40 Original article
Gait pattern in lean and obese adolescents Veronica Cimolina,b, Manuela Gallia,d, Luca Vismarab, Giorgio Albertinid, Alessandro Sartorioc and Paolo Capodagliob Obesity is the most common chronic disorder in children and adolescents. As walking is the most common daily task and is recommended for weight management, quantifying how obesity affects the biomechanics of gait provides important insight into the relationship between metabolic and mechanical energetics, mechanical loading and associated risk for musculoskeletal injury. This study quantitatively compared gait in 12 obese and 10 lean adolescents. Obese adolescents showed longer stance duration, excessive hip flexion during the whole gait cycle and an increased hip movement in the frontal plane compared with lean participants. In the obese, the knee was slightly extended in stance phase and the 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 a higher absorption at initial stance and higher values of power generation in the terminal stance. Because obese adolescents are encouraged to walk to increase their physical activity and
Introduction Obesity is the most common chronic disorder in children and adolescents in industrialized societies. In some countries, the prevalence of obesity in childhood and adolescence has become much higher than that of 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; Clément 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 towards obesity is inherited (Bouchard, 1996; Bray et al., 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 for 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 individuals whose BMI had been greater than the 75th percentile as adolescents (Calle et al., 1999). Childhood obesity seems to increase 0342-5282 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
energy expenditure level, injury prevention and rehabilitative programmes 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. International Journal of Rehabilitation Research 38:40–48 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. International Journal of Rehabilitation Research 2015, 38:40–48 Keywords: gait analysis, kinematics, kinetics, obesity, rehabilitation a
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, bOrthopaedic Rehabilitation Unit, Clinical Lab for Gait Analysis and Posture, Ospedale San Giuseppe, Piancavallo (VB), cDivision of Auxology and Experimental Laboratory for Auxo-Endocrinological Research, Istituto Auxologico Italiano, IRCCS, Ospedale San Giuseppe, Piancavallo (VB) and dIRCCS ‘San Raffaele Pisana’, Tosinvest Sanità, Rome, Italy Correspondence to Veronica Cimolin, PhD, Department of Electronics, Information and Bioengineering, Politecnico di Milano, p.za Leonardo da Vinci 32, 20133 Milan, Italy Tel: + 39 02 2399 3359; fax: + 39 02 2399 3360; e-mail: [email protected] Received 23 May 2014 Accepted 7 September 2014
the risk for subsequent morbidity whether or not obesity persists in adulthood (Bray et al., 1998; Bray, 1999; Pi-Sunyer et al., 1999; Barker, 2000). 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 and 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 because of 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 involve considerable levels of energy expenditure. Although 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. The literature on this topic is scanty, with a variety of investigations on the effects of obesity on the characteristics of walking yielding inconsistent results. To date, studies have included only adults and children (8–13 years old) and considered only spatiotemporal parameters (McGraw et al., 2000). To our knowledge, the only quantitative analysis on obese adolescents was conducted without DOI: 10.1097/MRR.0000000000000089
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Gait in obese adolescents Cimolin et al. 41
considering the whole gait cycle (McMillan et al., 2010). McGraw et al. (2000) demonstrated that obese boys spend a greater double stance phase during gait and have higher sway areas, energy and variability, especially in the medial/lateral direction as for posture. McMillan et al. (2010) showed significant differences between obese and normal weight adolescents at all lower limb joint kinematics; in particular, obese participants seem to use a gait strategy aimed to minimize 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 for musculoskeletal injury or pathology. Previous studies evidenced that muscle weakness may be one potential cause of the movement alterations (McMillan et al., 2010) associated with decreased stability caused by the increased noncontributory mass added to the system, instead of impairment of motor control system (McGraw et al., 2000). On the basis of previous findings and our direct clinical experience, we hypothesized that differences may be present between obese and normal weight adolescents. Identifying those differences also appears crucial for developing effective physical activity recommendations specific for these individuals, 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 spatiotemporal, kinematic and kinetic parameters of gait in agematched obese and normal weight individuals using 3D quantitative analysis of walking [3D-gait analysis (GA)] during the entire gait cycle.
