'Ai Pono for Obesity

Recruiting at 1 trial location

Overseen By Ki'i Aweau

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Hawaii

Disqualifiers: Cancer, Chronic pain, Pregnancy, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 36

1780 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

Native Hawaiians' traditional lifestyles and diets ensured the mutual health and well-being of the land and its inhabitants, which stand in stark contrast to the disproportionately high prevalence of diet-related, cardiometabolic diseases they experience today. In this project, the investigators will adapt and test an evidence-based multilevel intervention entitled PILI 'Āina to improve the self-management of prevalent cardiometabolic diseases and reduce risk factors for developing new diet-related illnesses and implement and evaluate the impact and sustainability of community-wide cooking demonstrations. The objectives of this project are to optimize the effectiveness and sustainability of PILI 'Āina, improve diet quality, cardiometabolic markers, promote traditional Native Hawaiian diets, and improve social cohesion.

Do I need to stop my current medications for the trial?

The trial information does not specify whether you need to stop taking your current medications.

Is 'Ai Pono (vagal blocking therapy) safe for treating obesity?

The ReCharge Trial showed that vagal blocking therapy (vBloc) is safe for treating moderate to severe obesity over a 24-month period. ^{1 2 3 4 5}

How does the 'Ai Pono treatment for obesity differ from other treatments?

'Ai Pono is unique because it likely incorporates artificial intelligence (AI) to enhance weight loss by regulating eating and exercise behaviors, offering personalized and timely recommendations, and optimizing goal-setting, which is not typically found in standard obesity treatments. ^{6 7 8 9 10}

What data supports the effectiveness of the treatment 'Ai Pono for obesity?

Research shows that using artificial intelligence, specifically reinforcement learning, can optimize weight loss treatments by adjusting interventions based on individual responses. This approach has been shown to achieve similar weight loss results at a lower cost compared to traditional methods, indicating promise for AI-based systems in managing obesity. ^{7 11 12 13 14}

Who Is on the Research Team?

Claire Ing, DrPH

Principal Investigator

Principal Investigator

Kulani DeSimone, B.S.

Principal Investigator

Graduate Assistant

Are You a Good Fit for This Trial?

This trial is for Native Hawaiian adults who are overweight or obese, can do moderate exercise like brisk walking for 150 minutes a week, and have type 2 diabetes, pre-diabetes, high blood pressure, or dyslipidemia. They must be willing to participate fully in the program and live in a participating homestead.

Inclusion Criteria

I live in a participating Hawaiian homestead.

My BMI is 25 or higher, indicating I am overweight or obese.

I can do activities like brisk walking for 150 minutes weekly.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Quarterly cooking demonstrations and cultural lessons aimed at improving diet quality and social cohesion

5 years

36 events over 5 years

Follow-up

Participants are monitored for changes in diet self-efficacy, social support, and other health markers

4 years

What Are the Treatments Tested in This Trial?

Interventions

• 'Ai Pono

Trial Overview 'Ai Pono involves community-wide cooking demonstrations aimed at promoting traditional Native Hawaiian diets to improve diet quality and cardiometabolic health markers while also enhancing social cohesion among participants.

How Is the Trial Designed?

Group I: Aim 3: Community Level Intervention Experimental Treatment 1 Intervention

Aim 3 is community-driven, supports the overall goal of the PILI 'Āina project, allows all members of the community to benefit, and is strongly promoted within the community. The investigators will design and implement 'Ai Pono, a community-level component that can operate in tandem with PILI 'Āina. This component will not be tested in the group-randomized trial. Instead, all 7 communities will implement 'Ai Pono for the full study period.

Who Is Running the Clinical Trial?

University of Hawaii

Lead Sponsor

Trials

122

Recruited

55,200+

National Institutes of Health (NIH)

Collaborator

Trials

2,896

Recruited

8,053,000+

National Heart, Lung, and Blood Institute (NHLBI)

Collaborator

Trials

3,987

Recruited

47,860,000+

American Heart Association

Collaborator

Trials

352

Recruited

6,196,000+

Published Research Related to This Trial

A randomized controlled trial involving 340 participants showed that a digital lifestyle coaching program led to a significant weight loss of 4.5 kg over 12 months compared to only 1.5 kg in the usual care group, indicating the efficacy of digital coaching for obesity management.

