| Literature DB >> 30498212 |
Jerry Guintivano1, Holly Krohn2, Carol Lewis3, Enda M Byrne4, Anjali K Henders4, Alexander Ploner5, Katherine Kirk6, Nicholas G Martin6, Jeannette Milgrom7, Naomi R Wray4,8, Patrick F Sullivan2,5,9, Samantha Meltzer-Brody2.
Abstract
Postpartum depression (PPD) is one of the most frequent complications of childbirth and particularly is suited to genetic investigation as it is more homogenous than major depression outside of the perinatal period. We developed an iOS app (PPD ACT) to recruit, consent, screen, and enable DNA collection from women with a lifetime history of PPD to sufficiently power genome-wide association studies. In 1 year, we recruited 7344 women with a history of PPD and have biobanked 2946 DNA samples from the US. This sample of PPD cases was notably severely affected and within 2 years of their worst episode of PPD. Clinical validation was performed within a hospital setting on a subset of participants and recall validity assessed 6-9 months after initial assessment to ensure reliability of screening tools. Here we detail the creation of the PPD ACT mobile app including design, ethical, security, and deployment considerations. We emphasize the importance of multidisciplinary collaboration to correctly implement such a research project. Additionally, we describe our ability to customize the PPD ACT platform to deploy internationally in order to collect a global sample of women with PPD.Entities:
Mesh:
Year: 2018 PMID: 30498212 PMCID: PMC6265256 DOI: 10.1038/s41398-018-0305-5
Source DB: PubMed Journal: Transl Psychiatry ISSN: 2158-3188 Impact factor: 6.222
Fig. 1PPD ACT participant flow and geographic distribution in the US.
a Flowchart of participants through the app and the number of participants that pass each step. b Images of PPD ACT app. c Geographic distribution of cases per 10,000 births (State-level birth rate data was taken from the National Vital Statistics Reports Final Births Report for 2015[42])
Fig. 2Cumulative PPD ACT US data over time
Fig. 3Characteristics of US ases and test–retest reliability.
a Distribution of EPDS Scores among US cases. b Number of children among US cases. c Number of years since worst episode at the time of enrollment. d Correlation of EPDS scores between test and retest (>6 months after initial screening)
Fig. 4Severity measures in US cases and respective EPDS scores
Fig. 5Characteristics of Australian cases and test–retest reliability.
a Distribution of EPDS Scores among Australian cases. b Number of children among Australian cases. c Number of years since worst episode at the time of enrollment. d Correlation of EPDS scores between test and retest (>6 months after initial screening)
Fig. 6Severity measures in Australian cases and respective EPDS scores