karaholloway.files.wordpress.com€¦ · Web viewImplementing the NCHIP Online Drinking Survey to Keele University and Researching Risk Factors for Alcohol Use Disorder. By. Kara

September 2014 Kara Holloway MSc Psychology

Implementing the NCHIP Online Drinking Survey to Keele University and Researching Risk Factors for

Alcohol Use Disorder

ByKara Holloway

Word count: 15,306

Submitted in partial fulfilment of the requirements for the degree of MSc

Psychology

September 20141


Abstract

Rationale: Research assessing the links between the alcohol hangover and its links to

alcohol use disorder (AUD) are sporadic and still in their infancy. This research will provide a

body of evidence from the UK, of which there is currently none. Furthermore, Keele

University has a recognised issue with alcohol on campus with the need to implement a

drugs and alcohol policy for students and staff; the data collection aspect of the survey will

provide information on student’s drinking patterns and behaviours.

Methods: An online survey consisting of the NCHIP, HSS and SMAST was emailed to every

student member of Keele University. From which participants drinking patterns and

behaviours were calculated, participants were categorised into those with a positive family

history of AUD (FHP) and those with a negative family history of AUD (FHN) and hangover

severity was derived. The survey was run in March and July in order to gain information

about drinking behaviours at differing times of the academic year.

Aims: Use the NCHIP to collect relevant statistics for the benefit of Keele University to

address issues of harmful and hazardous drinking amongst students. Use the NCHIP, HSS

and SMAST to assess links between the familial risk for AUD, the alcohol hangover and the

development of AUD.

Results: Students at Keele University are showing high levels of high risk drinking episodes

(29% of students engaging in high risk drinking at least once in the previous fortnight).

Furthermore, 13% of males and 21.5% of females displayed estimated blood alcohol levels

above a safe level. There was no difference in drinking patterns between FHP and FHN

participants however hangover severity was significantly higher in FHP participants

(p=0.012). We can predict that these participants will be more likely to engage in ‘hair of the

dog’ drinking in order to overcome the symptoms of the hangover which could lead to AUD.

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Acknowledgements

I would like to thank all the people who helped and supported me in the writing of this

dissertation.

Firstly, I would like to thank Dr Richard Stephens, my supervisor, for guiding me through

every aspect of this project. I would also like to thank you for all the support you have

provided me throughout my entire academic year at Keele University.

Secondly, I would like to thank Ian Munton and his team at Student Support for supporting

the project and distributing and encouraging participation of the survey. I would also like to

thank you for the Amazon vouchers you provided as an incentive to students.

Thirdly, I would like to thank my family for proof reading many stages of the written project,

and again for the support you have provided me whilst at Keele University.

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Table of Contents

Title page 1

Abstract 2

Acknowledgements 3

Table of contents 4

Introduction 5

Methods 16

Results 22

Discussion 37

References 44

Appendices Attached disc

Tables

1. Demographics and descriptives of participants

2. Drinking patterns of participants

3. Rates (frequency and %) of drinking related harm

4. Social impacts of drinking: experiences as a result of someone else’s drinking

5. Drinking behaviours: respondents who provided help to others

6. Rates (frequency and %) of drinking related benefits

7. Participants above safe limits for eBAC

8. Descriptives for FHN and FHP participants

9. Drinking patterns and behaviours of FHN and FHP participants

10. Mean & SD of hangover severity according to eBAC

11. Frequencies for positive consequences of drinking

Figures

1. Regression graph of hangover severity against drinks consumed for FHP participants

2. Regression graph of hangover severity against drinks consumed for FHN participants

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Introduction

Alcohol Use

Alcohol is Europe’s largest risk factor for disease burden and is associated with many

serious social and developmental issues (Global Strategy to Reduce Harmful Use of Alcohol,

2011). The NHS website states that women should not regularly drink more than 2-3 units a

day and men should not regularly drink more than 3-4 units a day (NHS, 2014). Here, they

define ‘regularly’ as drinking this amount every day or most days. Keeping within these

guidelines will limit the harm caused by alcohol consumption. Binge drinking for men is

defined as 8 or more units of alcohol which is the equivalent of three pints of strong beer. For

women, a binge drink is defined as 6 or more units which is the equivalent of two large

glasses of wine (Drink Aware, 2014). Binge drinking can lead to accidents, injuries and anti-

social and violent behaviour (Drink Aware, 2014) yet despite this it is a common occurrence

amongst young adults, in particular, students (Johnson, 2014).

The Drink Aware website (Drink Aware, 2014) states that alcohol is a factor in 30% of sexual

offences, 33% of burglaries and 50% of street crimes. Furthermore, binge drinking is most

common among 16-24 year olds, and emphasises the dangers of binge drinking when

young; those who binge drink in their teens are up to twice as likely to be binge drinkers 25

years later (The BMJ, 2005). The Institute of Alcohol Studies reported that 31% of 16-24

year olds drink above the recommended low-risk guidelines. Furthermore, a higher

proportion of 16 to 24 year-old women reported drinking more than the guidelines on at least

1 day in the previous week compared with the national average for all female age groups

[31% compared to 28% total] in 2011 (IAS, 2013). Stark (2007) interviewed 16-24 year olds

and reported that 33% classify themselves as binge drinkers, 22% have passed out five or

more times and 63% were more likely to have a one-night stand while drunk. These statistics

emphasis the danger of adolescent and young adult drinking, and highlights this age group

as at risk. Webb, Ashton, Kelly & Kamali (1996) issued 3075 second year students at

university with a questionnaire assessing drinking, use of illicit drugs, lifestyle variables and

ratings of depression and anxiety. It was reported that 61% of the males and 48% of the

females exceeded safe limits of alcohol consumption; emphasising the student age range to

be at high risk of negative alcohol consequences in the future. Johnson (2014) as part of the

learning collaborative on high risk drinking suggest several reasons as to why this age group

may be of risk; student living areas that provide and encourage high risk drinking such as

fraternities and sororities, the density of bars and clubs that surround campuses, the events

and rituals that are engrained in university life such as hazing, and inconsistent

communication and messaging about high risk drinking and consequential harm.

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The demand for alcohol research has been emphasised in the European Commission

Strategy released in 2006 (EU Alcohol Strategy, 2006). Section 5.4 and 5.5 state the need to

inform, educate and raise awareness on the impact of harmful and hazardous alcohol

consumption and to develop, support and maintain a common evidence base. The report

highlighted young people as a target audience for health education and awareness, raising

interventions and for the gaining of comparable information on alcohol consumption in young

people; achieved through the use of students in the current study. Section 5.5 also details

the demand for more defined effects of alcohol, both on health and socially and that gaps in

the research are filled, which has promoted recent research in the field. Since the

implementation of this strategy, there have been promising beginnings, for example, the link

between alcohol use and wider socio-economic issues has been brought more sharply into

focus. However, the First Progress Report on the Implementation of the EU Alcohol Strategy

(2009) stated that alcohol consumption still contributes to a substantial loss of productivity

and thus more research needs to be conducted to establish more definite effects of alcohol

consumption which will become more recognised by the public, and in particular, young

people. This strategy has been recognised in several recent studies that support the idea of

education through research. Zeigler et al. (2005) stated that adolescent alcohol consumption

is associated with implications in learning and intellectual development; and that they fully

support the view that action is justified to protect adolescents from this through marketing

and changing the normalisation of adolescent alcohol consumption.

The consequences of alcohol abuse are evident not just on a personal level but also on a

societal level. Stockwell (1998) stated that the greatest cost incurred in the workplace is

decreased productivity as a direct effect of alcohol-related absence and poor job

performance. According to a study carried out by Jarvilehto, Laakso and Virsu (1975), 1

million workdays are lost in Finland each year due to hangover-related issues. The NICE

guidelines are accredited, internationally recognised guidelines followed by clinicians. The

NICE guidelines (2013) identify alcohol use as a major health problem, and they advise the

government on various policies that may be implemented with the aim of reducing this

problem; specifically in regards to pricing, the way alcohol is marketed and availability. The

implications and dangers listed above are a few of a long list of drinking-related issues, yet

despite this, research around the alcohol hangover and its links to alcohol use disorder have

been sporadic and are in it’s infancy. Recently, attention has been paid to the link between

alcohol use and the hangover, and its potential links with familial risk and the development of

Alcohol Use Disorder (AUD).

The Alcohol Hangover

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The alcohol hangover has been known since Biblical times:

“Woe unto them that rise up early in the morning, that they may follow strong drink” (Isaiah,

5:11).

