Project description
Optimising wearable sensor data analysis for physical activity and inactivity
There is more of a focus on how a combination of physical activity, sedentary behaviour and sleep within a 24-hour period may have important implications for health. Wearable sensors provide rich data on these so-called 24/7 movement behaviours. However, new analysis techniques are needed to provide detailed insight into their links. With the support of the Marie Skłodowska-Curie Actions programme, the LABDA project will train 13 public health researchers to ultimately develop an open-source toolbox consisting of innovative analysis methods. The doctoral fellows’ research outcomes will result in improved, personalised public health recommendations and better personal wearable feedback involving 24/7 movement behaviour.
Objective
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).
Fields of science
Not validated
Not validated
- natural sciencescomputer and information sciencesdata science
- natural sciencescomputer and information sciencesinternetinternet of things
- medical and health scienceshealth sciencespublic healthepidemiology
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral NetworksCoordinator
1081 HV Amsterdam
Netherlands
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Participants (5)
5230 Odense M
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7491 Trondheim
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2311 EZ Leiden
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75654 Paris
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2200 Copenhagen
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
Partners (14)
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3721 MA Bilthoven
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4604 Kristiansand
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5020 Bergen
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PE28 0NJ Kimbolton
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EC4A 3BF London
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1050 Elsene
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1217 CM Hilversum
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110070 New Delhi
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1081 HV Amsterdam
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75006 Paris
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LE11 3TU Loughborough
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LE1 7RH Leicester
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G4 0BA Glasgow
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