Search

Chronic diseases are the leading cause of disability and mortality globally. In Australia, females are at heightened risk. This research explored the prevalence, patterns, and correlates of six key risk behaviors (physical inactivity, poor diet, recreational screen time, inadequate sleep, alcohol use, and smoking) among adolescent females and whether knowledge of health guidelines was associated with adherence. Adolescent females completed an anonymous online questionnaire ( N = 687; M age = 13.82). Logistic regression assessed the association between knowledge and adherence. A Latent Class Analysis (LCA) and three-step procedure identified risk behavior clusters and their correlates. Despite positive health self-ratings (77% good/very good), most participants reported insufficient moderate-to-vigorous physical activity (MVPA; 89%), vegetable intake (89%), and excessive screen time (63%). Knowledge of guidelines was associated with adherence for MVPA, vegetable intake, sleep, and alcohol abstinence. Three classes emerged: "moderate risk" (76%), "relatively active, healthy eaters" (19%), and "excessive screen users" (5%). These risk-behavior clusters were associated with perceived value of academic achievement and physical wellbeing. Adolescent females commonly perceive they are in good health, despite engaging in unhealthy behaviors. Public health interventions should utilize effective behavior change strategies, adopt a multiple health behavior change approach (MHBC), and be tailored to specific risk profiles and values among females.

Heavy and disordered alcohol consumption is a known risk factor for several health conditions and is associated with considerable disease burden. However, at low‐to‐moderate levels, evidence suggests that drinking is associated with reduced risk for certain health outcomes. Whether these findings represent genuine protective effects or mere methodological artifacts remains unclear, but has substantial consequences for policy and practice. This critical review introduces methodological advances capable of enhancing causal inference from observational research, focusing on the 'G‐methods' and Mendelian Randomization. We also present and evaluate recent research applying these methods and compare findings to the existing evidence base. Future directions are proposed for improving our causal understanding of the relationships between alcohol and long‐term health outcomes. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the effectiveness of a school‐based multiple health behaviour change e‐health intervention for modifying risk factors for chronic disease (secondary outcomes). Study design: Cluster randomised controlled trial. Setting, participants: Students (at baseline [2019]: year 7, 11–14 years old) at 71 Australian public, independent, and Catholic schools. Intervention: Health4Life: an e‐health school‐based multiple health behaviour change intervention for reducing increases in the six major behavioural risk factors for chronic disease: physical inactivity, poor diet, excessive recreational screen time, poor sleep, and use of alcohol and tobacco. It comprises six online video modules during health education class and a smartphone app. Main outcome measures: Comparison of Health4Life and usual health education with respect to their impact on changes in twelve secondary outcomes related to the six behavioural risk factors, assessed in surveys at baseline, immediately after the intervention, and 12 and 24 months after the intervention: binge drinking, discretionary food consumption risk, inadequate fruit and vegetable intake, difficulty falling asleep, and light physical activity frequency (categorical); tobacco smoking frequency, alcohol drinking frequency, alcohol‐related harm, daytime sleepiness, and time spent watching television and using electronic devices (continuous). Results: A total of 6640 year 7 students completed the baseline survey (Health4Life: 3610; control: 3030); 6454 (97.2%) completed at least one follow‐up survey, 5698 (85.8%) two or more follow‐up surveys. Health4Life was not statistically more effective than usual school health education for influencing changes in any of the twelve outcomes over 24 months; for example: fruit intake inadequate: odds ratio [OR], 1.08 (95% confidence interval [CI], 0.57–2.05); vegetable intake inadequate: OR, 0.97 (95% CI, 0.64–1.47); increased light physical activity: OR, 1.00 (95% CI, 0.72–1.38); tobacco use frequency: relative difference, 0.03 (95% CI, –0.58 to 0.64) days per 30 days; alcohol use frequency: relative difference, –0.34 (95% CI, –1.16 to 0.49) days per 30 days; device use time: relative difference, –0.07 (95% CI, –0.29 to 0.16) hours per day. Conclusions: Health4Life was not more effective than usual school year 7 health education for modifying adolescent risk factors for chronic disease. Future e‐health multiple health behaviour change intervention research should examine the timing and length of the intervention, as well as increasing the number of engagement strategies (eg, goal setting) during the intervention. Trial registration: Australian New Zealand Clinical Trials Registry: ACTRN12619000431123 (prospective). [ABSTRACT FROM AUTHOR]

