Diagnostics,
Год журнала:
2022,
Номер
12(2), С. 317 - 317
Опубликована: Янв. 27, 2022
This
study
examines
related
literature
to
propose
a
model
based
on
artificial
intelligence
(AI),
that
can
assist
in
the
diagnosis
of
depressive
disorder.
Depressive
disorder
be
diagnosed
through
self-report
questionnaire,
but
it
is
necessary
check
mood
and
confirm
consistency
subjective
objective
descriptions.
Smartphone-based
assistance
diagnosing
disorders
quickly
lead
their
identification
provide
data
for
intervention
provision.
Through
fast
region-based
convolutional
neural
networks
(R-CNN),
deep
learning
method
recognizes
vector-based
information,
devised
by
checking
position
change
eyes
lips,
guessing
emotions
accumulated
photos
participants
who
will
repeatedly
participate
Journal of Affective Disorders,
Год журнала:
2021,
Номер
287, С. 69 - 77
Опубликована: Март 18, 2021
The
Netherlands
Study
of
Depression
and
Anxiety
(NESDA,
www.nesda.nl)
is
a
longitudinal,
multi-site,
naturalistic,
case-control
cohort
study
set
up
to
examine
the
etiology,
course
consequences
depressive
anxiety
disorders.
This
paper
presents
profile
NESDA.
NESDA
sample
recruited
initially
2329
persons
with
remitted
or
current
DSM-IV
based
(major
disorder,
dysthymia)
and/or
disorder
(panic
social
phobia,
agoraphobia,
generalized
disorder),
367
their
siblings
652
healthy
controls,
yielding
total
3348
participants.
Half-day
face-to-face
assessments
participants
started
in
2004
since
then
have
been
repeated
six
times
over
period
9
years.
A
13-year
follow-up
assessment
ongoing,
at
what
time
we
also
recruit
offspring
Retention
rates
are
generally
high,
ranging
from
87.1%
(after
2
years)
69.4%
years).
Psychiatric
diagnostic
interviews
administered
all
assessments,
as
was
monitoring
clinical
characteristics,
psychosocial
functioning
somatic
health.
Assessed
etiological
factors
include
e.g.
early
environmental
risk
factors,
psychological
vulnerability
resilience
well
(neuro)biology
through
hypothesis-driven
biomarker
genome-wide
large-scale
'-omics'
neuroimaging
assessments.
naturalistic
design
allows
research
into
affective
disorders
but
limited
treatment
response
interpretation.
provides
strong
infrastructure
for
Its
data
used
many
scientific
papers
describing
either
NESDA-based
analyses
joint
collaborative
consortia-projects,
principle
available
researchers
outside
consortium.
This
PRISMA
systematic
literature
review
examined
the
use
of
digital
data
collection
methods
(including
ecological
momentary
assessment
[EMA],
experience
sampling
method
[ESM],
biomarkers,
passive
sensing,
mobile
ambulatory
assessment,
and
time-series
analysis),
emphasizing
on
phenotyping
(DP)
to
study
depression.
DP
is
defined
as
profile
health
information
objectively.Four
distinct
yet
interrelated
goals
underpin
this
study:
(a)
identify
empirical
research
examining
depression;
(b)
describe
different
technology
employed;
(c)
integrate
evidence
regarding
efficacy
in
examination,
diagnosis,
monitoring
depression
(d)
clarify
definitions
mental
records
terminology.Overall,
118
studies
were
assessed
eligible.
Considering
terms
employed,
"EMA",
"ESM",
"DP"
most
predominant.
A
variety
sources
reported,
including
voice,
language,
keyboard
typing
kinematics,
phone
calls
texts,
geocoded
activity,
actigraphy
sensor-related
recordings
(i.e.,
steps,
sleep,
circadian
rhythm),
self-reported
apps'
information.
Reviewed
employed
subjectively
objectively
recorded
combination
with
interviews
psychometric
scales.Findings
suggest
links
between
a
person's
Future
recommendations
include
deriving
consensus
definition
expanding
consider
broader
contextual
developmental
circumstances
relation
their
data/records.
Journal of Affective Disorders,
Год журнала:
2022,
Номер
320, С. 735 - 741
Опубликована: Окт. 19, 2022
To
describe
the
prevalence
of
depressive
symptoms
among
Chinese
college
students
and
examine
relationship
between
sleep
circadian
rhythm
disruption
(SCRD)
indicators
(chronotype,
social
jetlag,
duration)
symptoms.From
April
to
May
2019,
College
Student
Behavior
Health
Cohort
Study
was
conducted
from
2
universities
in
Anhui
Jiangxi
provinces.
