Völler Hartmann

Völler Hartmann Ticker der Bundes-CDU

Die Hartmann Reederei hat ihren Stammsitz in Leer. Als Teil der Hartmann-​Gruppe deckt das Unternehmen die gesamte Wertschöpfungskette der Seeschifffahrt. Die Hartmann Reederei ist eine deutsche Reederei mit Sitz in Leer (Ostfriesland)​. Sie ist eine von mehreren Reedereien der ebenfalls in Leer ansässigen. Die Unternehmen der Hartmann-Gruppe bilden ein starkes Netzwerk maritimer Spezialunternehmen. Sie bieten Dienstleistungen über die komplette. Hartmann Müller Partner Ihre Wirtschaftsanwälte in Zürich. Seit 35 Jahren kompetent vertreten. Als Anwaltskanzlei mit Fokus. Biografie. Persönliche Angaben: Geboren am 4. September in Memmingen​/Allgäu; römisch-katholisch, verheiratet mit Richard Müller, zwei erwachsene.

Völler Hartmann

AdolfBlumentritt, HorstВ Martin, BerndBГјthe, BernhardВ MГ¶ller, KurtGaida, JosefВ Petzing, W.В bis 09/92В Ullrich, AnnemarieTischer, RuthВ В VГ¶lker, Karl-HeinzВ ab 09/92В В Sitze)В Hartmann, VolkhardВ Beisheim, JoachimВ В Sabine Hartmann-Müller MdL. Hauptstraße 18 Waldshut-Tiengen. Telefon 26 Fax 26 E-Mail: [email protected] Die Hartmann Reederei ist eine deutsche Reederei mit Sitz in Leer (Ostfriesland)​. Sie ist eine von mehreren Reedereien der ebenfalls in Leer ansässigen.

Only effect sizes for accuracy measures were computed for this cognitive category. We note that individuals with a background in cognitive psychology may consider many of these as long-term memory tests due to differences in nomenclature across fields.

Tests in reasoning and crystallized intelligence assessed mental processes such as problem solving, vocabulary exercises, and other forms of crystallized cognitive ability.

We had originally intended to analyze tests of verbal fluency e. However, as only three articles to date contained such tests and met all inclusion criteria Binks et al.

It has been recommended that studies entered into a meta-analysis be coded for study quality Chalmers et al.

This is especially important when the pool of studies entered into the analysis is highly heterogeneous and the designs have varying threats to internal and external validity Detsky et al.

In our survey of the literature, we observed significant discrepancies in how experiments of SD are conducted and controlled, and thus deemed that this step was appropriate for our analysis.

For between-subjects studies, were subjects randomized to the sleep-deprived and control groups?

For repeated-measures studies, was the study order counterbalanced to avoid the potential confound of order effects?

Were the treatment and control groups equal in number? Were they treated similarly e. Were subjects randomly recruited from the population?

Was the study sample representative of the population, or did the experiment include only a particular subgroup of people e. Did the cognitive tests used appropriately capture the construct of interest?

Were they well validated? Were appropriate dependent measures used in the analysis? Was the study conducted in a sleep laboratory?

Were subjects monitored during their time in the study? Were their diet and activity controlled?

Were participants screened for good sleep history or absence of sleep disorders? Was sleep history monitored in the period prior to the experiment e.

Studies were assessed and coded by two independent raters Julian Lim and one other rater who was trained on the criteria above.

They assessed interrater reliability using intraclass correlation coefficients from a two-way mixed model with raters as fixed effects and studies as random effects.

The intraclass correlation coefficient for the entire sample was. In addition to coding for study quality, we recorded the following variables for use as potential moderators in the secondary analysis: length of SD and the times of test administration which were used to calculate circadian offset.

The primary metric of the meta-analysis is the effect size, which is a standardized estimate of the magnitude of the treatment effect.

We calculated all effect sizes in this analysis, d values using a baseline test and the test at the most extreme point of SD in the experiment with a few exceptions, noted in Table 1.

Instead, we estimated this term using the pre- and posttest standard deviations and correlations, as suggested by Morris and DeShon In cases where this information was not available, we reverted to Formula 1 as our best estimate of the effect size.

Where only t or F values were reported, we converted these to effect sizes following the recommendations of Rosenthal , as shown in Equations 2 and 3.

Where only p values were reported for t tests , we back-converted these to t values using statistical tables and applied Formula 3.

Once a d value was obtained, its mathematical sign was adjusted so that worse performance following SD was always reflected by a negative effect size.

As recommended by Hedges and Olkin , we next adjusted for effect size inflation in studies with small samples by applying the correction in Equation 4.

The difference in this correction for between- and within-subjects studies is accounted for by the differing degrees of freedom in the denominator of the equation.

In order to combine the results of between- and within-subjects designs, all effect sizes need to be converted into a single metric.

As the effect of interest in this study was that of SD on an individual over time, the within-subjects design is the appropriate standard of measurement for this analysis.

Pre- and posttest correlations from this investigation generally fell in the. We computed the within-studies variance due to sampling error Var e for each of the data sets using Equation 6 for within-subjects studies and Equation 7 for between-subjects studies.

Separate analyses were conducted for accuracy or lapses and speed for the cognitive domains of simple attention, complex attention, working memory, and processing speed.

Only accuracy measures were compiled for the domains of short-term memory and reasoning. We calculated the overall average effect size for each outcome measure type and domain using Equation 8.

Each effect size was weighted by the inverse of its sampling variance w i , thus giving relatively less weight to studies with small sample sizes.

Effect sizes were also weighted by their individual mean-adjusted study quality sq i ; i. This was clearly the case in our analysis; as a most basic example, the length of SD varied from 24 to 48 hr between studies, and it is known that the magnitude of performance deficits grows with escalating sleep pressure Doran et al.