The alcohol hangover occurs as the blood alcohol concentration (BAC) approaches zero, a

“puzzling post-intoxication phenomenon” (Penning, Nuland, Fliervoet, Olivier & Verster;

2010). Although a clear consensus on a definition of the alcohol hangover is lacking,

previous literature identified a common cluster of symptoms such as headache, sickness,

dizziness, nausea and diarrhoea (Wiese, Shlipak & Browner, 2000). More objective

indications reported are decreased occupational, cognitive, or visual–spatial skill

performance (Yesavage & Leirer, 1986; Kupari, 1983). Just as a clear definition is lacking so

is an elicit mechanism of the alcohol hangover. It is suggested that Acetaldehyde, the

dehydrogenated product of alcohol metabolism, might be responsible for hangover

symptoms (Bogin, Nostrant, Young, 1987; Tsukamoto, Kanegae, Saito, Nagoya, Shimamura

& Tainaka, 1991). It is also suggested that Congeners, the by-products of individual alcohol

preparations found in brandy and wine increase the frequency and severity of hangover

(Damrau & Goldberg, 1971; Pawan, 1973; Damrau & Liddy, 1960). The alcohol hangover

has also been associated with a dysregulated cytokine pathway which would produce

symptoms such as nausea, headache and diarrhoea. Studies involving experimentally

induced alcohol hangover have reported that alcohol increases levels of thromboxane B2

which in turn alters cytokine production (Kanasaho, Hillbom, Kaste & Vapaatalo, 1982).

Furthermore, changes in antidiuretic hormone levels have been observed in people with a

hangover; the more alcohol consumed the more inhibited the function of the antidiuretic

hormone becomes from which dehydration persists; and this is what gives rise to so many of

the symptoms of the alcohol hangover that get reported (Linkola, Ylikahri, Fyhquist &

Wallenius, 1978; Linkola, Fyhrquist & Ylikahri, 1979; Linkola, Fyhrquist, Nieminen, Weber &

Tontti, 1976; Hangover Cures, 2012).

Verster, Herwijnen, Olivier and Kahler (2009) developed the Dutch version of the brief young

adult alcohol consequences questionnaire and found that most commonly reported alcohol-

related consequence was the alcohol hangover (74.3%). As previously mentioned, 16-24

year olds are at particularly high risk of binge drinking behaviours and thus more susceptible

to hangovers. A recent study showed how college students often overestimated the number

of alcoholic drinks it would take to cause a hangover the following day showing a failure to

learn from past drinking experiences. Mallett et al. (2006) used self-report techniques to

assess drinking behaviour associated consequences of drinking in 303 college students.

They also asked students to estimate the number of drinks associated with the risk of

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experiencing future similar consequences. T tests revealed that students significantly

overestimated the number of drinks it would take to vomit, have unwanted sexual

experiences, experience hangover and black out (Mallett, Lee, Neighbors, Clarimer & Turrisi;

2006).

As a result of publications exploring the effects of the alcohol hangover on functioning,

several hangover scales have been produced; with the aim of identifying hangover severity

in various populations, and to improve the quality and reliability of research in the field

(Penning, McKinney, Bus, Olivier, Slot & Verster; 2013). One prominent scale, produced by

Slutske, Piasecki and Hunt-Carter in 2003, is the thirteen item Hangover Symptom Scale

(HSS) where subjects report hangover symptoms they have experienced in the past twelve

months. Each item focuses on one of thirteen hangover symptoms; participants are asked to

report in the past 12 months when they drank alcohol, how often they experienced the

symptom the next morning. Participants are then asked to state more specifically the number

of times they experienced that symptom the next morning after drinking alcohol as a sub

section to the question. Following the development of the HSS, Slutske, Piasecki and Hunt-

Carter (2003) reported that the HSS captures a valid set of adjectives describing common

hangover effects. More recently, Robertson, Piasecki, Slutske, Wood, Sher, Shiffman &

Heath (2012) corroborated the HSS as a valid tool for alcohol hangover research. They

reported that “higher HSS scores identify individuals who complain of real world hangovers

and who may be especially likely to display particular symptoms after a night of drinking.”

An interesting calculation that can help clinicians and researchers to make an approximation

on the blood alcohol level (BAC) of participants in order to obtain information about ethanol

exposure is the eBAC (estimated blood alcohol concentration). The formula to obtain the

eBAC takes into account the number of drinks consumed and the hours over which they

were consumed, as well as height, weight and sex of the participant. There are many other

extraneous variables to be considered when calculating the eBAC such as body type, food

consumed and medication however it provides a rough estimate of the blood concentration

in order to gain a higher level of understanding about a participant’s alcohol consumption.

Recent research has suggested that the alcohol hangover is experienced when the

individual’s eBAC reaches 0.1; this value can be used in research now to assess the

likelihood of a hangover occurring (Kruisselbrink, 2014).

The Alcohol Hangover & Familial Risk

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The National Institute of Alcohol Abuse and Alcoholism (2012), states the seriousness of

Alcohol Use Disorder (AUD) and highlights symptoms including craving (a need or urge to

drink), loss of control (being unable to stop drinking once started), physical dependence

(withdrawal symptoms such as nausea and dizziness) and tolerance (drinking more alcohol

but feeling the same effect as a lower dose would have previously).

According to the DSM-5, Alcohol Use Disorder is defined as the presence of at least two of

the following symptoms; alcohol is often taken in larger amounts or over a longer period than

intended, there is a persistent desire or unsuccessful efforts to cut down or control alcohol

use, a great deal of time is spent in activities necessary to obtain alcohol, use alcohol or

recover from its effects, craving or a strong urge to use alcohol, recurrent alcohol use

resulting in a failure to fulfil major role obligations, continued alcohol use despite having

persistent problems caused by alcohol, important activities given up because of alcohol use,

recurrent alcohol use in situations in which it is physically hazardous, alcohol use is

continued despite knowledge of physical or psychological problem, tolerance and withdrawal

(American Psychiatric Association, 2013). The severity of AUD is defined as either mild (2 or

3 symptoms), moderate (4 or 5 symptoms) or severe (6 or more symptoms).

The alcohol hangover can alter the development of alcohol use disorders, yet this factor has

received little attention in terms of current research; by 2005 only five studies had explored

the links between hangover and familial risk for AUD (Piasecki, Sher, Slutske & Jackson,

2005).

Piasecki et al. (2005) exposed that family history positive (FHP) participants tended to report

more hangover than family history negative (FHN) participants; even when drinking

frequency and sex was controlled in multilevel models. Furthermore, FHP participants

tending to be more impulsive and less sensitive to punishment cues, suggesting a longer

trial and error period to detect relationships between alcohol consumption and the negative

effects of the alcohol hangover. Similarly, in 1999, Span and Earlywine conducted a study

which sought to replicate work done on the relationship between risk for AUD and hangover.

Newlin and Pretorious (1990) and McCaul, Turkkan, Svilis and Bigelow (1991) had published

work previously suggesting that sons of alcoholics (SOA) report greater hangover symptoms

than sons of non-alcoholics (SONA). Span and Earlywine (1999) used the McAndrew scale

as an index of personality risk for alcoholism to compare the sons of alcoholics (SOA) and

sons of non-alcoholics (SONA). Participants also completed the McCaul, Turkkan, Svilis and

Bigelow (1991) and Newlin and Pretorious (1990) assessments of hangover. Subjects

attended two consecutive testing sessions where they consumed a placebo drink in one

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condition and alcohol in the other. It was concluded that the 20 SOAs reported significantly

greater hangover symptoms than SONAs. Furthermore, those with an increased risk for

alcoholism reported that they experienced more acute withdrawal and hangover which might

contribute to the development of problem drinking.

Over the past 25 years, researchers have used familial risk to enrich studies and encourage

future investigation into the risk factors for the development of AUD. Piasecki, Sher, Slutske

& Jackson (2005) conducted a longitudinal study where data was drawn from 3,156 college

freshman all of whom were screened for family history of AUD using the Short Michigan

Alcohol Screening Test (SMAST); this assesses paternal and maternal drinking problems.

The 26 item SMAST was developed in 1992 by Crews and Sher and it is commonly used to

identify familial risk of AUD; with questions relating to both the subjects mother’s (M-SMAST)

and father’s (F-SMAST) drinking patterns and behaviours. The questions are designed to

reveal problem behaviours exhibited by participants’ parents. Crew and Sher (1992) reported

that the overall reliability and validity of the F-SMAST and M-SMAST was good and

appropriate for researchers and clinicians interested in a parental history of AUD. They

compared those with a positive family history of alcohol use disorders (FHP), and those with

a negative family history (FHN) of Alcohol Use Disorder. Hangover was measured using the

Young Adult Alcohol Problem Screening Test (YAAPST) which is a 27-item questionnaire

designed to assess the frequency of a variety of negative consequences of drinking;

responses were between 0 to 9 where 0 indicated no hangover in the past year and 9

indicated hangover experienced 40 or more times in the past year.

The Alcohol Hangover as a Risk Factor for AUD

In 2004, Park assessed whether the alcohol hangover acted as a deterrent to further

consumption, as previously assumed, or if it in fact caused further consumption of alcohol.

They found that college students reviewed drinking more alcohol, once hung-over, as a

positive way to outweigh the negative consequences and that these positive feelings would

influence their future drinking decisions. Several studies have explored this further and again

found that experience of the alcohol hangover is not associated with reduced consumption;

furthermore, it has been suggested that not only will it fail to deter drinkers; it may even

increase their consumption the next morning (Mallett, Lee, Neighbors, Clarimer & Turrisi,

2006; Earlywine, 1993a). The idea of drinking alcohol in order to relieve symptoms of

previous alcohol consumption is commonly called ‘hair of the dog’ drinking. As previously

mentioned the alcohol hangover is associated with congeners in alcohol. Ethanol and

methanol are metabolised by the alcohol dehydrogenase and so drinking ethanol prevents or

delays the metabolism of methanol causing a temporary relief of symptoms (Ylikahri,

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Huttunen, Eriksson & Nikkila, 1974; Bentdsen, Wayne Jones & Helander, 1998). Robertson

(2012) stated that past hangovers predicted future hangovers, suggesting hangovers do not

necessarily discourage or inhibit future drinking. As alcohol relieves its own symptoms the

following day, it is a case of negative reinforcement which causes it’s consumers to engage

in ‘hair of the dog’ drinking. Negative reinforcement is when behaviour is strengthened by

avoiding an aversive stimulus; in this case, drinking alcohol is enforced by the lack of

hangover symptoms experienced.

