Wearables and Artificial Intelligence in Advanced Heart Failure Care
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Advanced Heart Failure. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Netherlands
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Advancing Proactive Care in Advanced Heart Failure: Integrating AI and Continuous Remote Monitoring for Early Detection of Heart Failure Deterioration
Overview
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.
Detailed description
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).
Primary outcome measures
- Algorithm Performance Metrics [Time frame: From enrollment to the end of the monitoring period at 1 year.]
Secondary outcome measures (2)
- Change in daily exercise duration [Time frame: From baseline to the end of the monitoring period at 1 year.]
- Perceived usability [Time frame: At 1-year]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Netherlands · 1 center
- UMC Utrecht — Utrecht
Publications
- Schots BBS, Pizarro CS, Arends BKO, Oerlemans MIFJ, Ahmetagic D, van der Harst P, van Es R. Deep learning for electrocardiogram interpretation: Bench to bedside. Eur J Clin Invest. 2025 Apr;55 Suppl 1(Suppl 1):e70002. doi: 10.1111/eci.70002. PMID 40191935
- Wang L, Zhou X. Detection of Congestive Heart Failure Based on LSTM-Based Deep Network via Short-Term RR Intervals. Sensors (Basel). 2019 Mar 28;19(7):1502. doi: 10.3390/s19071502. PMID 30925693
- Huang JD, Wang J, Ramsey E, Leavey G, Chico TJA, Condell J. Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. Sensors (Basel). 2022 Oct 20;22(20):8002. doi: 10.3390/s22208002. PMID 36298352
- Truby LK, Rogers JG. Advanced Heart Failure: Epidemiology, Diagnosis, and Therapeutic Approaches. JACC Heart Fail. 2020 Jul;8(7):523-536. doi: 10.1016/j.jchf.2020.01.014. Epub 2020 Jun 10. PMID 32535126
Identifiers
NCT: NCT07051356 · 24U-1521