ArticlePloS one2022
Machine learning-based predictive modeling of resilience to stressors in pregnant women during COVID-19: A prospective cohort study.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- Prenatal metabolic and mental health exposures and the association with neonatal brain dynamics.Pediatric research · 2026Article
- Predicting psychological resilience in older adults during the COVID-19 pandemic: a machine learning approach.The Gerontologist · 2025Article
- Stress and resilience during pregnancy: A comparative study between pregnant and non-pregnant women in Ethiopia.PLOS global public health · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the COVID-19 pandemic, pregnant women have been at high risk for psychological distress. Lifestyle factors may be modifiable elements to help reduce and promote resilience to prenatal stress. We used Machine-Learning (ML) algorithms applied to questionnaire data obtained from an international cohort of 804 pregnant women to determine whether physical activity and diet were resilience factors against prenatal stress, and whether stress levels were in turn predictive of sleep classes. A support vector machine accurately classified perceived stress levels in pregnant women based on physical activity behaviours and dietary behaviours. In turn, we classified hours of sleep based on perceived stress levels. This research adds to a developing consensus concerning physical activity and diet, and the association with prenatal stress and sleep in pregnant women. Predictive modeling using ML approaches may be used as a screening tool and to promote positive health behaviours for pregnant women.
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Registered trials
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