Trend change analysis of postural balance in Parkinson’s disease discriminates between medication state

Abstract

Background
Maintaining static balance is relevant and common in everyday life and it depends on a correct intersegmental coordination. A change or reduction in postural capacity has been linked to increased risk of falls. People with Parkinson’s disease (pwPD) experience motor symptoms affecting the maintenance of a stable posture. The aim of the study is to understand the intersegmental changes in postural sway and to apply a trend change analysis to uncover different movement strategies between pwPD and healthy adults.

Methods
In total, 61 healthy participants, 40 young (YO), 21 old participants (OP), and 29 pwPD (13 during medication off, PDoff; 23 during medication on, PDon) were included. Participants stood quietly for 10 s as part of the Short Physical Performance Battery. Inertial measurement units (IMU) at the head, sternum, and lumbar region were used to extract postural parameters and a trend change analysis (TCA) was performed to compare between groups.

Objective
This study aims to explore the potential application of TCA for the assessment of postural stability using IMUs, and secondly, to employ this analysis within the context of neurological diseases, specifically Parkinson’s disease.

Results
Comparison of sensors locations revealed significant differences between head, sternum and pelvis for almost all parameters and cohorts. When comparing PDon and PDoff, the TCA revealed differences that were not seen by any other parameter.

Conclusions
While all parameters could differentiate between sensor locations, no group differences could be uncovered except for the TCA that allowed to distinguish between the PD on/off. The potential of the TCA to assess disease progression, response to treatment or even the prodromal PD phase should be explored in future studies.

Trial registration
The research procedure was approved by the ethical committee of the Medical Faculty of Kiel University (D438/18). The study is registered in the German Clinical Trials Register (DRKS00022998).

Full text

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Wodarski et al. Journal of NeuroEngineering and Rehabilitation (2024) 21:112
https://doi.org/10.1186/s12984-024-01411-z Journal of NeuroEngineering
and Rehabilitation
*Correspondence:
Clint Hansen
C.Hansen@neurologie.uni-kiel.de
1Faculty of Biomedical Engineering, Department of Biomechatronics,
Silesian University of Technology, Gliwice, Poland
2Division of Surgery, Saarland University, 66421 Homburg, Germany
3Department of Neurology, Kiel University, 24105 Kiel, Germany
4Sky Sp. z o.o, Gliwice, Poland
Abstract
Background Maintaining static balance is relevant and common in everyday life and it depends on a correct
intersegmental coordination. A change or reduction in postural capacity has been linked to increased risk of falls.
People with Parkinson’s disease (pwPD) experience motor symptoms aecting the maintenance of a stable posture.
The aim of the study is to understand the intersegmental changes in postural sway and to apply a trend change
analysis to uncover dierent movement strategies between pwPD and healthy adults.
Methods In total, 61 healthy participants, 40 young (YO), 21 old participants (OP), and 29 pwPD (13 during
medication o, PDo; 23 during medication on, PDon) were included. Participants stood quietly for 10s as part of the
Short Physical Performance Battery. Inertial measurement units (IMU) at the head, sternum, and lumbar region were
used to extract postural parameters and a trend change analysis (TCA) was performed to compare between groups.
Objective This study aims to explore the potential application of TCA for the assessment of postural stability using
IMUs, and secondly, to employ this analysis within the context of neurological diseases, specically Parkinson’s disease.
Results Comparison of sensors locations revealed signicant dierences between head, sternum and pelvis for
almost all parameters and cohorts. When comparing PDon and PDo, the TCA revealed dierences that were not
seen by any other parameter.
Conclusions While all parameters could dierentiate between sensor locations, no group dierences could be
uncovered except for the TCA that allowed to distinguish between the PD on/o. The potential of the TCA to assess
disease progression, response to treatment or even the prodromal PD phase should be explored in future studies.
Trial registration The research procedure was approved by the ethical committee of the Medical Faculty of Kiel
University (D438/18). The study is registered in the German Clinical Trials Register (DRKS00022998).
