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.

A multidimensional investigation of sleep and biopsychosocialprofiles with associated neural signatures.

Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.

A multidimensional investigation of sleep and biopsychosocial profiles with associated neural signatures.

Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.

Higher handgrip strength is linked to higher salience ventral attention functional network segregation in older adults.

Converging evidence suggests that handgrip strength is linked to cognition in older adults, and this may be subserved by shared age-related changes in brain function and structure. However, the interplay among handgrip strength, brain functional connectivity, and cognitive function remains poorly elucidated. Hence, our study sought to examine these relationships in 148 community-dwelling older adults. Specifically, we examined functional segregation, a measure of functional brain organization sensitive to ageing and cognitive decline, and its associations with handgrip strength and cognitive function. We showed that higher handgrip strength was related to better processing speed, attention, and global cognition. Further, higher handgrip strength was associated with higher segregation of the salience/ventral attention network, driven particularly by higher salience/ventral attention intra-network functional connectivity of the right anterior insula to the left posterior insula/frontal operculum and right midcingulate/medial parietal cortex. Importantly, these handgrip strength-related inter-individual differences in salience/ventral attention network functional connectivity were linked to cognitive function, as revealed by functional decoding and brain-cognition association analyses. Our findings thus highlight the importance of the salience/ventral attention network in handgrip strength and cognition, and suggest that inter-individual differences in salience/ventral attention network segregation and intra-network connectivity could underpin the handgrip strength-cognition relationship in older adults.

Neurobehavioral functions during recurrent periods of sleep restriction: effects of intra-individual variability in sleep duration.

STUDY OBJECTIVES: To investigate whether neurobehavioral impairments are exacerbated during successive cycles of sleep restriction and recovery in young adults, and whether a variable short sleep schedule can mitigate these impairments relative to a stable one. METHODS: Fifty-two healthy young adults (25 males, aged: 21-28) were randomly assigned to the stable short sleep group, the variable short sleep group, or the control group in this laboratory-based study. They underwent two baseline nights of 8-hour time-in-bed (TIB), followed by two cycles of "weekday" sleep opportunity manipulation and "weekend" recovery (8-hour TIB). During each manipulation period, the stable short sleep and the control groups received 6- and 8-hour TIBs each night respectively, while the variable short sleep group received 8-hour, 4-hour, 8-hour, 4-hour, and 6-hour TIBs from the first to the fifth night. Neurobehavioral functions were assessed five times each day. RESULTS: The stable short sleep group showed faster vigilance deterioration in the second week of sleep restriction as compared to the first. This effect was not observed in the variable short sleep group. Subjective alertness and practice-based improvement in processing speed were attenuated in both short sleep groups. CONCLUSIONS: In young adults, more variable short sleep schedules incorporating days of prophylactic or recovery sleep might mitigate compounding vigilance deficits resulting from recurrent cycles of sleep restriction. However, processing speed and subjective sleepiness were still impaired in both short sleep schedules. Getting sufficient sleep consistently is the only way to ensure optimal neurobehavioral functioning. CLINICAL TRIAL: Performance, Mood, and Brain and Metabolic Functions During Different Sleep Schedules (STAVAR), https://www.clinicaltrials.gov/study/NCT04731662, NCT04731662.

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.

Assessing 'readiness' by tracking fluctuations in daily sleep duration and their effects on daily mood, motivation, and sleepiness.

