Internship in Epidemiology/Sports Science (Master2 student) - LM0919
1A-B, rue Thomas Edison, Strassen 1445, Luxembourg
Current knowledge on how health behaviours such as physical activity, sitting and sleep affect our health is based on self-report by questionnaires, which have limited validity, are prone to bias and enquire about selective aspects of these behaviours. Progresses in technology (e.g. accelerometry) and methods (e.g. compositional analysis) have changed the landscape. One of the most exciting aspects of accelerometers is that they theoretically allow for capturing nearly complete accounts of movement behaviour, including posture and activity type detection. Interest in 24-hour accelerometry to assess all movement behaviours is increasing but analyses are complicated. Actually, only few studies have used the devices collecting data in raw mode 24/7 to cover activity pattern of the whole day. Most accelerometer-based constructs of physical activity are based on total daily time spent in different intensity bands. However, ongoing advancements in accelerometers and data science have opened new avenues for incorporating a variety of other equally important characteristics of physical activity, like posture, activity types and sleep. These new approaches will allow to 1) define multidimensional profiles constructed across the key physical activity dimensions, 2) investigate the association between profiles and health outcomes, and 3) personalised future interventions in public health.
Our team aims at identifying approaches that have been developed so far to define 24-hour physical activity patterns using raw accelerometry data. There is a strong need for a systematic review on the available methods for the identification of physical activity patterns, which methods might be used in future studies on the association between physical activity and health conditions (e.g. cardiometabolic health, Diabetes, frailty), as well as in personalised interventions in public health. This work will provide consumer wearable device companies with decisive information on future developments in the data processing, as well as on relevant feedback to the end-user.
Training and research environment
This project falls within the framework of a close collaboration between the Sports Medicine Research Laboratory and the “Digital Epidemiology Hub”, which develops a transversal research activity within the Department of Population Health on modern approaches in digital epidemiology, at the frontier with data and computer sciences.
The Master student will directly contribute to the development of a large international initiative to integrate innovate digital data into modern epidemiological studies. He/she will lead a project of systematic review of the literature on the available methods for the identification of physical activity patterns. He/She will have to take in charge the literature review, the processing of the data, the data analysis and the writing of the systematic review. He/she will be supervised by Dr. L. Malisoux, Research Leader of the Sports Medicine Research Laboratory. This internship position may lead to a PhD opportunity in Sports Science (Health data science).
KEY SKILLS, EXPERIENCE AND QUALIFICATIONS
- Students will have the opportunity to work in an interactive and international scientific environment, attend conferences by eminent scientists from abroad, and present their own work during lab meetings.
- They will receive training in physical activity and digital epidemiology research, and will have the opportunity to gain skills in systematic review and scientific writing.
- Applicants must be affiliated to their own University
- English is mandatory.
- Master 2 students will receive 500 €/month unless they have their own funding source, e.g.Erasmus grant.
Available for 6-9 months (can be extended according to the specific requirements of the university) -full time - start date : 2020
- Doherty A, Jackson D, Hammerla N, et al. Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study. PLoS One. 2017;12:e0169649.
- Vaha-Ypya H, Vasankari T, Husu P, et al. A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. Clin Physiol Funct Imaging. 2015;35:64-70.
- Western MJ, Peacock OJ, Stathi A, et al. The understanding and interpretation of innovative technology-enabled multidimensional physical activity feedback in patients at risk of future chronic disease. PLoS One. 2015;10:e0126156.
- Stamatakis E, Koster A, Hamer M, et al. Emerging collaborative research platforms for the next generation of physical activity, sleep and exercise medicine guidelines: the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS). Br J Sports Med. 2019.
- Gupta N, Hallman DM, Dumuid D, et al. Movement behavior profiles and obesity: a latent profile analysis of 24-h time-use composition among Danish workers. Int J Obes (Lond). 2019.
- Fitzsimons CF, Kirk A, Baker G, et al. Using an individualised consultation and activPAL feedback to reduce sedentary time in older Scottish adults: results of a feasibility and pilot study. Prev Med. 2013;57:718-20.