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Nursing and Health Sciences (2015), 17, 134–142

Research Article

A cross-sectional investigation of depressive, anxiety, and stress symptoms and health-behavior participation in Australian university students Geoff P. Lovell, PhD, Kim Nash, BSocSc (Psych) (Hons), Rachael Sharman, PhD and Ben R. Lane, BSocSc (Psych) (Hons) School of Social Sciences, University of the Sunshine Coast, Maroochydore DC, Queensland, Australia

Abstract

Transitioning to university involves a major life change that can have implications for physical and mental health. This study had three objectives: first, assess the mental health and health-behavior participation of Australian university students; second, evaluate clustering of health behaviors; and third, examine how mental health relates to health behaviors. University students (n = 751) enrolled at an Australian regional university completed an online survey containing the Depression, Anxiety, and Stress Scales and a health-behavior questionnaire. Over one-third of students reported mild or higher mental illness symptoms and most reported engaging in multiple unhealthy behaviors. Furthermore, mental health was associated with unhealthy behaviors. For males, depressive symptoms were associated with skipping breakfast and poor sleep quality. For females, depressive symptoms were associated with skipping breakfast, inadequate vigorous physical activity, and short or long sleep hours. Stress symptoms in females were associated with healthy sleep hours, but poor sleep quality. Future research may consider whether an intervention targeting one or two key health behaviors has utility in improving participation in other health behaviors and mental health.

Key words

anxiety, depression, health behavior, mental health, stress, university students.

INTRODUCTION Attending university is potentially a major life transition. For many it involves moving away from home and family, and an increase in independence and autonomy that brings new choices, pressures, and problems. Studies examining students in the United Kingdom and USA have shown that attending university can present a significant threat to maintaining mental health (Bewick et al., 2008; Burris et al., 2009). Indeed, research has also found that students present with poorer mental health compared to peers in the general population (Roberts & Zelenyanski, 2002). Many factors have been shown to relate to mental health in university students, such as optimism, religiosity, and health values (Burris et al., 2009; Papazisis et al., 2013), and financial hardship and long working hours (Roberts et al., 1999). A problem with many of these factors is that they are not, or not easily, modifiable. Health behaviors are a modifiable factor and, generally, students tend to have poor compliance with health-behavior guidelines (e.g. Keller et al., 2008), perhaps partly through a loss of facilitating factors, such as parental Correspondence address: Ben R. Lane, School of Social Sciences, University of the Sunshine Coast, Locked bag 4, Maroochydore DC, QLD 4558, Australia. Email: [email protected] Received 30 September 2013; revision received 5 February 2014; accepted 26 March 2014

© 2014 Wiley Publishing Asia Pty Ltd.

encouragement (Hosseini et al., 2013). This is concerning given some health-behavior patterns, such as physical activity participation, are likely to establish in young adulthood and continue through life (Gordon-Larsen et al., 2004). Health behaviors include actions taken to maintain physical health, such as limiting alcohol and tobacco use, and engaging in healthy physical activity, diet, and sleep habits.The Australian Government has issued several health guidelines, mostly equivalent to guidelines internationally. These include having five vegetable servings and two fruits servings per day; engaging in 30 min of physical activity, most days per week; not smoking; and having no more than four standard drinks (10 g alcohol/drink) on a single occasion and no more than two standard drinks on a regular basis (Australian Government Department of Health, 2013; 2014; National Health and Medical Research Council, 2009; 2013; please note that the physical activity guidelines have changed since this study was conducted). The American College of Sports Medicine has specified that at least 20 min of vigorous activity, three days per week can also satisfy physical activity requirements (Garber et al., 2011). Past research has identified that health-behavior participation tends to cluster in that people who fail to meet guidelines for one health behavior are likely to fail guidelines for other behaviors. For instance, in a German university sample (n = 1262), more participants failed criteria for two (34.5%), doi: 10.1111/nhs.12147

