Digital Health,
Journal Year:
2025,
Volume and Issue:
11
Published: Jan. 1, 2025
Digital
biomarkers
are
quantifiable
and
objective
indicators
of
a
person's
physiological
function,
behavioral
state
or
treatment
response,
that
can
be
captured
using
connected
sensor
technologies
such
as
wearable
devices
mobile
apps.
We
envision
continuous
24-h
monitoring
the
underlying
processes
through
digital
enhance
early
diagnostics,
disease
management,
self-care
cardiometabolic
diseases.
Cardiometabolic
diseases,
which
include
combination
cardiovascular
metabolic
disorders,
represent
an
emerging
global
health
threat.
The
prevention
potential
diseases
is
around
80%,
indicating
promising
role
for
interventions
in
lifestyle
and/or
environmental
context.
Disruption
sleep
circadian
rhythms
increasingly
recognized
risk
factors
disease.
used
to
measure
clock,
is,
day
night,
quantify
not
only
patterns
but
also
diurnal
fluctuations
certain
processes.
In
this
way,
support
delivery
optimal
timed
medical
care.
Night-time
patterns,
blood
pressure
dipping,
predictive
outcomes.
addition,
period
provides
opportunity
with
relatively
low
influence
artifacts,
physical
activity
eating.
utilize
window
during
daily
life
enable
diagnosis
facilitate
remote
patient
monitoring,
self-management
people
This
review
describes
on
highlights
state-of-the-art
could
benefit
Sleep Health,
Journal Year:
2024,
Volume and Issue:
unknown
Published: April 1, 2024
Goal
and
aimsTo
test
sleep/wake
transition
detection
of
consumer
sleep
trackers
research-grade
actigraphy
during
nocturnal
simulated
peri-sleep
behavior
involving
minimal
movement.Focus
technologyOura
Ring
Gen
3,
Fitbit
Sense,
AXTRO
Fit
Xiaomi
Mi
Band
7,
ActiGraph
GT9X.Reference
technologyPolysomnography.SampleSixty-three
participants
(36
female)
aged
20-68.DesignParticipants
engaged
in
common
(reading
news
articles,
watching
videos,
exchanging
texts)
on
a
smartphone
before
after
the
period.
They
were
woken
up
night
to
complete
short
questionnaire
simulate
responding
an
incoming
message.Core
analyticsDetection
timing
accuracy
for
onset
times
wake
times.Additional
analytics
exploratory
analysesDiscrepancy
analysis
both
including
excluding
activity
periods.
Epoch-by-epoch
rate
extent
misclassification
periods.Core
outcomesOura
more
accurate
at
detecting
transitions
than
actigraph
lower-priced
tracker
devices.
Detection
was
less
reliable
with
lower
efficiency.Important
additional
outcomesWith
inclusion
periods,
specificity
Kappa
improved
significantly
Oura
Fitbit,
but
not
ActiGraph.
All
devices
misclassified
motionless
as
some
extent,
this
prevalent
Fitbit.Core
conclusionsPerformance
is
robust
nights
suboptimal
bedtime
routines
or
minor
disturbances.
Reduced
performance
low
efficiency
bolsters
concerns
that
these
are
fragmented
disturbed
sleep.
Biosensors,
Journal Year:
2024,
Volume and Issue:
14(4), P. 205 - 205
Published: April 20, 2024
Wearable
health
devices
(WHDs)
are
rapidly
gaining
ground
in
the
biomedical
field
due
to
their
ability
monitor
individual
physiological
state
everyday
life
scenarios,
while
providing
a
comfortable
wear
experience.
This
study
introduces
novel
wearable
device
capable
of
synchronously
acquiring
electrocardiographic
(ECG),
photoplethysmographic
(PPG),
galvanic
skin
response
(GSR)
and
motion
signals.
The
has
been
specifically
designed
be
worn
on
finger,
enabling
acquisition
all
biosignals
directly
fingertips,
offering
significant
advantage
being
very
easy
employed
by
users.
simultaneous
different
allows
extraction
important
indices,
such
as
heart
rate
(HR)
its
variability
(HRV),
pulse
arrival
time
(PAT),
GSR
level,
blood
oxygenation
level
(SpO2),
respiratory
rate,
well
detection,
assessment
states,
together
with
detection
potential
physical
mental
stress
conditions.
