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.
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Data availability
Deidentified participant data underlying the findings of this study, along with the data dictionary, will be made available upon reasonable request to the first author. Access to the raw data is restricted due to ethical and privacy constraints and will require a data access agreement. Access to the raw data may be granted to researchers whose proposed use of the data has been approved by the study team.
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Acknowledgements
A special acknowledgment is addressed to all the participants of the Providemus alz study, who have contributed their time and efforts to make this research a reality. The first author also thanks all the co-authors, and all the collaborators who helped build the tools and reasoning upon the collected data in this project, namely Dr. Alexandre De Masi, Dr. Paweł Prociow, and Dr. Clauirton De Siebra. The authors are grateful to the reviewers for their insightful and constructive feedback, which greatly improved the clarity, depth, and rigor of this manuscript. This research obtained funding from AGE-INT Swissuniversities, the Centre Universitaire d’Informatique of the University of Geneva, Société Académique de Genève, EU SHIELD (101156751), and by the Swiss National Centre of Competence in Research LIVES – Overcoming vulnerability: Life course perspectives, which is financed by the Swiss National Science Foundation (grant number: 51NF40-185901). During the preparation of this work, the author(s) used ChatGPT to help draft Python scripts for data analysis and refine the writing of specific areas of the manuscript. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. All data processing and analysis were conducted locally using non-AI-powered tools. No participant’s data was transmitted by any means to any AI tool.
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I.M. led the study design, participant recruitment and management, data collection, preprocessing, implementation of methods, statistical analysis, results interpretation, and drafted the manuscript. M.H. made substantial contributions to manuscript revision and improvement. The remaining authors (E.J.D., M.K., K.W.) provided critical feedback on the study design and supervised closely how the study was conducted. E.J.D., M.K., and K.W. also provided critical feedback and revisions to the manuscript draft and its development at various stages. All authors (I.M., M.H., E.J.D., M.K., K.W.) reviewed and approved the final version of the manuscript.
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Eric J. Daza is a full-time employee of Boehringer Ingelheim and is the founder and chief editor of Stats-of-1. Katarzyna Wac is an Associate Editor of npj Digital Medicine. She was not involved in any part of the peer-review process, editorial assessment, or decision-making for this manuscript. The other authors declare not having competing interests.
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Matias, I., Haas, M., Daza, E.J. et al. Digital biomarkers for brain health: passive and continuous assessment from wearable sensors. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02340-y
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DOI: https://doi.org/10.1038/s41746-026-02340-y
