1002 Examination of Wearable and Non-Wearable Consumer Sleep-Tracking Devices Versus Polysomnography

Chinoy, Evan D.; Huwa, Kirbie E; Snider, M N; Bessman, Sara C.; Cuellar, Joseph A; Cooper, Adam; Hirsch, Dale A.; Nardulli, Patrick J; Jameson, Jason; Drummond, S; Markwald, Rachel R.

doi:10.1093/sleep/zsz067.999

Cited by 4 publications

(3 citation statements)

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“…Over time, the validity of consumer‐grade wearable devices has improved, and some devices now include a heart rate sensor, which enables detection of some sleep stages. Some of the more recent devices also demonstrate similar agreement with gold‐standard polysomnography and research‐grade devices 152‐155 . However, one drawback of wrist‐worn devices is that they will likely need to be recharged during the assessment period.…”

Section: Digital Phenotyping: Mobile Technology To Collect Pghdmentioning

confidence: 79%

Innovations in research and clinical care using patient‐generated health data

Jim

Hoogland

Brownstein

et al. 2020

CA A Cancer J Clinicians

110

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Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

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Section: Digital Phenotyping: Mobile Technology To Collect Pghdmentioning

confidence: 79%

Innovations in research and clinical care using patient‐generated health data

Jim

Hoogland

Brownstein

et al. 2020

CA A Cancer J Clinicians

110

View full text Add to dashboard Cite

show abstract

“…Fitness trackers rather determine the waking and sleeping times, although they show the user the exact sleep phases. However, there are irregularities, for example, if users do not move while awake [29,30]. The analyzed sleeping phases of trackers for lifestyle purposes are rarely reliable (I6.1) [29].…”

Section: 2mentioning

confidence: 99%

“…However, there are irregularities, for example, if users do not move while awake [29,30]. The analyzed sleeping phases of trackers for lifestyle purposes are rarely reliable (I6.1) [29]. Thus, users may perceive days or nights worse with an evaluation of wearables than without, and vice versa [5].…”

Section: 2mentioning

confidence: 99%

A Healthy Lifestyle and the Adverse Impact of its Digitalization: The Dark Side of Using eHealth Technologies

Behne¹,

Teuteberg²

2020

WI2020 Zentrale Tracks

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eHealth technologies strongly influence healthy lifestyles. In addition to numerous advantages and positive effects, we analyze adverse effects of technologies that are used in everyday life to support a person's health or health management. Therefore, we conducted short interviews and a literature review to structure aspects of a healthy lifestyle in eight areas related to the potential technology usage. Based on that, this contribution presents a systematic collection of negative effects of eHealth technologies on end users or patients. With regard to our results, this contribution provides seven scientific and six practical implications.

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Components of normal human sleep

Johnson

Czeisler

2022

Foundations of Sleep Health

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1002 Examination of Wearable and Non-Wearable Consumer Sleep-Tracking Devices Versus Polysomnography

Cited by 4 publications

References 0 publications

Innovations in research and clinical care using patient‐generated health data

Innovations in research and clinical care using patient‐generated health data

A Healthy Lifestyle and the Adverse Impact of its Digitalization: The Dark Side of Using eHealth Technologies

Components of normal human sleep

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