Australia's University Students and Mental Health

Dr Wayne Usher - EPS
PREDICTIVE FACTORS
Physical Activity
Social & Emotional Wellbeing
Sporting Club
DOMAINS
Personal
University
Community
Home
Investigating patterns of association which measure
the level of influence predictive factors have on
Australia's university students' mental health status
MENTAL HEALTH
Status
Short title: Predictors of Australia’s university students’ mental health status

Presentation outline - What is research? Research is a process in which you engage
in a small set of logical steps.
1. Background – what is the situation?
2. Justification – what is the importance?
3. Research Question & Aims – what did I want to achieve?
4. Theoretical Framework – underpinnings (inform approach for data collection and data interpretation).
5. Methodology - principles that guided this research (positivism and Interpretivism).
6. Methods – data collection (mixed methods - quantitative & qualitative tools).
7. Methods – data analysis (mixed methods - quantitative & qualitative tools).
8. Results – findings (quantitative & qualitative).
9. Conclusion – bringing it together.
10. Recommendations - summary of recommendations.
11. Impact – what is the impact of this study?

BACKGROUND – what is the situation?
“University students’ health behaviours are typically influenced and shaped due to them entering a dynamic transitional period of new independence, that is
characterised by rapid, interrelated changes in body, mind, and social relationships and is seen as the period between ages 17 and 25 as a phase of “emerging
adulthood”.
 Professional experience: Course convenor (2005 – 17), First Year Advisor (2008 – 2011), Program Director / Advisor (2013 – 17).
This sub-population experiences……..
 Five times more likely to be diagnosed with a mental health (MH) issue as compared to the general public.
 National Health Priority Areas: 1) arthritis, 2) musculoskeletal conditions, 3) asthma, 4) cardiovascular health, 5) diabetes mellitus 6) injury
7) obesity and 8) mental health.
 Increased morbidity and mortality.
 Decreased physical activity (PA) levels.
 Increased sedentary behaviours (SB) associated with technology use.
 Mental health is defined as a state of well-being in which every individual realizes his or her own potential, can cope with the normal stresses of life, can work productively
and fruitfully, and is able to make a contribution to her or his community.
Sources: Binkowska-Bury, M., & Januszewicz, P. (2010), Fried, L. (2011), Veness, B.(2016), Stallman, H. (2011), World Health Organisation (2014).

JUSTIFICATION – what is the importance?
“Few Australian universities have made a significant commitment to improving their students’ mental health, failing to acknowledge
its innate connection with their teaching and research objectives”
(Veness, 2016,p.4).
Important to University:
 Lower educational achievement,
 Increased chance of enrolment cancellations,
 Decreased employment,
 Lower incomes and standard of living,
 Negative learning and teaching experience,
 Increased impairment.
Important to Economy:
 Estimated $16 trillion lost from the world’s economy in the next 20 years.
 Costs Australian businesses $10.9 billion per annum 12 million days / year of lost productivity.
Sources: Veness, B.(2016), Stallman, H. (2011), Keegan, R., et al (2013), World Health Organisation. (2014).
PA – Physical Activity
SEWB – Social & Emotional Wellbeing
SC – Sporting Club

JUSTIFICATION – what is the importance?
Literature indicating: Why these 3 predictive factors ?
Justification Conclusion Literature Review
Links between PA / sport participation and increased
SEWB and MH evident…
 Yes, a great deal of research exists which has established the link between
increased PA / sport participation and improved levels of SEWB and MH.
 However……..
Biddle et al., 2011, Peluso et al.,
2005, Booth et al., 1997, Biddle et
al., 2001
MH and Aust Uni students….  More contemporary research is required. Storrie et al., 2010, Stallman, 2010,
Reavley et al., 2010.
PA, Sport and Aust Uni students…..  More contemporary research required. El-Gilany et al., 2011, Leslie et al.,
2001, Steptoe et al., 1997, Irwin et
al., 2004, Gómez-López et al., 2010,
Buckworth et al., 2004.
SEWB and Aust Uni students… • More contemporary research required. Rosenthal et al., 2008, Eisenberg et
al., 2007, Brown et al., 2008,
Tosevski et al., 2010.
Combination is unique…..  Predictive factors (PA levels + SEWB + SC involvement) and their impact on this
subpopulations’ MH.
 Throughout various personal and social domains – (personal, home, university
and community).
Bronfenbrenner’s Ecological Systems
Theory (1979, 1991)
Literature suggests….  More rigorously controlled studies are needed to clarify the benefits of PA on
MH care.
 That the importance of exercise is not adequately understood or appreciated
by patients and MH professionals alike.
 That exercise is an often-neglected intervention in MH treatment and
prevention.
 Research investigating the mechanisms by which exercise exerts its effect on
mental health has not been extensively studied and are consequently not
fully understood (Biddle et al., 2011, Craft, 2015).
Paluska et al., 2000; Morgan et al.,
2013, Battaglia et al., 2016,
Sharma et al., 2006
Methodological approach is unique…..  Implementation of mixed methods approach unique – predominately singular
methodology.
 Aim – multidimensional approach (quant, qual and spatial).

