IMMORTAL D-R4.4 21/10/04

Deliverable D-R4.4 EXPERIMENTAL STUDIES ON THE EFFECTS OF LICIT AND ILLICIT DRUGS ON DRIVING PERFORMANCE, PSYCHOMOTOR SKILLS AND COGNITIVE FUNCTION

Public

IMMORTAL CONTRACT NO GMAI-2000-27043 SI2.319837

Project Co-ordinator: Prof. Bob Hockey, University of Leeds

Workpackage Leader: Inger Marie Bernhoft, Danish Transport Research Institute

Authors: JG Ramaekers, KPC Kuypers, Brain & Behavior Institute, Faculty of Psychology, Maastricht University CM Wood, GRJ Hockey, S Jamson, H Jamson, E Birch, School of Psychology, University of Leeds

Date: 28.09.2004

Project Funded by the European Commission under the Transport RTD Programme of the 5th Framework Programme

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School of Psychology, UNIV LEEDS DOCUMENT CONTROL INFORMATION

Title Experimental studies on the effects of licit and illicit drugs on driving performance, psychomotor skills and cognition

Author(s) JG Ramaekers, KPC Kuypers, CM Wood, GRJ Hockey, S Jamson, H Jamson, E Birch

Editor(s)

Date 28/09/2004

Report Number

Reference and version Version 1 number

Distribution Project Consortium

Availability Public

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QA check Nic Ward

Authorised by Bob Hockey

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ii IMMORTAL D-R4.4 21/10/04

Table of Contents

1. INTRODUCTION...... 10 1.1 GENERAL BACKGROUND...... 10 1.2 TASK DESCRIPTION...... 11 2. STUDY A: A PLACEBO CONTROLLED STUDY ON THE EFFECTS OF 3,4- METHYLENEDIOXYMETHAMPHETAMINE (MDMA) 75mg AND METHYLPHENIDATE 20mg ON ACTUAL DRIVING PERFORMANCE, VISUOSPATIAL ATTENTION AND MEMORY DURING INTOXICATION AND WITHDRAWAL ...... 11 2.1 INTRODUCTION ...... 11 2.2 AIMS OF THE STUDY...... 13 2.3 METHODS...... 14 2.3.1 Subjects...... 14 2.3.2 Design, doses and administration ...... 14 2.3.3 Procedures ...... 15 2.3.4 Actual driving tests ...... 15 2.3.5 Visuospatial attention ...... 16 2.3.6 Learning and working memory...... 17 2.3.7 Subjective evaluations...... 18 2.3.8 Pharmacokinetic assessments...... 18 2.3.9 Statistical analysis ...... 18 2.4 RESULTS ...... 19 2.4.1 Missing data and Failures to complete the driving tests ...... 19 2.4.2 Actual driving tests ...... 19 2.4.3 Visual spatial attention ...... 22 2.4.4 Learning and working memory...... 22 2.4.5 Subjective evaluations...... 26 2.4.6 Pharmacokinetics ...... 26 2.5 DISCUSSION ...... 26 2.6 ACKNOWLEDGEMENTS...... 31 2.7 REFERENCES ...... 31 3. STUDY B: INTERACTION EFFECTS OF 3,4-METHYLENEDIOXYMETHAMPHETAMINE (MDMA ) AND ALCOHOL ON ACTUAL DRIVING, PSYCHOMOTOR PERFORMANCE AND RISK TAKING BEHAVIOR...... 34 3.1 INTRODUCTION ...... 34 3.2 AIM OF THE STUDY ...... 35 3.3 METHODS...... 36 3.3.1 Subjects...... 36 3.3.2 Design, doses and administration ...... 36 3.3.3 Procedures ...... 37 3.3.4 Actual driving tests ...... 38 3.3.5 Psychomotor performance ...... 39 3.3.6 Impulsivity and risk taking behavior ...... 40 3.3.7 Subjective evaluations...... 41 3.3.8 Pharmacokinetic assessments...... 41 3.3.9 Statistical analysis ...... 41 3.4 RESULTS ...... 42 3.4.1 Missing values and failures to complete the driving tests ...... 42 3.4.2 Actual driving tests ...... 42 3.4.3 Psychomotor tests...... 44 3.4.4 Impulsivity and risk taking behavior ...... 44 3.4.5 Subjective evaluations...... 48

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3.4.6 Pharmacokinetics ...... 48 3.5 DISCUSSION ...... 49 3.6 ACKNOWLEDGEMENTS...... 52 3.7 REFERENCES ...... 52 4. STUDY C: THE EFFECT OF COLD VIRUS AND COLD VIRUS MEDICATION ON COGNITIVE AND DRIVING PERFORMANCE ...... 54 4.1 INTRODUCTION ...... 54 4.1.1 The common cold...... 54 4.2.1 Participants ...... 57 4.2.2 Medication ...... 57 4.2.3 Experimental design ...... 57 4.2.4 Experimental timetable ...... 57 4.2.5 Procedure ...... 59 4.2.6 Subjective measures ...... 59 4.2.7 Psychomotor performance...... 60 4.2.8 Driving performance ...... 62 4.3 Data analysis ...... 67 4.4 Results ...... 67 4.4.1 Subjective measures ...... 67 4.4.2 Psychomotor performance...... 69 4.4.3 Driving performance ...... 73 4.5 Conclusions...... 85 4.6 Discussion ...... 86 4.6.1 Psychomotor performance...... 86 4.6.2 Driving performance ...... 87 4.6.3 Limitations...... 88 4.7 References...... 89 Appendix A - Symptoms checklist ...... i Appendix B - Subjective state report...... iii Appendix C - Trail making task ...... iv Appendix D - Volunteer Information Sheet...... vi Appendix E - Subjective Awareness ...... ix Appendix F - Virtual Road Map...... i

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EXECUTIVE SUMMARY

The current deliverable D-R4.4 of the IMMORTAL project entails a series of experimental studies on the effects of licit and illicit drugs on cognition, psychomotor function and driving performance. The studies have been conducted by two research groups with strong backgrounds in Drugs and Driving research: i.e. the Experimental Psychopharmacology Unit, Brain & Behavior Institute at Maastricht University and the School of Psychology at Leeds University. The former group conducted 2 double-blind, placebo-controlled studies on the acute effects of 3,4-methylenedioxymethamphetamine (MDMA or ecstasy) alone or in combination with alcohol on actual driving performance as measured in on-the-road driving tests and in laboratory tests measuring psychological functions relevant to driving: i.e. impulsivity, risk taking, psychomotor speed, tracking, motion sensitivity and memory (Study A and B). The latter group performed a single-blind, placebo controlled study on the effect of a common over–the-counter cold remedy mixture of diphenhydramine hydrochloride 25mg, paracetamol 1000mg and pseudoephedrine 45mg, on simulated driving performance and psychomotor function (Study C).

Study A: A placebo controlled study on the effects of 3,4-methylene- dioxymethamphetamine (MDMA) 75mg and methylphenidate 20mg on actual driving performance, visuospatial attention and memory during intoxication and withdrawal.

3,4-methylenedioxymethamphetamine (MDMA) is currently one of the most popular drugs of abuse in Europe. Its increasing use over the last decade has led to concern regarding possible adverse effects on driving and cognition. The primary aim of the present study was to investigate the acute effects of MDMA on actual driving performance and cognition (i.e. visuospatial attention and memory). The second aim of the present study was to assess the effects of MDMA on driving and cognition during the withdrawal phase. Eighteen recreational MDMA-users (9 males, 9 females) aged 21-39 yrs participated in a double-blind, placebo-controlled, 3-way cross-over study. Drugs and placebo were administered on Day 1 of treatment (Intoxication phase). Cognitive and driving tests were conducted between 1,5-2,5 hrs and 3-5 hrs post drug respectively. Subjects returned the following day for a repetition of the cognitive and driving tests at the same times as on the day of treatment, i.e. 24 hrs later (Withdrawal phase). Actual on-the-road-driving tests consisted of a Road Tracking Test and a Car Following Test. Its main parameters were Standard Deviation of Lateral Position (SDLP), Time to Speed Adaptation (TSA), Brake Reaction Time (BRT) and Gain. Visuospatial processing and memory were assessed in a range of cognitive laboratory tests.

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Results demonstrated that MDMA and methylphenidate significantly decreased SDLP in the Road Tracking Tests by about 2 cm relative to placebo on Day 1. SDLP was not affected by Treatment or Period during withdrawal on Day 2. In addition, MDMA intoxication decreased performance in the Car-Following test as indicated by a significant rise in the ‘overshoot’ of the subjects’ response to speed decelerations of the leading vehicle. Cognitive tests furthermore demonstrated that a single dose of MDMA 75 mg impairs performance in spatial and verbal memory tasks. MDMA’s detrimental effect on spatial memory may be of particular relevance to the driver as it indicates a reduction in situation awareness or spatial orientation while driving under the influence. Collectively, these data indicate that MDMA possesses activating or stimulating properties that may improve driving performance in certain aspects of the driving task (road tracking) but cause impairment in others (car-following task).

Study B: Interaction effects of 3,4-methylenedioxymethamphetamine (MDMA or ecstasy) and alcohol on actual driving, psychomotor performance and risk taking behavior.

MDMA is frequently taken in combination with other recreational drugs, such as alcohol. It is presently unclear whether and how concomitant use of alcohol will affect MDMA effects on performance. The present study was designed to assess the effects of MDMA and alcohol, alone and in combination, on actual driving performance and risk taking behaviour. Eighteen recreational users of MDMA entered a double-blind, placebo-controlled, 6-way cross-over study. The treatments consisted of MDMA 0, 75 and 100 mg with and without alcohol. Alcohol dosing was designed to achieve a peak Blood Alcohol Concentration (BAC) of about 0.06 g/dl during laboratory testing and of about 0.05 g/dl during the actual driving tests. Laboratory tests of psychomotor function and risk taking behavior behaviour were conducted between 1.5-2.25 hrs post MDMA. Actual driving test, i.e. a Road Tracking Test and a Car-Following Test, were conducted between 3-5 hrs post MDMA. Both doses of MDMA significantly improved performance in the Road Tracking Test, as indicated by decrements in standard deviation of lateral position (SDLP) and standard deviation of speed. The stimulating effect of MDMA 100mg was more prominent when combined with alcohol whereas the stimulating effect of MDMA 75mg did not change in magnitude after alcohol co-administration. MDMA did not affect performance in the Car-Following test, or any other test measuring psychomotor function and risk taking behavior. Alcohol alone, significantly increased SDLP in the Road Tracking Test, brake reaction time in Car-Following and tracking performance in a Critical Tracking Task. Alcohol furthermore decreased inhibitory control in a Stop-Signal task, but increased inhibitory control in a Gambling paradigm of impulsivity. Collectively these data indicate that MDMA is a stimulant drug that may facilitate certain aspect of the driving task, i.e. road tracking, even when combined with a low dose of alcohol. However performance compensation after combined MDMA/alcohol administration was limited to a single driving parameter

6 IMMORTAL D-R4.4 21/10/04 and was never sufficient to fully overcome alcohol impairment in all driver related tasks.

Study C: The effect of cold virus and cold virus medication on cognitive and driving performance

Common cold infections are so widespread that there can be very few people who escape infection each year. Symptoms can last anywhere between 1 day and 2 weeks. Several studies have shown that the cold virus can impair attention and psychomotor performance related to driving. Many cold sufferers take some form of over the counter medication to relieve their symptoms. The present study was designed to assess the effect of cold virus and cold virus medication on driving performance and cognitive performance related to driving. Ninety-six participants took part in a single blind 2x2 between subjects study. Participants diagnosed with a common cold were compared, with and without medication, and to baseline conditions. The medication was typical of many over the counter cold remedies used in the UK and contained the following active ingredients; Diphenhydramine hydrochloride 25 mg, Paracetamol 1000 mg, and Pseudoephedrine hydrochloride 45 mg. Laboratory tests of psychomotor performance related to driving were conducted between 0.25 –1 hrs post ingestion of placebo or medication. Participants' performance was then tested using the Leeds driving simulator 1.25-2.5 hrs post ingestion of placebo or medication. The results from the cognitive tests were similar to previous findings and showed that volunteers suffering with a cold virus had slower reaction times and impaired visual search abilities. Cold sufferers also reported increased subjective fatigue and depression scores. Medication did not affect performance on the cognitive tasks but medicated volunteers did report higher scores of subjective fatigue. The results from the simulator tasks were somewhat mixed. Generally it seemed that cold sufferers taking medication could perform well in longitudinal control. Indeed, some results suggested that cold sufferers performed better in longitudinal control. Secondary tasks and lateral control, however, were often impaired by medication and sometimes further impaired by taking medication whilst having a cold. It seems that drivers compensate for the effects of medication by modifying their driving style. The extra effort applied to some driving aspects results in decreased performance in other aspects of driving such as lateral stability. Cold sufferers taking medication also performed poorly on awareness tasks during the simulated run again suggesting that although driving ability may appear adequate there may be less cognitive resources for additional secondary tasks.

Discussion

What all studies have in common is their use of well controlled, experimental study designs. The studies employed representative subject samples, i.e. recreational users of MDMA and cold sufferers, who went through

7 IMMORTAL D-R4.4 21/10/04 strict medical screening and selection procedures. They furthermore employed (mixed-model) cross-over designs which are generally preferred for their efficiency while providing maximal statistical power with relatively small sample sizes, and they proceeded from conventional laboratory testing of psychomotor skills and cognition to sophisticated driving simulators and actual driving tests for establishing the driving hazard potential of the respective drugs. A final similarity can be found in the potential scope of the problem that is addressed in these studies. Both MDMA and cold remedies are a widely used among recreational drug users and cold sufferers respectively. Many of these people will operate their vehicles while under the influence of their drug. Yet the effects of these drugs on driving ability have been poorly studied and were prior to the initiation of the IMMORTAL research projects, largely unknown. The MDMA studies demonstrated that MDMA significantly improved tracking control in a Road Tracking Tests. In addition, MDMA intoxication decreased performance in a Car-Following test as indicated by a significant rise in the ‘overshoot’ of the subjects’ response to speed decelerations of the leading vehicle. Cognitive tests furthermore demonstrated that a single dose of MDMA 75 mg impairs performance in spatial and verbal memory tasks. MDMA’s detrimental effect on spatial memory may be of particular relevance to the driver as it indicates a reduction in situation awareness or spatial orientation while driving under the influence. In general, MDMA mitigated the impairing effect of alcohol on one driving parameter, i.e. road tracking performance, but failed to affect alcohol induced impairment on a range of other parameters such as brake reaction rime and risk taking. Collectively, these data indicate that MDMA possesses activating or stimulating properties that may improve driving performance in certain aspects of the driving task but cause impairment in others Potential problem areas in cognitive function that have been identified in the present MDMA studies thus include time estimation of moving objects, spatial orientation and memory. Most of these problem areas have been indicated by laboratory tests and to a lesser degree by actual driving tests. The reason for this discrepancy may be related to the fact that on-the-road driving tests have primarily focussed on modelling (psycho)motor functions and have paid relatively little attention to the role of executive cognitive functions during driving. Consequently, actual driving tests have been very successful for assessing the impairing potential of sedative drugs on psychomotor function. MDMA however is a stimulant drug that has been shown to improve performance in a range of psychomotor tasks but causes impairment in some cognitive functions. The consequence might be be that existing driving test must be further developed in order to also include the assessment of the cognitive domains relevant to stimulant drugs such as MDMA. Candidate concepts are objective measurements of estimated time to collision and prospective memory during actual driving. The study on the effect of cold and cold remedy on driving ability showed volunteers suffering with a cold virus had slower reaction times and impaired visual search abilities. Medication did not affect performance on the cognitive tasks but impaired road tracking control in a driving simulator task. The data also

8 IMMORTAL D-R4.4 21/10/04 suggested that drivers compensate for the effects of medication by modifying their driving style. The extra effort applied to some driving aspects resulted in decreased performance in other aspects of driving such as lateral stability. Cold sufferers taking medication also performed poorly on awareness tasks during the simulated run again suggesting that although driving ability may appear adequate there may be less cognitive resources for additional secondary tasks. The impairing effect of cold remedy medication on road tracking performance is particularly noteworthy. Although many experimental studies have previously shown that diphenhydramine can impair psychomotor skills and driving performance, one would not necessarily have expected the same to occur in the present study. The reasons are twofold: 1) the dose of diphenhydramine in the present cold remedy formulation was rather low, i.e. 25mg, as compared to the usual doses that have been tested in previous studies, i.e. 50-100mg, and 2) the addition of a stimulant drug, i.e. pseudoephedrine, to the cold remedy formulation. The reason why stimulant drugs are usually added to cold remedy formulations is clear: to counteract drowsiness and somnolence induced by diphenhydramine. This study demonstrates however that the addition of pseudoephedrine, present in common cold remedies, does not fully compensate for the sedative potential of diphenhydramine.

Conclusions on tolerance levels The general recommendation coming from these studies is that users of MDMA and common cold remedies should be informed on these drugs’ potential to selectively affect cognitive function and performance at relevant aspects of the driver task. In the case of MDMA however on has to also draw the conclusion that single doses up to 100mg of MDMA are not likely to pose a great hazard in drivers due to its stimulating activities of psychomotor abilities. This perspective may change however for MDMA users taking higher doses or repeated doses of MDMA on one or successive nights, in hot environments at rave’s and dance- parties that may change a subjects response to MDMA during intoxication and withdrawal. MDMA’s detrimental effect on verbal and spatial memory may also be of relevance to the driver as it indicates a reduction in situation awareness or spatial orientation while driving under the influence. More research is needed to understand the influence of high en repeated dosing, environmental conditions and sleep deprivation on the effects of MDMA on driving performance. Combinations of MDMA (75 and 100mg) with low doses of alcohol however consistently impaired performance in a range of measures of actual driving, psychomotor function and risk taking behaviour. The implication is that any combination of alcohol and MDMA should always be avoided when operating a motor vehicle. In the case of diphenhydramine the study results are straightforward. Diphenhydramine produced driving impairment in subject suffering from flu, even though the drug was administered at the lowest dose available. The implication is that driving should always be contraindicated in drivers taking diphenhydramine.

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1. INTRODUCTION

1.1 GENERAL BACKGROUND

IMMORTAL specifies a research programme concerning the accident risk associated with different forms of driver impairment and the identification of ‘tolerance levels’ applied to licensing assessment and roadside impairment testing (including drug screening). The present research was conducted as part of workpackage on “Alcohol, drugs and medicines”. In the technical annex of IMMORTAL this workpage was decribed as follows:

“This workpackage considers the effects of using alcohol, drugs and medicines on driver impairment and traffic accidents. The workplan includes a combination of epidemiological and experimental methods to provide the basis to calculate accident risk and ‘tolerance levels’ with reference available data on fatal and injury accidents related to alcohol, drugs and medicines.

As the problem of alcohol in traffic has decreased over the last 10-15 years the present international situation is characterised by an increasing concern about drugs and driving and by various attempts to elucidate and intervene against this problem. It is recognised that drugs, even more than alcohol, is a complex issue. It is also recognised that drugs in combination with alcohol form an even greater problem because the combination will often result in a synergistic effect.

In spite of national differences, international literature indicates that licit and illicit drugs, often combined with alcohol, are increasingly found in dead and injured road users, e.g. in 10-20% of killed drivers (of motor vehicles, including cyclists) and often in combination with illegal alcohol concentrations. However, when it comes to describing the extent of the drug-driving problem, great uncertainty prevails, as experiments, roadside surveys and accident statistics are sparse and inadequate. For example, there are few valid baseline indications of drugs in non-accident cases from which to calculate accident risk and ‘tolerance levels’ relative to other ‘standards’ such as alcohol (BAC limits) or fatigue.

It is crucial to get more precise information on drugged driving in order to be able to change the law towards giving the police legal rights to conduct random tests for drugs in traffic. Moreover, better devices for screening at the roadside have been developed during recent years, which means that we will be able to produce more exact knowledge about the problem of drugged driving.”

The technical and scientific objectives of IMMORTAL are to:

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1. Investigate the influence of chronic and acute impairment factors on driving performance and accident risk; 2. Recommend criteria (‘tolerance levels’) for high risk categories of impairment; 3. Provide key information to support formulation of European policy on licensing assessment and roadside testing.

The present deliverable addresses objective No. 1 and 2. The central concepts here are acute impairment factors and accident risk.

1.2 TASK DESCRIPTION

The description of the task R4.4 is stated in the Technical Annex and deals with the experimental methods for establishing driver impairment. The task entails experiments on actual driving performance and risk taking by drivers under the influence of ecstacy (mdma) alone (study A) or in combination with alcohol (study B), and experiments on the effect of flue medication on simulated driving performance and cognitive function (study C). The main objectives of these studies are to indicate acute impairment factors and to suggest tolerance levels for the drugs under study.

