Browsing by Author "Ramakrishnan, Sridhar"
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- Item2B-Alert App: A mobile application for real-time individualized prediction of alertness.(0000-00-00) Reifman, Jaques; Ramakrishnan, Sridhar; Liu, Jianbo; Kapela, Adam; Doty, Tracy J; Balkin, Thomas J; Kumar, Kamal; Khitrov, Maxim YKnowing 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
- ItemCaffeine dosing strategies to optimize alertness during sleep loss.(0000-00-00) Vital-Lopez, Francisco G; Ramakrishnan, Sridhar; Doty, Tracy J; Balkin, Thomas J; Reifman, JaquesSleep loss which affects about one third of the US population can severely impair physical and neurobehavioural performance Although caffeine the most widely used stimulant in the world can mitigate these effects currently there are no tools to guide the timing and amount of caffeine consumption to optimize its benefits In this work we provide an optimization algorithm suited for mobile computing platforms to determine when and how much caffeine to consume so as to safely maximize neurobehavioural performance at the desired time of the day under any sleep loss condition The algorithm is based on our previously validated Unified Model of Performance which predicts the effect of caffeine consumption on a psychomotor vigilance task We assessed the algorithm by comparing the caffeine dosing strategies timing and amount it identified with the dosing strategies used in four experimental studies involving total and partial sleep loss Through computer simulations we showed that the algorithm yielded caffeine dosing strategies that enhanced performance of the predicted psychomotor vigilance task by up to 64 while using the same total amount of caffeine as in the original studies In addition the algorithm identified strategies that resulted in equivalent performance to that in the experimental studies while reducing caffeine consumption by up to 65 Our work provides the first quantitative caffeine optimization tool for designing effective strategies to maximize neurobehavioural performance and to avoid excessive caffeine consumption during any arbitrary sleep loss condition
- ItemCan a mathematical model predict an individual's trait-like response to both total and partial sleep loss?(2015-01-12) Ramakrishnan, Sridhar; Lu, Wei; Laxminarayan, Srinivas; Wesensten, Nancy J; Rupp, Tracy L; Balkin, Thomas J; Reifman, JaquesHumans display a trait like response to sleep loss However it is not known whether this trait like response can be captured by a mathematical model from only one sleep loss condition to facilitate neurobehavioural performance prediction of the same individual during a different sleep loss condition In this paper we investigated the extent to which the recently developed unified mathematical model of performance UMP captured such trait like features for different sleep loss conditions We used the UMP to develop two sets of individual specific models for 15 healthy adults who underwent two different sleep loss challenges order counterbalanced separated by 2 4 weeks i 64 h of total sleep deprivation TSD and ii chronic sleep restriction CSR of 7 days of 3 h nightly time in bed We then quantified the extent to which models developed using psychomotor vigilance task data under TSD predicted performance data under CSR and vice versa The results showed that the models customized to an individual under one sleep loss condition accurately predicted performance of the same individual under the other condition yielding on average up to 50 improvement over non individualized group average model predictions This finding supports the notion that the UMP captures an individual s trait like response to different sleep loss conditions
- ItemDose-dependent model of caffeine effects on human vigilance during total sleep deprivation.(2014-08-05) Ramakrishnan, Sridhar; Laxminarayan, Srinivas; Wesensten, Nancy J; Kamimori, Gary H; Balkin, Thomas J; Reifman, JaquesCaffeine is the most widely consumed stimulant to counter sleep loss effects While the pharmacokinetics of caffeine in the body is well understood its alertness restoring effects are still not well characterized In fact mathematical models capable of predicting the effects of varying doses of caffeine on objective measures of vigilance are not available In this paper we describe a phenomenological model of the dose dependent effects of caffeine on psychomotor vigilance task PVT performance of sleep deprived subjects We used the two process model of sleep regulation to quantify performance during sleep loss in the absence of caffeine and a dose dependent multiplier factor derived from the Hill equation to model the effects of single and repeated caffeine doses We developed and validated the model fits and predictions on PVT lapse number of reaction times exceeding 500 ms data from two separate laboratory studies At the population average level the model captured the effects of a range of caffeine doses 50 300 mg yielding up to a 90 improvement over the two process model Individual specific caffeine models on average predicted the effects up to 23 better than population average caffeine models The proposed model serves as a useful tool for predicting the dose dependent effects of caffeine on the PVT performance of sleep deprived subjects and therefore can be used for determining caffeine doses that optimize the timing and duration of peak performance
