Engineering topics

Lucica Arsintescu

Publications and source records attributed to Lucica Arsintescu.

14 records

NASA NTRS2022

Assessing Several Non-Traditional Data Sources for Value in Aviation Safety

The NASA System-Wide Safety (SWS) project and its predecessor projects have been developing Machine Learning (ML) algorithms for commercial aviation safety for many years. These algorithms have been applied to Flight Operations Quality Assurance (FOQA); radar track data (e.g., Threaded Track); and safety reports, including Aviation Safety Reporting System (ASRS) and Aviation Safety Action Plan (ASAP). SWS is working with partners to get access to other data that air carriers provide, such as maintenance data, and has been assisting carriers in working with other data, such as Line Operations Safety Audit (LOSA) data, using manual methods. However, the project has discussed whether there are other data that are not traditionally used in aviation safety analysis that may be useful. This paper discusses four sets of data and models that are not traditionally used in aviation safety but that have shown promise for such use. In the future, we plan to incorporate such data into ML algorithms to use with data that we have used before and determine the additional benefit that is actually achieved under different contexts from the inclusion of these non-traditional data sources.

Nikunj C. Oza↗

NASA NTRS2020

Mitigating fatigue on the flight deck: how is controlled rest used in practice?

Controlled Rest (CR) refers to a short, voluntary nap opportunity taken by pilots on the flight deck as a countermeasure to unanticipated sleepiness in flight. This study explores the profile of CR use in a long-haul commercial airline. Forty-four pilots wore actiwatches and filled in an application-based sleep/work diary for approximately 2 weeks resulting in complete records from 239 flights. Timing of sleep periods and flight schedules were analyzed relative to home-base time. Pearson correlations were used to assess the influence of pilot demographics on CR use. A mixed-effects logistic regression was used to analyze the impact of schedule factors on CR. CR was taken on 46% (n=110) of flights, with 80% (n=106/133) of all CR attempts estimated by actigraphy to have successfully achieved sleep. Average sleep duration during successful rest periods was estimated as 31.7 ± 12.2 minutes. CR was more frequent on return (60%, n=71) vs. outbound flights (33%, n=39); 2-pilot (69%, n=83) vs. >2-pilot flights (23%, n=27); <10h (63%, n=80) vs. >10h duration flights (27%, n=30); and night (55%, n=76) vs. day flights (34%, n=34) (all p≤0.001). There was no significant difference for direction of travel (eastbound: 51%, n= 57; westbound: 40%, n= 44; p=0.059). Of note, 22% (n=26) of augmented flights contained both CR and bunk rest. Data from this airline show that CR is most commonly used as a countermeasure to sleepiness on flights <10h duration (2-pilot crews) and home-base nighttime flights. Future studies are required to determine the generalizability of these results to other airlines.

sleepiness↗

NASA NTRSDate not supplied

The Influence of Self-Reported Distractions on the Psychomotor Vigilance Task (PVT) in Laboratory and Field Environments

Introduction: The Psychomotor Vigilance Task (PVT) is a measure of vigilant attention that is commonly used in laboratory environments to assess the neurobehavioral impact of sleep loss and circadian misalignment. The PVT has been increasing in popularity for use in field environments; however, the potential for distraction is higher in the field compared to the lab. It is unclear how distractions experienced by individuals taking the PVT in the real world may influence reaction time metrics. We investigated the influence of self-reported distraction on PVT outcomes across laboratory and field environments. Methods: We examined PVT data from five studies including short (n=36 participants, 3799 PVTs) and long-haul (n=75 participants, 3283 PVTs) airline personnel, control center personnel (n=5 participants, 96 PVTs), and healthy individuals who participated in a study involving at-home and laboratory assessments (n=12 participants, 486 and 310 PVTs). Individuals in all of the studies were asked to complete the five-minute NASA PVT at least three times daily. Participants were asked to indicate the number of distractions they experienced immediately after each PVT. Mean PVT reaction time (RT) and number of distractions were computed for each study and overall. Results: Participants reported more distractions in field environments compared to the lab (short-haul=1.29 +/- 1.48, long-haul=0.66 +/- 1.07, control-center=1.20 +/- 1.37, at-home=0.86 +/- 1.36, laboratory=0.46 +/- 1.07) Across all studies, we found that PVT RT slowed as self-reported distractions increased (all studies combined: 0 distractions=PVT RT 274.4ms; 1=284.6ms; 2=302.9ms; 3=321.2ms; >4=409.3ms). These findings were similar for healthy participants completing PVTs at home (0 distractions=287.1ms; 1=309.9ms; 2=328.3ms; 3=369.8ms; >4=385.1ms) but were less consistent during in-lab assessments (0 distractions=278.7ms; 1=316.2ms; 2=396.2ms; 3=370.4ms; >4=354.4ms). These findings were similar for other PVT outcomes. Conclusion: Participants reported more distractions in field environments compared to the laboratory. Our findings suggest that the number of distractions that individuals report experiencing while taking a PVT increases the reaction time registered by the device. Researchers should collect information about distractions during the PVT and should be aware that distractions may influence the recorded PVT reaction time.

