Sylvain Moreno

Background: Wearable technologies are rapidly transforming the health care landscape by enabling the tracking of real-time physiological and behavioral data used in healthy lifestyle interventions. Despite their widespread adoption in the consumer market, scientific validation of their impact on the brain remains limited, and factors influencing effects on cognition and adherence are not fully understood. This gap is critical, as consumers, health care providers, and researchers depend on these data to unlock the potential of wearables for cognitive support. Objective: We aim to assess the effectiveness, engagement, and acceptability of a commercially available smartwatch healthy lifestyle intervention on cognitive enhancement in healthy adults, using a convergent parallel mixed methods design. We seek to provide a comprehensive, replicable methodological framework to evaluate wearable technology interventions targeting cognitive enhancement in healthy adults. Methods: Healthy adults aged 18 to 40 years with a sedentary lifestyle (N=72) interested in participating in a healthy lifestyle intervention will be recruited in the Vancouver Metropolitan Area, BC, Canada. Participants will be randomly assigned into an intervention group using an Apple Watch or a control group following standard physical activity guidelines without an Apple Watch for a period of 4 weeks. Effectiveness will be evaluated through (1) cognitive function (primary outcome)—executive function, specifically inhibitory control, via electroencephalogram-based event-related potentials (N2/P3) during a go/no-go task; (2) resting-state brain activity via electroencephalogram spectral power; (3) behavioral performance (reaction time and accuracy); (4) perceived health (36-item Short Form Health Survey); and (5) qualitative analysis of factors affecting the intervention’s effectiveness. Engagement will be assessed through goal completion metrics and qualitative analysis of barriers, facilitators, and usage patterns. Acceptability will be assessed through System Usability Scale scores and qualitative analysis of factors affecting usability, satisfaction, and user preferences. This design integrates assessments before, during, and after intervention. Qualitative assessments will include weekly check-ins and semistructured interviews to obtain a comprehensive understanding of factors influencing the effectiveness, engagement, and acceptability of the intervention. Exploratory outcomes include descriptions of possible interactions between the 3 constructs and comparison of smartwatch-tracked vs self-reported goal completion. Results: The study received funding from the AGE-WELL Network Centres of Excellence (April 2020-March 2023) and was approved by the Simon Fraser University Research Ethics Board. Enrollment for the study is ongoing. As of October 2026, data have been collected from 48 participants and are currently being processed and analyzed. The first results are expected to be published in 2027. Conclusions: This study outlines a foundational framework for evaluating the effectiveness, engagement, and acceptability of wearable health interventions targeting cognitive enhancement in healthy adults. Furthermore, our mixed methods approach with randomized controlled comparison is expected to inform the design and development of effective, engaging, and friendly wearable brain health technologies.

Cognitive aging is a complex process influenced by diverse life experiences and environmental factors. However, some traditional studies have oversimplified this process by assuming that cognitive aging trajectories follow a uniform process and that all individuals will experience similar declines. This framework minimizes the impact of external factors, neglecting the diversity observed in the aging population. In fact, research has shown significant inter- and intraindividual variability in cognitive trajectories, with some individuals maintaining stable or even improving cognitive function, while others experience rapid decline. To address this gap, emerging research proposes promising alternatives to the homogenous modelling approaches used, focusing on the identification of latent classes of cognitive trajectories. In this work, we build on this by examining the complex interaction of heterogeneous cognitive trajectories with external factors during the aging process, using episodic memory as a measure of cognitive function. We use longitudinal data from 1746 individuals aged 60 and older, assessed at three times over eight years. Our findings revealed three distinct cognitive trajectories – low cognitive performance with early decline , unmodulated cognitive change and high cognitive performance with late decline – each uniquely influenced by specific lifestyle factors. These findings challenge the current theoretical model of cognitive aging by identifying that factors such as concentration activities and social engagement significantly influence the trajectories of low cognitive performance with early decline and high cognitive performance with late decline, whereas the trajectory of unmodulated cognitive change is largely unaffected by environmental influences. Overall, our results highlight the critical role of individual environmental susceptibility in shaping cognitive trajectories. This research provides key insights into the heterogeneity of cognitive aging and underscores the need for a research paradigm shift in understanding cognitive trajectories’ heterogeneity. While further research is required to determine how these findings translate into practice, tailoring interventions to these newly identified cognitive trajectories, we can significantly improve individual and public health outcomes through more precise and effective social prescribing interventions.

