2B-Alert App: A mobile application for real-time individualized prediction of alertness

When to sleep and consume caffeine to boost alertness

STUDY OBJECTIVES

Sleep loss can cause cognitive impairments that increase the risk of mistakes and accidents. However, existing guidelines to counteract the effects of sleep loss are generic and are not designed to address individual-specific conditions, leading to sub-optimal alertness levels. Here, we developed an optimization algorithm that automatically identifies sleep schedules and caffeine-dosing strategies to minimize alertness impairment due to sleep loss for desired times of the day.

METHODS

We combined our previous algorithms that separately optimize sleep or caffeine to simultaneously identify the best sleep schedules and caffeine doses that minimize alertness impairment at desired times. The optimization algorithm uses the predictions of the well-validated Unified Model of Performance to estimate the effectiveness and physiological feasibility of a large number of possible solutions and identify the best one. To assess the optimization algorithm, we used it to identify the best sleep schedules and caffeine-dosing strategies for four studies that exemplify common sleep-loss conditions and compared the predicted alertness-impairment reduction achieved by using the algorithm's recommendations against that achieved by following the U.S. Army caffeine guidelines.

RESULTS

Compared to the alertness-impairment levels in the original studies, the algorithm's recommendations reduced alertness impairment on average by 63%, an improvement of 24 percentage points over the U.S. Army caffeine guidelines.

CONCLUSIONS

We provide an optimization algorithm that simultaneously identifies effective and safe sleep schedules and caffeine-dosing strategies to minimize alertness impairment at user-specified times.

Electroencephalographic markers from routine sleep discriminate individuals who are vulnerable or resilient to sleep loss

Sleep loss impairs cognition; however, individuals differ in their response to sleep loss. Current methods to identify an individual's vulnerability to sleep loss involve time-consuming sleep-loss challenges and neurobehavioural tests. Here, we sought to identify electroencephalographic markers of sleep-loss vulnerability obtained from routine night sleep. We retrospectively analysed four studies in which 50 healthy young adults (21 women) completed a laboratory baseline-sleep phase followed by a sleep-loss challenge. After classifying subjects as resilient or vulnerable to sleep loss, we extracted three electroencephalographic features from four channels during the baseline nights, evaluated the discriminatory power of these features using the first two studies (discovery), and assessed reproducibility of the results using the remaining two studies (reproducibility). In the discovery analysis, we found that, compared to resilient subjects, vulnerable subjects exhibited: (1) higher slow-wave activity power in channel O1 (p < 0.0042, corrected for multiple comparisons) and in channels O2 and C3 (p < 0.05, uncorrected); (2) higher slow-wave activity rise rate in channels O1 and O2 (p < 0.05, uncorrected); and (3) lower sleep spindle frequency in channels C3 and C4 (p < 0.05, uncorrected). Our reproducibility analysis confirmed the discovery results on slow-wave activity power and slow-wave activity rise rate, and for these two electroencephalographic features we observed consistent group-difference trends across all four channels in both analyses. The higher slow-wave activity power and slow-wave activity rise rate in vulnerable individuals suggest that they have a persistently higher sleep pressure under normal rested conditions.

Individualised prediction of resilience and vulnerability to sleep loss using EEG features

It is well established that individuals differ in their response to sleep loss. However, existing methods to predict an individual's sleep-loss phenotype are not scalable or involve effort-dependent neurobehavioural tests. To overcome these limitations, we sought to predict an individual's level of resilience or vulnerability to sleep loss using electroencephalographic (EEG) features obtained from routine night sleep. To this end, we retrospectively analysed five studies in which 96 healthy young adults (41 women) completed a laboratory baseline-sleep phase followed by a sleep-loss challenge. After classifying subjects into sleep-loss phenotypic groups, we extracted two EEG features from the first sleep cycle (median duration: 1.6 h), slow-wave activity (SWA) power and SWA rise rate, from four channels during the baseline nights. Using these data, we developed two sets of logistic regression classifiers (resilient versus not-resilient and vulnerable versus not-vulnerable) to predict the probability of sleep-loss resilience or vulnerability, respectively, and evaluated model performance using test datasets not used in model development. Consistently, the most predictive features came from the left cerebral hemisphere. For the resilient versus not-resilient classifiers, we obtained an average testing performance of 0.68 for the area under the receiver operating characteristic curve, 0.72 for accuracy, 0.50 for sensitivity, 0.84 for specificity, 0.61 for positive predictive value, and 3.59 for likelihood ratio. We obtained similar performance for the vulnerable versus not-vulnerable classifiers. These results indicate that logistic regression classifiers based on SWA power and SWA rise rate from routine night sleep can largely predict an individual's sleep-loss phenotype.

