OBJECTIVES:Multidimensional sleep health captures the 24-hour experience of sleep as regularity, satisfaction, alertness, timing, efficiency, and duration. However, most evidence comes from adults. Adolescents have unique sleep needs that may not be fully represented. This study aimed to empirically identify and validate dimensions of adolescent sleep health and evaluate methods relevant to their assessment. METHODS:We analyzed subjective and objective sleep measures from 347 adolescents in the Penn State Child Cohort (16.3±2.2 years; 45.8% female; 22.2% race and ethnic minority). Exploratory factor analysis was conducted in a random 40% subsample to identify latent dimensions of self-reported, actigraphy, and polysomnography measures, followed by confirmatory factor analysis in the remaining 60% to test model validity. RESULTS:Exploratory factor analysis identified 6 dimensions: Breathing, Efficiency, Duration, Regularity/variability, Satisfaction, and Timing, collectively referred to as the BEDReST framework, explaining 63% of the variance. Confirmatory factor analysis of individual factors within the 6-factor structure demonstrated good to excellent fit (Comparative Fit Index = 0.95-1.00; Tucker-Lewis Index = 0.86-1.00; Root Mean Square Error of Approximation = 0.06-0.15; Standardized Root Mean Square Residual < 0.08). All dimensions, except Satisfaction, included objective measures. CONCLUSIONS:We empirically derived and validated 6 dimensions of adolescent sleep health, the BEDReST framework, using a multimethod approach. This approach highlighted the importance of including objective sleep data to comprehensively capture adolescent sleep health. The BEDReST framework extends existing models by incorporating sleep-disordered breathing and provides a foundation for refining the conceptualization and measurement of sleep health during adolescence.

Importance Many adolescents experience sleep that is too short and mis-timed for their circadian clock, which can adversely impact psychological and physical health. Feasible, targeted interventions to modify sleep behaviors and circadian timing could improve adolescent sleep and ultimately, health, and functioning. Objective To determine whether a novel intervention integrating chronotherapeutic approaches (sleep scheduling, morning bright light glasses, and evening blue-light blocking glasses) would increase weeknight sleep duration and shift circadian timing earlier in adolescents with late sleep. Design, Setting, and Participants This randomized clinical trial was conducted in a research setting in an academic medical center during school months (late August to mid-June) between 2018 and 2024. All analyses were intention to treat. Adolescents aged 16 to 19 years enrolled in a traditional high school who reported habitual weekend sleep onset later than 1 am . Participants were randomized to a Sleeping Late Teens Program or sleep monitoring control. Interventions The Sleeping Late Teens Program included 1 collaborative, problem-solving session (<1 hour) followed by 2 weeks of a personalized sleep schedule that shifted bedtimes and wake times earlier. Participants wore morning bright-light glasses for 30 to 60 minutes on waking and amber-tinted blue light–blocking glasses for 2 hours before bed. Main Outcomes and Measures Primary outcome measures included weeknight circadian timing indexed by salivary dim-light melatonin onset (DLMO), weeknight sleep duration measured with actigraphy, and circadian alignment operationalized as the interval between DLMO and midsleep (middle of the nocturnal sleep period). Results Among 86 participants, 44 were randomly assigned to the intervention group and 42 to the control group. Of these, 80 completed baseline procedures (40 in each group): mean (SD) age, 17.5 (0.7) years; 48 female (60%) and 32 male (40%). The intervention group showed statistically significant and clinically relevant changes in sleep and circadian metrics compared with the sleep monitoring control. After 2 weeks, participants randomized to the active intervention, compared with the control, had earlier circadian timing (45 minutes; β = −0.55; 95% CI, −0.79 to −0.31; P = .003) and longer weeknight sleep duration (47 minutes; β = 0.74; 95% CI, 0.30-1.18; P = .003). DLMO-midsleep alignment shortened by 18 minutes in the intervention group compared with an 8-minute lengthening in controls; however, this difference was not statistically significant (β = −0.35; 95% CI, −0.72 to 0.02; P = .20). Conclusion and Relevance In this randomized clinical trial, results show that a short-term intervention that combined sleep scheduling, morning bright-light glasses, and evening blue light–blocking glasses shifted circadian timing earlier and extended weeknight sleep duration in adolescents. Larger trials are needed to confirm the effectiveness of this intervention. Trial Registration ClinicalTrials.gov Identifier: NCT03806296