A complete list of studies and individual effect sizes is presented in Table 1. The total sample size for the analysis was 1,, with an average of Average effect sizes for each cognitive domain and outcome are presented in Table 2.

As there were relatively few studies in each of these categories, however, it is possible that the analysis had insufficient power to detect a significant effect for these outcomes.

As anticipated, the largest effects of 24 — 48 hr of SD were on tests of vigilance, or simple attention.

Effect sizes for complex attention and working memory fell in the moderate range, and tests of processing speed showed on average small but significant effects.

We performed analyses of variance on the aggregate effect sizes to test two sets of null hypotheses: first, that average effect sizes are identical across cognitive domains with separate analyses conducted for speed and accuracy , and second, that average effect sizes for speed and accuracy are identical within each cognitive domain.

As two of the cognitive domains short-term memory and reasoning contained only one outcome measure, we did not enter all information into a two-way analysis of variance.

The I 2 value is an index of the proportion of variance within each analysis that is due to between-studies differences; Higgins et al.

I 2 values in each analysis ranged from small reasoning: 5. Indeed, given that the number of hours of SD in these studies ranged from 24 to 48, and that several types of cognitive tests made up these collective indices, it would have been surprising to discover low I 2 values in this first-pass analysis.

Forest plots for a sample cognitive domain lapses in simple attention tests. See the supplemental materials file for references to the studies cited here.

Forest plots for a sample cognitive domain reaction times in simple attention tests. We coded three study variables to test their impact as moderators of the effect of SD.

Circadian time was estimated by plotting the time of test administration as a sinusoidal function with a hr period and a performance nadir at hr, with peak amplitude arbitrarily defined as 1.

Circadian offset was computed by subtracting the time of test administration for sleep-deprived subjects from time of test administration of the control group.

Homeostatic sleep pressure was estimated as the elapsed time between sleep offset and time of test administration. In cases where any of this information was not explicitly reported, or the testing time occurred over a range greater than 2 hr, we did not code these variables, and the effect size was excluded from the moderator analysis.

As there were insufficient cases to conduct separate metaregressions within each cognitive category, we combined all results for accuracy and reaction time effects, and conducted stepwise multiple regression within these two larger data sets, using the average effect size found for each cognitive domain as a covariate.

In contrast, the overall model for reaction time measures was not significant, indicating that none of the coded variables were a significant predictor of heterogeneity in this sample.

The results from our meta-analysis support the conclusions of previous reviews that short-term total SD has a significant deleterious effect across most cognitive domains.

Our current study represents an advance over previous meta-analyses in several important respects.

Second, we weighted each effect size on the basis of study quality, thus giving less influence to studies that may have been less well conducted.

Third, we had more stringent inclusion criteria than Philibert , which increased the homogeneity of our sample.

Finally, and most important, we classified behavioral tests into finer grained cognitive domains than previous meta-analyses, further increasing the similarity of studies within each subsample.

Overall, average effect sizes appear to fall along a continuum, with tasks of greater complexity affected relatively less after total SD.

The relative magnitude of effect sizes across cognitive domains was similar to those seen in the meta-analysis of Philibert , although the absolute size of these effects was smaller across all categories.

This is likely due to two reasons: We excluded all studies with a period of total SD greater than 48 hr, and we did not disattenuate effect sizes based on test—retest reliability of dependent measures.

As anticipated, the combined effect size for simple attention and vigilance tasks was the largest among all the categories studied.

In contrast, average effect sizes for complex attention and working memory tests fell into the moderate range. Although this pattern of results has been observed in the literature, this is, to our knowledge, the first time that this difference has been systematically investigated in a large body of studies.

Several points of interest arise on inspection of the group effect sizes of the complex cognitive tasks all categories other than simple attention.

First, we note that task performance in the complex attention category is relatively spared when compared with simple attention.

These data are compelling, as many of the complex attention tests differ from the simple attention tests in only a single cognitive process e.

This finding suggests that for tests of orienting or executive attention, performance is relatively preserved after SD either because of the greater salience of the bottom-up feed and thus the reduced need for internally motivated top-down control or because of the recruitment of additional mental operations.

However, we also observe that complexity alone is an inadequate construct with which to identify tasks that may not be as affected by SD, as there were still substantial effect size differences among complex tasks in different domains.

The nuances of these behavioral effects, as well as their neural correlates, should continue to be an interesting and fruitful area of study.

We failed to find significant effects in two of the categories tested. First, there was no effect of SD on accuracy measures in tests of reasoning and crystallized intelligence.

Crystallized abilities e. It is unsurprising, therefore, that outcomes on these tests are relatively unaffected by short-term SD. Second, the average effect size of the change in accuracy measures for tests of processing speed failed but only barely to reach statistical significance.

There are at least two potential explanations for this finding. Nearly all the tasks in the processing speed category were self-paced, as opposed to work paced, and several authors have commented on the differences between these two classes of tests.

Williams et al. Koslowsky and Babkoff also found a similar effect of work- versus self-paced tasks in their meta-analysis, although this increased effect size was seen only in studies with more than 48 hr of SD.

A less common explanation of the relatively preserved accuracy on processing speed tasks relates to the nature of the operations being performed in them.

These operations usually involve high levels of automaticity e. An important feature of the current meta-analysis was the separate aggregation of accuracy and reaction time measures.

This point is not intuitive and warrants further discussion. Figure 3 illustrates the curve representing the speed—accuracy trade-off in a typical cognitive test, as well as the downward shift of this curve following a period of SD.

The unexplored factor in this relationship is whether SD also biases subjects toward faster or slower responding, as represented by a shift along the lower curve.

