Stroke, IschemicNCT05795842
DELTA (Detecting and Predicting Atrial Fibrillation in Post-Stroke Patients)
An observational study of Stroke, Ischemic, run by Emory University at 1 site in United States.
Trial summary
Atrial Fibrillation (AF) is an abnormal heart rhythm. Because AF is often asymptomatic, it often remains undiagnosed in the early stages. Anticoagulant therapy greatly reduces the risks of stroke in patients diagnosed with AF. Read more about this study
- Who is it for?
- Ages 55 years and older
- Conditions the study is for
- Stroke, Ischemic
- What is being tested?
- wearable wristband model and Samsung Galaxy Watch 6
- Type of treatment
- Medical device
- How many people
- 500 participants
- Where
- 1 siteUnited States
- Study dates
- 21 Mar 2023to Dec 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 55 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Subjects 55 years of age or older, who are discharged from an index ischemic stroke, who are treated at one of the hospitals within the Emory Health Care System, and who initiate follow-up care at the Emory Stroke Clinic. The patient population should have clinically prescribed extended cardiac monitoring.
Who may be able to join
- Adults 55 years of age or older.
- Post-discharge with diagnostic of index ischemic stroke with uncertain cause.
- Subject must be treated at the Emory Stroke Clinic for follow-up treatment.
- Subject must be prescribed a clinical extended cardiac monitoring.
- Subject or their Legal Authorized Representative (LAR) must be willing and able to provide informed consent.
- Subject, family proxy, or caregiver must understand English and the instructions to manage and recharge the study wrist device.
Who cannot join
- Subject is younger than 55 years of age at the time of consent.
- No indication for clinical extended cardiac monitoring.
- Subject, family proxy, or caregiver unable to understand English and unable to follow the instructions on how to manage and recharge the study wrist device.
- Subject has a diagnosis of structural valve disease, endocarditis, aortic arch atheroma >3 mm, hypercoagulability, on lifelong anticoagulation, or has an active neoplastic disease
- Subject or LAR is not willing or able to provide informed consent.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Emory Clinic
Atlanta, Georgia, 30322
Corey Williams, 404-251-4060, corey.williams2@emory.edu
About this study
Official title: Develop and Validate Machine-Learning Algorithm to Detect Atrial Fibrillation With Wearable Devices
Atrial Fibrillation (AF) is an abnormal heart rhythm. Because AF is often asymptomatic, it often remains undiagnosed in the early stages. Anticoagulant therapy greatly reduces the risks of stroke in patients diagnosed with AF. However, diagnosis of AF requires long-term ambulatory monitoring procedures that are burdensome and/or expensive.
Smart devices (such as Apple or Fitbit) use light sensors (called "photoplethysmography" or PPG) and motion sensors (called "accelerometers") to continuously record biometric data, including heart rhythm. Smart devices are already widely adopted.
Read more
This study seeks to validate an investigational machine-learning software (also called "algorithms") for the long-term monitoring and detection of abnormal cardiac rhythms using biometric data collected from consumer smart devices.
The research team aims to enroll 500 subjects who are being followed after a stroke event of uncertain cause at the Emory Stroke Center. Subjects will undergo standard long-term cardiac monitoring (ECG), using FDA-approved wearable devices fitted with skin electrodes or implantable continuous recorders, and backed by FDA-approved software for abnormal rhythm detection.
Patients will wear a study-provided consumer wrist device at home, for the 30 days of ECG monitoring, 23 hours a day. At the end of the 30 days, the device data will be uploaded to a secure cloud server and will be analyzed offline using proprietary software (called "algorithms") and artificial intelligence strategies. Detection of AF events using the investigational algorithms will be compared to the results from the standard monitoring to assess their reliability. Attention will be paid to recorded motion artifacts that can affect the quality and reliability of recorded signals.
The ultimate aim is to establish that smart devices can potentially be used for monitoring purposes when used with specialized algorithms. Smart devices could offer an affordable alternative to standard-of-care cardiac monitoring.
