Effects of Exercise on Long-Range Autocorrelations in Parkinson's Disease

Locomotion of Parkinsonian Patient: Are There Relations Between the Long Range Autocorrelations and the Neurological Impairments, Walking Abilities and the Practice of Physical Exercise?

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).

Study Overview

• Status: Unknown
• Conditions: Parkinson's Disease
• Intervention / Treatment: Other: Physical Exercise

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.

• Study Type: Interventional
• Enrollment (Anticipated): 50
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Belgium
  • Brussels
    • Woluwé-Saint-Lambert, Brussels, Belgium, 1200
      • Recruiting
      • Université catholique de Louvain - Cliniques universitaires Saint-Luc
      • Contact: Thierry Lejeune, Professor; Phone Number: +32 2 764 16 48; Email: thierry.lejeune@uclouvain.be

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• 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
• Adequate vision and hearing, achieved using corrective lenses and/or hearing aid if required

Exclusion Criteria:

• Severe co-morbidity, other neurological problems, acute medical problems (e.g. MI, diabetes) and joint problems affecting mobility
• Unpredictable "Off"-periods (score >2, MDS-UPDRS item 4.5)

Study Plan

How is the study designed?

Design Details

• Allocation: Randomized
• Interventional Model: Crossover Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Physical Exercise; All patients will receive a circuit-group training including a specific work of balance, posture, gait, fitness, dual tasks and stretching.	Other: Physical Exercise; The physical exercise program will include 30 sessions of 60 minutes (twice a week). According to the recent guidelines, the program will include a specific work on balance, posture, gait, fitness, dual tasks and stretching.
No Intervention: Control; All patients will not change their physical activities

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
Balance Evaluation Systems Test (BESTest)	Change from baseline in balance measures at an expected average of 15 (T1), 30 (T2), 45 (T3) and 60 weeks (T4)

Secondary Outcome Measures

Outcome Measure	Time Frame
Movement Disorder Society-Unified Parkinson Disease Rating Scale (MDS-UPDRS)	Change from baseline in MDS-UPDRS at an expected average of 15 (T1), 30 (T2), 45 (T3) and 60 weeks (T4)
Six Minute Walk Distance (6-MWD)	Change from baseline in exercise tolerance at an expected average of 15 (T1), 30 (T2), 45 (T3) and 60 weeks (T4)
10 Meter Walk Test (10-MWT)	Change from baseline in walking speed, step lenght and cadence at an expected average of 15 (T1), 30 (T2), 45 (T3) and 60 weeks (T4)
Long-range autocorrelations	Change from baseline in long-range autocorrelations at an expected average of 15 (T1), 30 (T2), 45 (T3) and 60 weeks (T4)
Instrumented gait analysis	Change from baseline in gait parameters (kinematic, kinetic, electromyographic and energetic) at an expected average of 15 (T1), 30 (T2), 45 (T3) and 60 weeks (T4)
Impact on Participation and Autonomy Questionnaire (IPAQ)	Change from baseline in participation and quality of life at an expected average of 15 (T1), 30 (T2), 45 (T3) and 60 weeks (T4)
Activities-specific Balance Confidence Scale (ABC-Scale)	Change from baseline in subjective balance measures (fear of falling) at an expected average of 15 (T1), 30 (T2), 45 (T3) and 60 weeks (T4)

Collaborators and Investigators

• Sponsor: Cliniques universitaires Saint-Luc- Université Catholique de Louvain
• Investigators: Principal Investigator: Thibault B. Warlop, Doctor, Université Catholique de Louvain

