Relationship between chewing features and body mass index in young adolescents

Abstract

Summary Background Behavioural aspects of chewing may influence food intake, nutritional status and in turn body weight. Objectives The current study aimed to study chewing features in adolescents as they naturally occur in home‐based settings, and to test for a possible association with weight status. Methods Forty‐two adolescents (15.3 ± 1.3 years) were recruited (21 with healthy‐weight/21 with overweight). Using a smartphone‐assisted wearable electromyographic device, the chewing features of each participant were assessed over one evening, including the evening meal, in their natural home setting. Results The mean (±SD) for chewing pace was 1.53 ± 0.22 Hz, chewing power 30.1% ± 4.8%, number of chewing episodes 63.1 ± 36.7 and chewing time 11.0 ± 7.7 minutes. The chewing pace of the group with overweight was slower than that of healthy weight (−0.20 Hz; 95% CI, −0.06 to −0.33; P = .005) while their chewing time was shorter (−4.9 minutes; 95% CI, 0.2‐9.7; P = .044). A significant negative correlation was observed between BMI z‐score and chewing pace (R = −.41; P = .007), and between BMI z‐score and chewing time (R = −0.32; P = .039). Conclusion The current study suggests that adolescents who are overweight eat at a slower pace for a shorter period of time than their counterparts who are a healthy weight. This unexpected finding based on objective data appears to conflict with existing questionnaire findings but provides impetus for further work testing the effectiveness of changing eating behaviour as a weight‐management intervention in youth.

Home-Based Monitoring of Eating in Adolescents: A Pilot Study

“…A smart phone-assisted wireless EMG device developed by our research team was used to record chewing activity in the home setting without interfering with routine activities or chewing behavior [28,[31][32][33]. The EMG device was applied unilaterally on the preferred chewing side of the participant.…”

“…The electrodes of the EMG device were positioned on the skin over most prominent part of the masseter muscle during contraction and parallel to the main muscle fibers at a center-to-center distance of 20 mm. A third electrode acted as a right-leg drive for noise suppression and was 23.5 mm distal to the active electrodes [33]. A smartphone application (App) was developed in Android to enable visualization, calibration, and logging of EMG activity.…”

“…The user-friendly App was designed to allow the investigator to set threshold values for the detection of contraction episodes prior to each recording session. The received EMG data were stored in the internal memory of the smartphone [28,33].…”

“…designed to allow the investigator to set threshold values for the detection of contraction episodes prior to each recording session. The received EMG data were stored in the internal memory of the smartphone [28,33]. To normalize the raw EMG activity in the time domain, the maximum voluntary contraction was used as the reference standard.…”

“…The participants were asked to consume three pieces from a small packet of rice crackers (Peckish Cheese Rice Crackers 20 g, Menora, Co., Victoria, Australia); then they were asked to chew gum (Wrigley's Extra Chewing Gum Spearmint, Mars Incorporated, Mars Wrigley Confectionery, New South Wales, Australia) for one minute. This calibration is essential to calculate the chewing maximum chewing power and to set thresholds for automated identification of chewing activity using our previously developed algorithm [33].…”