Smartphone-Based Dietary Support for Obesity
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: The Miriam Hospital
Must not be taking: Weight loss medication
Disqualifiers: Pregnancy, Bariatric surgery, others
No Placebo Group
Trial Summary
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.
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.
12345Is 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.
35678How 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.
568910Eligibility 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 am between 18 and 70 years old.
I have been diagnosed with a condition that increases my risk for heart disease.
+1 more
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.
+6 more
Trial 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
Participant Groups
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.
6Treatment groups
Experimental Treatment
Active Control
Placebo Group
Group I: Self-regulationExperimental Treatment2 Interventions
Theory-driven intervention focused on providing skills to increase self-regulation
Group II: Self-efficacyExperimental Treatment2 Interventions
Theory-driven intervention focused on providing skills to increase self-efficacy for following dietary goals
Group III: MotivationExperimental Treatment2 Interventions
Theory-driven intervention focused on providing skills to increase motivation for following dietary goals
Group IV: Enhanced EducationExperimental Treatment2 Interventions
Theory-driven intervention focused on providing information about dietary quality and goals
Group V: Generic Risk AlertActive Control2 Interventions
A notification to alert participant of lapse risk, no additional intervention provided
Group VI: No InterventionPlacebo Group1 Intervention
No notification or intervention is delivered to the participant during lapse risk
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Weight Control and Diabetes Research CenterProvidence, RI
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Who Is Running the Clinical Trial?
The Miriam HospitalLead Sponsor
National Heart, Lung, and Blood Institute (NHLBI)Collaborator
References
Information technology in the service of diabetes prevention and treatment. [2022]In the year since the first ATTD yearbook was published the field of internet and cell phone interventions has made major advances. This chapter contains clinical studies and reviews of the state-of-the-art regarding the ability of technology-enabled self-management education and support to improve outcomes for patients with, or at risk for, diabetes. The reviews and papers in this chapter demonstrate increased understanding of the underlying basis for effective interventions - a prerequisite for improving the effectiveness and efficiency of these approaches. The research studies described demonstrate that internet interventions are effective for a variety of patients and for specific outcomes (e.g. diabetes self-management for teens as well as older patients, medication adherence, empowerment, psychosocial well-being, helping patients become more active, and helping patients lose weight and keep it off). As additional and more sophisticated studies are completed and the evidence base is expanded one can hope that payors will recognise their value and begin to pay for these treatments. That is what ultimately will bring effective treatments to those who need them. The associate editors' mission was to choose articles that would give the non-technology skilled reader a general overview of the field of information technology and the prevention and treatment of obesity and diabetes. Articles were selected because they either provided a significant review of the state-of the art of the field or were the results from studies that could give the reader a better understand of the benefits and challenges associated with information technology use in clinical settings.
[Influence of nutritional education on management of infantile-juvenile obesity]. [2013]To analyze the therapeutic response in a group of obese patients to a therapy program based on nutritional education, auto-management, and intensive follow-up.
Mobile eHealth interventions for obesity: a timely opportunity to leverage convergence trends. [2018]Obesity is often cited as the most prevalent chronic health condition and highest priority public health problem in the United States. There is a limited but growing body of evidence suggesting that mobile eHealth behavioral interventions, if properly designed, may be effective in promoting and sustaining successful weight loss and weight maintenance behavior changes. This paper reviews the current literature on the successes and failures of public health, provider-administered, and self-managed behavioral health interventions for weight loss. The prevailing theories of health behavior change are discussed from the perspective of how this knowledge can serve as an evidence base to inform the design of mobile eHealth weight loss interventions. Tailored informational interventions, which, in recent years, have proven to be the most effective form of conventional health behavior intervention for weight loss, are discussed. Lessons learned from the success of conventional tailored informational interventions and the early successes of desktop computer-assisted self-help weight management interventions are presented, as are design principles suggested by Social Cognitive Theory and the Social Marketing Model. Relevant computing and communications technology convergence trends are also discussed. The recent trends in rapid advancement, convergence, and public adoption of Web-enabled cellular telephone and wireless personal digital assistant (PDA) devices provide timely opportunities to deliver the mass customization capabilities, reach, and interactivity required for the development, administration, and adoption of effective population-level eHealth tailored informational interventions for obesity.
