Sleep as a construct in the postpartum period and its perceived importance to postpartum patients remain largely under explored. The aim of this concept elicitation study was to develop a conceptual framework for postpartum sleep based on the key themes (domains and subdomains) identified. Secondary aims were to determine the frequency of discussion of individual domains/subdomains among participants and provide exemplar patient quotes for the most frequently discussed subdomains. This study received Institutional Review Board approval from Stanford Lucile Packard Children's Hospital and the University of Arkansas for Medical Sciences. Semi-structured interviews were conducted with patients, partners and multidisciplinary experts until thematic saturation was achieved. All interviews were audio recorded and professionally transcribed and de-identified. Thematic codes (domains and subdomains of postpartum sleep) were derived from review of interview transcripts. Iterative thematic analysis of transcripts with constant comparison across cases was conducted systematically by ≥2 analysts. All transcripts were coded in Nvivo software and qualitatively analyzed to report frequency of domain/subdomain discussion and to identify exemplar patient quotes for individual subdomains. Interviews were conducted with 42 patients, 13 multidisciplinary experts, and 6 partners over a total of 20 h. Median interval between delivery and interview for the recruited postpartum women was 8 weeks (interquartile range 6-10 weeks, range 3-52 weeks). Analysis of all 61 participant interviews resulted in derivation of 10 domains (psychological, pharmacological, non-pharmacological, sleep interference, medical factors, feeding of neonate, sleep disruption, social factors, societal and cultural factors, and infant related factors) and 85 subdomains related to the construct of postpartum sleep. The three most frequently discussed domains were sleep disruption, non-pharmacological interventions to improve sleep, and medical factors related to sleep. The top 10 most frequently discussed subdomains were breast feeding/feeding, maternal awakenings, social support, childbirth experience, infant sleep routine, day time sleep, infant care (bottles, milk, diapers), sleep arrangements, chronotype, and nighttime sleep. This study provides a conceptual framework based on 10 domains and 85 subdomains, which can be used to comprehensively describe and study the complex construct of postpartum sleep. These findings can be used to counsel patients regarding postpartum sleep experiences, facilitate patient discussion in the postnatal period when assessing postpartum sleep experiences, guide development of new measures, and assess content validity of existing sleep measures.
Appropriate duration and high-quality sleep is essential for overall well-being and may be improved through engaging in daytime physical activity (PA). Yet the postpartum period is a particularly challenging time for these health behaviors, and sleep in this population has received little attention in scientific literature. This study examined the associations of daily PA and sedentary behavior (SED) with postpartum sleep measures, both obtained from wrist-worn ActiGraph devices. Data come from the Pregnancy Environment and Lifestyle Study-2 cohort, a racially and ethnically diverse longitudinal cohort of postpartum mothers who delivered at Kaiser Permanente Northern California in 2018-2019. Participants (N = 136) wore ActiGraph wGT3X-BT devices on their non-dominant wrists for 7 days at 6 months postpartum. ActiGraph data were processed in RStudio using validated algorithms for device wear time, PA metrics (i.e. minutes of moderate-to-vigorous physical activity [MVPA], light physical activity [LPA], and SED, bedrest), and sleep quality metrics (i.e. nightly sleep duration; frequency of sleep interruptions; sleep efficiency [defined total nocturnal sleep/total sleep period × 100%]; and average duration of sleep interruptions). We estimated associations using mixed-effects models in SAS, adjusted for participant characteristics and daytime bedrest. Participants engaged in a median of 66 min/day (Q1-Q3: 36.0-101.0) of MVPA and 340.0 min/day (Q1-Q3: 272.0-422.0) of SED; neither were associated with sleep duration. In the adjusted model, for every additional 10 min of daily LPA, there was 0.12 per cent higher sleep efficiency; in contrast, every additional 10 min of SED was associated with 0.10 per cent lower sleep efficiency (both p < .05). For each additional 10 min of daily MVPA, LPA, and SED, the average length of sleep interruptions was 0.65 min shorter, 0.52 min shorter, and 0.36 min longer, respectively (all p < .05). More daytime PA, regardless of intensity, may modestly improve sleep quality, while more daytime SED may diminish sleep quality at 6 months postpartum.
We examined the impact of a sleep extension intervention on multiple dimensions of sleep in adults with habitual short sleep duration. Thirty healthy participants (14 women; aged 23.1 ± 4.5 years; BMI 22.3 ± 2.2 kg/m2 [mean ± SD]) with <6.5 h sleep/night completed a 2-week baseline assessment followed by a 4-week sleep extension intervention (2 h/night increased time in bed). Wrist actigraphy, at-home electroencephalography (EEG; DREEM headband), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and daily quality/satisfaction and alertness Likert-scales quantified sleep. Baseline total sleep time (TST) was 5.5 ± 0.7 h. During sleep extension, actigraphy time in bed and TST increased (p < .001) by 60.8 ± 46.7 and 46.6 ± 41.1 minutes, respectively, sleep onset shifted earlier (p < .001) by 51.9 ± 64.5 minutes, with regularity similar to baseline. EEG showed increases (all p < .05) in TST (61.8 ± 75.6 minutes), Stage N1 (5.09 ± 6.51 minutes), Stage N2 (39.54 ± 41.78 minutes), rapid eye movement sleep (13.58 ± 28.86 minutes), and wakefulness after sleep onset (4.86 ± 8.03 minutes), with a nonsignificant decrease (p = .07) in sleep efficiency (-1.38 ± 4.15%). Subjective alertness, ISI, PSQI, and ESS each improved (all p < .05) during sleep extension. Our findings help establish efficacy of sleep extension as an experimental intervention the sleep field can leverage across diverse contexts to study potential health benefits of increasing free-living TST. During sleep extension, the largest effects were observed for improved TST and ESS. Alternatively, some sleep dimensions including sleep regularity remained unchanged, highlighting a potential need for developing multi-component interventions that can improve more dimensions of sleep as both short and irregular sleep are linked with adverse health outcomes. Biomarkers of Increased Free Living Sleep Time. URL: https://clinicaltrials.gov/study/NCT04214184. ClinicalTRIALS.gov ID: NCT04214184.
To assess the performance of a portable electroencephalography device for sleep monitoring against polysomnography. Fifty-six adults underwent one night of in-laboratory sleep recording with the Muse-S headband and simultaneous level 1 polysomnography. Muse-S data were scored by an automated sleep staging algorithm. A registered technologist, blind to the Muse-S automated sleep scoring, scored the polysomnography data. Good quality data were available for 47 (84 per cent) participants (53 per cent females; 20-71 years old; 17 per cent with sleep-related breathing disorder). Epoch-by-epoch analyses showed substantial agreement between the Muse-S and polysomnography (full night Cohen's Kappa = 0.76). Cohen's Kappa were in the fair agreement range for non-rapid eye movement (NREM) 1, substantial agreement range for NREM2 and NREM3, and near-perfect agreement range for rapid eye movement sleep and wake. Accuracy ranged from 88 per cent to 96 per cent across all sleep stages, with a sensitivity of 79-92 per cent and a specificity of 90-99 per cent. Similar results were observed in the subgroup with sleep-related breathing disorder. On average, the Muse-S had higher mean values than polysomnography for total sleep time (+6 min), NREM3 (+15 min), rapid eye movement sleep (+6 min), and sleep efficiency (+1.5 per cent), and lower mean values for sleep latency (-3 min), wake after sleep onset (-3 min), and light sleep (-14 min). When compared to standard polysomnography, the Muse-S performed well to measure sleep macroarchitecture. This portable device shows great potential as an accessible tool for sleep electroencephalography monitoring. More work is required to validate this tool in more diverse populations to ensure robustness across age, sex, neurological conditions, and sleep profiles.This article is part of the Consumer Sleep Technology Special Collection.
