Assessing knowledge, attitude, and dietary practice in association with prediabetes risk using objective clinical markers among Saudi adult population: A cross-sectional study
Medicine,
Journal Year:
2025,
Volume and Issue:
104(16), P. e42172 - e42172
Published: April 18, 2025
A
major
risk
for
developing
diabetes
is
prediabetes
(Pre-DM).
Assessing
knowledge,
attitude,
and
dietary
practice
(KAP)
regarding
Pre-DM
plays
a
crucial
role
in
decreasing
complications.
Limited
previous
studies
on
KAP
among
prediabetic
patients
exist.
This
study
aimed
to
determine
the
prevalence
of
using
glycosylated
hemoglobin
(HbA1c%)
indicator
as
well
degree
awareness
Saudi
participants
Jeddah
about
across
their
body
mass
index
(BMI)
categories.
cross-sectional
was
conducted
2
large
public
malls,
targeting
310
adults
aged
30
55
who
had
no
prior
diagnosis
or
any
chronic
disease.
valid
questionnaire
used
assess
KAP.
Data
were
collected
through
anthropometric
measurements,
including
BMI,
fat%,
trunk%,
waist
hip
ratio.
Random
blood
glucose
HbA1c%
also
measured
diagnose
Pre-DM.
The
data
analyzed
Statistical
Package
Social
Sciences
(SPSS).
In
final
analysis,
290
included.
found
23.1%
participants,
3.4%,
73.4%
normal.
Obesity
observed
be
strongly
associated
with
compared
normal
BMI
(
P
=
.04).
Out
44.8%
poor
knowledge
Pre-DM,
44.2%
them
overweight.
Additionally,
49.8%
total
sample
neutral
55.7%
being
obese.
53.4%
reported
good
practice,
33%
Furthermore,
it
that
significantly
.025)
but
not
attitude
>
.005).
results
demonstrated
average
trend
towards
level,
attitudes,
studied
Saudis
sample.
Interestingly,
only
correlated
suggesting
raising
essential
improving
prevention.
Longitudinal
larger
size
are
warranted
better
establish
causality
between
practices.
Language: Английский
Predictive Model Approach for Enhancing Diet Management for Diabetes Patients Through Artificial Intelligence
Advances in medical technologies and clinical practice book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 335 - 366
Published: Nov. 1, 2024
Diabetes
represents
a
severe
global
health
crisis
with
escalating
rates,
complications,
and
economic
impact.
Effective
management
requires
combination
of
nutrition,
physical
activity,
medication,
insulin
therapy,
but
challenges
like
limited
specialist
access
medication
adherence
hinder
optimal
glycemic
control.
Recent
advancements
in
digital
health,
especially
artificial
intelligence
(AI),
offer
promising
solutions.
This
study
explores
the
integration
AI
diabetes
through
Random
Forest
classifier
to
provide
personalized
dietary
recommendations.
The
Nutrition
Diet
Expert
System
(NDES)
achieved
impressive
results
96.48%
accuracy,
0.98
precision,
0.96
recall,
0.97
F1-score.
By
optimizing
food
intake,
management,
lifestyle
adjustments,
NDES
supports
stable
blood
glucose
levels,
healthy
weight,
improved
patient
out-comes.
Ongoing
continue
innovative
strategies
for
tackling
challenges.
Language: Английский