Data analysis
weight gain, Machine learning
EVENTS, EVENT
The data will be analyzed statistically. Appropriate descriptive statistics will be calculated for the background variables, WEL, PASE, weight change, EPDS, WHOQOL-BREF, PRAQ-R2, PSS-4, SOC-13, TFEQ-R18, 6-FQ, calories consumed, and the Ouras parameters. Variables like the EPDS, baseline BMI and Ouras wear time will be tested for their association with changes in self-efficacy in eating and physical activity. Mixed effect models will be used to evaluate change over time in self-efficacy in eating and physical activity and weight change. Simple mediation models will be used to assess calories consumed and MVPA as mediators between self-efficacy and weight change.Machine learning and event mining approaches are being exploited in the intervention to build personal models–such as sleep and physical activity–for each individual. The relationships between the collected parameters, including weight gain, physical activity, sleep, and stress will also be investigated. Signal processing and (for example, unsupervised and semi-supervised) machine learning techniques are being exploited to extract events–i.e., high abstraction level information–from the data collected via the Oura ring and the questionnaires. These events characterize pregnant women’s daily life or lifestyle: e.g., their physical activity during the day or stress levels during sleep and these events are defined based on different attributes in certain epochs varying from minutes to a day. The events will be divided into three categories: activity, health, and context. Subsequently, the relationships between the events and their attitudes will be investigated via statistical analysis techniques. For example, when an undesired event (e.g., stress) is detected during pregnancy, statistical analysis techniques (e.g., Chi-Square test) will be used to evaluate the relationships between the event and the mother’s activity, health, and context. Moreover, higher data abstraction will be performed to record events in a chronological order. Data analytic techniques ranging from simple rule-based algorithms to machine learning classifications will be developed to extract patterns from the collected data and events. To this end, machine-learning-based methods (e.g., autoencoders) will be developed to build personalized models, by which the pregnant woman’s real state is compared with the desired state.