Mental health and health behaviors

three (34.8%), or four health behaviors (18.2%; including inadequate diet, physical inactivity, tobacco use, and alcohol use) than for one (10.5%) or none (2%; Keller et al., 2008). Notably, the clustering in this university sample was more pronounced than that in various adult samples (Schuit et al., 2002; Poortinga, 2007). Clustering has also been shown through findings that white men and women who smoked were less likely to have adequate fruit and vegetable intake than those who did not smoke (Zhou & Oh, 2012). In turn, those who did have adequate fruit and vegetable intake were more likely to engage in regular physical activity. Potentially, the effort required to engage in one health behavior encourages engagement in other health behaviors or avoidance of risk behaviors. Although generally not presented in health guidelines, sleep behaviors have also been documented as an important physical and mental health consideration. College students form a population who face particular sleep challenges, stemming perhaps from a unique combination of social, academic, and work pressures (Pilcher et al., 1997). This is concerning given that quality sleep has been associated with psychological well-being; poor sleep quality may lead to poorer psychological well-being, while poor psychological well-being may contribute to poorer sleep quality (Steptoe et al., 2008). Sleep quality has also been associated with academic success in university students (Becker et al., 2008). Although research with non-clinical samples suggests sleep quality is possibly of greater importance to health and well-being (Pilcher et al., 1997), short or long sleep hours remain a notable symptom to some mental disorders, including major depressive disorder (American Psychiatric Association, 2000). Sleeping less than seven or more than nine hours per night has also been associated with greater mortality risk, although this link is explained mostly through comorbidities (Kripke et al., 2002). Thus, both quality and quantity should be considered when investigating associations with sleep. Studies have linked depressive symptoms in university students to lack of physical activity (Tyson et al., 2010; Elliot et al., 2012), tobacco use (Roberts et al., 2010), and poor diet behaviors and short or long sleep (Allgöwer et al., 2001). For instance, Allgöwer et al. found that European students with mild or higher depressive symptoms were more likely than students without depressive symptoms to be sedentary, not eat breakfast, have sleep hours outside 7 to 9 h, and not use a seat belt regularly. These relationships remained when controlling for social support. Females, but not males, with depressive symptoms were also more likely to smoke. The causal direction of these relationships is unclear. Feelings of depression likely reduce participation in healthy behaviors, but health behaviors may also affect such feelings. For instance, physical activity appears to be effective in alleviating depression (Blumenthal et al., 2007), but dysphoria associated with depression likely also leads to less activity (Elliot et al., 2012). Although the above described research suggests that relationships between depressive symptoms and health behaviors are a consistent finding, other variables may also be relevant to these relationships. For instance, Steptoe et al. (1996) found that the naturalistic stress of exam times was

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associated with less physical activity and more alcohol and tobacco consumption. Strine et al. (2005) found that frequent anxiety symptoms were related to smoking, drinking, physical inactivity, and obesity even when adjusting for frequent depressive symptoms. Given these findings, it is not clear which mental illness symptoms are most relevant to health-behavior participation. Depressive, anxiety, and stress symptoms likely constitute a general dimension of psychological distress and are often correlated (Henry & Crawford, 2005), however, they should be considered distinct constructs (Lovibond & Lovibond, 1995). Adjusting statistical analyses to accommodate these factors may help to isolate which factors are most important in determining health-behavior participation. Regarding other potential factors, research with students is advantaged as students form a relatively homogeneous population, having similar education and physical health conditions (Allgöwer et al., 2001). However, studies have often reported gender differences regarding health behaviors (Steptoe et al., 1996; Allgöwer et al., 2001). Many researchers have noted limited generalizability of findings or a need to examine samples of greater diversity (Burris et al., 2009; Schleicher et al., 2009; Roberts et al., 2010). University culture likely varies between countries, potentially affecting rates of mental illness symptoms and health-behavior participation. Australian students are unique in that a large proportion of mature-age students make up the population. This is evident in Australia-wide university statistics that have shown over 40% of students to be aged 24 years or older (Australian Government Department of Industry, Innovation, Climate Change, Science, Research and Tertiary Education, 2011. These mature-age students likely form a distinctive group compared with recent high-school graduates and, for instance, research has shown younger Australian students the most likely to binge drink (Rickwood et al., 2011). Australia is also distinct in having many universities in regional, low population-density areas. Students in such areas are particularly underresearched compared to those in metropolitan areas.

Study purposes Considering limited research with Australian students, this investigation’s purpose was first, to quantify students’ depression, anxiety, and stress levels and health-behavior participation; second, to evaluate the presence of potential health-behavior clustering; and third, to examine the relationships between health behaviors and mental health.