Keywords Parkinson Disease, Trend Change Index, Body balance, Postural Stability, Balance, Wearable sensors,
Neurology
Trend change analysis of postural balance
in Parkinson’s disease discriminates between
medication state
PiotrWodarski1, JacekJurkojć1, MartaChmura1, ElkeWarmerdam2, RobbinRomijnders3, Markus A.Hobert3,
WalterMaetzler3, KrzysztofCygoń4 and ClintHansen3*
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Wodarski et al. Journal of NeuroEngineering and Rehabilitation (2024) 21:112
Introduction
Maintaining an upright posture, or static balance, is a
fundamental aspect of human life that underscores the
intricate interconnections of the vestibular, visual, and
somatosensory systems within the central nervous sys-
tem [1]. Posture is more than the mere static alignment
of body segments; it represents a dynamic process char-
acterized by continuous adjustments to maintain stability
while performing various tasks. Maintaining upright pos-
ture becomes increasingly critical with aging and neuro-
logical disorders due to the gradual decline in postural
control, predisposing individuals to an elevated risk of
falls and associated injuries. is decline is inuenced by
a multitude of factors, encompassing alterations in sen-
sory input, muscle strength, joint exibility, and neural
processing [2]. As an example pwPD present profound
challenges to postural control [3] which is based on the
neurodegenerative character of the disease characterized
by the loss of dopaminergic neurons. e diculties with
balance are linked to the loss of dopaminergic neurons
aecting the basal ganglia which are essential to control
upright posture.
A particularly intriguing aspect of postural control
is the necessity for specic body segments to remain
stable while others adapt to accommodate external
demands. For instance, the head must remain stable to
preserve visual focus and spatial orientation [4], while
the pelvis may need to make adjustments to accommo-
date changes in terrain or task requirements [5]. Uncon-
sciously, humans stabilize their visual focus or gaze and
maintain awareness of their body position [6] but also
stabilize their head to ensure balance [7]. For example
Wallard et al. [8] found that children with cerebral palsy
exhibit greater head angle variability, suggesting a com-
pensatory strategy and Pozzo et al. [5] observed signi-
cant head stabilization during various locomotor tasks,
with the head compensating for translation and rota-
tion. People with mild traumatic brain injury revealed
increased sway of the center of mass and less head stabi-
lization compared with healthy controls [9]. In addition
Israeli-Korn et al. [10] showed that intersegmental coor-
dination patterns dier e.g. between Parkinson’s disease
and cerebellar ataxia. Honegger et al. [11] investigated
the coordination of the head with respect to the trunk,
pelvis, and lower leg during quiet stance after vestibular
loss. ey argue that such simplication, as proposed by
Fitzpatrick et al. [12] and Pinter et al. [13], may not fully
capture the complexity of postural control in these popu-
lations. Contrary to expectations, their ndings reveal
synchronous movements of the head and trunk among
healthy controls, suggesting that the presence of an intact
vestibular system does not necessarily confer greater sta-
bility to the head in space. Instead, the pelvis emerges as
a key stabilizing factor, as supported by earlier studies
[13, 14] and the present investigation. ese studies col-
lectively highlight the role of aligning of body segments
in postural control, particularly in individuals with motor
impairments introducing another layer of complexity to
our understanding of static balance. is raises the ques-
tion of how the body segments sway and are controlled
within the realm of quiet stance in dierent pathologies.
Inertial measurement units (IMUs) are small body-
mounted sensors containing accelerometers, gyroscopes
and magnetometers that can track 3D human movement
on a very granular level e.g. to measure balance [15, 16]
based on center of mass movements [17, 18]. eir reli-
ability and validity have been extensively examined [19,
20] and provide a tool to be used in combination with a
trend change analysis (TCA) [21]. TCA can detect the
small number of quick corrections, an increased fre-
quency of longer-duration corrections, and an elongation
in the displacement between successive postural correc-
tions. Adapted from techniques originally employed in
stock exchange analyses, the TCA facilitates the quanti-
cation of postural corrections in both the anteroposte-
rior (A/P) and mediolateral (M/L) directions. Moreover,
it allows for the calculation of the number of adaptations,
the time interval between successive posture corrections
[21] providing insights about the body’s responses to pos-
tural challenges [22].
e research presented herein aims to delve into the
intricate relationship between maintaining an upright
posture, PD, aging, and the dynamic adjustments involv-
ing intersegmental control. e objectives of this study
are twofold: Firstly, to explore the potential application of
TCA for the assessment of postural stability using IMUs,
and secondly, to employ this analysis within the context
of neurological diseases, specically PD. We hypothe-
sized that the TCA could dierentiate between persons
with PD (pwPD) and healthy adults and also distinguish,
in pwPD, between dopaminergic on (PDon) and dopami-
nergic o phases (PDo).
Methods
Participants
e experimental groups consisted of 61 healthy par-
ticipants, 40 young (YO), 21 old (OP) and 29 pwPD. e
demographic characteristics of the study participants are
presented in Table1.
All participants were either inpatients at the neuroge-
riatric ward of the Neurology Center at the University
Hospital Schleswig-Holstein, Campus Kiel, or spouses of
the patients or members of the professional team. pwPD
were diagnosed according to the Movement Disorder
Society clinical diagnostic criteria for Parkinson’s disease
[23, 24]. irteen pwPD participated as PDo (UPDRS
III score 24 ± 10), 23 as PDon (UPDRS III score 30 ± 20),
and 7 as both PDon (UPDRS III score 26 ± 10) and PDo
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Wodarski et al. Journal of NeuroEngineering and Rehabilitation (2024) 21:112
(UPDRS III score 27 ± 10). e sample size for this study
was predetermined based on prior research and the cur-
rent analysis is a secondary analysis of the previously
published data set [25–27].
e study was conducted according to the guidelines
of the Declaration of Helsinki and approved by the Eth-
ics Committee of Kiel University (D438/18) and all par-
ticipants provided written informed consent before
participation. Participants were excluded when their fall
risk was determined to be too high (> 2 falls in the pre-
vious week), corrected visual acuity was below 60%, they
scored ≤ 15 points in the Montreal Cognitive Assessment
(MoCA) test [24, 28], had current or past chronic sub-
stance abuse (except nicotine), and were not able to per-
form at least one of the walking tasks [25].
Protocol
Data from the IMU sensors were recorded using a
motion capture system (Noraxon USA Inc., myoMO-
TION 3.16, Scottsdale, AZ, USA) [25, 26]. e partici-
pants were asked to stand in an upright position with
their feet together, side-by-side and x their gaze on a
point on a white wall for 10s as part of the Short Physical
Performance Battery [25].
ree IMUs were attached to the body (pelvic, ster-
num and head) using elastic bands with a special hous-
ing for the IMU to clip into (see Fig.1). e research
procedure was approved by the ethical committee of
the Medical Faculty of Kiel University (D438/18). e
study is registered in the German Clinical Trials Register
(DRKS00022998).
Sensor data processing
e IMU data was processed by custom written scripts
using MATLAB (MathWorks, Nantick, MA) based
on methodology described by Mancini et al. [29]. e
parameters provided information about the sway jerki-
ness (JERK) (cm2/s5), the sway area (SURFACE) (cm2),
path (PATH) (cm), mean velocity (MV) (cm/s), range of
acceleration (RANGE) (cm/s2) and root mean square of
the acceleration (RMS) (cm/s2).
In addition, the TCA was applied. Acceleration signals
were ltered with a low-pass lter (7Hz low-pass Butter-
worth lter). e method is based on a Moving Average
Convergence Divergence (MACD) indicator calculation
algorithm and evaluates the relationships of exponential
moving averages (EMAs) for the recorded signal [21].
Calculations can be performed for any time-varying sig-
nal. In the case of the tests used, recorded acceleration
signals were used, the S signal is the acceleration signal.
In the rst step of calculations, for the signal S, the
MACD line was determined as the dierence between
two EMAs (Eq. 2) with lengths of 12 and 26 samples
according to Eq.1.
