Abstract
Study Objectives:
Sleep disturbances are common in people with Alzheimer’s disease (AD), and a reduction in slow-wave activity is the most striking underlying change. Acoustic stimulation has emerged as a promising approach to enhance slow-wave activity in healthy adults and people with amnestic mild cognitive impairment. In this phase 1 study we investigated, for the first time, the feasibility of acoustic stimulation in AD and piloted the effect on slow-wave sleep (SWS).
Methods:
Eleven adults with mild to moderate AD first wore the DREEM 2 headband for 2 nights to establish a baseline registration. Using machine learning, the DREEM 2 headband automatically scores sleep stages in real time. Subsequently, the participants wore the headband for 14 consecutive “stimulation nights” at home. During these nights, the device applied phase-locked acoustic stimulation of 40-dB pink noise delivered over 2 bone-conductance transducers targeted to the up-phase of the delta wave or SHAM, if it detected SWS in sufficiently high-quality data.
Results:
Results of the DREEM 2 headband algorithm show a significant average increase in SWS (minutes) [t(3.17) = 33.57, P = .019] between the beginning and end of the intervention, almost twice as much time was spent in SWS. Consensus scoring of electroencephalography data confirmed this trend of more time spent in SWS [t(2.4) = 26.07, P = .053].
Conclusions:
Our phase 1 study provided the first evidence that targeted acoustic stimuli is feasible and could increase SWS in AD significantly. Future studies should further test and optimize the effect of stimulation on SWS in AD in a large randomized controlled trial.
Citation:
Van den Bulcke L, Peeters A-M, Heremans E, et al. Acoustic stimulation as a promising technique to enhance slow-wave sleep in Alzheimer’s disease: results of a pilot study. J Clin Sleep Med. 2023;19(12):2107–2112.
Keywords: Alzheimer’s disease, sleep, slow-wave sleep, acoustic stimulation, wearable device
BRIEF SUMMARY
Current Knowledge/Study Rationale: Disturbances of sleep are a very common and debilitating symptom of Alzheimer’s disease. A reduction in slow-wave sleep is one of the most striking underlying changes.
Study Impact: Our study provides the first evidence that acoustic stimulation could lead to a considerable increase in slow-wave sleep in Alzheimer’s disease. Enhancement of slow-wave sleep in Alzheimer’s disease provides exciting perspectives because this may not only lead to better sleep without the risks associated with sleep-inducing medications and fewer mood, cognitive, and behavioral problems but could even, given the role of slow-wave sleep in the pathophysiology of Alzheimer’s disease, be considered a disease-modifying therapy in the future.
INTRODUCTION
Besides cognitive impairments, one of the most striking features of Alzheimer’s disease (AD) is sleep disturbances. These disturbances can lead to mood,1 behavior,2 and cognitive problems3 that reduce patients’ quality of life and increase caregivers’ burden.4 In AD, a reduction in the slow-wave sleep (SWS) stage (or N3) is one of the most notable changes in sleep.5 The glymphatic system, a system particularly active during SWS, has been described in rodents as a pathway underlying the clearance of solutes, including Aβ, from the brain’s extracellular space.6 In humans, studies have indicated evidence for pathways that closely resemble the glymphatic system outlined in rodents.7 A reduced clearance of pathological protein aggregates from the brain by the glymphatic system, influenced by a reduction in SWS, could be a shared phenomenon in neurodegeneration.8
In recent years, several, primarily single-night, in-laboratory interventions have reported the beneficial effect of acoustic stimuli on slow-wave enhancement and declarative memory consolidation in a (mostly young and healthy) small group of participants.9–13 Furthermore, mobile devices provide the opportunity to move findings from well-controlled laboratory studies to more longitudinally in-field applications. Patients with AD could be of specific interest because of their pronounced reductions of SWS; moreover, effective and safe treatment of sleep disturbances in dementia remains an unresolved challenge.14,15 Risks associated with the use of sleep-inducing medications in dementia include risk for falls, confusion, and declining ability to care for oneself.14 Slow-wave enhancement by acoustic stimuli may lead to better sleep without the risks associated with sleep-inducing medications and fewer mood and behavior problems and could even slow down the cognitive impairment. However, sound might also influence sleep in an undesirable way.16
To date, no study has investigated the effect of acoustic stimulation in patients with dementia. In this study, we focused on people with AD; the primary aims of our phase 1 trial were to examine the feasibility of acoustic stimulation in the home environment during multiple nights and pilot the effect on SWS by using a commercially available sleep monitoring and feedback-controlled slow-wave modulation device.
