Objective assessment of nap as a method to improve cognitive performance using a bio-mathematical model

INTRODUCTION

Fatigue in aviation has been an threat to aviation safety because of the resultant impairment in alertness and crew performance.^[1] Fatigue is known to affect many cognitive functions such as impairment of short-term memory, increased reaction time, and errors of omission.^[2] Significant decrement in cognitive performance invariably results in errors, some of which may prove fatal. It also endures a variety of physical, psychological, and physiological stressors that affects the operational effectiveness of the crew. Fatigue is a major contributor for aircrew errors^[3] and hence of aircraft accidents.^[4] As per Shappell and Weigman, 70% of aircraft accidents can be attributed to human error.^[5]

Aviation organizations have adopted multi-pronged approach to counter this challenge. Strategic napping is one such measure. Caldwell et al. have described that a well-planned nap is the best countermeasure for fatigue as this serves as a recuperative function to attenuate the effects of fatigue on performance.^[6] It was recommended that naps should be considered as a first-line approach toward preserving alertness on the job whenever 8 h of consolidated sleep would not be possible. In a survey conducted by Taneja, it was documented that a large number of IAF aircrew follow the practice of crew-room napping of variable duration and they felt that it improved the drowsiness and tiredness and also crew performance in the flight.^[7] While such survey report was based on the subjective assessment of the crew, it is imperative to study the beneficial effect of napping objectively. With limited studies on the subject available in the Indian military aviation, this study was envisaged with an aim to study the effectiveness of naps toward improving the crew effectiveness and cognitive performance using a validated biomathematical model.

MATERIAL AND METHODS

RESULTS

A total of 23 service personnel involved in various duties related to aviation had voluntarily participated in the study. Of them, only 15 (65.2%), who were found to be the “Daytime Habitual Napper (DTHN)” could be considered for analysis of data. Demographic details of these participants are displayed in Table 1.

Of the 50 Naps logged by the participants, 11 (22%) naps were less than 30 min, 14 (28%) were between 30 and 60 min, 15 (30%) were between 60 and 120 min, and only 10 (20%) were above 120 min. The sleep quality indicators for these nap-groups are displayed in Table 2.

A series of One-Way ANOVA was carried out to examine the differences among the nap-groups for various sleep quality indicators. The results of the same along with post hoc analysis for pair-wise comparisons are displayed in the Table 3.

The mean SE for the nap-groups 1, 2, 3 and 4 were 62.3%, 78%, 74.7%, and 76.3%, respectively. The first nap-groups, in which the participants were having naps for the duration of 30 min or less had the lowest SE in comparison to other groups and the differences were statistically significant whereas, those in the nap-groups 2, 3, and 4 were not statistically significant.

The enhancement in cognitive performance associated with napping was calculated by subtracting the post-nap values from the pre-nap values. The means (SD) of these differences are displayed in Table 4. One-Way ANOVA was carried out to examine the differences among the nap-groups for each cognitive performance indicators and the results are displayed in Table 5.

Post-nap gain in the effectiveness and other cognitive parameters was found to be different among the nap-groups in different cognitive domains such as EfG (F = 10.45, P = 0.000), CRG (F = 4.29, P = 0.009), ILR (F = 9.18, P = 0.000), IRT (F = 4.10, P = 0.012), and SRG (F = 4.81, P = 0.005). In comparison to the nap-group 1, the post-nap EfG and CRG were statistically significant for nap-groups 2, 3, and 4. No statistically significant differences were observed between the nap-group 2, 3, and 4. The similar group differences was also observed as far as improvement in the cognitive functions such as lapse rate (ILR) was concerned. However, the statistically significant SRG were observed for Group 3 and 4 and IRT was observed for Group 4 only.

To examine the influence of various demographic variables on the nap induced enhancement of cognitive performance, multiple regression analysis was carried out by adjusting the demographic variables, namely, age, seniority, marital status, weight, BMI, alcohol, and smoking habits. The results are displayed in Table 6.

Post-nap gain in the effectiveness and other cognitive parameters was found to be linked with nap duration with no significant influence from any of these demographic variables was confirmed for EfG (F = 35.35, P = 0.000, R² = 0.424), CRG (F = 10.13, P = 0.002, R² = 0.174), ILR (F = 27.26, P = 0.000, R2 = 0.362), IRT (F = 12.68, P = 0.000, R² = 0.209), and SRG (F = 8.34, P = 0.005, R² = 0.148).