Currently, the proposed mechanisms for the physical activity and mental health relationship fall into three main areas.
Proven links to the three mechanisms :
Why is physical activity and sporting involvement so linked to mental health?
1. Biochemical mechanisms - reported in the literature; the endorphin hypothesis and
the monoamine hypothesis.
2. Physiological mechanisms - include an improvement of physiological functioning
with a link to mental health from epidemiological studies and the thermogenic
hypothesis.
3. Psychological mechanisms - include the distraction hypothesis, self-efficacy theory,
mastery and social interaction.
JUSTIFICATION – what is the
importance?

RESEARCH QUESTION & AIMS - what I wanted to achieve?
Research Hypotheses : Statistically – significant associations (+ or -)…
Ha1. The level of involvement in PA will influence university students’ MH diagnosis and SEWB as measured by indicators.
Ha2. The level of involvement in SCs will influence university students’ MH diagnosis and SEWB as measured by indicators.
Ha3. The level of SEWB will influence university students’ MH diagnosis as measured by indicators.
Research Question: Exploratory study investigating……
What is the level of influence predictive factors (PA, SEWB, SC) have on Australia’s university students’ MH status?
Research Aims: Establishing patterns of associations (+ or -)…. assessing strength of evidence against the null effect.
1. Students’ MH Profile (quantitative):
a) Determine (snapshot) Australia’s university students’ MH profile / status.
2. Students’ Predictive Factor Profile (quantitative):
a) Implement psychometric measurement components to collect descriptive statistics.
b) Determine (snapshot) Australia’s university students’ predictive factor profile / status as measured against the four domains (personal, university, home, and community).
3. Correlations / Associations (quantitative) :
a) Establish if there are any significant correlations / associations ( + or -) between predictive factors and the influence these have on their MH.
4. Lived Experience (qualitative):
a) Establish what students are witnessing and experiencing concerning MH – recommendations,
b) Establishing (snapshot) Social – Ecological (personal, home, community, university) and Predictive Factor (PA, SEWB, SC) Profiles.

THEORETICAL FRAMEWORK - underpinnings (approach and interpretation)
When giving consideration to researching mental health, there is a need to understand the
multidimensional and multifaceted nature / approach that is required……The PREDICTIVE FACTORS
Sources: Bronfenbrenner, U (1994), Elder, J P et al (2007).
Although previous research has focused
on identifying risk factors for mental
disorders (or ill-being), recent research
has demonstrated a shift towards factors
predicting mental well-being (Wilhelm
K., 2010).
Predictive Factors: Predictive
factors describe something that
increases a person’s risk of developing a
condition or disease. (MAYO Clinic,
2016).

…another layer …..the many scales / models that potentially could be used to measure mental health…….
Sources: Bronfenbrenner, U (1994), Elder, J P et al (2007), Vella S (2015)
Each scale / model is
complex, but all display a
‘common thread’ when it
comes to measuring mental
health (wealth)…that is..
an individual's MH is
impacted on by many
‘spheres of influence’. Each
can be measured as to their
impact on MH…..generally
indicated as DOMAINS /
indicators.

The socio-ecological model assists in describing the relationships and patterns of associations by
examining the….
• interrelatedness of different spheres of social life.
• multidimensional interconnections between a number of domains (social settings), such as:
1) personal,
2) home,
3) university,
4) community.
• particular patterns of health behaviours throughout population groups. (i.e. university students).
For this study…Psychometric measurement components were based on adopting :
• Bronfenbrenner ‘s (1979,1994) socio-ecological model to collect and interpret descriptive statistics , concerned with
measuring:
1. Three predictive factors (PA levels ,SEWB, SC involvement), against
2. Four domains (personal, university, home, and community).
Sources: Urie Bronfenbrenner’s Ecological Systems Theory (1979), Kenneth McLeroy’s Ecological Model of Health Behaviours
(1988), Daniel Stokols’s Social Ecology Model of Health Promotion (1992, 2003), The work of these and other researchers has
been used and modified and has evolved into what is referred to as the social-ecological model.

Diagrammatical view of
theory

METHODOLOGY – positivism and interpretivism
The analysis benefited from the perceptions drawn from personal and first-hand experiences - the research used qualitative data to
enrich the quantitative data.
(Bronfenbrenner ‘s (1994), Rasch (2000), Merriam, 2002; Creswell, 2008)

METHODS – how the data was collected?
Recruitment Strategy
• Flyers
• Posters
• Broadcast emails
from all 5 student
services and
program directors
to students.

