Machine learning-based predictive modeling of resilience to stressors in pregnant women during COVID-19: A prospective cohort study

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...

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Bibliographic Details
Published in:PloS one 2022-08, Vol.17 (8), p.e0272862-e0272862
Main Authors: Nichols, Emily S, Pathak, Harini S, Bgeginski, Roberta, Mottola, Michelle F, Giroux, Isabelle, Van Lieshout, Ryan J, Mohsenzadeh, Yalda, Duerden, Emma G
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Biology and Life Sciences
Cohort analysis
Computer and Information Sciences
Coronaviruses
COVID-19
Diabetes
Diet
Eating behavior
Exercise
Feature selection
Food
Fruits
Gestational age
Health aspects
Hypotheses
Learning algorithms
Lifestyles
Machine learning
Medicine and Health Sciences
Modelling
Nutrition research
Pandemics
Physical activity
Physical fitness
Prediction models
Pregnancy
Pregnancy complications
Pregnant women
Prenatal experience
Psychological stress
Questionnaires
Resilience
Risk factors
Sleep
Social Sciences
Stress
Stress (Psychology)
Support vector machines
Womens health
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0272862