2. STUDY A: A PLACEBO CONTROLLED STUDY ON THE EFFECTS OF 3,4-METHYLENEDIOXYMETHAMPHETAMINE (MDMA) 75mg AND METHYLPHENIDATE 20mg ON ACTUAL DRIVING PERFORMANCE, VISUOSPATIAL ATTENTION AND MEMORY DURING INTOXICATION AND WITHDRAWAL

2.1 INTRODUCTION

3,4-methylenedioxymethamphetamine (MDMA or ecstasy) is currently one of the most popular drugs of abuse in Europe. Its increasing use over the last decade has led to concern regarding possible short and long term adverse effects. Reports of short term side effects have included physiological adverse reactions such as hyperthermia, hepatotoxicity and cardiac stimulation as well as adverse psychological reactions such as agitation, blurred vision, depression, anxiety aggression and sleep disturbances. Of even more concern are reports on neurotoxicity of MDMA in animal studies. In humans, there is growing evidence that heavy use of MDMA is associated with sleep disorders, depression, memory impairment and attention disorders that persist for prolonged periods during abstinence. These deficits are believed to be caused by MDMA induced neurotoxicity as evinced by depleted serotonin in MDMA users and by dose-

11 IMMORTAL D-R4.4 21/10/04 response relationships between MDMA exposure and severity of cognitive deficits (reviews: Cole and Sumnall 2003; Morgan 2000; Parrott 2001; Reneman et al 2001a) Given the large extend of its use, there are relatively few reports of death associated with MDMA use (Cole and Sumnall 2003; Logan and Couper 2001). In most cases MDMA seems to have contributed to death by evoking dehydration, sympathetic overactivity, hyperthermia and tachyarrhythmia in settings involving strenuous activities such as dancing all night, or by altering or impairing behaviour such as driving. It has been estimated that 6-35% of subjects using MDMA at raves or dance parties will drive home afterward (Riley SC 2001). Several fatal and non-fatal road-accidents have been reported in which MDMA was found in plasma of drivers, or those held responsible for the accident (review: Logan & Couper, 2003). Specific behavioural changes that have been reported in drivers under the influence of MDMA included speeding, jumping red lights, hallucinations/delusions and a sense of detachment from the real world (Schifano 1995). These case reports clearly stress the need to further investigate the putative effects of MDMA on driving performance or driving-related psychomotor performance under experimentally controlled conditions. Lamers et al (2003) have undertaken the first attempt to qualify the acute effects of MDMA on driving in a double-blind, placebo controlled study. They administered MDMA 75mg to 12 recreational users of MDMA and assessed driving related task performance through critical tracking, divided attention, time to collision and reaction time tasks. MDMA improved psychomotor performance, such as tracking and movement speed, but impaired the ability to predict time to collision in a visual motion perception task. Impaired performance in this task indicates a reduction in the subjects’ timing ability that may become of crucial importance in certain traffic situations, such as gap acceptance during overtaking or road-crossing maneuvers. Brookhuis et al (2004) used a quasi-experimental design to assess the effects of MDMA on simulated driving. Twenty-three subjects who had indicated that they regularly used MDMA were asked to complete test rides in an advanced driving simulator, shortly after they had self- administered MDMA (average dose 56 mg), just before going to a party. They were tested again after having visited the rave, while they were under the influence of MDMA and a number of different other active drugs. Participants were also tested sober, at a comparable time at night. Separately, a control group of non-drug users was included in the experiment. The authors report that basic vehicle control was only moderately affected by MDMA but that subjects were prepared to accept higher risks as compared to the control group. This was clear from gap acceptance data, while the ultimate indicator of unsafe driving, accident involvement or even causation, was increased by 100% and 150%, respectively. Collectively, these data indicate that MDMA may impair cognitive skills related to risk taking, but improve performance in tasks measuring psychomotor speed. The latter effect can presumably be attributed to the CNS stimulating effect of the drug. Long-term effects of MDMA on driving skills have been investigated in abstinent users. Lamers et al (2004) assessed simulated driving performance in

12 IMMORTAL D-R4.4 21/10/04 groups of MDMA/ THC users, THC users and age matched controls. Compared to the latter, MDMA users showed a poor emergency response to an illegal intersection incursion by another vehicle and entered the collision with higher speed. In addition, abstinent MDMA users were less perceptive of trajectory of travel in a visual motion perception task as compared to non-drug controls indicating a loss of heading perception (Rizzo 2004). Some of these effects may have been caused by residual drug effects as a small fraction of the subjects included in these studies tested positive for THC in urine on test days. Yet on the average, days of abstinence were comparable in both groups of drug users and inclusion of this potential confounder as a covariate in the statistical model did not change the study outcome. Overall, these data thus seem to suggest that MDMA causes persistent neurocognitive change relevant to driving that outlasts MDMA intoxication.

2.2 AIMS OF THE STUDY

The primary aim of the present study was to investigate the acute effects of MDMA on driving performance as measured in actual on-the-road driving tests in a placebo controlled study, in order to confirm and elaborate on previous findings from driving simulator studies and laboratory studies measuring skills related to driving. These actual driving tests have been developed to assess drug effects on a broad range of driving tasks at operational, tactical and strategical levels. The basic operational level includes highly automated behaviors such as road tracking performance; the tactical level vehicle maneuvering like overtaking, distance keeping and car-following; and the strategical level general plans, risk evaluation and anticipation of traffic. In addition, a range of laboratory tasks were added to measure visuospatial attention, memory and learning. A dopaminergic stimulant, methylphenidate, was chosen as an active control drug in the present design in order to evaluate the combined and separate contributions of dopaminergic and serotonergic counterparts of MDMA effects on driving. The second aim of the present study was to assess the effects of MDMA on driving during the withdrawal phase. Stimulant drugs are known to produce fatigue, loss of concentration and anxiety during the first days of abstinence. Performance of MDMA users during the withdrawal phase has never been assessed, but the drug is known to induce feelings of dysphoria and depression during the first days of abstinence. Mood changes are believed to result from a sharp decline in serotonin (5-HT) levels in the brain that occurs after massive 5HT release during MDMA use. The possibility may exist that withdrawal effects of MDMA may have a stronger impact on psychomotor and cognitive function of MDMA users, than the acute effects of this drug. The current study was therefore designed to assess psychomotor and actual driving performance of recreational MDMA users while under the influence of MDMA and while in the phase of withdrawal.

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2.3 METHODS

2.3.1 Subjects

Eighteen recreational MDMA-users (9 males, 9 females) aged 21-39 were recruited through advertisements in local newspapers. Initial screening was accomplished on the basis of a questionnaire on medical history and driving experience. Subjects who were accepted were examined by the medical supervisor, who also checked vitals signs and took blood and urine samples. Standard blood chemistry, haematological and drug screen tests were conducted on these samples. Inclusion criteria were: experience with the use of MDMA; free from psychotropic medication; good physical health as determined by examination and laboratory analysis; absence of any major medical (except OAC), endocrine and neurological condition; normal weight, body mass index (weight/length2) between 18 and 28 kg/m2 ; and written Informed Consent. Exclusion criteria were: history of drug abuse (other than the use of MDMA) or addiction; pregnancy or lactation; cardiovascular abnormalities as assessed by standard 12-lead ECG; excessive drinking (> 20 alcoholic consumptions a week); hypertension (diastolic> 100; systolic> 170); and history of psychiatric or neurological disorder. This study was conducted according to the code of ethics on human experimentation established by the declaration of Helsinki (1964) and amended in Edinburgh (2000). All subjects gave their informed consent, in writing. Approval for the study was obtained from the University’s Medical Ethics committee and the District Attorney of the City of Maastricht. A permit for obtaining, storing and administering MDMA was obtained from the Dutch drug enforcement administration. The subjects were paid for their participation.

2.3.2 Design, doses and administration

The study followed a double-blind, placebo-controlled, 3-way cross-over design. Complete balancing of the treatment orders yielded 6 treatment orders randomly assigned to 18 subjects. The treatments consisted of MDMA 75 mg, methylphenidate 20mg and placebo. Placebo, methylphenidate and MDMA were administered orally in identically appearing formulations. MDMA was administered as a 25 ml solution in bitter orange peel syrup, which was ingested at once. Methylphenidate 20 mg was given in a capsule containing 2 tablets of 10mg. Placebo capsule and syrup were administered along with the active treatments to insure blinding. Drugs and placebo were administered at 10:00 am. Driving tests were conducted between 3-5 hrs post drug. Visuospatial and memory tests were conducted between 1.5-2.5 hrs post drug. Subjects returned on the next day for a repetition of the laboratory and driving tests at the same times as on the day of treatment, i.e. 24 hrs later. The minimum period between successive treatments was two weeks (see Table 1).

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) g ) in g w in o w ll o o ll ) F ) o y r- y F r a r r- o ts C o a m s m ts n e e d e s C o t n e d ti ts m g a ts m t n a s d s d a r e n in g e n g t t a iv in t a in g is y l r k y l v in n r a D c r a ri k i to ti l a to ti D c m a a a r a a l a d r p u T r p a r a o s t d o s u T b o c a b o t d g a u A o a u c u L L A a r is (R is o D (v (v (R

0 1.5 – 2.5 h 3 – 5 h 25.5 – 26.5 h 27 – 29 h Time post Drug Day 1 (Intoxication phase) Day 2 (Withdrawal phase)

Table 1. Schematic representation of activities on Day 1 and Day 2 of each treatment condition

2.3.3 Procedures

Subjects were asked to refrain from any drugs starting 1 week before the medical screening and physical examination until two weeks after the last experimental session. The subjects were not allowed to use alcohol on the day prior to an experimental session and were requested to arrive at experimental sessions well rested. Drug and alcohol screens were performed before experimental sessions upon arrival of the subject. Subjects were transported from their homes to the laboratory or vice versa by one of the experimenters. Additional clinical blood chemistry, with particular reference to liver and renal function were conducted at day 11 after each treatment. During 14 days following each treatment subjects were asked to contact the medical supervisor or a member of the investigating team upon experiencing any sign of nausea, vomiting, intolerance to fatty food, yellowish colour of skin, or tiredness. All subjects received a training session prior to onset of the experimental sessions in order to familiarize them with the tests and procedures.

2.3.4 Actual driving tests

Road Tracking Test In the Road Tracking Test (O'Hanlon et al 1982) the subject operates a specially instrumented vehicle over a 100 km primary highway circuit while maintaining a constant speed (95 km/h) and a steady lateral position between the delineated boundaries of the right (slower) traffic lane. An electro- optical device mounted at the rear back of the car continuously measures lateral distance separating the vehicle and the left lane-line. This signal is digitised at a rate of 4 Hz and stored on an onboard computer disk file for later editing analysis. The off line editing routine involves removal of all data segments that reveal signal loss, disturbance or occurrence of passing manoeuvres. The remaining data are then used to calculate means and variances for lateral position and speed (SP). Standard deviation of lateral position (SDLP) is taken as the primary outcome variable. SDLP is a measure of road tracking error, in

15 IMMORTAL D-R4.4 21/10/04 practical terms, a composite index of allowed weaving, swerving and overcorrecting. The test duration is 1 hour.

Car-Following Test The Car Following Test (Brookhuis et al, 1994; Ramaekers et al 2002; Ramaekers and O'Hanlon 1994) involves the use of two vehicles. The preceding vehicle is under an investigator's control, and the following vehicle, the subject's. The test begins with the two vehicles travelling in tandem at speeds of 70 km/h on a secondary highway. Subjects attempt to drive 15-30 m behind the preceding vehicle and to maintain that headway as it executes a series of deceleration manoeuvres. During the test, the speed of the leading car is automatically controlled by a modified cruise-control system. At the beginning it is set to maintain a constant speed of 70 km/h, and by activating a microprocessor, the investigator can start sinusoidal speed changes reaching an amplitude of -10 km/h and returning to the starting level within 50 sec. The manoeuvre is repeated 6 times. Between deceleration manoeuvres, the investigator in the leading car randomly activates the brake lights of his vehicle by activating a second mode of the microprocessor. The brake lights then light for 3 seconds whereas the speed of the leading car remains constant at 70 km/h. The subject is instructed to react to brake lights by removing his/her foot from the speed pedal as fast as possible. This procedure will be repeated between 20-30 times throughout the test. Headway is continuously recorded by means of a DME 2000 optical distance sensor. That device is placed in the grill of the following vehicle and emits laser signals in the direction of a reflection board mounted on the leading vehicles towing bracket. Distance from the lead vehicle is deduced from the time lapse between the transmission and receipt of the signal at the receiving end of the distance sensor. The velocity of the leading vehicle and initiation times of speed manoeuvres and brake lights are transmitted via telemetry to the following vehicle and stored on a computer disk along with the velocity of the following vehicle, headway and response time to brake lights. Speed signals collected during speed manoeuvres enter a power spectral analysis for yielding phase- delay between the vehicle’s velocities at the manoeuvre cycle frequency (0.02) Hz. Phase delay converted to a measure of Time to Speed Adaptation (TSA, in msec), brake reaction time (BRT, in msec) and Gain are the major dependent variables. Gain is the amplification factor between the both speed signals collected from the leading and following vehicle and indicated the magnitude of undershoot or overshoot in reaction. Test duration is 25 minutes.

General driving proficiency Driving proficiency was scored by the driving instructor in retrospect after completion of the actual driving tests. In total 90 items were scored on a 6 item ordinal scale. Subscores were calculated for vehicle checks and handling, traffic manoeuvres and understanding traffic.

2.3.5 Visuospatial attention

Change Blindness Task Subjects are presented a series of 100 photographs of traffic situations. Each photograph is shown for 3 sec before a 300msec blank is presented. During the blank period a fixation dot appears that subjects have to

16 IMMORTAL D-R4.4 21/10/04 attend to. In the next 3 secs the original photo reappears with or without a superimposed change in the traffic scene. Changes appear in 80% of the cases and half of those either relevant or irrelevant to traffic. Fifty percent of the relevant and irrelevant changes occur at a position central or peripheral relative to the position of the fixation dot during the blank period. Number of correct detections and reaction time (RT) are the dependent variables.

Spatial Memory Task.The spatial memory task is based on a spatial localisation task (Vermeeren et al 1995). It measures short-term memory for non-verbal information. The subject is briefly shown a fixation point in the center of the computer display. Shortly thereafter, a target appears at a random location for 500msec. The subject’s task is to memorize the position of the target and, using a trackball, relocate the cursor as accurately as possible over that position. The cursor appears either immediately upon target offset or after a delay of 2 or 4 sec. The subject depresses a button to indicate that the cursor is at the recalled position of the target. The test consists of 75 trials, divided equally among 3 response delays. The sequence of delays is random. Regression lines will be calculated to describe localisation error (in mm) as a function of delay. The slope (SMT-SLP) reflects memory decay, and the intercept (SMT-ICT), the subject’s localisation error.

2.3.6 Learning and working memory

Word learning task (WLT) WLT is the Dutch language version of the standardized, clinically validated test for verbal memory. The test begins with the sequential presentation of 15 monosyllabic common nouns. Each word is shown on the computer display for 2 seconds and the subject is required to read it aloud. When the series ends, the subject is required to recall as many words as possible. The number correct is scored as the 1st trial score. Thereupon the same list is presented in the same manner on four successive occasions. Numbers correct are scored as before. The numbers correctly recalled in successive trails are summed to yield the total Immediate Free Recall Score (WLT-IR). After at least a 30-min delay, the subject is asked to name as many of the words he can still recall and the number correct is taken as his Delayed Recall Score (WLT-DR). WLT-DR is transformed into a percentage of WLT-IR to yield the relative recall score, WLT-RR. Finally, the subject is shown a series of 30 words on the computer display, comprising of the original set and 15 new words in random order. He/she responds to each at his/her own pace to indicate whether the given word was one of the original set. The number of correct recognitions and the average speed (ms) of correct recognitions are recorded as the Recognition Score and the Recognition Time, WLT-RS & WLT-RT, respectively.

Syntactic Reasoning Task (SRT) A series of 32 sentences are presented to the subject. Each describes the order of the two letters; e.g. “B follows A”. Each sentence is followed immediately by the same letters. Half of the time the order is the same as described by the preceding sentence, and the other half opposite.

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Sentence difficulty varies within the series, from simple active sentences as given above to more complicated sentences involving passives, negatives or both; e.g., “B is not followed by A”. The required response is to indicate as quickly as possible using appropriate push buttons whether or not the letter pair are in the same order as given in the preceding sentence (Baddeley 1968).

Digit-Symbol Substitution Task (DSST) DSST is a computerized version of the original paper and pencil test taken from the Wechsler Adult Intelligence Scale. The subject is shown an encoding scheme consisting of a row of squares at the top of the screen, wherein nine digits are randomly associated with particular symbols. The same symbols are presented in a fixed sequence at the bottom of the screen as a row of separate response buttons. The randomisation procedure is chosen such that symbols never appear at the same ordinal position within both rows. The encoding scheme and the response buttons remain visible while the subject is shown successive presentations of a single digit at the centre of the screen. The task is to match each digit with a symbol from the encoding list and click the corresponding response button. The number of digits correctly encoded within 3 minutes is the performance measure.

2.3.7 Subjective evaluations

Subjects filled out a number of subjective questionnaires. Sleep quality and sleep duration were measured using the Groningen Sleep Questionnaire (Mulder-Hajonides van der Meulen 1981). Mood was measured by means of the Hamilton-D and Profile of Mood Scale (POMS). The latter consists of 5 mood items: ie. depression, anger, tension, vigour and fatigue.

2.3.8 Pharmacokinetic assessments

MDMA was determined in plasma (15 ml) at 1.5, 5.5, 25.5 and 29.5 hrs post drug. Blood samples were placed on ice immediately, centrifuged later and frozen at -80°C until analyses for pharmacokinetic assessments. MDMA, MDA and methylphenidate concentrations were determined using solid phase extraction and gas chromatography with mass spectrometric detection with quantification limits of 5 ng/ml, 0.5 ng/ml and 5 ng/ml respectively.

2.3.9 Statistical analysis

Sample size was based on a power calculation for detecting a clinical relevant effect on the primary measure of this study, Standard Deviation of Lateral Position (SDLP). A change in SDLP of > 2 cm relative to placebo is considered to be clinically relevant. It is known from previous driving studies conducted by the present research group, that normal populations’ standard deviation in SDLP is 4.2 cm. Test-retest reliability is known to be at least r=0.70. Based on these numbers it has been shown that 18 subjects are sufficient for detecting a difference of 2cm at the alpha=.05 level with more than 90% power.

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The global model used in the analysis of variance (ANOVA) of all driving and cognitive parameters included Subject, Period and Treatments. Analyses of variance were conducted separately for data collected during Intoxication (Day 1) and Withdrawal (Day 2). If the model assumptions were violated, a suitable transformation or nonparametric method was considered. Where there were indications that the distribution of a variable departs substantially from normality, an appropriate approach to the analysis was performed. In case of an overall effect of Treatments, a subsequent analysis for comparing separate drug treatments was conducted using contrast tests with placebo as reference.

2.4 RESULTS

2.4.1 Missing data and Failures to complete the driving tests

Due to technical dysfunction no Road tracking and Car-Following data were collected for a single subject during Day 1 of placebo and methylphenidate treatment respectively. One driving test was terminated during placebo treatment before scheduled completion because the subject felt he was unable to continue.

2.4.2 Actual driving tests

Road Tracking Test. Mean (+ SE) standard deviation of lateral position (SDLP) recorded during the intoxication and withdrawal phase in every treatment condition are shown in Figure 1. ANOVA revealed that the overall effects of Treatment was highly significant during intoxication of the first day of Treatment (F2, 31=9,1; P<.001). Separate drug-placebo contrasts showed that this main effect was attributable to MDMA as well as methylphenidate. Both MDMA (p=.005) and methylphenidate (p<.001) decreased SDLP relative to placebo. SDLP was not affected by Treatment or Period during withdrawal on Day 2. Lateral position, speed and SD speed were not affected by Treatment and Period on both days of testing.

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MDMA Methylphenidate Placebo 24 Day 1 (Intoxication) Day 2 (Withdrawal)

22

20

18 ** **

16

14 SD Lateral Position (cm)

12

Figure 1. Mean (+SE) standard deviation of lateral position (SDLP, cm) in every treatment condition during intoxication and withdrawal (** = p<.05).

Car-Following Test. Mean (+SE) Gain is shown in Figure 2 for every treatment condition during intoxication and withdrawal. Overall, mean Gain differed significantly between Treatments (F2, 32=5.47; p=.009) on Day 1 during intoxication. Separate drug-placebo comparisons showed that MDMA (p=.005), but not methylphenidate, significantly increased (p=.005) the modulus or gain of the subjects’ response to speed decelerations of the leading vehicle. Gain did not differ between Treatments during withdrawal. Both Time to Speed Adaptation (TSA) and Brake Reaction Time (BRT) were not significantly affected by Treatments or Period on each day of testing.

General Driving Proficiency. Treatments and Periods did not affect vehicle checks and handling, manoeuvring in traffic and understanding traffic as judged by the driving instructor on both days of testing.

A summary of means and treatment effects in the Road Tracking and Car- Following Test is given in Table 2.

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MDMA Methylphenidate Placebo 1,4

1,3 Day 1 (Intoxication) Day 2 (Withdrawal)

1,2 **

1,1

1 (amplification factor)

0,9 Gain

0,8

Figure 2. Mean (+SE) Gain in every treatment condition during intoxication and withdrawal (** = p<.05).

Table 2. Summary of means (SE) and treatment effect in the Road Tracking and Car-Following Test. ANOVA Contrasts Treatments

Overall MDMA METH Tests Day MDMA METH PLA vs PLA Vs PLA Road Tracking Test SDLP (cm) I 17.1(0.6) 16.5(0.7) 18.8(0.9) < 0.001 0.005 <0.001 2 18.5(0.8) 19.2(0.9) 18.1(0.7) - - - LP (cm) I 2.8(7.2) 12.8(9.9) 3.6(8.6) - - - 2 1.1(7.2) 3.1(6.9) 1.8(7.6) - - - Speed 1 95.8(0.3) 95.8(0.2) 95.9(0.3) - - - (km/h) 2 95.9(0.4) 95.7(0.3) 95.9(0.2) - - - SD Speed 1 1.6(0.1) 1.6(0.1) 1.7(0.1) - - - (km/h) 2 1.6(0.1) 1.7(0.1) 1.6(0.1) - - - Car Following Test Gain 1 1.2(0.0) 1.1(0.0) 1.1(0.0) 0.009 0.005 - 2 1.1(0.0) 1.1(0.0) 1.1(0.0) - - - TSA (sec) 1 2.2(0.2) 1.8(0.2) 2.1(0.3) - - - 2 2.4(0.3) 2.0(0.1) 2.1(0.2) - - - BRT (msec) 1 621.2(61.5) 578.6(41.0) 573.3(31.8) - - - 2 602.4(44.2) 552.6(19.3) 548.1(23.9) - - -

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2.4.3 Visual spatial attention

Spatial Memory Task. Mean spatial error (± SE) and reaction times (± SE) by response delay in the spatial memory tests for each treatment are given in Figures 3 and 4. The intercept of the regression line of error on delay showed a significant effect of Treatments (F2, 32=3.49; p=.043) on Day 1 (intoxication). This main effect was primarily caused by MDMA which increased spatial error independent of response delay (p=.02). Slope of the regression line of error on delay was not affected by Treatments. Mean reaction time during spatial memory performance was also significantly affected by Treatments on Day 1(F2, 32=5.16; p=.011). Pair wise comparisons showed that MDMA decreased reaction time independent of response delay (p=.003). There was no effect of Treatments during withdrawal (day 2)

Change Blindness Task. Reaction time performance in the Change Blindness Task revealed overall effects of Content of change (relevant vs irrelevant) and Location of change (central vs peripheral) on each of the test days (F2,187>70; p=.000). Number of correct detections was affected by Content of change on both test days (F2,187>447; p=.000) and by Location of change on Day 2 (F2,187=20.2; p=.000). Changes relevant to traffic safety or driver performance were faster and more often detected than changes irrelevant to traffic. Changes in the central field of vision were faster and more frequently discovered than changes taking place in the peripheral field. Treatments did not affect performance. Interactions between Treatments and Content or Locus of change also failed to reach statistical significance.

