Health TrialsbyHealthPanel

AI-INFORMED PEER-MENTORING BEHAVIORAL INTERVENTION FOR OBESITY AND CARDIOVASCULAR DISEASE PRIMARY PREVENTION IN YOUTH

ConditionObesity (Disorder)
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age12-25
SponsorAikaterini Karaivazoglou

About this trial

The goal of this clinical trial is to learn if an AI-based peer-mentoring works to prevent obesity in adolescents and young adults. The main questions it aims to answer are:

Does the AI-based peer-mentoring improve dietary habits and increase physical activity in healthy individuals 12-25 yrs old? Does the AI-based peer-mentoring reduces the risk of obesity?

Researchers will compare the AI-based behavioral peer-mentoring intervention to traditional peer-mentoring and to health education intervention to see if AI-based peer-mentoring is more effective.

Participants will:

Follow either a structured AI-based peer-mentoring program or a traditional peer-mentoring program or health education sessions focusing on diet and physical activity They will be evaluated at baseline, at 6 months and at 12 months

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

None

Disqualifiers

None

Trial design

Design model

Parallel

Treatments tested in this trial

  • AI-informed behavioral intervention

    Behavioral

    A hybrid, human-delivered peer-mentoring intervention, combining in-person and remote interactions and supported by AI-informed tools that provide mentors with access to ethically governed, privacy-preserving participant indicators. The intervention will operate under a supervisor-in-the-loop model, ensuring participant safety, intervention quality and ethical compliance, mentor support, and timely escalation to professional care when risks exceed the scope of peer support.

  • Standard behavioral intervention

    Behavioral

    Standard in-person behavioral intervention

  • Health Education

    Other intervention

    Regular professional-delivered health educational sessions on nutrition and physical activity

Treatment groups

840 Participants
are divided into 3 treatment groups
Group A: AI-informed peer-mentoring behavioral interventionExperimental treatment 1 intervention
Group B: Standard peer-mentoringActive comparator 1 intervention
Group C: Health educational interventionActive comparator 1 intervention

Trial outcomes

Primary outcomes

1

Food Frequency Questionnaire

Self- or parent-reported questionnaire on the consumption of several food categories

Time frame
From study enrollment to study completion at 12 months
2

KIDMED INDEX

A questionnaire used to measure adherence to the Mediterranean diet in children and adolescents

Time frame
From study enrollment to study completion at 12 months
3

Accelerations measurement

Measurement of accelerations through an accelerometer to evaluate the degree of physical activity

Time frame
From study enrollment to study completion at 12 months
4

Healthy Diet Index

It is a measure of diet quality

Time frame
From study enrollment to study completion at 12 months

Secondary outcomes

1

BMI

Measurement of body mass index

Time frame
From study enrollment to study completion at 12 months
2

Waist circumference

Measurement of waist circumference

Time frame
From study enrollment to study completion at 12 months
3

Lipidemic profile

Measurement of plasma total cholesterol, TGs, LDL, HDL, non-HDL, ApoB levels

Time frame
From study enrollment to study completion at 12 months
4

Skin carotenoid levels

Spectroscopy-based measurement of skin carotenoid levels with the use of Veggie-Meter

Time frame
From study enrollment to study completion at 12 months

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Aikaterini Karaivazoglou

Lead sponsor

University of Patras

Sponsor institution

University of Eastern Finland

Collaborator

Savonia University of Applied Sciences

Collaborator

Source ClinicalTrials.gov

This trial is not recruiting at the moment. You can still explore other options: