BJMB
Brazilian Journal of Motor Behavior
Special issue:
Effects of aging on locomotor patterns
!
Barbieri et al.
2022
VOL.16
N.5
362 of 371
Gait velocity and stability are correlated to muscle and bone mass loss in people with
Parkinson’s disease: a preliminary study
FABIO A. BARBIERI
1
| MURILO H. FARIA
1
| LUCAS SIMIELI
1
| TIAGO PENEDO
1
| CARLOS A. KALVA-FILHO
1
| VICTOR
S. BERETTA
2
1
São Paulo State University (Unesp), School of Sciences, Department of Physical Education, Human Movement Research Laboratory (MOVI-LAB), Bauru, SP, Brazil.
2
São Paulo State University (Unesp), School of Technology and Sciences, Physical Education Department, Presidente Prudente, São Paulo, Brazil.
Correspondence to:!Prof. Dr. Fabio Augusto Barbieri, São Paulo State University (Unesp), School of Sciences, Department of Physical Education, Human Movement
Research Laboratory (MOVI-LAB).
Av. Eng. Luiz Edmundo Carrijo Coube, 14-01-Vargem Limpa, Bauru/SP, CEP: 17033-360, Brazil.
Phone: +55 14 3103-9612
email: fabio.barbieri@unesp.br
https://doi.org/10.20338/bjmb.v16i5.346
HIGHLIGTHS
• Reduced lean and bone mass were related to
gait deficits in Parkinson’s disease.
• Reduced lean and bone mass were related to
slower gait speed in Parkinson’s disease.
• Bone mass loss may cause gait instability in
people with Parkinson’s disease.
• Body composition should be monitored over
the gait disability in Parkinson’s disease.
ABBREVIATIONS
C7 7
th
cervical vertebra
DXA Dual-energy X-ray
H&Y Hoehn & Yahr scale
MMSE Mini-Mental State Examination
ON One hour after the participants had
taken their dopaminergic
medication
PD Parkinson’s disease
UPDRS-III Motor section of the Unified
Parkinson's Disease Rating Scale
PUBLICATION DATA
Received 10 11 2022
Accepted 10 12 2022
Published 15 12 2022
BACKGROUND: Parkinson’s disease (PD) exacerbates muscle and bone mass loss, which is associated with
several negative outcomes such as falls and disability. Thus, muscle and bone mass loss may be one
mechanism for the mediator role between gait impairments and PD.
AIM: To verify the relationship between the spatial-temporal gait parameters and the body composition of the
lower limbs in people with PD.
METHOD: Thirteen people with PD were evaluated on two different days: i) clinical and gait evaluation; ii) body
composition evaluation. The step length, width, duration and speed, the percentage in double support, and gait
velocity during walking at self-selected velocity. Dual-energy X-ray absorptiometry technique was used to
measure fat mass, lean mass, bone mass, and total mass, for the whole body, and separately for each limb.
Pearson’s correlation coefficients were applied between the spatial-temporal gait parameters and the variables
of body composition of lower limbs.
RESULTS: Higher lean and bone mass of both legs were related to faster gait velocity (r=0.6, p<0.03 and r=0.7,
p<0.01, respectively) and step speed (r=0.5, p<0.05 and r=0.65, p<0.02, respectively). Also, narrower step width
was related to the higher bone mass of both legs (r=0.6, p<0.03). However, muscle and bone mass did not
correlate with step length and duration, and percentage of double support.
CONCLUSION: Our findings suggest that the muscle and bone mass of the lower limbs are important body
characteristics for gait impairments in people with PD and should be monitored over the disease.
KEYWORDS: Walking | Body composition | Parkinson’s disease | Sarcopenia | Osteopenia
INTRODUCTION
Sarcopenia is commonly seen in older people, mainly in those with chronic
degenerative diseases, such as rheumatoid arthritis, type 2 diabetes mellitus, Alzheimer’s
and Parkinson’s disease (PD)
1
. It shows a high prevalence, becoming a serious global
public health concern
2
. Sarcopenia stems from an abnormal decline in quality, quantity,
and functionality (i.e., strength) of the muscle mass related to aging
3
. In addition, aging is
related to a gradual loss of bone mineral density, known as osteopenia
4
, which may
progress to osteoporosis
5
. Both sarcopenia and osteopenia are associated with several
negative outcomes such as falls, disability, poor quality of life, institutionalization,
hospitalization, and death
6
. Also, they reduce the ability to perform daily living activities,
BJMB! ! ! ! ! ! ! !
