The number of adults and children who are overweight or obese continues to increase and has reached epidemic proportions. Obesity is due to a sustained positive energy balance (energy intake >energy expenditure) and is typically coupled with low levels of physical activity (i.e. sedentary lifestyles). The combination of obesity and inactivity has resulted in increased morbidity and mortality from hypertension, stroke, coronary artery disease, dyslipidemia, type 2 diabetes, sleep apnea and a number of other conditions. To reduce these health risks, individuals are advised to monitor and manage their weight by altering lifestyle and engaging in daily physical activity. Posture allocation (time spent lying, sitting and standing) reflects an individuals lifestyle and is related to obesity, which suggests that the ability to monitor posture allocation may be useful for weight management. Instruments that monitor body weight and quantify physical activity (e.g. accelerometers) have proven to be beneficial for individuals engaged in weight management programs but have limited accuracy in free-living conditions. A single device that accurately monitors body weight, posture allocation, physical activity (i.e. movement), and energy expenditure would be an extremely useful tool for weight management. Our long-term objective is to develop a simple, inexpensive, unobtrusive device that can easily be incorporated into conventional footwear and can accurately measure body weight, posture allocation, physical activity and daily energy expenditure. Such a device could be used to quantify and modify physical activity and lifestyle behavior in overweight and obese individuals and others with sedentary lifestyles. We propose that by measuring plantar pressure distribution and acceleration of the foot, combined with a pattern recognition methodology for identifying posture allocation and movement, we can accurately and reliably measure several of the variables associated with successful weight management in free-living adults. The proposed methodology is based on inexpensive technology that would enable research, clinical and consumer applications for estimating daily energy expenditure and behavior modification.

Public Health Relevance

Obesity is a chief contributor to preventable deaths in the United States and poses a major health challenge associated with increased morbidity and mortality. Obesity is a result of positive energy balance and there is an urgent need to develop tools that can ultimately be used to prevent and treat obesity by changing physical activity behavior.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
Project #
5R43DK083229-02
Application #
7936259
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Densmore, Christine L
Project Start
2009-09-30
Project End
2011-12-31
Budget Start
2010-09-01
Budget End
2011-12-31
Support Year
2
Fiscal Year
2010
Total Cost
$134,014
Indirect Cost
Name
Smartmove, Inc.
Department
Type
DUNS #
809841633
City
Fort Collins
State
CO
Country
United States
Zip Code
80528
Sazonov, Edward; Hegde, Nagaraj; Browning, Raymond C et al. (2015) Posture and activity recognition and energy expenditure estimation in a wearable platform. IEEE J Biomed Health Inform 19:1339-46
Sazonova, Nadezhda; Browning, Raymond; Melanson, Edward et al. (2014) Posture and activity recognition and energy expenditure prediction in a wearable platform. Conf Proc IEEE Eng Med Biol Soc 2014:4163-7
Dannecker, Kathryn L; Sazonova, Nadezhda A; Melanson, Edward L et al. (2013) A comparison of energy expenditure estimation of several physical activity monitors. Med Sci Sports Exerc 45:2105-12
Sazonova, Nadezhda A; Browning, Raymond; Sazonov, Edward S (2011) Prediction of bodyweight and energy expenditure using point pressure and foot acceleration measurements. Open Biomed Eng J 5:110-5
Sazonova, Nadezhda; Browning, Raymond C; Sazonov, Edward (2011) Accurate prediction of energy expenditure using a shoe-based activity monitor. Med Sci Sports Exerc 43:1312-21