11, 13, 16, 17, 36). RESEARCH PROJECT Affecting about 1% of the population over the age of 60, Parkinson's disease (PD) is one of the most common neurodegenerative disorders. PD is progressive in nature, and so patients face increased difficulties with activities of daily living and various aspects of mobility such as gait, transfers, balance, and posture. Ultimately, this leads to decreased independence, inactivity, and social isolation, resulting in reduced quality of life. Consequently, the improvement of locomotion is one of the most important aims of the management of PD. The management of PD has traditionally centered on drug therapy, with levodopa viewed as the \"gold standard\" treatment. However, even with optimal medical management, parkinsonian patients experience deterioration in body function, daily activities and participation. For this reason, support has been increasing for the inclusion of rehabilitation therapies as an adjuvant to pharmacological and neurosurgical treatment. Indeed, regular physical activity slows down the progression and decrease the fall risk. Moreover, exercise has demonstrated its effectiveness for both preservation of functional abilities and prevention of complications (cardiovascular, osteoporosis,\u2026). Until now, few studies have included the analysis of variability in the functional assessment of patients presenting a neurological disease, such as PD. Yet, walking disorders and falls represent not only an important cost for the society but also a sizeable individual risk of morbi/mortality. An appropriate rehabilitation program should allow for reduction at once the risks and costs resulting from these disorders. The investigator hypothesize that the analysis of walking variability could be useful as clinical tool in the assessment of fall risk and as assessment tool of the therapeutic effectiveness (medication and/or physical exercise) in PD. Therefore, the aims of this study are (1) to assess the influence of physical exercise on human walking variability and (2) to study its potential correlations with walking abilities and neurological impairments of parkinsonian patients. Patients The investigators will recruit 50 patients with idiopathic Parkinson's disease from the department of Neurology of Cliniques universitaires Saint-Luc (Brussels, Belgium) The study is approved by the ethics committee. All patients will give informed written consent to the study. Eligibility criteria are: diagnosis idiopathic Parkinson (according to the Brain Bank criteria of the United Kingdom Parkinson's Disease Society), disease severity (according to modified Hoehn & Yahr stages I to IV), absence of dementia (Minimal Mini Mental State Examination score of 24 or higher), stable drug usage in the last 4 weeks and adequate vision and hearing, achieved using corrective lenses and/or hearing aid if required. Patients will be excluded if they have severe co-morbidity, other neurological problems, acute medical problems (e.g. MI, diabetes) and joint problems affecting mobility, and unpredictable \"Off\"-periods (score >2, MDS-UPDRS item 4.5). Procedure The present study is a controlled, randomized, single blinded clinical study with a crossover design. The control group will not change its usual physical activity whereas the intervention group will benefit from the physical exercise program. This latter will include 30 sessions of circuit-group training of 60 min (twice a week) spread over 15 weeks. Then, the two groups will be crossed. According to the recent guidelines, the program will include a specific work on balance, posture, gait, fitness, dual tasks and stretching. All sessions will be performed at an adequate intensity (i.e. 60-80% of predicted maximal heart rate). At least 512 cycles will be recorded (at a high sampling rate (512 Hz)) on a treadmill at a self-selected comfortable speed using a unidimensional accelerometer taped on the right lateral malleolus. Functional assessment based on the 3 domains of the International Classification of Functioning, Disability and Health (ICF) Patients will be assessed before intervention (T0) and at 15 (T1), 30 (T2), 45 (T3) and 60 weeks (T4) among the 3 ICF domains: Impairments will assessed by MDS-UPDRS, an instrumented gait analysis (kinematic, kinetic, electromyographic and energetic) (18), the 6 minute walk distance, the 10 meter walk test, the ABC-Scale and the BESTest (including the Functional Reach Test, the Push & Release and the Get Up & Go test). Activities, participation and quality of life will be evaluated the Impact on Participation and Autonomy Questionnaire (IPAQ) and a fall diary. Walking variability analysis Revolution time variability will be appreciated by classical and complex mathematical methods. Classical mathematical methods (standard deviation, coefficient of variation) allow for evaluating the fluctuation magnitude, while complex mathematical methods (long-range autocorrelations) assess the dynamics of fluctuations over time (3). The presence of long-range autocorrelations will be evaluated using the integrated approach proposed by Rangarajan and Ding and validated by Crevecoeur et al. in the context of physiological time series. These methods are described in greater details elsewhere. Briefly, the Hurst exponent (H) will be calculated using the rescaled range analysis and the \u03b1 exponent will be evaluated using the power