Drug misuse is a major health issue with wide-ranging economic and social implications. In Australia, tobacco use is responsible for 9% of the nation’s disease burden, while alcohol and illicit drug use account for 5.1% and 1.8% respectively (AIHW, 2016). The total economic and social cost of these drugs is estimated at more than $55 billion per annum (AIHW, 2011). Nonetheless, high proportions of Australians 14 years or older report recent (preceding 12 months) drug use: 14.9% are current smokers, 77.5% indicate consuming alcohol, and 15.6% have used illicit drugs (AIHW, 2017). While relatively few of these individuals will transition to dependent use, the factors influencing this transition, as well as those that may have a mitigating impact, remain unclear. Numerous studies have consistently reported the association between various cognitive factors and dependent drug use. For instance, compared to healthy controls, individuals dependent on various substances exhibit difficulties on tasks that assess executive function, particularly those related to decision-making and inhibitory control (Bickel et al., 2012; MacKillop et al., 2011; Smith et al., 2014; Verdejo-García et al., 2008). Alcohol and substance dependent individuals additionally show significant impairments across tasks that assess various types of learning and memory (Lundqvist, 2005; Potvin et al., 2014; Stavro et al., 2013). At the neural level, attention is focused on the interplay between bottom-up dopamine-mediated reward circuitry, and top-down prefrontal regions involved in self-control (Goldstein & Volkow, 2011; Verdejo-García et al., 2006). Substance dependent individuals show dysfunction across both systems (Goldstein & Volkow, 2011; Verdejo-García et al., 2006; Verdejo-García & Bechara, 2009). The cognitive deficits evident among substance dependent individuals are considered both a cause and consequence of drug use (De Wit, 2009). Longitudinal studies involving both children and adolescents have shown how executive function performance in early life predicts illicit drug use and the onset of problematic alcohol consumption, independent of other risk factors (Audrain-McGovern et al., 2009; Fernie et al., 2013; Kollins, 2003; Nigg et al., 2006; Verdejo-García et al., 2008). At the same time, drug taking appears to exacerbate cognitive issues related to memory, learning and executive function (Colzato et al., 2007; De Wit, 2009; Fillmore & Rush, 2002; Mendez et al., 2010; Verdejo-García et al., 2008). Given these findings, studies concerned with identifying non-dependent drug users at risk of transitioning to dependency often focus on intake patterns and their relationship to (usually single) assessments of executive function, memory and learning; such studies tend, however, to yield inconsistent results (Fernie et al., 2010; Field et al., 2007; Henges & Marczinski, 2012; MacKillop et al., 2011; Moreno et al., 2012; Murphy & Garavan, 2011; Poulton et al., 2016; Rossiter et al., 2012; Smith & Mattick, 2013). This may be due to a number of factors. For instance, intake is frequently recorded using retrospective summary surveys, which can underestimate consumption and are limited in their ability to capture within- person variability and change over time (Piasecki et al., 2007; Poulton et al., 2018; Runyan et al., 2013). At the sub-clinical level, it is possible variability of intake is linked to performance – or, indeed, variability of performance – on cognitive tasks. To investigate this proposition, we are utilising mobile-based ecological momentary assessment (EMA) – which involves using a smartphone app to collect data repeatedly, in close proximity to the event, and in the natural environment (Shiffman, 2009; Trull & Ebner-Priemer, 2013) – to assess both intake and cognition. We hope this enables a more detailed exploration of the in-the-moment cognitive antecedents and consequences of intake behaviour. EMA techniques additionally provide a unique opportunity to assess how individualized feedback about intake and cognition delivered in real-time might alter the trajectory of licit and illicit drug use over time. Early brief interventions have already been identified as a potentially effective means of curbing intake among non-dependent drug users, particularly when they are individualized, though a number of difficulties have been noted (Bernstein et al., 2009; Das et al., 2016; Riper et al., 2009; Tait & Hulse, 2003). Namely, there are limited professionals to deliver these interventions; it can be difficult contacting users; and, implementation and delivery costs can be high (Riper et al., 2009). Interventions delivered via smartphone apps potentially address these limitations.