MACD =EMA
S,12
−EMA
S,26 (Eq. 1)
Where EMAS,12 - faster exponential moving average for
signal S,
EMAS,26 - slower exponential moving average for signal
S
EMA=p0+(1−α)p1+(1−α)
2
p2+···+(1−α)
N
pN
1+(1 −α)+(1 −α)
2
+···+(1−α)
N
(Eq. 2)
Where, p0 – ultimate value, p1 – penultimate value, pN –
value preceding N periods, N = number of periods, α = a
smoothing coecient equal to 2/(N + 1).
In the next step, the signal line is calculated as an EMA
with a length of 9 samples from the MACD line signal in
accordance with Eq.3.
Signal line =EMA
MACDline,
9
(Eq. 3)
e intersection of the MACD line and the Signal line
determines the trend change points in the S signal. e
number of intersections determines the TCI (trend
changes index).
In the next step, the time intervals between successive
points of trend changes in the S signal were calculated. In
this way, the MACD_dT array was determined, the aver-
age value of which is the value of the TCI_dT. As a con-
sequence, the displacement between subsequent trend
change points were calculated and the results constitute
the MACD_dS array. e average value of the array is
the value of the TCI_dS (Fig.2). Finally, the correspond-
ing elements of the MACD_dS array were divided by
MACD_dT to obtain the MACD_dV array. e aver-
age value of the array is the value of the TCI_dV. In this
study, the displacement of the signal is the dierence
in the acceleration values between successive points of
trend change on the acceleration signal.
To summarize, TCI determines the number of trend
changes in the assumed research period, TCI_dT denes
the average time between detected trend changes, and
TCI_dS determines the average value of the acceleration
change between subsequent trend changes. Indices were
Table 1 Characteristics of study participants (YO: young, OP: old,
pwPD: persons with PD, w: women, m: men)
YO OP pwPD
N (w/m) 40 (20/20) 21 (11/10) 29 (18/11)
Age(w/m) [year] 29.5 ± 8.5 / 27.5 ± 7.1 72.5 ± 5.9 /
70.9 ± 6.0
63.2 ± 11.7 /
68.0 ± 7.3
Weight (w/m) [kg] 79.5 ± 11.5 / 66.3 ± 8.5 83.9 ± 13.3 /
68.9 ± 12.5
88.5 ± 15.3
/ 69.3 ± 14.4
Height (w/m) [m] 1.85 ± 0.08 / 1.73 ± 0.05 1.81 ± 0.08 /
1.66 ± 0.06
1.78 ± 0.07
/ 1.67 ± 0.06
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Wodarski et al. Journal of NeuroEngineering and Rehabilitation (2024) 21:112
determined for each of the three directions of measure-
ment, and then the resultant values were determined
i.e. for TCI as the sum of the number of trend changes
detected in each direction of the measured accelerations
(in the X, Y and Z axes), and for TCI_dT, TCI_dS, TCI_
dV as the square root of the sum of squares of the values
calculated in each direction.
Statistical analysis
e analyses were performed using Matlab R2022a and
JASP (Version 0.16.1 JASP Team (2022)) for all statistical
analyses.
e analysis aimed to investigate dierences between
sensor positions and cohorts within the dataset.
Shapiro-Wilk tests revealed signicant deviations from
normality (p < 0.05) across multiple groups and sensor
positions, thus prompting the utilization of non-para-
metric tests. Subsequently, a Kruskal-Wallis H Test were
employed to evaluate variations between cohorts and
sensor positions. In case of statistically signicant dier-
ences (p < 0.05) post-hoc analyses, utilizing Dunn’s test
with Bonferroni correction, were conducted to ascertain
specic group disparities.
Results
When comparing the individual parameters for each sen-
sor and each cohort (Table2), no dierences could be
found between the cohorts but signicant dierences
Fig. 1 Placement of the inertial measurement units on the head, sternum and pelvis
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Wodarski et al. Journal of NeuroEngineering and Rehabilitation (2024) 21:112
were uncovered between the sensor positions (Additional
le 1).
e sensor position diered for all cohorts and all
parameters except TCI and TCI_dT for PDon (Table3).
When comparing the PDon and PDo cohort (Table4)
only TCI & TCI_dT diered between the PDon and
PDo cohort. Signicant dierences were found between
the three sensor locations (Table5).
