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Clustering of Multiple Risk Behaviors Among a Sample of 18-Year-Old Australians and Associations With Mental Health Outcomes: A Latent Class Analysis

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Specialty Public Health
Date 2018 Jun 6
PMID 29868543
Citations 32
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Abstract

Introduction: Risk behaviors commonly co-occur, typically emerge in adolescence, and become entrenched by adulthood. This study investigated the clustering of established (physical inactivity, diet, smoking, and alcohol use) and emerging (sedentary behavior and sleep) chronic disease risk factors among young Australian adults, and examined how clusters relate to mental health.

Methods: The sample was derived from the long-term follow-up of a cohort of Australians. Participants were initially recruited at school as part of a cluster randomized controlled trial. A total of 853 participants (M = 18.88 years, SD = 0.42) completed an online self-report survey as part of the 5-year follow-up for the RCT. The survey assessed six behaviors (binge drinking and smoking in the past 6 months, moderate-to-vigorous physical activity/week, sitting time/day, fruit and vegetable intake/day, and sleep duration/night). Each behavior was represented by a dichotomous variable reflecting adherence to national guidelines. Exploratory analyses were conducted. Clusters were identified using latent class analysis.

Results: Three classes emerged: "moderate risk" (moderately likely to binge drink and not eat enough fruit, high probability of insufficient vegetable intake; Class 1, 52%); "inactive, non-smokers" (high probabilities of not meeting guidelines for physical activity, sitting time and fruit/vegetable consumption, very low probability of smoking; Class 2, 24%), and "smokers and binge drinkers" (high rates of smoking and binge drinking, poor fruit/vegetable intake; Class 3, 24%). There were significant differences between the classes in terms of psychological distress ( = 0.003), depression ( < 0.001), and anxiety ( = 0.003). Specifically, Class 3 ("smokers and binge drinkers") showed higher levels of distress, depression, and anxiety than Class 1 ("moderate risk"), while Class 2 ("inactive, non-smokers") had greater depression than the "moderate risk" group.

Discussion: Results indicate that risk behaviors are prevalent and clustered in 18-year old Australians. Mental health symptoms were significantly greater among the two classes that were characterized by high probabilities of engaging in multiple risk behaviors (Classes 2 and 3). An examination of the clustering of lifestyle risk behaviors is important to guide the development of preventive interventions. Our findings reinforce the importance of delivering multiple health interventions to reduce disease risk and improve mental well-being.

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References
1.
Ye Y, Wang P, Qu G, Yuan S, Phongsavan P, He Q . Associations between multiple health risk behaviors and mental health among Chinese college students. Psychol Health Med. 2015; 21(3):377-85. DOI: 10.1080/13548506.2015.1070955. View

2.
Hale D, Fitzgerald-Yau N, Viner R . A systematic review of effective interventions for reducing multiple health risk behaviors in adolescence. Am J Public Health. 2014; 104(5):e19-41. PMC: 3987586. DOI: 10.2105/AJPH.2014.301874. View

3.
Newton N, Teesson M, Barrett E, Slade T, Conrod P . The CAP study, evaluation of integrated universal and selective prevention strategies for youth alcohol misuse: study protocol of a cluster randomized controlled trial. BMC Psychiatry. 2012; 12:118. PMC: 3502100. DOI: 10.1186/1471-244X-12-118. View

4.
Teesson M, Newton N, Slade T, Carragher N, Barrett E, Champion K . Combined universal and selective prevention for adolescent alcohol use: a cluster randomized controlled trial. Psychol Med. 2017; 47(10):1761-1770. DOI: 10.1017/S0033291717000198. View

5.
Spring B, Moller A, Coons M . Multiple health behaviours: overview and implications. J Public Health (Oxf). 2012; 34 Suppl 1:i3-10. PMC: 3284863. DOI: 10.1093/pubmed/fdr111. View