Brazilian(Journal(of(Motor(Behavior(
(
Barbieri et al.
2022
VOL.16
N.5
363 of 371
Special issue:
Effects of aging on locomotor patterns
affecting gait velocity and stability
7
.
PD exacerbates sarcopenia and osteopenia. People with PD have three times
more chance of being sarcopenic than neurologically healthy older adults
1
. In addition,
osteopenia and osteoporosis are common in people with PD, affecting around 40% of
individuals
8
. The literature has suggested that few aspects are related to the exacerbated
sarcopenia and osteopenia conditions in PD
9
. Malnutrition results from reduced energy
intake
10
. The motor limitations caused by the PD progression
11
reduce the amount and
intensity of exercises, decreasing the mechanical load that the lower limb muscles exert on
the structure of the bones and that contributes to remodeling by stimulating osteoblastic
and osteoclastic activation
12
and keeping muscle mass and strength
13
. Also, the loss of
motor neurons that occurs in PD contributes to the genesis of sarcopenia, leading to
weakness and reduction in movements
14
.
The individuals with both PD and sarcopenia had greater difficulty performing
activities of daily living, such as brushing their teeth, taking a shower, and getting up from
bed
15
. In addition, a higher risk of falls is associated with people with PD and sarcopenia
9
.
The negative effects of PD and sarcopenia, such as reduced mobility, poor balance, and
reduced lower limb muscle strength, explain this association
16
. However, there is a lack of
literature on the effects of muscle and bone mass loss on gait parameters in people with
PD.
The step of people with PD is characterized by slower speed, shorter length,
longer duration and double support time, and wider width compared to neurologically
healthy older peers
17,18
. Despite gait impairments being related to motor symptoms of PD,
mainly rigidity and bradykinesia and basal ganglia deficits
19
, sarcopenia and osteopenia
can be important aspects to explain gait deficits in PD. Quantitative and qualitative
changes in muscle structure and function and bone mass are associated with slower gait
speed and reduced stability of gait in older adults
20
. Thus, sarcopenia and osteopenia may
be one mechanism for the mediator role between gait impairments and PD
21
. In addition,
sarcopenia and PD share common pathophysiological pathways for muscle fiber and bone
mass loss: inflammation, muscle autophagy, oxidative stress, and apoptosis
14
.
Considering that, understanding the effects of sarcopenia and osteopenia on gait
in people with PD is promising to improve interventions for walking impairments. Therefore,
this study aimed to verify the relationship between the spatial-temporal gait parameters
and the body composition (i.e., lean mass, fat mass, bone mass, and total mass) of the
lower limbs in people with PD. We expected that higher muscle and bone mass of the
lower limbs would be related to increased gait speed and step length, and reduced step
duration, double support time, and step width.
METHODS
Participants
Thirteen individuals with PD were recruited from the Ativa Parkinson Group at São
Paulo State University (Unesp – Bauru, SP, Brazil) to participate in this study. The
diagnosis of idiopathic PD was performed by an expert neurologist according to the UK
Parkinson’s Disease Brain Bank criteria. The exclusion criteria comprised (a) less than 60
years old, (b) other parkinsonism syndromes and/or neurological diseases, (c) rheumatic
or orthopedic impairments that affect gait, and (d) vestibular deficits and uncorrected
BJMB! ! ! ! ! ! ! !
Brazilian(Journal(of(Motor(Behavior(
(
Barbieri et al.
2022
VOL.16
N.5
364 of 371
Special issue:
Effects of aging on locomotor patterns
vision. In addition, individuals were included in the study if they were under dopaminergic
medication treatment for more than four months (using the same medication), exhibited no
signs of dementia, and were below four on the Hoehn & Yahr scale (H&Y)
22
. Written
informed consent was obtained from all participants according to the protocol approved by
the Ethical Committee of the School of Science at São Paulo State University, and it was
conducted according to the Declaration of Helsinki.
