TIMESPAN - TIMESPAN

News

18 May 2021

Kick-off TIMESPAN project

The EU-funded research project TIMESPAN kicked off on May 18th – 19th 2021. The meeting was held remotely and although being held virtually it was full of vivid discussions as well as soc...

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Events

23 June 2021

ART-CARMA study

Focus group is currently established. The KCL team around Jonna Kuntsi is currently establishing a focus group who should be involved in the discussions on how to incorporate remote technol...

About

Emerging evidence points at a significant association and shared genetic traits between adultattention-deficit/hyperactivity disorder (ADHD) and cardiometabolic conditions such as Obesity, Type2 Diabetes, and cardiovascular disease, which, when inadequately treated, can lead to adverse outcomes and significant costs to society. Various national guidelines on cardiometabolic disease already highlight the importance of concurrent psychiatric disorders, but there is a lack of knowledge around ADHD. This is problematic given that ADHD is a common and serious complex chronic condition among adults.
This is where TIMESPAN steps in, fostering improvements in risk stratification as well as treatments already available for patients with ADHD who also have cardiometabolic disease.

Objectives

Determine if and how ADHD in adults worsens prognosis and hampers the management of cardiometabolic disease, leveraging the largest data sets and population registries available world-wide.
Identify the cardiometabolic risks and benefits of multidisciplinary treatment approaches in patients with ADHD, performing advanced pharmacological and epidemiological analyses on available data as well as acquiring new and unique real-world data using active and passive apps for smartphones and a groundbreaking new advanced smartwatch for continuous health monitoring.
Pinpoint reasons for ADHD treatment discontinuity in adult patients with and without cardiometabolic disease. Capitalizing on so far unused real-world clinician’s data through new algorithms, created utilizing Machine Learning (ML) and natural language processing techniques in conjunction with using state-of-the-art genomic approaches.
Discern patients with ADHD at high-risk for poor cardiometabolic outcomes and treatment discontinuity by applying novel AI driven methods like deep learning neural networks (DLNNs) on existing large-scale cohort studies and linked electronic health record databases in multiple countries with different health care systems.
Identify optimized and personalized treatment approaches across multiple disciplines, to minimize harm and maximize positive changes in disease prognosis and to improve treatment discontinuity.
Improve clinical outcomes, as well as quality of life in adult ADHD patients with co-occurring cardiometabolic disease.