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The relationships between foot-joints range of motion and plantar pressure in the normal foot
S24
Abstracts / Gait & Posture 42S (2015) S1–S27
foot surgeries and increasing our understanding of the relationship between foot types and plantar load distribution.
O36 The relationships between foot-joints range of motion and plantar pressure in the normal foot
Reference
Paolo Caravaggi 1,∗ , Alberto Leardini 1 , Claudia Giacomozzi 2
[1] [2] [3] [4]
1
Movement Analysis Laboratory, Istituto Ortopedico Rizzoli, Bologna, Italy 2 Istituto Superiore di Sanità, Roma, Italy Introduction: Plantar load can be considered as a measure of the effectiveness of the foot to transmit forces at the foot/ground or at the foot/footwear interface via foot-joints motion. Plantar load during barefoot walking is uniformly distributed across foot regions, with peak pressures being normally located at the center of the heel, under the metatarsal heads and under the hallux. While morphological and functional measures have been shown to be correlated to plantar load [1,2], a comprehensive evaluation of the relationships between foot-joints mobility and plantar load regional parameters has yet to be done. Understanding the effects of joint motion on plantar pressure may improve our comprehension of foot pathologies and predict the outcome of orthopedic and rehabilitation treatments. Methods: This retrospective, consecutive cohort study was conducted on 21 children with diplegia (12 males, 9 females; mean age 12 y; GMFCS ranging between I and II) who were evaluated by GA. Data from an age-matched group of 26 typically developing children (15 males, 11 females; mean age 12y) were also analyzed. A set of summarizing indices was computed from GA data, including the height normalized walking speed, DLA, DPA and BEQ. Reference values from healthy children were obtained and compared to those of adults. Next, the dependency of indices values upon the level of impairment (healthy, GMFCS level I or GMFCS1 and GMFCS level II or GMFCS2 ) was assessed by the Kruskal–Wallis test and the Mann-Wilcoxon test for group comparisons. Results: Several kinematic parameters showed weak to moderate linear correlation (0.24 < |R| < 0.44) with the three baropodometric parameters, across all regions. Joints ROM and normalized walking speed explained 6–45% of the model variance (adjusted R2 ) for baropodometric parameters. Normalized walking speed was positively associated to mean and peak plantar pressure, whereas ROM was negatively associated to mean pressure in all regions (Table 1). Discussion: The present results provide novel insight into the relationship between foot-joints mobility and plantar load. While association between limited joint mobility and increased plantar pressure has been inferred in the diabetic foot [4], changes in soft tissue properties alter proprioception and make it difficult to isolate the effect of joint mobility on pressure distribution in this population. The present study has shown that, in the normally-arched feet, decreased foot-joints ROM is associated to larger mean and peak pressure in all foot regions. This information has the potential to improving rehabilitation programs, helping predict the outcome of
Table 1 The kinematic regressors and corresponding coefficients of the multiple linear regression model describing the relationship between mean pressure at the forefoot and foot-joints ROM during stance. ShFo (y) and ShCa (y) are respectively the shank-to-foot and shank-to-calcaneus frontal-plane ROM [3]. Regressor
Coefficient
SE
p-Value
ShFo (y) ShCa (y) Speed
−2.36 −5.14 69.64 R2 = 0.359
1.02 0.99 34.51 R2 (adj) = 0.333
0.023 1.77E−06 0.047 RMSE = 21.8
Moragh, Cavanagh. J Biomech 1999;32:359–70. Giacomozzi, et al. J Biomech 2014;47:2654–9. Leardini A, et al. Gait Posture 2007;25:453–62. Fernando D, et al. Diabetes Care 1991;14.
