Research Summary: How do lifeguards detect a drowning casualty?
Dec 31, 2021This article considers the mechanism by which lifeguards scan and detect drowning incidents.
How do lifeguards search for drowning incidents?
Two studies that measured eye movements (Page et al., 2011a; Lanagan-Leitzel & Moore, 2010) suggested experienced beach lifeguards’ eye movements were not significantly different from those of newly trained lifeguards. In a study involving UK beach lifeguards and real-life footage, no significant difference in eye movements between the beginning and end of the season (Smith, 2011). Laxton and Crundell (2018) also found that UK swimming pool lifeguards adopted broadly the same fixations and eye movements as non-lifeguards, lifesavers and trainers.
Experienced lifeguards were found to use longer and more variable fixations than novice lifeguards (Vansteenkiste et al., 2021). Increased fixation duration is thought to increase the quality and quantity of data that informs decision-making and would be expected to lead to higher detection rates.
Is head movement (scanning) a good indicator of lifeguard detection performance?
Of the lifeguards who fixated on the correct location in the final 3.5 seconds in both conditions, 40% (biased) and 42% (non-biased) did not detect the person disappearing. Page et al. (2011) reported that observers frequently miss changes to scenes they are observing.
Hunsucker and Davison (2013) found in a US documentary analysis of pool inspection records that 79% of scans took less than 30 seconds, and only 42% of scans were completed in under 15 seconds. The average scan time was 22 seconds.
A study of 14 YMCA swimming pools in the US supervised by American Red Cross lifeguards (Schwebel, Jones, Holder, and Marciani, 2011) found that risky behaviours decreased as scanning increased, as Harrell (2001) had previously found.
How do lifeguards identify hazards?
Lifeguards, trainers, experts, and non-lifeguards are less consistent in their definition of a hazard in a pool environment displayed via video footage than in real life.
In another video study of beach lifeguards, Smith et al. (2011) found that lifeguard instructors identified more hazards than experienced lifeguards and experienced lifeguards more than less experienced lifeguards. Large variations in what experienced lifeguards identified as hazardous were found (Smith, 2011). Lanagan-Leitzel (2019) asked a group of participants to provide numerical ratings of severity for some of the incidents identified in a 2012 video study (Lanagan-Leitzel, 2012). Events given a high severity rating by participants in Lanagan-Leitzel (2019) were identified as hazards at broadly the same rate as events given a lower severity rating (Lanagan-Leitzel, 2012), suggesting that agreement about what is a more or less serious hazard in the pool environment is inconsistently understood.
Does experience improve performance?
Experience does not appear to alter a lifeguard's scanning strategy (Smith, 2011).
In a video simulation study, experienced beach lifeguards were 4.9 times more likely than less experienced lifeguards to make a positive detection (Page et al., 2011). In a study involving UK beach lifeguards and real-life footage, no significant difference in a lifeguard's performance at the beginning and end of a season was identified (Smith, 2013). Laxton and Crundell (2018) found that lifeguard detection accuracy and response times were only marginally better than those of non-lifeguards and lifesavers.
Current results suggest experience does not alter scanning strategy but produces more accurate detection rates because of improvements to the contextual interpretation of visual information resulting from increased knowledge of what drowning casualties look like (Pia, 2017b; Vansteenkiste et al., 2021). Laxton and Crundell (2018) found that lifeguard detection accuracy and response times were only marginally better than non-lifeguards, further supporting the notion that knowing what to look for may be more important to detection rates than scanning techniques, particularly where drowning is passive, and signs are less obvious.
One study showed that only about a third of drowning incidents in lifeguarded facilities were detected by on-duty lifeguards; most are detected by non-lifeguard bystanders (Pelletier and Gilchrist, 2011). However, it cannot be said to be a failure of lifeguard detection where a proximate non-lifeguard responds quickly to a person in danger, and this can be difficult to ascertain in documentary analyses.
How do higher pool user numbers affect how lifeguards scan?
Page et al. (2011b) found no differences in the visual search patterns adopted by lifeguards viewing animated footage when observing 43, 53 and 63 heads in the water. Vansteenkiste et al. (2021) found the number of swimmers had no effect on fixation duration in their in situ study of beach lifeguards but did observe a difference in the direction of gaze lifeguards' adopted.
Scanning behaviour decreases with a lower child-adult ratio, more children in the pool, and later in the day (Harrell, 1999, 2001, 2003, 2006). An increase in scanning behaviour (i.e. vigilant with active head movement) appears to reduce the number of rule violations by pool users resulting in an overall increase in the safety of the pool (Harrell, 2001). Harrell (1999, 2001; Harrell & Boisvert, 2003) suggested that lifeguards tended to increase their head movement when the ratio of children to adults increased, indicating they perceived adults supervising more children as posing a greater risk.
How are detection rates affected by higher pool user numbers?
Laxton and Crundall (2018) found decreasing drowning detection accuracy as pool user numbers increased from three to six to nine swimmers in a pool.
References
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Citation: Jacklin, D. 2021. How do lifeguards detect a drowning casualty? Water Incident Research Hub, 27 December.