Read the detailed description
An estimated 1.6% - 6% of the population over age 65 have undiagnosed and often asymptomatic AF. Oral anticoagulant therapy (OAC) reduces the risks of ischemic stroke by 64% and all-cause mortality by 26% for those diagnosed with AF. Hence, not proactively diagnosing and treating AF will be too great an opportunity to miss. Opportunistic AF screening is endorsed as a cost-effective way of diagnosing AF at primary care facilities and/or pharmacies using various techniques. However, the benefits, costs, and potential harms of more powerful systematic AF screening remain a matter of debate. Continuous AF monitoring is also needed to characterize AF occurrence in terms of its burden and temporal relation to symptoms. On the other hand, technologies for continuous monitoring of AF need excellent acceptability by patients. Well-established ambulatory techniques (e.g., Holter) are not suited because of their poor wearability and short monitoring duration. Techniques of implantable loop recorders have advanced significantly to support AF monitoring. However, only some patients can experience the benefits of these techniques because of their associated high costs and invasiveness. Cutaneous ECG patches are clinically used for AF monitoring, but they last for 2 to 4 weeks and are limited to a selected patient population with approved reimbursement. Consumer-facing solutions exist to provide spot-check ECG with an accuracy on par with that of clinical ECG devices, but they are not continuous and are infeasible for patients with compromised fine motor functions.
In contrast to these techniques, PPG is much better positioned for passive AF monitoring because of its strong physiological premise and the practical consideration that PPG sensors are ubiquitously available in more than 71% of consumer wearable devices. However, because PPG is ubiquitously available on mainstream wearables with companion software capable of generating AF alerts, laypeople can readily use PPG to monitor themselves and take actions without clinician guidance. An untoward consequence of this approach is the potential inappropriate utilization of healthcare resources when following up on false AF detections by potentially millions of users. Unfortunately, algorithms described in 24 published papers have not yet achieved adequate precision that can effectively combat such a risk. For example, many studies reported an accuracy of > 95% but a 5% of error is still too high for a technology that will be used by millions of people to continuously monitor AF in free-living settings.
- Conditions
- Stroke, Ischemic
- Being tested
- wearable wristband model, Samsung Galaxy Watch 6, Standard of care extended ECG monitoring
- Keywords
- Cardiac monitoring, Atrial Fibrillation
- Medical subject headings
- Ischemic Stroke, Atrial Fibrillation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
wearable wristband modelDevice
Also called Algorithm Device
MOTO 360 smartwatch: is a specific consumer wearable wristband model (Motorola: MOTO 360), fitted with proprietary firmware (LifeQ) to collect continuous biometric signals, including PPG signals and 3-axis accelerometers in an ambulatory setting. The device is not a medical or diagnostic device, but rather a photoplethysmography (PPG) data collection device. PPG is a non-invasive technology that uses light to measure the change in the volume of blood beneath the skin that occurs as the heart beats. LifeQ has developed software that enables the collection of vital signs data from PPG technology.
Samsung Galaxy Watch 6Other
The Samsung Galaxy Watch6 will collect study data on physiological signals with a compatible Samsung Galaxy phone S21. The Samsung Galaxy Watch6 will include various models, the difference being the size of the watch face or the analog front end of the device.
The software device is installed on the Samsung Galaxy Watch. The app on the watch continuously records PPG and/or ECG data and transmits it. The phone app allows study staff to enter the subject ID, initiate data collection, and stop data collection sessions on the watch. It also receives and stores PPG and ECG data from the paired watch. The PPG app used in the study does not trigger irregular rhythm notifications or display rhythm classification. The data collected using the PPG app will support algorithm development.
Standard of care extended ECG monitoringDevice
Participants enrolled in the study are prescribed ambulatory ECG monitoring (Mobile Cardiac Outpatient Telemetry, Biotel e-Patch, or LINQ insertable cardiac monitor). If the patient is negative for Afib during their time wearing an ECG monitoring patch, then patients may proceed with LINQ insertable cardiac monitor, as part of their standard of care. These are standard-of-care FDA-approved devices and detection software. Researchers will rely on the final ECG report to identify arrhythmic events to use as a golden standard to evaluate the algorithm findings. Specifically, the raw data will be used for establishing and getting an accurate ground truth for the algorithm.
