Search Results (6)

Background/Aim: To date, there have been no data regarding the reproducibility or agreement of meal duration when a test meal is eaten. To confirm the reproducibility and agreement of the meal duration, number of chews, chewing tempo, and number of bites of a test meal, we performed a prospective observation study. Methods: We measured the duration, number of chews, chewing tempo, and number of bites of a test meal (salmon bento) among 33 participants (male: 15; female: 18) aged 20–60 years who ate twice at 2-week intervals to verify the agreement (by Bland-Altman (BA) analysis) and reproducibility (intraclass correlation coefficient (ICC)) by sex. Results: The meal duration (s) and number of bites (times) were significantly greater in the female group (560.4 (128.7) and 731.9 (266.3), p = 0.023; 17.1 (9.9) vs. 26.4 (13.7), p = 0.036), and the number of chews tended to be greater in the female group (752.5 (203.3) vs. 938.1 (375.9), p = 0.083). Meal duration was positively associated with the number of chews (0.64 [0.53, 0.74], p < 0.001) and bites (10.4 [5.1, 15.8], p < 0.001). For both sexes, the % error calculated via BA analysis was high for meal duration, the number of chews, and the chewing tempo (21.4 and 13.4%; 16.5 and 18.5%; and 6.8 and 5.3%, respectively) and low for the number of bites (37.9 and 68.9%). The ICCs were high for meal duration (0.73 and 0.90), the number of chews (0.76 and 0.89), and the chewing tempo (0.76 and 0.90), and low for the number of bites (0.84 and 0.69). Moreover, systematic and proportional errors were found only for the number of bites in the female group (median_difference with 95% CI: −9.00 (−13.00, −2.00); −0.320 (−0.45, −0.093)). Conclusions: Although the sample size was small due to the exploratory nature of the study, meal duration, number of chews, and chewing tempo had high reproducibility and agreement, at least when this test meal was consumed. These measures may indicate individual-specific eating behavior. Full article

Background: Dietary intake plays a crucial role in health research, yet existing methods for its measurement can lead to participant burden, lengthy recording, and human errors, and do not account for age-specific variations. Libro is a real-time diet-tracking mobile-based app offering flexible features. An automated food recording program within Libro was customized for young people vulnerable to eating misbehaviour. This preliminary study assessed its relative validity using a self-administered 24 h recall method as the reference method. Methods: The relative validity of Libro was tested by adopting a cross-over design that recorded food intake over a period of 3 non-consecutive weekdays and 1 weekend day with both methods. The participants were recruited online through a mental health research charity, and this study was conducted fully online. The primary outcome was the concordance of total energy intake between the two methods, with secondary outcomes focusing on the intake of protein, carbohydrates, fats, free sugars, fibre, and trans-fatty acids. Test–retest validity was assessed per method with the intraclass correlation coefficient; a Bland–Altman plot and t-test were performed to test agreement at the group level; correlation coefficient and cross-classification were performed to assess agreement at the individual level. Results: Forty-seven participants were included in the final analysis. The average intraclass correlation coefficient for energy intake measured by Libro over four days was 0.85 (95% CI: 0.76–0.91). Compared to Intake24, the average energy intake recorded using Libro was significantly lower (mean difference: −554 Kcal, 95% CI: −804.1 to −305.6 Kcal, p < 0.001), potentially driven by the reduced reporting of foods rich in free sugars. The correlation coefficient for average energy intake measured by Libro vs. Intake24 was 0.32 (95% CI: 0.03, 0.55), with only 27.7% of subjects classified in the same quartile with both methods (κ = 0.31, 95% CI: −0.03, 0.55). Concordance varied across specific dietary component measures. Conclusions: While Libro had good test–retest reliability if adopting a multiple administration method, it underreported energy and other aspects of dietary intake, along with poor classification performance compared to Intake24 in a population vulnerable to eating misbehaviour. We suggest that future studies improve user experience to increase compliance and data accuracy. Full article

