Meet the team

Derk-Jan Dijk

"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 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:

1. To validate and implement technology to characterise circadian and sleep disturbances in the home that can be scaled to be used in dementia and its preclinical phase.

2. To develop and validate approaches to integrating sleep and circadian disturbances with other sensor information to predict variation in behavioural and cognitive symptoms, and clinical events such as falls and disease progression. 

3. Team Members

Professor Anne Skeldon, Department of Mathematics, University of Surrey
Professor Adrian Hilton, Centre for Vision, Speech and Signal Processing, University of Surrey
De Kevin Wells,  Centre for Vision, Speech and Signal Processing, University of Surrey
Dr Ciro della Monica, Surrey Sleep Research Centre, University of Surrey

4. Collaborations

Within UK DRI:

Prof Payam Barnaghi, University of Surrey 

Dr Shirin Enshaeifar, University of Surrey 

Dr Ramin Nilforooshan, Surrey and Borders Partnership NHS Trust


Beyond UK DRI:

Prof Jason Warren, UCL

5. Topics

Circadian rhythms, sleep, accelerometer, home EEG, sensors

6. Techniques

EEG, wearable and passive technologies, accelerometers, machine learning, mathematical modelling 

7. Key publications

Dijk DJ, Landolt HP. Sleep physiology, circadian rhythms, waking performance and the development of sleep-wake therapeutics. Handb Exp Pharmacol. 2019. doi: 10.1007/164_2019_243.

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.

Lazar ZI, Dijk DJ, Lazar AS. Infraslow oscillations in human sleep spindle activity. J Neurosci Methods. 2019 ;316:22-34. doi: 10.1016/j.jneumeth.2018.12.002.

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.

Faulkner SM, Bee PE, Meyer N, Dijk DJ, Drake  RJ Light therapies to improve sleep in intrinsic circadian rhythm sleep disorders and neuro-psychiatric illness: a systematic review and meta-analysis. Sleep Medicine Reviews. Sleep Med Rev. 2019;46:108-123. doi: 10.1016/j.smrv.2019.04.012. 

Nollet M , Hicks H ,  McCarthy AP, Wu H, Möller-Levet CS , Laing EE,  Malki M , Lawless N, Wafford KA, Dijk DJ, Winsky-Sommerer  REM sleep’s unique associations with corticosterone regulation, apoptotic pathways and behavior in chronic stress in mice'  PNAS Proc Natl Acad Sci U S A. 2019;116(7):2733-2742. doi: 10.1073/pnas.1816456116. 

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

Sterr A, Ebajemito JK, Mikkelsen KB, Bonmati-Carrion MA, Santhi N, Atzori G, Revell V, Debener S, Dijk DJ, de Vos M. Sleep EEG Derived From Behind-the-Ear Electrodes (cEEGrid) Compared to Standard Polysomnography:  A Proof of Concept Study Front Hum Neurosci. 2018; 12:452. doi: 10.3389/fnhum.2018.00452.

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Latest tweets from @UKDRI

New Research Associate role with Dr Marco Brancaccio, UK DRI at Imperial - recruitment of circadian clocks in brain… https://t.co/OAhT2Vi4UA
09 Dec 2019 16:50