Experimental Protocol
People with PD performed the evaluations during the ON-medication state (one
hour after the participants had taken their dopaminergic medication
23
). The participants
were evaluated on two different days (three to five days apart): i) day 1 – clinical and gait
evaluation; ii) day 2 – body composition evaluation.
Clinical evaluation
A specialist evaluated the clinical characteristics of people with PD. First, an
anamnesis (clinical historical, cognition, and medication status) was performed. To
determine the degree and stage of disease, participants were evaluated by the motor
section of the Unified Parkinson's Disease Rating Scale (UPDRS-III)
24
and the H&Y
25
scale, respectively. In addition, cognitive screening for dementia was performed using
Mini-Mental State Examination
26,27
.
Gait evaluation
The spatial-temporal gait parameters were acquired through eight Vicon Motion
System® cameras (Bonita System Cameras), with a frequency of 100 Hz. Two passive
reflective markers were placed on each participant’s foot (second metatarsal and
calcaneus). Also, one marker was positioned in the 7
th
cervical vertebra (C7).
The participants were instructed to walk five times on an 8 x 3 m walkway at their
self-selected velocity. For safety purposes, an evaluator accompanied the participant
during the execution of the task. The data were filtered using a 5
th
-order low-pass digital
Butterworth filter (zero-lag) with a cut-off frequency of 6 Hz. The following gait parameters
were calculated: step length, step width, step duration, the percentage in double support
(time in double support normalized by step duration), and step speed. The spatial-temporal
parameters were calculated in four to six steps, and the average for each parameter was
calculated in each trial. In addition, the mean gait velocity was calculated by displacing a
marker located in C7.
Body composition evaluation
Dual-energy X-ray (DXA) absorptiometry technique (DXA Discovery, Hologic,
USA) with a Hologic Discovery total body scan, fan-beam densitometer, software QDR for
Windows version 12.5 (Hologic, Waltham, Massachusetts, USA) was used to measure
body composition. The DXA has a low coefficient of variation (bone mineral content =
0.6%, soft tissue without fat = 0.3%, fat mass, and body fat percentage = 2.5%)
28
, and the
radiation exposure during the procedure is less than 0.05 mRem (0.5 Sv). Before
performing the analyses with the participants, a phantom step with six acrylic and
aluminum fields of different thicknesses and known absorption properties were scanned to
serve as an external standard for the analysis of different tissue components following the
BJMB! ! ! ! ! ! ! !
Brazilian(Journal(of(Motor(Behavior(
(
Barbieri et al.
2022
VOL.16
N.5
365 of 371
Special issue:
Effects of aging on locomotor patterns
recommendations of the protocol described by the manufacturer, which is a validated
procedure for the general use of DXA
29
.
The segmental body composition was determined from the regional analysis of the
whole body. The lower limbs were assessed by separating the legs from the hip by an
inclined line crossing the hip joint. The participant was instructed to wear no metal objects,
including undergarments with metal parts, on the day of the examination. This orientation
was recalled and verified before the participant entered the room where they would carry
out the examination. Participants were placed supine in a table fixed from which X-rays
were emitted. A "sweep" was performed through a reader arm (cursor) that walked over
the body area of interest without touching the individual. The values of the body
composition of each participant were calculated by summing the values of the right and left
lower limbs for each variable. The values of the body composition measured were fat
mass, lean mass, bone mass, and total mass, for the whole body, and separately for each
limb.
Statistical analysis
The software SPSS 21.0 for Windows
®
was used for statistical analysis. The level
of significance was maintained at p < 0.05. The data showed normal distribution in the
Shapiro-Wilk test. To verify the correlation between the spatial-temporal gait parameters
and the variables of body composition of lower limbs, Pearson’s correlation coefficients
were applied. The correlation coefficients were classified as weak: 0 < r < 0.3, moderate:
0.4 < r < 0.6, and strong r > 0.7
30
.
RESULTS
General and clinical characteristics, spatial-temporal parameters, and body
composition parameters are presented in Table 1.
