Avoidance Schedule Research Articles

Dual-system free-operant avoidance: Extension of a theory.

Our theory of positively reinforced free-operant behavior (Perez & Dickinson, 2020) assumes that responding is controlled by two systems. One system is sensitive to the correlation between response and reinforcement rates and controls goal-directed behavior, whereas a habitual system learns by reward prediction error. We present an extension of this theory to the aversive domain that explains why free-operant avoidance responding increases with both the experienced rate of negative reinforcement and the difference between this rate and that programmed by the avoidance schedule. The theory also assumes that the habitual component is reinforced by the acquisition of aversive inhibitory properties by the feedback stimuli generated by responding, which then act as safety signals that reinforce habit performance. Our analysis suggests that the distinction between habitual and goal-directed control of rewarded behavior can also be applied to the aversive domain. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comparative Analysis of Methods of Evaluating Human Fatigue

AbstractThe present study used four different methods to estimate fatigue. Forty-seven volunteers (45 men and 2 women), 41.3 ± 7.5 years old, truck operators for 11.5 ± 6.0 years, were included. All participants accepted the invitation to be included in the study. Actigraphy and core temperature were evaluated. The 5-minute psychomotor vigilance test, the Karolinksa Sleepiness Scale (KSS), and the postural assessment using the Light Sonometer™ (Belo Horizonte, Minas Gerais, Brazil) were performed. Fatigue prediction was performed using the Fatigue Avoidance Scheduling Tool (FAST) program. In response to the Pittsburgh Sleep Quality Index (PSQI), 51.06% had good sleep quality and 48.94% had poor sleep quality with an average efficiency of 81.6%. In response to the actigraphy, workers slept an average of 7.2 hours a day with 93.5% efficiency. The workers' core body temperature (CBT) cosinor analysis showed a preserved circadian curve. Core body temperature showed differences between the 6 hours worked in each shift. Similarly, the light sound level meter showed lower risk scores for fatigue in day shifts. Only the variable of the fastest 10% of the Psychomotor Vigilance Test (PVT) showed worse results, while no significant differences were observed by the KSS. The risk analysis by FAST showed a strong influence of the circadian factor. In conclusion, each method has positive and negative points, and it is up to the evaluator/manager to identify the method that best suits the purpose of the evaluation, as well as the local culture and conditions. We recommend using different methods of risk assessment and management in combination with fatigue prediction by Sonometer as well as carrying out assessments, which enable researchers to estimate performance and fatigue throughout the working day, since these may change over the duration of the working day.

Objective assessment of fatigue, sleep and insomnia in breast cancer survivors on estrogen deprivation therapy.

TPS12145 Background: Fatigue, insomnia and sleep problems are common among breast cancer survivors (BCS). These symptoms remain underassessed, underreported and undertreated. Validated and objective measures of sleep metrics have yet to be widely incorporated into BCS studies to help examine the role that these play on patient patient reported fatigue and insomnia. While more than 50% of BCS experience fatigue and insomnia, most research on BCS fatigue is limited to short-term, self-report data that does not objectively assess sleep metrics. Estrogen Deprivation Therapy (EDT) has been shown to exacerbate sleep problems and contribute to perceptions of fatigue and insomnia, yet there is a paucity of research examining the specific effects of EDT on sleep metrics over time, especially objective measures. Wrist actigraphy is a non-invasive, objective measure of rest/activity cycles and sleep via a wrist-worn device. This study utilizes a novel instrument, called Readiwatch (Fatigue Sciences). It is widely validated in the military and in industry but has not been used in patient studies. The Readiwatch assesses fatigue and estimates cognitive performance, based on the Sleep Activity and Task Effectiveness (SAFTE) model developed in conjunction with the Department of Defense in 2004. The Readiwatch syncs with the Fatigue Avoidance Scheduling Tool (FAST) app on the participant’s smartphone to provide a score, called Readiscore, based on previous sleep and activity. It has been shown to have greater than 92% accuracy with lab polysomnography results. The Readiscore also serves as a “cognition” measure that represents the person’s cognitive capacity based on sleep metrics. This novel, objective feedback to the BCS could be very accessible and useful in community medical practice. Methods: This single-center, pilot study of BCS, receiving endocrine therapy, possessing a smartphone and Scoring >8 on the Insomnia Severity Index (ISI), has enrolled 25 of 50 planned participants. Participants wear the Readiwatch for two months to objectively assess their sleep metrics. Patient reported levels of fatigue and insomnia will be assessed prior to, midway, and following the two-month period. Participants document their self-reported experiences through surveys and track their own sleep and fatigue metrics using their smartphone app. At the conclusion of the study participants are provided with personalized feedback about their sleep metrics, along with detailed information related to sleep hygiene. Data analysis will model the effect of actigraphy fatigue measures on reported pain, anxiety, depression, and cognitive impairment and will also examine sleep metrics, perceptions of fatigue and insomnia scores in relation to type of EDT the patient is currently undergoing to determine if there are specific types of adjuvant therapy that are associated with fewer fatigue and insomnia problems.

