Dimitris Spachos

mEducator 3.0: Combining Semantic and Social Web Approaches in Sharing and Retrieving Medical Education Resources

Sharing of educational resources over the web has been a key development for both educators and learners in recent years. Pivotal roles in these developments have been played by following principles of the social/collaborative web, and more recently by exploiting advances in the semantic web front. In this paper an architecture that allows the sharing and retrieving of educational resources is proposed. In contrast to similar past attempts, this is now done without the need of copying or storing metadata in a centralised dataset/repository. Linked Data principles were used for exploring the metadata, while collaborative techniques were used in the systems that take advance of this architecture in order to enhance the end user experience, thereby offering a contemporary way to achieve resource adaptation and personalisation.

WHAAM: A mobile application for ubiquitous monitoring of ADHD behaviors

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common psychiatric conditions in childhood. Observation of the behavior of the young person plays a key role in Cognitive-Behavioural (CB) approaches to the treatment of ADHD, but traditional pen and pen-and-paper based methods have a number of disadvantages which inhibit their use in realworld situations. The WHAAM mobile application (WMA) provides teachers, parents and health experts with features to easily monitor behaviors in a Specific, Measurable, Attainable, Realistic and Timely (SMART) way.

A Pilot Medical Curriculum Analysis and Visualization According to Medbiquitous Standards

Curriculum design and implementation in higher medical education can be a great challenge. Although there are well-defined standards, such as the Curriculum Inventory and Competency Framework by MedBiquitous Consortium, existing systems are incapable of a visual representation of the various components, attributes, and relations. In this paper, we present the MEDCIN platform, a pilot tool which uses a standard-compliant curriculum data model to offer comprehensive and thorough analysis of a given curriculum. In addition, the ongoing research in challenging areas, such as the curriculum content comparison, can reveal valuable knowledge from existing data and transform the future of medical education.

Evaluation of Neurofeedback on ADHD Using Mobile Health Technologies

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common disorders that affect children. The existing therapeutic modalities have been characterized by side effects and short-term outcomes. Therefore, neurofeedback as non-invasive, non-pharmacological and painless biofeedback technique could be a promising treatment option. In this study, we present the experimental design of a project that aims to evaluate the neurofeedback effect on ADHD using a mobile health application called WHAAM. The application enables the network creation of individuals involved in child care (parents, teachers, health professionals) to collect information on the behavior of the child but further to evaluate the effectiveness of an intervention through provided quantitative - statistical reports. Our experimental design consists of five stages: (1) Inform parents concerning the project and complete of a consent form, (2) Select the child’s behaviors to be observed and organization of child’s network, (3) Gather data based on the predefined behaviors before neurofeedback, (4) Completion 20 session of neurofeedback with simultaneous behavior monitoring through WHAAM application, (5) Final data gathering based on the predefined behaviors after neurofeedback. The implementation of this study will introduce an innovative and objective way to evaluate an intervention with parallel involvement of a child care network in intervention’s supervision.

The future of mobile health ADHD applications

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common disorders that affect children. The diagnosis and the Cognitive-Behavioral treatment approaches are based on childs behavioral assessment through pen and pen-and-paper procedures. A number of mobile applications have been designed not only to replace traditional methods but also to provide more accurate, objective, direct and reliable recordings, better management of ADHD symptoms, education and training about the ADHD or even tools for ADHD diagnosis. The WHAAM application through a virtual network provides features to monitor behaviors in a SMART way (Specific, Measurable, Attainable, Realistic and Timely). In other words, creating a network of people involved in childs care (parents, educators, health professionals, relatives), WHAAM app allows data collection when the behavior occurs accompanied by information about its content and environment. Subsequently, gathered data is visualized and evaluated making possible an intervention planning and programming by the involved health professional. Additionally, the WHAAM app provides tools for evaluation of intervention efficacy. However, as emerging technologies came to facilitate healthcare delivery, there is a need for a continuous challenging and progress. Therefore, additional health data collection through advanced sensors and storage in big data hubs might be the new challenge of the future m-health applications.