| Study group | Role | What they receive |
|---|---|---|
| AFib monitoring learning algorithms | Participants will wear a prescribed (standard of care) ambulatory ECG monitoring (Biotel Patch or LINQ insertable cardiac monitor) and either a MOTO 360 smartwatch, fitted with proprietary firmware (LifeQ) to collect continuous biometric signals, including PPG signals and 3-axis accelerometers in an ambulatory setting or a Samsung Galaxy watch 6 paired with the Samsung Galaxy phone S21 to continuously record PPG and/or ECG data that can transmit data. Receives: Device: wearable wristband model; Other: Samsung Galaxy Watch 6; Device: Standard of care extended ECG monitoring |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity and specificity for detecting AF with PPG
Sensitivity and specificity of the algorithm will be calculated at study completion
Measured: At completion of the study up to five years
The algorithm concordance index or c-index for predicting AF compared with EHR data
The c-index is a metric to evaluate the predictions made by an algorithm. It is defined as the proportion of concordant pairs divided by the total number of possible evaluation pairs. For predicting AF with EHR data, researchers are targeting a higher c-index. Participants with a higher predicted probability of AF will have AF sooner than those with a lower predicted probability.
Measured: At completion of the study up to five years
Secondary outcomes (3)
Assess the characteristics and quality of long-term, continuous high-fidelity ambulatory photoplethysmographic (PPG) data using consumer wearable devices with PPG and accelerometers sensors.
Research team will report signal quality (a number between [0, 1]) for reach 30-second PPG strip and report its relationship with patient mobilities (based on acc signals), time of day.
Measured: Baseline and up to five years
Effect of wrist motion and skin tone on PPG signal
Research team will assess any cofounding effect of wrist motion and skin tone on PPG signal and on AF detection
Measured: At completion of the study up to five years
Validate atrial fibrillation (AF) pattern detection using investigational machine-learning algorithms from wearable devices in post-stroke patients.
The investigators aim to collect and process photoplethysmographic (PPG) signals from wearable devices compared to standard-of-care ECG-based automated detection in post-stroke patients. This is not a hypothesis-driven study but rather a signal database development project with the goal to collect PPG signals and monitoring data to support the development and validation of algorithms that will be useful to detect atrial fibrillation.
Measured: At completion of the study up to five years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Apr 2023
- Study start21 Mar 2023
- Primary completionDec 2027 (estimated)
- Study completionDec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Stroke, IschemicStudies of the same condition, open and under way.
- More trials run by Emory UniversityThe sponsor's other registered studies.
- 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
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Emory University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xiao Hu, Emory University
- Investigators
- Xiao Hu, PhD, Principal investigator, Emory University, School of Nursing
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05795842
- Sponsor's protocol ID
- STUDY00004530
- Other IDs
- 1R01HL166233-01, 2025P010026
- Status last verified
- Jan 2026
- First submitted
- 21 Mar 2023
- Last updated
- 21 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Yes: The research team plans to share de-identified individual participant data collected during the trial.
Study at a glance
From the registry record, last updated by the sponsor on 21 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Develop and Validate Machine-Learning Algorithm to Detect Atrial Fibrillation With Wearable Devices
- ClinicalTrials.gov ID
- NCT05795842
- Acronym / sponsor ID
- STUDY00004530
- Conditions
- Stroke, Ischemic
- Keywords
- Cardiac monitoring, Atrial Fibrillation
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Emory UniversityUniversity, hospital or other
- Collaborators
- Duke University, National Heart, Lung, and Blood Institute (NHLBI)
- Study start
- 21 Mar 2023 (actual)
- Primary completion
- Dec 2027 (estimated)
- Study completion
- Dec 2028 (estimated)
- First posted
- 3 Apr 2023 (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.
Atrial Fibrillation (AF) is an abnormal heart rhythm. Because AF is often asymptomatic, it often remains undiagnosed in the early stages. Anticoagulant therapy greatly reduces the risks of stroke in patients diagnosed with AF. However, diagnosis of AF requires long-term ambulatory monitoring procedures that are burdensome and/or expensive.
Smart devices (such as Apple or Fitbit) use light sensors (called "photoplethysmography" or PPG) and motion sensors (called "accelerometers") to continuously record biometric data, including heart rhythm. Smart devices are already widely adopted.
This study seeks to validate an investigational machine-learning software (also called "algorithms") for the long-term monitoring and detection of abnormal cardiac rhythms using biometric data collected from consumer smart devices.