Discussion
is study investigated postural stability of healthy
young, old controls and persons with PD in a static bal-
ance task using three dierent sensor locations. e aim
of the study was to analyze the upright posture and inter-
segmental adjustments, to evaluate whether the param-
eters could uncover distinct postural sway behavior
between the dierent cohorts. Our results conrmed that
both, the postural parameters and TCA, could uncover
sway dierences between the segments but only the TCA
could dierentiate between PDon and PDo.
e results of the current study show no group dier-
ences between the healthy adults and pwPD, conrming
results from a previous study investigating static sway
with increasing task diculty [27]. is is of interest as
PD is known for its altered postural reexes with a dis-
ruption of the precisely coordinated execution of agonist
and antagonist muscles (associated with bradykinesia
and rigidity), which results in diculty to maintain static
postural stability [30–32] due to a reduced margin of sta-
bility [33].
While pwPD have shown larger values for sway accel-
eration, jerk and sway velocity during postural balance
compared to age-matched healthy controls [29, 34] they
also show an increased jerkiness during the performance
of cognitive task [35], suggesting an interaction of cogni-
tive functions, including multisensory integration, with
static balance mechanisms. Our results highlight larger
motions from the head compared to the sternum and the
pelvis. e results convey with previous ndings [14] bas-
ing their ndings upon the biomechanical principal of a
double-inverted pendulum. e double-inverted pendu-
lum allows to be controlled by the ankles, the hip or both,
while assuming a rigid head-on-trunk coupling. Almost
all parameters were able to distinguish between sensor
position indicating the complex relationship between
the dynamic intersegmental adjustments and upright
posture. e results suggest that for a relative simple
and short balance tasks pwPD can perform control-like,
which could be related to the location of the pathology
within the central nervous system and its extensive com-
pensation possibilities [36] and by using alternative path-
ways or even networks [37].
ere is some evidence that dopaminergic medication
can improve static sway [38, 39]. However, there are not
many IMU-based studies available that can show these
dierences. One reason may be that the parameters cur-
rently assessed for this performance are not covering dis-
ease-relevant changes. Here we introduced TCA in the
analysis of static sway in PDon and PDo, and could in
fact detect signicant dierences only with this approach
(but not with the conventional parameters). We found
Fig. 2 Graphical explanation of the Trend Change Index (TCI), the delta time between successive TCIs (MACD_dT) as well as the delta space between
successive TCIs (MACD_dS) in an acceleration signal from a sensor on the pelvis with an observation phase of about 3s. Seven trend changes (indicated
by the seven red dots) are shown. All determined MACD_dTs were used to calculate TCI_dT and all MACD_dSs to calculate TCI_dS according to the
procedure described in the text
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Wodarski et al. Journal of NeuroEngineering and Rehabilitation (2024) 21:112
Table 2 Values of individual parameters (M - median, QR/2 - the half of coecient quartile of variation)
Head Pelvis Sternum
YO OP PD on PD o YO OP PD on PD o YO OP PD on PD o
JERK [cm2/s5] M30.78 37.54 28.52 28.06 7.64 10.48 8.28 8.08 9.22 12.12 10.26 8.59
QR/2 [%] 190 123 56 60 58 54 67 59 109 58 55 50
MV
[cm/s]
M24.17 29.66 26.50 26.97 12.15 13.32 14.39 15.57 14.25 16.78 16.80 15.98
QR/2 [%] 38 29 22 24 31 34 29 31 31 21 17 24
PATH
[cm]
