Naval Health Research Center

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.

Generation of Swine Flu As the newly emerged influenza virus starts its journey to infect the world's human population, the genetic secrets of the 2009 outbreak of swine influenza A(H1N1) are being revealed. In extensive phylogenetic analyses, Garten et al. (p. 197 , published online 22 May) confirm that of the eight elements of the virus, the basic components encoded by the hemagglutinin, nucleoprotein, and nonstructural genes originated in birds and transferred to pigs in 1918. Subsequently, these formed a triple reassortant with the RNA polymerase PB1 that transferred from birds in 1968 to humans and then to pigs in 1998, coupled with RNA polymerases PA and PB2 that transferred from birds to pigs in 1998. The neuraminidase and matrix protein genes that complete the virus came from birds and entered pigs in 1979. The analysis offers insights into drug susceptibility and virulence, as well as raising the possibility of hitherto unknown factors determining host specificity. A significant question is, what is the potential for the H1 component of the current seasonal flu vaccine to act as a booster? Apart from the need for ongoing sequencing to monitor for the emergence of new reassortants, future pig populations need to be closely monitored for emerging influenza viruses.

Importance: The rapid expansion of virtual health care has caused a surge in patient messages concomitant with more work and burnout among health care professionals. Artificial intelligence (AI) assistants could potentially aid in creating answers to patient questions by drafting responses that could be reviewed by clinicians. Objective: To evaluate the ability of an AI chatbot assistant (ChatGPT), released in November 2022, to provide quality and empathetic responses to patient questions. Design, Setting, and Participants: In this cross-sectional study, a public and nonidentifiable database of questions from a public social media forum (Reddit's r/AskDocs) was used to randomly draw 195 exchanges from October 2022 where a verified physician responded to a public question. Chatbot responses were generated by entering the original question into a fresh session (without prior questions having been asked in the session) on December 22 and 23, 2022. The original question along with anonymized and randomly ordered physician and chatbot responses were evaluated in triplicate by a team of licensed health care professionals. Evaluators chose "which response was better" and judged both "the quality of information provided" (very poor, poor, acceptable, good, or very good) and "the empathy or bedside manner provided" (not empathetic, slightly empathetic, moderately empathetic, empathetic, and very empathetic). Mean outcomes were ordered on a 1 to 5 scale and compared between chatbot and physicians. Results: Of the 195 questions and responses, evaluators preferred chatbot responses to physician responses in 78.6% (95% CI, 75.0%-81.8%) of the 585 evaluations. Mean (IQR) physician responses were significantly shorter than chatbot responses (52 [17-62] words vs 211 [168-245] words; t = 25.4; P < .001). Chatbot responses were rated of significantly higher quality than physician responses (t = 13.3; P < .001). The proportion of responses rated as good or very good quality (≥ 4), for instance, was higher for chatbot than physicians (chatbot: 78.5%, 95% CI, 72.3%-84.1%; physicians: 22.1%, 95% CI, 16.4%-28.2%;). This amounted to 3.6 times higher prevalence of good or very good quality responses for the chatbot. Chatbot responses were also rated significantly more empathetic than physician responses (t = 18.9; P < .001). The proportion of responses rated empathetic or very empathetic (≥4) was higher for chatbot than for physicians (physicians: 4.6%, 95% CI, 2.1%-7.7%; chatbot: 45.1%, 95% CI, 38.5%-51.8%; physicians: 4.6%, 95% CI, 2.1%-7.7%). This amounted to 9.8 times higher prevalence of empathetic or very empathetic responses for the chatbot. Conclusions: In this cross-sectional study, a chatbot generated quality and empathetic responses to patient questions posed in an online forum. Further exploration of this technology is warranted in clinical settings, such as using chatbot to draft responses that physicians could then edit. Randomized trials could assess further if using AI assistants might improve responses, lower clinician burnout, and improve patient outcomes.

Because of the distance between the skull and brain and their dier-ent resistivities, electroencephalographic (EEG) data collected from any point on the human scalp includes activity generated within a large brain area. This spatial smearing of EEG data by volume conduction does not involve signicant time delays, however, sug-gesting that the Independent Component Analysis (ICA) algorithm of Bell and Sejnowski [1] is suitable for performing blind source sep-aration on EEG data. The ICA algorithm separates the problem of source identication from that of source localization. First results of applying the ICA algorithm to EEG and event-related potential (ERP) data collected during a sustained auditory detection task show: (1) ICA training is insensitive to dierent random seeds. (2) ICA may be used to segregate obvious artifactual EEG components (line and muscle noise, eye movements) from other sources. (3) ICA is capable of isolating overlapping EEG phenomena, including al-pha and theta bursts and spatially-separable ERP components, to separate ICA channels. (4) Nonstationarities in EEG and behav-ioral state can be tracked using ICA via changes in the amount of residual correlation between ICA-ltered output channels.