Participants and methods Participants
In this observational study, obese (BMI > 97th percentile or > 2 SD from the mean for age and sex) and normal weight (BMI was between the 25th and 75th percentile) children were recruited from the beginning of January 2008 through December 2008, matched for age and height. In particular, we considered 14 obese boys (mean age 15.71 ± 14.65 years; BMI 32.89 ± 3.67 kg/m2; BMISDS 2.46 ± 0.44 kg/m2; body mass 91.79 ± 13.41 kg; height 1.65 ± 0.05 m) and 10 normal weight and agematched adolescent boys (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 individuals were musculoskeletal, neuromuscular or cardiopulmonary conditions other than obesity that would hinder mobility capacity or contraindicate the integrated weight management programme. Obese individuals were recruited from among patients admitted to a hospital-based integrated weight management programme at the Division of Auxology, Istituto Auxologico Italiano, Piancavallo (VB),
Italy. They were not involved in regular physical daily activities before the admission to the hospital (< 1–2 h/ week). With regard to education, three individuals had scholastic problems (repeating a year). All patients had absence of relevant physical comorbidities hampering the execution of the tests; no one suffered from diabetes and hypertension. No individual suffered from pain, headache, balance disorders or any other symptoms hampering the execution of the tests. Exclusion criteria for the nonobese controls included history of cardiovascular, neurological or musculoskeletal disorders. The nonobese 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 from the participants and their parents. Experimental set-up
The complete evaluation consisted of clinical examination, video recording and 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 six 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 nonobese individuals were assessed at the Movement Analysis Lab of the IRCCS ‘San Raffaele Pisana’, Tosinvest Sanità, Rome, Italy, using a 12-camera optoelectronic system (ELITE2002; BTS, Milan, Italy) with a sampling rate of 100 Hz, two force platforms (Kistler, CH) and two TV camera video system (BTS) 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 pertaining to the same participants 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) of the two healthy participants was not statistically different. To evaluate the kinematics of each body segment, passive markers were positioned on the participant’s body according to Helen Hayes marker set by an experienced operator in a manner consistent with the literature (Kadaba et al., 1990; Davis et al., 1991) and to reduce potential sources of bias due to incorrect marker placement. This marker set was chosen as the protocol of choice to acquire the movement of lower limbs based on the study by 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
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42 International Journal of Rehabilitation Research
2015, Vol 38 No 1
barefoot at their own natural pace (self-selected speed) along a walkway containing the force platforms at the midpoint. Five acquisitions comprehensive of kinematic and kinetic data were collected for each patient 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. Therefore, the joint rotation angles that are determined clinically are pelvic obliquity (PO)-tilt-rotation, hip adduction/abductionflexion/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; for example, 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 individual’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 joint angles, following ‘inverse dynamic problem’ procedure analysis. Joint moments and powers were normalized for body weight and reported in newton-metres per kilogram. All graphs obtained from GA were normalized as a percentage of gait cycle (0–100). For each participant (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 spatiotemporal, 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, Piancavallo (VB), Italy], data were exported in .txt and .xls files. From these data formats we identified and calculated some parameters (time/distance parameters, angles joint values in specific gait cycle instant and 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 nonobese group the mean value (SD and coefficient of variation) for the right side and the left side was computed separately, evidencing the dominant and nondominant side.
Statistical analysis
All of the previously defined parameters were computed for each participant and then the median and quartile range values of all the indices were calculated for each participant and then for each group. With the proposed sample sizes, the study will have a power of 81%. The Kolmogorov–Smirnov test was used to verify whether the parameters were normally distributed; the parameters were not normally distributed, and therefore we used nonparametric analysis. Data of the right and left sides and dominant and nondominant legs were compared using the Wilcoxon signed-rank test. Obese and nonobese data were compared using the Mann–Whitney U-test to detect significant differences. Null hypotheses were rejected when P values were below 0.05.
Results All patients included in the study showed good compliance; two patients of the obese group were not included in the study as their GA data did not include accurate kinetics. Therefore, 12 children composed the obese group (mean age 15.83 ± 14.75 years; 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 the obese group were significantly different from that of the nonobese group. An initial comparison between the parameter values of the right versus left limb and dominant versus nondominant 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 spatiotemporal, kinematic and kinetic indices considered in this study for obese and nonobese participants are reported. Spatiotemporal parameters
Obese participants were characterized by slightly higher stance duration compared with nonobese participants (P < 0.05), whereas no statistical differences were found in terms of velocity and step length, which was similar in the two groups (P > 0.05) (Table 2). Kinematic parameters
As for the pelvic joint, the obese were characterized by a significantly higher pelvic excursion in planes of the movement (sagittal, frontal and transversal planes) as compared with the control group (P < 0.05). Obese participants exhibited hip range of motion (ROM) on the sagittal plane [hip flex-extension (HFE)-ROM index] close to physiological values, and on the frontal plane [hip abduction/adduction (HAA)-ROM index] increased as compared with lean participants. Obese participants showed a slightly extended knee at the initial contact and at midstance. In the swing phase, the maximum value of knee flexion was not statistically different between the two groups, and the knee ROM was similar to controls.
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Gait in obese adolescents Cimolin et al. 43
Table 1
Gait parameters and descriptors
Gait parameters Spatiotemporal parameters Velocity (m/s) Cadence (steps/min) %stance (%gait cycle) Step length Kinematics (deg.) PT-ROM PO-ROM PR-ROM HFE-ROM HAA-ROM KIC KmSt KMSw KFE-ROM AIC AMSt AmSt AMSw ADP-ROM Kinetics HPM (W/hg) KmIC (N m/kg) KMSt (N m/kg) KmSt (N m/kg) AM (N m/kg) APm (W/kg) APM (W/kg) APMnorm [W s/(kg m)]
Description Mean velocity of progression (m/s) equal to step length times cadence (steps/min) Rate at which a person walks (steps/min) Duration of the stance phase, expressed as % of the gait cycle Longitudinal distance from one foot strike to the next one (mm) 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 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 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 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 The range of motion at the hip joint on the frontal plane (hip abduction/adduction graph) during the gait cycle, calculated as the difference between the maximum and minimum values of the plot Value of knee flexion-extension angle (knee position on sagittal plane) at initial contact, representing the position of the knee joint at the beginning of the gait cycle Minimum of knee flexion (knee position on sagittal plane) in midstance, representing the extension ability of the knee during this phase of the gait cycle Peak of knee flexion (knee position on sagittal plane) in swing phase, representing the flexion ability of the knee joint during this phase of the gait cycle The range of motion at the 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 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 Peak of ankle dorsiflexion (on sagittal plane) during stance phase, representing the dorsiflexion ability of ankle joint during this phase of gait cycle Minimum value of the ankle joint angle (on sagittal plane) in stance phase, representing the plantar flexion ability of the ankle joint at toe-off Peak of ankle dorsiflexion (on sagittal plane) during swing phase, representing the dorsiflexion ability of the ankle joint in this phase of the gait cycle The range of motion at the ankle joint on the sagittal plane (ankle dorsiplantar flexion graph) during the gait cycle, calculated as the difference between the maximum and minimum values of the plot The maximum value of hip power generation during the first part of the stance phase The minimum value of knee moment at the initial contact The maximum value of knee moment during the early stance The minimum value of knee moment during the late stance The peak of plantar flexion moment of ankle joint in the second half of stance Minimum value of absorbed ankle power in early stance and midstance, when the muscle is contracting eccentrically and absorbing energy (minimum value of negative ankle power) 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 The maximum ankle power during terminal stance normalized to the velocity of progression
A, ankle; ADP, ankle dorsiplantar flexion; AIC, ankle at IC; AP, ankle power; HAA, hip abduction/adduction; HFE, hip flex-extension; IC, initial contact; K, knee; KFE, knee flex-extension; KIC, knee at IC; M, maximum value; m, minimum value; PO, pelvic obliquity; PR, pelvic rotation; PT, pelvic tilt; ROM, range of motion; St, stance; Sw, swing.
