Evaluation of the Remote Intervention for Diet and Exercise (RIDE)
Condition: Overweight; Obesity
MeSH terms: Overweight
Study type: Interventional

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.
    

Intervention name(s): RIDE e-health application
Intervention type(s): Behavioral
Intervention description(s): 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.
Allocation: Randomized
Intervention model: Parallel Assignment
Masking: Single (Investigator)
Primary purpose: Treatment
Keywords: Body weight changes; Overweight; Obesity