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КартаВръзкиТемиБългарияНощта (K-TRIO)Мария Склодовска-КюриЗа ученициЗа учителиМетодологияИзненадай ме
HEДокторантска мрежа2023–2027

LABDA · Learning network for Advanced Behavioural Data Analysis

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период
2023-02-01 → 2027-01-31
Финансиране от ЕС
2 881 037 €
Участници
20
Схема
HORIZON-TMA-MSCA-DN

Линиите свързват координатора с партньорите.

Накратко на български

Връзката между здравето и цялодневното движение, като съчетанието от физическа активност, сън и време за седене, се анализира чрез данни от сензори. Това помага за създаването на по-точни здравни препоръки и по-добри обратни връзки в носимите устройства.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Learning network for Advanced Behavioural Data Analysis

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 data. 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. 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. Objective LABDA aims 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 sound analysis methods and apply these in various contexts. Via training-through-research, 12 doctoral fellows collaboratively work towards (i) sound and accessible methods for advanced 24/7 movement behaviour data analysis, (ii) linking multimodal data, and (iii) a taxonomy to enable interoperability and data harmonisation. Impact Results are combined in an open source LABDA toolbox supporting the accessibility of advanced analysis methods, including a decision tree to guide users to the optimal method for their (research) question and data. LABDA will gain evidence informing 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).

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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).

Оригинален текст от CORDIS (на английски).

Участници

Връзки

Данни: CORDIS, © Европейски съюз