Generative AI-Based Health Education for Older Adults With Sarcopenic Obesity

NCT07782320 | Not Yet Recruiting

• 1 other identifier: N202404120
• Study Type: interventional
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

Sarcopenic obesity is a major public health concern among community-dwelling older adult populations.Encouraging healthy behavior modification through health education emerges as an effective strategy for preventing and treating sarcopenic obesity. Generative Artificial Intelligence (AI) offers an innovative opportunity to tailor health education for aging populations. This study aims to explore the effectiveness of Personalized AI-generated Multimedia Health Education in community-dwelling older adults with sarcopenic obesity. The study comprises two phases, with 280 participants in Study 1 and 180 in Study 2.Study 1, a cluster randomized controlled trial, explores the feasibility, acceptability, and efficacy of different AI-generated multimedia, including images, sounds, and videos. Study 2 employs a three-armed,individually randomized controlled trial design, creating Personalized AI-generated Multimedia Health Education based on participant preferences. All materials focus on behavioral risk factors, delivered by social media-based AI chatbots. The intervention is conducted once a day, five days a week for 12 weeks.Structured questionnaires and objective instruments collect data before and after the intervention. The study outcome includes behavioral and psychological factors, quality of life, and sarcopenic obesity indicators. Statistical analyses include descriptive analyses, Chi-square tests, t-tests, One-way analysis of variance, path models, and generalized estimating equations. This study anticipates that Personalized AI-generated Multimedia Health Education will be a feasible, acceptable, and effective intervention for older adults. The study results are expected to demonstrate a significant improvement in study outcomes in the experimental group. Personalized AI-generated multimedia health education could be an easy-to-use,enjoyable, and effective strategy for health promotion and sarcopenic obesity prevention.

Conditions

Sarcopenic Obesity

Outcome Measures

Primary Outcomes (4)

• The Chinese version of the Physical Activity Scale for the Elderly

  This questionnaire consists of 12 items assessing physical activity (PA) over the past 7 days, including leisure-time, household, and occupational activities. Total, light-, moderate-, and vigorous-intensity PA can be calculated in metabolic equivalents of task-minutes per week (MET-min/week).

  Baseline, midpoint (the 6th week), and post-intervention (the 13rd week)

• General Dietary Behavior Inventory

  A total of 16 items measure participants' healthy behaviors using a 5-point bipolar scale.The score of each item is summed up to a total score, with a higher score representing healthier dietary behavior.

  Baseline, midpoint (the 6th week), and post-intervention (the 13rd week)

• The skeletal muscle index (SMI)

  A body composition analyzer using bioelectrical impedance analysis technology is conducted. A higher score (kg/m2) of the skeletal muscle index (SMI) indicates greater muscle mass for sarcopenic indicators.

  Baseline, midpoint (the 6th week), and post-intervention (the 13rd week)

• Muscle Strength

  Muscle strength is assessed using a handheld grip device (SANKA, Japan). Participants are asked to stand, allow the wrist and arm of the dominant hand to hang straight down, and maintain maximum strength for more than 3 seconds. This measurement is repeated three times, and the maximum value (kg) is used as the muscle strength.

  Baseline, midpoint (the 6th week), and post-intervention (the 13rd week)

Secondary Outcomes (2)

• The Health-Promoting Lifestyle Profile II

  Baseline, midpoint (the 6th week), and post-intervention (the 13rd week)

• World Health Organization (WHO)- Quality of Life Scale

  Baseline, midpoint (the 6th week), and post-intervention (the 13rd week)

Study Arms (8)

No Intervention

NO INTERVENTION

Generative Artificial Intelligence (AI)-based text materials

ACTIVE COMPARATOR

Behavioral: Generative AI-based text materials

Generative AI-based video materials

EXPERIMENTAL

Behavioral: Generative AI-based video materials

Generative AI-based images materials

EXPERIMENTAL

Behavioral: Generative AI-based images materials

Generative AI-based voice materials

EXPERIMENTAL

Behavioral: Generative AI-based voice materials

Comparator Group

NO INTERVENTION

Non-Personalized Gen-AI MHE

EXPERIMENTAL

Behavioral: Non-Personalized Gen-AI MHE

Personalized Gen-AI MHE

EXPERIMENTAL

Behavioral: Personalized Gen-AI MHE

Interventions

Generative AI-based text materials BEHAVIORAL

Participants receive a text message regarding the health education topic.

Generative Artificial Intelligence (AI)-based text materials

Generative AI-based video materials BEHAVIORAL

Participants receive a video message , accompanied by AI-generated background music at the beginning and end. Male avatars are used for PA and SO education, and female avatars are used for HD education.

Generative AI-based video materials

Generative AI-based images materials BEHAVIORAL

Participants receive one Gen-AI-generated image concerning the health education topic, formatted as a health poster.

Generative AI-based images materials

Generative AI-based voice materials BEHAVIORAL

Participants receive a podcast-style voice message regarding the health education topic. The recording incorporates AI-generated background voice. Male voices are utilized for physical activity (PA) education, while female voices are used for healthy diet (HD) and sarcopenic obesity (SO) education.

Generative AI-based voice materials

Non-Personalized Gen-AI MHE BEHAVIORAL

Participants receive non-personalized multimodal health education generated by generative AI.

Non-Personalized Gen-AI MHE

Personalized Gen-AI MHE BEHAVIORAL

Participants receive personalized multimodal health education tailored through generative AI.

Personalized Gen-AI MHE

Eligibility Criteria

• Age: 50 Years - 99 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Age: 60 years or older
• Smartphone ownership with internet connectivity
• Appendicular fat-free mass (AFFM) calculated by the equation: AFFM = 14.529 + (17.989 \* height)+ (0.1307 \* fat mass). The cut-off value corresponds to a residual ≤ 3.4 in the equation
• Ability to read, listen, and understand health education materials with normal cognitive function (MMSE score ≥ 25) and normal sensory function.

You may not qualify if:

• Functional dependency
• Current residence in long-term care facilities or hospitals
• Presence of serious diagnosed diseases, disabilities, or mental health issues requiring medical treatment that might influence the study process.

Sponsors & Collaborators

• Taipei Medical University: lead

Study Sites (1)

Taipei Medical University

Taipei, 110, Taiwan

Location

Central Study Contacts

Hsin-Yen Yen, PhD

CONTACT

886-2-2736-1661; kenji@tmu.edu.tw

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 16, 2026
• First Posted: August 24, 2026
• Study Start (Estimated): October 1, 2026
• Primary Completion (Estimated): December 31, 2029
• Study Completion (Estimated): December 31, 2029
• Last Updated: August 24, 2026

Record last verified: 2026-01

Locations

TW (1)