2.4.4 Learning and working memory

Word Learning Test. Mean free recall scores by treatment during intoxication and withdrawal are shown in Figure 5. Statistical analyses revealed an overall effect of Treatment on immediate recall (F2,34=3.08; p=.059) and delayed recall (F2,34=12.57; p=.000) on Day 1 of testing. Drug-placebo contrasts revealed that immediate recall (p=.031) and delayed (p=.002) recall significantly decreased after treatment with MDMA. Methylphenidate did not affect word learning.

Syntactical Reasoning Task. Performance in the SRT was not affected by Treatments or Period on any of the test days.

Digit Symbol Substitution Task. Performance in the DSST was not affected by Treatments or Period on any of the test days

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MDMA Methylphenidate Placebo

12 Day 1 (Intoxication) Day 2 (Withdrawal) 11

10

9

8

7

Spatial error (mm) 6

5

4

0 2 4 0 2 4 Response delay (sec)

Figure 3. Mean (± SE) spatial error by response delay for each treatment during intoxication and withdrawal.

MDMA Methylphenidate Placebo 2,2 Day 1 (Intoxication) Day 2 (Withdrawal)

2

1,8

Reaction Time (sec) 1,6

1,4 0 2 4 0 2 4 Response delay (sec)

Figure 4. Mean (± SE) reaction time by response delay for each treatment during intoxication and withdrawal.

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MDMA Methylphenidate Placebo 15

13

11

9

7

Words recalled (number) 5 Day 1 (Intoxication) Day 2 (Withdrawal) 3 1 2 3 4 5 DR 1 2 3 4 5 DR Trials

Figure 5. Mean (± SE) immediate and delayed recall scores in every treatment condition during intoxication and withdrawal. Immediate recall was assessed over 5 learning trials whereas delayed recall (DR) was measured 30 minutes after the last learning trial.

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Table 3. Summary of means (SE) and treatment effects in cognitive tasks and subjective evaluations. ANOVA Contrasts Treatments Overall MDMA METH vs vs Tests Day MDMA METH PLA PLA PLA Visuospatial Attention Spatial Memory Task Intercept 1 6.2(0.7) 5.5(0.6) 5.4(0.7) 0.043 0.02 - 2 4.9(0.6) 5.8(0.6) 5.5(0.6) - - - Slope 1 1.1(0.1) 0.9(0.1) 1.0(0.1) - - - 2 1.1(0.2) 1.1(0.2) 0.9(0.1) - - - RT (msec) 1 1616.4(74.7) 1716.2(81.5) 1829.0(108.2) 0.011 0.003 - 2 1783.9(110.5) 1820.4(108.3) 1756.2(99.8) - - - Change Blindness Task # Correct 1 50.5(1.5) 50.3(1.4) 49.9(1.7) - - - 2 53.6(1.4) 52.6(1.0) 52.7(1.7) - - - RT(msec) 1 786.3(29.2) 798.5(31.5) 834.3(40.4) - - - 2 771.7(32.8) 801.1(38.0) 782.5(39.6) - - - Learning and working memory Word Learning Task IR (#) 1 53.7(2.4) 59.1(2) 58.3(2.1) 0.059 0.031 - 2 57.8(1.8) 57.2(2.4) 56.3(2.9) - - - DR (#) 1 11.2(0.8) 11.6(0.7) 12.7(0.7) 0.000 0.002 - 2 12.6(0.5) 10.8(0.9) 11.6(1) - - - Syntactic Reasoning Task # Correct 1 24.1(1.2) 24.8(1.3) 24.9(1.4) - - - 2 24.4(1.4) 25.3(1.4) 25.2(1.5) - - - RT(msec) 1 1458.6(89.9) 1474.5(98.4) 1531.6(78.0) - - - 2 1347.7(97.9) 1430.5(90.2) 1410.5(93.8) - - - Digital Symbol Substitution Task Score (#) 1 84.9(3.3) 85.9(3.8) 86.9(3.6) - - - 2 88.6(3.8) 89.7(3.2) 91.4(3.5) - - - Subjective Evaluations Sleep Questionnaire Quality 1 2.2(0.6) 3.6(0.9) 2.7(0.7) - - - 2 1.7(0.6) 2.0(0.5) 1.5(0.8) - - - Duration 1 6.9(0.2) 6.8(0.3) 7.7(0.5) - - - 2 7.8(0.3) 7.2(0.3) 7.3(0.4) - - - Hamilton Depression Scale Score 1 1.7(0.5) 1.4(0.4) 1.7(0.6) - - - 2 1.3(0.3) 1.0(0.4) 1.3(0.4) - - - Profile of Mood Scale Depression 1 64.8(1.9) 64.5(2.1) 61.0(3.1) - - - 2 61.9(2.9) 62.3(2.7) 64.6(2.7) - - - Anger 1 58.3(1.2) 57.7(1.7) 55.1(2.4) - - - 2 55.6(2.5) 55.0(2.3) 56.4(2.0) - - - Fatigue 1 41.3(2.9) 38.6(2.8) 36.0(3.2) - - - 2 37.3(3.0) 39.0(2.6) 42.9(2.4) 0.044 0.014 - Vigor 1 12.9(2.1) 15.5(2.2) 18.1(2.7) - - - 2 18.2(2.3) 16.1(2.2) 13.0(2.0) 0.014 0.009 - Tension 1 45.0(2.0) 47.0(1.8) 47.8(1.7) - - - 2 47.2(1.8) 47.8(1.7) 48.4(1.7) - - -

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2.4.5 Subjective evaluations

Subjective evaluation of subjective sleep quality and sleep duration did not differ between Treatments on days 1 and 2. On average, subjects slept between 6.7 – 7.8 hrs during the nights before testing. Hamilton depression ratings did not significantly differ between Treatments on both test days. Profile Of Moods Scale (POMS) rating revealed overall Treatment effects on vigour and fatigue on day 2 of treatment (withdrawal). Drug-placebo contrast showed that MDMA significantly increased feelings of fatigue (p=.017) and reduced feelings of vigour (p=.004) during the withdrawal phase. Methylphenidate tended to produce similar feelings but these just failed to reach statistical significance. Other items of the POMS, i.e. depression, anger and tension were not affected by Treatments.

2.4.6 Pharmacokinetics

Descriptive parameters of MDMA, MDMA and methylphenidate concentrations (ng/ml) during intoxication and withdrawal are given in Table 4.

Table 4. Mean (SD) concentrations (ng/ml) of MDMA, MDA and ritalinic acid during intocation and withdrawal (N=18) Day Time post MDMA 75mg Methylphenidate 20 drug (hrs) mg MDMA MDA Ritalinic acid 1 (intoxication) 1.5 113.4 2.9 (0.9) 95.9 (78.4) (37.4) 1 5.5 95.0 (31.6) 5.7 (1.8) 146.7 (49.8) 2 (withdrawal) 25.5 11.6 (9.3) 2.1 (1.2) 22.9 (7.0) 2 29.5 6.1 (1.5) 1.4 (1.0) 14.5 (5.0)

2.5 DISCUSSION

Actual driving data This was the first study to assess the effects of MDMA (ecstasy) on actual driving performance in normal traffic. The main implications of the results from the Road Tracking and Car Following tests are twofold. First, it was shown that MDMA improved road tracking performance as indicated by a significant decrease in SDLP, relative to placebo. Second, it was shown that MDMA decreased Car- Following performance as indicated by a significant increase in the subjects’ gain of their response to speed decelerations of the leading vehicle. Gain is the amplification factor between the both speed signals collected from the leading and following vehicle and indicates the magnitude of overshoot in reaction. MDMA did not affect phase delay or reaction time of the subjects’ motor response to speed decelerations, but it did increase the error between the actual and the desired outcome of their compensatory motor response. Collectively, these data indicate that MDMA possesses activating or stimulating properties

26 IMMORTAL D-R4.4 21/10/04 that that may improve performance in certain aspects of the driving task but cause impairment in others. This finding corroborates earlier results from experimental, (quasi) controlled studies with MDMA. Lamers et al (2003) demonstrated that MDMA 75mg improved psychomotor performance, such as tracking and movement speed, but impaired the ability to predict time to collision in a visual motion perception task. Brookhuis et al (2004) assessed simulated driving performance of MDMA users returning from a rave party. The authors report that basic vehicle control was only moderately affected by MDMA (average dose 56mg) but that subjects were prepared to accept higher risks as compared to the control group. It is of interest to note that the effects of MDMA on driving differed from those of the comparator drug, methylphenidate. The latter is a well known stimulant drug that was expected to produce performance improvement as shown in the Road Tracking Test. Yet, in contrast to MDMA, it did not affect performance in the Car-Following task. This qualitative difference between these drugs’ effects on driving may be related to differences in their pharmacological profiles. Methylphenidate can be classified a pure dopaminergic drug that achieves its stimulant effect by promoting the release of dopamine from presynaptic neurons and blocking its reuptake from the synaptic cleft. MDMA produces similar actions within dopaminergic projection systems, but in addition also increases serotonergic turnover in the brain by blocking serotonergic reuptake transporters. MDMA’s specific effect on gain of responding may thus be under serotonergic control, whereas the stimulating effect of both MDMA and methylphenidate on road tracking performance may result from common dopaminergic input. Driving performance during the withdrawal phase did not differ between treatments. Subjects did report increase in fatigue and loss of vigor the day following treatment administration but apparently the magnitude of these side effects was not sufficient to produce driving impairment. The lack of a strong withdrawal effect may in part be related to the fact that the subjects were treated with single doses and had normal sleep hours in the night between test days. In real life, MDMA users often take multiple doses of the drug, on successive days and nights, at the cost of a substantial sleep loss. In such scenario, withdrawal effects might be more prominent as compared to the present study.

Visuospatial processing and memory Research on the effects of MDMA on visuospatial processing and memory has provided mixed results. Several studies have demonstrated that MDMA users perform worse on visuospatial memory tasks as compared to non-drug using control (Fox et al 2002; Fox et al 2001; Verkes et al 2001; Wareing et al 2004) whereas others (Gouzoulis-Mayfrank 2000; Morgan 1998) found no significant differences between MDMA users and controls. Wareing et al (2004) have suggested that these apparently contradictive results may be related to differences in task characteristics of visuospatial memory tests employed in these studies. Visuospatial task performance showing no difference between MDMA users and controls primarily involved storage of spatial information

27 IMMORTAL D-R4.4 21/10/04 whereas tasks sensitive to differences between these groups involved storage plus explicit goal oriented processing. All of these studies have been performed in MDMA users who were not under the influence during the time of testing. The question of causal relationship between MDMA use and impairment of visuospatial attention tasks therefore remains a matter of controversy. These is some evidence neurotoxic damage in humans (McCann et al 1998; Reneman et al 2002) but it is unclear whether these effects may be confounded by polydrug use and lifestyle factors typical to MDMA users. The present study was designed to circumvent such problems of interpretation by assessing the effects of MDMA on spatial memory performance during intoxication as well as during a period of abstinence or withdrawal. In addition, two visuospatial tasks were selected that are designed to selectively measure 1) storage of spatial information (Spatial Memory Task) and 2) central processing of visuospatial information (Change Blindness Task) in order to assess the role of task characteristics on MDMA’s effect on visuospatial task performance. Results from these test unequivocally demonstrated a causal relation between MDMA intoxication and spatial memory performance. Localisation error in the Spatial Memory task significantly increased after MDMA administration indicating impairment in the short term storage of visuo-spatial information. However performance in the Change Blindness Task remained unaffected by treatments. The subjects’ detection rates of changes in traffic scenes were similar during MDMA, methylphenidate and placebo treatment, independent of the content or location of change. The latter manipulations did affect performance in the Change Blindness Task. Changes relevant to traffic safety or driver performance were faster and more often detected than changes irrelevant to traffic. Changes in the central field of vision were faster and more frequently discovered than changes taking place in the peripheral field. Collectively, these data indicate that MDMA does not affect processing of visuospatial information but solely impairs storage of such information in short-term memory. MDMA’s detrimental effect on spatial memory may also be of relevance to the driver as it indicates a reduction in situation awareness or spatial orientation while driving under the influence.

Learning and verbal memory The most consistent finding in recreational and abstinent ecstasy users are learning and verbal memory deficits in a range of neuropsychological tests (reviews: Cole and Sumnall 2003; Morgan 2000; Parrott 2001). The prototypical example being a reduction in performance on immediate and delayed word recall tasks. Verbal memory deficits appear most evident in heavy MDMA users who have been abstinent for over 6 month. In many studies light and novice ecstasy users displayed no memory impairments. The cause of these persistent memory deficits has been a matter of continuous debate. There is some evidence that these deficits are caused by MDMA induced neurotoxicity as indicated by depleted serotonin levels in MDMA users and by dose response relationships between the extend of exposure to MDMA and the magnitude of memory

28 IMMORTAL D-R4.4 21/10/04 impairment. Reneman et al (2000) reported a significant up-regulation in post- synaptic 5HT2 receptor in occipital regions of 5 abstinent MDMA users, possibly as a compensatory response to low synaptic 5HT levels. In addition, mean cortical 5HT2A receptor binding correlated positively with memory performance. However this association could not be replicated in a follow-up study employing a larger subject population (Reneman et al 2001a). Memory impairment was also not associated with binding to cortical 5HT transporters or duration of abstinence which suggests that memory deficits in MDMA occur independent of serotonergic neurotoxicity (Reneman et al 2001b). In addition, many of the studies showing long term memory impairment in MDMA users failed to control for polydrug use of their subject which may have seriously confounded the interpretation of their results. Cannabis in particular has been shown to account for some of the cognitive deficits observed in MDMA users (Croft et al 2001). Because of interpretational problems in studies employing abstinent users it remains unclear at present how memory deficits in MDMA users are related to MDMA induced changes in their serotonergic system. An alternative approach would be to study the effects of MDMA on memory performance during acute intoxication in placebo controlled, double-blind studies. Such approach would circumvent the problem of polydrug use by experimental control over drug administration, and would be able to test the assumption that serotonergic suppletion or depletion is associated with changes in memory performance. In the present study it was hypothesized that memory performance would actually increase during acute MDMA intoxication because of the synaptic surplus in 5- HT that becomes available through MDMA induced presynaptic release of 5-HT. In addition it was predicted that memory performance would decline 24 hrs later during the crash or withdrawal phase as a result of a short-term depletion in 5-HT resources that has been reported to occur in the days following MDMA use (Curran and Travill 1997; Parrott and Lasky 1998). Results from the present study did not confirm our hypotheses. MDMA did not improve memory performance during acute intoxication and it did not decrease memory performance during withdrawal. Instead, performance on immediate recall and delayed word recall task decreased during acute MDMA intoxication and remained unaffected during withdrawal. These results seem to indicate that memory performance during MDMA intoxication is not directly affected by changes in the availability of 5-HT in the synaptic cleft that are caused by blockade or reversal of 5-HT transporters. It is of interest to note that our original hypotheses were mainly based on other studies that have employed more selective manipulations of synaptic availability of 5-HT than in the present study. Single dose administration of escitalopram, a selective inhibitor of the 5- HT reuptake transporter that increases synaptic 5-HT availability has been demonstrated to improve memory consolidation in healthy volunteers as measured in a delayed word recall task (Harmer et al 2002). In contrast, performance in delayed word recall has been shown to decrease in subjects with low level of synaptic 5HT in tryptophane depletion studies (Riedel et al 1999; Schmitt et al 2000). However, none of these manipulations affected short-term memory as measured in immediate word recall tasks. What seems typical

29 IMMORTAL D-R4.4 21/10/04 however to MDMA induced memory deficits is a reduction in both immediate and delayed word recall. This impairment pattern was shown in the present study during acute MDMA intoxication and in previous studies assessing memory in abstinent MDMA users. The qualitative differences between memory impairments observed after MDMA intoxication and tryptophane depletion raises the question whether MDMA exerts its detrimental effect on memory through other mechanisms then synaptic 5-HT depletion alone. Candidate mechanisms include direct and indirect activation of postsynaptic 5-HT2 and 5HT1A receptors respectively. It is clear from animal studies that 5HT2 receptors mediate learning and memory processes, although it is not completely clear whether 5HT2 receptor drugs achieve their facilitating and impairing effects through agonism, antagonism or inverse agonism (Meneses 2002). Still, there appears to be some consensus that 5HT2A/2C blockade improves learning whereas 5HT2 receptor agonists such as mCPP have been shown to decrease learning and memory in rats (Meneses 1999). The same mechanism might be responsible for the decrement in learning and delayed recall in the present experiment, as it has been previously demonstrated that MDMA possesses a moderate affinity for activating the 5-HT2A receptor (Sadzot et al 1989). A second candidate mechanism is indirect activation of the post-synaptic 5HT1A receptor. The latter has also showed to modulate memory performance in animals and humans (Meneses 1999; Yasuno et al 2003). PET studies have shown a negative correlation between memory function and 5HT1A receptor agonists binding in the hippocampus. More specifically, the 5HT1A agonist tandospirone dose dependently impaired performance in an immediate and delayed word recall task (Yasuno et al 2003). The memory impairing effects of 5HT1A agonist thus appear identical to those produced by MDMA. It is not known whether MDMA also acts as a direct 5-HT1A receptor agonist. However it may achieve the same net results by indirectly stimulating postsynaptic 5HT1A receptors by the short-term increase in availability of synaptic 5HT during intoxication.

In summary, it can be concluded that single doses of MDMA produced a variety of performance changes in tests measuring actual driving, visuospatial processing and memory. The driving data indicated that MDMA possesses activating or stimulating properties that that may improve performance in certain aspects of the driving task, i.e. road tracking, but cause impairment in others, i.e. car-following. It was also shown that a single dose of MDMA reduces performance in spatial memory and word learning tasks. MDMA’s detrimental effect on spatial memory may also be of relevance to the driver as it indicates a reduction in situation awareness or spatial orientation while driving under the influence.

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2.6 ACKNOWLEDGEMENTS

We would like to thank Nele Samyn, Gert De Boeck and Marleen Laloup from NICC, Brussels, for analyzing MDMA and methylphenidate plasma samples.

2.7 REFERENCES

Baddeley A (1968): A 3 minute test based on grammatical transformation. Psychon Sci 10:341-342. Brookhuis KA, De Waard D, Samyn N (2004): Effects of MDMA (ecstasy), and multiple drugs use on (simulated) driving performance and traffic safety. Psychopharmacology (Berl). Brookhuis KA, De Waard D, Mulder LJ (1994) Measuring driving performance by carfollowing in traffic. Ergonomics 37: 427-434 Cole JC, Sumnall HR (2003): Altered states: the clinical effects of Ecstasy. Pharmacol Ther 98:35-58. Croft RJ, Mackay AJ, Mills AT, Gruzelier JG (2001): The relative contributions of ecstasy and cannabis to cognitive impairment. Psychopharmacology (Berl) 153:373-9. Curran HV, Travill RA (1997): Mood and cognitive effects of +/-3,4- methylenedioxymethamphetamine (MDMA, 'ecstasy'): week-end 'high' followed by mid-week low. Addiction 92:821-31. Fox HC, McLean A, Turner JJ, Parrott AC, Rogers R, Sahakian BJ (2002): Neuropsychological evidence of a relatively selective profile of temporal dysfunction in drug-free MDMA ("ecstasy") polydrug users. Psychopharmacology (Berl) 162:203-14. Fox HC, Parrott AC, Turner JJ (2001): Ecstasy use: cognitive deficits related to dosage rather than self-reported problematic use of the drug. J Psychopharmacol 15:273-81. Gouzoulis-Mayfrank E, Dauman, J, Tuchtenhagen, F, Pelz, S, Becker, S, Kunert, HJ, Frimm B, Sass, H (2000): Impiared cognitive performance in drug free users of recreational ecstasy (MDMA). J Neurol Neurosurg psychiatry 68:719-725. Harmer CJ, Bhagwagar Z, Cowen PJ, Goodwin GM (2002): Acute administration of citalopram facilitates memory consolidation in healthy volunteers. Psychopharmacology (Berl) 163:106-10. Lamers CT, Ramaekers JG, Muntjewerff ND, et al (2003): Dissociable effects of a single dose of ecstasy (MDMA) on psychomotor skills and attentional performance. J Psychopharmacol 17:379-87. Lamers CTJ, Rizzo M., Bechara, A., Ramaekers, J.G. (2004): Simulated driving and attention in repeated users of MDMA and THC as compared to THC users and non drug using controls. Submitted for publication. Logan BK, Couper FJ (2001): 3,4-Methylenedioxymethamphetamine (MDMA, ecstasy) and driving impairment. J Forensic Sci 46:1426-33.

