Auditory deep sleep stimulation in older adults at home: a randomized crossover trial.

Background: Auditory stimulation has emerged as a promising tool to enhance non-invasively sleep slow waves, deep sleep brain oscillations that are tightly linked to sleep restoration and are diminished with age. While auditory stimulation showed a beneficial effect in lab-based studies, it remains unclear whether this stimulation approach could translate to real-life settings.
Methods: We present a fully remote, randomized, cross-over trial in healthy adults aged 62-78 years (clinicaltrials.gov: NCT03420677). We assessed slow wave activity as the primary outcome and sleep architecture and daily functions, e.g., vigilance and mood as secondary outcomes, after a two-week mobile auditory slow wave stimulation period and a two-week Sham period, interleaved with a two-week washout period. Participants were randomized in terms of which intervention condition will take place first using a blocked design to guarantee balance. Participants and experimenters performing the assessments were blinded to the condition.
Results: Out of 33 enrolled and screened participants, we report data of 16 participants that received identical intervention. We demonstrate a robust and significant enhancement of slow wave activity on the group-level based on two different auditory stimulation approaches with minor effects on sleep architecture and daily functions. We further highlight the existence of pronounced inter- and intra-individual differences in the slow wave response to auditory stimulation and establish predictions thereof. Conclusions: While slow wave enhancement in healthy older adults is possible in fully remote settings, pronounced inter-individual differences in the response to auditory stimulation exist. Novel personalization solutions are needed to address these differences and our findings will guide future designs to effectively deliver auditory sleep stimulations using wearable technology.

Introduction

While basic and applied research in promoting human health has increased substantially in the last decades, its translation into clinical applications with access for a wider public has lagged behind[1-3]. In contrast, there is an ever-growing range of mobile health devices and technologies that found their way into the consumer market and into people’s homes, yet clinical validations of most of these applications is lacking[4,5]. With these technologies, an important platform has been developed to move the findings from well-controlled lab studies to in-field applications[6]. However, to establish the potential and limitations of preventive and therapeutic health approaches in real-life settings, studies are needed that identify individual differences, establish predictors for successful applications, and elucidate intended and unintended effects of the prolonged intervention in different contexts.

In this regard, translating in-lab methods that promote healthy sleep have gained increased attention because sleep could be a key player in promoting brain and body health up until old age[7,8]. Sleep in older adults is hallmarked by reduced sleep duration, increased sleep fragmentation, and reduced sleep efficiency[9-11]. Most pronouncedly, sleep becomes more superficial with age, which is reflected in a decrease of sleep slow waves[12,13]. Slow waves are dominant low-frequency (i.e., <4 Hz) brain rhythms during deep non-rapid eye movement (NREM) sleep, which are considered to mirror sleep intensity and are fundamental for healthy and consolidated sleep[14]. They have been implicated in promoting brain plasticity[15], memory formation[16], immune-supportive functions[17], and have recently been inversely related to amyloid burden in older adults[18,19]. Therefore, deep sleep enhancement strategies will potentially contribute to healthy aging, which is highly relevant considering the growing proportion of older people in our society. Specifically, the enhancement of slow waves might be an ideal target to improve sleep quality. Consequently, applications that enhance slow waves in a non-pharmacological, non-invasive, and physiological way have become a central topic in sleep research.

Among others, the application of acoustic stimuli during sleep have crystallized as a very promising avenue. In 2013, Ngo and colleagues[20] reported that acoustic stimuli specifically targeted to the up-phase of the endogenous slow wave can enhance slow wave amplitude along with improved declarative memory in young participants. Thereafter, several in-lab studies from different research groups replicated the beneficial effect of phase-targeted or rhythmic (i.e., around 1 Hz) acoustic stimuli to boost slow waves mainly in young participants, but also in middle-aged, and older populations[20-30]. These slow wave enhancement effects particularly benefited declarative memory consolidation[20-25,31], but single studies have also reported benefits for executive[29], immune-supportive[17], and autonomic functions[28]. Collectively, there is convincing evidence that slow waves can be enhanced in in-lab settings during single session applications along with some specific brain and body functions. Owing to these first in-lab results, this deep sleep modulation approach has already found its way into the consumer-market[32,33]. However, no auditory sleep stimulation technology has yet established itself as certified medical wearable. To date, no controlled, randomized clinical trial exists that investigated the effect of auditory up-phase stimulation over multiple nights, in at-home settings and its effects on sleep and daytime functioning.

In order to overcome this gap, we recently developed a portable sleep monitoring and feedback-controlled slow wave modulation device with research-grade accuracy[34]. Here, we used this system in the first randomized controlled cross-over clinical trial applying auditory stimulation during slow wave sleep over multiple weeks in healthy older adults between 60 and 80 years of age and without diagnosed sleep disorders in order to establish effectiveness thereof in enhancing slow wave activity as our primary outcome. Older adults are of specific interest because of their reduced slow waves and their less pronounced potential to benefit from auditory stimulation as revealed in an in-lab study[35]. The device was worn at home and self-applied by the participants. Their sleep schedule was not restricted to a specific schedule. In addition, we implemented mobile phone based daily ecological momentary assessments (EMA) of mood, subjective sleep quality, and vigilance to determine the direct effects of stimulation on these secondary outcome parameters. We demonstrate on the group level that slow waves can be enhanced over multiple days in older adults, also in non-controlled, in-home settings, and for two different stimulation protocols. We establish predictors that define participants with strong and weak responses to auditory stimulation, and predictors that relate to nightly variance within participants. While slow wave enhancement in healthy older adults is possible in at-home settings and could potentially benefit related functions, we must consider that clear inter-individual differences in the response to auditory stimulation exists and novel personalization solutions are needed to address these differences to achieve high efficacy and effectiveness in the field.

