Smartphone-Based Dietary Support for Obesity

Overseen By Stephanie P Goldstein, PhD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: The Miriam Hospital

Must not be taking: Weight loss medication

Disqualifiers: Pregnancy, Bariatric surgery, others

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

Compensation: Varies

Number of Visits: 4

Duration: 3 months

204 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial does not specify whether you need to stop taking your current medications, but you cannot participate if you are currently taking weight loss medication.

What data supports the effectiveness of the treatment Smartphone-Based Dietary Support for Obesity?

Research shows that mobile eHealth interventions, which include smartphone-based approaches, can effectively promote weight loss and maintenance by encouraging behavior changes. Additionally, tailored informational interventions and self-management education have been successful in helping individuals lose weight and improve dietary habits. ^{1 2 3 4 5}

Is smartphone-based dietary support for obesity safe for humans?

The research on mobile and web-based interventions for obesity, which includes smartphone-based dietary support, generally shows positive effects on weight management and dietary habits. While the studies focus on effectiveness, they do not report any significant safety concerns, suggesting these interventions are generally safe for human use. ^{3 5 6 7 8}

How is the Smartphone-Based Dietary Support for Obesity treatment different from other obesity treatments?

This treatment is unique because it uses a smartphone app to provide personalized dietary support, combining education, motivation, and self-monitoring to help people manage their weight. It emphasizes self-regulation and tailored advice, which has been shown to be more effective than non-tailored interventions in supporting weight loss. ^{5 6 8 9 10}

What is the purpose of this trial?

This project targets dietary lapses (instances of nonadherence to dietary goals), a major cause of poor outcomes during behavioral obesity treatment, which is a recommended first-line intervention for cardiovascular disease. The investigators propose to conduct a micro-randomized trial (MRT) to empirically optimize a smartphone-based just-in-time adaptive intervention (JITAI) that monitors risk and intervenes on lapses as needed. By evaluating the immediate, proximal effect of four theory-driven interventions on lapse behavior, the project will: (a) produce a scalable, finalized JITAI that has the greatest potential to show clear clinical impact in future trials; and (b) inform the development of more sophisticated theoretical models of adherence behavior more broadly. Therefore, this study has three goals. First the investigators aim to compare the effects of delivering any intervention to no intervention on the occurrence of lapse. Second, the investigators aim to compare the effects of specific theory-driven interventions to one another to determine which ones are best for preventing lapses. Within this second aim, the investigators also aim to examine other factors that may influence the effectiveness of interventions (e.g., time, location). Lastly, the investigators will use the data from this MRT to customize intervention delivery in future versions of this JITAI Patients will be recruited through various methods including advertisements in local media, targeted online advertising, advertisements in medical and minority communities, and direct mailers. All participants will receive a well-established 3-month online obesity treatment program, with 3 months of no-treatment follow-up. In conjunction, they will use a smartphone-based JITAI consisting of: 1) repeated daily surveys assess lapses and relevant triggers; 2) a machine learning algorithm that uses information from the surveys to determine real-time lapse risk; \& 3) interventions to counter lapse risk. When an individual is at risk for lapsing she will be randomly assigned to no intervention, a generic risk alert, or one of 4 theory-driven interventions with interactive skills training. The outcome of interest will be the occurrence (or lack thereof) of dietary lapse, as measured both subjectively (i.e., reported by the participant in the daily surveys) and objectively (i.e., via wrist-based intake monitoring), in the hours following randomization initiated by heightened lapse risk.

Eligibility Criteria

This trial is for adults aged 18-70 with a BMI of 25-50 and at least one cardiovascular risk factor like prediabetes, type 2 diabetes, high cholesterol, or hypertension. They must be able to walk two blocks without stopping and not be in another weight loss program or have conditions that affect study participation.

Inclusion Criteria

Your body mass index (BMI) falls between 25 and 50 kg/m².

I have been diagnosed with a condition that increases my risk for heart disease.

I can walk 2 city blocks without needing to stop.