behavior↗

NASA NTRSDate not supplied

Pilot Workload is Associated with Long Duty Days and Multiple Flight Legs

Pilot workload is a real concern throughout aviation, especially in short-haul operations due to short flights and multiple flight legs. Our goal was to examine the workload experienced by pilots during normal operations in a short-haul airline. Thirty pilots flew a roster consisting of a cycle of five days of mid-day start time duty hours with many flight legs (baseline block) followed by four days off, five early start time duty hours followed by three days off, five mid-day time starts with many legs followed by three days off and then five early start time followed by one day off. The pilots provided evaluations of workload by scoring the six subscales of NASA-TLX (i.e., Mental Demand, Performance, Physical Demand, Effort, Temporal Demand, and Frustration) on their duty days during each flight and once during rest days. The analyses included the raw TLX scores for each demand, the mean raw TLX, and the overall weighted TLX mean for duty days and rest days. We found that mental demand was significantly lower for each duty schedule and rest relative to baseline (early start 1st block p = 0.016, late duty p = 0.017, early start 2nd block p < 0.001, rest p < 0.001). Temporal demand was significantly lower than baseline on early duty 1st block (p = 0.02), early start 2nd block (p < 0.001), and rest (p < 0.001). Effort was significantly lower than baseline only during early start 1st block (p = 0.014) and early start 2nd block (p < 0.001). Frustration was significantly lower during early start 2nd block relative to baseline (p = 0. 039). Physical demand and performance were higher relative to baseline during rest days (p < 0.001). Mean raw TLX showed significantly lower workload for early start 1st block (p = 0.011) and early start 2nd block (p < 0.001). The overall weighted TLX showed significantly lower workload for early start 1st block (p = 0.004), early start 2nd block (p < 0.001) and rest (p < 0.001). We found that pilots experienced higher workload on longer duty days with multiple flight legs. Pilots experienced lower mental demand, temporal demand, and effort on short duty days even though when their duty started earlier in the day. During days off pilots experienced higher physical demand and higher performance.

short-haul↗

NASA NTRSDate not supplied

Controlled Rest: Investigating the Use of an In-Flight Sleepiness Countermeasure

INTRODUCTION: Sleepiness is commonly reported amongst commercial airline pilots and is recognized as a safety risk due to its impact on performance. Controlled Rest (CR) refers to a short, voluntary nap opportunity taken by pilots on the flight deck as a countermeasure to unanticipated sleepiness in flight. This study explores the profile of CR use in a long-haul commercial airline. METHODS: Forty-four pilots filled in an application-based sleep/work diary and wore actiwatches for approximately 2 weeks. Complete data sets from 239 flights including sleep diaries, actigraphy, and schedules were merged and analyzed. Sleep diary entries were used to set CR intervals in the actigraphy software, which was then used to predict sleep within these intervals. All time-stamps of sleep periods and flight schedules were adjusted for home-base time of the pilots. Pearson Correlations were used to assess the influence of pilot demographics on CR use. A mixed-effects logistic regression was used to analyze the impact of schedule factors on CR. RESULTS: Pilots reported taking CR on 46% (n=110) of observed flights. Average CR attempt duration was 43.1 ± 11.0 minutes. Eighty-percent (n=106/133) of all CR attempts were estimated by actigraphy to have successfully achieved sleep with an average sleep duration during successful rest periods of 31.7 ± 12.2 minutes. Captains reported taking CR on 38% of flights (n=39/102), compared to First Officers reporting 52% (n=71/137) of flights with CR (p=0.131). Age, experience, BMI, and sleep need were not associated with the percentage of flights with CR (all p>0.244). The following schedule factors were associated with a higher frequency of CR: night (55%, n=76) vs. day flights (34%, n=34); <10h (63%, n=80) vs. >10h duration flights (27%, n=30); return (60%, n=71) vs. outbound flights (33%, n=39); and 2-pilot (69%, n=83) vs. >2-pilot flights (23%, n=27) (all p≤0.001). There was a trend for more CR on eastbound flights, but this was not significant (eastbound: 51%, n= 57; westbound: 40%, n= 44; p=0.059). Of note, 22% (n=26) of augmented flights (>2-pilots) contained both CR and Bunk Rest (in a designated rest facility). DISCUSSION: Data from this airline show that pilots commonly use CR to mitigate sleepiness in-flight, especially on flights <10h duration and during home-base nighttime flights. Future studies are required to determine generalizability of these results to other airlines.

