Abstract

Depressive symptoms are common among older adults and can significantly impact their quality of life. Yet, many older adults face barriers to accessing psychological treatment. Internet-based cognitive behavioural therapy (iCBT) is a promising alternative to face-to-face treatments, but its feasibility among older adults is less researched.

Abstract
The current surge in the development of web applications highlights the necessity of incorporating user-specific preferences into the design process. An innovative approach to improving these applications involves the analysis of interaction data recorded by browsers, such as the number of mouse clicks and keystrokes. The data thus obtained provides valuable insight into user behavior, enabling effective personalization of web applications. The WebTraceSense project proposes the development of a web platform designed to facilitate the customization of the visualization of interaction data from websites. The platform will include a dynamic visualization component, which will support the identification of user behaviors, and a DevOps cycle, which will help streamline software cycle processes. This article presents a case study for the examination of user interaction logs from a visual data analysis tool, utilizing the functionalities of the WebTraceSense platform to facilitate the identification of behavioral trace patterns.

Abstract
This research investigates the use of artificial intelligence algorithms to identify behavioural patterns in computer use, with the aim of detecting trends that help to flag cases of depression by analysing the human-computer interaction records of these users, thereby increasing the quality of the data for early detection of these situations. Following design science methodology, a case study will be conducted using an existing mental health screening questionnaire, integrating an artificial intelligence layer to map mouse and keyboard interactions, followed by machine learning analysis of the records. The results of the machine learning assisted questionnaires will be compared with the results of the questionnaires without the mapping. If there is a significant difference, this model could be useful for making predictions about emotional states, contributing to the field of artificial intelligence and helping to prevent depression, which is the focus of the research, although the aim is to look at mental health in a global way.

Abstract
Enhancing the reproducibility and comprehension of adaptive immune receptor repertoire sequencing (AIRR-seq) data analysis is critical for scientific progress. This study presents guidelines for reproducible AIRR-seq data analysis, and a collection of ready-to-use pipelines with comprehensive documentation. To this end, ten common pipelines were implemented using ViaFoundry, a user-friendly interface for pipeline management and automation. This is accompanied by versioned containers, documentation and archiving capabilities. The automation of pre-processing analysis steps and the ability to modify pipeline parameters according to specific research needs are emphasized. AIRR-seq data analysis is highly sensitive to varying parameters and setups; using the guidelines presented here, the ability to reproduce previously published results is demonstrated. This work promotes transparency, reproducibility, and collaboration in AIRR-seq data analysis, serving as a model for handling and documenting bioinformatics pipelines in other research domains.

Abstract
Background: Preterm birth is a global health concern. Its adverse consequences may persist throughout the life course, exerting a potentially heavy burden on families, health systems, and societies. In high-income countries, the first children who benefited from improved care are now adults entering middle age. However, there is a clear gap in the knowledge regarding the long-term outcomes of individuals born preterm. Objective: This study aimed to assess the feasibility of recruiting and following up an e-cohort of adults born preterm worldwide and provide estimations of participation, characteristics of participants, the acceptability of questions, and the quality of data collected. Methods: We implemented a prospective, open, observational, and international e-cohort pilot study (Health of Adult People Born Preterm-an e-Cohort Pilot Study [HAPP-e]). Inclusion criteria were being an adult (aged =18 years), born preterm (<37 weeks of gestation), having internet access and an email address, and understanding at least 1 of the available languages. A large, multifaceted, and multilingual communication strategy was established. Between December 2019 and June 2021, inclusion and repeated data collection were performed using a secured web platform. We provided descriptive statistics regarding participation in the e-cohort, namely, the number of persons who registered on the platform, signed the consent form, initiated and completed the baseline questionnaire, and initiated and completed the follow-up questionnaire. We also described the main characteristics of the HAPP-e participants and provided an assessment of the quality of the data and the acceptability of sensitive questions. Results: As of December 31, 2020, a total of 1004 persons had registered on the platform, leading to 527 accounts with a confirmed email and 333 signed consent forms. A total of 333 participants initiated the baseline questionnaire. All participants were invited to follow-up, and 35.7% (119/333) consented to participate, of whom 97.5% (116/119) initiated the follow-up questionnaire. Completion rates were very high both at baseline (296/333, 88.9%) and at follow-up (112/116, 96.6%). This sample of adults born preterm in 34 countries covered a wide range of sociodemographic and health characteristics. The gestational age at birth ranged from 23+6 to 36+6 weeks (median 32, IQR 29-35 weeks). Only 2.1% (7/333) of the participants had previously participated in a cohort of individuals born preterm. Women (252/333, 75.7%) and highly educated participants (235/327, 71.9%) were also overrepresented. Good quality data were collected thanks to validation controls implemented on the web platform. The acceptability of potentially sensitive questions was excellent, as very few participants chose the I prefer not to say option when available. Conclusions: Although we identified room for improvement in specific procedures, this pilot study confirmed the great potential for recruiting a large and diverse sample of adults born preterm worldwide, thereby advancing research on adults born preterm.