The kinematic analysis showed a plantar flexed position at initial contact and at toe-off, leading to a greater ankle ROM during stance phase in the obese group (Table 2 and Fig. 2). Kinetic parameters
Obese individuals were characterized by higher values of maximum power generated at hip level during the first part of the stance phase. In terms of ankle kinetics, no differences were found in the peak of plantar flexion moment during the second half of stance (AMM index; obese 1.45 ± 0.31 vs. 1.39 ± 0.30 N m/kg, P = 0.07). Obese individuals were also characterized by higher absorbed power at initial stance and higher values of ankle power generation in terminal stance; however, the APM index normalized 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 initial contact: obese − 0.24 ± 0.06 vs. − 0.27 ± 0.13 N m/kg; maximum value of knee moment during the early stance: obese 0.17 ± 0.14 vs. 0.14 ± 0.13 N m/kg; and minimum value of knee moment during the late stance: obese − 0.15 ± 014 vs. − 0.25 ±0.16 N m/kg) (Table 3).
Discussion The purpose of this study was to investigate the lower extremity biomechanics, in terms of spatiotemporal 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 may be present between obese and normal weight adolescents. In line with previous reports on obese children (Hills and Parker, 1991; McGraw et al., 2000), we observed, as for spatiotemporal parameters, longer stance duration, indicating some degree of underlying postural instability.
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44 International Journal of Rehabilitation Research
2015, Vol 38 No 1
Fig. 1
Assessed for eligibility (n = 24) •
Obese adolescents (n = 14)
•
Normal weight adolescents (n = 10) Excluded (n = 0) •
Not meeting inclusion crtieria (n = 0)
•
Declined to participate (n = 0)
•
Other reasons (n = 0)
Evaluated using 3D-gait analysis (n = 24) •
Obese adolescents (n = 14)
•
Normal weight adolescents (n = 10) Excluded (n = 2) •
Obese adolescents (n = 2) → data without kinetics
•
Normal weight adolescents (n = 0)
Analysed (n = 22) •
Obese adolescents (n = 12)
•
Normal weight adolescents (n = 10)
Flow diagram describing the formation of the study groups.
Spatiotemporal and kinematic parameters of the study groups
Table 2
Table 3
Kinetic parameters of the study groups Obese group
Spatiotemporal parameters %stance (%gait cycle) Step length (mm) Velocity (m/s) Pelvis (deg.) PT-ROM PO-ROM PR-ROM Hip joint (deg.) HIC HmSt HFE-ROM HAA-ROM Knee joint (deg.) KIC KmSt KMSw KFE-ROM Ankle joint and foot (deg.) AIC AMSt AmSt ADP-ROM AMSw
Obese group
Lean group
62.74 (1.71)* 0.61 (0.06) 1.08 (0.11)
58.60 (2.66) 0.58 (0.07) 0.95 (0.16)
7.90 (1.64)* 7.88 (3.36)* 11.89 (5.25)*
4.86 (4.36) 6.96 (3.15) 8.47 (3.31)
43.59 − 1.46 42.12 16.05
(11.08)* (10.77)* (9.87) (4.47)*
26.70 − 11.07 43.52 11.08
(7.04) (7.93) (4.76) (4.10)
0.98 − 5.07 44.99 53.36
(7.19)* (3.31)* (14.68) (7.02)
4.88 0.96 52.04 54.03
(6.21) (6.27) (7.10) (11.51)
− 4.23 12.79 − 17.68 28.71 2.62
(5.52)* (5.41) (8.64)* (8.46)* (4.28)
− 1.97 16.23 − 9.28 23.73 2.94
(3.87) (3.85) (6.99) (7.23) (3.88)
Data are expressed as median (quartile range). A, ankle; ADP, ankle dorsiplantar flexion; AIC, ankle at IC; H, hip; HAA, hip abduction/adduction; HFE, hip flex-extension; HIC, hip at IC; IC, initial contact; K, knee; KFE, knee flex-extension; KIC, knee at IC; M, maximum value; m, minimum value; PO, pelvic obliquity; PT, pelvic tilt; ROM, range of motion; St, stance; Sw, swing. *P < 0.05, obese versus lean group.
HPM (W/kg) APm (W/kg) APM (W/kg) APMnorm [W s/(kg m)]
1.76 − 1.04 4.23 3.89
(1.17)* (0.30)* (0.92)* (0.80)
Lean group 0.58 − 0.52 3.29 3.57
(0.26) (0.18) (0.85) (0.90)
Data are expressed as median (quartile range). AP, ankle power; HP, hip power; M, maximum value; m, minimum value. *P < 0.05, obese versus lean group.
