Gonçalo Campos Gonçalves

Abstract
Background: Online psychological interventions like behavioural activation (BA) can be provided with or without human support. Unguided online interventions require no human contact and are therefore easier to implement on a large scale than guided interventions. However, effectiveness and adherence rates to these interventions are generally lower. One way to increase adherence to unguided online interventions is to offer automated motivational support. Objective: This pilot randomized controlled trial (RCT) examined whether adherence to unguided online BA for low mood could be improved by adding automated support in the form of smartphone-delivered personalized motivational messages or a motivational virtual coach. Methods: A three-arm pilot RCT (n = 106) was conducted that compared an online intervention delivered with automated motivational support by a virtual coach (n = 35), or by automated personalized messages on their smartphone (n = 35), to the same intervention without support (control condition; n = 36). The primary outcome was level of adherence, operationalized as (1) the number of webpages of the intervention visited, and (2) the number of mood ratings completed on the smartphone application, both retrieved from participants' logfiles. Secondary outcomes were satisfaction with the intervention (CSQ-I), usability (SUS) depression scores (HADS), and motivation for treatment (SMFL), measured through online questionnaires administered at baseline or after 4 weeks. Results: Adherence was moderate overall, with participants visiting on average 23 pages of 55 webpages and completing on average 50 of 84 requested mood ratings. No evidence for differences in adherence rates were observed between the intervention conditions and the control condition. Satisfaction with the intervention was moderate to high. Usability scores were below the desirable threshold of 68. Depression symptoms did not change significantly across all participants (p = .053). No significant changes in motivation were found over time or between groups. Conclusions: Adding automated support to unguided online BA for depression did not improve overall adherence. The limited effectiveness may reflect a misalignment between the motivational strategies and the needs of the target population, who reported mild symptoms and high intrinsic motivation. The findings highlight the need to further improve both the quality of automated support and the usability of online platforms. Future research should explore additional adherence-related factors and investigate how personalization can better address different symptom severities in unguided mental health interventions. Trial registration: International Clinical Trials Registry Platform: trialsearch.who.int/Trial2.aspx?TrialID=NL8110. © 2025 The Authors

Abstract
Motivation The validity of epidemiologic findings can be increased using triangulation, i.e. comparison of findings across contexts, and by having sufficiently large amounts of relevant data to analyse. However, access to data is often constrained by practical considerations and by ethico-legal and data governance restrictions. Gaining access to such data can be time-consuming due to the governance requirements associated with data access requests to institutions in different jurisdictions.Results DataSHIELD is a software solution that enables remote analysis without the need for data transfer (federated analysis). DataSHIELD is a scientifically mature, open-source data access and analysis platform aligned with the 'Five Safes' framework, the international framework governing safe research access to data. It allows real-time analysis while mitigating disclosure risk through an active multi-layer system of disclosure-preventing mechanisms. This combination of real-time remote statistical analysis, disclosure prevention mechanisms, and federation capabilities makes DataSHIELD a solution for addressing many of the technical and regulatory challenges in performing the large-scale statistical analysis of health and biomedical data. This paper describes the key components that comprise the disclosure protection system of DataSHIELD. These broadly fall into three classes: (i) system protection elements, (ii) analysis protection elements, and (iii) governance protection elements.Availability and implementation Information about the DataSHIELD software is available in https://datashield.org/ and https://github.com/datashield.

Abstract
Over the years, sensitive data has been growing in software systems. To comply with ethical and legal requirements, the General Data Protection Regulation (GDPR) recommends using pseudonymization and anonymization techniques to ensure appropriate protection and privacy of personal data. Many anonymization techniques have been described in the literature, such as generalization or suppression, but deciding which methods to use in different contexts is not a straightforward task. Furthermore, anonymization poses two major challenges: choosing adequate techniques for a given context and achieving an optimal level of privacy while maintaining the utility of the data for the context within which it is meant to be used. To address these challenges, this paper describes four new design patterns: Generalization, Hierarchical Generalization, Suppress Outliers, and Relocate Outliers, building on existing literature to offer solutions for common anonymization challenges, including avoiding linkage attacks and managing the privacy-utility trade-off. © 2024 Copyright held by the owner/author(s).

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.

Abstract
Objectives: Healthcare professionals' high risk of infection and burnout in the first months of the COVID19 pandemic probably hindered their much-needed preparedness to respond. We aimed to inform how individual and institutional factors contributed for the preparedness to respond during the first months of a public health emergency. Study design: Cross-sectional study. Methods: We surveyed healthcare workers from a Local Health Unit in Portugal, which comprises primary health care centers and hospital services, including public health units and intensive care units, in the second and third months of the COVID-19 epidemic in Portugal. The 460 answers, completed by 252 participants (about 10% of the healthcare workers), were analyzed using descriptive statistics and multiple logistic regressions. We estimated adjusted odds ratios for the readiness and willingness to respond. Results: Readiness to respond was associated with the perception of adequate infrastructures (aOR = 4.04, P < 0.005), lack of access to personal protective equipment (aOR = 0.26, P < 0.05) and organization (aOR = 0.31, P < 0.05). The willingness to act was associated with the perception of not being able to make a difference (aOR = 0.05, P < 0.005), risk of work-related burnout (aOR = 21.21, P < 0.01) and experiencing colleagues or patients' deaths due to COVID-19 (aOR = 0.24, P < 0.05). Conclusions: Adequate organization, infrastructures, and access to personal protective equipment may be crucial for workers' preparedness in a new public health emergency, as well workers' understanding of their roles and expected impact. These factors, together with the risk of work-related burnout, shall be taken into account in the planning of the response of healthcare institutions in future public health emergencies.