Exclusion Criteria

Plans to become pregnant within 6 months of enrolling

Has any condition that would result in inability to follow the study protocol, including terminal illness, substance abuse, eating disorder (not including Binge Eating Disorder) and untreated major psychiatric illness.

I have had weight loss surgery in the past.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

1 visit (in-person or virtual)

Baseline Assessment

Participants complete baseline questionnaires and self-reported logs of dietary intake and ecological momentary assessments (EMA) for 1 week before the baseline assessment.

1 week

1 visit (in-person or virtual)

Treatment

Participants receive 3 months of online behavioral obesity treatment (BOT) and use the JITAI system to monitor and intervene on dietary lapses.

3 months

Daily interaction with JITAI, 1 assessment visit at 3 months

Follow-up

Participants continue using the JITAI system without the behavioral obesity treatment for an additional 3 months.

3 months

1 assessment visit at 6 months

Treatment Details

Interventions

• Enhanced Education
• Generic Risk Alert (Active Comparator)
• Motivation
• Online Behavioral Obesity Treatment
• Self-Efficacy
• Self-Regulation

Trial Overview The trial tests a smartphone-based intervention to prevent dietary lapses during obesity treatment. It uses daily surveys, machine learning for lapse risk assessment, and various interventions when there's a high risk of lapsing. The effectiveness of these interventions will be compared.

Participant Groups

Group I: Self-regulation Experimental Treatment 2 Interventions

Theory-driven intervention focused on providing skills to increase self-regulation

Group II: Self-efficacy Experimental Treatment 2 Interventions

Theory-driven intervention focused on providing skills to increase self-efficacy for following dietary goals

Group III: Motivation Experimental Treatment 2 Interventions

Theory-driven intervention focused on providing skills to increase motivation for following dietary goals

Group IV: Enhanced Education Experimental Treatment 2 Interventions

Theory-driven intervention focused on providing information about dietary quality and goals

Group V: Generic Risk Alert Active Control 2 Interventions

A notification to alert participant of lapse risk, no additional intervention provided

Group VI: No Intervention Placebo Group 1 Intervention

No notification or intervention is delivered to the participant during lapse risk

Who Is Running the Clinical Trial?

The Miriam Hospital

Lead Sponsor

Trials

252

Recruited

39,200+

National Heart, Lung, and Blood Institute (NHLBI)

Collaborator

Trials

3,987

Recruited

47,860,000+

Findings from Research

Recent advancements in internet and cell phone interventions have shown that technology-enabled self-management education can significantly improve outcomes for patients with diabetes, including better self-management, medication adherence, and weight loss.

Research indicates that these interventions are effective across various age groups, from teens to older adults, suggesting a broad applicability that could lead to increased recognition and reimbursement from payors for these valuable treatments.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Information technology in the service of diabetes prevention and treatment. Kaufman, N.[2022]

In a study of 60 obese patients over 24 months, those who received a therapy program focused on nutritional education and intensive follow-up showed a significant decrease in body mass index (%BMI), especially in females after 18 months.

The program had a higher success rate in females (40.5%) compared to males (5.5%), indicating that gender may influence the effectiveness of obesity management strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Influence of nutritional education on management of infantile-juvenile obesity]. Durá Travé, T.[2013]

Mobile eHealth behavioral interventions have the potential to effectively promote and sustain weight loss and maintenance, especially when they are well-designed and tailored to individual needs.

The review highlights that tailored informational interventions have been the most successful conventional methods for weight loss, and the advancements in mobile technology present new opportunities to enhance these interventions for broader public health impact.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Mobile eHealth interventions for obesity: a timely opportunity to leverage convergence trends. Tufano, JT., Karras, BT.[2018]

The combination of motivational interviewing and nutrition psychoeducation (MINP) led to significant weight loss in adults with overweight/obesity over a 3-month period, with 21% of participants achieving at least 5% weight loss by the end of treatment.