The Negative Effects of Travel on Student Athletes Through Sleep and Circadian Disruption

Collegiate athletes must satisfy the academic obligations common to all undergraduates, but they have the additional structural and social stressors of extensive practice time, competition schedules, and frequent travel away from their home campus. Clearly such stressors can have negative impacts on both their academic and athletic performances as well as on their health. These concerns are made more acute by recent proposals and decisions to reorganize major collegiate athletic conferences. These rearrangements will require more multi-day travel that interferes with the academic work and personal schedules of athletes. Of particular concern is additional east-west travel that results in circadian rhythm disruptions commonly called jet lag that contribute to the loss of amount as well as quality of sleep. Circadian misalignment and sleep deprivation and/or sleep disturbances have profound effects on physical and mental health and performance. We, as concerned scientists and physicians with relevant expertise, developed this white paper to raise awareness of these challenges to the wellbeing of our student-athletes and their co-travelers. We also offer practical steps to mitigate the negative consequences of collegiate travel schedules. We discuss the importance of bedtime protocols, the availability of early afternoon naps, and adherence to scheduled lighting exposure protocols before, during, and after travel, with support from wearables and apps. We call upon departments of athletics to engage with sleep and circadian experts to advise and help design tailored implementation of these mitigating practices that could contribute to the current and long-term health and wellbeing of their students and their staff members.

Individualisation method of biomathematical model of fatigue for predicting individual performance in mild and irregular sleep deprivation

Individual biomathematical models of fatigue (BMMF) are promising tools for detecting fatigue and possible incidents. Existing individual BMMFs have been validated in laboratory experiments in which subjects experience total sleep deprivation (TSD) and regular chronic sleep deprivation (CSD). However, some shift populations experience mild sleep deprivation (MSD) or irregular sleep deprivation (ISD) in real life. We employed the adaptive momentum estimation algorithm to adjust the classical SAFTE model for an individual. Model individualisation can be performed in real-time when new performance data are collected. The individual SAFTE model was compared with existing BMMFs in TSD, CSD, MSD, and ISD. The validation results show that the individual SAFTE model has advantages in MSD and ISD datasets collected from officers and truck drivers in real life. This study expands previous research results on the real-time individualisation of BMMFs and exposes individual BMMFs to various sleep-deprivation conditions in the field. Practitioner summary: This study proposes an individual biomathematical models of fatigue to predict human performance in mild and irregular sleep deprivation. The validation results in both laboratory and field show the proposed model has advantages over existing models when predicting officers' and truck drivers' performance in real life.

Assessment of the unified model of performance: accuracy of group-average and individualised alertness predictions

To be effective as a key component of fatigue-management systems, biomathematical models that predict alertness impairment as a function of time of day, sleep history, and caffeine consumption must demonstrate the ability to make accurate predictions across a range of sleep-loss and caffeine schedules. Here, we assessed the ability of the previously reported unified model of performance (UMP) to predict alertness impairment at the group-average and individualised levels in a comprehensive set of 12 studies, including 22 sleep and caffeine conditions, for a total of 301 unique subjects. Given sleep and caffeine schedules, the UMP predicted alertness impairment based on the psychomotor vigilance test (PVT) for the duration of the schedule. To quantify prediction performance, we computed the root mean square error (RMSE) between model predictions and PVT data, and the fraction of measured PVTs that fell within the models' prediction intervals (PIs). For the group-average model predictions, the overall RMSE was 43 ms (range 15-74 ms) and the fraction of PVTs within the PIs was 80% (range 41%-100%). At the individualised level, the UMP could predict alertness for 81% of the subjects, with an overall average RMSE of 64 ms (range 32-147 ms) and fraction of PVTs within the PIs conservatively estimated as 71% (range 41%-100%). Altogether, these results suggest that, for the group-average model and 81% of the individualised models, in three out of four PVT measurements we cannot distinguish between study data and model predictions.

