Using a wearable EEG device to examine age trends in sleep macro- and micro-architecture across adolescence

Using a wearable EEG device to examine age trends in sleep macro- and micro-architecture across adolescence | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Using a wearable EEG device to examine age trends in sleep macro- and micro-architecture across adolescence Sanna Lokhandwala , Rebecca Hayes , Soumya Sathe , Isabelle Elder , Mary Corcoran , Beatriz Horta , Maya Fray-Witzer , Lauren Keller , Simey Chan , Peter Franzen , Daniel Buysse , Brant P. Hasler , Jessica Levenson , Meredith L. Wallace , Duncan B Clark , Ronette G. Blake , Adriane Soehner , Maria Jalbrzikowski doi: https://doi.org/10.1101/2025.10.10.681690 Sanna Lokhandwala 1 Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital 2 Department of Psychiatry, Harvard Medical School Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rebecca Hayes 1 Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital 2 Department of Psychiatry, Harvard Medical School Find this author on Google Scholar Find this author on PubMed Search for this author on this site Soumya Sathe 1 Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital 2 Department of Psychiatry, Harvard Medical School Find this author on Google Scholar Find this author on PubMed Search for this author on this site Isabelle Elder 1 Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital 2 Department of Psychiatry, Harvard Medical School Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mary Corcoran 1 Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital 2 Department of Psychiatry, Harvard Medical School Find this author on Google Scholar Find this author on PubMed Search for this author on this site Beatriz Horta 1 Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital 2 Department of Psychiatry, Harvard Medical School Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maya Fray-Witzer 3 Department of Psychiatry, University of Pittsburgh School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lauren Keller 3 Department of Psychiatry, University of Pittsburgh School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Simey Chan 3 Department of Psychiatry, University of Pittsburgh School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter Franzen 3 Department of Psychiatry, University of Pittsburgh School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Buysse 3 Department of Psychiatry, University of Pittsburgh School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brant P. Hasler 3 Department of Psychiatry, University of Pittsburgh School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jessica Levenson 3 Department of Psychiatry, University of Pittsburgh School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Meredith L. Wallace 3 Department of Psychiatry, University of Pittsburgh School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Duncan B Clark 3 Department of Psychiatry, University of Pittsburgh School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ronette G. Blake 3 Department of Psychiatry, University of Pittsburgh School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Adriane Soehner 3 Department of Psychiatry, University of Pittsburgh School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria Jalbrzikowski 1 Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital 2 Department of Psychiatry, Harvard Medical School Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: maria.jalbrzikowski{at}childrens.harvard.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT 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. Statement of Significance Adolescence is a dynamic period characterized by changes in sleep physiology and behavior. While polysomnography has long been widely used for capturing age-related trends, it is resource-intensive and laboratory-bound, which limits the ability to track sleep in an accessible, scalable, and ecologically valid manner. Here, we used a sleep EEG headband, the Dreem3, to examine age-related trends in sleep macro- and micro-architecture across late childhood, adolescence, and early adulthood. We assessed sleep features with previously replicated age effects and explored age associations in other macro- and micro-architecture measures. The at-home wearable sleep EEG device replicated many of the age trends seen in traditional polysomnography. Leveraging accessible sleep EEG devices may provide a more scalable and comprehensive understanding of how sleep changes over adolescence. INTRODUCTION The transition from late childhood to adulthood is a unique developmental period marked by brain maturation and sleep changes. Sleep physiology features, which are sensitive markers of brain development, undergo prominent transformations in the second decade of life 1 , 2 . Sleep physiology plays an active role in sculpting the maturation of the brain 3 , predicts long-term brain, behavior, and health trajectories 4 – 6 , and is modifiable with