Evaluation of the Remote Intervention for Diet and Exercise (RIDE)
| Status: | Completed |
|---|---|
| Conditions: | Obesity Weight Loss |
| Therapuetic Areas: | Endocrinology |
| Healthy: | No |
| Age Range: | 18 - 65 |
| Updated: | 4/21/2016 |
| Start Date: | May 2009 |
| End Date: | January 2011 |
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.
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.
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.
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.
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.
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