The research team aims to enroll 500 subjects who are being followed after a stroke event of uncertain cause at the Emory Stroke Center. Subjects will undergo standard long-term cardiac monitoring (ECG), using FDA-approved wearable devices fitted with skin electrodes or implantable continuous recorders, and backed by FDA-approved software for abnormal rhythm detection.
Patients will wear a study-provided consumer wrist device at home, for the 30 days of ECG monitoring, 23 hours a day. At the end of the 30 days, the device data will be uploaded to a secure cloud server and will be analyzed offline using proprietary software (called "algorithms") and artificial intelligence strategies. Detection of AF events using the investigational algorithms will be compared to the results from the standard monitoring to assess their reliability. Attention will be paid to recorded motion artifacts that can affect the quality and reliability of recorded signals.
The ultimate aim is to establish that smart devices can potentially be used for monitoring purposes when used with specialized algorithms. Smart devices could offer an affordable alternative to standard-of-care cardiac monitoring.
Detailed description
An estimated 1.6% - 6% of the population over age 65 have undiagnosed and often asymptomatic AF. Oral anticoagulant therapy (OAC) reduces the risks of ischemic stroke by 64% and all-cause mortality by 26% for those diagnosed with AF. Hence, not proactively diagnosing and treating AF will be too great an opportunity to miss. Opportunistic AF screening is endorsed as a cost-effective way of diagnosing AF at primary care facilities and/or pharmacies using various techniques. However, the benefits, costs, and potential harms of more powerful systematic AF screening remain a matter of debate. Continuous AF monitoring is also needed to characterize AF occurrence in terms of its burden and temporal relation to symptoms. On the other hand, technologies for continuous monitoring of AF need excellent acceptability by patients. Well-established ambulatory techniques (e.g., Holter) are not suited because of their poor wearability and short monitoring duration. Techniques of implantable loop recorders have advanced significantly to support AF monitoring. However, only some patients can experience the benefits of these techniques because of their associated high costs and invasiveness. Cutaneous ECG patches are clinically used for AF monitoring, but they last for 2 to 4 weeks and are limited to a selected patient population with approved reimbursement. Consumer-facing solutions exist to provide spot-check ECG with an accuracy on par with that of clinical ECG devices, but they are not continuous and are infeasible for patients with compromised fine motor functions.
In contrast to these techniques, PPG is much better positioned for passive AF monitoring because of its strong physiological premise and the practical consideration that PPG sensors are ubiquitously available in more than 71% of consumer wearable devices. However, because PPG is ubiquitously available on mainstream wearables with companion software capable of generating AF alerts, laypeople can readily use PPG to monitor themselves and take actions without clinician guidance. An untoward consequence of this approach is the potential inappropriate utilization of healthcare resources when following up on false AF detections by potentially millions of users. Unfortunately, algorithms described in 24 published papers have not yet achieved adequate precision that can effectively combat such a risk. For example, many studies reported an accuracy of > 95% but a 5% of error is still too high for a technology that will be used by millions of people to continuously monitor AF in free-living settings.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AFib monitoring learning algorithms | – | Device: wearable wristband model, Other: Samsung Galaxy Watch 6, Device: Standard of care extended ECG monitoringParticipants will wear a prescribed (standard of care) ambulatory ECG monitoring (Biotel Patch or LINQ insertable cardiac monitor) and either a MOTO 360 smartwatch, fitted with proprietary firmware (LifeQ) to collect continuous biometric signals, including PPG signals and 3-axis accelerometers in an ambulatory setting or a Samsung Galaxy watch 6 paired with the Samsung Galaxy phone S21 to continuously record PPG and/or ECG data that can transmit data. |
Who may qualify
- Ages
- 55 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Subjects 55 years of age or older, who are discharged from an index ischemic stroke, who are treated at one of the hospitals within the Emory Health Care System, and who initiate follow-up care at the Emory Stroke Clinic. The patient population should have clinically prescribed extended cardiac monitoring.
Key inclusion criteria (6 listed)
- Adults 55 years of age or older.
- Post-discharge with diagnostic of index ischemic stroke with uncertain cause.
- Subject must be treated at the Emory Stroke Clinic for follow-up treatment.
- Subject must be prescribed a clinical extended cardiac monitoring.
