The goal of this observational study is to evaluate whether AI-based analyses of wearable sensor data can identify early signs of deterioration leading to hospitalization in patients with advanced heart failure.
The main questions it aims to answer are:
* Can AI-driven analysis of wearable data detect physiological or behavioral changes associated with impending hospital admissions?
* Does wearable-based remote monitoring influence daily exercise duration in patients with advanced heart failure.
* Is wearable-based remote monitoring usable and acceptable for patients with advanced heart failure in a real-world setting?
Participants will wear a wrist-worn (Fitbit) device continuously for one year and will use an eHealth app to answer question about their symptoms. Participant's physical activity, heart rate, heart rate variability, respiratory rate, sleep quality, and symptomatic status will be monitored remotely.
Eligibility
Sex
ALL
Min age
18 Years
Max age
—
Healthy volunteers
No
Inclusion Criteria:
* \>18 years.
* Diagnosis of advanced heart failure, including at least one of the following major criteria.
* LVAD implanted
* Included on the waiting list for Heart transplant
* Meeting the European Society of CArdiology criteria for advanced HF:
* Severe and persistent symptoms of heart failure \[NYHA class III or IV\].
* Severe cardiac dysfunction: according to ESC guidelines definition
* ≥ 1 unplanned visit or hospitalization in the last 12 months requiring IV treatment.
* Have access to a mobile phone or tablet with an operating system iSO 15 or Android 9 (or posterior versions of these systems).
Exclusion Criteria:
* Impossibility to provide inform consent.
* Impossibility to self-report data due to physical or mental disability.
Primary outcome measure(s)
Algorithm Performance Metrics — From enrollment to the end of the monitoring period at 1 year. Algorithm's performance to detect imminent admission in patients with advanced HF will be measured by means of the following parameters: Accuracy, sensitivity, specificity, negative predictive value, positive predictive value, area under the ROC curve.
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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