Predicting maternal social loneliness by passive sensing with wearable devices

BackgroundMaternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness non-invasively through wearable devices and passive sensing provides opportunities to prevent or reduce the impact of loneliness on the health and well-being of the mother and her child. ObjectiveThe aim of this study is to use objective health data collected passively by a wearable device to predict maternal (social) loneliness during pregnancy and the postpartum period based on and to identify the important objective physiological parameters in loneliness detection. MethodsWe conducted a longitudinal study using smartwatches to continuously collect physiological data from 31 women during pregnancy and the postpartum period. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire in gestational week 36 and again at 12 weeks postpartum. Responses to this questionnaire and the background information of the participants were collected via our customized cross-platform mobile application. We leveraged participants smartwatch data from the 7 days before and the day of their completion of the UCLA questionnaire for loneliness prediction. We categorized the loneliness scores from the UCLA questionnaire as loneliness (scores [&ge;] 12) and non-loneliness (scores < 12). We developed decision tree and gradient boosting models to predict loneliness. We evaluated the models by using a leave-one-participant-out cross validation. Moreover, we discussed the importance of extracted health parameters in our models for loneliness prediction. ResultsThe gradient boosting and decision tree models predicted maternal social loneliness with weighted F1 scores of 0.871 and 0.897, respectively. Our results also show that loneliness is highly associated with activity intensity, activity distribution during the day, resting heart rate (HR), and resting heart rate variability (HRV). ConclusionOur results show the potential benefit and feasibility of using passive sensing with a smartwatch to predict maternal loneliness. Our developed machine learning models achieved a high F1 score for loneliness prediction. We also show that intensity of activity, activity pattern, and resting HR and HRV are good predictors of loneliness. These results indicate the intervention opportunities made available by wearable devices and predictive models to improve maternal well-being by early detection of loneliness.