This is an ongoing study that aims to investigate whether individual differences in cognitive function are related to licit and illicit drug use in the general population. Specifically, we seek to assess if cognitive factors – related to memory, learning, and executive function – are associated with particular patterns of drug use, including occasional, social and binge intake behaviours. We additionally hope to determine if these cognitive factors can predict changes in drug use over time and if feedback about cognition and/or intake precipitates reduced drug-taking behaviour. The current study aimed to assess whether personalised impulsivity feedback would augment the benefit of alcohol-related feedback. It also aimed to validate whether differences in impulsivity can be observed between groups of binge and non-binge drinkers.

Participants included 111 first-year university students, 102 of which completed all baseline measures and were randomly allocated to one of three conditions: no feedback, alcohol use feedback only, and impulsivity combined with alcohol use feedback. Seventy-four participants completed follow-up measures at 6 weeks post-intervention.

Data collection was cross-sectional, intensive (14 days), and longitudinal. Participants were requested to take part in two intensive assessment periods. They were recruited remotely. At the commencement of each assessment period, participants were required to complete online baseline (Time 1) or subsequent (Time 2) surveys. They were then required to complete a cognitive stop-signal task online. Finally, they were asked to download an app and record alcohol intake for a period of 14 days. Feedback condition participants were provided with feedback about their alcohol intake and/or cognition at the conclusion of each assessment period. Control condition participants received no feedback about their intake or cognition.

This is an ongoing study. Key variables of interest are binge drinking, total alcohol consumption, choice impulsivity, and response inhibition. Choice impulsivity is indexed by the Monetary Choice Questionnaire (MCQ) overall k-score; higher scores are indicative of greater impulsive decision making. Response inhibition is indexed by stop-signal reaction time, which is derived from Stop-Signal Task (SST) data. Longer stop signal reaction times are indicative of reduced response inhibition. Prior to primary hypothesis testing, the three groups will be compared on key demographic, alcohol use, and impulsivity variables measured at Time 1. A one-way analysis of variance (ANOVA) will be conducted to test the groups are matched on continuous variables, with a chi-square test of independence used for gender. The primary hypotheses will be tested using a mixed between- within subjects ANOVA for each alcohol use variable, with three feedback conditions (No Feedback, Alcohol Feedback, and Alcohol + Impulsivity Feedback) and two time periods (Time 1 and Time 2). Effect sizes will be computed using partial eta squared and interpreted according to Cohen's guidelines: .01 = small, .06 = moderate, and .14 = large effect (Cohen, 1988).

We expect binge drinking and total alcohol consumption to be reduced at Time 2 across all groups as recording alcohol consumption has been found to reduce alcohol intake over time. We expect participants in the Alcohol Feedback group to have reduced drinking to a greater extent than those in the No Feedback group, and those in the Alcohol + Impulsivity Feedback group to have reduced drinking to a greater extent than those in any other group. Data from S1 will be analysed and form part of 4th year theses in 2021. Data will continue to be collected in S2 with a view to publishing results early in 2022.

There is a longstanding debate in episodic memory research about whether it is possible to remember contextual details of an event if the event itself is not recognized. Many notable models of recognition and source memory have predicted that such a phenomenon is not possible, while others have suggested that it is entirely possible. To complicate the matter, several empirical studies utilizing different methodologies have reported source memory for unrecognized items that is both at chance-level and above chance.

The aim of our research is to determine whether source memory exists for unrecognized items. It is hypothesized that we will observe above-chance source accuracy for unrecognized items across the majority of conditions in the experiment.

A total of 281 REP participants completed the study.

Participants completed 6 cycles of a memory task, with each involving a study phase and test phase. In the study phase, participants were presented with several words on a computer screen, one at a time. Half the words were presented in the bottom-left corner and half the words were presented in the top-right corner. During the test phase, participants were presented with each of the words from the study phase and an equal amount of words that had not been seen before. For each word, participants were first asked to determine whether it was a “NEW” word or an “OLD” word. Following this, they were asked to determine whether the word was initially seen in the bottom-left corner or the top-right corner of the screen.

When the source memory trials are conditionalized on items that were not recognized (i.e., they were labelled as “NEW”), it appears that accuracy is quite low, but still slightly above chance.

The findings of the experiment suggest that it is possible to retrieve contextual (i.e., source) details of an event, even if the event itself is not recognized. These findings support models of recognition and source memory that involve multivariate signal detection theory, as they allow for such a phenomenon to occur. The results from this study will be written up as a journal article for publication, and will be presented at various conferences on psychological research.