A study to determine the possible links between subjective (fatigue questionnaires) and objective (bio-mathematical fatigue prevention model) fatigue detection methods

Introduction: Application of objective fatigue detection tools in aviation bases is limited. This study was envisaged to conduct a systematic comparative analysis between a well-established objective method and short fatigue questionnaires which are used in fatigue research to employ them as a fatigue screening tool for aviation personnel. Material and Methods: Thirty-eight aviation personnel volunteered for this cross-sectional observational study. Work-rest/sleep data collected using actigraphy over 1 week were fed to a PC running Fatigue Avoidance Scheduling Tool. Objective fatigue parameters in the form of Fatigue Risk Time (FRT) and Fatigue Free Occupational Time (FFOT) were retrieved. Fatigue questionnaires Groningen Sleep Quality Scale (GSQS) for assessing sleep quality and Stanford Sleepiness Scale (SSS) to detect day-time sleepiness were used as subjective fatigue parameters. Comparative analysis was carried out using appropriate statistical tests. Results: A consistent Total Sleep Time (TST) ranging from 353 to 378 min in the week of the study with no statistically significant differences between the nights were recorded. The increasing trend of FRT and decreasing trend of FFOT over the week were observed. The GSQS, SSS (morning), and SSS (afternoon) also demonstrated a progressive increase in the scores, but only the increase from day 1 to day 2 was statistically significant. Conclusion: Gradual increase in FRT with a reciprocal decrease in FFOT, which was observed, in this study, could be attributed to a progressive increase in sleep debt over the week. A consistent TST of the duration, which is less than the optimal duration of 7–8 h for night sleep, can lead to a gradual increase in sleep debt. The regression equations computed for FFOT was: FFOT = 657 + (0.24 × TST in min) – {(27 × Morning GSQS) + (73 × Day factor)}. This regression equation could be used to extrapolate the fatigue free occupation time for aviation personnel. The study has confirmed the effectiveness of both GSQS and SSS as the fatigue prevention tool and their application in the field setup, especially in the absence of any objective fatigue detection tool.

Biomathematical modeling for the prediction of sleep behavior and comparison against cognitive performance in firefighters

Biomathematical fatigue models, like the Sleep, Activity, Fatigue, and Task Effectiveness Fatigue Avoidance Scheduling Tool (SAFTE-FAST) could be adapted to serve as a fatigue mitigation tool for firefighting operations. The first aim of this study was to compare model predictions of effectiveness (Predicted Effectiveness) to actual Psychomotor Vigilance Task (PVT) speed (Actual Effectiveness) in a firefighter population. SAFTE-FAST also has a sleep prediction algorithm called AutoSleep that can be calibrated against actigraphy data in order to determine the best fit parameters for sleep estimation. The second aim of this study was to explore the ability of AutoSleep to predict sleep timing and duration in firefighters. N = 238 firefighters provided sleep and work data, with a subset of N = 151 firefighters who also provided PVT data, for up to two weeks. Firefighters slept between 6 and 8 h per 24-hour period whether on or off-shift. Linear regression revealed significant correlations between Actigraphy Predicted Effectiveness and PVT Actual Effectiveness scores (R2 = 0.68, p = 0.012). Best-fit AutoSleep parameters matched sleep duration in 99 % of predictions and sleep timing for 75 % of predicted sleep events compared to actigraphy, but there was no significant correlation between AutoSleep Predicted Effectiveness and PVT Actual Effectiveness scores (R2 = 0.34, p = 0.13). These findings highlight areas where the model must be refined in order to reliably predict firefighter performance. This study is the first step in adapting SAFTE-FAST for work shifts lasting 24 h or longer and an important step towards fatigue risk management for first responders.

Improving performance on night shift: a study of resident sleep strategies.

