Use of a gamified website to increase pain neurophysiology knowledge and improve satisfaction and motivation among students studying for a degree in physiotherapy: a quasi-experimental study.

BACKGROUND: The scientific evidence highlights the difficulties that healthcare professionals experience when managing patients with chronic pain. One of the causes of this difficulty could be related to the acquired training and the lack of knowledge about the neurophysiology of pain. In the present study, we assessed the effectiveness of a gamified web platform in acquiring knowledge about pain neurophysiology and determining the satisfaction and motivation of students of the Degree in Physiotherapy at the University of Lleida. METHODS: A quasi-experimental study was carried out with a sample of 60 students who had access to a gamified web platform that included notes, videos, and clinical cases prepared by the teaching staff and was based on a previous study that included patients and healthcare professionals. RESULTS: The results show that after the intervention, there was a statistically significant increase in knowledge about the neurophysiology of pain, and the effect size was in the desired area of ​​effect. Likewise, many students considered that their motivation had increased as a result of the methodology used in the present study. CONCLUSIONS: The results support the use of this methodology to promote knowledge about the neurophysiology of pain while improving students' motivation.

Corrigendum: Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way.

[This corrects the article on p. 85 in vol. 10, PMID: 27594831.].

Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way.

Technical advances, particularly the integration of wearable and embedded sensors, facilitate tracking of physiological responses in a less intrusive way. Currently, there are many devices that allow gathering biometric measurements from human beings, such as EEG Headsets or Health Bracelets. The massive data sets generated by tracking of EEG and physiology may be used, among other things, to infer knowledge about human moods and emotions. Apart from direct biometric signal measurement, eye tracking systems are nowadays capable of determining the point of gaze of the users when interacting in ICT environments, which provides an added value research on many different areas, such as psychology or marketing. We present a process in which devices for eye tracking, biometric, and EEG signal measurements are synchronously used for studying both basic and complex emotions. We selected the least intrusive devices for different signal data collection given the study requirements and cost constraints, so users would behave in the most natural way possible. On the one hand, we have been able to determine basic emotions participants were experiencing by means of valence and arousal. On the other hand, a complex emotion such as empathy has also been detected. To validate the usefulness of this approach, a study involving forty-four people has been carried out, where they were exposed to a series of affective stimuli while their EEG activity, biometric signals, and eye position were synchronously recorded to detect self-regulation. The hypothesis of the work was that people who self-regulated would show significantly different results when analyzing their EEG data. Participants were divided into two groups depending on whether Electro Dermal Activity (EDA) data indicated they self-regulated or not. The comparison of the results obtained using different machine learning algorithms for emotion recognition shows that using EEG activity alone as a predictor for self-regulation does not allow properly determining whether a person in self-regulation its emotions while watching affective stimuli. However, adequately combining different data sources in a synchronous way to detect emotions makes it possible to overcome the limitations of single detection methods.