METHODS
Thirteen patients (5 females, mean age 76.31 [standard deviation (SD) 5.39] years, range: 63–84 years) living at home and their partners were included. The involvement of partners allowed for a more reliable application of the study intervention. Participants fulfilled the criteria of a neurocognitive disorder due to AD, according to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition, ranging from mild to moderate (as defined by a score of 1 or 2 on the Clinical Dementia Rating Scale).17 Exclusion criteria included other types of dementia, unstable medical or psychiatric conditions, and alcohol or substance abuse. The presence of preexisting sleep disturbances and use of sleep-inducing medication were not exclusion criteria; however, no alterations in the use of psychopharmacological drugs were made during the study. The study was approved by the Ethics Committee of UZ Leuven and UPC KU Leuven (S65612) and conducted in accordance with the Declaration of Helsinki and its later amendments. Written informed consent was obtained from all participants.
Eligible participants were equipped with a commercially available headband device (DREEM 2, Rythm SAS, Paris, France). The device was worn at home and participants respectively activated and deactivated the device themselves at their habitual sleep and wake time. The device uses machine learning to assess the data of its built-in sensors, including 5 electroencephalography (EEG) dry electrodes, a 3-dimensional accelerometer, and a pulse oximeter, and automatically scores sleep stages. Additionally, the device has an inbuilt feature enabling acoustic stimulation through bone conduction. Participants first wore the device for 2 consecutive nights with the feature for stimulation turned off to establish a baseline registration. Then, they wore the headband for 14 consecutive “stimulation nights.” The device applied phase-locked acoustic stimulation targeting slow-wave up-phases at the 45° ascending condition. During these nights, the device either stimulated with 2 consecutive SO phase-locked stimulation of 40-dB pink noise (STIM) or only marked the wave (SHAM) if the device detected SWS in sufficiently high-quality data. SHAM and STIM, each 50%, were randomly displayed throughout the night.18 The device has proven to be accurate in detecting N3 sleep compared to polysomnography (PSG) (specificity: 0.90, sensibility: 0.70) and has a precise algorithm for stimulation (45 ± 52° reached on average for a 45° targeting).18 At the end of the study, participants were systematically debriefed to identify any adverse events and technical issues. A representation of the DREEM headband device and the flowchart of the study intervention are shown in Figure 1.
Figure 1. Representation of the DREEM 2 headband device and the flowchart of the study intervention.
(A) We used the DREEM 2 headband device (DH) to record sleep data and to stimulate the slow-wave activity by closed-loop acoustic stimulation. The DH was worn at home and self-applied by participants. Participants activated and deactivated the device at their habitual sleep and wake time. The device uses machine learning to assess the data of its built-in sensors, including 5 electroencephalography dry electrodes, a 3-dimensional accelerometer, and a pulse oximeter, and automatically scores sleep stages. Additionally, the device has an inbuilt feature enabling acoustic stimulation through bone conduction. (B) Participants first wore the device for 2 consecutive nights with the feature for stimulation turned off to establish a baseline registration (OFF). Then, they wore the headband for 14 consecutive “stimulation nights.” The device applied phase-locked acoustic stimulation targeting slow wave up-phases at the 45° ascending condition. During these nights, the device either stimulated with 2 consecutive SO phase-locked stimulation of 40-dB pink noise (STIM) or only marked the wave (SHAM) if the device detected SWS in sufficiently high-quality data. SHAM and STIM, each 50%, were randomly displayed throughout the night.