It is generally acknowledged that the latter could possibly be related mainly to anthropometrics in which an excess of mass imposes per se functional limitations. In addition, 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 (Capodaglio et al., 2010; Cimolin et al., 2010, 2011a, 2011b; Menegoni et al., 2011). A cautious gait pattern has been previously described in obese adults (De Souza et al., 2005; Lai et al., 2008; Capodaglio et al., 2010; Cimolin et al., 2010, 2011a, 2011b; Menegoni et al., 2011): some authors (Capodaglio et al., 2010; Cimolin et al., 2010, 2011a, 2011b; Menegoni 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
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Gait in obese adolescents Cimolin et al. 45
Fig. 2
Pelvic obliquity
Deg.
20 15
50
10
40
5
30 20
0 −5
0
50
100
10
−10
0
−15
−10
−20
−20
Deg.
0
Pelvic rotation
20
50
10
40
5
30
0 0
50
100
50
100
Hip flex-extension
60
15
−5
Pelvic tilt
60
20 10
−10
0
−15
−10
−20
−20
0
50
100
%gait cycle
Ankle dorsiplantar flexion
60 50 40 Deg.
30 20 10 0 −10 −20
0
50
100
%gait cycle
Pelvic obliquity, pelvic tilt, pelvic rotation, hip flex-extension and ankle dorsiplantar flexion plots of a representative obese adolescent (dashed line) and mean value of the control group (solid line) are reported.
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 a lower plantar flexion angle at toe-off and no differences in terms of ankle plantar flexion moment peak and ankle power peak normalized to walking velocity when compared with the control group. Other kinematics and kinetics reports in obese versus nonobese individuals have been inconsistent: no differences in motion, but lower muscle moments at the ankle (Browning and Kram, 2007), lower plantar flexion and greater dorsal flexion angles (Spyropoulos et al., 1991), greater plantar flexion motion
and higher ankle moments (DeVita and Hortobágyi, 2003), lower plantar flexion moments (Gushue et al., 2005; McMillan et al., 2010). Several significant findings were noted in the sagittal plane at knee level. In line with a previous report (McMillan et al., 2010), individuals who were obese had less flexion at initial contact, and no significant differences in sagittal plane angles have been reported (Browning and Kram, 2007). Obese individuals 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
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46 International Journal of Rehabilitation Research
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joint structure. However, neither knee extensor strength nor knee stability was directly measured in this study. Participants who were obese had a normal ROM on the sagittal plane (HFE-ROM index) and higher hip generation power during early stance (HPM index), as compared with 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 adolescents but to children (8–10 years) (McGraw et al., 2000) or adults (>30 years) (Spyropoulos et al., 1991; Browning and Kram, 2007). In the frontal plane, hip excursion (HAA-ROM index) was higher in the obese group than in the lean group. This strategy, directly linked to the pelvis movement in the frontal plane (POROM index), may be because of the presence of excessive adipose tissue inside the thighs, as previously suggested by 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 the terminal phase of stance, as demonstrated by the 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 a 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, as our convenience sample consisted only of male participants, whereas there were mainly female participants in McMillan et al.’s (2010) study, and to methodological differences. Other authors in fact assessed using video recording (Browning and Kram, 2005) or treadmill walking (Spyropoulos et al., 1991; DeVita and Hortobágyi, 2003), whereas our data were collected while walking on the ground in a motion analysis laboratory at self-selected velocity. Speculation 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 participants 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 a 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, as the measurements served mainly as functional outcome of the rehabilitation programme and were not specifically aimed at research purposes and hence no adhoc protocol had been implemented. The literature showed that this marker set could be influenced by inaccurate marker placement and soft tissue artefact (Della Croce et al., 2005; Baker, 2006). Of particular concern, especially in obese participants, is the reliance on anterior superior iliac spine 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, the literature demonstrated that pelvic and hip ranges of motion, knee flex-extension and ankle dorsiflexion 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 in order to ensure that these parameters are not affected by errors, excluding those influenced by inaccurate marker placement because of the presence of fat. Conclusion
In our paper, significant differences were found in the gait pattern of obese adolescents when compared with normal weight individuals. The fact that notable differences can be measured even in a nonparticularly demanding, with regard to joint kinetics, and natural task such as walking should be borne 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 is known to be higher in obese individuals, may well increase further. Because adolescents who are obese are recommended to walk daily 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 clarify whether the prescription of walking is appropriate and safe and generates 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 as stabilizing the ankle and leading to improved balance (Capodaglio et al., 2009, 2011a, 2011b; Vismara et al., 2010; Cimolin et al., 2011a, 2011b) and gait (Cimolin et al., 2010) in genetically obese individuals. From our present data, it seems that obese adolescents
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Gait in obese adolescents Cimolin et al. 47
would benefit from comprehensive inpatient and outpatient rehabilitation programmes 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 limitation of this study is the small sample size and the lack of female obese participants. Our findings could therefore not be generalized to both genders. Further additional studies on female participants will be needed to clarify the plausible sexrelated differences of these parameters. Another limitation is represented by the choice of the marker set, previously discussed. Future studies that report kinematic data using obese individuals should need to clearly identify marker placement and skeletal model development procedures and how errors regarding marker placement were addressed. A combination of 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) is suggested to improve the accuracy of marker-based motion capture.
Acknowledgements The authors acknowledge Eng. Carlotta Mondadori and Chiara Montanari for their valuable contribution. Conflicts of interest
There are no conflicts of interest.