METHODS Design This study used a cross-sectional design to assess correlations between health-behavior participation and mental health. These variables were operationalized through a self-report questionnaire delivered online to university students. © 2014 Wiley Publishing Asia Pty Ltd.

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Table 1.

G. P. Lovell et al.

Male and female participant characteristics

Characteristic Caucasian Born in Australia Studies full-time Age 18–25 26–45 46–63 Year at university 1st 2nd 3rd Honors Post-graduate Work Full-time Part-time No work Receives government benefits

Male (n = 163) n (%)

Female (n = 588) n (%)

Overall (n = 751) n (%)

129 (79) 117 (74) 134 (82)

466 (79) 466 (79) 474 (81)

595 (79) 583 (78) 608 (81)

86 (53) 53 (33) 21 (13)

353 (60) 171 (29) 57 (10)

439 (59) 224 (30) 78 (10)

77 (47) 31 (19) 35 (22) 4 (3) 16 (10)

262 (45) 139 (24) 129 (22) 9 (2) 49 (8)

339 (45) 170 (23) 164 (22) 13 (2) 65 (9)

16 (10) 82 (50) 65 (40) 49 (30)

36 (6) 337 (57) 215 (37) 173 (29)

52 (7) 419 (56) 280 (37) 222 (30)

Participants All students enrolled at a regional Australian university, the University of the Sunshine Coast, Queensland, over age 18 years were invited via email to take part in the online survey. Seven hundred and fifty-one participants (588 females, 163 males) completed the survey, with ages ranging between 18 and 63 years (M = 28.00, SD = 11.06). This sample represented over 11% of the University population. Females, undergraduates, and older students were slightly overrepresented in the sample compared to the population. Females composed 78.8% of the sample (65.5% in the population), undergraduates composed 90.1% of the sample (78.4% in the population), and the average age in the sample was 28 years (25 years in the population). Additional participant characteristics data are presented in Table 1.

Ethical considerations The Human Research Ethics Committee at the University of the Sunshine Coast granted ethics approval prior to commencement of the investigation. Participants accessed an information sheet and gave informed consent before beginning the survey. The information sheet informed participants that their responses were anonymous and there was no way to link responses back to an individual.

Data collection Students enrolled at the University of the Sunshine Coast, Queensland, for the 2010 academic year, received an allstudent email before the intersemester break and again in week two of semester two, 2010, asking for volunteers to complete an anonymous online survey. The survey took approximately 15 min to complete. © 2014 Wiley Publishing Asia Pty Ltd.

Depressive, anxiety, and stress symptoms were assessed using the Depression, Anxiety, and Stress Scale short form (DASS-21; Lovibond & Lovibond, 1995; Henry & Crawford, 2005). The subscales of DASS-21 have been shown to have adequate construct validity through confirmatory factor analysis and convergence with other measures of depressive and anxiety symptoms (Henry & Crawford, 2005). For the present sample, Cronbach alphas for the depression (α = 0.91), anxiety (α = 0.78), and stress (α = 0.85) subscales all showed high internal consistency. A 32-item health-behavior survey was constructed consisting of the Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989) to measure sleep quality and duration, and eight items used in previous research to measure diet, physical activity, alcohol consumption, and tobacco use health behaviors. The PSQI consists of 24 items and measured sleep across seven dimensions of subjective sleep quality including sleep disturbances, sleep latency, sleep duration, daytime dysfunction, habitual sleep efficiency, and use of sleep medication (Buysse et al., 1989). Participants are asked to respond based on their experiences on most days and nights of the past month. Scores from the seven dimensions sum to provide a global sleep-quality score; scores over five indicate probable poor sleep quality (Buysse et al., 1989). The PSQI has shown high internal consistency, test-retest reliability, and internal homogeneity (Grandner et al., 2006). Cronbach alpha for the present sample indicated acceptable internal consistency (α = 0.68). Eating behavior was assessed by asking participants if they usually ate breakfast daily or sometimes and how many servings of fruit and vegetables they would eat per day. Eating breakfast daily and having greater than or equal to five servings of vegetables and two servings of fruit indicated meeting the respective guidelines. Physical activity was assessed similar to Pate et al. (1996); participants were asked “How

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Table 2. Student mental health data compared to population data

Sample This study (n = 751) Population (n = 497)