BACKGROUND Wearable technologies, such as the Apple Watch, are rapidly transforming the healthcare landscape by enabling the tracking of real-time physiological and behavioral data used in healthy lifestyle interventions. Despite their widespread adoption in the consumer market, scientific validation of their impact on the brain remains limited, and factors influencing adherence and effects on cognition are not fully understood. This gap is critical as consumers, healthcare providers, and researchers depend on this data to unlock the potential of wearables for cognitive support. OBJECTIVE In this study, we aim to assess the cognitive and behavioral effect of a commercially available wearable technology, the Apple Watch, on the brain, while exploring user experience (UX) factors influencing adherence, engagement, and acceptability of wearable healthy lifestyle interventions. Our goal is to provide comprehensive guidelines to evaluate the effect of wearable technologies on the brain, which could potentially shape the future of brain health interventions. METHODS Healthy adults aged 18-40 with a sedentary lifestyle (n=72) will be recruited in the Vancouver Metropolitan Area, BC, Canada. Participants will be randomized into an intervention group following a healthy lifestyle intervention using an Apple Watch or a control group following standard physical activity guidelines without an Apple Watch over a period of four weeks. We will employ a convergent parallel mixed methods design to investigate three constructs of a wearable healthy lifestyle intervention: effectiveness, engagement, and acceptability. This design integrates pre-, during, and post-intervention assessments combining quantitative and qualitative approaches. Effectiveness will be evaluated with quantitative assessments including perceived health (SF-36), behavioral performance (Go/No-Go task) and cognitive activity (EEG-based ERPs and resting-state activity). Engagement and acceptability will be assessed using questionnaires (goals completion and System Usability Scale, respectively). Qualitative assessments will include weekly check-ins and semi-structured interviews to capture UX factors influencing the acceptability of the intervention. Secondary outcomes include descriptions of possible interactions between the three constructs. RESULTS The study received funding by the AGE-WELL Network Centres of Excellence (April 2020–March 2023) and the Pacific Economic Development Agency of Canada (April 2022–March 2025) and was approved by Simon Fraser University’s Research Ethics Board. Enrollment for the study is ongoing. The data collected from participants who have successfully completed the intervention is currently being processed and analyzed. The first results are expected to be published by the end of 2025. CONCLUSIONS This study outlines a foundational framework for evaluating the effect of wearable health interventions on the brain. Further, we hope our mixed methods approach will inform the design and development of effective, engaging, and friendly wearable brain health technologies.

The brain age gap (BAG), the divergence of an individual’s neurobiologically predicted brain age from their chronological age, is a key indicator of brain health. While BAG can be derived from diverse brain metrics, its interpretation often polarizes between early-life trait influences and current state-dependent factors like cognitive decline. Here, we propose an integrative framework that moves beyond single summary statistics by considering the full distribution of brain metrics across regions or time. We distinguish between a neural system’s “baseline” (typical values, e.g., mean) and its “capacity” (extreme values, e.g., maximum) within these distributions. To test this, we analyzed resting-state magnetoencephalography (MEG) from the Cam-CAN adult cohort, focusing on cross-frequency coupling (CFC) within functional MRI-defined networks. We derived network-specific CFC baseline (mean) and capacity (maximum) measures. Separate brain age prediction models were trained for each measure. The resulting BAGs (baseline-BAG and capacity-BAG) for each network were then correlated with cognitive performance on a picture priming task. Both baseline-BAG and capacity-BAG profiles showed associations with cognitive scores, with younger predicted brain age correlating with better performance. However, capacity-BAG exhibited more conclusive relationships, suggesting that metrics reflecting a neural system’s peak operational ability (capacity) may better capture an individual’s current cognitive state. These findings indicate that brain age models emphasizing neural capacity, rather than just baseline activity, could offer a more sensitive lens for understanding the state-dependent aspects of brain ageing.

Introduction: Machine performance has surpassed human capabilities in various tasks, yet the opacity of complex models limits their adoption in critical fields such as healthcare. Explainable AI (XAI) has emerged to address this by enhancing transparency and trust in AI decision-making. However, a persistent gap exists between interpretability and performance, as black-box models, such as deep neural networks, often outperform white-box models, such as regression-based approaches. To bridge this gap, the Explainable Boosting Machine (EBM), a class of generalized additive models has been introduced, combining the strengths of interpretable and high-performing models. EBM may be particularly well-suited for cognitive health research, where traditional models struggle to capture nonlinear effects in cognitive aging and account for inter- and intra-individual variability.Methods: This cross-sectional study applies EBM to investigate the relationship between demographic, environmental, and lifestyle factors, and cognitive performance in a sample of 3,482 healthy older adults. The EBM’s performance is compared against Logistic Regression, Support Vector Machines, Random Forests, Multilayer Perceptron, and Extreme Gradient Boosting, evaluating predictive accuracy and interpretability.Results: The findings reveal that EBM provides valuable insights into cognitive aging, surpassing traditional models while maintaining competitive accuracy with more complex machine learning approaches. Notably, EBM highlights variations in how lifestyle activities impact cognitive performance, particularly differences between engaging in and refraining from specific activities, challenging regression-based assumptions. Moreover, our results show that the effects of lifestyle factors are heterogeneous across cognitive groups, with some individuals demonstrating significant cognitive changes while others remain resilient to these influences.Discussion: These findings highlight EBM’s potential in cognitive aging research, offering both interpretability and accuracy to inform personalized strategies for mitigating cognitive decline. By bridging the gap between explainability and performance, this study advances the use of XAI in healthcare and cognitive aging research.