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Spyropoulos P, Pisciotta JC, Pavlou KN, Cairns MA, Simon SR (1991). Biomechanical gait analysis in obese men. Arch Phys Med Rehabil 72:1065–1070. Stagni R, Leardini A, Cappozzo A, Grazia Benedetti M, Cappello A (2000). Effects of hip joint centre mislocation on gait analysis results. J Biomech 33:1479–1487. Vismara L, Cimolin V, Grugni G, Galli M, Parisio C, Sibilia O, Capodaglio P (2010). Effectiveness of a 6-month home-based training program in Prader–Willi patients. Res Dev Disabil 31:1373–1379. Von Kries R, Koletzko B, Sauerwald T, von Mutius E, Barnert D, Grunert V, von Voss H (1999). Breast feeding and obesity: cross sectional study. BMJ 319:147–150. Wearing SC, Hennig EM, Byrne NM, Steele JR, Hills AP (2006). The biomechanics of restricted movement in adult obesity. Obes Rev 7:13–24.
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Gait pattern in lean and obese adolescents Veronica Cimolina,b, Manuela Gallia,d, Luca Vismarab, Giorgio Albertinid, Alessandro Sartorioc and Paolo Capodagliob Obesity is the most common chronic disorder in children and adolescents. As walking is the most common daily task and is recommended for weight management, quantifying how obesity affects the biomechanics of gait provides important insight into the relationship between metabolic and mechanical energetics, mechanical loading and associated risk for musculoskeletal injury. This study quantitatively compared gait in 12 obese and 10 lean adolescents. Obese adolescents showed longer stance duration, excessive hip flexion during the whole gait cycle and an increased hip movement in the frontal plane compared with lean participants. In the obese, the knee was slightly extended in stance phase and the 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 a higher absorption at initial stance and higher values of power generation in the terminal stance. Because obese adolescents are encouraged to walk to increase their physical activity and
Introduction Obesity is the most common chronic disorder in children and adolescents in industrialized societies. In some countries, the prevalence of obesity in childhood and adolescence has become much higher than that of 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; Clément 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 towards obesity is inherited (Bouchard, 1996; Bray et al., 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 for 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 individuals whose BMI had been greater than the 75th percentile as adolescents (Calle et al., 1999). Childhood obesity seems to increase 0342-5282 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
energy expenditure level, injury prevention and rehabilitative programmes 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. International Journal of Rehabilitation Research 38:40–48 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. International Journal of Rehabilitation Research 2015, 38:40–48 Keywords: gait analysis, kinematics, kinetics, obesity, rehabilitation a
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, bOrthopaedic Rehabilitation Unit, Clinical Lab for Gait Analysis and Posture, Ospedale San Giuseppe, Piancavallo (VB), cDivision of Auxology and Experimental Laboratory for Auxo-Endocrinological Research, Istituto Auxologico Italiano, IRCCS, Ospedale San Giuseppe, Piancavallo (VB) and dIRCCS ‘San Raffaele Pisana’, Tosinvest Sanità, Rome, Italy Correspondence to Veronica Cimolin, PhD, Department of Electronics, Information and Bioengineering, Politecnico di Milano, p.za Leonardo da Vinci 32, 20133 Milan, Italy Tel: + 39 02 2399 3359; fax: + 39 02 2399 3360; e-mail: [email protected] Received 23 May 2014 Accepted 7 September 2014
the risk for subsequent morbidity whether or not obesity persists in adulthood (Bray et al., 1998; Bray, 1999; Pi-Sunyer et al., 1999; Barker, 2000). 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 and 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 because of 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 involve considerable levels of energy expenditure. Although 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. The literature on this topic is scanty, with a variety of investigations on the effects of obesity on the characteristics of walking yielding inconsistent results. To date, studies have included only adults and children (8–13 years old) and considered only spatiotemporal parameters (McGraw et al., 2000). To our knowledge, the only quantitative analysis on obese adolescents was conducted without DOI: 10.1097/MRR.0000000000000089
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Gait in obese adolescents Cimolin et al. 41
considering the whole gait cycle (McMillan et al., 2010). McGraw et al. (2000) demonstrated that obese boys spend a greater double stance phase during gait and have higher sway areas, energy and variability, especially in the medial/lateral direction as for posture. McMillan et al. (2010) showed significant differences between obese and normal weight adolescents at all lower limb joint kinematics; in particular, obese participants seem to use a gait strategy aimed to minimize 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 for musculoskeletal injury or pathology. Previous studies evidenced that muscle weakness may be one potential cause of the movement alterations (McMillan et al., 2010) associated with decreased stability caused by the increased noncontributory mass added to the system, instead of impairment of motor control system (McGraw et al., 2000). On the basis of previous findings and our direct clinical experience, we hypothesized that differences may be present between obese and normal weight adolescents. Identifying those differences also appears crucial for developing effective physical activity recommendations specific for these individuals, 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 spatiotemporal, kinematic and kinetic parameters of gait in agematched obese and normal weight individuals using 3D quantitative analysis of walking [3D-gait analysis (GA)] during the entire gait cycle.
