September 2014
Kara Holloway
MSc Psychology
Implementing the NCHIP Online
Drinking Survey to Keele University
and Researching Risk Factors for
Alcohol Use Disorder
By
Kara Holloway
Word count: 15,306
Submitted in partial fulfilment of the
requirements for the degree of MSc
Psychology
September 2014
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Kara Holloway
MSc Psychology
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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MSc Psychology
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 antisocial 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 alcoholrelated 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 HuntCarter (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
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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 emailed 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
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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).
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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 MSMAST 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 emails 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 Participants
MARCH Wave 1
JULY Wave 2
Frequency
%
Frequency
%
Size of sample
10,000
10,000
surveyed
Survey response
273
310
totals
Survey response rate
2.73%
3.1%
Year of Study
1st
2nd
3rd
4th/5th
Post-graduate
103
50
61
14
45
37.7
18.3
22.4
5.1
16.5
92
68
79
18
53
29.7
21.9
25.5
5.8
17.1
Age
18
19
20
21
22
23+
26
46
62
53
22
64
9.5
16.9
22.7
19.4
8.1
23.4
15
57
65
63
31
79
4.8
18.4
21
20.3
10
25.6
Sex
Male
Female
101
172
37
63
123
187
39.7
60.3
Live on Campus
Yes
No
108
165
39.6
60.4
122
188
39.4
60.6
Halls of Resident
Support Assistant
Yes
No
15
258
5.5
94.5
7
303
2.3
97.7
Average Module % last
term
Less than 40
40-49
50-59
60-69
70+
6
63
118
76
10
2.2
23.1
43.2
27.8
3.7
1
8
75
151
75
0.3
2.6
24.2
48.7
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
Number of drinks
perceived to be
moderate when
partying
Drinking Rates &
Hangover Severity
Usual Drinking Rate
(drinks/hour)
Highest Volume
Drinking Rate
(drinks/hour)
Hangover severity
rating
eBAC
High Risk Drinking
Episodes*
Dry this month
Didn’t HRD
1-2 times
3-5 times
6+ times
MARCH – Wave 1
Mean
SD
4.48
1.789
JULY – Wave 2
Mean
SD
4.47
1.905
1.24
0.578
1.15
0.731
1.673
1.028
1.52
0.981
2.02
1.686
1.50
1.649
0.04
0.771
0.06
0.559
Frequency
%
Frequency
%
23
78
81
49
17
8.4
28.6
29.7
17.9
6.2
36
86
93
40
7
11.6
27.7
29.9
12.9
2.3
Mixed alcohol with
80
35.6
68
51
energy drink
Didn’t mix alcohol
145
64.4
158
21.9
with energy drink
* defined as 5 or more drinks for a male and 4 or more drinks for a female in one
occasion
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Table 3. Rates (Frequency and %) of Drinking Related Harm
Reference Sample:
Drinkers
Been nauseated or
vomited
Got into an argument
or fight
Got in trouble with
security/police
Had unprotected sex
Physically injured
yourself
Physically injured
another person
Required medical
attention
Did something you
later regretted
Had a mild hangover
Had a severe hangover
Forgot where you were
or what you did
Fell behind in
University work
Missed a class
Performed poorly on a
test or important
project
Seriously considered
suicide
MARCH – Wave 1
Frequency
%
225
100
JULY – Wave 2
Frequency
%
262
100
101
44.9
123
46.2
50
22.2
55
21
14
6.2
19
7.3
29
68
12.9
30.2
36
67
13.7
25.6
12
5.3
11
4.2
10
4.4
11
4.2
75
33.3
103
39.3
133
84
77
59.1
37.3
34.2
177
86
94
67.6
32.8
35.9
39
17.3
15
5.7
66
10
29.3
4.4
65
14
24.8
5.3
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).
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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.
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Table 4. Social Impacts of drinking: Experiences as a result of someone else’s
drinking
Full sample
Was prevented from
enjoying an event
Was made to feel
unsafe in your
surroundings
Had a drink spilled on
you
Experienced an
unwanted sexual
advance
Been insulted or
humiliated
Got into an argument
or fight
Were physically
injured by someone
who had been
drinking
Got into trouble with
security/police
Had to “baby-sit” or
take care of another
student who drank too
much
Had unprotected sex
Had your property
damaged
Had your sleep
interrupted
Had your studying
interrupted
None of these applied
to me
Frequency
273
%
100
Frequency
310
%
100
78
28.6
82
26.5
57
20.9
57
18.4
158
57.9
151
48.7
49
17.9
47
15.2
32
11.7
36
11.6
28
10.3
32
10.3
14
5.1
14
4.5
3
0.7
7
2.3
103
37.7
109
35.2
13
15
4.8
5.5
13
7
4.2
2.3
127
46.5
127
41.0
62
22.7
57
18.4
61
22.3
98
31.6
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Table 5. Drinking Behaviours: Respondents who provided help to other drinkers
Full sample
MARCH – Wave 1
Frequency
%
273
100
JULY – Wave 2
Frequency
%
310
100
Was around people
who were drinking
Wasn’t around
people who were
drinking
243
89
269
86.8
30
11
41
13.2
Sample: Was around
people who were
drinking
Was around people
who were drinking,
but didn’t need to
assist anyone
Suggested they drink
more slowly
Suggested that they
stopped drinking
Took away their car
keys
Got additional
assistance (e.g.
