Wearables and Artificial Intelligence in Advanced Heart Failure Care

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-07-31

Study Completion Date

2027-08-31

Brief Summary

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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.

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Detailed Description

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Advanced heart failure (HF) is characterized by persistent and progressive symptoms despite optimal, guideline-directed medical therapy. Although improvements in care have been achieved, mortality remains high, and recurrent hospitalizations continue to significantly impact patients' morbidity and quality of life. Timely recognition of early signs of clinical deterioration remains a challenge. Innovative approaches that enable early identification of patients at increased risk of readmission may support proactive interventions and help reduce the need for hospitalization.

In the WAI-HF study, we will investigate whether AI-driven analysis wearable data can identify changes that precede hospital admission in patients with advanced heart failure. The wrist-worn device measures several physiological parameters including heart rate, heart rate variability, respiratory rate, skin temperature, 1-lead electrocardiogram, and sleep quality. Data collected in the remote monitoring including continuous data derived from the wearable device and symptomatic data collected in the eHealth app, will be used to develop a predictive model.

The study will be conducted according to the principles of the Declaration of Helsinki (64th WMA General Assembly, Fortaleza, Brazil, October 2013), to 'gedragscode gezondheidsonderzoek', and in accordance with the EU GDPR (General Data Protection Regulation).

Conditions

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Advanced Heart Failure

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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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).