Background:
The
majority
of
research
on
emotion
regulation
processes
has
been
restricted
to
controlled
laboratory
settings
that
use
experimental
paradigms
investigate
short-term
outcomes.
A
true
understanding
requires
an
unobtrusive,
ecologically
valid
assessment
the
construct
as
it
occurs
in
environment.
Digital
phenotyping
is
a
novel
method
for
evaluating
human
behavior
naturalistic
settings.
Objective:
This
study
aimed
evaluate
whether
smartphone-based
digital
data
predicts
individual
differences
both
in-lab
and
settings.Methods:
During
session,
unselected
university
student
participants
(N
=
69)
completed
self-report
questionnaires
measuring
trait
well
state
affect
following
baseline
period,
negative
mood
induction,
recovery
period.
Smartphone-based
were
then
collected
over
course
7-day
follow-up.
Variation
global
positioning
system
(GPS)
distance
mobile
power
level
examined
predictors
longitudinal
variation
affect,
regulation,
depression.
Results:
Results
showed
GPS
was
significantly
associated
with
cognitive
reappraisal
(b
-0.0004,
SE
0.0002,
p
.02)
0.
005,
0.002,
.01)
time.
also
time
-4.98,
1.72,
.005)
marginally
-29.58,
16.73,
.08)
Cluster
classification
analyses
accurately
classified
two
clusters
high
sensitivity
(.95
.96
respectively)
specificity
(.86
.97
respectively).
together
did
not
predict
current
depressive
symptoms
(ps
>
.05).Conclusions:
Overall,
findings
provide
initial
foundational
predicting
results
suggest
operationalizations
modeling
approaches
are
particularly
important
factors
consider
when
implementing
methodology
mental
health
such
regulation.
JMIR Formative Research,
Год журнала:
2023,
Номер
7, С. e45309 - e45309
Опубликована: Март 6, 2023
Despite
significant
research
done
on
youth
experiencing
homelessness,
few
studies
have
examined
movement
patterns
and
digital
habits
in
this
population.
Examining
these
behaviors
may
provide
useful
data
to
design
new
health
intervention
models
for
homelessness.
Specifically,
passive
collection
(data
collected
without
extra
steps
a
user)
insights
into
lived
experience
user
needs
putting
an
additional
burden
homelessness
inform
design.The
objective
of
study
was
explore
mobile
phone
Wi-Fi
usage
GPS
location
among
Additionally,
we
further
the
relationship
between
as
correlated
with
depression
posttraumatic
stress
disorder
(PTSD)
symptoms.A
total
35
adolescent
young
adult
participants
were
recruited
from
general
community
that
included
installing
sensor
acquisition
app
(Purple
Robot)
up
6
months.
Of
participants,
19
had
sufficient
conduct
analyses.
At
baseline,
completed
self-reported
measures
(Patient
Health
Questionnaire-9
[PHQ-9])
PTSD
(PTSD
Checklist
DSM-5
[PCL-5]).
Behavioral
features
developed
extracted
data.Almost
all
(18/19,
95%)
used
private
networks
most
their
noncellular
connectivity.
Greater
associated
higher
PCL-5
score
(P=.006).
entropy,
representing
amount
variability
time
spent
across
identified
clusters,
also
severity
both
(P=.007)
PHQ-9
(P=.045)
scores.Location
demonstrated
associations
symptoms,
while
only
symptom
severity.
While
be
conducted
establish
consistency
findings,
they
suggest
offer
could
tailor
interventions.
BACKGROUND
The
objective,
unobtrusively
collected
GPS
features
(eg,
homestay
and
distance)
from
everyday
devices
like
smartphones
may
offer
a
promising
augmentation
to
current
assessment
tools
for
depression.
However,
date,
there
is
no
systematic
meta-analytical
evidence
on
the
associations
between
OBJECTIVE
This
study
aimed
investigate
between-person
within-person
correlations
mobility
activity
depressive
symptoms,
critically
review
quality
potential
publication
bias
in
field.
METHODS
We
searched
MEDLINE,
PsycINFO,
Embase,
CENTRAL,
ACM,
IEEE
Xplore,
PubMed,
Web
of
Science
identify
eligible
articles
focusing
depression
December
6,
2022,
March
24,
2023.
Inclusion
exclusion
criteria
were
applied
2-stage
inclusion
process
conducted
by
2
independent
reviewers
(YT
JK).
To
be
eligible,
studies
needed
report
wearable-based
variables
total
symptoms
measured
with
validated
questionnaire.
Studies
underage
persons
other
mental
health
disorders
excluded.
Between-
analyzed
using
random
effects
models.
Study
was
determined
comparing
against
STROBE
(Strengthening
Reporting
Observational
Epidemiology)
guidelines.
Publication
investigated
Egger
test
funnel
plots.
RESULTS
A
k=19
involving
N=2930
participants
included
analysis.
mean
age
38.42
(SD
18.96)
years
59.64%
22.99%)
being
female.
Significant
identified:
distance
(<i>r</i>=–0.25,
95%
CI
–0.29
–0.21),
normalized
entropy
(<i>r</i>–0.17,
–0.04),
location
variance
–0.26
(<i>r</i>=–0.13,
–0.23
number
clusters
(<i>r</i>=–0.11,
–0.18
–0.03),
(<i>r</i>=0.10,
0.00
0.19).
reporting
within-correlations
(k=3)
too
heterogeneous
conduct
meta-analysis.
deficiency
research
standards
all
followed
exploratory
observational
designs,
but
referenced
or
fully
adhered
international
guidelines
(STROBE).
