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Potential Mediators of a School-Based Digital Intervention Targeting Six Lifestyle Risk Behaviours in a Cluster Randomised Controlled Trial of Australian Adolescents.
O'Dean, SM, Sunderland, M, Smout, S, Slade, T, Chapman, C, Gardner, LA, Thornton, L, Newton, NC, Teesson, M, Champion, KE
Prevention science : the official journal of the Society for Prevention Research. 2024;(2):347-357
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Abstract
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.
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The Health4Life e-health intervention for modifying lifestyle risk behaviours of adolescents: secondary outcomes of a cluster randomised controlled trial.
O'Dean, S, Sunderland, M, Newton, N, Gardner, L, Teesson, M, Chapman, C, Thornton, L, Slade, T, Hides, L, McBride, N, et al
The Medical journal of Australia. 2024
Abstract
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).
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A systematic review of goal attainment scaling implementation practices by caregivers in randomized controlled trials.
Cheema, K, Dunn, T, Chapman, C, Rockwood, K, Howlett, SE, Sevinc, G
Journal of patient-reported outcomes. 2024;(1):37
Abstract
BACKGROUND Goal attainment scaling (GAS), an established individualized, patient-centred outcome measure, is used to capture the patient's voice. Although first introduced ~60 years ago, there are few published guidelines for implementing GAS, and almost none for its use when caregivers GAS is implemented with caregiver input. We conducted a systematic review of studies that implemented GAS with caregiver input; and examined variations in GAS implementation, analysis, and reporting. METHODS Literature was retrieved from Medline, Embase, Cochrane, PsycInfo and CINAHL databases. We included randomized controlled trials (published between 1968 and November 2022) that used GAS as an outcome measure and involved caregiver input during goal setting. RESULTS Of the 2610 studies imported for screening, 21 met the inclusion criteria. Most studies employed GAS as a primary outcome. The majority (76%) had children as study participants. The most common disorders represented were cerebral palsy, developmental disorders, and dementia/Alzheimer's disease. The traditional five-point GAS scale, with levels from -2 to +2, was most often implemented, with -1 level typically being the baseline. However, most studies omitted essential GAS details from their reports including the number of goals set, number of attainment levels and whether any training was given to GAS facilitators. CONCLUSIONS GAS with caregiver input has been used in a limited number of randomized controlled trials, primarily in pediatric patients and adults with dementia. There is a variability in GAS implementation and many crucial details related to the specifics of GAS implementation are omitted from reports, which may limit reproducibility. Here we propose catalog that may be utilized when reporting research results pertaining to GAS with caregivers to enhance the application of this patient-centered outcome measure.
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Health4Life eHealth intervention to modify multiple lifestyle risk behaviours among adolescent students in Australia: a cluster-randomised controlled trial.
Champion, KE, Newton, NC, Gardner, LA, Chapman, C, Thornton, L, Slade, T, Sunderland, M, Hides, L, McBride, N, O'Dean, S, et al
The Lancet. Digital health. 2023;(5):e276-e287
Abstract
BACKGROUND Lifestyle risk behaviours are prevalent among adolescents and commonly co-occur, but current intervention approaches tend to focus on single risk behaviours. This study aimed to evaluate the efficacy of the eHealth intervention Health4Life in modifying six key lifestyle risk behaviours (ie, alcohol use, tobacco smoking, recreational screen time, physical inactivity, poor diet, and poor sleep, known as the Big 6) among adolescents. METHODS We conducted a cluster-randomised controlled trial in secondary schools that had a minimum of 30 year 7 students, in three Australian states. A biostatistician randomly allocated schools (1:1) to Health4Life (a six-module, web-based programme and accompanying smartphone app) or an active control group (usual health education) with the Blockrand function in R, stratified by site and school gender composition. All students aged 11-13 years who were fluent in English and attended participating schools were eligible. Teachers, students, and researchers were not masked to allocation. Primary outcomes were alcohol use, tobacco use, recreational screen time, moderate to vigorous physical activity (MVPA), sugar-sweetened beverage intake, and sleep duration at 24 months, measured by self-report surveys, and analysed in all students who were eligible at baseline. Latent growth models estimated between-group change over time. This trial is registered with the Australian New Zealand Clinical Trials Registry (ACTRN12619000431123). FINDINGS Between April 1, 2019, and Sept 27, 2019, we recruited 85 schools (9280 students), of which 71 schools with 6640 eligible students (36 schools [3610 students] assigned to the intervention and 35 [3030 students] to the control) completed the baseline survey. 14 schools were excluded from the final analysis or withdrew, mostly due to a lack of time. We found no between-group differences for alcohol use (odds ratio 1·24, 95% CI 0·58-2·64), smoking (1·68, 0·76-3·72), screen time (0·79, 0·59-1·06), MVPA (0·82, 0·62-1·09), sugar-sweetened beverage intake (1·02, 0·82-1·26), or sleep (0·91, 0·72-1·14) at 24 months. No adverse events were reported during this trial. INTERPRETATION Health4Life was not effective in modifying risk behaviours. Our results provide new knowledge about eHealth multiple health behaviour change interventions. However, further research is needed to improve efficacy. FUNDING Paul Ramsay Foundation, the Australian National Health and Medical Research Council, the Australian Government Department of Health and Aged Care, and the US National Institutes of Health.
