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      Development and Testing of the MyHealthyPregnancy App: A Behavioral Decision Research-Based Tool for Assessing and Communicating Pregnancy Risk

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          Despite significant advances in medical interventions and health care delivery, preterm births in the United States are on the rise. Existing research has identified important, seemingly simple precautions that could significantly reduce preterm birth risk. However, it has proven difficult to communicate even these simple recommendations to women in need of them. Our objective was to draw on methods from behavioral decision research to develop a personalized smartphone app-based medical communication tool to assess and communicate pregnancy risks related to preterm birth.


          A longitudinal, prospective pilot study was designed to develop an engaging, usable smartphone app that communicates personalized pregnancy risk and gathers risk data, with the goal of decreasing preterm birth rates in a typically hard-to-engage patient population.


          We used semistructured interviews and user testing to develop a smartphone app based on an approach founded in behavioral decision research. For usability evaluation, 16 participants were recruited from the outpatient clinic at a major academic hospital specializing in high-risk pregnancies and provided a smartphone with the preloaded app and a digital weight scale. Through the app, participants were queried daily to assess behavioral risks, mood, and symptomology associated with preterm birth risk. Participants also completed monthly phone interviews to report technical problems and their views on the app’s usefulness.


          App use was higher among participants at higher risk, as reflected in reporting poorer daily moods (Odds ratio, OR 1.20, 95% CI 0.99-1.47, P=.08), being more likely to smoke (OR 4.00, 95% CI 0.93-16.9, P=.06), being earlier in their pregnancy (OR 1.07, 95% CI 1.02-1.12, P=.005), and having a lower body mass index (OR 1.07, 95% CI 1.00-1.15, P=.05). Participant-reported intention to breastfeed increased from baseline to the end of the trial, t15=−2.76, P=.01. Participants’ attendance at prenatal appointments was 84% compared with the clinic norm of 50%, indicating a conservatively estimated cost savings of ~US $450/patient over 3 months.


          Our app is an engaging method for assessing and communicating risk during pregnancy in a typically hard-to-reach population, providing accessible and personalized distant obstetrical care, designed to target preterm birth risk, specifically.

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          Most cited references 16

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          Behavioral Decision Research: A Constructive Processing Perspective

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            Validation of the Edinburgh Postnatal Depression Scale (EPDS) in non-postnatal women.

             J. Cox (1996)
            This paper reports the validation of the EPDS against a Research Diagnostic Criteria diagnosis of Major and Minor depression. The EPDS was administered to non-postnatal women with older children (mean age of youngest child 3 years 9 months) and to postnatal women (baby aged 6 months). All who scored 9 or above and one third of low scorers were interviewed, using Goldberg's Clinical Interview Schedule. The study confirmed good user acceptability of the EPDS when administered as a postal questionnaire (92% response rate). The EPDS was found to have satisfactory sensitivity (79%) and specificity (85%). Our findings suggest that the EPDS take a place alongside other screening scales for depression in Community samples. It is proposed that when used in these settings it is referred to as the Edinburgh Depression Scale.
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              Designing for usability: key principles and what designers think


                Author and article information

                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                April 2017
                10 April 2017
                : 5
                : 4
                1Department of Engineering and Public Policy Carnegie Mellon University Pittsburgh, PAUnited States
                2Magee-Womens Research Institute Department of OBGYN and Reproductive Sciences University of Pittsburgh Pittsburgh, PAUnited States
                3Department of Microbiology and Molecular Genetics University of Pittsburgh Pittsburgh, PAUnited States
                Author notes
                Corresponding Author: Tamar Krishnamurti tamar@
                ©Tamar Krishnamurti, Alexander L Davis, Gabrielle Wong-Parodi, Baruch Fischhoff, Yoel Sadovsky, Hyagriv N Simhan. Originally published in JMIR Mhealth and Uhealth (, 10.04.2017.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.

                Original Paper
                Original Paper

                mhealth, pregnancy, premature birth, decision making


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