GAIT PATTERN IN LEAN AND OBESE ADOLESCENTS
Short title: Gait in obese adolescents
Veronica Cimolin
1,2, Manuela Galli
1,3, Luca Vismara
2, Giorgio
Albertini
3, Alessandro Sartorio
4, Paolo Capodaglio
21 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, p.zza Leonardo
Da Vinci 32, 20133, Milano, Italy
2 Istituto Auxologico Italiano, IRCCS, Orthopaedic Rehabilitation Unit and Clinical Lab for Gait
Analysis and Posture, Ospedale San Giuseppe, Via Cadorna 90, I-28824, Piancavallo (VB), Italy
3 IRCCS “San Raffaele Pisana”, Tosinvest Sanità, Roma, Italy
4 Istituto Auxologico Italiano, IRCCS, Division of Auxology and Experimental Laboratory for
Auxo-endocrinological Research, Ospedale San Giuseppe, Via Cadorna 90, I-28824, Piancavallo (VB), Italy
Veronica Cimolin, PhD
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, p.zza Leonardo Da Vinci 32, 20133, Milano, Italy
Istituto Auxologico Italiano, IRCCS, Orthopaedic Rehabilitation Unit and Clinical Lab for Gait Analysis and Posture, Ospedale San Giuseppe, Via Cadorna 90, I-28824, Piancavallo (VB), Italy Manuela Galli, Prof.
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, p.zza Leonardo Da Vinci 32, 20133, Milano, Italy
IRCCS “San Raffaele Pisana”, Tosinvest Sanità, Roma, Italy Luca Vismara, MS
Istituto Auxologico Italiano, IRCCS, Orthopaedic Rehabilitation Unit and Clinical Lab for Gait Analysis and Posture, Ospedale San Giuseppe, Via Cadorna 90, I-28824, Piancavallo (VB), Italy
Giorgio Albertini, Prof.
IRCCS “San Raffaele Pisana”, Tosinvest Sanità, Roma, Italy Alessandro Sartorio, MD
Istituto Auxologico Italiano, IRCCS, Division of Auxology and Experimental Laboratory for Auxo-endocrinological Research, Ospedale San Giuseppe, Via Cadorna 90, I-28824, Piancavallo (VB), Italy
Paolo Capodaglio, MD
Istituto Auxologico Italiano, IRCCS, Orthopaedic Rehabilitation Unit and Clinical Lab for Gait Analysis and Posture, Ospedale San Giuseppe, Via Cadorna 90, I-28824, Piancavallo (VB), Italy
Corresponding author: Veronica Cimolin,
Department of Electronics, Information and Bioengineering, Politecnico di Milano, p.za Leonardo da Vinci 32, 20133 Milano, Italy
Tel: +39-02 23993359, Fax: +39-02 23993360
GAIT PATTERN IN LEAN AND OBESE ADOLESCENTS
Abstract
Obesity is the most common chronic disorder in children and
adolescents. Since walking is the most common daily task and is
recommended for weight management, quantifying how obesity
affects biomechanics of gait provides important insight into
relationship between metabolic and mechanical energetics,
mechanical loading and associated risk of musculoskeletal injury.
This study quantitatively compared gait in 12 obese and 10 lean
adolescent. Obese adolescents showed longer stance duration,
excessive hip flexion during the whole gait cycle and an increased
hip movement in frontal plane than lean participants. In the obese,
the knee was slightly extended in stance phase and ankle was in a
plantar flexed position at initial contact and at toe-off, with a
greater ankle range of motion. Kinetic data showed higher values
of maximum power generated at hip level during the stance phase;
ankle power displayed higher absorption at initial stance and
higher values of power generation in terminal stance. Because
obese adolescents are encouraged to walk in order to increase their
physical activity and energy expenditure level, injury prevention
and rehabilitative programs should take our findings into
consideration and include specific strengthening of the lower limb
proximal and distal muscles together with weight loss and
reconditioning interventions.
Keywords: Obesity, Gait Analysis, Rehabilitation, kinematics,
kinetics
Introduction
Obesity is the most common chronic disorder in children and adolescents in the industrialized societies. In some countries, the prevalence of obesity in childhood and adolescence has become much higher than that of the allergic disorders including both asthma and eczema (Kiess et al., 2001). As the incidence and severity of obesity continues to increase in children and adolescents (Ogden et al., 2010), the determination of the physiological and functional consequences of this epidemic becomes ever more pressing. Multiple factors are related to its high incidence, and both genetic/endogenous (Bouchard, 1996; Clement et al., 1998; Farooqi et al., 1999) and environmental/exogenous factors contribute to the development of a high degree of body fatness early in life. In fact, twin studies suggest that at least 50% of the tendency toward obesity is inherited (Bouchard, 1996; Bray, 1998). There is also increasing evidence that responsiveness to dietary intervention is genetically determined (Boulton et al., 1999; von Kries et al., 1999). A central distribution of body fat is associated with a higher risk of morbidity and mortality (Bray et al 1998; Calle et al., 1999). In addition, and most importantly, an increased risk of death from cardiovascular disease in adults has been found in subjects whose Body Mass Index (BMI) had been greater than the 75th percentile as adolescents (Calle et al., 1999). Childhood obesity seems to
increase the risk of subsequent morbidity whether or not obesity persists in adulthood (Bray et al., 1998; Barker, 2000; Bay, 1999; Pi-Sunyer et al, 1999). An increased body mass is reported to have negative influences on many activities of daily living, including the control of postural stability, sit-to-stand and locomotion (Hills & Parker, 1991; Galli et al., 2000; Sibella et al., 2003). Obesity modifies body geometry, increases the mass of limb segments (Rodacki et al., 2005), and enhances the predisposition to injury due to constraints on the biomechanics of activities of daily living (Wearing et al., 2006).
Walking is the most popular form of physical activity for weight management because it is easy to perform and can require up to considerable levels of energy expenditure. While studies that investigate how obesity affects the physiological responses to locomotion are essential and ongoing (Browning et al., 2006; Peyrot et al., 2009), biomechanical studies are also critical. Literature on this topic is scanty, a variety of investigations of the effects of obesity on characteristics of walking yielding inconsistent results. To date, studies have included only adults and children (8–13 years old) and considered only spatio-temporal parameters (McGraw et al., 2000). To our knowledge, the only quantitative analysis on obese adolescents was conducted without considering the whole gait cycle (McMillan et al., 2010). McGraw et al. (McGraw et al., 2000) demonstrated that obese boys spent greater double stance phase during gait and higher sway areas, energy and variability
especially in medial/lateral direction as for posture. McMillan et al. (McMillan et al., 2010) showed significant differences between obese and normal weight adolescents at all lower limb joints kinematics; in particular obese participants seem to use a gait strategy aimed to minimise joint moments, especially at knee and hip level.
Quantifying how obesity affects the biomechanics of gait provides important insight into the relationship between metabolic and mechanical energetics, mechanical loading (e.g. joint loads), and the associated risk of musculoskeletal injury and/or pathology. Previous studies evidenced that muscle weakness may be one potential cause of the movement alterations (McMillan et al., 2010) associated to decreased stability caused by the increased non-contributory mass added to the system, instead of impairment of motor control system (McGraw et al., 2000). According to previous findings and our direct clinical experience, we hypothesised that differences may be present between obese and normal weight adolescents. Identifying those differences appears also crucial for developing effective physical activity recommendations specific for these subjects, aimed both to achieve the energy expenditure goals and to reduce the risk of musculoskeletal injury. The aim of our study was therefore to identify, quantify and compare the spatio-temporal, kinematic and kinetic parameters of gait in age-matched obese and normal-weight individuals using 3D quantitative analysis of walking (3D-Gait analysis) during the entire gait cycle.
