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Abstract ObjectiveTo understand community perspectives on the effects of high ambient temperature on the health and wellbeing of neonates, and impacts on post-partum women and infant care in Kilifi. DesignQualitative study using key informant interviews, in-depth interviews and focus group discussions with pregnant and postpartum women (n = 22), mothers-in-law (n = 19), male spouses (n = 20), community health volunteers (CHVs) (n = 22) and stakeholders from health and government ministries (n = 16). SettingsWe conducted our research in Kilifi County in Kenya’s Coast Province. The area is largely rural and during summer, air temperatures can reach 37˚C and rarely go below 23˚C. Data analysisData were analyzed in NVivo 12, using both inductive and deductive approaches. ResultsHigh ambient temperature is perceived by community members to have direct and indirect health pathways in pregnancy and postpartum periods, including on the neonates. The direct impacts include injuries on the neonate’s skin and in the mouth, leading to discomfort and affecting breastfeeding and sleeping. Participants described babies as “having no peace”. Heat effects were perceived to be amplified by indoor air pollution and heat from indoor cooking fires. Community members believed that exclusive breastfeeding was not practical in conditions of extreme heat because it lowered breast milk production, which was, in turn, linked to a low scarcity of food and time spend by mothers away from their neonates performing household chores. Kangaroo Mother Care (KMC) was also negatively affected. Participants reported that postpartum women took longer to heal in the heat, were exhausted most of the time and tended not to attend postnatal care. ConclusionsHigh ambient temperatures affect postpartum women and their neonates through direct and indirect pathways. Discomfort makes it difficult for the mother to care for the baby. Multi-sectoral policies and programs are required to mitigate the negative impacts of high ambient temperatures on maternal and neonatal health in rural Kilifi and similar settings.
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Maternal Social Loneliness Detection Using Passive Sensing Through Continuous Monitoring in Everyday Settings: Longitudinal Study
BackgroundMaternal 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. 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 and 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 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. ResultsThe 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. ConclusionsOur 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.
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- Award ID(s):
- 1831918
- PAR ID:
- 10486071
- Publisher / Repository:
- JMIR
- Date Published:
- Journal Name:
- JMIR Formative Research
- Volume:
- 7
- ISSN:
- 2561-326X
- Page Range / eLocation ID:
- e47950
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2196/47950