METHODS – how the data was collected?
Participants
• Participants (N = 2326) were drawn from across five major Australian universities.
• Participants were invited by a number of email broadcasts to complete an anonymous online survey.
• The survey could be accessed by an embedded hyperlink, with the survey being hosted at a University.
• Ethics Committee approved (Ref No: EDN/56/15/HREC).
Survey design
• Cross-sectional, (Mixed Methods - Concurrent Triangulation): This is a multistrand design in which both QUAL and QUAN data
are collected and analysed to answer a single type of research question/s (either QUAL or QUAN). The final inferences are based
on both data analysis results. The two types of data are collected independently at the same time or with a time lag. The
combinations and comparisons of multiple data sources, data collection and analysis procedures, research methods, or inferences
that occur at the end of a study.
- Study design, consisting of an online survey, collecting participants’ personal variables (demographic and health
behaviours) was implemented at a point in time (snapshot of population – 6 weeks).
• Demographic information:
- Included participants’ gender, age, type of study, family status, employment status and residential location (postcode); whilst
health behaviours included collecting participants’ drinking, smoking habits and sedentary behaviour (SB) and predictive factor
profile.
• Measurement components:
- Collected based on adopting a socio-ecological model approach (Bronfenbrenner, 1994). Descriptive statistics were drawn
from measuring three predictive factors (PA levels, SEWB and SC involvement), against four domains (personal,
university, home, and community).
• Self-reflecting questions for each domain
- Resulting in detailed participant profiling. Statements using a 5-point Likert scale for agreement was used
(Strongly disagree=1, Disagree=2, Neutral=3, Agree=4, Strongly agree=5). The online survey consisted of 29 items.
• Open and closed questions:
- Used to collect both qualitative and quantitative data. Used to confirm, cross-validate, and corroborate findings within study.
Data collection is concurrent.Dr Wayne Usher - EPS

METHODS – how was the data analysed?
Quantitative Analysis
• Data analysis:
- Statistical Package for the Social Sciences (SPSS) (PASW20).
• Frequencies:
- Used for the initial data analysis to provide a profile of respondents based on personal variables and health
behaviours.
Validity and Reliability:
1. Factor loadings were visible if .250 or above, with the threshold for acceptable loadings set at .300. Identify the
underlying relationships between measured variables.
2. Bartlett’s test of sphericity (α = p<.05) and KMO=>.800. For indicators, the measurement of sampling adequacy
(Kaiser-Meyer-Olkin) and significance level of Bartlett's test of sphericity indicated that there were significant
relationships amongst items (p = 0.001) , and that the data were suitable for factor analysis.
3. Cronbach’s Alpha procedure was utilised to provide evidence for the Internal Consistency reliability of the factor
structure of identified factors (CA =>.700 acceptable).
4. Spearman’s rho correlation matrix was used to examine the strength of the association between personal
variables. A r = between 0.20 and 0.50 is acceptable.
5. P values indicated statistically significant associations (Sig. level p < 0.05* and p < 0.01**).
6. Optimal scaling was used to further explore associations between predictive factors, domains and the MH status
of participants.

METHODS – how was the data analysed?
Qualitative Analysis
• Participants (N=932).
• Asked to share their experiences (lived or witnessed) concerning MH issues amongst university
students.’
• Leximancer software:
- Was used to assist in the analysis process.
- Analyses text documents to identify the high level of concepts in text.
- Key themes and related concepts associated with the open ended question was identified.
- Provide possible solutions and recommendations.
• Qualitative data analysis procedures provided an important tool to understand hidden information, build a
profile of a university students who suffers from a MH issue and what possible strategies / professional
help they are responding to
(Establish socio-ecological and predictive factor profiles)
This qualitative study design will be located within the interpretive paradigm (Merriam, 2002;
Creswell, 2008) and provides a framework for this research, by way of how the data is: 1) collected,
2) analysed, 3) results presented and 4) results interpreted.

RESULTS – findings – demographic breakdown
Personal Variables
(demographics and health behaviors)
N %
Male 741 32
Female 1585 68
17 – 20 age 744 32
21 – 24 age 661 28
25 – 28 age 370 16
29 – 33 age 254 11
34 – 37 age 104 4.5
38 – 41 age 62 3
42 + age 131 6
Undergraduate 1553 67
Postgraduate 773 33
Part Time Work 1153 50
Aboriginal or Torres Strait Islander 30 1
Socioeconomic Status lower level (1 – 5 steps) 591 25
Socioeconomic Status upper level (6 - 10 steps) 1181 50
First in family to attend university 636 27
People living at home (M = 3) 604 26
Consistently drink > 10 standard alcoholic drinks / week (< 14 / week) 163 7
Smokers 125 5
Both drinker and smoker 250 11
Diagnosed mental health illness by a health professional 571 25
Sedentary behavior – hours / week (M = 31, SD = 24) 0-168 hours / week of SB
Personal variables (demographics and health behaviors)
With the aim of developing a body of responses, cases were eliminated iteratively until a stable base of 2326 that answered all questions was attained.
Table 1 represents a specific breakdown of personal variables (demographics and health behaviors) related to participants (n = 2326).
This study identified that
25% (n = 571)
of participants indicated that
they have been diagnosed
with a mental health (MH)
illness by a Health
Professional.