M 4.75 2.57

Depression SD

Median

M

3 1

3.95 1.74

5.03 3.86

Anxiety SD 3.81 2.78

Median

M

3 1

7.01 3.99

Stress SD

Median

4.61 4.24

7 3

Population data sourced from Crawford et al. (2011).

many of the last 14 days have you done at least 20 min of hard exercise that made you breathe heavily and your heart beat fast?” and “How many days in a usual week do you accumulate 30 min or more of moderately strenuous physical activity such as walking or going to the gym?” Participants who indicated three or more days of vigorous activity and five or more days of moderate activity were classified as meeting the respective guideline. To measure alcohol consumption, participants were provided with a standard drinks guide and one question similar to Paschall et al. (2005): “Using the scale above, the last time you consumed alcohol, how many standard drinks did you have?” Participants selected from the options of zero, one to four, five to eight, and nine or more. Selections involving five or more standard drinks resulted in classification as a binge drinker. Two items measured tobacco use similar to Schleicher et al. (2009): “How many days in the last month have you smoked a cigarette?” and “On the days that you have smoked, how many cigarettes did you smoke on those days?” Any amount of tobacco use resulted in classification as a smoker.

Data analysis The Statistical Package for the Social Sciences (SPSS) was used to analyze the data. Students’ mental health was assessed via inspection of depression, anxiety, and stress scores on the DASS-21. Means and standard deviations were determined and scores were also categorized according to the presence or absence of mild or higher symptoms. Student health-behavior data were converted into dichotomies of meeting or not meeting the respective guideline, according to the guidelines outlined in the “Data collection” section. Sleep behavior was categorized according to sleep quality (scoring five or higher on the PSQI) and short or long sleep hours (outside 7 to 9 h). To assess the second research objective, respondents were grouped according to their combination of meeting guidelines for four health behaviors based on previous research (vegetable consumption, physical activity, smoking status, and alcohol use). Frequencies for each category were then determined. For the third research objective, separate logistic regression analyses were conducted for each health behavior (smoking, physical inactivity, no vigorous activity, no breakfast, low fruit intake, low vegetable intake, alcohol binging, short or long sleep hours, and poor sleep quality), with depression, anxiety, and stress symptoms and age being entered as covariates. Separate analyses were also con-

ducted for males and females. The presence of no depression, no anxiety, or no stress symptoms were used as reference categories for the respective odds ratios reported. An adjusted odds ratio greater than 1.00 indicates that those with mental illness symptoms (either depression, anxiety, or stress) were more likely to report the unhealthy behavior habit compared to those without symptoms, whereas an adjusted odds ratio less than 1.00 indicates that those with mental illness symptoms were less likely to report the unhealthy behavior habit compared to those without symptoms. Statistical significance was assessed with an alpha level set at 0.05. Cross-tabulations were also run to determine the frequencies for each category.

RESULTS Student depression, anxiety, and stress levels and health-behavior participation Depression, anxiety, and stress scores for the student sample were higher than scores in a normative Australian general population sample (Crawford et al., 2011; see Table 2). Mild or higher symptoms were present for 21.8% of the sample for depression, 28.5% of the sample for anxiety, and 26.5% of the sample for stress. Overall, 39.8% of students reported mild or higher symptoms for any subscale. The percentages of students meeting health-behavior guidelines are presented in Table 3.

Clustering between health behaviors Most respondents failed to meet guidelines for two of the four factors (46.2%), followed by failure to meet three (24.8%) and one (21.4%; see Table 4). Small percentages failed guidelines for no (3.6%) or all four risk factors (4.0%). The combinations with the most respondents were nonsmokers with safe alcohol use but insufficient vegetable consumption and physical inactivity (36.2%) and nonsmokers who binge drank while also having insufficient vegetable consumption and physical inactivity (17.7%).

Associations between mental health and health behaviors Females with depressive symptoms were more likely to skip breakfast (OR = 1.66, P = 0.047), avoid vigorous physical activity (OR = 1.96, P = 0.044), or have unhealthy sleep hours © 2014 Wiley Publishing Asia Pty Ltd.

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Table 3.