Objective The number of adolescents and young adults (AYA) being identified with depressive symptoms is increasing. Unfortunately, there is a paucity of behavioral health (BH) care options for this population, and obtaining care is more challenging in underserved areas. Digital cognitive–behavioral interventions (dCBIs) have been effective in decreasing self-reported depressive symptoms in pediatric patients. We examined the effectiveness of augmenting therapy with dCBI as compared to therapy alone in a multi-site randomized controlled trial (RCT). Method A multi-site RCT was conducted among patients 16 to 22 years of age who were being treated for depressive symptoms within BH collaborative care provided in pediatric primary care settings in Pittsburgh, PA, Boston, MA, and San Diego, CA. Patients were randomized to treatment as usual (TAU) or TAU+dCBI. Data obtained from the dCBI app included the number and type of sessions completed, and engagement with an asynchronous coach within the dCBI. Outcomes of interest were scores on the Patient Health Questionnaire (PHQ-9), the Children’s Depression Rating Scale—Revised (CDRS-R), the Generalized Anxiety Disorder (GAD-7), the Satisfaction with Life Scale (SWLS), and Children’s Global Assessment Scale (CGAS), obtained at baseline, 6 weeks, and 12 weeks. Intent-to-treat analyses used linear mixed-effects models to test the effect of treatment group, adjusting for gender, race, and other treatment (antidepressants, behavioral therapy) at baseline. Additional per protocol analyses examined treatment differences when the analysis was restricted to engaged users. Sensitivity analyses further examined the effect among those without other BH treatment at baseline. Results A total of 185 participants were randomized and enrolled: 73 in TAU and 112 in TAU+dCBI. Mean age was 17.8 (1.66) years; 57% were White, 75% were female, and 48% had public insurance. Among participants randomized to TAU+dCBI, the mean number of techniques used was 5.5 ± 5.7. A total of 61% (n = 68) used at least 3 techniques and were considered engaged users. In intent-to-treat (n = 112) and per protocol (n = 107) analyses, there were no statistically significant differences between TAU and TAU+dCBI on any of the outcome measurements. However, among those without other treatment at baseline (n = 36), those randomized to TAU+dCBI had a significantly greater decrease in CDRS-R over 12 weeks relative to TAU. In this subgroup of 36 participants, the estimated within-person change for TAU+dCBI was d (95% CI) = 0.86 (0.59, 1.13) vs only 0.19 (−0.20, 0.58) for TAU (β = −0.21, 95% CI = −0.35, −0.06, p = .01, for the group-by-time interaction term). Conclusion We did not find a significantly different change in depressive symptoms between patients randomized to TAU+dCBI vs TAU. However, sensitivity analyses suggested that the app may be more effective among subgroups without other treatment options for behavioral health. As only 60.7% of those randomized to the TAU+dCBI study arm engaged with the app, future studies should include ways to promote engagement. The potential effects of dCBI should be further explored, as apps offer a convenient, non-stigmatizing means of accessing therapy techniques to supplement traditional therapy. Given the digital acceptance and familiarity among AYAs, this could be a promising means of providing needed access to therapy at a time when therapy options are scarce. Clinical trial registration information Study to Compare the Use of a Behavioral Health App Versus Care and Usual for 16-22 Year Olds with Depression; https://clinicaltrials.gov/study/NCT05159713

The transition from childhood to adolescence heralds a marked escalation in pediatric mental health risk, as well as major developmental shifts in sleep. Poor sleep health is a common, causal, and modifiable transdiagnostic mental health symptom and risk factor in youth. Yet, unraveling the sleep-mental health risk relationship over adolescence has proven to be deceptively challenging. Sleep health arises from complex biopsychosocial processes and can be measured across multiple methods and time scales. The interplay between profound developmental shifts in sleep over adolescence and the high-dimensional nature of sleep measurement often leads to significant data heterogeneity. As a result, computational approaches are necessary to parse out typical variation from at-risk patterns that may reflect warning signs of emerging mental illness. In this review, we propose sleep signatures as a strategy to characterize sleep health and accurately predict psychiatric outcomes in adolescence. Sleep signatures are within-person combinations of multiple sleep features that more holistically characterize individual-level patterns of sleep health. We propose the multidimensional sleep health framework as a basis for sleep signature development and discuss unique complexities in sleep measurement for adolescents, highlighting classic sleep measurement methods and new opportunities provided by modern wearable and smartphone-based sleep monitoring. Next, we review computational techniques to derive multidimensional, multimodal sleep signatures in a developmental context, focusing on variable-centered (factor analysis) and person-centered (clustering) approaches. Finally, we offer a roadmap for leveraging these approaches to identify sleep signatures salient to adolescent mental health through the ongoing Pediatric Precision Sleep Network project.