Participants and methods Participants
In this observational study, obese (BMI > 97th percentile or > 2 SD from the mean for age and sex) and normal weight (BMI was between the 25th and 75th percentile) children were recruited from the beginning of January 2008 through December 2008, matched for age and height. In particular, we considered 14 obese boys (mean age 15.71 ± 14.65 years; BMI 32.89 ± 3.67 kg/m2; BMISDS 2.46 ± 0.44 kg/m2; body mass 91.79 ± 13.41 kg; height 1.65 ± 0.05 m) and 10 normal weight and agematched adolescent boys (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 individuals were musculoskeletal, neuromuscular or cardiopulmonary conditions other than obesity that would hinder mobility capacity or contraindicate the integrated weight management programme. Obese individuals were recruited from among patients admitted to a hospital-based integrated weight management programme at the Division of Auxology, Istituto Auxologico Italiano, Piancavallo (VB),
Italy. They were not involved in regular physical daily activities before the admission to the hospital (< 1–2 h/ week). With regard to education, three individuals had scholastic problems (repeating a year). All patients had absence of relevant physical comorbidities hampering the execution of the tests; no one suffered from diabetes and hypertension. No individual suffered from pain, headache, balance disorders or any other symptoms hampering the execution of the tests. Exclusion criteria for the nonobese controls included history of cardiovascular, neurological or musculoskeletal disorders. The nonobese 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 from the participants and their parents. Experimental set-up
The complete evaluation consisted of clinical examination, video recording and 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 six 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 nonobese individuals were assessed at the Movement Analysis Lab of the IRCCS ‘San Raffaele Pisana’, Tosinvest Sanità, Rome, Italy, using a 12-camera optoelectronic system (ELITE2002; BTS, Milan, Italy) with a sampling rate of 100 Hz, two force platforms (Kistler, CH) and two TV camera video system (BTS) 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 pertaining to the same participants 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) of the two healthy participants was not statistically different. To evaluate the kinematics of each body segment, passive markers were positioned on the participant’s body according to Helen Hayes marker set by an experienced operator in a manner consistent with the literature (Kadaba et al., 1990; Davis et al., 1991) and to reduce potential sources of bias due to incorrect marker placement. This marker set was chosen as the protocol of choice to acquire the movement of lower limbs based on the study by 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
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barefoot at their own natural pace (self-selected speed) along a walkway containing the force platforms at the midpoint. Five acquisitions comprehensive of kinematic and kinetic data were collected for each patient 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. Therefore, the joint rotation angles that are determined clinically are pelvic obliquity (PO)-tilt-rotation, hip adduction/abductionflexion/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; for example, 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 individual’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 joint angles, following ‘inverse dynamic problem’ procedure analysis. Joint moments and powers were normalized for body weight and reported in newton-metres per kilogram. All graphs obtained from GA were normalized as a percentage of gait cycle (0–100). For each participant (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 spatiotemporal, 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, Piancavallo (VB), Italy], data were exported in .txt and .xls files. From these data formats we identified and calculated some parameters (time/distance parameters, angles joint values in specific gait cycle instant and 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 nonobese group the mean value (SD and coefficient of variation) for the right side and the left side was computed separately, evidencing the dominant and nondominant side.
Statistical analysis
All of the previously defined parameters were computed for each participant and then the median and quartile range values of all the indices were calculated for each participant and then for each group. With the proposed sample sizes, the study will have a power of 81%. The Kolmogorov–Smirnov test was used to verify whether the parameters were normally distributed; the parameters were not normally distributed, and therefore we used nonparametric analysis. Data of the right and left sides and dominant and nondominant legs were compared using the Wilcoxon signed-rank test. Obese and nonobese data were compared using the Mann–Whitney U-test to detect significant differences. Null hypotheses were rejected when P values were below 0.05.
Results All patients included in the study showed good compliance; two patients of the obese group were not included in the study as their GA data did not include accurate kinetics. Therefore, 12 children composed the obese group (mean age 15.83 ± 14.75 years; 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 the obese group were significantly different from that of the nonobese group. An initial comparison between the parameter values of the right versus left limb and dominant versus nondominant 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 spatiotemporal, kinematic and kinetic indices considered in this study for obese and nonobese participants are reported. Spatiotemporal parameters
Obese participants were characterized by slightly higher stance duration compared with nonobese participants (P < 0.05), whereas no statistical differences were found in terms of velocity and step length, which was similar in the two groups (P > 0.05) (Table 2). Kinematic parameters
As for the pelvic joint, the obese were characterized by a significantly higher pelvic excursion in planes of the movement (sagittal, frontal and transversal planes) as compared with the control group (P < 0.05). Obese participants exhibited hip range of motion (ROM) on the sagittal plane [hip flex-extension (HFE)-ROM index] close to physiological values, and on the frontal plane [hip abduction/adduction (HAA)-ROM index] increased as compared with lean participants. Obese participants showed a slightly extended knee at the initial contact and at midstance. In the swing phase, the maximum value of knee flexion was not statistically different between the two groups, and the knee ROM was similar to controls.
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Gait in obese adolescents Cimolin et al. 43
Table 1
Gait parameters and descriptors
Gait parameters Spatiotemporal parameters Velocity (m/s) Cadence (steps/min) %stance (%gait cycle) Step length Kinematics (deg.) PT-ROM PO-ROM PR-ROM HFE-ROM HAA-ROM KIC KmSt KMSw KFE-ROM AIC AMSt AmSt AMSw ADP-ROM Kinetics HPM (W/hg) KmIC (N m/kg) KMSt (N m/kg) KmSt (N m/kg) AM (N m/kg) APm (W/kg) APM (W/kg) APMnorm [W s/(kg m)]
Description Mean velocity of progression (m/s) equal to step length times cadence (steps/min) Rate at which a person walks (steps/min) Duration of the stance phase, expressed as % of the gait cycle Longitudinal distance from one foot strike to the next one (mm) 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 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 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 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 The range of motion at the hip joint on the frontal plane (hip abduction/adduction graph) during the gait cycle, calculated as the difference between the maximum and minimum values of the plot Value of knee flexion-extension angle (knee position on sagittal plane) at initial contact, representing the position of the knee joint at the beginning of the gait cycle Minimum of knee flexion (knee position on sagittal plane) in midstance, representing the extension ability of the knee during this phase of the gait cycle Peak of knee flexion (knee position on sagittal plane) in swing phase, representing the flexion ability of the knee joint during this phase of the gait cycle The range of motion at the 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 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 Peak of ankle dorsiflexion (on sagittal plane) during stance phase, representing the dorsiflexion ability of ankle joint during this phase of gait cycle Minimum value of the ankle joint angle (on sagittal plane) in stance phase, representing the plantar flexion ability of the ankle joint at toe-off Peak of ankle dorsiflexion (on sagittal plane) during swing phase, representing the dorsiflexion ability of the ankle joint in this phase of the gait cycle The range of motion at the ankle joint on the sagittal plane (ankle dorsiplantar flexion graph) during the gait cycle, calculated as the difference between the maximum and minimum values of the plot The maximum value of hip power generation during the first part of the stance phase The minimum value of knee moment at the initial contact The maximum value of knee moment during the early stance The minimum value of knee moment during the late stance The peak of plantar flexion moment of ankle joint in the second half of stance Minimum value of absorbed ankle power in early stance and midstance, when the muscle is contracting eccentrically and absorbing energy (minimum value of negative ankle power) 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 The maximum ankle power during terminal stance normalized to the velocity of progression
A, ankle; ADP, ankle dorsiplantar flexion; AIC, ankle at IC; AP, ankle power; HAA, hip abduction/adduction; HFE, hip flex-extension; IC, initial contact; K, knee; KFE, knee flex-extension; KIC, knee at IC; M, maximum value; m, minimum value; PO, pelvic obliquity; PR, pelvic rotation; PT, pelvic tilt; ROM, range of motion; St, stance; Sw, swing.
