Detection of Medication Taking Using a Wrist-Worn Commercially Available Wearable Device
Amy I. Laughlin, Quy Cao, Richard Bryson, Virginia Haughey, Rashad Abdul-Salaam, Virgilio Gonzenbach, Mridini Rudraraju, Igor Eydman, Christopher M. Tweed, Glenn J. Fala, Kash Patel, Kevin R. Fox, C. William Hanson, Justin E. Bekelman, Haochang Shou- General Medicine
PURPOSE
Medication nonadherence is a persistent and costly problem across health care. Measures of medication adherence are ineffective. Methods such as self-report, prescription claims data, or smart pill bottles have been used to monitor medication adherence, but these are subject to recall bias, lack real-time feedback, and are often expensive.
METHODS
We proposed a method for monitoring medication adherence using a commercially available wearable device. Passively collected motion data were analyzed on the basis of the Movelet algorithm, a dictionary learning framework that builds person-specific chapters of movements from short frames of elemental activities within the movements. We adapted and extended the Movelet method to construct a within-patient prediction model that identifies medication-taking behaviors.
RESULTS
Using 15 activity features recorded from wrist-worn wearable devices of 10 patients with breast cancer on endocrine therapy, we demonstrated that medication-taking behavior can be predicted in a controlled clinical environment with a median accuracy of 85%.
CONCLUSION
These results in a patient-specific population are exemplar of the potential to measure real-time medication adherence using a wrist-worn commercially available wearable device.