Implementation Framework to Increase Patient Adherence in Clinical Trials Utilizing Wearable Devices

Wearable devices are increasingly integrated into clinical trials to collect continuous physiologic data in inpatient, outpatient, and home settings. Consistent patient adherence is essential to ensure high-quality, analyzable data, yet challenges such as digital literacy, device syncing, and socioeconomic disparities persist. Lessons learned from an R01-funded trial at Massachusetts General Hospital, which incorporated wearable devices to extend a machine learning algorithm for monitoring postoperative events after cardiothoracic surgery, offer insight into strategies to improve adherence.

Insights from the design and implementation of the NIH-NHLBI-funded Wearables Clinical Trial at Massachusetts General Hospital and Case Western Reserve University were synthesized with findings from a focused literature review. This process was used to develop a practical framework of strategies to optimize adherence in clinical trials using wearable devices.

Key strategies include defining clear inclusion and exclusion criteria, offering both virtual and in-person recruitment, reinforcing digital literacy early and repeatedly, establishing regular data monitoring checkpoints, and maintaining targeted participant contact to support accountability. These approaches may enhance participant engagement, reduce data missingness, and promote equitable participation.

Strategies that address technological barriers, participant engagement, and data monitoring can enhance adherence in clinical trials using wearable devices. The proposed framework provides guidance for optimizing study design, improving data quality, and expanding trial accessibility across diverse patient populations. Lessons learned from wearable device integration can inform future studies and facilitate broader adoption of digital health technologies in clinical research.

Not applicable: this article does not report trial results.