Wearables and Artificial Intelligence in Advanced Heart Failure Care

NCT07051356 | Not Yet Recruiting

• 1 other identifier: 24U-1521
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Advanced Heart Failure

Keywords

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

Outcome Measures

Primary Outcomes (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.

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

Secondary Outcomes (2)

• Change in daily exercise duration

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

• Perceived usability

  At 1-year

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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).

You may qualify if:

• \>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).

You may not qualify if:

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

Sponsors & Collaborators

• UMC Utrecht: lead
• Health Holland: collaborator
• Viduet Health: collaborator

Study Sites (1)

UMC Utrecht

Utrecht, Netherlands

Location

Related Publications (4)

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor. Head of the department of Cardiology, UMC Utrecht.

Study Record Dates

• First Submitted: May 26, 2025
• First Posted: July 4, 2025
• Study Start: July 1, 2025
• Primary Completion (Estimated): August 1, 2027
• Study Completion (Estimated): August 1, 2027
• Last Updated: July 4, 2025

Record last verified: 2025-06

Locations

NL (1)