Publications

Introduction: Digital phenotyping, the real-time quantification of human phenotype in situ via digital devices, offers opportunities to understand how behavior change interventions influence brain and mental health. Interoception, chronic stress, and self-regulation are key domains, benefiting from real-world, continuous assessment beyond what traditional methods can provide. Objective: The aim of this scoping review was to map and synthesize the literature of the last five years on the use of digital phenotyping to measure or predict interoception, chronic stress, and self-regulation in adults. We focused on the types of devices and sensors employed, the psychological domains targeted, the nature of the data collected, feature extraction, data processing methods, and technological platforms utilized. Methods: Following Joanna Briggs Institute methodology and PRISMA-ScR guidelines, we systematically searched PubMed, Web of Science, and Scopus, complemented with Google Scholar. Eligibility criteria included studies published since 2018, using smartphones or commercial wearables to assess or predict interoception, chronic stress, or self-regulation in adults. Results: From 850 retrieved records, 18 studies met inclusion criteria. Of these, 11 addressed chronic stress or stress reactivity, five self-regulation, and two interoception. Thirteen studies used wearable devices, three used smartphones, and two combined both approaches. Ecological momentary assessment (EMA) via smartphones was applied in eight studies. Heart rate variability (HRV) was the most common physiological measure (n = 14), followed by electrodermal activity and heart rate (n = 4 each). Nine studies analyzed behavioral data, including smartphone use, sleep, and activity. Six studies applied machine learning models, though only three reported classification accuracy (56.8%–79%). Eight used statistical methods to link features with stress or interoception, while four examined self-regulation using predefined features without identifying new biomarkers. Discussion: This review highlights that the field is still in its early stages, with most work focused on chronic stress and predominantly reliant on wearable devices. Integration of smartphone sensing and long-term monitoring remains limited, and analytical performance is modest. Nevertheless, the ubiquity of smartphones and wearables positions digital phenotyping as a promising, scalable approach for assessing brain and mental health in daily life. Future research should emphasize multimodal, longer-term data collection, innovative analytic methods, and transparent reporting.

Psychological characteristics are associated with varying dementia risk and protective factors. To determine whether these characteristics aggregate into psychological profiles and whether these profiles differentially relate to aging health, we conducted a cross-sectional investigation in two independent middle-aged (51.4 ± 7.0 years (mean ± s.d.); N = 750) and older adult (71.1 ± 5.9 years; N = 282) cohorts, supplemented by longitudinal analyses in the former. Using a person-centered approach, three profiles emerged in both cohorts: those with low protective characteristics (profile 1), high risk characteristics (profile 2) and well-balanced characteristics (profile 3). Profile 1 showed the worst objective cognition in older age and middle age (at follow-up), and most rapid cortical thinning. Profile 2 exhibited the worst mental health symptomology and lowest sleep quality in both older age and middle age. We identified profile-dependent divergent patterns of associations that may suggest two distinct paths for mental, cognitive and brain health, emphasizing the need for comprehensive psychological assessments in dementia prevention research to identify groups for more personalized behavior-change strategies.

La adherencia a estilos de vida saludables es crucial para la promoción de la salud cerebral, pero su heterogeneidad entre individuos limita la efectividad de las intervenciones generalizadas. Este estudio ha identificado y caracterizado perfiles de adherencia multidominio en 1.545 participantes de la cohorte Barcelona Brain Health Initiative mediante análisis de clustering mediante K-Medoids y distancia de Gower. El análisis reveló cuatro arquetipos conductuales estables (índice Jaccard = 0.926): adherencia general baja, adherencia selectiva con foco en dieta, adherencia selectiva con foco en actividades estructuradas, y adherencia holística con énfasis social. Estos perfiles mostraron asociaciones significativas con variables sociodemográficas y psicológicas: el perfil de baja adherencia se asoció con menor edad, nivel educativo, inestabilidad familiar y un perfil psicológico de vulnerabilidad (alto neuroticismo, baja autoeficacia y propósito vital), mientras que los perfiles de alta adherencia se relacionaron con mayores recursos psicológicos protectores. Los resultados demuestran que la adherencia sigue patrones conductuales ligados a factores contextuales y psicológicos, proporcionando una base empírica para el diseño de intervenciones personalizadas que aborden las barreras específicas de cada perfil de usuario y optimicen así la efectividad de las estrategias de promoción de la salud cerebral.