Materials and methods
Participants
In this observational study, obese (BMI greater than 97th centile or greater than 2 standard
deviations from the mean for the age and gender) and normal weighted (BMI comprised between the 25th and 75th centile) children were recruited from the beginning of January 2008 through
December 2008, matched for age and height. In particular, we considered 14 obese males (mean age: 15.71+14.65years; BMI: 32.89+3.67 Kg/m2; BMI-SDS: 2.46+0.44 Kg/m2; body mass:
91.79+13.41 Kg; height: 1.65+0.05 m) and 10 normal weighted and age-matched adolescent males (mean age: 14.75+3.54 years; BMI: 20.29+3.71 Kg/m2; body mass: 51.64+16.44 Kg; height: 1.62+0.11 m; Tanner stage: 3-5).
Exclusion criteria for the obese subjects were musculoskeletal, neuromuscular, and/or cardiopulmonary conditions other than obesity that would hinder mobility capacity or contraindicate the integrated weight management program. Obese subjects were recruited among patients admitted to a hospital based integrated weight management program at the Division of Auxology, Istituto Auxologico Italiano, Piancavallo (VB). They were not involved in regular physical daily activities before the admission to the Hospital (less than 1-2 hours/week). As
concerns education, 3 individuals had scholastic problems (repeating a year). All the patients had absence of relevant physical comorbidities hampering the execution of the tests, no one suffered from diabetes and hypertension. No subjects suffered from pain, headache, balance disorders and/or any other symptoms hampering the execution of the tests.
Exclusion criteria for the non-obese controls included history of cardiovascular, neurological or musculoskeletal disorders. The non-obese individuals showed normal flexibility and muscle strength and no obvious gait abnormalities.
The study was approved by the Ethics Research Committee of the
Institute. Written informed consent was obtained by the
participants and by their parents.
Experimental Set-up
The complete evaluation consisted of: clinical examination, video
recording and 3D Gait Analysis (GA). The obese participants were
evaluated at the Movement Analysis Lab of Ospedale San
Giuseppe, Istituto Auxologico Italiano, Piancavallo (VB), Italy,
using an optoelectronic system with 6 cameras (460 VICON,
Oxford Metrics Ltd., Oxford, UK) with a sampling rate of 100 Hz,
and two force platforms (Kistler, CH; length: 600 mm; width: 400
mm) with a sampling rate of 500 Hz. The non-obese individuals
were assessed at the Movement Analysis Lab of the IRCCS “San
Raffaele Pisana”, Tosinvest Sanità, Roma, Italy, using a
12-camera optoelectronic system (ELITE2002, BTS, Milan, Italy)
with a sampling rate of 100 Hz, two force platforms (Kistler, CH)
and 2 TV camera Video system (BTS, Italy) synchronized with
the system and the platforms for video recording. As previously
published (Cimolin et al., 2010), data originating from the two
laboratory settings can be compared. In this paper, data of the
same subjects acquired in the two different laboratories were
matched and the authors verified that marker placement and data
collection procedures in the two laboratories are similar and the
output (kinematic and kinetic data) the two healthy subjects were
not statistically different.
To evaluate the kinematics of each body segment, passive markers
were positioned on the participants’ body according to Helen
Hayes Marker set by an experienced operator in a manner
consistent with the literature (Davis, et al., 1991; Kadaba et al.,
1989) and in order to reduce potential sources of bias due to an
incorrect markers placement. This marker-set was chosen as the
protocol of choice to acquire the movement of lower limbs based
on Ferrari et al. (Ferrari et al., 2008). In total, 16 reflective
markers were placed on the skin overlying bony landmarks or on
specific anatomical positions. After placement of the markers,
individuals were asked to walk barefoot at their own natural pace
(self-selected speed) along a walkway containing the force
platforms at the mid-point. Five acquisitions comprehensive of
kinematic and kinetic data were collected for each patient in order
to guarantee reproducibility of the results.
