Influence of mid-afternoon nap duration and sleep parameters on memory encoding, mood, processing speed, and vigilance.

STUDY OBJECTIVES: To determine how mid-afternoon naps of differing durations benefit memory encoding, vigilance, speed of processing (SOP), mood, and sleepiness; to evaluate if these benefits extend past 3 hr post-awakening and to examine how sleep macrostructure during naps modulate these benefits. METHODS: Following short habitual sleep, 32 young adults underwent four experimental conditions in randomized order: wake; naps of 10 min, 30 min, and 60 min duration verified with polysomnography. A 10-min test battery was delivered at a pre-nap baseline, and at 5 min, 30 min, 60 min, and 240 min post-nap. Participants encoded pictures 90 min post-nap and were tested for recognition 210 min later. RESULTS: Naps ranging from 10 to 60 min increased positive mood and alleviated self-reported sleepiness up to 240 min post-nap. Compared to waking, only naps of 30 min improved memory encoding. Improvements in vigilance were moderate, and benefits for SOP were not observed. Sleep inertia was observed for the 30 min to 60 min naps but was resolved within 30 min after waking. We found no significant associations between sleep macrostructure and memory benefits. CONCLUSIONS: With short habitual sleep, naps ranging from 10 to 60 min had clear and lasting benefits for positive mood and self-reported sleepiness/alertness. Cognitive improvements were moderate, with only the 30 min nap showing benefits for memory encoding. While there is no clear "winning" nap duration, a 30 min nap appears to have the best trade-off between practicability and benefit. CLINICAL TRIAL ID: Effects of Varying Duration of Naps on Cognitive Performance and Memory Encoding, https://www.clinicaltrials.gov/ct2/show/NCT04984824, NCT04984824.

Multi-Night at-Home Evaluation of Improved Sleep Detection and Classification with a Memory-Enhanced Consumer Sleep Tracker.

Purpose: To evaluate the benefits of applying an improved sleep detection and staging algorithm on minimally processed multi-sensor wearable data collected from older generation hardware. Patients and Methods: 58 healthy, East Asian adults aged 23-69 years (M = 37.10, SD = 13.03, 32 males), each underwent 3 nights of PSG at home, wearing 2nd Generation Oura Rings equipped with additional memory to store raw data from accelerometer, infra-red photoplethysmography and temperature sensors. 2-stage and 4-stage sleep classifications using a new machine-learning algorithm (Gen3) trained on a diverse and independent dataset were compared to the existing consumer algorithm (Gen2) for whole-night and epoch-by-epoch metrics. Results: Gen 3 outperformed its predecessor with a mean (SD) accuracy of 92.6% (0.04), sensitivity of 94.9% (0.03), and specificity of 78.5% (0.11); corresponding to a 3%, 2.8% and 6.2% improvement from Gen2 across the three nights, with Cohen's d values >0.39, t values >2.69, and p values <0.01. Notably, Gen 3 showed robust performance comparable to PSG in its assessment of sleep latency, light sleep, rapid eye movement (REM), and wake after sleep onset (WASO) duration. Participants <40 years of age benefited more from the upgrade with less measurement bias for total sleep time (TST), WASO, light sleep and sleep efficiency compared to those ≥40 years. Males showed greater improvements on TST and REM sleep measurement bias compared to females, while females benefitted more for deep sleep measures compared to males. Conclusion: These results affirm the benefits of applying machine learning and a diverse training dataset to improve sleep measurement of a consumer wearable device. Importantly, collecting raw data with appropriate hardware allows for future advancements in algorithm development or sleep physiology to be retrospectively applied to enhance the value of longitudinal sleep studies.

Sleep after learning aids the consolidation of factual knowledge, but not relearning.

STUDY OBJECTIVES: Sleep strengthens and reorganizes declarative memories, but the extent to which these processes benefit subsequent relearning of the same material remains unknown. It is also unclear whether sleep-memory effects translate to educationally realistic learning tasks and improve long-term learning outcomes. METHODS: Young adults learned factual knowledge in two learning sessions that were 12 h apart and separated by either nocturnal sleep (n = 26) or daytime wakefulness (n = 26). Memory before and after the retention interval was compared to assess the effect of sleep on consolidation, while memory before and after the second learning session was compared to assess relearning. A final test 1 week later assessed whether there was any long-term advantage to sleeping between two study sessions. RESULTS: Sleep significantly enhanced consolidation of factual knowledge (p = 0.01, d = 0.72), but groups did not differ in their capacity to relearn the materials (p = 0.72, d = 0.10). After 1 week, a numerical memory advantage remained for the sleep group but was no longer significant (p = 0.21, d = 0.35). CONCLUSIONS: Reduced forgetting after sleep is a robust finding that extends to our ecologically valid learning task, but we found no evidence that sleep enhances relearning. Our findings can exclude a large effect of sleep on long-term memory after 1 week, but hint at a smaller effect, leaving open the possibility of practical benefits from organizing study sessions around nocturnal sleep. These findings highlight the importance of revisiting key sleep-memory effects to assess their relevance to long-term learning outcomes with naturalistic learning materials.

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

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 epoch-by-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. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Title: The Cognitive and Metabolic Effects of Sleep Restriction in Adolescents; Identifier: NCT03333512; URL: https://clinicaltrials.gov/ct2/show/NCT03333512. CITATION: Lee XK, Chee NIYN, Ong JL, Teo TB, van Rijn E, Lo JC, Chee MWL. Validation of a consumer sleep wearable device with actigraphy and polysomnography in adolescents across sleep opportunity manipulations. J Clin Sleep Med. 2019;15(9):1337-1346.