Abstract Importance: Irregular sleep-wake patterns have been associated with poor health and cognitive outcomes, yet evidence linking 24-hour sleep-wake regularity to cognitive decline or dementia remains inconsistent. Particularly, regularity can be measured as regularity of rest-wake, sleep-wake or overall 24-hour activity, but it is unclear which aspects are most relevant for cognitive aging. Objective: To assess associations of rest-wake, sleep-wake, and 24-hour activity regularity with cognitive decline and dementia risk. Design: Observational prospective study comprised of six US and European cohorts: MrOS (sleep study between 2003-2005, mean follow-up: 7.1 years), Rotterdam Study (2004-2007, 11.6 years), MESA (2010-2013, 8.2 years), MAP (2005-2018, 7.2 years), Whitehall II (2012-2013, 6.9 years), and UKB (2013-2015, 7.9 years). Setting: Cohort-specific estimates were pooled using random-effects meta-analysis. Analyses were done between June 2025 and March 2026. Participants 74,733 dementia-free adults with multi-day actigraphy were included across cohorts: MrOS (age: 67-96 years, female:0%), MESA (54-95y, female:54.6%), Rotterdam Study (46-98y, female:55.0%), MAP (56-100y, female:77.1%), Whitehall II (59-83y, female:25.9%), and UKB (55-78y, female:55.5%). Exposure: Day-to-day rest-wake regularity (Rest Regularity Index, RRI), day-to-day sleep-wake regularity (Sleep Regularity Index, SRI), and 24-hour activity regularity (Interdaily Stability, IS) were derived from multi-day actigraphy. Main Outcome: Outcomes were risk of dementia and changes in global cognition. Results: Across six cohorts, 1,906 dementia cases occurred among 74,733 participants. After adjusting for demographics, health behaviors, depressive symptoms and cardiovascular comorbidities, each 1-SD higher regularity score was associated with an 9-14% lower dementia risk (pooled hazard ratios: RRI 0.86 95%CI: [0.79-0.95]; SRI 0.87[0.79-0.97]; IS: 0.91[0.88-0.95]). Associations were approximately linear. Age-stratified analyses showed directionally stronger associations among adults aged < 65, although meta-regression did not support an interaction(p > 0.55). Greater regularity was associated with modestly slower decline in global cognition (pooled β per 1-SD higher score of RRI per year: 0.003, 95%CI [0.001-0.006]). Conclusions & Relevance: Greater regularity of rest-wake, sleep-wake, and 24-hour activity rhythms was associated with lower dementia risk and modestly slower global cognitive decline. These findings suggest that 24-hour sleep-wake regularity is a relevant behavioral marker of cognitive aging and may inform future efforts to identify or intervene on early risk.

Increases in slow-wave activity (SWA) during sleep are associated with improvements in overnight memory retention and cognitive performance in young adults. Links between SWA and overnight improvements in cognition among older populations, however, are poorly understood. This proof-of-concept pilot study used a single session of excitatory repetitive transcranial magnetic stimulation (rTMS) during wakefulness to increase SWA during subsequent sleep in older adults with subjective cognitive decline. We examined whether this single night increase in SWA led to improvements in overnight memory retention, executive function, and vigilant attention. Participants (mean age = 70 years, SD = 5.2) with subjective reports of cognitive decline participated in a 40-min session of active (n = 11) or Sham (n = 9) high frequency (10 Hz) rTMS applied to the left dorsolateral prefrontal cortex. Participants spent two nights in a sleep lab pre- and post-rTMS with high-density EEG to assess changes in SWA. A behavioral task battery examining overnight memory retention (Word Pair Task, Face-Profession, Object Recognition), executive control (Stroop, Sternberg, task-switching) and vigilant attention (Psychomotor Vigilance Task) were also assessed pre- and post-rTMS. Overall reaction time (RT) on the Stroop task, across task conditions, improved between sessions with rTMS, but not with Sham. There was no evidence of improved performance on other cognitive domains with rTMS. Increased fronto-parietal SWA in the first NREM period was associated with the improvement in overall Stroop RT. Stroop congruent and neutral trial RT, Stroop neutral trial accuracy, and Sternberg working memory RT were also associated with increased whole night and first NREM period SWA. These preliminary results suggest that acute increases in SWA are associated with improved processing speed in older adults at risk for cognitive decline, as evidenced by an improvement in overall Stroop RT as opposed to improvements on specific task conditions. Multi-session rTMS studies and randomized controlled trials are needed to fully assess the effect of enhanced SWA through rTMS on improvements in overnight memory retention and executive function.

Slow-wave activity (0.5–4 Hz electroencephalographic activity) during non-rapid eye movement sleep is consistently associated with better cognitive performance in older adults. Slow-wave activity is known to regulate synaptic plasticity and may thereby mitigate excitotoxicity and accumulation of Alzheimer’s pathology. Paradoxically, longer total sleep times in older adults are often associated with poorer cognition and general health. Conventional behavioral sleep treatments robustly increase sleep efficiency and sleep time, but do not consistently enhance slow-wave activity and have shown only subtle effects on cognition. Thus, enhancement of slow-wave activity may be a critical target for sleep-based cognitive enhancement. The Alzheimer’s Pathways Sleep Study (ALPS) uses a novel time-in-bed (TiB) restriction intervention designed to increase slow-wave activity through homeostatic sleep drive and assesses improvements in measures of excitotoxic hippocampal hyperactivation, plasma levels of amyloid beta (Aβ), and overnight memory retention. ALPS is a randomized controlled trial designed to increase slow-wave activity behaviorally in older adults with poor sleep. Target enrollment is 116 participants aged 65–85. Participants are randomized to a TiB restriction intervention or an attention-matched control intervention. Participants randomized to TiB restriction follow a sleep schedule restricting their TiB to 85

Importance:Many adolescents experience sleep that is too short and mis-timed for their circadian clock, which can adversely impact psychological and physical health. Feasible, targeted interventions to modify sleep behaviors and circadian timing could improve adolescent sleep and ultimately, health, and functioning. Objective:To determine whether a novel intervention integrating chronotherapeutic approaches (sleep scheduling, morning bright light glasses, and evening blue-light blocking glasses) would increase weeknight sleep duration and shift circadian timing earlier in adolescents with late sleep. Design, Setting, and Participants:This randomized clinical trial was conducted in a research setting in an academic medical center during school months (late August to mid-June) between 2018 and 2024. All analyses were intention to treat. Adolescents aged 16 to 19 years enrolled in a traditional high school who reported habitual weekend sleep onset later than 1 am. Participants were randomized to a Sleeping Late Teens Program or sleep monitoring control. Interventions:The Sleeping Late Teens Program included 1 collaborative, problem-solving session (<1 hour) followed by 2 weeks of a personalized sleep schedule that shifted bedtimes and wake times earlier. Participants wore morning bright-light glasses for 30 to 60 minutes on waking and amber-tinted blue light-blocking glasses for 2 hours before bed. Main Outcomes and Measures:Primary outcome measures included weeknight circadian timing indexed by salivary dim-light melatonin onset (DLMO), weeknight sleep duration measured with actigraphy, and circadian alignment operationalized as the interval between DLMO and midsleep (middle of the nocturnal sleep period). Results:Among 86 participants, 44 were randomly assigned to the intervention group and 42 to the control group. Of these, 80 completed baseline procedures (40 in each group): mean (SD) age, 17.5 (0.7) years; 48 female (60%) and 32 male (40%). The intervention group showed statistically significant and clinically relevant changes in sleep and circadian metrics compared with the sleep monitoring control. After 2 weeks, participants randomized to the active intervention, compared with the control, had earlier circadian timing (45 minutes; β = -0.55; 95% CI, -0.79 to -0.31; P = .003) and longer weeknight sleep duration (47 minutes; β = 0.74; 95% CI, 0.30-1.18; P = .003). DLMO-midsleep alignment shortened by 18 minutes in the intervention group compared with an 8-minute lengthening in controls; however, this difference was not statistically significant (β = -0.35; 95% CI, -0.72 to 0.02; P = .20). Conclusion and Relevance:In this randomized clinical trial, results show that a short-term intervention that combined sleep scheduling, morning bright-light glasses, and evening blue light-blocking glasses shifted circadian timing earlier and extended weeknight sleep duration in adolescents. Larger trials are needed to confirm the effectiveness of this intervention. Trial Registration:ClinicalTrials.gov Identifier: NCT03806296.