The
current
study
used
data
third
follow-up
study.
Questionnaire
content
includes
socio-demographic
lifestyle
information.
Social
jetlag
duration
were
calculated
by
answering
question
about
time.
Chronotype
assessed
Morning
Evening
(MEQ-5).
Depressive
evaluated
Patient
9
(PHQ-9).
A
Chi-square
test
proportion
symptoms.
Multinomial
logistic
regression
model
explore
associations
with
symptoms.The
mild
depression,
moderate
above
depression
18.8
%
6.9
%.
types
(E-types)
short
significantly
associated
depression.
Stratified
analysis
indicated
that
E-types
jetlag≥2
h
(OR
=
5.67,
95
CI:
1.83-17.51),
as
well
stratified
duration<8
5.10,
1.88-13.87).The
findings
suggest
are
more
severe
when
multiple
SCRD
out
whack.
intervention
programs
should
consider
context
multidimensional
aspects
student
sleep.
npj Digital Medicine,
Год журнала:
2023,
Номер
6(1)
Опубликована: Фев. 17, 2023
Abstract
Recent
growth
in
digital
technologies
has
enabled
the
recruitment
and
monitoring
of
large
diverse
populations
remote
health
studies.
However,
generalizability
inference
drawn
from
remotely
collected
data
could
be
severely
impacted
by
uneven
participant
engagement
attrition
over
course
study.
We
report
findings
on
long-term
retention
patterns
a
multinational
observational
study
for
depression
containing
active
(surveys)
passive
sensor
via
Android
smartphones,
Fitbit
devices
614
participants
up
to
2
years.
Majority
(67.6%)
continued
remain
engaged
after
43
weeks.
Unsupervised
clustering
participants’
apps
usage
showed
3
distinct
subgroups
each
stream.
found:
(i)
least
group
had
highest
severity
(4
PHQ8
points
higher)
across
all
streams;
(ii)
(completed
4
bi-weekly
surveys)
took
significantly
longer
respond
survey
notifications
(3.8
h
more)
were
5
years
younger
compared
most
20
surveys);
(iii)
considerable
proportion
(44.6%)
who
stopped
completing
surveys
8
weeks
share
(average
42
weeks).
Additionally,
multivariate
survival
models
age,
ownership
brand
sites
associated
with
Together
these
inform
design
future
studies
enable
equitable
balanced
collection
populations.
There
is
currently
a
deficit
of
knowledge
about
how
to
define,
quantify,
and
measure
different
aspects
daily
routine
disruptions
amid
large-scale
disasters
like
COVID-19,
which
psychiatric
symptoms
were
more
related
the
disruptions.
This
study
aims
conduct
systematic
review
meta-analysis
on
probable
positive
associations
between
mental
disorders
COVID-19
pandemic
factors
that
moderated
associations.
Personality and Mental Health,
Год журнала:
2025,
Номер
19(1)
Опубликована: Фев. 1, 2025
ABSTRACT
Given
the
lack
of
sufficient
studies
exploring
nature
sleep
problems
from
perspective
alternative
model
personality
disorders
(AMPD)
proposed
by
DSM‐5,
present
study
is
aimed
at
determining
associations
between
five
trait
domains
such
as
negative
affectivity
and
(insomnia,
parasomnia,
hypersomnia,
circadian
rhythm
disorder,
restless
legs
syndrome,
sleep‐disordered
breathing)
in
an
adult
population.
Adults
aged
18–65
western
Iran
were
invited
to
via
virtual
platforms
(
N
=
928;
62%
female)
responded
online
Brief
Form
Personality
Inventory
for
DSM‐5
Holland
Sleep
Disorder
Questionnaire
assess
problems.
The
regression
analyses
indicated
that
AMPD
could
significantly
predict
both
specific
R
2
ranges
0.13
0.17;
all
p
≤
0.001)
total
score
0.23;
<
0.001).
Psychoticism
β
0.26
0.39;
0.14
0.29;
0.002)
strongest
associated
with
findings
highlighted
links
maladaptive
multiple
unique
profiles
each
problem
are
useful
selecting
treatments
tailored
adults.