Independently and blinded from the DREEM annotations, 2 expert sleep scorers also manually scored the raw EEG data. SeqSleepNet was used to aid the manual scoring of the sleep experts based on predictions from a machine-learning model.19 Using sequences of 10 consecutive epochs, we pretrained SeqSleepNet on an open-access dataset of 25 adult volunteers measured with the DREEM headband.20 We used 4 EEG channels (Fp1-O1, Fp1-F7, F8-F7, F7-O1), accelerometry, and respiration of this dataset consisting of simultaneous PSG and DREEM headband measurements. Sleep stages are annotated based on PSG. Using the PSG annotations as ground truth, we trained the network on the DREEM data of this source dataset in a supervised way. Then, we used unsupervised adversarial domain adaptation to adapt SeqSleepNet to each participant of our dataset.21 The automatic sleep stage classification was followed by 2 independent manual expert reviews of these classifications based on visual scoring of the raw EEG data. Scorings were based on the American Academy of Sleep Medicine guidelines.22 If the scorings of the 2 sleep experts were not concordant, a third scorer made the final consensus decision.
Duration (in minutes) and percentage of time spent in stage N3 of sleep, total sleep time (TST) (in minutes), sleep onset latency (in minutes), and wake after sleep onset (in minutes) was calculated for nights 2 and 16, the last night of the experimental procedure. To overcome the first-night effect, a common occurrence when using PSG, the initial night of registration was discarded from the analysis. First-night recordings are often characterized by decreased TST, lower sleep efficiencies, reduced rapid eye movement sleep, and longer rapid eye movement latencies, an effect that is resolved during the second night of recording.23 The results from the second night were thus used as baseline registration.
For between-night differences, paired 2-sample t tests were used. Where normality assumptions were violated, a nonparametric Wilcoxon signed-rank test for paired data was applied. Pairwise deletion was implemented for handling missing data.
RESULTS
Of the 13 participants, 2 discontinued the study after 3 nights of recording for, respectively, experiencing pain behind the left ear and increased agitation during the day. Eleven participants completed the entire protocol. Data of 4 nights were excluded from the final analysis dataset, 2 due to an unusually short total record time compared to previously recorded nights (less than 2.5 hours) as a result of the removal or dislodging of the device during that specific night, 1 due to internal memory storage issues of the device, and 1 due to the refusal of the participant to wear the device during that night. Sleep data of the remaining 7 participants who had sufficient data for both night 2 and night 16 were subsequently analyzed. The basic demographics of these participants are shown in Table 1.
Table 1.
Demographics of the study participants (n = 7).
| Sex | |
| Male | 5 (71.43) |
| Female | 2 (28.57) |
| Age, years | |
| 60–70 | 1 (14.29) |
| 70–80 | 4 (57.14) |
| 80–90 | 2 (28.57) |
| Staging dementia (CDR*) | |
| Very mild dementia (3.0–4.0) | 0 (0.00) |
| Mild dementia (4.5–9.0) | 4 (57.14) |
| Moderate dementia (9.5–15.5) | 3 (42.86) |
| Severe dementia (16–18) | 0 (0.00) |
| Highest level of attained education | |
| Uneducated | 0 (0.00) |
| Primary school | 0 (0.00) |
| High school | 3 (42.86) |
| Graduate | 4 (57.14) |
| Smoking | |
| Active | 0 (0.00) |
| Stopped | 2 (28.57) |
| Never | 5 (71.43) |
| Alcohol use | |
| Never | 0 (0.00) |
| Daily | 5 (71.43) |
| Weekly | 2 (28.57) |
| Monthly | 0 (0.00) |
| Coffee consumption | |
| Never | 0 (0.00) |
| 1–5 cups/day | 7 (100.00) |
| >5 cups/day | 0 (0.00) |
Values presented as n (%). *Staging dementia using Clinical Dementia Rating scale sum of boxes scores.