spectral density of the time series. For each time series, both methods will be applied to sequences of 512 consecutive gait strides. In theory, the exponents H and \u03b1 are asymptotically related by the relation H. Hence, the integrated approach consists of separately computing H and \u03b1, and verifying that these two parameters are consistent through the equation d=H-(1+\u03b1)/2=0. A value of d \u2264 0.10 is considered acceptable since the asymptotic parameters are evaluated on finite time series. In summary, the following three conditions must be satisfied to conclude for the presence of long-range autocorrelations : H > 0.5; \u03b1 is significantly different from 0 and lower than 1; and d \u2264 0.10 When inconsistencies appear between H and \u03b1, the investigators will use the randomly shuffled surrogate data test to reject the null hypothesis that the series under investigation has no temporal structure (i.e. uncorrelated random process). PERSPECTIVES By studying the influence of physical exercise on human walking variability and its potential correlations with walking abilities and neurological impairments of parkinsonian patients, the investigators hope to demonstrate that the analysis of walking variability could be use as a clinical tool in the assessment of fall risk and as an assessment tool of the therapeutic effectiveness (medication and/or physical exercise) in PD.", 
    "id": "sg:clinicaltrial.NCT02419768", 
    "keywords": [
      "locomotion", 
      "parkinsonian patient", 
      "long range", 
      "neurological impairment", 
      "practice", 
      "exercise", 
      "Parkinson Disease", 
      "common neurodegenerative disorder", 
      "gait", 
      "stride length", 
      "disorder", 
      "high risk", 
      "modification", 
      "variability", 
      "magnitude", 
      "SD", 
      "temporal organization", 
      "healthy human", 
      "interdependency", 
      "cycle", 
      "span", 
      "stride", 
      "Numerous observation", 
      "relation", 
      "functional ability", 
      "drug", 
      "rehabilitation", 
      "important way", 
      "clinical study", 
      "exercise program", 
      "session", 
      "guideline", 
      "long-range correlation", 
      "exponent", 
      "functional assessment", 
      "classification", 
      "ICF", 
      "common feature", 
      "human movement", 
      "repetition", 
      "task", 
      "human", 
      "signal", 
      "heartbeat", 
      "neuronal activity", 
      "complexity", 
      "complex manner", 
      "fluctuation", 
      "correlation", 
      "memory", 
      "temporal structure", 
      "time series", 
      "mathematical method", 
      "short time", 
      "data point", 
      "distribution", 
      "standard deviation", 
      "coefficient", 
      "long time series", 
      "latter method", 
      "physiological signal", 
      "respiratory rhythm", 
      "Motor Activity", 
      "inter-dependency", 
      "large number", 
      "intrinsic property", 
      "biological system", 
      "Formal Social Control", 
      "Recent study", 
      "optimal range", 
      "capability", 
      "adaptation", 
      "stress", 
      "human body", 
      "temporal dynamic", 
      "marker", 
      "health", 
      "breakdown", 
      "index", 
      "pathological condition", 
      "human heart", 
      "instance", 
      "deviation", 
      "optimum", 
      "randomness", 
      "atrial fibrillation", 
      "regularity", 
      "heart failure", 
      "adaptive capability", 
      "alongside", 
      "Central Nervous System Disease", 
      "neurodegenerative disease", 
      "Huntington", 
      "disease", 
      "origin", 
      "control mechanism", 
      "research project", 
      "population", 
      "age", 
      "nature", 
      "patient", 
      "increased difficulty", 
      "aspect", 
      "mobility", 
      "transfer", 
      "balance", 
      "posture", 
      "independence", 
      "inactivity", 
      "social isolation", 
      "reduced quality", 
      "life", 
      "improvement", 
      "management", 
      "Drug Therapy", 
      "Levodopa", 
      "gold standard treatment", 
      "optimal medical management", 
      "deterioration", 
      "body function", 
      "participation", 
      "inclusion", 
      "rehabilitation therapy", 
      "regular physical activity", 
      "decrease", 
      "risk", 
      "effectiveness", 
      "preservation", 
      "prevention", 
      "complication", 
      "osteoporosis", 
      "neurological disease", 
      "society", 
      "individual risk", 
      "rehabilitation program", 
      "reduction", 
      "clinical tool", 
      "assessment", 
      "assessment tool", 
      "therapeutic effectiveness", 
      "medication", 
      "potential correlation", 
      "idiopathic Parkinson's", 
      "neurology", 
      "saint", 
      "Brussels", 
      "Belgium", 
      "ethic committee", 
      "consent", 
      "eligibility criterion", 
      "Parkinson", 
      "brain bank", 
      "criterion", 
      "Parkinson's", 
      "disease severity", 
      "stage", 
      "absence", 
      "dementia", 
      "mini", 
      "mental state", 
      "score", 
      "drug usage", 
      "hearing", 
      "lens", 
      "co-morbidities", 
      "neurological problem", 
      "medical problem", 
      "MI", 
      "diabetes", 
      "joint problem", 
      "period", 
      "method", 