RESULTS – findings – Aim #1 : Students’ MH Profile (quantitative):
Rho correlation coefficient matrix was utilised to examine the extent of associations between a MH diagnosis / issues and participants’ personal variables. From
this study, a profile of Australia’s university student who is more inclined to suffer an MH diagnosis / issue has emerged (Table 2).
It has been identified that being……..
1) female,
2) age (17 – 25),
3) consistently consuming more than 10 standard alcoholic drinks each week,
4) being a smoker,
5) spending more hours per week on SB,
6) low socio economic status (SES), had a significant positive association with having been diagnosed with a MH issue.
In contrast, being …..
1) postgraduate student,
2) having more people living at home with them, had a significant negative association with having been diagnosed with a MH issue.
Statistics Spearman Rho Sig. (2-tailed)
Females .268** 0.000
Age (17 - 25) .201** 0.000
Postgraduate students -.255** 0.008
Number of people who live at home with you -.293** 0.000
Do you have part time paid employment? 0.084 0.102
Are you the first person in your family to attend a
university?
0.061 0.053
Do you consistently consume > 10 standard alcoholic drinks
each week?
.315** 0.001
Are you a smoker? .292** 0.000
How many hours per week do you spend on sedentary
activities?
.271** 0.001
Socioeconomic Status (Lower SES) .278** 0.001
Table 2: Spearman’s Rho for associations between MH diagnosis / issue and personal variables (N=2326)
•Correlations (**) were identified as being significant (positively or negatively) at the 0.01 and 0.05 levels (2-tailed)
For significance levels, the smaller
the decimal fraction, the higher the
level of significance.
.05 = five in hundred probability of
achieving this outcome by chance.
.01 = one in hundred probability of
.001 = one in thousand probability of
.000 = less than one in thousand
probability of achieving this
outcome by chance.
A slight to moderate relationship (.20 -.50) is acceptable. Consideration to sample size and nature of study – caution
interpreting results – not absolute. Due to large sample size, any relationship between variables is considered significant.
Aim to establish patterns of associations. The findings will help focus strategies for improving MH interventions.

RESULTS – findings - Aim # 2: Students’ Predictive Factor Profile (quantitative):
Personal SEWB (emotions/states of
being) were measured by specific
questions to explore strength of
associations between items:
• Happiness
• Resilience
• Body Image

RESULTS – findings - Aim # 2: Students’ Predictive Factor Profile (quantitative):

RESULTS – findings - Aim # 3: Correlations / associations (quantitative)
From the correlation matrix it has been identified that there are significant associations (positive / negative) between Australia’s university
students who reported higher levels of personal PA and / or SC involvement, in that they were less inclined to be diagnosed with a MH issue.
• Participants who identified higher personal PA, had a negative association to being diagnosed with MH issue.
• Participants who identified higher personal PA, had a negative association to negative feelings / involvement in SC.
• Participants who identified with negative feelings / involvement in SC, had a positive association to being diagnosed with a MH issue.
There is a significant associations (Sig. level p < 0.05* and p < 0.01**) between respondent’s level of importance for personal PA levels and participation in SC and them being diagnosed
with a mental health issue. A low Pearson correlation coefficient does not mean that no relationship exists between the variables. The variables may have a nonlinear relationship.
Sig (2-tailed) indicates level of
significance.
The standard ANOVA language
referred to significance at .05 (less
than .05) or .01 (less than .01) levels.
SPSS provides exact level of
significance.
Sig. level p < 0.05* and p < 0.01**
Correlations Personal PA
Negative feelings about
sporting clubs
Has a health
professional ever
diagnosed you with
a mental health
issue?
Personal PA Pearson Correlation 1 -.217** -.227*
Sig. (2-tailed) .000 .030
N 2326 2326 2326
Negative feelings about
club sports
Pearson Correlation -.217** 1 .117**
Sig. (2-tailed) .000 .000
N 2326 2326 2326
Has a health professional
ever diagnosed you with a
mental health issue?
Pearson Correlation -.227* .117** 1
Sig. (2-tailed) .030 .000
N 2326 2326 2326

RESULTS – findings - Aim # 3: Correlations / Associations (quantitative)
The strongest correlation is between negative personal SEWB and negative feelings / participation in SCs, with these lines
closely aligned. From the data, it can therefore be inferred, that higher identified rates of participation in SC involvement
positively influences Australia’s university students’ SEWB status.
Here one can see that the most closely aligned vectors are between negative personal SEWB and negative
feelings / involvement in club sports. One would expect these two variables to be correlated at a significant level.

(Sig. level is p < .05* and p < 0.01**)
.000 = less than one in thousand probability of achieving this outcome by chance.
A slight to moderate relationship (.20 -.50) is acceptable. Consideration to sample size and nature of study. Due to large
sample size, any relationship between variables is considered significant. Aim to establish patterns of associations. The
findings will help focus strategies for improving MH interventions.
Viewing the Correlation Matrix one
can start to ……establish patterns of
association between the personal
domain across PA, SEWB and SC.