G. P. Lovell et al.

Percentages of male and female students not meeting health-behavior guidelines

Health behavior

Male (n = 163) (%)

Female (n = 588) (%)

Overall (n = 751) (%)

16.0 66.9 71.8 33.7 55.8 92.0 42.9 41.1 25.8

13.3 73.1 77.4 32.7 53.9 82.3 31.6 47.4 34.9

13.8 71.8 76.2 32.9 54.3 84.4 34.1 46.1 32.9

Smokes Physically inactive No vigorous activity No breakfast Low fruit Low vegetables Alcohol binges Short or long sleep Poor sleep quality

Table 4.

Combinations of health-risk behaviors for Australian students

Number of risk behaviors

Vegetable consumption

Physical activity

Smoking

Alcohol use

N

Percent of sample

4 3

− − − − +

− − − + −

− − + − −

− + − − −

2

− − + − + +

− + − + − +

+ − − + + −

+ + + − − −

1

− + + +

+ − + +

+ + − +

+ + + −

0 Total

+

+

+

+

30 35 133 11 7 186 272 9 4 45 11 6 347 99 47 2 13 161 27 751

4.0 4.7 17.7 1.5 0.9 24.8 36.2 1.2 0.5 6.0 1.5 0.8 46.2 13.2 6.3 0.3 1.7 21.4 3.6 100

3 total

2 total

1 total

+, indicates meeting guidelines; −, indicates not meeting guidelines.

(OR = 1.98, P = 0.007) than were females without depressive symptoms. Females with stress symptoms were more likely to have poor sleep quality (OR = 2.18, P = 0.002), but less likely to have unhealthy sleep hours (OR = 0.59, P = 0.033) compared to females without stress symptoms. No associations for other variables were significant (see Table 5). Males with depressive symptoms were more likely to skip breakfast (OR = 6.29, P = 0.003) and have poor sleep quality (OR = 4.24, P = 0.014) than were males without depressive symptoms (see Table 6).

Australian university sample and quantify depressive, anxiety, and stress symptoms and health-behavior participation. Just under two-thirds of students who responded to the survey reported depressive, anxiety, and stress symptoms in the normal range. Although this comprises most students, it indicates a sizeable portion, greater than for the general Australian population (Crawford et al., 2011), who reported noteworthy mental illness symptoms. Past research has also found that students present with poor psychological well-being (Bewick et al., 2008; Burris et al., 2009), which suggests that the pressure of university on student mental health is a consistent phenomenon across countries.

DISCUSSION Health-behavior participation has been recognized to relate to mental health (Allgöwer et al., 2001; Schleicher et al., 2009). This study aimed to extend these findings to an © 2014 Wiley Publishing Asia Pty Ltd.

Health-behavior participation Most students reported insufficient physical activity (moderate or vigorous intensity) and vegetable consumption.

16 (13) 95 (77) 108 (87) 54 (44) 68 (55) 108 (87) 44 (36) 69 (56) 49 (40)

62 (13) 335 (72) 347 (75) 138 (30) 249 (54) 376 (81) 142 (31) 210 (45) 156 (34)

2 0.60 0.11 4.05 3.94 0.77 0.27 0.00 7.22 1.86

0.440 0.740 0.044 0.047 0.379 0.603 0.961 0.007 0.173

Depression χ2 P 0.76 (0.37, 1.54) 0.91 (0.52, 1.59) 1.96 (1.02, 3.79) 1.66 (1.01, 2.73) 0.80 (0.49, 1.31) 1.20 (0.61, 2.35) 1.01 (0.59, 1.73) 1.98 (1.20, 3.25) 0.70 (0.42, 1.17)

AOR† (95% CI) 28 (16) 136 (79) 141 (82) 64 (37) 103 (60) 149 (87) 61 (36) 84 (49) 75 (44)

3 50 (12) 294 (71) 314 (76) 128 (31) 214 (51) 335 (81) 125 (30) 195 (47) 130 (31)

4 2.39 1.58 0.02 0.07 2.46 0.36 0.00 0.04 1.05

0.122 0.208 0.903 0.794 0.117 0.548 0.986 0.849 0.305

Anxiety χ2 P 1.63 (0.88, 3.04) 1.40 (0.83, 2.35) 1.03 (0.60, 1.78) 0.94 (0.59, 1.51) 1.43 (0.91, 2.25) 1.20 (0.66, 2.21) 1.00 (0.61, 1.62) 1.04 (0.67, 1.64) 1.27 (0.80, 2.02)

