"Sleep and circadian rhythm are major determinants of quality of life and new technologies to improve sleep will contribute to the wellbeing of persons with dementia and their carers." Derk-Jan Dijk
UK DRI Group Leader
Professor of Sleep and Physiology and Director of the Surrey Sleep Research Centre, Prof Derk-Jan Dijk is an expert in sleep and circadian rhythms. Obtaining his PhD from the University of Groningen, Netherlands, he went on to conduct research at Harvard Medical School and Brigham & Women’s Hospital, USA. Joining the University of Surrey in 1999, he founded the Surrey Sleep Research Centre in 2003. Derk-Jan has won numerous awards, including a Distinguished Scientist Award from the Sleep Research Society in 2015, and was elected Fellow of the Academy of Medical Sciences in 2018. As part of the UK DRI Care Research & Technology Centre at Imperial in collaboration with the University of Surrey, he will lead a novel programme of research developing new technologies to enhance sleep for people with dementia.
1. At a glance
Improving sleep quality for people living with dementia
Disturbed sleep is a common symptom for people living with dementia but we still don’t know whether this is a consequence of the condition or a driving factor in the disease progression. Prof Derk-Jan Dijk is developing new technologies that can measure a person’s sleep and wake patterns at home, which will enable large state-of-the-art studies and investigations into new interventions to enhance healthy sleep and circadian rhythm in people with dementia.
The team are exploring the potential of wearable devices that can sense movements and physiological signals – which could offer a low-cost approach for sleep research. They will determine whether using these devices is scientifically robust and then explore the potential to add on sensors that measure variables such as exposure to light. They will then apply mathematical modelling and machine learning approaches to the data to understand the relationship between sleep, light and dementia symptoms to design new interventions.
They will also investigate wearable devices that can record a person’s electrical brain activity – to identify one that can accurately assess key features of sleep. The team will test whether playing specially selected sounds into an earpiece sensor can help improve disrupted sleep patterns in dementia.
The team will also develop passive devices such as movement sensors attached to beds and automated video analysis, integrating these with data collected from other home sensors monitoring behaviour. They hope to then use these insights to improve sleep quality.
2. Scientific goals
A common symptom for people living with dementia is disturbed sleep. However, there is still little known about whether sleep is just a consequence of dementia, and therefore could be utilised as an indicator of early disease, or a driver in pathogenesis of the disease. Both these explanations may be true but either way, there is a wealth of knowledge still to uncover in this field.
This UK DRI programme, led by Prof Derk-Jan Dijk, will validate and develop technology to quantify vigilance states, sleep symptoms and physiology across the 24-hour day (i.e. circadian rhythm). The team will validate the new technologies developed under rigorous sleep lab conditions prior to testing in the home environment.
Four parallel strands will be pursued:
Circadian Assessment: The team will validate accelerometers as a low-cost approach to assessing circadian sleep rhythms in order to facilitate their use in the monitoring of large populations and the evaluation of interventions. Parameters derived from accelerometers will be compared against EEG. Automated scoring algorithms will be developed, facilitating the investigation of circadian sleep rhythms in large samples, including UK Biobank (N>100K). They will extend the functionality of accelerometers by adding light exposure sensors to quantify circadian stimulation. Mathematical modelling will be used to understand the relationship between light, sleep and clinical state. Accurate home assessment of circadian rhythm will facilitate new treatments for disturbed sleep, for example by clarifying the optimal timing and duration of naps and the predictors of nocturnal wandering.
Home EEG monitoring: The team will evaluate devices capable of prolonged (ideally 24 hours) home EEG recording. Requirements to be met are the accurate assessment of sleep stages (REM and NREM), sleep continuity and EEG features during sleep and wakefulness. Slow waves and REM sleep are often abnormal in dementia and may be modulated by appropriately timed acoustic stimuli. They will, therefore, test whether acoustic stimulation coupled to home EEG can be used as a therapeutic modality to enhance healthy sleep.
Contactless sleep and circadian assessment: In addition to the focus on wearable technology, they will develop passive monitoring for sleep and circadian rhythm that can be scaled independently of patient compliance. They will evaluate passive approaches to sleep assessment using movement sensors attached to beds and sheets, automated video analysis and integration of multi-modal data produced by the ‘Healthy Home system’ including home radar. The team envisage using this technology to modify sleep quality, which might be achieved without the need for wearable technology, for example by an automated adjustment of bed position to change a person’s posture and reduce apnoea.
Technology evaluation: New technology will be evaluated in the Living Home, part of the Clinical Research Building at the University of Surrey. Validation will initially be conducted in small groups of cognitively healthy older participants. The most promising technologies will be evaluated in dementia/mild cognitive impairment (MCI) patients, with either overnight stays in the Living Home or in patients’ homes.