sleepiness↗

NASA NTRSDate not supplied

Changes in Alertness and Performance Over Time During Long-Haul Flying Across Multiple Time Zones

BACKGROUND: Long-haul pilots experience high levels of fatigue and circadian disruptions due to long work hours and flying over multiple time zones. The aim of this study was to describe changes in alertness and performance among flight crews during long-haul flights crossing multiple time zones. METHODS: All pilots flying long-haul operations from a single airline were eligible to participate. All participants collected data for ~2 weeks of their normal work schedule within airline operations, with at least two long-haul rotations, including rest days and layovers. Participants wore an Actiwach throughout the entire study period and completed a sleep diary (at bedtime, upon waking up and after each nap). Each participant completed a 5-min Psychomotor Vigilance Task (PVT) and a Karolinska Sleepiness Scale (KSS) pre-flight, on top-of-descent (TOD; inflight) and at the end of each flight (post-flight). Response speed (1/RT x 1000), lapses (RT > 500 ms), and alertness were assessed over time pre-flight, TOD, post-flight, using mixed-effects models with participant as a random factor. Linear models were assumed for response speed and alertness, while a negative binomial distribution was specified for lapses due to overdispersion. RESULTS: Forty-four long-haul pilots participated in the study (5 female; mean age 44.25 ± 10.06 yrs; mean flight hours 9834.3; ± 5334.1 hrs). Lapses increased post-flight relative to pre-flight (F(2, 665) = 3.67, p < 0.05). There was a main effect of response speed (F(2, 665) = 21.45, p < 0.001) with slower speed inflight and postflight compared to preflight (p < 0.001). The KSS increased over time from M = 4.02 (± 1.35) preflight to M = 5.15 (± 1.58) inflight, to M = 6.7 (± 1.51) postflight (F(2, 701) = 182.63, p < 0.001). DISCUSSION: Our preliminary analyses showed that both performance and subjective alertness worsened from the beginning to the end of a flight. Additional analyses will be conducted to investigate the changes in alertness and performance by direction of travel, sleep history, and flight timing and duration.

long-haul↗

NASA NTRSDate not supplied

Short-haul fatigue: A focus group study

Researchers from NASA Ames Research Center, together with research scientists from CAMI, are preparing to conduct a study to evaluate fatigue during short-haul operations. In order to develop the scope of our study, we conducted a series of focus groups across multiple US airlines. Participants were recruited through emails distributed by airline safety teams and union representatives. We conducted 14 focus groups in early 2022 for a total of 90 participants across four airlines. Participants were asked to identify short-haul pairings and operations that they felt: a) elevated fatigue, b) were not fatiguing, and c) were important to study. Data were collected anonymously and coded using content analysis techniques to identify main themes. This analysis is ongoing.

short-haul↗

NASA NTRSDate not supplied

Sleep during layovers of different lengths after long-haul flying across multiple time zones in different geographic directions