Preliminary
measurements
have
conducted
healthy
subjects
using
measurement
protocol
consisting
resting
states
(i.e.,
SUPINE
SIT)
alternated
conditions
STAND
WALK).
Statistical
analyses
carried
out
among
distributions
indices
extracted
time,
frequency,
information
domains,
evaluated
under
results
our
demonstrate
capability
detect
changes
between
rest
conditions,
thereby
encouraging
use
for
assessing
individuals’
state.
Furthermore,
possibility
performing
synchronous
acquisitions
PPG
ECG
signals
allowed
us
compare
HRV
(PRV)
so
corroborate
reliability
PRV
analysis
stationary
Finally,
confirms
already
known
limitations
during
activities,
suggesting
algorithms
artifact
correction.
npj Digital Medicine,
Journal Year:
2024,
Volume and Issue:
7(1)
Published: May 23, 2024
Abstract
Data
from
commercial
off-the-shelf
(COTS)
wearables
leveraged
with
machine
learning
algorithms
provide
an
unprecedented
potential
for
the
early
detection
of
adverse
physiological
events.
However,
several
challenges
inhibit
this
potential,
including
(1)
heterogeneity
among
and
within
participants
that
make
scaling
to
a
general
population
less
precise,
(2)
confounders
lead
incorrect
assumptions
regarding
participant’s
healthy
state,
(3)
noise
in
data
at
sensor
level
limits
sensitivity
algorithms,
(4)
imprecision
self-reported
labels
misrepresent
true
values
associated
given
event.
The
goal
study
was
two-fold:
characterize
performance
such
presence
these
insights
researchers
on
limitations
opportunities,
subsequently
devise
address
each
challenge
offer
future
opportunities
advancement.
Our
proposed
include
techniques
build
determining
suitable
baselines
participant
capture
important
changes
label
correction
as
it
pertains
participant-reported
identifiers.
work
is
validated
potentially
one
largest
datasets
available,
obtained
8000+
1.3+
million
hours
wearable
captured
Oura
smart
rings.
Leveraging
extensive
dataset,
we
achieve
pre-symptomatic
COVID-19
receiver
operator
characteristic
(ROC)
area
under
curve
(AUC)
0.725
without
techniques,
0.739
baseline
correction,
0.740
training
set,
0.777
both
test
set.
Using
same
respective
paradigms,
ROC
AUCs
0.919,
0.938,
0.943
0.994
fever,
0.574,
0.611,
0.601,
0.635
shortness
breath.
These
improvements
across
almost
all
metrics
events,
PR
AUC,
75%
specificity,
precision
recall.
ring
allows
continuous
monitoring
event
onset,
further
demonstrate
improvement
average
3.5
days
4.1
before
reported
positive
result.
Digital Health,
Journal Year:
2025,
Volume and Issue:
11
Published: Jan. 1, 2025
Digital
biomarkers
are
quantifiable
and
objective
indicators
of
a
person's
physiological
function,
behavioral
state
or
treatment
response,
that
can
be
captured
using
connected
sensor
technologies
such
as
wearable
devices
mobile
apps.
We
envision
continuous
24-h
monitoring
the
underlying
processes
through
digital
enhance
early
diagnostics,
disease
management,
self-care
cardiometabolic
diseases.
Cardiometabolic
diseases,
which
include
combination
cardiovascular
metabolic
disorders,
represent
an
emerging
global
health
threat.
The
prevention
potential
diseases
is
around
80%,
indicating
promising
role
for
interventions
in
lifestyle
and/or
environmental
context.
Disruption
sleep
circadian
rhythms
increasingly
recognized
risk
factors
disease.
used
to
measure
clock,
is,
day
night,
quantify
not
only
patterns
but
also
diurnal
fluctuations
certain
processes.
In
this
way,
support
delivery
optimal
timed
medical
care.
Night-time
patterns,
blood
pressure
dipping,
predictive
outcomes.
addition,
period
provides
opportunity
with
relatively
low
influence
artifacts,
physical
activity
eating.
utilize
window
during
daily
life
enable
diagnosis
facilitate
remote
patient
monitoring,
self-management
people
This
review
describes
on
highlights
state-of-the-art
could
benefit