For instance, increases in the number of commission errors or false alarms on simple reaction time tests after SD have been attributed to increased disinhibition Dorrian et al.

Illustration of two possible ways in which sleep deprivation SD can affect speed and accuracy variables.

Two sources of change may potentially occur following a period of total SD: a downward shift of the performance curve and a movement along the curve.

A movement along the curve i. As it turns out, the results of our analysis show remarkable agreement between accuracy and reaction time measures in each cognitive category: Overall, there was no significant effect when comparing accuracy and reaction time across the sample.

This finding suggests that, on average, SD does not bias subjects toward either faster or more accurate responding, although this claim cannot be made of any individual cognitive test.

Of the three moderator variables studied, only hours awake homeostatic sleep drive or sleep pressure was a significant moderator of the effect of SD, and only for accuracy, not reaction time variables.

A likely explanation for this negative result is that much of the observed heterogeneity is due to the variety of cognitive tests in each sample.

If this assertion is correct, it implies that the amount of impairment on tests that putatively assess the same cognitive domain may still differ considerably following SD.

In other words, the validity of these tests in assessing the cognitive process may not be as high after SD.

For example, total SD is known to exacerbate the time-on-task effect Doran et al. To obtain an objective standard of impairment, therefore, it may be necessary to establish norms on several of the most commonly used tests in each domain.

Although it would have been interesting to test the moderating effect of self-paced and work-paced paradigms in this analysis, these variables were highly confounded with cognitive domain i.

From the data obtained in the main effects, however, we can infer that the differential effects of self-paced versus work paced on accuracy and reaction time measures are unlikely to be significant as suggested in previous meta-analyses.

Instead, it is possible that these effects are present only under certain conditions e. As stated in the introduction, the chief objective of this meta-analysis was not to rule out any particular theoretical model but to direct attention to which of these models may have the greatest importance in explaining the real-world consequences of total SD.

Although total SD does produce statistically significant differences in most cognitive domains, the largest effects are seen in tests of simple, sustained attention.

Moreover, relatively brief failures of vigilance may potentially lead to disastrous consequences. We argue, therefore, that this cognitive module is of the greatest practical concern in combating SD-related problems in real-world situations.

A striking feature of this deficit in sustained attention is how rapidly large changes emerge. Although our analysis was restricted to subjects who had gone a single night without sleep, effect sizes were still large for both speed and accuracy measures on simple attention tasks.

These findings support the data showing that deficits in sustained attention often presage the other observable cognitive effects of SD and may have considerable utility as an early warning system for imminent cognitive failure.

This cognitive component should therefore be one of the primary targets of assessment for work fitness and a basis for decisions on whether subsequent countermeasures should be applied.

On the next rung of the hierarchy, we note that tests of working memory and other tests of executive attention are also robustly affected by one night of SD.

Considerable research has been conducted over the past several decades to assess the effects of SD on decision making and its component subprocesses e.

Finally, although tests of processing speed and cognitive throughput such as the Digit Symbol Substitution Test are commonly used in SD paradigms, the results of this analysis demonstrate that their effects are relatively small compared with those of other tests.

The implication of this finding is that changes in processing speed may be theoretically interesting but not of great practical significance in explaining and predicting real-world cognitive failures Monk, This analysis contains a small number of limitations that may have affected the validity of the conclusions drawn.

As we were able to obtain only a small amount of unpublished data, it is possible that there was a bias in the analysis toward effect sizes that reached statistical significance.

We are, therefore, relatively confident that the study was not greatly affected by publication bias.

Although every effort was made in this analysis to classify studies into appropriate and meaningful categories, it is clear that with the possible exception of simple attention, pure assays of most of the cognitive domains we have identified do not exist.

Moreover, there remained numerous dissimilarities among the forms and characteristics of the tests within each category e. As discussed, this is the most likely reason why heterogeneity was in the moderate range for almost all categories studied.

Despite these drawbacks, we propose that our taxonomy is a useful heuristic for several reasons.

First, significant between-categories differences were found in the meta-analysis, suggesting that we have captured meaningful constructs with the classification we employed.

Second, we have stayed faithful to categories that are well defined in the neuropsychological literature.

In many cases, focal deficits on these tests have been observed in patients with specific pathologies or injuries e. Finally, several of the domains studied here have relatively high external validity.

For instance, the challenge in simple attention tasks is similar to the real-world demands on air traffic controllers, and tasks such as the Psychomotor Vigilance Test have been shown to correlate highly with other indicators of dangerous, drowsy driving Dinges et al.

We were not able to study a number of moderator effects that may be important predictors of the residual intradomain heterogeneity.

Task duration is likely to be one of these factors, with longer tasks associated with greater effect sizes due to the presence of the time-on-task effect.

We were unable to code this moderator chiefly because many articles did not report task length and because of the variability in time to completion for all tasks that were self-paced.

As we have already mentioned, the difference between self-paced and work-paced tests was highly confounded with cognitive domain, making it unfeasible to test this as a moderator.

Finally, a substantial number of studies entered into this meta-analysis reported only accuracy or reaction time as a dependent variable in their final published work.

As a result, we could not conduct paired comparisons of these measures to assess their reliability.

We encourage authors publishing in this field in the future to consider reporting both accuracy and reaction time measures where appropriate so that their relationship after SD can be better explored.

We also suggest that, wherever possible, data from individual test bouts and not just omnibus F values for a series of bouts be reported, so as to enable the inclusion of more studies in future quantitative syntheses.

The results of this analysis have revealed the pattern of effects across cognitive domains and outcomes after a period of short-term total SD.

Overall, there was a significant difference among cognitive domains, but not between speed and accuracy, suggesting that SD has differential effects on different cognitive processes but does not bias subjects toward either faster or more accurate responding in any of these domains.