1. There are significant (P < 0.05* and p < 0.01**) associations between variables ...indicating that one variable does impact / have a strong association with the other.
2. An α of 0.05 indicates that the risk of concluding that a correlation exists—when, actually, no correlation exists—is 5%.
3. There is sufficient evidence at the α level (0.05) to conclude that there is a significant linear association between variables. In short, if one (-) variable increases, the other variable
decreases with the same magnitude, and vice versa.
4. With a p = .000 it goes to indicate that less than one in one thousand probability of achieving this outcome by chance.
5. A low Pearson correlation coefficient (r) does not mean that no relationship exists between the variables. The variables may have a nonlinear relationship.
TO SUMMARISE --- The patterns of association indicate ---- Three important points from data (+ 1 assumption) –
1. Higher reported levels of the 3 Predictive Factors (personal) negatively associated with a MH diagnosis (significant associations – p=.000).
2. Level of Personal PA could be seen as a predictor (benchmark) for determining a university students’ personal SEWB, SBs and MH status (significant associations – p=.000).
3. Happiness --- Perhaps more research as to the ‘pursuit of HAPPINESS’?.....What does it mean to be HAPPY? ….. How to achieve HAPPINESS?
• What will make Australia’s early 21st century university students HAPPY? (longitudinal studies).
4. Hope or a sense of hopelessness in early 21st century – esp with youth….What increases a persons HOPE?
0.000 = less than one in thousand probability of achieving this outcome by chance.
A slight to moderate relationship (.20 -.50) is acceptable.. Consideration to sample size and nature of study. Due to large
sample size, any relationship between variables is considered significant. Aim to establish patterns of associations. The
findings will help focus strategies for improving MH interventions.
Addressing Research Question (level
of influence) and Research Aims
(patterns of associations).
P-value ≤ α…The correlation is
statistically significant.
Domain N Factor Association Correlation (r)
P value (p)
Higher reported levels
Personal SEWB
2326 - MH diagnosis
+ Happiness
+ Resilience
+ Body image
r = -.319**, p=.000
r = -.376**, p=.000
r = -.383**, p=.000
+Happiness and Resilience r = .586**, p=.000
+ Happiness and Body Image r = .434**, p=.000
+ Resilience and Body Image r = .324**, p=.000
Personal PA
2326 - MH diagnosis r= -.227*, p=.030
- Sedentary behaviour r= -.239**, p=.000
- Prefer technology over PA r= -.276**, p=.000
+Happiness r= .356**, p=.000
+ Resilience r= .315**, p=.000
+ Body Image r= .363**, p=.000
+ SC enjoyment r=.229**, p.000
Personal SC Involvement
2326 - MH diagnosis r= -.214**, p=.000
+ Happiness r=.386**, p=.000
+ Resilience r=.321**, p=.000
+ Body Image r=.210**, p.000

Aim to establish patterns of associations. The findings will help focus strategies for improving Predictive Factors.
Establishing patterns of association
between all 4 domains across PA,
SEWB and SC.
There were significant
associations (positive) between
participants’ PA and SEWB
profiles (across the 4
domains).
One could assume that
university students who
undertake more PA (make it a
priority) have more positive
SEWB profiles across the 4
domains – personal, home,
university, community.
Significant ‘noise’ (p<0.01**)
created from the patterns of
associations.

Aim to establish patterns of associations. The findings will help focus strategies for improving Predictive Factors.
Establishing patterns of association
The level of involvement in PA will influence university students’ MH diagnosis and SEWB as measured by indicators.
The level of involvement in SCs will influence university students’ MH diagnosis and SEWB as measured by indicators.
The level of SEWB will influence university students’ MH diagnosis as measured by indicators.
Summary:
Data patterns suggest:
……higher levels of PA and / or SC involvement indicate significant
negative associations (influence) with a MH diagnosis.
..….. higher levels of (+) SEWB indicate significant
negative association (influence) with a MH diagnosis.
….. higher levels of PA and / or SC involvement indicate significant
positive associations (influence) with (+) SEWB.

RESULTS – findings - Aim # 4: Lived Experience (qualitative):
The analysis indicated that the content was frequently attributed to the terms:
issues, time and people.
As indicated in Figure 1, there were about twice as many hits related to the thematic
concept of issues as to time, with a smaller number again related to the thematic
concept of people.
Students had no prompting associated with 4 domains
(personal, university, home, community) other than the
previous quantitative questions. Open ended question was
asking for their personal ‘lived or witnessed’ experiences
with MH amongst university students.

As illustrated in Figure 2, with 100% of concepts visible and with thematic size set at 75%, the three major thematic concepts of people, time
and issues overlap.
The thematic concepts:
Issues - students, cause, stress, depression, anxiety, mental, diagnosed, problems, and environmental.
Time - semester, degree, study, university, academic, experience, work and pressure.
People - friends, family and relationship.
To an extent one could propose that the related concepts go to re-enforce /
support the theoretical approach that has underpinned this research =
SOCIO-ECOLOGICAL Model…….that is, findings align with the 4 domains:
personal, home, community (?) and university.