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.

Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected-and undetected-target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.

The purpose of this prospective study was to determine whether an association exists between foot structure and the development of musculoskeletal overuse injuries. The study group was a well-defined cohort of 449 trainees at the Naval Special Warfare Training Center in Coronado, California. Before beginning training, measurements were made of ankle motion, subtalar motion, and the static (standing) and dynamic (walking) characteristics of the foot arch. The subjects were tracked prospectively for injuries throughout training. We identified risk factors that predispose people to lower extremity overuse injuries. These risk factors include dynamic pes planus, pes cavus, restricted ankle dorsiflexion, and increased hindfoot inversion, all of which are subject to intervention and possible correction.

CONTEXT: High rates of alcohol misuse after deployment have been reported among personnel returning from past conflicts, yet investigations of alcohol misuse after return from the current wars in Iraq and Afghanistan are lacking. OBJECTIVES: To determine whether deployment with combat exposures was associated with new-onset or continued alcohol consumption, binge drinking, and alcohol-related problems. DESIGN, SETTING, AND PARTICIPANTS: Data were from Millennium Cohort Study participants who completed both a baseline (July 2001 to June 2003; n=77,047) and follow-up (June 2004 to February 2006; n=55,021) questionnaire (follow-up response rate = 71.4%). After we applied exclusion criteria, our analyses included 48,481 participants (active duty, n = 26,613; Reserve or National Guard, n = 21,868). Of these, 5510 deployed with combat exposures, 5661 deployed without combat exposures, and 37 310 did not deploy. MAIN OUTCOME MEASURES: New-onset and continued heavy weekly drinking, binge drinking, and alcohol-related problems at follow-up. RESULTS: Baseline prevalence of heavy weekly drinking, binge drinking, and alcohol-related problems among Reserve or National Guard personnel who deployed with combat exposures was 9.0%, 53.6%, and 15.2%, respectively; follow-up prevalence was 12.5%, 53.0%, and 11.9%, respectively; and new-onset rates were 8.8%, 25.6%, and 7.1%, respectively. Among active-duty personnel, new-onset rates were 6.0%, 26.6%, and 4.8%, respectively. Reserve and National Guard personnel who deployed and reported combat exposures were significantly more likely to experience new-onset heavy weekly drinking (odds ratio [OR], 1.63; 95% confidence interval [CI], 1.36-1.96), binge drinking (OR, 1.46; 95% CI, 1.24-1.71), and alcohol-related problems (OR, 1.63; 95% CI, 1.33-2.01) compared with nondeployed personnel. The youngest members of the cohort were at highest risk for all alcohol-related outcomes. CONCLUSION: Reserve and National Guard personnel and younger service members who deploy with reported combat exposures are at increased risk of new-onset heavy weekly drinking, binge drinking, and alcohol-related problems.

OBJECTIVE: To describe new onset and persistence of self reported post-traumatic stress disorder symptoms in a large population based military cohort, many of whom were deployed in support of the wars in Iraq and Afghanistan. DESIGN: Prospective cohort analysis. SETTING AND PARTICIPANTS: Survey enrolment data from the millennium cohort (July 2001 to June 2003) obtained before the wars in Iraq and Afghanistan. Follow-up (June 2004 to February 2006) data on health outcomes collected from 50 184 participants. MAIN OUTCOME MEASURES: Self reported post-traumatic stress disorder as measured by the posttraumatic stress disorder checklist-civilian version using Diagnostic and Statistical Manual of Mental Disorders, fourth edition criteria. RESULTS: More than 40% of the cohort were deployed between 2001 and 2006; between baseline and follow-up, 24% deployed for the first time in support of the wars in Iraq and Afghanistan. New incidence rates of 10-13 cases of post-traumatic stress disorder per 1000 person years occurred in the millennium cohort. New onset self reported post-traumatic stress disorder symptoms or diagnosis were identified in 7.6-8.7% of deployers who reported combat exposures, 1.4-2.1% of deployers who did not report combat exposures, and 2.3-3.0% of non-deployers. Among those with self reported symptoms of post-traumatic stress disorder at baseline, deployment did not affect persistence of symptoms. CONCLUSIONS: After adjustment for baseline characteristics, these prospective data indicate a threefold increase in new onset self reported post-traumatic stress disorder symptoms or diagnosis among deployed military personnel who reported combat exposures. The findings define the importance of post-traumatic stress disorder in this population and emphasise that specific combat exposures, rather than deployment itself, significantly affect the onset of symptoms of post-traumatic stress disorder after deployment.