In the debrief interviews with the 11 participants no severe side effects were reported. The device was found to be comfortable to wear, with only 4 nights in total (out of 176) of nonuse due to patient refusal. However, 60% of the participants reported that the headband became loose overnight at least once during the study. Suggested improvements included adding adjustable straps. Additionally, 80% of patients expressed the need for clear guidelines on pairing the headband with a personal device through Bluetooth and suggested simpler pairing methods.
The results generated by the DREEM headband showed a significant increase in the time spent in N3 (minutes) on night 16 (mean [M] = 68.64, SD = 46.64) compared to night 2 (M = 35.07, SD = 38.67), t(3.17) = 33.57, P = .019. Thus, on night 16, almost twice as much time was spent in N3 compared to night 2. Also, an increase in the percentage of TST spent in N3 (percent) on night 16 (M = 15.80, SD = 9.90) compared to night 2 (M = 8.76, SD = 9.98) could be seen; t(2.39) = 7.04, P = .054. The consensus scoring of the raw EEG data confirms this trend of more time spent in N3 (minutes) on night 16 (M = 66.93, SD = 15.28) compared to night 2 (M = 40.86, SD = 27.28), t(2.4) = 26.07, P = .053. Additionally, on night 16 the percentage of TST spent in N3 (M = 14.35, SD = 3.15) compared to night 2 (M = 8.83, SD = 4.92) was significantly greater; t(2.74) = 5.52, P = .034. This indicates a 1.63-fold increase in the percentage N3 of TST between our intervention’s beginning and end. There were no significant differences between baseline registration and night 16 in TST, sleep onset latency, or wake after sleep onset (see Table 2).
Table 2.
Sleep macrostructure for nights 2 and 16.
| DREEM | Consensus | |||||
|---|---|---|---|---|---|---|
| Night 2 | Night 16 | P | Night 2 | Night 16 | P | |
| Total sleep time (min) | 397.43 (79.39) | 421.29 (65.23) | .394 | 468.18 (82.73) | 473.91 (70.39) | .774 |
| Sleep onset latency (min) | 41.07 (40.52) | 30.64 (38.47) | .236 | 12.32 (4.93) | 9.30 (3.71) | .093 |
| Wake after sleep onset (min) | 116.79 (45.19) | 88.86 (73.17) | .237 | 77.68 (43.43) | 60.34 (28.36) | .275 |
| Stage N3 (min) | 35.07 (38.67) | 68.64 (46.64) | .019 | 40.86 (27.28) | 66.93 (15.28) | .053 |
| Stage N3 (%) | 8.76 (9.98) | 15.80 (9.90) | .054 | 8.83 (4.92) | 14.35 (3.15) | .034 |
Two-sided P values (standard deviation) are given for the paired t-tests/nonparametric Wilcoxon signed-rank test for the DREEM results of sleep onset latency and wake after sleep onset. The results as shown represent respectively those by the embedded automatic algorithm of the DREEM headband (DREEM) and those of the consensus scoring of the raw electroencephalography (Consensus) data.
The results of the scoring by the algorithm of the DREEM headband and those of the consensus scoring are shown in Table 2. A representation of the effect of the intervention on N3, both in absolute time and percentage of total sleep, is shown in Figure 2.
Figure 2. Representation of the effect of the intervention on N3 both in absolute time and percentage of total sleep.
(Left) Results by the embedded automatic algorithm of the DREEM headband (DREEM). (Right) Results of the consensus scoring of the raw electroencephalography (CONSENSUS) data.
DISCUSSION
We provide the first evidence that using a self-applied device at home for multiple nights targeting SWS by acoustic stimuli is a usable and acceptable approach in AD and could considerably increase SWS by a factor of 1.6–2. Although enhancement of SWS was considered an important intervention target, no study has previously investigated the potential to enhance SWS by acoustic stimulation in patients with dementia.15 It was considered questionable whether enhancement of slow waves would be feasible in patients with AD, given the reduced baseline SWS and increased sleep fragmentation.10 Moreover, given the reduced anticipated patient participation of patients with AD, using a self-applied headband device at home was considered challenging.
