Activity-Aware Prompting to Improve Medication Adherence in Heart Failure Patients

NCT04152031 | Completed

• 1 other identifier: 16243-001
• Study Type: interventional
• Target: 40
• Locations: 0 countries
• Sites: N/A

Brief Summary

The long-term objective of this project is to improve human health and impact health care delivery by developing intelligent technologies that aid with health monitoring and intervention. The immediate objective of this project is to design, evaluate and validate machine learning-based software algorithms that recognize daily activities, provide activity-aware medicine reminder interventions and provide insights on intervention timings that yield successful compliance. The investigators hypothesize that many individuals with needs for medicine intervention can be more compliant with their medicine regimen if prompts are provided at the right times and in the right context. The investigators plan to accomplish these objectives by 1) enhancing and validating software algorithms that recognize daily activities and activity transitions, 2) developing and validating activity-aware medicine prompting interventions for mobile devices, and 3) designing technologies to analyze medicine reminder successes and failures. The proposed work will partner real-time methodologies for validation and algorithmic development with smart phone data, utilize novel activity discovery algorithms, and employ activity recognition and prediction algorithms in the development of activity-aware prompting.

Conditions

Heart Failure; Cardiovascular Diseases

Outcome Measures

Primary Outcomes (1)

• Medication adherence rate

  The Russell's adherence score will be used to measure medication adherence rate. A 3-hour window centered on the prescribed dosing time will be considered. A dose taken within this time window will be given a full score for that dosing time; a dose taken outside the window but within a 6 hour window will be given a half score for that dosing time; and missed doses will receive a score of 0. Each participant will be assigned a score from 0.0 to 1.0 for each day. The scores for each subject will be averaged to obtain weekly adherence rates. The overall adherence rate will be computed by taking an average other the entire study period.

  Through study completion, an average of 1 year

Interventions

Prompting OTHER

Participants receive medication reminders on a smartphone. The reminders are generated through machine learning algorithms that automate the process of medication prompting according to successful medication contexts that occurred in the past.

Eligibility Criteria

• Age: 21 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• have a diagnosis of HF and recently hospitalized for HF exacerbation
• age ≥ 21 years;
• live independently (not in an institutional setting); and
• willing to carry the smartphone throughout the day.

You may not qualify if:

• any serious co-morbidities (e.g. malignancy, neurological disorder),
• impaired cognition,
• inability to understand, read, write, or speak English or Spanish
• major or uncorrected hearing or vision loss.

Sponsors & Collaborators

• Washington State University: lead
• University of California, Irvine: collaborator

MeSH Terms

Conditions

Heart Failure; Cardiovascular Diseases

Condition Hierarchy (Ancestors)

Heart Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: OTHER
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Adjunct Faculty

Study Record Dates

• First Submitted: October 22, 2019
• First Posted: November 5, 2019
• Study Start: October 20, 2016
• Primary Completion: August 5, 2019
• Study Completion: August 5, 2019
• Last Updated: April 27, 2023

Record last verified: 2023-04

Data Sharing

• IPD Sharing: Will not share