Lifestyle risk behaviours—physical inactivity, poor diet, poor sleep, recreational screen time, and alcohol and tobacco use—collectively known as the "Big 6" emerge during adolescence and significantly contribute to chronic disease development into adulthood. To address this issue, the Health4Life program targeted the Big 6 risk behaviours simultaneously via a co-designed eHealth school-based multiple health behaviour change (MHBC) intervention. This study used multiple causal mediation analysis to investigate some potential mediators of Health4Life's effects on the Big 6 primary outcomes from a cluster randomised controlled trial of Health4Life among Australian school children. Mediators of knowledge, behavioural intentions, self-efficacy, and self-control were assessed. The results revealed a complex pattern of mediation effects across different outcomes. Whilst there was a direct effect of the intervention on reducing moderate-to-vigorous physical activity risk, the impact on sleep duration appeared to occur indirectly through the hypothesised mediators. Conversely, for alcohol and tobacco use, both direct and indirect effects were observed in opposite directions cancelling out the total effect (competitive partial mediation). The intervention's effects on alcohol and tobacco use highlighted complexities, suggesting the involvement of additional undetected mediators. However, little evidence supported mediation for screen time and sugar-sweetened beverage intake risk. These findings emphasise the need for tailored approaches when addressing different risk behaviours and designing effective interventions to target multiple health risk behaviours. The trial was pre-registered with the Australian and New Zealand Clinical Trials Registry: ACTRN12619000431123. [ABSTRACT FROM AUTHOR]

Objective: Physical inactivity, sugar sweetened beverage consumption, alcohol use, smoking, poor sleep and excessive recreational screen time (the 'Big 6' lifestyle risk behaviours) often co-occur and are key risk factors for psychopathology. However, the best fitting latent structure of the Big 6 is unknown and links between multiple lifestyle risk behaviours and hierarchical dimensions of psychopathology have not been explored among adolescents. This study aimed to address these gaps in the literature. Methods: Confirmatory factor analysis, latent class analysis and factor mixture models were conducted among 6640 students (M age = 12.7 years) to identify the latent structure of the Big 6 lifestyle risk behaviours. Structural equation models were then used to examine associations with psychopathology. Results: A mixture model with three classes, capturing mean differences in a single latent factor indexing overall risk behaviours, emerged as the best fitting model. This included relatively low-risk (Class 1: 30%), moderate-risk (Class 2: 67%) and high-risk (Class 3: 3%) classes. Students high on externalizing demonstrated significantly greater odds of membership to the high-risk class (odds ratio = 8.75, 99% confidence interval = [3.30, 23.26]) and moderate-risk class (odds ratio = 2.93, 99% confidence interval = [1.43, 5.97]) in comparison to the low-risk class. Similarly, students high on internalizing demonstrated significantly higher odds of membership to the high-risk class (odds ratio = 1.89, 99% confidence interval = [1.06, 3.37]) and the moderate-risk class (odds ratio = 1.66, 99% confidence interval = [1.03, 2.67]) in comparison to the low-risk class. Associations between lower order factors of psychopathology and lifestyle risk behaviours were mostly accounted for by the more parsimonious higher order factors. Conclusion: Classes representing differences in probabilities of the Big 6 lifestyle risk behaviours relate to varying levels of hierarchical dimensions of psychopathology, suggesting multiple health behaviour change and transdiagnostic intervention approaches may be valuable for reducing risk of psychopathology. [ABSTRACT FROM AUTHOR]

Background: Parents play an influential role in the health behaviors of their children, such as physical activity, dietary intake, sleep, screen time, and substance use. However, further research is needed to inform the development of more effective and engaging parent-based interventions targeting adolescent risk behaviors. Objective: This study aimed to assess parents' knowledge about adolescent risk behaviors, barriers and facilitators to engaging in healthy behaviors, and preferences for a parent-based prevention intervention. Methods: An anonymous web-based survey was conducted from June 2022 to August 2022. Eligible participants were parents of children aged 11 to 18 years and were residing in Australia at the time of this study. The survey assessed the parents' perceived and actual knowledge about Australian health guidelines for youth, parent and adolescent engagement in health behaviors, parenting style and attitudes, barriers and facilitators to engaging in healthy behaviors, and delivery and component preferences for a parent-based preventive intervention. Descriptive statistics and logistic regressions were conducted to analyze the data. Results: A total of 179 eligible participants completed the survey. The mean age of the parents was 42.22 (SD 7.03) years, and 63.1% (101/160) were female. Parent-reported sleep duration was high for both parents (mean 8.31, SD 1.00 hours) and adolescents (mean 9.18, SD 0.94 hours). However, the proportion of parents who reported that their child met the national recommendations for physical activity (5/149, 3.4%), vegetable intake (7/126, 5.6%), and weekend recreational screen time (7/130, 5.4%) was very low. Overall, parents' perceived knowledge of health guidelines was moderate, ranging from 50.6% (80/158) for screen time to 72.8% (115/158) for sleep guidelines (for children aged 5-13 years). Actual knowledge was lowest for vegetable intake and physical activity, with only 44.2% (46/104) and 42% (31/74) of parents reporting correct guidelines for these behaviors, respectively. The key issues of concern reported by parents were excessive use of technology, mental health, e-cigarette use, and negative peer relationships. The top-rated delivery method for a parent-based intervention was via a website (53/129, 41.1%). The highest rated intervention component was opportunities for goal-setting (89/126, 70.7% rated very or extremely important), and other important program features were ease of use (89/122, 72.9%), paced learning (79/126, 62.7%), and appropriate program length (74/126, 58.8%). Conclusions: The findings suggest that such interventions should be brief and web based and should aim to increase parental knowledge of health guidelines; provide opportunities for skill-building, such as goal-setting; and include effective behavior change techniques, such as motivational interviewing and social support. This study will inform the development of future parent-based preventive interventions to prevent multiple lifestyle risk behaviors among adolescents. [ABSTRACT FROM AUTHOR]

