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Personalized Blood Pressure Care Using IoMTs and Artificial Intelligence

Primary Purpose

Pre-hypertension

Status
Unknown status
Phase
Not Applicable
Locations
United States
Study Type
Interventional
Intervention
AI-Based Lifestyle Recommendations
No Lifestyle Recommendations
Sponsored by
University of California, San Diego
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional prevention trial for Pre-hypertension

Eligibility Criteria

18 Years - undefined (Adult, Older Adult)All SexesAccepts Healthy Volunteers

Inclusion Criteria:

  • age ≥18 years
  • SBP between 130 mmHg to 139 mmHg or/and DBP between 80 to 89 mmHg in a research office
  • speaking and reading English
  • having an iPhone 8 or newer or an Android x or newer

Exclusion Criteria:

  • currently taking antihypertensive medication
  • self-reported diagnosis of coronary heart disease, medical condition or other physical problem necessitating special attention in an exercise program (e.g., cancer, eating disorder, uncontrolled diabetes)
  • current participation in a lifestyle modification program or research study
  • self-report of being currently pregnant

Sites / Locations

  • University of California, San DiegoRecruiting

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

Active Comparator

Arm Label

AI-Based Lifestyle Recommendations Group

Control Group

Arm Description

Participants in this group receive AI-based, personalized lifestyle recommendations based on analysis of their activity tracker and blood pressure data.

Participants in this group do not receive the lifestyle recommendations, but are provided with an identical activity tracker and blood pressure monitor.

Outcomes

Primary Outcome Measures

Average change in systolic blood pressure (SBP) in experimental vs. control group
Average SBP during the first and last week of a patient's enrollment will be used to calculate SBP change for each patient.
Average change in diastolic blood pressure (DBP) in experimental vs. control group
Average DBP during the first and last week of a patient's enrollment will be used to calculate DBP change for each patient.

Secondary Outcome Measures

Number of patients in experimental group that followed lifestyle recommendations
This secondary outcome measure is interested in assessing patient engagement within the experimental group. Data from the activity tracker (steps, sleep quality, active minutes, etc.) will be used to assess whether a patient followed the lifestyle recommendations.

Full Information

First Posted
August 10, 2020
Last Updated
September 27, 2021
Sponsor
University of California, San Diego
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1. Study Identification

Unique Protocol Identification Number
NCT04543656
Brief Title
Personalized Blood Pressure Care Using IoMTs and Artificial Intelligence
Official Title
Personalized Blood Pressure Care for Prehypertensive Patients Using IoMTs and Artificial Intelligence
Study Type
Interventional

2. Study Status

Record Verification Date
September 2021
Overall Recruitment Status
Unknown status
Study Start Date
October 1, 2020 (Actual)
Primary Completion Date
June 2022 (Anticipated)
Study Completion Date
June 2022 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
University of California, San Diego

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No

5. Study Description

Brief Summary
In this RCT, up to 100 adults classified as pre-hypertensive will be randomized to either an artificial intelligence (AI) based lifestyle intervention group or an active control group with a 1 to 1 ratio. Both groups will receive an identical activity tracker (Samsung Galaxy Watch) and BP monitor (Omron Evolv). The AI intervention group will receive automated and personalized lifestyle recommendations based on their lifestyle (e.g. sleep, exercise and diet) and blood pressure (BP) data, involving an automated analytics engine using statistics and machine learning. The active control group will not receive these lifestyle recommendations. The investigators aim to assess objectively the effectiveness of the AI-based personalized lifestyle recommendations on the patients BP.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Pre-hypertension

7. Study Design

Primary Purpose
Prevention
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Masking
Participant
Allocation
Randomized
Enrollment
100 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
AI-Based Lifestyle Recommendations Group
Arm Type
Experimental
Arm Description
Participants in this group receive AI-based, personalized lifestyle recommendations based on analysis of their activity tracker and blood pressure data.
Arm Title
Control Group
Arm Type
Active Comparator
Arm Description
Participants in this group do not receive the lifestyle recommendations, but are provided with an identical activity tracker and blood pressure monitor.
Intervention Type
Behavioral
Intervention Name(s)
AI-Based Lifestyle Recommendations
Intervention Description
The intervention provides participants with automated and personalized lifestyle recommendations involving a sophisticated analytics engine using advanced statistics and machine learning.
Intervention Type
Other
Intervention Name(s)
No Lifestyle Recommendations
Intervention Description
The control group receives an identical activity tracker and BP monitor in order to objectively assess the effectiveness of the experimental group intervention.
Primary Outcome Measure Information:
Title
Average change in systolic blood pressure (SBP) in experimental vs. control group
Description
Average SBP during the first and last week of a patient's enrollment will be used to calculate SBP change for each patient.
Time Frame
Initial 2 week data collection, weekly lifestyle recommendations, 6 months total
Title
Average change in diastolic blood pressure (DBP) in experimental vs. control group
Description
Average DBP during the first and last week of a patient's enrollment will be used to calculate DBP change for each patient.
Time Frame
Initial 2 week data collection, weekly lifestyle recommendations, 6 months total
Secondary Outcome Measure Information:
Title
Number of patients in experimental group that followed lifestyle recommendations
Description
This secondary outcome measure is interested in assessing patient engagement within the experimental group. Data from the activity tracker (steps, sleep quality, active minutes, etc.) will be used to assess whether a patient followed the lifestyle recommendations.
Time Frame
Initial 2 week data collection, weekly lifestyle recommendations, 6 months total

10. Eligibility

Sex
All
Minimum Age & Unit of Time
18 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
Eligibility Criteria
Inclusion Criteria: age ≥18 years SBP between 130 mmHg to 139 mmHg or/and DBP between 80 to 89 mmHg in a research office speaking and reading English having an iPhone 8 or newer or an Android x or newer Exclusion Criteria: currently taking antihypertensive medication self-reported diagnosis of coronary heart disease, medical condition or other physical problem necessitating special attention in an exercise program (e.g., cancer, eating disorder, uncontrolled diabetes) current participation in a lifestyle modification program or research study self-report of being currently pregnant
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Sujit Dey, PhD
Phone
8587617518
Email
dey@eng.ucsd.edu
Facility Information:
Facility Name
University of California, San Diego
City
La Jolla
State/Province
California
ZIP/Postal Code
92093
Country
United States
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Sujit Dey

12. IPD Sharing Statement

Plan to Share IPD
No
Links:
URL
https://ieeexplore.ieee.org/abstract/document/8822927
Description
Offline and Online Learning Techniques for Personalized Blood Pressure Prediction and Health Behavior Recommendations

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Personalized Blood Pressure Care Using IoMTs and Artificial Intelligence

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