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McCann UD, Szabo Z, Scheffel U, Dannals RF, Ricaurte GA (1998): Positron emission tomographic evidence of toxic effect of MDMA ("Ecstasy") on brain serotonin neurons in human beings. Lancet 352:1433-7. Meneses A (1999): 5-HT system and cognition. Neurosci Biobehav Rev 23:1111- 25. Meneses A (2002): Involvement of 5-HT(2A/2B/2C) receptors on memory formation: simple agonism, antagonism, or inverse agonism? Cell Mol Neurobiol 22:675-88. Morgan MJ (1998): Recreational use of "ecstasy" (MDMA) is associated with elevated impulsivity. Neuropsychopharmacology 19:252-64. Morgan MJ (2000): Ecstasy (MDMA): a review of its possible persistent psychological effects. Psychopharmacology (Berl) 152:230-48. Mulder-Hajonides van der Meulen W (1981): Measurements of subjective sleep, International European Sleep Congres: Elsevier, Amsterdam. O'Hanlon JF, Haak TW, Blaauw GJ, Riemersma JB (1982): Diazepam impairs lateral position control in highway driving. Science 217:79-81. Parrott AC (2001): Human psychopharmacology of Ecstasy (MDMA): a review of 15 years of empirical research. Hum Psychopharmacol 16:557-577. Parrott AC, Lasky J (1998): Ecstasy (MDMA) effects upon mood and cognition: before, during and after a Saturday night dance. Psychopharmacology (Berl) 139:261-8. Ramaekers G, Lamers J, Verhey F, et al (2002): A comparative study of the effects of carbamazepine and the NMDA receptor antagonist remacemide on road tracking and car-following performance in actual traffic. Psychopharmacology (Berl) 159:203-10. Ramaekers JG, O'Hanlon JF (1994): Acrivastine, terfenadine and diphenhydramine effects on driving performance as a function of dose and time after dosing. Eur J Clin Pharmacol 47:261-6. Reneman L, Booij J, Majoie CB, Van Den Brink W, Den Heeten GJ (2001a): Investigating the potential neurotoxicity of Ecstasy (MDMA): an imaging approach. Hum Psychopharmacol 16:579-588. Reneman L, Booij J, Schmand B, van den Brink W, Gunning B (2000): Memory disturbances in "Ecstasy" users are correlated with an altered brain serotonin neurotransmission. Psychopharmacology (Berl) 148:322-4. Reneman L, Endert E, de Bruin K, et al (2002): The acute and chronic effects of MDMA ("ecstasy") on cortical 5-HT2A receptors in rat and human brain. Neuropsychopharmacology 26:387-96. Reneman L, Lavalaye J, Schmand B, et al (2001b): Cortical serotonin transporter density and verbal memory in individuals who stopped using 3,4- methylenedioxymethamphetamine (MDMA or "ecstasy"): preliminary findings. Arch Gen Psychiatry 58:901-6. Riedel WJ, Klaassen T, Deutz NE, van Someren A, van Praag HM (1999): Tryptophan depletion in normal volunteers produces selective impairment in memory consolidation. Psychopharmacology (Berl) 141:362-9. Riley SC jC, Gregory D, Dingle H, Cadger M (2001): Patetrns of recreational drug use at dance events in Edinburgh, Scotland. Addcition 96:1035.

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Rizzo M, Lamers, C.T.J., Sauer, C.G., Ramaekers, J.G., Bechara, A., Andersen, G.J. (2004): Loss of motion sensitivity for the perception of heading in abstinent ectasy and marijuana users. Submitted for publication. Sadzot B, Baraban JM, Glennon RA, et al (1989): Hallucinogenic drug interactions at human brain 5-HT2 receptors: implications for treating LSD- induced hallucinogenesis. Psychopharmacology (Berl) 98:495-9. Schifano F (1995): Dangereous driving and MDMA. J Serotonin Rese 1:53. Schmitt JA, Jorissen BL, Sobczak S, et al (2000): Tryptophan depletion impairs memory consolidation but improves focussed attention in healthy young volunteers. J Psychopharmacol 14:21-9. Verkes RJ, Gijsman HJ, Pieters MS, et al (2001): Cognitive performance and serotonergic function in users of ecstasy. Psychopharmacology (Berl) 153:196-202. Vermeeren A, Jackson JL, Muntjewerff ND, Quint PJ, Harrison EM, O'Hanlon JF (1995): Comparison of acute alprazolam (0.25, 0.50 and 1.0 mg) effects versus those of lorazepam 2 mg and placebo on memory in healthy volunteers using laboratory and telephone tests. Psychopharmacology (Berl) 118:1-9. Wareing M, Murphy PN, Fisk JE (2004): Visuospatial memory impairments in users of MDMA ('ecstasy'). Psychopharmacology (Berl). Yasuno F, Suhara T, Nakayama T, et al (2003): Inhibitory effect of hippocampal 5-HT1A receptors on human explicit memory. Am J Psychiatry 160:334- 40.

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3. STUDY B: INTERACTION EFFECTS OF 3,4- METHYLENEDIOXYMETHAMPHETAMINE (MDMA ) AND ALCOHOL ON ACTUAL DRIVING, PSYCHOMOTOR PERFORMANCE AND RISK TAKING BEHAVIOR.

3.1 INTRODUCTION

MDMA is frequently used in combination with psychoactive drugs. Surveys in Australia (Topp et al 1999) and Spain (Gamella 1997) have indicated that 40- 60% percent of MDMA users consumed alcohol concomitantly. It has also been estimated that 6-35% of subjects using MDMA at raves or dance parties will drive home afterward (Riley et al 2001). Several fatal and non-fatal road-accidents have been reported in which MDMA was found in plasma of drivers, or those held responsible for the accident (review: Logan and Couper 2001) . Specific behavioral changes that have been reported in drivers under the influence of MDMA included speeding, jumping red lights, hallucinations/delusions and a sense of detachment from the real world (Schifano 1995) Experimental studies have shown that single doses of MDMA produce esophoria (Cami et al 2000) and impairs its users’ capacity to estimate time a time to collision of moving objects (Lamers et al 2003). However MDMA was also shown to improve performance on range of psychomotor tasks such as reaction time and tracking performance indicating that the drug has stimulating properties as well (Lamers et al 2003). The experimental study employing actual on-the-road driving test (Ramaekers and Kuypers, this deliverable, chapter 2) confirmed this dualistic profile of the drug. A single dose of MDMA 75 mg improved vehicle control in a road tracking tests but induced overreacting in a car following task. Collectively, these data indicated that MDMA possesses activating or stimulating properties that may improve driving performance in certain aspects of the driving task but cause impairment in others. The role of alcohol use in MDMA induced performance changes is presently unclear. Only 2 studies have addressed alcohol-MDMA interactions in humans. Hernandez-Lopez et al (Hernandez-Lopez et al 2002) showed that MDMA reversed subjective sedation induced by alcohol. However, objective psychomotor impairment after alcohol as measured in a digit-symbol substitution task was not revered by concomitant MDMA use. Brookhuis et al (Brookhuis et al 2004) conducted a quasi-controlled study to assess driving performance of MDMA users in an advanced driving simulator before and after visiting a rave party. All subjects indicated that they had taken MDMA (average dose: 56mg) just before going to the party, and 30-40% of them also attested to the concomitant use of alcohol and/or marijuana. When retuning from the party, most of the subjects had taken additional doses of MDMA (70%), marijuana (80%) or alcohol (90%). The participants were also tested sober, at a comparable time as the first MDMA ride. The results indicated that driving performance was not

34 IMMORTAL D-R4.4 21/10/04 greatly affected prior to the rave party, but deteriorated during the night after multiple drug use. The most striking result was the apparent decreased sense for risk taking. This data suggest that combined use of MDMA and alcohol or marijuana produces more severe driving impairment than MDMA alone. However, it should be noted that these data are also confounded by time of testing. Driving impairment was primarily observed when subjects had build up a full-night of sleep loss, at the end of the rave. It can thus not be excluded that sleep by itself may also have played a major role in driver impairment observed after multiple drugs use. Placebo controlled studies specifically designed to assess the role of combined MDMA and alcohol use in driver impairment have not yet been conducted. Yet it has become increasingly clear that the effects of many drugs, when taken concurrently are not necessarily predictable on the basis of knowledge of their effects when given alone. When MDMA users take several drugs concurrently, they face the problem of knowing whether a specific combination of drugs causes an interaction. A drug interaction refers to the possibility that one drug may alter the intensity of pharmacological effects of another drug given concurrently. The net results may be enhanced or diminished effects of one or both drugs or the appearance of a new effect that has not been observed with either drug alone.

3.2 AIM OF THE STUDY

The aim of the present study is to assess the potential interaction between MDMA and alcohol at the performance level. Primary variables are measures taken during actual driving tests (see below) and laboratory measures of risk taking and impulsivity. The actual driving tests have been developed to assess drug effects on a broad range of driving tasks at operational, tactical and strategical levels. The basic operational level includes highly automated behaviors such as road tracking performance; the tactical level vehicle maneuvering like overtaking, distance keeping and car-following; and the strategical level general plans, risk evaluation and anticipation of traffic. In addition, a number of laboratory tests were added to measure skills related to driving such as tracking performance, time to contact estimation and impulsivity or risk taking behaviour. Impulsivity was either defined as commission error or the failure to inhibit a response in rapid response model (Continuous Performance Task and Stop Signal task) or as the inability to wait for a larger reward (Gambling Task). Studies that have examined the effect of MDMA on impulsivity have provided conflicting results. McCann et al (McCann et al 1994) reported a decrement in impulsivity ratings of MDMA users as measured by the Multidimensional Personality Questionnaire whereas others reported elevated scores of impulsivity in heavy MDMA users, using different measures of impulsivity (Morgan 2000 ,Parrott, 2003 #72). Elevated levels of impulsiveness have been associated with lower levels of 5HT and CSF-HIAA (Linnoila et al 1983) and with dopaminergic activation in the prefrontal cortex leading to a

35 IMMORTAL D-R4.4 21/10/04 reduction in “inhibitory control” over behavioural functions (review: Jentsch & Taylor, 1999). MDMA induced impulsivity could be accounted for by both mechanism. Low CSF-5HIAA levels have been observed in abstinent and recent MDMA users, whereas dopaminergic turnover is temporarily elevated during the MDMA intoxication (Morgan 2000 ,Parrott, 2003 #72). Despite mounting evidence that inhibitory control may be impaired in MDMA users, no research has directly assessed the effects of acute MDMA administration on inhibitory control in humans.

3.3 METHODS

3.3.1 Subjects

Eighteen recreational MDMA-users (9 males, 9 females) aged 20-37 were recruited through advertisements in local newspapers. Initial screening was accomplished on the basis of a questionnaire on medical history and driving experience. Subjects who were accepted were examined by the medical supervisor, who also checked vitals signs and took blood and urine samples. Standard blood chemistry, haematological and drug screen tests were conducted on these samples. Inclusion criteria were: experience with the use of MDMA; free from psychotropic medication; good physical health as determined by examination and laboratory analysis; absence of any major medical (except OAC), endocrine and neurological condition; normal weight, body mass index (weight/length2) between 18 and 28 kg/m2 ; and written Informed Consent. Exclusion criteria were: history of drug abuse (other than the use of MDMA) or addiction; pregnancy or lactation; cardiovascular abnormalities as assessed by standard 12-lead ECG; excessive drinking (> 20 alcoholic consumptions a week); hypertension (diastolic> 100; systolic> 170); and history of psychiatric or neurological disorder. This study was conducted according to the code of ethics on human experimentation established by the declaration of Helsinki (1964) and amended in Edinburgh (2000). All subjects gave their informed consent, in writing. Approval for the study was obtained from the University’s Medical Ethics committee and the District Attorney of the City of Maastricht. A permit for obtaining, storing and administering MDMA was be obtained from the Dutch drug enforcement administration. The subjects were paid for their participation.

3.3.2 Design, doses and administration

The study followed a double-blind, placebo-controlled, 6-way cross-over design. Complete balancing of the treatment orders yielded 6 treatment orders randomly assigned to 18 subjects. The treatments consisted of MDMA 0, 75 and 100 mg with and without alcohol. MDMA and MDMA placebo were administered orally in identically appearing formulations. MDMA was administered as a 25 ml solution in bitter orange peel syrup, which was ingested at once. Alcohol dosing was designed to achieve a peak Blood Alcohol Concentration (BAC) of about 0.6

36 IMMORTAL D-R4.4 21/10/04 mg/ml during laboratory testing and of about 0.5 mg/ml during the driving tests. Subjects’ BAC was monitored regularly at 15 min intervals for 30-90 min after cessation of drinking using a Lion SD-4 Breath Alcohol Analyzer. At 90 min post alcohol, those failing to achieve the expected BAC levels prior to the driving test were given a first booster dose of 0.05-0.2 g/kg in the same proportion to the mixer whereas other would receive the mixer alone. A second booster dose, calculated to increase BAC by 0.10 mg/ml, was given prior to the Road Tracking Test. Flavoured orange juice was be given at the same times and in the same volumes in the placebo alcohol conditions. The minimum wash-out period between successive treatments was one week. Subjects were always tested in pairs. Laboratory tests were conducted between 1.5-2.25 hrs post drug. Driving tests were conducted between 3-5 hrs post drug. Half of the subject commenced with the Car-Following Tests followed by the Road Tracking Test, whereas the other half conducted both driving test in the reversed order. This procedure was necessary because two Car-Following Tests cannot be conducted simultaneously when testing pairs of subjects.

Table 1. Schematic representation of activities on test days

Activity Actual Time Time post alcohol Time post drug (hrs) (hrs) Arrival at test facility 9:00 Drug screen 9.00 Standard breakfast 9:00 Drug administration 10:00 0 Sleep questionnaires 10:30 0.5 Alcohol administration* 10:45-11:00 0 1 Blood sample /saliva 11:30 0.5 1.5 Laboratory tests / 11.30-12:15 0.5 – 1.25 1.5-2.25 subjective evaluations Alcohol booster dose 12:15 1.25 2.25 1/ Lunch Transport to Driving 12:30 1.5 2.5 site Car-Following Test 13:00-13:30 or 2-2.5 or 3.5-4 3-3.5 or 4.5-5 14:30-15:00 Alcohol booster dose 2 13:30 2.5 3.5 Road Tracking Test 13:30-14:30 2.5-3.5 3.5-4.5 Transport UM/home 15:00 Blood sample/saliva 15:30 4.5 5.5

3.3.3 Procedures

Subjects were asked to refrain from any drugs starting 1 week before the medical screening and physical examination until two weeks after the last experimental session. The subjects were not allowed to use alcohol on the day prior to an

37 IMMORTAL D-R4.4 21/10/04 experimental session and were requested to arrive at experimental sessions well rested. Drug and alcohol screens were be performed in experimental sessions upon arrival of the subject. Subjects were transported from their homes to the laboratory or vice versa by one of the experimenters. Additional clinical blood chemistry, with particular reference to liver and renal function were conducted at day 7 after each treatment. During 7 days following each treatment subjects were asked to contact the medical supervisor or a member of the investigating team upon experiencing any sign of nausea, vomiting, intolerance to fatty food, yellowish colour of skin, or tiredness. All subjects received a training session prior to onset of the experimental sessions in order to familiarize them with the tests and procedures.

3.3.4 Actual driving tests

Road Tracking Test In the Road Tracking Test (O'Hanlon et al 1982) the subjects operate a specially instrumented vehicle over a 100 km primary highway circuit while maintaining a constant speed (95 km/h) and a steady lateral position between the delineated boundaries of the right (slower) traffic lane. An electro- optical device mounted at the rear back of the car continuously measures lateral distance separating the vehicle and the left lane-line. This signal is digitised at a rate of 4 Hz and stored on an onboard computer disk file for later editing analysis. The off line editing routine involves removal of all data segments that reveal signal loss, disturbance or occurrence of passing manoeuvres. The remaining data are then used to calculate means and variances for lateral position and speed (SP). Standard deviation of lateral position (SDLP) is taken as the primary outcome variable. SDLP is a measure of road tracking error, in practical terms, a composite index of allowed weaving, swerving and overcorrecting. The test duration is 1 hour.

Car-Following Test The Car Following Test (Brookhuis et al, 1994; Ramaekers et al 2002; Ramaekers and O'Hanlon 1994) involves the use of two vehicles. The preceding vehicle is under an investigator's control, and the following vehicle, the subject's. The test begins with the two vehicles travelling in tandem at speeds of 70 km/h on a secondary highway. Subjects attempt to drive 15-30 m behind the preceding vehicle and to maintain that headway as it executes a series of deceleration manoeuvres. During the test, the speed of the leading car is automatically controlled by a modified cruise-control system. At the beginning it is set to maintain a constant speed of 70 km/h, and by activating a microprocessor, the investigator can start sinusoidal speed changes reaching an amplitude of -10 km/h and returning to the starting level within 50 sec. The manoeuvre is repeated 6 times. Between deceleration manoeuvres, the investigator in the leading car randomly activates the brake lights of his vehicle by activating a second mode of the microprocessor. The brake lights then light for 3 seconds whereas the speed of the leading car remains constant at 70 km/h. The subject is instructed to react to brake lights by removing his/her foot from the speed pedal as fast as possible. This procedure will be repeated between 20-30 times throughout the test.

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Headway is continuously recorded by means of a DME 2000 optical distance sensor. That device is placed in the grill of the following vehicle and emits laser signals in the direction of a reflection board mounted on the leading vehicles towing bracket. Distance from the lead vehicle is deduced from the time lapse between the transmission and receipt of the signal at the receiving end of the distance sensor. The velocity of the leading vehicle and initiation times of speed manoeuvres and brake lights are transmitted via telemetry to the following vehicle and stored on a computer disk along with the velocity of the following vehicle, headway and response time to brake lights. Speed signals collected during speed manoeuvres enter a power spectral analysis for yielding phase- delay between the vehicle’s velocities at the manoeuvre cycle frequency (0.02) Hz. Phase delay converted to a measure of Time to Speed Adaptation (TSA, in msec), brake reaction time (BRT, in msec) and Gain are the major dependent variables. Gain is the amplification factor between the both speed signals collected from the leading and following vehicle and indicated the magnitude of undershoot or overshoot in reaction. Test duration is 25 minutes.

General driving proficiency. Driving proficiency is scored by the driving instructor in retrospect after completion of the actual driving tests. In total 137 items are scored dichotomously as pass or fail. Total test performance is scored by the number of items scored as ”pass”. Subscores are calculated for vehicle checks and handling, traffic manoeuvres and understanding traffic.

3.3.5 Psychomotor performance

Critical Tracking Task (CTT) CTT measures the subject’s ability to control a displayed error signal in a first-order compensatory tracking task. Error is displayed as a horizontal deviation of a cursor from the midpoint on a horizontal, linear scale. Compensatory joystick movements null the error by returning the cursor to the midpoint. The frequency at which the subject loses the control is the critical frequency or λc. The test includes five trials of which the lowest and the highest score are removed ; the average of the remaining scores is taken as the final score(Jex et al 1966).

Object Movement Estimation under Divided Attention (OMEDA) In the OMEDA task (Read 2000) subject have to estimate time to contact (TTC) of a moving object to a fixed point. The subject is seated in front of a computer screen. The corners of the screen are covered by green triangles and a yellow circle occludes the centre of the screen. The occlusion circle varies in size per trial (2, 100 or 200 pixels). From one of the corners, a red dot (target) travels towards the centre of the screen. Once it encounters the edge of the occlusion circle, it travels underneath and is no longer visible. The primary task of the subjects is to estimate at what point in time the target reaches the centre of the computer screen, by pressing a foot pedal. During movement of the targets 5 geometrical shapes appear; one on top of the occlusion circle and one on top of each of the triangles in the corners. The secondary task of the subject is to press a button in case the geometric shape at the occlusion circle matches one of the others. TTC

39 IMMORTAL D-R4.4 21/10/04 error, defined as the mean difference between estimated and actual TTC is the dependent measure.

3.3.6 Impulsivity and risk taking behavior

The Gambling Task. The subject sees 4 decks of cards on a computer screen labeled A, B, C, and D at the top end of each deck. With a mouse, the subject can click on a card on any of the four decks. Each deck of cards is programmed to have 40 cards. The gains and losses for each card selection are set so that in each block of 10 cards from deck A or deck B over the course of trials there is a total gain of $1000 (interrupted by unpredictable losses with a total of $1250. For decks C and D, the gains and losses for each card selection are set so that in each block of 10 cards there is a total gain of $500, interrupted by losses totalling $250 (gains and losses all refer to virtual money). Thus decks A and B are “disadvantageous” in the long term, while decks C and D are “advantageous” in the long term. The majority of normal people choose advantageously on this task (i.e., select more cards from the advantageous relative to the disadvantageous decks). Patients with frontal lobe lesions do the opposite, i.e., select more disadvantageous cards. The sensitivity, reliability, and validity of this task in detecting decision-making impairments has been tested in neurological as well as psychiatric populations. Thus there is one dependent measure that we collect from this task: net score (total # of cards picked from C and D minus total # of cards picked from A and B). Parallel versions of the gambling task are used over 6 treatment sessions (Bechara et al 2001) .

The Continuous Performance test (CPT). The CPT Conners' Continuous Performance Test is a computerized measure of vigilance (attention). However, the task provides a measure of the ability to inhibit or suppress a habit response. During the test, a series of letters (A, E, H, L, K and X) appear one at a time in the center of the computer screen, and participants are instructed to press the spacebar at the appearance of each letter (Go Condition). However, ff the letter “X” was preceded by “A” subjects had to inhibit their prepared responses (No Go condition). The CPT is most often used in studies on attention- deficit/hyperactivity disorder (ADHD), but we will use it here to measure one of the mechanisms of impulse control. Dependent variables of the CPT include average and overall reaction times (RT), % hits, risk taking, perceptual sensitivity (d’), and errors of omission (not pushing the spacebar for a letter other than X) and commission (pushing the spacebar for an X). For the purposes of this investigation, we focus on “errors of commission” because it reflects the poor ability to suppress/inhibit an automatic response.

Stop Signal Task (SST). This task requires subjects to make quick key responses to visually presented go signals and to inhibit any response when a visual stop signal is suddenly presented. The current test is adapted from an earlier version of Fillmore et al (Fillmore et al 2002). The go signals are four 1.5 cm letters (ABCD) presented one at a time in the center of a computer screen. Subjects are required to respond to each letter as quickly as possible by pressing

40 IMMORTAL D-R4.4 21/10/04 on of two response buttons. One button is pressed to indicate that “A” or “C” appeared and the other to indicate “B” or “D”. Letters are displayed for 500 msec and the computer screen is blank for 1.5 sec interstimulus interval before the next letter is displayed. This provides a period of 2 sec in which the subject can respond to a letter. A single test consists of 176 trials in which each of the 4 letter stimuli is presented equally often. A stop signal occurred in 48 trials during a test. The stop signal consists of visual cue, i.e. “*”, that appears in one of the four corners of the screen. Subjects are required to withhold any response in case a stop-signal is presented. Stop signals are presented 12 times at each of the four delays after the onset of a letter: 50, 150, 250 and 350 msec. Trials always begin with a 500 msec preparation interval in which a fixation point appears at the center of the screen. The task lasts about 10 minutes. Dependant variable is the proportion of inhibited responses on stop trials (IR). IR data were subjected to conventional arsin (X’=2 arcsin X0.5) transformation before entering the statistical analyses.

