Toward Objective Monitoring of Ingestive Behavior in Free‐living Population

Abstract

IntroductIonRates of overweight and obesity are increasing globally. The World Health Organization estimated that there were ~1.6 billion overweight and at least 400 million obese adults worldwide in 2005 and that there will be 2.3 billion overweight and 700 million obese adults worldwide by 2015 (ref. 1). Overweight and obese individuals have an increased risk of developing chronic diseases such as type 2 diabetes, cardiovascular disease, and cancer (2-5).Overweight and obesity result from an imbalance between energy intake and energy expenditure, but the etiology of that imbalance and the underlying mechanisms are still incompletely understood. Our physiology, our behavior, and our environment must all be considered in understanding the etiology of obesity. There is a debate about the relative importance of genetic/physiological factors and environmental factors in the etiology of obesity. Clearly there are genetic/physiological contributions to obesity (6-9) but some weight gain can be attributed to an environment that provides an abundance of inexpensive, highly palatable, and energy dense foods, while requiring only minimal levels of physical activity (10-13). Part of our lack of understanding of the etiology of obesity is the fact that most weight gain likely occurs due to very small differences between energy intake and energy expenditure, necessitating very accurate measurements of energy intake and energy expenditure.A variety of methods are available for accurate and objective measurement of energy expenditure and its components, including doubly labeled water, indirect calorimetry, and accelerometry (14-16). These techniques can be used in the laboratory and in free-living populations. Energy and food intake can be accurately monitored in the laboratory by directly measuring consumed food. It is currently not possible to accurately monitor food intake in free-living populations. Several methods have been proposed to measure free-living food intake including observation, weighed food records, estimated records, diet history, food-frequency questionnaires, food recall methods, etc. (17). In a review of 43 studies comparing these methods to indirect measurement using doubly labeled water, the majority suffered from underestimation of energy intake on the order of 0.83 (ratio of intake estimate to energy expenditure) (18).There is an urgent need for innovative strategies for the accurate assessment of free-living energy intake and monitoring of

Bite count rates in free-living individuals: new insights from a portable sensor

“…These results are concordant with conclusions derived from a chewing and swallowing sensor, which found that there appeared to be no correlation between mass per chew/swallow and BMI [1]. Although to our knowledge, experiments comparing time length of meals and BMI have not been conducted, several studies manipulated eating rates using universal eating monitors and compared meal duration in obese and lean participants [17,18].…”

“…Feedback data on body weight and total daily intake has been used to successfully modify weight behavior [1]. However, these apps do not including timing and speed of eating behavior.…”

Automatic Ingestion Monitor: A Novel Wearable Device for Monitoring of Ingestive Behavior

“…Estimation of the mass ingested could be achieved through the counts of chews [13] and the type and caloric density of food could potentially be determined by adding a camera triggered by the detection algorithm [19]. Ingested mass could also be predicted by acoustic recognition of chewing cycles and food types.…”

“…The size selected for the epoch was found to present the best tradeoff between the frequency of physiological events such as bites, chewing and swallowing and time resolution of FI monitoring [11], [13]. The mean absolute value (MAV) of the signal SF 1 ( t ) within e i was computed as $${\mathit{M AV}}_{e_i}=\frac{1}{N}true\sum _{k=1}^{N}false|x_{k}false|$$where x k is the k th sample in an epoch e i of SF 1 ( t ) containing a total of N samples.…”

“…Our approach proposes the use of HtM gestures, chews and swallows as indicators of FI (objectively detecting timing and duration of each instance of FI, number of bites and chews in each eating episode, characterizing eating frequency and mass of ingestion) [11], [13]. Monitoring of swallowing activities by acoustical means was integrated with machine learning algorithms in [20] and [21] to create classification models, which detected periods of FI with more than 85% accuracy.…”