Introduction: Long-haul pilots experience high levels of fatigue and circadian disruption due to long work hours and trans-meridian travel. The aim of this study was to characterize sleep timing and duration during layovers of different lengths after crossing multiple time zones. Methods: All pilots flying long-haul operations from a single airline were eligible to participate. While following their normal work schedule within airline operations, pilots collected data for ~2 weeks including at least two long-haul rotations, with rest days and layovers. Participants wore an Actiwach (Phillips Respironics) throughout the entire study period and completed a sleep diary (at bedtime and upon waking up). Based on the data, we categorized layovers as follows: layover type 1: 34 h after crossing five time zones in westward direction; layover type 2: of 34 h, no time zone change; layover type 3: 55 h after crossing six time zones in eastward direction. Each flight left the home base at the same time of day (23:00) and the flight duration was between 10.5 and 12 hrs. We calculated descriptive statistics for each category. Results. Forty-four long-haul pilots participated in the study (5 female; mean age 44.25 ± 10.06 yrs; mean flight hours 9834.3 ± 5334.1 hrs). We found that for layover type 1, the mean sleep duration (h) per 24 h was 6.90 (±1.04), mean sleep efficiency (%) was 81.08 (±10.89) and mean Wake After Sleep Onset (WASO, min) was 33.23 (±20.42); for layover type 2, the mean sleep duration (h) per 24 h was 6.91 (±1.06), mean sleep efficiency (%) was 81.71 (±6.80), and mean WASO (min) was 30.10 (±11.00); for layover type 3, the mean sleep duration (h) was 7.07 (±1.32), mean sleep efficiency (%) was 83.93 (±6.04), and mean WAS (min) was 36.94 (±17.95). Conclusion: Our preliminary analyses showed that sleep duration and sleep efficiency were similar for the layovers of same length regardless time zone change. Additional analyses will be conducted to investigate sleep on additional layovers of different lengths and the effects of sleep obtained during layover on the performance and alertness on the return flights.

long-haul↗

NASA NTRSDate not supplied

The View From the Flight Deck: Pilot Perspectives on Fatigue in Short-Haul Operations

INTRODUCTION: There are few studies investigating the impact of fatigue in short-haul flight operations conducted under United States (US) Federal Aviation Regulations (FAR) Part 117 flight and duty limitations and rest requirements. In order to understand the fatigue factors unique to short-haul operations, we conducted a series of focus groups across four major commercial passenger airlines in the US. The outcomes of this study were intended to inform the scope of a larger study of fatigue in short-haul operations. METHODS: Ninety short-haul pilots were recruited through emails distributed by airline safety teams and labor representatives. Fourteen focus groups were conducted via an online conferencing platform in which participants were asked to identify, specific to short-haul: a) schedules and operations that lead to elevated fatigue; b) schedules and operations that are not fatiguing, and c) important fatigue factors to study. Data were collected anonymously and coded using conventional qualitative content analysis, with axial coding and summative analysis used to identify main themes and over-arching categories. RESULTS: Participants had an average of 12,348 (6,483) lifetime flying hours with 71 (14.5) hours of monthly flying. Forty-six percent of participants were captains. The six fatigue factor categories identified were: circadian disruption (e.g., circadian switches, redeyes), high workload (e.g., hassle factors, number of flights per duty), inadequate rest opportunity (e.g., minimum rest layovers, quality of rest facilities), schedule changes (e.g., unpredictability), regulation and policy issues (e.g., scheduling up to FAR 117 limits), and long sits (e.g., long wait times between flights). DISCUSSION: A field study informed by these results and designed to investigate the prevalence and impact of these factors in US short-haul operations is currently underway.

aviation↗

NASA NTRSDate not supplied

Can Pre-Flight Subjective Sleepiness Predict the Use of Controlled Rest on the Flight Deck?

Pilot sleepiness in flight continues to be an issue in aviation due to irregular and extended working hours. In some regions of the world, pilots are allowed to use an in-flight countermeasure called controlled rest, which involves taking a short nap on the flight deck. The use of this countermeasure in practice is understudied. We aimed to investigate whether subjective sleepiness ratings taken pre-flight are predictive of controlled rest use.

