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NCT Number: NCT07051356

Wearables and Artificial Intelligence in Advanced Heart Failure Care

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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Key information

Conditions

Advanced Heart Failure

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

UMC Utrecht

Utrecht, Netherlands

About this study

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).

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. Algorithm Performance Metrics

    Time frame: 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.

Secondary outcomes

  1. Change in daily exercise duration

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

    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.

  2. Perceived usability

    Time frame: At 1-year

    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.

Sponsors and collaborators

Lead sponsor

UMC Utrecht

Other

Collaborators

  • Health Holland
  • Viduet Health

Registry information

Official study title

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

Acronym: WAI-HF

Important dates

Study start
2025
Primary completion
2027
Study completion
2027
First posted
Jul 4, 2025
Registry last updated
Jul 4, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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