Data analysis
The limb rotation algorithm is based on the determination of Euler
angles with a y-x-z axis rotation sequence. The joint rotation
angles routinely obtained correspond to flexion/extension,
adduction/abduction and internal/external rotation respectively. So
the joint rotation angles that are determined clinically are pelvic
obliquity-tilt-rotation, hip ad/abduction-flexion/extension-rotation,
knee flexion/extension, ankle plantar/dorsiflexion and foot
rotation. The pelvic angles are absolute angles, referenced to the
initially fixed laboratory coordinate system; the hip, knee and
ankle angles are all relative angles, e.g., the three hip angles
describe the orientation of the thigh with respect to the pelvis; the
foot rotation angle is an absolute angle, referenced to the
laboratory, which indicates the position of the subject’s foot with
respect to the direction of progression. The Newton-Euler
formulation was used to compute the joint forces and moments
based on anthropometric characteristics and the joint angles,
following “inverse dynamic problem” procedure analysis. Joint
moments and powers were normalized for body weight and
reported in Newton-meters per kilogram. All graphs obtained from
GA were normalized as % of gait cycle (0-100). For each
participants (both pathological and healthy ones) starting from the
five trials collected, three consistent trials for both lower limbs
able to evidence the same gait pattern (from spatio-temporal,
kinematic and kinetic point of view) were extracted and
considered for the analysis. Using specific software (BTS
EliteClinic, version 3.4.109, for the Movement Analysis Lab of
IRCCS “San Raffaele Pisana”, Tosinvest Sanità, and Polygon
Application, version 2.4, for the Movement Analysis Lab of San
Giuseppe Hospital, Istituto Auxologico Italiano, data were
exported in .txt and .xls files. From these data format we identified
and calculated some parameters (time/distance parameters, angles
joint values in specific gait cycle instant, peak values in joint
power graphs). This procedure was performed by the same
operator to ensure data reproducibility and to reduce potential
sources of bias. The computed parameters are listed in Table 1.
These parameters were selected among the indices that were
demonstrated to be not significantly affected by errors due to skin
artefacts and marker placement errors (Kirtley, 2002).
For each participant of the obese and non-obese group the mean
value (standard deviation and coefficient of variation) for the right
side and for the left side was computed separately, evidencing the
dominant and non-dominant side.
Statistical analysis
participant and then the median and quartile range values of all the
indexes were calculated before for each subject and then for each
group. With the proposed sample sizes, the study will have a
power of 81%. Kolmogorov–Smirnov tests were used to verify if
the parameters were normally distributed; the parameters were not
normally distributed, so we used nonparametric analysis. Data of
the right and the left side and dominant and non-dominant leg
were compared using Wilcoxon signed rank test. Obese and
non-obese data were compared using Mann-Whitney U tests, in order
to detect significant differences. Null hypotheses were rejected
when probabilities were below 0.05.
Results
All patients included in the study showed a good compliance; 2
patients of the obese group was not included in the study as their
Gait Analysis data did not included accurate kinetics. Therefore,
12 children composed the obese group (mean age:
15.83+14.75years; BMI: 32.87+3.45 Kg/m2; BMI-SDS:
2.48+0.49 Kg/m2; body mass: 91.73+13.42 Kg; height: 1.66+0.06
m) (Fig. 1). The two groups were similar in terms of age and
height; BMI and weight in obese group were significantly
different from non-obese participants. An initial comparison
between the parameter values of the right vs. left limb and
dominant vs. non-dominant leg was performed for all individuals.
No statistical differences were found between the two limbs; data
from both sides were then pooled. In Tables 2 and 3, the median
and quartile range values of the spatio-temporal, kinematic and
kinetic indexes considered in this study for obese and non-obese
participants are reported.
Spatio-temporal parameters (Table 2)
Obese participants were characterised by slightly higher stance duration than non-obese ones (p< 0.05), while no statistical differences were found in terms of velocity and step length, which was similar in the two groups (p> 0.05).
Kinematic parameters (Table 2) (Figure 2)
As for the pelvic joint, obese were characterised by significantly higher pelvic excursion in all the planes of the movement (sagittal, frontal and transversal planes) as compared to the control group (p< 0.05). Obese exhibited the hip range of motion on the sagittal plane (HFE-ROM index) close to physiological values, and on the frontal plane (HAA-ROM index) increased as compared to lean participants. Obese showed a slightly extended knee at the initial contact and at mid-stance. In the swing phase, the maximum value of knee flexion was not statistical different between the two groups, and the knee range of motion was similar to controls. The kinematic analysis showed a plantar flexed position at initial contact and at toe-off, leading to greater ankle range of motion dur-ing stance phase in the obese group.