STUDY OBJECTIVES: Consumer sleep trackers issue daily guidance on 'readiness' without clear empirical basis. We investigated how self-rated mood, motivation, and sleepiness (MMS) levels are affected by daily fluctuations in sleep duration, timing, and efficiency and overall sleep regularity. We also determined how temporally specific these associations are. METHODS: 119 healthy university students (64 female, mean age = 22.54 ± 1.74 years) wore a wearable sleep tracker and undertook twice-daily smartphone-delivered ecological momentary assessment of mood, motivation, and sleepiness at post-wake and pre-bedtime timings for 2-6 weeks. Naps and their duration were reported daily. Nocturnal sleep on 2471 nights were examined using multilevel models to uncover within-subject and between-subject associations between sleep duration, timing, efficiency, and nap duration on following day MMS ratings. Time-lagged analyses examined the temporal specificity of these associations. Linear regression models investigated associations between MMS ratings and sleep variability, controlling for sleep duration. RESULTS: Nocturnal sleep durations were short (6.03 ± 0.71 h), and bedtimes were late (1:42AM ± 1:05). Within-subjects, nocturnal sleep longer than a person's average was associated with better mood, higher motivation, and lower sleepiness after waking. Effects of such longer sleep duration lingered for mood and sleepiness till the pre-bedtime window (all Ps < .005) but did not extend to the next day. Between-subjects, higher intraindividual sleep variability, but not sleep duration, was associated with poorer mood and lower motivation after waking. Longer average sleep duration was associated with less sleepiness after waking and lower motivation pre-bedtime (all Ps < .05). Longer naps reduced post-nap sleepiness and improved mood. Controlling for nocturnal sleep duration, longer naps also associated with lower post-waking sleepiness on the following day. CONCLUSIONS: Positive connections between nocturnal sleep and nap duration with MMS are temporally circumscribed, lending credence to the construction of sleep-based, daily 'readiness' scores. Higher sleep duration variability lowers an individual's post waking mood and motivation. CLINICAL TRIAL ID: ClinicalTrials.gov NCT04880629.

Nocturnal Blood Pressure Estimation from Sleep Plethysmography Using Machine Learning.

BACKGROUND: Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP during sleep. We describe a machine-learning-based algorithm for predicting nocturnal BP using single-channel fingertip plethysmography (PPG) in healthy adults. METHODS: Sixty-eight healthy adults with no apparent sleep or CVD (53% male), with a median (IQR) age of 29 (23-46 years), underwent overnight polysomnography (PSG) with fingertip PPG and ambulatory blood pressure monitoring (ABPM). Features based on pulse morphology were extracted from the PPG waveforms. Random forest models were used to predict night-time systolic blood pressure (SBP) and diastolic blood pressure (DBP). RESULTS: Our model achieved the highest out-of-sample performance with a window length of 7 s across window lengths explored (60 s, 30 s, 15 s, 7 s, and 3 s). The mean absolute error (MAE ± STD) was 5.72 ± 4.51 mmHg for SBP and 4.52 ± 3.60 mmHg for DBP. Similarly, the root mean square error (RMSE ± STD) was 6.47 ± 1.88 mmHg for SBP and 4.62 ± 1.17 mmHg for DBP. The mean correlation coefficient between measured and predicted values was 0.87 for SBP and 0.86 for DBP. Based on Shapley additive explanation (SHAP) values, the most important PPG waveform feature was the stiffness index, a marker that reflects the change in arterial stiffness. CONCLUSION: Our results highlight the potential of machine learning-based nocturnal BP prediction using single-channel fingertip PPG in healthy adults. The accuracy of the predictions demonstrated that our cuffless method was able to capture the dynamic and complex relationship between PPG waveform characteristics and BP during sleep, which may provide a scalable, convenient, economical, and non-invasive means to continuously monitor blood pressure.

Country differences in nocturnal sleep variability: Observations from a large-scale, long-term sleep wearable study.

STUDY OBJECTIVES: Country or regional differences in sleep duration are well-known, but few large-scale studies have specifically evaluated sleep variability, either across the work week, or in terms of differences in weekday and weekend sleep. METHODS: Sleep measures, obtained over 50 million night's sleep from ∼220,000 wearable device users in 35 countries, were analysed. Each person contributed an average of ∼242 nights of data. Multiple regression was used to assess the impact country of residence had on sleep duration, timing, efficiency, weekday sleep variability, weekend sleep extension and social jetlag. RESULTS: Nocturnal sleep was shorter and had a later onset in Asia than other regions. Despite this, sleep efficiency was lower and weekday sleep variability was higher. Weekend sleep extension was longer in Europe and the USA than in Asia, and was only partially related to weekday sleep duration. There were also cross-country differences in social jetlag although the regional differences were less distinct than for weekend sleep extension. CONCLUSIONS: In addition to regional differences in sleep duration, cross-country differences in sleep variability and weekend sleep extension suggest that using the latter as an indicator of sleep debt may need to be reconsidered. In countries exhibiting both short sleep and high weekday sleep variability, a culturally different means of coping with inadequate sleep is likely. Country or region differences in culture, particularly those related to work, merit closer examination as factors influencing the variability in normative sleep patterns around the world.

A multicentre study on grey matter morphometric biomarkers for classifying early schizophrenia and parkinson's disease psychosis.

