logo
ResearchBunny Logo
Development of digital measures for nighttime scratch and sleep using wrist-worn wearable devices

Medicine and Health

Development of digital measures for nighttime scratch and sleep using wrist-worn wearable devices

N. Mahadevan, Y. Christakis, et al.

This innovative study delves into the challenges faced by patients with atopic dermatitis, focusing on nighttime scratching and sleep disturbances. Conducted by a team of researchers from Pfizer and the University of Rochester, the research introduces a groundbreaking method utilizing wrist-worn accelerometer data to objectively measure scratching and sleep, demonstrating strong correlations with established sleep measures.

00:00
00:00
Playback language: English
Abstract
Patients with atopic dermatitis experience increased nocturnal pruritus leading to scratching and sleep disturbances. This study developed and validated a method using wrist-worn accelerometer data to continuously measure nighttime scratching and sleep quantity. Heuristic and machine learning algorithms were used to detect sleep intention, sleep-wake states, and scratching episodes. Results showed strong correlations between sensor-derived measures and reference polysomnography and video data, supporting the use of wearables for objective, continuous measurement of nighttime scratching and sleep.
Publisher
npj Digital Medicine
Published On
Mar 03, 2021
Authors
Nikhil Mahadevan, Yiorgos Christakis, Junrui Di, Jonathan Bruno, Yao Zhang, E. Ray Dorsey, Wilfred R. Pigeon, Lisa A. Beck, Kevin Thomas, Yaqi Liu, Madisen Wicker, Chris Brooks, Nina Shaafi Kabiri, Jaspreet Bhangu, Carrie Northcott, Shyamal Patel
Tags
atopic dermatitis
nocturnal pruritus
scratching
sleep measurement
wearable technology
machine learning
accelerometer data

Related Publications

Explore these studies to deepen your understanding of the subject.

Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery
Medicine and Health

Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery

C. Boissin, L. Laflamme, et al.

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets
Medicine and Health

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

X. Xue, S. Chinnaperumal, et al.

Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing
Medicine and Health

Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing

K. Leung, J. T. Wu, et al.

Highly durable and sustainable heterogeneous fabric using in-and-out fluorinated urethane coating for elimination of bacteria and oil-water separation
Engineering and Technology

Highly durable and sustainable heterogeneous fabric using in-and-out fluorinated urethane coating for elimination of bacteria and oil-water separation

M. Choi, S. Park, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny