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Sleep disturbances are associated with adverse perinatal outcomes. Thus, it is necessary to understand the continuous patterns of sleep during pregnancy and how moderators such as maternal age and pre-pregnancy body mass index impact sleep.

This study aimed to examine the continuous changes in sleep parameters objectively (i.e. sleep stages, total sleep time, and awake time) in pregnant women and to describe the impact of maternal age and/or pre-pregnancy body mass index as moderators of these objective sleep parameters.

This was a longitudinal observational design.

Seventeen women with a singleton pregnancy participated in this study. Mixed model repeated measures were used to describe weekly patterns, while aggregated changes describe these three pregnancy periods (10–19, 20–29, and 30–39 gestational weeks).

For the weekly patterns, we found significantly decreased deep (1.26 ± 0.18 min/week, p < 0.001), light (0.72 ± 0.37 min/week, p = 0.05), and total sleep time (1.56 ± 0.47 min/week, p < 0.001) as well as increased awake time (1.32 ± 0.34 min/week, p < 0.001). For the aggregated changes, we found similar patterns to weekly changes. Women (⩾30 years) had an even greater decrease in deep sleep (1.50 ± 0.22 min/week, p < 0.001) than those younger (0.84 ± 0.29 min/week, p = 0.04). Women who were both overweight/obese and ⩾30 years experienced an increase in rapid eye movement sleep (0.84 ± 0.31 min/week, p = 0.008), but those of normal weight (<30 years) did not.

This study appears to be the first to describe continuous changes in sleep parameters during pregnancy at home. Our study provides preliminary evidence that sleep parameters could be potential non-invasive physiological markers predicting perinatal outcomes.

Maternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness noninvasively 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.

The 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 and identify the important objective physiological parameters in loneliness detection.

We 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 post partum. Responses to this questionnaire and background information of the participants were collected through our customized cross-platform mobile app. 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≥12) and nonloneliness (scores<12). We developed decision tree and gradient-boosting models to predict loneliness. We evaluated the models by using leave-one-participant-out cross-validation. Moreover, we discussed the importance of extracted health parameters in our models for loneliness prediction.

The gradient boosting and decision tree models predicted maternal social loneliness with weighted F1-scores of 0.897 and 0.872, respectively. Our results also show that loneliness is highly associated with activity intensity and activity distribution during the day. In addition, resting heart rate (HR) and resting HR variability (HRV) were correlated with loneliness.

Our 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 through early detection of loneliness.