Importance:Sleep behavior markedly shifts in adolescence, increasing vulnerability to mental health disorders. Although sleep health is understood to be multidimensional, adolescent-specific sleep health dimensions have not been empirically validated and their relevance to transdiagnostic mental health outcomes is unknown. Objective:To identify sleep health dimensions using Fitbit devices in a large sample of young adolescents and assess concurrent and prospective associations between sleep health dimensions and transdiagnostic mental health outcomes. Design, Setting, and Participants:Multicenter longitudinal cohort study using data from 3393 participants in the Adolescent Brain Cognitive Development (ABCD) Study (Data Release 5.1, collected 2018-2020), including early adolescents (ages 11-13 years) within the US. Exploratory factor analysis (EFA) was used to identify sleep health dimensions and confirmatory factor analysis (CFA) to confirm the factor structure in an independent subsample. Linear mixed-effects models were used to test concurrent and prospective associations between sleep dimensions and mental health outcomes at 1-year follow-up. Statistical analysis was conducted from January to November 2025. Exposures:Objective sleep data collected for up to 21 (range, 7-21) days, using wearable Fitbit devices. Main Outcomes and Measures:Transdiagnostic mental health outcomes assessed via the Child Behavior Checklist and Brief Problem Monitor (internalizing and externalizing symptoms), Prodromal Questionnaire-Brief Child Version (psychoticlike symptoms), and 10-item Mania Scale (mania symptoms). Results:The 3393 participants (49% female; median age, 12 years) were split into EFA and CFA subsamples. Six sleep factors were identified using EFA: irregularity, timing, social jetlag, duration, weekend oversleep, and continuity. CFA confirmed this factor structure. All variables loaded strongly (≥0.64) onto at least 1 factor (factor 1 loadings, 0.64-0.98; factor 2, 0.96-0.98; factor 3, 0.95-0.97; factor 4, -0.86 to 1.01; factor 5, 0.68-0.93; factor 6, 0.82-0.94). Greater sleep irregularity was associated with transdiagnostic mental health symptoms cross-sectionally, but not prospectively (β, 0.06 [95% CI, 0.02-0.10] to 0.12 [95% CI, 0.08-0.16]). Shorter duration was associated with total, internalizing, externalizing, and attention symptoms cross-sectionally (β, -0.06 [95% CI, -0.10 to -0.01] to -0.11 [95% CI, -0.15 to -0.06]) and total, attention, and psychotic symptoms 1 year later. Conclusions and Relevance:In this study, wearable Fitbit data provide empirical support for multidimensional frameworks of sleep health in adolescence. Although effect sizes were small, sleep irregularity and duration emerged as key dimensions with relevance to mental health. These findings establish a foundation for future investigations, including examining within-person patterns of the 6 dimensions, extending to older adolescence, investigating associations with other health outcomes, replicating with research-grade actigraphy devices, and suggesting potential targets for pediatric sleep interventions.

In adult samples, tightly-controlled laboratory studies indicate the presence of circadian rhythms in positive (and negative) affect. Naturalistic studies also suggest the presence of diurnal positive affect rhythms in adults, the characteristics (acrophase, mesor, and amplitude) of which vary by self-report circadian preference-greater evening preference is associated with later acrophase, lower mesor, and lower amplitude in positive affect. We examined the extent to which diurnal affect rhythms are associated with 4 different measures of circadian timing, including dim light melatonin onset, in a sample of high-school adolescents who reported at least one drink of alcohol in their lifetime (N = 126, 17.3 ± 0.87 years, 55.6% female). Cosinor models found support for robust diurnal rhythms in positive, but not negative, affect. The overall modeled positive affect rhythm had an acrophase at 3:39 PM, a mesor of 9.77, and an amplitude of 1.61. Later circadian timing was associated with later acrophase in positive affect rhythms across the following measures: circadian preference (3:00 PM vs 4:20 PM, p < .001), chronotype (3:20 PM vs 4:11 PM, p = .014), and actigraphy-based midsleep (3:08 PM vs 4:16 PM, p = .014). We did not find significant associations between circadian phase (dim light melatonin onset) and positive affect rhythms. We also explored weekday-weekend differences in positive affect rhythms, finding significantly higher mesor (9.71 vs 9.99, p = .004) and lower amplitude (1.69 vs 1.26, p = .008) on the weekends than weekdays. In sum, compared to their peers, adolescents with later sleep and circadian timing experience a delayed peak in positive affect during the day, which may have consequences for behavioral activation and depressed mood. These findings underscore the importance of considering the role of sleep and circadian factors in affective processes during adolescence.

Recent work supports the efficacy of Behavioral Activation (BA) over Treatment as Usual (TAU) for the negative symptoms (NS) of Schizophrenia Spectrum Disorder (SSD). To inform precision medicine approaches, we aimed to identify factors that moderate the effect of BA versus TAU. We performed discovery-based moderators analyses, leveraging data from a recently completed randomized controlled trial that found BA had efficacy over TAU for NS. Potential moderators were pre-treatment symptom items from common symptom scales, including items from the Clinical Assessment Interview for Negative Symptoms [13], the Positive and Negative Syndrome Scale [14] (PANSS), and the Brief Negative Symptom Scale. Seven PANSS items emerged as potential negative moderators, wherein greater severity of these symptoms was associated with lower efficacy of BA versus TAU (effect size estimate range: -0.45 to -0.25). These negative moderators were conceptual disorganization, hallucinatory behavior, grandiosity, anxiety, tension, uncooperativeness, and unusual thought content. Using total scores on these items as a combined moderator revealed a minority subgroup, composed of 20% of the overall sample, in whom rates of response to TAU and BA were roughly equal (response rates: TAU, 60%; BA, 50%). In contrast, among the majority subgroup (80% of the sample), BA had clear effects above and beyond the TAU condition (response rates: TAU, 20%; BA, 58%). These findings showcase how moderator research informs precision medicine. Specifically, NS appear malleable by adding BA to TAU among patients without uncooperativeness, anxiety, and positive symptoms. However, in the presence of these moderating symptoms, TAU may be equally efficacious.