A similar regression analysis was also carried out to examine the influence of fatigue inducing factors on the post-nap gain in the effectiveness and the associated cognitive performance. The details of the descriptive data for all fatigue inducing factors namely “Total sleep time in the last 24 h,” “Chronic Sleep Debt,” “Out of Phase Sleep (OOPS)” along with “SE (Nap),” and “SE (Duration)” are displayed in Table 7 and the results of the regression analysis are displayed in Table 8.

The variables with significant influence on the post-nap cognitive performance gain were “Total sleep time in the last 24 h” for EfG, CRG, ILR, and IRT; “Out of Phase Sleep (OOPS)” for CRG, ILR, and SRG; “SE (Nap)” for EfG, CRG, ILR, IRT, and SRG; and “Nap (Duration)” for EfG, ILR, IRT, and SRG. The only variable which was found to have no such influence was “Chronic Sleep Debt.” R² indicates the amount of variation in the gain in the cognitive performance that can be explained by the respective fatigue inducing variables. In presence of significant R², these fatigue inducing variables could be considered as the covariates for the Nap (Duration), which was the primary predictor for nap-induced cognitive performance gain.

To control such variations in the gains in the different cognitive parameters, these parameters were subjected to a dimension reduction analysis, namely, “Principal Component Analysis” and a single cognitive factor was extracted (named as Combined Cognition Factor). On repetition of regression analysis with Combined COG Factor, only two fatigue inducing factors, namely, SE (Nap) (P = 0.000) and Sleep in last 24 h (P = 0.042) were found to be significant covariates for nap-induced performance enhancement.

DISCUSSION

The sleep data were collected through a wrist based actigraphy device and fed into SAFTE based software called FAST. The nap episodes were identified and were subjected for analysis. About 65.2% of the studied population were found to be the “Day-time Habitual Napper (DTHN)” with a history minimum of 3 or more day-time naps in the week. Earlier studies had documented the DTHN among 35–42% of Indian military aircrew and 68% of general population.^[7,12] Considering the napping to be an effective fatigue prevention strategy, this proportion of DTHN in our study could be encouraging.

Of the 50 Naps logged by the participants, 11 (22%) naps were less than 30 min, 14 (28%) were between 30 and 60 min, 15 (30%) were between 60 and 120 min, and only 10 (20%) were above 120 min. In a questionnaire survey in IAF by Taneja, it was reported that 31.3–37.1% of aircrew used to nap for duration of 30–60 min, which was comparable to our results.^[7] Although the nap of longer duration is beneficial toward restoration of sleep debt,^[13] the sleep opportunity to have such longer naps may not be available to the crew in an active operational set-up. Therefore, it is prudent to find out the optimal duration of nap and the associated benefits toward the crew effectiveness.

Like sleep, the naps are also qualified by the sleep quality indicators such as SE, SOL, number of arousals/awakenings, and percentage of time wasted as arousals. The average SE for the nap-groups 1, 2, 3, and 4 were 62.3%, 78%, 74.7%, and 76.3%, respectively. As per the National Sleep Foundation, sleep is considered optimal if its efficiency is more than 75%. Our results indicated suboptimal SE for the naps less than 30 min. No statistically significant difference was also observed with naps between 30 to 60 min with those more than 60 min. However, any Nap with a duration more than 30 min was associated with a significantly higher SE in comparison to the nap-group having naps of less than 30 min duration. Interestingly, the other sleep quality indicators such as SOL, wake percentage, and bouts of awakenings were not statistically different among the nap-groups and no group in particular had registered a poor-quality nap.

All nap-groups had registered the gain/improvements in all the domains of cognitive performance. EfG, CRG, SRG, ILR, and IRT were observed by all nap-groups but in a different quantum. When analyzed by One-Way ANOVA, nap-groups 2, 3, and 4 were not different statistically. However, all had statistically significant gain in comparison to the nap-group 1. This finding provided an indication that a significant difference in the improvement in cognitive performance was linked to the nap duration of 30 min. Any nap more than 30 min was associated with optimal gain in cognitive performance in comparison to the nap less than 30 min. Second, naps more than 60 min did not show any added advantages in the form of cognitive gain in comparison with the naps between 30 and 60 min.

