Precision Nudging for Hypertension Management: Observational Study

NCT06354192 | Active Not Recruiting

• 1 other identifier: 0450
• Study Type: observational
• Target: 12,000
• Locations: 1 country
• Sites: 1

Brief Summary

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).

Conditions

Hypertension

Keywords

Medication adherence; Self-monitoring of blood pressure; Clinical encounters; Health equity; Precision nudging; Digital health intervention; Behavioral reinforcement learning

Outcome Measures

Primary Outcomes (2)

• Clinical encounter behavioral outcome

  Adherence with recommended clinical encounters given eligibility based on blood pressure readings and medication

  0-18 months

• Blood pressure reading clinical outcome

  Blood pressure reading

  0-18 months

Secondary Outcomes (3)

• Engagement

  0-18 months

• Medication adherence behavioral outcome

  0-18 months

• Self-monitoring of blood pressure behavioral outcome

  0-18 months

Study Arms (1)

Precision Nudging for Hypertension Management

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

Behavioral: Precision Nudging for Hypertension Management

Interventions

Precision Nudging for Hypertension Management 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.

Also known as: Digital Health Intervention, Artificial Intelligence Driven Intervention

Precision Nudging for Hypertension Management

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This observational study will follow participants enrolled in a digital health intervention for hypertension management at one of nine Cone Health Community Access Clinics.

You may qualify if:

• 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)

You may not qualify if:

• Non-English speaking.
• Globally unsubscribed.
• Locally unsubscribed.
• Deceased.

Sponsors & Collaborators

• Lirio: lead
• Cone Health: collaborator

Study Sites (1)

Cone Health clinics

Greensboro, North Carolina, 27401, United States

Location

MeSH Terms

Conditions

Hypertension; Medication Adherence

Condition Hierarchy (Ancestors)

Vascular Diseases; Cardiovascular Diseases; Patient Compliance; Patient Acceptance of Health Care; Treatment Adherence and Compliance; Health Behavior; Behavior

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 3, 2024
• First Posted: April 9, 2024
• Study Start: May 1, 2024
• Primary Completion: June 26, 2026
• Study Completion (Estimated): November 1, 2026
• Last Updated: June 26, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)