Heart FailureNCT04152031
Activity-Aware Prompting to Improve Medication Adherence in Heart Failure Patients
A study testing Prompting in people with Heart Failure and Cardiovascular Diseases, run by Washington State University.
Trial 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. Read more about this study
- Who is it for?
- Ages 21 years and older
- Conditions the study is for
- Heart Failure, Cardiovascular Diseases
- What is being tested?
- Prompting
- How many people
- 40 participants
- Where
- No sites listed yet
- Study dates
- 20 Oct 2016to 5 Aug 2019
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 21 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
About this study
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.
Read the detailed description
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. They will validate the hypothesis by designing and evaluating 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 first aim of the project is to expand and validate software algorithms that recognize daily activities and activity transitions with mobile devices. The hypothesis is that daily behavior contexts can be characterized and tracked with minimal user input using machine learning combined with automated activity discovery. In earlier work, the investigators had demonstrated the success of our algorithms in smart homes. In this project, they propose to adapt the techniques for mobile devices.
The second aim of the project is to develop activity-sensitive medicine prompting and assess the impact of activity-sensitive prompting on the primary outcome of medication adherence rates and the secondary outcome of quality of life. To this end, this goal can be decomposed into two tasks including (a) developing activity-sensitive prompting; (b) assessing the impact of activity-sensitive prompting on patient outcomes. The investigators will combine an activity prompting interface with activity recognition to deliver prompts in contexts with demonstrated success.
Finally, in the third aim, the investigators design machine learning algorithms to analyze medicine reminder success and failure situations. They hypothesize that machine learning techniques can be used to automatically predict prompt compliance by using computer algorithms to learn how to distinguish successful from unsuccessful prompt situations. In their approach, the investigators utilize sensor data to analyze daily behavior and link behavior context with medicine adherence.
- Conditions
- Heart Failure, Cardiovascular Diseases
- Being tested
- Prompting
- Medical subject headings
- Heart Failure, Cardiovascular Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
PromptingOther
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Nov 2019
- Study start20 Oct 2016
- Primary completion5 Aug 2019
- Study completion5 Aug 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Heart FailureStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 40 participants (actual)
Sponsor and investigators
- Lead sponsor
- Washington State University
- Sponsor type
- University, hospital or other
- Collaborators
- University of California, Irvine
- Responsible party
- Principal investigator, Hassan Ghasemzadeh, Washington State University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04152031
- Sponsor's protocol ID
- 16243-001
- Status last verified
- Apr 2023
- First submitted
- 22 Oct 2019
- Last updated
- 27 Apr 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: We do not expect to share the data for this feasibility study. We, however, consider any requests for data access from other investigators. Those requesting the data will provide a data-sharing agreement to protect the participant's privacy and will receive only de-identified data.
Study at a glance
From the registry record, last updated by the sponsor on 27 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Activity-Aware Prompting to Improve Medication Adherence in Heart Failure Patients
- ClinicalTrials.gov ID
- NCT04152031
- Acronym / sponsor ID
- 16243-001
- Conditions
- Heart Failure, Cardiovascular Diseases
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 40 participants (actual)
- Lead sponsor
- Washington State UniversityUniversity, hospital or other
- Collaborators
- University of California, Irvine
- Study start
- 20 Oct 2016 (actual)
- Primary completion
- 5 Aug 2019 (actual)
- Study completion
- 5 Aug 2019 (actual)
- First posted
- 5 Nov 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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. They will validate the hypothesis by designing and evaluating 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 first aim of the project is to expand and validate software algorithms that recognize daily activities and activity transitions with mobile devices. The hypothesis is that daily behavior contexts can be characterized and tracked with minimal user input using machine learning combined with automated activity discovery. In earlier work, the investigators had demonstrated the success of our algorithms in smart homes. In this project, they propose to adapt the techniques for mobile devices.
The second aim of the project is to develop activity-sensitive medicine prompting and assess the impact of activity-sensitive prompting on the primary outcome of medication adherence rates and the secondary outcome of quality of life. To this end, this goal can be decomposed into two tasks including (a) developing activity-sensitive prompting; (b) assessing the impact of activity-sensitive prompting on patient outcomes. The investigators will combine an activity prompting interface with activity recognition to deliver prompts in contexts with demonstrated success.
Finally, in the third aim, the investigators design machine learning algorithms to analyze medicine reminder success and failure situations. They hypothesize that machine learning techniques can be used to automatically predict prompt compliance by using computer algorithms to learn how to distinguish successful from unsuccessful prompt situations. In their approach, the investigators utilize sensor data to analyze daily behavior and link behavior context with medicine adherence.
Study design
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
Who may qualify
- Ages
- 21 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| PromptingParticipants 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. | Other | – |
Primary outcome
Medication adherence rate
Time frame: Through study completion, an average of 1 year
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Apr 2023.
- Individual participant dataSharing planned: No. We do not expect to share the data for this feasibility study. We, however, consider any requests for data access from other investigators. Those requesting the data will provide a data-sharing agreement to protect the participant's privacy and will receive only de-identified data.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing Prompting in people with Heart Failure and Cardiovascular Diseases, run by Washington State University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 27 Apr 2023. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Medication adherence rate (time frame: Through study completion, an average of 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Apr 2023; AzTrials last refreshed its copy on 6 Nov 2026.