Evidence is limited on the role of caffeine intake in the relationship between sleep quality and the incidence of major adverse cardiovascular events (MACE) particularly in patients with sleep breathing disorders. Therefore, this study's primary aim was to determine the potential confounding effects of total caffeine consumption on the relationship between sleep quality parameters (total sleep time [TST], sleep efficiency [SE], sleep latency [SL], daytime sleepiness, and wakefulness after sleep onset [WASO]) and MACE. This study is a secondary analysis of data from the Sleep Heart Health Study (SHHS). Sleep assessments (TST, SE, SL, daytime sleepiness, and WASO) were performed objectively using in-home polysomnography. Caffeine was measured using a survey asking about the average number of cups/cans/glasses of tea, soda, and coffee consumed per regular day and during the last night before polysomnography. A final sample of 5628 participants was included in SHHS Visit 1 (78 per cent White/Caucasian; 54 per cent female). Cumulative incidence rates measured over 10.9 ± 2.8 years were 15.1 per cent for MACE and 19.7 per cent for all-cause mortality. In univariate models, all sleep measures except SL were associated with MACE; but after adjustment, only TST remained a significant predictor (odds ratio [OR] = 1.122, p = .011). No confounding effect of caffeine was observed in the associations between sleep measures and MACE. Moderate-high intake attenuated MACE risk among individuals with greater daytime sleepiness (OR = 0.91, p = .011). Caffeine was not a confounding factor in the relationship between sleep measures and MACE. While exploratory analyses suggested potential modification of the association between hypersomnolence and cardiovascular outcomes, these effects were attenuated after statistical adjustment and correction for multiple testing and should be interpreted cautiously.
Disturbed sleep and daytime sleepiness have been associated with subjective cognitive decline (SCD), which often precedes objective cognitive deficits and clinical dementia. Due to heterogeneity of sleep problems, researchers have used phenotype-based approaches to cluster individuals based on a group of sleep symptoms. These clusters have been associated with continuous positive airway pressure (CPAP) efficacy and cardiovascular incidence risk, but less work has related them to SCD. We sought to examine the relationships between sleep phenotypes and SCD among a diverse sample of Hispanic/Latino individuals. We used data from the Hispanic Community Health Study/Study of Latinos, a large dataset of Hispanic/Latino communities. We included 15 sleep symptoms and 4 cardiovascular measures in our latent class analysis model to find the optimal cluster size. SCD was assessed using the Everyday Cognition Scale. We ran survey weighted linear regression models to assess domain specific and global SCD across the groups. A total of N = 5551 individuals were included in the analysis. The 3-class solution was the best fit, and groups were consistent with previous work. Those in the sleepiness-disturbed sleep (N = 943) or disturbed sleep (N = 1886) group had more SCD across all domains, including global, compared to those in the minimally symptomatic (N = 2722) group. Adjusting for social, cardiovascular factors, and obstructive sleep apnea attenuated but did not fully explain associations. Daytime sleepiness and disturbed sleep clusters were associated with worse SCD. These findings suggest that sleep disturbances, regardless of sleep apnea, could have significant implications for cognitive health in diverse Hispanic/Latino populations.
