Influence of Quadrato Motor Training on Salivary proNGF and proBDNF

Previous studies demonstrated exercise-induced modulation of neurotrophins, such as Nerve Growth Factor (NGF) and Brain-Derived Neurotrophic Factor (BDNF). Yet, no study that we are aware of has examined their change as a function of different training paradigms. In addition, the understanding of the possible training-induced relationship between NGF and BDNF change is still lacking. Consequently, in the current study we examined the effect of a Walking Training (WT) and of Quadrato Motor Training (QMT) on NGF and BDNF precursors (proNGF and proBDNF). QMT is a specifically structured sensorimotor training that involves sequences of movements based on verbal commands, that was previously reported to improve spatial cognition, reflectivity, creativity as well as emotion regulation and general self-efficacy. In addition, QMT was reported to induce electrophysiological and morphological changes, suggesting stimulation of neuroplasticity processes. In two previous independent studies we reported QMT-induced changes in the salivary proNGF and proBDNF levels. Our present results demonstrate that following 12 weeks of daily QMT practice, proNGF level increases while proBDNF showed no significant change. More importantly, while no correlation between the two neurotrophins prior to training was detectable, there was a significant correlation between change in proNGF and proBDNF levels. Taken together the current results suggest that the two neurotrophins undergo a complex modulation, likely related to the different pathways by which they are produced and regulated. Since variations of these neurotrophins have been previously linked to depression, stress and anxiety, the current study may have practical implications and aid in understanding the possible physiological mechanisms that mediate improved well-being, and the dynamic change of neurotrophins as a result of training

Moods and Their Relevance to Systems Usage Models within Organizations: An Extended Framework

Traditionally, information systems (IS) usage models have examined user behavior within a cognitive framework, that is, these models suggest that a user’s cognition influences his/her IS usage behavior. Research over the past three decades has shown that mood, one’s global feeling state at a given time, can significantly impact a person’s cognitive processes. Mood effects on cognition are particularly relevant to organizational settings. Because moods are pervasive, they provide a stable context for cognitive processes that influence behavior at work; therefore, the inclusion of mood in individual IS usage models that support organizational tasks is both relevant and necessary. Because positive mood can enhance performance under certain circumstances, mood management is also relevant to IS usage models. Thus, we highlight how moods can be managed via IS and propose a model that takes into account users’ moods at the time they work with a system. This model provides an extended framework for incorporating relevant mood literature into current IS usage behavioral models. With this model, researchers can examine certain aspects of the model (such as how IS design can influence user feeling states or how users’ moods can impact their behavior), or conduct more comprehensive research using the entire model. This model can contribute to theory by providing a more complete picture of user behavior, and contribute to practice by helping mangers plan for desired outcomes

User Experience-driven Innovation—Theory and Practice: Introduction to Special Issue

This special issue is motivated by the need for user experience (UX) innovation ecosystems and the difficulty of creating and maintaining them. The papers in this special issue reflect the emerging views of the scholars and industry experts involved in one such UX innovation ecosystem. Authors presented the papers at the 2019 UX Symposium (https://uxsym.org/), an annual conference with the mission to continue to build its vibrant community of scholars and practitioners dedicated to advancing UX theory, education, and practice

Using Audio Augmented Reality to Support Decision Making

Audio augmented reality (AR) is becoming increasingly popular as a medium for communicating additional layers of information about a user’s environment. This extended abstract reports the preliminary results of a study examining the impact of an audio AR enabled mobile decision aid, FoodGlance, on users’ decision quality as well as their overall experience of the decision-making process. Research shows that people often find nutrition facts labels confusing and difficult to use [1]. FoodGlance was designed to make it easier for people to choose healthy products based on their dietary needs and preferences. FoodGlance uses OCR technology to extract textual information from nutrition fact labels and then translates it into simple feedback [2]. In this study, the feedback was provided in three different ways: audio AR, visual, and the combination of the two. Audio AR was provided via Bose glasses. Visual feedback, consistent with our previous study, was displayed as a pop-up on the smartphone showing thumbs up (good amount) or thumps down (bad amount) for sugar, fat, and protein separately [2]. For this study we used the FDA’s 5-20 percent daily value rule to design feedback. The feedback engine in FoodGlance, however, is not limited to this rule and can be modified by users

Interpersonal Model of Online Textual Persuasion

As with other forms of human communication, text-based computer-mediated communication (CMC) media, such as email, instant messaging, and online texting, are often used as a means to persuade others. However, unlike most other media, which feature structural bias in their support for either interpersonal or broadcast communication modes, text- based CMC supports both modes. As a result, CMC text messages frequently have ambiguous origins. We argue that individuals respond to this ambiguity by categorizing these messages based on characteristics that distinguish interpersonal messages from broadcast messages, and receivers tend to comply to a greater extent with those messages that they perceive as interpersonal. Based on these arguments, we present a fundamentally new online textual persuasion model. In empirically testing the model in an online experiment that we assessed with structural equation modeling, we found that it exhibited strong explanatory power and additional utility in augmenting existing online persuasion models. The results offer important theoretical contributions to human-computer interaction research generally and provide practical specific insights for improving persuasive communication via text-based CMC