Barreiras e facilitadores da adesão em intervenções psicológicas baseadas na internet com adolescentes: uma revisão sistemática

Orientação: Diogo LamelaA investigação tem vindo a demonstrar alguma evidência empírica acerca do potencial das intervenções psicológicas mediadas pela internet/tecnologia na promoção da saúde mental em adolescentes. Contudo, pouco se sabe em relação a potenciais barreiras e facilitadores identificados pelos adolescentes na adesão a este tipo de intervenções. Desta forma, esta revisão sistemática pretendeu rever sistematicamente a investigação produzida até ao momento sobre estas variáveis clínicas, bem como potenciais moderadores associados. Foram triados artigos de uma base de dados (Web of Science), publicados entre janeiro de 2008 a janeiro de 2019, de modo a selecionar estudos que analisavam, quantitativamente, barreiras ou facilitadores percebidos à adesão em adolescentes (dos 12 aos 18 anos). Dos 2220 artigos triados inicialmente, cinco cumpriram os critérios de inclusão. Foram identificadas barreiras relacionadas com fatores pessoais e internos (e.g., esquecimento, desinteresse), design e conteúdo dos programas (e.g., dúvidas quanto à relevância e utilidade do próprio programa, problemas de utilização ou compreensão do conteúdo), e a privacidade e estigma. Foram identificados como facilitadores da adesão, a natureza interativa das intervenções digitais. Não foram identificados moderadores nos estudos incluídos. Implicações para a prática clínica e estudos futuros foram discutidos. Será importante que estudos futuros explorem, de forma mais rigorosa, algumas das variáveis anteriormente mencionadas, bem como moderadores e/ou caraterísticas mais especificas, com o objetivo de adaptar os programas de intervenção online/digitais disponíveis ou futuros, tornando-os mais adequados para os adolescentes como população-alvo.Research has been demonstrating some empirical evidence about the potential of internet/technology mediated psychological interventions in promoting mental health in adolescents. However, little is known about potential barriers and facilitators identified by adolescents in adhering to this type of intervention. Thus, this systematic review aimed to systematically review the research produced so far on these clinical variables, as well as potential associated moderators. Articles from a database (Web of Science), published from January 2008 to January 2019, were screened to select studies that quantitatively analyzed perceived barriers or facilitators of adherence in adolescents (12 to 18 years). Of the 2220 articles initially screened, five met the inclusion criteria. Barriers related to personal and internal factors (e.g., forgetfulness, disinterest), program design and content (e.g., doubts about the relevance and usefulness of the program itself, problems of use or understanding of the content), and privacy and stigma have been identified. Adherence facilitators have identified the interactive nature of digital interventions. No moderators were identified in the included studies. Implications for clinical practice and future studies were discussed. It will be important for future studies to more accurately explore some of the variables mentioned above, as well as more specific moderators and/or characteristics, in order to adapt available or future online/digital intervention programs, making them more suitable for adolescents as the target population

Evaluation of methods for the reconstruction of specific models from omics data

Recent efforts have allowed the development of genome-scale metabolic models for several organisms, including humans. These models have been used to predict cellular metabolism under the framework of constraint-based modeling. The application in health related research has spanned the issues of drug discovery, biomarker identification and targeting diseases such as cancer or Alzheimer. However, the human organism includes several cell types, each one with a different metabolic profile and functions. So, it is imperative to develop tissue-specific metabolic models. This challenge was addressed by several approaches, namely the Model-Building Algorithm, Metabolic Context specificity Assessed by Deterministic Reaction Evaluation and Task-driven Integrative Network Inference for Tissues methods. All these approaches use a generic model as a template and integrate evidences from omics data, literature and/or network analysis to infer the tissue specific metabolic model. Nevertheless, their results have not yet been adequately and critically evaluated and compared. We analysed the consistency between several omics data sources and reconstructed metabolic models of hepatocytes using different methods and distinct data sources as inputs. The results show that omics data sources have a poor overlapping and, in some cases, are contradictory. Additionally, the hepatocyte metabolic models generated are dependent on the combination of method and omics data source. Finally, we conclude that reliable methods for a priori omics data integration are required to support human cells models reconstruction

Computational tools for strain optimization by adding reactions

This paper introduces a new plug-in for the OptFlux Metabolic Engineering platform, aimed at finding suitable sets of reactions to add to the genomes of microbes (wild type strain), as well as finding complementary sets of deletions, so that the mutant becomes able to overproduce compounds with industrial interest, while preserving their viability. The optimization methods used are Evolutionary Algorithms and Simulated Annealing. The usefulness of this plug-in is demonstrated by a case study, regarding the production of vanillin by the bacterium E. coli

In silico strain optimization by adding reactions to metabolic models

Nowadays, the concerns about the environment and the needs to increase the productivity at low costs, demand for the search of new ways to produce compounds with industrial interest. Based on the increasing knowledge of biological processes, through genome sequencing projects, and high-throughput experimental techniques as well as the available computational tools, the use of microorganisms has been considered as an approach to produce desirable compounds. However, this usually requires to manipulate these organisms by genetic engineering and/ or changing the enviromental conditions to make the production of these compounds possible. In many cases, it is necessary to enrich the genetic material of those microbes with hereologous pathways from other species and consequently adding the potential to produce novel compounds. This paper introduces a new plug-in for the OptFlux Metabolic Engineering platform, aimed at finding suitable sets of reactions to add to the genomes of selected microbes (wild type strain), as well as finding complementary sets of deletions, so that the mutant becomes able to overproduce compounds with industrial interest, while preserving their viability. The necessity of adding reactions to the metabolic model arises from existing gaps in the original model or motivated by the productions of new compounds by the organism. The optimization methods used are metaheuristics such as Evolutionary Algorithms and Simulated Annealing. The usefulness of this plug-in is demonstrated by a case study, regarding the production of vanillin by the bacterium E. coli.This work is supported by project PTDC/EIA-EIA/115176/2009, funded by Portuguese FCT and Programa COMPETE

Customer relationship management at Sport Lisboa e Benfica: football customer segmentation

The purpose of this study is to consolidate the knowledge and learning from the field lab at SL Benfica and propose a conceptual framework for customer-based lifecycle segmentation that suits the club’s needs and its customer base. Relevant customer relationship management and segmentation literature was reviewed and served as the basis for the proposed segmentation method supported by 78 qualitative semi-structured interviews with Benfica supporters. Benfica needs to improve its targeting and marketing efforts and realize the true potential behind certain segments of customers within its database by applying a customer-based lifecycle segmentation technique as proposed in this study