Changes in sleep with aging are associated with risk for Alzheimer's and other neurological diseases, risk of accidents, and can be a predictor of health decline. For this reason, continuous sleep monitoring is of great interest for researchers, clinicians, and family members. The objective of this study was to assess the validity of consumer sleep-tracking devices in older relative to young adults. Analyses were based on one night of sleep assessed in young (19-24 years; n = 13) and older adults (56-80 years; n = 19). Participants wore sleep-tracking wearables (Fitbit Sense 2, Oura Ring) and nearables (Withings Sleep Mat, Sleep Score Max) were positioned nearby. Sleep measures were compared to polysomnography. Results suggest that devices may be less accurate in older relative to young adults in commonly reported measures. In older adults, devices underestimated total sleep time (Fitbit bias = -74.5 minutes, p=.012; Oura bias = -75.5, p = <0.0001; Withings bias = -45.7, p=.083; Sleep Score Max bias = -56.5, p=.001) and wake after sleep onset (Fitbit bias = -44.1, p=.012; Oura bias = -19.8, p=.2823; Withings bias = -32.1, p=.129; Sleep Score Max bias = -71.6, p=.006) and overestimated deep sleep time (Fitbit bias = -29.3, p=.013; Oura bias = 71.5, p=.001; Withings bias = 97.4, p = <0.0001; Sleep Score Max bias = 88.8, p = <0.0001). Devices performed poorly in identifying individual sleep stages, particularly deep sleep. Limits of agreement were generally greater in older adults than younger adults across all measures, suggesting less precision in measuring older adults' sleep. Older adults interested in tracking their sleep and clinicians and researchers using consumer devices as a replacement for polysomnography should use caution when interpreting results from these devices.This paper is part of the Consumer Sleep Technology Collection.
Research has examined acute sleep effects at the immediate transition onto Daylight-Saving Time (DST), culminating in a sentiment that it should be abolished. These effects could be theorized to continue well-into the DST period. The current article aimed to address this gap, investigating the effect of DST on sleep during the middle-late stages (2-4 and 6 months) of the DST period. A retrospective, cross-sectional design was used to compare subjective data of two nationwide surveys of the Australian population; one population-representative sample, and a convenience sample of those with chronic Insomnia. Respondents were categorized based on whether they were from a DST state or permanent Standard Time (ST) state. We then compared sleep behavior and tendencies in sleep health between DST and ST states. Overall, both samples consistently demonstrated that those from DST states tended to go to bed later and, particularly, rise at later clock times than those from ST states. Importantly, despite a delay in the timing of sleep we found no differences in reported Total Sleep Time nor Sleep Onset Latency; and no sign of impairment on any related health estimates. Very few sleep health variables reached significance (p < .05), and the vast majority of them suggested those from DST states were less impaired than their ST counterparts. We have found no evidence of impairment associated with DST well-into the DST period. Future studies should measure sleep and associated daytime functioning longitudinally and objectively to accurately assess the possible duration of any potential acute DST effect.
Sleep disturbance is common among patients with cancer and is linked to significant morbidity, poorer quality of life and reduced survival in this population. The Pittsburgh Sleep Quality Index (PSQI) can identify poor sleep quality in this population, although a higher threshold value may be required compared to the general population. The brief PSQI (bPSQI), consisting of six of the original 19 items, offers a quicker and simpler tool. The bPSQI has demonstrated comparable accuracy in identifying poor sleep in the general population but remains unexplored in patients with advanced cancer. This observational study of 65 patients with advanced cancer reiterates the prevalence of sleep disturbance, demonstrates good internal consistency of the bPSQI and notes higher bPSQI scores with increasing subjective sleep-related distress. Although significant associations were noted between the bPSQI global score, individual bPSQI item scores and single-item sleep disturbance questions, the study cautions against the use of single-item questions in detecting sleep problems. Discrepancies were noted between subjective and objective sleep assessments. The findings support a higher threshold value for identifying poor sleep quality using the bPSQI. Although limited by a small sample size, the findings emphasize the need for further validation of the bPSQI in this population and to ensure that assessment methods align with research aims.
Sleep monitoring outside of clinics could enhance care for insomnia and other sleep disorders but requires home systems that are easily operable and provide consistent data quality over multiple nights. We assessed the Waveband for usability by participants and feasibility of obtaining multi-night sleep data in the home setting. 15 subjects with insomnia wore the Waveband electroencephalogram headband and an FDA-cleared wearable home sleep testing device, WatchPAT ONE ("WP1") for three nights. Usability was assessed via the System Usability Scale (SUS). Feasibility of participants to collect data was evaluated by examining stability of measured total sleep time in relation to measurements from the reference device (WP1) and data quality as evaluated by three human experts. Average SUS score was 69.7, meeting the 68-point threshold for good usability. Total sleep time recorded by the Waveband and WP1 devices showed a correlation of 87.3 per cent. All the recordings had an average of over 7 scorable hours of data per night. Waveband demonstrated good usability by patients, was operable by patients, and generated interpretable data that provided stable sleep estimates across nights, comparable to an established home sleep testing device. The device has potential to advance patient care, sleep research, and clinical trials by enabling longitudinal ambulatory sleep assessment.