non-invasive biobehavioral intervention 7 – 10 . Delineating the physiological sleep changes that occur over adolescence in a scalable way is critical for understanding developmental variability and how aberrant individual-level health trajectories manifest. Sleep macro- and micro-architecture undergo dramatic changes over adolescence 1 , 11 , 12 . Sleep macro-architecture is based on standard sleep staging via polysomnography (PSG) and refers to the overall structure and organization of sleep across the entire sleep period. Total sleep time (TST), percent of time in N3 (N3%), and rapid-eye movement (REM) latency decrease with increasing age 13 – 21 . In contrast, there is a concomitant rise in percent time spent in N2 (N2%) and wake after sleep onset (WASO) 14 , 15 , 22 . Sleep micro-architecture captures specific electrophysiological features within different sleep stages 23 . Cross-sectional studies find significant age effects in slow wave sleep characteristics, with decreases in NREM delta activity, delta power, and slow wave amplitude and slope with increasing age 11 , 12 , 24 , 25 . Longitudinal studies have further confirmed this decline across adolescence 16 , 26 . For instance, one longitudinal study of adolescents 9 to 17 years of age identified that NREM delta power (1-4 Hz) was maintained between 9 to 11 years, followed by a steep decline between 11 and 12 years 26 . In contrast, other studies find that sigma power (12-15 Hz) and various spindle characteristics increase over adolescence 14 , 27 – 30 . Kozhemiako and colleagues (2024) found that absolute sigma power and fast spindle density increased with increasing age. These developmental shifts in sleep EEG patterns during adolescence are linked to brain maturational processes which influence long-term brain, behavior, and health trajectories 4 – 6 . Age trends in sleep macro- and micro-architecture have historically been captured using laboratory-based PSG. Multiple challenges associated with PSG make it prohibitive to measure age effects for large-scale use and incorporate in community settings. Standard PSG is costly, requires highly trained staff, and is time-consuming to set up, conduct, and score, making it impractical for multi-night home-based recordings on a large scale. Generalizability of lab-based PSG is also a concern, as in-lab PSG has known ‘first night effects’ due to sleeping in a new environment 31 . First night effects are characterized by shorter and more fragmented sleep 32 , potentially preventing researchers from reliably capturing an individuals’ habitual sleep patterns, raising ecological validity questions. Among adolescents in particular, multiple nights of sleep recording may be needed to obtain stable and valid sleep EEG estimates 33 . Meta-analytic and longitudinal data point to weekday-weekend differences in sleep EEG characteristics in adolescents 16 , 22 . Further, sleep loss, sleep fragmentation, or sleep timing shifts – common occurrences in adolescents 34 – can introduce variability in sleep stage duration and sleep micro-architecture 35 , reducing the reliability of sleep endpoints drawn from a single-night recording. Indeed, measures of within-subject variability from at-home wearable sleep devices may more accurately capture adolescent sleep than laboratory PSG 36 . In a sample of adults, Arnal and colleagues (2020) found that an at-home, wireless, sleep monitoring device, the Dreem headband, was able to (1) acquire EEG signals that strongly correlate with EEG signals from PSG, and (2) perform automatic sleep staging with accuracy similar to that of expert scorers. Similarly, in a recent study of adolescents and young adults, data from the Dreem device provided sleep estimates that are moderately stable between nights, and these estimates are similar between youth and adults 36 . These results highlight that adopting a more portable, inexpensive, and scalable approach to sleep EEG results in similar outcomes as PSG for the sleep field 37 . It is critical to translate sleep-based risk assessment from research settings to community settings to have the greatest impact 38 , 39 . By incorporating sleep assessment in community settings, we could identify those at highest risk (e.g., for psychiatric illness) earlier, reach individuals traditionally underserved by academic medical centers, and provide a more accurate picture of age trends in the general population 40 , 41 . Using accessible, in-home sleep measures not only satisfies service users’ preference for community settings and easily implemented assessments 40 but also removes logistical barriers that prevent individuals (often under-represented or minoritized) from participating in research or seeking clinical care, such as lack of transportation, difficulty finding childcare, or lengthy time commitments. To this end, the current study aims to address these theoretical and technical gaps by attempting to replicate known age-related trends in sleep macro- and micro-architecture in young people