Limitations
At-home devices provide the opportunity for longitudinal in-field applications. However, they suffer from the absence of control over the subjects’ behavior (eg, pairing difficulties, refusal to wear the device, removal or dislodging of the device). These have been previously reported and present a continuous challenge to measure sleep in people with dementia.24 Feasibility measures may include the implication of a cognitively healthy partner in the study design, clear guidance on how to pair the headband with a personal device via Bluetooth, and general troubleshooting for technical issues. To ensure comfort and accuracy of data, adjustable straps should also be provided.
Because we chose a commercially available device no individualized alterations to the closed-loop stimulation technique could be made. We thus cannot exclude that an even more significant effect on SWS could be achieved with further (personalized) adjustments to the stimulation technique. Finally, because the primary aim of this phase 1 study was to investigate the feasibility of acoustic stimulation in AD and pilot the effect on SWS, our study was conducted in a small patient cohort without a control group.
Future directions
We have shown that acoustic stimulation could be a feasible and promising technique to enhance SWS in AD. Given the encouraging results of our phase 1 study, a large, randomized control trial is warranted to further test and optimize the effect of acoustic stimulation on SWS in AD. Enhancement of slow waves in AD provides exciting perspectives, because improving sleep could possibly also result in improved mood, cognition, and quality of life of patients and their caregivers. A decrease in slow-wave activity, a quantitative measurement of SWS, has furthermore been correlated with the severity of cognitive decline and reduced cerebrospinal fluid clearance of Aβ and tau tracers in patients with AD.5,25 Because SWS seems to play an important role in the pathophysiology of AD, possibly mediated by the glymphatic system, enhancement of slow waves in AD could even be considered a disease-modifying therapy in the future.
DISCLOSURE STATEMENT
All authors have seen and approved the manuscript. This work was supported by the Funds Malou Malou, Perano, Georgette Paulus, JMJS Breugelmans and Gabrielle, François and Christian De Mesmaeker, managed by the King Baudouin Foundation of Belgium (no. 2021-J1990130-222081) to Maarten Van Den Bossche and Laura Van den Bulcke and by the Klinische onderzoeks- en opleidingsraad (KOOR) of the University Hospitals Leuven to Maarten Van Den Bossche. The authors report no conflicts of interest.
EDITOR’S NOTE
The Emerging Technologies section focuses on new tools and techniques of potential utility in the diagnosis and management of any and all sleep disorders. The technologies may not yet be marketed, and indeed may only exist in prototype form. Some preliminary evidence of efficacy must be available, which can consist of small pilot studies or even data from animal studies, but definitive evidence of efficacy will not be required, and the submissions will be reviewed according to this standard. The intent is to alert readers of Journal of Clinical Sleep Medicine of promising technology that is in early stages of development. With this information, the reader may wish to (1) contact the author(s) in order to offer assistance in more definitive studies of the technology; (2) use the ideas underlying the technology to develop novel approaches of their own (with due respect for any patent issues); and (3) focus on subsequent publications involving the technology in order to determine when and if it is suitable for application to their own clinical practice. The Journal of Clinical Sleep Medicine and the American Academy of Sleep Medicine expressly do not endorse or represent that any of the technology described in the Emerging Technologies section has proven efficacy or effectiveness in the treatment of human disease, nor that any required regulatory approval has been obtained.
ACKNOWLEDGMENTS
The authors thank all participating patients, their partners, and their treating physicians.
ABBREVIATIONS
- AD
Alzheimer’s disease
- EEG
electroencephalography
- M
mean
- PSG
polysomnography
- SD
standard deviation
- SWS
slow-wave sleep
- TST
total sleep time
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