Performance of wearable sleep trackers during nocturnal sleep and periods of simulated real-world smartphone use.

GOAL AND AIMS: To test sleep/wake transition detection of consumer sleep trackers and research-grade actigraphy during nocturnal sleep and simulated peri-sleep behavior involving minimal movement. FOCUS TECHNOLOGY: Oura Ring Gen 3, Fitbit Sense, AXTRO Fit 3, Xiaomi Mi Band 7, and ActiGraph GT9X. REFERENCE TECHNOLOGY: Polysomnography. SAMPLE: Sixty-three participants (36 female) aged 20-68. DESIGN: Participants engaged in common peri-sleep behavior (reading news articles, watching videos, and exchanging texts) on a smartphone before and after the sleep period. They were woken up during the night to complete a short questionnaire to simulate responding to an incoming message. CORE ANALYTICS: Detection and timing accuracy for the sleep onset times and wake times. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Discrepancy analysis both including and excluding the peri-sleep activity periods. Epoch-by-epoch analysis of rate and extent of wake misclassification during peri-sleep activity periods. CORE OUTCOMES: Oura and Fitbit were more accurate at detecting sleep/wake transitions than the actigraph and the lower-priced consumer sleep tracker devices. Detection accuracy was less reliable in participants with lower sleep efficiency. IMPORTANT ADDITIONAL OUTCOMES: With inclusion of peri-sleep periods, specificity and Kappa improved significantly for Oura and Fitbit, but not ActiGraph. All devices misclassified motionless wake as sleep to some extent, but this was less prevalent for Oura and Fitbit. CORE CONCLUSIONS: Performance of Oura and Fitbit is robust on nights with suboptimal bedtime routines or minor sleep disturbances. Reduced performance on nights with low sleep efficiency bolsters concerns that these devices are less accurate for fragmented or disturbed sleep.

Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices.

AIMS: Evaluate the performance of 6 wearable sleep trackers across 4 classes (EEG-based headband, research-grade actigraphy, iteratively improved consumer tracker, low-cost consumer tracker). FOCUS TECHNOLOGY: Dreem 3 headband, Actigraph GT9X, Oura Ring Gen3, Fitbit Sense, Xiaomi Mi Band 7, Axtro Fit3. REFERENCE TECHNOLOGY: In-lab polysomnography with 3-reader, consensus sleep scoring. SAMPLE: Sixty participants (26 males) across 3 age groups (18-30, 31-50, and 51-70years). DESIGN: Overnight in a sleep laboratory from habitual sleep time to wake time. CORE ANALYTICS: Discrepancy and epoch-by-epoch analyses for sleep/wake (2-stage) and sleep-stage (4-stage; wake/light/deep/rapid eye movement) classification (devices vs. polysomnography). CORE OUTCOMES: EEG-based Dreem performed the best (2-stage kappa=0.76, 4-stage kappa=0.76-0.86) with the lowest total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset discrepancies vs. polysomnography. This was followed by the iteratively improved consumer trackers: Oura (2-stage kappa=0.64, 4-stage kappa=0.55-0.70) and Fitbit (2-stage kappa=0.58, 4-stage kappa=0.45-0.60) which had comparable total sleep time and sleep efficiency discrepancies that outperformed accelerometry-only Actigraph (2-stage kappa=0.47). The low-cost consumer trackers had poorest overall performance (2-stage kappa<0.31, 4-stage kappa<0.33). IMPORTANT ADDITIONAL OUTCOMES: Proportional biases were driven by nights with poorer sleep (longer sleep onset latencies and/or wake after sleep onset). CORE CONCLUSION: EEG-based Dreem is recommended when evaluating poor quality sleep or when highest accuracy sleep-staging is required. Iteratively improved non-EEG sleep trackers (Oura, Fitbit) balance classification accuracy with well-tolerated, and economic deployment at-scale, and are recommended for studies involving mostly healthy sleepers. The low-cost trackers, can log time in bed but are not recommended for research use.

Advantage conferred by overnight sleep on schema-related memory may last only a day.

Study Objectives: Sleep contributes to declarative memory consolidation. Independently, schemas benefit memory. Here we investigated how sleep compared with active wake benefits schema consolidation 12 and 24 hours after initial learning. Methods: Fifty-three adolescents (age: 15-19 years) randomly assigned into sleep and active wake groups participated in a schema-learning protocol based on transitive inference (i.e. If B > C and C > D then B > D). Participants were tested immediately after learning and following 12-, and 24-hour intervals of wake or sleep for both the adjacent (e.g. B-C, C-D; relational memory) and inference pairs: (e.g.: B-D, B-E, and C-E). Memory performance following the respective 12- and 24-hour intervals were analyzed using a mixed ANOVA with schema (schema, no-schema) as the within-participant factor, and condition (sleep, wake) as the between-participant factor. Results: Twelve hours after learning, there were significant main effects of condition (sleep, wake) and schema, as well as a significant interaction, whereby schema-related memory was significantly better in the sleep condition compared to wake. Higher sleep spindle density was most consistently associated with greater overnight schema-related memory benefit. After 24 hours, the memory advantage of initial sleep was diminished. Conclusions: Overnight sleep preferentially benefits schema-related memory consolidation following initial learning compared with active wake, but this advantage may be eroded after a subsequent night of sleep. This is possibly due to delayed consolidation that might occur during subsequent sleep opportunities in the wake group. Clinical Trial Information: Name: Investigating Preferred Nap Schedules for Adolescents (NFS5) URL: https://clinicaltrials.gov/ct2/show/NCT04044885. Registration: NCT04044885.

