ABSTRACT
BACKGROUND: Eating alone has been significantly associated with psychological distress. However, there is no research that evaluates the effects or relation of eating together online to autonomic nervous system functions. METHODS: This is a randomized, open-label, controlled, pilot study conducted among healthy volunteers. Participants were randomized into either an eating together online group or an eating-alone group. The effect of eating together on autonomic nervous functions was evaluated and compared with that of the control (eating alone). The primary endpoint was the change in the standard deviation of the normal-to-normal interval (SDNN) scores among heart rate variabilities (HRV) before and after eating. Physiological synchrony was investigated based on changes in the SDNN scores. RESULTS: A total of 31 women and 25 men (mean age, 36.6 [SD = 9.9] years) were included in the study. In the comparison between the aforementioned groups, two-way analysis of variance revealed interactions between time and group on SDNN scores. SDNN scores in the eating together online group increased in the first and second halves of eating time (F[1,216], P < 0.001 and F[1,216], P = 0.022). Moreover, high correlations were observed in the changes in each pair before and during the first half of eating time as well as before and during the second half of eating time (r = 0.642, P = 0.013 and r = 0.579, P = 0.030). These were statistically significantly higher than those in the eating-alone group (P = 0.005 and P = 0.040). CONCLUSIONS: The experience of eating together online increased HRV during eating. Variations in pairs were correlated and may have induced physiological synchrony. TRIAL REGISTRATION: The University Hospital Medical Information Network Clinical Trials Registry, UMIN000045161. Registered September 1, 2021. https://center6.umin.ac.jp/cgi-open-bin/icdr/ctr_view.cgi?recptno=R000051592 .
ABSTRACT
While eye gaze data contain promising clues for inferring the interests of viewers of digital catalog content, viewers often dynamically switch their focus of attention. As a result, a direct application of conventional behavior analysis techniques, such as topic models, tends to be affected by items or attributes of little or no interest to the viewer. To overcome this limitation, we need to identify "when" the user compares items and to detect "which attribute types/values" reflect the user's interest. This paper proposes a novel two-step approach to addressing these needs. Specifically, we introduce a likelihood-based short-term analysis method as the first step of the approach to simultaneously determine comparison phases of browsing and detect the attributes on which the viewer focuses, even when the attributes cannot be directly obtained from gaze points. Using probabilistic latent semantic analysis, we show that this short-term analysis step greatly improves the results of the subsequent step. The effectiveness of the framework is demonstrated in terms of the capability to extract combinations of attributes relevant to the viewer's interest, which we call aspects, and also to estimate the interest described by these aspects.
