Présentation faite dans le cadre de la JdCHE 20-21 du 25-02-21

1. Changes in stride interval variability induced by Charcot foot:
A pilot study
F. Dierick (1,2)
, S. Colson (3)
, L. Orioli (4,5)
, B. Vandeleene (4)
, C. Detrembleur (6)
, F. Buisseret (1,3,7)
(1) CeREF, Chaussée de Binche 159, 7000 Mons, Belgium.
(2)
Centre National de Rééducation Fonctionnelle et de Réadaptation - Rehazenter, Laboratoire d'Analyse du Mouvement et de la Posture (LAMP), Luxembourg, Grand-Duché de Luxembourg.
(3)
Laboratoire Forme et Fonctionnement Humain, HELHa, Rue Trieu Kaisin 136, 6061 Montignies-sur-Sambre, Belgium.
(4)
Department of Endocrinology and Nutrition, Cliniques Universitaires Saint-Luc, avenue Hippocrate 10, 1200 Brussels, Belgium.
(5) Department of Endocrinology, Diabetology and Nutrition, IREC, UCLouvain, avenue Hippocrate 55, Brussels, Belgium.
(6)
Neuro Musculo Skeletal Lab (Brussels), Institut de recherche expérimentale et Clinique, UClouvain, Belgium.
(7) Service de Physique Nucléaire et Subnucléaire, Université de Mons, UMONS Research Institute for Complex Systems, Place du Parc 20, 7000 Mons, Belgium.
Introduction
Figure 1: Typical traces of 200 consecutive stride intervals (SI) in a healthy participant
(CTRL) and a patient with Charcot foot (Charcot) walking on a treadmill.
Stride interval variability (SI)
Stride interval = duration of a gait cycle for 2 consecutive steps.
Ideally more than 256 consecutive strides;
References
[1] Hausdorff, JM, Peng, CK, Ladin, Z, Wei, JY, & Goldberger, AL (1995). Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. Journal of Applied Physiology, 78(1), 349-358. doi:10.1152/jappl.1995.78.1.349.
[2] Gates, DH, Su, J., & Dingwell, JB (2007). Possible biomechanical origins of the long-range correlations in stride intervals of walking. Physica A: Statistical Mechanics and Its Applications, 380, 259-270. doi:10.1016/j.physa.2007.02.061.
[3] Moon, Y, Sung, J, An, R, Hernandez, ME, & Sosnoff, JJ. (2016). Gait variability in people with neurological disorders : A systematic review and meta-analysis. Human Movement Science, 47, 197-208. doi:10.1016/j.humov.2016.03.010.
[4] Gates, DH, & Dingwell, JB. (2007). Peripheral neuropathy does not alter the fractal dynamics of stride intervals of gait. Journal of Applied Physiology, 102(3), 965-971. doi:10.1152/japplphysiol.00413.2006
[5] Crevecoeur, F, Bollens, B, Detrembleur, C, & Lejeune, TM. (2010). Towards a “gold-standard” approach to address the presence of long-range auto-correlation in physiological time series. Journal of Neuroscience Methods, 192(1), 163-172. doi:10.1016/j.jneumeth.2010.07.017.
[6] Goldberger, AL. et al. (2002). Fractal dynamics in physiology : Alterations with disease and aging. Proceedings of the National Academy of Sciences, 99(1), 2466-2472. doi:10.1073/pnas.012579499. Data available online: https://physionet.org/content/gaitndd/1.0.0/ .
Patients with Charcot foot
Human walk is a periodic phenomenon whose variability is not random.
It shows long-range autocorrelations behavior [1].
Origin still unclear. Contributions from
• Biomechanics: Inverted pendulum model of walk [2].
• Central nervous system: Neurodegenerative diseases alter variability of walk [3].
• Peripheral nervous system ? No modification from peripheral neuropathy [4].
Aim of this pilot study: To assess the impact of Charcot foot on the variability of walk.
Contact
Buisseret Fabien
E-mail : buisseretf@helha.be
Assessment of SI variability by computation of [5]:
• Average SI (T)
• Coefficient of variation, CV =
!"
#
, with SD the standard
deviation of SI time series Magnitude of variability.
• Hurst exponent (H) with rescaled range analysis
Predictibility of variability
• Sample entropy (S) Complexity of variability
Charcot
CTRL
0.9
1.2
1.5
1.8
0 50 100 150 200
Cycle
SI
(s)
Charcot foot: complication of diabetes in patients with neuropathy.
Severe foot deformity, ulcer formation.
Our group: 4 patients with Charcot foot, Eichenholtz stage 3.
• Age 57 ± 3.1 years
• BMI 28.2 ± 5.3 kg.m-2
• Falling Risk Assessment Tool (FRAT) 7 [6.5-7.5]
Protocol: Walk 15 minutes on a treadmill at spontaneous speed with own
orthopaedic shoes. Homemade accelerometer (100Hz sampling frequency)
fixed on right ankle
computation of SI time series.
Results & Discussion
●
●
●
●
●
●
●
● ●
●
Charcot CTRL
0.025
0.050
0.075
CV
●
●
●
●
●
●
●
●
●
●
Charcot Sains
1.00
1.25
1.50
1.75
S
●
●
●
●
● ●
●
●
●
●
Charcot CTRL
0.5
0.6
0.7
0.8
H
●
●
●
●
●
●
●
●
●
●
Charcot CTRL
0.75
1.00
1.25
1.50
1.75
T
(s)
Control group
6 healthy adults, SI time series taken from [6]:
• Age 60.0 ± 10.2 years
• BMI 22.1 ± 2.6 kg.m-2
Patients with Charcot foot have a significantly larger SI than CTRL group.
• Slower walk than healthy adults.
• Impact of treadmill ? Spontaneous speed of 1.7 ± 0.7 km h-1 on the treadmill against 3.6 ± 0.4
km h-1 on flat ground.
The variability of walk in patients with Charcot foot is globally
• Larger
Significantly larger CV. Any deviation from healthy state increases CV, e.g. central nervous system
pathologies [3]. Poorer control of gait cycle.
• More stereotyped
H closer to 1, more predictible variability at long-range. Poorer adaptability due to lack of
proprioceptive feedback stiff orthopaedic shoes + neuropathy ?
• More complex
Higher sample entropy. Distribution of SI fluctuations closer to random process.
… than that of healthy adults with similar age.
What about the risk of fall ?
• Weak according to FRAT
• Modified SI variability could suggest a potential risk of fall for long walks.
And about the orthopaedic shoes ?
• Designed to improve plantar pressure profile
• Still they do not lead to « normal » SI variability.
Figure 2: Individual results (points) and first and third quartiles (rectangles)
for Charcot and CTRL groups. Significant differences between medians
(Mann-Whitney, p<0.05) are marked by lines linking the medians.
Introduction