Abstract: While there has been much discussion around the use of Artificial Intelligence (AI) for multilingual translations in other areas, recommendations pertaining specifically to the use of AI in the context of Clinical Outcome Assessment (COA) translation, linguistic validation, and electronic migration within clinical trials are lacking. Without published recommendations or guidelines, stakeholders involved in the COA translation process may be hesitant to explore or include AI. To address this gap, the AI Working Group of the ISOQOL TCA-SIG conducted a study to assess the landscape of AI in this specific context aimed at proposing recommendations for potential implementation of AI in COA translation, linguistic validation and electronic migration processes. The study consisted of three parts: (1) a literature review targeting studies using AI in COA translation; (2) a survey among relevant stakeholders assessing perceptions of AI use in COA translation; and (3) interviews with AI subject matter experts (SMEs). Survey responses were received from a total of 50 individuals from a wide variety of stakeholder groups, including COA copyright holders, representatives from pharmaceutical company COA/HEOR teams, respondents holding roles associated with the COA translation, eCOA, and AI industries, and authors of the 2005 ISPOR task force article on linguistic validation methodology. Survey data provided detailed feedback regarding the appropriateness of using AI during all reviewed process steps. Results of the literature review and AI expert interviews provided additional depth and nuance, allowing for the generation of detailed recommendations covering the use of AI within linguistic validation and eCOA migration processes. When assessing the potential use of AI tools within the linguistic validation process, it is important to consider not only the capabilities of the technology, but also the degree to which use of AI may or may not align with the spirit and intent of existing linguistic validation guidelines. The recommendations included in this manuscript are designed to balance considerations of technological capability and improved efficiency with concerns related to intellectual property protection, data privacy/security, and the goal of keeping patients at the center of outcomes research.

Abstract: Continuous and scalable monitoring of cognition and affective states is critical for the early detection of brain health, which is currently limited by the burden of active assessments. This study investigated the potential of consumer-grade wearable and mobile technologies to passively predict 21 cognitive and mental health outcomes in real-world conditions. We collected data from 82 cognitively healthy adults, including passively measured behaviour, physiology, and environmental exposures longitudinally, for 10 months. Active data were gathered in four waves using validated patient- and performance-reported outcomes. Data quality assurance involved a data filtering resulting in average wearable data coverage of 96% per day. Artificial Intelligence-powered prediction was applied, and performance was assessed using subject- and wave-dependent cross-validation. Cognitive and affective outcomes were predicted with low scaled errors. Patient-reported outcomes were more predictable than performance-based ones. Environmental and physiological metrics emerged as the most informative predictors. Passive multimodal data captured meaningful variability in cognition and affect, demonstrating the feasibility of low-burden, scalable approaches to continuous brain-health monitoring. Feature-importance analyses suggested that environmental exposures better explained inter-individual differences, whereas physiological and behavioural rhythms captured within-person changes. These findings highlight the potential of everyday technologies for population-level tracking of brain-health and deviations from expected trajectories.

Abstract: Ego depletion refers to the idea that self-control and decision-making abilities become depleted or diminished after engaging in prolonged or demanding cognitive tasks. When conducting research with humans, states such as stress are usually asked directly to the participants by using ecological momentary assessment (EMA). However, as those answers are self-reported, they are prone to bias due to the lack of user commitment or effort to assess the stress levels the best they can. This paper investigates the relationship between ego depletion and stress level reporting, specifically focusing on the bias in self-reported stress levels. The hypothesis tested in this study suggests that higher levels of ego depletion lead to greater bias in self-reported stress levels. Data collected by Berrocal et al. using EMA were analyzed to examine this hypothesis. The dataset included self-reporting stress levels by individuals being assessed and stress level reports from their peers. Data was collected using a mobile app, incorporating passive smartphone usage data alongside EMA responses. The hypothesis was tested and studied employing artificial intelligence algorithms. The results partially confirmed the initial hypothesis, revealing that lower ego depletion was associated with reduced bias in stress level reporting. Notably, when analyzing data from working days, the morning period demonstrated the least bias compared to the afternoon. These findings suggest that individuals have higher self-capacity and willingness to provide accurate stress level assessments during the morning hours. Challenges encountered in the research included limitations related to the holidays considered and potential confounders such as flexible time schedules or post-lunch sleepiness. This paper releases its data publicly, allowing for further examination and replication of the findings. Future research is encouraged to expand upon these conclusions.

Abstract: Ego depletion refers to the idea that self-control and decision-making abilities become depleted or diminished after engaging in prolonged or demanding cognitive tasks. When conducting research with humans, states such as stress are usually asked directly to the participants by using ecological momentary assessment (EMA). However, as those answers are self-reported, they are prone to bias due to the lack of user commitment or effort to assess the stress levels the best they can. This paper investigates the relationship between ego depletion and stress level reporting, specifically focusing on the bias in self-reported stress levels. The hypothesis tested in this study suggests that higher levels of ego depletion lead to greater bias in self-reported stress levels. Data collected by Berrocal et al. using EMA were analyzed to examine this hypothesis. The dataset included self-reporting stress levels by individuals being assessed and stress level reports from their peers. Data was collected using a mobile app, incorporating passive smartphone usage data alongside EMA responses. The hypothesis was tested and studied employing artificial intelligence algorithms. The results partially confirmed the initial hypothesis, revealing that lower ego depletion was associated with reduced bias in stress level reporting. Notably, when analyzing data from working days, the morning period demonstrated the least bias compared to the afternoon. These findings suggest that individuals have higher self-capacity and willingness to provide accurate stress level assessments during the morning hours. Challenges encountered in the research included limitations related to the holidays considered and potential confounders such as flexible time schedules or post-lunch sleepiness. This paper releases its data publicly, allowing for further examination and replication of the findings. Future research is encouraged to expand upon these conclusions.