Wearable Device Heart Rate and Activity Data in an Unsupervised Approach to Personalized Sleep Monitoring: Algorithm Validation.

Jiaxing Liu,Boya Lai,Yang Zhao,Kwok Leung Tsui,Hailiang Wang

doi:10.2196/18370

Abstract

BackgroundThe proliferation of wearable devices that collect activity and heart rate data has facilitated new ways to measure sleeping and waking durations unobtrusively and longitudinally. Most existing sleep/wake identification algorithms are based on activity only and are trained on expensive and laboriously annotated polysomnography (PSG). Heart rate can also be reflective of sleep/wake transitions, which has motivated its investigation herein in an unsupervised algorithm. Moreover, it is necessary to develop a personalized approach to deal with interindividual variance in sleep/wake patterns.ObjectiveWe aimed to develop an unsupervised personalized sleep/wake identification algorithm using multifaceted data to explore the benefits of incorporating both heart rate and activity level in these types of algorithms and to compare this approach’s output with that of an existing commercial wearable device’s algorithms.MethodsIn this study, a total of 14 community-dwelling older adults wore wearable devices (Fitbit Alta; Fitbit Inc) 24 hours a day and 7 days a week over period of 3 months during which their heart rate and activity data were collected. After preprocessing the data, a model was developed to distinguish sleep/wake states based on each individual’s data. We proposed the use of hidden Markov models and compared different modeling schemes. With the best model selected, sleep/wake patterns were characterized by estimated parameters in hidden Markov models, and sleep/wake states were identified.ResultsWhen applying our proposed algorithm on a daily basis, we found there were significant differences in estimated parameters between weekday models and weekend models for some participants.ConclusionsOur unsupervised approach can be effectively implemented based on an individual’s multifaceted sleep-related data from a commercial wearable device. A personalized model is shown to be necessary given the interindividual variability in estimated parameters.

Highlights

Sleep plays a vital role in maintaining health [1,2]
Short sleep duration may lead to adverse health outcomes, such as obesity, insulin resistance, depression, hypertension, and cardiovascular disease [4,5,6,7,8]
Polysomnography is the gold standard for sleep duration and sleep quality assessment; various devices are used to record multiple body functions such as brain activity, eye movements, skeletal muscle movement, and heart rhythm during sleep, and it is typically done in a sleep lab or clinic [11]

Summary

Introduction

Background Sleep plays a vital role in maintaining health [1,2]. Adequate sleep can help to maintain a high quality of life [3]. Health care professionals use these physiological measures to assess sleep states, the cost of overnight PSG may range from US $600 to $5000 each night [12]. Such assessment is expensive and intrusive for consumers, and labor-intensive and resource-demanding for health care professionals to achieve [13,14,15], making it hard to use for long-term sleep monitoring at home. The proliferation of wearable devices that collect activity and heart rate data has facilitated new ways to measure sleeping and waking durations unobtrusively and longitudinally. It is necessary to develop a personalized approach to deal with interindividual variance in sleep/wake patterns

Objectives

Methods

Results

Conclusion