security/police)
Stayed with them
until they got home
safely
Kept them awake
until you knew they
were OK
Gave them water /
other non-alcoholic
drink
Gave them
something to eat
Put them to bed on
their side
Kept them warm
Monitored their
breathing to make
sure it wasn’t
abnormally shallow
or slow
243
100
269
100
129
53
157
58.4
30
12.3
33
12.3
64
26.3
69
25.7
9
3.7
12
4.5
18
7.4
12
4.5
83
34.2
71
26.4
24
9.9
28
10.4
92
37.9
90
33.5
48
19.8
52
19.3
47
19.3
51
19
32
16
13.2
6.6
25
20
9.3
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.
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Table 6. Rates (frequency and %) of Drinking Related Benefits
Sample: Drinkers
Enjoyed the taste of
what you drank
Enjoyed the feeling
you got from
drinking
Talked to someone
you probably
wouldn’t have
spoken to otherwise
Was able to tell a
funny story or joke
that made others
laugh
Was able to express
thoughts and
feelings you
normally keep to
yourself
Noticed a release of
tension in your
muscles and nerves
Felt like you had lots
of energy
Forgot your worries
Had a good time that
wouldn’t have been
as much fun sober
Felt more attractive
Facilitated a
romantic encounter
MARCH – Wave 1
Frequency
%
225
100
JULY – Wave 2
Frequency
%
262
100
194
86.2
240
91.6
170
75.6
201
76.7
141
62.7
161
61.5
110
48.9
129
49.2
98
43.6
137
52.3
101
44.9
126
48.1
89
39.6
106
40.5
113
126
50.2
56
139
135
53.1
51.5
62
63
27.6
28
90
78
34.4
29.8
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Table 7. Participants Above Safe Limits for eBAC
MARCH – Wave 1
Frequency
%
JULY – Wave 2
Frequency
%
Above safe
Males
34
12.45
43
13.87
limit for
Females
71
26.01
53
17.10
eBAC
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 FSMAST 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
FHP
Mean
Age
Age first
intoxication
SD
Mean
23.18
14.91
SD
3.935
1.57
%
21.41
13.97
3.343
5.354
Sample
Frequency
162
100
Frequency
34
100
Male
Female
58
104
35.8
64.2
11
23
34.3
65.7
Live on Campus
Live off Campus
71
91
43.8
56.2
10
24
28.6
71.4
Undergraduates
Postgraduates
136
26
84
16
25
9
71.4
28.6
2.5
25.9
40.1
30.2
1.2
1
4
14
12
3
2.8
11.3
42.8
34.7
8.5
Average Module Mark Last Year
3:3
4
2:2
42
2:1
65
1:1
49
Fail
2
%
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.
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Table 9. Drinking Patterns and Behaviours of FHN and FHP participants
FHN
Mean
FHP
SD
Mean
No. times
drank last 4
weeks
No. times
high risk
drinking last 2
weeks
No. drinks
normal night
No. of hours
drinking
normal night
No. drinks
heavy night
No. of hours
drinking
heavy night
Hangover
severity
Drinks
perceived
moderate
eBAC
6.11
4.913
8.58
2.939
1.629
P
value
0.204
1.38
1.908
2.03
7.934
0.739
0.391
4.86
3.052
4.54
3.036
2.102
0.226
4
1.903
3.79
1.414
0.092
0.762
7.78
4.532
8.96
4.648
1.366
0.244
5.07
2.519
5.67
2.220
1.996
0.160
1.48
1.572
2.38
1.740
6.446
0.012
4.44
1.788
4.67
1.926
0.420
0.518
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
34
SD
F
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MSc Psychology
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.
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4. eBAC and Hangover Severity – ANOVA
An ANOVA was run to assess hangover severity of those with an 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.
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Table 11. Frequencies for Positive Consequences of Drinking
FHN
Enjoyed
the taste
of what
you drank
Enjoyed
the feeling
you got
from
drinking
Noticed a
release of
tension in
muscles
and
nerves
Felt like
you had a
lot of
energy
FHP
No
40
Yes
122
No
20
Yes
13
X2
16.601
P value
0.000
61
101
14
19
0.264
0.608
99
63
24
9
1.588
0.208
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
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MSc Psychology
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).
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MSc Psychology
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
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MSc Psychology
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.
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MSc Psychology
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
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MSc Psychology
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.
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MSc Psychology
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