As some of the known key moderators of this effect did not explain the remaining between-studies variance, we infer that that the remaining heterogeneity is due to intertest differences and that test characteristics can influence the level of performance in the sleep-deprived state even when they are ostensibly assessing the same cognitive domain.

Finally, our results indicate that simple attention is the cognitive domain most strongly affected by short-term SD. Although decrements in other cognitive modules such as decision-making and memory processes no doubt contribute to real-world errors and accidents, the results of this analysis argue that deficits in sustained attention may represent the most parsimonious explanation for these occurrences.

Thus, in light of these and other data, we believe that countermeasures targeting this cognitive module may be the most efficient means of accident prevention in industries where SD poses a significant safety risk.

David F. We wish to thank Oo Htaik for his assistance in coding study quality and moderator variables. In cases where reliability information for a particular test was not available, we first searched the literature for tests that were highly similar to the one used, then as a last resort used the average reliability from tests within the respective cognitive domain.

In all cases, separate reliability coefficients were located and used for accuracy and reaction time measures. For the purposes of comparison, the supplemental materials table reports pooled effect sizes for each cognitive domain with and without these study-quality weights.

References marked with an asterisk indicate studies included in the meta-analysis that are discussed in the text.

National Center for Biotechnology Information , U. Psychol Bull. Author manuscript; available in PMC Feb Julian Lim and David F.

Author information Copyright and License information Disclaimer. Copyright notice. The publisher's final edited version of this article is available at Psychol Bull.

See other articles in PMC that cite the published article. Keywords: sleep deprivation, meta-analysis, attention, working memory, processing speed.

The Neuropsychological Hypothesis Several reviewers have suggested that SD has domain-specific effects on cognition, with particular focus on tasks mediated by prefrontal cortex PFC function.

The Vigilance Hypothesis Finally, other reviewers have singled out arousal and vigilance as general factors that explain much of the variance in cognitive deficits following sleep loss.

Prior Meta-Analyses To our knowledge, three meta-analyses have been conducted to date on the effects of SD on performance.

Method Study Selection Our primary collection of literature was gathered by searching online electronic databases for articles relevant to our topic of interest through December These criteria were as follows: Participants in the study must all have been healthy adults aged 18 years and older.

Table 1 List of Studies and Effect Sizes. Open in a separate window. Effect sizes were calculated on the basis of change from baseline performance to performance at the melatonin midpoint, which was around hr for each subject.

Tasks were performed while subjects underwent positron emission tomography scanning. Cognitive Domains Each cognitive test was assigned a domain according to the classification scheme below.

Complex attention Tests in complex attention assessed all attentional processes more demanding than those in the first category e.

Processing speed Tests in processing speed primarily assessed cognitive throughput or processing speed, requiring multiple repetitions of a rehearsed process within a fixed period.

Short-term memory Tests in short-term memory involved the encoding, maintenance, and retrieval of information.

Reasoning and crystallized intelligence Tests in reasoning and crystallized intelligence assessed mental processes such as problem solving, vocabulary exercises, and other forms of crystallized cognitive ability.

Verbal fluency We had originally intended to analyze tests of verbal fluency e. Coding for Study Quality It has been recommended that studies entered into a meta-analysis be coded for study quality Chalmers et al.

Adequacy of control group Were the treatment and control groups equal in number? Subject recruitment Were subjects randomly recruited from the population?

Quality of statistical analysis Were appropriate statistical tests used to analyze the data?

Adequacy of measures used Did the cognitive tests used appropriately capture the construct of interest? Adequacy of control over SD Was the study conducted in a sleep laboratory?

Adequacy of control over sleep history Were participants screened for good sleep history or absence of sleep disorders?

Coding Reliability Studies were assessed and coded by two independent raters Julian Lim and one other rater who was trained on the criteria above.

Other Study Variables In addition to coding for study quality, we recorded the following variables for use as potential moderators in the secondary analysis: length of SD and the times of test administration which were used to calculate circadian offset.

Effect Size Calculation The primary metric of the meta-analysis is the effect size, which is a standardized estimate of the magnitude of the treatment effect.

Calculation of Sampling Variance We computed the within-studies variance due to sampling error Var e for each of the data sets using Equation 6 for within-subjects studies and Equation 7 for between-subjects studies.

Meta-Analysis Procedure Separate analyses were conducted for accuracy or lapses and speed for the cognitive domains of simple attention, complex attention, working memory, and processing speed.

Results A complete list of studies and individual effect sizes is presented in Table 1. Aggregate Effect Sizes Average effect sizes for each cognitive domain and outcome are presented in Table 2.

Cognitive domain z Simple attention 0. Figure 1. Figure 2. Moderator Analyses We coded three study variables to test their impact as moderators of the effect of SD.

Discussion The results from our meta-analysis support the conclusions of previous reviews that short-term total SD has a significant deleterious effect across most cognitive domains.

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Butz, J. Welge, G. Plehn, A. Meissner, H. Trappe, D. Horstkotte, L. Sager, H. Schunkert, V. Kriegeskorte, T. Eisele, C.

Herholz, T. Rampp, M. Schneider, C. Kadel, S. Kronberg, A. Motz, A. Deneke Bochum , S. Nowak, B. Misselwitz, A. Erdogan, R.

Funck, W. Irnich, C. Israel, H. Olbrich, H. Schmidt, J. Sperzel, M. Ismer, W. Trautmann, M. Just-Teetzmann, S. Schade, A. Knaus, R. Riedel, B.

Weyerbrock, C. Kolb, R. Dietl, M. Morgenstern, M. Fleckenstein, W. Hopperdietzel, C. Mackes, S. Silber, H. Kolb, J.

Dietl, R. Schmidt, S. Weyerbrock, M. Mackes, B. Binner, J. Bodky, I. Szendey, M. Maunz, A. Klein, M. Jetter, C. Kolb, G.