Methods

Participants

Overall, 33 participants were enrolled and screened (first participant enrolled: 07.05.2018, last participant out: 05.03.2019, study has been completed). An overview of the trial profile of the enrolled participants is shown in Supplementary Fig. S1. Here, we report data of 16 participants that underwent the whole experimental procedure of auditory up-phase slow wave stimulation over multiple weeks and had sufficient data for analysis. This dataset was analyzed as part of the planned interim analysis of this clinical trial (NCT03420677), however, also represents the final results because the study was terminated early due to the COVID-19 pandemic. The included participants (nine males, seven females, age [mean ± SEM (standard error of the mean)]: 69.5 ± 1.3 years, age range: 62–78 years) were all in good general health, non-smokers and had a stable home situation that allowed for reliable application of intervention for the duration of the study. None of them had a presence or history of a psychiatric/neurologic disorder, presence of a diagnosed sleep disorder or an internal disorder. Participants were further excluded if they used on-label sleep medication, showed known or suspected non-compliance, drug or alcohol abuse, cannabis or nicotine intake, or excessive caffeine consumption. We further confirmed with basic audiometry that they were able to hear at least the intervention sound at 50 dB on both ears (hearing threshold [mean ± SEM]: 44.1 ± 0.8 dB, range: 40–50 dB). The participants were recruited from the community using advertisements on different platforms/newspapers and at meet-ups. The recruitment was further supported by the University Zurich Research Priority Program “Dynamics of Healthy Aging” and the Senior University. All participants were compensated for their participation. The study was approved by the cantonal Ethics Committee Zurich (reference number: KEK ZH, BASEC 2017-01436), the Swiss Agency for Therapeutic Products Swissmedic (reference number: 10000181/2017-MD-0027) and was conducted in accordance with the Declaration of Helsinki. The study was registered at ClinicalTrials.gov (NCT03420677). All the participants provided written informed consent before participation. Participants completing the full study duration received at least 400 Swiss Francs for participation. In addition, up to 200 Swiss Francs could be earned proportionally to the completed tasks during the study. The identifiable person in Fig. 1a, who was not a participant in this study, provided written consent to publish this image.

Fig. 1

Study and auditory stimulation design.

a Participants first underwent a screening phase to familiarize with the in-home MHSL-Sleepbandv2 (MHSL-SB) use and the mobile phone assessments. In the experimental phase, two-week intervention periods of either Sham (no auditory stimulation) or Verum (auditory stimulation) were conducted in a randomized, crossover design. Daytime recordings reported here (hallmarked by a sun) included subjective sleep quality assessment in the morning, a psychomotor vigilance test (PVT) at 2 pm, the Karolinska sleepiness scale (KSS) in the morning, at 2 pm, and 8 pm, and a mood assessment in the morning and at 8 pm. Nocturnal recordings (hallmarked by a moon) included the nightly application of the MHSL-SB that recorded Fpz-A2 (electroencephalography [EEG]), left and right electrooculography (EOG), and chin electromyography (EMG). The identifiable person in this figure provided written consent to publish this image. b The MHSL-SB performed a sleep classification and defined stable NREM sleep and used a phase-locked-loop (PLL) to precisely time the slow wave modulation during up-phase of the slow wave. During Sham, only triggers were saved to mark slow wave up-phases, during Verum 50 ms pink noise bursts were applied (illustrated as red dots). The defined PLL target phase was around 45°. In each intervention period two types of application were used, the continuous and windowed approach. In the windowed approach, 6 s ON and 6 s OFF windows were alternated, during OFF no slow waves were targeted (but marked), during ON slow wave up-phases were targeted and either stimulated with pink noise bursts (Verum) or only marked (Sham). During the continuous approach, all targeted slow wave up-phases were either stimulated (Verum) or marked (Sham). Each of these approaches lasted for 7 days in a counter-balanced design resulting in four possible order combinations that were counter-balanced across participants: (1) Verum ON, Verum ONOFF, Sham ON, Sham ONOFF, (nsubjects = 4) (2) Sham ON, Sham ONOFF, Verum ON, Verum ONOFF, (nsubjects = 3) (3) Verum ONOFF, Verum ON, Sham ONOFF, Sham ON, (nsubjects = 5) (4) Sham ONOFF, Sham ON, Verum ONOFF, Verum ON (nsubjects = 4).

