Literature DB >> 33574229

Early identification of postpartum depression using demographic, clinical, and digital phenotyping.

Juergen Dukart1,2, Natalia Chechko3,4, Lisa Hahn5,6, Simon B Eickhoff5,6, Ute Habel7,8, Elmar Stickeler9, Patricia Schnakenberg5,7, Tamme W Goecke9,10, Susanne Stickel7, Matthias Franz11.   

Abstract

Postpartum depression (PPD) and adjustment disorder (AD) affect up to 25% of women after childbirth. However, there are no accurate screening tools for either disorder to identify at-risk mothers and enable them to benefit from early intervention. Combinations of anamnestic, clinical, and remote assessments were evaluated for an early and accurate identification of PPD and AD. Two cohorts of mothers giving birth were included in the study (N = 308 and N = 193). At baseline, participants underwent a detailed sociodemographic-anamnestic and clinical interview. Remote assessments were collected over 12 weeks comprising mood and stress levels as well as depression and attachment scores. At 12 weeks postpartum, an experienced clinician assigned the participants to three distinct groups: women with PPD, women with AD, and healthy controls (HC). Combinations of these assessments were assessed for an early an accurate detection of PPD and AD in the first cohort and, after pre-registration, validated in a prospective second cohort. Combinations of postnatal depression, attachment (for AD) and mood scores at week 3 achieved balanced accuracies of 93 and 79% for differentiation of PPD and AD from HC in the validation cohort and balanced accuracies of 87 and 91% in the first cohort. Differentiation between AD and PPD, with a balanced accuracy of 73% was possible at week 6 based on mood levels only with a balanced accuracy of 73% in the validation cohort and a balanced accuracy of 76% in the first cohort. Combinations of in clinic and remote self-assessments allow for early and accurate detection of PPD and AD as early as three weeks postpartum, enabling early intervention to the benefit of both mothers and children.

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Mesh:

Year:  2021        PMID: 33574229      PMCID: PMC7878890          DOI: 10.1038/s41398-021-01245-6

Source DB:  PubMed          Journal:  Transl Psychiatry        ISSN: 2158-3188            Impact factor:   6.222


  28 in total

1.  Prediction of postpartum depression using multilayer perceptrons and pruning.

Authors:  Salvador Tortajada; Juan M García-Gomez; Javier Vicente; Julio Sanjuán; Rosa de Frutos; Rocío Martín-Santos; Luisa García-Esteve; Isolde Gornemann; Alfonso Gutiérrez-Zotes; Francesca Canellas; Angel Carracedo; Monica Gratacos; Roser Guillamat; Enrique Baca-García; Montserrat Robles
Journal:  Methods Inf Med       Date:  2009-03-31       Impact factor: 2.176

2.  Postpartum depression: what we know.

Authors:  Michael W O'Hara
Journal:  J Clin Psychol       Date:  2009-12

Review 3.  Perinatal Depression: Embracing Variability toward Better Treatment and Outcomes.

Authors:  Liisa A M Galea; Vibe G Frokjaer
Journal:  Neuron       Date:  2019-04-03       Impact factor: 17.173

4.  New parents and mental disorders: a population-based register study.

Authors:  Trine Munk-Olsen; Thomas Munk Laursen; Carsten Bøcker Pedersen; Ole Mors; Preben Bo Mortensen
Journal:  JAMA       Date:  2006-12-06       Impact factor: 56.272

5.  The onset of postpartum depression: Implications for clinical screening in obstetrical and primary care.

Authors:  Zachary N Stowe; Amy L Hostetter; D Jeffrey Newport
Journal:  Am J Obstet Gynecol       Date:  2005-02       Impact factor: 8.661

6.  Breastfeeding duration and postpartum psychological adjustment: role of maternal attachment styles.

Authors:  Ipek Akman; M Kemal Kuscu; Ziya Yurdakul; Nihal Ozdemir; Mine Solakoğlu; Lale Orhon; Aytül Karabekiroğlu; Eren Ozek
Journal:  J Paediatr Child Health       Date:  2008-06       Impact factor: 1.954

Review 7.  Postpartum depression: current status and future directions.

Authors:  Michael W O'Hara; Jennifer E McCabe
Journal:  Annu Rev Clin Psychol       Date:  2013-02-01       Impact factor: 18.561

Review 8.  Depression: the benefits of early and appropriate treatment.

Authors:  Aron Halfin
Journal:  Am J Manag Care       Date:  2007-11       Impact factor: 2.229

9.  Economic and Health Predictors of National Postpartum Depression Prevalence: A Systematic Review, Meta-analysis, and Meta-Regression of 291 Studies from 56 Countries.

Authors:  Jennifer Hahn-Holbrook; Taylor Cornwell-Hinrichs; Itzel Anaya
Journal:  Front Psychiatry       Date:  2018-02-01       Impact factor: 4.157

10.  Prevalence of postpartum depression and interventions utilized for its management.

Authors:  Reindolf Anokye; Enoch Acheampong; Amy Budu-Ainooson; Edmund Isaac Obeng; Adjei Gyimah Akwasi
Journal:  Ann Gen Psychiatry       Date:  2018-05-09       Impact factor: 3.455

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  5 in total

1.  Characterization of Depressive Symptom Trajectories in Women between Childbirth and Diagnosis.

Authors:  Natalia Chechko; Susanne Stickel; Elena Losse; Aliaksandra Shymanskaya; Ute Habel
Journal:  J Pers Med       Date:  2022-03-28

2.  Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol.

Authors:  Alkistis Skalkidou; Fotios C Papadopoulos; Ayesha M Bilal; Emma Fransson; Emma Bränn; Allison Eriksson; Mengyu Zhong; Karin Gidén; Ulf Elofsson; Cathrine Axfors
Journal:  BMJ Open       Date:  2022-04-27       Impact factor: 3.006

3.  The effectiveness of Tai Chi for postpartum depression: A protocol for systematic review and meta-analysis.

Authors:  Haoyu Tian; Shengnan Han; Jing Hu; Xiangyu Peng; Wei Zhang; Wanyu Wang; Xianghua Qi; Jing Teng
Journal:  Medicine (Baltimore)       Date:  2021-12-10       Impact factor: 1.817

4.  Threats to Global Mental Health From Unregulated Digital Phenotyping and Neuromarketing: Recommendations for COVID-19 Era and Beyond.

Authors:  Hossein Akbarialiabad; Bahar Bastani; Mohammad Hossein Taghrir; Shahram Paydar; Nasrollah Ghahramani; Manasi Kumar
Journal:  Front Psychiatry       Date:  2021-09-14       Impact factor: 4.157

5.  The expectant brain-pregnancy leads to changes in brain morphology in the early postpartum period.

Authors:  Natalia Chechko; Jürgen Dukart; Svetlana Tchaikovski; Christian Enzensberger; Irene Neuner; Susanne Stickel
Journal:  Cereb Cortex       Date:  2022-09-04       Impact factor: 4.861

  5 in total

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