Clusters of health behaviours in Queensland adults are associated with different socio-demographic characteristics.

BACKGROUND: The co-occurrence of unhealthy lifestyles, calls for interventions that target multiple health behaviours. This study investigates the clustering of health behaviours and examines demographic differences between each cluster. METHODS: In total, 934 adults from Queensland, Australia completed a cross-sectional survey assessing multiple health behaviours. A two-step hierarchical cluster analysis using multiple iterations identified the optimal number of clusters and the subset of distinguishing health behaviour variables. Univariate analyses of variance and chi-squared tests assessed difference in health behaviours by socio-demographic factors and clusters. RESULTS: Three clusters were identified: the 'lower risk' cluster (n = 436) reported the healthiest profile and met all public health guidelines. The 'elevated risk' cluster (n = 105) reported a range of unhealthy behaviours such as excessive alcohol consumption, sitting time, fast-food consumption, smoking, inactivity and a lack of fruit and vegetables. The 'moderate risk behaviour' cluster (n = 393) demonstrated some unhealthy behaviours with low physical activity levels and poor dietary outcomes. The 'elevated risk' cluster were significantly younger and more socio-economically disadvantaged than both the 'lower and moderate risk' clusters. DISCUSSION: Younger people who live in more deprived areas were largely within the 'elevated risk' cluster and represent an important population for MHBC interventions given their wide range of unhealthy behaviours.

Can you elaborate on that? Addressing participants' need for cognition in computer-tailored health behavior interventions.

Computer-tailored interventions, which deliver health messages adjusted based on characteristics of the message recipient, can effectively improve a range of health behaviours. Typically, the content of the message is tailored to user demographics, health behaviours and social cognitive factors (e.g., intentions, attitudes, self-efficacy, perceived social support) to increase message relevance, and thus the extent to which the message is read, considered and translated into attitude and behaviour change. Some researchers have suggested that the efficacy of computer-tailored interventions may be further enhanced by adapting messages to suit recipients' need for cognition (NFC) - a personality trait describing how individuals tend to process information. However, the likely impact of doing so, especially when tailored in conjunction with other variables, requires further consideration. It is possible that intervention effects may be reduced in some circumstances due to interactions with other variables (e.g., perceived relevance) that also influence information processing. From a practical point of view, it is also necessary to consider how to optimally operationalise and measure NFC if it is to be a useful tailoring variable. This paper aims to facilitate further research in this area by critically examining these issues based on relevant theories and existing evidence.

Designing more engaging computer-tailored physical activity behaviour change interventions for breast cancer survivors: lessons from the iMove More for Life study.

BACKGROUND: Participating in regular physical activity is a recommended cancer recovery strategy for breast cancer survivors. However, tailored support services are not widely available and most survivors are insufficiently active to obtain health benefits. Delivering tailored programs via the Internet offers one promising approach. However, recent evaluations of such programs suggest that major improvements are needed to ensure programs meet the needs of users and are delivered in an engaging way. Understanding participants' experiences with current programs can help to inform the next generation of systems. PURPOSE: The purposes of this study are to explore breast cancer survivor's perspectives of and experiences using a novel computer-tailored intervention and to describe recommendations for future iterations. METHODS: Qualitative data from a sub-sample of iMove More for Life study participants were analysed thematically to identify key themes. Participants long-term goals for participating in the program were explored by analysing open-ended data extracted from action plans completed during the intervention (n = 370). Participants negative and positive perceptions of the website and recommendations for improvement were explored using data extracted from open-ended survey items collected at the immediate intervention follow-up (n = 156). RESULTS: The majority of participants reported multi-faceted goals, consisting of two or more outcomes they hoped to achieve within a year. While clear themes were identified (e.g. 'being satisfied with body weight'), there was considerable variability in the scope of the goal (e.g. desired weight loss ranged from 2 to 30 kg). Participants' perceptions of the website were mixed, but clear indications were provided of how intervention content and structure could be improved. CONCLUSIONS: This study provides insight into how to better accommodate breast cancer survivors in the future and ultimately design more engaging computer-tailored interventions.