PhD on long-term monitoring of sleep disorder symptoms with digital tools

Introduction

Are you eager to work on digital monitoring tools and data science for medical applications? Are you fascinated about sleep? In this project TU/e works together with Sleep Center Kempenhaeghe and Takeda to improve long-term, low-obtrusive monitoring of the sleep disorder narcolepsy and facilitate management of daytime complaints using digital health technology.

Job Description

Narcolepsy is a chronic sleep disorder that typically imposes a substantial burden on those affected. People with narcolepsy often experience a broad range of symptoms, such as excessive daytime sleepiness, cataplexy, hypnagogic hallucinations, sleep paralysis, and fragmented nocturnal sleep. Moreover, the spectrum of narcolepsy also includes symptoms that manifest more during daytime such as automatic behavior, cognitive impairments or mood disturbances. Symptoms greatly vary among patients and day-to-day variance can be considerable. Available narcolepsy questionnaires do not cover the full symptom spectrum and do not - or only retrospectively - capture symptom variability. Therefore, there is a clinical need for tools to monitor narcolepsy-related symptoms over time, to assess, describe, and predict the associated burden in daily life and evaluate the effect of treatment on everyday behaviors and experiences during sleep and wake.

M-Health offers the opportunity to monitor user experiences during their daily routine over prolonged time, in a scalable manner. Sleep Medicine Center Kempenhaeghe developed the Narcolepsy Monitor, a digital research tool to gain insight into both the individual and synergetic burden of 20 narcolepsy symptoms over time. In this project, we aim to detailly expand our knowledge of the narcolepsy symptom spectrum, gain a better understanding of the inter-relation between patients’ symptoms and lived experiences, and advance longitudinal monitoring of disease burden. A better understanding of the symptoms and their dynamics over time as well as interindividual variability in the ecologies of specific symptoms can help to tailor treatment to the individual patient.

To facilitate the monitoring of narcolepsy-related symptoms with an m-Health application, it is important to understand which symptoms should be logged, and with which frequency, to map and better understand patients' lived experiences. Moreover, to mitigate user burden, monitoring should occur as minimally obtrusive and as engaging for a large audience as possible. Unobtrusive sensor technologies could complement self-reported data and may reduce the sampling frequency of self-reports.

The current project has three main aims, contributing to monitoring narcolepsy with an mHealth application:

• Analyzing the Narcolepsy Monitor database to identify symptom clusters and to quantify dynamics of longitudinal disease burden. We will investigate associations between symptoms experienced by narcolepsy patients and assess temporal dynamics in the symptoms from a 24/7 perspective. To this end, we will analyze variations in symptom severity within days, across days, weeks, and explore variance over longer timeframes such as months. This will help us learn about which symptoms to monitor and at what time resolution.
• Advancing insights about the integration of sensor data by analyzing self-reported data augmented with wearable data obtained through a new data collection campaign.
• Understanding patient’s and healthcare professionals’ preferences in patient monitoring and co-creating and analyzing dashboards tailored to the needs of the different user groups (patients and healthcare professionals).

In this project, you will work in a multidisciplinary team which combines expertise on sleep medicine in general and narcolepsy in particular, together with longitudinal monitoring and digital health technology. This research will be conducted in both the Advanced Sleep Monitoring research group (Biomedical Diagnostics lab, Electrical Engineering) and the Human Technology interaction group (Industrial Engineering & Innovation Sciences) at the TU/e. You will work in close collaboration with Takeda and Sleep Medicine Center Kempenhaeghe. Kempenhaeghe is a tertiary expertise center for sleep medicine, with specific experience with diagnosis and treatment of narcolepsy patients. Takeda is an international pharmaceutical company which has a narcolepsy medication in development.  

Job Requirements

• A master’s degree (or an equivalent university degree) in data science, human-technology interaction, neuroscience, technical medicine or psychology (or related fields).
• Experience with advanced statistical modelling
• A research-oriented attitude.
• Ability to work in an interdisciplinary team and interested in collaborating with clinical and industrial partners.
• Experience with (clinical) data collection
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level).
• Fluent in spoken Dutch (required for interaction with patients and clinical staff during data collection campaign)

Conditions of Employment

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you: 

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment. 
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 3,059 - max. € 3,881).  
• A year-end bonus of 8.3% and annual vacation pay of 8%. 
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process. 
• An excellent technical infrastructure, on-campus children's day care and sports facilities.  
• An allowance for commuting, working from home and internet costs. 
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates. 

Information

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Merel van Gilst (m.m.v.gilst@tue.nl).

Visit our website for more information about the application process or the conditions of employment. You can also contact HR Services EE (hrservices.ee@tue.nl).

Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video.

Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Application

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

Please note

• You can apply online. We will not process applications sent by email and/or post. 
• A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check, please consult the National Knowledge Security Guidelines.
• Please do not contact us for unsolicited services.