Byte by bite: Use of a mobile Bite Counter and weekly behavioral challenges to promote weight loss

Turner‐McGrievy, Gabrielle; Boutté, Alycia K.; Crimarco, Anthony; Wilcox, Sara; Hutto, Brent; Hoover, Adam; Muth, Eric R.

doi:10.1016/j.smhl.2017.03.004

Cited by 20 publications

(15 citation statements)

References 46 publications

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“…Increasingly, studies rely on off-the-shelf devices such as smart watches, demonstrating that such devices are considered reliable and accurate for detecting eating behaviour (e.g., [61,62,67]). This has important implications for the real-world feasibility of using this technology for dietary assessment and monitoring [86]. In particular, because watches have been worn on the wrist for more than a century, using wearable sensors on the wrist is an unobtrusive solution for collecting movement data.…”

Section: Discussionmentioning

confidence: 99%

Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review

et al. 2019

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Wearable motion tracking sensors are now widely used to monitor physical activity, and have recently gained more attention in dietary monitoring research. The aim of this review is to synthesise research to date that utilises upper limb motion tracking sensors, either individually or in combination with other technologies (e.g., cameras, microphones), to objectively assess eating behaviour. Eleven electronic databases were searched in January 2019, and 653 distinct records were obtained. Including 10 studies found in backward and forward searches, a total of 69 studies met the inclusion criteria, with 28 published since 2017. Fifty studies were conducted exclusively in laboratory settings, 13 exclusively in free-living settings, and three in both settings. The most commonly used motion sensor was an accelerometer (64) worn on the wrist (60) or lower arm (5), while in most studies (45), accelerometers were used in combination with gyroscopes. Twenty-six studies used commercial-grade smartwatches or fitness bands, 11 used professional grade devices, and 32 used standalone sensor chipsets. The most used machine learning approaches were Support Vector Machine (SVM, n = 21), Random Forest (n = 19), Decision Tree (n = 16), Hidden Markov Model (HMM, n = 10) algorithms, and from 2017 Deep Learning (n = 5). While comparisons of the detection models are not valid due to the use of different datasets, the models that consider the sequential context of data across time, such as HMM and Deep Learning, show promising results for eating activity detection. We discuss opportunities for future research and emerging applications in the context of dietary assessment and monitoring.

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Section: Discussionmentioning

confidence: 99%

Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review

et al. 2019

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“…As an example, subjects enrolled in the present study had a mean energy intake of 718 kcal, adding the energy (168 kcal) of a medium-sized carbonated beverage (which was excluded from the analysis), resulted in an energy intake of 886 kcal from a single meal, which represents almost half of the daily energy requirements (considering a daily requirement of 2000 kcal). Anyway, despite the potential limitations identified in the performance of the device, Turner-McGrievy et al have shown that the use of the Bite Counter significantly correlates with weight loss [26,27], confirming the potentials of AFIM-BC devices employment in weight management programs.…”

Section: Discussionmentioning

confidence: 99%

Food Composition Impacts the Accuracy of Wearable Devices When Estimating Energy Intake from Energy-Dense Food

et al. 2019

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The present study aimed to assess the feasibility and reliability of an a3utomatic food intake measurement device in estimating energy intake from energy-dense foods. Eighteen volunteers aged 20–36 years were recruited from the University of Padova. The device used in the present study was the Bite Counter (Bite Technologies, Pendleton, USA). The rationale of the device is that the wrist movements occurring in the act of bringing food to the mouth present unique patterns that are recognized and recorded by the Bite Counter. Subjects were asked to wear the Bite Counter on the wrist of the dominant hand, to turn the device on before the first bite and to turn it off once he or she finished his or her meal. The accuracy of caloric intake was significantly different among the methods used. In addition, the device’s accuracy in estimating energy intake varied according to the type and amount of macronutrients present, and the difference was independent of the number of bites recorded. Further research is needed to overcome the current limitations of wearable devices in estimating caloric intake, which is not independent of the food being eaten.

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“…This device also measures the number of bites taken when the user presses a button to turn the device into bite count mode and stops counting bites when the user presses the same button to turn off the bite count mode . The device also automatically tracks the number of steps taken . Participants randomised into the WD were educated on the use and application of the ELMM device and were provided with a demonstration of how to download the device software onto their computers, as well as how to open the software to read goal setting pages and data.…”

Section: Methodsmentioning

confidence: 99%

Examining changes in respiratory exchange ratio within an 8‐week weight loss intervention

Beatty

Melanson

2019

J Human Nutrition Diet

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Background: Maintaining weight loss is difficult, partly as a result of accompanying reductions in fat oxidation. The present study examined fat oxidation [reflected by respiratory exchange ratio (RER)] within an 8-week, self-led weight loss intervention. Changes in RER, body fat (BF%) and estimated energy expenditure (EE) were examined. Methods: Twenty-two adults [13 females, nine males; mean (SD) age 34.6 (16.5) years; body mass index 32.0 (4.3) kg m À2 ] received a self-directed workbook; twelve were also randomised to receive a self-monitoring wristworn device. At weeks 0 and 8, RER (indirect calorimetry), BF% (BodPod) and estimated EE [7-day physical activity recall (PAR-EE) were collected. Participants were pooled and paired t-tests were used to examine changes over time. Correlations explored associations among variables. Participants were then dichotomised into weight loss group (WL) or weight stable/gainers group (WSG) and eating behaviours [Intuitive Eating Scale (IES-2)] were examined by 2 9 2 repeated measures multivariate analysis of covariance. Results: There were no significant changes in RER, body fat percentage and PAR-EE. A significant negative association was found between week 8 PAR-EE and week 8 RER, as well as between BF% change and RER change. There was a significant time by WL versus WSG group effect of IES-2 scores, with the WL group self-reporting significantly increased scores in Eating for Physical Reasons rather than Emotional Reasons (EPR) subscale. Conclusions: Increased physical activity after an 8-week weight loss intervention was associated with a higher fasting fat oxidation. Participants who increased EPR scores were more successful in weight loss than those without a change in this subscale. 737

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Byte by bite: Use of a mobile Bite Counter and weekly behavioral challenges to promote weight loss

Cited by 20 publications

References 46 publications

Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review

Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review

Food Composition Impacts the Accuracy of Wearable Devices When Estimating Energy Intake from Energy-Dense Food

Examining changes in respiratory exchange ratio within an 8‐week weight loss intervention

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