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

Hahn, Lisa; Eickhoff, Simon B.; Habel, Ute; Stickeler, Elmar; Schnakenberg, Patricia; Goecke, Tamme W.; Stickel, Susanne; Franz, Matthias; Dukart, Juergen; Chechko, Natalia

doi:10.1038/s41398-021-01245-6

Cited by 25 publications

(24 citation statements)

References 33 publications

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“…It also has the potential to provide precision medicine for the field of psychiatry 22 , better predicting what treatments will work for a particular patient. Although research in digital phenotyping in postpartum women is limited, one study successfully used digitally administered measures to generate three phenotypes of postpartum women, which allowed for early detection of postpartum depression 23 . A review of digital phenotyping for mood disorders in a general population found that digital phenotyping not only has the potential for early detection of mood disorders, but also the capability to capture the nuances of distinct phenotypes of mental illness within a large class.…”

Section: Promisementioning

confidence: 99%

Digital mental health for postpartum women: perils, pitfalls, and promise

Feldman

Perret

2023

npj Digit. Med.

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Postpartum mental health conditions are a public health concern, affecting a large number of reproductive-age women and their families. Postpartum depression alone affects at least 14% of new mothers and their families. However, very little has been written about how advances in digital mental health can benefit women in the postpartum period, or how those advances may poorly serve this vulnerable population. This manuscript takes a high-level view of the advances in different areas of digital mental health, including telehealth, apps, and digital phenotyping. In this comment, we explore ways in which digital interventions for postpartum mental health may help with connection to treatment, accessibility, agency, and ease of access. We also note particular concerns for how digital postpartum mental health may encounter issues of low-quality resources, ethical considerations, and equity considerations. We provide suggestions for how to leverage the promise and avoid the pitfalls of digital mental health for postpartum women.

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Section: Promisementioning

confidence: 99%

Digital mental health for postpartum women: perils, pitfalls, and promise

Feldman

Perret

2023

npj Digit. Med.

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“…However, mental health has been a primary area for the investment and development of this technology. Mental health applications of digital phenotyping include analysis of sleep patterns to predict episodes of relapse in schizophrenia [21], early identification of postpartum depression [22], use of keystroke patterns to predict episodes of mania [23], movement or linguistic analysis to predict episodes of depression [24,25], and social media data used to identify drinking and tobacco abstinence behaviors [26]. Mental health applications have been a primary focus of digital phenotyping projects in part because of the ease with which mobile technology can be used to gather massive amounts of fine-grained behavioral data from the user at any time and in any location [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study

Martinez-Martin¹,

Greely²,

Cho³

2021

JMIR Mhealth Uhealth

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Background Digital phenotyping (also known as personal sensing, intelligent sensing, or body computing) involves the collection of biometric and personal data in situ from digital devices, such as smartphones, wearables, or social media, to measure behavior or other health indicators. The collected data are analyzed to generate moment-by-moment quantification of a person’s mental state and potentially predict future mental states. Digital phenotyping projects incorporate data from multiple sources, such as electronic health records, biometric scans, or genetic testing. As digital phenotyping tools can be used to study and predict behavior, they are of increasing interest for a range of consumer, government, and health care applications. In clinical care, digital phenotyping is expected to improve mental health diagnoses and treatment. At the same time, mental health applications of digital phenotyping present significant areas of ethical concern, particularly in terms of privacy and data protection, consent, bias, and accountability. Objective This study aims to develop consensus statements regarding key areas of ethical guidance for mental health applications of digital phenotyping in the United States. Methods We used a modified Delphi technique to identify the emerging ethical challenges posed by digital phenotyping for mental health applications and to formulate guidance for addressing these challenges. Experts in digital phenotyping, data science, mental health, law, and ethics participated as panelists in the study. The panel arrived at consensus recommendations through an iterative process involving interviews and surveys. The panelists focused primarily on clinical applications for digital phenotyping for mental health but also included recommendations regarding transparency and data protection to address potential areas of misuse of digital phenotyping data outside of the health care domain. Results The findings of this study showed strong agreement related to these ethical issues in the development of mental health applications of digital phenotyping: privacy, transparency, consent, accountability, and fairness. Consensus regarding the recommendation statements was strongest when the guidance was stated broadly enough to accommodate a range of potential applications. The privacy and data protection issues that the Delphi participants found particularly critical to address related to the perceived inadequacies of current regulations and frameworks for protecting sensitive personal information and the potential for sale and analysis of personal data outside of health systems. Conclusions The Delphi study found agreement on a number of ethical issues to prioritize in the development of digital phenotyping for mental health applications. The Delphi consensus statements identified general recommendations and principles regarding the ethical application of digital phenotyping to mental health. As digital phenotyping for mental health is implemented in clinical care, there remains a need for empirical research and consultation with relevant stakeholders to further understand and address relevant ethical issues.

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“…Additionally, the early postpartum period is a time known for emotional lability, and depressive and anxious symptoms assessed at this time should be viewed differently than the same symptoms seen prenatally or at later postpartum weeks. Recent research suggests that the EPDS at early postpartum is not reliably able to differentiate between diagnosable postpartum depression and adjustment disorder, but it is a useful tool in differentiating both trajectories from healthy controls [ 35 ]. Importantly, evidence suggests that EPDS scores within the first two days to one week postpartum have a strong predictive power for detecting later postpartum depression [ 36 , 37 ].…”

Section: Discussionmentioning

confidence: 99%

Psychosocial predictors of early postpartum depressive and anxious symptoms in primiparous women and their partners

Henshaw

Cooper

Wood

et al. 2023

BMC Pregnancy Childbirth

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Background While the majority of research on postpartum depressive and anxious symptoms has focused on mothers, a growing body of research suggests a need to understand the role of the partner’s health and relationship quality as predictors of postpartum maternal depression, while also better understanding correlates of partner or paternal depression in the postpartum period. The purpose of the current study is to evaluate mother and partner stress, anxiety, mood, infant care support, and relationship quality as predictors of perinatal depressive and anxious symptoms in first time mothers and partners during the postpartum hospital stay. Methods First time parent couples (n = 116) completed a survey during the two-day postpartum stay in a Midwest hospital. Depressive (EPDS) and anxiety symptoms (DASS-21-Anxiety) were assessed in both mothers and partners. Hierarchical linear regression was used to evaluate relationship satisfaction, partner infant care support, stress, and co-parent mood as predictors of mood in mothers and partners separately. Results Stress was a predictor of anxiety and depression symptoms in both mothers and partners. Additionally, co-parent anxiety significantly predicted anxiety in both mothers and partners. Maternal relationship satisfaction was a predictor of the partner’s depressive symptoms, and maternal perceptions of partner infant support predicted maternal depressive symptoms. Conclusions Together, these results suggest that stress, relationship satisfaction, and co-parent mood are related to depressive and anxious symptoms in mothers and partner, underscoring the need to continue exploring mother and partner mental health in a dyadic framework.

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

Cited by 25 publications

References 33 publications

Digital mental health for postpartum women: perils, pitfalls, and promise

Digital mental health for postpartum women: perils, pitfalls, and promise

Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study

Psychosocial predictors of early postpartum depressive and anxious symptoms in primiparous women and their partners

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