The r-values of Pearson’s correlation and significant p-values are presented in
Table 2. Faster gait velocity was strong and moderate related to higher bone mass and
lean mass of both legs, respectively. In addition, wider step width was moderately related
to lower bone mass of both legs, while faster step speed was moderately related to higher
lean mass for both legs. Step length, step duration, and the percentage of double support
were not related to body composition parameters (p > 0.05).
BJMB! ! ! ! ! ! ! !
Brazilian(Journal(of(Motor(Behavior(
(
Barbieri et al.
2022
VOL.16
N.5
366 of 371
Special issue:
Effects of aging on locomotor patterns
Table 1. Means, standard deviations, and maximum and minimum values (in brackets) of general and
clinical characteristics, body composition, and spatial-temporal gait parameters.
General and clinical characteristics
Age (years)
67 ± 9 [56 – 84]
Height (m)
1.60 ± 0.08 [1.44 – 1.70]
Time of disease (years)
5.3 ± 3.7 [1 – 13]
H&Y (pts)
2.2 ± 0.4 [2 – 3]
UPDRS-III (pts)
25.7 ± 10.4 [15 – 50]
MMSE (pts)
27.2 ± 2.8 [21 – 30]
Body composition
Body mass (kg)
63.8 ± 11.6 [46.7 – 80.2]
Body fat mass (kg)
23.5 ± 8.6 [11.5 – 40.7]
Body lean mass (kg)
40.2 ± 6.6 [29.5 – 51.7]
Body bone mass (kg)
1.1 ± 0.24 [1.58 – 0.69]
Fat mass (kg)
Left limb
4.1 ± 1.3 [1.9 – 6.3]
Right limb
4.3 ± 1.3 [ 2.1 – 7.1]
Lean mass (kg)
Left limb
6.4 ± 1.1 [4.6 – 8.9]
Right limb
6.4 ± 1.3 [4.2 – 9.2]
Bone mass (kg)
Left limb
0.4 ± 0.1 [0.2 – 0.6]
Right limb
0.4 ± 0.2 [0.2 – 0.8]
Total mass (kg)
Left limb
10.5 ± 1.7 [8.4 – 12.9]
Right limb
10.7 ± 1.9 [8.2 – 13.6]
Spatial-temporal gait parameters
Mean gait velocity (m/s)
0.92 ± 0.19 [0.61 – 1.19]
Step length (cm)
48.11 ± 8.20 [31.32 – 57.25]
Step duration (s)
0.51 ± 0.02 [0.47 – 0.57]
Step speed (cm/s)
94.08 ± 18.96 [62.71 – 122.89]
Step width (cm)
13.30 ± 3.54 [8.55 – 19.56]
Double support time (%)
36.77 ± 4.62 [28.22 – 39.84]
H&Y - Hoehn & Yahr scale; UPDRS III – motor part of Unified Parkinson's Disease Rating Scale;
MMSE - Mini-Mental State Examination.
Table 2. Relationship between spatial-temporal gait parameters and lower limbs composition. r and p-values are presented in each column. (*) statistically significant.
Lower limb
Mean velocity
Step length
Step width
Step
duration
Step speed
Percentage
in double
support
Bone mass
Left
r = 0.70 (p < 0.01)*
r = 0.55
r = - 0.59 (p < 0.04)*
r = - 0.47
r = 0.65 (p < 0.02)*
r = - 0.05
Right
r = 0.71 (p < 0.01)*
r = 0.52
r = - 0.62 (p < 0.03)*
r = - 0.52
r = 0.65 (p < 0.02)*
r = - 0.07
Fat mass
Left
r = 0.042
r = 0.26
r = - 0.06
r = 0.52
r = 0.01
r = 0.29
Right
r = 0.004
r = 0.22
r = - 0.06
r = 0.54
r = - 0.02
r = 0.31
Lean mass
Left
r = 0.64 (p < 0.03)*
r = 0.48
r = - 0.55
r = - 0.43
r = 0.58 (p < 0.05)*
r = 0.09
Right
r = 0.62 (p < 0.03)*
r = 0.49
r = - 0.55
r = - 0.35
r = 0.56 (p < 0.05)*
r = 0.14
Total mass
Left
r = 0.52
r = 0.54
r = - 0.46
r = 0.02
r = 0.45
r = 0.25
Right
r = 0.49
r = 0.52
r = - 0.47
r = 0.04
r = 0.42
r = 0.28
BJMB! ! ! ! ! ! ! !