While recovery sleep can ameliorate the negative impacts of total sleep deprivation (TSD) on cognitive functioning, the effects of post-TSD sleep on different forms of emotional functioning remain unknown. Here, we investigated the effects of TSD and post-TSD recovery sleep on emotional memory processing. Participants viewed scenes with negative or neutral central objects overlain on neutral backgrounds. The scene components were then presented separately for recognition testing. Participants in the TSD (n = 46) and Sleep (n = 22) conditions encoded the scenes the morning after the sleep manipulation (~10:00) and recognition memory was tested for half of the scene components after a short delay (Recog_1, ~10:45). Twenty of the TSD participants then received a 90-min nap opportunity (TSDNap). All participants then completed a second recognition test on the remaining images (Recog_2, ~14:00). At Recog_1, all TSD participants showed worse overall memory compared to sleep participants. Specifically, memory was significantly worse for every scene component except neutral objects during Recog_1. At Recog_2, while memory deteriorated further for all scene components in the TSDNoNap group, the TSDNap group showed no memory decline and had improved memory for negative objects, matching the sleep group at Recog_2. Post-TSD recovery sleep preserves and restores memory functioning to the level seen in typically rested individuals. But extending TSD leads to continued memory deterioration, highlighting the importance of sleep in healthy emotional memory functioning. This paper is part of the Festschrift in honor of Dr. Robert Stickgold.
Evaluate the effect and safety of alpha rhythm-guided repetitive transcranial magnetic stimulation (α-rTMS) on sleep difficulties in children with autism spectrum disorder (ASD). Twenty children (6-12 years old; 16 males; 4 females) with ASD level 2 were randomly assigned (1:1 ratio) to a treatment group (TG) or a waitlist control group (WLCG) (T1). The TG received ten α-rTMS sessions over two weeks, while the WLCG acted as control for that period (T2). Next, the WLCG received α-rTMS for two weeks (T3). All study participants were followed up at one (T4) and four (T5) months. Sleep difficulties were measured using the Children's Sleep Habit Questionnaire (CSHQ), Actigraphy, and Polysomnography (PSG). Group-by-time interactions indicated that the TG had greater improvements than the WLCG in total CSHQ score (p=.008) and, bedtime resistance (p=.003), sleep onset delay (p=.004), and sleep duration (p=.003) subdomain scores. When the WLCG received the α-rTMS, there were improvements in their sleep-disordered breathing (p=.001), parasomnia (p=.002) and sleep duration (p=.018) subdomain scores, while PSG data showed improved Waking After Sleep Onset (WASO) (p=.014), Sleep efficiency (p=.046), and N2 stage (p=.039). The improved CSHQ scores persisted, with actigraphy data showing significant improvement in WASO at T4 and T5. Side effects of α-rTMS were mild and transient. This RCT study presents preliminary evidence on the effect and safety of α-rTMS in improving subjective sleep difficulties in children with ASD, with effects lasting up to four months post-intervention. Further studies using a larger sample size and sham-controlled group are warranted. The trial was registered on July 11, 2023 within the Australian New Zealand Clinical Trials Registry (ANZCTR) https://www.anzctr.org.au/TrialSearch.aspx with registration number: ACTRN12623000757617.