Psychotic symptoms occur in a majority of schizophrenia patients and in ~50% of all Parkinson's disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little is known, however, about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as in schizophrenia and PD. The present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals in at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms. SCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD, PDN and Con-PD, as well as PDN and PDP, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC ~0.80) of FEP and Con-Psy, and fair performance (AUC ~0.72) when differentiating PDP from Con-PD. Importantly, the best performance was found in partly the same networks, including the thalamus. Alterations within selected SCNs may be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.

Insights into vascular physiology from sleep photoplethysmography.

STUDY OBJECTIVES: Photoplethysmography (PPG) in consumer sleep trackers is now widely available and used to assess heart rate variability (HRV) for sleep staging. However, PPG waveform changes during sleep can also inform about vascular elasticity in healthy persons who constitute a majority of users. To assess its potential value, we traced the evolution of PPG pulse waveform during sleep alongside measurements of HRV and blood pressure (BP). METHODS: Seventy-eight healthy adults (50% male, median [IQR range] age: 29.5 [23.0, 43.8]) underwent overnight polysomnography (PSG) with fingertip PPG, ambulatory blood pressure monitoring, and electrocardiography (ECG). Selected PPG features that reflect arterial stiffness: systolic to diastolic distance (∆T_norm), normalized rising slope (Rslope) and normalized reflection index (RI) were derived using a custom-built algorithm. Pulse arrival time (PAT) was calculated using ECG and PPG signals. The effect of sleep stage on these measures of arterial elasticity and how this pattern of sleep stage evolution differed with participant age were investigated. RESULTS: BP, heart rate (HR) and PAT were reduced with deeper non-REM sleep but these changes were unaffected by the age range tested. After adjusting for lowered HR, ∆T_norm, Rslope, and RI showed significant effects of sleep stage, whereby deeper sleep was associated with lower arterial stiffness. Age was significantly correlated with the amount of sleep-related change in ∆T_norm, Rslope, and RI, and remained a significant predictor of RI after adjustment for sex, body mass index, office BP, and sleep efficiency. CONCLUSIONS: The current findings indicate that the magnitude of sleep-related change in PPG waveform can provide useful information about vascular elasticity and age effects on this in healthy adults.

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.

Working-from-home persistently influences sleep and physical activity 2 years after the Covid-19 pandemic onset: a longitudinal sleep tracker and electronic diary-based study.

Objective: Working from home (WFH) has become common place since the Covid-19 pandemic. Early studies observed population-level shifts in sleep patterns (later and longer sleep) and physical activity (reduced PA), during home confinement. Other studies found these changes to depend on the proportion of days that individuals WFH (vs. work from office; WFO). Here, we examined the effects of WFH on sleep and activity patterns in the transition to normality during the later stages of the Covid-19 pandemic (Aug 2021-Jan 2022). Methods: Two-hundred and twenty-five working adults enrolled in a public health study were followed for 22 weeks. Sleep and activity data were collected with a consumer fitness tracker (Fitbit Versa 2). Over three 2-week periods (Phase 1/week 1-2: August 16-29, 2021; Phase 2/week 11-12: October 25-November 7, 2021; Phase 3/week 21-22: January 3-16, 2022), participants provided daily Fitbit sleep and activity records. Additionally, they completed daily phone-based ecological momentary assessment (EMA), providing ratings of sleep quality, wellbeing (mood, stress, motivation), and information on daily work arrangements (WFH, WFO, no work). Work arrangement data were used to examine the effects of WFH vs. WFO on sleep, activity, and wellbeing. Results: The proportion of WFH vs. WFO days fluctuated over the three measurement periods, mirroring evolving Covid restrictions. Across all three measurement periods WFH days were robustly associated with later bedtimes (+14.7 min), later wake times (+42.3 min), and longer Total Sleep Time (+20.2 min), compared to WFO days. Sleep efficiency was not affected. WFH was further associated with lower daily step count than WFO (-2,471 steps/day). WFH was associated with higher wellbeing ratings compared to WFO for those participants who had no children. However, for participants with children, these differences were not present. Conclusion: Pandemic-initiated changes in sleep and physical activity were sustained during the later stage of the pandemic. These changes could have longer term effects, and conscious effort is encouraged to harness the benefits (i.e., longer sleep), and mitigate the pitfalls (i.e., less physical activity). These findings are relevant for public health as hybrid WHF is likely to persist in a post-pandemic world.