To identify sleep strategies of internal medicine residents transitioning to night shift and report their effect on performance. Residents logged hours of sleep and work starting 3 days prior to the first night shift and continuing through the next 8 days. Cohorts were defined by sleep logs and compared separately by transition strategy, total hours of sleep, amount of sleep occurring at work, weekend sleep schedule, and residency training year. Data from logs were entered into the Fatigue Avoidance Scheduling Tool to measure predicted Performance Effectiveness (PE) during each night shift. Twenty-three residents were evaluated. The Sleep Banking transition strategy (n = 2) had higher PE (mean = 88.6%) than all other sleep strategies combined (n = 21, mean = 80.9%; P = .016). Additionally, residents who slept an average of 8-9 hours daily during their week of night shifts had a higher mean PE compared to those who slept < 6 hours (86.8% vs 78.6%; P = .014). Residents who engaged in Sleep Banking prior to the first night shift had higher PE and spent less time above a 0.05% blood alcohol concentration equivalent compared to all other strategies. Similarly, PE and time spent above a 0.05% blood alcohol concentration equivalent improved with increased average hours slept per day during the week of night shifts. Optimizing performance on night shift through the adoption of efficacious sleep strategies is imperative to mitigate patient safety issues that may result from poor alertness and cognitive abilities. Cushman P, Scheuller HS, Cushman J, Markert RJ. Improving performance on night shift: a study of resident sleep strategies. J Clin Sleep Med. 2023;19(5):935-940.

Modelling the root causes of fatigue and associated risk factors in the Brazilian regular aviation industry

This work evaluates the potential root causes of fatigue and its relationships with accident risks using a bio-mathematical model approach and a robust sample (N = 8476) of aircrew rosters from the Brazilian regular aviation, extracted from the Fadigômetro database. The fatigue outcomes derive from the software Sleep, Activity, Fatigue, and Task Effectiveness Fatigue Avoidance Scheduling Tool (SAFTE-FAST), which considers the homeostatic process, circadian rhythms and the sleep inertia. The analyses include data from January 2019 until March 2020 and show relevant group effects comparing early 2019 and 2020, with the latter presenting lower fatigue outcomes in most cases. The average minimum SAFTE-FAST effectiveness during critical phases of flight (departures and arrivals) decreases cubically with the number of shifts that elapse totally or partially between mid-night and 6 a.m. within a 30-day period (NNS). As a consequence, the relative fatigue risk increases by 23.3% (95% CI, 20.4–26.2%) when increasing NNS from 1 to 13. The average maximum equivalent wakefulness in critical phases also increases cubically with the number of night shifts and exceeds 24 h for rosters with NNS above 10. The average fatigue hazard area in critical phases of flight varies quadratically with the number of departures and arrivals within 2 and 6 a.m. (NWocl). These findings demonstrate that both NNS and NWocl represent potential root causes of fatigue and therefore should be considered key performance indicators and kept as low as reasonably practical when building aircrew rosters, in order to properly manage the fatigue risk. The effectiveness scores obtained at 30-minute time intervals allowed a model estimate for the relative fatigue risk as a function of the time of the day, whose averaged values show reasonable qualitative agreement with previous measurements of pilot errors in the cockpit. Moreover, the 2019 data revealed a risk exposure factor two times (14%) higher than the figures reported by de Mello et al. (2008) (7%), within 0h00 do 05h59. Tailored analyses of the SAFTE-FAST inputs for afternoon naps before night shifts, commuting from home to station and vice-versa, and bedtime before early-start shifts were carried out using the responses of a questionnaire. The average fatigue hazard area in critical phases of flight increases by 43 to 63% switching off the afternoon naps, 14 to 21% increasing the commuting from one to two hours and 35 to 54% switching off the advanced bedtime criterion, with significant group effects in all cases (p<0.001), evidencing the need of a better and more accurate understanding of these parameters when modelling fatigue risk factors. The non-linear relationships between SAFTE-FAST outcomes and key roster’s parameters, such as NNS and NWocl, provided a novel statistical approach for risk assessment and led to few safety recommendations to the aviation sector regarding regulatory reviews and fatigue risk management and mitigation policies.