Table 9 is a collection of a few individual commentaries and recommendations concerning the current status of Australian
university students’ mental health status.
Participant comment (lived experience) Participant recommendations
I often feel isolated at university and find it hard to
make the step from uni friends to friends that can spend
time together outside of uni. In high school, I was
thoroughly involved in sport, physical activity,
community service and leadership. I am yet to find
something in the uni that can fulfill these needs.
The university needs to devote resources to ensuring
that all students can access social events – for all
ages and programs. The sporting program needs to be
more affordable and more variety needed.
In my undergrad studies I had really bad depression
and failed two subjects because I couldn't even get
myself to move out of bed. I was numbly watching
deadlines go past. I was previously high-achieving and
a perfectionist. I didn't make any friends at uni and I
felt like no one was interested in talking to me at
tutorials.
More support networks and individualised approach
to mental health serves in universities. This is a major
issue for uni students, but little is being done.
Causes can relate to loneliness, isolation and the use of
coping mechanisms such as alcohol, drugs and
pornography.
Potential solutions and strategies are having a
support network of family and friends and avoiding
alcohol, drugs and pornography.
Students do not really talk about their mental health at
uni. It is something that is 'assumed' for a particular
student that is stressed or visibly anxious. I am not sure
how this would be supported as I see too many factors
that contribute here, including stress, family, need to
succeed, peers, lecturers,alleged bullying, and
general home life issues (just to name a few).
More ways for students to talk about mental health
issues. More services on campus that allow students
to get individual help quickly.
Thematic analysis using Creswell’s (2008)
Visual Model of the Coding Process in
Qualitative Research

RESULTS – findings-Aim # 4: Lived Experience (qualitative):
SOCIO – ECOLOGICAL PROFILE – Personal, Community, Home, University (N = 932)
Recommendation:
Strategies that engage
COMMUNITY and
HOME initiatives (early
diagnosis, prevention,
support) …outside of
university.
Question: ‘I am
involved in
community
projects?’ scored
very low in
agreement.
Little reference to
the importance of
community and
home connections
to support
individuals with
mental health
issues.
‘PERFECT STORM’

RESULTS – findings -Aim # 4: Lived Experience (qualitative):
SOCIO – ECOLOGICAL THEMATIC PROFILE

PREDICTIVE FACTOR PROFILE – Physical Activity, Social Emotional Wellbeing, Sporting Club (N = 932)
Recommendation:
Strategies that engage
Physical Activity and
Sporting Club
initiatives (increase
access and promote
the positive health
benefits of PA and SC
involvement)
…throughout
university and
community.
Questions: ‘I am too
busy and fees are
too expensive to
participate in SC?’
scored very high in
agreement.
Little reference to
the importance of
physical activity
and sporting club
involvement as a
benefit for
promoting mental
health.
Many students living in
‘quiet desperation’
The silent epidemic

PREDICTIVE FACTOR THEMATIC PROFILE

CONCLUSION – bringing it together
Preliminary findings suggest…..
SEWB Predictive Factor
 Generally happy, as measured against the four domains, being….
1) personal {M=3.73, A%=56, SA%=16}, ( r = .586**, p = .000, r = .436**, p = .000),
2) university {M=3.85, A%=59, SA%=16} (r = .422**, p = .000),
3) home {M=4.08, A%=52, SA%=31} ( r = .367**, p = .000),
4) community {M=3.98, A%=62, SA%=20} (r = .367**, p = .000).
 Substantial, ‘at risk’, pockets - of students with MH issues, being 25% of participants.
Number of students who reported that they suffer from either,
1) high anxiety (45%),
2) loneliness (30%),
3) hurt feelings (34%),
4) an inability to cope with negative life situations (39%).
5) I tend to get angry a lot (16%).
6) I self harm as a way of coping (6%).
 Warning signs - the above negative SEWB indicators influence a MH diagnosis amongst university students.
 Summary - gender, age, health behaviours, higher positive SEWB predictive factors (happiness, resilience and body image)
were found to be significantly associated with and influence a MH diagnosis.
“Few Australian universities have made a significant commitment to
improving their students’ mental health, failing to acknowledge its innate
connection with their teaching and research objectives”(p.4).
Personality Traits – perhaps a need to address / measure
PT as a way of determining mental health and suicide
tendencies? Develop ways to empower individuals around
resilience, emotional intelligence / regulation.
SUICIDE TENDENCIES vs MENTAL HEALTH
Initial step forward.
Implementing strategies to promote emotional
resilience and regulation --- combined with
increasing PA levels and SC involvement.
PA and SC provide positive vehicles for this.

