Gait characteristics in people with Friedreich ataxia: daily life versus clinic measures
Frontiers in Neurology,
Год журнала:
2025,
Номер
16
Опубликована: Март 17, 2025
Gait
assessments
in
a
clinical
setting
may
not
accurately
reflect
mobility
everyday
life.
To
better
understand
gait
during
daily
life,
we
compared
measures
that
discriminated
Friedreich
ataxia
(FRDA)
from
healthy
control
(HC)
subjects
prescribed
clinic
tests
and
free,
daily-life
monitoring.
We
recruited
9
people
with
FRDA
(median
age:
20,
IQR
[12,
48]
years).
A
comparative
subject
cohort
of
was
sampled
using
propensity
matching
on
age
18
[13,
22]
Subjects
wore
3
inertial
sensors
(one
each
foot
lower
back)
the
laboratory
2-min
walk
at
natural
pace,
followed
by
7
days
For
life
analysis,
total
99,216
strides
across
1,008
h
recording
were
included.
Mann-Whitney
U
test
area
under
curve
(AUC)
differences
between
HC
when
assessed
Pairwise
Wilcoxon
also
if
participants
exhibited
different
metric
values
two
environments.
The
group
levels
activity.
Measures
best
characteristics
differed
Variation
elevation
feet
midswing
in-clinic
(Clinic
AUC
=
1,
Home
0.69),
whereas
slow
speed
performed
(Home
Clinic
0.64).
Of
17
tested,
11
had
an
>
0.8
8
>0.8
home.
Variability
swing
time
0.97,
0.94)
double-support
0.94,
most
sensitive
specific
for
both
Digital
are
However,
more
free-living
versus
gait,
suggesting
does
gait.
Язык: Английский
Predictive machine learning and multimodal data to develop highly sensitive, composite biomarkers of disease progression in Friedreich Ataxia
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 4, 2025
Abstract
Friedreich
Ataxia
(FRDA)
is
a
rare,
inherited
progressive
movement
disorder
for
which
there
currently
no
cure.
The
field
urgently
requires
more
sensitive,
objective,
and
clinically
relevant
biomarkers
to
enhance
the
evaluation
of
treatment
efficacy
in
clinical
trials
speed
up
process
drug
discovery.
This
study
pioneers
development
relevant,
multidomain,
fully
objective
composite
disease
severity
progression,
using
multimodal
neuroimaging
background
data
(i.e.,
demographic,
history,
genetics).
Data
from
31
individuals
with
FRDA
controls
longitudinal
natural
history
IMAGE-FRDA,
were
included.
Using
an
elasticnet
predictive
machine
learning
(ML)
regression
model,
we
derived
weighted
combination
background,
structural
MRI,
diffusion
quantitative
susceptibility
imaging
(QSM)
measures
that
predicted
Rating
Scale
(FARS)
high
accuracy
(R²
=
0.79,
root
mean
square
error
(RMSE)
13.19).
also
exhibited
strong
sensitivity
progression
over
two
years
(Cohen's
d
1.12),
outperforming
FARS
score
alone
(d
0.88).
approach
was
validated
Assessment
(SARA),
demonstrating
potential
robustness
ML-derived
composites
surpass
individual
act
as
complementary
or
surrogate
markers
progression.
However,
further
validation,
refinement,
integration
additional
modalities
will
open
new
opportunities
translating
these
into
practice
FRDA,
well
other
rare
neurodegenerative
diseases.
Язык: Английский
At-home wearable-based monitoring predicts clinical measures and biological biomarkers of disease severity in Friedreich’s Ataxia
Communications Medicine,
Год журнала:
2024,
Номер
4(1)
Опубликована: Окт. 29, 2024
Язык: Английский
At-home wearables and machine learning capture motor impairment and progression in adult ataxias
medRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 29, 2024
A
significant
barrier
to
developing
disease-modifying
therapies
for
spinocerebellar
ataxias
(SCAs)
and
multiple
system
atrophy
of
the
cerebellar
type
(MSA-C)
is
scarcity
tools
sensitively
measure
disease
progression
in
clinical
trials.
Wearable
sensors
worn
continuously
during
natural
behavior
at
home
have
potential
produce
ecologically
valid
precise
measures
motor
function
by
leveraging
frequent
numerous
high-resolution
samples
behavior.
Here
we
test
whether
movement-building
block
characteristics
(i.e.,
submovements),
obtained
from
wrist
ankle
home,
can
capture
SCAs
MSA-C,
as
recently
shown
amyotrophic
lateral
sclerosis
(ALS)
ataxia
telangiectasia
(A-T).
Remotely
collected
cross-sectional
(
Язык: Английский