Kinetic parameters (Table 3)
Obese individuals were characterised by higher values of maximum power generated at hip level during the first part of stance phase. In terms of ankle kinetics, no differences were found in peak of plantar flexion moment during the second half of stance (AMM index; obese: 1.45+0.31 N*m/Kg vs. 1.39+0.30 N*m/Kg, p= 0.07). Obese individuals were also characterised by higher absorbed power at initial stance and higher values of ankle power generation in terminal stance; however, the APM index normalised to the velocity of progression did not reveal significant differences among groups. All these differences are statistically significant (p< 0.05). No significant differences were found in terms of knee moment (p> 0.05) (minimum value of knee moment at the initial contact: obese: -0.24+0.06 N*m/Kg vs. -0.27+0.13 N*m/Kg; maximum value of knee moment during the early stance: obese: 0.17+0.14 N*m/Kg vs. 0.14+0.13 N*m/Kg; minimum value of knee moment
during the late stance: obese: -0.15+014 N*m/Kg vs. -0.25+0.16 N*m/Kg). Discussion
The purpose of this study was to investigate the lower extremity
biomechanics, in terms of spatio-temporal parameters, kinematics
and kinetics during the entire gait cycle (stance and swing phase)
in obese versus normal weight adolescents. Our hypothesis, based
on previous findings, was that differences in gait pattern maybe
present between obese and normal weight adolescents. In line with
previous reports on obese children (Hills & Parker, 1991; McGraw
et al., 2000), we observed, as for spatio-temporal parameters,
longer stance duration, indicating some degree of underlying
postural instability. It is generally acknowledged that the latter
could possibly be related mainly to anthropometrics, where an
excess of mass imposes per se functional limitations. On top of
that, a reduced muscle strength of the lower limbs normalized to
body mass (Capodaglio et al., 2009) imposes biomechanical
changes in the gait pattern causing a less effective locomotion
(Cimolin et al., 2010; Capodaglio et al., 2010; Menegoni et al.,
2011; Cimolin et al., 2011). A cautious gait pattern has been
previously described in obese adults (Cimolin et al., 2010;
Capodaglio et al., 2010; Menegoni et al., 2011; Cimolin et al.,
2011; De Souza et al., 2005; Lai et al., 2008): some authors
(Cimolin et al., 2010; Capodaglio et al., 2010; Menegoni et al.,
2011; Cimolin et al., 2011) have suggested that poor balance and
reduced muscle strength normalized per body mass may to a large
extent account for the impaired gait. Other factors, such as the
inertial contribution of the excessive volumes and masses of the
different body segments to gait abnormalities or sensory input
alterations (Menegoni et al., 2009), have received scanty attention
in the literature.
As for ankle kinematics, we found lower plantar flexion angle at
toe-off, no differences in terms of ankle plantar flexion moment
peak and ankle power peak normalised to walking velocity when
compared to the control group. Other kinematics and kinetics
reports in obese versus non-obese subjects have been inconsistent:
no differences in motion, but lower muscle moments at the ankle
(Browning & Kram, 2007), lower plantar flexion and greater
dorsal flexion angles (Spyropoulos et al., 1991); greater plantar
flexion motion and higher ankle moments (DeVita & Hortobagyi,
2003), lower plantar flexion moments (McMillan et al., 2010;
Gushue et al., 2005).
