Clinical trial NCT04198857: Development and Testing of a Mobile Health Application for Management of Gestational Diabetes
Official title: Development and Testing of a Mobile Health Application for Management of Gestational Diabetes
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Diabetes, Gestational
Telemedicine
Lead sponsor: Rutgers, The State University of New Jersey
Sponsor class: OTHER
Collaborators: name: Nepal Health Research Council
name: University of Connecticut
name: Fogarty International Center of the National Institute of Health
name: Dhulikhel Hospital
First posted: 2019-12-13
Enrollment: 220
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: A user-centered design approach, here we propose to develop a culturally-appropriate smartphone application (app) to support self-management of GDM (GDM-DH), and test its usability and preliminary efficacy, among patients in a tertiary level hospital.
Primary purpose: SUPPORTIVE_CARE

Brief summary: Adequate control and management of gestational diabetes mellitus (GDM) during pregnancy is critical to mitigate its short- and long-term health consequences in women and their children and may serve as a key strategy to curb the escalating type 2 diabetes epidemic in low- and middle-income countries (LMICs). Taking a user-centered design approach, here the study investigators propose to develop a culturally-appropriate smartphone application (app) to support self-management of GDM, and additionally, test its usability and preliminary efficacy, among patients in a peri-urban hospital setting in Nepal. App-based lifestyle interventions for GDM management are not common, especially in LMICs where its prevalence is rapidly increasing, and as such, study findings will have important public health relevance for a broader population.

Detailed description: The prevalence of gestational diabetes mellitus (GDM) is rapidly increasing worldwide, particularly in low- and middle-income countries (LMICs). Tight glycemic control via diet and lifestyle modification is critical to treating GDM and preventing its adverse health consequences, including increased risk of type 2 diabetes (T2D), in women and their children. However, in many resource-limited countries including Nepal, time for diet/lifestyle counseling often competes with other components of antenatal care. Mobile health (mHealth) technology can be leveraged to promote healthy behaviors, and support self-management and treatment of GDM, but this approach has not been tried previously in any LMICs. Taking a user-centered design approach, here the study investigators propose to develop a culturally-appropriate smartphone application (app) to support self-management of GDM (GDM-DH), and test its usability and preliminary efficacy, among patients in a tertiary level, university hospital of Kathmandu University, Nepal. Based on the Social Cognitive Theory framework for behavior change, this app will assist in self-management of GDM by increasing the patient's knowledge and self-efficacy to adhere to the recommended diet and physical activity regimens. The app will also help clinicians by generating easily digestible visual displays of patient data and behaviors, which can aid in their clinical decision-making and counseling. In the requirements gathering phase, 6 GDM patients will be recruited into a focus group to view paper prototypes and provide feedback on its features and functions. Additional questions will be asked about their perceived barriers, facilitators, and strategies for lifestyle modification. Key informant interviews will also be conducted with 5 clinicians (gynecologists, dietician, physical therapist) and 3 family members, asking them to provide feedback on the GDM-DH prototype, especially pertaining to the usefulness and format of the graphic summaries of patient data. After revising paper prototypes and developing the first digital prototype, six additional patients with GDM will be recruited for two rounds of usability testing including think-aloud protocol and focus group discussions. Final prototype will be developed following an iterative process of product design and user testing. After developing the app, the investigators will recruit 120 women who are newly diagnosed with GDM, and randomly assign them to one of two treatment conditions: either (A) standard care + telemonitoring alone, or (B) GDM-DH app + standard care + telemonitoring, from 30 weeks of gestation to delivery. All participants will receive bio-monitoring devices for the self-monitoring of blood pressure and blood glucose. Although the fundamental elements of the telemonitoring support are the same between the two groups, patients in the app group will be able to leverage the technological features in the app to aid with self-monitoring.

In addition to 120 women with GDM, we will also be enrolling 100 women who do not have GDM but failed the glucose challenge test (GCT) in the first screening step of the 2-step diagnostic test for GDM. Women who had high GCT (thus failed the GCT screening) but are not diagnosed GDM are also at increased risk of adverse pregnancy outcomes and are also recommended to adopt diet/lifestyle modification in our study site (Dhulikhel Hospital). In addition to randomizing 120 GDM women in a clinical trial, we will also randomize 100 high GCT women to one of two treatment conditions: either (A) standard care alone or (B) GDM-DH app + standard care, for the rest of their pregnancies.

Block randomization will be utilized to split GDM patients and high GCT patients evenly amongst the two treatment groups. The 120 GDM women will be randomized into two groups of 60. The high GCT women will be randomized into two groups of 50. Patients in Treatment A will receive standard care alone, regardless of diagnosis. Patients in Treatment B will receive GDM-DH app + standard care. All women diagnosed with GDM will receive telemonitoring in addition to their standard care, whereas women diagnosed with high GCT only will not.

Eligibility criteria: Inclusion Criteria:

* receive antenatal care at Dhulikhel Hospital
* receive a GDM diagnosis based on Carpenter-Coustan criteria or fail the Glucose Challenge Test only
* less than 30 gestational weeks into pregnancy
* own a smartphone
* have internet connectivity at home
* can understand and read Nepali

Exclusion Criteria:

• Patients with learning difficulties or vision/hearing impairments

Primary outcomes:
measure: Maternal blood glucose levels at 6 weeks postpartum
timeFrame: up to 22 weeks after recruitment
measure: Neonatal birth weight
timeFrame: up to 16 weeks after recruitment
measure: Primary cesarean delivery
timeFrame: up to 16 weeks after recruitment
measure: Neonatal hypoglycemia
timeFrame: up to 16 weeks after recruitment
Secondary outcomes:
measure: Maternal gestational weight gain
timeFrame: up to 16 weeks after recruitment
measure: Usability of the GDM app
timeFrame: up to 22 weeks after recruitment
measure: Acceptability
timeFrame: up to 22 weeks after recruitment
measure: Usability of telemonitoring
timeFrame: up to 22 weeks after recruitment
measure: Self-monitoring blood pressure adherence
timeFrame: recruitment to delivery
measure: Self-monitoring glucose adherence
timeFrame: recruitment to delivery
measure: App Usage
timeFrame: recruitment to delivery
measure: Antenatal care adherence
timeFrame: recruitment to delivery
measure: Apgar scale
timeFrame: up to 16 weeks after recruitment
measure: NICU admittance
timeFrame: up to 16 weeks after recruitment
measure: Proportion of insulin prescription
timeFrame: up to 16 weeks after recruitment
measure: Induction of labor
timeFrame: up to 16 weeks after recruitment
measure: Glucose readings over glycemic target
timeFrame: up to 16 weeks after recruitment
Other outcomes:
Locations:
city: Dhulikhel
country: Nepal
facility: Dhulikhel Hospital, Kathmandu University Hospital
state: Kavrepalanchok
Study officials:
affiliation: Rutgers University
name: Shristi Rawal, PhD
role: PRINCIPAL_INVESTIGATOR
References: