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.

Introduction: Lifestyle factors have demonstrated a significant contribution to resilience against cognitive decline and age-related diseases. However, the understanding of how combinations of modifiable lifestyle behaviors relate to cognitive trajectories across the lifespan remains limited. This study aims to explore the relationship between lifestyle trajectories—including cognitive activity, physical exercise, sleep, socialization, nutrition, alcohol consumption, tobacco use, and body mass index (BMI)—and cognitive performance in healthy middle-aged adults.Methods: Data were obtained from the Barcelona Brain Health Initiative (BBHI), an ongoing longitudinal prospective cohort study. Participants completed repeated self-reports on lifestyle factors and underwent in-person neuropsychological assessments. Kml3d clustering was applied to the longitudinal lifestyle data to identify distinct profiles. Cognitive performance was then analyzed across these lifestyle clusters to evaluate associations between lifestyle patterns and cognitive status.Results: The results revealed that adherence to healthy lifestyle patterns was strongly associated with better cognitive performance. Specifically, individuals following profiles characterized by higher engagement in cognitive and physical activities, healthier nutrition, better psychological health, and stronger socialization showed superior cognitive status. Moreover, the findings underscored that adhering to a higher number of healthy behaviors had a cumulative positive impact on cognition. Across the studied period—spanning middle age to older adulthood—cognitive trajectories were generally stable.Discussion: This study highlights that k-means clustering of longitudinal lifestyle data can successfully identify meaningful lifestyle profiles associated with cognitive status in middle-aged adults. The results suggest that specific combinations of modifiable lifestyle factors may exert a more pronounced influence on maintaining cognitive health. These findings provide promising insights for the development and personalization of lifestyle interventions aimed at enhancing brain health and resilience to cognitive decline.

Brain Health is defined as the development and preservation of optimal brain integrity and neural network functioning for a given age. Recent studies have related healthy habits with better maintenance of brain health across the lifespan. As a part of the Barcelona Brain Health Initiative (BBHI), a mHealth platform has been developed with the purpose of helping people to improve and monitor their healthy habits, facilitating the delivery of health coaching strategies. A decision support system (DSS), named Intelligent Coaching Assistant (ICA), has been developed to ease the work of professional brain health coaches, helping them design and monitor adherence to multidomain interventions in a more efficient manner. Personalized recommendations are based on users’ current healthy habits, individual preferences, and motivational aspects. Taking these inputs, an initial user profile is defined, and the ICA applies an algorithm for determining the most suitable personalized intervention plan. An initial validation has been done focusing on assessing the feasibility and usability of the solution, involving 20 participants for three weeks. We conclude that this kind of technology-based intervention is feasible and implementable in real-world settings. Importantly, the personalized intervention proposal generated by the DSS is feasible and its acceptability and usability are high.

Background: As the world population continues to age, the prevalence of neurological diseases, such as dementia, poses a significant challenge to society. Detecting cognitive impairment at an early stage is vital in preserving and enhancing cognitive function. Digital tools, particularly mHealth, offer a practical solution for large-scale population screening and prompt follow-up assessments of cognitive function, thus overcoming economic and time limitations. Objective: In this work, two versions of a digital solution called Guttmann Cognitest^® were tested. Methods: Two hundred and one middle-aged adults used the first version (Group A), while 132 used the second one, which included improved tutorials and practice screens (Group B). This second version was also validated in an older age group (Group C). Results: This digital solution was found to be highly satisfactory in terms of usability and feasibility, with good acceptability among all three groups. Specifically for Group B, the system usability scale score obtained classifies the solution as the best imaginable in terms of usability. Conclusions: Guttmann Cognitest^® has been shown to be effective and well-perceived, with a high potential for sustained engagement in tracking changes in cognitive function.

BACKGROUND Digital phenotyping, the real-time quantification of human phenotype in situ via digital devices, offers new opportunities to understand how behavior change interventions influence brain health. This approach has gained increasing interest in recent years, especially in brain health research. Interoception, chronic stress, and self-regulation are key domains, and benefit 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. The review addressed specific questions concerning the types of devices and sensors used, the psychological domains targeted, the nature of the data collected, feature extraction and intended targets, data processing methods, and the technological platforms utilized. METHODS We conducted a scoping review following the Joanna Briggs Institute (JBI) methodology and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Eligibility criteria included studies using digital phenotyping to assess or predict interoception, chronic stress, or self-regulation in adults aged 21–65 years, based on data collected via smartphones or commercial wearable devices. Studies published since 2018 were considered. A systematic search of PubMed, Web of Science, and Scopus retrieved 850 records; one additional study was identified through Google Scholar. Two reviewers independently screened titles, abstracts, and full texts. Eighteen studies met the inclusion criteria. Results were synthesized narratively. RESULTS Of the 18 studies included, 11 addressed chronic stress or stress reactivity, 5 self-regulation, and 2 interoception. To gather physiological and behavioral data, 13 used wearable devices, 3 smartphones, and 2 both; 8 used smartphones for ecological momentary assessment (EMA). Heart rate variability (HRV) was the most common physiological measure (n=14), followed by electrodermal activity (EDA) and heart rate (HR) (n=4 each). Behavioral data (e.g., smartphone use, sleep, activity) was analyzed in 9 studies. Six studies used machine learning models, but only 3 reported classification accuracy (56.8–79%). Eight applied statistical methods to link features with stress or interoception, while 4 assessed self-regulation using predefined features without identifying new biomarkers. CONCLUSIONS This scoping review highlights that research on digital phenotyping for interoception, chronic stress, and self-regulation is still in its early stages, with most studies focusing on chronic stress and relying primarily on wearable devices. Integration of smartphone sensing and long-term monitoring remains limited, and reported analytical performance is generally modest. Despite these challenges, the widespread use of smartphones and wearables positions digital phenotyping as a promising and scalable approach for assessing brain health in daily life. Future research should emphasize longer-term, multimodal data collection, innovative analytical methods, and transparent reporting to advance the field. CLINICALTRIAL

Prehabilitation (PRH) is a preoperative process aimed at optimizing patients' functional capacity to improve surgical outcomes and overall well-being. While its physical and cognitive benefits are increasingly documented, its emotional impact, particularly in neuro-oncology patients, remains less explored. This study assessed the psychological effects of a PRH program on 29 brain tumor patients. The primary outcome, emotional well-being, was measured using quality of life and emotional distress metrices. Secondary outcomes included perceived stress levels and control attitudes. Additionally, qualitative data from structured interviews provided further insights into the psychological effects of the intervention. The results indicated significant improvements in quality of life and reductions in emotional distress, particularly among women. While perceived stress levels remained stable, control attitudes showed an increase. Qualitative analysis further highlighted the positive changes in the control sense and identified additional factors, such as the importance of social support sources during the PRH process. Overall, these findings suggest that PRH interventions play a significant role in enhancing emotional well-being among neuro-oncological patients in the preoperative phase. These results underscore the importance of implementing comprehensive and personalized PRH approaches to optimize clinical status both before and after surgery, thereby promoting sustained psychological benefits in this population. This study is based on data collected at Institut Guttmann in Barcelona in the context of the Prehabilita project (ClinicalTrials.gov identifier: NCT05844605 ; registration date: 06/05/2023).