Clinical trial NCT00883350: Evaluation of the Remote Intervention for Diet and Exercise (RIDE)
Official title: Design and Evaluation of the Remote Intervention for Diet and Exercise (RIDE)
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Overweight
Obesity
Keywords: Body weight changes
Overweight
Obesity
Lead sponsor: Pennington Biomedical Research Center
Sponsor class: OTHER
Collaborators: name: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
First posted: 2009-04-17
Enrollment: 40
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: TREATMENT

Brief summary: A large proportion of the adult population in the United States qualifies for weight loss treatment based on the NIH treatment recommendations, but traditional clinic-based weight loss treatments have a number of limitations. For example, access to healthcare facilities is limited among people living in rural communities and people of low socioeconomic status, yet a disproportionate number of these people would benefit from services. Internet-based weight loss interventions have been used to deliver services to these populations, but these "e-Health" interventions suffer from a number of limitations and produce only modest weight loss. The limitations associated with internet-based interventions include decreased use of the internet application over time; patients must logon to the internet to receive treatment recommendations, yet few patients regularly logon to the application and this negatively affects treatment outcome. An additional limitation is the quality of self-reported food intake, exercise, and body weight data that participants enter into the internet application or report to their online counselor. Self-reported data are associated with error and accurate data are needed to formulate effective treatment recommendations for participants. Lastly, most applications rely on asynchronous communications between the patient and the counselor, and patients do not always receive personalized treatment recommendations in a reasonable amount of time (1 to 3 days), which limits the extent to which the recommendations result in behavior change and weight loss.

The purpose of the proposed pilot and feasibility project is to test the efficacy of the Remote Intervention for Diet and Exercise (RIDE) e-Health application at promoting weight loss compared to a control condition. The RIDE e-Health application addresses the limitations of internet-based interventions that are noted above. The application relies on novel technology to collect near real-time food intake, body weight, and exercise data from participants while they reside in their free-living environments. These data are transmitted to the researchers in near real-time: food intake data are collected and transmitted with camera and Bluetoothenabled cell phones using the Remote Food Photography method that was developed by this laboratory, body weight data is automatically transmitted daily from a bathroom scale using the same phones, and accelerometry is used to collect exercise data that is transmitted via the internet. These data are analyzed and personalized treatment recommendations are sent to the participant in a timely manner, e.g., every 1 to 3 days, using the cell phones. The RIDE e-Health application was developed based on learning and behavioral theory to maximize behavior change and weight loss. The findings of this study will have significant implications for the affordable delivery of effective weight management interventions to patients with limited access to health care.

Detailed description: The prevalence of overweight and obesity has increased significantly over the past four decades, resulting in 66% of the adult population in the United States (U.S.) being classified as overweight or obese (Wang, 2007).

Moreover, there is an over-representation of overweight and obesity among rural and low socioeconomicstatus groups (Wang, 2007). Consequently, a large proportion of the adult population in the U.S. qualifies for weight loss treatment based on the NIH treatment recommendations (NHLBI, 1998). Nevertheless, traditional weight loss treatments have a number of limitations, including lifestyle change (diet, exercise, and behavior therapy), which is one of the first options for treating overweight and obesity. First, delivering clinical services to the number of individuals who qualify for treatment would overwhelm the healthcare system. Second, many people who qualify for and would benefit from treatment cannot obtain services due to financial limitations or geographic location. Third, lifestyle change requires a significant time-commitment on the part of the patient and a team of professionals, resulting in fairly costly treatment. Despite the cost, lifestyle change fails to consistently promote long-term weight loss maintenance and the amount of weight lost in the short-term frequently fails to meet patient expectations (Foster, 1997). Lastly, lifestyle change typically involves meeting with the patient regularly, e.g., fortnightly, and patients do not always receive timely feedback about modifying behaviors to achieve an energy deficit. This is a significant limitation since learning theory indicates that behavior change is fostered by receiving specific feedback that is temporally contiguous to the target behavior. Feedback that is delayed or unspecific is less effective at inducing behavior change (Schultz, 2006).

The application of novel technology to health problems has improved some areas of health care. For example, telemedicine applications have been used to monitor the vital signs of victims of mass casualty disasters (Gao, 2005). Technology-based methodologies have also been applied to weight loss treatments in an effort to improve treatment efficacy and more affordably deliver services to individuals with limited health care access, such as people living in rural communities. To date, these "e-Health" applications have focused primarily on internet-based interventions, which have been found to produce only modest weight loss (Weinstein, 2006; Williamson, 2006).

Our group has conducted many internet-based weight management studies (Williamson, 2006; Stewart, 2008; Williamson, 2008; Williamson, 2007) and we have identified limitations that negatively affect the efficacy of e-Health applications. First, patients must logon to the internet to report their progress and data (e.g., amount of food intake) and to receive treatment recommendations, yet few patients regularly logon to the internet application. Second, most e-Health applications rely on the participant to self-report food intake and exercise data, and these self-reported data are typically inaccurate (Schoeller, 1990). Consequently, the quality of the feedback that the participant receives is limited by the poor quality of the self-reported data. Third, no application has been able to: a) obtain accurate free-living food intake, exercise, and body weight data from participants in near real-time, b) evaluate these data as they are received, and c) provide specific feedback to participants in a reasonable amount of time (1 to 3 days).

