atrial fibrillation
Conditions
Interventions
None listed
Sponsors
Keio University Hospital
Eligibility
Sex/Gender
All
Inclusion criteria
Inclusion criteria: - Is an atrial fibrillation patient receiving treatment at the Department of Cardiology, KEIO University Hospital. - Aged >= 20 years (no gender restrictions). - Has access to an iPhone (iOS > 13.0) and Apple Watch (watchOS > 6.0) and can download the research application from the App Store (Japan) (iPhone and Apple Watch can be borrowed). - Is a Japanese person who can understand Japanese. - Is capable of understanding the content of this study and giving his/her written consent.
Exclusion criteria
Exclusion criteria: - Is unable to wear an Apple Watch while sleeping. - Is unable to operate the research application. - Is scheduled to be transferred or moved. - Has been deemed an inappropriate subject by the research supervisor or office.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Analysis of the degree of agreement between Apple Watch heart data collected while subjects are at rest or sleeping (pulse rates, irregular heartbeat notifications, and ECG recordings) and 14-day ECG patch data (atrial fibrillation events). | — |
Secondary
| Measure | Time frame |
|---|---|
| - Construction and evaluation of a machine-learning algorithm to identify the optimal timing of pulse/ECG recording for arrhythmia detection. - Evaluation of the relevance of Apple Watch healthcare data and 14-day ECG patch data, profile data, questionnaire data, and information from medical records, including data from transtelephonic monitors. | — |
Countries
Japan
Contacts
Public ContactTakehiro Kimura
Keio University School of Medicine Department of Cardiology
Outcome results
None listed