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.

Dissociable changes in sleep architecture with mindfulness and sleep hygiene intervention in older adults: Secondary and exploratory analysis of polysomnography data from the Mindfulness Sleep Therapy (MIST) trial.

OBJECTIVES: We conducted a secondary analysis of the Mindfulness Sleep Therapy study, a randomized controlled trial testing Mindfulness-Based Therapy for Insomnia (MBTI) against a sleep hygiene education and exercise program (SHEEP). We investigated whether the interventions led to changes in sleep macroarchitecture (N2, N3 and REM), and microarchitecture (sleep fragmentation, slow wave activity, spectral band power) measured by ambulatory polysomnography (PSG). METHODS: 48 MBTI and 46 SHEEP participants provided usable PSG and subjective sleep quality data both pre- and post intervention. The interventions consisted of 8 weekly 2-hour group sessions, and daily practice. PSG data were staged according to the American Academy of Sleep Medicine criteria by 2 technicians blind to time point and condition. Repeated-measures ANOVA and permutation analysis were used to test for differences over time and between the interventions. RESULTS: Self-reported sleep quality improved in both study groups. We observed significant increases in N2 in MBTI but not SHEEP (p = .045), and significant increases in N3 in SHEEP but not MBTI (p = .012). No significant differences over time or between group were observed in N1, REM, or sleep fragmentation. Higher frequency non-REM EEG power decreased in SHEEP but not MBTI. Slow wave activity and slow wave activity dissipation did not differ over time or between groups. Among all variables, significant time by group interactions were observed in only N3 and non-REM alpha power. CONCLUSIONS: MBTI and sleep hygiene education had different effects on sleep macro and microarchitecture, suggesting that the underlying mechanisms of mindfulness training in improving sleep quality may differ from traditional interventions.

Mindfulness-Based Training Does Not Improve Neuropsychological Outcomes in Mild Cognitive Impairment More Than Spontaneous Reversion Rates: A Randomized Controlled Trial.

BACKGROUND: Current pharmacological and behavioral treatment options for mild cognitive impairment (MCI) are limited, motivating a search for alternative therapies that might slow the progression of cognitive decline. OBJECTIVE: We investigated the effectiveness of a cognition-focused mindfulness-based intervention. METHODS: An open-label, three arm randomized controlled trial was conducted at a public tertiary medical center. Older persons (ages 45-75; N = 76) diagnosed with MCI were recruited and randomized into either mindfulness-based training (MBT), cognitive rehabilitation therapy (CRT), or treatment as usual (TAU). Participants in the intervention arms received 8 weekly 2-h sessions delivered in a group setting and engaged in home practice. Primary outcomes measures included changes in index scores for attention, immediate memory, and delayed memory as measured using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Depression was a secondary outcome. RESULTS: Using intent-to-treat analysis, we found that participants receiving MBT showed significant improvements in global cognition (d = 0.26; [95%CI 0.03-0.56]) and delayed memory (d = 0.36; [95%CI 0.17-0.57]), with significantly greater improvements in delayed memory than CRT (ηp2 = 0.10). However, there was no benefit of MBT over TAU. No change in depression was observed in the MBT group. Reductions in depression were associated with improvements in cognitive functioning in the MBT group only. CONCLUSION: Our results suggest that a cognition-focused MBT did not improve cognitive functioning in MCI patients substantially more than spontaneous reversion rates, possibly as mood symptoms were not significantly alleviated in this group.

Schema-driven memory benefits boost transitive inference in older adults.

Age-related cognitive deficits may be diminished by tapping into prior knowledge structures. We investigated age-related differences in the formation and updating of schemas and examined whether the memory benefits of recently acquired schemas would be preserved in older adults. Data were collected from 60 older adults (M = 66.2, SD = 9.3 years) and 59 adolescents recruited from Singapore's top schools (M = 16.6, SD = 0.9 years) who learnt the age hierarchy of six galaxies to criterion by viewing neighboring pairs one at a time, for example A-B, D-E, C-D, enabling the formation of a schema via transitive inference (i.e., if B > C and C > D then B > D). Once learning reached criterion, two new sets of galaxies were presented: one comprising four galaxies from the schema intercalated with four new galaxies (schema condition) and the other comprising eight unfamiliar galaxies (no schema condition). Participants were then tested on both neighboring pairs (noninference) and inference pairs: (B-D, B-E, C-E). Compared with adolescents, older adults required a significantly higher number of learning blocks to successfully form a schema. Nonetheless, the acquired schema significantly aided relational memory and facilitated the making of novel inferences in older adults. This schema benefit was particularly pronounced in supporting novel inferences, boosting performance in older adults to levels comparable with adolescents (59% vs. 61%). These results suggest that older adults can effectively form new schemas with extended practice. Schema-driven memory benefits are preserved with aging and appear to mitigate age-related memory deficits, optimizing cognitive performance in making novel inferences. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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.

Multi-Night Sleep Restriction Impairs Long-Term Retention of Factual Knowledge in Adolescents.