      "present study", 
      "Cross-Over Study", 
      "control group", 
      "intervention group", 
      "circuit", 
      "min", 
      "recent guideline", 
      "specific work", 
      "fitness", 
      "dual task", 
      "stretching", 
      "intensity", 
      "heart rate", 
      "high sampling rate", 
      "Hz", 
      "treadmill", 
      "speed", 
      "accelerometer", 
      "right", 
      "domain", 
      "intervention", 
      "T0", 
      "T1", 
      "T2", 
      "T3", 
      "T4", 
      "impairment", 
      "gait analysis", 
      "distance", 
      "meter", 
      "scale", 
      "Reach", 
      "release", 
      "Go", 
      "time variability", 
      "variation", 
      "dynamic", 
      "integrated approach", 
      "Ding", 
      "great detail", 
      "spectral density", 
      "sequence", 
      "theory", 
      "parameter", 
      "asymptotics", 
      "finite time", 
      "summary", 
      "condition", 
      "inconsistency", 
      "surrogate", 
      "null hypothesis", 
      "random process", 
      "studying", 
      "investigator hope"
    ], 
    "name": "Locomotion of Parkinsonian Patient: Are There Relations Between the Long Range Autocorrelations and the Neurological Impairments, Walking Abilities and the Practice of Physical Exercise?", 
    "sameAs": [
      "https://app.dimensions.ai/details/clinical_trial/NCT02419768"
    ], 
    "sdDataset": "clinical_trials", 
    "sdDatePublished": "2019-03-07T15:25", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "file:///pack/app/us_ct_data_00018.json", 
    "sponsor": [
      {
        "id": "https://www.grid.ac/institutes/grid.7942.8", 
        "type": "Organization"
      }
    ], 
    "startDate": "2014-06-01T00:00:00Z", 
    "subjectOf": [
      {
        "id": "https://doi.org/10.1073/pnas.012579499", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004887097"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10439-013-0834-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008101414", 
          "https://doi.org/10.1007/s10439-013-0834-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/14651858.cd002817.pub4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008439308"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/japplphysiol.00413.2006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009218995"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmicm0810287", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010487682"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.humov.2010.07.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020643644"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/14651858.cd007146.pub3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021234815"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.gaitpost.2010.11.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022747929"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.humov.2011.06.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028184444"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mds.22141", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031889217"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.gaitpost.2010.06.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034212208"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.humov.2007.05.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034623796"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0378-4371(01)00460-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035410389"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mds.870130310", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040661741"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jneumeth.2010.07.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042878086"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neucli.2008.02.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048555523"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroscience.2012.02.039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049577963"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mds.23530", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049784527"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.1000856", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051232437"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.166141", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057739643"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1075763150", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12984-017-0226-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083854089", 
          "https://doi.org/10.1186/s12984-017-0226-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12984-017-0226-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083854089", 
          "https://doi.org/10.1186/s12984-017-0226-1"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "type": "MedicalStudy", 
    "url": "https://clinicaltrials.gov/show/NCT02419768"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/clinicaltrial.NCT02419768'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/clinicaltrial.NCT02419768'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/clinicaltrial.NCT02419768'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/clinicaltrial.NCT02419768'


 

This table displays all metadata directly associated to this object as RDF triples.