Experimental procedure

The experimental procedure is summarized in Fig. 1. Participants completed a six-week randomized, crossover experimental protocol in which they had two weeks of auditory slow wave stimulation (Verum) and two weeks of Sham interleaved with a two-week washout period (Fig. 1a). A crossover design was chosen because the within-participant variation of sleep is less than the between-participant variation and therefore, fewer participants are required for this study compared to a randomized parallel-group design. Possible disadvantages of the crossover design such as larger dropout rate, carry-over effects, or instability of the participant’s condition were not expected in this study. The study was conducted in Switzerland at the participant’s home. All participants first underwent a screening phase and during an initial home visit several demographic questionnaires were answered. They were further extensively instructed on how to use the intervention device and the EMA on the mobile phone. We also performed a basic audiometry to assure that their hearing ability was good enough to perceive the sound level of the intervention device on both ears. The first night habituated the participant to the MHSL-SleepBandv2 (MHSL-SB, ETH Zurich, Zurich, Switzerland) and used EMA. If participants fulfilled all criteria and agreed to participate in the experimental sessions, a baseline period followed. Thereafter, the experimental phase started, including two 2-week intervention periods that were interleaved with a 2-week washout period. One intervention period was including auditory stimulation of slow wave sleep (Verum), the other one was not including auditory stimulation (Sham). The intervention periods were further subdivided into two different stimulation approaches (windowed and continuous, counter-balanced order) that are summarized in Fig. 1 and will be explained in the paragraph MHSL-SB and auditory stimulation. The condition applied was in a counter-balanced, randomized, and double-blind design. Randomization was performed by the study coordinator that was not involved in recruitment or data collection but only analysis. Participants were randomized in terms of which intervention condition will take place first, having four different options (Verum ON, Verum ONOFF, Sham ON, Sham ONOFF) using a blocked design to guarantee balance. After the first condition was defined, the following conditions were set (Sham always followed Sham, and ON/ONOFF were kept in the same order, the same was for Verum). Gender was taken into account when performing randomization in order to achieve an equal gender balance. The randomization order was predefined in blocks for both genders before the study started and then participants were randomly allocated by the study coordinator to the first condition in sequence after their successful enrollment upon passing screening performed by trained and blinded research assistants. The randomization aimed at 25% (n = 4) start per condition, however, due to intermediate drop-outs (see data exclusion above) and gender imbalance, this counter-balance was not perfectly achieved but very close to the aimed distribution: Start Sham ONOFF: 4 (2f, 2m), Sham ON: 3 (1f, 2m), Verum ONOFF: 5 (2f, 3m), Verum ON: 4 (2f, 2f). Each start, the middle, and end of an intervention period was accompanied by home visits during which experimenters provided or collected the MHSL-SB, respectively. The participants and experimenters performing the assessments and providing the device/instructions were blinded to the conditions. The study coordinator, PI, and lead engineer had to know conditions for device settings and safety reasons. Experimenters performing the assessments were blinded to the performed intervention by restricting access to device settings only to the lead engineer and randomization list access only to the study coordinator. Participants were blinded by having for both Sham and Verum conditions exactly the same application procedures of the device and the exact same physical appearance of the device independent of intervention setting. During the complete intervention period of 14 nights, participants were wearing the MHSL-SB during the night to collect EEG, EOG, and EMG data and to perform auditory stimulation in case of the Verum condition. The next day they performed EMAs in the morning, at 2 pm and at 8 pm (see paragraph Daytime assessments with Mobile Phone Apps). Collectively, data reported in this manuscript include the recordings of the MHSL-SB during the intervention period and the EMA collected in the morning (sleep quality), 2 pm and 8 pm, which represent our secondary outcome variables. Additional assessments of the quality of life questionnaires and cognitive tasks were performed before and after each intervention period of the study that will not be reported here. The analysis of these questionnaires and cognitive outcomes are underpowered due to the early termination because of the COVID-19 pandemic to draw meaningful conclusions from these parameters. Furthermore, cardiovascular assessments were performed daily but will not be reported here.

MHSL-SB and auditory stimulation

Biosignals were recorded using the MHSL-SB, a mobile and configurable system for real-time sleep recording and processing which also implemented the auditory slow wave stimulation[34]. One single EEG signal (Fpz-A2) was used for real-time processing, while two EOG and two EMG signals were recorded for post-processing purposes. Single-use auto-adhesive electrodes (Neuroline 720, Ambu A/S, DK) enabled self-application while keeping signal quality high. Biosignals were sampled at 250 Hz using a 24-bit analog-to-digital converter that featured an on-chip anti-aliasing filter. The raw biosignals including AC and DC components were saved to an SD card and further piped to the embedded real-time algorithms for auditory stimulation. To produce precise auditory stimulation, the MHSL-SB implemented a set of algorithms. The single EEG signal was processed to eliminate power line noise (first-order notch filter, 50 Hz). Then, overall, a stimulation decision logic triggered the auditory stimuli when NREM sleep, slow wave sleep (SWS), low beta power, and EEG target phase conditions were simultaneously met. First, the NREM sleep algorithm categorized the EEG data as NREM sleep or not-NREM sleep (awake and REM) based on the spectral power at different frequency bands: low delta ([2-4] Hz), high delta ([3-5] Hz), and high beta ([20-30] Hz). If low and high delta powers were above, and beta power below predefined and fixed thresholds during a specific window length, the EEG signal was classified as NREM sleep. When the EEG data was classified as NREM sleep for the first time, this algorithm immediately enabled the follow-up algorithms. However, stimulation triggers were enabled only after 10 min of consecutive NREM sleep is detected to ensure stable NREM sleep. This 10 min period was also used as control baseline. For the follow-up NREM sleep classification periods the stable NREM sleep time was decreased to 3 min. Metrics regarding algorithm performance are summarized in Supplementary Table S1. Algorithm performance was quantified using precision, specificity, and recall. Precision indicated the rate of correctly predicted NREM sleep samples. Specificity indicated the ability of the sleep classification algorithms to identify not-NREM sleep correctly. Finally, the recall metric indicated the correct NREM sleep classification rate when the data was also scored as NREM sleep. All metrics were defined as relative numbers ranging from 0 to 1.