Based on learning theory, this ability would result in superior behavior change (Schultz, 2006) and subsequent weight loss.

The purpose of the proposed pilot and feasibility study is to test the efficacy of the Remote Intervention for Diet and Exercise (RIDE) e-Health application at promoting weight loss. The RIDE e-Health application addresses the limitations of internet-based interventions noted above. The application relies on novel technology to collect near real-time food intake, body weight, and exercise data from participants while they reside in their free-living environments. These data are transmitted to the researchers in near real-time: food intake data are collected and transmitted with camera and Bluetooth-enabled cell phones, body weight data are automatically transmitted from a bathroom scale using the same phones, and accelerometry is used to collect exercise data that is transmitted via the internet. These data are analyzed and personalized treatment recommendations are sent to the participant via the cell phone in a timely manner, e.g., every 1 to 3 days.

Eligibility criteria: Inclusion Criteria:

* Body mass index (BMI) is \> 25 kg/m2 and \< 35 kg/m2.
* Willing to use cell phones provided by the PBRC or personal cell phones to take pictures of foods during the study and to receive messages from study personnel.
* Willing to wear an activity monitor on your shoe and to use the internet to send information as frequently as once daily.
* Willing to weigh on a scale provided by the PBRC as frequently as once per day
* Willing to accept random assignment to either the e-Health (RIDE group) or control group.
* Weight stable, defined as no greater than 4.4 lbs. (2 kg) weight change over the previous 60 days.

Exclusion Criteria:

* Diagnosed with a chronic disease that affects body weight, appetite, or metabolism, namely diabetes, cardiovascular disease, cancer, and thyroid diseases or conditions.
* Currently in a weight loss program.
* Unable to engage in moderate intensity exercise.
* Unable to diet or exercise due to your medical history or current health status.
* Current use of prescriptions or over-the-counter medications or herbal products that affect appetite, body weight, or metabolism (e.g., weight loss medications such as sibutramine, antipsychotic medications such as olanzapine, ephedrine, and diuretics).
* Diagnosed with uncontrolled hypertension (high blood pressure), defined as systolic blood pressure \>159 mmHg \& diastolic blood pressure \>99 mmHg.
* For females, current pregnancy, or plans to become pregnant in the duration of the study.

Primary outcomes:
measure: Percent Change in Body Weight From Baseline to 12 Weeks
timeFrame: 12 weeks
Secondary outcomes:
Other outcomes:
Locations:
city: Baton Rouge
country: United States
facility: Pennington Biomedical Research Center
state: Louisiana
Study officials:
affiliation: Pennington Biomedical Research Center
name: Corby K Martin, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Wang Y, Beydoun MA. The obesity epidemic in the United States--gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiol Rev. 2007;29:6-28. doi: 10.1093/epirev/mxm007. Epub 2007 May 17.
citation: Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults--The Evidence Report. National Institutes of Health. Obes Res. 1998 Sep;6 Suppl 2:51S-209S. No abstract available.
citation: Foster GD, Wadden TA, Vogt RA, Brewer G. What is a reasonable weight loss? Patients' expectations and evaluations of obesity treatment outcomes. J Consult Clin Psychol. 1997 Feb;65(1):79-85. doi: 10.1037//0022-006x.65.1.79.
citation: Schultz W. Behavioral theories and the neurophysiology of reward. Annu Rev Psychol. 2006;57:87-115. doi: 10.1146/annurev.psych.56.091103.070229.
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citation: Williamson DA, Champagne CM, Harsha D, Han H, Martin CK, Newton R Jr, Stewart TM, Ryan DH. Louisiana (LA) Health: design and methods for a childhood obesity prevention program in rural schools. Contemp Clin Trials. 2008 Sep;29(5):783-95. doi: 10.1016/j.cct.2008.03.004. Epub 2008 Mar 26.
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citation: Schoeller DA. How accurate is self-reported dietary energy intake? Nutr Rev. 1990 Oct;48(10):373-9. doi: 10.1111/j.1753-4887.1990.tb02882.x.
citation: Martin CK, Han H, Coulon SM, Allen HR, Champagne CM, Anton SD. A novel method to remotely measure food intake of free-living individuals in real time: the remote food photography method. Br J Nutr. 2009 Feb;101(3):446-56. doi: 10.1017/S0007114508027438. Epub 2008 Jul 11.
citation: Martin CK, Miller AC, Thomas DM, Champagne CM, Han H, Church T. Efficacy of SmartLoss, a smartphone-based weight loss intervention: results from a randomized controlled trial. Obesity (Silver Spring). 2015 May;23(5):935-42. doi: 10.1002/oby.21063.