The aim of this laboratory-based study was to understand the effect of total sleep deprivation (TSD) on decision-making using a reversal learning task with unannounced contingency reversals in those with chronic insomnia compared to healthy sleepers. Twenty-eight individuals completed the study, 15 with chronic insomnia (7 underwent TSD) and 13 healthy sleepers (7 underwent TSD). Participants were in the laboratory for 5 days/4 nights. Following baseline sleep, participants underwent 38 h of TSD or another nighttime sleep opportunity, followed by a recovery sleep opportunity for all participants. Mixed-effects ANOVAs with fixed effects of condition (healthy TSD, insomnia TSD, healthy control, or insomnia control), session (baseline or TSD/control), phase (pre or post-reversal), block (1-4), and their interactions were run to assess performance. There were significant effects of day, condition, phase, and their interaction (all p<.01). Participants performed better when rested and pre-reversal (i.e. before the rule change). While both TSD groups showed poorer performance post-reversal (i.e. after the rule change) during TSD, the TSD insomnia group showed relatively intact performance pre-reversal. TSD led to significant overall impairment on a reversal learning decision task for healthy sleepers and those with insomnia. However, the nature of impairment differed between groups. Hyperarousal may have conferred a protective effect on those with insomnia, resulting in preserved decision-making pre-reversal. These findings have important implications for our understanding of the nature of cognitive impairment associated with chronic insomnia.
It is not clear whether gender differences in self-reported sleep reflect polysomnographical differences. We here investigated gender differences in polysomnography (PSG) variables and their association with rated sleep quality for recorded sleep. The participants were 238 women and 238 men who were recorded for a night with PSG (home recordings), and provided sleep quality ratings for the recorded night. Analyses of variance showed that women reported significantly lower sleep quality than men, but it showed significantly better PSG sleep (fewer awakenings per hour, lower N1%, longer total sleep time, higher sleep efficiency, and more N3%, among others). However, men underestimated their objective number of awakenings and had a shorter objective time awake per objective awakening (6.4 ± .6 vs 8.2 ± .6 minutes for women, p < .05). Men with short awakenings (<7.8 min per awakening) had a high self-reported sleep quality, in contrast to men with long awakenings or women regardless of the duration of awakenings. When men with short awakenings were excluded, self-reported sleep quality no longer differed between genders. Gender differences in PSG variables increased with age. In addition, better self-reported sleep quality was associated with "better" PSG values for both genders. In conclusion, women reported poorer sleep quality than men but showed better objective sleep. It is suggested that men's better self-reported sleep is associated with an inability to perceive/remember short awakenings. The findings open a new view of gender differences in sleep, and indicate a need for experimental studies on gender differences in the perception of awakenings, their duration, and rated sleep quality.
Intrusive memories are common following trauma exposure. However, they can develop into a distressing symptom of posttraumatic stress disorder (PTSD). Recent research examining the role of sleep in the development of intrusive memories demonstrates overnight sleep or a daytime nap following analog trauma exposure (ie, a trauma film), compared to a similar period of wake, results in fewer intrusive memories in the following week. This is consistent with research examining participants who have recently experienced trauma, demonstrating sleep disturbances are highly associated with the development of intrusive memories. However, the mechanisms underlying this are not well understood. Identifying those mechanisms is critical to improving interventions to reduce intrusive memories, and thus, the impacts of posttraumatic stress disorder. This study aims to investigate two potential mechanisms underlying the relationship between sleep and intrusive memories; namely, whether sleep-related memory consolidation and/or executive control over spontaneous cognition explains this relationship. These aims will be investigated through an experimental trauma film study in a healthy adult population (n = 114), where we will compare the effects of a 2-h nap versus 2-h controlled waking period on intrusive memories for the trauma film. In the subsequent week, we will examine the relationship between sleep the previous night and intrusive memories the following day. The outcomes of this project will provide important insights regarding the mechanism(s) explaining the relationship between sleep and intrusive memory frequency. Findings will provide further direction for research investigating sleep-related effects on intrusive memories and may have implications for intervention post trauma exposure.
