Can We Reverse the Obesity Epidemic in the World? Concept of A New Approach to Dieting

Original Article: Kurt M. Tamaru, MD, FAAFP, MBA

Scope of the Problem

A global economic analysis of obesity conducted in 2014, indicated that nearly 30% of the worlds population are either obese or overweight.  This comprises nearly 2.1 billion individuals in the world today. One review of the literature speculates that if the incidence of obesity rates continue, nearly half of the world’s population will be overweight or obese by year 2030. 

The direct related medical cost for obesity in the United States alone is estimated to be as high as $147 billion excluding lost productivity and quality of life issues that it impacts on individuals, families, communities, and the economy.  As such, the aggregated indirect and direct costs across the globe have been calculated to be more than 2.0 trillion U.S. dollars of economic impact just in 2014 alone.  Aside from the economic impact, overweight and obesity are consider risk factors for other conditions such Hypertension, Type 2 Diabetes, Coronary Heart Disease, Stroke, Osteoarthritis/Musculoskeletal disorders, Gallbladder Disease, Obstructive Sleep Apnea, and some Cancers. 

Causes of Obesity

While overweight and obesity can be multi-factorial in cause, including medical conditions, drugs, genetics, inactivity, and food intake.  Arguably, one of the major factors contributing to the incidence of overweight and obesity is the higher caloric intake of foods. Some speculate that the greater use of highly processes foods might explain this growing trend but behavioral and lifestyle issues often are underlying factors that are difficult to address. As such, the ability to influence and change the eating and dietary habits and behaviors is extremely challenging and often the primary point of failure for many individuals to maintain a lower Body Mass Index (BMI) after attempting dieting or lifestyle modifications.

Why Fitbits Exercise Monitoring Devices Have Failed to Make an Impact

With the advent of monitoring devices that can measure the energy expenditures of individuals on a constant basis, we would hope to see greater permanence to healthier weights and BMI’s but current trends fail to reflect such longevity in the data.  While expenditures of energy are one side of the weight equation, the other key factor is energy input which has been traditionally monitored through self reported diet logs.  As such, these methodologies have been a struggle due to inaccuracy and under reporting or lack of consistency in reporting.  Furthermore, no consistent and economical real time feedback to modify the behavior of eating can be evoked without constant visual and real time monitoring.  However, in recent years, two technological advances will change our ability to address these issues in food intake reporting and real time behavioral feedback. These two key technologies are the prevalence of the “smart phone” and devices and data transmitted through the Internet of Things (IoT)

 How the IoT and Data Can Impact Eating Behavior

Prevalence of data inputs that can measure proxies or triggers of eating activity such as heart monitors, accelerometers, and GPS coordinates, along with personal data sources from various social media and smart phone applications have created a consortium of data points that are real time and can be aggregated to determine eating activities, locations, time, date, duration, and sources of food likely being ingested.  With these data points alone serving as activity markers for eating, one can then compile an accurate chronological accounting of eating that can be presented back to the individual in a real time manner.   

Concept of the Food Monitoring Devices

Current devices in the consumer market place such as fitbits, watches, and smart phones, have multiple data sources including heart rate, movement, and GPS location. All of these devices have been designed to specifically tract output activity but fail to monitor energy input such as caloric intake or activities related to calorie consumption. Ability to aggregate these sources of existing data to a back end database and applying real time machine learning has the ability to identify pattern learning and behaviors to differentiate activities strongly correlated to eating. The identified event assumption is then feedback to the individual user via their personal mobile device through text, voice, or active application downloaded to the phone for validation. This immediate feedback loop yields greater impact on modifying behavior at the time of greatest impact and learning.

Application Beyond Food Intake

Logically, one would assume that application of this form of pattern learning to modify conditioned response learning behaviors can be applied across many other health care related activities to improve hypertension, diabetes, smoking cessation, and addiction behaviors.... stay tuned.

About the Author - Dr Tamaru is managing partner at KT Health Services which is a boutique consulting and advisory health care services company. KT Health Services has been engaged in development of consumer related health care devices specific to this issue of obesity and food related intake. For more information, contact our offices at Toll Free phone 800-222-5959