Main objectives and research goals:
- Identify valid and affordable technology to measure sleep and circadian rhythms at-scale and longterm in PLWD
- Identify aspects of sleep and circadian disturbance that predict cognitive function and psychiatric symptoms
- Design and deliver interventions to improve sleep within a clinical neuroscience context
3. Team Members
Dr Ullrich Bartsch (Academic)
Dr Vikki Revell (Academic)
Prof Anne Skeldon (Academic)
Dr Ines Violante (Academic)
Dr Hana Hassanin (Clinical Consultant)
Iris Wood Camper (Clinical Support)
Dr Ciro Della Monica (Postdoctoral Researcher)
Dr Valeria Jaramillo (Postdoctoral Researcher)
Dr Ivan Kiskin (Postdoctoral Researcher)
Dr Kiran GR Kumar (Postdoctoral Researcher)
Dr Sara Mohammadi Mahvash (Postdoctoral Researcher)
Dr Thalia Rodriguez Garcia (Postdoctoral Researcher)
James Woolley (Project Officer)
Giuseppe Atzori (Staff Scientist)
Damion Lambert (Staff Scientist)
Henry Hebron (PhD Student)
Elaheh Kalantari (PhD Student)
Within UK DRI:
- Dr Ramin Nilforooshan, Surrey and Borders Partnership NHS Trust
- Prof Henrik Zetterberg, University College London
- Dr Nir Grossman, Imperial College London
- Prof William Wisden, Imperial College London
- Prof David Sharp, Care Research & Technology
- Prof Timothy Constandinou, Care Research & Technology
- Prof Ravi Vaidyanathan, Care Research & Technology
Beyond UK DRI:
- Prof Jason Warren, UCL
- Prof Nicholas Frank, Imperial College London
- Professor Naji Tabet, Brighton and Sussex Medical School
- Professor Tim Denison, Oxford University
Circadian rhythms, sleep, accelerometer, home EEG, sensors
EEG, wearable and passive technologies, accelerometers, machine learning, mathematical modelling
7. Key publications
Balouch S, Dijk DAD, Rusted J , Skene SS, Tabet N, Dijk DJ . Night-to-night variation in sleep associates with day-to-day variation in vigilance, cognition, memory, and behavioral problems in Alzheimer’s disease. Alzheimer’s & Dementia: Diagnosis, Assessment, and Disease Monitoring. 2022 May 16;14(1):e12303. doi: 10.1002/dad2.12303. eCollection 2022.
Meyer N, Harvey AG, Lockley SW, Dijk DJ. Circadian rhythms and disorders of the timing of sleep. Lancet. 2022 Sep 14:S0140-6736(22)00877-7 doi: 10.1016/S0140-6736(22)00877-7
De Oliveira P, Cella C, Locker N, Ravindran KKG, Mendis A, Wafford K, Gilmour G, Dijk DJ Winsky-Sommerer R. Improved sleep, memory, and cellular pathological features of tauopathy, including 1 the NLRP3 inflammasome, after chronic administration of trazodone in rTg4510 mice. J Neuroscience 2022 Apr 20;42(16):3494-3509. doi: 10.1523/JNEUROSCI.2162-21.2022.
Skeldon A, Dijk DJ, Meyer N, Wulff K. Extracting circadian and sleep parameters from longitudinal data in schizophrenia for the design of pragmatic light interventions. Schizophr Bull. 2021 Nov 10:sbab124. doi: 10.1093/schbul/sbab124
Muto V, Koshmanova E, Ghaemmaghami P, Jaspar M, Meyer C, Elansary M, Van Egroo M, Chylinski D, Berthomier C, Brandewinder M, Mouraux C, Schmidt C, Hammad G, Coppieters W, Ahariz N, Degueldre C, Luxen A, Salmon E, Phillips C, Archer SN, Yengo L, Byrne E, Collette F, Georges M, Dijk DJ, Maquet P, Visscher PM, Vandewalle G. Alzheimer's disease genetic risk and sleep phenotypes in healthy young men: association with more slow waves and daytime sleepiness. Sleep. 2021 Jan 21;44(1):zsaa137. doi: 10.1093/sleep/zsaa137
Mohammadi SM, Enshaeifar S, Hilton A, Dijk DJ, Wells K. Transfer Learning for Clinical Sleep Pose Detection using a Single 2D IR Camera. IEEE Trans Neural Syst Rehabil Eng. 2020 Dec 30;PP. doi: 10.1109/TNSRE.2020.3048121.
Winsky-Sommerer R, de Oliveira P, Loomis S, Wafford K, Dijk DJ, Gilmour G. Disturbances of sleep quality, timing and structure and their relationship with other neuropsychiatric symptoms in Alzheimer's disease and schizophrenia: Insights from studies in patient populations and animal models. Neurosci Biobehav Rev. 2019 97:112-137. doi: 10.1016/j.neubiorev.2018.09.027.
Laing EE, Moller-Levet CS, Dijk DJ, Archer SN. Identifying and validating blood mRNA biomarkers for acute and chronic insufficient sleep in humans: a machine learning approach. 2019 ;42(1). doi: 10.1093/sleep/zsy186.
Mikkelsen KB, Ebajemito JK, Bonmati-Carrion MA, Santhi N, Revell VL, Atzori G, della Monica C, Debener S, Dijk DJ, Sterr A, de Vos M. Machine learning derived sleep-wake staging from around-the-ear EEG outperforms manual scoring and actigraphy. J Sleep Res. 2019 ;28(2):e12786. doi: 10.1111/jsr.12786.
Mohammadi SM, Alnowami M, Khan S, Dijk DJ, Hilton A, Wells K Sleep Posture Classification using a Convolutional Neural Network. Conf Proc IEEE Eng Med Biol Soc. 2018 ;2018:1-4. doi: 10.1109/EMBC.2018.8513009