Maternal Parenting Stress in the Face of Early Regulatory Disorders in Infancy: A Machine Learning Approach to Identify What Matters Most
Objective: Early regulatory disorders (ERD) in infancy are typically associated with high parenting stress (PS). Theoretical and empirical literature suggests a wide range of factors that may contribute to PS related to ERD. The aim of this study was to identify key predictors of maternal PS within...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-08-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2021.663285/full |
| _version_ | 1818650115823370240 |
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| author | Anna K. Georg Paul Schröder-Pfeifer Paul Schröder-Pfeifer Manfred Cierpka Svenja Taubner |
| author_facet | Anna K. Georg Paul Schröder-Pfeifer Paul Schröder-Pfeifer Manfred Cierpka Svenja Taubner |
| author_sort | Anna K. Georg |
| collection | DOAJ |
| description | Objective: Early regulatory disorders (ERD) in infancy are typically associated with high parenting stress (PS). Theoretical and empirical literature suggests a wide range of factors that may contribute to PS related to ERD. The aim of this study was to identify key predictors of maternal PS within a large predictor data set in a sample of N = 135 mothers of infants diagnosed with ERD.Methods: We used machine learning to identify relevant predictors. Maternal PS was assessed with the Parenting Stress Index. The multivariate dataset assessed cross-sectionally consisted of 464 self-reported and clinically rated variables covering mother-reported psychological distress, maternal self-efficacy, parental reflective functioning, socio-demographics, each parent's history of illness, recent significant life events, former miscarriage/abortion, pregnancy, obstetric history, infants' medical history, development, and social environment. Variables were drawn from behavioral diaries on regulatory symptoms and parental co-regulative behavior as well as a clinical interview which was utilized to diagnose ERD and to assess clinically rated regulatory symptoms, quality of parent–infant relationship, organic/biological and psychosocial risks, and social–emotional functioning.Results: The final prediction model identified 11 important variables summing up to the areas maternal self-efficacy, psychological distress (particularly depression and anger-hostility), infant regulatory symptoms (particularly duration of fussing/crying), and age-appropriate physical development. The RMSE (i.e., prediction accuracy) of the final model applied to the test set was 21.72 (R2 = 0.58).Conclusions: This study suggests that among behavioral, environmental, developmental, parent–infant relationship, and mental health variables, a mother's higher self-efficacy, psychological distress symptoms particularly depression and anger symptoms, symptoms in the child particularly fussing/crying symptoms, and age-inappropriate physical development are associated with higher maternal PS. With these factors identified, clinicians may more efficiently assess a mother's PS related to ERD in a low-risk help-seeking sample. |
| first_indexed | 2024-12-17T01:45:05Z |
| format | Article |
| id | doaj.art-4afb34e8a7824368901d82a0aca83566 |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-12-17T01:45:05Z |
| publishDate | 2021-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-4afb34e8a7824368901d82a0aca835662022-12-21T22:08:12ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402021-08-011210.3389/fpsyt.2021.663285663285Maternal Parenting Stress in the Face of Early Regulatory Disorders in Infancy: A Machine Learning Approach to Identify What Matters MostAnna K. Georg0Paul Schröder-Pfeifer1Paul Schröder-Pfeifer2Manfred Cierpka3Svenja Taubner4Institute for Psychosocial Prevention, Centre for Psychosocial Medicine, Heidelberg University Hospital, Heidelberg, GermanyInstitute for Psychosocial Prevention, Centre for Psychosocial Medicine, Heidelberg University Hospital, Heidelberg, GermanyPsychological Institute, University Heidelberg, Heidelberg, GermanyInstitute for Psychosocial Prevention, Centre for Psychosocial Medicine, Heidelberg University Hospital, Heidelberg, GermanyInstitute for Psychosocial Prevention, Centre for Psychosocial Medicine, Heidelberg University Hospital, Heidelberg, GermanyObjective: Early regulatory disorders (ERD) in infancy are typically associated with high parenting stress (PS). Theoretical and empirical literature suggests a wide range of factors that may contribute to PS related to ERD. The aim of this study was to identify key predictors of maternal PS within a large predictor data set in a sample of N = 135 mothers of infants diagnosed with ERD.Methods: We used machine learning to identify relevant predictors. Maternal PS was assessed with the Parenting Stress Index. The multivariate dataset assessed cross-sectionally consisted of 464 self-reported and clinically rated variables covering mother-reported psychological distress, maternal self-efficacy, parental reflective functioning, socio-demographics, each parent's history of illness, recent significant life events, former miscarriage/abortion, pregnancy, obstetric history, infants' medical history, development, and social environment. Variables were drawn from behavioral diaries on regulatory symptoms and parental co-regulative behavior as well as a clinical interview which was utilized to diagnose ERD and to assess clinically rated regulatory symptoms, quality of parent–infant relationship, organic/biological and psychosocial risks, and social–emotional functioning.Results: The final prediction model identified 11 important variables summing up to the areas maternal self-efficacy, psychological distress (particularly depression and anger-hostility), infant regulatory symptoms (particularly duration of fussing/crying), and age-appropriate physical development. The RMSE (i.e., prediction accuracy) of the final model applied to the test set was 21.72 (R2 = 0.58).Conclusions: This study suggests that among behavioral, environmental, developmental, parent–infant relationship, and mental health variables, a mother's higher self-efficacy, psychological distress symptoms particularly depression and anger symptoms, symptoms in the child particularly fussing/crying symptoms, and age-inappropriate physical development are associated with higher maternal PS. With these factors identified, clinicians may more efficiently assess a mother's PS related to ERD in a low-risk help-seeking sample.https://www.frontiersin.org/articles/10.3389/fpsyt.2021.663285/fullearly regulatory disordersmachine learning algorithmsparenting stressparental self-efficacyinfant mental health diagnostic |
| spellingShingle | Anna K. Georg Paul Schröder-Pfeifer Paul Schröder-Pfeifer Manfred Cierpka Svenja Taubner Maternal Parenting Stress in the Face of Early Regulatory Disorders in Infancy: A Machine Learning Approach to Identify What Matters Most Frontiers in Psychiatry early regulatory disorders machine learning algorithms parenting stress parental self-efficacy infant mental health diagnostic |
| title | Maternal Parenting Stress in the Face of Early Regulatory Disorders in Infancy: A Machine Learning Approach to Identify What Matters Most |
| title_full | Maternal Parenting Stress in the Face of Early Regulatory Disorders in Infancy: A Machine Learning Approach to Identify What Matters Most |
| title_fullStr | Maternal Parenting Stress in the Face of Early Regulatory Disorders in Infancy: A Machine Learning Approach to Identify What Matters Most |
| title_full_unstemmed | Maternal Parenting Stress in the Face of Early Regulatory Disorders in Infancy: A Machine Learning Approach to Identify What Matters Most |
| title_short | Maternal Parenting Stress in the Face of Early Regulatory Disorders in Infancy: A Machine Learning Approach to Identify What Matters Most |
| title_sort | maternal parenting stress in the face of early regulatory disorders in infancy a machine learning approach to identify what matters most |
| topic | early regulatory disorders machine learning algorithms parenting stress parental self-efficacy infant mental health diagnostic |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2021.663285/full |
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