No abstract

User centred evaluation of health apps to manage mood disorders:EvalDepApps project

Traditonal learning and assessment systems are overwhelmed when it comes to addressing the complex and mult-dimensional problems of clinical communicaton and professional practce. This paper shows results of a training program in clinical communicaton under Problem Based Learning (PBL) methodology and correlaton between student self-assessment and teachers assessment. This involves a qualitatve-quanttatve cross-sectonal study in usual practce in the 2nd year of the degree in Medicine. Teaching methodology is PBL, including 15 associate professors and 90 students. Educatonal tools for learning: PBL cases and seminars (video recorded, theoretcal-practcal lectures). Assessment tools: Tutorials on those cases worked on PBL (40%), knowledge test (30%), assessment of a case with PBL methodology (20%) and video recording report (10%). Communicaton skills are evidenced by CICCA-D scale (Connect-Understand-Identfy-Agree-Help-Decision). Variables: academic performance, score on CICCA-D and academic methodological assessment. The analysis is carried out using descriptve statstcs, calculatng the intra-class correlaton coefcients and weighted Kappa index with quadratc weights. 92.2% of students passed the course on the frst round. In a range between 0 and 34 points students' self-assessment scored 13 (SD ± 5) points and teachers' 16 (SD ±7). A weak (21%-41%) or poor (< 20%) correlaton was obtained between teachers and students for all questons on CICCA-D. The authors suggest a summatve assessment using diferent instruments and techniques to assess clinical communicaton skills from the frst year onwards, and highlight the key role of self-assessment, peer assessment and the use of video recording techniques along with feedback in formatve assessment.

IntroductionE-health offers the opportunity of supporting the management of several diseases, but most of these tools are far from being based on scientific evidence and demonstrating their effectiveness and efficacy. The PSICODEM Project aims to develop a mobile personalized clinical decision support system (CDSS) based on evidence for contributing to e-health interventions addressed to the management of dementia that require not only a pharmacological approach but also psychosocial interventions for improving patients’ quality of life and reducing emotional, cognitive and behavioral symptoms. The present communication focuses on the identification of the evidence on which the CDSS algorithm will be developed.MethodsThree systematic reviews were carried out in order to identify the existing scientific evidence published in relation to the effectiveness of behavioral, emotional and cognitive therapies addressing dementia (January 2009 to December 2017). The main databases were consulted (PubMed, Cochrane Library, PsychoInfo) and only randomized control trials (RCT) were considered. Articles were reviewed by two independent reviewers. The quality of the selected publications was assessed according to the SIGN criteria.ResultsForty-seven RCTs were selected for cognitive therapies, thirty-two for emotional ones and fifteen for behavioral interventions. Those therapies with more support of evidence were skills training for cognitive therapies and reminiscence interventions for emotional interventions; however, in behavioral interventions a variety of therapeutically approaches were found. Wide differences were found between studies in terms of types and levels of dementia, forms of intervention (number, length and frequency of sessions) and outcome measures.ConclusionsIn-depth analysis of evidence will allow the identification of those interventions more appropriate for each patient according to their symptoms and level of dementia. According to this evidence, the mobile CDSS algorithm will be developed. Additionally, these findings point out the gaps in psychosocial intervention research.

IntroductioneHealth is a new approach for managing several health conditions, but up to now not so many interventions have shown their efficacy/effectiveness. The AUTAPP Project tries to add knowledge in eHealth interventions targeted to Mental Health disorders, specifically Autism Spectrum Disorder (ASD) management that requires complex interventions that integrate different psychosocial interventions. AUTAPP aims to develop an evidence based Clinical Decision Support System (CDSS) using mobile technology for improving the decision process on psychosocial therapies in ASD. This study aimed to identify recommendations on which the algorithm of the CDSS will be developed.MethodsA systematic review (November 2009-November 2018) was carried out to identify the existing scientific evidence published in relation to the effectiveness of: (i) early detection protocols; (ii) assessment tools; (iii) existing non-pharmacological therapies. Main databases were consulted (PubMed, Cochrane Library, PsychoInfo). Articles were reviewed by two independent reviewers. The quality of included publications and recommendations were assessed according to SIGN criteria.ResultsA total number of 147 publications were included (477 identified): 96 for non-pharmacological therapies, 33 for assessment tools and eighteen for early detection. Regarding early detection and assessment, 12 recommendations were identified and six obtained the highest level (A), such as the convenience of multidisciplinary diagnosis teams and the usefulness of the Modified Checklist for Autism in Toddlers (M-CHAT) for ASD confirmation. For non-pharmacological therapies, 16 recommendations were collected. Those with higher levels of recommendations were family, environmental and educational (three As and one B). Interventions with lower levels of recommendation (C) were interventions which exercise, computers and neurological approaches.ConclusionsThis systematic review allows both to identify gaps and opportunities in psychosocial interventions research and be the base for the CDSS algorithm. In the future professionals, careers and people diagnosed with ASD will validate the mobile CDSS.