Psychedelic-assisted therapy shows promise for treating various mental health conditions; however, its reliance on intensive psychological preparation limits its broader application. Digital health interventions have the potential to address this limitation by providing structured, accessible and scalable preparation solutions. This randomised controlled feasibility trial aims to evaluate the feasibility and preliminary efficacy of the Digital Intervention for Psychedelic Preparation (DIPP), a 21-day mobile-accessible programme designed to prepare individuals for psychedelic experiences.

The study will recruit 40 non-treatment-seeking adults without a clinical diagnosis, randomly assigning them to one of two conditions: (1) DIPP-MEDITATE, which combines daily guided meditation with background music or (2) DIPP-MUSIC, which provides the same background music without guided meditation. Both groups will complete the 21-day digital intervention remotely. Following the intervention, participants will attend an in-person supervised psilocybin session, receiving a standardised 25 mg dose. Primary outcomes focus on feasibility metrics including recruitment efficiency, participant retention and adherence to the intervention protocol. Secondary outcomes assess subjective feasibility, acceptability and preliminary efficacy, specifically evaluating psychedelic preparedness, the quality of the psychedelic experience and changes in wellbeing, with follow-up assessments at 2 weeks, and at 3, 6 and 9 months post-session. Exploratory measures include neuroimaging, physiological, cognitive and psychological assessments, as well as voice note experience sampling through a chatbot (referred to as ‘DIPP-bot’) to monitor inner speech, thought and emotional states during the intervention and follow-up periods.

Approved by UCL Research Ethics Committee (ID: 19113/003), this study follows the Declaration of Helsinki. Results will be published in peer-reviewed journals and presented at conferences. Confidentiality will be maintained throughout.

NCT06815653.

The Novara Cohort Study (NCS) was established to investigate the biological, psychological and social factors that influence ageing in the general population. The study aims to identify early risk factors for frailty, allostatic load and cognitive decline, and to uncover molecular and functional markers of accelerated biological ageing. NCS addresses the need for detailed life-course data from Southern Europe to support personalised prevention and early diagnosis, and to promote healthy longevity.

NCS is a population-based, longitudinal cohort in the Novara province (Northern Italy), originally enrolling adults aged 35 and older. The inclusion criteria were later expanded to encompass all residents aged 18 and over, facilitating the study of ageing trajectories from early adulthood onward. As of mid-2025, about 1000 participants have been enrolled, and recruitment is ongoing. The cohort’s diversity in age, employment status and health conditions enhances its value for life-course analysis.

Following a pilot phase in 2022–2023, the whole study protocol now includes detailed demographic, clinical, behavioural, cognitive and psychosocial data, along with biological samples stored in the UPO Biobank. The protocol incorporates validated tools, comprehensive physical and cognitive assessments, and over 90 laboratory biomarkers covering inflammation, metabolism, hormonal function and coagulation. Additionally, a subset of participants underwent advanced inflammatory profiling by simultaneous measurement of 92 immune-related proteins and comprehensive genomic profiling using Illumina Single Nucleotide Polymorphism (SNP) arrays, capturing common genetic variation across multiple biological domains. Preliminary results demonstrate the feasibility of integrating deep phenotyping, reveal the roles of frailty in ageing and show initial evidence of age-related changes in inflammatory proteins.

NCS plans to enrol at least 10 000 participants and will conduct long-term follow-up using both passive methods, such as linking with clinical records and administrative health databases, and active in-person reassessments. Future phases will integrate clinical, behavioural and cognitive data with large-scale omics analyses, including genomics, proteomics, metabolomics and transcriptomics. Machine learning techniques will be employed to model biological age, identify early signs of age-related decline and develop personalised prevention strategies. By combining high-resolution phenotyping with multidimensional data, NCS aims to find modifiable risk factors and molecular signatures of ageing, supporting national and European research efforts and encouraging collaborative studies through open data-sharing frameworks.