TIMESPAN - TIMESPAN

News

22 July 2024

World Brain Day 2024

TIMESPAN celebrates World Brain Day. #WBD2024 is dedicated to Brain Health and Prevention We aim to improve the clinical management of chronic cardiometabolic diseases, such as obesity o...

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News

24 June 2024

TIMESPAN Podcast: Observations from Finnish ADHD Association, with Nina Hovén (ep 2)

Welcome to the TIMESPAN Podcast! A format that gives a stage to different perspectives of people with ADHD. In our second episode we join Nina Hovén as she shares insights about how ADHD i...

TIMESPAN at #ECNP2024

We're excited to share that TIMESPAN members will be present at prestigious ECNP Congress 2024 in Milan, Italy. Held from 21st to 24th September 2024, the congress promises to be the larges...

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.

About

Emerging evidence points at a significant association and shared genetic traits between adult attention-deficit / hyperactivity disorder (ADHD) and cardiometabolic conditions such as obesity, type 2 diabetes, and cardiovascular disease, which, when inadequately treated, can lead to adverse outcomes and significant costs for 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, serious, and complex chronic condition among adults. This is where TIMESPAN steps in, fostering improvements in risk stratification and of the treatments for patients with ADHD who also have a 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.