AOR‡ (95% CI) 23 (14) 125 (78) 134 (84) 64 (40) 93 (58) 140 (88) 60 (38) 73 (46) 77 (48)

5 55 (13) 305 (71) 321 (75) 128 (30) 224 (52) 344 (80) 126 (29) 206 (48) 128 (30)

6 0.03 0.58 0.55 0.99 0.12 0.66 0.89 4.56 9.68

0.854 0.447 0.457 0.321 0.731 0.417 0.346 0.033 0.002

Stress χ2 P

1.07 (0.54, 2.11) 1.24 (0.71, 2.16) 1.25 (0.99, 1.03) 1.29 (0.78, 2.12) 1.09 (0.67, 1.76) 1.31 (0.68, 2.54) 1.28 (0.77, 2.15) 0.59 (0.36, 0.96) 2.18 (1.34, 3.57)

AOR§ (95% CI)

9 (23) 28 (70) 31 (78) 22 (55) 26 (65) 39 (98) 20 (50) 16 (40) 19 (48)

17 (14) 81 (66) 86 (70) 33 (27) 65 (53) 111 (90) 50 (41) 51 (42) 23 (19)

2 1.37 0.45 1.65 8.96 0.18 1.59 1.06 1.20 6.05

0.241 0.502 0.199 0.003 0.671 0.208 0.304 0.273 0.014

Depression χ2 P 2.21 (0.59, 8.28) 1.46 (0.48, 4.45) 2.20 (0.66, 7.35) 6.29 (1.89, 20.95) 1.26 (0.43, 3.72) 5.06 (0.41, 62.98) 1.83 (0.58, 5.79) 1.86 (0.61, 5.63) 4.24 (1.34, 13.34)

AOR† (95% CI) 8 (19) 28 (67) 30 (71) 18 (43) 29 (69) 40 (95) 19 (45) 15 (36) 17 (41)

3 18 (15) 81 (67) 87 (72) 37 (31) 62 (51) 110 (91) 51 (42) 52 (43) 25 (21)

4 0.04 0.53 1.21 0.18 2.10 0.02 0.71 0.70 0.08

0.836 0.465 0.271 0.668 0.147 0.891 0.400 0.403 0.784

Anxiety χ2 P

1.16 (0.29, 4.63) 0.66 (0.22, 2.01) 0.51 (0.16, 1.68) 0.77 (0.23, 2.54) 2.29 (0.75, 7.01) 0.87 (0.11, 6.86) 1.00 (0.19, 1.95) 0.62 (0.20, 1.90) 0.84 (0.25, 2.82)

AOR‡ (95% CI)

7 (18) 27 (69) 29 (74) 15 (39) 23 (59) 37 (95) 17 (44) 13 (33) 17 (44)

5

19 (15) 82 (66) 88 (71) 40 (32) 68 (55) 113 (91) 53 (43) 54 (44) 25 (20)

6

0.02 0.01 0.00 1.03 1.66 0.03 0.47 1.55 0.92

0.893 0.911 0.968 0.311 0.198 0.876 0.492 0.213 0.338

Stress χ2 P

0.92 (0.25, 3.30) 1.06 (0.38, 2.98) 1.02 (0.34, 3.05) 0.56 (0.18, 1.72) 0.50 (0.17, 1.44) 0.86 (0.12, 6.07) 0.69 (0.34, 2.01) 0.52 (0.18, 1.46) 1.68 (0.58, 4.85)

AOR§ (95% CI)

Simple logistic regression, n = 163. In each respective instance, participants with no depression, no anxiety, or no stress are the reference group. 1, depression n (%); 2, no depression n (%); 3, anxiety n (%); 4, no anxiety n (%); 5, stress n (%); 6, no stress n (%). †AOR, Adjusted odds ratio; adjusted for age, anxiety, and stress. ‡AOR, adjusted for age, depression, and stress. §AOR, adjusted for age, depression, and anxiety. Bold text indicates significant differences for those with versus without mental illness symptoms.

Smokes Physically inactive No vigorous activity Skips breakfast Low fruit Low vegetables Alcohol binges Short or long sleep Poor sleep quality

1

Health behaviors for male students with or without depressive, anxiety, or stress symptoms

Health behavior

Table 6.

