Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography

Bhat, Sushanth; Ferraris, Ambra; Gupta, Divya; Mozafarian, Mona; DeBari, Vincent A.; Gushway-Henry, Neola; Gowda, Satish P.; Polos, Peter; Rubinstein, Mitchell; Seidu, Huzaifa; Chokroverty, Sudhansu

doi:10.5664/jcsm.4840

Cited by 113 publications

(84 citation statements)

References 23 publications

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“…Our methodology and results can be used as the baseline for further studies looking into predicting sleep quality from mobile and wearable devices. This is a source of major concern, since many sleep apps in the making predict with unclear methodology and performance [50-52]. Although more studies are highlighting the increasing reliability of consumer sleep wearables [31], we do not know how they calculate or predict sleep quality parameters.…”

Section: Discussionmentioning

confidence: 99%

Sleep Quality Prediction From Wearable Data Using Deep Learning

Sathyanarayana¹,

Joty²,

Fernández-Luque³

et al. 2016

JMIR Mhealth Uhealth

173

124

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BackgroundThe importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science.ObjectiveThe objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality.MethodsActigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM).ResultsDeep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional linear regression. CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional linear regression (0.6463).ConclusionsDeep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.

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Section: Discussionmentioning

confidence: 99%

Sleep Quality Prediction From Wearable Data Using Deep Learning

Sathyanarayana¹,

Joty²,

Fernández-Luque³

et al. 2016

JMIR Mhealth Uhealth

173

124

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“…Of course, there have been many studies devoted to validating hardware devices and reporting on their accuracy and precision. Some studies devoted to validating actigraphs are [45,46,47,48,49,50,51,52,53,54]. They all study the correlation between one commercial actigraph and a PSG (the patient wore the actigraph during the PSG).…”

Section: A Critical Discussion About Accuracy and Validationmentioning

confidence: 99%

Peer Review #1 of "A survey on sleep assessment methods (v0.2)"

2018

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Purpose.A literature review is presented that aims to summarize and compare current methods to evaluate sleep.Methods. Current sleep assessment methods have been classified according to different criteria; e.g., objective (polysomnography, actigraphy…) vs. subjective (sleep questionnaires, diaries…), contact vs. contactless devices, and need for medical assistance vs. self-assessment. A comparison of validation studies is carried out for each method, identifying their sensitivity and specificity reported in the literature. Finally, the state of the market has also been reviewed with respect to customers' opinions about current sleep apps. Results.A taxonomy that classifies the sleep detection methods. A description of each method that includes the tendencies of their underlying technologies analyzed in accordance with the literature. A comparison in terms of precision of existing validation studies and reports.Discussion. In order of accuracy, sleep detection methods may be arranged as follows: Questionnaire < Sleep diary < Contactless devices < Contact devices < PolysomnographyA literature review suggests that current subjective methods present a sensitivity between 73% and 97.7%, while their specificity ranges in the interval 50%-96%. Objective methods such as actigraphy present a sensibility higher than 90%. However, their specificity is low compared to their sensitivity, being one of the limitations of such technology. Moreover, there are other factors, such as the patient's perception of her or his sleep, that can be provided only by subjective methods. Therefore, sleep detection methods should be combined to produce a synergy between objective and subjective methods. The review of the market indicates the most valued sleep apps, but it also identifies problems and gaps, e.g., many hardware devices have not been validated and (especially software apps) should be studied before their clinical use.PeerJ reviewing PDF |

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“…This is achieved by translating collected sensor data into predefined knowledge (e.g., class label), providing an inexpensive and objective alternative to manual sleep stage scoring 95 . Similarly, through its automated analysis capabilities, AI can provide wellness and lifestyle recommendations based on the interpretation of data collected from wearable devices and mobile apps 96,97 , enable clinicians and researchers to track changes in sleep patterns from people's homes 98 or interact with smart-home set-ups to provide better quality sleep through the adjustment of lights and temperature in rooms 99 . Here, we discuss methods of AI-based sleep modelling.…”

Section: Artificial Intelligence-based Sleep Modellingmentioning

confidence: 99%

The future of sleep health: a data-driven revolution in sleep science and medicine

et al. 2020

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In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-ofthe-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

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Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography

Cited by 113 publications

References 23 publications

Sleep Quality Prediction From Wearable Data Using Deep Learning

Sleep Quality Prediction From Wearable Data Using Deep Learning

Peer Review #1 of "A survey on sleep assessment methods (v0.2)"

The future of sleep health: a data-driven revolution in sleep science and medicine

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