http://dx.doi.org/10.1016/j.gaitpost.2015.07.050 O37 A foot finite element model integrating porous media approach and gait analysis: A step forward in the study of the diabetic foot disease Mattia Pizzocaro 1,∗ , Annamaria Guiotto 2 , Giuseppe Sciumè 3 , Zimi Sawacha 2 , Claudio Cobelli 2 , D.P. Boso 1 , B.A. Schrefler 1 1 Department of Civil, Environmental and Architectural Engineering, University of Padua, Padova, Italy 2 Department of Information Engineering, University of Padua, Padova, Italy 3 Department of Innovation Engineering, University of Salento, Lecce, Italy
Introduction: The main target in the treatment of the diabetic foot disease is the prevention of ulceration onset. To this aim, three main things must be taken into account: the reduction of the pressure peaks, the treatment of peripheral vascular disease and neuropathy. In fact, one of the primary causes of the foot ulceration is the excessive mechanical loading of the foot, due to the modification of its structure and tissue properties. Methods: The innovative aspect of our approach consists in modeling plantar tissue as a porous medium [1] in a finite element (FE) model of the foot driven by gait analysis data. In literature it is usually modeled by means of elastic or hyperelastic constitutive laws. With our method the real visco-elastic behavior of the soft tissue is suitably considered. In our analysis a patient specific load vs. time is applied and a transient solution is obtained. In this way, time is not a mere integration variable, but it keeps its physical meaning. Therefore, due to the impact of the duration of the gait cycle on the solution, modifications of gait in diabetic patients can be properly taken into account. The load history, the boundary conditions and the foot morphology are taken from the experimental measurements: the acquisition of in-vivo foot kinematics and kinetics during gait (as in [2]) together with magnetic resonance imaging give the input data of the model [3]. Plantar pressures simultaneously acquired during gait are the gold standard for the validation of the simulated plantar pressures [2]. As regards the mathematical framework, the upscaling from the microstructure is obtained by means of the Thermodynamically Constrained Averaging Theory (TCAT) [4,5], so that the system of the governing equations can be written at macroscopic level. In the tissue model, a bi-phase system is taken into account: a solid phase, made of tissue cells and extracellular matrix, and a liquid phase (interstitial fluid), which fully saturates the medium. The equations are discretized in space by using finite element method, and in time with finite difference method. The primary nodal variables are the interstitial fluid pressure and the displacement vector. Results: The main results of the finite element analyses are the distribution of pressure peaks in the entire foot, and in particular within the plantar tissue medium. Thanks to the porous media approach, total stresses can be split into effective stresses inside
Abstracts / Gait & Posture 42S (2015) S1–S27
foot surgeries and increasing our understanding of the relationship between foot types and plantar load distribution.
O36 The relationships between foot-joints range of motion and plantar pressure in the normal foot
Reference
Paolo Caravaggi 1,∗ , Alberto Leardini 1 , Claudia Giacomozzi 2
[1] [2] [3] [4]
1
Movement Analysis Laboratory, Istituto Ortopedico Rizzoli, Bologna, Italy 2 Istituto Superiore di Sanità, Roma, Italy Introduction: Plantar load can be considered as a measure of the effectiveness of the foot to transmit forces at the foot/ground or at the foot/footwear interface via foot-joints motion. Plantar load during barefoot walking is uniformly distributed across foot regions, with peak pressures being normally located at the center of the heel, under the metatarsal heads and under the hallux. While morphological and functional measures have been shown to be correlated to plantar load [1,2], a comprehensive evaluation of the relationships between foot-joints mobility and plantar load regional parameters has yet to be done. Understanding the effects of joint motion on plantar pressure may improve our comprehension of foot pathologies and predict the outcome of orthopedic and rehabilitation treatments. Methods: This retrospective, consecutive cohort study was conducted on 21 children with diplegia (12 males, 9 females; mean age 12 y; GMFCS ranging between I and II) who were evaluated by GA. Data from an age-matched group of 26 typically developing children (15 males, 11 females; mean age 12y) were also analyzed. A set of summarizing indices was computed from GA data, including the height normalized walking speed, DLA, DPA and BEQ. Reference values from healthy children were obtained and compared to those of adults. Next, the dependency of indices values upon the level of impairment (healthy, GMFCS level I or GMFCS1 and GMFCS level II or GMFCS2 ) was assessed by the Kruskal–Wallis test and the Mann-Wilcoxon test for group comparisons. Results: Several kinematic parameters showed weak to moderate linear correlation (0.24 < |R| < 0.44) with the three baropodometric parameters, across all regions. Joints ROM and normalized walking speed explained 6–45% of the model variance (adjusted R2 ) for baropodometric parameters. Normalized walking speed was positively associated to mean and peak plantar pressure, whereas ROM was negatively associated to mean pressure in all regions (Table 1). Discussion: The present results provide novel insight into the relationship between foot-joints mobility and plantar load. While association between limited joint mobility and increased plantar pressure has been inferred in the diabetic foot [4], changes in soft tissue properties alter proprioception and make it difficult to isolate the effect of joint mobility on pressure distribution in this population. The present study has shown that, in the normally-arched feet, decreased foot-joints ROM is associated to larger mean and peak pressure in all foot regions. This information has the potential to improving rehabilitation programs, helping predict the outcome of
Table 1 The kinematic regressors and corresponding coefficients of the multiple linear regression model describing the relationship between mean pressure at the forefoot and foot-joints ROM during stance. ShFo (y) and ShCa (y) are respectively the shank-to-foot and shank-to-calcaneus frontal-plane ROM [3]. Regressor
Coefficient
SE
p-Value
ShFo (y) ShCa (y) Speed
−2.36 −5.14 69.64 R2 = 0.359
1.02 0.99 34.51 R2 (adj) = 0.333
0.023 1.77E−06 0.047 RMSE = 21.8
Moragh, Cavanagh. J Biomech 1999;32:359–70. Giacomozzi, et al. J Biomech 2014;47:2654–9. Leardini A, et al. Gait Posture 2007;25:453–62. Fernando D, et al. Diabetes Care 1991;14.
http://dx.doi.org/10.1016/j.gaitpost.2015.07.050 O37 A foot finite element model integrating porous media approach and gait analysis: A step forward in the study of the diabetic foot disease Mattia Pizzocaro 1,∗ , Annamaria Guiotto 2 , Giuseppe Sciumè 3 , Zimi Sawacha 2 , Claudio Cobelli 2 , D.P. Boso 1 , B.A. Schrefler 1 1 Department of Civil, Environmental and Architectural Engineering, University of Padua, Padova, Italy 2 Department of Information Engineering, University of Padua, Padova, Italy 3 Department of Innovation Engineering, University of Salento, Lecce, Italy
Introduction: The main target in the treatment of the diabetic foot disease is the prevention of ulceration onset. To this aim, three main things must be taken into account: the reduction of the pressure peaks, the treatment of peripheral vascular disease and neuropathy. In fact, one of the primary causes of the foot ulceration is the excessive mechanical loading of the foot, due to the modification of its structure and tissue properties. Methods: The innovative aspect of our approach consists in modeling plantar tissue as a porous medium [1] in a finite element (FE) model of the foot driven by gait analysis data. In literature it is usually modeled by means of elastic or hyperelastic constitutive laws. With our method the real visco-elastic behavior of the soft tissue is suitably considered. In our analysis a patient specific load vs. time is applied and a transient solution is obtained. In this way, time is not a mere integration variable, but it keeps its physical meaning. Therefore, due to the impact of the duration of the gait cycle on the solution, modifications of gait in diabetic patients can be properly taken into account. The load history, the boundary conditions and the foot morphology are taken from the experimental measurements: the acquisition of in-vivo foot kinematics and kinetics during gait (as in [2]) together with magnetic resonance imaging give the input data of the model [3]. Plantar pressures simultaneously acquired during gait are the gold standard for the validation of the simulated plantar pressures [2]. As regards the mathematical framework, the upscaling from the microstructure is obtained by means of the Thermodynamically Constrained Averaging Theory (TCAT) [4,5], so that the system of the governing equations can be written at macroscopic level. In the tissue model, a bi-phase system is taken into account: a solid phase, made of tissue cells and extracellular matrix, and a liquid phase (interstitial fluid), which fully saturates the medium. The equations are discretized in space by using finite element method, and in time with finite difference method. The primary nodal variables are the interstitial fluid pressure and the displacement vector. Results: The main results of the finite element analyses are the distribution of pressure peaks in the entire foot, and in particular within the plantar tissue medium. Thanks to the porous media approach, total stresses can be split into effective stresses inside