- ItemPC-PVT 2.0: An updated platform for psychomotor vigilance task testing, analysis, prediction, and visualization.(0000-00-00) Reifman, Jaques; Kumar, Kamal; Khitrov, Maxim Y; Liu, Jianbo; Ramakrishnan, SridharBACKGROUND The psychomotor vigilance task PVT has been widely used to assess the effects of sleep deprivation on human neurobehavioral performance To facilitate research in this field we previously developed the PC PVT a freely available software system analogous to the gold standard PVT 192 that in addition to allowing for simple visual reaction time RT tests also allows for near real time PVT analysis prediction and visualization in a personal computer PC NEW METHOD Here we present the PC PVT 2 0 for Windows 10 operating system which has the capability to couple PVT tests of a study protocol with the study s sleep wake and caffeine schedules and make real time individualized predictions of PVT performance for such schedules We characterized the accuracy and precision of the software in measuring RT using 44 distinct combinations of PC hardware system configurations RESULTS We found that 15 system configurations measured RTs with an average delay of less than 10 ms an error comparable to that of the PVT 192 To achieve such small delays the system configuration should always use a gaming mouse as the means to respond to visual stimuli We recommend using a discrete graphical processing unit for desktop PCs and an external monitor for laptop PCs COMPARISON WITH EXISTING METHOD This update integrates a study s sleep wake and caffeine schedules with the testing software facilitating testing and outcome visualization and provides near real time individualized PVT predictions for any sleep loss condition considering caffeine effects CONCLUSIONS The software with its enhanced PVT analysis visualization and prediction capabilities can be freely downloaded from https pcpvt bhsai org
- ItemRandomized, double-blind, placebo-controlled, crossover study of the effects of repeated-dose caffeine on neurobehavioral performance during 48 h of total sleep deprivation.(0000-00-00) Hansen, Devon A; Ramakrishnan, Sridhar; Satterfield, Brieann C; Wesensten, Nancy J; Layton, Matthew E; Reifman, Jaques; Van Dongen, Hans P ARATIONALE Caffeine is widely used as a countermeasure against neurobehavioral impairment during sleep deprivation However little is known about the pharmacodynamic profile of caffeine administered repeatedly during total sleep deprivation OBJECTIVES To investigate the effects of repeated caffeine dosing on neurobehavioral performance during sleep deprivation we conducted a laboratory based randomized double blind placebo controlled crossover multi dose study of repeated caffeine administration during 48 h of sleep deprivation Twelve healthy adults mean age 27 4 years six women completed an 18 consecutive day in laboratory study consisting of three 48 h total sleep deprivation periods separated by 3 day recovery periods During each sleep deprivation period subjects were awakened at 07 00 and administered caffeine gum 0 200 or 300 mg at 6 18 30 and 42 h of wakefulness The Psychomotor Vigilance Test and Karolinska Sleepiness Scale were administered every 2 h RESULTS The 200 and 300 mg doses of caffeine mitigated neurobehavioral impairment across the sleep deprivation period approaching two fold performance improvements relative to placebo immediately after the nighttime gum administrations No substantive differences were noted between the 200 mg and 300 mg caffeine doses and adverse effects were minimal CONCLUSIONS The neurobehavioral effects of repeated caffeine dosing during sleep deprivation were most evident during the circadian alertness trough i e at night The difference between the 200 mg and 300 mg doses in terms of the mitigation of performance impairment was small Neither caffeine dose fully restored performance to well rested levels These findings inform the development of biomathematical models that more accurately account for the time of day and sleep pressure dependent effects of caffeine on neurobehavioral performance during sleep loss