Background: Six core behavioural risk factors (poor diet, physical activity, sedentary behaviour, alcohol misuse, smoking and unhealthy sleep patterns) have been identified as strong determinants of chronic disease, such as cardiovascular disease, diabetes and cancers. Smartphones have the potential to provide a real-time, pervasive, unobtrusive and cost-effective way to measure health behaviours and deliver instant feedback to users. Despite this, validity of using smartphones to measure these six key behaviours is largely unknown. The proposed systematic review aims to address this gap by identifying existing smartphone-based approaches to measure these health behaviours and critically appraising, comparing and summarizing the quality of their measurement properties. Methods: A systematic search of the Ovid MEDLINE, Embase (Elsevier), Cochrane Library (Wiley), PsychINFO (EBSCOhost), CINAHL (EBSCOHost), Web of Science (Clarivate), SPORTDiscus (EBSCOhost) and IEEE Xplore Digital Library databases will be conducted from January 2007 to March 2020. Eligible studies will be those written in English that measure at least one of the six health behaviours of interest via a smartphone and report on at least one measurement property. The primary outcomes will be validity, reliability and/or responsiveness of these measurement approaches. A secondary outcome will be the feasibility (e.g. user burden, usability and cost) of identified approaches. No restrictions will be placed on the participant population or study design. Two reviewers will independently screen studies for eligibility, extract data and assess the risk of bias. The study methodological quality (or bias) will be appraised using an appropriate tool. Our results will be described in a narrative synthesis. If feasible, random effects meta-analysis will be conducted where appropriate. Discussion: The results from this review will provide important information about the types of smartphone-based approaches currently available to measure the core behavioural risk factors for chronic disease and the quality of their measurement properties. It will allow recommendations on the most suitable and effective measures of these lifestyle behaviours using smartphones. Valid and reliable measurement of these behaviours and risk factor opens the door to targeted and real-time delivery of health behaviour interventions, providing unprecedented opportunities to offset the trajectory toward chronic disease. Systematic review registration: PROSPERO: CRD42019122242 [ABSTRACT FROM AUTHOR]

Background: Earlier engagement in risk-taking behaviors has been associated with more severe mental health problems across development. However, sex differences in risk-outcome relationships remain underexplored and mental health outcomes spanning both ends of the internalizing and externalizing spectra are rarely considered within the same sample. The present study examined associations between age at initiation of alcohol use, illicit drug use, and sexual intercourse, and symptoms of internalizing, externalizing, depression and self-harm, for males and females.Methods: The present study analyzed self-report survey data from a nationally representative sample of Australian adolescents (N=2,950). Logistic regression models were conducted separately for males and females to test the association of early (age 15 or younger) and concurrent (age 16-17) initiation of alcohol use, illicit drug use and sexual behavior with symptoms of internalizing, externalizing, depression and self-harm at age 16-17.Results: Risk-taking behaviors were associated with all mental health outcomes. Generally, earlier initiation of risk-taking behaviors demonstrated stronger associations with mental health outcomes than concurrent initiation. Associations between risk-taking behaviors and mental health outcomes varied by participant sex and mental health outcome.Limitations: Mechanisms underlying the relationship between risk-taking behaviors and mental health problems were not tested and the sample had not yet reached early adulthood.Conclusions: Risk-taking behaviors, particularly when initiated early, share links with multiple mental health problems in adolescence. Public health strategies to delay the onset of risk-taking behaviors may therefore incur multiple benefits as might an integrated approach to mental health prevention programs for youth. [ABSTRACT FROM AUTHOR]