Roessingh Research and Development (RRD) is a research and development Small Medium Enterprise in the area of rehabilitation technology and digital health with strong formalized links to one of the largest rehabilitation centers in the Netherlands (Roessingh Rehabilitation Center) and the University of Twente. The mission of RRD is to carry out scientific research and contribute to its valorisation and implementation in clinical practice, by close cooperation with clinical centres and industry.

RRD is a pioneer in the field of coaching of physical activity in the prevention and management of chronic diseases. In the last 10 years we have performed more than 50 studies in which physical activity data was measured objectively with wearable sensors for periods ranging between 1 and 12 weeks. The target group varied from pediatric rehabilitation (e.g. children with cerebral palsy or asthma), to physical rehabilitation (e.g. COPD and chronic pain) or promotion of Active Ageing among older adults.

Most studies looking at physical activity patterns often compare two points in time, for example, the first and the last week of an intervention. Everything that happens in the between is unknown. In other words, we know the physical activity of the patient at the subject and at the end points, but we do not know what happened in the between. Did the patient have a fast recovery and reached a plateau soon after surgery? Were there periods of relapse? A second problem with this approach is that it does not allow for a look at the trajectory of each individual patient.

Conventional methods for physical activity analysis look at metrics of physical activity, such as total counts/steps at the end of day, or number of minutes in each activity intensity category. A new line of research, so called compositional analysis of physical activity, is now looking at physical activity as part of a pattern over 24-hour period, including: sleep time, sedentary time and light, moderate- or vigorous-intensity physical activity. The idea is that the relative amount of time spend in each category is compared, and not the absolute values.

Complexity analysis is another method gaining interest in the recent years. In complexity analysis, the changes of stages are investigated. In a practical example, one can look at how often the subject changes from sitting to standing position, or from sedentary to light active intensity.


The challenge within this assignment is to investigate how each one of the data analysis methods contribute to a better understanding of individual patterns of daily physical activity: (1) conventional analysis; (2) compositional analysis of 24-hours physical activity data; (3) complexity analysis.


The data used in this assignment was collected within the final evaluation of the Council of Coaches project. Council of Coaches is an online platform is which multiple virtual coaches form a personal council that supports older adults in their health and well-being. One of main coaches is dedicated to the promotion of physical activity in daily life supported by the data collected from a wristband activity tracker. To perform this assignment, you will get access to the anonymized data of more than 50 participants in the Netherlands and Scotland, who participated in the study for a period between 5 and 9 weeks.


  • Student in Biomedical Engineering (or similar) with strong interest in data analysis / signal processing;
  • Fluency in written and spoken English language is a must;
  • Proactive and independent student.


  • The dataset to be used in this assignment is already available (>50 participants over a period of 5-9 weeks). The student is not expected to perform data collection;
  • The student will be integrated in a multidisciplinary team;
  • It is the intention to work towards a scientific article to be published at the end of the research.

Relevant resources

  • Dumuid, D., Stanford, T. E., Martin-Fernández, J.-A., Pedišić, Ž., Maher, C. A., Lewis, L. K., … Olds, T. (2018). Compositional data analysis for physical activity, sedentary time and sleep research. Statistical Methods in Medical Research, 27(12), 3726–3738.
  • Paraschiv-Ionescu, A., Perruchoud, C., Buchser, E., & Aminian, K. (2012). Barcoding human physical activity to assess chronic pain conditions. PloS one, 7(2), e32239. doi:10.1371/journal.pone.0032239
  • Open Source Compositional Analysis of 24 hour Time Use and Movement Behavior Data


If you are interested in the assignment or have any questions please contact Miriam Cabrita (