The kinematic analysis showed a plantar flexed position at initial contact and at toe-off, leading to a greater ankle ROM during stance phase in the obese group (Table 2 and Fig. 2). Kinetic parameters
Obese individuals were characterized by higher values of maximum power generated at hip level during the first part of the stance phase. In terms of ankle kinetics, no differences were found in the peak of plantar flexion moment during the second half of stance (AMM index; obese 1.45 ± 0.31 vs. 1.39 ± 0.30 N m/kg, P = 0.07). Obese individuals were also characterized by higher absorbed power at initial stance and higher values of ankle power generation in terminal stance; however, the APM index normalized 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 initial contact: obese − 0.24 ± 0.06 vs. − 0.27 ± 0.13 N m/kg; maximum value of knee moment during the early stance: obese 0.17 ± 0.14 vs. 0.14 ± 0.13 N m/kg; and minimum value of knee moment during the late stance: obese − 0.15 ± 014 vs. − 0.25 ±0.16 N m/kg) (Table 3).
Discussion The purpose of this study was to investigate the lower extremity biomechanics, in terms of spatiotemporal 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 may be present between obese and normal weight adolescents. In line with previous reports on obese children (Hills and Parker, 1991; McGraw et al., 2000), we observed, as for spatiotemporal parameters, longer stance duration, indicating some degree of underlying postural instability.
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44 International Journal of Rehabilitation Research
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Fig. 1
Assessed for eligibility (n = 24) •
Obese adolescents (n = 14)
•
Normal weight adolescents (n = 10) Excluded (n = 0) •
Not meeting inclusion crtieria (n = 0)
•
Declined to participate (n = 0)
•
Other reasons (n = 0)
Evaluated using 3D-gait analysis (n = 24) •
Obese adolescents (n = 14)
•
Normal weight adolescents (n = 10) Excluded (n = 2) •
Obese adolescents (n = 2) → data without kinetics
•
Normal weight adolescents (n = 0)
Analysed (n = 22) •
Obese adolescents (n = 12)
•
Normal weight adolescents (n = 10)
Flow diagram describing the formation of the study groups.
Spatiotemporal and kinematic parameters of the study groups
Table 2
Table 3
Kinetic parameters of the study groups Obese group
Spatiotemporal parameters %stance (%gait cycle) Step length (mm) Velocity (m/s) Pelvis (deg.) PT-ROM PO-ROM PR-ROM Hip joint (deg.) HIC HmSt HFE-ROM HAA-ROM Knee joint (deg.) KIC KmSt KMSw KFE-ROM Ankle joint and foot (deg.) AIC AMSt AmSt ADP-ROM AMSw
Obese group
Lean group
62.74 (1.71)* 0.61 (0.06) 1.08 (0.11)
58.60 (2.66) 0.58 (0.07) 0.95 (0.16)
7.90 (1.64)* 7.88 (3.36)* 11.89 (5.25)*
4.86 (4.36) 6.96 (3.15) 8.47 (3.31)
43.59 − 1.46 42.12 16.05
(11.08)* (10.77)* (9.87) (4.47)*
26.70 − 11.07 43.52 11.08
(7.04) (7.93) (4.76) (4.10)
0.98 − 5.07 44.99 53.36
(7.19)* (3.31)* (14.68) (7.02)
4.88 0.96 52.04 54.03
(6.21) (6.27) (7.10) (11.51)
− 4.23 12.79 − 17.68 28.71 2.62
(5.52)* (5.41) (8.64)* (8.46)* (4.28)
− 1.97 16.23 − 9.28 23.73 2.94
(3.87) (3.85) (6.99) (7.23) (3.88)
Data are expressed as median (quartile range). A, ankle; ADP, ankle dorsiplantar flexion; AIC, ankle at IC; H, hip; HAA, hip abduction/adduction; HFE, hip flex-extension; HIC, hip at IC; IC, initial contact; K, knee; KFE, knee flex-extension; KIC, knee at IC; M, maximum value; m, minimum value; PO, pelvic obliquity; PT, pelvic tilt; ROM, range of motion; St, stance; Sw, swing. *P < 0.05, obese versus lean group.
HPM (W/kg) APm (W/kg) APM (W/kg) APMnorm [W s/(kg m)]
1.76 − 1.04 4.23 3.89
(1.17)* (0.30)* (0.92)* (0.80)
Lean group 0.58 − 0.52 3.29 3.57
(0.26) (0.18) (0.85) (0.90)
Data are expressed as median (quartile range). AP, ankle power; HP, hip power; M, maximum value; m, minimum value. *P < 0.05, obese versus lean group.