Simple logistic regression, n = 588. In each respective instance, participants with no depression, no anxiety, or no stress are the reference group. 1, depression n (%); 2, no depression n (%); 3, anxiety n (%); 4, no anxiety n (%); 5, stress n (%); 6, no stress n (%). †AOR, adjusted odds ratio; adjusted for age, anxiety, and stress. ‡AOR, adjusted for age, depression, and stress. §AOR, adjusted for age, depression, and anxiety. Bold text indicates significant differences for those with versus without mental illness symptoms.

Smokes Physically inactive No vigorous activity Skips breakfast Low fruit Low vegetables Alcohol binges Short or long sleep Poor sleep quality

1

Health behaviors for female students with or without depressive, anxiety, or stress symptoms

Health behavior

Table 5.

Mental health and health behaviors 139

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Potentially, the time and financial pressures of student life could contribute to these high rates. Fewer of both sexes reported smoking behavior compared with students in USA (Schleicher et al., 2009; Roberts et al., 2010), Germany (Keller et al., 2008), and other European countries (Steptoe & Wardle, 2001). These differences may reflect Australia’s strong restrictions on where smoking is permissible, heavy antismoking marketing, and smoking-specific taxes, with this last point being especially relevant to the financial pressures in student life. On initial inspection, alcohol binging rates also appeared low. However, when student data were analyzed according to age groups, 18 to 24 year olds (56% males, 42% females) reported binge drinking levels similar to German students (Keller et al., 2008), but still less than USA students (Roberts et al., 2010). These figures dropped by more than half in the older age group (27% males, 17% females), which perhaps highlights the unique quality of the Australian university population as encompassing a wider age range than universities in other countries. Overall, the rates of healthbehavior participation are concerning and demonstrate that Australian students would make a suitable target for healthbehavior intervention.

Health-behavior clustering The health-behavior data were also analyzed according to level of clustering, as per previous research (Schuit et al., 2002; Poortinga, 2007; Keller et al., 2008). The results showed clustering, with 75% of the sample reporting a failure to meet guidelines for two or more of the four chosen risk behaviors (vegetable consumption, physical activity, smoking, and alcohol use), and 29% reporting three or four. Combinations including both insufficient vegetable consumption and physical activity were the most common. This perhaps reflects the fact that the effort it takes to engage in positive health behaviors (i.e., physical activity and vegetable consumption) is arguably greater than the effort to avoid health-damaging behaviors. This study’s findings of insufficient vegetable intake and physical inactivity clustering are similar to Poortinga’s (2007) English study, but not Keller et al.’s (2008) German university study, in which failure to meet fruit and vegetable guidelines corresponded most with binge drinking or Schuit et al.’s (2002) Dutch study where this failure was most likely to occur in isolation. Individuals who smoked were especially prone to other health-risk behaviors, more commonly showing two or more other risks than one or no other risks. Similarly, those who reported a binge alcohol session were far more likely to also have insufficient vegetable intake and physical inactivity than any combination with other factors. Thus, it appears that for those who willingly engage in unhealthy behaviors (smoking and alcohol binging), engagement in the health-promoting behaviors (vegetable consumption and physical activity) is also likely to be low. Taken together, these findings suggest that many people could benefit from a single intervention targeting multiple health behaviors. © 2014 Wiley Publishing Asia Pty Ltd.