311 TRIPLES      15 PREDICATES      262 URIs      234 LITERALS      1 BLANK NODES

Subject Predicate Object
1 sg:clinicaltrial.NCT02419768 schema:about anzsrc-for:3053
2 schema:description Parkinson's disease (PD) is one of the most common neurodegenerative disorders. The parkinsonian gait is characterized by reducted stride length and gait speed, postural disorders (with a high risk of falling) and a modification of stride duration variability. This variability can be assessed by its magnitude (SD and CV) and its temporal organization (long-range autocorrelations). Healthy human gait presents with an interdependency between consecutive cycles that can span over hundreds of strides (long-range autocorrelations). Numerous observations plead for a relation between long-range autocorrelations and functional abilities of the system. Complementary to drugs, rehabilitation becomes an important way to treat PD. The aim of our study is to assess by a controlled, randomized, single blinded clinical study, the effect of physical exercise on stride duration variability, neurological impairments and walking abilities of parkinsonian patients. Physical exercise program will include 30 sessions spread over 15 weeks following the guidelines. Long-range correlations analysis, including the study of Hurst and α exponents, will be performed on a minimum of 512 consecutive cycles. Finally, the functional assessment of the parkinsonian patient will be done according to International Classification of Functioning Disability and Health (ICF). Detailed Description BACKGROUND One of the most common features of human movement is its variability across multiple repetition of the same rhythmic task (1). In humans, many periodic signals, such as gait, heartbeat, respiratory and neuronal activities are characterized by their temporal complexity, fluctuating in a complex manner over time. Although fluctuations between cycles could appear to vary randomly, without apparent correlations between cycles, healthy systems possess the memory of preceding values of the series displaying a complex temporal structure. In order to assess variability in physiological time series, several mathematical methods can be used. On one hand, classical mathematical methods, usually applied on shorter time series (tens of data points), quantify the fluctuation magnitude in a set of values independently of their order in the distribution, by computing the standard deviation (SD) and the coefficient of variation (CV). On the other hand, more complex mathematical methods, applied on longer time series (≥512 cycles), can be used to assess the fluctuation dynamics over time (3). These latter methods have demonstrated that variability of numerous physiological signals (cardiac and respiratory rhythm or locomotor activities e.g.) exhibit long-range autocorrelations, whereby the statistical inter-dependency between cycles spans of a very large number of cycles (14). This temporal organization of variability is thus an intrinsic property within numerous biological systems. Moreover, it could provide insight into the neurophysiological organization and into the regulation of these systems (32). Recent studies claimed that these fluctuations, included in an optimal range, would represent the underlying physiologic capability to make flexible adaptations to everyday stresses placed on the human body (32). Therefore, the presence of such temporal dynamics is thought to be a critical marker of health and their breakdown as an index of pathological condition (18, 25, 32). In human heart rate for instance, deviations from an optimum of variability in either the direction of randomness (atrial fibrillation e.g.) or the over-regularity (congestive heart failure e.g.) indicate the loss of the adaptive capabilities of the system (9, 32). Alongside, some central nervous system diseases influence the variability, especially, of gait. Indeed, neurodegenerative disorders such as Parkinson and Huntington diseases are characterized, among others, by a modification of walking variability (observed by a breakdown of long-range autocorrelations) and a high risk of falling. Although the origin of long-range autocorrelation remains unknown, their breakdown in such diseases suggests a central control mechanism (8, 11, 13, 16, 17, 36). RESEARCH PROJECT Affecting about 1% of the population over the age of 60, Parkinson's disease (PD) is one of the most common neurodegenerative disorders. PD is progressive in nature, and so patients face increased difficulties with activities of daily living and various aspects of mobility such as gait, transfers, balance, and posture. Ultimately, this leads to decreased independence, inactivity, and social isolation, resulting in reduced quality of life. Consequently, the improvement of locomotion is one of the most important aims of the management of PD. The management of PD has traditionally centered on drug therapy, with levodopa viewed as the "gold standard" treatment. However, even with optimal medical management, parkinsonian patients experience deterioration in body function, daily activities and participation. For this reason, support has been increasing for the inclusion of rehabilitation therapies as an adjuvant to pharmacological and neurosurgical treatment. Indeed, regular physical activity slows down the progression and decrease the fall risk. Moreover, exercise has demonstrated its effectiveness for both preservation of functional abilities and prevention of complications (cardiovascular, osteoporosis,…). Until now, few studies have included the analysis of variability in the functional assessment of patients presenting a neurological disease, such as PD. Yet, walking disorders and falls represent not only an important cost for the society but also a sizeable individual risk of morbi/mortality. An appropriate rehabilitation program should allow for reduction at once the risks and costs resulting from these disorders. The investigator hypothesize that the analysis of walking variability could be useful as clinical tool in the assessment of fall risk and as assessment tool of the therapeutic effectiveness (medication and/or physical exercise) in PD. Therefore, the aims of this study are (1) to assess the influence of physical exercise on human walking variability and (2) to study its potential correlations with walking abilities and neurological impairments of parkinsonian patients. Patients The investigators will recruit 50 patients with idiopathic Parkinson's disease from the department of Neurology of Cliniques universitaires Saint-Luc (Brussels, Belgium) The study is approved by the ethics committee. All patients will give informed written consent to the study. Eligibility criteria are: diagnosis idiopathic Parkinson (according to the Brain Bank criteria of the United Kingdom Parkinson's Disease Society), disease severity (according to modified Hoehn & Yahr stages I to IV), absence of dementia (Minimal Mini Mental State Examination score of 24 or higher), stable drug usage in the last 4 weeks and adequate vision and hearing, achieved using corrective lenses and/or hearing aid if required. Patients will be excluded if they have severe co-morbidity, other neurological problems, acute medical problems (e.g. MI, diabetes) and joint problems affecting mobility, and unpredictable "Off"-periods (score >2, MDS-UPDRS item 4.5). Procedure The present study is a controlled, randomized, single blinded clinical study with a crossover design. The control group will not change its usual physical activity whereas the intervention group will benefit from the physical exercise program. This latter will include 30 sessions of circuit-group training of 60 min (twice a week) spread over 15 weeks. Then, the two groups will be crossed. According to the recent guidelines, the program will include a specific work on balance, posture, gait, fitness, dual tasks and stretching. All sessions will be performed at an adequate intensity (i.e. 60-80% of predicted maximal heart rate). At least 512 cycles will be recorded (at a high sampling rate (512 Hz)) on a treadmill at a self-selected comfortable speed using a unidimensional accelerometer taped on the right lateral malleolus. Functional assessment based on the 3 domains of the International Classification of Functioning, Disability and Health (ICF) Patients will be assessed before intervention (T0) and at 15 (T1), 30 (T2), 45 (T3) and 60 weeks (T4) among the 3 ICF domains: Impairments will assessed by MDS-UPDRS, an instrumented gait analysis (kinematic, kinetic, electromyographic and energetic) (18), the 6 minute walk distance, the 10 meter walk test, the ABC-Scale and the BESTest (including the Functional Reach Test, the Push & Release and the Get Up & Go test). Activities, participation and quality of life will be evaluated the Impact on Participation and Autonomy Questionnaire (IPAQ) and a fall diary. Walking variability analysis Revolution time variability will be appreciated by classical and complex mathematical methods. Classical mathematical methods (standard deviation, coefficient of variation) allow for evaluating the fluctuation magnitude, while complex mathematical methods (long-range autocorrelations) assess the dynamics of fluctuations over time (3). The presence of long-range autocorrelations will be evaluated using the integrated approach proposed by Rangarajan and Ding and validated by Crevecoeur et al. in the context of physiological time series. These methods are described in greater details elsewhere. Briefly, the Hurst exponent (H) will be calculated using the rescaled range analysis and the α exponent will be evaluated using the power spectral density of the time series. For each time series, both methods will be applied to sequences of 512 consecutive gait strides. In theory, the exponents H and α are asymptotically related by the relation H. Hence, the integrated approach consists of separately computing H and α, and verifying that these two parameters are consistent through the equation d=H-(1+α)/2=0. A value of d ≤ 0.10 is considered acceptable since the asymptotic parameters are evaluated on finite time series. In summary, the following three conditions must be satisfied to conclude for the presence of long-range autocorrelations : H > 0.5; α is significantly different from 0 and lower than 1; and d ≤ 0.10 When inconsistencies appear between H and α, the investigators will use the randomly shuffled surrogate data test to reject the null hypothesis that the series under investigation has no temporal structure (i.e. uncorrelated random process). PERSPECTIVES By studying the influence of physical exercise on human walking variability and its potential correlations with walking abilities and neurological impairments of parkinsonian patients, the investigators hope to demonstrate that the analysis of walking variability could be use as a clinical tool in the assessment of fall risk and as an assessment tool of the therapeutic effectiveness (medication and/or physical exercise) in PD.
