OverweightRIDENCT00883350
Evaluation of the Remote Intervention for Diet and Exercise (RIDE)
A study testing RIDE e-health application in people with Overweight and Obesity, run by Pennington Biomedical Research Center at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Overweight, Obesity
- What is being tested?
- RIDE e-health application
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 40 participants
- Where
- 1 siteUnited States
- Study dates
- May 2009to Jan 2011
- Phase
- Interventional
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
United States1 site
Pennington Biomedical Research Center
Baton Rouge, Louisiana, 70808
About this study
Official title: 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.
Read the 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.
- Conditions
- Overweight, Obesity
- Being tested
- RIDE e-health application
- Keywords
- Body weight changes, Overweight, Obesity
- Medical subject headings
- Overweight, Obesity, Body Weight Changes
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
RIDE e-health applicationBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| RIDE | Experimental | Participants randomized to utilize the RIDE e-health application for the duration of the 12 week intervention. Receives: Behavioral: RIDE e-health application |
| Control | No intervention | 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 being measured
The primary outcome is the main question the study is designed to answer.
Percent Change in Body Weight From Baseline to 12 Weeks
weight loss calculated as a percentage of initial weight measured at baseline [((weight at 12 weeks-weight at baseline)/weight at baseline) x 100]
Measured: 12 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Apr 2009 (estimated)
- Study startMay 2009
- Primary completionJan 2011
- Study completionJan 2011
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (13)Papers the sponsor has linked to this study.
- Other trials for OverweightStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Investigator
- Enrolment
- 40 participants (actual)
Sponsor and investigators
- Lead sponsor
- Pennington Biomedical Research Center
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Corby K. Martin, Pennington Biomedical Research Center
- Investigators
- Corby K Martin, PhD, Principal investigator, Pennington Biomedical Research Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT00883350
- Sponsor's protocol ID
- PBRC 28023
- Other IDs
- 1R03DK083533
- Acronym
- RIDE
- Status last verified
- Nov 2024
- First submitted
- 15 Apr 2009
- Last updated
- 3 Dec 2024
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- Yes
Publications
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.
PubMed 17510091Clinical 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.
PubMed 9813653Foster 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.
PubMed 9103737Schultz W. Behavioral theories and the neurophysiology of reward. Annu Rev Psychol. 2006;57:87-115. doi: 10.1146/annurev.psych.56.091103.070229.
PubMed 16318590Gao 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.
PubMed 17282121Weinstein 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.
PubMed 16823276Williamson 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.
PubMed 16899804Stewart 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.
PubMed 19885186Williamson 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.
PubMed 18448393Williamson 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.
PubMed 17426326Schoeller 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.
PubMed 2082216Martin 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.
PubMed 18616837Martin 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.
PubMed 25919921
Study at a glance
From the registry record, last updated by the sponsor on 3 Dec 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Design and Evaluation of the Remote Intervention for Diet and Exercise (RIDE)
- ClinicalTrials.gov ID
- NCT00883350
- Acronym / sponsor ID
- RIDE / PBRC 28023
- Conditions
- Overweight, Obesity
- Keywords
- Body weight changes, Overweight, Obesity
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 40 participants (actual)
- Lead sponsor
- Pennington Biomedical Research CenterUniversity, hospital or other
- Collaborators
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
- Study start
- May 2009
- Primary completion
- Jan 2011 (actual)
- Study completion
- Jan 2011 (actual)
- First posted
- 17 Apr 2009 (estimated)
- Results
- Posted 3 Dec 2024
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (investigator)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| RIDE | Experimental | Behavioral: RIDE e-health applicationParticipants randomized to utilize the RIDE e-health application for the duration of the 12 week intervention. |
| Control | No intervention | –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]. |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- 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.
Key exclusion criteria (7 listed)
- 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).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| RIDE e-health applicationThe 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. | Behavioural | RIDE |
Primary outcome
Percent Change in Body Weight From Baseline to 12 Weeks
Time frame: 12 weeks
weight loss calculated as a percentage of initial weight measured at baseline [((weight at 12 weeks-weight at baseline)/weight at baseline) x 100]
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Dec 2024.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Background: PubMed 17510091Wang 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.
- Background: PubMed 9813653Clinical 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.
- Background: PubMed 9103737Foster 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.
- Background: PubMed 16318590Schultz W. Behavioral theories and the neurophysiology of reward. Annu Rev Psychol. 2006;57:87-115. doi: 10.1146/annurev.psych.56.091103.070229.
- Background: PubMed 17282121Gao 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.
- Background: PubMed 16823276Weinstein 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.
- Background: PubMed 16899804Williamson 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.
- Background: PubMed 19885186Stewart 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.
- Background: PubMed 18448393Williamson 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.
- Background: PubMed 17426326Williamson 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.
- Background: PubMed 2082216Schoeller 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.
- Background: PubMed 18616837Martin 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Corby K Martin, PhD principal investigator
Pennington Biomedical Research Center
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing RIDE e-health application in people with Overweight and Obesity, run by Pennington Biomedical Research Center at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 3 Dec 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 3 Dec 2024). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Percent Change in Body Weight From Baseline to 12 Weeks (time frame: 12 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT00883350, which also lists 13 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Dec 2024.