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Project HealthDesign: Estrellita - An Application for Tracking Observations of Daily Living Related to Preterm Infants, 2011-2012

Resource Type
Dataset : clinical data, images: photographs, drawings, graphical representations, text, observational data, survey data
  • Flatley Brennan, Patricia (University of Wisconsin-Madison)
  • Hayes, Gillian R. (University of California-Irvine)
  • Cheng-Ramos, Karen G. (University of California-Irvine)
Other Title
  • Archival Version (Subtitle)
Collective Title
  • Project HealthDesign Series
Publication Date
Funding Reference
  • Robert Wood Johnson Foundation
  • National Science Foundation
Free Keywords
health care; health information technology; observations of daily living; postpartum depression; premature births; stress
  • Abstract

    More than 12 percent of all United States births each year are preterm. To improve the care of these infants, which have an increased risk of serious developmental and chronic health problems, the Estrellita team created a mobile phone application to monitor premature infants and their caregivers. This app allows caregivers to better understand the infant care process and to more easily interact with clinicians about themselves and the care of their infants. Caregivers used the Estrellita app to record their own stress levels and mood and their infants' observations of daily living (ODL) such as baby fussiness, diapering, weight, and bonding activities with the babies. In addition, the app allowed the caregivers to track clinical appointments, review the ODL data, and send and receive text messages from clinicians.
  • Methods

    ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Checked for undocumented or out-of-range codes..
  • Methods

    Presence of Common Scales: Edinburgh Postnatal Depression Scale (EPDS) Fussy-o-meter MoodMap Perceived Stress Scale
  • Table of Contents


    • DS0: Study-Level Files
    • DS1: Appointments Data
    • DS2: Diapers Data
    • DS3: Weight Data
    • DS4: Survey Responses Data
    • DS5: General Indicators Data
    • DS6: Logs Data
    • DS7: Posts Data
Temporal Coverage
  • 2011 / 2012
    Time period: 2011--2012
Geographic Coverage
  • Orange County, California
Sampled Universe
Premature infants and their caregivers.
A convenience sample of eight infants and their caregivers was selected from neonatal intensive care units in the Orange County, California area.
Collection Mode
  • Estrellita (formerly FitBaby) was a project of Project HealthDesign, a national program of the Robert Wood Johnson Foundation designed to stimulate innovation in the development of person health records systems. Additional information about Estrellita is available on the Project HealthDesign Web site.

    Additional support for data cleaning and depositing the data with ICPSR came from the Smart Asthma Management program funded by the National Science Foundation.

Funding insitution(s): Robert Wood Johnson Foundation (67163). National Science Foundation (IIS-1343969).
One or more files in this study are not available for download due to special restrictions; consult the study documentation to learn more on how to obtain the data.
Alternative Identifiers
  • 36029 (Type: ICPSR Study Number)
  • Is previous version of
    DOI: 10.3886/ICPSR36029.v1

Update Metadata: 2015-08-05 | Issue Number: 5 | Registration Date: 2015-06-29

Flatley Brennan, Patricia; Hayes, Gillian R.; Cheng-Ramos, Karen G. (2015): Project HealthDesign: Estrellita - An Application for Tracking Observations of Daily Living Related to Preterm Infants, 2011-2012. Archival Version. Project HealthDesign Series. Version: v0. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset.