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 Vol. 17; no. 8; p. 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:	Journal Article
Language:	English
Published:	San Francisco Public Library of Science 11-08-2022 Public Library of Science (PLoS)
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 Canada
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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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0272862