This study investigated the production process of ready-to-eat crayfish, focusing on changes in sensory quality, pH, total volatile base nitrogen (TVB-N), total viable count (TVC), acid value (AV), springiness, and hardness during storage at 4 °C, 25 °C, and 37 °C. A shelf-life prediction model was developed using the Arrhenius model. The optimal crayfish formula was determined to be 0.12% spices, 0.80% salt, and a stewing time of 70 min, which achieved the highest sensory score of 9.25 points. This combination resulted in shrimp meat with an intact texture, a soft and smooth taste, and rich spicy and briny flavors. A Pearson correlation analysis showed significant correlations among TVB-N, TVC, AV, springiness, and hardness. When fitting each indicator with zero-order, first-order, and second-order kinetics, TVB-N, AV, and springiness aligned more closely with the zero-order kinetics model, while TVC and hardness fit better with the first-order kinetics model. The Arrhenius equation-based shelf-life model demonstrated an error margin of 9.1% between predicted and actual quality indicators, confirming its feasibility for predicting the quality and shelf life of spicy crayfish. These findings provide a crucial theoretical basis for the intelligent prediction of storage and distribution conditions for ready-to-eat crayfish. Full article

We present an eating detection algorithm for wearable sensors based on first detecting chewing cycles and subsequently estimating eating phases. We term the corresponding algorithm class as a bottom-up approach. We evaluated the algorithm using electromyographic (EMG) recordings from diet-monitoring eyeglasses in free-living and compared the bottom-up approach against two top-down algorithms. We show that the F1 score was no longer the primary relevant evaluation metric when retrieval rates exceeded approx. 90%. Instead, detection timing errors provided more important insight into detection performance. In 122 hours of free-living EMG data from 10 participants, a total of 44 eating occasions were detected, with a maximum F1 score of 99.2%. Average detection timing errors of the bottom-up algorithm were 2.4 ± 0.4 s and 4.3 ± 0.4 s for the start and end of eating occasions, respectively. Our bottom-up algorithm has the potential to work with different wearable sensors that provide chewing cycle data. We suggest that the research community report timing errors (e.g., using the metrics described in this work). Full article

Eating behavior can have an important effect on, and be correlated with, obesity and eating disorders. Eating behavior is usually estimated through self-reporting measures, despite their limitations in reliability, based on ease of collection and analysis. A better and widely used alternative is the objective analysis of eating during meals based on human annotations of in-meal behavioral events (e.g., bites). However, this methodology is time-consuming and often affected by human error, limiting its scalability and cost-effectiveness for large-scale research. To remedy the latter, a novel “Rapid Automatic Bite Detection” (RABiD) algorithm that extracts and processes skeletal features from videos was trained in a video meal dataset (59 individuals; 85 meals; three different foods) to automatically measure meal duration and bites. In these settings, RABiD achieved near perfect agreement between algorithmic and human annotations (Cohen’s kappa κ = 0.894; F1-score: 0.948). Moreover, RABiD was used to analyze an independent eating behavior experiment (18 female participants; 45 meals; three different foods) and results showed excellent correlation between algorithmic and human annotations. The analyses revealed that, despite the changes in food (hash vs. meatballs), the total meal duration remained the same, while the number of bites were significantly reduced. Finally, a descriptive meal-progress analysis revealed that different types of food affect bite frequency, although overall bite patterns remain similar (the outcomes were the same for RABiD and manual). Subjects took bites more frequently at the beginning and the end of meals but were slower in-between. On a methodological level, RABiD offers a valid, fully automatic alternative to human meal-video annotations for the experimental analysis of human eating behavior, at a fraction of the cost and the required time, without any loss of information and data fidelity. Full article

We intend to develop earphone-type wearable devices to measure occlusal force by measuring ear canal movement using an ear sensor that we developed. The proposed device can measure occlusal force during eating. In this work, we simultaneously measured the ear canal movement (ear sensor value), the surface electromyography (EMG) of the masseter muscle and the occlusal force six times from five subjects as a basic study toward occlusal force meter development. Using the results, we investigated the correlation coefficient between the ear sensor value and the occlusal force, and the partial correlation coefficient between ear sensor values. Additionally, we investigated the average of the partial correlation coefficient and the absolute value of the average for each subject. The absolute value results indicated strong correlation, with correlation coefficients exceeding 0.9514 for all subjects. The subjects showed a lowest partial correlation coefficient of 0.6161 and a highest value of 0.8286. This was also indicative of correlation. We then estimated the occlusal force via a single regression analysis for each subject. Evaluation of the proposed method via the cross-validation method indicated that the root-mean-square error when comparing actual values with estimates for the five subjects ranged from 0.0338 to 0.0969. Full article