Dataset Information

A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine.

ABSTRACT:

Study objectives

Existing mathematical models of neurobehavioral performance cannot predict the beneficial effects of caffeine across the spectrum of sleep loss conditions, limiting their practical utility. Here, we closed this research gap by integrating a model of caffeine effects with the recently validated unified model of performance (UMP) into a single, unified modeling framework. We then assessed the accuracy of this new UMP in predicting performance across multiple studies.

Methods

We hypothesized that the pharmacodynamics of caffeine vary similarly during both wakefulness and sleep, and that caffeine has a multiplicative effect on performance. Accordingly, to represent the effects of caffeine in the UMP, we multiplied a dose-dependent caffeine factor (which accounts for the pharmacokinetics and pharmacodynamics of caffeine) to the performance estimated in the absence of caffeine. We assessed the UMP predictions in 14 distinct laboratory- and field-study conditions, including 7 different sleep-loss schedules (from 5 h of sleep per night to continuous sleep loss for 85 h) and 6 different caffeine doses (from placebo to repeated 200 mg doses to a single dose of 600 mg).

Results

The UMP accurately predicted group-average psychomotor vigilance task performance data across the different sleep loss and caffeine conditions (6% < error < 27%), yielding greater accuracy for mild and moderate sleep loss conditions than for more severe cases. Overall, accounting for the effects of caffeine resulted in improved predictions (after caffeine consumption) by up to 70%.

Conclusions

The UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance.

SUBMITTER: Ramakrishnan S

PROVIDER: S-EPMC5020365 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Publications

A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine.

Ramakrishnan Sridhar S Wesensten Nancy J NJ Kamimori Gary H GH Moon James E JE Balkin Thomas J TJ Reifman Jaques J

Sleep 20161001 10

<h4>Study objectives</h4>Existing mathematical models of neurobehavioral performance cannot predict the beneficial effects of caffeine across the spectrum of sleep loss conditions, limiting their practical utility. Here, we closed this research gap by integrating a model of caffeine effects with the recently validated unified model of performance (UMP) into a single, unified modeling framework. We then assessed the accuracy of this new UMP in predicting performance across multiple studies.<h4>Me ...[more]

PMID: 27397562

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Project description:Observationally, higher caffeine consumption is associated with poorer sleep and insomnia. We investigated whether these associations are a result of shared genetic risk factors and/or (possibly bidirectional) causal effects. Summary-level data were available from genome-wide association studies on caffeine intake (n = 91 462), plasma caffeine and caffeine metabolic rate (n = 9876), sleep duration and chronotype (being a "morning" versus an "evening" person) (n = 128 266), and insomnia complaints (n = 113 006). First, genetic correlations were calculated, reflecting the extent to which genetic variants influencing caffeine consumption and those influencing sleep overlap. Next, causal effects were estimated with bidirectional, two-sample Mendelian randomization. This approach utilizes the genetic variants most robustly associated with an exposure variable as an "instrument" to test causal effects. Estimates from individual variants were combined using inverse-variance weighted meta-analysis, weighted median regression and MR-Egger regression. We found no clear evidence for a genetic correlation between caffeine intake and sleep duration (rg = 0.000, p = .998), chronotype (rg = 0.086, p = .192) or insomnia complaints (rg = -0.034, p = .700). For plasma caffeine and caffeine metabolic rate, genetic correlations could not be calculated because of the small sample size. Mendelian randomization did not support causal effects of caffeine intake on sleep, or vice versa. There was weak evidence that higher plasma caffeine levels causally decrease the odds of being a morning person. Although caffeine may acutely affect sleep when taken shortly before bedtime, our findings suggest that a sustained pattern of high caffeine consumption is more likely to be associated with poorer sleep through shared environmental factors. Future research should identify such environments, which could aid the development of interventions to improve sleep.

Project description:BACKGROUND:It has been suggested that polymorphisms within CYP1A2 impact inter-individual variation in the response to caffeine. The purpose of this study was to explore the acute effects of caffeine on resistance exercise, jumping, and sprinting performance in a sample of resistance-trained men, and to examine the influence of genetic variation of CYP1A2 (rs762551) on the individual variation in responses to caffeine ingestion. METHODS:Twenty-two men were included as participants (AA homozygotes n = 13; C-allele carriers n = 9) and were tested after the ingestion of caffeine (3 mg/kg of body mass) and a placebo. Exercise performance was assessed with the following outcomes: (a) movement velocity and power output in the bench press exercise with loads of 25, 50, 75, and 90% of one-repetition maximum (1RM); (b) quality and quantity of performed repetitions in the bench press exercise performed to muscular failure with 85% 1RM; (c) vertical jump height in a countermovement jump test; and (d) power output in a Wingate test. RESULTS:Compared to placebo, caffeine ingestion enhanced: (a) movement velocity and power output across all loads (effect size [ES]: 0.20-0.61; p < 0.05 for all); (b) the quality and quantity of performed repetitions with 85% of 1RM (ES: 0.27-0.85; p < 0.001 for all); (c) vertical jump height (ES: 0.15; p = 0.017); and (d) power output in the Wingate test (ES: 0.33-0.44; p < 0.05 for all). We did not find a significant genotype × caffeine interaction effect (p-values ranged from 0.094 to 0.994) in any of the analyzed performance outcomes. CONCLUSIONS:Resistance-trained men may experience acute improvements in resistance exercise, jumping, and sprinting performance following the ingestion of caffeine. The comparisons of the effects of caffeine on exercise performance between individuals with the AA genotype and AC/CC genotypes found no significant differences. TRIAL REGISTRATION:Australian New Zealand Clinical Trials Registry. ID: ACTRN12619000885190.

Dataset Information

A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine.

Study objectives

Methods

Results

Conclusions

Publications

A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine.

Similar Datasets

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