Objective assessment of fatigue among aviation personnel using a bio-mathematical model: An experimental study

BackgroundThere are many subjective and objective tools to detect, assess, and quantify fatigue. This study is a novice attempt to assess the occupational fatigue among the aviation personnel employing a computerized work-rest schedule tool integrated with actigraphy. MethodsThirty-eight aviation personnel were assessed for their sleep by using an actigraphy device. A work-rest scheduling software program called Fatigue Avoidance Scheduling Tool (FAST) was used to obtain fatigue parameters like Fatigue Risk Time (FRT), Fatigue Free Time (FFT), and Fatigue Free Occupational Time (FFOT). ResultsThe percentages of crew having a night sleep of the duration of more than 6 hours were 50% (Mon), 44.7% (Tue), 44.7% (Wed), and 47.3% (Thu) for weekdays and 65.8% (Fri), 57.9% (Sat), and 57.9% (Sun) for the weekend. There was a gradual increase in FRT, FFT, and FFOT from Day 1 to Day 5 of the week, and the differences were statistically significant. ConclusionIncrease in the FRT with a reciprocal drop of FFT and FFOT was observed with the progress of the week. Total Sleep Time (TST) of less than 8 hours could be the reason for a gradual increase in sleep debt, leading to fatigue depicted as increase in fatigue risk parameter FRT and gradual decrease in fatigue preventing parameters like FFT and FFOT. It was further confirmed by regression analysis in which TST was found to be a statistically significant predictor for all fatigue parameters. Regression equation for FFOT as 498.53 + (0.39 x TST) - (58.8 x Day of the week) can be used.

0022 Improving predicted effectiveness of shiftworkers in watchfloors

Abstract Introduction In support of 24/7 hour operations around the globe, Navy and Marine Corps shore-based watchfloors provide information, intelligence, and technical support to the warfighter. Personnel working on these watchfloors are challenged to obtain consistent sleep due to shift working. The study objectives were to model the predicted effectiveness (PE) of the current watch schedule and recommend a new schedule to improve sleep, safety, and performance. Methods In a 3-week longitudinal, naturalistic study, we assessed the work/rest patterns and well-being of watchstanders while on their typical watch schedule. Participants (N=11, age:19-33yrs) completed an online sleep/activity log for the study period. Standardized tools (Pittsburgh Sleep Quality Index--PSQI, Epworth Sleepiness Scale--ESS, Insomnia Severity Index--ISI, Profile of Mood States--POMS) were completed pre/post-study. Based on study findings combined with information gathered from the command leadership, we designed an improved watch schedule. The Fatigue Avoidance Scheduling Tool (version 3.3.01T) employing the Sleep, Activity, Fatigue, and Task Effectiveness model (SAFTE/FAST) was used to model PE in the legacy and newly improved watch schedules. Results The median daily reported sleep duration was 6hrs (range: 5-8.5hrs). Nine participants (82%) were classified as poor sleepers and six (55%) had elevated daytime sleepiness. Four (36%) participants had moderately severe/severe insomnia symptoms. In terms of mood, 10 (91%) participants had a lower vigor-activity score than the adult norms, whereas eight (73%) scored higher than the adult norms for fatigue. The mid-shift watches had low PE that dropped steeply throughout the shift. The timing of low PE coincided with the period in which personnel was briefing leadership and/or commuting from work. Conclusion Based on our findings, we recommended a schedule that enables more regular sleep patterns. Compared to the legacy schedule, the new schedule increased both the number of week days and weekend days off, with 2-3 full weekends off per month. Also, PE was improved and the trough in PE did not occur during the commute. Lastly, we recommended that the new watch schedule should rotate less frequently with longer times on an assigned shift. Support (If Any) This work was supported in part by the Naval Medical Research Center's Naval Advanced Medical Development Program.

0136 Firefighter Sleep Behavior and Psychomotor Vigilance Compared to Biomathematical Predictions of Performance

Abstract Introduction Firefighters work a demanding 24-hour job in which fatigue may compromise safety. Biomathematical fatigue modeling software applications, like the Sleep, Activity, Fatigue, and Task Effectiveness Fatigue Avoidance Scheduling Tool (SAFTE-FAST) could be adapted to serve as a fatigue mitigation tool for firefighting operations. SAFTE-FAST predicts task effectiveness using objective work and sleep data. Model predictions of effectiveness (Predicted Effectiveness) have not been previously compared against actual Psychomotor Vigilance Task (PVT) speed (Actual Effectiveness) in a firefighter population. Methods Total sleep time across the 24-hour day (TST24) was monitored continuously throughout a 2-week study period in firefighters working 24-hour shifts using a sleep-tracking actigraphy device (Zulu Watch, Institutes for Behavior Resources). Speed on an 8-minute PVT was assessed daily on on-shift and off-shift days. Mean speeds &amp;gt;2 standard deviations above the grand distribution were excluded. SAFTE-FAST calculated Predicted Effectiveness (PVT Speed [1/Reaction Time] expressed as a % of individual optimum performance) from objective sleep and work data. Actual Effectiveness was computed as a percentage of the median of an individual’s top 10% fastest PVT Speeds. Paired samples t-test explored differences between on-shift and off-shift PVT Speed and TST24. Linear regression explored correlation of Predicted Effectiveness to Actual Effectiveness across 5% Effectiveness bins. Results Two hundred twenty (N=220) firefighters provided sleep, work, and PVT data for 1-2 weeks (Mean: 8; Range: 1-15 days) between 9/2020 to 8/2021. On-shift days accounted for 37% of study days (M: 3; R: 0-10 on-shift days). PVT Speed and TST24 were not significantly different between on-shift (Speed: 2.43±0.47; TST24: 404±163 minutes) and off-shift days (Speed: 2.44±0.48; TST24: 396±156 min; all p&amp;gt;0.1). Linear regression across 5% SAFTE prediction bins revealed significant correlations between SAFTE Predicted Effectiveness and PVT Actual Effectiveness (R2=0.861, p&amp;lt;.001). Conclusion Firefighters averaged less than 7 hours of sleep per 24-hour period whether on or off-shift. Average PVT speed was similar to performance from chronically-sleep-deprived or alcohol-impaired individuals. SAFTE-FAST Predicted Effectiveness and PVT Actual Effectiveness were distributed similarly across 5% bins. While preliminary, these findings suggest that firefighters may suffer from chronic sleep debt and impaired psychomotor vigilance that can be predicted using biomathematical modelling. Support (If Any) N/A

Predictive Biomathematical Modeling Compared to Objective Sleep During COVID-19 Humanitarian Flights.

BACKGROUND: Biomathematical modeling software like the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model and Fatigue Avoidance Scheduling Tool (FAST) help carriers predict fatigue risk for planned rosters. The ability of a biomathematical model to accurately estimate fatigue risk during unprecedented operations, such as COVID-19 humanitarian ultra-long-range flights, is unknown. Azul Cargo Express organized and conducted five separate humanitarian missions to China between May and July 2020. Prior to conducting the missions, a sleep-prediction algorithm (AutoSleep) within SAFTE-FAST was used to predict in-flight sleep duration and pilot effectiveness. Here we compare AutoSleep predictions against pilots' sleep diary and a sleep-tracking actigraphy device (Zulu watch, Institutes for Behavior Resources) from Azul's COVID-19 humanitarian missions.METHODS: Pilots wore Zulu watches throughout the mission period and reported sleep duration for their in-flight rest periods using a sleep diary. Agreement between AutoSleep, diary, and Zulu watch measures was compared using intraclass correlation coefficients (ICC). Goodness-of-fit between predicted effectiveness distribution between scenarios was evaluated using the R² statistic.RESULTS: A total of 20 (N = 20) pilots flying across 5 humanitarian missions provided sleep diary and actigraphy data. ICC and R² values were >0.90, indicating excellent agreement between sleep measures and predicted effectiveness distribution, respectively.DISCUSSION: Biomathematical predictions of in-flight sleep during unprecedented humanitarian missions were in agreement with actual sleep patterns during flights. These findings indicate that biomathematical models may retain accuracy even under extreme circumstances. Pilots may overestimate the amount of sleep that they receive during extreme flight-duty periods, which could constitute a fatigue risk.Devine JK, Garcia CR, Simoes AS, Guelere MR, de Godoy B, Silva DS, Pacheco PC, Choynowski J, Hursh SR. Predictive biomathematical modeling compared to objective sleep during COVID-19 humanitarian flights. Aerosp Med Hum Perform. 2022; 93(1):4-12.