Several significant findings were noted in the sagittal plane at
knee level. In line with a previous report (McMillan et al., 2010),
subjects who were obese had less flexion at initial contact, and no
significant differences in sagittal plane angles have also been
reported (Browning & Kram, 2007). Obese subjects may flex the
knee less to compensate for relative knee extensor weakness, or
else for less effective knee extensor contraction, given the more
abducted knee alignment in stance. A relatively extended knee
during stance may also be a compensation for instability within
the knee joint structure. However, neither knee extensor strength
nor knee stability were directly measured in this study. Subjects
who were obese had a normal range of motion on sagittal plane
(HFE-ROM index) and higher hip generation power during early
stance (HPM index), as compared to the control group. Other
authors have reported no difference in sagittal plane kinematics
and kinetics at hip level (McGraw et al., 2000; McMillan et al.,
2010). However, previous studies did not refer to obese adolescent
but to children (8-10 years) (McGraw et al., 2000) or adults (older
than 30 years) (Browning & Kram, 2007; Spyropoulos et al.,
1991). In the frontal plane, hip excursion (HAA-ROM index) was
higher in obese than lean group. This strategy, directly linked to
the pelvis movement in the frontal plane (PO-ROM index), may
be due to the presence of excessive adipose tissue inside the
thighs, as previously suggested by Spyropoulos et al.
(Spyropoulos et al., 1991), which appears to produce the typical
external rotation of the hip during stance (Cimolin et al., 2010).
This result showed that the reduced level of ankle power
generation (APM index) resulted in greater amounts of work
exercised by muscle groups of the hip. The hip flexors produced
increased values during terminal phase of stance, as demonstrated
by HPM index. This means that our obese patients did not derive
as much muscle power from their ankle plantar flexors as their
lean counterparts. According to our data, obese patients show a
sort of proximal compensation strategy finalized to walking by
generating muscle power mainly from hip flexion rather than from
push-off of foot and ankle. Such strategy may also account for an
increased energetic cost of locomotion, leading to a less functional
and economical gait pattern, as previously published (Peyrot et al.,
2009). Our data showed no differences in terms of knee moment,
in contrast with previous published data (McMillan et al., 2010).
Conflicting results could be related to a selection bias, since our
convenience sample consisted only of males, whereas there were
mainly females in McMillan’s study (McMillan et al., 2010), and
to methodological differences. Other authors in fact, assessed
using videorecording (Browning & Kram, 2005) or treadmill
walking (Spyropoulos et al., 1991; DeVita & Hortobagyi, 2003),
while our data were collected while walking on the ground in a
motion analysis laboratory at self-selected velocity.
Speculations should however bear in mind the challenges associated with quantifying motions of a skeletal system covered by layers of abundant soft tissue, such as in subjects who are obese. Skin artefacts and marker placement errors are, in fact, known as potential confounders of movement data. In the literature, almost all of the studies reporting lower extremity kinematics/kinetics in obese individuals during gait have used standard gait marker set for clinical applications (Davis et al., 1991) as it is the most commonly used technique for gait data acquisition and reduction in clinics (Ferrari et al., 2008). Our data were collected using the same standard marker set since the measurements served mainly as functional outcome of the rehabilitation program and were not specifically aimed at research purposes and hence no ad hoc protocol had been implemented.
Literature showed that this marker-set could be influenced by inaccurate marker placement and soft tissue artefact (Baker, 2006; Della Croce et al., 2005). Of particular concern, especially in obese subjects, is the reliance on Anterior Superior Iliac Spine (ASIS) and/or greater trochanter markers to establish the pelvis and hip joint centres: inaccurate localization of the bony landmarks may in fact lead to inaccurate estimates of the joint centres and to errors in the resultant kinematics/kinetics (at hip and knee level in particular) (Stagni et al., 2000). However, literature demonstrated that pelvic and hip ranges of motion, knee flex-extension and ankle dorsi-flexion plots are not significantly affected by these errors (Kirtley, 2002). For this reason in our analysis, we selected and analysed only the demonstrated reliable measures so to be sure that these parameters are not affected by errors, excluding those influenced by inaccurate marker placement due to the presence of fat. Conclusions
In our paper, significant differences were found in the gait pattern of obese adolescents when compared to normal weight individuals. The fact that notable differences can be measured even in a non-particularly demanding, with regard to joint kinetics, and natural task such as walking should be born in mind. Differences could supposedly become even greater under more demanding but yet commonly used loading conditions, such as negotiating stairs, involving greater power generation at all joints in the lower limb. Under those conditions the risk of musculoskeletal injury or fall, which are known to be higher in obese subjects, may well further increase.
