Association of number of bites and eating speed with energy intake: Wearable technology results under free-living conditions

Abstract

Personalized weight management strategies are gaining interest. However, knowledge is limited regarding eating habits and association with energy intake, and current technologies limit assessment in free-living situations. We assessed associations between eating behavior and time of day with energy intake using a wearable camera under free-living conditions and explored if obesity modifies the associations. Sixteen participants (50% with obesity) recorded free-living eating behaviors using a wearable fish-eye camera for 14 days. Videos were viewed by trained annotators who confirmed number of bites, eating speed, and time of day for each eating episode. Energy intake was determined by a trained dietitian performing 24-h diet recalls. Greater number of bites, reduced eating speed, and increased BMI significantly predicted higher energy intake among all participants (P < 0.05, each). There were no significant interactions between obesity and number of bites, eating speed, or time of day (p > 0.05). Greater number of bites and reduced eating speed were significantly associated with higher energy intake in participants without obesity. Results show that under free-living conditions, more bites and slower eating speed predicted higher energy intake when examining consumption of foods with beverages. Obesity did not modify these associations. Findings highlight how eating behaviors can impact energy balance and can inform weight management interventions using wearable technology.

Introduction

The prevalence of obesity has steadily increased in the United States, with obesity affecting 42% of adults in 2018 (Hales CM, 2020). The growth is believed to be largely driven by excessive calorie intake and overeating (“Preventing Weight Gain.," 2020). Thus, weight management strategies often focus on restriction of calories and food intake (Williams et al., 2019), while also applying behavioral strategies to prevent overeating (Raynor & Champagne, 2016). There is increasing interest in identifying effective strategies related to the duration (e.g., eating speed, number of bites) and timing (i.e., time of day) of eating episodes to prevent excessive energy intake in the treatment of adults who are overweight or with obesity (Ferriday et al., 2015; Raynor & Champagne, 2016; Sonoda et al., 2018). Despite the absence of definitive evidence to support the manipulation of eating duration and timing to decrease energy intake (Raynor & Champagne, 2016), strategies targeting eating behaviors and timing are often advised for weight management (Kinsey & Ormsbee, 2015). The mixed evidence has rendered conclusions unclear regarding the relationship between eating behaviors and energy intake.

Preliminary trials investigating the effect of duration of eating on energy intake among adults show a positive association between eating speed and/or number of bites with energy intake (Andrade, Greene, & Melanson, 2008; Hurst & Fukuda, 2018; Scisco, Muth, Dong, & Hoover, 2011; Shah et al., 2014), while faster self-reported eating speed has been linked with higher body mass index (BMI) in observational studies (Leong, Gray, & Horwath, 2016; Leong, Madden, Gray, Waters, & Horwath, 2011; Otsuka et al., 2006; Sasaki, Katagiri, Tsuji, Shimoda, & Amano, 2003; Tanihara et al., 2011; van den Boer et al., 2017). Some studies also support the use of timing to curb energy intake. Notably, nighttime eating habits are associated with higher energy intake, higher BMI, and/or obesity (Baron, Reid, Kern, & Zee, 2011; Berg et al., 2009; Reid, Baron, & Zee, 2014; Wang et al., 2014; Yoshida, Eguchi, Nagaoka, Ito, & Ogino, 2018), and eating later in the day has been associated with slower weight loss than having earlier meals (Garaulet et al., 2013; Jakubowicz, Barnea, Wainstein, & Froy, 2013; Kahleova, Lloren, Mashchak, Hill, & Fraser, 2017). However, there is some ambiguity about the efficacy of intervening on eating duration and timing. A few trials have shown either negative or no associations between eating speed and energy intake (Andrade, Kresge, Teixeira, Baptista, & Melanson, 2012; Spiegel, Kaplan, Tomassini, & Stellar, 1993; Yeomans, Gray, Mitchell, & True, 1997), and one randomized controlled trial reports that consuming most of one's energy intake earlier in the day resulted in greater weight loss (Jakubowicz et al., 2013). The heterogeneity in the literature and limited generalizability might be attributed to the paucity of randomized controlled trials designed to test these hypotheses (Raynor & Champagne, 2016), as well as the small number of eating observations, use of self-reports of eating behaviors, and/or cultural differences in the food supply as many of these studies were conducted in Asian countries (Andrade et al., 2012; Baron et al., 2011; Berg et al., 2009; Garaulet et al., 2013; Jakubowicz et al., 2013; Kahleova et al., 2017; Leong et al., 2016; Leong et al., 2011; Otsuka et al., 2006; Reid et al., 2014; Sasaki et al., 2003; Spiegel et al., 1993; Tanihara et al., 2011; van den Boer et al., 2017; Wang et al., 2014; Yeomans et al., 1997; Yoshida et al., 2018). Further, there has been limited focus on differences in associations between eating speed and energy intake between individuals with and without obesity, despite evidence that weight status may influence the effect of eating speed on energy intake (Shah et al., 2014). Recent evidence suggests that eating slowly reduces energy intake during a controlled meal within the normal weight group (Shah et al., 2014), although it is uncertain whether these 2-day observations in a laboratory are generalizable to free-living conditions. A greater understanding of the modifying effect of BMI on eating behaviors and energy intake is necessary, especially with the use of objective assessments in free-living conditions.

Technological advances are being made to objectively measure caloric intake to detect eating behaviors that correlate with energy intake (Alshurafa et al., 2019). Two studies used a wrist-worn device to count bites through hand-to-mouth gestures in free-living conditions have shown modest correlation between number of bites and caloric intake (Dong, Hoover, Scisco, & Muth, 2012; Scisco, Muth, & Hoover, 2014). However, these experiments rely on participants to turn on/off the camera and do not allow for visually confirming the eating gestures detected by the device. Given that hand-to-mouth gestures during eating episodes do not always represent an eating gesture (Zhang, Alharbi, Nicholson, & Alshurafa, 2017), the validity of number of bites is important to confirm visually. To visually confirm usual eating behaviors, we employ a wearable fish-eye camera that enables us to investigate the association of eating speed, number of bites, and time of day with caloric intake in people with and without obesity. We hypothesize that faster eating speed and eating later in the day are associated with higher energy intake in participants, irrespective of weight status.