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.