Publications and helpful links

General Publications

• KARVONEN MJ, KENTALA E, MUSTALA O. The effects of training on heart rate; a longitudinal study. Ann Med Exp Biol Fenn. 1957;35(3):307-15. No abstract available.
• Gillespie LD, Robertson MC, Gillespie WJ, Sherrington C, Gates S, Clemson LM, Lamb SE. Interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev. 2012 Sep 12;2012(9):CD007146. doi: 10.1002/14651858.CD007146.pub3.
• Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos. 1995;5(1):82-7. doi: 10.1063/1.166141.
• Hausdorff JM. Gait dynamics, fractals and falls: finding meaning in the stride-to-stride fluctuations of human walking. Hum Mov Sci. 2007 Aug;26(4):555-89. doi: 10.1016/j.humov.2007.05.003. Epub 2007 Jul 5.
• Stergiou N, Decker LM. Human movement variability, nonlinear dynamics, and pathology: is there a connection? Hum Mov Sci. 2011 Oct;30(5):869-88. doi: 10.1016/j.humov.2011.06.002. Epub 2011 Jul 29.
• Hausdorff JM, Cudkowicz ME, Firtion R, Wei JY, Goldberger AL. Gait variability and basal ganglia disorders: stride-to-stride variations of gait cycle timing in Parkinson's disease and Huntington's disease. Mov Disord. 1998 May;13(3):428-37. doi: 10.1002/mds.870130310. Erratum In: Mov Disord 1998 Jul;13(4):757.
• Goldberger AL, Amaral LA, Hausdorff JM, Ivanov PCh, Peng CK, Stanley HE. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci U S A. 2002 Feb 19;99 Suppl 1(Suppl 1):2466-72. doi: 10.1073/pnas.012579499.
• Bollens B, Crevecoeur F, Nguyen V, Detrembleur C, Lejeune T. Does human gait exhibit comparable and reproducible long-range autocorrelations on level ground and on treadmill? Gait Posture. 2010 Jul;32(3):369-73. doi: 10.1016/j.gaitpost.2010.06.011. Epub 2010 Jul 15.
• Bollens B, Crevecoeur F, Detrembleur C, Guillery E, Lejeune T. Effects of age and walking speed on long-range autocorrelations and fluctuation magnitude of stride duration. Neuroscience. 2012 May 17;210:234-42. doi: 10.1016/j.neuroscience.2012.02.039. Epub 2012 Mar 5.
• Crevecoeur F, Bollens B, Detrembleur C, Lejeune TM. Towards a "gold-standard" approach to address the presence of long-range auto-correlation in physiological time series. J Neurosci Methods. 2010 Sep 30;192(1):163-72. doi: 10.1016/j.jneumeth.2010.07.017. Epub 2010 Jul 21.
• Diniz A, Wijnants ML, Torre K, Barreiros J, Crato N, Bosman AM, Hasselman F, Cox RF, Van Orden GC, Delignieres D. Contemporary theories of 1/f noise in motor control. Hum Mov Sci. 2011 Oct;30(5):889-905. doi: 10.1016/j.humov.2010.07.006. Epub 2010 Dec 31.
• Dingwell JB, John J, Cusumano JP. Do humans optimally exploit redundancy to control step variability in walking? PLoS Comput Biol. 2010 Jul 15;6(7):e1000856. doi: 10.1371/journal.pcbi.1000856.
• Gates DH, Dingwell JB. Peripheral neuropathy does not alter the fractal dynamics of stride intervals of gait. J Appl Physiol (1985). 2007 Mar;102(3):965-71. doi: 10.1152/japplphysiol.00413.2006. Epub 2006 Nov 16.
• Hausdorff JM, Ashkenazy Y, Peng CK, Ivanov PC, Stanley HE, Goldberger AL. When human walking becomes random walking: fractal analysis and modeling of gait rhythm fluctuations. Physica A. 2001 Dec 15;302(1-4):138-47. doi: 10.1016/s0378-4371(01)00460-5.
• Paterson K, Hill K, Lythgo N. Stride dynamics, gait variability and prospective falls risk in active community dwelling older women. Gait Posture. 2011 Feb;33(2):251-5. doi: 10.1016/j.gaitpost.2010.11.014. Epub 2010 Dec 16.
• Keus SH, Munneke M, Nijkrake MJ, Kwakkel G, Bloem BR. Physical therapy in Parkinson's disease: evolution and future challenges. Mov Disord. 2009 Jan 15;24(1):1-14. doi: 10.1002/mds.22141.
• Tomlinson CL, Patel S, Meek C, Herd CP, Clarke CE, Stowe R, Shah L, Sackley CM, Deane KH, Wheatley K, Ives N. Physiotherapy versus placebo or no intervention in Parkinson's disease. Cochrane Database Syst Rev. 2013 Sep 10;2013(9):CD002817. doi: 10.1002/14651858.CD002817.pub4.
• Snijders AH, Bloem BR. Images in clinical medicine. Cycling for freezing of gait. N Engl J Med. 2010 Apr 1;362(13):e46. doi: 10.1056/NEJMicm0810287. No abstract available.
• Snijders AH, Toni I, Ruzicka E, Bloem BR. Bicycling breaks the ice for freezers of gait. Mov Disord. 2011 Feb 15;26(3):367-71. doi: 10.1002/mds.23530. Epub 2011 Feb 1.
• Warlop TB, Bollens B, Crevecoeur F, Detrembleur C, Lejeune TM. Dynamics of revolution time variability in cycling pattern: voluntary intent can alter the long-range autocorrelations. Ann Biomed Eng. 2013 Aug;41(8):1604-12. doi: 10.1007/s10439-013-0834-2. Epub 2013 May 28.
• Stoquart G, Detrembleur C, Lejeune T. Effect of speed on kinematic, kinetic, electromyographic and energetic reference values during treadmill walking. Neurophysiol Clin. 2008 Apr;38(2):105-16. doi: 10.1016/j.neucli.2008.02.002. Epub 2008 Mar 6.
• Warlop T, Detrembleur C, Buxes Lopez M, Stoquart G, Lejeune T, Jeanjean A. Does Nordic Walking restore the temporal organization of gait variability in Parkinson's disease? J Neuroeng Rehabil. 2017 Feb 21;14(1):17. doi: 10.1186/s12984-017-0226-1.

Study record dates

Study Major Dates

• Study Start: June 1, 2014
• Primary Completion (Anticipated): May 1, 2016

Study Registration Dates

• First Submitted: April 14, 2015
• First Submitted That Met QC Criteria: April 16, 2015
• First Posted (Estimate): April 17, 2015

Study Record Updates

• Last Update Posted (Estimate): January 28, 2016
• Last Update Submitted That Met QC Criteria: January 27, 2016
• Last Verified: January 1, 2016

More Information

Terms related to this study

• Keywords: ICF; Parkinson's Disease; Physical Exercise; Variability; Long-range autocorrelations
• Additional Relevant MeSH Terms: Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Parkinsonian Disorders; Basal Ganglia Diseases; Movement Disorders; Synucleinopathies; Neurodegenerative Diseases; Parkinson Disease
• Other Study ID Numbers: IONS - Warlop - 01