Sensor-Based Interventions for Obesity

No longer recruiting at 1 trial location

Overseen By Olly Richards, BSc

Age: 18+

Sex: Any

Trial Phase: Phase 2

Sponsor: The Miriam Hospital

Must not be taking: Appetite suppressants

Disqualifiers: Diabetes, Eating disorder, Pregnancy, others

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

Prior Safety Data This treatment has passed at least one previous human trial

Compensation: Varies

Number of Visits: 2

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to determine if a special sensor device worn on glasses can help people change their eating habits. The device, called an Automatic Ingestion Monitor (AIM), tracks how much and how fast participants eat and sends messages to their smartphones to encourage eating less or more slowly. The trial seeks individuals with a BMI between 27 and 45 who have maintained stable weight for the past six months and use a smartphone.

As a Phase 2 trial, this research measures the device's effectiveness in an initial, smaller group, offering participants a chance to contribute to innovative weight management solutions.

Will I have to stop taking my current medications?

The trial does not specify if you need to stop taking your current medications, but it excludes those using medications known to affect energy regulation or appetite. It's best to discuss your specific medications with the trial coordinators.

What prior data suggests that the Automatic Ingestion Monitor (AIM) device is safe for use in changing eating behavior?

Research shows that the Automatic Ingestion Monitor (AIM) device, used in the Eat Less Intervention, is easy to use and doesn't cause problems. Previous studies have found that devices like AIM can help people eat less without any harm. Worn on eyeglasses, the device has been safely used in other studies to track eating habits.

For the Eat Slower Intervention, studies have shown that eating more slowly can increase feelings of fullness and improve memory of food intake, with no negative effects reported. Eating slower is also linked to preventing obesity, making it a safe and helpful practice.

Since this trial is in an early stage, it aims to learn more about the safety and effectiveness of these methods. So far, both approaches seem safe and easy for participants to handle. ^{1 2 3 4 5}

Why are researchers excited about this trial's treatments?

Researchers are excited about using sensor-based interventions for obesity because these treatments offer a unique approach compared to traditional methods like diet changes or medication. The Eat Less and Eat Slower interventions use a novel device called the Automatic Ingestion Monitor (AIM), which is worn on eyeglasses. This sensor tracks eating habits and sends real-time feedback to your smartphone to help you adjust your eating behavior. Unlike standard treatments, which often rely on self-reporting and discipline, this technology provides immediate, personalized guidance, potentially leading to more effective and sustainable weight management.

What evidence suggests that this sensor device is effective for changing eating behavior in obesity?

This trial will evaluate sensor-based interventions for obesity, including the Eat Less Intervention and the Eat Slower Intervention. Studies have shown that just-in-time adaptive interventions (JITAIs), like the Eat Less Intervention, can help people eat less and maintain their diets. Treatments for obesity often result in a 5% to 10% weight loss. Research on the Eat Slower Intervention found that eating more slowly aids in weight control. Specifically, one study showed that people who ate more slowly were less likely to become obese and felt fuller after meals. Both methods use technology to promote healthier eating habits, leading to effective weight management. ^{1 2 3 5 6}

Are You a Good Fit for This Trial?

This trial is for individuals struggling with obesity who are willing to use a sensor device on eyeglasses and a smartphone app. Participants will be observed for one week without intervention, followed by two weeks of testing eating behavior interventions.

Inclusion Criteria

Body mass index (BMI) between 27 and 45 kg/m-squared

Weight stable within 5% in the past 6 months

Self-reports basic familiarity with use of a smartphone

Exclusion Criteria

Current pregnancy or lactation within the past 1 year, or planning a pregnancy during the study participation period

Consuming a medically-prescribed or unusual diet (e.g., macrobiotic)

Food allergies or sensitivities, or other conditions which result in avoidance of a wide range of foods (e.g. celiac disease, tree nut allergy)

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

1-2 weeks

Observation

Participants wear the Automatic Ingestion Monitor (AIM) device for one week without intervention to monitor baseline eating behavior

1 week

1 visit (in-person) for device setup

Intervention

Participants test behavioral interventions focused on eating, receiving messages on their smartphone based on AIM data

2 weeks

1 visit (in-person) for intervention setup, ongoing virtual monitoring

Follow-up

Participants are monitored for changes in eating behavior and energy intake after the intervention

2 weeks

What Are the Treatments Tested in This Trial?

