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LABDA

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Cerrado

Learning network for Advanced Behavioural Data Analysis

BACKGROUND Recently, there has been a paradigm shift from the isolated focus on the health impact of single behaviours (physical activity, sedentary behaviour, sleep) to the combined health effects of 24/7 movement behaviours. Tec... ver más

31/01/2027

STICHTING AMSTERDA...

Presupuesto desconocido

Líder del proyecto

STICHTING AMSTERDAM UMC No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-02-01

HORIZON-MSCA-2021-DN-01-01: MSCA Doctoral Networks 2021 ExpectedOutcome:

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Cerrada hace 3 años

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STICHTING AMSTERDAM UMC

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Duración del proyecto: 47 meses Fecha Inicio: 2023-02-01
Fecha Fin: 2027-01-31

Líder del proyecto

STICHTING AMSTERDAM UMC No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto BACKGROUND Recently, there has been a paradigm shift from the isolated focus on the health impact of single behaviours (physical activity, sedentary behaviour, sleep) to the combined health effects of 24/7 movement behaviours. Technological advancements have led to wearable sensors providing rich time-series. Such large-scale data require novel analysis methods to provide detailed insight into the links between multidimensional 24/7 movement behaviour and health, potential relevant subgroups, and relevant behavioural characteristics to target in interventions. CONSORTIUM In LABDA, leading researchers in advanced movement behaviour data analysis at the intersection of data science, method development, epidemiology, public health, and wearable technology are brought together to address this challenge. AIM: To train a new generation of creative and innovative public health researchers with strong analytical and data science skills, and a deep understanding of all aspects of wearable sensor data analysis, that are able to develop innovative analysis methods and apply these in various contexts. WORK PLAN Via training-through-research, 13 doctoral fellows establish novel methods for advanced 24/7 movement behaviour data analysis and assess the added value of linking multimodal data. They develop a joint taxonomy to enable interoperability and data harmonisation. Results are combined in an open source LABDA toolbox of advanced analysis methods, including a decision tree to guide researchers and other users to the optimal method for their (research) question. IMPACT The open source toolbox of advanced analysis methods will lead to optimised, tailored public health recommendations and improved personal wearable feedback concerning 24/7 movement behaviour. After the project, LABDA fellows will be in an excellent position to pursue careers in academia (epidemiology, data science), commercial business (wearable technology, consultancy), or government (public health policy).

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