Brazilian(Journal(of(Motor(Behavior(
(
Barbieri et al.
2022
VOL.16
N.5
367 of 371
Special issue:
Effects of aging on locomotor patterns
DISCUSSION
This is a preliminary study that first time directly investigated the relationship
between lean and bone mass in the lower limbs and gait in people with PD. We
demonstrated that reduced lean and bone mass were related to slower gait speed and
reduced stability in people with PD (i.e., greater step width), which corroborated our
hypothesis. Specifically, faster gait velocity and step speed were related to higher bone
mass and lean mass of both legs, while narrower step width was related to the higher bone
mass of both legs. However, muscle and bone mass did not correlate with step length and
duration, and percentage of double support. Our findings suggest that muscle and bone
mass of the lower limbs are important body characteristics for gait impairments in people
with PD and should be monitored over the disease.
Reduced bone and muscle mass change the spatial-temporal gait parameters in
people with PD. It is well established in the literature that gait impairments in PD reflect
dysfunctions of cortico-basal ganglia-brainstem circuits 19. However, our findings seem to
show that changes in bone and muscle mass are also related to gait impairments in PD. A
previous study also reported that the decline in bone and muscle mass are related to the
stage of disease and reduced mobility in people with PD. Walking involves muscle activity
from the lower limbs, and other parts of the body, such as the trunk and upper limbs
muscles. Reduced muscular strength is known to be significantly and independently
associated with functional impairment, walking speed, balance, mobility tasks, physical
performance, and all-cause mortality
31
. Gait speed and stability performance are related to
muscle mass quality, which is important for independence
21
. Landi et al.
32
reported that a
reduction in lower limb muscle mass is directly related to lower strength of the legs, which
is correlated to slower gait speed and longer double support time in older people. People
with PD also reduce the strength of the lower limb
33
. Thus, the combination of inadequate
control of dorsiflexors
34
and muscle weakness
14
caused by PD can contribute to changes
in gait speed and stability.
Reduction in bone mass is also correlated to walking changes. Older women with
reduced bone mass in the hip, spine, and forearm walk with slow gait speed and large step
time and stance time
35
. Also, low bone mass is correlated to less power generation at the
hip and ankle as well as, less power absorption at the hip and knee, and stability during
walking
36
. Frailty older individuals show reduced bone mass, increasing the risk of falls
37
.
Thus, the slower gait speed and wider step width are possible gait adaptations used by
people with PD to deal with lower bone mass. However, despite the wider step width being
an efficient strategy to increase stability
38,39
, reduced gait speed does not cope with an
increased margin of stability, and thus higher stability
40
. Therefore, bone mass loss can be
related to gait instability in people with PD.
An important limitation of the present study is the small sample of individuals with
PD, which could impact the power of our statistical analysis. However, we found a
moderate correlation and a significant association (regression) with a small sample, which
is very promising. Also, we did not include neurologically older adults which did not allow
us to assume that the effects are related to PD and not to aging. Thus, future studies
should compare the associations of bone and muscle mass with gait parameters
considering older adults with and without PD. Finally, lean mass is all mass without fat (fat-
free mass), which involves not only muscles but also bones (data shown), tendons and
BJMB! ! ! ! ! ! ! !
Brazilian(Journal(of(Motor(Behavior(
(
Barbieri et al.
2022
VOL.16
N.5
368 of 371
Special issue:
Effects of aging on locomotor patterns
ligaments (data not shown). So, it should be considered when our findings are analyzed.