using the Dreem3 headband. We considered sleep measures as having a “replicated” age effect if at least two independent studies reported consistent relationships in the same direction (Table S1 & S2). Specifically, we examined eight sleep macro-architecture features: TST, WASO, N2%, N3%, REM%, REM latency, sleep efficiency (SE), and time in bed (TIB) 11 , 13 – 16 , 21 , 42 , 43 . We examined fifteen sleep micro-architecture features, including NREM and REM spectral power (absolute delta, sigma, theta, and beta power) and oscillatory characteristics (slow spindle amplitude, density, duration, fast spindle amplitude, density, and duration, and slow oscillation-spindle coupling magnitude (slow and fast spindles) 3 , 11 , 13 , 24 – 27 , 42 , 44 – 49 . Finally, we explored several sleep macro- and micro-architecture features with less established age effects (i.e., no study has examined age associated trends in these features or we did not find at least two papers showing consistent age effects). MATERIALS and METHODS Participants University of Pittsburgh (PITT)-Study 1 Twenty-three healthy adolescents (13-16 years-old) were enrolled in a Dreem pilot study at the University of Pittsburgh. Participants were recruited from a larger parent protocol (P50DA046346) focused on understanding relationships between adolescent sleep-circadian rhythms and neurobehavioral mechanisms of substance use risk. The University of Pittsburgh Human Rights Protection Office (HRPO) approved the study. Researchers recruited participants through a local participant registry, community advertisements, newsletters, and social media posts. University of Pittsburgh (PITT)-Study 2 Forty-five adolescents and young adults (16-24 years-old) were enrolled in a second Dreem pilot study at the University of Pittsburgh. Participants were recruited from a larger parent protocol focused on understanding relationships between sleep-circadian rhythms and neurobehavioral mechanisms of mood disorder risk (R01MH124828). The University of Pittsburgh HRPO approved this study, which used recruitment methods that were similar to those in PITT-Study 1. Boston Children’s Hospital (BCH) We also recruited forty healthy individuals (9-26 years-old) for a Dreem pilot study at Boston Children’s Hospital. We used the following recruitment methods: flyers, previous study participation, and the Precision Link Biobank 50 for Healthy Discovery registries, and online posts. The Boston Children’s Hospital IRB approved all study procedures. Analytic Sample We had 108 participants enrolled across the three studies. Of the sixty-eight individuals enrolled at the PITT sites, sixty-three had Dreem data. We excluded an additional three PITT participants for having less than three hours of total sleep time for their available sleep recording. Forty individuals enrolled at BCH and all had Dreem data. Given that both sites followed similar sleep data collection protocols, used the same equipment, and participants were drawn from similar populations, we decided to combine data across sites. We included a total of 100 participants for the final analysis. Protocol After consent (18+) or caregiver consent and youth assent (<18yr), participants completed study-specific eligibility evaluation procedures and a survey battery. We then taught the participants how to use the Dreem headband and complete daily electronic sleep diaries. In PITT-Study 1 and BCH, participants completed 3-4 consecutive nights of overnight Dreem sleep monitoring at self-selected habitual sleep times at home. In PITT-Study 2, participants completed at least 3-4 consecutive nights of Dreem monitoring: one in lab, two at-home, and then one final night of concurrent Dreem-PSG recording. We used all available nights in the present analyses. Data were collected across weekdays and weekends, spanning both the summer and academic year. Sleep Measures Sleep EEG (Dreem3 Headband) The Dreem device is a wireless headband worn during sleep. It records, stores, and analyzes physiological data in real time without any connection (e.g., Bluetooth). The device contains five EEG sensors (all sampled at 250 Hz, 0.4-35 Hz bandpass filter): two frontal sensors (F7, F8), two occipital sensors (O1, O2), and one ground sensor on the frontal band (FpZ location). Additional details can be found in the supplemental text (S3) and Arnal et al., 2020. Data Processing Sleep Staging The Dreem automated algorithm performed AASM sleep stage classification, which researchers have validated against PSG and manual sleep staging by expert technicians in healthy adult participants 51 . In brief, the Dreem sleep staging algorithm uses a combination of features derived from EEG, pulse oximeter, and accelerometer signals to determine the probability that each epoch belongs to each sleep stage. To predict the sleep stage for a given epoch, the algorithm incorporates features extracted from the current and past 30 epochs. In a validation study, the Dreem sleep staging algorithm correctly classified sleep