One mechanism that could underlie the relationship between the hangover and alcohol

consumption is the physiological effects of alcohol on FHP participants when the limb of

BAC is rising compared to when the limb is falling. Rising limb effects suggest that as your

BAC increases, so does your sensitivity to drinking consequences. In 1999, Leonard and

Blane wrote that sons of alcoholics show hypersensitivity to alcohol’s effects during the rising

limb of the BAC curve when the pleasurable and activating consequences of the drinking

dominate. When the BAC curve displays a falling limb, the negative and depressing effects

of alcohol dominate. Furthermore, McKinney and Coyle (2006) reported that during the rising

limb of the blood alcohol curve, alcohol has a stimulating effect by affecting

neurotransmitters that control the sleep/wake cycle. Sher (1991) reported that FHP

participants may exhibit stronger physiological reactions to a variety of stimuli when

compared to FHN participants. Furthermore, Fin et al. (1990) reported that children of

alcoholics display greater responses to stimuli regardless of the nature of the stimulus.

There is a wealth of evidence suggesting that the rising limb of the BAC curve correlates

with a hypersensitive response in FHP participants for a range of stimuli including the

physiological consequences of drinking, however, it is suggested that the exact mechanism

underlying the rising limb effects are replicated and clarified (Sayette, 1999).

Alcohol use disorders are genetic, suggesting they run in families (Dawson, Harford & Grant,

1992) Hussong, Bauer and Chassin (2008) reported that children of alcoholics progressed

more quickly from adolescent drinking to the onset of an AUD when compared to children of

non-alcoholics which was a direct result of their parent’s externalising symptoms and

drinking patterns. Therefore, it is imperative to examine familial links in terms of severity of

hangovers and the various alleviating behaviours for this negative consequence. Earlywine

(1993b) investigated hangover as a potential mediator of the relation between personality

and drinking problems. The subjects were 178 undergraduates who were asked to complete

a questionnaire assessing drinking patterns, familial risk for AUD, personality risk for AUD

and hangover. Earlywine found that drinking problems only correlate with those who

experience severe hangovers, not moderate hangovers, thus indicating that those who suffer

from an AUD suffer from greater hangovers than those who do not suffer from an AUD. It is

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then suggested that as those who are at risk for AUD experience the worst hangovers might

then consume alcohol again in order to relieve the symptoms.

One factor to be considered in terms of the hangover being a risk factor for AUD is the

withdrawal effects of alcohol. Newlin & Pretorious (1990) assessed alcohol-induced

hangovers among college men; using both sons of alcoholics and sons of non-alcoholics.

The sons of alcoholics reported significantly worse hangovers to the extent where the

hangover represented an acute withdrawal from alcohol. From this, Newlin and Pretorious

have suggested that patients with frequent hangovers are at the greatest risk for further

alcohol dependence. McCaul, Turkkan, Svikis and Bigelow (1991) extended previous

research investigating the breadth of addiction risk conferred by a positive family history.

Previous findings reported that FHP participants showed greater sensitivity across a variety

of subjective measures than FHN participants. Data in the present study examined hangover

effects from a controlled dose of alcohol, up to 3 to 18 hours after the administration of the

drink. Withdrawal was assessed, amongst other factors, through a combination of mood

analogue scales, drug effect self-reports and the Subjective High Assessment Scale. They

reported that positive familial-risk participants reported extended intoxication and greater

withdrawal effects following alcohol. Furthermore, these withdrawal effects persisted longer

than those reported by those at negative familial-risk. Baker, Piper, McCarthy, Majeskie and

Fiore (2004) presented the reformulated negative reinforcement model of addiction. This

explores how early in a participant’s drug use, instances of drug-contingent relief of

withdrawal are demonstrated to be influences towards the development of drug dependence.

Theoretically, this explains how using the drug as a response to its own effects can easily

lead that consumer to dependence i.e. drinking alcohol to alleviate symptoms of a hangover.

The withdrawal relief model proposed by Span and Earlywine (1999) states that high risk

participants of alcoholism will report a more severe hangover; showing withdrawal effects

similar to those presented by Newlin and Pretorious (1990), and suggesting that individuals

will then drink alcohol again to alleviate the severe symptoms; potentially leading to AUD

(Span & Earlywine, 1999). Both expectancies of hangover and personality types were

assessed and found no differences between sons of alcoholics and sons of non-alcoholics;

reducing the effect of external influences. Verster (2009) argued that finding a cure for the

alcohol hangover can result in more excessive drinking episodes, supporting the notion of

‘hair of the dog’ drinking. Earlywine (1993a) supported this by stating that those who

experience greater hangover may drink more alcohol to alleviate adverse effects.

Furthermore, in 2010, a survey revealed that 11% of participants had engaged in hair of the

dog drinking; highlighting the percentage of that population at risk of AUD from this

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mechanism (Verster, Keurten, Olivier & Laar; 2010). However, it is important to identify the

difference between the alcohol hangover and withdrawal to prevent conflicting ideas. The

hangover occurs after a single session of alcohol consumption; no matter how many times

these sessions have occurred. Withdrawal, on the other hand, depends on a prolonged

duration of alcohol abuse (Penning, Nuland, Fliervoet, Olivier & Verster; 2010).

Relevance to Keele University

Keele University, Staffordshire, has a regularly-updated alcohol policy currently in place

(Human Resources Department, 2011) illustrating that it has been a recognised problem at

the university to the extent where a policy needed to be implemented. Keele University

would benefit greatly from a better understanding of students’ drinking patterns and

consequent behaviours; creating a clearer focus for action. Once various analyses have

been conducted, Keele University can then use the results to compare levels of drinking

against those at other institutions, and also compare Keele student’s drinking levels to safe

regulations and guidelines, provided by the government, to assess the extent to which

students are engaging in binge drinking.

The Student Support and Development Services at Keele University provide students with

any support they require during their time at Keele University from advice and support to

dealing with a physical or mental health disability. The head of Student Support, Ian Munton,

has been extremely involved in the process of the survey as he has recognised the need for

increased research and findings on student drinking. Ian Munton and his team are

responsible for the distribution of the survey as they have access to email addresses of the

entire student body. Furthermore, it was Student Support who were generous enough to

provide the two £25 Amazon vouchers offered as mild incentives for students to complete

the survey. Alongside the release of the survey in March, Ian Munton and his team ran a

month long alcohol campaign at the Student’s Union where topics such as hangover, drink

driving and Police Community Support were focused upon to raise awareness amongst

students. This alcohol campaign not only raised awareness of critical issues which relate to

the survey but also raised the validity of the survey; by emphasising that Student Support

encourage participation and takes the topic seriously.

Further analysis would reveal gender differences; allowing Student Support to run a more

gender directed approach to tackling binge drinking. Finally, with the survey being run twice

in the year, it will be possible to identify changes in drinking levels throughout the year. No

university in the UK has a survey like this, therefore once the data has been collected it is

possible that other institutions would be interested in Keele’s example and wish to be

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involved in the project; representatives at Edge Hill University have already shown interest in

using the survey to assess drinking patterns and behaviours at their university.

Present Study

Research surrounding links between familial risk, the alcohol hangover and the development

of Alcohol Use Disorder (AUD) is still in its infancy and has been explored little and

sporadically. It appears the consensus to be that those at highest familial risk for AUD will

display significantly greater hangover than those who are not (Piasecki, Sher, Slutske &

Jackson, 2005; Span & Earlywine, 1999). Furthermore, there is growing evidence that those

who suffer from severe hangovers will then engage in hair of the dog drinking; and this is

what can lead consumers to the onset of AUD; alleviating symptoms of alcohol use with

alcohol (Baker, Piper, McCarthy, Majeskie and Fiore, 2004; Newlin & Pretorious, 1990). It is

imperative that more research is conducted assessing the links between the alcohol

hangover and the development of AUD as there is a gap in the literature for more in-depth

analysis, especially using at risk samples such as students (Stark, 2007).

As emphasised in the European Commission Strategy released in 2006, there is an

increased need to educate, raise awareness and support and maintain the evidence base on

alcohol research findings. This supports the need for a piece of research such as this one

due to the sporadic nature of the existing literature on the specific topic.

This survey will provide a deeper understanding of the topic, and not only this, but it will be

the first study of its kind to be implemented in the UK. Furthermore, the NCHIP has not been

applied outside of the USA before and thus this study presents an opportunity to gather

detailed and thorough information about UK students drinking behaviours, which has never

been done before to this extent. Combined with on-going research in the department, it will

provide a body of UK evidence, as currently there is none. This suggests that studies

conducted elsewhere, for example in the United States, are potentially not generalisable to

UK samples; for example, drinking culture in the UK is much more embedded in an

undergraduates university experience compared to the USA, also, the legal drinking age in

the UK is much lower than in the USA. This research aims to provide a solution to this flaw.

