Clinical trial

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

Opening soon · Not applicable · 1 countries · Registry ID NCT07782320

Opening soonNot applicableInterventional

What this study is about

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.

A promising-looking record is not the same as confirmed eligibility. The study team must review the full criteria and current recruitment status.

Basic eligibility

Age50 Years to 99 Years

SexAll

Healthy volunteersAccepted

ConditionSarcopenic Obesity

Full registry criteria

Inclusion Criteria: 1. . Age: 60 years or older 2. . Smartphone ownership with internet connectivity 3. . 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 4. . Ability to read, listen, and understand health education materials with normal cognitive function (MMSE score ≥ 25) and normal sensory function. Exclusion Criteria: 1. . Functional dependency 2. . Current residence in long-term care facilities or hospitals 3. . Presence of serious diagnosed diseases, disabilities, or mental health issues requiring medical treatment that might influence the study process.

Treatments and study arms

Generative AI-based text materials

Behavioral

Participants receive a text message regarding the health education topic.

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 images materials

Behavioral

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

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.

Non-Personalized Gen-AI MHE

Behavioral

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

Personalized Gen-AI MHE

Behavioral

Participants receive personalized multimodal health education tailored through generative AI.

Primary outcomes

The Chinese version of the Physical Activity Scale for the ElderlyBaseline, midpoint (the 6th week), and post-intervention (the 13rd week)

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).

General Dietary Behavior InventoryBaseline, midpoint (the 6th week), and post-intervention (the 13rd week)

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.

The skeletal muscle index (SMI)Baseline, midpoint (the 6th week), and post-intervention (the 13rd week)

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.

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

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.

Study locations

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Taipei Medical University🇹🇼 Taipei, Taiwan

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Hsin-Yen Yen, PhDContact

Email · Call 886-2-2736-166