To increase stimulation accuracy during deep NREM sleep, a SWS classification and beta power detection algorithm were implemented. When delta power overpassed a predefined threshold condition, the EEG signal was classified as SWS. Since power increment in the beta frequency range has been related to awake, light sleep, artifacts, and the presence of arousals, a beta power detection algorithm prevented stimulations when beta power was exceeding a pre-defined threshold in the last second.

The algorithm used to deliver the auditory stimulation in phase with the SW consisted of a first-order phase-locked loop (PLL) that estimated the EEG phase in real-time from the preprocessed EEG signal. The PLL algorithm is a control system that estimates the EEG phase in real time at each sample point. Therefore, there is no time window used for phase estimation. Since high-amplitude, low-frequency slow waves predominate during NREM sleep, the PLL can lock in phase with the EEG signal even in presence of other frequency components. Therefore, the PLL was applied in real time over the recorded and notch-filtered data that was further pre-processed with the high-pass filter at 0.1 Hz, avoiding extra undesired non-linear phase shifts induced by pre-processing filters. When the estimated phase reached the target stimulation phase (45°), the EEG phase condition was satisfied. The PLL parameters were optimized to maximize the stimulation triggers in the SW up-phase (from 0° to 90° phase range) and increased the phase accuracy around the target phase.

To avoid waking the participants due to the auditory stimuli, the volume of the stimuli was adapted to the depth of sleep and stimulus related detected arousal. The initial volume was set to 52 dB (default value). If no arousal was detected after seven consecutive tones and a sufficient sleep depth was reached, the volume increased in small steps (1.5 dB) until it reached a predefined maximum volume (60 dB). When an arousal was detected during two consecutive stimuli, the volume decreased by 1.5 dB until a predefined minimum of 46 dB. In addition, when minimum delta power was not satisfied, the volume was set to the default value.

In parallel, the system featured three reinforcement conditions that disabled stimulations when: (1) the preprocessed EEG had a negative amplitude to prevent stimulations in the slow wave down-phase; (2) a minimum time of 0.5 s was not accomplished from the previous delivered stimuli to prevent multiple stimulations on one slow wave; and (3) the EEG amplitude was above 300 µV indicating the possible presence of artefacts.

Finally, two different stimulation approaches were implemented: (1) ON or continuous approach, where stimulations were continuously applied, and (2) ONOFF or windowed approach where stimulations were enabled for an interval of 6 s (ON window), and immediately thereafter disabled for another 6 s (OFF window). We selected this window length to be comparable to previous publications using an ON/OFF windowed approach with windows that had on average the duration of ~6 s[24,36].

EMA on mobile phone

Android Apps built with lambdanative[37] were used to collect phone data. Data collected by phone was directly transferred and stored in REDCap[38,39]. After each intervention night, participants completed in the morning an EMA regarding their perceived sleep quality, bedtime, device usability/blinding, sleepiness, and their momentary mood. At 2 pm they completed again an assessment of sleepiness and performed the PVT on the phone. At 8 pm they rated their sleepiness and their mood for the whole day. In this manuscript, we only report the results from two subjective sleep quality questions, the sleepiness at different time points during the day, the mood in the morning and at 8 pm, and the results of the PVT. Participants had to rate their overall perceived sleep quality on a 7-point Likert scale ranging from 0: very bad sleep quality to 6: very good sleep quality, and their overall perceived sleep depth on a 5-point Likert scale ranging from 0: very superficial to 4: very deep. Sleepiness was assessed using the KSS[40]. We used the “Mehrdimensionale Befindlichkeitsfragebogen” (MDBF, Steyer et al. 1997) to assess a summed mood score[41]. PVT (Dinges & Powell, 1985) is used to test attention, vigilance, and recovery from sleep[42]. For this study, we have developed a mobile PVT app using the touchscreen interface of the mobile phone with a mean maximal latency of 15.8 ms (sd 5.8 ms). During 10 min participants had to tap as fast as possible with their dominant index finger on a predefined area on the screen as soon as a counter appeared indicating the stimulus onset time in ms. Taps were excluded that had a reaction time (RT) equal or above 3000 ms or below 100 ms. We focused on three outcome measures that were indicated by Basner and Dinges (2011) to be most sensitive (e.g., most pronounced effect size) under sleep restriction conditions: mean speed (1/RT), fastest 10% reaction time, and slowest 10% of speed[43]. In this manuscript, we only included assessments of the days that had a good-quality/functional EEG assessment and stimulation the night before. Furthermore, assessments at 2 pm and 8 pm were only included if the start of the assessment was not deviating more than ±75 min from the scheduled time. Of note, on the last day after intervention, 2 pm and 8 pm assessments were consistently missing because home visits were mostly scheduled before 2 pm. A number of included subjects and assessments per EMA are summarized conditions in Supplementary Table S2. These EMA assessments of sleep quality, sleepiness, mood, and vigilance represent our secondary study outcomes.