Keele University has found the need to implement an alcohol and drugs policy suggesting

that is it a recognised problem with students. Student Support also plan to run a

corresponding alcohol campaign to increase the awareness of such topics and encourage

participation in the survey, showing the lengths the University are going to address the issue;

illustrating the demand for an intervention.

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The aims of the research are: 1) use the NCHIP (NCHIP, 2011), the HSS (Slutske, Piasecki

& Hunt-Carter; 2003) and the F-SMAST and M-SMAST (Crew & Sher, 1992) to collect

relevant statistics for the benefit of Keele University to address student drinking patterns and

behaviours and 2) use the NCHIP (NCHIP, 2011), the HSS (Slutske, Piasecki & Hunt-Carter;

2003) and the F-SMAST and M-SMAST (Crew & Sher, 1992) to assess links between the

familial risk for the alcohol hangover and the development of AUD. We hypothesise that the

participants at highest familial risk for AUD as identified by the NCHIP, HSS and SMAST will

report the greatest hangover symptoms, and thus present the highest risk for development of

AUD.

The survey consisting of the NCHIP, the HSS and the SMAST will be distributed online to

every student member at Keele, once in March and once in July. From this, participants can

be categorised into those who have a positive family history of AUD (FHP) and those who do

not (FHN), from which hangover severity can be compared. Furthermore, information about

student’s drinking patterns and behaviours can inform Keele University and Student Support

on levels of hazardous and harmful drinking amongst its students. The two waves of the

survey means that data can be collected from two opposing times of the academic year;

mid-semester and during the examination period, from which levels of drinking amongst

students can be compared and contrasted in order to create much more focused

interventions.

The National College Health Improvement Program (NCHIP)

The National College Health Improvement Program was developed in 2011 at Dartmouth

University and addressed a new way of looking at the problem of harmful and hazardous

drinking amongst students. The survey uses comprehensive evaluation and measurement

techniques to identify and implement the most effective ways to tackle the problem (NCHIP,

2011). The program relies on the Breakthrough Series Model which encourages face-to-face

learning sessions to share outcomes of the survey; this helps to address high-risk drinking

during ‘action periods’ where various campus teams engage in ‘plan-do-study-act’ cycles (a

novel way to address a problem is tested on a small scale, from which rapid changes can be

made based on the results). All members share a commitment to gathering and sharing

evidence retrieved from their respective institutes, measuring their outcomes and feeding

back to a collaborative system. The NCHIP thus allows rapid information sharing so that

effective and practical strategies can be gained which suit each institutes circumstances -

this has shown significant success.

Two years since the implementation, Dartmouth has collaborated with 32 colleges in

America and has demonstrated great success; with the number of students treated for

15


extreme alcohol intoxication being reduced by more than half (Platt, 2013). The collaborative

members of the program identify various cultural and geographic factors which shape

alcohol consumption; with those on urban campuses drinking mainly in off-campus bars, but

those in residential campuses drinking mainly in residences. Annual events, celebrations,

sports and fraternities are also identified as environments where alcohol is strongly

associated (NCHIP, 2011).

With appreciation to contacts at Acadia University, Canada, there is an opportunity to extend

the NCHIP for the first time into a British University, Keele. At Acadia University, the NCHIP

revealed that peer to peer influence is one of the most effective ways to change behaviour;

and thus have developed appropriate messaging in regards to responsible behaviour which

has been proven to be very influential and effective (Platt, 2013). Working in collaboration

with the head of Student Support and Developmental Services, Ian Munton, the survey can

be distributed through the Keele student body and analysed to discover ways that binge

drinking can be addressed in a university setting.

Methods

Participants

The participants for this study will be recruited using opportunistic sampling; with every

student member of Keele University being invited to take part. The majority of participants

will be undergraduate degree members of the university, aging typically 18-21, however,

mature students will also be invited to participate. Approximately 10,000 students will be e-

mailed by Student Support requesting their participation in the online survey, with a roughly

even split between males and females. Recruitment will be aided by an alcohol campaign

run by Ian Munton, head of Student Support and Development Services at Keele University,

as this will increase face validity in the eyes of the students and awareness of the survey.

Other recruitment methods will include posters, social media campaigns and other various

forms of advertising. The survey has no exclusion criteria; even students who don’t drink will

be encouraged to participate as a control measure. The inclusion criterion is broad; any

students with a Keele University e-mail address will be invited to take part in the study. As a

mild incentive, participants will be offered to enter into a prize draw to win an Amazon

voucher; Student Support is providing two £25 Amazon vouchers for two participants (if an

e-mail address is provided). As the survey is being used as a data collection tool for Keele

University specifically it will be a very representative sample, however, student survey

fatigue may impact negatively on the response rate (Porter, Whitcomb & Weitzer, 2004).

Student Support reported that at Keele, the average response rate for a survey of this nature

is 2.5-5%. In terms of a research project, 16-21 year olds have been highlighted as a risk

16


population for binge drinking (IAS, 2013) and thus will be generalisable to the research

hypothesis. In order to assess risk factors for AUD in the general UK student population

however, it would require other academic institutions such as Universities and Colleges to

partake in the survey in order to obtain more generalisable data.

Materials

All students will be required to have access to their university e-mail account in order to

receive their invitation and thus the link to participate. Three questionnaires make up the

overall survey.

The NCHIP was developed by the Dartmouth Institute for Health Policy and Clinical Practice

in 2011. Colleges and universities from around the USA use the NCHIP to address the issue

of high risk drinking through the use of comprehensive evaluation and measurement

techniques. The survey consists of 25 items. Questions 1-5 focus on gathering information

on the participants normal drinking behaviour, questions asked include ‘how many times did

you drink in the past month?’ and ‘on the days that you drank, how many drinks did you

normally have?’. These items can be used to calculate a normal drinking rate of participants

and assess number of high risk drinking episodes in the previous two weeks. Questions 6-9

then proceed to ask the participant about their highest volume drinking episode, questions

asked include ‘what is the largest number of drinks you can recall having on one occasion in

the past month?’ and ‘on this occasion, over how many hours did you drink?’. These items

allow the researcher to calculate a high volume drinking rate and assess perceived hangover

severity following such a session. Questions 10-13 ask participants to state which of the

positive and negative consequences of drinking they have experienced in the last month and

also the social impacts experiences as a direct impact of someone else’s drinking. At this

point, participants select options from a list and can select as many as they feel is required.

Question 14-15 assess participants helping behaviours towards others who have drank such

as getting them some water or taking away their car keys. The options here are again

presented in a list format. Question 16 onwards collects various participant demographics

such as height, weight, age and sex in order to gain an understanding of who is responding

to the survey and to calculate participant related statistics such as the estimated blood

alcohol level. The NCHIP has been tried and tested at Dartmouth College and directly from

this a combination of strategies has been implemented at a number of levels; individual,

interpersonal, institutional and community. Dartmouth College has now experienced a

significant reduction in the number of students medically transported with BACs greater than

0.25g/dL (a near total loss of motor function) between 2011 and 2013 (NCHIP, 2011).

17


Furthermore, Cornell University reported an 8% relative reduction in high risk drinking from a

baseline wave at the start of the academic year to the end of the academic year. These

statistics show the reliability and validity of the survey in University settings for the promotion

of alternative and more appropriate interventions than those previously in place at academic

institutions.

The Short Michigan Alcohol Screening Test (SMAST) was developed by Crew & Sher in

1992. This test is employed to gather and assess information about an individual’s father’s

(F-SMAST) and mother’s (M-SMAST) alcohol abuse. There are 26 items in total, 13

questions about the participant’s father’s alcohol consumption and 13 about the participant’s

mother’s. Questions include “Do you feel your mother/father has been a normal drinker?”,

“Was your mother/father able to stop drinking when they wanted to?” and “Has your

mother/father ever neglected his obligations, family, or work for two or more days in a row

because he was drinking?” For the majority of the questions, if the participant answers yes

this indicates evidence of some problematic behaviour. For these questions, ‘yes’ will scored

as 1 and ‘no’ scored as 0, this is reversed for the questions where a ‘yes’ response does not

indicate any problematic behaviour (‘yes’=0 and ‘no’=1). A total from the F-SMAST and M-

SMAST is calculated separately. If participants score 5 or above in either of the tests then

they are categorised as family history positive (FHP), those who score 0 or 1 in both tests

are categorised as family history negative (FHN) (Piasecki et al. 2005). Crews and Sher

(1992) report that the overall reliability and validity of the F-SMAST and M-SMAST is quite

good; in particular, the M-SMAST demonstrated high reliability as well as validity showing

good internal consistency, stability and convergence with parallel instruments. More recently,

Crews & Sher (2006) emphasized the validity and reliability again, stating that it is

appropriate for use by clinicians and researchers who are interested in assessing parental

history of AUD. Barry and Fleming (1993) reported a moderate Cronbach’s alpha of 0.85

suggesting good internal reliability of the questionnaire.