Sleep is complex and variable, yet insomnia research and treatment often rely on averages-either across nights or across individuals. Such approaches risk obscuring dynamic features that characterize insomnia as a disorder and its unique manifestation in individuals. In this study, we explore disorder-specific (group-level) and person-specific (individual-level) dynamic phenomena of insomnia among people with insomnia. We analyzed 8 weeks of sleep diary data from 61 participants with insomnia. Four domains of sleep dynamics were examined at group- and individual-levels: (1) night-to-night variability, (2) temporal dependency of sleep quality, (3) stability of sleep complaints, (4) weekday-weekend variability. We correlated these domains with insomnia severity, pre-sleep arousal, and sleep-related safety behavior. At the group-level, insomnia was characterized by (1) night-to-night fluctuations in sleep parameters, (2) unpredictable sleep quality, (3) frequent co-occurrence and fluctuations in type of sleep complaints, and (4) different sleep patterns on weekdays and weekends. These disorder-specific dynamic phenomena showed medium-sized significant correlations with insomnia indices, ranging from r = -0.25 to 0.41. At the individual-level, all four domains varied markedly across individuals. While the group-level characterizations were fitting for some participants, others showed patterns clearly distinct. We developed a Shiny application which allows readers to explore individual sleep profiles (https://uvasobe.shinyapps.io/PersonalSleepExplorer/). Sleep in insomnia varies from night-to-night and person-to-person. Reliance on averages across nights and across individuals may obscure fluctuations of potential clinical relevance. We call for broader use of sleep diaries to capture dynamic patterns of insomnia and for investigation of their clinical utility. Sleep Restriction Treatment for Insomnia.URL: https://clinicaltrials.gov/study/NCT05548907. NCT05548907.
We investigated daily associations between step count and sleep quality across trimesters using wearable devices. Participants (N = 243; pre-pregnancy body mass index≥ 25 kg/m2) from a mobile health randomized clinical trial intervention arm, wore Fitbits day and night from ~8 weeks' gestation- delivery. Devices tracked daily step count (primary exposure), moderate to vigorous physical activity (MVPA) and light physical activity (LPA) (secondary exposures), and sleep measures (duration, stage length, efficiency, awakenings, midpoint and multidimensional sleep score). Covariate-adjusted mixed effects models estimated daily associations between movement and sleep outcomes, stratified by trimester. Participants averaged 5795 steps/day. In trimester 1, step count (per 1000) was associated with shorter sleep duration (-23 min, odds ratio [OR] = 0.84, 95% confidence interval [CI] = -38.7 to -8.6). In the trimester 2, step count was associated with shorter sleep duration (-22 min, CI = -27.6 to -16.4), shorter light sleep (-10 min, CI = -13.3 to -6.6), longer deep (+4 min, CI = 2.7 to 6.1), and rapid eye movement (REM) sleep (+6 min, CI = 3.5 to 7.7). In trimester 3, step count was associated with lower odds of poor sleep (OR = 0.84, CI = 0.70 to 1.00), shorter light sleep (-14 min, CI = -18.4 to -9.3), longer deep (+6 min, CI = 3.3 to 7.8), and REM sleep (+8 min, CI = 5.5 to 11.4), and more awakenings (+0.9, CI = 0.4 to 1.4). Associations of MVPA and LPA with sleep were smaller in magnitude but relatively consistent with step count. Higher daily step count was associated with higher quality sleep in the following night during second and third trimesters. These findings highlight step count as a potential target to support prenatal sleep quality.