Hybrid sleep scoring

Automated sleep scoring was performed for all nights using a deep learning classification that was followed by a manual expert review of classifications with low confidence scores. The Fpz-A2 derivation, sampled at 250 Hz, was preprocessed with a 0.5–38 Hz band pass filter. We designed the deep learning architecture as a fusion of two established sleep scoring neural networks adapted to work with each other. The first network used a sequence of convolutional layers followed by recurrent layers, as introduced by the DeepSleepNet[44], but altered such that the training is performed in a single stage and the network uses a many-to-many input-output relation. The second network replaced the convolutional layers with another set of recurrent layers as seen in the SeqSleepNet[45]. A sequence of 20 epochs was used as input for the models and by using a one stride in a many-to-many output scheme the network produced 20 predictions per epoch from each base model for a total of 40 individual predictions in the form of a likelihood distribution over all labels. The multiple predictions were combined into a single distribution through probabilistic multiplicative aggregation from which the final prediction was selected as the maximum likelihood label. To train the machine-learning algorithm, four expert raters scored manually a training set of 98 nights from the 24 subjects (7 subjects 9 nights each, and 17 subjects 2 nights each applying AASM sleep scoring guidelines[46] on 20-s epochs, however picking up all EEG information from the FPz-A2 derivation (84% inter-rater agreement). Both base networks were trained with the Adam optimization algorithm using a learning rate of 0.0001, a batch size of 32 for 50 learning epochs for the convolution-recurrent model and 10 learning epochs for the recurrent-recurrent model. Dropout was used for both networks with a probability of 50 and 75%. The final model selection was done through assessment of the accuracy on a left out validation set every 100 training steps. This model was then used to score the remaining nights. To further increase the scoring quality, a confidence score was assigned to each automatically scored epoch by comparing the likeliness of each predicted class. The N2 or N3 epochs that obtained low confidence scores (8.8%) were presented to an expert for rescoring while blinded to the predicted class. This manual re-scoring was done by an expert not involved in the study, and he was not informed about the conditions and intention of the study. This approach increased the classification accuracy of N2 and N3 in the training set from 75 to 78.8%.

All nights were excluded in the sleep architecture analysis that had either more than 20 min bad EEG signal quality, only partial recordings present or had electrodes not yet connected when recording started or disconnected before recording was stopped for more than 20 min. The number of included nights per condition are summarized in Supplementary Table S2.

EEG analysis

Before spectral analysis was performed, EEG was high-pass filtered at 0.5 Hz and low-pass filtered at 40 Hz using FIR filters provided by the EEGLAB toolbox[47,48] for MATLAB (The MathWorks, Inc., Natick, MA). Thereafter, the EEG was either analyzed with a consecutive analysis or ON-OFF analysis method. For both methods, spectral power of 6-s epochs was calculated using the MATLAB pwelch function with 4 s window length with 50% overlap. We analyzed SWA, that is EEG power in the slow wave frequency range, as our primary outcome variable to investigate the main effect of the auditory stimulation on brain oscillations. This is among the most consistently reported findings in previous in-lab studies using auditory slow wave sleep modulation (reviewed in Grimaldi et al.[27]). Furthermore, for the spectral density analysis in Figs. 2a and 3a, we further normalized the power spectra by its cumulative power up to 30 Hz as to be comparable with previous auditory stimulation studies[20,21] and to define our slow wave activity (SWA) frequency range of interest. For all other spectral-related analysis, absolute power values were used in the designated frequency band. For all spectral related analysis (ON-OFF analysis and consecutive analysis) only 6-s epochs that were recognized by both, the device algorithm and the hybrid scoring as N2 or N3 NREM sleep for the entire 6 s were included. Furthermore, in order to avoid outlier affecting the calculated spectral analysis, only epochs that were not identified as artifacts by the semiautomatic artifact rejection[49,50] and by an automatic outlier detection in the 6 s epochs (isoutlier function of MATLAB) were included. The ON-OFF analysis reported in Figs. 2b, c, d, and 3 was based on windows defined as ON or OFF by the MHSL-SB algorithm during the windowed approach nights. We considered the ON and OFF windows that entirely coincides with the hybrid scoring and MHSL-SB stable NREM sleep classification. Thereafter, we discarded windows with artefacts or high beta power detected by the MHSL-SB algorithm and windows without stimulation (or trigger in the case of Sham nights). To analyze whether there is a dose-dependent effect, the ON and OFF windows were binned and compared by the number of stimulations occurring during the ON and OFF windows (1–2, 3–4, and more than 4 stimulations). Thereafter, specifically windows with more than 2 stimulations were considered for analysis. The consecutive analysis method was applied in Figs. 2a, 4 and 5 for both the continuous and windowed stimulation approach to allow for comparability between approaches. Here, the whole recording was divided in consecutive 6-s epochs and for each epoch number of stimulations were defined. For Figs. 2a and 4a, c all epochs that entirely coincided with the hybrid scoring and MHSL-SB stable NREM sleep classification were included. For Figs. 4b, d and 5, epochs that fulfill the above criteria and had >2 stimulations were selected. Of note, spectral analysis of the robust NREM baseline (before stimulation started) was using consecutive 6-s epochs that entirely coincided with the hybrid scoring and MHSL-SB stable NREM sleep classification and had no artifacts (see above).