PURPOSE: Sleep deprivation is associated with increased forgetting of declarative memories. Sleep restriction across multiple consecutive nights is prevalent in adolescents, but questions remain as to whether this pattern of sleep impairs memory for material typically learned in the classroom and the time course of retention beyond a few days. METHODS: Adolescents aged 15-18 years (n = 29) were given 5 hours sleep opportunity each night for 5 consecutive nights (sleep restricted group; SR), simulating a school week containing insufficient sleep. After the fourth night of restriction, participants learned detailed facts about different species of arthropod across a 6-hour period. Retention was tested 30 minutes and 3 days after learning and contrasted with a control group (n = 30) who had 9 hours sleep opportunity every night of the study. A subset of participants (SR, n = 14; control, n = 22) completed a surprise test 42 days after learning. RESULTS: Memory was significantly impaired in the SR group relative to controls, with 26% increased forgetting at the 30-minute test (t(57) = 2.54, p = .014, d = .66), 34% at the Day 3 test (t(57) = 2.65, p = .010, d = .69), and 65% at the Day 42 test (t(34) = 3.22, p = .003, d = 1.17). Vigilance was also significantly impaired after 4 nights of restricted sleep (p < .05), but did not correlate significantly with memory (p > .05). CONCLUSION: Long-term retention of classroom material is significantly compromised when adolescents learn after being sleep restricted, reinforcing the importance of keeping good sleep habits to optimize learning.

Does splitting sleep improve long-term memory in chronically sleep deprived adolescents?

Sleep aids the encoding and consolidation of declarative memories, but many adolescents do not obtain the recommended amount of sleep each night. After a normal night of sleep, there is abundant evidence that a daytime nap enhances the consolidation of material learned before sleep and also improves the encoding of new information upon waking. However, it remains unclear how learning is affected when sleep is split between nocturnal and daytime nap periods during a typical school week of restricted sleep. We compared long-term memory in 58 adolescents who underwent two simulated school weeks of suboptimal continuous (6.5 h nocturnal sleep opportunity) or split sleep (5 h nocturnal sleep +1.5 h daytime nap at 14:00). In the first week, participants encoded pictures in the late afternoon on Day 5 and were tested after 2-nights of recovery sleep. On 3 consecutive days in the second week, participants learned about six species of amphibians in the morning, and six different amphibians in the late afternoon. Testing was performed in the evening following a night of recovery sleep. In the first week, the split sleep group recognized more pictures. In the second week, they remembered more facts about species learned in the afternoon. Groups did not differ for species learned in the morning. This suggests that under conditions of sleep restriction, a split sleep schedule benefits learning after a nap opportunity without impairing morning learning, despite less preceding nocturnal sleep. While not replacing adequate nocturnal sleep, a split sleep schedule may be beneficial for chronically sleep restricted learners.

The long-term memory benefits of a daytime nap compared with cramming.

Study Objectives: Daytime naps benefit long-term memory relative to taking a break and remaining awake. However, the use of naps as a practical way to improve learning has not been examined, in particular, how memory following a nap compares with spending the equivalent amount of time cramming. Methods: Young adults learned detailed factual knowledge in sessions that flanked 1 hr spent napping (n = 27), taking a break (n = 27), or cramming that information (n = 30). Recall was examined 30 min and 1 week after learning. Results: When tested 30 min after learning, cramming and napping led to significantly better memory than taking a break. After a week, napping maintained this significant advantage, but cramming did not. Conclusions: These findings demonstrate the longer-term benefits of napping for retention of memoranda akin to what students encounter daily and encourage more widespread adoption of napping in education.

Positive Effects of Mindfulness-Based Training on Energy Maintenance and the EEG Correlates of Sustained Attention in a Cohort of Nurses.

Mindfulness based training (MBT) is becoming increasingly popular as a means to improve general wellbeing through developing enhanced control over metacognitive processes. In this preliminary study, we tested a cohort of 36 nurses (mean age = 30.3, SD = 8.52; 2 male) who participated in an 8-week MBT intervention to examine the improvements in sustained attention and its energetic costs that may result from MBT. Changes in sustained attention were measured using the psychomotor vigilance task (PVT) and electroencephalography (EEG) was collected both during PVT performance, and during a brief period of meditation. As there was substantial variability in training attendance, this variable was used a covariate in all analyses. Following the MBT program, we observed changes in alpha power across all scalp regions during meditation that were correlated with attendance. Similarly, PVT performance worsened over the 8-week period, but that this decline was mitigated by good attendance on the MBT program. The subjective energy depletion due to PVT performance (measured using self-report on Likert-type scales) was also less in regular attendees. Finally, changes in known EEG markers of attention during PVT performance (P300 and alpha-band event-related desynchronization) paralleled these behavioral shifts. Taken together, our data suggest that sustained attention and its associated costs may be negatively affected over time in the nursing profession, but that regular attendance of MBT may help to attenuate these effects. However, as this study contained no control condition, we cannot rule out that other factors (e.g., motivation, placebo effects) may also account for our findings.