ArticleJMIR formative research2023
Maternal Social Loneliness Detection Using Passive Sensing Through Continuous Monitoring in Everyday Settings: Longitudinal Study.
Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Perinatal Mental Health Detection and Prediction Using Mobile Sensing Data: Systematic Review.JMIR mental health · 2026Pooled it
- Digital Phenotyping in Health Research: Scoping Review of Methods, Gaps, and Opportunities.Journal of medical Internet research · 2026Article
- Development and User-Centered Evaluation of Smart Systems for Loneliness Monitoring in Older Adults: Mixed Methods Study.Journal of medical Internet research · 2026Article
- mHealth apps for maternal mental well-being among pregnant and postpartum women: a systematic review.mHealth · 2026Review
- Ecological Momentary Assessment to Measure Social Connectedness in Older Adults: Integrative Review.Journal of medical Internet research · 2025Review
- Unlocking the Potential of Wear Time of a Wearable Device to Enhance Postpartum Depression Screening and Detection: Cross-Sectional Study.JMIR formative research · 2025Article
- Immigration stress and maternal sensitivity in a Mexican immigrant sample: The role of parasympathetic activity and familism value.Cultural diversity & ethnic minority psychology · 2024Article
- Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life.Scientific reports · 2024Article
- Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study.JMIR mHealth and uHealth · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 F
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 F
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.