Obesity is a global health issue, estimated to cause nearly three million annual deaths worldwide. A key risk factor is poor diet, therefore it is important to investigate ways of improving dietary decisions and preferences. Previous studies suggest that experiencing positive emotions can increase people's intake of foods high in calories. This study aims to determine whether the regulation of positive emotions can lead to healthier or unhealthier dietary decisions.

How does the downregulation (decreasing the intensity) and upregulation (increasing the intensity) of positive emotions affect people's willingness to consume foods? Does this apply to foods in general, or only foods that are considered to be unhealthy?

402 REP participants completed the study in total. 35 participants were excluded for reporting that they had dietary requirements or history of an eating disorder. A further 17 were excluded for showing little desire to consume any foods in the study.

Part 1 was an online demographics survey. In part 2, participants completed a series of trials. In each trial, they were instructed to either let their positive emotions unfold naturally, or regulate their emotional response (either increase or decrease their positive emotions, depending on a randomly allocated condition). They then viewed an image selected to induce positive emotions, followed by a food image, and indicated how much they wanted to consume the food in that moment.

Linear mixed effects model analyses will be run to determine whether willingness to consume foods differed according to trial type (look vs. regulate) and strategy type (upregulation vs. downregulation). This analysis will also look at whether these effects differed according to tastiness and healthiness. It is expected that participants will report greater willingness to consume unhealthy foods when increasing their positive emotional response, and greater willingness to consume healthy foods when decreasing their positive emotional response.

The results of this study may help to further our understanding of interventions to improve dietary decision-making, and lead to potentially better treatments for obesity, reducing people’s risk of multiple diseases. The results will be communicated in journal articles, conference presentations, and a PhD thesis.

This project investigates people’s emotional experiences in daily life, how these emotional experiences change in response to everyday events and social interactions, and how people seek to manage their everyday emotional experiences. The aim of this study is to build on previous research by investigating new aspects of emotional experience and management, as well as using more fine-grained measurement approaches. When we manage our emotions—a process termed emotion regulation—we try to change how we feel to how we want to feel, using various strategies. Emotion regulation can be personal, which involves regulating our own emotional states using self-directed strategies. Emotion regulation can also be interpersonal, which involves regulating one’s own emotional states with input from other people or regulating other people’s emotional states. This project investigates (a) how people individually regulate their own emotions (personal emotion regulation); (b) how people interpersonally regulate their own and others’ emotions (interpersonal emotion regulation); and (c) best practices for measuring emotions and emotion regulation in everyday life.

This research project is being conducted by a team of researchers, including 8 fourth-year psychology students, and is therefore designed to address a number of distinct research questions / hypotheses relating to (inter)personal emotion regulation, emotion dynamics, and the assessment of emotions in daily life. The specific research questions are all related to the following three key themes: 1. What strategies do people use to regulate their own emotions in daily life? 2. What strategies do people use to regulate other people’s emotions in daily life? 3. How do methodological factors (such as the type of items used to assess emotional state) influence the measurement of emotions in everyday life?

138 participants signed up for the study via the REP. Of this number, 86 participants fully completed Part 1 of our study and 78 completed Part 2 of the study. As the per the advertised inclusion/exclusion criteria, participants were excluded if they were not residing in AEST time-zone; did not have an iPhone (iOS 8 and above) or Android (6.0 and above, except Oppo, Huawei or Realme) phone; were not willing/able to install the SEMA3 app; were not available to complete Parts 1 and 2 of the study. Participants were excluded from Part 2 if they failed a pre-specified number of attention-checks during Part 1.

For Part 1, participants were asked to complete an online survey (approx. 30 minutes) comprising various self-report questionnaires assessing individual differences in personality, well-being, habitual use of emotion regulation strategies, and labelling of emotions. For Part 2, participants downloaded the SEMA3 smartphone app, which was programmed to send them several short experience sampling surveys (approx. 2-3 minutes each) over 7 days.

Results are not yet available as data collection is ongoing. However, our preliminary descriptive statistics indicate that participants completed approximately 70% of all scheduled smartphone surveys. Because the smartphone survey data are nested (momentary smartphone surveys nested within people), we will analyse data using multilevel models (e.g., using the lme4 and/or nlme R packages; and/or using Mplus).

We anticipate that this research will be beneficial to the wider community. Emotion regulation is thought to be central to psychological and social functioning. Understanding the emotion regulation process more fully is important in applied settings and to eventual therapeutic interventions. Finally, participants may benefit directly by participating in this research as they may gain insight into their own experience and regulation of emotions in daily life. We aim to increase this potential benefit by providing participants with the opportunity for a personalised report of their responses (in Part 2), which will allow them to reflect on their own responses across the week. Participants in our previous studies have found this very beneficial.