Fig. 2

Slow wave effect of auditory stimulation in the windowed stimulation approach.

a Ratio (mean, shaded area SEM) between Verum and Sham in the windowed approach of the normalized spectral density (power divided by cumulative electroencephalography [EEG] power up to 30 Hz) during non-rapid eye movement (NREM) sleep (device and offline detected, consecutive analysis see methods) shows a significant cluster of frequency bins in the low-frequency range of 0.75–1.25 Hz (lowSWA). P-values (fixed factor condition, cond) were derived from a robust linear mixed model (see methods) performed for each frequency bin separately. b Error bars (mean ± SEM) with data points representing individual participants of ON-OFF window analysis of lowSWA for the difference between ON-OFF, ON windows, and OFF windows (see methods). Windows with stimulations (triggers or tones) were sorted and matched for their number of stimulations (stim#, 1–2 stimulations (1–2 stim), 3–4 stimulations (3–4 stim), and more than 4 stimulations (>4 stim)) and compared between Verum and Sham condition. Overall robust linear mixed model results (model includes average of nights) are summarized in the gray panels. The y-axis has been condensed to better visualize the error bars despite outliers. In case of significant main or interaction effects, a robust linear mixed-effects model was calculated for each stimulation bin separately (robust linear mixed-effect model [LMM], model includes all nights, *indicate p < 0.05 **indicate p < 0.01, stars in brackets illustrate exploratory analysis because of non-significant interaction effect). Effect of ON-OFF difference is driven by a significant increase of lowSWA in the ON window as indicated by a significant main effect of condition in a robustLMM, but not the OFF window. Following this significant overall model, separate robust LMM including all nights depict that specifically stimulation bins with more than 2 stimulations (3–4 stim, >4stim) show a significant condition effect in the ON window (*indicate p < 0.05). c In an additional analysis of all the ON-OFF window differences with more than 2 stims, we focused on whether there is a consistent difference between Verum and Sham for all seven nights. Results are shown as error bars (mean ± SEM) and data points represent individual nights of 16 participants. The y-axis has been condensed to better visualize the error bars despite outliers. In a robust LMM with factors nights, conditions, and control fixed and random factors (see methods) revealed no significant interaction night x condition (p > 0.1) and a main effect condition (p < 0.001), indicating a significant effect between Sham and Verum across all seven nights. Solid lines refer to ON window results, long dashed lines to ON-OFF difference and short dashed lines to OFF window results. Data underlying this figure is provided in Supplementary Data 1.

Fig. 3

Predictive factors for across and with subject variability in ON-OFF difference in low-frequency slow wave activity (lowSWA) during the windowed approach.

a Individual (all nights of nsubjects = 16) ON-OFF difference (calculated using ON-OFF analysis, see methods) of lowSWA for all windows with >2 stimulations sorted by averaged lowSWA (red horizontal lines) during Verum. Strong responders refer to the upper 50% and weak responders to the lower 50% of participants. Clear ON-OFF differences during Verum condition across the majority of nights are seen for strong responders but not that clearly for weak responders. (S = Sham, V = Verum). b Linear discrimination analysis (LDA) using 10-fold cross-validation. All good quality nights during Verum ONOFF were included in the model and labeled as weak or strong responder night (dependent variable for classification). For lowSWA and %NREM N3 (non-rapid eye movement stage 3) during baseline, number of stimulations during NREM sleep and mean sound volume (prediction factors) a separate LDA model was run and accuracy in predicting weak and strong responder nights were plotted in blue. * indicate p < 0.05 comparing accuracy achieved by the prediction factors to chance level (No Information Rate). c Comparison of lowSWA ON-OFF difference between weak and strong responder (factor resp) for windows that only included sound volume at 52 dB (baseline lowest level) and for different stimulation bins (factor stim#) using a robust linear mixed-effect model (LMM) illustrated as error bars (mean ± SEM) with data points representing individual participants. The y-axis has been condensed to better visualize the error bars despite outliers. d–f Repeated measures (rm) correlations to investigate the prediction of nightly lowSWA ON-OFF difference variance. d Predictor baseline lowSWA for all good quality Verum ONOFF nights. e Predictor number of NREM sleep stimuli for all good quality Verum ONOFF nights. f Predictor number of NREM sleep stimuli for all good quality Verum ONOFF nights in strong responder subjects. Data underlying this figure is provided in Supplementary Data 2.

Fig. 4

Slow wave spectral analysis in the continuous approach in comparison/relation to the windowed approach using the consecutive analysis method.

a Ratio (mean, shaded area SEM) between Verum and Sham of the normalized spectral density (power divided by cumulative EEG power up to 30 Hz) during non-rapid eye movement (NREM) sleep (device and offline detected) shows a significant cluster of frequency bins in the low-frequency range of 1–1.25 Hz. P-values (fixed factor condition) were derived from a robust LMM (see methods) performed for each frequency bin separately. b Slow wave activity in the low-frequency range of 0.75–1.25 Hz (lowSWA) in windows containing more than 2 stimulations for Sham and Verum, for weak and strong responders separately illustrated as error bars (mean ± SEM) with data points representing individual participants. Robust linear mixed model results comparing conditions (factor cond) and the interaction of the condition (Verum vs Sham) and responder (factor resp, weak vs strong) are depicted in the gray panel. c LowSWA summed overall device and offline detected NREM sleep periods (energy, lowSWE) for Verum ON (continuous approach) Verum ONOFF (windowed approach), for weak and strong responders separately illustrated as error bars (mean ± SEM) with data points representing individual participants. Overall robust LMM results (all night included in model) regarding approach (appr) and the interaction of approach and responder are summarized in the gray panel. d LowSWA in windows containing more than 2 stimulations comparing windowed and continuous approaches in responders (nsubjects = 7), for Verum and Sham separately illustrated as error bars (mean ± SEM) with data points representing individual participants. Robust LMM comparing approaches (continuous vs windowed) are summarized in the gray panel. Data underlying this figure is provided in Supplementary Data 3.

