5 results
PP40 Health Apps To Manage Depression: Can We Separate The Grain From The Chaff? EvalDepApps Project
- Carme Carrion, Ariadna Sales-Masnou, Sophie Eis, Noemí Robles, Elisa Puigdomènech, Andrea Duarte-Díaz, Josep Vidal-Alaball, Lilisbeth Perestelo, Meritxell Davins, Oriol Solà-Morales
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- Journal:
- International Journal of Technology Assessment in Health Care / Volume 38 / Issue S1 / December 2022
- Published online by Cambridge University Press:
- 23 December 2022, pp. S53-S54
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Introduction
The use of mobile applications in the treatment of health issues is more frequently becoming common practice. Apps are fast, versatile, and manageable tools that allow the empowerment of patients and professionals, and can reduce the possible stigmatization suffered by some patients, mainly in mental health. There are more than 325,000 health apps on the market, but their impact remains unclear. There are several initiatives to define how health applications should be assessed, however, all of them address only partial aspects of the evaluation. The theoretical frameworks existing to date highlight the need to develop new tools and methodologies to assess mobile applications whose objective is the management of specific pathologies.
MethodsThe primary goal of the EvalDepApps project is to develop and pilot an assessment tool for mobile applications whose main objectives are the treatment, monitoring or social support of people suffering from depression. The project is inspired by the results and lessons learnt from a previous project, EVALAPPS, whose central aim was to develop a tool to assess health apps targeted toward the management of overweight and obesity. The first steps of the EvalDepApps project are: (i) to explore and characterize the current landscape of mobile applications available in the market to treat depression through a systematic appraisal, and (ii) to review the existing evidence about the effectiveness and safety of these applications through systematic research of the existing evidence.
ResultsPreliminary results show that all the depression management studies were by design based on cognitive-behavioral therapy (CBT) interventions (n=17) and the main management tools included in the services (web or apps) are psychoeducation and coaching (14), together with self-monitoring and feedback messaging (13).
ConclusionsMoreover, although health apps seem to be an interesting strategy to treat depression, there are very few apps available on the markets (30) and the supporting evidence is very limited. This result uncovers a need for further systematic and clinically oriented validation and testing of such apps.
PP166 A Mobile Clinical Decision Support System for Autism Spectrum Disorder
- Noemí Robles, Carme Carrion i Ribas, Montserrat Pàmias, Isabel Parra, Jordi Conesa, Antoni Perez-Navarro, Marc Alabert, Marta Aymerich
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- Journal:
- International Journal of Technology Assessment in Health Care / Volume 35 / Issue S1 / 2019
- Published online by Cambridge University Press:
- 31 December 2019, pp. 68-69
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Introduction
eHealth 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.
VP29 Designing A Mobile Clinical Decision Support System For Dementia
- Noemí Robles, Carme Carrion i Ribas, Marta Aymerich
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- Journal:
- International Journal of Technology Assessment in Health Care / Volume 35 / Issue S1 / 2019
- Published online by Cambridge University Press:
- 31 December 2019, p. 83
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Introduction
E-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.
OP62 Let's Co-design A Tool To Assess Overweight And Obesity Health Apps
- Elisa Puigdomenech Puig, Noemí Robles, Corpus Gomez, Francesc Saigí-Rubió, Alberto Zamora, Montse Moharra, Guillem Paluzié, Mariona Balfegó, Guillem Cuatrecasas, Carme Carrion i Ribas
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- Journal:
- International Journal of Technology Assessment in Health Care / Volume 35 / Issue S1 / 2019
- Published online by Cambridge University Press:
- 31 December 2019, p. 15
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Introduction
There are more than 320,000 accessible health apps, with the most downloaded of those related to physical exercise and weight control. However the initiatives for their validation address only partial aspects of the evaluation. The EVALAPPS project aims to develop an assessment tool for overweight and obesity management apps, based on the evaluation of efficacy, effectiveness and safety. In the present phase of the project, the team is co-creating the assessment tool considering both the evidence and the expertise of professionals (co-creation process).
MethodsProposed co-creation methodology includes: 1) a modified Delphi process for selecting the assessment criteria. Criteria were identified through a) an exhaustive review of the criteria used by several mHealth assessment tools and b) a systematic review of efficacy, safety and effectiveness criteria used in mHealth interventions that assess overweight and obesity management. 2) a co-creation session using “Design Thinking” techniques for defining the final content and appearance of the tool (November 2018).
ResultsTen dimensions and 133 criteria were identified, both in relation to the outputs (Usability, Clinical Effectiveness, Security, Development, etc.) and the outcomes (such as weight loss, number of steps). Of those, 114 were included in the modified Delphi, in which 31 professionals participated. A set of 63 criteria were selected as candidates for being part of the tool. Criteria mainly belonged to Security (22%) and Usability dimensions (14%), followed by Quality (11%), and outcomes related to Activity (11%) and Physical status (11%). Once the co-creation session has been performed, the final tool will be developed.
ConclusionsRelevant criteria to evaluate the efficacy and safety of mHealth interventions in the management of overweight and obesity have been identified. Once the tool is developed it will be user tested and piloted on users of overweight and obesity management apps.
VP89 Assessing mHealth: Proposal Of A New Framework
- Elisa Puigdomenech Puig, Santiago Gómez, Carme Carrión, Cari Almazan, Mireia Espallargues
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- Journal:
- International Journal of Technology Assessment in Health Care / Volume 33 / Issue S1 / 2017
- Published online by Cambridge University Press:
- 12 January 2018, p. 190
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INTRODUCTION:
The use of health apps is rapidly increasing. They intend to promote health or to treat diseases; in some cases, substituting medical duties. No specific frameworks to assess mHealth solutions in a broad scope and in a comprehensive way have been identified. We aim to propose a framework for mHealth assessment.
METHODS:The framework development was based on:
• Literature review to identify existing assessment models including the evaluation of health effects
• Exploratory analysis with experts and user group discussions
• Definition of the assessment model, following the domains of health technology assessment.
RESULTS:Existing frameworks are mainly focused on certification criteria. Professionals and users agreed on the need to undertake mHealth assessments as to better inform user decisions. Assessments should be sensible to continuous changes of these technologies and be undertaken by independent organizations.
The proposed framework offers a step-by-step process by which any mHealth solution can be categorized and analyzed, according to: (i) Risk classification matrix: combining intervention type and patient type, (ii) Users: patients, professionals, informal caregivers individually or all of these together and (iii) Integration: stand-alone, fully integrated.
The model has four evaluation domains: technical maturity, risks, benefits and resources needed, including the commonly accepted evaluation perspectives: technical, contents, clinical/health, user perspective, organizational and socio-economic. Sub-domains are defined as: end-user, organization, healthcare system and community (society as a whole). Aspects to be assessed are selected according to the purpose of the evaluation (intended use / intended impact) and vary depending on the type of the mHealth solution: product or service.
CONCLUSIONS:The mHealth assessment process is needed and should be: (i) continuous/iterative, providing timely conclusions and recommendations for improvement, (ii) inclusive/collaborative, involving all stakeholders,and (iii) constantly adapting to standards. The proposed framework is intended to support informed decisions when developing, integrating, selecting, recommending, or adopting mHealth solutions.