RESUMO
Background: Major Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies. Methods: This prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source "Pothos" air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels. Results: Following initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness. Conclusion: This study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.
RESUMO
Nowadays, we live in the "data era" where the use of statistical or data analysis software is inevitable, in any research field. This means that the choice of the right software tool or platform is a strategic issue for a research department. Nevertheless, in many cases decision makers do not pay the right attention to a comprehensive and appropriate evaluation of what the market offers. Indeed, the choice still depends on few factors like, for instance, researcher's personal inclination, e.g., which software have been used at the university or is already known. This is not wrong in principle, but in some cases it's not enough at all and might lead to a "dead end" situation, typically after months or years of investments already done on the wrong software. This article, far from being a full and complete guide to statistical software evaluation, aims to illustrate some key points of the decision process and introduce an extended range of factors which can help to undertake the right choice, at least in potential. There is not enough literature about that topic, most of the time underestimated, both in the traditional literature and even in the so called "gray literature", even if some documents or short pages can be found online. Anyhow, it seems there is not a common and known standpoint about the process of software evaluation from the final user perspective. We suggests a multi-factor analysis leading to an evaluation matrix tool, to be intended as a flexible and customizable tool, aimed to provide a clearer picture of the software alternatives available, not in abstract but related to the researcher's own context and needs. This method is a result of about twenty years of experience of the author in the field of evaluating and using technical-computing software and partially arises from a research made about such topics as part of a project funded by European Commission under the Lifelong Learning Programme 2011.