Precision Nudging for Hypertension Management: Observational Study

NCT ID: NCT06354192

Last Updated: 2025-11-25

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

12000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-05-01

Study Completion Date

2026-11-01

Brief Summary

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The purpose of the proposed observational study is to explore the relations between data-driven personalization and equitable health outcomes in a digital health intervention (DHI) for hypertension management. In the current intervention, behavioral reinforcement learning is applied to personalize intervention content to maximize the behavioral outcomes of three target behaviors critical for effective hypertension management: clinical encounters, medication adherence, and self-monitoring of blood pressure (SMBP).

Detailed Description

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Conditions

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Hypertension

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

PROSPECTIVE

Study Groups

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Precision Nudging for Hypertension Management

All participants in this study will receive the digital health intervention.

Precision Nudging for Hypertension Management

Intervention Type BEHAVIORAL

The hypertension digital health intervention under study combines behavioral science theory with a type of machine learning called reinforcement learning to enable precision nudging: the selection, assembly, and delivery of behavioral science-based messages created to address barriers to, and promote, management of hypertension. The behavioral design process used for the hypertension DHI intentionally includes research on traditionally underserved populations, such as racial and ethnic minorities and people with lower incomes or educational attainment. By including the right ingredients to meet a diverse set of needs and by effectively personalizing the content each individual sees based on their own reactions, the intervention aims to improve health equity by matching each individual to the right behavioral approach.

Interventions

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Precision Nudging for Hypertension Management

The hypertension digital health intervention under study combines behavioral science theory with a type of machine learning called reinforcement learning to enable precision nudging: the selection, assembly, and delivery of behavioral science-based messages created to address barriers to, and promote, management of hypertension. The behavioral design process used for the hypertension DHI intentionally includes research on traditionally underserved populations, such as racial and ethnic minorities and people with lower incomes or educational attainment. By including the right ingredients to meet a diverse set of needs and by effectively personalizing the content each individual sees based on their own reactions, the intervention aims to improve health equity by matching each individual to the right behavioral approach.

Intervention Type BEHAVIORAL

Other Intervention Names

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Digital Health Intervention Artificial Intelligence Driven Intervention

Eligibility Criteria

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Inclusion Criteria

* Patient of Cone Health.
* Hypertension condition considered "uncontrolled" (signaled by an eligibility indicator).
* Most recent blood pressure reading is at or higher than 160/90 (lowered to 140/90 in April 2025 in accordance with approved protocol)

Exclusion Criteria

* Non-English speaking.
* Globally unsubscribed.
* Locally unsubscribed.
* Deceased.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Cone Health

OTHER

Sponsor Role collaborator

Lirio

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Cone Health clinics

Greensboro, North Carolina, United States

Site Status RECRUITING

Countries

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United States

Central Contacts

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Amy Bucher, PhD

Role: CONTACT

865-839-6158

Sarah Deedat, PhD

Role: CONTACT

865-243-8000

Facility Contacts

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Amy Bucher, PhD

Role: primary

865.243.8000

Sarah Deedat, PhD

Role: backup

865.243.8000

Other Identifiers

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0450

Identifier Type: -

Identifier Source: org_study_id

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