Employability assessment of computerized fatigue avoidance tool used with and without actigraphy among naval aircrew

BackgroundFatigue Avoidance Scheduling Tool (FAST) is a computerized fatigue prevention tool, which was developed based on a Bio-mathematical model called SAPTE (Sleep, Activity, Performance and Task Effectiveness). Similarly, actigraphy technology is used as a tool for sleep evaluation. This study was an attempt to assess the employability of FAST (both independently and integrated with actigraphy) for detecting operational fatigue by determining the ‘Measures for diagnostic accuracy’. Methods103 pre-flight medical checks from 51 aircrew were evaluated to detect fatigue both clinically and by using objective measures (FAST and actigraphy integrated FAST). The ‘measures of diagnostic accuracy’ namely sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), likelihood ratio positive (LR +ve), likelihood ratio negative (LR -ve) and Youden’s Index were determined and compared. ResultsThe diagnostic measures for FAST were: sensitivity (68.4%), specificity (92.8%), PPV (68.4%), NPV (92.8%), LR+ve (9.5), LR-ve (0.34) and Youden’s Index (0.61). For actigraphy integrated FAST these values were: sensitivity (84.2%), specificity (96.4%), PPV (94.2%), NPV (94.4%), LR+ve (23.5), LR-ve (0.16) and Youden’s Index (0.80). ConclusionThe increase in sensitivity (from 68.4% to 84.2%), specificity (from 92.8% to 96.4%), PPV (from 68.4% to 94.2%), NPV (92.8% to 94.4%), LR+ve (9.5 to 23.5) and Youden’s Index (0.61 to 0.80) indicated that actigraphy integrated FAST is a better screening tool in comparison to the independently employed FAST.

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

Introduction: Strategic naps are considered as efficacious means of maintaining performance and reducing the individual’s sleep debt. It can reduce subjective feelings of fatigue and improve performance and alertness. However, literature is scant on assessment of naps and associated cognitive performance in the Indian military aviation scenario. This study is an attempt to assess the nap duration and its objective assessment on gain in performance, if any. Material and Methods: In this cross-sectional observational study, sleep data were collected from 23 aviation personnel in a military flying base using actigraphy device. The actigraphic data were then fed into a software called fatigue avoidance scheduling tool. The nap duration and its effect on cognitive parameters were analyzed. Results: About 65.2% of the participant were found to be Day-Time Habitual Nappers. Of the 50 Naps logged by these 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. Post-nap gain in the effectiveness and other cognitive parameters was found to be different in different cognitive domains. Conclusion: Naps more than 30 min had the optimal efficiency. The nap-induced gain in the task effectiveness and cognitive performance was confirmed. While the performance enhancement was significant for the naps more than 30 min, naps more than 60 min did not have any added advantages.

286 Pilot Sleep During Brazil-to-China COVID-19 Humanitarian Mission Flights Compared to Biomathematical Predictions of Sleep

Introductionn response to the COVID-19 pandemic, Azul Airlines organized and conducted five separate humanitarian missions to China between May and July, 2020. Each mission consisted of 4 flight legs between 11-15 hours long crewed by a team of 8 pilots. Each pilot was given a 9-hour sleep opportunity during the flight period. Prior to conducting the missions, a sleep-prediction algorithm (AutoSleep) within the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model Fatigue Avoidance Scheduling Tool (FAST) was used to predict in-flight time in bed (TIB) and total sleep time (TST). During missions, pilots wore a wrist actigraph and completed a sleep diary. These analyses compare the accuracy of SAFTE-FAST AutoSleep predictions against pilots’ sleep diary and actigraphy from Azul’s COVID-19 humanitarian missions.MethodsPilots wore a sleep-tracking actigraphy device (Zulu Watch, Institutes for Behavior Resources), and reported the TIB and sleep quality of their in-flight rest periods using a sleep diary. Diary TST was estimated from TIB and sleep quality. AutoSleep, diary, and actigraphy measures were compared using paired samples t-tests. Agreement was compared using intraclass correlation coefficients (ICC).ResultsTwenty (n=20) pilots flying across 5 humanitarian missions provided sleep diary and actigraphy data. AutoSleep predictions of TIB (235±20 minutes) and TST (193±16 minutes) were significantly lower than diary (TIB: 330±123, t=6.80, p≤0.001; TST: 262±108, t=5.60, p≤0.001) and comparable to actigraphy (TIB: 246±127, t=0.78, p=0.43; TST: 212±113, t=1.59, p=0.12). ICC values were >0.90, indicating excellent agreement, for TIB (0.94) and TST (0.91).ConclusionBiomathematical predictions of in-flight sleep during unprecedented humanitarian missions were in agreement with actual sleep patterns during flights. These findings indicate that biomathematical models may retain accuracy even under extreme circumstances like the COVID-19 pandemic. Pilots may overestimate the amount of sleep that they receive during extreme flights-duty periods, which could constitute a fatigue risk.Support (if any)NA

The Importance of Validating Sleep Behavior Models for Fatigue Management Software in Military Aviation.