Interventions

• Eat Less Intervention
• Eat Slower Intervention

Trial Overview

The study tests if the Automatic Ingestion Monitor (AIM) combined with smartphone messages can help people eat less and slow down their eating. The AIM tracks eating habits, and based on this data, personalized messages aim to modify behavior over two weeks.

How Is the Trial Designed?

Group I: Just-in-time Adaptive Intervention Experimental Treatment 2 Interventions

A sensor device called an Automatic Ingestion Monitor (AIM) will be worn on eyeglasses to monitor eating. Information from the device will be used to send messages to a smartphone to change eating behavior.

Who Is Running the Clinical Trial?

The Miriam Hospital

Lead Sponsor

Trials

252

Recruited

39,200+

Published Research Related to This Trial

Digital self-monitoring of diet and physical activity is effective in promoting weight loss, with a mean difference of -2.87 kg and a significant reduction in calorie intake of approximately 182 calories per day, based on a meta-analysis of 12 randomized controlled trials involving adults with obesity or overweight.

Tailored interventions that provide personalized advice were found to be significantly more effective than non-tailored approaches, highlighting the importance of customization in digital self-monitoring programs for better outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Does self-monitoring diet and physical activity behaviors using digital technology support adults with obesity or overweight to lose weight? A systematic literature review with meta-analysis. Berry, R., Kassavou, A., Sutton, S.[2021]

The automatic ingestion monitor (AIM) is a novel wearable device that accurately tracks food intake and ingestive behavior in real-life settings, achieving an impressive average accuracy of 89.8% based on data from 12 subjects over 24 hours.

The AIM integrates multiple sensors, including a jaw motion sensor, hand gesture sensor, and accelerometer, using advanced sensor fusion and pattern recognition methods, making it a promising tool for studying obesity and eating disorders.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Automatic ingestion monitor: a novel wearable device for monitoring of ingestive behavior. Fontana, JM., Farooq, M., Sazonov, E.[2021]

In a 12-month randomized controlled trial involving 106 obese adolescents, the use of the Mandometer feedback device significantly reduced body mass index (BMI) compared to standard care, with effects maintained even 18 months after the intervention.

Participants using the Mandometer not only had lower BMI but also reduced their meal sizes and showed improved levels of good cholesterol (HDL), indicating both weight loss and potential metabolic benefits.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Treatment of childhood obesity by retraining eating behaviour: randomised controlled trial. Ford, AL., Bergh, C., Södersten, P., et al.[2021]

A study using an instrumented paper-and-pencil diary revealed that participants often reported self-monitoring their eating behaviors even when they had not actually opened the diary, indicating a discrepancy between self-reported and actual monitoring.

The findings suggest that electronically recorded self-monitoring patterns can help develop strategies to improve adherence to self-monitoring, which is crucial for effective weight loss management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Ancillary study to the PREFER trial: a descriptive study of participants' patterns of self-monitoring--rationale, design and preliminary experiences. Burke, LE., Sereika, S., Choo, J., et al.[2007]

The OnTrack smartphone app, designed to predict and prevent dietary lapses using machine learning, was found to be feasible and acceptable among 43 participants in an 8-week weight loss program, with high user satisfaction reported.

Participants using OnTrack experienced an average weight loss of 3.13% and a reduction in unplanned dietary lapses, indicating the app's potential effectiveness in supporting weight management efforts.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

OnTrack: development and feasibility of a smartphone app designed to predict and prevent dietary lapses. Forman, EM., Goldstein, SP., Zhang, F., et al.[2023]

A study involving 18 participants showed that providing real-time feedback on chew counts can significantly reduce the amount of food consumed during meals without impacting feelings of hunger or fullness.