Despite such limitations, our preliminary study extends the current literature by
providing an understanding of the influence of bone and muscle mass on gait parameters
in people with PD. A practical application of our study is related to the gait rehabilitation
program. It must be incorporated exercises that keep muscle and bone mass and the
lower limb to improve walking in people with PD. Exercise, especially with a mechanical
load that contributes to remodeling
9
fosters a vicious cycle related to an increase in gait
speed, and functional independence
31
. Also, good nutrition should be considered for
keeping muscle and bone mass and improving gait in PD
10
.
CONCLUSION
The body composition is related to spatial-temporal parameters in people with PD.
Faster gait speed and reduced stability (i.e., wider step width) are correlated to higher
bone and muscle mass in PD. These findings reinforce the need to exercise in PD to
preserve muscle and bone mass for improving walking behavior.
REFERENCES
1. Ponsoni A, Sardeli AV, Costa FP, Mourão LF. Prevalence of sarcopenia in Parkinson’s
disease: A systematic review and meta-analysis. Geriatr Nurs. 2022;49:44-49.
doi:10.1016/j.gerinurse.2022.11.006
2. Shafiee G, Keshtkar A, Soltani A, Ahadi Z, Larijani B, Heshmat R. Prevalence of
sarcopenia in the world: a systematic review and meta- analysis of general population
studies. J Diabetes Metab Disord. 2017;16:21. doi:10.1186/s40200-017-0302-x
3. Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on
definition and diagnosis. Age Ageing. 2019;48(1):16-31. doi:10.1093/ageing/afy169
4. Zhang J, Morgan SL, Saag KG. Osteopenia: debates and dilemmas. Curr Rheumatol Rep.
2013;15(12):384. doi:10.1007/s11926-013-0384-5
5. Zeng Q, Li N, Wang Q, et al. The Prevalence of Osteoporosis in China, a Nationwide,
Multicenter DXA Survey. J Bone Miner Res. 2019;34(10):1789-1797.
doi:10.1002/jbmr.3757
6. Bahat G, Tufan A, Tufan F, et al. Cut-off points to identify sarcopenia according to
European Working Group on Sarcopenia in Older People (EWGSOP) definition. Clin Nutr.
2016;35(6):1557-1563. doi:10.1016/j.clnu.2016.02.002
7. Barichella M, Pinelli G, Iorio L, et al. Sarcopenia and Dynapenia in Patients With
Parkinsonism. J Am Med Dir Assoc. 2016;17(7):640-646. doi:10.1016/j.jamda.2016.03.016
8. Wood B, Walker R. Osteoporosis in Parkinson’s disease. Mov Disord. 2005;20(12):1636-
1640. doi:https://doi.org/10.1002/mds.20643
9. Lima DP, de Almeida SB, Bonfadini J de C, et al. Clinical correlates of sarcopenia and falls
in Parkinson’s disease. PLoS One. 2020;15(3):e0227238.
doi:10.1371/journal.pone.0227238
BJMB! ! ! ! ! ! ! !
Brazilian(Journal(of(Motor(Behavior(
(
Barbieri et al.
2022
VOL.16
N.5
369 of 371
Special issue:
Effects of aging on locomotor patterns
10. Kwan P. Sarcopenia, a neurogenic syndrome? J Aging Res. 2013;2013:791679.
doi:10.1155/2013/791679
11. van Nimwegen M, Speelman AD, Hofman-van Rossum EJM, et al. Physical inactivity in
Parkinson’s disease. J Neurol. 2011;258(12):2214-2221. doi:10.1007/s00415-011-6097-7
12. Bellido T. Osteocyte-driven bone remodeling. Calcif Tissue Int. 2014;94(1):25-34.
doi:10.1007/s00223-013-9774-y
13. Kamide N, Fukuda M, Miura H. The relationship between bone density and the physical
performance of ambulatory patients with Parkinson’s disease. J Physiol Anthropol.
2008;27(1):7-10. doi:10.2114/jpa2.27.7
14. Vetrano DL, Pisciotta MS, Laudisio A, et al. Sarcopenia in Parkinson Disease: Comparison
of Different Criteria and Association With Disease Severity. J Am Med Dir Assoc.
2018;19(6):523-527. doi:10.1016/j.jamda.2017.12.005
15. da Luz MCL, Bezerra GKA, Asano AGC, Chaves de Lemos M da C, Cabral PC.
