Table of contents

The course is connected to the following study programs

Teaching language

English

Prerequisites

Adimtted to a PhD-program

Recommended previous knowledge

  • EX-606 Researching Health and Sport Sciences: Philosophical and Methodological Foundations, or equivalent
  • ME-626 General Quantitative Research Methods, or equivalent

Learning outcomes

On successful completion of the course, the candidate will have acquired:

Advanced knowledge about:

  • Current technologies and data collection methods employed in high-level sports (endurance, strength/power and team sports)
  • Relevance and interpretation of laboratory versus field-based assessments
  • Training data analytics applied to performance monitoring and development
  • Distributed data collection approaches
  • Validity and reliability of commonly used metrics in high-level sport
  • Integrating and interpreting physiological and psychological metrics
  • Artificial Intelligence/machine learning in sport science

Skills

  • Can critically evaluate, disseminate results within data analysis and interpretation in high-level sport
  • Can critically evaluate assessment methods used for physiological and psychological data metrics in high-level sports
  • Can formulate relevant research questions and plan the implementation of data analysis and interpretation in high-level sport research and interventions
  • Can use Application Program Interfaces (APIs) in data acquisition analytics

General competence

  • Can communicate relevant knowledge through scientific and popular scientific channels

Contents

  • General to sport-specific: Progression from laboratory to field-based assessments
  • Testing and monitoring athletes in endurance, strength/power and team sports settings
  • Using day-to-day data to optimize individual training load: Stress/Recovery balance
  • Opportunities and limitations of using Artificial Intelligence in sport
  • Development and opportunities with distributed/crowdsourcing data methods
  • Effective interpretation and presentation of data for coaches and athletes
  • The evolution of sport and the future of e-sport

Teaching and learning methods

Over five days, teaching and learning methods will consists of a combination of lectures, group work, practical workshops, work tasks, discussions, and presentations based on course topics and the candidates own research experiences and work.

Examination requirements

Participation in compulsory lectures, group work, practical workshops, work tasks, discussions and presentations as stated in the course description

Examinations

Individual two-weeks home examination based on use of Application Program in Interfaces (APIs) in sport science dat acquisition, aggregation and analytics, will be assessed as pass/fail.
(Passed on minium B-level).

Offered as a free-standing course

Yes, for external PhD-candidates (maximum 10 seats / participants)

Other information

The PhD-course is offered 17 - 21 March, 2025.
Physical or digital participation.

Contact persons:
Matthew Spencer (matthew.spencer@uia.no) and Stephen Seiler (stephen.seiler@uia.no)

Last updated from FS (Felles studentsystem) Oct. 15, 2026 1:34:31 PM