ID 2454: Sensor-based Measurement of Fear Avoidance in Chronic Primary Lower Back Pain during Standardized Movement Tasks

18. December 2024

Bachelor’s / Master’s Thesis / Research Internship / Project

Fear avoidance (FA) beliefs (i.e. movements may lead to pain/injury) motivate patients with chronic primary low back pain (CPLBP) to avoid physical activity and may thereby contribute to the maintenance of pain and disability. Patients’ confrontation with unavoidable movements should act as a stressor and lead to psychophysiological stress responses (heart rate, skin conductance) and to a guarded task execution (EMG and movement patterns). The vast amount of FA research used self-report measures; few studies investigated neural or psychophysiological correlates but none integrated these parameters. In this project, we aim to assess whether movement-related fear in CPLBP patients translates into discernable patterns of the patients’ movements and stress response during a range of standard tasks.

The goal of this thesis will be to investigate whether fear avoidance can be assessed from sensor-based measurements during standardized movement tasks in healthy individuals, where CPLBP is simulated using a back pain simulator.

 

We always have open topics for various types of theses in this project! If you are interested in working with us, please use the application form to apply for the topic. We will then get in contact with you and together, we can identify a suitable topic for you. You can have a look at our group page to get an overview of previous student topics in this area.

Tasks

  • You will work in an interdisciplinary team with psychologists and engineers
  • You have the opportunity to apply your data science knowledge to practical applications in psychology and chronic pain research
  • Depending on the topic and scope of your project, you will:
    • Support in data collection
    • Implement different algorithms into an existing data analysis framework
    • Perform statistical and machine learning-based data analysis
    • Document your code in a clear and structured manner

 

Requirements (depending on the topic and type of project)

  • Interest in diving into the field of data science and psychology (especially biopsychology, psychophysiology, and behavioral psychology)
  • Knowledge in data processing, data analysis, and data visualization using Python (or willingness to learn such!)
  • Further skills:
    • Knowledge of machine and deep learning, (Bayesian) statistics, etc.
  • German skills for supporting in data collection

Supervisors

Robert Richer, M. Sc.

PhD Candidate & Group Head

Isabella Hebel, M.Sc.

Chair of Clinical Psychology and Behavioral Health Therapy

Annika Heuler, M.Sc.

Chair of Clinical Psychology and Behavioral Health Therapy

Please use the application form to apply for the topic. We will then get in contact with you.