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.

The value of large-scale studies of sleep and cognition.

Large-scale, global studies providing representative characterisation of sleep and its relation to cognitive performance are rare. In a recent study using a video game to assess spatial navigation, Coutrot and colleagues found that, among participants aged 54-70 years, optimal wayfinding performance was associated with a self-reported sleep duration of 7 h. The results also showed that, while the effects of age and gender on sleep duration were consistent across countries, geography and/or culture significantly modulated sleep duration, a finding that merits further study.

Adherence to 24-Hour Movement Recommendations and Health Indicators in Early Adolescence: Cross-Sectional and Longitudinal Associations in the Adolescent Brain Cognitive Development Study.

PURPOSE: Adherence to 24-hour movement guidelines of ≥60 minutes of physical activity, ≤2 hours of screen time, and 9-11 hours of sleep has been shown to benefit cognitive, physical, and psychosocial health in children and young adolescents aged 5-13 years. However, these findings have mostly been based on cross-sectional studies or relatively small samples and the associations between adherence to guidelines and brain structure remain to be evaluated. METHODS: Data from the Adolescent Brain Cognitive Development℠ (ABCD) study of 10,574 early adolescents aged 9-14 years from September 2016 to January 2021 were used to examine whether adherence to 24-hour movement guidelines benefits cognition (general cognitive ability, executive function, and learning/memory assessed by the National Institutes of Health Toolbox neurocognitive battery), body mass index, psychosocial health (internalizing, externalizing, and total problems from the parent-reported Child Behavior Checklist), and magnetic resonance imaging-derived brain morphometric measures at baseline (T1), ∼2 years later (T2), and longitudinally from T1 to T2 (T2-T1). Multivariable linear mixed models were used, with adjustments for sociodemographic confounders. Time elapsed and T1 outcome measures were also controlled for in longitudinal models. RESULTS: Better cognitive scores, fewer behavioral problems, lower adiposity levels, and greater gray matter volumes were observed in those who met both sleep and screen time recommendations compared to those who met none. Longitudinal follow-up further supports these findings; participants who met both recommendations at T1 and T2 evidenced better outcome measures than those who met none. DISCUSSION: These findings support consideration of integrated rather than isolated movement recommendations across the day in early adolescence for better cognitive, physical and psychosocial health. Although the associations between physical activity and health indicators were less consistent in this study, the significant findings from sleep and screen time demonstrate the importance of considering movement recommendations in an integrated rather than isolated manner for adolescent health. It is recommended that movement behaviors be simultaneously targeted for better developmental outcomes.

Associations between objectively measured sleep parameters and cognition in healthy older adults: A meta-analysis.

Multiple studies have examined associations between sleep and cognition in older adults, but a majority of these depend on self-reports on sleep and utilize cognitive tests that assess overall cognitive function. The current meta-analysis involved 72 independent studies and sought to quantify associations between objectively measured sleep parameters and cognitive performance in healthy older adults. Both sleep macrostructure (e.g., sleep duration, continuity, and stages) and microstructure (e.g., slow wave activity and spindle activity) were evaluated. For macrostructure, lower restlessness at night was associated with better memory performance (r = 0.43, p = 0.02), while lower sleep onset latency was associated with better executive functioning (r = 0.28, p = 0.03). Greater relative amount of N2 and REM sleep, but not N3, positively correlated with cognitive performance. The association between microstructure and cognition in older adults was marginally significant. This relationship was moderated by age (z = 0.07, p < 0.01), education (z = 0.26, p = 0.03), and percentage of female participants (z = 0.01, p < 0.01). The current meta-analysis emphasizes the importance of considering objective sleep measures to understand the relationship between sleep and cognition in healthy older adults. These results also form a base from which researchers using wearable sleep technology and measuring behavior through computerized testing tools can evaluate their findings.

A randomized-controlled trial of a digital, small incentive-based intervention for working adults with short sleep.