Background: Digital health interventions (DHIs) are changing the dynamic of health care by providing personalized, private, and instantaneous solutions to end users. However, the explosion of digital health has been fraught with challenges. The approach to co-design with end users varies across a diverse domain of stakeholders, often resulting in siloed approaches with no clear consensus. The concept of validating user experiences contrasts greatly between digital stakeholders (ie, user experience and retention) and health stakeholders (ie, safety and efficacy). Several methodologies and frameworks are being implemented to address this challenge to varying degrees of success. Objective: We aimed to broadly examine the advancements and challenges to co-design DHIs with end users over the last decade. This task was undertaken to identify the key problem areas at the domain level, with the ultimate goal of creating recommendations for better approaches to co-design DHIs with end users. Methods: We conducted a systematic search of key databases for co-design studies involving end users in DHIs. Searches were divided into 3 relevant streams: health behavior, user experience, and digital methodologies and frameworks. The eligibility criteria were guided by the PerSPEcTiF framework and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. In line with this framework, studies were included in this review that (1) address research on DHIs; (2) focus on interaction and co-design with end users; (3) explain results such that uptake, effectiveness, satisfaction, and health outcomes are discernible, positively or negatively; and (4) describe actionable procedures for better DHI design. The search was conducted in a diverse group of 6 bibliographical databases from January 2015 to May 2024: PsycINFO, PubMed (MEDLINE), Web of Science, CINAHL, Institute of Electrical and Electronics Engineers Xplore, and Scopus. From the 13,961 studies initially screened for titles and abstracts, 489 (3.6%) were eligible for a full-text screening, of which 171 (1.2%) studies matched the inclusion criteria and were included in a qualitative synthesis. Results: Of the 171 studies analyzed across 52 journals, we found 5 different research approaches, spanning 8 different digital health solution types and 5 different design methodologies. These studies identified several core themes when co-designing with end users: advancements, which included participatory co-design; challenges, which included participatory co-design, environment and context, testing, and cost and scale; and gaps, which included a pragmatic hybridized framework and industry implementability. Conclusions: This research supports a pragmatic shift toward using mixed methods approaches at scale, methods that are primed to take advantage of the emerging big data era of digital health co-design. This organic outlook should blend the vision of digital health co-designers with the pragmatism of Agile design methodology and the rigor of health care metrics. Trial Registration: PROSPERO CRD42021238164; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021238164 International Registered Report Identifier (IRRID): RR2-10.2196/28083

Background/Objectives: Children with Autism Spectrum Disorder (ASD) face challenges in social communication due to difficulties in considering context, processing information, and interpreting social cues. This study aims to explore the neural processes related to pragmatic language communication in children with ASD and address the research question of how functional brain connectivity operates during complex pragmatic language tasks. Methods: We examined differences in brain functional connectivity between children with ASD and typically developing peers while they engaged in video recordings of spoken language tasks. We focused on two types of speech acts: semantic and pragmatic. Results: Our results showed differences between groups during the pragmatic and semantic language processing, indicating more idiosyncratic connectivity in children with ASD in the Left Somatomotor and Left Limbic networks, suggesting that these networks play a role in task-dependent functional connectivity. Additionally, these functional differences were mainly localized to the left hemisphere.

Cognitive aging is a complex and dynamic process characterized by changes due to genetics and environmental factors, including lifestyle choices and environmental exposure, which contribute to the heterogeneity observed in cognitive outcomes. This heterogeneity is particularly pronounced among older adults, with some individuals maintaining stable cognitive function while others experience complex, non-linear changes, making it difficult to identify meaningful decline accurately. Current research methods range from population-level modeling to individual-specific assessments. In this work, we review these methodologies and propose that population subtyping should be considered as a viable alternative. This approach relies on early individual-specific detection methods that can lead to an improved understanding of changes in individual cognitive trajectories. The improved understanding of cognitive trajectories through population subtyping can lead to the identification of meaningful changes and the determination of timely, effective interventions. This approach can aid in informing policy decisions and in developing targeted interventions that promote cognitive health, ultimately contributing to a more personalized understanding of the aging process within society and reducing the burden on healthcare systems.

Autism Spectrum Disorder (ASD) is characterized by both atypical functional brain connectivity and cognitive challenges across multiple cognitive domains. The relationship between task-dependent brain connectivity and cognitive abilities, however, remains poorly understood. In this study, children with ASD and their typically developing (TD) peers engaged in semantic and pragmatic language tasks while their task-dependent brain connectivity was mapped and compared. A multivariate statistical approach revealed associations between connectivity and psychometric assessments of relevant cognitive abilities. While both groups exhibited brain–behavior correlations, the nature of these associations diverged, particularly in the directionality of overall correlations across various psychometric categories. Specifically, greater disparities in functional connectivity between the groups were linked to larger differences in Autism Questionnaire, BRIEF, MSCS, and SRS-2 scores but smaller differences in WASI, pragmatic language, and Theory of Mind scores. Our findings suggest that children with ASD utilize distinct neural communication patterns for language processing. Although networks recruited by children with ASD may appear less efficient than those typically engaged, they could serve as compensatory mechanisms for potential disruptions in conventional brain networks.