Article
Version 1
Preserved in Portico This version is not peer-reviewed
Expert Demand for Consumer Sleep Technology Features and Wearable Devices
Version 1
: Received: 27 December 2021 / Approved: 28 December 2021 / Online: 28 December 2021 (13:58:14 CET)
A peer-reviewed article of this Preprint also exists.
Journal reference: IoT 2022
DOI: 10.3390/iot3020018
Abstract
Global demand for sleep-tracking wearables, or consumer sleep technologies (CSTs), is steadily increasing. CST marketing campaigns often feature a scientific component, but the scientific relevancy and monetary value of CST features within the sleep research community remains unquantified. Sleep medicine experts were recruited through social media and nonprobability sampling techniques to complete a survey identifying sleep metrics and device features that are most desirable to the scientific community. A hypothetical purchase task (HPT) estimated economic valuation for devices with different features by price. Forty-six (N=46) respondents with an average of 10±6 years’ experience conducting research in real-world settings completed the online survey. Total sleep time was ranked as the most important measure of sleep followed by objective sleep quality while sleep architecture/depth and diagnostic information were ranked as least important. Experts preferred wrist-worn devices that could reliably determine sleep episodes as short as 20 minutes. Economic value was greater for hypothetical devices with longer battery life. These data set a precedent to determine how scientific relevance of a product impacts the potential market value of a CST device. This is the first known attempt to establish consensus opinion or economic valuation for scientifically-desirable CST features and metrics using expert elicitation.
Keywords
behavioral economics; wearables; consumer sleep technology; Internet of Things; economical survey; expert elicitation
Subject
BEHAVIORAL SCIENCES, Other
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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