Background Actigraphs are wrist-worn devices that record tri-axial accelerometry data used clinically and in research studies. The expense of research-grade actigraphs, however, limit their widespread adoption, especially in clinical settings. Tri-axial accelerometer-based consumer wearable devices have gained worldwide popularity and hold potential for a cost-effective alternative. The lack of independent validation of minute-to-minute accelerometer data with polysomnographic data or even research-grade actigraphs, as well as access to raw data has hindered the utility and acceptance of consumer-grade actigraphs. Methods Sleep clinic patients wore a consumer-grade wearable (Huami Arc) on their non-dominant wrist while undergoing an overnight polysomnography (PSG) study. The sample was split into two, 20 in a training group and 21 in a testing group. In addition to the Arc, the testing group also wore a research-grade actigraph (Philips Actiwatch Spectrum). Sleep was scored for each 60-s epoch on both devices using the Cole-Kripke algorithm. Results Based on analysis of our training group, Arc and PSG data were aligned best when a threshold of 10 units was used to examine the Arc data. Using this threshold value in our testing group, the Arc has an accuracy of 90.3%±4.3%, sleep sensitivity (or wake specificity) of 95.5%±3.5%, and sleep specificity (wake sensitivity) of 55.6%±22.7%. Compared to PSG, Actiwatch has an accuracy of 88.7%±4.5%, sleep sensitivity of 92.6%±5.2%, and sleep specificity of 60.5%±20.2%, comparable to that observed in the Arc. Conclusions An optimized sleep/wake threshold value was identified for a consumer-grade wearable Arc trained by PSG data. By applying this sleep/wake threshold value for Arc generated accelerometer data, when compared to PSG, sleep and wake estimates were adequate and comparable to those generated by a clinical-grade actigraph. As with other actigraphs, sleep specificity plateaus due to limitations in distinguishing wake without movement from sleep. Further studies are needed to evaluate the Arc’s ability to differentiate between sleep and wake using other sources of data available from the Arc, such as high resolution accelerometry and photoplethysmography.

Background: Actigraphs are widely used portable wrist-worn devices that record tri-axial accelerometry data. These data can be used to approximate amount and timing of sleep and wake. Their clinical utility is limited, however, by their expense. Tri-axial accelerometer-based consumer wearable devices (so-called fitness monitors) have gained popularity and could represent cost-effective research alternatives to more expensive devices. Lack of independent validation of minute-to-minute accelerometer data for consumer devices has hindered their utility and acceptance. Methods: We studied a consumer-grade wearable device, Arc (Huami Inc., Mountain View CA), for which minuteto-minute accelerometer data (vector magnitude) could be obtained. Twelve healthy participants and 19 sleep clinic patients wore on their non-dominant wrist, both an Arc and a research-grade actigraph (Actiwatch Spectrum, Philips, Bend OR) continuously over a period of 48 h in free-living conditions. Time-stamped data from each participant were aligned and the Cole-Kripke algorithm was used to assign a state of "sleep" or "wake" for each minute-long epoch recorded by the Arc. The auto and low scoring settings on the Actiwatch software (Actiware) were used to determine sleep and wake from the Actiwatch data and were used as the comparators. Receiver operating characteristic curves were used to optimize the relationship between the devices. Results: Minute-by-minute Arc and Actiwatch data were highly correlated (r = 0.94, Spearman correlation) over the 48-h study period. Treating the Actiwatch auto scoring as the gold standard for determination of sleep and wake, Arc has an overall accuracy of 99.0% ± 0.17% (SEM), a sensitivity of 99.4% ± 0.19%, and a specificity of 84.5% ± 1.9% for the determination of sleep. As compared to the Actiwatch low scoring, Arc has an overall accuracy of 95.2% ± 0.36%, a sensitivity of 95.7% ± 0.47%, and a specificity of 91.7% ± 0.60% for the determination of sleep. Conclusions: The Arc, a consumer wearable device in which minute-by-minute activity data could be collected and compared, yielded fundamentally similar sleep scoring metrics as compared to a commonly used clinical-grade actigraph (Actiwatch). We found high degrees of agreement in minute-to-minute data scoring for sleep and wake periods between the two devices.

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