A background can be found here: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please see: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please see: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please see: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please see: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please see: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Anxiety disorders are a kind of mental disorder defined by problematic anxiety, fear and avoidance (Craske & Stein, 2016). They are the most common type of mental disorder worldwide, develop early in life, and tend to be chronic. Those with anxiety disorders have a heightened sense of threat and danger that is excessive and not in proportion to the situation of concern. Exposure therapy is an effective psychological treatment for anxiety disorders. Exposure means approaching a feared (but actually safe) situation or thing (as in “exposing” yourself to what you are scared of). Exposure therapy involves approaching feared (but actually safe) situations with the support of a therapist until it is learned that these situations are safe. Decades or research and clinical experience demonstrates that exposure therapy improves all types of anxiety disorder. Despite this, researchers are not sure how and why exposure works. The leading view currently is that exposure works by providing experiences where clients can learn that their fears don't come true. The technical term for when a fear doesn't come true is "threat prediction-error". Discrepancy between anxious predictions and actual outcome vary from being a lot worse than expected, as-expected, to a lot better than expected. Current theory posits that larger threat prediction error during an exposure will lead to more safety learning. Exposure therapists should thus try to create conditions where people are surprised by the outcome and see that their fears don’t come true to the greatest extent possible. Evidence supporting this theory of exposure therapy comes from basic fear learning research with animals and humans and using controlled experimental procedures in laboratory settings. This fear learning research often involves participants viewing trials of abstract geometric shapes on a computer screen. Some shapes pair with an electric shock or loud noise and some don't. Researchers measure fear to see how it changes through different stimuli combinations. This research shows that fear in this kind of procedure reduces after threat prediction errors. Some researchers assume that the findings from this kind of research will translate to the "real-world" context of therapy in the clinic. However, findings may not transfer to the more complex and "messy" settings that characterise everyday life. We need more research to determine whether the threat prediction error model of exposure translates to real-world contexts outside of the laboratory. It is important to know the therapeutic mechanisms of our treatments. If we don't know the true treatment mechanisms, we may be subject clients to unnecessary procedures. Instead, we could drop unneeded procedures to focus on the real mechanisms of change. This study aims to address these gaps by exploring whether threat prediction error is associated with anxiety reduction through a procedure that resembles exposure therapy in the clinic. This procedure involves give six short speeches to a researcher over two days and rating anxiety, threat perception, and perception of threat outcome for each speech.

Questions: 1. How often do speeches turn out better, worse, and as-expected? 2. How does threat expectancy and perceived outcome relate to speech anxiety change over time? 3. How does social anxiety symptom level relate to speech anxiety, threat prediction, and perceived outcome? Hypotheses: 1. Anxiety, threat expectancy, and perceived outcome scores will vary significantly between participants, and also with within participants over time. 2. Discrepancy between speech threat expectancy and perceived outcome will relate to speech anxiety change over time. i. Speech anxiety will be lower after speeches that went better than expected. The better the outcome was compared to what was expected, the lower anxiety will be at the next speech. ii. Speech anxiety will be higher after speeches that went worse than expected. The worse the outcome was compared to what was expected, the higher anxiety will be at the next speech. iii. Speech anxiety will remain similar after speeches that went as-expected. 3. Higher social anxiety symptoms will be associated with higher overall levels of speech anxiety, threat expectancy, and perceived outcome.

Twenty two people participated in the study this semester through REP. Five completed part one only, two withdrew before completing part one, and fifteen people completed part one and two. We need 100 participants in total and will keep recruiting next semester.

It is a longitudinal and observational study conducted remotely via Zoom. Participants give six 3 minute speeches to a researcher with one minute to prepare, while rating their anxiety, sense of threat, and threat outcome. These six speeches are done across two sessions separated by a week (3x speeches per session). Social anxiety and mood questionnaires are completed before each session.

Multi level modelling will be used where threat expectancy, threat outcome, and anxiety at time X will be entered as predictors of anxiety at time X + 1. A discrepancy effect will be supported if threat expectancy and outcome predictors are both significant and opposite in sign (Humberg et al, 2018), and this is what we are expecting. We will also test whether the joint relationship between threat expectancy / outcome and anxiety is more complex than a simple discrepancy effect by adding a set of higher order terms to the model (e.g see Nestler et al, 2019) and evaluating whether this improves fit with the data (Edwards, 2002). If adding higher order terms do not improve fit, then it allows more confidence in establishing that the relationship between the two predictors and anxiety represent a simple discrepancy effect.