In a NASA publication, Dr Rosekind and his associates had confirmed that 30–40 min of cockpit naps could prevent many instances of attention lapses and microsleeps encountered by the crew during the long-haul flights.^[14] In another study, Angus et al. had documented an increase in the correct responses in a cognitive test performed after a 2-h nap among subjects who had exhibited drop in correct responses while following a continuous work period.^[15] In a similar study, Caldwell et al. had documented that the group having a 2 h nap before a long overnight work period exhibited lesser decrease in alertness in comparison to the group who had not taken any nap.^[16] Caldwell Jr. in his book named “Fatigue in aviation” had mentioned that “although 2–4-h naps are considered optimal for arresting performance declines associated with continuous work without sleep, even the short naps (20–30 min in length) have been found to enhance the productivity and safety of sleep-deprived personnel.”^[17]

In a survey among the IAF aircrew, Taneja observed that large number of fixed wing aircrew (Fighter – 65% and Transport – 72.9%) reported napping to enhance crew performance in a flight by reducing drowsiness, or feelings of tiredness.^[7] Although there are evidence, which suggests that strategic naps can reduce subjective feelings of fatigue and improve performance and alertness,^[13] our study has provided the same objectively.

To examine the influence of various demographic variables on the nap induced enhancement of cognitive performance, multiple regression analysis was carried out by adjusting the demographic variables, namely, age, seniority, marital status, weight, BMI, alcohol, and smoking habits. Post-nap gain in the effectiveness and other cognitive parameters was found to be linked with nap duration with no significant influence from any of these demographic variables.

The quantity and the quality of the sleep and the naps could vary depending on the age, gender, physical constituency such as body weight and BMI. Alcohol and many drugs can also influence the sleep. Therefore, the influence of such parameters required to be adjusted before confirming the main effects of naps on enhancement of cognitive performance. However, such confounding effects were not observed in our study.

Similarly, the fatigue inducing factors could independently influence the study outcome as the covariates. “Total sleep time in the last 24 h,” “Chronic Sleep Debt,” and “Out of Phase Sleep (OOPS)” along with “SE (Nap)” were derived from the biomathematical model software and were also examined for their influence as covariates. Since the influences were different for different domains of the cognitive function, a single cognitive factor was computed by a dimension reduction analysis using PCA and this extracted factor which was called “Combined Cognition Factor” was regressed with these factors. Only two fatigue inducing factors namely SE (Nap) (P = 0.000) and “Sleep in last 24 hours” (P = 0.042) were found to be the significant covariates for nap-induced performance enhancement.

In the past, investigators had brought out that the nap duration alone might not be a good indicator to explain the variable responses in performance enhancement following a nap.^[18] The results of our study, could confirm at least two additional factors such as SE (Nap) and “sleep in past 24 h” could vary the nap induced gain in cognitive performance. The influence of Out of Phase Sleep (OOPS) during the post-nap restoration of cognitive reserve was also a take home message from this study.

CONCLUSION

Sleep deprivation is a fact of life for all aviation personnel especially those who are involved in military flying. Apart from the high tempo operational flying, the flying efforts involved during training, VIP missions, and carrier borne flying operations will possess a situation of restricted sleep opportunities. Under such circumstances, napping could be handy as a short-term fatigue prevention strategy. Our study, which was undertaken with an aim to carry out an objective assessment of naps taken by the aviation crew, could reiterate some of the established facts and register few new findings. A good proportion of crew is aware of this fact and follows judiciously. Our study has indicated existence of such practice among the aviation crew though the duration of naps varies from person to person, time of napping, and the circumstances for the naps. Like sleep, naps are also qualified based on SE and most of these naps are optimum if the duration is more than 30 min. Depending on the duration and the efficiency, naps help in enhancing the task effectiveness and cognitive performance of the subject. While the performance enhancement is significant for the naps more than 30 min, naps more than 60 min may not have the added advantages of yielding more gain in the cognitive performance apart from the risk of getting sleep inertia. Unlike sleep, the nap induced cognitive gain had little influence by the demographic variables. However, the factors such as “sleep in past 24 h,” “out of phase sleep” could affect the post-nap gain in cognitive functions.