Ritscher, G. Lotze, K. Lang, L. Binner, V. Schibgilla, S. Weyerbrock, R. Ventura, G. Reischl, B. Sperzel, J. Scheiner, J. Bondke, A.

Borges, A. Grohmann, C. Melzer, G. Baumann Berlin P Detailed onset scenario analysis in pacemaker patients with 12 paroxysmal atrial fibrillation K.

Mortensen, M. Aydin, H. Klemm, N. Gosau, P. Peitsmeyer, A. Schuchert, T. Meinertz, S. Willems, R. Boguschewski, A.

Hafer, J. Boguschewski, J. Brill, M. Stockmann, R. Pfaffenberger, M. Gwechenberger, B. Richter, H.

Ehnert, K. Heinroth, S. Nuding, P. Wasmer, P. Milberg, G. Breithardt, L. Ehnert, P. Schirdewahn, S.

Nuding, K. Werdan, K. Wasmer, S. Zellerhoff, P. Milberg, L. Kranig, E. Wolff, J. Kopf, J. Kettering, Chr. Kampmann, H.

Mollnau, K. Kreitner, T. Marschang, M. Servatius, T. Rostock, D. Steven, B. Lutomsky, I. Drewitz, K. Konstantinidou, B.

Schmidt, F. Ouyang, B. Kuck, K. Thiem, G. Lutter, A. Rahimi, J. Richardt Bad Segeberg , H. Sherif, M. Schwarz, H. Beurich, A. Khattab, H.

Breithardt, H. Rittger, A. Sinha, M. Schmidt, G. Dietinger, A. Jakob, G. Simonis, R. Lange, M. Wiemer, S.

Eckert, L. Faber, T. Bitter, D. Franke, H. Ulbricht, B. Gremmler, M. Hoffmann, A. Hafer, H. Schunkert, F.

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Lim and Dinges also spotlighted vigilant attention as a cognitive process that is consistently and robustly affected by total SD.

There is strong experimental evidence for these assertions. Tests of sustained attention e. Finally, models of attention often stress that vigilance and sustained attention are fundamentally important to many higher aspects of cognition and that these higher processes will necessarily decline if a subject is not able to sustain a sufficient level of vigilance while performing a task Sturm et al.

The three models discussed are above not mutually incompatible. One could argue that the controlled attention hypothesis and the vigilance hypothesis merely take different perspectives in explaining the same set of phenomena and that the neuropsychological hypothesis, though consistent with both of these models, accounts for effects above and beyond what may be expected from either.

As a result, certain theorists have proposed a more integrative approach in interpreting the available data.

For instance, Boonstra, Stins, Daffertshofer, and Beek suggested that impairment in the PFC following a period of SD may underlie changes in both executive functioning and attention, stressing the role of the PFC in the interaction between top-down and bottom-up processes.

If we believe that there is some predictive power in all of these models, a new and more pragmatic question arises: To what degree are different cognitive functions impaired?

Without a standardized basis of comparison, there is no way to assess the relative importance of each of these theoretical approaches.

Knowledge of the effect sizes associated with each of these impairments may be of use in determining the targets for intervention in real-life situations so as to minimize the deleterious effects of SD on workers in the field.

The meta-analysis M. This method entails a systematic search for all articles related to a topic that meet a preordained set of inclusion criteria, calculating the effect sizes in all studies that meet these criteria and accumulating these effect sizes by weighting them on the basis of their sample sizes.

This final step uses an estimate of sampling variance to give greater weight to studies with larger samples, thus providing a more unbiased estimate of the true effect size of a given manipulation.

To our knowledge, three meta-analyses have been conducted to date on the effects of SD on performance. Koslowsky and Babkoff summarized 27 studies and reported that total SD showed greater correlations with performance as the duration of deprivation increased, and that speed or latency variables were generally affected more than accuracy measures, arguing that these data support the lapse hypothesis of Williams et al.

The same pattern was seen with task duration, with short tasks being more adversely affected after short periods of SD, and the reverse true of long tasks.

These authors found that partial SD in which a restricted amount of sleep is allowed every night had a more pronounced effect on cognition overall than total SD.

Most recently, Philibert conducted a meta-analysis to assess the effects of SD on cognitive and clinical performance in physicians and nonphysicians.

This final analysis was the most comprehensive and methodologically sophisticated of the three, with 60 studies and 5, individual effect indices included.

The studies described above have a number of weaknesses that remain to be addressed. First, two of these meta-analyses excepting Koslowsky and Babkoff, aggregated performance variables measuring accuracy and speed into a single category when summarizing effect sizes.

There is little empirical evidence that speed and accuracy are uniformly affected by SD, and aggregating the two outcome types may result in the loss of interesting information.

Second, the cognitive categories in these previous analyses were relatively coarse; for example, no distinction was made in the Philibert analysis between working memory and short-term memory paradigms.

Finally, none of the previous analyses performed attempted to control for differences in study quality or took into account the interindividual differences present in cognitive responses to total SD.

Our purpose in conducting the current meta-analysis was thus twofold: a to investigate the relative magnitude of the effects of SD on different cognitive domains and b to explore whether the effects on accuracy and reaction time measures were different in any of these domains.

The overarching motivation for this analysis was to uncover evidence that may inform our understanding of the effects of short-term acute SD on cognition and thus aid in assessing the relative importance of current theoretical models.

Our primary collection of literature was gathered by searching online electronic databases for articles relevant to our topic of interest through December In each of these databases, we conducted a search using a combination of the following terms: sleep deprivation or sleep restriction and cognition , attention , memory , performance , vigilance , and executive function 12 combinations in all.

This search yielded 4, hits in total. We next scanned the abstracts of these articles to determine their suitability for inclusion in the analysis.

In total, of the articles were empirical studies that employed SD as a primary independent variable and used at least one cognitive measure as a dependent outcome.

We subsequently obtained the full text of these articles to determine whether they met full inclusion criteria. These criteria were as follows:.

The study must have included as its primary manipulation a specified length of total SD not exceeding 48 hr. The study must have included as a dependent measure at least one test of basic cognitive function, and the description of the test must have been specific enough for us to classify it as an assay of a particular cognitive domain we elaborate on this point further below.

There was sufficient statistical information in the study for the calculation of effect sizes. Because of the restrictions imposed by Criterion 3, a number of subareas within the realm of SD research necessarily had to be omitted from this analysis.

A survey of the literature on SD and decision making revealed that outcome variables on these tests did not form a cluster that was homogeneous enough to warrant a quantitative synthesis.

Moreover, it is unclear how outcome variables from standardized decision-making tests e. In addition to this online literature search, we obtained data from several other sources.

We conducted hand searches of the journal Sleep and the Journal of Sleep Research from to We also reviewed the reference lists of the major review articles on SD and cognitive performance that have been published over the last several years.

We received additional data from one of these laboratories, as well as replies from all but one of the remaining investigators informing us that they did not have suitable data for inclusion.

In total, 70 articles and data sets met inclusion criteria and were included in the meta-analysis see Table 1. Among these, numerous data sets contained more than one cognitive outcome; these were coded separately, according to the recommendations of Hunter and Schmidt Altogether, aggregated effect sizes and 5, individual effect sizes were calculated from these data sets.

For some imaging i. Dashes indicate data not reported. Each cognitive test was assigned a domain according to the classification scheme below.

This was the only category in which effect sizes were calculated for lapses and omissions instead of accuracy.

Tests in complex attention assessed all attentional processes more demanding than those in the first category e.

Tests in processing speed primarily assessed cognitive throughput or processing speed, requiring multiple repetitions of a rehearsed process within a fixed period.

Tests in short-term memory involved the encoding, maintenance, and retrieval of information. The amount of information to be stored had to exceed working memory capacity, and maintenance typically occurred over a longer period.

This domain was further subdivided into short-term memory recall and short-term memory recognition. Only effect sizes for accuracy measures were computed for this cognitive category.

We note that individuals with a background in cognitive psychology may consider many of these as long-term memory tests due to differences in nomenclature across fields.

Tests in reasoning and crystallized intelligence assessed mental processes such as problem solving, vocabulary exercises, and other forms of crystallized cognitive ability.

We had originally intended to analyze tests of verbal fluency e. However, as only three articles to date contained such tests and met all inclusion criteria Binks et al.

It has been recommended that studies entered into a meta-analysis be coded for study quality Chalmers et al.

This is especially important when the pool of studies entered into the analysis is highly heterogeneous and the designs have varying threats to internal and external validity Detsky et al.

In our survey of the literature, we observed significant discrepancies in how experiments of SD are conducted and controlled, and thus deemed that this step was appropriate for our analysis.

For between-subjects studies, were subjects randomized to the sleep-deprived and control groups? For repeated-measures studies, was the study order counterbalanced to avoid the potential confound of order effects?

Were the treatment and control groups equal in number? Were they treated similarly e. Were subjects randomly recruited from the population?

Was the study sample representative of the population, or did the experiment include only a particular subgroup of people e.

Did the cognitive tests used appropriately capture the construct of interest? Were they well validated? Were appropriate dependent measures used in the analysis?

Was the study conducted in a sleep laboratory? Were subjects monitored during their time in the study? Were their diet and activity controlled?

Were participants screened for good sleep history or absence of sleep disorders? Was sleep history monitored in the period prior to the experiment e.

Studies were assessed and coded by two independent raters Julian Lim and one other rater who was trained on the criteria above.

They assessed interrater reliability using intraclass correlation coefficients from a two-way mixed model with raters as fixed effects and studies as random effects.

The intraclass correlation coefficient for the entire sample was. In addition to coding for study quality, we recorded the following variables for use as potential moderators in the secondary analysis: length of SD and the times of test administration which were used to calculate circadian offset.

The primary metric of the meta-analysis is the effect size, which is a standardized estimate of the magnitude of the treatment effect.

We calculated all effect sizes in this analysis, d values using a baseline test and the test at the most extreme point of SD in the experiment with a few exceptions, noted in Table 1.

Instead, we estimated this term using the pre- and posttest standard deviations and correlations, as suggested by Morris and DeShon In cases where this information was not available, we reverted to Formula 1 as our best estimate of the effect size.

Where only t or F values were reported, we converted these to effect sizes following the recommendations of Rosenthal , as shown in Equations 2 and 3.

Where only p values were reported for t tests , we back-converted these to t values using statistical tables and applied Formula 3.

Once a d value was obtained, its mathematical sign was adjusted so that worse performance following SD was always reflected by a negative effect size.

As recommended by Hedges and Olkin , we next adjusted for effect size inflation in studies with small samples by applying the correction in Equation 4.

The difference in this correction for between- and within-subjects studies is accounted for by the differing degrees of freedom in the denominator of the equation.

In order to combine the results of between- and within-subjects designs, all effect sizes need to be converted into a single metric.

As the effect of interest in this study was that of SD on an individual over time, the within-subjects design is the appropriate standard of measurement for this analysis.

Pre- and posttest correlations from this investigation generally fell in the. We computed the within-studies variance due to sampling error Var e for each of the data sets using Equation 6 for within-subjects studies and Equation 7 for between-subjects studies.

Separate analyses were conducted for accuracy or lapses and speed for the cognitive domains of simple attention, complex attention, working memory, and processing speed.