stages at a level equivalent to manual sleep scoring by experts (83.8%) 51 . We examined total sleep time (TST), wake after sleep onset (WASO), and percent time spent in N1 (N1%), N2 (N2%), N3 (N3%), and REM (REM%), REM latency (time from sleep onset to the first REM epoch), time in bed (TIB), sleep efficiency (SE; TST/TIB), stage transition index from NREM to REM (TI-S), total sleep cycles, total NREM-REM cycle duration, total NREM cycle duration, and total REM cycle duration. EEG preprocessing We processed sleep EEG data using the open-source package Luna ( http://zzz.bwh.harvard.edu/luna/ ). DH sleep EEG was sampled at 250 Hz and band-pass filtered (0.3Hz, 35Hz) in the original records. Primary analysis focused on F7-O2 and F8-O1 channels. We first used Luna to convert all data to μV. Within N2, N3, and REM epochs (based on DH automated staging), we identified and removed epochs with artifacts based on the following criteria: a) delta power greater than 2.5 times the local average or beta power more than 2.0 times the local average, identified using Welch’s method, b) maximum amplitudes over 200 uV for more than 5% of the epoch, c) flat or clipped signals for more than 5% of the epoch, and d) signals 3.5 standard deviations from the mean for any of the three Hjorth parameters (i.e., activity, mobility, and complexity) 52 . We performed outlier removal based on Hjorth parameters twice for each record, following prior work 14 . We excluded channels if >50% of the epochs contained identified artifacts. In addition, we excluded records if there was <180 min of total sleep time (TST) because such records are likely to reflect unique circumstances and not typical sleep. After artifact rejection, we also used Luna to perform power spectral analysis, spindle detection, and slow oscillation. Spectral power estimation We used Welch’s method to estimate spectral power for NREM (N2, N3) and REM separately. For each 30-second epoch, we applied a Fast Fourier Transform using 4-second segments (0.25 spectral resolution) windowing with a Tukey taper (50%); consecutive segments overlapped by 50% (2 seconds). We summarized spectral power in the traditional frequency bands: delta [1-4Hz], theta [4-8 Hz], alpha [8-12 Hz], sigma [12-15 Hz], and beta [15-30 Hz]. For each channel (F8-O1, F7-O2), we computed average power in each band for each epoch by taking 30-seconds of signal, and using Welch’s method of overlapping windows (i.e., 4-second windows within 30-second epochs), then across NREM (N2, N3) epochs separately and REM epochs separately. Relative power for each band was computed with respect to the total absolute power. Spindle detection Consistent with other reports 14 , 53 , 54 , we detected spindles in the “slow” (11 Hz) and “fast” (15 Hz) frequencies. We computed spindle density (number of spindles per minute), amplitude (based on maximum peak-to-peak amplitude), duration (seconds), mean integrated spindle activity (ISA) per spindle (average amplitude and duration of an individual’s spindles), spindle chirp (within-spindle change in oscillatory frequency), and mean spindle frequency in Hz. Slow oscillation detection We identified slow oscillations based on the EEG signals band-pass filtered between 0.5 and 4 Hz (as described in 14 , 55 ). Briefly, the following temporal criteria to define slow oscillations: 1) a consecutive zero-crossing leading to negative peak was between 0.5 and 1.5 seconds; and 2) a zero-crossing leading to positive peak was not longer than 1 second. We used two approaches to measure slow oscillation amplitude: 1) an adaptive/relative threshold required that negative peak and peak-to-peak amplitudes be greater than twice the mean (for that individual/channel) and 2) an absolute threshold required a negative peak amplitude larger than -40 μV, and peak-to-peak amplitude larger than 75 μV. We estimated slow oscillation density (count per minute), mean amplitude of the negative peak, peak-to-peak amplitude, duration, and the upward slope of negative peak for each channel. Slow oscillation-spindle coupling We analyzed slow oscillation-spindle overlap for each channel and characterized coupling in Luna . First, we determined the proportion of spindles that coincide with a slow oscillation. Next, using the filter-Hilbert method, we calculated the slow oscillation phase at the peak of each spindle and averaged these values across slow oscillations to obtain the coupling angle for each channel. To evaluate the consistency of phase coupling between slow oscillations and spindles, we used inter-trial phase clustering to measure coupling magnitude. We z-transformed the overlap and magnitude metrics by comparing them against a null distribution generated from 10,000 random permutations, where the time series indices were shuffled 14 . Statistical analysis Exploratory Locally Estimated Scatterplot Smoothing (LOESS) plots suggested most variables displayed linear associations in our sample. Thus, to estimate age-related changes in sleep macro- and