3.3.7 Subjective evaluations

Subjects filled out a profile of mood scale (POMS) and a visual analog scale measuring impulsivity. Sleep quality and duration were measured using the Groningen Sleep Questionnaire (Mulder-Hajonides van der Meulen 1981)

3.3.8 Pharmacokinetic assessments

MDMA was determined in plasma (15 ml) and saliva (1-2 ml) at 1,5 and 5,5 hrs post drug. Saliva was collected by spitting in a dry polypropylene tube, preferably without any chemical stimulation. Blood samples were placed on ice immediately, centrifuged later and frozen at -80°C until analyses for pharmacokinetic assessments. MDMA and MDA concentrations were determined using solid phase extraction and gas chromatography with mass spectrometric detection with quantification limits of 5 ng/ml and 0.5 ng/ml respectively. BAC concentrations were assessed every 15 minutes during the first 2 hrs after drinking and prior to and after completion of the driving test using a Lion SD-4 Breath Alcohol Analyser.

3.3.9 Statistical analysis

Sample size was based on a power calculation for detecting a clinical relevant effect on the primary measure of this study, Standard Deviation of Lateral Position (SDLP). A change in SDLP of > 2 cm relative to placebo is considered to be clinically relevant. It is known from previous driving studies conducted by the present research group, that normal populations’ standard deviation in SDLP is 4.2 cm. Test-retest reliability is known to be at least r=0.70. Based on these numbers it has been shown that 18 subjects are sufficient for detecting a difference of 2cm at the alpha=.05 level with more than 90% power. Data were analyzed by means of GLM, repeated measures, with Alcohol (2 levels) and MDMA (3 levels) as main factors. Separate drug-placebo contrast

41 IMMORTAL D-R4.4 21/10/04 were conducted following an overall effect of MDMA or the interaction between MDMA and Alcohol. If the model assumptions were violated, a suitable transformation or nonparametric method was considered. Where there were indications that the distribution of a variable departs substantially from normality, a Friedman analyses was performed to establish the overall effect of Treatments. If significant, a subsequent analysis for comparing separate drug treatments was conducted using Wilcoxon ranked sign tests with placebo as reference.

3.4 RESULTS

3.4.1 Missing values and failures to complete the driving tests

Four driving test were terminated before scheduled completion because the driving instructor felt the subjects were unable to continue. These failures to complete the driving test occurred during treatment with alcohol alone (2x), MDMA75 and alcohol combined (1x) and placebo (1x). Due to technical dysfunctions no data were collected in the CTT, OMEDA, Gambling and CPT tasks on a single occasion. These missing values were replaced by their particular treatment means in the statistical analysis. SST data of one subject was excluded from analysis because of inadequate task performance.

3.4.2 Actual driving tests

Road Tracking Test. Mean (+ SE) SDLP values recorded during every treatment condition are shown in Figure 1. MANOVA revealed significant main effects of MDMA (F2,34 =12.116; p< 0.001), Alcohol (F1, 17 =33.374; p< 0.001) and their interaction (F2,34 =3.270; p= 0.05). Both doses of MDMA significantly decreased mean SDLP by approximately 2 cm in comparison to placebo. Alcohol increased mean SDLP by about 3 cm. Contrast analysis of the Alcohol-MDMA interaction showed that the stimulating effect of MDMA 100mg on SDLP was more prominent when combined with alcohol whereas the stimulating effect of MDMA 75mg did not change in magnitude after alcohol co-administration (p= 0.014). In addition, a main effect of MDMA was found on SDSP (F2,34= 5.382; p< 0.05). Separate contrast testing demonstrated that both MDMA doses significantly decreased SDSP by about 0.2 km/h relative to placebo (p<.05).

42 IMMORTAL D-R4.4 21/10/04

28 Without alcohol With alcohol 26

24

22

20

18

SDLateral Position (cm) 16

14 PLA MDMA MDMA PLA MDMA MDMA 75mg 100mg 75mg 100mg

Figure 1. Mean (+SE) standard deviation of lateral position (SDLP, cm) in every treatment condition .

700

690 680 Without alcohol With alcohol 670 660 650 640 630 620 610

600 590 580 570 560 550 540

Brake reaction time (msec) 530

520 510

500 PLA MDMA MDMA PLA MDMA MDMA 75mg 100mg 75mg 100mg

Figure 2. Mean (+SE) brake reaction time (BRT, msec) in every treatment condition .

Car-following test. MANOVA revealed a main effect of Alcohol (F1, 17 = 5.757; p< 0.05) on BRT. On the average, alcohol reduced brake reaction time by 40 ms. No significant treatment effects were found on TSA or gain. Mean (+SE) brake reaction times are shown in Figure 2.

43 IMMORTAL D-R4.4 21/10/04

General driving proficiency. MDMA and Alcohol did not affect vehicle checks and handling, manoeuvring in traffic and understanding traffic as judged by the driving instructor.

3.4.3 Psychomotor tests

Mean (+ SE) lambda-c values recorded in the Critical Tracking Task (CTT) during every treatment condition are shown in Figure 3. MANOVA revealed significant main effects of Alcohol (F1, 17 =17.53; p= 0.001), but not of MDMA. Task performance in the Object Movement Estimation under Divided Attention task (OMEDA) was not affected by MDMA, Alcohol or their interaction.

5

4,8 Without alcohol With alcohol

4,6

4,4

4,2

4

3,8

3,6

Lambda-c (rad/sec) 3,4

3,2

3 PLA MDMA MDMA PLA MDMA MDMA 75mg 100mg 75mg 100mg

Figure 3. Mean (+SE) lambda-c in the CTT during every treatment condition .

3.4.4 Impulsivity and risk taking behavior

Mean (+SE) values recorded in the Gambling and Stop Signal task during every treatment condition are shown in Figures 4 and 5 respectively. MANOVA revealed significant main effects of Alcohol on both the Gambling (F1, 17 =3.28; p= 0.08), and the Stop Signal task (SST) (F1, 16 =7.06; p=0.017), but no effects of MDMA and MDMA + Alcohol. Performance in the Continuous Performance test (CPT) task was not affected by MDMA or the interaction of MDMA + Alcohol. Alcohol tended to decrease the percentage of correct response inhibitions (F1, 17 =4.14; p=0.058),

44 IMMORTAL D-R4.4 21/10/04

15

12 Without alcohol With alcohol

9

6

3

0

-3

-6

-9 Net score (correct-incorrect) Net score -12

-15 PLA MDMA MDMA PLA MDMA MDMA 75mg 100mg 75mg 100mg

Figure 4. Mean (+SE) net score on the Gambling task in every treatment condition .

90 Without alcohol With alcohol

85

80

75 Inhibitory responses (%)

70 PLA MDMA MDMA PLA MDMA MDMA 75mg 100mg 75mg 100mg

Figure 5. Mean (+SE) percentage of correct inhibitory responses during the SST in every treatment condition .

A summary of means (SE) and Treatment effects in tests of actual driving, psychomotor function and risk taking behaviour is given in Tables 2 and 3

45 IMMORTAL D-R4.4 21/10/04

Table 2. Summary of means (SE) and Treatment effects in tests of actual driving, psychomotor function and risk taking behavior .

Treatments Repeated measures MANOVA Overall effects Comparisons Alcohol-Placebo Alcohol vs placebo MDMA- MDMA- MDMA x MDMA MDMA Tests Placebo MDMA 75 MDMA 100 Placebo MDMA 75 MDMA 100 MDMA Alcohol Alcohol 75 100 Road Tracking Test SDLP 20.6(0.9) 18.4(0.6) 19.0(0.7) 23.5(0.9) 22.2(0.9) 20.2(0.7) <0.001 <0.001 0.05 0.007 0 LP -7.8(3.4) -5.1(5.2) -6.1(3.6) -3.5(3.9) -8.0(3.8) 3.6(7.5) - - - - - Speed 96.6(0.2) 96.2(0.3) 96.3(0.2) 96.4(0.3) 96.7(0.2) 96.4(0.4) - - - - -

SD(Speed) 2.0(0.1) 1.8(0.1) 1.8(0.2) 2.1(0.1) 1.8(0.1) 1.7(0.1) <0.05 - - 0.011 0.014

Car Following Test Gain 1.1(0.0) 1.1(0.0) 1.1(0.0) 1.1(0.0) 1.1(0.0) 1.1(0.0) - - - - - TSA 1.9(0.2) 2.2(0.2) 2.2(0.2) 2.5(0.3) 2.0(0.1) 2.4(0.1) - - - - - BRT 565.0(30.6) 547.0(20.1) 548.5(29.3) 584.6(28.3) 581.5(24.8) 624.8(39.6) - 0.05 - - - Psychomotor tests CTT

λc 4.2(0.1) 4.3(0.1) 4.1(0.1) 3.8(0.2) 3.9(0.1) 3.8(0.2) - 0.001 - - - OMEDA TTC error 0.4(0.1) 0.5(0.1) 0.5(0.1) 0.5(0.1) 0.5(0.1) 0.5(0.1) - - - - -

Impulsivity and risk taking Gambling Score -10.7(2.9) -4.2(3.0) -3.4(4.9) 1.6(4.9) -3.9(3.1) 1.8(5.0) - 0.08 - - - SST IR 9.9(0.6) 10.0(0.5) 9.6(0.6) 9.2(0.7) 10.0(0.4) 9.4(0.6) - 0.017 - - - CPT IR 82.1(3.1) 83.0(2.9) 84.3(3.4) 79.1(3.6) 81.6(3.8) 80.2(2.4) - 0.058 - - -

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Treatments Repeated measures MANOVA Overall effects Comparisons vs Alcohol-Placebo Alcohol placebo MDMA- MDMA MDMA MDMA- MDMA MDMA MDMA x MDMA MDMA Tests Placebo 75 100 Placebo 75 100 Drug Alcohol Alcohol 75 100 Subjective Evaluations Sleep Questionnaire Duration 7.1(0.2) 6.9(0.3) 7.0(0.3) 6.8(0.3) 6.7(0.3) 6.6(0.3) - - - - - Profile of Mood Scale Depression 65.2(2.3) 63.7(2.6) 63.8(2.8) 57.6(3.2) 65.5(2.0) 63.7(2.5) - - - - - Anger 56.6(1.7) 55.0(2.5) 55.3(2.5) 50.8(3.0) 56.5(1.9) 57.0(2.0) - - - - - Fatigue 39.2(3.0) 41.6(3.1) 41.2(3.0) 32.5(3.7) 39.6(3.0) 38.2(2.6) - - - - - Vigor 16.4(2.6) 14.6(2.6) 14.3(2.7) 22.5(3.2) 15.4(2.3) 15.8(1.8) - - - - - Tension 50.6(1.6) 48.1(2.1) 46.9(1.8) 44.2(2.2) 49.1(1.6) 46.3(1.9) - - 0.048 - - VAS Impulsivity 6.1(0.6) 5.2(0.7) 4.7(0.6) 6.1(0.6) 4.9(0.6) 4.2(0.6) 0.006 - - 0.055 0.001

Table 3. Summary of means (SE) and Treatment effects on subjective evaluations of sleep and mood.

47 IMMORTAL D-R4.4 21/10/04

3.4.5 Subjective evaluations

Mean sleep duration in the evening prior to test days ranged from 6.5-7.1 hrs and did differ significantly between treatments. Non-parametric Friedman analyses of sleep quality an overall difference between treatments (χ2=13.10; p=.02). Sleep quality ratings ranged from 1-3.5. Separate drug-placebo comparisons indicated a slight reduction in sleep quality in the evening prior to the MDMA75/Alcohol administration (Z=-2.26; p=0.023). Mood ratings (Profile of Moods Scale, POMS) revealed an interaction effect of MDMA and alcohol on tension (F2,34 =3.33; p= 0.048). MDMA increased feelings of tension, but less so in combination with alcohol. Feelings of depression, anger, fatigue and vigor did not differ between treatments MDMA also slight increased subjective feelings of impulsivity as measured on a 10cm visual analog scale (F1,17 =6.0; p= 0.006).

3.4.6 Pharmacokinetics

Mean blood alcohol concentrations (BAC) did not significantly differ between treatments. Mean BACs during performance in the Road Tracking Task were respectively 0.37, 0.41 and 0.42 mg/ml following placebo, MDMA 75mg and MDMA 100mg. During Car-Following performance, mean BACs varied from 0.38, 0.43 and 0.41 mg/ml respectively following placebo, MDMA 75mg and MDMA 100mg.

Descriptive parameters of blood alcohol concentration (BAC), MDMA and its main metabolite MDA are given in Tables 4 and 5.

Table 4. Mean (SD) blood alcohol concentrations (BAC, mg/ml) as a function of time post dosing in alcohol related treatment conditions (N=18) Time post Time post Activity ALC MDMA 75/ MDMA 100/ drinking (hrs) drug (hrs) ALC ALC 0.25 1.25 0.57 (0.19) 0.50 (0.13) 0.51 (0.16) 0.5 1.5 Onset laboratory tests 0.65 (0.19) 0.56 (0.12) 0.57 (0.13) 0.75 1.75 0.64 (0.16) 0.59 (0.11) 0.61 (0.11) 1 2 0.64 (0.16) 0.59 (0.11) 0.59 (0.12) 1.25 2.25 End laboratory tests 0.56 (0.16) 0.56 (0.08) 0.58 (0.10) 1.5 3.5 Booster dose 1 2 3 Onset driving tests 0.39 (0.08) 0.42 (0.08) 0.43 (0.08) 2 or 3,5 3 or 4,5 Onset Car-Following 0.38 (0.02) 0.43 (0.03) 0.41 (0.02) Test 2.5 3.5 Booster dose 2 2.5 3.5 Onset Road Tracking 0.37 (0.02) 0.41 (0.02) 0.42 (0.02) Test 4 5 End driving tests 0.29 (0.10) 0.36 (0.07) 0.34 (0.11)

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Table 5. Mean (SD) concentrations (ng/ml) of MDMA and MDA at 1,5 and 5,5 hrs post dosing in every treatment condition (N=18) PLA MDMA 75 MDMA 100 ALC MDMA 75/ MDMA 100/ ALC ALC 1,5 hrs post drug (onset laboratory tests) MDMA 0 137.4 (31.9) 191.8 (49.1) 0 147.1 (36.3) 208.5 (45.7) MDA 0 3.5 (1.2) 4.5 (1.5) 0 3.4 (1.0) 4.7 (1.4) 5,5 hrs post drug (end driving tests) MDMA 0 113.5 (31.2) 163.0 (40.7) 0 107.9 (25.4) 152.6 (40.8) MDA 0 6.8 (1.7) 9.0 (1.9) 0 6.5 (1.5) 8.8 (2.1)

3.5 DISCUSSION

Actual driving and psychomotor performance

The Road Tracking Test revealed stimulatory effects of both doses of MDMA as indicated by significant reductions in mean SDLP and SDSP. Both doses produced a mean reduction in SDLP of about 2cm relative to placebo, which is similar to the change observed after a single dose of MDMA 75mg in a previous study (Ramaekers and Kuypers, this deliverable). Present data thus confirms that performance in the Road Tracking Test improves under the influence of MDMA. Alcohol on the other hand significantly decreased performance in the Road Tracking Test. Relative to placebo, mean SDLP increased by about 3 cm when subjects operated the vehicle under the influence of alcohol alone at mean BACs of about 0.4 mg/ml. Combined use with MDMA however counteracted the impairing effects of alcohol, particularly after a high dose of MDMA. Alcohol related increments in SDLP dropped to 1,5 cm in combination with MDMA 75mg and even reduced to zero in combination with MDMA 100mg. These data seem to indicate that the stimulatory effect of MDMA on road tracking performance was present both in the absence and in the presence of alcohol, and perhaps even stronger so in the presence of alcohol as suggested by a significant MDMA x Alcohol interaction. Performance in the Car-Following task was not affected by MDMA. Alcohol however significantly increased brake reaction time (BRT). The subjects’ mean BAC during the Car-Following task was about 0.4 mg/ml and did not differ between alcohol treatments. Relative to placebo, BRT increased by 20 msec when alcohol was given alone or in combination with MDMA 75mg and by as much as 60 msec in combination with the higher dose of MDMA 100mg. These data thus seem to suggest that MDMA may worsen the impairing effects of alcohol on reaction time performance. However, the interaction term MDMA x Alcohol failed to reach significance at the statistical level. In any case, it is clear that MDMA did not reduce the impairing effects of alcohol in the Car-Following task. General driving proficiency and performance in two psychomotor tasks, i.e. critical tracking and OMEDA were also not affected by MDMA. It is noteworthy that both critical tracking and OMEDA task performance were shown to change after acute MDMA administration in a previous study by our group (Lamers et al 2003) . That study showed that a single dose of MDMA 75mg improved tracking performance and

49 IMMORTAL D-R4.4 21/10/04

decreased the subjects’ ability to estimate time to contact in the OMEDA task. The discrepancy in the results of the OMEDA task may be related to a difference in task complexity. In the present study time to collision estimates were based single object movements, whereas in the previous study such estimates also included simultaneous movement of multiple objects. The latter approach may have increased the tasks sensitivity for measuring drug effects. The discrepancy between the tracking task results in the present and previous study may be related to time of test administration. Critical tracking (CTT) was assessed at 1,5 hrs post drug in the present study and at 1, 2, 3 and 5 hrs post drug in the study by Lamers et al (2003). In the latter study the effect of MDMA was prominent at each time of testing except at 1 hr post drug. Perhaps assessment of critical tracking performance should have been scheduled at 2 hrs post drug or later in the present study for MDMA to produce a measurable effect. Assessment between 2 and 5 hrs would perhaps have better coincided with MDMA plasma levels which have been shown to be maximal with this same time range (Lamers et al 2003). This explanation however remains highly speculative since MDMA/MDA plasma concentrations in the present study were comparable at the onset and completion of each test session, which suggests that drug levels were stable throughout testing. Alcohol on the other hand significantly impaired critical tracking performance, as expected. Combined administration of MDMA and alcohol produced impairments in the critical tracking task that were comparable to alcohol alone. Alcohol induced impairment was thus not mitigated by co-administration of MDMA. Collectively these data indicate that MDMA is stimulant drug that may facilitate certain aspect of the driving task, i.e. road tracking, even when combined with a low dose of alcohol. However performance compensation after combined MDMA/alcohol administration was limited to a single driving parameter and was never sufficient to fully overcome alcohol impairment in all driver tasks.

Impulsivity and risk taking behavior

The main findings of this study on impulsivity and risk taking behavior were 1) that acute doses of MDMA did not affect behavioral inhibition in any of the three laboratory tests of impulsivity (i.e gambling, stop signal and continuous performance task); 2) that a moderate dose of alcohol impaired the subjects’ ability to inhibit responses in the Stop – Signal and Continuous Performance paradigms and 3) that a moderate dose of alcohol tended to improve the subjects’ decision making in the gambling task. The absence of any performance effect of MDMA doses on any of these tasks indicates that single doses of this drug do not increase impulsive behavior. There was even some marginal indication that MDMA actually improved ’inhibitory control’ in the gambling task. Subject made more correct responses after both doses of MDMA relative to placebo but these mean differences failed to reach statistical significance. The present study has been the first to assess the acute effects of MDMA on impulsive behavior employing the task described above. In two tasks, i.e. the stop signal task and the continuous performance task, impulsivity was defined as the inability to conform responses to environmental context leading to errors of commission. In the third task, i.e

50 IMMORTAL D-R4.4 21/10/04

the gambling task, impulsivity was defined as the inability to delay reward leading to a tendency to choose immediate small rewards over larger delayed rewards. Only two other studies (Liechti et al 2001; Vollenweider et al 1999) have assessed the acute effects of MDMA on tasks that are slightly related to the present concepts of impulsivity. These studies employed prepulse inhibition (PPI) of the acoustic startle response as an operational measure of sensory motor gating. PPI is the unlearned suppression of startle when the startle stimulus is preceded by warning signal. Deficits in PPI have been associated with a decrement in the ability to filter irrelevant sensory information and a failure to suppress or inhibit a corresponding response. Both studies demonstrated that a single, recreational dose of MDMA slightly improved prepulse inhibition. It was furthermore shown that the rise in PPI was probably related to release of presynaptic 5HT, since citalopram, a selective serotonin reuptake inhibitor, mitigates the MDMA induced increase in PPI (Liechti et al 2001). It is not clear at present how measures of PPI are related to measures of impulsivity employed in the current study. What seems clear however is that acute MDMA intoxication does not impair response inhibition in any of these conceptual models of impulsivity, and may increase performance in some. The effects of MDMA on impulsivity thus seem more neutral than those of dopaminergic stimulants such as d-amphetamine and cocaine. D-amphetamine was show to decrease impulsivity in a wider range of tasks similar to the ones used in the current study (de Wit et al 2000; de Wit et al 2002). Cocaine on the other hand was shown to impair the ability to inhibit behavioral responses, thus increase impulsivity, in a stop-signal paradigm (Fillmore et al 2002) . The relative lack of effects of MDMA on measures of impulsivity may reflect the fact that MDMA exerts its effect primarily through serotonergic stimulation, whereas cocaine and d-amphetamine exert their effects through dopaminergic stimulation. The effects of alcohol on measures of impulsivity were intriguing. Alcohol reduced performance in the stop-signal and continuous performance tasks but almost significantly improved performance in the gambling task. Similar findings have been reported in the scientific literature. De Wit et al (2000) reported that alcohol produced a dose related impairment of behavioral inhibition in a stop-signal paradigm, suggesting increased impulsivity. However the opposite effect of alcohol has also been reported in models where impulsivity is defined as the inability to wait for a larger reward. A recent study, Ortner et al (2003) employed a delay-discounting task where healthy volunteers made a series of hypothetical choices between a small, immediate reward and a large, delayed reward. In the alcohol condition, subjects discounted delayed reward at lower rates than during the sober condition, indicating the alcohol led to more cautious decision making under these condition. Conceptually, the delay discounting task is strongly related to the gambling task that was employed in the current study. Results from both tests can thus be considered as mutually supportive. Our results thus suggest that alcohol may increase certain forms of impulsive behavior while decreasing other forms of impulsive behaviors. The dissociation between the present measures of impulsivity strongly suggests that “impulsivity” is not a unitary concept that is under control of a single underlying process.

