Showing papers by "Andreas Triantafyllidis published in 2020"

COVID-19 Mobile Apps: A Systematic Review of the Literature.

Abstract: Background: A vast amount of mobile apps have been developed during the past few months in an attempt to “flatten the curve” of the increasing number of COVID-19 cases. Objective: This systematic review aims to shed light into studies found in the scientific literature that have used and evaluated mobile apps for the prevention, management, treatment, or follow-up of COVID-19. Methods: We searched the bibliographic databases Global Literature on Coronavirus Disease, PubMed, and Scopus to identify papers focusing on mobile apps for COVID-19 that show evidence of their real-life use and have been developed involving clinical professionals in their design or validation. Results: Mobile apps have been implemented for training, information sharing, risk assessment, self-management of symptoms, contact tracing, home monitoring, and decision making, rapidly offering effective and usable tools for managing the COVID-19 pandemic. Conclusions: Mobile apps are considered to be a valuable tool for citizens, health professionals, and decision makers in facing critical challenges imposed by the pandemic, such as reducing the burden on hospitals, providing access to credible information, tracking the symptoms and mental health of individuals, and discovering new predictors.

Detection of Mild Cognitive Impairment in an At-Risk Group of Older Adults: Can a Novel Self-Administered Serious Game-Based Screening Test Improve Diagnostic Accuracy?

Abstract: Background Literature supports the use of serious games and virtual environments to assess cognitive functions and detect cognitive decline. This promising assessment method, however, has not yet been translated into self-administered screening instruments for pre-clinical dementia. Objective The aim of this study is to assess the performance of a novel self-administered serious game-based test, namely the Virtual Supermarket Test (VST), in detecting mild cognitive impairment (MCI) in a sample of older adults with subjective memory complaints (SMC), in comparison with two well-established screening instruments, the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE). Methods Two groups, one of healthy older adults with SMC (N = 48) and one of MCI patients (N = 47) were recruited from day centers for cognitive disorders and administered the VST, the MoCA, the MMSE, and an extended pencil and paper neuropsychological test battery. Results The VST displayed a correct classification rate (CCR) of 81.91% when differentiating between MCI patients and older adults with SMC, while the MoCA displayed of CCR of 72.04% and the MMSE displayed a CCR of 64.89%. Conclusion The three instruments assessed in this study displayed significantly different performances in differentiating between healthy older adults with SMC and MCI patients. The VST displayed a good CCR, while the MoCA displayed an average CCR and the MMSE displayed a poor CCR. The VST appears to be a robust tool for detecting MCI in a population of older adults with SMC.

A Social Robot-based Platform towards Automated Diet Tracking

Abstract: Diet tracking via self-reports or manual taking of meal photos might be difficult, time-consuming, and discouraging, especially for children, which limits the potential of long-term dietary assessment. We present the design and development of a proof of concept of an automated and unobtrusive system for diet tracking integrating: a) a social robot programmed to automatically capture photos of food and motivate children, b) a deep learning model based on Google Inception V3, applied for the use case of image-based fruit recognition, c) a RESTful microservice architecture deployed to deliver the model outcomes to a platform aiming at childhood obesity prevention. We illustrate the feasibility and virtue of this approach, towards the development of the next-generation computer-assisted systems for automated diet tracking.

Smart Algorithm for Unhealthy Behavior Detection in Health Parameters

Abstract: Obesity is one of the most significant public health problems of the 21st century, having been recognized by the World Health Organization as the epidemic of this century. This problem has been affecting men, women, and children of all races and all ages, particularly in urban areas. OCARIoT is a research and development project financed by the Rede Nacional de Pesquisa (Brazil) and the European Union to conceive a technological solution based on the Internet of Things to face Childhood Obesity. An OCARIoT solution is to use a Decision Support System to encourage children to have healthy habits, with the help of IoT devices. This work presents the development of an algorithm for detecting unhealthy trends and forecast values in time series, which will be a tool to assist the Decision Support System.