Validation of a Consumer Sleep Wearable Device With Actigraphy and Polysomnography in Adolescents Across Sleep Opportunity Manipulations

Abstract

Study Objectives: To compare the quality and consistency in sleep measurement of a consumer wearable device and a research-grade actigraph with polysomnography (PSG) in adolescents. Methods: Fifty-eight healthy adolescents (aged 15-19 years; 30 males) underwent overnight PSG while wearing both a Fitbit Alta HR and a Philips Respironics Actiwatch 2 (AW2) for 5 nights, with either 5 hours or 6.5 hours time in bed (TIB) and for 4 nights with 9 hours TIB. AW2 data were evaluated using two different wake and immobility thresholds. Discrepancies in estimated total sleep time (TST) and wake after sleep onset (WASO) between devices and PSG, as well as epochby-epoch agreements in sleep/wake classification, were assessed. Fitbit-generated sleep staging was compared to PSG. Results: Fitbit and AW2 under default settings similarly underestimated TST and overestimated WASO (TST: medium setting (M10) ≤ 38 minutes, Fitbit ≤ 47 minutes; WASO: M10 ≤ 38 minutes; Fitbit ≤ 42 minutes). AW2 at the high motion threshold setting provided readings closest to PSG (TST: ≤ 12 minutes; WASO: ≤ 18 minutes). Sensitivity for detecting sleep was ≥ 90% for both wearable devices and further improved to 95% by using the high threshold (H5) setting for the AW2 (0.95). Wake detection specificity was highest in Fitbit (≥ 0.88), followed by the AW2 at M10 (≥ 0.80) and H5 thresholds (≤ 0.73). In addition, Fitbit inconsistently estimated stage N1 + N2 sleep depending on TIB, underestimated stage N3 sleep (21-46 min), but was comparable to PSG for rapid eye movement sleep. Fitbit sensitivity values for the detection of N1 + N2, N3 and rapid eye movement sleep were ≥ 0.68, ≥ 0.50, and ≥ 0.72, respectively. Conclusions: A consumer-grade wearable device can measure sleep duration as well as a research actigraph. However, sleep staging would benefit from further refinement before these methods can be reliably used for adolescents.

Sleep in Myopic and Non-Myopic Children

“…The IS parameter varies from 0 to 1 and quantifies how well synchronized these rhythms are to external zeitgebers, such as the lightdark cycle (with higher IS values indicating stronger synchronization and more stable rhythms), whereas the IV parameter provides an assessment of the degree of fragmentation of the rest-activity patterns (with a value of 0 indicating a perfect sign wave rhythm and a value of two indicating Gaussian noise) with higher values considered to be markers of sleep-wake disturbances. 35,36 The Actiwatch has been shown to have >90% sensitivity for detecting sleep in adolescents, 37 with previous validation studies reporting no significant differences in sleep parameters between the Actiwatch and polysomnography. 38,39 Note that the sleep duration reported here was the time that the software determined the child to be asleep, and not just in bed; therefore sleep duration is not the same as the duration between bedtime and wake time.…”

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

“…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.…”

Evaluation of Sleep Problems or Disorders Using Sleep Questionnaires

“…Physicians should know about the characteristics and limitations of different sleep questionnaires, and they should combine sleep questionnaires with other evaluation tools when diagnosing patients. Recently, various sleep questionnaires and sleep diaries have been developed into mobile app versions, and consumer wearable devices for easier use [38,39].…”