ICSPP Thematic Submission Guidelines

ICSPP Thematic Submission Guidelines


All submissions must follow the instructions listed below and be submitted online by midnight on Sunday 12th March 2023 (Greenwich Mean Time). Please note that thematic submissions will only be formally accepted into the programme if the presenting author has registered and paid by the 31st May 2023.

Abstract Submission Closed


  • All authors must approve submission of the thematic session.
  • Abstracts do not need to declare potential conflict of interests, but conflicts of interest must be declared in the presentation.


  • Thematic sessions will take the format of a series of oral presentations.
  • Sessions will be 90 minutes in total and must include time for panel questions and / or discussion.
  • Each session should include four to six speakers with a nominated chair to lead the session. The chair can be one of the speakers or an alternate person. Submitting authors can decide the final format of their session.
  • Example formats could include: four 20-minute presentations with 10 minutes for discussion and questions; five 15-minute presentations with 15 minutes for discussion and questions; six 12-minute presentations with 18 minutes for discussion and questions.
  • Submitting authors are encouraged to include presenters from more than one country.
  • Sessions should be an authoritative overview of a topic.
  • The proposals must be submitted in English.
  • Notification will be emailed only to the presenting author.
  • References can be provided and placed at the end of the submission but are not required.
  • The use of abbreviations and acronyms should be kept to an absolute minimum and avoided where possible.

The thematic session proposal should be organised under the following headings: Title, Description, Background, Speakers and Titles, and Military Impact. An example thematic proposal is presented below.

Title: A short description of the session.

Description (150 words maximum): Provide an overview of the session including the aims and scope.

Background (150 words maximum): A short background to the topic and should identify any knowledge or research gaps.

Speakers and Titles: Provide the names of the speakers, their intended titles, and the proposed session format.

Military Impact (150 words maximum): Describe how the session impacts Defence.



Authors will need to select two of the following themes that best fit their submission.

  • Environmental Stressors, Exposures, and Injuries
  • Data Analytics and Predictive Modelling
  • Physical Training
  • Musculoskeletal Injury and Physiology
  • Nutrition and Metabolism
  • Futures Science and Technology
  • Human Augmentation
  • Trial Design, Methods, Conduct, and Reporting
  • Female Physiology
  • Physical Performance
  • Cognitive Performance
  • Psychological Resilience and Performance
  • Health and Wellbeing
  • Mental Health
  • Epidemiology
  • Human Machine Teaming
  • Neurobiology



TITLE: The use of statistical learning to predict health and performance outcomes in Servicewomen.

SESSION DESCRIPTION: This session will explore the use of supervised and unsupervised statistical learning methods to predict health and performance outcomes in Servicewomen. The session will provide an overview of statistical learning techniques, as well as their merits and limitations, with a focus on models used both for inference and prediction; techniques for model validation will also be discussed. A range of health and performance outcomes relevant to Servicewomen will be highlighted. The session aims to highlight the evidence where statistical learning prediction models can be used to highlight individual risk and improve outcomes in Servicewomen as well as to identify where research is lacking. Case studies where prediction models have been successfully implemented to help inform military decision making and / or aid performance will also be provided. The aim of the session will be to improve understanding of how statistical learning techniques can be used to improve the health and performance of Servicewomen.

BACKGROUND: Servicewomen are at increased risk of musculoskeletal injuries and, on average, have poorer physical performance than their male counterparts. With the recent opening of combat roles to women in the United Kingdom, United States, Australia, and other nations, women are employed in more physically arduous roles and are required to complete the most physically arduous military courses. Arduous training courses may come with increased risk of musculoskeletal injuries and other health outcomes including menstrual disturbances. These roles also require greater levels of physical performance. The ability to understand predictive factors and / or make individual predictions for success in a training course, meeting a physical employment standard, or developing a health outcome will help militaries better manage the health and performance of Servicewomen. Statistical learning methods have long been used for statistical inference and the same models can also be used to predict outcomes in new observations.


  • Prof A Scientist, Chair, British Army [20 minutes]. Statistical learning methods to predict physical employment standards performance from routine physical performance tests.
  • Dr J Smith, US Army [20 minutes]. Factors affecting menstrual function in Servicewomen: a statistical learning approach for identifying menstrual dysfunction.
  • Prof J Doe, British Army [20 minutes]. A comparison of machine learning classification models to predict arduous training course success in men and women.
  • Dr NE Body, University College London  [ 20 minutes]. The use of statistical learning to predict musculoskeletal injuries during basic military training.
  • Discussion [10 minutes].

MILITARY IMPACT: With the employment of women expanding across many militaries, coupled with recent increased interest in statistical learning methods, this session is a highly topical thematic session. Militaries typically collect routine health and performance data across selection, training, and through career that could be used to better manage the health and performance of Service personnel; statistical learning methods provide one means to gain insight from these data. The impact of this work extends far beyond military employment, with implications for athlete, arduous occupation, and wider female, populations. Practical recommendations on risk factors for, and the use of prediction models to detect, health and performance outcomes will be presented within each talk based on the best available evidence. Where evidence is lacking, areas for novel future research will be identified.