micro-architecture, we used linear mixed models, implementing the lme4 package (v 1.1.35.4) in R version 4.4.1. Further, because prior research indicates that sleep physiology may differ between males and females 13 , 56 , we examined sex effects as well. We included age, sex, and channel (F7 and F8) in the model as fixed effects, and random intercepts for participants nested within site to account for clustering by site and repeated measures across nights. We explored including an autoregressive correlation structure across nights which yielded very similar estimates; therefore, all reported results are from the simpler model with random intercepts for subjects nested within site. We had eight macro- and fifteen micro-architecture measures as our primary outcome variables. Because we conducted twenty-three comparisons, we used False Discovery Rate (FDR) to correct for multiple comparisons and used a q-value below 0.05 to define statistical significance. Sleep characteristics can vary between weeknights vs weekends and summer vs school year due to social schedules, circadian misalignment, and recovery from prior sleep restriction 57 – 59 . Therefore, to further explore potential confounds, we also ran post hoc models that included weekday/weekend night status and summer vs. school year as fixed effects in the primary model. We first examined sleep macro-architecture (N=8 variables) and micro-architecture measures (N=15 variables) with known age effects from traditional PSG (i.e., at least two studies with consistent age effects; Table S1 & S2). We next explored macro-architecture variables with lesser-known age effects (i.e., unexplored, less than two studies with consistent age-effects; N=5 variables): stage transition index from NREM to REM (TI-S), total number of sleep cycles, total cycle duration, total NREM cycle duration, and total REM cycle duration. We further explored micro-architecture with lesser-known age effects (N=19 variables): slow oscillation duration, slow oscillation peak-to-peak amplitude, relative NREM delta, sigma, theta, and beta power, relative REM delta, sigma, theta, beta, alpha power, and absolute REM alpha and beta power; integrated slow spindle activity, integrated fast spindle activity, slow and fast spindle chirp, and slow and fast spindle frequency based on FFT. RESULTS Sample Description Table 1 includes the descriptive characteristics for the study sample (Table S4 for details). The sample was 53% female, and most participants identified as White and Non-Hispanic or Latino. View this table: View inline View popup Download powerpoint Table 1. Sample demographics of 100 participants, ages 9-26 years old. Replication of Known Age Effects with At-Home EEG Device in Adolescents - Sleep Macro-Architecture Similar to previous traditional PSG work 13 – 15 , 20 , 21 , 42 , we found a significant effect of age on N2% [β = 0.23, q = .011], with percent of time spent in N2 increasing with increasing age. There was also a statistically significant effect of age on N3% [β = -0.37, q<.001], and REM latency [β = -0.25, q = .003], with these sleep features decreasing with increasing age ( Figure 1 ; Table 2 for results; Table S4 for additional sleep variable descriptives). There was no main effect of sex for any of the variables following FDR correction (all q s>.220; Figure S1). In post hoc analyses, results were largely similar when accounting for the effect of weekday/weekend night status or summer/school season (Tables S5-S6). View this table: View inline View popup Download powerpoint Table 2. Linear mixed-effects model for the effect of age on sleep macro-architecture across adolescence Download figure Open in new tab Figure 1. Associations between sleep macro-architecture and age (averaged across nights and channels). (A) non-rapid eye movement (NREM) stage 2 (N2%) sleep, (B) NREM stage 3 (N3%) sleep, (C) rapid-eye movement (REM) latency. Replication of Known Age Effects with at-home EEG Device in adolescents - Sleep Micro-Architecture Our models revealed significant effects of age on absolute NREM delta power [β = -0.42, q<.001], which decreased with increasing age. Additionally, slow spindle density [β = -0.40, q <.001], slow spindle duration [β = -0.43, q <.001], and fast spindle duration [β = -0.18, q = .005] all decreased with increasing age. Lastly, the magnitude of slow oscillation-spindle coupling for slow [β = 0.20, q =0.003] and fast [β=0.14, q =.047] spindles increased with increasing age ( Figure 2 ; Table 3 for results). There was no main effect of sex for any of the variables following FDR correction (all q s>.462; Figure S2). In post hoc analyses, results were largely similar when examining the effect of weekday/weekend night or summer/school season (Table S7 & S8). View this table: View inline View popup Download powerpoint Table 3. Linear mixed-effects model for the effect of age on sleep micro-architecture across adolescence Download figure Open in new tab Figure 2. Associations between sleep micro-architecture and