79%
(k=15)
underpowered
detect
small
correlation
(<i>r</i>=.20).
Results
showed
bias.
CONCLUSIONS
Our
results
provide
Hence,
diagnostics
benefit
adding
as
an
integral
part
future
expert
tools.
confirmatory
further
are
needed.
In
addition,
methodological
needs
improve.
CLINICALTRIAL
OSF
Registeries
cwder;
https://osf.io/cwder
medRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Окт. 5, 2023
Abstract
Background
Smartphone-based
digital
phenotyping
enables
potentially
clinically
relevant
information
to
be
collected
as
individuals
go
about
their
day.
This
could
improve
monitoring
and
interventions
for
people
with
Major
Depressive
Disorder
(MDD).
The
aim
of
this
systematic
review
was
investigate
current
features
methods
used
in
MDD.
Methods
We
searched
PubMed,
PsycINFO,
Embase,
Scopus
Web
Science
(10/11/2023)
articles
including:
(1)
MDD
population,
(2)
smartphone-based
features,
(3)
validated
ratings.
Risk
bias
assessed
using
several
sources.
Studies
were
compared
within
analysis
goals
(correlating
depression,
predicting
symptom
severity,
diagnosis,
mood
state/episode,
other).
Twenty-four
studies
(9801
participants)
included.
Results
achieved
moderate
performance.
Common
themes
included
challenges
from
complex
missing
data
(leading
a
risk
bias),
lack
external
validation.
Discussion
made
progress
towards
relating
phenotypes
clinical
variables,
often
focusing
on
time-averaged
features.
investigating
temporal
dynamics
more
directly
may
beneficial
patient
monitoring.
European
Research
Council
consolidator
grant:
101001118,
Prospero:
CRD42022346264,
Open
Framework:
https://osf.io/s7ay4
Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Апрель 19, 2024
Abstract
Prior
research
has
shown
that
changes
in
seasons
and
weather
can
have
a
significant
impact
on
depression
severity.
However,
findings
are
inconsistent
across
populations,
the
interplay
between
weather,
behavior,
not
been
fully
quantified.
This
study
analyzed
real-world
data
from
428
participants
(a
subset;
68.7%
of
cohort)
RADAR-MDD
longitudinal
mobile
health
to
investigate
seasonal
variations
(measured
through
remote
validated
assessment
-
PHQ-8)
examine
potential
dynamic
changes,
physical
activity
(monitored
via
wearables),
The
clustering
PHQ-8
scores
identified
four
distinct
severity:
one
stable
trend
three
varying
patterns
where
peaks
different
seasons.
Among
these
patterns,
within
had
oldest
average
age
(p
=
0.002)
lowest
baseline
score
0.003).
Mediation
analysis
assessing
indirect
effect
showed
differences
among
with
affective
responses
weather.
Specifically,
temperature
day
length
significantly
influenced
severity,
which
turn
impacted
levels
<
0.001).
For
instance,
negative
correlation
severity
temperature,
10°C
increase
led
total
daily
step
count
rise
655.4,
comprised
461.7
steps
directly
due
itself
193.7
because
decreased
depressive
(1.9
decrease
PHQ-8).
In
contrast,
for
those
positive
correlation,
262.3-step
rise;
however,
it
was
offset
by
141.3-step
increased
(2.1
higher
temperatures,
culminating
an
insignificant
overall
121
steps.
These
illustrate
heterogeneity
individuals'
underscoring
necessity
personalized
approaches
help
understand
environmental
factors
effectiveness
behavioral
treatments.
Background:
The
majority
of
research
on
emotion
regulation
processes
has
been
restricted
to
controlled
laboratory
settings
that
use
experimental
paradigms
investigate
short-term
outcomes.
A
true
understanding
requires
an
unobtrusive,
ecologically
valid
assessment
the
construct
as
it
occurs
in
environment.
Digital
phenotyping
is
a
novel
method
for
evaluating
human
behavior
naturalistic
settings.
Objective:
This
study
aimed
evaluate
whether
smartphone-based
digital
data
predicts
individual
differences
both
in-lab
and
settings.Methods:
During
session,
unselected
university
student
participants
(N
=
69)
completed
self-report
questionnaires
measuring
trait
well
state
affect
following
baseline
period,
negative
mood
induction,
recovery
period.
Smartphone-based
were
then
collected
over
course
7-day
follow-up.
Variation
global
positioning
system
(GPS)
distance
mobile
power
level
examined
predictors
longitudinal
variation
affect,
regulation,
depression.
Results:
Results
showed
GPS
was
significantly
associated
with
cognitive
reappraisal
(b
-0.0004,
SE
0.0002,
p
.02)
0.
005,
0.002,
.01)
time.
also
time
-4.98,
1.72,
.005)
marginally
-29.58,
16.73,
.08)
Cluster
classification
analyses
accurately
classified
two
clusters
high
sensitivity
(.95
.96
respectively)
specificity
(.86
.97
respectively).
together
did
not
predict
current
depressive
symptoms
(ps
>
.05).Conclusions:
Overall,
findings
provide
initial
foundational
predicting
results
suggest
operationalizations
modeling
approaches
are
particularly
important
factors
consider
when
implementing
methodology
mental
health
such
regulation.