The purpose of this study is to assess the acceptability and feasibility of the use of MyHealthyPregnancy (MHP) 3.0 in clinical practice over a 1 year period of time. The investigators will pilot MHP 3.0 (Spanish and English language options) at practice sites at the Obstetrix Medical Group of Houston, Texas and the UPMC (University of Pittsburgh Medical Center) Healthcare System. Patient and care team feedback will be assessed throughout this study. Patients at Obstetrix Medical Group of Houston will be assessed using 4 short acceptability and feasibility surveys administered by the Naima Health team using a link for an online survey during the study term: at baseline, week 14-18, week 24-28, and week 34-36. A subset of participants at UPMC will also be asked to complete user surveys.

Start Date20 Jun 2022

Sponsor / Collaborator

Naima Health LLCStartup

[+2]

NCT05502510 / CompletedNot Applicable

Assessing the Effectiveness and Efficacy of the MyHealthyPregnancy Application

One in ten births will occur prior to 37 weeks of gestation leading to serious complications such as problems with lung, heart and brain function and an increased risk of infant mortality. Solutions exist to treat risk factors related to preterm birth but these solutions require timely identification of the risks which is often not possible within regular prenatal care. This study will evaluate MyHealthyPregnancy, an application that monitors for common risks associated with preterm birth and recommends solutions to the expectant mother and care team.

Start Date23 Sep 2019

Sponsor / Collaborator

Naima Health LLCStartup

[+1]

100 Clinical Results associated with Naima Health LLC

0 Patents (Medical) associated with Naima Health LLC

Literatures (Medical) associated with Naima Health LLC

01 Jan 2022·Procedia Computer Science

Identification of maternal depression risk from natural language collected in a mobile health app

Article

Author: Krishnamurti, Tamar ; Rodriguez, Samantha ; Hayani, Laila ; Davis, Alexander L ; Allen, Kristen

Depression is one of the most common pregnancy complications, affecting approximately 15% of pregnant people. While valid psychometric measures of depression risk exist, they are not consistently administered at routine prenatal care, exacerbating the problem of adequate detection. The language we use in daily life offers a window into our psychological wellbeing. In this longitudinal observational cohort study of prenatal patients using a prenatal care mobile health app, we examine how features of app-entered natural language and other app-entered patient-reported data may be used as indicators for validated depression risk measures. Patient participants (n=1091) were prescribed a prenatal care app as part of a quality improvement initiative in the UPMC healthcare system from September 2019 - May 2022. Natural language from open-ended writing prompts in the app and self-reported daily mood, were entered by patients using the tool. Participants also completed a validated measure of depression risk - the Edinburgh Postnatal Depression Scale (EPDS) - at least once in their pregnancy. A variety of natural language processing tools were used to score sentiment, categorize topics, and capture other semantic and syntactic information from text entries. LASSO was used to model the relationship between the natural language features and depression risk. Open-ended text within a 30-day and 60-day timeframe of completing an EPDS was found to be moderately predictive of moderate to severe depression risk (AUROC=0.66 and 0.67, for each respective timeframe). When combined with average daily reported mood, open-ended text showed good predictive power (AUROC=0.87). Consistently predictive language features across all models included themes of "money" and "sadness." The combination of natural language and other user-reported data collected through a mobile health app offers an opportunity for identifying depression risk among a pregnant population.

01 Oct 2021·Health Informatics JournalQ4 · MEDICINE

Digital medicines for adherence support: A conceptual framework and qualitative study of adherence among chronically ill patients

Q4 · MEDICINE

Article

Author: Davis, Alexander L ; Castillo, Anabel F ; Krishnamurti, Tamar ; Fischhoff, Baruch

Digital medicine programs (DMPs) are emerging technologies that use sensor-enabled medicine to detect when patients have taken their medication and then provide feedback about adherence. We use qualitative methods to understand how patients change their behavioral patterns while participating in a DMP intervention. An influence diagram outlining the factors hypothesized to affect adherence in DMPs constructed from prior scientific research and expert input was created. Subsequently, we conducted semi-structured interviews with 10 patients to see if their experience supported the relationships outlined in the model. We identified three pathways by which DMPs are likely to change behavior around medication adherence: (1) providing patients and providers with accurate, personalized information about adherence; (2) improving patient–provider interactions by structuring them around this information; and (3) facilitating routines and habits for medication use. Chronically ill patients often fail to adhere to drug regimens. Patients in a DMP intervention used the DMP-provided information to better understand drug efficacy and collaborated with their physician to develop adherence strategies. DMPs can promote medication adherence among patients who are willing to use them and may be most effective if physicians are active partners in the DMP.

Adapting a Pregnancy App to Address Disparities in Healthcare Access Among an Emerging Latino Community: Qualitative Study using Implementation Science Frameworks

Author: Davis, Alexander L. ; Castillo, Anabel F. ; Krishnamurti, Tamar

AbstractBackground: Digital mobile health (mHealth) applications are a popular form of prenatal education and care delivery in the U.S.; yet there are few Spanish language options for native speakers. Furthermore, existing applications do not consider cultural differences and disparities in healthcare access, including those specific to Emerging Latino Communities. Objective: This research uses iterative qualitative methods based on the Transcreation and Health Equity Implementation Framework to assess the needs of Latino pregnant individuals for the design of a mHealth pregnancy application. We then adapt an existing application, initially developed for native English speakers, to meet those needs.Methods: We use a three-step culturally-sensitive process to translate a mHealth app to meet the needs of pregnant Latino immigrants living in the United States. Interviews with stakeholders (n = 12) who advocate for the needs of pregnant individuals in an emerging Latino community were used as inputs to identify domains of possible disparities in access to prenatal care. We then conducted semi-structured interviews with peripartum Latino users (n = 14) to understand their perspectives within those domains. We identified a list of topics to create educational material for the modified App and implemented a systematic translation approach to ensure that the new version was acceptable for immigrants from different countries in Latin America. Results: The interviews with stakeholders revealed seven critical domains that need to be addressed in an adapted prenatal app: language barriers, financial barriers, social support, immigration status, cultural differences, healthcare accessibility, and connection to population-specific language-appropriate local resources. The interviews with peripartum women informed how the existing content in the App could be adjusted or built upon to address these issues, including providing information on accessing language-appropriate care and community support. Finally, we used a systematic approach to translate the existing application and create new content. This work illustrates an approach for culturally-sensitive language translation of digital tools focusing on addressing existing health disparities.

100 Deals associated with Naima Health LLC

100 Translational Medicine associated with Naima Health LLC

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