age (averaged across nights and channels). (A) non-rapid eye movement (NREM) absolute delta power density, (B) slow spindle duration, (C) slow spindle density, (D) fast spindle duration, (E) slow oscillation (SO)-slow spindle coupling magnitude, (F) SO-fast spindle coupling magnitude. Exploratory analyses We also explored the extent to which additional macro- and micro-architecture measures showed age effects in our sample. Out of twenty-four measures examined (and after correcting for multiple comparisons), we found a significant age-associated relationship with seven measures. We found an increase in relative NREM sigma [β = 0.26, q = .004], relative REM sigma [β = 0.19, q = .020], and relative REM alpha [β = 0.25, q=.002] power with increasing age. Total NREM cycle duration [β = -0.22, q = .015], median slow oscillation peak to peak amplitude [β = -0.43, q<.001], relative NREM delta power [β = -0.27, q = .006], and integrated slow spindle activity [β = -0.41, q.054). View this table: View inline View popup Download powerpoint Table 4. Exploratory: Linear mixed-effects model for the effect of age on sleep macro- and micro-architecture with lesser-known age effects across adolescence DISCUSSION We assessed how sleep changes across adolescence when measured with an at-home sleep EEG device. Using the Dreem3 headband, we captured macro- and micro-architecture age effects previously characterized using lab-based PSG. We were able to replicate known age effects in macro-architecture variables N2%, N3%, and REM latency. Further, in line with previous PSG studies examining age trends in micro-architecture, we found age-related associations with delta power and spindle characteristics (e.g., slow and fast spindle duration). Additionally, we explored age-related effects in macro- and micro-level variables with limited prior evidence of age associations (e.g., slow and fast spindle FFT) and identified several additional age-related relationships. Taken together, these results suggest many of the known age effects observed in lab-based PSG in youth may be captured through at-home wearable sleep EEG recordings, and specifically, with the Dreem headband. Furthermore, wearable devices may enable researchers to explore lesser-known age effects associated with other sleep physiology variables at scale. These results demonstrate the potential for using at-home sleep EEG wearables in young people and provide a foundation for examining and leveraging sleep physiology trajectories as indicators of subsequent behavioral and mental health trajectories. Of the twenty-three macro- and micro-architecture sleep variables with “replicated” age associations (i.e., at least two independent PSG studies reported consistent relationships in the same direction), we found age relationships in close to half of these trends. Similar to PSG studies 11 , 13 – 15 , 22 , 42 , 60 , we found N2% increases with increasing age while N3% and REM latency decreases with increasing age. For micro-architecture, we found that absolute NREM delta power, slow oscillation-slow and -fast spindle coupling magnitude increase with increasing age, while slow spindle duration, slow spindle density, and fast spindle duration decreased with increasing age. Observing age effects which are also seen in PSG provides additional evidence that the Dreem headband is capturing physiologically meaningful EEG features. Thus, at-home wearable sleep EEG headbands offer a valuable alternative to lab-based PSG in understanding developmental effects on sleep physiology. Additionally, wearables allow researchers and clinicians to focus on longitudinal and ecologically valid data as such devices allow for multi-night, at-home recordings, which PSG rarely affords due to cost and logistics. This makes wearables ideal for not only studying group-level comparisons but within-person effects as well. We did not replicate several age effects reported in PSG studies. For example, we did not replicate age effects of TST, WASO, sleep efficiency, NREM theta and beta power, and several REM spectral and spindle features. Both study design characteristics and device features may contribute to these non-replications. Our study enabled us to capture naturalistic sleep patterns in the home environment whereas PSG studies over adolescence have predominantly been conducted in the lab, often with fixed sleep timing windows. Such fixed timing may or may not align with subjects’ preferred sleep timing, and this circadian misalignment could lead to, for example, elevated sleep onset latency and curtailed REM. While our design may better capture real-world sleep (e.g., allowing for self-selected bedtimes and wake times), it may also magnify the influence of certain confounds, such as control of ambient light and screen exposure before bedtime. The discrepancies between findings from at-home vs. in-lab sleep studies may reveal important differences between sleep capacity (i.e., more controlled and standardized conditions in-lab) and sleep expression (i.e., more ecologically valid but less controlled home studies). In contrast to PSG, Dreem has dry (and fewer) EEG electrodes and relies on a proprietary automated algorithm for sleep staging. Dry-EEG electrodes embedded in the Dreem headband may introduce variability in electrode contact and impedance. In turn, a noisy signal may not only compromise WASO and sleep efficiency estimations (e.g., masking alpha activity) but influence spectral power as well 61 , which may explain only seeing age effects in one frequency band. The absence of expected age-related associations with some REM and spindle features may reflect limitations of Dreem’s automated staging algorithm. Dreem has shown only moderate agreement between the device and PSG for multi-stage classification and some EEG band power (i.e., sigma power), and an overestimation of REM 62 . Algorithmic misclassifications of REM and sigma power could attenuate detection of age-related effects in REM and spindle features. We also found that our age effects in NREM theta and beta power and relative REM sigma power were in the opposite direction from the literature 26 , 42 , 63 . This may be due to limited electrode sites. The Dreem headband records from frontal and occipital sites while PSG records from multiple derivations, including central and parietal, where spectral activity may be more reliably detected. Specifically, PSG typically detects age-related effects in sleep using central derivations such as C3-M2 and C4-M1 64 – 66 . Different referencing schemes also impact EEG micro-architecture. For instance, Dreem references the frontal channels to O1 and O2 (i.e., bipolar recording) which is likely to introduce more alpha and attenuate overall signal amplitude. In contrast, traditional PSG typically references channels to the contralateral mastoid (i.e., M1/M2; referential recording) because they are considered relatively ‘electrically neutral.’ Considering these differences, future studies could incorporate measurement error modeling to better understand and account for discrepancies in age-related effects in sleep measures. More generally, Dreem may be more prone to artifact due to limited impedance control and greater susceptibility to the impact of movement, sweat, and hair on the EEG signal, which can distort physiological differences (e.g., more high frequency signal related to electrodes slipping off the head) 67 . We also explored age-related associations in a range of additional “lesser known” sleep measures. Of the twenty-four measures, we found age associations with seven variables. We found a decrease in total NREM cycle duration, median slow oscillation peak to peak amplitude, relative NREM delta power, and integrated slow spindle activity with increasing age while relative NREM sigma and relative REM sigma and alpha power increased with increasing age. Interestingly, we found more age associations with relative power in most frequency bands versus absolute power. It may be that relative power remains robust across measurement methods because it is internally normalized (e.g., sigma power as a % of total power). These results suggest that while certain age-related changes in sleep are well-documented, novel findings in other variables could point to other aspects of NREM and REM sleep that change with age in previously unrecognized yet meaningful ways. The study has several limitations. While our study is larger than similar validation studies 51 , 68 , 69 , a larger sample than ours would provide greater reliability and generalizability of age trends during this developmental period, particularly if including a sample more diverse across different racial and ethnic identities that have been historically underrepresented in this literature. Further, as the study is cross-sectional, we cannot definitively state the observed age effects are due to developmental changes. We used linear mixed models because exploratory visualization showed linear relationships within our sample. However, some developmental changes in sleep are nonlinear 30 , 45 , and such patterns may be more accurately captured with longitudinal analyses 70 and massive samples sizes 14 . Future longitudinal sleep EEG studies of young people using an at-home wearable sleep device will extend our understanding of developmental trajectories in sleep physiology. Further, the current study used an auto staging algorithm validated against PSG in adults 51 , but not adolescents. However, the Dreem headband provides moderately stable sleep data across nights for adolescents and adults 36 . Ideally, if we did have not have practical or budgetary constraints, we would have collected PSG and Dreem data in the entire sample, to allow for direct comparison of Dreem findings with the in-lab standard. Interestingly, we did not find a main effect of sex in any macro- or micro-architecture measures of sleep. Sex differences in macro-architecture variables such as TST, SE, and WASO in adolescence have largely been derived from actigraphy