Wearables Detect Malaria Early in a Controlled Human-Infection Study

OBJECTIVE

Observational studies on the use of commercially available wearable devices for infection detection lack the rigor of controlled clinical studies, where time of exposure and onset of infection are exactly known. Towards that end, we carried out a feasibility study using a commercial smartwatch for monitoring of heart rate, skin temperature, and body acceleration on subjects as they underwent a controlled human malaria infection (CHMI) challenge.

METHODS

Ten subjects underwent CHMI and were asked to wear the smartwatch for at least 12 hours/day from 2 weeks pre-challenge to 4 weeks post-challenge. Using these data, we developed 2B-Healthy, a Bayesian-based infection prediction algorithm that estimates a probability of infection. We also collected data from eight control subjects for 4 weeks to assess the false-positive rate of 2B-Healthy.

RESULTS

Nine of 10 CHMI subjects developed parasitemia, with an average time to parasitemia of 12 days. 2B-Healthy detected infection in seven of nine subjects (78% sensitivity), where in six subjects it detected infection 6 days before parasitemia (on average). In the eight control subjects, we obtained a false-positive rate of 6%/week.

CONCLUSION

The 2B-Healthy algorithm was able to reliably detect infection prior to the onset of symptoms using data collected from a commercial smartwatch in a controlled human infection study.

SIGNIFICANCE

Our findings demonstrate the feasibility of wearables as a screening device to provide early warning of infection and support further research on the use of the 2B-Healthy algorithm as the basis for a wearable infection-detection platform.

How sleep can help maximize human potential: The role of leaders

OBJECTIVE

During multi-domain operations (MDO), soldiers need the physical supremacy, cognitive dominance, and emotional resilience to help defend and win our nation's wars. Optimal sleep has been shown to boost physical performance and cognitive processing. This manuscript will discuss how recent advances in sleep science strongly argue for the integration of sleep planning into military operations.

DESIGN

Review article.

METHODS

We reviewed the current understanding of how sleep affects Soldier readiness, how sleep and pain are interrelated, and unique challenges to obtaining adequate sleep in military training environments. We then address solutions that can be implemented by leaders and individuals to manage warfighter fatigue and optimize unit performance.

RESULTS

Since sleep is foundational to soldier health and readiness, improving warfighter fatigue management is a priority for leaders.

CONCLUSION

To succeed in MDO, military personnel require physical supremacy, cognitive dominance, and emotional resilience to fight and win. Sleep science is a rapidly emerging field, and the clear implications for maximizing human performance argue strongly for more deliberate integration into military training and operations. Leaders that incorporate sleep and fatigue management into the planning and execution phases of operations will help facilitate mission priorities and prove a powerful force multiplier.

Optimal Sleep and Work Schedules to Maximize Alertness

STUDY OBJECTIVES

Working outside the conventional "9-to-5" shift may lead to reduced sleep and alertness impairment. Here, we developed an optimization algorithm to identify sleep and work schedules that minimize alertness impairment during work hours, while reducing impairment during non-work hours.

METHODS

The optimization algorithm searches among a large number of possible sleep and work schedules and estimates their effectiveness in mitigating alertness impairment using the Unified Model of Performance (UMP). To this end, the UMP, and its extensions to estimate sleep latency and sleep duration, predicts the time course of alertness of each potential schedule and their physiological feasibility. We assessed the algorithm by simulating four experimental studies, where we compared alertness levels during work periods for sleep schedules proposed by the algorithm against those used in the studies. In addition, in one of the studies we assessed the algorithm's ability to simultaneously optimize sleep and work schedules.

RESULTS

Using the same amount of sleep as in the studies but distributing it optimally, the sleep schedules proposed by the optimization algorithm reduced alertness impairment during work periods by an average of 29%. Similarly, simultaneously optimized sleep and work schedules, for a recovery period following a chronic sleep restriction challenge, accelerated the return to baseline levels by two days when compared to the conventional 9-to-5 work schedule.

CONCLUSIONS

Our work provides the first quantitative tool to optimize sleep and work schedules and extends the capabilities of existing fatigue-management tools.