The Hangover Symptom Scale (HSS) was developed by Slutske, Piasecki and Hunt-Carter

in 2003. This scale is used to assess the psychological and subjective effects commonly

experienced the morning after drinking. This scale is a 13 item scale; each question

represents a different hangover symptom and participants are required to state how many

times in the past year they have experienced these symptoms. Items take the format of

“Within the last 12 months when you drank alcohol, how often did you feel more tired than

usual the next morning?” with each question stating a different symptom such as headache,

nausea and sensitivity to light. Each question has two parts; part 1 requires participants to

select their answer from a list of ‘never’, ‘occasionally’, ‘about half the time’, ‘most of the

time’ and ‘every time’. The second part of the question then requires participants to select

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more specifically how many times in the past 12 months this occurred, for example ‘2 times

or less’, ‘3-11 times’, ’12-51 times’ or ’52 times or more’. For each item, participants were

given a score of 1 if they had experienced that particular hangover symptom in the past 12

months. Scores were then totalled to give the total hangover score. For the present study,

only the first part of the question was used; participant could score 0-13. Once the scale was

developed, Slutske, Piasecki and Hunt-Carter (2003) reported that the HSS captures a valid

set of adjectives describing common hangover effects and that the scale is appropriate for

laboratory and survey investigations into hangover. In 2012, Robertson, Piasecki, Slutske,

Wood, Sher, Shiffman & Heath reaffirmed that the HSS is a valid tool for hangover research

by representing ‘real world’ hangovers. Participants who endorsed the HSS headache and

nausea items were especially likely to report the elevations of corresponding symptoms in

diary records. Penning et al. (2013) reported that the Cronbach’s alpha score for the HSS

was acceptable (0.8) with a relatively high predictive validity (R2=081.5%) suggesting that

the HSS has “sufficient efficacy in in measuring overall hangover severity” (Penning,

McKinney, Bus, Olivier, Slot & Verster, 2013; 808).

The study was conducted via an online questionnaire hosted by Google Docs – the same

platform that has been used to deliver the NCHIP survey in colleges and universities across

the US. The European Commission’s Directive on Data Protection prohibits the transfer of

personal data to non-European Union countries that do not meet the European Union (EU)

“adequacy” standard for privacy protection. The Safe Harbor Framework was designed to

bridge the gap between EU data protection law and the different way that data protection is

handled in the US (EU Safe Harbor, 2012), and provide a means for US organisations to

comply with EU data protection directive. Google participates in the Safe Harbor Framework

of the European Union and the United States (Certification and Data Privacy, 2011).

Other materials included posters for advertisement and the survey was made more salient to

students with access to various forms of social media such as Facebook or Twitter. Once the

survey was distributed, SPSS software was necessary to collate descriptive statistics and

perform a series of ANOVAs, regression, chi square, general linear model and calculations

of other variables such as estimated blood alcohol level.

Procedure

The first stage of the process was the initial meetings with Dr Stephens to address initial

links between familial risks for the alcohol hangover, these occurred in October 2013; at this

point the three questionnaires were decided upon. Once the survey was complied, meetings

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with the head of Student Support and Development to discuss distribution of the survey took

place in November 2013; here a corresponding alcohol campaign was developed and

incentives for the participants offered. The final stage before the survey was circulated was

the ethics submission; general principles of the BPS Ethical Guidelines will be adhered to

and the process included application to both the School of Psychology Ethics Committee

(SPEC) and the University Ethical Review Panel.

Once the ethics was approved, recruitment began, utilising posters, social media and e-

mails so that the survey was distributed to the entire student body at Keele University. In

order to maintain confidentiality, participants were asked to volunteer an e-mail address only

if they wish to be entered into the prize draw for the £25 Amazon vouchers.

All students received an e-mail requesting their participation in the survey. This e-mail

contained an invite letter; stating the scales being utilised, the aims of the research and the

benefits of taking part. If they chose to take part in the study, they had the option to click on

a web link which directed them to Google Drive, upon which is the survey. The first page of

the survey is composed of a consent form where participants are provided with the aims of

the research once more, why they have been chosen, what will happen if they take part and

various benefits and information about the risks (participants are introduced to the fact that

there are no risks but the survey may include some sensitive topics). Information about the

participant’s data is then displayed and the participant is informed of who they should

contact should there be any problems. The final part of the consent form is the consent

agreement; a tick box list for participants to show that they confirm that they have read and

understood the information, that they are 18 years or above, they understand their

participation is voluntary and can withdraw, they agree to take part in the study, they

understand that the only identifying information that will be asked of them is their e-mail

address (should they wish to be entered into the prize draw) and that they allow the use of

their data to be used for future research projects. Once consent has been provided, the next

pages of the survey will contain the three questionnaires; the NCHIP, the HSS and the

SMASTs. The three questionnaires took approximately 20 minutes to complete. Once the

participants have finished the survey a final screen will show appropriate debrief information

including the details of the principle investigator (Dr Richard Stephens) should the participant

have any further questions or wish to withdraw. Also relevant services provided by the

university will be listed should any concerning issues be raised through completion of the

questionnaire for an individual. Furthermore, the final page will provide participants with a

space to insert their e-mail address if they wish to be entered into the prize draw for the

Amazon vouchers. Once the survey is complete, the participants can exit out of Google

Docs.

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The next stage of the procedure was the statistical analysis of the data collected. To analyse

the survey various descriptive statistics were implemented including the drinking frequency

of students, percentage of light to heavy drinkers and drinking patterns compared to UK

drinking guidelines. These were then be presented to Student Support in a clear and easy to

understand format utilising diagrams and charts; this then allows Ian Munton and team to

identify risk behaviours and take action to prevent them occurring. ANOVAs were utilised to

compare differences on a range of variables between FHP and FHN participants (variables

include number of high risk drinking episodes, normal drinking rate, peak drinking rate and

hangover severity). A regression was ran to observe if there is a link between hangover

symptom score and amount of alcohol consumed to assess units consumed affects

hangover symptoms or if there is another variable to consider such as familial risk. The

eBAC was calculated to compare student’s eBACs against safe limits, and also to assess

the relationship between eBAC and hangover symptoms. Finally, a chi squared test was

utilised to assess the frequency of rising limb effects occurring during and immediately

following alcohol consumption such as noticing a release in tension and having lots of

energy.

The survey ran twice in the academic year, once in March and once in July. The second

wave of the survey in July only consisted of the NCHIP. This added to the benefit for Keele

University by assessing whether there are times of the year when students are most likely to

binge drink, and thus be at highest risk for AUD. This then enabled Student Support to plan

alcohol awareness weeks for those high risk stretches of the semester.

Design

The design of the study is an online survey design where patterns of consumption are

addressed, and risk factors of AUD are explored: particularly the alcohol hangover. It is

understood that employing an online questionnaire reduces the level of control when

compared to laboratory studies, including issues around verification. The benefit however of

using an online survey is that a large sample can be accumulated efficiently, and

subsequent controlled research can occur if need be.

Dissemination

Dissemination will include a written dissertation, a short presentation to various students and

staff at Keele University and also a booklet of relevant statistics, graphs and charts for the

use of Student Support and Development.

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Results

As the project is two-fold, I present the findings in two sections; results from the survey for

the benefit of Keele University and the results for the research question regarding risk

factors for AUD.

An alpha level of 0.05 was used for all statistical tests.

The Survey

1. Demographics

Table 1 shows the various participant demographics including year of study, age, gender,

whether they live on campus, if they are a halls of resident support assistant and average

module % last term for wave 1 and wave 2.

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Table 1. Demographics and Descriptives of ParticipantsMARCH Wave 1 JULY Wave 2

Frequency

% Frequency %

Size of sample surveyed

10,000 10,000

Survey response totals

273 310

Survey response rate 2.73% 3.1%

Year of Study1st 103 37.7 92 29.72nd 50 18.3 68 21.93rd 61 22.4 79 25.54th/5th 14 5.1 18 5.8Post-graduate 45 16.5 53 17.1

Age18 26 9.5 15 4.819 46 16.9 57 18.420 62 22.7 65 2121 53 19.4 63 20.322 22 8.1 31 1023+ 64 23.4 79 25.6

SexMale 101 37 123 39.7Female 172 63 187 60.3

Live on CampusYes 108 39.6 122 39.4No 165 60.4 188 60.6

Halls of Resident Support AssistantYes 15 5.5 7 2.3No 258 94.5 303 97.7

Average Module % last termLess than 40 6 2.2 1 0.340-49 63 23.1 8 2.650-59 118 43.2 75 24.260-69 76 27.8 151 48.770+ 10 3.7 75 24.2

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A series of chi square tests revealed there were no significant differences between age, sex,

living on/off campus and year of study between participants in wave 1 and 2 (p>0.05). There

was, however, a significant difference between whether participants were a halls of resident

assistant and the wave of the survey X2(1)=4.26, p=0.039 with there being more halls of

resident assistants in wave 1. Similarly, there was a significant difference between average

module grade and wave of the survey also X2(4)=10.44, p=0.034 in that module grade was

much higher in wave 2.

2. Drinking patterns in students

Table 2 show various drinking behaviours reported by students including high risk drinking

episodes, drinking rates and energy drink consumption for wave 1 and wave 2.

A high risk drinking episode was defined as 5 or more drinks for a male and 4 or more drinks

for a female in one occasion. A series of t-tests revealed no significant differences between

mean number of drinks perceived to be moderate, mean usual drinking rate, mean highest

volume drinking rate, mean hangover or mean eBAC for participants in wave 1 to

participants in wave 2 (p>0.05). Chi square also revealed no significant differences between

number of high risk drinking episodes or whether participants mixed alcohol with energy

drinks between the wave of the survey (p>0.05).

3. Consequences of Drinking

Table 3 shows the frequency of participants who reported experiencing various drinking

related harms for wave 1 and wave 2.

Harm caused by alcohol consumption was the same for both waves with participants

reporting feeling nauseated/vomiting and forgot where they were or what they did. Wave 1

participants also selected having a severe hangover whereas wave 2 participants selected

doing something they later regretted.

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Table 2. Drinking Patterns of Participants

MARCH – Wave 1 JULY – Wave 2Mean SD Mean SD

Number of drinks perceived to be moderate when partying

4.48 1.789 4.47 1.905

Drinking Rates & Hangover SeverityUsual Drinking Rate (drinks/hour)

1.24 0.578 1.15 0.731

Highest Volume Drinking Rate (drinks/hour)

1.673 1.028 1.52 0.981

Hangover severity rating

2.02 1.686 1.50 1.649

eBAC 0.04 0.771 0.06 0.559

Frequency % Frequency %High Risk Drinking Episodes*Dry this month 23 8.4 36 11.6Didn’t HRD 78 28.6 86 27.71-2 times 81 29.7 93 29.93-5 times 49 17.9 40 12.96+ times 17 6.2 7 2.3

Mixed alcohol with energy drink

80 35.6 68 51

Didn’t mix alcohol with energy drink

145 64.4 158 21.9

* defined as 5 or more drinks for a male and 4 or more drinks for a female in one occasion

25


Table 3. Rates (Frequency and %) of Drinking Related Harm

MARCH – Wave 1 JULY – Wave 2Frequency % Frequency %

Reference Sample: Drinkers

225 100 262 100

Been nauseated or vomited

101 44.9 123 46.2

Got into an argument or fight

50 22.2 55 21

Got in trouble with security/police

14 6.2 19 7.3

Had unprotected sex 29 12.9 36 13.7Physically injured yourself

68 30.2 67 25.6

Physically injured another person

12 5.3 11 4.2

Required medical attention

10 4.4 11 4.2

Did something you later regretted

75 33.3 103 39.3

Had a mild hangover 133 59.1 177 67.6Had a severe hangover 84 37.3 86 32.8Forgot where you were or what you did

77 34.2 94 35.9

Fell behind in University work

39 17.3 15 5.7

Missed a class 66 29.3 65 24.8Performed poorly on a test or important project

10 4.4 14 5.3

Seriously considered suicide

10 4.4 9 3.4

Chi square analyses revealed a significant difference between frequency of experiencing of

a mild hangover and the wave of the survey X2(1)=3.913, p=0.048 with higher frequency of

participants reporting mild hangover in wave 2. Furthermore, there was a significant

difference in frequency between falling behind in University work and the wave of the survey

X2(1)=15.532, p=0.00 with many more participants reporting falling behind in University work

in wave 1. There were no other significant interactions between harm caused by alcohol

consumption and the wave of the survey (p>0.05).

26


Table 4 shows the social impacts experienced as a direct result of someone else’s drinking

for wave 1 and wave 2.

The same three most prevalent social impacts of others drinking for both waves were;

having a drink spilt on you, having your sleep interrupted and having to baby-sit another

student.

Chi square revealed a significant difference between whether participants had a drink spilled

on them and the wave of the survey X2(1)=5.117, p=0.024 with more participants reporting

this in wave 1. Furthermore, there was a significant difference between whether participants

had had their property damaged and the wave of the study X2(1)=4.225, p=0.04 with more

participants reporting this in wave 1. Similarly, there was a significant difference between

whether participants had experienced none of the above social impacts and the wave of the

study X2(1)=6.158, p=0.013 with more of the participants reporting having experienced none

of the social impacts in wave 2. There were no significant differences between the remaining

social impacts and the wave of the study (p>0.05).

Table 5 shows behaviours participants have had to provide to help others who had

consumed alcohol for both wave 1 and wave 2 data.

27


Table 4. Social Impacts of drinking: Experiences as a result of someone else’s drinking

Frequency % Frequency %Full sample 273 100 310 100

Was prevented from enjoying an event

78 28.6 82 26.5

Was made to feel unsafe in your surroundings

57 20.9 57 18.4

Had a drink spilled on you

158 57.9 151 48.7

Experienced an unwanted sexual advance

49 17.9 47 15.2

Been insulted or humiliated

32 11.7 36 11.6

Got into an argument or fight

28 10.3 32 10.3

Were physically injured by someone who had been drinking

14 5.1 14 4.5

Got into trouble with security/police

3 0.7 7 2.3

Had to “baby-sit” or take care of another student who drank too much

103 37.7 109 35.2

Had unprotected sex 13 4.8 13 4.2Had your property damaged

15 5.5 7 2.3

Had your sleep interrupted

127 46.5 127 41.0

Had your studying interrupted

62 22.7 57 18.4

None of these applied to me

61 22.3 98 31.6

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Table 5. Drinking Behaviours: Respondents who provided help to other drinkers


Full sample 273 100 310 100

Was around people who were drinking

243 89 269 86.8

Wasn’t around people who were drinking

30 11 41 13.2

Sample: Was around people who were drinking

243 100 269 100

Was around people who were drinking, but didn’t need to assist anyone

129 53 157 58.4

Suggested they drink more slowly

30 12.3 33 12.3

Suggested that they stopped drinking

64 26.3 69 25.7

Took away their car keys

9 3.7 12 4.5

Got additional assistance (e.g. security/police)

18 7.4 12 4.5

Stayed with them until they got home safely

83 34.2 71 26.4

Kept them awake until you knew they were OK

24 9.9 28 10.4

Gave them water / other non-alcoholic drink

92 37.9 90 33.5

Gave them something to eat

48 19.8 52 19.3

Put them to bed on their side

47 19.3 51 19

Kept them warm 32 13.2 25 9.3Monitored their breathing to make sure it wasn’t abnormally shallow or slow

16 6.6 20 7.4

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In addition, the same three helping behaviours were deemed to be most prevalent to

students in both wave 1 and wave 2 of the survey were giving someone water or another

non-alcoholic drink, staying with them until they got home safely and suggested that they

stopped drinking.

Chi square analyses revealed a significant difference between participants staying with

someone until they got home safely and the wave of the study X2(1)=0.4313, p=0.038 with

more participants engaging in this helping behaviour in wave 1. There were no other

significant interactions between the remaining helping behaviours and the wave of the study

(p>0.05).

Table 6 examines the various drinking related benefits reported by wave 1 and wave 2

participants.

The three most common benefits of drinking to students in both waves 1 and 2 were

enjoying the taste of the drinks, enjoying the feeling you got from drinking and talking to

someone they probably wouldn’t have otherwise.

Chi squared analyses revealed no significant differences between the benefits of drinking

and the wave of the survey (p>0.05).

4. eBAC for students

A ‘safe’ limit for eBAC was calculated for females based on a weight of 67.5kg, a height of

162.5cm and 4 drinks over a period of 2.5 hours. The value of a safe limit for eBAC for

females is 0.079 or below. Similarly, a ‘safe’ limit for eBAC was calculated for males based

on a weight of 79.8kg, a height of 175cm and 5 drinks over a period of 2.5 hours. The value

of a safe limit for eBAC for males is 0.068.

Table 7 shows the frequency and percentage of participants who had an eBAC of above the

safe calculated limits.

30


Table 6. Rates (frequency and %) of Drinking Related Benefits


Sample: Drinkers 225 100 262 100

Enjoyed the taste of what you drank

194 86.2 240 91.6

Enjoyed the feeling you got from drinking

170 75.6 201 76.7

Talked to someone you probably wouldn’t have spoken to otherwise

141 62.7 161 61.5

Was able to tell a funny story or joke that made others laugh

110 48.9 129 49.2

Was able to express thoughts and feelings you normally keep to yourself

98 43.6 137 52.3

Noticed a release of tension in your muscles and nerves

101 44.9 126 48.1

Felt like you had lots of energy

89 39.6 106 40.5

Forgot your worries 113 50.2 139 53.1Had a good time that wouldn’t have been as much fun sober

126 56 135 51.5

Felt more attractive 62 27.6 90 34.4Facilitated a romantic encounter

63 28 78 29.8

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Table 7. Participants Above Safe Limits for eBAC

MARCH – Wave 1 JULY – Wave 2

Frequency % Frequency %

Above safe limit for eBAC

Males 34 12.45 43 13.87

Females 71 26.01 53 17.10

Chi square analyses revealed there was a significant difference between the number of

females above the safe limit for eBAC and the wave of the study X2(1)=6.883, p=0.009 which

was significantly higher in wave 1 than it was in wave 2. This was not observed for the male

participants (p>0.05).

Risk Factors for AUD

1. FHN vs. FHN Demographics

Participants were categorised as family history negative (FHN) participants if they had an F-

SMAST and M-SMAST score of 1 or below (n=162). Participants were categorised as family

history positive (FHP) participants if they had an F-SMAST or M-SMAST score of 5 or above

(n=33).

Table 8 shows the descriptives for FHN and FHP participants.

A t-test revealed no significant difference between the ages or the age of first intoxication of

FHP and FHN participants. A chi square analysis revealed a significant difference between

failing last year of FHP and FHN participants X2(1)=6.773, p=0.009 with more FHP

participants failing the year than FHN participants. No other variables significantly differed

from the participant group (p>0.05).

2. FHN vs. FHP drinking patterns

Table 9 shows drinking patterns and behaviours of FHN and FHN participants.

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Table 8. Descriptives for FHN and FHP Participants

FHN FHPMean SD Mean SD

Age 21.41 3.343 23.18 3.935Age first intoxication

13.97 5.354 14.91 1.57

Frequency % Frequency %Sample 162 100 34 100

Male 58 35.8 11 34.3Female 104 64.2 23 65.7

Live on Campus 71 43.8 10 28.6Live off Campus 91 56.2 24 71.4

Undergraduates 136 84 25 71.4Postgraduates 26 16 9 28.6

Average Module Mark Last Year3:3 4 2.5 1 2.82:2 42 25.9 4 11.32:1 65 40.1 14 42.81:1 49 30.2 12 34.7Fail 2 1.2 3 8.5

An ANOVA revealed a significant difference for hangover severity between FHP and FHN

participants F(1,160)=6.446, p=0.012 with FHP participants displaying significantly higher

hangover severity (mean=2.38) compared to FHN participants (mean=1.48). No other

variables’ means were significantly different (p>0.05).

3. Hangover & Drinks Consumed - Regression

A regression is used to predict an outcome variable from a single predictor variable. Here,

we use a regression model to predict hangover severity from the number of drinks

consumed for both FHP and FHN participants.

33


Table 9. Drinking Patterns and Behaviours of FHN and FHP participants

FHN FHPMean SD Mean SD F P

valueNo. times drank last 4 weeks

6.11 4.913 8.58 2.939 1.629 0.204

No. times high risk drinking last 2 weeks

1.38 1.908 2.03 7.934 0.739 0.391

No. drinks normal night

4.86 3.052 4.54 3.036 2.102 0.226

No. of hours drinking normal night

4 1.903 3.79 1.414 0.092 0.762

No. drinks heavy night

7.78 4.532 8.96 4.648 1.366 0.244

No. of hours drinking heavy night

5.07 2.519 5.67 2.220 1.996 0.160

Hangover severity

1.48 1.572 2.38 1.740 6.446 0.012

Drinks perceived moderate

4.44 1.788 4.67 1.926 0.420 0.518

eBAC 0.001 0.991 0.062 0.069 0.125 0.724

HSS Score 5.531 3.745 6.941 4.249 3.799 0.053

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Figure 1 shows a regression between the hangover severity and number of drinks consumed

for FHP participants. The regression proved to be insignificant

b=0.051,f(1,22)=0.166,p=0.688.

Figure 2 shows a regression between the hangover severity and the number of drinks

consumed for FHN participants. The regression proved to be significant b=0.126, f(1,

37)=8.692, p=0.004.

4. eBAC and Hangover Severity – ANOVA

An ANOVA was run

to assess hangover

severity of those with an

35


eBAC of below 0.1 (group 1) and those above 0.1 (group 2) as referred to by Kruisselbrink

(2014).

Table 10. Mean & SD of Hangover Severity according to eBAC

Mean SD

Group 1 1.85 1.700

Group 2 2.25 1.616

The ANOVA revealed no significant difference between hangover severity between group 1

and group 2 F(1,187)=2.610, p=0.108.

5. eBAC and Hangover Severity - General Linear Model

A univariate general linear model was utilised to assess the relationship between the eBAC

and hangover severity for FHN and FHP participants. The dependent variable for the model

was hangover severity with the fixed factor being the group participants belonged to (FHP or

FHN). eBAC was included into the model as a covariate.

The general linear model revealed no significant interaction between hangover severity and

group with eBAC as a covariate F(1, 158)=2.137, p=0.146.

6. Rising Limb Effects – Chi Square

A chi square was utilised to assess frequencies of participants reporting various sensitivities

to drinking consequences for FHP participants and FHN participants. The four

consequences were: enjoying the taste of what you drank, enjoying the feeling you got from

drinking, noticing a release of tension in your muscles or nerves and felt like you had lots of

energy.

Table 11. Frequencies for Positive Consequences of Drinking

36


FHN FHPNo Yes No Yes X2 P value

Enjoyed the taste of what you drank

40 122 20 13 16.601 0.000

Enjoyed the feeling you got from drinking

61 101 14 19 0.264 0.608

Noticed a release of tension in muscles and nerves

99 63 24 9 1.588 0.208

Felt like you had a lot of energy

104 58 26 7 2.626 0.105

Chi square analyses revealed no significant differences in frequency of responses between

FHP and FHN participants for three effects; enjoyed the feeling you got from drinking,

noticing a release of tension in your muscles or nerves and felt like you had lots of energy

(p>0.05). However, there was a significant difference between frequency of FHN participants

and FHP participants who reported enjoying the taste of what they drank X2(1, 1)=16.601,

p=0.000. 122 out of 162 FHN participants reported enjoying the taste; however, only 13 of

33 FHP participants reported enjoying the taste.

Discussion

The Survey

The survey was used to obtain data on Keele students drinking patterns and behaviours

which it was extremely successful in doing. A full report was produced in order to provide Ian

Munton and his team at Student Support with graphical and tabular information from which

interventions to reduce harmful and hazardous drinking on campus could be planned and

implemented.

Participants were fairly similar over the two waves of the survey, with slight differences in the

average module grade. Average module grade was much higher in the second wave of the

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study; however this could be due to the fact that for the first wave of the study participants

had not sat January exams or received grades back and so used a predicted grade,

however by July they would have received final grades and thus could explain the difference.

In terms of the drinking patterns and behaviours of participants there were no significant

differences between participants in wave 1 and wave 2 suggesting that people are still

drinking similar amounts throughout the progress of the academic year. Having contacted

the bar manager at the Student’s Union I can report the numbers attending nights out at the

Union decreases by a substantial amount during exam time and slowly grows towards the

end of exam time as more students finish their exams. The same is for January when the

first semester exams are set.

Webb, Ashton, Kelly & Kamali (1996) reported that 61% of males and 48% of women from

their second year university students exceeded safe limits of alcohol consumption. One

piece of information that can be extracted from the report in order to compare Keele to these

levels of consumption is the frequency of high risk drinking amongst students in the past

fortnight. In the first wave 53.8% of participants engaged in high risk drinking at least once in

the past fortnight, and 45.1% of wave two participants engaged in high risk drinking at least

once in the past fortnight. Furthermore, 6.2% of first wave participants and 2.3% of second

wave participants reported high risk drinking episodes 6+ times in the past two weeks which

shows extreme levels of harmful and hazardous drinking. This data also mirrors high levels

of drinking seen in other institutions suggesting that student drinking is a common issue

(Webb, Aston, Kelly & Kamali, 1996; Stark, 2007; The IAS, 2013). Student Support can now

use this information to compare to other levels of drinking in other institutions, and to also

gage the level of action required with the aim of reducing these numbers. Dartmouth

University and other institutions which partook in the survey have been able to reduce the

number of students treated for extreme alcohol intoxication being reduced by more than half

(Platt, 2013); emphasising the power of the survey. This data is supported by the eBAC

calculations which show that a large percentage of participants are displaying an eBAC

which is unsafe. This data supports research from the Institute of Alcohol Studies, who

reported that 31% of 16-24 year olds drink above the recommended low-risk guidelines.

The survey requires participants to state various negative and positive consequences of, as

well as the social impacts of other’s drinking, which again can be used by Student Support to

mould and focus interventions on the consequences of drinking that most affects its

students. In both waves, participants placed a huge emphasis on feeling nauseated and

forgetting what they did the night before which could be focused upon in intervention

messages sent by the University as a deterrent for drinking large amounts. Furthermore, the

38


most prevalent benefits of drinking were enjoying the taste, enjoying the feeling you got from

drinking and talking to someone you probably wouldn’t have otherwise. This information can

aid Student Support in providing alternative activities for students that produce the same

benefits e.g. producing non-alcoholic drinks which also taste nice and provide an

atmosphere where people have the opportunity to speak to new people such as speed

dating.

Keele University has a regularly updated alcohol policy which shows that it has been a

recognised problem on campus with, on occasion, fatal consequences. This information

collected will be invaluable for Student Support who has been struggling to combat harmful

drinking on campus. The responses from the survey will provide a renewed push towards

tackling the issue, from which Ian Munton and his team can create and implement novel

approaches to reducing harmful drinking; from providing alternative activities with the same

associated benefits of drinking to focused messaging to students on days when drinking is

most common.

Hangover & AUD

Aside from being a tool for data collection, the survey was also utilised to assess risk factors

for Alcohol Use Disorders. This survey was an important first step in gathering data which

explores the relationship between familial risk, AUD and the alcohol hangover. From this,

there is now a reasonable and substantial base for future research to build upon and

replicate in order to confirm or challenge findings.

The SMAST (Crew & Sher, 1992) scored enabled me to categorise participants into either

those who were FHP, FHN or neither. The FHN and FHP participants characteristics

revealed no significant differences in the age sex and level of study, indicating that the

demographically the participants were all similar. In terms of the drinking patterns and

behaviours of the FHP and FHN participants they were also very similar, with no significant

difference between drinking rates, high risk drinking episodes and drinks perceived to be

moderate on a night out. I found evidence that FHP participants tended to report more

severe hangovers than FHN participants, even though there was no difference in drinking

levels and patterns. The present findings partially replicate and support previous research

stating that family history positive (FHP) participants report more severe hangover than

family history negative (FHN) participants (Piasecki, Sher, Slutske & Jackson, 2005; Newlin

& Pretorious, 1990; McCaul, Turkkam, Svilis & Bigelow, 1991).

39


The replication being partial is due to the fact that the significant difference was only

observed from the hangover severity item on the NCHIP; HSS score, although very close

(p=0.053) was not significantly different for FHP and FHN participants as predicted.

A regression was conducted to assess whether as the number of drinks consumed

increased so did hangover severity the following morning. For FHP participants there was no

significant relationship between the two variables, however, there was one for FHN

participants. This would suggest that for those FHP participants, there are other factors

influencing their perceived hangover severity other than it being purely down to the alcohol

consumed. However, with the FHN participants, there is a positive regression; the more

drinks that are consumed, the greater the hangover severity, suggesting that their

experienced hangover is reflects on the amount of alcohol consumed. Similarly, Piasecki et

al. (2005) found that when drinking frequency and various demographics were controlled,

FHP participants tended to report more hangover than FHN participants. This research

reflects similar findings as even though FHN and FHP participant’s drinking frequencies

were no different, FHP participant’s hangover severity was significantly larger. This could

suggest that FHP participants may have an underlying genetic mechanism or alternatively a

learned effect which causes them to experience hangovers regardless of the number of

alcoholic drinks consumed; proposing they are at the highest risk of consuming further

alcohol to relieve the symptoms of the hangover. This supports existing research which

suggests that hangovers do not always deter drinking but can actually facilitate it due to its

ability to relief its own symptoms the following day (Mallett, Lee, Neighbors, Clarimer &

Turrisi, 2006; Earlywine, 1993a; Robertson, 2012).

According to a paper written by Kruisselbrink (2014), hangovers are experienced when you

reach a BAC of 0.1 or greater. An ANOVA was utilised to check mean hangover scores

between those who had an eBAC below 0.1 and those with an eBAC 0.1 and above. We

found that there was no significant difference between hangover scores for those with low

eBACs compared to those with higher eBACs, and so Kruisselbrink’s research is not

replicated. Further analysis using a General Linear Model to assess the relationship between

eBAC and hangover severity for FHP and FHN participants also revealed no significant

interaction. The eBAC calculation is a fairly new development in alcohol research and so

further study will reveal the occurrence of this relationship between eBAC and hangover

severity.

One aspect of alcohol research which requires more investigation is the rising limb effects of

the BAC and the increased sensitivity to the consequences of drinking. Leonard and Blane

(1999) reported that sons of alcoholics display a hypersensitivity to drinking consequences

40

http://kinesiology.acadiau.ca/darren-kruisselbrink.html


when the BAC curve rises, and this is the time pleasurable consequences of drinking

dominate. We selected four physiological positive consequences of drinking reported by

students; enjoyed the taste of what you drank, enjoyed the feeling you got from drinking,

noticed a release of tension in muscles and nerves and felt like you had lots of energy in

order to obtain information about participants sensitivities to these consequences and

assess differences between FHN and FHP participants. We found there was no difference in

the frequency of participants reporting three of the four consequences between FHP and

FHN individuals (enjoyed the feeling, noticed a release in tension, felt like you had lots of

energy) however, there was a significant difference in the frequency of FHN and FHP

participants who reported enjoying the taste of what they drank. The data reveals that FHP

participants reported enjoying the taste of what they were drinking significantly fewer times

than FHN participants. This shows that the FHP participants in the present study showed a

decreased sensitivity to the taste of alcohol which does not support Leonard and Blane’s

(1999) work. This could suggest that FHP participants have different motivations for drinking

as they are displaying harmful levels of alcohol consumption yet very little report to enjoy the

taste. One possibility for a motivation for drinking in FHP participants is to relieve symptoms

of a hangover. Earlywine (1993a) stated that those who experience the greatest hangovers

may drink alcohol to alleviate the adverse effects of previous alcohol consumption and thus

this could provide a potential motivation for drinking in FHP participants. Furthermore, in

2010, a survey revealed that 11% of participants had engaged in hair of the dog drinking

(Verster, Keurten, Olivier & Laar; 2010) which shows that this behaviour does occur and is a

possibility in explaining the differences in frequencies of experiencing various consequences

of drinking.

The survey was very effective at collecting a variety of data from participants which was

used to explore the idea that the alcohol hangover is a risk factor for AUD. I was able to

obtain various participant demographics in order to obtain an idea of who responded to the

survey and if this reflects a naturalistic sample of the university, which it did. Furthermore,

using the SMAST I was able to categorise participants into the two main groups; FHP and

FHN from which responses could be compared and contrasted in order to address the

research question. The HSS meant I could calculate a hangover score in a reliable and valid

way, which mean that numeric score could be involved in the analysis in order to assess

hangover severity. The NCHIP data allowed me to calculate the eBAC for participants which

proved to be very valuable in understanding the relationship between ethanol exposure and

the alcohol hangover. The three questionnaires worked together complimentary to provide a

uniform set of data from which the research question of assessing risk factors for alcohol use

disorder could be explored.

41


Utilising an online survey design meant that the survey was extremely easy to administer to

students and meant that a large number of participants could be contacted simultaneously;

this was particularly useful in the present study as it meant the entire student body at Keele

University could be sent the survey and asked to complete. Furthermore, the online data set

meant that a variety of statistical tests could be run easily on the data using the SPSS

software including ANOVAs, chi square and regression. However, one disadvantage of

survey design is that participants may feel discouraged to provide answers that paint

themselves or others in an unfavourable light, for example, the SMAST items which refer to

participant’s parent’s drinking behaviours are of a sensitive nature and thus may make the

participant feel uncomfortable, causing a bias in responses. One way I attempted to

overcome this is to emphasise the confidential nature of the responses and that it is only

members of the research team who have access to the data. Furthermore, the details of the

principal investigator were provided should anyone have any issues and wish to withdraw

their data set.

One limitation to the study was the lack of option for students to express drinks consumed in

terms of units or simply type of drink. The majority of information available to people in

regards to drinking alcohol uses units to demonstrate safe levels and calculate individual

drinking behaviours and thus an option for individuals to include this would have not only

aided statistical analyses in the current study, but could also have simultaneously raised

awareness on the number of units in drinks and how individuals may compare to the safe

limits encouraged by professionals. Future research could utilise a similar survey but simply

alter the question format to ask for drinks in terms of units, providing guidance for

participants as to how many units are in common drinks consumed.

A further limitation of the project is the low response rate. Although I was assured by Ian

Munton that this level of response is normal for a survey of its kind at Keele University it still

didn’t capture a large proportion of students. A response rate this low lowers the

generalizability of the data to all students at Keele as there is only a small percentage of the

target sample who participated in the study; for example, my sample could consist purely of

people who were more likely to take the time to respond which biases my results. However,

the data I collected provides others with a good base to build upon in future research. One

was to potentially improve the response rate is to take advantage of the benefits of social

media; the survey could be mentioned by Student Support on their Facebook or Twitter

account to build up awareness and gain people’s interest in the survey and the findings.

Another potential way to increase the response rate is to personalise the study and make the

survey as meaningful to participants as possible. Ways to achieve this is to ensure to

emphasise how the findings will benefit students and the University and to reference

42


people’s names who are familiar to the participants e.g. utilising Student Support to send the

email rather than myself, as a student. Finally, once the survey is sent out, gentle reminder

emails could have been utilised to prompt participants to complete the survey as they might

not have had time when the email was sent out but may find time now reminded.

It would appear that the alcohol hangover is a risk factor for AUD, as those who report the

greatest hangovers tend to have a positive family history of alcohol use issues and thus are

more likely to engage in hair of the dog drinking in order to overcome the effects of the

hangover. However, it is important to remember that is cannot be explicitly stated from this

research that the hangover leads certain individuals to engage in ‘hair of the dog’ drinking;

other factors should be considered such as personality factors. For example, Piasecki et al.

(2005) reported that FHP participants were more impulsive and less sensitive to punishment

cues, suggesting that FHP participants may have an inability to effectively learn from their

actions.

In the future, research could focus on more explicitly dividing the alcohol hangover and

withdrawal symptoms into two clear constructs, and also further exploring the idea of hair of

the dog drinking to see how often this occurs in participants who are of high familial risk for

AUD.

43


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