Evaluation of the Remote Intervention for Diet and Exercise (RIDE) (RIDE)

Design and Evaluation of the Remote Intervention for Diet and Exercise (RIDE)

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.

Study Overview

• Status: Completed
• Conditions: Obesity; Overweight
• Intervention / Treatment: Behavioral: RIDE e-health application

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.

• Study Type: Interventional
• Enrollment (Actual): 40
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Louisiana
    • Baton Rouge, Louisiana, United States, 70808
      • Pennington Biomedical Research Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 65 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: RIDE; Participants randomized to utilize the RIDE e-health application for the duration of the 12 week intervention.	Behavioral: RIDE e-health application; The RIDE e-Health application utilizes the latest technology to obtain near real-time food intake, body weight, and exercise data from participants living in their natural environment. The application also provides personalized and timely feedback and treatment recommendations based on participants' data. The application relies on the Remote Food Photography Method (Martin, 2009), which was developed by our research team, to collect freeliving food intake data that is transmitted to the researchers in near realtime using a camera and Bluetooth-enabled cell phone. A scale is used to collect daily body weight data from participants and these data are automatically transmitted to the researchers via the same cell phone. The e-Health application collects exercise data from participants and these data are delivered to the researchers via the internet; personalized feedback and treatment recommendations are sent to the participant every 1 to 3 days via the cell phone.
No Intervention: Control; Participants assigned to the Health-Ed (control) group will receive health information via the cell phone throughout the 84-day study. We have generated numerous health information tips for other studies on a variety of topics, including stress management, the benefits of eating fruits and vegetables, etc. [6-9]. These lessons will be modified for delivery via cell phone. We have found that participants assigned to these health information control groups report being satisfied with the information and their assignment. Importantly, our data also indicate that such health information results in very little behavior change or weight loss, e.g., [6].

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
Change in body weight, measured as percent of baseline body weight	12 weeks

Collaborators and Investigators

• Sponsor: Pennington Biomedical Research Center
• Collaborators: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
• Investigators: Principal Investigator: Corby K Martin, PhD, Pennington Biomedical Research Center

Publications and helpful links

General Publications

• 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. Erratum In: Obes Res 1998 Nov;6(6):464.
• Schultz W. Behavioral theories and the neurophysiology of reward. Annu Rev Psychol. 2006;57:87-115. doi: 10.1146/annurev.psych.56.091103.070229.
• 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.
• 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.
• Gao T, Greenspan D, Welsh M, Juang R, Alm A. Vital signs monitoring and patient tracking over a wireless network. Conf Proc IEEE Eng Med Biol Soc. 2005;2006:102-5. doi: 10.1109/IEMBS.2005.1616352.
• Weinstein PK. A review of weight loss programs delivered via the Internet. J Cardiovasc Nurs. 2006 Jul-Aug;21(4):251-8; quiz 259-60. doi: 10.1097/00005082-200607000-00003. Erratum In: J Cardiovasc Nurs. 2007 Mar-Apr;22(2):137.
• Williamson DA, Walden HM, White MA, York-Crowe E, Newton RL Jr, Alfonso A, Gordon S, Ryan D. Two-year internet-based randomized controlled trial for weight loss in African-American girls. Obesity (Silver Spring). 2006 Jul;14(7):1231-43. doi: 10.1038/oby.2006.140.
• Stewart T, May S, Allen HR, Bathalon CG, Lavergne G, Sigrist L, Ryan D, Williamson DA. Development of an internet/population-based weight management program for the U.S. Army. J Diabetes Sci Technol. 2008 Jan;2(1):116-26. doi: 10.1177/193229680800200117.
• 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.
• Williamson DA, Copeland AL, Anton SD, Champagne C, Han H, Lewis L, Martin C, Newton RL Jr, Sothern M, Stewart T, Ryan D. Wise Mind project: a school-based environmental approach for preventing weight gain in children. Obesity (Silver Spring). 2007 Apr;15(4):906-17. doi: 10.1038/oby.2007.597.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start: May 1, 2009
• Primary Completion (Actual): January 1, 2011
• Study Completion (Actual): January 1, 2011

Study Registration Dates

• First Submitted: April 15, 2009
• First Submitted That Met QC Criteria: April 15, 2009
• First Posted (Estimate): April 17, 2009

Study Record Updates

• Last Update Posted (Estimate): January 18, 2016
• Last Update Submitted That Met QC Criteria: January 15, 2016
• Last Verified: January 1, 2016

More Information

Terms related to this study

• Keywords: Obesity; Overweight; Body weight changes
• Additional Relevant MeSH Terms: Body Weight; Overweight
• Other Study ID Numbers: PBRC 28023; 1R03DK083533 (U.S. NIH Grant/Contract)