The propensity for air mobility missions to exhaust aircrews is strongly dependent on operational tempo. Most flying is performed during periods of low to moderate operational tempo, but a major flight safety risk can emerge when operational tempo becomes very high. This risk can be managed by software tools that contain fatigue and sleep behavior modeling, but optimization/validation of the model using the specific target population is required to ensure that the modeled predictions are accurate. The goal of the study was to validate the sleep behavior model settings for a fatigue modeling tool that is used within the RCAF, the Fatigue Avoidance Scheduling Tool, taking into account the organizational requirements for pre- and postflight routines, especially within the Air Mobility force. Four Royal Canadian Air Force Air Mobility Squadrons from Canadian Forces Base Trenton took part in this trial over a 3-month period (May 3 to August 21, 2016). All 22 missions of the trial included long-range transmeridian flights. All members of the participating aircrew wore wrist actigraphs to measure their sleep. We compared cognitive effectiveness modeling scenarios (preharmonization) based on the SAFTE-FAST sleep behavior model with its default settings against cognitive effectiveness modeling scenarios based on actigraphically-measured sleep. The measured sleep was then harmonized against the predicted sleep to optimize accuracy of the sleep behavior algorithm. During the harmonization process, the "Autosleep" prediction settings were optimized to match the actigraphically-measured sleep timings. Prior to the harmonization effort, the sleep behavior algorithm overpredicted the sleep obtained by CAF Aircrews. The most significant adjustment to the sleep behavior model was the increase in commute time to account for briefing, flight planning, debriefing, and postflight activities. Following harmonization, the sleep behavior model provided nearly perfect estimates of overall fatigue risk against missions modeled with actigraphically-measured sleep. For both measured and predicted sleep, most of the time in flight was in a low-fatigue, high-cognitive effectiveness state (90%-95% cognitive effectiveness). Current Fatigue Risk Management Systems require accurate fatigue and sleep behavior modeling, which can only be achieved by studying specific target populations to determine their culture of work/rest routines, and optimizing sleep behavior model settings accordingly.

Does DRO type matter?: Cycle versus resetting contingencies in eliminating responding

Following lever-press training on variable-interval 30-s schedules, rats were exposed to three types of schedules designed to eliminate lever pressing. The first two were variations on what is called a differential-reinforcement-of-other-behavior (DRO, “zero rate”, or [target response] omission schedule) schedule. Under both variations, reinforcers were scheduled to occur in different conditions after either fixed or variable inter-reinforcer intervals (IRIs). Under one variation each lever press reset the time interval (i.e., “resetting DRO”) and under the other a reinforcer delivery scheduled at the end of an IRI was cancelled by the first response during the IRI (i.e., “cycle DRO”). In another condition reinforcers were delivered independently of responding after fixed or variable time periods. Each of the DRO procedures reduced response rates quickly and to near zero across ten sessions. The time schedules also reduced responding, albeit at a slower rate. The results extend the analogy of omission training to freeoperant avoidance to shock-deletion avoidance schedules.

Effective Data Transmission and Control Based on Social Communication in Social Opportunistic Complex Networks

In opportunistic complex networks, information transmission between nodes is inevitable through broadcast. The purpose of broadcasting is to distribute data from source nodes to all nodes in the network. In opportunistic complex networks, it is mainly used for routing discovery and releasing important notifications. However, when a large number of nodes in the opportunistic complex networks are transmitting information at the same time, signal interference will inevitably occur. Therefore, we propose a low-latency broadcast algorithm for opportunistic complex networks based on successive interference cancellation techniques to improve propagation delay. With this kind of algorithm, when the social network is broadcasting, this algorithm analyzes whether the conditions for successive interference cancellation are satisfied between the broadcast links on the assigned transmission time slice. If the conditions are met, they are scheduled at the same time slice, and interference avoidance scheduling is performed when conditions are not met. Through comparison experiments with other classic algorithms of opportunistic complex networks, this method has outstanding performance in reducing energy consumption and improving information transmission efficiency.