BACKGROUND: To develop content validity of a comprehensive patient-reported outcome (PRO) measure following current best scientific methodology to standardize assessment of influenza (flu) symptoms in clinical research. METHODS: Stage I (Concept Elicitation): 1:1 telephone interviews with influenza-positive adults (≥18 years) in the US and Mexico within 7 days of diagnosis. Participants described symptom type, character, severity, and duration. Content analysis identified themes and developed the draft Flu-PRO instrument. Stage II (Cognitive Interviewing): The Flu-PRO was administered to a unique set of influenza-positive adults within 14 days of diagnosis; telephone interviews addressed completeness, respondent interpretation of items and ease of use. RESULTS: Samples: Stage I: N = 46 adults (16 US, 30 Mexico); mean (SD) age: 38 (19), 39 (14) years; % female: 56%, 73%; race: 69% White, 97% Mestizo. Stage II: N = 34 adults (12 US, 22 Mexico); age: 37 (14), 39 (11) years; % female: 50%, 50%; race: 58% White, 100% Mestizo. SYMPTOMS: Symptoms identified by >50%: coughing, weak or tired, throat symptoms, congestion, headache, weakness, sweating, chills, general discomfort, runny nose, chest (trouble breathing), difficulty sleeping, and body aches or pains. No new content was uncovered during Stage II; participants easily understood the instrument. CONCLUSIONS: Results show the 37-item Flu-PRO is a content valid measure of influenza symptoms in adults with a confirmed diagnosis of influenza. Research is underway to evaluate the suitability of the instrument for children and adolescents. This work can form the basis for future quantitative tests of reliability, validity, and responsiveness to evaluate the measurement properties of Flu-PRO for use in clinical trials and epidemiology studies.

In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, we show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEG measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.

A method is given for determining the time course and spatial extent of consistently and transiently task-related activations from other physiological and artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed three-dimensional spatial distribution of brain voxel values (a "map") and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the fMRI trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other ICA components were related to physiological pulsations, head movements, or machine noise. By using higher-order statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis (PCA). ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance.

Broad personality traits may be important predictors of health behavior patterns. Two studies are reported which examined the associations between five major personality dimensions and four major health behavior dimensions. Prior associations between health behaviors and neurotic and extraverted personality tendencies generally were replicated. However, Conscientiousness and Agreeableness, two domains of personality that have received little research attention, emerged as important personality predictors of health behaviors. The results indicate that personality is a reliable predictor of health behavior patterns. It is suggested that the importance of personality has been underestimated in past research by the failure to consider appropriate health behavior criteria and the omission of important personality dimensions, such as Conscientiousness and Agreeableness, when studying health behavior patterns.

BACKGROUND: Post-traumatic stress disorder (PTSD) is a common adverse mental health outcome among seriously injured civilians and military personnel who are survivors of trauma. Pharmacotherapy in the aftermath of serious physical injury or exposure to traumatic events may be effective for the secondary prevention of PTSD. METHODS: We identified 696 injured U.S. military personnel without serious traumatic brain injury from the Navy-Marine Corps Combat Trauma Registry Expeditionary Medical Encounter Database. Complete data on medications administered were available for all personnel selected. The diagnosis of PTSD was obtained from the Career History Archival Medical and Personnel System and verified in a review of medical records. RESULTS: Among the 696 patients studied, 243 received a diagnosis of PTSD and 453 did not. The use of morphine during early resuscitation and trauma care was significantly associated with a lower risk of PTSD after injury. Among the patients in whom PTSD developed, 61% received morphine; among those in whom PTSD did not develop, 76% received morphine (odds ratio, 0.47; P<0.001). This association remained significant after adjustment for injury severity, age, mechanism of injury, status with respect to amputation, and selected injury-related clinical factors. CONCLUSIONS: Our findings suggest that the use of morphine during trauma care may reduce the risk of subsequent development of PTSD after serious injury.

Severe contamination of electroencephalographic (EEG) activity by eye movements, blinks, muscle, heart and line noise is a serious problem for EEG interpretation and analysis. Rejecting contaminated EEG segments results in a considerable loss of information and may be impractical for clinical data. Many methods have been proposed to remove eye movement and blink artifacts from EEG recordings. Often regression in the time or frequency domain is performed on simultaneous EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. However, EOG records also contain brain signals [1, 2], so regressing out EOG activity inevitably involves subtracting a portion of the relevant EEG signal from each recording as well. Regression cannot be used to remove muscle noise or line noise, since these have no reference channels. Here, we propose a new and generally applicable method for removing a wide variety of artifa...

BACKGROUND: Most previous attempts to determine the psychological cost of military deployment have been limited by reliance on convenience samples, lack of pre-deployment data or confidentiality and cross-sectional designs. AIMS: This study addressed these limitations using a population-based, prospective cohort of U.S. military personnel deployed in support of the operations in Iraq and Afghanistan. METHOD: The sample consisted of U.S. military service members in all branches including active duty, reserve and national guard who deployed once (n = 3393) or multiple times (n = 4394). Self-reported symptoms of post-traumatic stress were obtained prior to deployment and at two follow-ups spaced 3 years apart. Data were examined for longitudinal trajectories using latent growth mixture modelling. RESULTS: Each analysis revealed remarkably similar post-traumatic stress trajectories across time. The most common pattern was low-stable post-traumatic stress or resilience (83.1% single deployers, 84.9% multiple deployers), moderate-improving (8.0%, 8.5%), then worsening-chronic post-traumatic stress (6.7%, 4.5%), high-stable (2.2% single deployers only) and high-improving (2.2% multiple deployers only). Covariates associated with each trajectory were identified. CONCLUSIONS: The final models exhibited similar types of trajectories for single and multiple deployers; most notably, the stable trajectory of low post-traumatic stress preto post-deployment, or resilience, was exceptionally high. Several factors predicting trajectories were identified, which we hope will assist in future research aimed at decreasing the risk of post-traumatic stress disorder among deployers.

We have discovered a ca. 40-Hz transient magnetic oscillatory response, evoked in the human brain by the onset of auditory stimuli, consisting of four or more cycles locked in phase to stimulus onset in approximately the 20- to 130-ms poststimulus interval. The response originates in the supratemporal auditory cortex, some millimeters deeper and anterior to the source of the larger-amplitude slow-wave M100 component of the evoked magnetic field and moves in a posterior arcing trajectory 1 cm or more in length. The oscillatory cortical activation elicited by auditory stimuli may be similar to the gamma-band cortical oscillations elicited by olfactory and visual stimuli and may represent an essential component of auditory perceptual processing.

STUDY OBJECTIVES: Consumer sleep-tracking devices are widely used and becoming more technologically advanced, creating strong interest from researchers and clinicians for their possible use as alternatives to standard actigraphy. We, therefore, tested the performance of many of the latest consumer sleep-tracking devices, alongside actigraphy, versus the gold-standard sleep assessment technique, polysomnography (PSG). METHODS: In total, 34 healthy young adults (22 women; 28.1 ± 3.9 years, mean ± SD) were tested on three consecutive nights (including a disrupted sleep condition) in a sleep laboratory with PSG, along with actigraphy (Philips Respironics Actiwatch 2) and a subset of consumer sleep-tracking devices. Altogether, four wearable (Fatigue Science Readiband, Fitbit Alta HR, Garmin Fenix 5S, Garmin Vivosmart 3) and three nonwearable (EarlySense Live, ResMed S+, SleepScore Max) devices were tested. Sleep/wake summary and epoch-by-epoch agreement measures were compared with PSG. RESULTS: Most devices (Fatigue Science Readiband, Fitbit Alta HR, EarlySense Live, ResMed S+, SleepScore Max) performed as well as or better than actigraphy on sleep/wake performance measures, while the Garmin devices performed worse. Overall, epoch-by-epoch sensitivity was high (all ≥0.93), specificity was low-to-medium (0.18-0.54), sleep stage comparisons were mixed, and devices tended to perform worse on nights with poorer/disrupted sleep. CONCLUSIONS: Consumer sleep-tracking devices exhibited high performance in detecting sleep, and most performed equivalent to (or better than) actigraphy in detecting wake. Device sleep stage assessments were inconsistent. Findings indicate that many newer sleep-tracking devices demonstrate promising performance for tracking sleep and wake. Devices should be tested in different populations and settings to further examine their wider validity and utility.