WEALTH Objectives
WEALTH Objectives and partners responsible
Our overall aim is to develop data processing methods for research grade and commercially available accelerometer device data. We will test the feasibility of linking ML generated data with data obtained using Ecological Momentary Assessment (EMA) methods, to produce an integrated data collection system that will simultaneously capture physical behaviours and eating behaviours and their interrelation.
The objective of the WEALTH project is to develop standardised methods to derive daily physical and eating behaviours from wearable sensors and evaluate the interaction of both behaviours, i.e. identifying unhealthy multi-behavioural patterns and contextual prompts such as eating while walking or snacking behaviour while sitting. To develop and validate machine learning models, 600 participants will be recruited to wear a range of state-of-the-art accelerometer devices including wearables for 7 days. Using laboratory protocols, observation of behaviour classification and a free-living assessment period, behaviours will be labelled using feedback to event-triggered Ecological Momentary Assessment (EMA). This will lead to the development of an integrated data collection and processing infrastructure to disseminate the standardized analysis of PB and EB from a range of state-of-the-art devices to support further research and surveillance systems.
| Objective No. | Description | Partner(s) responsible for the objective |
|---|---|---|
| 0 | Objective 0.1: To coordinate, promote and facilitate interrelations between WPs, the communication between WP leads and members of the WEALTH consortium, and to ensure appropriate ethics and data management | UL, BIPS & UHK |
| 1 | Objective 1.1: To develop and test a protocol for the measurement of physical and eating behaviours and the context in which they occur by combining EMA and sensor data in an independent feasibility sample in each WEALTH centre. | UHK, UCC & UGent |
| 2 | Objective 2.1: To implement data collection studies for the development of ML models (WP3).
Objective 2.2: Collect data to be used in WP4 to determine the feasibility of the use of a combined ML and EMA measurement. |
UL & UHK |
| 3 | Objective 3.1: Training and validation of ML models to predict eating, and physical behaviours as well as EE derived from tri-axial accelerometry and further sensors from protocol and free-living data collected in WP2.
Objective 3.2: Development of web-based solution for collection and data processing of raw sensor data from wearable devices and open-source provision of ML algorithms and preliminary data analysis in order to classify PB and EB. |
BIPS & UL |
| 4 | Objective 4.1: To assess the feasibility and acceptability by citizens and usability by analysing entities of innovative measures developed in WEALTH.
Objective 4.2: To identify multi-behavioural patterns using device-based PA measures predicted from ML algorithms (developed in WP3) and data provided by EMA (as collected in WP2). |
UP13 & UGent |
| 5 | Objective 5.1: To use both integrated and end-of-project knowledge translation strategies to disseminate the findings of WEALTH as broadly as possible.
Objective 5.2: To encourage maximum use of this new knowledge and new methods, an online toolbox will be created. |
UL & UGent |