Actigraphy is a popular behavioral sleep assessment tool in research and clinical practice. Hierarchical hand-scoring approaches remain the standard for actigraphy rest interval estimation, but can be impractical for large cohort studies and suffer from reproducibility problems. We developed a semi-automated pipeline (actiSleep) to set rest intervals consistent with best-practice hand-scoring algorithms incorporating event marker, diary, light, and activity data. To evaluate actiSleep performance, we used data from an observational study of 51 adolescents (14-19yr), with and without family history of bipolar disorder. Participants completed 2 weeks of wrist actigraphy and daily sleep diary. We first hand-scored records using a standardized hierarchical algorithm incorporating event marker, diary, light, and activity data. We then compared the hand-scored rest intervals to those from actiSleep and two automated activity-based algorithms ('Activity-Merged', 'Activity-Only'). Activity-Only used activity-based sleep estimation and Activity-Merged joined closely adjacent rest intervals. For rest onset, rest offset, and rest duration, all algorithms had strong mean agreement with hand-scoring: actiSleep estimates were within 1-3 minutes, Activity-Merged within 2-4 minutes, and Activity-Only within 7-14 minutes. However, actiSleep had notably better (narrower) margins of agreement with hand-scoring, as evidenced by Bland-Altman plots, and greater positive predictive value and true positive rates for rest detection, especially in the 60 minutes surrounding the onset and offset of the rest interval. The actiSleep algorithm successfully estimates actigraphy rest intervals comparable to hand-scoring while avoiding pitfalls of activity-only algorithms. actiSleep has potential to replace hand-scoring for research in adolescents but requires further testing and validation in other samples.

BACKGROUND:Traumatic brain injury (TBI) in the U.S. military can result in lasting health issues, with insomnia being a common symptom that worsens recovery, cognitive function, and performance, especially when combined with common co-occurring conditions like chronic pain, post-traumatic stress disorder (PTSD), and depression. Insomnia may be an important intervention target for managing post-concussive symptoms and overall functioning in service members who have sustained a TBI. However, the standard of care for the treatment of insomnia, Cognitive Behavioral Therapy for Insomnia (CBTI), is not widely available in military health care settings. The aim of this paper is to describe the design and analysis plan of the clinical trial to evaluate and compare two methods for delivering CBTI including in-person CBTI or CBTI delivered remotely via a clinician-supervised digital platform in a sample of active-duty service members presenting for care in a military TBI specialty clinic. METHODS:This is a phase II, randomized clinical trial designed to evaluate and compare the effects of CBTI (in-person or via a digital health platform) on sleep, behavioral health, and cognitive functions relative to treatment as usual among a sample of service members with a history of TBI. The effectiveness of in-person CBTI and CBTI delivered via a digital health platform, relative to treatment as usual, will be compared at baseline, after the six-week intervention, and again three months later on symptoms of insomnia, sleep quality, post-concussive symptoms, neurocognitive functioning, and psychological health. DISCUSSION:TBI is common in military personnel, often leading to insomnia that affects health and performance. While CBTI is the first-line recommended treatment for insomnia, CBTI is rarely implemented as the standard of care in military TBI specialty clinics, highlighting the need to assess its role in treating post-concussion symptoms and related issues. Clinical trials evaluating insomnia treatment in U.S. military service members with a history of TBI are essential to inform clinical practice for military TBI patients affected by insomnia and to potentially improve recovery, duty readiness, and cognitive function in this population. TRIAL REGISTRATION:ClinicalTrials.gov: NCT06867666. Registered on 2/26/2025.