M241.69 296.59 265.04 269.73 121.47 133.16 143.92 155.67 142.52 167.84 168.02 159.75
QR/2 [%] 38 29 22 24 31 34 29 31 31 21 17 24
RMS [cm/s2] M2.72 3.18 2.11 3.13 0.91 1.21 1.14 1.17 1.15 1.47 1.43 1.34
QR/2 [%] 67 72 83 36 25 34 38 14 44 41 38 29
SURFACE [cm2/s4] M35.96 81.86 35.94 71.10 7.09 10.40 10.90 11.22 10.49 18.07 17.19 14.17
QR/2 [%] 229 145 209 74 49 77 48 29 89 84 70 61
RANGE
[cm/s2]
M10.73 12.02 7.83 9.38 2.97 4.29 4.19 3.38 3.43 5.61 4.37 4.46
QR/2 [%] 78 89 64 73 41 52 44 15 72 53 43 24
TCI
[no]
M258 272 269 302 295 295 284 317 296 301 287 310
QR/2 [%] 9 5 4 4 4 6 7 5 3 5 7 2
TCI_dT
[s]
M0.21 0.20 0.19 0.17 0.18 0.18 0.18 0.16 0.18 0.17 0.18 0.17
QR/2 [%] 8 4 5 4 4 6 9 5 4 5 6 2
TCI_dS [cm] M2.08 2.29 1.89 1.76 0.88 0.74 1.02 0.97 1.01 0.96 1.02 1.02
QR/2 [%] 59 40 35 23 36 46 44 27 35 36 35 18
TCI_dV
[cm/s]
M15.63 19.49 15.90 17.22 8.26 6.90 9.59 9.20 9.32 8.45 11.18 10.04
QR/2 [%] 48 32 30 25 34 56 39 26 35 44 25 19
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Wodarski et al. Journal of NeuroEngineering and Rehabilitation (2024) 21:112
a higher number of TCIs and smaller TCI_dT values in
PDo compared to PDon. is is coherent with previ-
ous results obtained for COP measurements showing an
increase in TCIs and reduction of TCI_dT in pwPD com-
pared to healthy individuals [40]. In our view, this per-
spective also aligns with a pathomechanistic standpoint.
Previous research, as indicated by Bizid et al. [41], sug-
gests that low frequencies are predominantly associated
with visuo-vestibular regulation, while high frequencies
are associated with proprioceptive regulation. Addi-
tionally, it is well-established that visual perception
and integration are strongly dopamine-dependent [42].
erefore, we hypothesize that the results observed
through TCA most likely reect visual decits resulting
from a dopaminergic decit. is is particularly evident,
Table 3 Sensor parameters to dierentiate between groups and sensor positions in controls and PDon. The H-statistics of the Kruskall-
Wallis test as well as the degree of freedom and signicance levels are reported within the tables
Parameters Group level Sensor
position
YO post hoc
p < 0.05
OP post hoc
p < 0.05
PDon post hoc
p < 0.05
JERK n.s. H(2) = 60.29,
p < 0.001
head vs. sternum and pelvis head vs. sternum and pelvis head vs. sternum and pelvis
MV n.s. H(2) = 70.87,
p < 0.001
head vs. sternum and pelvis head vs. sternum and pelvis head vs. sternum and pelvis
PATH n.s. H(2) = 70.87,
p < 0.001
head vs. sternum and pelvis head vs. sternum and pelvis head vs. sternum and pelvis
RMS n.s. H(2) = 73.18,
p < 0.001
head vs. sternum and pelvis head vs. sternum and pelvis head vs. sternum and pelvis
SURFACE n.s. H(2) = 69.59,
p < 0.001
head vs. sternum and pelvis head vs. pelvis head vs. sternum and pelvis
RANGE n.s. H(2) = 54.82,
p < 0.001
head vs. sternum and pelvis head vs. pelvis head vs. sternum and pelvis
TCI n.s. H(2) = 44.27,
p < 0.001
head vs. sternum and pelvis head vs. sternum and pelvis
TCI_dT n.s. H(2) = 57.37,
p < 0.001
head vs. sternum and pelvis head vs. sternum and pelvis
TCI_dS n.s. H(2) = 79,63,
p < 0.001
head vs. sternum and pelvis head vs. sternum and pelvis head vs. sternum and pelvis
TCI_dV n.s. H(2) = 58.94,
p < 0.001
head vs. sternum and pelvis head vs. sternum and pelvis head vs. sternum and pelvis
Table 4 Values of parameter for 7 pwPD tested “on” and “o” (M - median, QR/2 - the half of coecient quartile of variation)
Head Pelvis Sternum
PD o PD on PD o PD on PD o PD on
JERK [cm2/s5] M34.71 44.54 13.08 8.07 8.87 10.26
QR/2 [%] 67 27 79 6 267 39
MV
[cm/s]
M32.82 29.79 18.66 14.39 16.57 16.80
QR/2 [%] 17 15 28 6 59 10
PATH
[cm]
M328.20 297.90 186.62 143.92 165.71 168.02
QR/2 [%] 17 15 28 6 59 10
RMS [cm/s2] M3.27 3.67 1.23 1.53 1.84 1.69
