| Autor: |
Laughlin, Amy I., Cao, Quy, Bryson, Richard, Haughey, Virginia, Abdul-Salaam, Rashad, Gonzenbach, Virgilio, Rudraraju, Mridini, Eydman, Igor, Tweed, Christopher M., Fala, Glenn J., Patel, Kash, Fox, Kevin R., Hanson, C. William, Bekelman, Justin E., Shou, Haochang |
| Předmět: |
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| Zdroj: |
JCO Clinical Cancer Informatics; 12/21/2023, Vol. 7, p1-9, 9p |
| Abstrakt: |
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. Wrist-worn tech can detect when breast cancer patients take medications based on hand motions. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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