G. P. Lovell et al.

Associations between mental health and health behaviors In combining the health behavior and mental-health data, this study found several notable associations. Eating no breakfast for males was the single strongest predictor of symptoms of mental illness, effectively showing for this sample over six times the chance of concurrent depressive symptoms. Similarly high, having poor sleep quality was associated with over four times the chance of concurrent depressive symptoms. For females, not eating breakfast was also significantly associated with depressive symptoms, but with a smaller effect size. Reporting no regular participation in vigorous physical activity also related to female depressive symptoms, as did short or long sleep hours. After controlling for depression and anxiety, presence of stress symptoms predicted greater likelihood of healthy sleep hours, but also poor sleep quality. The strong results for not eating breakfast are notable considering research linking breakfast skipping with body weight outcomes, poorer overall diet composition, and longterm health risks (see Giovannini et al., 2008, for review). They also correspond with significant results from previous research (Allgöwer et al., 2001). However, it is unclear whether the reason for the breakfast skipping and depressive symptoms association is a direct or indirect link, such that shared correlations with higher body weight may explain the relationship (Zhao et al., 2009).The current study is limited in exploring this further as body mass index was not measured, but the results suggest future research into breakfast skipping should consider potential mental health associations as well. It is interesting that poor sleep quality related to depressive symptoms in males, but stress in females. Sleep disturbances are symptoms of clinical depression, and thus a relationship would be expected (American Psychiatric Association, 2000). Similarly, studies have reported an association between moderate physical activity and depressive symptoms (Tyson et al., 2010; Elliot et al., 2012). It may be that current and sustained activity predicts future rather than current mental health. This study’s design prevents evaluation of such a hypothesis. However, the result that vigorous, but not moderate, activity was associated with depressive symptoms may relate to findings of a dose–response relationship in that more activity leads to greater effects (Harbour et al., 2008). The reason that these results appeared for females but not males is unclear, given past studies have not reported gender differences (Harbour et al., 2008; Elliot et al., 2012). In addition to individual associations, there was a general trend between not meeting health-behavior guidelines and experiencing mental-illness symptoms. Across depression, anxiety, and stress, in most cases the percentage of students not meeting health-behavior guidelines was higher in individuals with rather than without symptoms. This trend corresponds with previous research examining university student depressive and anxiety symptoms (Allgöwer et al., 2001; Strine et al., 2005). However, when adjusting the odds ratios, for some health behaviors, anxiety and stress became predictors of greater participation. This highlights that depression

Mental health and health behaviors

levels may better explain links between mental health and health-behavior participation than anxiety or stress levels. The general dysphoria and lack of desire to engage in activity associated with depressive symptoms provides an intuitive explanation for lower participation rates. This study cannot make causal assumptions and, as other researchers have noted, the relationships between these variables are likely bidirectional and there could be other mediating factors (Allgöwer et al., 2001).

Limitations This discussion highlights a major limitation of this study – its cross-sectional approach. Longitudinal or experimental research would be useful in furthering understanding of potential causal relationships (Allgöwer et al., 2001). A second point to note is that, except for sleep behavior and although based in previous research, the measures for health behaviors were brief. This brevity likely helped avoid fatigue effects, but outcome variables may have lacked the finesse potentially obtained with longer measures. Another noteworthy point is that demands during a university semester can vary greatly. Past research found higher emotional distress near examination periods (Steptoe et al., 1996). Participants in the current study were recruited before and early in the semester when few have assessments or high workloads. Thus, university pressures and mental illness symptoms were likely comparatively low and engagement in time-expensive health behavior comparatively high.These variations warrant further investigation. Sourcing participants from one university is another challenge for this study, limiting generalizability to other Australian university students, especially those in different geographical contexts, such as metropolitan based universities. Further research with other samples and multiple data-collection points would help to clarify the mental-health and health-behavior relationships.

CONCLUSION This study has highlighted some important findings regarding the mental health and physical health of Australian university students. The mental health and rates of health-behavior participation of Australian students are concerning and appear to warrant intervention. Many participants presented with two or more risky health behaviors rather than one or none, suggesting clustering of health behaviors. In addition, it appears that depressive symptoms relate to participation across a range of health behaviors, more so than do anxiety or stress symptoms. These findings have implications for future research or interventions. Specifically, they raise the question whether changing one or two key health behaviors may result in improvements in others through the observed clustering. For example, if students adopt a healthy diet, they may also feel motivated to avoid “ruining” this effort through binge drinking or smoking. Alternatively, through greater awareness of deficiencies for one health behavior, students may become more mindful of their other behaviors. This study’s findings also highlight the potential role of mental health, particularly

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depressive symptoms, in these relationships. To investigate these findings further, interventions could be designed around one or two key health behaviors, such as diet and physical activity. Assessing changes in mental health and participation in other health behaviors could help clarify possible causal links.

CONTRIBUTIONS Study Design: GL and KN. Data Collection and Analysis: GL, KN, RS, and BL. Manuscript Writing: RS and BL.

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A cross-sectional investigation of depressive, anxiety, and stress symptoms and health-behavior participation in Australian university students.

Transitioning to university involves a major life change that can have implications for physical and mental health. This study had three objectives: f...
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