Objectives

To assess, in terms of self-efficacy in weight management, the effectiveness of the SLIM lifestyle intervention among overweight or obese women during pregnancy and after delivery, and further to exploit machine learning and event mining approaches to build personalized models. Additionally, the aim is to evaluate the implementation of the SLIM intervention.

Methods

This prospective trial, which is a non-randomized, quasi-experimental, pre-post intervention, includes an embedded mixed-method process evaluation. The SLIM Intervention is delivered by public health nurses (n = 9) working in maternity clinics. The public health nurses recruited overweight women (n = 54) at their first antenatal visit using convenience sampling. The core components of the intervention i.e. health technology, motivational interviewing, feedback, and goal setting, are utilized in antenatal visits in maternity clinics starting from gestational week 15 or less and continuing to 12 weeks after delivery. Mixed effect models are used to evaluate change over time in self-efficacy, weight management and weight change. Simple mediation models are used to assess calories consumed and moderate to vigorous physical activity (MVPA) as mediators between self-efficacy and weight change. Signal processing and machine learning techniques are exploited to extract events from the data collected via the Oura ring and smartphone-based questionnaires.

Discussion

The SLIM intervention was developed in collaboration with overweight women and public health nurses working in maternity clinics. This study evaluates the effectiveness of the intervention among overweight women in increasing self-efficacy and achieving a healthy weight; thus, impacting the healthy lifestyle and long-term health of the whole family. The long-term objective is to contribute to women's health by supporting weight-management through behavior change via interventions conducted in maternity clinics.

Trial registration

The trial was registered at the Clinicaltrials.gov register platform (ID NCT04826861) on 17 March 2021.