Summary
Digital
health
interventions
can
support
the
treatment
of
overweight
and
obesity
in
children
adolescents,
yet
primary
research
systematic
reviews
leave
uncertain
evidence.
In
this
umbrella
review
meta‐analyses,
we
methodologically
appraise
investigate
effects
digital
used
to
manage
children.
Systematic
searches
were
conducted
July
2023
Medline
(Ovid),
CINAHL
(EBSCOhost),
Cochrane,
EMBASE
PsycINFO
Epistemonikos
Web
Science
(Core
Collection).
Reports
on
experiences
and/or
effectiveness
aimed
at
treating
with
or
aged
0
19
years
their
parents
eligible
for
inclusion.
Screening,
data
extraction,
methodological
appraisal
blinded
pairs
researchers.
total,
identified
2927
citations,
which
16
10
reporting
162
distinct
studies,
included.
Effects
anthropometric
measures
all
small
when
analyzing
BMI
BMI‐z‐scores
combined.
Future
should
strive
conduct
more
homogeneous
solid
research,
employing
robust
designs,
standardized
outcomes,
a
longer
follow‐up
time.
Designing
future
larger
extent
include
end‐users
ensure
usability
relevance
population,
adding
significance
that
are
evaluated.
Otology & Neurotology,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 4, 2025
Objective
To
examine
the
quality
of
information
provided
by
artificial
intelligence
platforms
ChatGPT-4
and
Claude
2
surrounding
management
vestibular
schwannomas.
Study
design
Cross-sectional.
Setting
Skull
base
surgeons
were
involved
from
different
centers
countries.
Intervention
Thirty-six
questions
regarding
schwannoma
tested.
Artificial
responses
subsequently
evaluated
19
lateral
skull
using
Quality
Assessment
Medical
Intelligence
(QAMAI)
questionnaire,
assessing
“Accuracy,”
“Clarity,”
“Relevance,”
“Completeness,”
“Sources,”
“Usefulness.”
Main
Outcome
Measure
The
scores
answers
both
chatbots
collected
analyzed
Student
t
test.
Analysis
grouped
stakeholders
was
performed
with
McNemar
Stuart-Maxwell
test
used
to
compare
reading
level
among
chatbots.
Intraclass
correlation
coefficient
calculated.
Results
demonstrated
significantly
improved
over
in
14
36
(38.9%)
questions,
whereas
higher-quality
for
only
observed
(5.6%)
answers.
Chatbots
exhibited
variation
across
dimensions
“Usefulness,”
demonstrating
a
statistically
significant
superior
performance.
However,
no
difference
found
assessment
“Sources.”
Additionally,
at
lower
grade
level.
Conclusions
failed
consistently
provide
accurate
schwannoma,
although
achieved
higher
most
parameters.
These
findings
demonstrate
potential
misinformation
patients
seeking
through
these
platforms.
Medicina,
Год журнала:
2025,
Номер
61(2), С. 358 - 358
Опубликована: Фев. 19, 2025
Greater
than
650
million
individuals
worldwide
are
categorized
as
obese,
which
is
associated
with
significant
health,
economic,
and
social
challenges.
Given
its
overlap
leading
comorbidities
such
heart
disease,
innovative
solutions
necessary
to
improve
risk
prediction
management
strategies.
In
recent
years,
artificial
intelligence
(AI)
machine
learning
(ML)
have
emerged
powerful
tools
in
healthcare,
offering
novel
approaches
chronic
disease
prevention.
This
narrative
review
explores
the
role
of
AI/ML
obesity
management,
a
special
focus
on
childhood
obesity.
We
begin
by
examining
multifactorial
nature
obesity,
including
genetic,
behavioral,
environmental
factors,
limitations
traditional
predict
treat
morbidity
Next,
we
analyze
techniques
commonly
used
risk,
particularly
minimizing
risk.
shift
application
comparing
perspectives
from
healthcare
providers
versus
patients.
From
provider's
perspective,
offer
real-time
data
electronic
medical
records,
wearables,
health
apps
stratify
patient
customize
treatment
plans,
enhance
clinical
decision
making.
patient's
AI/ML-driven
interventions
personalized
coaching
long-term
engagement
management.
Finally,
address
key
challenges,
determinants
embracing
while
our
recommendations
based
literature
review.
Summary
Digital
health
interventions
can
support
the
treatment
of
overweight
and
obesity
in
children
adolescents,
yet
primary
research
systematic
reviews
leave
uncertain
evidence.
In
this
umbrella
review
meta‐analyses,
we
methodologically
appraise
investigate
effects
digital
used
to
manage
children.
Systematic
searches
were
conducted
July
2023
Medline
(Ovid),
CINAHL
(EBSCOhost),
Cochrane,
EMBASE
PsycINFO
Epistemonikos
Web
Science
(Core
Collection).
Reports
on
experiences
and/or
effectiveness
aimed
at
treating
with
or
aged
0
19
years
their
parents
eligible
for
inclusion.
Screening,
data
extraction,
methodological
appraisal
blinded
pairs
researchers.
total,
identified
2927
citations,
which
16
10
reporting
162
distinct
studies,
included.
Effects
anthropometric
measures
all
small
when
analyzing
BMI
BMI‐z‐scores
combined.
Future
should
strive
conduct
more
homogeneous
solid
research,
employing
robust
designs,
standardized
outcomes,
a
longer
follow‐up
time.
Designing
future
larger
extent
include
end‐users
ensure
usability
relevance
population,
adding
significance
that
are
evaluated.
