An Analysis of Agricultural Pilots Seasonal Sleep Deficits

An Analysis of Agricultural Pilots Seasonal Sleep Deficits

In 2009, Gregory and Barbosa conducted a survey into the effects of seasonal sleep deficits on agricultural pilots based in Louisiana (1).

Comparison of On and Off Season Sleep Management

Table one is a comparison between off season and on season sleep patterns. As well as on task demands, pilots are also usually required to perform their own administrative and operational tasks, which also add to sleep management issues. Pilots tend to skew older as well (this is indicative of the industry norm as equipment costs, training costs and limited opportunities are dissuading younger pilots) which according to the SLEEP model means they are able to function better on less sleep (for example, as people age, their requirements for sleep incrementally reduce from 7.5hrs at 30 years to 6.02 at 60 years).

Table 1: Survey Results of On and Off Season Sleeping Habits
Normal Sleep Period (Hrs) Mean 1 Std Dev
<3 3 4 5 6 7 8+
Off Season - - 2 3 3 4 8 6.1 1.42
On Season 7.43 11.57 - 1 - - - 2.73 2 1.48 2
1. Mean average is based on hours slept weighted by occurrence
2. When data outlier for On Season results is ignored, Mean = 2.65 standard deviation = 0.5 (N-19)

This data shows that there is a close correlation by pilots in how they manage their on/off season sleeping habits. What is apparent is that there is a wide dispersion in hours slept by pilots in the off-season, however in peak season pilots are affected by limited sleep with only one pilot (N-1, a data outlier) managing more than 3 hours per night. Therefore 95% (N-19) of the sample were sleeping for three hours or less when ‘in season’.

Table 2: Comparison of Sleep Requirements and Actual Sleep Deficits by Age
Age Number in Survey (N) Sleep Need by Age in Hrs (SLEEP model) Sleep Deficit – Mean (2.73) *
26-30 1 7.55 4.82
31-35 2 7.24 4.51
36-40 3 6.97 4.24
41-45 2 6.72 3.99
46-50 4 6.51 3.76
51-55 1 6.33 3.6
56-60 3 6.16 3.43
60+ 4 6.02 3.29
Total 20 *Age divisions and sleep need determined by SLEEP model, sleep deficit determined by subtracting weighted mean (actual sleep) from sleep need

Table two illustrates the relationship between the pilots data and the sleep recommendations endorsed by the SLEEP models. This illustrates that there are more older pilots (who as mentioned earlier do not require as much sleep as younger pilots), however when the mean average of 2.73hrs of sleep per night is taken into account, even those pilots best suited to less sleep are not achieving 50% of their requirement. Over a prolonged period, this will cause fatigue with the individual experiencing degraded physiological and psychological capacities placing their decision making and cognitive reaction times at slower rates.

Caffeine as a Fatigue Countermeasure

Whilst the only effective countermeasure for fatigue is sleep, studies also showed that caffeine can reduce the chances of having an accident by lowering the accident probability ratio.

Table 3: Effects of Caffeine on the Odds of an Accident after Two Weeks Productive Flying with Sleep Deficit *
Age No Caffeine Caffeine Accident Odds Ratio
26-30 40.5 29.0 1 : 0.72
31-35 39.8 28.2 1 : 0.71
36-40 39.6 27.9 1 : 0.7
41-45 39.3 27.6 1 : 0.7
46-50 38.9 27.3 1 : 0.7
51-55 38.5 26.8 1 : 0.69
56-60 38.0 26.3 1 : 0.69
60+ 37.5 25.8 1 : 0.69
Age Weighted Average Accident Reduction when Caffeine is Consumed 1 : 0.699 or 30%
*It is important that readers understand that the 30% reduction in accident occurrence is not an overall reduction in the odds of an accident occurring, but in the odds of an accident being caused by fatigue over one per chance. An accident can still occur per chance due to meteorological, structural or other issues, but mitigating fatigue helps alleviate a major source of aerial agriculture accidents.

Whilst caffeine assists in the temporary alleviation of fatigue symptoms, the only effective countermeasure is decent REM sleep. Therefore it is recommended that under the SLEEP model, pilots avoid consuming alcohol and moderate any medication which may inhibit their ability to have an effective and restful REM sleep. Whilst operational pressures may make it difficult to fully implement, pilots should be aware of Fatigue Risk Management Systems (FRMS) and understand the risks of continual long duty days with inadequate rest. Small naps of up to 40mins in periods where operations may be delayed will also assist in fatigue management.

Research Methods

Research Sample

The research sample involved acquiring the details of all aerial agricultural pilots registered with the Louisiana Department of Agriculture and Forestry (LDAF). A total population of 251 pilots were contacted and 66 pilots indicated they would participate in the survey. Of the 66, 20 actually completed the survey (8% of the total population).

Data Collation Methods

'On season' sleep data was collected through diary method. Comparative 'off season' sleep data was provided by recollection of those sampled. The study also had a questionnaire, however the data from this is not related to this part of the analysis.

Data Analysis

Surveyed data relied upon the actual collation of on season sleep hours and comparing this to the participant’s recollection of their normal off season sleeping habits. Understandably, the off season sleep data is based on memory and open to bias or error, however trends indicate that most pilots have responded with correlation. As there are only the two variables (on/off season), correlations are evident in the comparison of range and standard deviation. When the data outlier is ignored in 'on season' sleep periods, the standard deviation falls to 0.5 and the mean average hours slept to 2.61 (based on N-19).

The SLEEP model statistics that illustrate the effects of continual sleep deprivation utilise the coefficient of determination to predict risk modelling and effects. Based on 12 equations of task and cognitive degradation, the SLEEP model has a R2 value in the range between 0.99 and 0.8 (With an average mean R2= 0.96) and a significance level of 0.01 across its determinations. Therefore the model concludes that there are concerns with pilot welfare when the survey results show that pilots are sleep deficient.

The accident odds ratio is determined by dividing the probability of having an accident while suffering sleep deficit by the probability of an accident occurring per chance. The data was used to justify that caffeine is an effective fatigue mitigating agent and will reduce the risk reduction of a fatigue based accident after two weeks by 30%.

Further Analysis Opportunities

Although small, the level of correlation was obvious and when qualitative research (such as agricultural aviation accident reports) is considered, this survey illustrates that there is validation for more research into agricultural pilots seasonal sleep deficits. This sample can be considered a pilot study and a basis towards further analysis into aviation and aerial agricultural human factors.

1. Gregory, J. M., & Barbosa, R. N., (2010). Seasonal Sleep Effects on Louisiana Aerial Applicators' Safety. Journal of Agricultural Safety and Health, 16 (1), 53-64.
2. Wanganui Coroner's Court (2008). Inquest into the Death of Jonathan Peter Lourie and Richard Sinclair McRae: Findings of Coroner., 29th July 2008.

Want to know more?

SLEEP (Sleep Loss Effects on Everyday Performance) Model
CASA FRMS (Fatigue Risk Management System) and Fatigue Pamphlet

zkbofzkbof Mike Stokes

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