Introduction We previously developed a brief, behavioral intervention (Sleep Promotion Program, SPP) that demonstrated feasibility, acceptability, and increased self-reported sleep. Here, we tested the effects of SPP on the trial’s secondary outcomes: sleep duration, regularity, and timing as measured by actigraphy and conducted a secondary analysis of SPP’s effects on rest-activity rhythms. Methods Forty participants (ages 13-15) with insufficient and irregular sleep randomized to SPP-continuation (n = 22), or to sleep monitoring-SPP (n = 18), had analyzable actigraphy data. SPP included sleep psychoeducation and one individual clinician session. Participants wore an actigraph for 1-2 weeks during baseline, Period 1, and Period 2. We hypothesized SPP would lead to longer and more regular sleep from baseline to Period 1 compared to sleep monitoring that would be sustained at Period 2. We used multilevel models with a treatment by time interaction to test whether SPP improved sleep relative to sleep monitoring. We also compared sleep changes from Period 1 to Period 2 among participants randomized to SPP-continuation. Results Participants receiving SPP decreased the difference in weekend-weekday sleep onset by 54-minute (β = 1.1; p = .03) from baseline to Period 1 (i.e., increased regularity). Increased regularity was maintained at Period 2. Total sleep time did not change significantly. Conclusion SPP regularized weekend-weekday sleep onset timing in adolescents with insufficient and irregular sleep but did not change actigraphic sleep duration, which differed from increases in self-reported sleep duration previously reported. Regularizing sleep may be an attainable first step to improving adolescent sleep. Future work is needed to test whether sleep regularity predicts adolescent health outcomes. Clinical trials Targeted Intervention for Insufficient Sleep among Typically-Developing Adolescents (TAPAS); https://clinicaltrials.gov/ct2/show/NCT04163003; NCT04163003.

BackgroundConsidering the multidimensional nature of self-report sleep health may improve identification of those at risk of accelerated cognitive decline and dementia.ObjectiveWe compared how composite measures of multidimensional sleep health relate to cognitive performance and the risk of dementia over time in older adults.MethodsSelf-reported indicators of sleep health domains (satisfaction, alertness, timing, efficiency, and duration) were measured in 7892 Rotterdam Study (RS) participants (mean ± SD age: 69.5 ± 8.9 years, 58.2% female) and 1601 Rush Memory and Aging Project and Minority Aging Research Project (MAP/MARS) participants (79.5 ± 7.9 years, 77.3% female). Sleep items were harmonized and used to derive a sleep health score (number of adverse sleep health items) and sleep health clusters (with latent class analysis). During follow-up, multiple cognitive tests were performed repeatedly and participants were followed for incident all-cause dementia. Relationships of sleep health with cognitive decline (linear mixed models) and risk of dementia (Cox proportional hazards models) were assessed in both samples, adjusting for covariates.ResultsThree sleep health clusters were identified: average sleep, inefficient sleep, and poor sleep. During follow-up of 10.6 ± 4.5 years in RS and 5.3 ± 2.9 years in MAP/MARS, 1148 (14.5%) and 286 (19.8%) participants developed dementia, respectively. Multidimensional sleep health scores and clusters were not significantly associated with accelerated cognitive decline or the risk of dementia in either sample (Hazard Ratios [HRs] between 0.72-1.15).ConclusionsFindings suggest composite measures of self-reported multidimensional sleep health need refinement to be useful in identifying older adults at risk of accelerated cognitive decline and dementia.

Adolescence is a period of distinct maturational changes in sleep physiology. Age-related trends in sleep physiology have been captured using laboratory-based polysomnography, a method limited by logistical burden and high cost. We tested the ability of the Dreem3 sleep EEG headband to replicate established age effects in sleep physiology from late childhood through early adulthood. Typically developing youth (N = 100, 9–26 years) completed 3–4 consecutive nights of at-home sleep recording. We estimated age-related trends across eight macro-architecture and 15 micro-architecture variables with known age effects, and conducted exploratory analyses of 24 additional variables. Dreem3 replicated established age trends, including increases in non-rapid eye movement (NREM) stage 2%, and decreases in N3%, time in bed, NREM delta and theta power with increasing age. Exploratory analysis revealed age effects in twelve additional variables, including decreases in spindle activity with increasing age. Sleep EEG wearables offer an accessible way to characterize sleep physiology development.