Because adolescents who are obese are recommended to walk daily in order to increase their physical activity and energy expenditure levels, it appears crucial to unveil the physiological and biomechanical mechanisms underneath this atypical gait pattern. This would indeed help clarifying whether prescription of walking is appropriate and safe and generating evidence-based background
for developing possible specific rehabilitation strategies. Previous papers have shown that targeted rehabilitation interventions focusing on specific functional limitations may indeed effectively enhance function and minimize disability: strengthening of the distal muscles was demonstrated stabilizing the ankle lead to improved balance (Capodaglio et al., 2009; Capodaglio et al., 2011; Cimolin et al., 2011; Capodaglio et al., 2011; Vismara et al., 2010) and gait (Cimolin et al., 2010) in genetically obese subjects. From our present data, it seems that obese adolescent would benefit from comprehensive in-and out-patient rehabilitation programs encompassing, together with weight management and physical conditioning, specific proximal (hip flexors and extensors) and distal (plantar flexors and extensors) muscle strengthening of the lower limbs aimed at improving balance and gait.
Given the high prevalence of musculoskeletal disorders (Capodaglio et al., 2010) and the increased risk of fall (Menegoni et al., 2009) in the obese population, the data presented in this study may serve as a basis for planning appropriate and effective injury prevention and rehabilitation interventions. The main limit of this study is the small sample size and the lack of female obese subjects. Our findings could therefore not be generalised to both genders. Further additional studies on female subjects will be needed to clarify the plausible gender-related differences of these parameters. Another limit is represented by the choice of the marker set, previosuly discussed. Future researches that report kinematic data using obese subjects should need to clearly identify marker placement and skeletal model development procedures and how errors regarding marker placement were addressed. A combination of using dual-energy X-ray absorptiometry images for determining inter-ASIS distance and estimating segment inertial parameters (Chambers et al., 2010), a sacral marker cluster and digitized pelvic anatomical landmarks (Segal et al., 2009) are suggested to improve the accuracy of marker-based motion capture.
Acknowledgments
The authors would like to acknowledge Eng. Carlotta Mondadori and Chiara Montanari for their valuable contribution.
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Tables
Table 1, Gait parameters and descriptors
Gait Parameter Description
Spatio-temporal parameters
Velocity (m/s) mean velocity of progression (m/s) equal to step length times cadence (ste/min); Cadence (step/min) Rate at which a person walks (step/min)
% stance (%gait cycle) duration of the stance phase, expressed as % of the gait cycle Step length longitudinal distance from one foot strike to the next one (mm)
Kinematics (degrees)
PT-ROM the range of motion at pelvis on the sagittal plane (Pelvic Tilt graph) during the gait cycle, calculated as the difference between the maximum and minimum values of the plot
PO-ROM the range of motion at pelvis on the frontal plane (Pelvic Obliquity graph) during the gait cycle, calculated as the difference between the maximum and minimum values of the plot PR-ROM the range of motion at pelvis on the transversal plane (Pelvic Rotation graph) during the gait
cycle, calculated as the difference between the maximum and minimum values of the plot HFE-ROM the range of motion at hip joint on the sagittal plane (Hip Flex-Extension graph) during the
gait cycle, calculated as the difference between the maximum and minimum values of the plot