Fig. 5

Repeated measures correlation of mood with low-frequency slow wave activity (lowSWA) and rapid eye movement (REM) sleep.

Repeated measures (rm) correlation of (a) slow wave activity in the low-frequency range of 0.75–1.25 Hz (lowSWA) (calculated using the consecutive analysis (see methods), windows with >2 stimulations) changes in Verum relative to Sham and (b) REM sleep changes relative to Sham with mood changes relative to Sham. Mood assessments were used from the morning assessments (lower values indicate a more negative mood). All nights during Verum were taken (pooled approaches in this correlation) relative to the averaged values of the respective Sham period (Sham ONOFF in windowed Verum nights, Sham ON in continuous Verum nights). Data underlying this figure is provided in Supplementary Data 4.

Auditory evoked responses of all stimuli (tones or triggers) were calculated. For the windowed approach these responses were analyzed for all ON and OFF windows and again differentiated the windows for their respective number of stimulations. We also calculated the ON-OFF difference by subtracting the average OFF ERP from the ON ERP for the respective stimulation numbers.

Arousal were automatically detected during hybrid scored NREM N1-N3 sleep and REM sleep using a published, open-source algorithm[51,52]. In short, the algorithm uses one raw EEG and EMG derivation and detects arousal-based EEG power in the alpha (8–12 Hz) and in the beta (>16 Hz) bands. Of note, all nights that had more than 20 min bad EEG signal quality or had only partial recordings present were excluded from the EEG statistical analysis. The number of included nights per condition is summarized in Supplementary Table S2.

Statistics and reproducibility

Statistical analyses were performed using the statistical software R[53] (R Foundation for Statistical Computing, Vienna, Austria). In order to establish the effect of condition, approach, and responder on sleep and daily functioning, we employed robust linear mixed-effect models (LMM) using the R-package robustlmm[54,55] for all interval-scaled variables. Robust statistical methods have the advantage that they provide accurate p-values even if some assumptions (e.g., normal distribution) are violated[56]. We used the Kenward–Roger approximation to estimate F, p-values, nominator, and denominator degrees of freedom[57]. For ordinal data obtained from the questionnaires (sleep quality, KSS), cumulative linked mixed models were employed using the R-package ordinal[58,59] and p-values obtained using the Likelihood Ratio Test. Depending on analysis in focus, different fixed and random factors were used. For Fig. 2a, b (post-hoc testing), and c fixed factors condition (2 levels: Verum, Sham), night (7 levels), period order (2 levels), and nested random factor of subject and condition were included. Because condition or the interaction of the factors condition and night were the focus of the analysis respective F and p-values were reported. In Fig. 2b, nights per condition were averaged and model included the fixed factors condition, number of stimulations of the respective ON and OFF window (3 levels: 1–2 stimulations, 3–4 stimulations, >4 stimulations), period order, and nested random factor of subject and condition. For Fig. 3c, analysis was performed for Sham and Verum separately, averaged nights per condition were included with fixed factors responder (2 levels: weak, strong), number of stimulations, and the random factor subjects. For Fig. 4a fixed factors condition (2 levels: Verum, Sham), night (7 levels), period order (2 levels), and nested random factor of subject and condition were included. Results reported in 4b, and in Table 1 and Supplementary Table S3 included the fixed factors condition, responder, night, period order, and nested random factor of subject and condition. In Fig. 4c, fixed factors approach (2 levels: ON, ONOFF), responder, night, period order, and nested random factor of subject and approach were included in the mixed model. Finally, Fig. 4c applies a model for responders only, for both Sham and Verum condition separately, including the factors approach, night, period order, and nested random factor of subject and approach were included in the linear mixed model. Subject S039 and all related nights were excluded from analysis that included the continuous approach because the participant had complete data loss during the Verum ON condition.

Table 1

Daytime assessments for the windowed and continuous approach.

	Windowed (ONOFF) approach						Continous (ON) approach
	Weak responder		Strong responder				Weak responder		Strong responder
	Sham	Verum	Sham	Verum	statistics		Sham	Verum	Sham	Verum	statistics
	Mean (SEM)	Mean (SEM)	Mean (SEM)	Mean (SEM)	pcond	pcondxresp	Mean (SEM)	Mean (SEM)	Mean (SEM)	Mean (SEM)	pcond	pcondxresp
Sleep questionnaire
Sleep qualitya	4.6 (0.34)	4.56 (0.30)	3.96 (0.25)	3.87 (0.27)	0.40	0.46	4.49 (0.33)	4.34 (0.32)	3.62 (0.33)	3.91 (0.3)	0.54	0.08*
Sleep deptha	3.05 (0.25)	2.96 (0.21)	2.67 (0.16)	2.52 (0.15)	0.12	0.66	2.97 (0.22)	2.76 (0.16)	2.52 (0.11)	2.43 (0.18)	0.17	0.35
MDBF
Mood morningb	102.11 (3.10)	98.47 (4.52)	100.67 (5.44)	96.79 (5.84)	0.021*	0.74	102.68 (2.76)	100.27 (3.82)	98.08 (5.94)	94.83 (6.04)	0.011*	0.55
Mood whole dayb	104.12 (2.45)	96.39 (6.2)	101.34 (4.94)	98.99 (4.94)	0.074*	0.55	103.36 (2.7)	103.31 (2.93)	100.99 (5.09)	95.36 (5.88)	0.038*	0.18
Sleepiness
KSS moringa	3.09 (0.24)	3.07 (0.20)	3.61 (0.22)	3.76 (0.33)	0.77	0.81	3.01 (0.18)	3.1 (0.19)	3.85 (0.3)	3.8 (0.32)	0.98	0.36
KSS 2pma	3.04 (0.27)	2.99 (0.28)	3.29 (0.18)	3.33 (0.39)	0.94	0.71	3.1 (0.35)	2.93 (0.28)	3.21 (0.16)	3.42 (0.27)	0.96	0.48
KSS eveninga	3.3 (0.30)	3.14 (0.18)	3.7 (0.20)	3.87 (0.23)	0.41	0.71	3.38 (0.38)	2.85 (0.23)	3.39 (0.21)	4.12 (0.43)	0.57	0.02*
PVT
Speed (1/RT)b	3.14 (0.1)	3.1 (0.1)	3.05 (0.12)	3.01 (0.09)	0.17	0.86	3.15 (0.1)	3.1 (0.09)	3.07 (0.13)	3.01 (0.08)	0.50	0.97
10% slowest RT (ms)b	378.55 (16.13)	381.72 (15.04)	401.86 (24.9)	414.38 (18.56)	0.22	0.85	377.96 (20.23)	382.77 (15.93)	406.02 (30.91)	418.26 (16.84)	0.80	0.47
10% fastest speed (1/RT)b	3.53 (0.09)	3.47 (0.10)	3.51 (0.10)	3.47 (0.08)	0.029*	0.73	3.51 (0.09)	3.48 (0.08)	3.52 (0.11)	3.46 (0.08)	0.77	0.88

Mood was assessed by the “Mehrdimensionaler Befindlichkeitsfragebogen” (MDBF). Sleepiness was assessed by the Karolinska Sleepiness Scale (KSS). PVT refers to a mobile version of the psychomotor vigilance task (10 min duration) to assess reaction time (RT). Cumulative link mixed model for ordinal data was used for all variables marked with a. Robust linear mixed models were performed to compare Sham to Verum nights for all interval scaled variables marked with b. P-value of factor Condition (cond, Sham vs Verum) and the interaction of condition and responder (resp, weak vs. strong) for each approach is reported in the statistics columns. Significant and trend-level p-values are marked with an asterisk (*).

For the evoked response analysis in Supplementary Figs. S2 and S3 we implemented a LMM with fixed factors condition (2 levels: Verum, Sham), night (7 levels), period order (2 levels), and nested random factor of subject and condition for Supplementary Figs. S2a and S3a. In Supplementary Figs. S2b and S4b we focused on the interaction between responder and condition. Supplementary Fig. S3b and d included the fixed factors approach (2 levels: ON, ONOFF), night (7 levels), period order (2 levels), and nested random factor of subject and approach. Please note that non-robust statistics were used here due to too extensive computation time for this analysis. However, the p-values obtained were extremely small and independent of whether a robust or non-robust approach is used the outcomes would have been similar.

In order to classify whether lowSWA baseline and stimulation characteristics of each night during Verum are predictive for whether they belonged to a weak or a strong responder, we performed a linear discrimination analysis (LDA, Fig. 3b) using a 10-fold cross-validation with the R-package caret[60,61]. To establish whether the obtained accuracies were better than the No Information Rate (best guess without additional information) a one-tailed binomial test was performed.

To assess whether nightly variance within subjects of an outcome parameter of interest (e.g., lowSWA ON-OFF difference) was influenced by specific predictive factors (e.g., baseline lowSWA), we performed repeated-measures correlation using the R-package rmcorr[62,63]. This type of correlation takes multiple measures of participants into account and calculates the common intra-individual association between two measures. The package establishes the best linear fit for each participant’s regression lines with the same slope but varying intercepts. This statistical approach was used in Figs. 3d–f and 5. Importantly, repeated measures correlation only considers the intra-individual associations and the provided r- and p-value refers to the individual fitted, solid lines in the presented figures and not on the dashed lines that represent the inter-individual association or group-level association. This analysis also highlights that it is important to distinguish inter- and intra-individual associations as they do not necessarily need to go in the same direction[62].

Overall, no corrections of multiple comparisons were performed, and we therefore specifically focused on effects that were consistently observable across approaches. P-values < 0.05 were considered significant and a p-value < 0.1 and ≥0.05 as trend-level (two-sided assessments, otherwise noted). Figures were done in MATLAB or in R using the R-package ggplot2[64,65]. For truncating and condensing the y-axis of figures with outlying values, the R-package ggbreak[66] was used.

Reproducibility: Our multiple nights of stimulation, and the two stimulation protocols that target the up-phase of sleep slow waves (just dose-dependent differences) provide robust findings because (1) across multiple nights the effect on SWA was seen and (2) findings in windowed stimulation approach are reproduced in continuous approach. For the performed interim analysis that is reported here, our sample size was based on previously published lab studies that showed in a within subject design, a significant effect of auditory stimulation on slow waves. Several landmark papers had ~13 participants that reported a significant effect on sleep upon auditory stimulation in different age groups (e.g. Young: Ngo et al.[20]; Besedovsky et al.[17]; Older adults: Papalambros et al.[24]). We used 16 participants to match with this number and to allow for a counter-balanced study design.