The findings from this study will help determine whether threat prediction-errors are related to safety learning in settings that are similar to those in exposure therapy. Basic research in the laboratory suggests that they will be, but this proposition hasn't been tested enough in naturalistic settings. To our knowledge, no other studies to date have explored this explicitly in the area of social anxiety. Giving a speech is more complex than experimental procedures used in basic fear learning research, so it can't be assumed that safety learning works the same way in these two scenarios. Clinicians are conducting exposure based on the prediction error model, so it is important to know if this practice is correct or whether they should focus their practice on other mechanisms. This study will contribute to knowledge about how fear and safety learning works for humans in everyday settings. Achieving these aims will help refine understanding of exposure therapy mechanisms and may lead to better treatment outcomes for people living with anxiety. Anxiety disorders are the most common mental disorder worldwide, so research that may help improve outcomes for these issues is important.

Please see: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please see: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please see: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please see: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please see: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please see: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Many bilinguals report that processing emotion (happy) and emotion-laden (death) words in a second language (L2) doesn't have the same emotional impact as processing the same words in their native language. Several explanations why this might be the case can be considered. It could be that the meaning of L2 is only incompletely acquired (i.e. Perfetti's lexical quality hypothesis). This is not unreasonable because many emotion words are abstract, and the meaning of abstract words is relational and contextually defined, making them difficult to process. Others have proposed an embodiment explanation (e.g. the Affective Embodiment Account, Kousta et al., 2011). According to this explanation, emotional terms are difficult in L2 because non-native speakers acquire these words in a classroom setting rather than an environment where these emotions are experienced. In addition, it could be a combination of factors that explain why emotion terms are represented differently in L2.

The goal of this study was to collection affective norms for both valence (good/positive vs bad/negative) and arousal (high vs low arousal) for a large set of English words and their Mandarin translations. We also collected data about language background and history, together with an English vocabulary test. We expected that ratings would be different between native and non-native speakers. In line with previous cross-cultural work, we expected high arousing words to be perceived less positive for Mandarin speakers. According to the lexical quality hypothesis we expected that more proficient participants would have ratings that resemble native speakers.

A total of 165 native English speakers and 182 native Mandarin speakers judged English words. There were also 76 native Mandarin speakers who judged the Mandarin translation equivalents of the English words.

The study consisted of several questionnaires to measure language experience and history, together with a normed vocabulary test (LexTale) in which participants had to decide whether a word is an existing English word or not. Participants completed both valence and arousal ratings for a subset of nearly 1000 words using a 9-point bipolar rating scale. In addition, we also used a control by having a group of Mandarin speakers judging English words and a separate Mandarin group judging Mandarin translations of these words.

Data collection is still ongoing as we aim to have a balanced sample of male and female raters and the sample size for male Mandarin speakers is not sufficient. Balancing the norms for gender is important as previous work suggests systematic variability as a function of gender. So far our results confirm that valence rating are highly reliable, but arousal ratings require larger samples to be achieve comparable levels of reliability. Since we are still collecting data it is too early to draw conclusions about systematic differences between native and non-native speakers or between highly and moderatly proficient speakers.

Lexical norms for bilinguals are often limited to Indo-European languages, whereas similar norms for widely spoken language like Mandarin are lacking. Apart from theoretical contributions regarding the role of emotion processing in L2, we expect these norms to be used in other psycholinguistic studies that aim to manipulate or control the emotionality of stimuli words in lexical processing tasks.

Research has found that insomnia symptoms and severity are associated with higher levels of perfectionism and specific aspects of perfectionism. However, it remains unknown whether this relationship exists only with the perception of poor sleep or whether the same relationship can be found with objective sleep measures.

1. Explore the relationship between perfectionism and insomnia using objective sleep parameters obtained from actigraphy. 2. Investigate the association between facets of multidimensional perfectionism and sleep disturbances. 3. Explore pathways whereby perfectionism may influence sleep through anxiety, stress, depression and sleep-related cognitions.

210 participants signed up for this pre-screening survey. Exclusion criteria: 1. Current diagnosis of psychosis, bipolar affective disorder, substance use disorder (including alcohol use disorder), central nervous system disorder, prior head injury and shift work. 2. Medication use (including sedatives, hypnotics and antidepressants) that may affect sleep may be permitted to participate if usage pattern is unstable.

