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.

Reopening after lockdown: the influence of working-from-home and digital device use on sleep, physical activity, and wellbeing following COVID-19 lockdown and reopening.

STUDY OBJECTIVES: COVID-19 lockdowns drastically affected sleep, physical activity, and wellbeing. We studied how these behaviors evolved during reopening the possible contributions of continued working from home and smartphone usage. METHODS: Participants (N = 198) were studied through the lockdown and subsequent reopening period, using a wearable sleep/activity tracker, smartphone-delivered ecological momentary assessment (EMA), and passive smartphone usage tracking. Work/study location was obtained through daily EMA ascertainment. RESULTS: Upon reopening, earlier, shorter sleep and increased physical activity were observed, alongside increased self-rated stress and poorer evening mood ratings. These reopening changes were affected by post-lockdown work arrangements and patterns of smartphone usage. Individuals who returned to work or school in-person tended toward larger shifts to earlier sleep and wake timings. Returning to in-person work/school also correlated with more physical activity. Contrary to expectation, there was no decrease in objectively measured smartphone usage after reopening. A cluster analysis showed that persons with relatively heavier smartphone use prior to bedtime had later sleep timings and lower physical activity. CONCLUSIONS: These observations indicate that the reopening after lockdown was accompanied by earlier sleep timing, increased physical activity, and altered mental wellbeing. Moreover, these changes were affected by work/study arrangements and smartphone usage patterns.

Splitting sleep between the night and a daytime nap reduces homeostatic sleep pressure and enhances long-term memory.

Daytime naps have been linked with enhanced memory encoding and consolidation. It remains unclear how a daily napping schedule impacts learning throughout the day, and whether these effects are the same for well-rested and sleep restricted individuals. We compared memory in 112 adolescents who underwent two simulated school weeks containing 8 or 6.5 h sleep opportunities each day. Sleep episodes were nocturnal or split between nocturnal sleep and a 90-min afternoon nap, creating four experimental groups: 8 h-continuous, 8 h-split, 6.5 h-continuous and 6.5 h-split. Declarative memory was assessed with picture encoding and an educationally realistic factual knowledge task. Splitting sleep significantly enhanced afternoon picture encoding and factual knowledge under both 6.5 h and 8 h durations. Splitting sleep also significantly reduced slow-wave energy during nocturnal sleep, suggesting lower homeostatic sleep pressure during the day. There was no negative impact of the split sleep schedule on morning performance, despite a reduction in nocturnal sleep. These findings suggest that naps could be incorporated into a daily sleep schedule that provides sufficient sleep and benefits learning.

Memory performance following napping in habitual and non-habitual nappers.

STUDY OBJECTIVES: Afternoon naps benefit memory but this may depend on whether one is a habitual napper (HN; ≥1 nap/week) or non-habitual napper (NN). Here, we investigated whether a nap would benefit HN and NN differently, as well as whether HN would be more adversely affected by nap restriction compared to NN. METHODS: Forty-six participants in the nap condition (HN-nap: n = 25, NN-nap: n = 21) took a 90-min nap (14:00-15:30 pm) on experimental days while 46 participants in the Wake condition (HN-wake: n = 24, NN-wake: n = 22) remained awake in the afternoon. Memory tasks were administered after the nap to assess short-term topographical memory and long-term memory in the form of picture encoding and factual knowledge learning respectively. RESULTS: An afternoon nap boosted picture encoding and factual knowledge learning irrespective of whether one habitually napped (main effects of condition (nap/wake): ps < 0.037). However, we found a significant interaction for the hippocampal-dependent topographical memory task (p = 0.039) wherein a nap, relative to wake, benefitted habitual nappers (HN-nap vs HN-wake: p = 0.003) compared to non-habitual nappers (NN-nap vs. NN-wake: p = 0.918). Notably for this task, habitual nappers' performance significantly declined if they were not allowed to nap (HN-wake vs NN-wake: p = 0.037). CONCLUSIONS: Contrary to concerns that napping may be disadvantageous for non-habitual nappers, we found that an afternoon nap was beneficial for long-term memory tasks even if one did not habitually nap. Naps were especially beneficial for habitual nappers performing a short-term topographical memory task, as it restored the decline that would otherwise have been incurred without a nap. CLINICAL TRIAL INFORMATION: NCT04044885.

Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data.

Using polysomnography over multiple weeks to characterize an individual's habitual sleep behavior while accurate, is difficult to upscale. As an alternative, we integrated sleep measurements from a consumer sleep-tracker, smartphone-based ecological momentary assessment, and user-phone interactions in 198 participants for 2 months. User retention averaged >80% for all three modalities. Agreement in bed and wake time estimates across modalities was high (rho = 0.81-0.92) and were adrift of one another for an average of 4 min, providing redundant sleep measurement. On the ~23% of nights where discrepancies between modalities exceeded 1 h, k-means clustering revealed three patterns, each consistently expressed within a given individual. The three corresponding groups that emerged differed systematically in age, sleep timing, time in bed, and peri-sleep phone usage. Hence, contrary to being problematic, discrepant data across measurement modalities facilitated the identification of stable interindividual differences in sleep behavior, underscoring its utility to characterizing population sleep and peri-sleep behavior.

Cognitive effects of split and continuous sleep schedules in adolescents differ according to total sleep opportunity.

STUDY OBJECTIVES: We compared the basic cognitive functions of adolescents undergoing split (nocturnal sleep + daytime nap) and continuous nocturnal sleep schedules when total sleep opportunity was either below or within the recommended range (i.e. 6.5 or 8 h). METHODS: Adolescent participants (age: 15-19 year) in the 8-h split (n = 24) and continuous (n = 29) sleep groups were compared with 6.5-h split and continuous sleep groups from a previous study (n = 58). These protocols involved two baseline nights (9-h time-in-bed [TIB]), 5 nights of sleep manipulation, 2 recovery nights (9-h TIB), followed by a second cycle of sleep manipulation (3 nights) and recovery (2 nights). Cognitive performance, subjective sleepiness, and mood were evaluated daily; sleep was assessed using polysomnography. RESULTS: Splitting 6.5 h of sleep with a mid-afternoon nap offered a boost to cognitive function compared to continuous nocturnal sleep. However, when total TIB across 24 h increased to 8 h, the split and continuous sleep groups performed comparably in tests evaluating vigilance, working memory, executive function, processing speed, subjective sleepiness, and mood. CONCLUSIONS: In adolescents, the effects of split sleep on basic cognitive functions vary by the amount of total sleep obtained. As long as the total sleep opportunity across 24 h is within the recommended range, students may fulfill sleep requirements by adopting a split sleep schedule consisting of a shorter period of nocturnal sleep combined with a mid-afternoon nap, without significant impact on basic cognitive functions. CLINICAL TRIAL REGISTRATION: NCT04044885.

Evaluation of an interactive school-based sleep education program: a cluster-randomized controlled trial.

OBJECTIVES: Shortened sleep has negative consequences on adolescents' well-being. The present study evaluated an interactive school-based sleep education program (SEP) aimed at increasing adolescent sleep duration. DESIGN AND INTERVENTION: A cluster-randomized controlled trial with 12 clusters (classes) was used. The intervention group received a SEP and the active control group received a healthy living program (HLP). Both groups underwent a 4-week class-based education program. The SEP students learned about the importance of sleep, the barriers to getting enough sleep, and how to improve their time management to increase their sleep opportunity. The HLP students learned about various health-related topics not including sleep. PARTICIPANTS: A total of 210 students (mean age = 14.04 ± 0.32 years) were randomly assigned to the SEP (n = 102) or the HLP (n = 108) group, with 6 classes per group. MEASUREMENTS: Sleep (actigraphically measured), sleep knowledge, and time usage were assessed using linear mixed models at three time points: baseline, immediately after intervention, and 1-month follow-up. RESULTS: Sleep knowledge improved at follow-up in the SEP relative to the HLP group (p = .017). Although students were receptive of the program and self-reported the intention to create more time for sleep, no changes in sleep were found following the SEP. Some benefit may have been masked by exam preparations at the follow-up evaluation. CONCLUSIONS: Sleep education alone may not be sufficient to change sleep behavior. A combination of sleep education, starting school later, and parental involvement may be needed to encourage and enable changes in adolescent sleep duration.