Studies of sleep slow oscillations (SOs, 0.5-1.5 Hz) have emphasized their importance for cognition and health, and their variable spatial organization. We have introduced a data-driven method to analyze SOs as events that differentiate in their space-time co-emergence on the electrode manifold. This approach has identified properties of SO organization that are relevant to function, and that can change in clinical populations. In this work, we share a software and user manual that will allow the sleep research community to leverage our method directly in their own datasets. The work formalizes which dataset properties are necessary to deploy our method in terms of number of participants (N) and count of electrodes (E), and share parameterization strategies. We applied our algorithm to two datasets of nighttime sleep in healthy adults: Set1 (N = 22, E = 58) and Set2 (N = 34, E = 24). Roles of E and N values were tested by down-sampling electrodes to 24 and 8 channels, reflecting standard caps, and by randomly selecting subsets of participants. Early vs complete nighttime sleep was evaluated by truncating sets to 90 min after the first detected SO. Clustering outputs from tests were compared to original dataset outputs. Successful identification of SO profiles was evaluated with an index of similarity to ideal centroid masks. We found that identification of SO profiles required at least 22 participants and at least a 24 head-electrode montage, whereas 8 head-electrodes configurations, typical of clinically acquired sleep, were not sufficient. Furthermore, early nighttime sleep was sufficient for successful identification of SO profiles.
Pediatric anxiety disorders (including Obsessive Compulsive Disorder) are prevalent and impairing. Youth with anxiety disorders frequently experience sleep disturbances. Exposure, the primary component of gold-standard Cognitive Behavioral Therapy (CBT) for treating anxiety disorders, works by harnessing fear extinction learning. Given that sleep plays a critical role in the consolidation and retrieval of emotional memories, we hypothesize that shorter sleep quantity and greater sleep disruption are associated with psychophysiological responses indicating reduced fear extinction learning and reduced fear extinction recall in adolescents with anxiety and OCD. In this protocol paper, we describe a pilot study testing this hypothesis in a clinical sample of adolescents participating in a CBT-based partial hospital program (PHP) dedicated to the treatment of anxiety disorders. Participants complete a multi-method sleep assessment over 10 days during the first portion of their admission in the program (within the first 4 weeks) and at the end of their stay (at least over 5-7 days before discharge). Standardized clinical interviews and sleep questionnaires are coupled with multi-modal at-home sleep monitoring using sleep diaries, patch-based actigraphy, and wearable sleep electroencephalography (EEG). Participants also complete a computerized task assessing initial fear learning (day 1), fear extinction learning (day 2), and extinction recall (day 3) as measured by skin conductance responses (SCR). This use of multi-method sleep assessments in a clinical sample of youths with more clinically severe anxiety disorders is innovative and, to our knowledge, has not yet been done.
Sleep is vitally important to maintain cognitive function, particularly in shift-work contexts. Sleep trackers can reliably estimate sleep, but the relationship between estimated sleep and specific cognitive domains is unclear. This study examined associations between estimated sleep and subsequent cognitive performance during a simulated night-shift protocol. Twenty-four participants (mean [SD] age = 28[9] years) attended the sleep laboratory twice, for an 8-day simulated shift-work experimental protocol under two lighting conditions (standard- vs. circadian-informed lighting). Following a baseline sleep, participants remained awake for 27 h and transitioned to sleeping between 10:00 and 19:00 with cognitive testing between 00:00 and 08:00 for four days. Tests included the Balloon Analogue Risk Task, Continuous Performance task, Digit Symbol Substitution Test (DSST), Iowa Gambling task, Operation-Span task, Psychomotor Vigilance test, Stroop task, Tower of London task, and Trail Making test. Sleep was assessed using an under-mattress sensor (Withings Sleep Analyzer). Linear and non-linear models were used to test associations between estimated sleep and cognitive performance. Significant associations were found between sleep metrics and PVT reaction time (R2 = .13, p = .008), Operation-Span arithmetic errors (.15, p = .023), proportion correct on DSST and Stroop tasks (.06, p = .045; .09, p = .003), and Stroop reaction time (.19, p = .004). Random forest models demonstrated that vigilance, working memory, and mental arithmetic were associated with estimated sleep architecture, snoring, and cardiovascular function. Sleep trackers could inform next-day cognitive performance, particularly vigilance, mental arithmetic, and working memory. In so doing, they may enable more informed interpretations of device-derived sleep and management of sleep-related cognitive impairment in future models. This paper is part of the Consumer Sleep Technology Collection.