Targeting a 25% reduction in chew counts led to decreased mass and energy intake, suggesting that wearable sensor systems could be effective tools for managing food intake and developing personalized dietary strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Reduction of energy intake using just-in-time feedback from a wearable sensor system. Farooq, M., McCrory, MA., Sazonov, E.[2019]

Current mobile health (mHealth) technologies have the potential to significantly enhance real-time interventions for obesity-related behaviors, but most efforts still rely on basic SMS interventions instead of utilizing advanced real-time sensing capabilities.

The paper highlights five studies that explore innovative uses of new technologies for monitoring diet, physical activity, and sleep, while also addressing the challenges that mHealth obesity research must overcome to fully leverage these advancements.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Innovations in the Use of Interactive Technology to Support Weight Management. Spruijt-Metz, D., Wen, CK., O'Reilly, G., et al.[2019]

In a 12-week pilot study with 39 participants, all groups using self-monitoring (SM) of food intake, whether with or without real-time feedback (FB) or in-person sessions, achieved significant weight loss, averaging around 3% of their body weight.

Adherence to dietary self-monitoring was similar across all groups, indicating that the addition of real-time feedback or in-person sessions did not significantly enhance adherence or weight loss outcomes, suggesting that self-monitoring alone can be effective.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The SMARTER pilot study: Testing feasibility of real-time feedback for dietary self-monitoring. Burke, LE., Zheng, Y., Ma, Q., et al.[2022]

The OnTrack (OT) smartphone app, which uses machine learning to predict dietary lapses and deliver tailored interventions, was found to be more effective in promoting weight loss among participants using the Weight Watchers (WW) Beyond the Scale program, resulting in an average weight loss of 4.7% compared to 2.6% for those using WW alone.

Participants using OT reported higher satisfaction and engagement with the intervention, indicating that machine learning-powered just-in-time adaptive interventions can enhance weight management strategies by addressing real-time challenges.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Randomized controlled trial of OnTrack, a just-in-time adaptive intervention designed to enhance weight loss. Forman, EM., Goldstein, SP., Crochiere, RJ., et al.[2020]

Citations

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pmc.ncbi.nlm.nih.gov

pmc.ncbi.nlm.nih.gov/articles/PMC8691411/

Optimizing a Just-in-Time Adaptive Intervention to Improve ...

Behavioral obesity treatment (BOT), a first-line intervention for overweight and obesity, typically produces a 5% to 10% reduction in initial body weight [1,2].

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withpower.com

withpower.com/trial/sensor-based-interventions-for-obesity-e4db9

Sensor-Based Interventions for Obesity

Research shows that using just-in-time adaptive interventions (JITAIs) through mobile technology can help reduce food intake and prevent dietary lapses, leading ...

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centerwatch.com

centerwatch.com/clinical-trials/listings/NCT06547320/sensor-based-just-in-time-adaptive-interventions-jitais-targeting-eating-behavior

Sensor-based Just-in Time Adaptive Interventions (JITAIs) ...

The purpose of this study is to determine whether a sensor device called an Automatic Ingestion Monitor (AIM) that is worn on eyeglasses can ...

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reporter.nih.gov

reporter.nih.gov/project-details/10005321

Project Details

We previously developed a wearable sensor, the Automatic Ingestion Monitor (AIM), that automatically and accurately detects eating and characterizes meal ...

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clinicaltrials.gov

clinicaltrials.gov/study/NCT04784585

Miro-randomized Trial for Optimizing a JITAI to Reduce ...

The purpose of the 6-month micro-randomized Trial (MRT) is to optimize a just-in-time adaptive intervention (JITAI) for dietary lapses by evaluating the effects ...

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researchgate.net

researchgate.net/publication/371518283_Investigating_the_Design_of_Just-in-Time_Adaptive_Interventions_JITAIs_Messages_Targeting_Eating_Behaviors

Investigating the Design of Just-in-Time Adaptive ...

Most studies monitor and estimate total meal duration by tracking changes in food weight over time, providing an objective measure of ingestion pace.