Sensor-Based Interventions for Obesity

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

90 Participants Needed

Trial Summary

What is the purpose of this trial?

The purpose of this study is to determine whether a sensor device called an Automatic Ingestion Monitor (AIM) that is worn on eyeglasses can be used with a smartphone to change eating behavior. Participants will wear the device for one week of no-intervention observation. They will then test behavioral interventions focused on eating for two weeks. The researchers hypothesize that messages sent to a smartphone that are based on information from the AIM can reduce the amount of food that is eaten and slow eating.

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 data supports the effectiveness of the Sensor-Based Interventions for Obesity treatment?

Research shows that using just-in-time adaptive interventions (JITAIs) through mobile technology can help reduce food intake and prevent dietary lapses, leading to weight loss. A study with a smartphone app called OnTrack demonstrated that participants experienced weight loss and fewer unplanned eating lapses, indicating the potential effectiveness of similar sensor-based interventions for obesity. ^{1 2 3 4 5}

Is the Sensor-Based Intervention for Obesity safe for humans?

The studies on sensor-based interventions, like the Automatic Ingestion Monitor (AIM) and just-in-time adaptive interventions (JITAIs), do not report any safety concerns, suggesting they are generally safe for human use. ^{1 2 6 7 8}

How is the Sensor-Based Interventions for Obesity treatment different from other treatments?

This treatment is unique because it uses real-time data from sensors to provide just-in-time adaptive interventions (JITAIs) that help individuals eat less and eat slower, unlike traditional methods that rely on static advice or delayed feedback. ^{1 2 3 7 9}

Eligibility Criteria

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

I do not have any unmanaged conditions like hypothyroidism that affect my weight or eating.

I have been diagnosed with diabetes.

Timeline

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

Treatment Details

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.

Participant Groups

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+

Findings from Research

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]

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]

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]

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 (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]

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 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]

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]

References

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

Innovations in the Use of Interactive Technology to Support Weight Management. [2019]

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

Reduction of energy intake using just-in-time feedback from a wearable sensor system. [2019]

3. England pubmed.ncbi.nlm.nih.gov

OnTrack: development and feasibility of a smartphone app designed to predict and prevent dietary lapses. [2023]

4. England pubmed.ncbi.nlm.nih.gov

Treatment of childhood obesity by retraining eating behaviour: randomised controlled trial. [2021]

5. United States 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. [2007]

6. England pubmed.ncbi.nlm.nih.gov

Randomized controlled trial of OnTrack, a just-in-time adaptive intervention designed to enhance weight loss. [2020]

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

The SMARTER pilot study: Testing feasibility of real-time feedback for dietary self-monitoring. [2022]

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

Automatic ingestion monitor: a novel wearable device for monitoring of ingestive behavior. [2021]

9. England 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. [2021]