QR/2 [%] 21 51 11 21 28 16
SURFACE [cm2/s4] M77.93 113.99 11.33 12.86 27.54 24.67
QR/2 [%] 55 81 31 21 44 35
RANGE
[cm/s2]
M13.87 10.17 3.35 4.27 5.20 5.75
QR/2 [%] 40 83 15 33 21 14
TCI
[no]
M308 275 316 279 310 277
QR/2 [%] 3 3 4 6 2 3
TCI_dT
[s]
M0.17 0.19 0.16 0.19 0.17 0.19
QR/2 [%] 4 3 5 5 2 3
TCI_dS [cm] M1.85 2.71 1.11 1.05 1.10 1.27
QR/2 [%] 21 26 26 18 26 23
TCI_dV
[cm/s]
M20.48 17.68 11.55 9.77 10.68 11.18
QR/2 [%] 15 27 32 8 27 19
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 8 of 9
Wodarski et al. Journal of NeuroEngineering and Rehabilitation (2024) 21:112
given that lower leg proprioceptive performance does not
appear to be inuenced by dopaminergic treatment [43].
Limitations
It would be worthwhile to mention limitations of the cur-
rent study. First, the number of pwPD measured in both
medication states was relatively low, potentially limiting
the generalizability of ndings and the ability to cap-
ture the full spectrum of balance-related issues in PD.
Another constraint lies in the brief 10-second measure-
ment duration, which may not provide a comprehensive
representation of individuals’ balance control capabili-
ties, particularly in dynamic real-world scenarios. Addi-
tionally, the use of a side-by-side stance as a measure may
cause limitations as it may not be challenging enough
to detect subtle dierences between cohorts or uncover
changes in postural control based on intersegmental
coordination. ese limitations emphasize the need for
cautious interpretation of results and highlight areas for
future research to address these constraints and provide
a more nuanced understanding of balance control in Par-
kinson’s disease and other relevant populations. Never-
theless, considering these limitations, it is all the more
remarkable given that the TCA parameters were eective
in distinguishing between PD on and PD o.
Clinical implication
is study investigated static sway in healthy individuals
and pwPD using three sensor locations. Results show that
postural parameters eectively distinguish between seg-
ments. However, and even more relevant, the introduc-
tion of TCA proves instrumental in detecting signicant
dierences between PDon and o medication, showcas-
ing its potential in assessing disease-relevant changes not
captured by conventional parameters.
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12984-024-01411-z.
Supplementary Material 1
Acknowledgements
We thank all study participants for their support and engagement.
Author contributions
RR and MAH and WM and CH and EW and MCh and PW and JJ: made the
conception RR and MAH and EW and CH: data acquisitionPW and JJ and MCh
and CH: analysisPW and WM and JJ and CH: interpretation of dataPW and JJ
and KC: creation of new software used in the workWM and JJ and PW and CH:
have drafted the work PW and JJ and RR and MAH and WM and CH and EW
and MCh and KC: substantively revised the manuscrypt.
Funding
Open Access funding enabled and organized by Projekt DEAL. The publication
is supported by the Rector’s habilitation grant implemented under the
Excellence Initiative - Research University program. Silesian University of
Technology, grant number: 07/030/SDU/10-07-01.
Open Access funding enabled and organized by Projekt DEAL.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
The research procedure was approved by the ethical committee of the
Medical Faculty of Kiel University (D438/18). The study is registered in the
German Clinical Trials Register (DRKS00022998).
Consent for publication
All authors express their full consent to publication of the material.
Competing interests
The authors declare no competing interests.
Received: 19 December 2023 / Accepted: 20 June 2024
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