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3.6 ACKNOWLEDGEMENTS

We would like to thank Nele Samyn, Gert De Boeck and Marleen Laloup from NICC, Brussels, for analyzing MDMA plasma samples.

3.7 REFERENCES

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Linnoila M, Virkkunen M, Scheinin M, Nuutila A, Rimon R, Goodwin FK (1983): Low cerebrospinal fluid 5-hydroxyindoleacetic acid concentration differentiates impulsive from nonimpulsive violent behavior. Life Sci 33:2609-14. Logan BK, Couper FJ (2001): 3,4-Methylenedioxymethamphetamine (MDMA, ecstasy) and driving impairment. J Forensic Sci 46:1426-33. McCann UD, Ridenour A, Shaham Y, Ricaurte GA (1994): Serotonin neurotoxicity after (+/-)3,4-methylenedioxymethamphetamine (MDMA; "Ecstasy"): a controlled study in humans. Neuropsychopharmacology 10:129-38. Morgan MJ (2000): Ecstasy (MDMA): a review of its possible persistent psychological effects. Psychopharmacology (Berl) 152:230-48. Mulder-Hajonides van der Meulen W (1981): Measurements of subjective sleep, International European Sleep Congres: Elsevier, Amsterdam. O'Hanlon JF, Haak TW, Blaauw GJ, Riemersma JB (1982): Diazepam impairs lateral position control in highway driving. Science 217:79-81. Ortner CN, MacDonald TK, Olmstead MC (2003): Alcohol intoxication reduces impulsivity in the delay-discounting paradigm. Alcohol Alcohol 38:151-6. Ramaekers G, Lamers J, Verhey F, et al (2002): A comparative study of the effects of carbamazepine and the NMDA receptor antagonist remacemide on road tracking and car-following performance in actual traffic. Psychopharmacology (Berl) 159:203-10. Ramaekers JG, Kuypers, K.P.C. (2004): A placebo controlled study on the effects of 3,4-methylene-dioxymethamphetamine (MDMA) 75mg and methylphenidate 20mg on actual driving performance, visuospatial attention and memory during intoxication and withdrawal.: Experimental Psychopharmacology Unit, Maastricht University. Ramaekers JG, O'Hanlon JF (1994): Acrivastine, terfenadine and diphenhydramine effects on driving performance as a function of dose and time after dosing. Eur J Clin Pharmacol 47:261-6. Read NL, Ward, N.J., Parkes, A.M. (2000): The role of dynamic tests in assessing the fitness to drive of healthy and cognitively impaired elderly. Journal of Traffic Medicine 28:34-35S. Riley SC, James C, Gregory D, Dingle H, Cadger M (2001): Patterns of recreational drug use at dance events in Edinburgh, Scotland. Addiction 96:1035-47. Schifano F (1995): Dangereous driving and MDMa. J Serotonin Rese 1:53. Topp L, Hando J, Dillon P, Roche A, Solowij N (1999): Ecstasy use in Australia: patterns of use and associated harm. Drug Alcohol Depend 55:105-15. Vollenweider FX, Remensberger S, Hell D, Geyer MA (1999): Opposite effects of 3,4- methylenedioxymethamphetamine (MDMA) on sensorimotor gating in rats versus healthy humans. Psychopharmacology (Berl) 143:365-72.

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4. STUDY C: THE EFFECT OF COLD VIRUS AND COLD VIRUS MEDICATION ON COGNITIVE AND DRIVING PERFORMANCE

4.1 INTRODUCTION

4.1.1 The common cold

It is reported that the common cold can lower well-being and impair mental functioning (Grant, 1972; Tye, 1960), and several studies have showed impairment of psychomotor skills related to driving (Bower, 2003; Drake, 2000). Using a variable foreperiod (period between warning and onset of the stimulus) simple reaction time task Clark et al (1978) and Cook (1995) found slower reaction times with colds. Bower (2003) also reported significant differences in mean reaction times between healthy controls and symptomatic patients, but only in the first week of illness; in the second week, a convalescent phase, differences were not significant. These studies suggest impairments in simple motor tasks that could be influential on driving performance (e.g., the time taken to respond to an emergency). In addition, the deficits in motor skills related to hand-eye co-ordination may affect driving in terms of lateral control, including lane excursions and steering instability. Smith et al (1998) found that slowing of simple reaction times was uncorrelated with subjective reports of symptom severity, duration of illness and nasal secretion. Clearly this has implications for an individual’s assessment of their ability to drive in the context of their illness. In addition, task-related motivation may produce slow responses to motor tasks that could be viewed in terms of muscular control.

The effects of such a minor illness upon driving performance merits investigation because of the high cognitive demand and psychomotor skills required. Driving is a multi-aspect task demanding mental alertness, visual, auditory and kinaesthetic information processing, eye-hand co-ordination, and manual dexterity (Michon, 1985). These considerable demands make it possible that a minor illness, such as a cold, may have a significant impact on driving ability. This is of particular concern because of the high prevalence of cold virus.

4.1.2 Cold remedies

A large number of people suffering from a cold virus will take some form of over the counter medication to relieve their symptoms. Cold remedies, which merely treat the symptoms of a cold and not the illness per se may exacerbate the detrimental effects of the cold virus (Smith et al 1998). Many cold remedies containing first generation anti- histamines have been reported to have depressant effects, such as drowsiness, sedation, fatigue, disturbed co-ordination, confusion and impaired psychomotor performance (Gengo et al, 1989; Clarke and Nicholson, 1978).

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A common combination of active ingredients used in cold remedies includes paracetamol, pseudoephedrine hydrochloride, and diphenhydramine hydrochloride. Gevins (2001) and Smith (1996) examined the effect of diphenhydramine on simple motor tasks. The results of these two studies were not consistent. Gevins (2001) found no effect of diphenhydramine on reaction time, whilst Smith (1996) did find a significant effect. Gaillard (1988), Gevins (2001) and Grant (1972) report deficits on a measure of tracking under divided attention conditions in participants taking diphenhydramine in contrast to a placebo and first generation anti-histamine. Gaillard (1988) and Gevins (2001) also report deficits in vigilance as measured by a continuous performance test and a cognitive shifting attention test. Gaillard (1988) found that mean hit reaction times were slower on an Aeromedical Vigilance Test in a diphenhydramine condition.

Deficits in higher cognitive functioning are also reported. Gengo (1989) used a backwards working memory task and showed that participants taking diphenhydramine performed the task more slowly. On a vigilance task reflecting ‘real world’ job demands, Hall (1996) found that participants in the diphenhydramine condition made more errors than a second generation anti- histamine or placebo. Kay (1997) compared the effects of diphenhydramine and second generation anti-histamines on driving performance using a driving simulator. Pair-wise comparisons demonstrated that after taking diphenhydramine, participants undertook car-following with significantly less coherence than after taking second generation antihistamine, placebo or alcohol. In addition, steering instability was increased in the diphenhydramine condition in contrast to placebo and fexofenadine (a second generation antihistamine). In the diphenhydramine condition, lane keeping (steering instability and crossing the centre line) was also impaired. This difference, however, was relatively small.

All the studies mentioned above report an increase in subjective sleepiness ratings, including significantly higher ratings of fatigue, lower motivation, lower activity and poorer performance ratings. The subjective reports, however, do not always correlate with the impaired level of psychomotor performance. This suggests that an individual may lack awareness of their reduced level of functioning. This has dangerous implications for assessing ability to drive: drivers are warned ‘may cause drowsiness’; yet if an individual is unable to assess this, the warning may be insufficient.

In summary, the research suggests that whilst the cold virus affects simple motor tasks, higher cognitive functioning remains intact (Gevins, 2001, Grant, 1972, Barrow et al 1990). In contrast, diphenhydramine may cause deficits in both kinds of task, with the effect more pronounced for higher cognitive processing. In addition, a general decrease in alertness and performance rating is often reported.

4.1.3 Aim of the study

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The aim of this study was to conduct an experimental investigation into the effects of the cold virus and cold medication on psychomotor and driving performance. In addition, the interacting effect these two variables had on performance was studied in order to ascertain whether the ingestion of cold medication had a mediating effect on the effects of the cold virus. Psychomotor performance related to driving was tested using three lab-based tasks whilst driving performance was assessed using the Leeds driving simulator. In addition, subjective ratings were used to elicit self-reported states.

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4.2 Method

4.2.1 Participants

Ninety six participants aged between 18-25 took part in the experiment (52 females, 44 males). Participants were recruited through poster advertisements in chemists and highly populated areas close to Leeds University. Initial screening was accomplished on the basis of a questionnaire. Screening questions were as follows:

1) Symptoms include nasal discharge, obstruction of nasal breathing, swelling of the sinus membranes, sneezing, sore throat, cough, and headache. 2) Symptoms do not include fever or have lasted longer than 2 weeks. 3) Aged 18 – 35 4) Current UK driver with license (averaging over 5000 miles per year) 5) Not allergic to anything in cold medication

If participants met the inclusion criteria they were invited to take part in the study. A follow up symptom checklist was administered to determine the presence of a cold virus. Ethical approval was obtained from the Leeds University Psychology department.

4.2.2 Medication

Participants were given either a medicated drink or a placebo drink. The medicated drink consisted of Diphenhydramine hydrochloride 25 mg, Paracetamol 1000 mg, and Pseudoephedrine hydrochloride 45 mg dissolved in 100ml of warm water. These ingredients and concentrations are found in a wide range of over the counter cold remedies. The placebo consisted of 100 ml of flavoured water. The water was flavoured with peppermint and 1 sweetener. A menthol crystal was also rubbed on underneath the container to give a medicated scent.

4.2.3 Experimental design

The experiment was a single blind 2x2 between subjects design with 24 participants per group. Healthy participants were randomly allocated to one of the two healthy conditions and cold sufferers were randomly allocated to one of the two cold conditions. Participants were considered healthy if they scored 3 or less on the cold symptoms from the symptom check list. They were considered to be cold sufferers if they scored 8 or more on the cold symptoms within the symptom check list.

4.2.4 Experimental timetable

The experimental timetable was designed to capitalise on the pharmacodynamics of diphenhydramine plasma concentrations following ingestion of a 25mg dose. Diphenhydramine plasma concentrations rapidly

57 IMMORTAL D-R4.4 21/10/04 rise up to a peak at about 2 hrs. Following initial peak there is a slow decline, see Figure 1 (taken from Scavone et al, 1998). Blood levels of diphenhydramine could not be measured directly due to ethical restrictions.

Plasma Diphenhydramine (ng/ml) following 25 mg dose

30 25 20 15

(ng/ml) 10 5 0 Plasma Diphenhydramine 012345678910 Hours After Dose

Figure 1 Pharmacodynamics of 25mg Diphenhydramine

Laboratory tests were conducted 20 mins following ingestion of the liquid and lasted around 1 hour. The simulator testing then followed between 1.25-2.5 hours. An indication of the approximate blood diphenhydramine level is shown on the right of table. This is an approximation based on the profile of diphenhydramine plasma concentration following an ingestion of 25mg (Scavone et al, 1998) where 1 is the lowest concentration and 9 the highest.

Table 1details the experimental timetable and shows it’s correspondence with approximated drug plasma concentrations.

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Post ingestion Test Drug/plasma time (mins) concentration 20 Subjective state (1) 3 25 Omeda practice 4 30 Omeda Part A 5 40 Omeda Part B 6 60 VRT 7 75 Trail making (Parts A&B) 8 77 Subjective state (2) 9 80 Simulator practice 9 90 Straights (A) 9 95 Corners (A) 8 100 Braking (1) 8 102 Choice RT (A) 8 115 Coherence (A) 7 116 Situation Awareness (A) 7 120 Wind gust 7 125 Anticipatory braking event 6 127 Situation Awareness (B) 6 130 Braking (2) 6 135 Straights (B) 5 140 Corners (B) 5

145 Simulator experiment Choice RT (B) 5 Subjective state (3)

Table 1 Experimental Timetable

4.2.5 Procedure

Volunteers were asked to refrain from taking any medication before and on the day of testing. They were also asked to refrain from taking any other stimulates before starting the experiment (e.g. caffeine). Volunteers were provided with information regarding the experiment prior to taking part via phone screening. Volunteers were also provided with an information sheet before consenting to take part in the experiment. Volunteers were asked to sign a consent form if they were happy to take part in the experiment. Volunteers were informed that they could withdraw at any point without any reason should they wish. After the testing session volunteers were debriefed and paid. Volunteers received £10 per hour of their time. The total testing procedure usually lasted between 2 and 2.5 hours (depending on volunteers driving speed).

4.2.6 Subjective measures

Symptom check list: The symptom checklist is a visual analogue scoring sheet developed by Smith (1998) to determine the presence of a cold virus. A list of symptoms is presented and participants are asked to score the severity of their symptoms. If they scored 8 or more on symptoms typical of a cold

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virus they were included in the cold group. If they scored 3 or more on symptoms not specific to cold virus they were excluded from the study. (Appendix A)

Subjective states: The subjective state questionnaire was developed through combining subjective state measures of strain from Hockey et al (1989) & Maule et al (2000). Volunteers were asked to rate their subjective states on a scale of 1-9. Volunteers were also asked to rate on a scale of 1-9 the amount of effort they dedicated to the laboratory and simulator tasks. Three separate indices of strain were used; depression, anxiety and fatigue. Volunteers were asked to complete the questionnaire before starting the lab tasks, before starting the simulator run and again at the end of the testing session. Subtracting the scores from the first and second phases from those of the final phase gives as indication of changes in strain over the session (Appendix B).

4.2.7 Psychomotor performance

Object Movement Estimation under Divided Attention (OMEDA): OMEDA (Read, 2000) is a computerised dual-task with two parts. Part 1 allows experimenters to obtain an individual’s error in Time-To-Collision (TTC) estimation. Different target speeds can be simulated, as can various degrees of occlusion. A secondary task is also incorporated in the form of a visual divided attention task. This requires the identification of peripheral duplication of a stimulus presented centrally (in this case geometrical shapes).

Part 2 provides a quantified estimate of collision detection error under various degrees of occlusion and for a series of target speeds, with the same secondary task as for Part 1. Figure 2 shows a typical OMEDA display.

Part 1 Part 2 Figure 2 OMEDA task

Participants do not need to be computer literate in order to be able to do this task, as the response keys are a foot pedal (for the primary task) and a hand button (for the secondary task).

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In Part 1 the participant is presented with a computer screen where the corners are covered by green triangles and in the centre of the screen is a yellow circle. The yellow circle varies in size between two and 250 pixels. From one of the four corners (randomly allocated), a red target, in the form of a circle travels towards the middle of the screen. Once it reaches the edge of the yellow circle, it travels underneath it and it is not visible. Therefore, the larger the circle, the more difficult the task is, due to a longer occlusion time. The participant is asked to estimate exactly when the target reaches the middle of the computer screen. They are instructed to press a foot pedal at the exact point the target reaches the middle.

To order to simulate divided attention, whilst participants are estimating when the target reaches the middle of the screen, they are required to complete a pattern matching task. When the target is moving, five shapes appear on the screen (one overlaid on the yellow circle and one in each of the four corners). Participants are instructed to press a hand button immediately if the shape in the middle matches any of those in the four corners of the screen.

Data collected are target speed, size of the occlusion circle, time under the occlusion circle, actual time to contact (TTC), estimated TTC and TTC error, errors in shape detection.

In Part 2, the participants are presented with the same screen as in Part 1. However, the primary task now involves two targets moving towards the centre of the screen, emerging at different times and travelling at different speeds. The targets reach the centre of the screen either at the same time (a hit), almost at the same time (a near miss) or at a noticeable time difference (a miss). The participant is required to press the foot pedal only if and when the targets reach the centre of the screen at the same time (i.e. only for hits). The secondary task is the same as for Part 1. The data collected includes the error in estimating TTC and the error in shape estimation, under different occlusions and target speeds.

Visual Reaction Time (VRT): Visual Reaction Time (VRT) was used as a measure of reaction time over time. The version used was a computerised version of a VRT task described in Dinges and Powell (1985). The VRT task was carried out on a PC using the E-Prime software. E-Prime is a graphical experiment generator for Windows 95/98/ME. E-Prime consists of a suite of applications to design, generate, run, collect data, edit and analyze the data. The participant is seated in front of a computer screen. The stimulus is the display of the words ‘HIT X’; at which point the participant is instructed to press the X key using their dominate index finger. The inter-stimulus interval on the task varies randomly from 1 to 10 seconds. There are 100 stimulus presentations and the task lasts between 12 and 15mins (dependant on reaction time) Volunteers are instructed to respond to the stimulus as quickly as possible.

Trail Making task:Trail making is the most commonly used test of attention search and sequencing. The Trail Making Test (Reitan, 1958) has 2 parts. Part A requires the volunteers to link 25 ascending consecutive numbers that

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are arranged randomly over an A4 page. Part B is the same as part A except the circles contain letters and numbers and the objective is to link them alternately in ascending order. The time required to complete the test is usually taken as the measure of performance. (Appendix C)

4.2.8 Driving performance

The tasks discussed below were carried out using the Leeds Driving Simulator. Figure 3, shows a view from out side of the Leeds Driving Simulator.

Figure 3 The Leeds Driving Simulator

The Leeds Driving Simulator is currently based on a complete Rover 216GTi with all of its basic controls and dashboard instrumentation still fully operational. On a 2.5m radius, cylindrical screen in front of the driver is projected a real-time, fully textured and anti-aliased, 3-D graphical scene of the virtual world. This scene is generated by a SGI Onyx2 Infinite Reality2 graphical workstation. The frame rate is a fixed to a constant 60Hz. A Roland digital sound sampler creates realistic sounds of engine and other noises via two speakers mounted close to each forward road wheel. Although the simulator is fixed-base, feedback is given by steering torques and speeds at the steering wheel. Data is collected at 60Hz and includes information of the behaviour of the driver (i.e. driver controls), that of the car (position, speed, accelerations etc.) and other autonomous vehicles in the scene (e.g. identity, position, speed).

Participants were given an instruction sheet before starting the experiment (Appendix D). The instruction sheet provided information regarding the speed

62 IMMORTAL D-R4.4 21/10/04 limit of the simulated roads (60mph), but did not insist that they complied. They were asked to drive as they would normally and not to experiment with the simulator. Instructions for the tasks detailed below were also provided in the instruction sheet. Before the experimental run they had a 10-minute practice drive where they could practice tasks and ask the experimenter any questions. The experimenter accompanied the participant in the passenger seat during the practice session. The participant was asked to drive alone during the experimental session.

Longitudinal control: Lateral Control or rather changes in speed, has been used as a surrogate for accident risk. Several studies have shown that there is a clear relationship between the speed level and the number of accidents and that a small change in mean traffic speed result in significant changes in the number of injury accidents (e.g. Salusjärvi, 1988; Finch, Kompfner, Lockwood and Maycock, 1994). Finch et al.’s summary of the US and German interstate/autobahn evidence is that a 1 mph decrease in mean traffic speed leads to a reduction in fatalities in the order of 8-10%. However, the relationship between speed and accidents is likely to be dependent on variables other than just mean speed. Some evidence suggests that accident rate rises with increases in speed variance, rather than mean speed. Munden (1967) and Hauer (1971) cite a U-shaped relationship between the accident rate and speed for drivers, with the highest accident rates being associated with the fastest and slowest drivers.

Some studies have found that as workload increases, speed decreases - thus giving the impression of increased safety. Such decreases in speed have been reported for conversation tasks (Jordan and Johnson, 1993), car phone tasks (Brown et al 1969) and navigation tasks (Van Winsum et al. 1989). However, whilst decreases in speed may theoretically improve safety, they may in fact indicate a decrease in performance due to a high level of workload, or in the case of illness - a decrease in the ability to perform at baseline levels. In the present experiment driver speed data was collected during free speed driving sections.

In the present study critical events were introduced to measure the drivers braking performance in 2 situations. The first braking event consisted of an unexpected flock of sheep in the driver’s path; the second braking event consisted of unexpected roadworks blocking the driver’s path. The time between the critical event trigger and brake pedal activation was used as an indication of braking performance.

The coherence task was introduced by Brookhuis et al (1994). The task is used as a measure of attention and perception performance in driving. Coherence is the correlation between the speed of the participants and the lead vehicles. High coherence suggests that the following driver is able to maintain a relatively uniform headway to the lead vehicle, whereas a driver with a low coherence has more variability in headway. The Modulus is the amplification factor between the speed of the participant’s and lead vehicles. It indicates the magnitude of overshoot in reaction to deceleration of the lead

63 IMMORTAL D-R4.4 21/10/04 vehicle. Phase shift between the two speed signals indicates the delay in the response of the participant’s vehicle.

“According to Brookhuis et al. (1994), phase shift is the most important measure in a car-following scenario as it illustrates the reaction time of the participant driver to the deceleration of the lead vehicle. Coherence is mainly a conditional measure—it indicates whether speed changes of the lead car were followed at all. The phase shift measure is meaningful only if coherence is substantial.”

Figure 4 (reproduced from De Waard & Brookhuis (2000)) shows the speed of a lead and following vehicle in a simple car following scenario. The coherence of the speed signals in the left-hand and right-hand plots is the same. The modulus indicates that in the left-hand plot there is some overshoot, suggesting that the following driver is having difficulty in matching the speed of the lead car. The phase shift between the two plots differs dramatically (the following driver in the right-hand plot is reacting much later than the driver in the left-hand plot to changes in the lead car speed. It is evident that along with modulus, phase shift is also a most relevant measure of car following behaviour.

lead vehicle following vehicle lead vehicle following vehicle speed speed

time time coherence = 0.89 coherence = 0.89 modulus = 1.09 modulus = 0.61 phase shift = 4.2 phase shift = 10.8

Figure 4 Speed signals in two separate car following scenarios

During the coherence task in this study, the participant was asked to try to maintain what they considered to be a safe headway from the car in front. The lead car varied its speed by + 20 mph to – 20mph from the speed it was travelling when entering the event trigger. This speed variation cycles with a frequency of around 0.03Hz. The participant was prevented from overtaking the lead car through the use of double white lines. The task lasted about 10 minutes and took place after each choice reaction time task.

Lateral control: Time Headway [sec] to a lead vehicle is defined as the time to collide into the lead vehicle if it stops dead. Time Headway is a measure of longitudinal risk margin. The closer and faster a driver travels behind a lead

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vehicle, the less the chance of managing to avoid a collision if the lead vehicle reduces its speed. The proportion of the time where the time headway is less than 1 sec has been used as a risk indicator for car following situations. In the present experiment the distance to the lead vehicle is defined as the distance between the front bumper of the driver and the rear bumper of the lead car. Time headway was measured whilst the driver was following the lead car during the curved and straight sections of the simulated route.

Less lateral control may be observed as an increase in standard deviation of lateral position (SDLP). Kay (2000) reported an increase in SDLP in drivers taking 50mg of diphenhydramine. SDLP was measured on the straight and curved sections of the simulated route. It should be noted that this measure can be related to speed changes, such that decreases in speed can be accompanied by increased lateral control.

In the present experiment an artificial ‘Wind Gust’ was implemented. A perpendicular cross-wind was presented from the left lasting 5 sec, emulating a wind speed of 15 m/s. Vehicle correction and overall time to regain control of the vehicle were measured.

Choice reaction time (CRT): A choice reaction time (CRT) task was used to assess drivers’ reaction time to additional stimulus during driving. During this task a square appeared on the screen superimposed over the simulated environment. The square appeared at a random time to the participant, but was in fact triggered by the position of the car. The square was either blue (shifted to the left hand side of the screen) or red (shifted to the right hand side of the screen), as shown in Figure 5. Corresponding blue and red buttons were fixed on the centre of the steering wheel and drivers were instructed to press the appropriate button as quickly and accurately as they could. There were 15 presentations of each coloured square. Accuracy and reaction times were measured. This task was performed twice during the run.

Figure 5 Simulator choice reaction time task

Situation awareness (SA): Endsley (1988) describes SA as “a person’s state of knowledge or mental model of the situation around them”. This definition is further expanded to include “the perception of the elements in the

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environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future”. Endsley (1988) has characterised three fundamental aspects of SA, which are referred to as “levels.”

• Level 1 SA comprises perception of the elements in the environment based on the processes of search, detection, recognition and identification of the relevant status, features, and attributes of the environment that are pertinent to the goals in hand. In the present study such features included the location and dynamics of one’s own and other vehicles. • Level 2 SA comprises comprehension of the current situation, which is achieved through the integration and synthesis of information acquired through Level 1. The individual goes beyond the simple knowledge that the information is present in the situation and seeks to determine its significance and meaning. In the present study a lorry was triggered to pull out from a left hand junction just before the screen went blank. Drivers were asked questions about the features of the lorry and its significance. SA was scored correctly if they recalled the features and moving status of the lorry. • Level 3 SA entails projection of the future status of the situation, based on the ability of an individual to anticipate or envisage the future status or actions of the elements in the environment. A driver who is functioning at this level of SA might be involved in the prediction of traffic behaviour and updating route planning due to traffic conditions. In the present study the driver was approaching a junction before the screen went blank. Participants were asked to predict how long it would take them to get to the junction. Participants were scored as correct if the error was less than 2 seconds..

SA Probes: At two points in the simulated drive the screen went blank and the driver was asked a series of questions designed to evaluate Situation Awareness (SA). This had a maximum of 12 (Appendix E). The driver was aware that this would occur at some point in the run, but had no idea when this would be. The SA questions were designed to assess the 3 levels of Situation Awareness described by Endsley (1988).

In the driving domain, problems of poor situation awareness could arise at any of the three levels. At Level 1, there may be detection errors arising from inattention, internal distraction or inappropriate lookout. At Level 2, drivers may fail to comprehend the meaning or significance of the detected information. At Level 3 there may be failures to appropriately extrapolate the current situation to the future and to plan appropriately. In sum, these SA errors tell us that drivers are not perceiving, not incorporating relevant info, or not projecting and planning appropriately.

Behavioural measure of SA.This event can be described as ‘anticipatory braking’. An HGV turns across lead vehicle causing it to brake. The HGV begins to move when the driver is 7 sec from the intersection. The lead vehicle begins to brake at 0.5 g when the driver is 5 sec from the intersection.

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The drivers' awareness of events happening in front of the lead car can be measured by looking at their anticipatory braking. If the driver is aware of the HGV turning in front they will begin to brake or decelerate, before the lead vehicle does.

4.3 Data analysis

The data were checked for normality and homogeneity of variance using the Kolmogorov-Smirnov and Levene tests respectively. The data were analysed using a factorial analysis of variance (ANOVA) to determine the effect of two between subjects factors (Cold, Medication) on a number of dependant variables. In addition, interaction effects were examined in order to discover if the effect of having or not having a cold was mediated by taking medication. Table 2 shows the sample sizes in each experimental group.

Group Placebo Medication Healthy 24 24 Cold 24 24 Table 2 Shows the experimental conditions and sample sizes

4.4 Results

There are three parts in the result section: Results regarding subjective measures, psychomotor performance and driving performance.

4.4.1 Subjective measures

Symptom check list: An ANOVA was performed to ensure that there was a significant difference between the severity of symptoms reported by the healthy and cold groups (F[1,92]=289.76;p<0.05). There was no difference between the medicated and placebo group (F<1). The mean symptom scores reported for each group are shown in Table 3 below.

Group Mean SD Healthy Placebo 0.92 1.41 Healthy Medication 1.54 1.90 Cold Placebo 17.08 6.13 Cold Medication 17.29 6.40

Table 3 Shows the mean reported symptom scores

Subjective states: A visual analogue scale was used to rate volunteers' subjective strain states (depression, anxiety, fatigue, and effort used) at different times during the experimental procedure: before the psychomotor experiments, before the simulator experiment, and at the end of the experiment.

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Figure 6 shows the mean subjective state scores for the healthy and cold groups before and after the experimental session. There was a statistically significant increase in subject state score as time on task increased. This was found for both depression (F[1,92]= 30.48; p<0.001) and fatigue (F[1,92]=105.67; p<0.001). There were no changes in anxiety. Cold sufferers’ had depression scores which increased at a similar rate to those of healthy volunteers, but their initial and final ratings were significantly higher (F[1,92]=12.71; p<0.05). Cold sufferers also had a higher level of fatigue than that of healthy volunteers (F[1,92]=22.80; p<0.001).

Figure 7 shows that there was also a significant effect of medication over time on fatigue (F[1,92]= 6.748; p<0.001): Medicated volunteers reported a significant increase in fatigue more quickly than non-medicated volunteers.

8 7.5 7 Healthy (Depression) 6.5 Cold (Depression) 6 Healthy (Anxiety) 5.5 5 Cold (Anxiety) 4.5 Healthy (Fatigue)

subjective scores 4 Cold (Fatigue) 3.5 3 Start End

Figure 6 Mean subjective scores before and after the experimental session (Healthy Vs Cold group)

7.5 Placebo (Depression) 7 6.5 Medication (Depression) 6 Placebo (Anxiety) 5.5 5 Medication (Anxiety) 4.5 4 subjective scores Placebo (Fatigue) 3.5 3 Medication (Fatigue) Start End

Figure 7 Mean subjective scores before and after the experimental session (Placebo Vs Medication group)

Interestingly, although not significant (F[1,92]=3.71; n.s.), there was a trend for cold sufferers to report more effort dedicated to the driving task than

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healthy volunteers (healthy mean = 7.3, cold mean = 7.8). This increase in reported effort dedicated to the task may account for the parallel in crease in fatigue. There was also a significant effect of medication on the amount of effort reported from the driving experiment (F[1,92]=5.12; p<0.05). Taking medication reduced the amount of subjective effort dedicated to the driving experiment.

4.4.2 Psychomotor performance

Object Movement Estimation under Divided Attention OMEDA: Performance data on both parts of the OMEDA task are presented below. Part 1 of the task provides an indication of accuracy in terms of time-to-collision (TTC) estimates of a moving target. Performance on the secondary task was also recorded, using the number of errors made in identifying the presence of a matching shape in the periphery of the screen. Part 2 of OMEDA provides data relating to the ability to detect a collision between two moving targets. Table 4 shows the mean time-to-collision error and percentage of shape identification errors.

Healthy Healthy Cold Cold P +Placebo +Medication +Placebo +Medication

Absolute error of TTC 0.7 0.65 0.67 0.64 p>0.05 (OMEDA Part 1) (secs) Shape identification 6.9% 4.5% 2.8% 4.7% p>0.05 error (OMEDA Part 1) (%)

Absolute error of TTC 0.49 0.55 0.55 0.56 p>0.05 (OMEDA Part 2) (secs) Shape identification 3% 1.9% 2.5% 2.4% p>0.05 error (OMEDA Part 2) (%)

Table 4 Mean TTC & % shape matching error for OMEDA task

There were no significant differences between the groups. This may be due partly to a ceiling effect, as participants generally found the task easy to complete. Participants found the primary task sufficiently easy that they were able to devote considerable attention to the secondary task, with very few identification errors across the whole sample.

When the task became more difficult (Part 2) the overall error rate increased, but there were no significant differences between groups. Analysis of these data was performed using a χ2 test. The hit and error rates for the detection of a collision were calculated, and are shown by category in Table 5.

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Healthy Healthy Cold Cold P +Placebo +Medication +Placebo +Medication

Missed collisions 7.1% 7.2% 8.6% 5.7% p>0.05 Detected collisions 26.2% 26.2% 24.8% 27.7% p>0.05 Correct misses 30.3% 26.3% 36.2% 28.6% p>0.05 False hits 36.4% 40.3% 30.4% 38% p>0.05

Table 5 Hit and error rates in collision detection task

In conclusion, no significant differences were found in the OMEDA task. Again, this may be because it was not sufficiently difficult. Part B of the task may have been too complex to tap into any simple performance decrements (such as reaction time).

Visual Reaction Time Task: The results of the ANOVA showed a significant main effect of Cold (F[1,92]= 5.60; p<0.05) indicating that overall cold sufferers had significantly slower reaction times than healthy participants. There was no significant effect of Medication (F<1), and no interaction (F<1). Figure 8 shows the mean reaction time for each group.

450

400

350

Mean Reaction Time (s) 300

ion at Placebo edic Placebo + M Medication ld + thy ld + thy + Co heal al Co He

Figure 8 Mean reaction time

Figure 9 shows how mean reaction time increased over time. Each time period represents a group of 25 responses. Time 1 is an average of the first 25 responses and Time 4 an average of the last 25 responses.

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410 400 390 380 healthy 370 cold 360 350

mean reaction time (ms) 340 330

Time 1 Time 2 Time 3 Time 4

Figure 9 Mean reaction time by time on task

A trend analysis of the visual reaction time (VRT) data was carried out to determine the pattern over the duration of the task. All participants showed a significant linear slowing in reaction time as time increased (F[1,92]=61.25; p<0.001). ANOVA revealed that those suffering with a cold had significantly slower reaction times than healthy participants (F[1,92]=5.66; p<0.05), but medication had no significant effects (F<1). There was also no interaction between cold subjects with and without medication (F<1).

Impairments in reaction time tasks are often found in the slower responses, whilst the faster responses remain unaffected. This is thought to be due to lapses of central executive control. The quicker reaction times may not be as affected as drivers are concentrating their efforts. For this reason effects were examined separately for the fastest and slowest 10%.

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500 healthy 400 cold 300

reaction time (ms) 200

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0 quickest slowest

Figure 10 Mean Fastest and slowest reaction times

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There were no significant differences between the healthy and cold sufferers when the means of the fastest reaction times (RTs) were compared (F<1). There was, however, a significant difference in the means of the slowest reaction times (F[1,92]=7.48; p<0.05). This suggests that cold virus impaired volunteers could perform well when concentrating and attending to the task, but may have had more lapses in reaction time (slow reactions). Figure 10 shows the mean fastest and the slowest reaction times for the healthy volunteers and cold sufferers.

Figure 11 shows fastest and slowest mean reaction times in the first and second half of the experiment. As previously noted, RTs were slower generally later in the task.

650 600 550

500 healthy quickest 450 healthy slowest 400 cold quickest 350 cold slowest

reaction time (ms) 300 250 200 1st half 2nd half

Figure 11 Fastest and slowest RTs for 1st and 2nd half of the test

Figure 12 shows the mean completion times for parts A and B of the Trail making task, see appendix C.

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40 healthy cold 30 Time (secs) 20

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0 Part A Part B

Figure 12 Mean completion times for Trail Making test

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The results for both parts of the Trail Making task were within the normative range stated in Reitan (1958). The distribution of results for part A of the task was shown to be slightly skewed. The distribution for part B was normal. Part A and B were examined in turn and the results are as follows:

Part A: The results of the ANOVA showed a significant main effect of Cold (F[1,92]=11.68; p<0.001) indicating that cold sufferers were significantly slower at completing the task than healthy participants. There was no significant effect of Medication (F<1), and no interaction (F<1).

Part B: The results from the second part of the trail making task were similar to Part A. The results of the ANOVA showed a significant main effect of cold (F[1,92]=4.91; p<0.05) indicating that cold sufferers were significantly slower than healthy participants. Again, there was no significant effect of medication or interaction (both F<1).

4.4.3 Driving performance

Coherence task: There were two coherence events in the virtual road network. Prior to each, the lead vehicle maintained 3s headway. It then reduced its speed to either 50 mph or its start speed –10mph, whichever was the lower. It then went through five repetitions of a sine wave with a period of 60s and an amplitude of ±10mph. Data from the first cycle was ignored. Data with maximum coherence under 0.3 were also ignored. Analysis was by repeated measures ANOVA with one between-subjects factor (Order, to test for fatigue effects) and two between factors (Cold, Medication). 13 participants were omitted as the coherence scenario did not occur on during their drive due to technical difficulties.

Mean headway. 7 outliers were removed from the dataset. Data were normally distributed for both the first event (KS =.06, p>0.2) and the second event (KS=.07, p>0.2). Figure 13 shows the mean headway during the coherence task.

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4 3.5 3 2.5 2 1.5 1 Headways (secs) 0.5 0 first second first second first second first second

placebo medication placebo medication

healthy cold

Figure 13 Mean headway during coherence task

There was a strong main effect of Order (F[1, 80]=8.79; p<0.01) such that drivers drover faster with time. In terms of following behaviour, headway decreased significantly with time. There was no main effect of Cold (F<1) or Medication (F<1). There was an interesting interaction of Cold and Medication (F[1, 80]=6.036; p<0.05): drivers increased headway with medication when they had a cold (safer driving), whereas those without a cold reduced headway with medication (more aggressive).

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Zero shift coherence. Five volunteers were removed because of outlying first event values, and a further 6 for outlying second event values. Data were not normally distributed for either the first event (KS= 0.15, p<0.001) or second event (KS= 0.13, p=.002). Figure 14 shows the zero shift coherence during the coherence task.

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Zero shift coherence 0.1 0 first second first second first second first second

placebo medication placebo medication

healthy cold

Figure 14 Zero shift coherence during coherence task

For coherence (zero shift coherence) there was no main effect of Cold (F<1) or Medication (F<1). There was interaction of Cold and Medication (F[1,80]=6.026; p<0.05): for healthy drivers, medication improved performance (coherence increased) whilst for cold sufferers, medication impaired performance. Phase shift also showed the same performance characteristics with a significant Cold by Medication interaction (F[1,74]=4.446; p<0.05).

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Standard deviation of lane position (SDLP). Data were almost normally distributed for either the first event (KS=.101, p=0.02) but not for the second event (KS=.130, p=.001). Figure 15 shows the standard deviation of lane position during coherence task.

0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 first second first second first second first second

placebo medication placebo medication Standard deviation of lane position (m) healthy cold

Figure 15 Standard deviation of lane position during coherence task

SDLP was affected by fatigue (F[1,87]=47.43; p<0.001 ) and drivers’ lane keeping deteriorated over time. Taking Medication significantly impaired drivers’ lateral ability as medicated drivers wobbled more during the coherence task (F[1,87]=6.77; p<0.05).

Choice reaction time (CRT):There were two CRT events in the virtual road network. During each, 30 red and 30 blue blocks appeared over approximately10 min, each requiring a separate push-button response from the driver. The lead vehicle maintained 3 sec headway throughout. A repeated measures ANOVA was used with one between-subjects factor (Order, to test for fatigue effects) and two between factors (Cold, Medication). Nine volunteers did not encounter the second presentation of the CRT task due to technical difficulties and their data were removed.

Response accuracy. Data were not normally distributed for either the first event (KS=.254, p<0.001) or the second event (KS=.333, p<0.001). Figure 16 shows the number of correct responses during the CRT task.

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60 58 56 54 52 50 48 46

Number of correct responses 44 first second first second first second first second

placebo medication placebo medication

healthy cold

Figure 16 Number of correct responses for CRT task

There was a main effect of Order (F[1,87]=8.343; p<0.05) indicating learning of the task demands. No effects of cold were found (F<1). There was, however, a trend for medicated volunteers to have impaired response accuracy (F[1,87]=3.878; p=0.052). No interaction of Cold and Medication was found (F<1).

Response time (RT). Data were not normally distributed for either the first event (KS=.134, p<0.001) or the second event (KS=.158, p<0.001). Figure 17, shows the mean response time to the presented squares in the CRT task.

700 600 500 400 300 200 100 Mean reaction time (ms) 0 first second first second first second first second

placebo medication placebo medication

healthy cold

Figure 17 Mean RT - CRT task

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There was no effect of Cold on RT ((F<1), while Medication significantly slowed RT (F[1,87]=6.893; p<0.05). There was also an interaction of Cold and Medication (F[1,87]=4.430; p<0.05) such that the slowing of RTs with Medication was more pronounced for the healthy group.

Time to complete CRT section. Five volunteers were removed from the analysis as they were extreme outliers. Data were not normally distributed for either the first event (KS=.140, p<0.001) or the second event (KS=0.112, p=0.007. Figure 18 shows the mean time taken to complete the CRT section of road.

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placebo medication placebo medication

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Figure 18 Mean time to complete CRT task

There was no effect of Cold on driving speed (F<1), however, there was a trend to suggest that as drivers became fatigued, they tended to slow down. This effect was very small. There was an effect of medication on time to complete the CRT scenario, such that Medicated drivers drove slower and took significantly longer to complete the CRT section (F[1,87]=4.866; p<0.05). No interaction of Cold and Medication was found (F<1).

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Standard deviation of speed. Five volunteers were removed as extreme outliers. Data were almost normally distributed the first event (KS=0.098, p=.039) and were normally distributed for the second event (KS=0.086, p=.167). Figure 19 shows standard deviation of speed during the section of road where subjects performed the CRT task.

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placebo medication placebo medication

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Figure 19 Standard deviation of speed

Variations in speed indicate driver stability. A lower standard deviation of speed suggests ‘better’ driving. An interaction of Order and Cold (F[1,82]= 5.305; p<0.05) was found. Surprisingly, those drivers without a cold had more difficulty maintaining a constant speed with fatigue than those with a cold, who had less variation in speed over time. A three way interaction of Order, Cold and Medication was also found (F[1,82]=8.796; p<0.05). In the second section of the CRT scenario volunteers suffering with a cold and taking medication had increased deviations in speed suggesting ‘poorer’ driving stability.

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Standard deviation of lane position. 8 volunteers were removed as extreme outliers. Data were almost normally distributed for the first event (KS=.119, p=0.016) and second event (KS=.116, p=0.017). Figure 20 shows standard deviation of lane position during the CRT section of road.

0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 first second first second first second first second

Standard deviation of lane position (m) Standard placebo medication placebo medication healthy cold

Figure 20 Standard deviation of lane position

Time exposed time to line crossing (TETLC) <0.5 sec. 1 volunteer was removed from the analysis as an extreme outlier (high value). Data were not normally distributed for either the first event (KS=.150, p<0.001) or for the second event (KS=.124, p=.002).

Figure 21 shows time exposed to line crossing during the CRT section of road.

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Figure 21 Time exposed to line crossing

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The lateral measures proved to be highly informative. There was a strong effect of fatigue on ability to maintain lane position. SDLP and TETLC show significantly reduced lateral control with time (F[1,79]=50.23; p<0.001)(F[1,86]=20.938; p<0.001).

SDLP and TETLC showed a reasonable effect of Medication such that medicated drivers were less able in lateral control (F[1,79]=6.625; p<0.05), (F[1,86]=3.214; p=0.077). TETLC showed an interaction of Cold and Medication (F[1,86]=4.252; p<0.05). The effect of Medication on cold-free drivers significantly increased TETLC (reduced performance), however, for the cold-ridden drivers, medication had little effect.

Number of steering reversals (> 1º /minute). Due to relatively low attentional primary driving demands, or due to attentional demands of secondary tasks, drivers do not pay continuous attention to the lane-tracking (steering) task. Steering-reversal rate (McLean & Hoffmann, 1975) can record this phenomenon quantitively. Reversal rate is defined as the number of changes in steering wheel direction per minute. At least an angle difference of 1º between steering end values is required for the reversal to count. Higher reversal rate indicates a higher level of driver workload.

2 volunteers were removed from the analysis as extreme outliers. Data were normally distributed for the first event (KS=.080, p>.2) and for the second event (KS=.061, p>.2). Figure 22 shows the number of steering reversals (over 1o) per minute during the CRT scenario.

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Number of steering reversals per minute healthy cold

Figure 22 Number of rapid steering reversals per minute

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Number of rapid steering movements (> 20º /sec) per minute. Data were skewed for both the first event (KS=.289, p<0.001) and for the second event (KS=.320, p<0.001).

Steering reversal rate tends to show how hard a driver is working at maintaining a lane position. Lots of small steering corrections are indicated by a high value of reversal rate. The number of small corrections reduced with time. This would explain the worsening steering performance (F[1,85]=4.357; p<0.05). There was a solid trend showing an interaction of Order and Cold (F[1,85]=3.476; p=0.066). Healthy drivers could raise their performance throughout whilst the cold sufferers made many small corrections early on, but this reduced dramatically for the second scenario.

