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Advanced Heart FailureWAI-HFNCT07051356

Wearables and Artificial Intelligence in Advanced Heart Failure Care

An observational study of Advanced Heart Failure, run by UMC Utrecht at 1 site in Netherlands.

Not yet recruiting

Opening soon. The study is approved but enrolment has not started yet. Sites may already take enquiries.

Sites and contacts
Official record
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Trial summary

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. Read more about this study

Who is it for?
Ages 18 years and older
Conditions the study is for
Advanced Heart Failure
How many people
200 participants
Where
1 siteNetherlands
Study dates
Jul 2025to Aug 2027
Phase
Observational
Are there results?
Not yet: the study is under way

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 years and older
  • All sexes
  • No healthy volunteers
  • Adult, Older adult

Study population: Patients who are under care at the UMC Utrecht due to advanced heart failure, are on the wating list for heart transplant or have received an LVAD as treatment (either as bridge to transplant or as destination therapy).

Who may be able to join

  • >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].
Show all 9Show fewer
  • 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).

Who cannot join

  • Impossibility to provide inform consent.
  • Impossibility to self-report data due to physical or mental disability.

Trial locations and contacts

1 site in 1 country. Contact the study team or a site near you to ask about taking part.

Netherlands1 site

  • UMC Utrecht

    Utrecht

Other trials in Netherlands

About this study

Official title: Advancing Proactive Care in Advanced Heart Failure: Integrating AI and Continuous Remote Monitoring for Early Detection of Heart Failure Deterioration

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:

Read more
  • 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.

Read the 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).

Keywords
Advanced heart failure, Wearable device, Artificial intelligence, Remote monitoring, Left ventricular assist device

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Algorithm Performance Metrics

    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.

    Measured: From enrollment to the end of the monitoring period at 1 year.

Secondary outcomes (2)

  1. Change in daily exercise duration

    Daily exercise duration will be measured using the wrist worn-device. Exercise duration will be measured in minutes of activity per day and step count per day.

    Measured: From baseline to the end of the monitoring period at 1 year.

  2. Perceived usability

    Perceived usability will be assessed by means of The System Usability Scale (SUS). SUS is a widely used, validated tool for assessing the usability of a system, product, or technology. SUS is a 10-item questionnaire with statements about the system being evaluated. Each item is rated on a 5-point Likert scale (from Strongly Disagree [1] to Strongly Agree [5]). The single score ranges from 0 to 100 where higher scores indicate better usability.

    Measured: At 1-year

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted4 Jul 2025
  2. Study startJul 2025 (estimated)
  3. Primary completionAug 2027 (estimated)
  4. Study completionAug 2027 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Prospective
Enrolment
200 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT07051356
Sponsor's protocol ID
24U-1521
Acronym
WAI-HF
Status last verified
Jun 2025
First submitted
26 May 2025
Last updated
4 Jul 2025
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No

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.

    PubMed 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.

    PubMed 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.

    PubMed 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.

    PubMed 32535126

Questions about this trial

What is this clinical trial studying?

An observational study of Advanced Heart Failure, run by UMC Utrecht at 1 site in Netherlands.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 9 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: opening soon, as of the sponsor's last update on 4 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in Netherlands. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Algorithm Performance Metrics (time frame: From enrollment to the end of the monitoring period at 1 year.). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT07051356, which also lists 4 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Jul 2025.

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