Military nutrition research: Contemporary issues, state of the science and future directions

The importance of diet and nutrition to military readiness and performance has been recognized for centuries as dietary nutrients sustain health, protect against illness, and promote resilience, performance and recovery. Contemporary military nutrition research is increasingly inter-disciplinary with emphasis often placed on the broad topics of (1) determining operational nutrition requirements in all environments, (2) characterizing nutritional practices of military personnel relative to the required (role/environment) standards, and (3) developing strategies for improving nutrient delivery and individual choices. This review discusses contemporary issues shared internationally by military nutrition research programmes, and highlights emerging topics likely to influence future military nutrition research and policy. Contemporary issues include improving the diet quality of military personnel, optimizing operational rations, and increasing understanding of biological factors influencing nutrient requirements. Emerging areas include the burgeoning field of precision nutrition and its technological enablers.

Models for predicting sleep latency and sleep duration

STUDY OBJECTIVES

Planning effective sleep-wake schedules for civilian and military settings depends on the ability to predict the extent to which restorative sleep is likely for a specified sleep period. Here, we developed and validated two mathematical models, one for predicting sleep latency and a second for predicting sleep duration, as decision aids to predict efficacious sleep periods.

METHODS

We extended the Unified Model of Performance (UMP), a well-validated mathematical model of neurobehavioral performance, to predict sleep latency and sleep duration, which vary nonlinearly as a function of the homeostatic sleep pressure and the circadian rhythm. To this end, we used the UMP to predict the time course of neurobehavioral performance under different conditions. We developed and validated the models using experimental data from 317 unique subjects from 24 different studies, which included sleep conditions spanning the entire circadian cycle.

RESULTS

The sleep-latency and sleep-duration models accounted for 42% and 84% of the variance in the data, respectively, and yielded acceptable average prediction errors for planning sleep schedules (4.0 min for sleep latency and 0.8 h for sleep duration). Importantly, we identified conditions under which small shifts in sleep onset timing result in disproportionately large differences in sleep duration-knowledge that may be applied to improve performance, safety, and sustainability in civilian and military operations.

CONCLUSIONS

These models extend the capabilities of existing predictive fatigue-management tools, allowing users to anticipate the most opportune times to schedule sleep periods.

Walter Reed Army Institute of Research (WRAIR): Fifty Years of Achievements That Impact Science and Society

Thirty-four historical achievements since 1970 that emanated from scientific research at the Walter Army Institute of Research are identified and documented. Impact areas include vaccines, drug development, and clinical assays to prevent or treat infectious diseases; neuropsychiatric management of warrior performance and combat casualty; blood delivery management; and radiation protection.

Smartphone-assisted experimentation as a didactic strategy to maintain practical lessons in remote education: alternatives for physiology education during the COVID-19 pandemic

Online and distance education may be dismissed by educators who argue that these methods are not equivalent to traditional face-to-face education due to the lack of laboratory classes. However, smartphone-assisted experimentation is an innovative and powerful didactic tool that helps educators in the teaching process of physiology, particularly in situations with a lack of financial support for purchasing laboratory equipment, or lack of support for homework and assignments, distance learning courses, and emergency remote education, such as during the COVID-19 pandemic. Therefore, we present the concept of the mobile learning laboratory (MobLeLab), which is a collection of smartphone applications that allow scientific data collection, such as physiological variables, for educational purposes. The three types of MobLeLabs (simulators, built-in, and plug-in) are presented, as well as ideas on how to use smartphone sensors to collect physiological data. Additionally, we elaborate on the principles of the protocols for physiology education with MobLeLabs and discuss their importance to fostering scientific method reasoning by students.