Only accuracy measures were compiled for the domains of short-term memory and reasoning. We calculated the overall average effect size for each outcome measure type and domain using Equation 8.

Each effect size was weighted by the inverse of its sampling variance w i , thus giving relatively less weight to studies with small sample sizes.

Effect sizes were also weighted by their individual mean-adjusted study quality sq i ; i. This was clearly the case in our analysis; as a most basic example, the length of SD varied from 24 to 48 hr between studies, and it is known that the magnitude of performance deficits grows with escalating sleep pressure Doran et al.

A complete list of studies and individual effect sizes is presented in Table 1. The total sample size for the analysis was 1,, with an average of Average effect sizes for each cognitive domain and outcome are presented in Table 2.

As there were relatively few studies in each of these categories, however, it is possible that the analysis had insufficient power to detect a significant effect for these outcomes.

As anticipated, the largest effects of 24 — 48 hr of SD were on tests of vigilance, or simple attention. Effect sizes for complex attention and working memory fell in the moderate range, and tests of processing speed showed on average small but significant effects.

We performed analyses of variance on the aggregate effect sizes to test two sets of null hypotheses: first, that average effect sizes are identical across cognitive domains with separate analyses conducted for speed and accuracy , and second, that average effect sizes for speed and accuracy are identical within each cognitive domain.

As two of the cognitive domains short-term memory and reasoning contained only one outcome measure, we did not enter all information into a two-way analysis of variance.

The I 2 value is an index of the proportion of variance within each analysis that is due to between-studies differences; Higgins et al.

I 2 values in each analysis ranged from small reasoning: 5. Indeed, given that the number of hours of SD in these studies ranged from 24 to 48, and that several types of cognitive tests made up these collective indices, it would have been surprising to discover low I 2 values in this first-pass analysis.

Forest plots for a sample cognitive domain lapses in simple attention tests. See the supplemental materials file for references to the studies cited here.

Forest plots for a sample cognitive domain reaction times in simple attention tests. We coded three study variables to test their impact as moderators of the effect of SD.

Circadian time was estimated by plotting the time of test administration as a sinusoidal function with a hr period and a performance nadir at hr, with peak amplitude arbitrarily defined as 1.

Circadian offset was computed by subtracting the time of test administration for sleep-deprived subjects from time of test administration of the control group.

Homeostatic sleep pressure was estimated as the elapsed time between sleep offset and time of test administration. In cases where any of this information was not explicitly reported, or the testing time occurred over a range greater than 2 hr, we did not code these variables, and the effect size was excluded from the moderator analysis.

As there were insufficient cases to conduct separate metaregressions within each cognitive category, we combined all results for accuracy and reaction time effects, and conducted stepwise multiple regression within these two larger data sets, using the average effect size found for each cognitive domain as a covariate.

In contrast, the overall model for reaction time measures was not significant, indicating that none of the coded variables were a significant predictor of heterogeneity in this sample.

The results from our meta-analysis support the conclusions of previous reviews that short-term total SD has a significant deleterious effect across most cognitive domains.

Our current study represents an advance over previous meta-analyses in several important respects. Second, we weighted each effect size on the basis of study quality, thus giving less influence to studies that may have been less well conducted.

Third, we had more stringent inclusion criteria than Philibert , which increased the homogeneity of our sample.

Finally, and most important, we classified behavioral tests into finer grained cognitive domains than previous meta-analyses, further increasing the similarity of studies within each subsample.

Overall, average effect sizes appear to fall along a continuum, with tasks of greater complexity affected relatively less after total SD.

The relative magnitude of effect sizes across cognitive domains was similar to those seen in the meta-analysis of Philibert , although the absolute size of these effects was smaller across all categories.

This is likely due to two reasons: We excluded all studies with a period of total SD greater than 48 hr, and we did not disattenuate effect sizes based on test—retest reliability of dependent measures.

As anticipated, the combined effect size for simple attention and vigilance tasks was the largest among all the categories studied. In contrast, average effect sizes for complex attention and working memory tests fell into the moderate range.

Although this pattern of results has been observed in the literature, this is, to our knowledge, the first time that this difference has been systematically investigated in a large body of studies.

Several points of interest arise on inspection of the group effect sizes of the complex cognitive tasks all categories other than simple attention.

First, we note that task performance in the complex attention category is relatively spared when compared with simple attention.

These data are compelling, as many of the complex attention tests differ from the simple attention tests in only a single cognitive process e.

This finding suggests that for tests of orienting or executive attention, performance is relatively preserved after SD either because of the greater salience of the bottom-up feed and thus the reduced need for internally motivated top-down control or because of the recruitment of additional mental operations.

However, we also observe that complexity alone is an inadequate construct with which to identify tasks that may not be as affected by SD, as there were still substantial effect size differences among complex tasks in different domains.

The nuances of these behavioral effects, as well as their neural correlates, should continue to be an interesting and fruitful area of study.

We failed to find significant effects in two of the categories tested. First, there was no effect of SD on accuracy measures in tests of reasoning and crystallized intelligence.

Crystallized abilities e. It is unsurprising, therefore, that outcomes on these tests are relatively unaffected by short-term SD.

Second, the average effect size of the change in accuracy measures for tests of processing speed failed but only barely to reach statistical significance.

There are at least two potential explanations for this finding. Nearly all the tasks in the processing speed category were self-paced, as opposed to work paced, and several authors have commented on the differences between these two classes of tests.

Williams et al. Koslowsky and Babkoff also found a similar effect of work- versus self-paced tasks in their meta-analysis, although this increased effect size was seen only in studies with more than 48 hr of SD.

A less common explanation of the relatively preserved accuracy on processing speed tasks relates to the nature of the operations being performed in them.