Using mobile applications in continuing medical education

We describe the design, development and usage of a mobile hybrid application (UROMobile), with a high usability factor, in an interactive medical school with more than 120 attendees. UROMobile application offers support for several learning activities, as well as program information, general evaluation forms, profile information and many more. The evaluation results shows that there is a successful case study on how to use a mobile application in the education field.

Color features for image fingerprinting

Image fingerprinting systems aim to extract unique and robust image descriptors (in analogy to human fingerprints). They search for images that are not only perceptually similar but replicas of an image generated through mild image processing operations. In this paper, we examine the use of color descriptors based on a 24-color quantized palette for image fingerprinting. Comparisons are provided between different similarity measures methods as well as regarding the use of color-only and spatial chromatic histograms.

Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol

BackgroundSmoking is one of the most avoidable health risk factors, and yet the quitting success rates are low. The usage of tailored health messages to support quitting has been proved to increase quitting success rates. Technology can provide convenient means to deliver tailored health messages. Health recommender systems are information-filtering algorithms that can choose the most relevant health-related items—for instance, motivational messages aimed at smoking cessation—for each user based on his or her profile. The goals of this study are to analyze the perceived quality of an mHealth recommender system aimed at smoking cessation, and to assess the level of engagement with the messages delivered to users via this medium.MethodsPatients participating in a smoking cessation program will be provided with a mobile app to receive tailored motivational health messages selected by a health recommender system, based on their profile retrieved from an electronic health record as the initial knowledge source. Patients’ feedback on the messages and their interactions with the app will be analyzed and evaluated following an observational prospective methodology to a) assess the perceived quality of the mobile-based health recommender system and the messages, using the precision and time-to-read metrics and an 18-item questionnaire delivered to all patients who complete the program, and b) measure patient engagement with the mobile-based health recommender system using aggregated data analytic metrics like session frequency and, to determine the individual-level engagement, the rate of read messages for each user. This paper details the implementation and evaluation protocol that will be followed.DiscussionThis study will explore whether a health recommender system algorithm integrated with an electronic health record can predict which tailored motivational health messages patients would prefer and consider to be of a good quality, encouraging them to engage with the system. The outcomes of this study will help future researchers design better tailored motivational message-sending recommender systems for smoking cessation to increase patient engagement, reduce attrition, and, as a result, increase the rates of smoking cessation.Trial registrationThe trial was registered at clinicaltrials.org under the ClinicalTrials.gov identifier NCT03206619 on July 2nd 2017. Retrospectively registered.

Assessing Emotional Impact of Biofeedback and Neurofeedback Training in Smokers During a Smoking Cessation Project

This pilot study was conducted in the framework of SmokeFreeBrain project and it aimed at assessing the subjective emotional impact of skin temperature training and neurofeedback training on smokers by means of the AffectLecture application. The current paper constitutes a proof-of-concept, exploring the case of a single participant. The intervention consists of 5 sessions of biofeedback followed by 20 sessions of neurofeedback. Both pre- and post- biofeedback and neurofeedback training subjective scores of the participants mood were collected through the application. Based on our results, biofeedback training seems to promote alterations in mood, which are then maintained in the baseline mood scoring before neurofeedback training. Additionally, mood seems to be preserved after neurofeedback training. However, significant correlations between scoring and training performance have not been indicated.

Evaluating the AffectLecture Mobile App within an Elementary School Class Teaching Process

Elementary school students experience significant personal changes; their bodies change, as well as their inner selves. Their emotional status can be affected by both intrinsic and extrinsic factors; therefore, it is vital to highlight the emotional factors that influence the learning process, as a negative emotional status may lead to reduced motivation and low school performance, whereas a positive one may bring the opposite results. The aim of this study is to evaluate the effects of the teaching process onto the students emotional status, and how it affects their academic performance. The study was conducted on 15 3rd-grade elementary school students in Greece. The students scores on weekly reviewing tests were used, in the subjects of Language, Math, History, and Environmental Study. The affective state of the students was measured by the AffectLecture app of the AUTh Medical Physics lab. The results indicate that after attending the Environmental Study class, students have a more positive emotional status; on the other hand, this wasnt the case with the other subjects. Additionally, the students emotions and their test marks, exhibit a strong positive correlation. These findings suggest that the affective state of students and how it alters should be taken into account by education professionals, researchers and policy makers.