Detection of Medication Taking Using a Wrist-Worn Commercially Available Wearable Device.
JCO Clin Cancer Inform
; 7: e2200107, 2023 09.
Article
em En
| MEDLINE
| ID: mdl-38127730
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Punho
/
Dispositivos Eletrônicos Vestíveis
Limite:
Humans
Idioma:
En
Revista:
JCO Clin Cancer Inform
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Panamá