These operations usually involve high levels of automaticity e. An important feature of the current meta-analysis was the separate aggregation of accuracy and reaction time measures.

This point is not intuitive and warrants further discussion. Figure 3 illustrates the curve representing the speed—accuracy trade-off in a typical cognitive test, as well as the downward shift of this curve following a period of SD.

The unexplored factor in this relationship is whether SD also biases subjects toward faster or slower responding, as represented by a shift along the lower curve.

For instance, increases in the number of commission errors or false alarms on simple reaction time tests after SD have been attributed to increased disinhibition Dorrian et al.

Illustration of two possible ways in which sleep deprivation SD can affect speed and accuracy variables. Two sources of change may potentially occur following a period of total SD: a downward shift of the performance curve and a movement along the curve.

A movement along the curve i. As it turns out, the results of our analysis show remarkable agreement between accuracy and reaction time measures in each cognitive category: Overall, there was no significant effect when comparing accuracy and reaction time across the sample.

This finding suggests that, on average, SD does not bias subjects toward either faster or more accurate responding, although this claim cannot be made of any individual cognitive test.

Of the three moderator variables studied, only hours awake homeostatic sleep drive or sleep pressure was a significant moderator of the effect of SD, and only for accuracy, not reaction time variables.

A likely explanation for this negative result is that much of the observed heterogeneity is due to the variety of cognitive tests in each sample.

If this assertion is correct, it implies that the amount of impairment on tests that putatively assess the same cognitive domain may still differ considerably following SD.

In other words, the validity of these tests in assessing the cognitive process may not be as high after SD. For example, total SD is known to exacerbate the time-on-task effect Doran et al.

To obtain an objective standard of impairment, therefore, it may be necessary to establish norms on several of the most commonly used tests in each domain.

Although it would have been interesting to test the moderating effect of self-paced and work-paced paradigms in this analysis, these variables were highly confounded with cognitive domain i.

From the data obtained in the main effects, however, we can infer that the differential effects of self-paced versus work paced on accuracy and reaction time measures are unlikely to be significant as suggested in previous meta-analyses.

Instead, it is possible that these effects are present only under certain conditions e. As stated in the introduction, the chief objective of this meta-analysis was not to rule out any particular theoretical model but to direct attention to which of these models may have the greatest importance in explaining the real-world consequences of total SD.

Although total SD does produce statistically significant differences in most cognitive domains, the largest effects are seen in tests of simple, sustained attention.

Moreover, relatively brief failures of vigilance may potentially lead to disastrous consequences.

We argue, therefore, that this cognitive module is of the greatest practical concern in combating SD-related problems in real-world situations.

A striking feature of this deficit in sustained attention is how rapidly large changes emerge. Although our analysis was restricted to subjects who had gone a single night without sleep, effect sizes were still large for both speed and accuracy measures on simple attention tasks.

These findings support the data showing that deficits in sustained attention often presage the other observable cognitive effects of SD and may have considerable utility as an early warning system for imminent cognitive failure.

This cognitive component should therefore be one of the primary targets of assessment for work fitness and a basis for decisions on whether subsequent countermeasures should be applied.

On the next rung of the hierarchy, we note that tests of working memory and other tests of executive attention are also robustly affected by one night of SD.

Considerable research has been conducted over the past several decades to assess the effects of SD on decision making and its component subprocesses e.

Finally, although tests of processing speed and cognitive throughput such as the Digit Symbol Substitution Test are commonly used in SD paradigms, the results of this analysis demonstrate that their effects are relatively small compared with those of other tests.

The implication of this finding is that changes in processing speed may be theoretically interesting but not of great practical significance in explaining and predicting real-world cognitive failures Monk, This analysis contains a small number of limitations that may have affected the validity of the conclusions drawn.

As we were able to obtain only a small amount of unpublished data, it is possible that there was a bias in the analysis toward effect sizes that reached statistical significance.

We are, therefore, relatively confident that the study was not greatly affected by publication bias. Although every effort was made in this analysis to classify studies into appropriate and meaningful categories, it is clear that with the possible exception of simple attention, pure assays of most of the cognitive domains we have identified do not exist.

Moreover, there remained numerous dissimilarities among the forms and characteristics of the tests within each category e.

As discussed, this is the most likely reason why heterogeneity was in the moderate range for almost all categories studied.

Despite these drawbacks, we propose that our taxonomy is a useful heuristic for several reasons. First, significant between-categories differences were found in the meta-analysis, suggesting that we have captured meaningful constructs with the classification we employed.

Second, we have stayed faithful to categories that are well defined in the neuropsychological literature. In many cases, focal deficits on these tests have been observed in patients with specific pathologies or injuries e.

Finally, several of the domains studied here have relatively high external validity. For instance, the challenge in simple attention tasks is similar to the real-world demands on air traffic controllers, and tasks such as the Psychomotor Vigilance Test have been shown to correlate highly with other indicators of dangerous, drowsy driving Dinges et al.

We were not able to study a number of moderator effects that may be important predictors of the residual intradomain heterogeneity.

Task duration is likely to be one of these factors, with longer tasks associated with greater effect sizes due to the presence of the time-on-task effect.

We were unable to code this moderator chiefly because many articles did not report task length and because of the variability in time to completion for all tasks that were self-paced.

As we have already mentioned, the difference between self-paced and work-paced tests was highly confounded with cognitive domain, making it unfeasible to test this as a moderator.

Finally, a substantial number of studies entered into this meta-analysis reported only accuracy or reaction time as a dependent variable in their final published work.

As a result, we could not conduct paired comparisons of these measures to assess their reliability. We encourage authors publishing in this field in the future to consider reporting both accuracy and reaction time measures where appropriate so that their relationship after SD can be better explored.

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