Adolescence and young adulthood are critical periods for the emergence of bipolar disorder (BD). Instability in sleep and rest-activity rhythms is associated with elevated risk for BD, yet little is known about the neural correlates of this vulnerability. White matter organization in fronto-limbic pathways, which support mood regulation and show sensitivity to sleep/rest-activity rhythm disruption, offers a promising avenue for investigation. Participants (16-24y;N=112) recruited across a spectrum of mania vulnerability (MOODS-SRL) completed 14 days of actigraphy followed by a neuroimaging assessment. Diffusion MRI was used to derive Neurite Orientation Dispersion and Density Imaging, focusing on the Orientation Dispersion Index (ODI) of the cingulum bundle, uncinate fasciculus, and forceps minor. Clinician-rated symptoms of mania and depression were assessed at baseline and 6-months follow-up. We evaluated whether the links between actigraphy-derived sleep duration variability, sleep onset variability, and Circadian Function Index (CFI; index of circadian rhythm robustness) and white matter ODI were moderated by baseline mania vulnerability, and tested whether baseline ODI predicted 6-month mood symptoms. At higher levels of mania vulnerability, lower CFI (greater rest-activity instability) associated with higher ODI (greater white matter disorganization) in the cingulum bundle (β=-0.22;P=0.029) and uncinate fasciculus (β=-0.22;P=0.020). Moreover, higher uncinate fasciculus ODI predicted greater mania symptoms at 6-month follow-up and fully mediated the CFI-mania symptom association (β=-0.10;95%CI[-0.14,-0.02]). Rest-activity instability may disrupt fronto-limbic white matter, increasing risk to mania in those at elevated vulnerability for BD. Stabilizing rest-activity rhythms in at-risk individuals may help preserve white matter integrity and mitigate BD risk.

Study objectives:Adolescence is a period of distinct maturational changes in sleep characteristics. Historically, age trends in sleep physiology have been captured using laboratory-based polysomnography (PSG). However, multiple challenges associated with PSG, including logistical issues, budgetary constraints and ecological validity questions, limit large-scale use. The current study aims to address these challenges by using the Dreem3 headband to measure sleep at home and replicate well-established age-related trends in sleep physiology from late childhood through early adulthood. Methods:100 typically developing youth (9-26 years) wore a sleep electroencephalography (EEG) device (Dreem3) for 3-4 consecutive nights at home. Sleep EEG data were processed using the Luna pipeline. We used linear mixed models to estimate age-related trends across 8 macro-architecture and 15 micro-architecture variables previously found to be associated with age, and explored age relationships in 24 additional macro- and micro-architecture variables. Results:At-home sleep studies using Dreem3 replicated established age trends in sleep macro- and micro-architecture, including decreases in percent time spent in non-rapid eye movement (NREM) stage 3 (N3%) sleep and decreases in NREM delta power with increasing age. Exploratory analysis revealed age effects in seven other variables, including decreases in integrated slow spindle activity and NREM cycle duration with increasing age. Conclusion:Sleep EEG wearables may offer an accessible way to characterize sleep physiology development in large cohorts, setting the stage for understanding how deviations from normative age patterns may put young people at risk for adverse outcomes.

The impact of alcohol on sleep is complex and nuanced, often (but not always) showing initial sedation followed by later disruption alongside other more subtle changes, as demonstrated in both laboratory- and home-based studies. However, prior naturalistic work has relied on subjective measures, small samples, and/or participants with co-occurring sleep problems, limiting generalizability. Additionally, retrospective designs constrain fine-grained, day-to-day analyses. Here, we leverage real-time ecological momentary assessment (EMA) reports of alcohol consumption to examine its relationship with actigraphically measured daily sleep among young adults reporting episodic heavy drinking. Young adults aged 21-30 years (N=88) engaging in episodic heavy drinking (4+/5+ drinks/night at least weekly) participated in a two-phase, 9-day EMA study assessing naturalistic alcohol consumption and sleep. Sleep was objectively measured by wrist-worn actigraphy devices. Linear mixed-effects models assessed day-to-day associations between drinks consumed and actigraphic sleep outcomes, adjusting for covariates including age, sex assigned at birth, race, ethnicity, and baseline alcohol use. On average, participants consumed 5.3±0.2 drinks per drinking day (over 3.0±0.1 drinking days) in each 9-day EMA period. Higher alcohol consumption was associated with shorter sleep onset latency (SOL; square-root-transformed; ß_std=-0.13, p< 0.001) and later midsleep time (log-transformed; ß_std=0.12, p< 0.001). No significant associations were found with total sleep time (TST), sleep efficiency (SE), wake after sleep onset (WASO), sleep fragmentation, or snooze time (all p>0.05). All relationships were unaffected by sex assigned at birth. This study extends prior work investigating day-to-day alcohol–sleep relationships, replicating alcohol-related delayed sleep timing and revealing shorter SOL with increased drink consumption, a finding previously only seen in laboratory-based studies. We did not observe expected disruptions in sleep continuity, which may reflect the complexity of within-night changes (initial sedation followed by later disruption) that our methods do not fully capture. Furthermore, delayed sleep timing may exacerbate alcohol-related morbidity if morning obligations constrain adequate rest. Future analyses will incorporate within-night changes, next day functioning, and consider co-use of cannabis, nicotine, caffeine, and other substances to fully elucidate these complex interactions. T32HL082610, T32MH018951, R01AA026249