Determinant factors of sarcopenia in individuals with Parkinson’s disease. Neurol Sci.
2021;42(3):979-985. doi:10.1007/s10072-020-04601-4
16. Canning CG, Paul SS, Nieuwboer A. Prevention of falls in Parkinson’s disease: a review of
fall risk factors and the role of physical interventions. Neurodegener Dis Manag.
2014;4(3):203-221. doi:10.2217/nmt.14.22
17. Simieli L, Gobbi LTB, Orcioli-Silva D, et al. The variability of the steps preceding obstacle
avoidance (approach phase) is dependent on the height of the obstacle in people with
Parkinson’s disease. Grahn JA, ed. PLoS One. 2017;12(9):e0184134.
doi:10.1371/journal.pone.0184134
18. Mirelman A, Bonato P, Camicioli R, et al. Gait impairments in Parkinson’s disease. Lancet
Neurol. 2019;18(7):697-708. doi:10.1016/S1474-4422(19)30044-4
19. Takakusaki K. Functional Neuroanatomy for Posture and Gait Control. J Mov Disord.
2017;10(1):1-17. doi:10.14802/jmd.16062
20. Larsson L, Degens H, Li M, et al. Sarcopenia: Aging-Related Loss of Muscle Mass and
Function. Physiol Rev. 2019;99(1):427-511. doi:10.1152/physrev.00061.2017
21. Perez-Sousa MA, Venegas-Sanabria LC, Chavarro-Carvajal DA, et al. Gait speed as a
mediator of the effect of sarcopenia on dependency in activities of daily living. J Cachexia
Sarcopenia Muscle. 2019;10(5):1009-1015. doi:10.1002/jcsm.12444
22. Hoehn MM, Yahr MD. Parkinsonism: onset, progression, and mortality. Neurology.
1967;17(5):427-427. doi:10.1212/WNL.17.5.427
23. Araújo-Silva F, Santinelli FB, Felipe I. Imaizumi L, et al. Temporal dynamics of cortical
activity and postural control in response to the first levodopa dose of the day in people with
Parkinson’s disease. Brain Res. 2022;1775(July 2021):147727.
doi:10.1016/j.brainres.2021.147727
24. Martínez-Martín P, Gil-Nagel A, Gracia LM, Gómez JB, Martínez-Sarriés J, Bermejo F.
Unified Parkinson’s disease rating scale characteristics and structure. Mov Disord.
1994;9(1):76-83. doi:10.1002/mds.870090112
BJMB! ! ! ! ! ! ! !
Brazilian(Journal(of(Motor(Behavior(
(
Barbieri et al.
2022
VOL.16
N.5
370 of 371
Special issue:
Effects of aging on locomotor patterns
25. Schenkman M, Wei Zhu C, Cutson TM, Whetten-Goldstein K. Longitudinal evaluation of
economic and physical impact of Parkinson’s disease. Park Relat Disord. 2001;8(1):41-50.
doi:10.1016/S1353-8020(00)00079-1
26. Almeida OP. The Mini-Mental State Examination and the Diagnosis of Dementia in Brazil.
Arq Neuropsiquiatr. 1998;56(3 B):605-612. doi:10.1590/S0004-282X1998000400014
27. Brucki SMD, Nitrin R, Caramelli P, Bertolucci PHF, Okamoto IH. Suggestions for utilization
of the mini-mental state examination in Brazil. Arq Neuropsiquiatr. 2003;61(3 B):777-781.
doi:10.1590/s0004-282x2003000500014
28. Bilsborough JC, Greenway K, Opar D, Livingstone S, Cordy J, Coutts AJ. The accuracy
and precision of DXA for assessing body composition in team sport athletes. J Sports Sci.
