Lifeguards play a crucial role in drowning prevention. However, current U.K. lifeguard qualifications are limited in training and assessing visual surveillance skills, and little is known about how lifeguards successfully detect drowning swimmers. To improve our understanding of lifeguard visual search skill, and explore the potential for improving this skill through training, this thesis had the following aims: (a) to identify whether visual skills for drowning detection improve with lifeguard experience, (b) to understand why such differences occur, and (c) design and valid a visual training intervention to improve drowning detection on the basis of these results.
The first two studies investigated drowning-detection skills of participants with differing levels of lifeguard experience in a dynamic search task with simulated drownings. Lifeguards were found to detect drownings faster and more often than non-lifeguards. In three follow-up studies these results were replicated with more naturalistic stimuli. Video footage from an American wave pool was extracted, which showed genuine instances of swimmer distress. Results again demonstrated lifeguard superiority in detecting the drowning targets.
Eye tracking measures, recorded on both the simulated and naturalistic clips, failed to reveal any differences between lifeguards and non-lifeguards, suggesting that superior drowning detection for lifeguards did not result from better scanning strategies per se.
Following this, two cognitive mechanisms that may underlie drowning-detection skill were investigated. Lifeguard and non-lifeguard performance on Multiple Object Avoidance (MOA) and Functional Field of View (FFOV) tests was assessed. Although lifeguards had better MOA task performance compared to non-lifeguards, only the lifeguards’ accuracy at detecting the central target in the FFOV task predicted performance on a subsequent drowning detection task. It was concluded that superior drowning detection was a result of better classification recognition of drowning swimmers (which was the central task in the FFOV test).
Based on these findings the final experiment explored the effectiveness of an intense classification training task to improve drowning detection. An intervention was designed that required participants to differentiate between videos of isolated drowning and non-drowning swimmers. Non-lifeguards trained in this intervention showed greater improvement on a subsequent drowning-detection task compared to untrained control participants, who completed an active-control task.
The results of this thesis suggest that drowning-detection skill can be reliably assessed, and that foveal processing of drowning characteristics is key to lifeguards' superior performance. Isolating and training this key sub-skill improves drowning-detection performance and offers a method for training future lifeguards
