OBJECTIVE: Maintaining plasma glucose homeostasis is vital for mammalian survival, but the masticatory function, which influences glucose regulation, has, to our knowledge, been overlooked.
METHODS: In this study, we investigated the relationship between the glycemic response curve and chewing performance in a group of 8 individuals who consumed 80 g of apple. A device called \"Chewing\" utilizing electromyographic (EMG) technology quantitatively assesses chewing pattern, while glycemic response is analyzed using continuous glucose monitoring. We assessed chewing pattern characterizing chewing time (tchew), number of bites (nchew), work (w), power (wr), and chewing cycles (tcyc). Moreover, we measured the principal features of the glycemic response curve, including the area under the curve (α) and the mean time to reach the glycemic peak (tmean). We used linear regression models to examine the correlations between these variables.
RESULTS: tchew, nchew, and wr were correlated with α (R2 =  0.44,   P  <  0.05 for tchew and nchew, P  <  0.001 for wr), and tmean was correlated with tchew (R2  =  0.25,  P  <  0.05). These findings suggest that increasing chewing time and power, while reducing the number of chews, resulted in a wider glycemic curve and an earlier attainment of the glycemic peak.
CONCLUSIONS: These results emphasize the influence of proper chewing techniques on blood sugar levels. Implementing correct chewing habits could serve as an additional approach to managing the glycemic curve, particularly for individuals with diabetes.

Chewing is essential in regulating metabolism and initiating digestion. Various methods have been used to examine chewing, including analyzing chewing sounds and using piezoelectric sensors to detect muscle contractions. However, these methods struggle to distinguish chewing from other movements. Electromyography (EMG) has proven to be an accurate solution, although it requires sensors attached to the skin. Existing EMG devices focus on detecting the act of chewing or classifying foods and do not provide self-awareness of chewing habits. We developed a non-invasive device that evaluates a personalized chewing style by analyzing various aspects, like chewing time, cycle time, work rate, number of chews and work. It was tested in a case study comparing the chewing pattern of smokers and non-smokers, as smoking can alter chewing habits. Previous studies have shown that smokers exhibit reduced chewing speed, but other aspects of chewing were overlooked. The goal of this study is to present the device and provide additional insights into the effects of smoking on chewing patterns by considering multiple chewing features. Statistical analysis revealed significant differences, as non-smokers had more chews and higher work values, indicating more efficient chewing. The device provides valuable insights into personalized chewing profiles and could modify unhealthy chewing habits.