STUDY OBJECTIVES: We evaluated the efficacy of a digitally delivered, small and scalable incentive-based intervention program on sleep and wellbeing in short-sleeping, working adults. METHODS: A 22-week, parallel-group, randomized-controlled trial was conducted on 21-40 y participants gifted with FitbitTM devices to measure sleep for ≥2 years, as part of a broader healthy lifestyle study. About 225 short sleepers (141 males; average time-in-bed, TIB < 7h) were randomly assigned in a 2:1 ratio to Goal-Setting or Control groups. The Goal-Setting group received health vouchers (~USD 0.24) for meeting each sleep goal (i.e. increasing weeknight TIB by 30 min/sleeping before midnight).The study spanned three phases: (1) 2-week Baseline, (2) 10-week Intervention, and (3) 10-week Follow-Up. Wellbeing questionnaires were administered on Weeks 1-2, 11-12, and 21-22. RESULTS: Baseline weeknight TIB (mean ±â€…SD) was 387 ±â€…43 min (Goal-Setting) and 399 ±â€…44 min (Control), while bedtime was 00:53 ±â€…01:13 (Goal-Setting), and 00:38 ±â€…00:56 (Control). No difference in sleep outcomes was observed at study endpoints, but exploratory week-by-week analysis showed that on Weeks 3-5, TIB in the Goal-Setting group increased (9-18 min; ps < 0.05) while on Week 5, bedtimes shifted earlier (15 min; p < 0.01) compared to Baseline. Morning sleepiness was reduced in the Goal-Setting group (mean[SEM] = -3.17(1.53); p = 0.04) compared to Baseline, although between-group differences were not significant (p = 0.62). Main barriers to sleeping longer were work hours (35%), followed by leisure activities (23%) and family commitments (22%). CONCLUSION: Our program resulted in encouraging subjective sleep improvements and short-term sleep extension, but sustained transformation of sleep will probably require structural measures to overcome significant obstacles to sleep. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04878380 (hiSG Sleep Health Study (hiSG-SHS); https://clinicaltrials.gov/ct2/show/NCT04878380).

Understanding the Need for Sleep to Improve Cognition.

The restorative function of sleep is shaped by its duration, timing, continuity, subjective quality, and efficiency. Current sleep recommendations specify only nocturnal duration and have been largely derived from sleep self-reports that can be imprecise and miss relevant details. Sleep duration, preferred timing, and ability to withstand sleep deprivation are heritable traits whose expression may change with age and affect the optimal sleep prescription for an individual. Prevailing societal norms and circumstances related to work and relationships interact to influence sleep opportunity and quality. The value of allocating time for sleep is revealed by the impact of its restriction on behavior, functional brain imaging, sleep macrostructure, and late-life cognition. Augmentation of sleep slow oscillations and spindles have been proposed for enhancing sleep quality, but they inconsistently achieve their goal. Crafting bespoke sleep recommendations could benefit from large-scale, longitudinal collection of objective sleep data integrated with behavioral and self-reported data.

Bedtime procrastination and chronotype differentially predict adolescent sleep on school nights and non-school nights.

OBJECTIVES: Bedtime procrastination (BTP) refers to the tendency to delay sleep beyond an intended bedtime, in favor of continuing evening activities. BTP has been associated with negative sleep outcomes (later timing, shorter duration, poorer quality), and is viewed as a problem of exercising self-control. BTP could be particularly challenging in adolescents, given the combined effects of increasing bedtime autonomy, later chronotype, and a still developing self-control capacity. Thus far, research on BTP has only been based on self-report measures. Here we examined the influence of BTP on adolescent sleep, using objective measures of sleep. METHODS: About 121 adolescents aged 14-19 years completed a survey on BTP, sleep quality, chronotype, and mental health. Subsequently, habitual sleep was actigraphically monitored for up to 2 weeks, and participants completed a temporal discounting task (a proxy for impulsivity). Associations between BTP, chronotype, and actigraphy-measured sleep were examined for school nights and non-school nights separately. RESULTS: Greater BTP was associated with poorer subjective sleep, eveningness chronotype, and higher daytime fatigue, as well as higher anxiety/depression scores. Measured using actigraphy, greater BTP predicted later bedtimes and shorter sleep duration on school nights, even when controlling for chronotype. On non-school nights, eveningness chronotype, but not BTP, predicted later bedtimes and wake-up times. BTP was not correlated with temporal discounting. CONCLUSIONS: Bedtime procrastination exerts significant influence on subjective and objective sleep measures in adolescents. Its effects are most prominent on school nights and can be separated from the effects of chronotype, which has stronger effects on sleep timing on non-school nights.