3 schema:keywords Belgium
4 Brussels
5 Central Nervous System Disease
6 Cross-Over Study
7 Ding
8 Drug Therapy
9 Formal Social Control
10 Go
11 Huntington
12 Hz
13 ICF
14 Levodopa
15 MI
16 Motor Activity
17 Numerous observation
18 Parkinson
19 Parkinson Disease
20 Parkinson's
21 Reach
22 Recent study
23 SD
24 T0
25 T1
26 T2
27 T3
28 T4
29 absence
30 accelerometer
31 adaptation
32 adaptive capability
33 age
34 alongside
35 aspect
36 assessment
37 assessment tool
38 asymptotics
39 atrial fibrillation
40 balance
41 biological system
42 body function
43 brain bank
44 breakdown
45 capability
46 circuit
47 classification
48 clinical study
49 clinical tool
50 co-morbidities
51 coefficient
52 common feature
53 common neurodegenerative disorder
54 complex manner
55 complexity
56 complication
57 condition
58 consent
59 control group
60 control mechanism
61 correlation
62 criterion
63 cycle
64 data point
65 decrease
66 dementia
67 deterioration
68 deviation
69 diabetes
70 disease
71 disease severity
72 disorder
73 distance
74 distribution
75 domain
76 drug
77 drug usage
78 dual task
79 dynamic
80 effectiveness
81 eligibility criterion
82 ethic committee
83 exercise
84 exercise program
85 exponent
86 finite time
87 fitness
88 fluctuation
89 functional ability
90 functional assessment
91 gait
92 gait analysis
93 gold standard treatment
94 great detail
95 guideline
96 health
97 healthy human
98 hearing
99 heart failure
100 heart rate
101 heartbeat
102 high risk
103 high sampling rate
104 human
105 human body
106 human heart
107 human movement
108 idiopathic Parkinson's
109 impairment
110 important way
111 improvement
112 inactivity
113 inclusion
114 inconsistency
115 increased difficulty
116 independence
117 index
118 individual risk
119 instance
120 integrated approach
121 intensity
122 inter-dependency
123 interdependency
124 intervention
125 intervention group
126 intrinsic property
127 investigator hope
128 joint problem
129 large number
130 latter method
131 lens
132 life
133 locomotion
134 long range
135 long time series
136 long-range correlation
137 magnitude
138 management
139 marker
140 mathematical method
141 medical problem
142 medication
143 memory
144 mental state
145 meter
146 method
147 min
148 mini
149 mobility
150 modification
151 nature
152 neurodegenerative disease
153 neurological disease
154 neurological impairment
155 neurological problem
156 neurology
157 neuronal activity
158 null hypothesis
159 optimal medical management
160 optimal range
161 optimum
162 origin
163 osteoporosis
164 parameter
165 parkinsonian patient
166 participation
167 pathological condition
168 patient
169 period
170 physiological signal
171 population
172 posture
173 potential correlation
174 practice
175 present study
176 preservation
177 prevention
178 random process
179 randomness
180 recent guideline
181 reduced quality
182 reduction
183 regular physical activity
184 regularity
185 rehabilitation
186 rehabilitation program
187 rehabilitation therapy
188 relation
189 release
190 repetition
191 research project
192 respiratory rhythm
193 right
194 risk
195 saint
196 scale
197 score
198 sequence
199 session
200 short time
201 signal
202 social isolation
203 society
204 span
205 specific work
206 spectral density
207 speed
208 stage
209 standard deviation
210 stress
211 stretching
212 stride
213 stride length
214 studying
215 summary
216 surrogate
217 task
218 temporal dynamic
219 temporal organization
220 temporal structure
221 theory
222 therapeutic effectiveness
223 time series
224 time variability
225 transfer
226 treadmill
227 variability
228 variation
229 schema:name Locomotion of Parkinsonian Patient: Are There Relations Between the Long Range Autocorrelations and the Neurological Impairments, Walking Abilities and the Practice of Physical Exercise?