PA Predictive Factor
 Australian PA guidelines - at least 60 min of moderate to vigorous PA every day, with a maximum of two hours screen-
based activity for entertainment/non-educational purposes a day.
 Failing to meet National PA guidelines, with….
1) only 36% partaking in daily moderate and 12% in daily vigorous PA levels,
2) the preferred levels were recorded at 2 times per week for moderate (70%) and vigorous (31%) PA.
 Indicate, that they….
1) feel happier after undertaking PA {M=4.24, A%=40, SA%=44}, (r=.356, p = .000),
2) wish they could do more PA {M=4.07, A%=54, SA%=30},
3) are happy with the university PA infrastructure {M=3.65, A%=43, SA%=18},
4) are happy with the community PA infrastructure {M=3.76, A%=51, SA%=18},
5) do most PA at home {M=3.37, A%=36, SA%=18}.
 Summary – Personal PA predictive factor was found to have an statistically significant association with a MH diagnosis…..Seems
to be a prediction of Australia’s university students’ MH status, SEWB, SC involvement and SBs - (p = .000).
“Although people with depression tend to be less physically active
than non-depressed individuals, increased aerobic exercise /
strength training reduces depressive symptoms significantly” (p.35).
IMPORTANT – another layer to the socio-ecological model, that is
POLICY IMPLEMENTATION

SC Predictive Factors
 Indicate….
1) fees to expensive to play club sport {M=3.83, A%=52, SA%=20},
2) to busy to play club sport {M=3.53, A%=41, SA%=19}.
 Summary - higher identified rates of personal SC involvement positively influences Australia’s university students’ MH and
SEWB status – (p = .000).
“Emotional wellbeing is one aspect of good mental health, and there is evidence to support the notion that
emotional wellbeing supports sport participation and athletic performance, and vice versa. Sport participation
appears to have an influence on one’s psycho-social development, even from a very young age and
particularly during fundamental (i.e. not elite) stages of sport development” (p.34).

RECOMMENDATIONS – summary of recommendations.
NEW DELIVERY MODELS
We know that certain aspects of university life and a students’ transition into adult hood will go unchanged – that is,
there will always be ---- assessment stress, limited money, physical and psychological changes, changing social
circles, pressures to work and live, moving away from parents and home, increased autonomy, poor health choices
etc…THEREFORE we need to acknowledge this and … create new ways of working……
1. MENTAL HEALTH SERVICES
• More MH services and more access to these services seem to be common recommendation.
• To a large degree traditional delivery and services to address MH not working.
• A new delivery model is needed….what does it look like?
 Online (ubiquitous, meets demands of youth),
 Easy access,
 Readily accessible via different modes of communication,
 In combination with f2f,
 View differently how we predict MH and SEWB – personality traits and biomarkers,
 Provides levels of referrals – high to low – linking all aspects of socio-ecological approach (home, university,
community and personal). What does this look like?
2. PHYSICAL ACTIVITY
• National standards in PA difficult to impossible to achieve (60 minutes of mod to vig PA / day),
• Promote more widely the inherent benefits of PA on MH and SEWB.
• A new delivery model is needed…what does it look like?
 Psycho/physical benefits to 7 minute workouts,
 Campus 7 minute PA workout centres / facilities,
 Health professionals prescribing PA as an approach to MH (in combination with medication),
 Incentives for undertaking more PA – connected to health insurance.
 Level of Personal PA could be seen as a predictor (benchmark) for determining a university students’ SEWB, SBs
and MH status (significant associations – p=.000).

RECOMMENDATIONS – summary of recommendations.
NEW DELIVERY MODELS
1. STRONGER COMMUNITY / SOCIAL LINKS
• Embedding in university programs more opportunities / expectations for students to undertake Community
projects – Community Internship Programs.
• Promoting the inherent benefits to community engagement for MH and SEWB – decrease isolation.
• More f2f social groups on campus or meeting places off campus – decrease isolation.
2. SPORTING CLUB INVOLVEMENT
• Identified high fees and limited time as main issues to decreased SC involvement,
• A new delivery model is needed…what does it look like?
 Health professionals prescribing SC as an approach to MH (in combination with medication),
 Incentives for undertaking more SC – connected to health insurance,
 Lower fees,
 Promotion as to inherent benefits to SC involvement – psychosocial,
 Embedding in university programs more opportunities / expectations for students to undertake SC
involvement – Sporting Internship Programs.
3. NEW RESEARCH DIRECTIONS
• Large longitudinal studies looking into the ‘Pursuit of Happiness’ for Australia’s university students -
measured again against the four domains – community, home, personal and university.
• University overly concerned with determining ‘student satisfaction’ not happiness.
• What does it look like for early 21st century university students?
• How can it be achieved?
• What MH and SEWB strategies are rhetoric and what are realities?

UC team recommends aerobic and strength
training for maintaining mental health
2017 – CAMPUS REVIEW -
• evidence is strong enough to recommend prescribing both aerobic and
resistance exercise to improve brain health in people over 50.
Uni students’ mental health under radar
2017 – CAMPUS REVIEW –
• “When I first started university, I found it very difficult … I found it a very isolating place,” he said,
adding he suffered anxiety symptoms, panic attacks and had disrupted sleep, which caused him to
miss classes.
• “A lack of sleep, poor diet, drug and alcohol use, financial stress, work/study balance, living away
from family and performance pressures are among the risk factors that can result in, or exacerbate,
mental ill-health and psychological distress among university students,” the report said.
• It found some university staff lack an understanding of the seriousness of mental health conditions
and of the best ways to respond to students’ needs.
• The report said universities need to offer youth mental health training to tutors and administration
staff who come into frequent contact with students.
• It also called for institutions to ensure students have easy access to online mental health services.
• The report also urged the federal government to increase support for students with mental ill-
health.