It is generally acknowledged that the latter could possibly be related mainly to anthropometrics in which an excess of mass imposes per se functional limitations. In addition, 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 (Capodaglio et al., 2010; Cimolin et al., 2010, 2011a, 2011b; Menegoni et al., 2011). A cautious gait pattern has been previously described in obese adults (De Souza et al., 2005; Lai et al., 2008; Capodaglio et al., 2010; Cimolin et al., 2010, 2011a, 2011b; Menegoni et al., 2011): some authors (Capodaglio et al., 2010; Cimolin et al., 2010, 2011a, 2011b; Menegoni 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
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Gait in obese adolescents Cimolin et al. 45
Fig. 2
Pelvic obliquity
Deg.
20 15
50
10
40
5
30 20
0 −5
0
50
100
10
−10
0
−15
−10
−20
−20
Deg.
0
Pelvic rotation
20
50
10
40
5
30
0 0
50
100
50
100
Hip flex-extension
60
15
−5
Pelvic tilt
60
20 10
−10
0
−15
−10
−20
−20
0
50
100
%gait cycle
Ankle dorsiplantar flexion
60 50 40 Deg.
30 20 10 0 −10 −20
0
50
100
%gait cycle
Pelvic obliquity, pelvic tilt, pelvic rotation, hip flex-extension and ankle dorsiplantar flexion plots of a representative obese adolescent (dashed line) and mean value of the control group (solid line) are reported.
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 a lower plantar flexion angle at toe-off and no differences in terms of ankle plantar flexion moment peak and ankle power peak normalized to walking velocity when compared with the control group. Other kinematics and kinetics reports in obese versus nonobese individuals have been inconsistent: no differences in motion, but lower muscle moments at the ankle (Browning and Kram, 2007), lower plantar flexion and greater dorsal flexion angles (Spyropoulos et al., 1991), greater plantar flexion motion
and higher ankle moments (DeVita and Hortobágyi, 2003), lower plantar flexion moments (Gushue et al., 2005; McMillan et al., 2010). Several significant findings were noted in the sagittal plane at knee level. In line with a previous report (McMillan et al., 2010), individuals who were obese had less flexion at initial contact, and no significant differences in sagittal plane angles have been reported (Browning and Kram, 2007). Obese individuals 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
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46 International Journal of Rehabilitation Research
2015, Vol 38 No 1
joint structure. However, neither knee extensor strength nor knee stability was directly measured in this study. Participants who were obese had a normal ROM on the sagittal plane (HFE-ROM index) and higher hip generation power during early stance (HPM index), as compared with 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 adolescents but to children (8–10 years) (McGraw et al., 2000) or adults (>30 years) (Spyropoulos et al., 1991; Browning and Kram, 2007). In the frontal plane, hip excursion (HAA-ROM index) was higher in the obese group than in the lean group. This strategy, directly linked to the pelvis movement in the frontal plane (POROM index), may be because of the presence of excessive adipose tissue inside the thighs, as previously suggested by 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 the terminal phase of stance, as demonstrated by the 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 a 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, as our convenience sample consisted only of male participants, whereas there were mainly female participants in McMillan et al.’s (2010) study, and to methodological differences. Other authors in fact assessed using video recording (Browning and Kram, 2005) or treadmill walking (Spyropoulos et al., 1991; DeVita and Hortobágyi, 2003), whereas our data were collected while walking on the ground in a motion analysis laboratory at self-selected velocity. Speculation 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 participants 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 a 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, as the measurements served mainly as functional outcome of the rehabilitation programme and were not specifically aimed at research purposes and hence no adhoc protocol had been implemented. The literature showed that this marker set could be influenced by inaccurate marker placement and soft tissue artefact (Della Croce et al., 2005; Baker, 2006). Of particular concern, especially in obese participants, is the reliance on anterior superior iliac spine 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, the literature demonstrated that pelvic and hip ranges of motion, knee flex-extension and ankle dorsiflexion 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 in order to ensure that these parameters are not affected by errors, excluding those influenced by inaccurate marker placement because of the presence of fat. Conclusion
In our paper, significant differences were found in the gait pattern of obese adolescents when compared with normal weight individuals. The fact that notable differences can be measured even in a nonparticularly demanding, with regard to joint kinetics, and natural task such as walking should be borne 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 is known to be higher in obese individuals, may well increase further. Because adolescents who are obese are recommended to walk daily 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 clarify whether the prescription of walking is appropriate and safe and generates 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 as stabilizing the ankle and leading to improved balance (Capodaglio et al., 2009, 2011a, 2011b; Vismara et al., 2010; Cimolin et al., 2011a, 2011b) and gait (Cimolin et al., 2010) in genetically obese individuals. From our present data, it seems that obese adolescents
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Gait in obese adolescents Cimolin et al. 47
would benefit from comprehensive inpatient and outpatient rehabilitation programmes 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 limitation of this study is the small sample size and the lack of female obese participants. Our findings could therefore not be generalized to both genders. Further additional studies on female participants will be needed to clarify the plausible sexrelated differences of these parameters. Another limitation is represented by the choice of the marker set, previously discussed. Future studies that report kinematic data using obese individuals should need to clearly identify marker placement and skeletal model development procedures and how errors regarding marker placement were addressed. A combination of 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) is suggested to improve the accuracy of marker-based motion capture.
Acknowledgements The authors acknowledge Eng. Carlotta Mondadori and Chiara Montanari for their valuable contribution. Conflicts of interest
There are no conflicts of interest.
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