and self-report 71 – 73 and these measures may be more likely to reveal these effects. Future studies and device developments will benefit if we are able to include additional EEG derivations (e.g., central sites), allowing for more comprehensive assessment of sleep physiology and potential sex-related effects. The study has several strengths as well. Despite the naturalistic variation in sleep timing and duration, we observed and replicated significant associations between age and sleep macro- and micro-architecture. This suggests that age related changes in sleep physiology are robust enough to be detected even in less controlled, real-world settings. Our, study also has strong ecological validity: Participants sleep in their own homes most nights, which increases generalizability to naturalistic sleep and captures habitual sleep patterns. Considering that childhood through young adulthood is a time of sleep and circadian changes, wearable sleep EEG offers the ability to capture these nuanced changes while limiting participant burden. Wearables offer feasibility and scalability. Wearables are more cost effective and accessible than lab-based PSG studies, enabling recruitment of larger, more diverse populations. Wearable devices also allow for longitudinal monitoring as they can be used over multiple nights for long stretches of time, allowing for long-term trends assessment. Taken together, these results illustrate that at-home wearable sleep devices can be used to capture age effects in sleep physiology across adolescent development. In sum, the study leveraged sleep EEG-based wearable technology to examine age-related associations in adolescent sleep outside of the lab environment. Wearable technology offers a more ecologically valid, accessible, and scalable approach to assessing developmental trajectories in sleep than lab-based PSG. Delineating normative models of sleep macro- and micro-architecture over adolescence with scalable sleep EEG devices can provide an essential template for developmentally informed sleep risk assessment and early interventions. To establish normative sleep physiology growth charts, future work would benefit from larger and more diverse samples. Our results highlight the promise of wearable sleep EEG wearable devices as a scalable tool for not just developmental sleep research but clinical assessment as well. DISCLOSURE STATEMENT Financial disclosure This work was supported by the National Institute of Mental Health (5R01MH129636-04, 5R01MH124828), the National Heart Lung and Blood Institute (5R01HL169318-02), the National Institute on Drug Abuse (5P50DA046346-03), the Tommy Fuss Center for Neuropsychiatric Research Next Generation Award, and SL was supported by the National Research Service Award (2T32MH112510) Nonfinancial disclosure The authors do not have any conflicts of interest to disclose. ACKNOWLEDGMENTS The authors thank all participants and families in this study. They also thank all research assistants who contributed to data collection. Funder Information Declared National Institute for Mental Health , 5R01MH129636-04 , 5R01MH124828 National Heart Lung and Blood Institute , 5R01HL169318-02 National Institute on Drug Abuse , 5P50DA046346-03 Ruth L. Kirschstein National Research Service Award Institutional Research Training Award , 2T32MH112510 Tommy Fuss Center for Neuropsychiatric Research Next Generation Award REFERENCES 1. ↵ Feinberg , I. , & Campbell , I. G. ( 2010 ). Sleep EEG changes during adolescence: an index of a fundamental brain reorganization . Brain and Cognition , 72 ( 1 ), 56 – 65 . OpenUrl CrossRef PubMed Web of Science 2. ↵ Jaramillo , V. , Volk , C. , Maric , A. , Furrer , M. , Fattinger , S. , Kurth , S. , … & Huber , R. ( 2020 ). Characterization of overnight slow-wave slope changes across development in an age-, amplitude-, and region-dependent manner . Sleep , 43 ( 9 ), zsaa038 . OpenUrl CrossRef PubMed 3. ↵ Campbell , I. G. , Grimm , K. J. , De Bie , E. , & Feinberg , I. ( 2012 ). 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Share Using a wearable EEG device to examine age trends in sleep macro- and micro-architecture across adolescence Sanna Lokhandwala , Rebecca Hayes , Soumya Sathe , Isabelle Elder , Mary Corcoran , Beatriz Horta , Maya Fray-Witzer , Lauren Keller , Simey Chan , Peter Franzen , Daniel Buysse , Brant P. Hasler , Jessica Levenson , Meredith L. Wallace , Duncan B Clark , Ronette G. Blake , Adriane Soehner , Maria Jalbrzikowski bioRxiv 2025.10.10.681690; doi: https://doi.org/10.1101/2025.10.10.681690 Share This Article: Copy Citation Tools Using a wearable EEG device to examine age trends in sleep macro- and micro-architecture across adolescence Sanna Lokhandwala , Rebecca Hayes , Soumya Sathe , Isabelle Elder , Mary Corcoran , Beatriz Horta , Maya Fray-Witzer , Lauren Keller , Simey Chan , Peter Franzen , Daniel Buysse , Brant P. Hasler , Jessica Levenson , Meredith L. Wallace , Duncan B Clark , Ronette G. 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