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.

Multi-Night Validation of a Sleep Tracking Ring in Adolescents Compared with a Research Actigraph and Polysomnography.

BACKGROUND: Wearable devices have tremendous potential for large-scale longitudinal measurement of sleep, but their accuracy needs to be validated. We compared the performance of the multisensor Oura ring (Oura Health Oy, Oulu, Finland) to polysomnography (PSG) and a research actigraph in healthy adolescents. METHODS: Fifty-three adolescents (28 females; aged 15-19 years) underwent overnight PSG monitoring while wearing both an Oura ring and Actiwatch 2 (Philips Respironics, USA). Measurements were made over multiple nights and across three levels of sleep opportunity (5 nights with either 6.5 or 8h, and 3 nights with 9h). Actiwatch data at two sensitivity settings were analyzed. Discrepancies in estimated sleep measures as well as sleep-wake, and sleep stage agreements were evaluated using Bland-Altman plots and epoch-by-epoch (EBE) analyses. RESULTS: Compared with PSG, Oura consistently underestimated TST by an average of 32.8 to 47.3 minutes (Ps < 0.001) across the different TIB conditions; Actiwatch 2 at its default setting underestimated TST by 25.8 to 33.9 minutes. Oura significantly overestimated WASO by an average of 30.7 to 46.3 minutes. It was comparable to Actiwatch 2 at default sensitivity in the 6.5, and 8h TIB conditions. Relative to PSG, Oura significantly underestimated REM sleep (12.8 to 19.5 minutes) and light sleep (51.1 to 81.2 minutes) but overestimated N3 by 31.5 to 46.8 minutes (Ps < 0.01). EBE analyses demonstrated excellent sleep-wake accuracies, specificities, and sensitivities - between 0.88 and 0.89 across all TIBs. CONCLUSION: The Oura ring yielded comparable sleep measurement to research grade actigraphy at the latter's default settings. Sleep staging needs improvement. However, the device appears adequate for characterizing the effect of sleep duration manipulation on adolescent sleep macro-architecture.

Prognostic impact of different definitions of metabolic syndrome in predicting cardiovascular events in a cohort of non-diabetic Tehranian adults.

BACKGROUND: The applicability of different definitions of metabolic syndrome (MetS) in predicting cardiovascular diseases (CVD) remains questionable. The aim of this study was to compare predictive ability of different definitions of MetS for CVD in non-diabetic subjects. METHODS: In this community-based study, 5198 non-diabetic subjects aged ≥ 30 years (mean age 45.6 years, 45% men) free of CVD at baseline were followed for a median of 9.3 years to assess risk for CVD. We assessed the predictability of definitions of the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III), the International Diabetes Federation (IDF), the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI), and the joint interim statement (JIS) on development of CVD. Hazard ratios (HRs) were calculated using Cox proportional-hazards models. The receiver operating characteristic (ROC) curve was also used to compare discriminative power of these MetS definitions in predicting CVD events. RESULTS: Compared to other definitions, the JIS identified more participants (41.8%) having MetS. First CVD events occurred in 311 subjects. After adjustment for potential confounders, the HRs of the NCEP-ATP III, AHA/NHLBI, IDF and JIS definitions for incident CVD were 1.55 (1.21-2.00), 1.73 (1.35-2.20), 1.54 (1.22-1.94) and 1.70 (1.34-2.17), respectively. All definitions showed higher HRs for females in comparison to males (P<0.05). ROC analysis showed no significant difference in the discriminative power of different MetS definitions in predicting CVD events (P>0.05). CONCLUSIONS: In the current study, compared to each other none of the definitions showed a superior discriminative power in predicting CVD; although, all definitions were more predictive in females than in males.

Association between moderate renal insufficiency and cardiovascular events in a general population: Tehran lipid and glucose study.

BACKGROUND: Chronic kidney disease(CKD) has been proposed as a risk factor for cardiovascular disease (CVD). There is conflicting evidence among community based studies regarding the association between CKD and CVD. Furthermore, in order to assess the possible interaction between CKD and BMI, we also examined the association between CKD and CVD, across different BMI categories. METHODS: The risk of CVD events was evaluated in a large cohort of participants selected from the Tehran Lipid and Glucose Study. Participants(mean age, 47.4 years) free of previous CVD were followed up for 9.1 years. GFR ml/min per 1.73 m(2) was estimated using the MDRD formula. RESULTS: Of the 6,209 participants, 22.2%(1381) had CKD with eGFR ml/min per 1.73 m(2) <60 at baseline. Almost all of them (99%) were in stage 3a. Moderate renal insufficiency only predicted CVD outcomes independently when we adjusted for age and sex. After further adjustment, the presence of moderate CKD lost its statistical significance to confer an independent increased risk of CVD events with a hazard ratio of: HR: 1.14, CI 95% 0.91-1.42. Furthermore, when participants were categorized according to CKD status and BMI groups, after further adjustment, no interaction was found(P = 0.2). CONCLUSION: CKD was not an independent risk factor for CVD events in a community-based study in a Tehranian population and the higher prevalence of CVD in subjects with mild to moderate renal insufficiency might be due to the co-occurrence of traditional CVD risk factors in this group.