There was a definite effect of Order and Medication such that medicated were unresponsive throughout (F[1,85]=5.097; p<.05), were the effect of fatigue reduced the level of responsiveness of the non-Medicated drivers.

However, the most interesting finding was the interaction of Cold and Medication (F[1,85]=5.61; p<.05). For the healthy volunteers, medication showed an increased responsiveness, whilst medication reduced the responsiveness of the cold sufferers.

Situation Awareness (SA): At two points in the simulated run there was a break and drivers were asked a number of questions referring to the immediate preceding events. Situation Awareness was scored as percentage correct. Healthy participants scored significantly higher on the Situation Awareness task than cold sufferers. (F[1,89]= 16.04; p<0.001). Medication also significantly affected Situation Awareness performance. Participants taking medication performed poorly compared to non-medicated participants (F[1,89]=19.070; p<0.001). There was also an interaction effect (F[1,89]=4.39; p<0.05), implying that the effect of taking medication was different, depending on whether the participant was suffering from a cold or not. Those suffering from a cold (and who took medication) did not exhibit the same amount of degradation in performance as those in the healthy group (who also took medication). The effect of the medication was more prominent in the healthy group of participants. Figure 23 shows the mean percentage of answers correctly given to the 12 SA probes.

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100 90 80 70 60 50 ; 40 30 20

Stuatonal Awareness (%) 10 0

acebo acebo + Pl + Pl Medication Medication hy d alt Col ld + e Co H ealthy + H

Figure 23 Mean percentage correct SA scores A number of other measures were also taken during the simulator run, but there were no significant findings. A full list of the simulator measures are detailed in Table 6.

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Driving Simulator Event Cold Medication Interaction F (significance) Comments (cold) Wind Gust (Recovery Time 0.25o & 0.5o) reduced X X (F(1,87)=4.702,p=0.33)(F(1,87)=12.24,p=.001) Cold improves ability to regain control? CRT Driving Measures Time to complete section X increased X (F(1,87)=4.866,p=0.30) Standard deviation of speed reduced X increased (F(1,82)=5.305,p=0.24)(F(1,82)=8.76,p=004) Standard deviation of lane position X Increased X (F(1,79)=6.625,p=0.013) Lane excursions X X X Time exposed to line crossing X X* X Medication almost sig increase in TETLC Number of Steering reversals X X reduced (F(1,85)=5.097, p=0.27) (F(1,86)=3.213,p=0.077) Number of rapid steering movements X X X CRT push-button response Response accuracy X X* X Medication almost sig improved response Response time X increased increased (F(1,87)=6.893,p=0.010)(F(1,87)=4.430, accuracy (F(1,87)=3.878,p=0.052, slowing of RT p=0.038) was much more pronounced for healthy group. Discrete events Event start speed X X X Brake reaction time X X X Minimum time headway X X X Minimum distance headway X X X Minimum time to collision x x x Anticipatory braking event Brake reaction time X X X Minimum time headway X X X Minimum distance headway X X X Minimum time to collision X X X Following Straight - Mean speed X X X Following Straight - Mean Headway X X X Coherence Mean speed X X X Mean Headway X X X For healthy drivers, medication improved Maximum coherence X x X performance whilst for cold suffers medication Zero shift coherence X x increased (F(1,74)=4.392, p=0.40) worsened performance. Phase shift X X X Medication improved healthy drivers, did not have Modulus X X X much effect on cold suffers Standard deviation of lane position X increased X Medicated drivers impaired lateral control. Lane excursions X X X (F(1,87)=6.770, p=011) Time exposed to line crossing (<0.5s) X X X Number of steering reversals (over 1o) X X X Number of rapid steering movements X X X Table 6 Effects of Cold and/or Medication on driving performance

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4.5 Conclusions

The results suggest that cold virus and medication to relieve its symptoms impairs performance on simple laboratory tasks that measure cognitive performance. These changes in performance are not transparent in simple driving tasks, and basic driving abilities may not be significantly affected. Impairments, however, are found in secondary driving tasks such as awareness and non-primary reaction time tasks. Secondary tasks are impaired significantly by the presence of a cold virus and can be further impaired by taking over the counter cold medication. Furthermore it seems that cold sufferers taking medication are unaware of the subtle deficits in their driving performance. These findings suggest that over the counter medication labelling is necessary. The labelling should ensure that drivers are informed that although they may feel safe to drive, they may in-fact be more dangerous and less aware than non-medicated drivers. Further research comparing the effects of cold medication with a given dose of alcohol and/or sleep deprivation would be useful to determine the relative importance of our findings.

The main findings are summarised below:

• Cognitive measures of simple reaction time show that cold sufferers are slower than healthy volunteers.

• Cognitive measures of visual search time show that cold sufferers are slower than healthy volunteers.

• Neither cold virus nor medication seems to affect basic driving ability.

• Secondary tasks such as awareness and reaction times not related to the primary driving task are impaired by medication.

• Taking medication whilst suffering with a cold may further impair secondary tasks such as awareness and reaction times not related to the primary driving task.

• Medication labelling should stress that drivers may feel ok to drive, but in-fact be more dangerous and less aware than non-medicated drivers.

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4.6 Discussion

The manipulation check results showed that there was a significant difference between the severity of cold virus symptoms reported by the cold sufferers and healthy volunteers. The two groups could therefore be compared validly for the purposes of the present experiment.

4.6.1 Psychomotor performance

Healthy volunteers performed significantly better on both parts of the Trail Making task than cold sufferers. This suggests that cold sufferers have poorer visual search abilities than healthy volunteers. Impairments in visual search ability were also reported by Smith (1998) and Weiler (2000). Cold virus also impaired performance on a visual reaction time task. Using a simple visual reaction time task that lasted around 12 minutes, we found that all participants reaction time slowed as time went on. Cold sufferers were, however, significantly slower at the start and end of the test. Participants' quickest and slowest reaction times were compared. Interestingly the performance deficit seemed to be in slow reaction times. No significant difference was found between their quickest reaction times suggesting that cold sufferers could respond as quickly as healthy ones. However, cold sufferers had impaired slow reaction times compared to healthy volunteers. This finding suggests that cold sufferers had more ‘lapses’ in response than healthy volunteers. This is of particular interest when considering driving ability as it suggests that cold sufferers are more likely to ‘lapse’ and miss a critical event than healthy volunteers.

No significant differences were found between the groups' performance on the OMEDA task. This may have been due to ceiling effects with all volunteers performing at a high level. It’s also possible that it failed to ‘tap’ into the performance deficits due to the nature of the task. OMEDA is a divided attention task and previous research has suggested that cold virus does not impair divided attention functioning. Research has, however, suggested that diphenhydramine impairs divided attention. The present study failed to find such impairment. This may have been as a result of using a lower concentration of diphenhydramine than other studies.

The laboratory tasks revealed impairments in the groups suffering with a cold virus, but did not show any impairment caused by the medication. One reason for this could be the pharmacodynamics of diphenhydramine. Diphenhydramine does not reach its peak plasma concentration until around one hour post ingestion. The simulated driving tasks followed the laboratory experiments and some effects of Medication were found in these tasks.

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4.6.2 Driving performance

A choice reaction time task (CRT) was presented to drivers and driving performance was measured during this task. The event lasted around 20 minutes (2 sessions of 10 minutes) and analyses provided an insight into ‘spare’ cognitive capacity during driving. Actual driving performance was also analysed to provide measures of longitudinal and lateral performance.

Medication was found to significantly affect the accuracy of performance of the CRT task. Volunteers taking the medication had fewer correct responses than volunteers talking a placebo. The Medicated group also reacted to the stimuli significantly slower than the non-medicated. This effect of Medication seemed to impair healthy volunteers much more than cold sufferers. Interestingly medicated participants slowed down during the CRT task and took longer to complete the task. This suggests that drivers were attempting to compensate for the increased cognitive demand. In contrast, during normal driving (standard driving on a straight section of open road) there was no difference in speed under any of the four conditions. Although medicated drivers decreased speed throughout the CRT task it did not improve their response time to the presented stimulus. These results suggest that medicated drivers ability to respond to expected events is impaired.

The results from the coherence section of the drive also point to some kind of compensatory behaviour in the medicated groups. Medication caused retardation of coherence in healthy drivers, but improved coherence ability in cold suffers. All the medicated drivers, however, had increased standard deviation of lateral position. Increased steering wheel corrections and rapid steering corrections in the second coherence task were also found in the Medicated group. Again these results suggest that medicated drivers are able to compensate in some areas of driving and perform as well as non- medicated drivers in longitudinal performance. As for lateral performance, however, cold sufferers and medicated drivers held a less stable position on the road compared to non-medicated drivers. Kay (2000) also found that lateral stability was impaired by a small dose (25mg) of diphenhydramine.

Fatigue effects.

There was no effect of Cold on driving speed, however, there was a trend to suggest that as drivers became fatigued, they tended to slow down. This effect was very small. There was an effect of Medication on time to complete the CRT scenario, such that Medicated drivers drove slower and took significantly longer to complete. The variation of driving speed (‘better’ driving tends to suggest a low standard deviation of speed) during the scenario showed an interaction of Order and Medication. Surprisingly, those drivers without a cold had more difficulty maintaining a constant speed with fatigue than those with a cold, who had less variation in speed over time.

Situation Awareness task

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In the simulated run there were two occasions where the projection screen was blanked and drivers were asked about events occurring immediately before. Healthy participants scored significantly higher on the Situation Awareness task than cold sufferers. Medication significantly affected Situation Awareness performance. Participants taking medication performed poorly compared to non-medicated participants.

There was also an interaction effect implying that the effect of taking medication was different, depending on whether the participant was suffering from a cold or not. Those suffering from a cold (and who took medication) did not exhibit the same amount of degradation in performance as those in the healthy group (who also took medication). The effect of the medication was more prominent in the healthy group of participants. This finding was also apparent in the CRT results. Again the interaction of Cold and Medication was such that lowering of response time with Medication was much more pronounced for the Healthy group.

4.6.3 Limitations

The present study looked a combination of active ingredients commonly used in over the counter cold medications in the UK. The amount used was the recommend dose for adults. Although this approach had ecological validity, it may have been useful to ‘top up’ the medication throughout the experiment so that a constant medication/ plasma level could be investigated. This was not possible in the present experiment due to ethical constraints.

The present experiment used a single blind between subjects design. An improved design would be a double blind within subjects approach. This was not carried out for a number of reasons. A single blind rather than double blind method was used due to the nature of the medication and availability of laboratory staff. The placebo and medicated drink needed to be prepared and ingested shortly after otherwise the drink cooled and tasted unpleasant. An additional member of staff was not always available to mix the drink for the main experimenter. A single blind approach was therefore considered necessary.

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4.7 References

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Salusjärvi M. (1988). The speed limit experiments on public roads in Finland. In Proceedings of Roads and Traffic Safety on Two Continents in Gothenburg, Sweden. VTI Rapport 332A, Swedish Road Traffic Research Institute.

Scavone, J.M., Greenblatt, D.J., Haramtz, J.S., Engelhardt, N, & Shader, R.I. (1998) Pharmacokinetics and pharmacodynamics of diphenhydramine 25mg in young and elderly volunteers. Journal of Clinical Pharmacology, 38: 603- 609

Schweitzer, P.K., Muehlbach, M.J, & Walsh, J.K. (1994) Sleepiness and performance during three-day administration of certirizine or diphenhydramine. Journal of Allergy and Clinical Immunology, 94 (4): 716-724

Simons, K.J, & Simons, F.E.R. (1996) H1-receptor antagonists: Pharmacokinetics and clinical pharmacology. In F.E.R. Simons Histamine and h1-receptor antagonists in allergic disease. New York: Marcel Ekker

Smith, A., Thomas, M et al (2000) After-effects of the common cold on mood and performance. Ergonomics, 43(9): 1342-1349

Smith, A., Thomas, M, Kent, J & Nicholson, K. (1998) Effects of the common cold on mood and performance. Psychoneuroendocrinology, 23 (7): 733-739

Smith, A., Harvey, I., Richmond, P., Peters, T.J., Thomas, M & Brockman, P. (1994) Upper respiratory tract illness and accidents. Occupational Medicine, 44: 141-144

Smith, A.P., Tyrell, D.A.J, Coyle, K, & Al-Nakib, W. (1998) The effects of experimentally induced respiratory virus infections on performance. Psychological Medicine, 18: 65-71

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Appendix A - Symptoms checklist

Please indicate whether or not you have any of the following symptoms at the moment and circle the appropriate number to rate the severity of them

Rating Scales

Not Present 0 Mild 1 Moderate 2 Severe 3 Very Severe 4

Physical weakness 0 1 2 3 4 Excessive fatigue 0 1 2 3 4 Legs feeling heavy 0 1 2 3 4 Muscle pain in back, arms or 0 1 2 3 4 legs Pain in chest* 0 1 2 3 4 Painful joints 0 1 2 3 4 Nausea 0 1 2 3 4 Indigestion 0 1 2 3 4 Bloated stomach 0 1 2 3 4 Wind 0 1 2 3 4 Diarrhoea 0 1 2 3 4 Sore throat* 0 1 2 3 4 Headache* 0 1 2 3 4 Earache 0 1 2 3 4 Sneezing* 0 1 2 3 4 Sore eyes 0 1 2 3 4 Runny nose* 0 1 2 3 4 Blocked nose* 0 1 2 3 4 Hoarseness* 0 1 2 3 4 Cough* 0 1 2 3 4

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Rating Scales

Not Present 0 Mild 1 Moderate 2 Severe 3 Very Severe 4

Sensitive to noise 0 1 2 3 4 Sensitive to light 0 1 2 3 4 Feeling hot/cold* 0 1 2 3 4 Sweating* 0 1 2 3 4 Shivering* 0 1 2 3 4 Fever* 0 1 2 3 4 Phlegm* 0 1 2 3 4 Swollen glands 0 1 2 3 4 Racing heart 0 1 2 3 4 Insomnia 0 1 2 3 4 Depression 0 1 2 3 4 Anxiety/Panic feelings 0 1 2 3 4 Loss of concentration 0 1 2 3 4 Loss of memory 0 1 2 3 4 Allergies 0 1 2 3 4 Dizziness 0 1 2 3 4 Faintness 0 1 2 3 4 Loss of appetite 0 1 2 3 4 Other 1 (please name) 0 1 2 3 4 ______0 1 2 3 4 Other 2 (please name) ______0 1 2 3 4 Other 3 (please name) ______

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Appendix B - Subjective state report

Feelings We would like to know how you are feeling at the present moment. Please indicate your state for each of the 6 scales (represented by pairs of adjectives) by circling one number for each.

enthusiastic 1 2 3 4 5 6 7 8 9 miserable weary 1 2 3 4 5 6 7 8 9 lively relaxed 1 2 3 4 5 6 7 8 9 tense depressed 1 2 3 4 5 6 7 8 9 elated energetic 1 2 3 4 5 6 7 8 9 tired on edge 1 2 3 4 5 6 7 8 9 at ease

Effort We would like to know how much mental effort you put into the task (concentrating hard, trying actively to keep on top of what you were required to do, etc. Please indicate how much mental effort you did exert in the session (compared to your typical experience), by circling one of the numbers on the scale.

Not very much 1 2 3 4 5 6 7 8 9 As much effort as I Effort at all possibly could

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Appendix C - Trail making task

Part A

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Part B

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Appendix D - Volunteer Information Sheet

School of Psychology, University of Leeds

Invitation to participate in the research study: The effects of cold virus and cold medications on driving and cognitive performance.

We are a group of researchers looking at what factors can affect a person’s driving. Through this document we are inviting you to take part in our research study.

Before you decide if you want to help us with our research or not, it is important for you to understand why we are doing it and what it involves. We would be grateful if you could take time to read the following information carefully. Please also contact us if, at any point, you feel that you need more information or a clarification and we will be very happy to discuss it with you. We would be extremely happy if you could help us with our research and we urge you to take time in deciding whether or not you wish to take part.

PLEASE TURN OVER AND READ MORE ABOUT THIS STUDY.

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This copy of the Volunteer Information sheet is for you to keep.

Study Background.

We are testing the effects of cold virus and cold virus medication on performance. We are particularly interested in the effects on driving performance. To study this we will test volunteers with and without a cold and with and without cold medication. If you choose to take part in the experiment you will be asked to consume a flavoured drink. The drink will be either a placebo, which will do nothing or a drink containing a cold medication. Neither you nor the experimenter will know which is which. The cold treatment drink will contain common over the counter medication and the dose will be within the recommended dose stated by the product manufacturers. Over the next year we will invite 100 people like you to help us with this study.

Do you have to take part?

You do NOT have to take part in this study unless you want to. If you decide to help us, you will be given this information sheet to keep and we will also ask you to sign a consent form to show that you do intend to take part. Even if you decide to take part, you are still free to withdraw at any time and without giving a reason and this will not affect the standard of care you receive.

What will happen to you if you take part?

If you decide to help us, you will be required to visit the University of Leeds, School of Psychology. A visit will be arranged at a time that is suitable for both you and the researchers. We will reimburse any travel costs, provide you with a taxi after the study, and pay for any medication used. You will also be paid £20 for your time upon completion of the experiment.

While at the University of Leeds, we will give you the flavoured drink and then familiarise you with the driving simulator. This is a real Rover car with everything working as usual, except the wheels. The road in front of you will be projected on screens. One of us will be seated in the car with you to make sure we answer any questions you may have. We will drive together for as long as it takes you to get used to the car. You will have the opportunity to have a practice session on the simulator. There will then be a break. Following the break you will be asked to complete a few simple tests and a questionnaire. Some of the tests are pen and paper based and ask you to do things like draw lines between numbers spread on a page or will ask you to name objects we point to. Some tests will be on a computer, but you do not have to know how to use computers to be able to do these tests. They are looking at things such as how quickly you press a button when an object appears on the screen. These tests should take about 1 hour to complete.

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After you complete these tests, we will ask you to drive the simulator. We will give you directions of where you need to drive and then you will drive for about 50 minutes. After the drive you will be given another short questionnaire to complete. At the end of this, you would have completed the whole research and be ready to return home.

What are the possible advantages and disadvantages of taking part?

The main benefit of this study should be to future drivers, since the study is intended to help improve the way that decisions are made about who is and who is not safe to drive. Although there is no direct clinical benefit to you personally, we do hope you will also enjoy driving our simulator, which is one of only two top simulators in this country.

Taking part in our study brings extremely few risks. About 1 in 100 people who drive a simulator will feel light headed or slightly unwell. To prevent this, we give you the chance to drive the simulator with one of us in the car first, and we ask you to stop driving at once if you feel even the slightest discomfort. We also keep an eye on you while you drive alone through cameras in the car that focus on your face.

In the very unlikely event that something goes wrong, the University of Leeds provides cover for damages.

Will your participation in this study be kept confidential?

We will treat any information about you, obtained during the course of this research, in the STRICTEST CONFIDENCE. Any information about you will have your name and address removed so that you cannot be recognised from it. This information will be used for research purposes only. It will not be available to anyone except the research team. The results of any tests you do for this study will NOT be passed on to anyone else.

We will be more than happy to provide you with a copy of the results when they become available, and if you would like us to do this, please let us know when you contact us to take part in the study. The final report is likely to be available to download via the internet, should you wish to see it. The report will not have any information about you personally.

This study is funded by the European Commission and is organised by the University of Leeds.

Thank you for reading the information we have provided. If, after due consideration, you would like to take part in our research, please ring Chris Wood on telephone 0113 343 6681. We are grateful for your help, without which this research would not be possible.

For any other enquiries, please contact Chris Wood at the University of Leeds, School of Psychology, Woodhouse Lane, Leeds LS2 9JT, telephone 0113 343 6681.

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Appendix E - Subjective Awareness

Part 1 What type of vehicle passed you just before the screen went blank? (lorry) What colour was it? (red) What was the logo on it? (kit kat) What was the car in front doing when the screen went blank? (braking) How many vehicles were behind you when the screen went blank? (1) What type of vehicle/s were they/ was it? (police car) What was/ were the vehicle/s behind doing when the screen went blank? (flashing lights) What speed were you doing just before the screen went blank? (+/- 10% is ok) Actual speed: ______Estimated speed: ______How long would it be before you reached the junction? (1-3 secs)

Part 2 What was the truck doing before the screen went blank? (turning onto drivers road) What colour was it? (green) What was the logo on it (Eddie Stobart)

ix Driver starts with the lead car in front and continues to follow the lead car throughout the virtual road network. The virtual road network The lead car moves away as the driverIMMORTAL star tsD-R4.4 t o accelerate. (the lead car speeds 21/10/04 emulates a rural road up to 60 mph and then maintains a 3second headway from the driver. environment. The road has a 60mph speed limit. The driver ROADWORKS: This is the first of the discrete events. The lead car slows down over can drive freely, but the lead a 30s period and initial deceleration of 0.5g. The lead car then moves around the car maintains a 3 second Section A roadworks and the driver follows. headway.

Appendix F - Virtual Road Map

During this section of the network the choice reaction time task was presented to the driver. 30 red and 30 blue blocks appeared over approximately 10mins, requiring an appropriate push button response This is the first part of the SA event. The driver is following the lead car at from the driver. The lead vehicle approximately 60 mph with a 3s headway. A police car approaches from maintained a 3s headway behind flashing it’s lights; a red lorry with a ‘kit-kat’ logo passes on the opposite side of the road; the lead car begins to brake. Following these events the screen blanks. This is the first of the two coherence events. Prior to the This is the wind gust event. Both cars are start of the task the lead vehicle maintains a 3s headway. It then travelling at around 60mph. The lead car reduces it’s speed to either maintaining a headway of 3s. The wind comes from the drivers left with an 50mph or it’s start speed – 10mph, whichever is slowest. It instantaneous velocity of 20m/s. After 5s the wind instantaneously drops back to zero then has 5 repetitions of a sign wave with a period of 60s and an Junction amplitude of +10mph. This event occurs at the approach to the HGV intersection. A HGV crosses the lead vehicle causing it to brake at 0.5g for 3s. The HGV began to move when the driver was 7s from the intersection. The lead vehicle begins to brake when the driver is 5s from the intersection. Junction The second part of the Section A is SA event occurs here. repeated, but A green Eddie Stobart sheep obstruct Lorry begins to turn the road rather into the drivers path thani road just before the screen works. goes blank.