2014;32(19):1821-1828. doi:10.1080/02640414.2014.926380
29. Tataranni PA, Ravussin E. Use of dual-energy X-ray absorptiometry in obese individuals.
Am J Clin Nutr. 1995;62(4):730-734. doi:10.1093/ajcn/62.4.730
30. Cohen J. Statistical Power Analysis for the Behavioral Sciences (2nd Ed.). (Routledge,
ed.). Routledge; 1988. doi:10.4324/9780203771587
31. Wilson D, Jackson T, Sapey E, Lord JM. Frailty and sarcopenia: The potential role of an
aged immune system. Ageing Res Rev. 2017;36:1-10. doi:10.1016/j.arr.2017.01.006
32. Landi F, Liperoti R, Fusco D, et al. Prevalence and risk factors of sarcopenia among
nursing home older residents. J Gerontol A Biol Sci Med Sci. 2012;67(1):48-55.
doi:10.1093/gerona/glr035
33. Pereira VAI, Barbieri FA, Zagatto AM, et al. Muscle Fatigue Does Not Change the Effects
on Lower Limbs Strength Caused by Aging and Parkinson’s Disease. Aging Dis.
2018;9(6):988. doi:10.14336/AD.2018.0203
34. Pistacchi M, Gioulis M, Sanson F, et al. Gait analysis and clinical correlations in early
Parkinson’s disease. Funct Neurol. 2017;32(1):28-34. doi:10.11138/FNeur/2017.32.1.028
35. Palombaro KM, Hack LM, Mangione KK, et al. Gait variability detects women in early
postmenopause with low bone mineral density. Phys Ther. 2009;89(12):1315-1326.
doi:10.2522/ptj.20080401
36. Eldeeb AM, Khodair AS. Three-dimensional analysis of gait in postmenopausal women
with low bone mineral density. J Neuroeng Rehabil. 2014;11:55. doi:10.1186/1743-0003-
11-55
37. Cheng M-H, Chang S-F. Frailty as a Risk Factor for Falls Among Community Dwelling
People: Evidence From a Meta-Analysis. J Nurs Scholarsh. 2017;49(5):529-536.
doi:10.1111/jnu.12322
38. Barbieri FA, Beretta SS, Pereira VAI, et al. Recovery of gait after quadriceps muscle
fatigue. Gait Posture. 2016;43:270-274. doi:10.1016/j.gaitpost.2015.10.015
39. Santos PCR, Lamoth C, Vuillerme N, Barbieri FA. Faster walking under muscle fatigability:
a strategy to improve stability or a warm-up effect? Brazilian J Mot Behav. 2021;15(3):149-
152. doi:10.20338/bjmb.v15i3.241
BJMB! ! ! ! ! ! ! !
Brazilian(Journal(of(Motor(Behavior(
(
Barbieri et al.
2022
VOL.16
N.5
371 of 371
Special issue:
Effects of aging on locomotor patterns
40. Hak L, Houdijk H, Steenbrink F, et al. Speeding up or slowing down?: Gait adaptations to
preserve gait stability in response to balance perturbations. Gait Posture. 2012;36(2):260-
264. doi:10.1016/J.GAITPOST.2012.03.005
Citation: Barbieri FA, Faria MH, Simieli L, Penedo T, Kalva-Filho CA, Beretta VS. (2022).!Gait velocity and stability are
correlated to muscle and bone mass loss in people with Parkinson’s disease: a preliminary study. Brazilian Journal of
Motor Behavior, 16(5):362-371.
Editor-in-chief: Dr Fabio Augusto Barbieri - São Paulo State University (UNESP), Bauru, SP, Brazil. !
Associate editors: Dr José Angelo Barela - São Paulo State University (UNESP), Rio Claro, SP, Brazil; Dr Natalia
Madalena Rinaldi - Federal University of Espírito Santo (UFES), Vitória, ES, Brazil; Dr Renato de Moraes – University
of São Paulo (USP), Ribeirão Preto, SP, Brazil.
Guest editors: Dr Paulo Cezar Rocha dos Santos - Weizmann Institute of Science, Rehovot, Israel; Dr Diego Orcioli
Silva - São Paulo State University (UNESP), Rio Claro, SP, Brazil.
Copyright:© 2022 Barbieri, Faria, Simieli, Penedo, Kalva-Filho and Beretta and BJMB. This is an open-access article
distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives 4.0 International
License which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and
source are credited.
Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Competing interests: The authors have declared that no competing interests exist.
DOI:!https://doi.org/10.20338/bjmb.v16i5.346