aviation↗

NASA NTRSDate not supplied

Investigating the Causes and Consequences of Controlled Rest on the Flight Deck

INTRODUCTION: Long and irregular working hours can lead to fatigue in aviation operations. In some regions, a short nap taken on the flight deck (known as controlled rest) can be used as a countermeasure to unexpected in-flight sleepiness. We aimed to investigate the impact of taking controlled rest on self-reported sleepiness at top-of-descent. METHODS: Data from 120 long-haul (> 6 h flight duration), unaugmented flights were analyzed (n = 31 pilots). Pilots wore actigraphs and completed sleep logs before and during trips. At pre-flight and top-of-descent, pilots completed a Karolinska Sleepiness Scale (KSS). A mixed-effects model was used to assess the impact of controlled rest on KSS at top-of-descent. Sleep duration in the 48 hours prior to departure, timing of the flight (day vs. night), and pre-flight KSS scores were included as covariates. RESULTS: Due to missing data, complete data from 83 flights (n = 29 participants) were available in the analyses of the KSS. There were no differences by controlled rest status for KSS scores at top-of-descent (estimated marginal means ± SEM with controlled rest: 5.10 ± 0.22, without controlled rest: 5.37 ± 0.29; p = .45, η2p = 0.01). DISCUSSION: Our results suggest that there is no difference in self-reported sleepiness at top-of-descent on flights in which controlled rest was taken compared to flights without controlled rest. Further research is necessary to determine the impact of controlled rest on objective measures of performance at top-of-descent.

aviation↗

NASA NTRSDate not supplied

The Impact of Controlled Rest on Neurobehavioral Outcomes at Top-of-Descent

INTRODUCTION: Long and irregular working hours can lead to fatigue in aviation operations. In some regions, a short nap taken on the flight deck (known as controlled rest) can be used as a countermeasure to unexpected in-flight sleepiness. We aimed to investigate the impact of taking controlled rest on neurobehavioral measures at top-of-descent. METHODS: Data from 120 long-haul (> 6 h flight duration), unaugmented flights were analyzed (n = 31 pilots). Pilots wore actigraphs and completed sleep logs before and during trips. At pre-flight and top-of-descent, pilots completed a 5-minute psychomotor vigilance task (PVT) and Karolinska Sleepiness Scale (KSS). A series of mixed-effects models were conducted to assess the impact of controlled rest on outcome measures at top-of-descent. Sleep duration in the prior 48 hours, timing of the flight, and pre-flight scores for each measure were included as covariates. RESULTS: Due to missing data, complete data from 76 flights (n = 28 participants) were available in the models examining the PVT metrics, and data from 83 flights (n = 29 participants) were available in the analyses of the KSS. Pilots who took controlled rest had faster response speeds at top-of-descent (p = .03, η2p = 0.07; estimated marginal mean [EMM] = 4.19, standard error [SE] = 0.07, 95% CI [4.08, 4.29]) than those who did not take controlled rest (EMM = 4.00, SE = 0.05, 95% CI [3.86, 4.14]). There were no differences by controlled rest status for KSS scores and PVT lapses (p values > .05, η2p values ≤ 0.01). DISCUSSION: Our results suggest that taking controlled rest may improve vigilant attention at critical phases of flight and, thus, may be a useful fatigue management tool during unaugmented flights. Further research is necessary to determine the impact of factors on the decision to take controlled rest (e.g., airline culture, personal preference) and how the controlled rest policy is applied in practice.

aviation↗

NASA NTRSDate not supplied

The Impact of Controlled Rest on Self-Reported Sleepiness at Top-of-Descent

aviation↗

NASA NTRSDate not supplied

Cabin Crew Alertness and Performance During Long-Haul Flights

INTRODUCTION: -Sleep loss and circadian disruption pose a significant risk in aviation. -Previous literature has shown that inflight rest facilities influence alertness and performance among pilots, but few studies have evaluated cabin crew. -The aim of this research was to assess alertness and performance among cabin crew members sleeping in different rest locations during a long-haul out-and-back trip. METHODS: -Twenty-nine cabin crewmembers flew the same long-haul route (outbound and inbound). -Participants were randomly assigned to fly on an aircraft with a bunk in both directions or to fly an aircraft with a bunk in one direction and with a high comfort jump seat (HCJS) in the other direction. -Throughout the study they completed a Karolinska Sleepiness Scale (KSS) and a 5-minute Psychomotor Vigilance Task (PVT) at the beginning and at the end of each flight. RESULTS: -A series of mixed-effects models were performed to assess the changes in KSS and PVT when crewmembers slept in the bunk during both directions of flight (bunk-only) compared to when sleep was obtained in the HCJS during one direction and bunk in the other (bunk + HCJS). -There was no significant difference in KSS alertness between the two conditions. - There were no significant differences in PVT response speed or lapses between the two conditions. CONCLUSIONS: -Limitations: small number of participants. Our study stopped abruptly because of the COVID-19 pandemic which limited our sample size. -Further research is needed to understand how other factors such as duty start time and workload might influence the sleep of cabin crewmembers during long-haul flights.

long-haul↗