230 schema:sameAs https://app.dimensions.ai/details/clinical_trial/NCT02419768
231 schema:sdDatePublished 2019-03-07T15:25
232 schema:sdLicense https://scigraph.springernature.com/explorer/license/
233 schema:sdPublisher N6ed03f652cbb48cf9a8750ce4b32d2eb
234 schema:sponsor https://www.grid.ac/institutes/grid.7942.8
235 schema:startDate 2014-06-01T00:00:00Z
236 schema:subjectOf sg:pub.10.1007/s10439-013-0834-2
237 sg:pub.10.1186/s12984-017-0226-1
238 https://app.dimensions.ai/details/publication/pub.1075763150
239 https://doi.org/10.1002/14651858.cd002817.pub4
240 https://doi.org/10.1002/14651858.cd007146.pub3
241 https://doi.org/10.1002/mds.22141
242 https://doi.org/10.1002/mds.23530
243 https://doi.org/10.1002/mds.870130310
244 https://doi.org/10.1016/j.gaitpost.2010.06.011
245 https://doi.org/10.1016/j.gaitpost.2010.11.014
246 https://doi.org/10.1016/j.humov.2007.05.003
247 https://doi.org/10.1016/j.humov.2010.07.006
248 https://doi.org/10.1016/j.humov.2011.06.002
249 https://doi.org/10.1016/j.jneumeth.2010.07.017
250 https://doi.org/10.1016/j.neucli.2008.02.002
251 https://doi.org/10.1016/j.neuroscience.2012.02.039
252 https://doi.org/10.1016/s0378-4371(01)00460-5
253 https://doi.org/10.1056/nejmicm0810287
254 https://doi.org/10.1063/1.166141
255 https://doi.org/10.1073/pnas.012579499
256 https://doi.org/10.1152/japplphysiol.00413.2006
257 https://doi.org/10.1371/journal.pcbi.1000856
258 schema:url https://clinicaltrials.gov/show/NCT02419768
259 sgo:license sg:explorer/license/
260 sgo:sdDataset clinical_trials
261 rdf:type schema:MedicalStudy
262 N6ed03f652cbb48cf9a8750ce4b32d2eb schema:name Springer Nature - SN SciGraph project
263 rdf:type schema:Organization
264 anzsrc-for:3053 schema:inDefinedTermSet anzsrc-for:
265 rdf:type schema:DefinedTerm
266 sg:pub.10.1007/s10439-013-0834-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008101414
267 https://doi.org/10.1007/s10439-013-0834-2
268 rdf:type schema:CreativeWork
269 sg:pub.10.1186/s12984-017-0226-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083854089
270 https://doi.org/10.1186/s12984-017-0226-1
271 rdf:type schema:CreativeWork
272 https://app.dimensions.ai/details/publication/pub.1075763150 schema:CreativeWork
273 https://doi.org/10.1002/14651858.cd002817.pub4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008439308
274 rdf:type schema:CreativeWork
275 https://doi.org/10.1002/14651858.cd007146.pub3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021234815
276 rdf:type schema:CreativeWork
277 https://doi.org/10.1002/mds.22141 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031889217
278 rdf:type schema:CreativeWork
279 https://doi.org/10.1002/mds.23530 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049784527
280 rdf:type schema:CreativeWork
281 https://doi.org/10.1002/mds.870130310 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040661741
282 rdf:type schema:CreativeWork
283 https://doi.org/10.1016/j.gaitpost.2010.06.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034212208
284 rdf:type schema:CreativeWork
285 https://doi.org/10.1016/j.gaitpost.2010.11.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022747929
286 rdf:type schema:CreativeWork
287 https://doi.org/10.1016/j.humov.2007.05.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034623796
288 rdf:type schema:CreativeWork
289 https://doi.org/10.1016/j.humov.2010.07.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020643644
290 rdf:type schema:CreativeWork
291 https://doi.org/10.1016/j.humov.2011.06.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028184444
292 rdf:type schema:CreativeWork
293 https://doi.org/10.1016/j.jneumeth.2010.07.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042878086
294 rdf:type schema:CreativeWork
295 https://doi.org/10.1016/j.neucli.2008.02.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048555523
296 rdf:type schema:CreativeWork
297 https://doi.org/10.1016/j.neuroscience.2012.02.039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049577963
298 rdf:type schema:CreativeWork
299 https://doi.org/10.1016/s0378-4371(01)00460-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035410389
300 rdf:type schema:CreativeWork
301 https://doi.org/10.1056/nejmicm0810287 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010487682
302 rdf:type schema:CreativeWork
303 https://doi.org/10.1063/1.166141 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057739643
304 rdf:type schema:CreativeWork
305 https://doi.org/10.1073/pnas.012579499 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004887097
306 rdf:type schema:CreativeWork
307 https://doi.org/10.1152/japplphysiol.00413.2006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009218995
308 rdf:type schema:CreativeWork
309 https://doi.org/10.1371/journal.pcbi.1000856 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051232437
310 rdf:type schema:CreativeWork
311 https://www.grid.ac/institutes/grid.7942.8 schema:Organization
 




Preview window. Press ESC to close (or click here)


...