EChronic pain is a complex, multidimensional condition that significantly impacts patients' quality of life and requires long-term management. To address existing gaps in digital health tools for this population, we developed a digital platform that integrates a web interface for professionals and a mobile application for patients. The platform was developed with open-source technologies (Angular, TypeScript, Node.js, PostgreSQL, ECharts) and designed using a User-Centered Design methodology, ensuring the active involvement of patients and clinicians to enhance usability and clinical relevance. It provides a multidimensional assessment of patients' conditions across key clinical domains, using validated questionnaires, clinical recommendations, and interactive visualizations. The platform was technically validated to ensure correct functionality and responsive behaviour. In addition, two rounds of usability testing were conducted, including a pilot test with real patients (n=10) that yielded an average System Usability Scale (SUS) score of 67.75. This result confirms the platform's feasibility and its potential to serve as a robust, usable tool to support patient self-management and clinical practice.

Introduction: Understanding the impact of different lifestyle trajectories on health preservation and disease risk is crucial for effective interventions. Methods: This study analyzed lifestyle engagement over five years in 3,013 healthy adults aged 40-70 from the Barcelona Brain Health Initiative using K-means clustering. Nine modifiable risk factors were considered, including cognitive, physical, and social activity, vital plan, diet, obesity, smoking, alcohol consumption, and sleep. Self-reported diagnoses of new diseases at different time-points after baseline allowed to explore the association between these five profiles and health outcomes. Results: The data-driven analysis classified subjects into five lifestyle profiles, revealing associations with health behaviors and risk factors. Those exhibiting high scores in health-promoting behaviors and low-risk behaviors, demonstrate a reduced likelihood of developing diseases (p < 0.001). In contrast, profiles with risky habits showed distinct risks for psychiatric, neurological, and cardiovascular diseases. Participant’s lifestyle trajectories remained relatively stable over time. Discussion: Our findings have identified risk for distinct diseases associated to specific lifestyle patterns. These results could help in the personalization of interventions based on data-driven observation of behavioral patterns and policies that promote a healthy lifestyle and can lead to better health outcomes for people in an aging society.

El cálculo preciso del consumo máximo de oxígeno (VO2max) es esencial para evaluar la aptitud cardiorrespiratoria y la salud general de un individuo. Tradicionalmente, la medición del VO2max requiere que los pacientes se sometan a pruebas de ejercicio extenuantes en entornos clínicos, lo que limita significativamente la accesibilidad y la conveniencia. En este trabajo de investigación se presenta un modelo de estimación del valor del VO2max basado en inteligencia artificial, que permite evaluar a las personas de forma remota mediante cuestionarios autoadministrados o midiendo variables antropométricas, evitando así la necesidad de someterse a pruebas físicas. Para realizar esta investigación, se han utilizado datos recogidos por la Barcelona Brain Health Initiative (BBHI) que alimentan varios algoritmos de inteligencia artificial con el fin de comparar y seleccionar el que ofrece mejor rendimiento. BBHI es un estudio longitudinal prospectivo en curso patrocinado por el Institut Guttmann centrado en identificar los determinantes de la salud cerebral. Este trabajo demuestra que es posible definir e implementar algoritmos basados en inteligencia artificial para la estimación del VO2max, logrando resultados prometedores y superando el estado del arte.

En los últimos años, la comunidad científica ha investigado sobre la influencia de los hábitos de vida, conductas y características de cada persona en el bienestar, mantenimiento de la salud y en la posibilidad de desarrollar enfermedades neurológicas y psiquiátricas. Los estudios realizados han demostrado que factores modificables relacionados con el estilo de vida influyen de forma determinante, siendo la calidad y los hábitos de sueño uno de esos factores. El sueño impacta en la salud, bienestar y productividad de las personas. Así, la cantidad de tiempo que dormimos es un factor predictivo de la función cognitiva influyendo incluso en la estructura del cerebro. Con el fin de extraer información sobre qué variables afectan más a la calidad de sueño, y por extensión a la salud y el bienestar de las personas, este trabajo de investigación describe la aplicación de técnicas de machine learning e Inteligencia Artificial Explicable (XAI) sobre el conjunto de datos de la Barcelona Brain Health Initiative, un estudio longitudinal y prospectivo de cohortes de base poblacional en el que participan más de 6000 voluntarios entre 40 y 70 años. Como resultado, la relevancia percibida de la rutina diaria, la percepción de llevar una vida plena y la preocupación por las opiniones ajenas, entre otros, podrían ser factores relacionados con la calidad de sueño.