Effects of Bite Count Feedback from a Wearable Device and Goal Setting on Consumption in Young Adults

Abstract

Background New technologies are emerging that may be able to help individuals engage in healthier eating behaviors. One paradigm to test the efficacy of a technology is to determine its effect relative to environmental cues that are known to cause individuals to overeat. Objective The purpose of this work was to independently investigate two questions: 1) How does the presence of a technology that provides bite count feedback alter eating behavior?; and 2) How does the presence of a technology that provides bite count feedback paired with a goal alter eating behavior? Design Two studies investigated these research questions. The first study tested the effects of a large and small plate crossed with the presence or absence of a device that provided bite count feedback on intake. The second study tested the effects of a bite count goal with bite count feedback again crossed with plate size on intake. Both studies used a 2×2 between subjects design. Participants/setting In the first study, 94 subjects (62 female, Age 19.0±1.6 years, BMI 23.04±3.6) consumed lunch in a laboratory. The second study examined 99 subjects (56 female, Age 18.5±1.5 years, BMI 22.73±2.70) under the same conditions. Intervention In both studies subjects consumed a single-course meal, using either a small or large plate. In the first study participants either wore or did not wear an automated bite counting device. In the second study all participants wore the bite counting device and were given either a low bite count goal (12 bites) or a high bite count goal (22 bites). Statistical Analyses Effect of PLATE SIZE, FEEDBACK, and GOAL on consumption (grams) and number of bites taken were assessed using 2×2 ANOVAs. As adjunct measures, the effects of serving size, bite size (grams per bite), post-meal satiety and satiety change were also assessed. Results In the first study there was a main effect of PLATE SIZE on grams consumed and number of bites taken such that eating from a large plate led to greater consumption (p=.001) and a greater number of bites (p=.001). There was also a main effect of FEEDBACK on consumption and number of bites taken such that those who received feedback consumed less (p=.011) and took fewer bites (p<.001). In the second study there was a main effect of PLATE SIZE on consumption such that those eating from a large plate consumed more (p=.003) but did not take more bites. Further analysis revealed a main effect of GOAL on number of bites taken such that those who received the low goal took fewer bites (p<.001) but did not consume less. Conclusion Providing feedback on the number of bites taken from a wearable intake monitor can reduce overall intake during a single meal. Regarding the first research question, providing feedback significantly reduced intake in both plate size groups and reduced the overall number of bites taken. Regarding the second research question, participants were successful in eating to their goals. However, individuals in the low goal condition appeared to compensate for the restric...

Multi-sensor ecological momentary assessment of behavioral and psychosocial predictors of weight loss following bariatric surgery: study protocol for a multicenter prospective longitudinal evaluation

“…As such, variables of interest are daily frequency, and duration of eating bouts, as well as bite count. The strengths of the Bite Counter are that it provides objective measurement on aspects of eating that are difficult to self-report (especially for individuals with overweight/obesity) [ 32 ], it is unobtrusive compared to other objective methods of assessing eating behavior, it was developed using sophisticated and systematic methods, and the display can be inactivated for assessment and activated for intervention purposes [ 33 , 34 ].…”

Mobile Ecological Momentary Diet Assessment Methods for Behavioral Research: Systematic Review (Preprint)

“…Because of the known limitation of existing dietary assessment methods, the research community is motivated to develop new solutions aimed at (semi-)automating the assessment of dietary intake. While the automated methods of real-time image-based detection [14][15][16][17][18][19][20][21][22][23][24] and real-time detection of food intake by biomechanical sensors or hand-held devices [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] have seen significant progress [15,24] in terms of identifying foods and estimating portion sizes [14][15][16][17][18][19][20][21][22][23][24] detecting wrist or hand motion [25][26][27][28]…”

“…image-assisted and image-based assessment [14][15][16][17][18][19][20][21][22][23][24] and the detection of food intake by biomechanical sensors or hand-held devices [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. Significant progress has been made in image-assisted and image-based food recording that has resulted in the improved accuracy of dietary self-report [15,24].…”

“…Significant progress has been made in image-assisted and image-based food recording that has resulted in the improved accuracy of dietary self-report [15,24]. Similarly, wearable technology including gyroscopes, microphones, and mechanical or electrical impedance sensors have been adapted to detect wrist or hand motion [25][26][27][28][29][30][31] or patterns of chewing or swallowing indicative of food intake (eg, number of bites) [32][33][34][35][36][37][38][39][40][41]. However, design and proof-of-concept data suggest the automation of dietary assessment remains out of reach for the time being.…”

“…However, design and proof-of-concept data suggest the automation of dietary assessment remains out of reach for the time being. For instance, the mean detection accuracy of image detection and wearable devices have been acceptable in controlled settings (range = 73-99%) [18,26,30,31,34,35,[38][39][40][41][42][43][44][45][46][47][48] but limited testing has been done in natural settings [27].…”