Tracing Pilots' Situation Assessment by Neuroadaptive Cognitive Modeling

This study presents the integration of a passive brain-computer interface (pBCI) and cognitive modeling as a method to trace pilots' perception and processing of auditory alerts and messages during operations. Missing alerts on the flight deck can result in out-of-the-loop problems that can lead to accidents. By tracing pilots' perception and responses to alerts, cognitive assistance can be provided based on individual needs to ensure they maintain adequate situation awareness. Data from 24 participating aircrew in a simulated flight study that included multiple alerts and air traffic control messages in single pilot setup are presented. A classifier was trained to identify pilots' neurophysiological reactions to alerts and messages from participants' electroencephalogram (EEG). A neuroadaptive ACT-R model using EEG data was compared to a conventional normative model regarding accuracy in representing individual pilots. Results show that passive BCI can distinguish between alerts that are processed by the pilot as task-relevant or irrelevant in the cockpit based on the recorded EEG. The neuroadaptive model's integration of this data resulted in significantly higher performance of 87% overall accuracy in representing individual pilots' responses to alerts and messages compared to 72% accuracy of a normative model that did not consider EEG data. We conclude that neuroadaptive technology allows for implicit measurement and tracing of pilots' perception and processing of alerts on the flight deck. Careful handling of uncertainties inherent to passive BCI and cognitive modeling shows how the representation of pilot cognitive states can be improved iteratively for providing assistance.

Poor Subjective Sleep Quality Is Associated with Poor Occupational Outcomes in Elite Soldiers

We aimed to assess the relationship between subjective sleep quality and occupationally-relevant outcomes in military personnel. Participants were from an elite unit of US Army soldiers who worked extended (~30 h) shifts (with minimal recovery time between shifts) during 3-week work sessions. Questionnaires assessing subjective sleep quality during the month prior (Pittsburgh Sleep Quality Index [PSQI]) were administered at the beginning of the session. Occupational outcomes (emotional exhaustion, functional impairment, role overload, daytime sleepiness) were assessed on the final day of the session. Regression analyses were conducted to link sleep quality and occupational outcomes. The study sample participants had relatively poor sleep prior to the exercise (PSQI Global score average = 6.3 ± 3.1). Higher PSQI Global Scores prior to the work session longitudinally predicted daytime sleepiness (f2: 0.56) after the work session. PSQI component 7, which queries daytime dysfunction attributed to poor sleep quality, longitudinally predicted emotional exhaustion, functional impairment, and role overload (f2 range: 0.19-0.70). In conclusion, poor sleep quality-in aggregation with occupationally-mandated sleep loss-is predictive of poorer subsequent occupational outcomes. Future work should aim to increase sleep opportunities prior to occupationally-mandated sleep loss in order to build resilience when sleep loss is unavoidable.

Precision Medicine for Sleep Loss and Fatigue Management

Sleep loss is a widespread phenomenon and a public health threat. Sleep disorders, medical conditions, lifestyles, and occupational factors all contribute to insufficient sleep. Regardless of the underlying cause, insufficient sleep has well-defined consequences and the severity of said consequences partially influenced by individual characteristics. It is here where precision medicine needs to understand and define sleep insufficiency in hopes for personalizing medical approach to improve patient outcomes. Following a discussion on causes and consequences of sleep loss, this article discusses tools for assessing sleep sufficiency, mitigating strategies to sleep loss, and sleep loss in the context of fatigue management.

2B-Alert App: A mobile application for real-time individualized prediction of alertness

Knowing how an individual responds to sleep deprivation is a requirement for developing personalized fatigue management strategies. Here we describe and validate the 2B‐Alert App, the first mobile application that progressively learns an individual’s trait‐like response to sleep deprivation in real time, to generate increasingly more accurate individualized predictions of alertness. We incorporated a Bayesian learning algorithm within the validated Unified Model of Performance to automatically and gradually adapt the model parameters to an individual after each psychomotor vigilance test. We implemented the resulting model and the psychomotor vigilance test as a smartphone application (2B‐Alert App), and prospectively validated its performance in a 62‐hr total sleep deprivation study in which 21 participants used the app to perform psychomotor vigilance tests every 3 hr and obtain real‐time individualized predictions after each test. The temporal profiles of reaction times on the app‐conducted psychomotor vigilance tests were well correlated with and as sensitive as those obtained with a previously characterized psychomotor vigilance test device. The app progressively learned each individual’s trait‐like response to sleep deprivation throughout the study, yielding increasingly more accurate predictions of alertness for the last 24 hr of total sleep deprivation as the number of psychomotor vigilance tests increased. After only 12 psychomotor vigilance tests, the accuracy of the model predictions was comparable to the peak accuracy obtained using all psychomotor vigilance tests. With the ability to make real‐time individualized predictions of the effects of sleep deprivation on future alertness, the 2B‐Alert App can be used to tailor personalized fatigue management strategies, facilitating self‐management of alertness and safety in operational and non‐operational settings.