Both groups experienced reductions in hemoglobin A1c levels, suggesting improvements in blood sugar control, but there was no significant difference between the groups, highlighting that while weight loss was greater in the intervention group, blood sugar improvements were similar for both.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Long-term Weight Loss in a Primary Care-Anchored eHealth Lifestyle Coaching Program: Randomized Controlled Trial. Hesseldal, L., Christensen, JR., Olesen, TB., et al.[2022]

A new study will evaluate whether a 12-month weight loss program using reinforcement learning (AI) can achieve similar weight loss results as traditional behavioral weight loss methods, but at a lower cost, involving 336 adults with overweight or obesity.

Preliminary results showed that participants using the AI system needed significantly less coaching while achieving nearly the same weight loss as those in the standard program, suggesting that AI could make weight loss support more accessible.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using artificial intelligence to optimize delivery of weight loss treatment: Protocol for an efficacy and cost-effectiveness trial. Forman, EM., Berry, MP., Butryn, ML., et al.[2023]

New generation anti-obesity medications, such as orlistat, phentermine/topiramate, and liraglutide 3.0 mg, have varying safety profiles, with some presenting significant risks like fetal toxicity and gallstone disease, while others mainly cause transient tolerability issues.

Despite their effectiveness in weight loss, the prescription rates for these medications are low, partly due to safety concerns and the need for more long-term data to better understand their risk-benefit profiles.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Safety and tolerability of new-generation anti-obesity medications: a narrative review. Patel, DK., Stanford, FC.[2023]

A total of 25 anti-obesity medications were withdrawn between 1964 and 2009, primarily due to adverse reactions related to their effects on monoamine neurotransmitters, with 80% of withdrawals supported by case reports.

The most common reasons for withdrawal included psychiatric disturbances, cardiotoxicity, and drug dependence, raising concerns about the safety of using medications that target neurotransmitters for obesity management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Post-marketing withdrawal of anti-obesity medicinal products because of adverse drug reactions: a systematic review. Onakpoya, IJ., Heneghan, CJ., Aronson, JK.[2022]

A study involving 22 participants from a weight management clinic in Singapore identified key features that weight loss apps should include for a multi-ethnic population with overweight and obesity, such as comprehensive calorie counters and individualized behavior change recommendations.

Participants emphasized the importance of flexibility in food logging and the need for apps to provide just-in-time reminders about the consequences of their choices, suggesting that future apps could benefit from artificial intelligence to offer personalized support for weight management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Essential elements of weight loss apps for a multi-ethnic population with high BMI: a qualitative study with practical recommendations. Chew, HSJ., Lim, SL., Kim, G., et al.[2023]

Citations

1. United States pubmed.ncbi.nlm.nih.gov

Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss? [2020]

2. Canada pubmed.ncbi.nlm.nih.gov

Long-term Weight Loss in a Primary Care-Anchored eHealth Lifestyle Coaching Program: Randomized Controlled Trial. [2022]

3. Canada pubmed.ncbi.nlm.nih.gov

The Use of Artificial Intelligence-Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations. [2022]

4. United States pubmed.ncbi.nlm.nih.gov

Using artificial intelligence to optimize delivery of weight loss treatment: Protocol for an efficacy and cost-effectiveness trial. [2023]

5. Switzerland pubmed.ncbi.nlm.nih.gov

Predicting weight loss success on a new Nordic diet: an untargeted multi-platform metabolomics and machine learning approach. [2023]

6. United States pubmed.ncbi.nlm.nih.gov

Vagal Blocking for Obesity Control: a Possible Mechanism-Of-Action. [2018]

7. United States pubmed.ncbi.nlm.nih.gov

Two-Year Outcomes of Vagal Nerve Blocking (vBloc) for the Treatment of Obesity in the ReCharge Trial. [2018]

8. England pubmed.ncbi.nlm.nih.gov

Safety and tolerability of new-generation anti-obesity medications: a narrative review. [2023]

9. England pubmed.ncbi.nlm.nih.gov

Latest approaches for the treatment of obesity. [2015]

10. England pubmed.ncbi.nlm.nih.gov

Post-marketing withdrawal of anti-obesity medicinal products because of adverse drug reactions: a systematic review. [2022]

11. England pubmed.ncbi.nlm.nih.gov

The potential of artificial intelligence in enhancing adult weight loss: a scoping review. [2023]

12. England pubmed.ncbi.nlm.nih.gov

Essential elements of weight loss apps for a multi-ethnic population with high BMI: a qualitative study with practical recommendations. [2023]

13. Switzerland pubmed.ncbi.nlm.nih.gov

A Smartphone Healthcare Application, CALO mama Plus, to Promote Weight Loss: A Randomized Controlled Trial. [2022]

14. United States pubmed.ncbi.nlm.nih.gov

The worldwide obesity epidemic. [2022]