JUN Mobile App for Pregnancy

Overseen By Allison Crawford, PhD

Age: 18 - 65

Sex: Female

Trial Phase: Academic

Sponsor: The University of Texas Health Science Center at San Antonio

Disqualifiers: Non-women, Late pregnancy

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

50 Participants Needed

Trial Summary

Do I need to stop my current medications to join the trial?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the JUN_Pregnancy Mobile Medical App treatment?

The research on similar mobile apps, like ObGynCare, shows that they can help pregnant women by providing nutrition advice, tracking health data like weight and blood pressure, and improving communication with doctors. This suggests that the JUN_Pregnancy app might also be effective in supporting pregnant women. ^{1 2 3 4 5}

How does the JUN Mobile App for Pregnancy differ from other treatments for pregnancy-related conditions?

The JUN Mobile App for Pregnancy is unique because it leverages mobile technology to provide pregnant women with information and self-care tools, which can include features like stress reduction, nutrition management, and communication with healthcare providers. Unlike traditional treatments that may involve medication or in-person consultations, this app offers a convenient, digital approach to managing pregnancy health and complications such as preeclampsia. ^{1 6 7 8 9}

What is the purpose of this trial?

This research activity looks at the safety of an investigational intervention, a mobile health (mHealth) app called JUN, that uses artificial intelligence (AI) to track symptoms and give time-sensitive feedback to pregnant women. JUN is an intervention being developed to provide education and assist with decision making during pregnancy. The researchers hope to learn differences in how effectively the participant use of the app is amongst women with and without criminal justice oversight such as adult probation or parole.

Research Team

Allison Crawford, PhD

Principal Investigator

The University of Texas Health Science Center at San Antonio

Eligibility Criteria

This trial is for pregnant women interested in using a mobile health app called JUN, which provides AI-driven feedback and education during pregnancy. Women under criminal justice oversight like probation or parole are also included to compare usage effectiveness.

Inclusion Criteria

I am a pregnant woman aged between 18 and 50.

Within the first 6 months of pregnancy

Either on community supervision or without criminal system involvement

Exclusion Criteria

I do not identify as a woman.

Later pregnancy, beyond the first 6 months

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Participants use the JUN mHealth app to track symptoms and receive feedback

3 months

Monthly virtual check-ins

Follow-up

Participants are monitored for self-efficacy and health practices after using the app

4 weeks

Treatment Details

Interventions

• JUN_Pregnancy Mobile Medical App

Trial Overview The study tests the safety and efficacy of the JUN mHealth app in tracking pregnancy symptoms and offering timely advice. It aims to understand how well participants use the app, especially comparing those with criminal justice involvement.

Participant Groups

Group I: Pregnant without Criminal Justice Oversight Experimental Treatment 1 Intervention

Pregnant women who are without criminal justice oversight

Group II: Pregnant with Criminal Justice Oversight Experimental Treatment 1 Intervention

Pregnant women who are under Community Supervision (CS), on probation or parole with criminal justice oversight

Who Is Running the Clinical Trial?

The University of Texas Health Science Center at San Antonio

Lead Sponsor

Trials

486

Recruited

92,500+

The University of Texas at San Antonio

Collaborator

Trials

24

Recruited

7,600+

Findings from Research

Pregnant women at high risk for gestational diabetes mellitus (GDM) expressed a strong desire for user-friendly mobile health apps that offer personalized features, accurate information, and interactive designs to help prevent GDM.

The study identified key app functionalities that could support healthy living habits, including professional and peer support, practical tools, and effective reminder systems, which are essential for improving user engagement and app effectiveness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Assessing the Views and Needs of People at High Risk of Gestational Diabetes Mellitus for the Development of Mobile Health Apps: Descriptive Qualitative Study. Duan, B., Liu, Z., Liu, W., et al.[2022]

References

1. Australia pubmed.ncbi.nlm.nih.gov

A review of pregnancy apps freely available in the Google Play Store. [2021]

2. United States pubmed.ncbi.nlm.nih.gov

An Evaluation of Mobile Applications for Reproductive Endocrinology and Infertility Providers. [2018]

3. Korea (South) pubmed.ncbi.nlm.nih.gov

Development and Preliminary Evaluation of Smartphone Application-Based Nutrition Education Material for Twin Pregnancy. [2023]

4. Netherlands pubmed.ncbi.nlm.nih.gov

Integrated System for Monitoring and Prevention in Obstetrics-Gynaecology. [2017]

5. United States pubmed.ncbi.nlm.nih.gov

Rating Pregnancy Wheel Applications Using the APPLICATIONS Scoring System. [2022]

6. Iran pubmed.ncbi.nlm.nih.gov

The Design and Evaluation of a Mobile based Application to Facilitate Self-care for Pregnant Women with Preeclampsia during COVID-19 Prevalence. [2021]

7. United States pubmed.ncbi.nlm.nih.gov

Evaluation of Mobile Applications Intended to Aid in Conception Using a Systematic Review Framework. [2020]

8. Indonesia pubmed.ncbi.nlm.nih.gov

Human-In-The-Loop application design for early detection of pregnancy danger signs. [2023]

9. Canada pubmed.ncbi.nlm.nih.gov

Assessing the Views and Needs of People at High Risk of Gestational Diabetes Mellitus for the Development of Mobile Health Apps: Descriptive Qualitative Study. [2022]