IDEALLY - A Multi-Dimensional framework to evaluate the extent and distribution of MH aspects – spatial pattering to give a more
detailed view of MH and predictive factors….assist in the allocation of resources …ensure preventative strategies are tailored to
fit areas of need and not wasted in areas of limited need.
Usher, W., Gudes, O., & Parekh, S. (2016). Exploring the use of technology pathways to
access health information by Australian university students: a multi - dimensional
approach. Health Information Management Journal, 45, 5–15.
METHODOLOGY – multi - dimensional

IMPACT – what is the impact of the study?
Stage 1: Quantitative data – Health Promotion International - JIF = 1.8 (Published)
Qualitative data – Studies in Higher Education – JIF = 1.3 (Under review)
Stage 2: Publish further findings using Multi-Dimensional framework (Usher, et al., 2015).
Stage 3.
• Larger competitive grants – Physical activity levels in school aged students across South
East Queensland = Physical Activity NAPLAN + SEWB research.
• Working with Industry Partners (NRL) – grassroots physical literacy programs for engaging
inactive students across Early Child Care, Primary and Secondary settings…inherent
benefits of PA on MH.

THE END
TERMINOLOGY
• Health Status: Health is a continuum, and extends the notion of health to include states of positive well being. Health is a state of complete physical, mental and
social wellbeing and not merely the absence of disease or infirmity. Health is often defined in terms of its negative aspect (e.g. ill-health), and a key focus of the
health area of concern is the presence or absence of sickness, disease, injury and disability within the population (WHO, 2014).
• Physical Activity (PA): Any bodily movements performed by skeletal muscles that result in an increase in energy expenditure (ABS, 2008).
• Moderate Physical Activity (MPA): Requires a moderate amount of effort and noticeably accelerates the heart rate (e.g. brisk walking, carrying or moving loads,
and active involvement in drill, games and sports) (WHO, 2014).
• Sedentary Behaviour (SB): Activity that results in almost no increase in energy expenditure, and usually involves sitting or lying down (ABS, 2008).
• Socioecological Model: describes the interrelatedness of different spheres of social life and the interactions between individuals and their environments
(Bronfenbrenner, 1994).
• Social and Emotional Wellbeing (SEWB): The general state of being happy, feeling safe, having positive relationships with others, being interested in the welfare
of others, and being involved in and striving to do one’s best in a wide range of activities (e.g. art, music, sport, exercise). (Bernard et al., 2007).
• Sporting Club (SC): An activity involving physical exertion, skill and/or hand-eye coordination as the primary focus of the activity, with elements of competition
where rules and patterns of behaviour governing the activity exist formally through organisations. Club sport is organised externally to school, where training and
completion is after school and on weekends. High level of rules and training” (ABS, 2008).
• Vigorous Physical Activity (VPA): Requires a large amount of effort and causes rapid breathing and a substantial increase in heart rate with a noticeably higher
increase in heart rate (e.g. running, carrying or moving heavy loads, active involvement in competitive games and sports) (WHO, 2014).
• Psychometrics: is the field of study concerned with the theory and technique of psychological measurement, which includes the measurement of knowledge,
abilities, attitudes, and personality traits. The field is primarily concerned with the study of differences between individuals.
• Predictive Factors: Predictive factors describe something that increases a person’s risk of developing a condition or disease. (National Cancer Institution, 2016).

 Well-reasoned statistical arguments contain much more than the value of a single number and whether that number exceeds an arbitrary
threshold. The ASA statement is intended to steer research into a ‘post p<0.05 era. When a task is not seen in a meaningful context it is
experienced as being arbitrary.
The post p<0.05 era
 Good statistical practice is an essential component of good scientific practice …. “principles of good study design and
conduct, a variety of numerical and graphical summaries of data, understanding of the phenomenon under study,
interpretation of results in context, complete reporting and proper logical and quantitative understanding of what data
summaries mean.”
1. Background – what is the situation?
2. Justification – what is the importance?
3. Research Question & Aims – what did I want to achieve?
4. Theoretical Framework – underpinnings (inform approach for data collection and data interpretation).
5. Methodology - principles that guided this research (positivism and Interpretivism).
6. Methods – data collection (mixed methods - quantitative & qualitative tools).
7. Methods – data analysis (mixed methods - quantitative & qualitative tools).
8. Results – findings (quantitative & qualitative).
9. Conclusion – bringing it together.
10. Recommendations - summary of recommendations.
11. Impact – what is the impact of this study?

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Australia's University Students and Mental Health