Discriminative Stimulus Effects of (R)(−)‐DOI and AM8936, a Synthetic Cannabinoid

Increased abuse of synthetic cannabinoids (SCBs) continues to be a public health concern. Preclinical studies have identified SCBs as being THC‐like; however, occurrences of emesis, hallucinations, and seizures have been reported with their usage. These studies examined the premise that the hallucinogenic effects of AM8936, a highly potent synthetic CB1 agonist, resemble the discriminative stimulus effects of another hallucinogen, the 5HT2A agonist (R)(−)2,5‐dimethoxy‐4‐iodoamphetamine (DOI). Male and female Sprague‐Dawley rats (n=6/group) were trained to discriminate either 0.18 mg/kg AM8936 or 0.56 mg/kg DOI from saline under a shock avoidance schedule of reinforcement. In both DOI and AM8936 trained subjects, their respective training dose produced full (&gt;80%) substitution. In the DOI trained subjects, AM8936 (0.032 – 0.18 mg/kg), delta‐9‐tetrahydrocannabinol (Δ9‐THC) (1 – 10 mg/kg), and JWH‐018 (0.1 – 3.2 mg/kg) produced dose‐dependent increases in DOI‐lever responding, but only JWH‐018 and AM8936 produced full substitution. In the AM8936 trained subjects, DOI (0.56 – 3.2 m/k), Δ9‐THC (1 – 10 mg/kg), and JWH‐018 (0.1 – 10 mg/kg) produced dose‐dependent increases in AM8936‐lever responding, but only JWH‐018 produced full (&gt;80%) substitution. Doses of DOI that produced AM8936‐appropriate responding (0.56 – 3.2 mg/kg) were higher than those that resulted in DOI‐appropriate responding (0.1 – 0.8 mg/kg). In addition, the mu‐opioid receptor agonist morphine (1 – 10 mg/kg) (negative control) engendered less than 20% DOI‐lever responding or AM8936‐lever responding up to doses that produced behavioral disruption. These studies indicate that there is bi‐directional overlap in the discriminative stimulus effects of SCBs and a 5‐HT2A agonist, suggesting similarities in their subjective effects.Support or Funding InformationAcknowledgementsFunded by NIH/NIDA: DA043700

Optimal Schedules of Light Exposure for Multiple Individuals for Quick Circadian Alignment

The invention of quick transportation modes that allow trans-meridian travel has caused circadian misalignment to be a common problem amongst people today. This leads to lower cognitive alertness in the short term and increases the risks of other maladies in the long term. Light, when applied at correct levels and times, can shift and re-entrain the circadian clock to the local time zone, and minimize the negative impact of any circadian misalignment. In this paper, we developed a new method (algorithm) to calculate common optimal light schedules of light exposure and avoidance, to quickly re-entrain the circadian systems of a group of individuals who have different internal circadian parameters. We used an experimentally validated mathematical model to define a target circadian phase, from which, our optimization algorithm iteratively adjusts the switching times of a bang-bang light input (restricted to two light levels) to minimize the difference between the current phase of all individuals and the target phase, within a set time period. The proposed algorithm generated light schedules that successfully minimizes the re-entrainment time of all the individuals with phase shifts up to 12 hours of delay or advancement.

An Analytical Framework for IEEE 802.15.6-Based Wireless Body Area Networks With Instantaneous Delay Constraints and Shadowing Interruptions

In this paper, a novel queueing analytical framework is proposed to evaluate the performance of IEEE 802.15.6-based carrier-sense multiple access with collision avoidance (CSMA/CA) scheduling in wireless body area networks with joint consideration of instantaneous delay constraints and body shadowing effects. Specifically, we develop an absorbing Markov chain to model the medium access process with error controls that is defined by the IEEE standard and design a random time-limited single vacation to describe the potential body shadowing interruption process. To guarantee the timeliness of received packets and avoid energy waste for transmitting valueless packets, an instantaneous delay constraint is carefully considered, which is then characterized by an overdeadline packet dropping process with a predefined waiting deadline. In our analysis, a Markovian arrival process is also adopted to capture the correlation of arrival traffic, and all other random processes are modeled by phase-type distributions, which make our framework more general and comprehensive. To address the inherent complexity of the original model, based on the transient queueing analysis, we develop a buffer-overflowing queueing principle to approximate the overdeadline packet dropping principle by solving a buffer length optimization problem. After that, we construct a multidimensional discrete-time Markov chain to analyze the stationary distribution through the matrix-geometric method. Performance measures, including the average delay, waiting time distribution, and packet transmission failure probability, are derived. The accuracy of our proposed analytical framework is validated by extensive simulations.