A New Qualitative Typology to Classify Treading Water Movement Patterns

Abstract

This study proposes a new qualitative typology that can be used to classify learners treading water into different skill-based categories. To establish the typology, 38 participants were videotaped while treading water and their movement patterns were qualitatively analyzed by two experienced biomechanists. 13 sport science students were then asked to classify eight of the original participants after watching a brief tutorial video about how to use the typology. To examine intra-rater consistency, each participant was presented in a random order three times. Generalizability (G) and Decision (D) studies were performed to estimate the importance variance due to rater, occasion, video and the interactions between them, and to determine the reliability of the raters' answers. A typology of five general classes of coordination was defined amongst the original 38 participants. The G-study showed an accurate and reliable assessment of different pattern type, with a percentage of correct classification of 80.1%, an overall Fleiss' Kappa coefficient K = 0.6, and an overall generalizability φ coefficient of 0.99. This study showed that the new typology proposed to characterize the behaviour of individuals treading water was both accurate and highly reliable. Movement pattern classification using the typology might help practitioners distinguish between different skill-based behaviours and potentially guide instruction of key aquatic survival skills. Key pointsTreading water behavioral adaptation can be classified along two dimensions: the type of force created (drag vs lift), and the frequency of the force impulsesBased on these concepts, 9 behavioral types can be identified, providing the basis for a typologyProvided with macroscopic descriptors (movements of the limb relative to the water, and synchronous vs asynchronous movements), analysts can characterize behavioral type accurately and reliably.

Keywords: Generalizability theory; clinical education; lifesaving.

Figure 1.

The four synchronized camera views used to analyse the movement patterns