HAA-ROM the range of motion at hip joint on the frontal plane (Hip Ab-Adduction graph) during the gait cycle, calculated as the difference between the maximum and minimum values of the plot
KIC value of Knee Flexion-Extension angle (knee position on sagittal plane) at initial contact, representing the position of knee joint at the beginning of gait cycle
KmSt minimum of knee flexion (knee position on sagittal plane) in mid-stance, representing the extension ability of knee during this phase of gait cycle
KMSw peak of knee flexion (knee position on sagittal plane) in swing phase, representing the flexion ability of knee joint during this phase of gait cycle
KFE-ROM the range of motion at knee joint on the sagittal plane (Knee Flex-Extension graph) during the gait cycle, calculated as the difference between the maximum and minimum values of the plot
AIC value of the ankle joint angle (on sagittal plane) at the initial contact, representing the position of knee joint at the beginning of gait cycle
AMSt peak of ankle dorsiflexion (on sagittal plane) during stance phase, representing the dorsiflexion ability of ankle joint during this phase of gait cycle
AmSt minimum value of the ankle joint angle (on sagittal plane) in stance phase, representing the plantarflexion ability of ankle joint at toe-off
AMSw peak of ankle dorsiflexion (on sagittal plane) during swing phase, representing the dorsiflexion ability of ankle joint in this phase of gait cycle
ADP-ROM the range of motion at ankle joint on the sagittal plane (Ankle Dorsi-Plantarflexion graph) during the gait cycle, calculated as the difference between the maximum and minimum values of the plot
Kinetics
HPM (W/Kg) The maximum value of hip power generation during the first part of the stance phase KMmin IC (N*m/Kg) the minimum value of knee moment at the initial contact
KMMax St (N*m/Kg) the maximum value of knee moment during the early stance KMmin St (N*m/Kg) the minimum value of knee moment during the late stance
AMMax (N*m/Kg) the peak of plantar flexion moment of ankle joint in the second half of stance
APmin (W/Kg) minimum value of absorbed ankle power in early stance and mid-stance, when muscle is contracting eccentrically and absorbing energy (minimum value of negative ankle power) APMax (W/Kg) the maximum value of generated ankle power during terminal stance (maximum value of
positive ankle power during terminal stance) representing the push-off ability of the foot during walking
Table 2, Spatio-temporal and kinematic parameters of the study
groups.
OBESE GROUP LEAN GROUP
Spatio-temporal parameters
%stance (% gait cycle) 62.74 (1.71)* 58.60 (2.66)
Step length (mm) 0.61 (0.06) 0.58 (0.07) Velocity (m/s) 1.08 (0.11) 0.95 (0.16) Pelvis (°) PT-ROM 7.90 (1.64)* 4.86 (4.36) PO-ROM 7.88 (3.36)* 6.96 (3.15) PR-ROM 11.89 (5.25)* 8.47 (3.31) Hip joint (°) HIC 43.59 (11.08)* 26.70 (7.04) HmSt -1.46 (10.77)* -11.07 (7.93) HFE-ROM 42.12 (9.87) 43.52 (4.76) HAA-ROM 16.05 (4.47)* 11.08 (4.10) Knee joint (°) KIC 0.98 (7.19)* 4.88 (6.21) KmSt -5.07 (3.31)* 0.96 (6.27) KMSw 44.99 (14.68) 52.04 (7.10) KFE-ROM 53.36 (7.02) 54.03 (11.51)
Ankle joint and foot (°)
AIC -4.23 (5.52)* -1.97 (3.87)
AMSt 12.79 (5.41) 16.23 (3.85)
AmSt -17.68 (8.64)* -9.28 (6.99)
ADP-ROM 28.71 (8.46)* 23.73 (7.23)
AMSw 2.62 (4.28) 2.94 (3.88)
Data are expressed as median (quartile range).
*= p< 0.05, obese vs. lean group.
(ROM, Range Of Motion; PT, Pelvic Tilt; PO, Pelvic Obliquity;
HIC, Hip at IC; HFE, Hip Flex-Extension; HAA, Hip
Ab-Adduction; KIC, Knee at IC; KFE, Knee Flex-Extension; AIC,
Ankle at IC; ADP, Ankle Dorsi-Plantarflexion; IC, Initial Contact;
St, Stance; Sw, Swing; M, maximum value; m, minimum value)
Table 3, Kinetic parameters of the study groups.
OBESE
GROUP GROUPLEAN
HPM (W/Kg) 1.76 (1.17)* 0.58 (0.26)
APm (W/Kg) -1.04 (0.30)* -0.52 (0.18)
APM (W/Kg) 4.23 (0.92)* 3.29 (0.85)
APMnorm (W*s/(kg*m)) 3.89 (0.80) 3.57 (0.90)