Examining motivational interviewing plus nutrition psychoeducation for weight loss in primary care. [2019]Our previous randomized controlled trial found that nutrition psychoeducation (NP), an attention-control condition, produced statistically significantly more weight loss than usual care (UC), whereas motivational interviewing (MI) did not. NP, MI, and UC resulted in medium-large, medium, and negligible effects on weight loss, respectively. To examine whether weight loss could be further improved by combining MI and NP, the current study evaluated the scalable combination (MINP) with accessible web-based materials.
Behavioral Nutrition Interventions Using e- and m-Health Communication Technologies: A Narrative Review. [2019]e- and m-Health communication technologies are now common approaches to improving population health. The efficacy of behavioral nutrition interventions using e-health technologies to decrease fat intake and increase fruit and vegetable intake was demonstrated in studies conducted from 2005 to 2009, with approximately 75% of trials showing positive effects. By 2010, an increasing number of behavioral nutrition interventions were focusing on body weight. The early emphasis on interventions that were highly computer tailored shifted to personalized electronic interventions that included weight and behavioral self-monitoring as key features. More diverse target audiences began to participate, and mobile components were added to interventions. Little progress has been made on using objective measures rather than self-reported measures of dietary behavior. A challenge for nutritionists is to link with the private sector in the design, use, and evaluation of the many electronic devices that are now available in the marketplace for nutrition monitoring and behavioral change.
Computer-assisted dieting: effects of a randomized nutrition intervention. [2019]To compare the effects of a computer-assisted dieting intervention (CAD) with and without self-management training on dieting among 55 overweight and obese adults.
The use of web-based interventions to prevent excessive weight gain. [2012]We reviewed web-based interventions for overweight and obesity prevention. A literature search was conducted using seven electronic databases. Manually searched articles were also included. Thirty studies fulfilled the inclusion criteria. Of these, 13 studied physical activity, eight studied dietary practices and nine studied a combination of physical activity and dietary practice. Twenty-eight of the studies (93%) reported positive changes in moderate to vigorous physical activity level, fruit and vegetable intake and psychological factors. A meta-analysis showed there were improvements, though not significant, in fruit and vegetable consumption (standardised mean difference, SMD = 0.61; 95% CI =-0.13 to 1.35) and physical activity (SMD = 0.15; 95% CI =-0.06 to 0.35). The review suggests that web-based interventions are a useful educational tool for increasing awareness and making healthy behaviour changes in relation to an excessive weight gain problem.
A pilot randomized trial of simplified versus standard calorie dietary self-monitoring in a mobile weight loss intervention. [2023]This study tested the efficacy of a lower-burden, simplified dietary self-monitoring approach compared with a standard calorie monitoring approach for self-monitoring adherence and weight loss in a mobile-delivered behavioral weight loss intervention.
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]Establish whether digital self-monitoring of diet and physical activity is effective at supporting weight loss, increasing physical activity and improving eating behavior in adults with obesity or overweight, and determine the intervention components that might explain variations in its effectiveness. A systematic search of MEDLINE, Embase, PsycINFO, Web of Science, Scopus, Cinahl, and CENTRAL identified 4068 studies, of which 12 randomized controlled trials were eligible and included in the review. A random-effect meta-analysis evaluated intervention effectiveness and subgroup analyses tested for effective intervention content. Twelve studies were included in the review and meta-analysis. Digital self-monitoring of both diet and physical activity had a statistically significant effect at supporting weight loss (mean difference [MD] = -2.87 [95% CI -3.78, -1.96], P < 0.001, I2  = 69%), improving moderate physical activity (standardized mean difference [SMD] = 0.44 [95% CI 0.26, 0.62], P < 0.001, I2  = 0%), and reducing calorie intake (MD = -181.71 [95% CI -304.72, -58.70], P < 0.01, I2  = 0%). Tailored interventions were significantly more effective than nontailored interventions (x2  = 12.92, P < 0.001). Digital self-monitoring of physical activity and diet is an effective intervention to support weight loss in adults with obesity or overweight. This effect is significantly associated with tailored advice. Future studies should use rigorous designs to explore intervention effectiveness to support weight loss as an adjunct to weight management services.
Detailed Versus Simplified Dietary Self-monitoring in a Digital Weight Loss Intervention Among Racial and Ethnic Minority Adults: Fully Remote, Randomized Pilot Study. [2023]Detailed self-monitoring (or tracking) of dietary intake is a popular and effective weight loss approach that can be delivered via digital tools, although engagement declines over time. Simplifying the experience of self-monitoring diet may counteract this decline in engagement. Testing these strategies among racial and ethnic minority groups is important as these groups are often disproportionately affected by obesity yet underrepresented in behavioral obesity treatment.