All questionnaires were completed online via Qualtrics.

The REP survey is used as a pre-screening survey to find potential participants to participate in the two-week actigraphy study. Some eligible participants will be invited to proceed with the next phase of the study via email in semester 2.

This project aims to contribute to the understanding of the relationship between perfectionism and sleep. Perfectionism may be a factor in determining the success of cognitive-behavioural therapy for insomnia (CBT-I) and incorporating perfectionism in CBT-I may benefit insomniacs who are highly perfectionistic. Results are planned for journal publication.

This research aims to understand the behaviours, attitudes and preferences of podcast listeners regarding different types of podcasts, including podcasts on the topic of mental health. It also aims to understand the relationship between listening to podcasts and different aspects of mental health.

1. What are the demographics of people who choose to listen to podcasts on the topic of mental health (‘mental health podcasts’, compared to general podcast listeners? 2. What are the behaviours, attitudes and preferences of podcast listeners regarding mental health podcasts? Do these differ based on occupation or lived experience of mental health issues? 3. Do people who listen to mental health podcasts experience different levels of stigma, internalised stigma and mental health literacy compared to more general podcast listeners?

There were 360 REP signups. 288 completed the survey. 250 REP students were included in the final dataset after accounting for speeders and failing attention check measures. Please note that there seemed to be some kind of error where I (Elise) was being prompted to credit students manually who may not have actually completed the survey. Hence, the number of signups do not match the number of completed responses. After some back and forth I discovered an error in the redirect code, meaning people were credited automatically, but I am not sure if the error persisted after this was fixed.

15-30 minute online survey covering podcast listening habits and preferences, degree of stigma and internalised stigma, and mental health literacy.

Data analysis in process. It is hypothesised that: • People with lived experience of mental health issues will be most willing to listen to a new mental health podcast, or a podcast on mental health stigma, compared to people from other target groups • Healthcare professionals will have a stronger preference for shorter episodes and fewer episodes compared to the other target groups • People who listen to mental health podcasts will have lower levels of internalised stigma and general stigmatising attitudes, and higher mental health literacy, compared to people who do not listen to mental health podcasts (controlling for socially desirable responding pattern).

The findings will feed into the next phase of my (Elise's) PhD, around designing a podcast-based intervention. I am also hoping to public these findings in a journal.

Approximately 70% of the population is exposed to a traumatic experience during their lifetime. However, only 4% of the population go on to develop posttraumatic stress disorder (PTSD). Sleep is a critical process linked to mental and physical restoration across numerous systems (Cox & Olatunji, 2016) and approximately 70% of people with PTSD also have co-occurring sleep problems (Ohayon & Shapiro, 2000). The relationship between sleep and PTSD is bidirectional, with sleep problems increasing the risk of PTSD, and PTSD increasing the risk of sleep difficulties. This study aims to investigate the link between sleep and PTSD in a university student population.

1) What is the relationship between PTSD symptoms and sleep in undergraduate students? 2) Are there any factors potentially moderating the relationship found in Aim 1? 3) Is there a difference between non-trauma exposed, trauma exposed, and individuals with PTSD with regards to sleep? It is expected that there will be a significant linear relationship between PTSD symptoms and sleep. Further, it is expected that dimensional analysis between PTSD score and sleep score will reveal a significant relationship.

The final sample included 720 participants (557 female, 163 male, aged 16-66 years; M = 19.82, SD = 4.56).

Participants anonymously completed a 30-minute online survey consisting of a battery of questionnaires: PTSD Checklist for DSM-5, Pittsburgh Sleep Quality Index, Morningness-Eveningness Questionnaire, Epworth Sleepiness Scale, Insomnia Severity Index, DASS-21, Audit Alcohol Screening Tool, and Positive and Negative Affect Schedule.

40% of the total sample responded with no prior traumatic experiences, 15% with one, 23% with two, 11% responded with three, 7% with four, 3% with five and 1% with six traumatic experiences. Further examination of the PCL-5 revealed that 29% have probable PTSD (PCL-5 cut-off score: 33). Finally, participants had a mean global PSQI score of 6.57 (SD = 3.23), which is above the cut-off score of 5 indicating reduced sleep quality across the sample.

Final results and implications are pending. Results will be communicated through Honours and PhD theses.