Participants with binge-eating disorder (BED) showed greater improvements in disordered eating and depression compared to those without BED, indicating that MINP may be particularly beneficial for individuals struggling with these issues.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Examining motivational interviewing plus nutrition psychoeducation for weight loss in primary care. Barnes, RD., Ivezaj, V., Martino, S., et al.[2019]

Between 2005 and 2009, about 75% of studies showed that e-health technologies effectively helped reduce fat intake and increase fruit and vegetable consumption in behavioral nutrition interventions.

By 2010, interventions began to focus more on body weight management and included personalized features like self-monitoring, but there has been limited progress in using objective dietary behavior measures instead of self-reports.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Behavioral Nutrition Interventions Using e- and m-Health Communication Technologies: A Narrative Review. Olson, CM.[2019]

In a study involving 55 overweight and obese adults, both a computer-assisted dieting intervention (CAD) and a combined intervention with self-management training (CAD+G) led to reductions in energy, fat, and carbohydrate intake.

The CAD+G group was more effective than the CAD-only group in reducing fat intake, increasing vegetable consumption, and maintaining fruit intake, suggesting that self-management training enhances the effectiveness of computer-assisted dieting.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer-assisted dieting: effects of a randomized nutrition intervention. Schroder, KE.[2019]

A review of 30 studies on web-based interventions for preventing overweight and obesity found that 93% reported positive changes in physical activity levels, dietary habits, and psychological factors.

While the meta-analysis indicated improvements in fruit and vegetable consumption and physical activity, these changes were not statistically significant, suggesting that web-based interventions can raise awareness and encourage healthy behaviors, but may need further optimization for greater impact.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The use of web-based interventions to prevent excessive weight gain. Maon, S., Edirippulige, S., Ware, R., et al.[2012]

A study involving 72 participants with overweight or obesity found that both simplified dietary self-monitoring and standard calorie tracking led to clinically meaningful weight loss after 6 months, with average weight losses of 5.7% and 4.0%, respectively.

There were no significant differences in adherence to dietary tracking or caloric intake between the two groups, suggesting that simplified monitoring of high-calorie foods could be an effective alternative to traditional calorie counting.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A pilot randomized trial of simplified versus standard calorie dietary self-monitoring in a mobile weight loss intervention. Nezami, BT., Hurley, L., Power, J., et al.[2023]

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]

A digital weight loss intervention using either detailed or simplified dietary self-monitoring was found to be feasible and acceptable among racial and ethnic minority adults, with a high retention rate of 92% over 3 months.

Both intervention groups achieved similar weight loss results, with participants losing an average of approximately 3.4 kg, indicating that simplified self-monitoring may enhance engagement without sacrificing efficacy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Detailed Versus Simplified Dietary Self-monitoring in a Digital Weight Loss Intervention Among Racial and Ethnic Minority Adults: Fully Remote, Randomized Pilot Study. Patel, ML., Cleare, AE., Smith, CM., et al.[2023]

References

1. England pubmed.ncbi.nlm.nih.gov

Information technology in the service of diabetes prevention and treatment. [2022]

2. Spain pubmed.ncbi.nlm.nih.gov

[Influence of nutritional education on management of infantile-juvenile obesity]. [2013]

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

Mobile eHealth interventions for obesity: a timely opportunity to leverage convergence trends. [2018]

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

Examining motivational interviewing plus nutrition psychoeducation for weight loss in primary care. [2019]

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

Behavioral Nutrition Interventions Using e- and m-Health Communication Technologies: A Narrative Review. [2019]

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

Computer-assisted dieting: effects of a randomized nutrition intervention. [2019]

7. England pubmed.ncbi.nlm.nih.gov

The use of web-based interventions to prevent excessive weight gain. [2012]

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

A pilot randomized trial of simplified versus standard calorie dietary self-monitoring in a mobile weight loss intervention. [2023]

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]

10. Canada pubmed.ncbi.nlm.nih.gov

Detailed Versus Simplified Dietary Self-monitoring in a Digital Weight Loss Intervention Among Racial and Ethnic Minority Adults: Fully Remote, Randomized Pilot Study. [2023]