Using Apple Watch data and machine learning to predict heart failure outcomes
Uses Apple Watch data to predict heart failure worsening in adults.
Plain-language title generated from the registry record
Registry title: Prediction of Heart-Failure with Machine Learning
Key facts
- Where
- Goettigen, GermanySee the site
Where in Germany
- University Medical Center GoettingenGoettigen, GermanyTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- University Medical Center Goettingen
- Trial code
- NCT06819618
- Record updated
- 11 February 2025
- Original description
In this monocentric observational study the research question is to what extent data collected via Apple Watch can predict the heart failure status of decompensated HF patients. For this purpose, physiological data from the Apple Watch (such as single-lead electrocardiogram, SpO2, respiratory rate, step count, nighttime temperature, etc.) will be extracted and used as predictor variables to forecast outcomes like risk of decompensation and rehospitalization within the follow-up period. Since this is a data-driven study, additional data collected as part of guideline-compliant treatment will also be included.Read full original descriptionShow less
Predicting Heart Failure Recovery by Wearables and Machine Learning
In this monocentric observational study the research question is to what extent data collected via Apple Watch can predict the heart failure status of decompensated HF patients. For this purpose, physiological data from the Apple Watch (such as single-lead electrocardiogram, SpO2, respiratory rate, step count, nighttime temperature, etc.) will be extracted and used as predictor variables to forecast outcomes like risk of decompensation and rehospitalization within the follow-up period. Since this is a data-driven study, additional data collected as part of guideline-compliant treatment will also be included.
Inclusion: age over 17; HFrEF with LV-EF under 41; hospitalized for decompensated heart failure with a) nTproBNP over 1000 AND b) willing to participate AND c) at least one out of three clinical signs (edema, pleural effusion, ascites).
Exclusion: life expectancy under 6 months due to non-cardiac conditions; inability to use smartwatch; severe valvular lesions.