- Subject or their Legal Authorized Representative (LAR) must be willing and able to provide informed consent.
Key exclusion criteria (5 listed)
- Subject is younger than 55 years of age at the time of consent.
- No indication for clinical extended cardiac monitoring.
- Subject, family proxy, or caregiver unable to understand English and unable to follow the instructions on how to manage and recharge the study wrist device.
- Subject has a diagnosis of structural valve disease, endocarditis, aortic arch atheroma >3 mm, hypercoagulability, on lifelong anticoagulation, or has an active neoplastic disease
- Subject or LAR is not willing or able to provide informed consent.
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 |
|---|---|---|
| wearable wristband modelAlso: Algorithm DeviceMOTO 360 smartwatch: is a specific consumer wearable wristband model (Motorola: MOTO 360), fitted with proprietary firmware (LifeQ) to collect continuous biometric signals, including PPG signals and 3-axis accelerometers in an ambulatory setting. The device is not a medical or diagnostic device, but rather a photoplethysmography (PPG) data collection device. PPG is a non-invasive technology that uses light to measure the change in the volume of blood beneath the skin that occurs as the heart beats. LifeQ has developed software that enables the collection of vital signs data from PPG technology. | Device | AFib monitoring learning algorithms |
| Samsung Galaxy Watch 6The Samsung Galaxy Watch6 will collect study data on physiological signals with a compatible Samsung Galaxy phone S21. The Samsung Galaxy Watch6 will include various models, the difference being the size of the watch face or the analog front end of the device. The software device is installed on the Samsung Galaxy Watch. The app on the watch continuously records PPG and/or ECG data and transmits it. The phone app allows study staff to enter the subject ID, initiate data collection, and stop data collection sessions on the watch. It also receives and stores PPG and ECG data from the paired watch. The PPG app used in the study does not trigger irregular rhythm notifications or display rhythm classification. The data collected using the PPG app will support algorithm development. | Other | AFib monitoring learning algorithms |
| Standard of care extended ECG monitoringParticipants enrolled in the study are prescribed ambulatory ECG monitoring (Mobile Cardiac Outpatient Telemetry, Biotel e-Patch, or LINQ insertable cardiac monitor). If the patient is negative for Afib during their time wearing an ECG monitoring patch, then patients may proceed with LINQ insertable cardiac monitor, as part of their standard of care. These are standard-of-care FDA-approved devices and detection software. Researchers will rely on the final ECG report to identify arrhythmic events to use as a golden standard to evaluate the algorithm findings. Specifically, the raw data will be used for establishing and getting an accurate ground truth for the algorithm. | Device | AFib monitoring learning algorithms |
Primary outcome
Sensitivity and specificity for detecting AF with PPG
Time frame: At completion of the study up to five years
Sensitivity and specificity of the algorithm will be calculated at study completion
The algorithm concordance index or c-index for predicting AF compared with EHR data
Time frame: At completion of the study up to five years
The c-index is a metric to evaluate the predictions made by an algorithm. It is defined as the proportion of concordant pairs divided by the total number of possible evaluation pairs. For predicting AF with EHR data, researchers are targeting a higher c-index. Participants with a higher predicted probability of AF will have AF sooner than those with a lower predicted probability.
3 secondary outcomes
Assess the characteristics and quality of long-term, continuous high-fidelity ambulatory photoplethysmographic (PPG) data using consumer wearable devices with PPG and accelerometers sensors.
Time frame: Baseline and up to five years
Effect of wrist motion and skin tone on PPG signal
Time frame: At completion of the study up to five years
Validate atrial fibrillation (AF) pattern detection using investigational machine-learning algorithms from wearable devices in post-stroke patients.
Time frame: At completion of the study up to five years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Jan 2026.
- Individual participant dataSharing planned: Yes. The research team plans to share de-identified individual participant data collected during the trial.
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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.
Xiao Hu, PhD principal investigator
Emory University, School of Nursing
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Stroke, Ischemic, run by Emory University at 1 site in United States.
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 6 inclusion and 5 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: looking for participants now, as of the sponsor's last update on 21 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Xiao Hu, PhD (404-712-8520, xhu40@emory.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity and specificity for detecting AF with PPG (time frame: At completion of the study up to five years). The study lists 2 primary outcomes in all. 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 21 Jan 2026.