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Lifestyle risk behaviours among adolescents: a two-year longitudinal study of the impact of the COVID-19 pandemic.
Gardner, LA, Debenham, J, Newton, NC, Chapman, C, Wylie, FE, Osman, B, Teesson, M, Champion, KE
BMJ open. 2022;12(6):e060309
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Plain language summary
The global spread of COVID-19 and subsequent lockdown measures have presented challenges worldwide. Previous research has highlighted the importance of six key lifestyle behaviours, including diet, physical activity, sleep, sedentary behaviour (including recreational screen time), alcohol use and smoking—collectively referred to as the ‘Big 6’—for the short-term and long-term health of adolescents. The aim of this study was to examine changes in the prevalence of six key chronic disease risk factors from before to during the COVID-19 pandemic, and also to explore whether differences over time are associated with gender and lockdown status. This study is a prospective cohort study among a large and geographically diverse sample of adolescents. The sample included 983 students (girls = 54.8%) from 22 schools. Results show that: - over the 2-year period, the prevalence of excessive recreational screen time, insufficient fruit intake and alcohol and tobacco use increased. - alcohol use increased more among girls compared to boys. - the prevalence of insufficient sleep reduced in the overall sample; yet, increased among girls. - being in lockdown was associated with improvements in sugar-sweetened beverages consumption and discretionary food intake. Authors conclude that supporting young people to improve or maintain their health behaviours, regardless of the course of the pandemic, is important, alongside targeted research and intervention efforts to support groups that may be disproportionately impacted, such as adolescent girls.
Abstract
OBJECTIVE To examine changes in the prevalence of six key chronic disease risk factors (the "Big 6"), from before (2019) to during (2021) the COVID-19 pandemic, among a large and geographically diverse sample of adolescents, and whether differences over time are associated with lockdown status and gender. DESIGN Prospective cohort study. SETTING Three Australian states (New South Wales, Queensland and Western Australia) spanning over 3000 km. PARTICIPANTS 983 adolescents (baseline Mage=12.6, SD=0.5, 54.8% girl) drawn from the control group of the Health4Life Study. PRIMARY OUTCOMES The prevalence of physical inactivity, poor diet (insufficient fruit and vegetable intake, high sugar-sweetened beverage intake, high discretionary food intake), poor sleep, excessive recreational screen time, alcohol use and tobacco use. RESULTS The prevalence of excessive recreational screen time (prevalence ratios (PR)=1.06, 95% CI=1.03 to 1.11), insufficient fruit intake (PR=1.50, 95% CI=1.26 to 1.79), and alcohol (PR=4.34, 95% CI=2.82 to 6.67) and tobacco use (PR=4.05 95% CI=1.86 to 8.84) increased over the 2-year period, with alcohol use increasing more among girls (PR=2.34, 95% CI=1.19 to 4.62). The prevalence of insufficient sleep declined across the full sample (PR=0.74, 95% CI=0.68 to 0.81); however, increased among girls (PR=1.24, 95% CI=1.10 to 1.41). The prevalence of high sugar-sweetened beverage (PR=0.61, 95% CI=0.64 to 0.83) and discretionary food consumption (PR=0.73, 95% CI=0.64 to 0.83) reduced among those subjected to stay-at-home orders, compared with those not in lockdown. CONCLUSION Lifestyle risk behaviours, particularly excessive recreational screen time, poor diet, physical inactivity and poor sleep, are prevalent among adolescents. Young people must be supported to find ways to improve or maintain their health, regardless of the course of the pandemic. Targeted approaches to support groups that may be disproportionately impacted, such as adolescent girls, are needed. TRIAL REGISTRATION NUMBER Australian New Zealand Clinical Trials Registry (ACTRN12619000431123).
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Lifestyle risks for chronic disease among Australian adolescents: a cross-sectional survey.
Champion, KE, Chapman, C, Gardner, LA, Sunderland, M, Newton, NC, Smout, S, Thornton, LK, Hides, L, McBride, N, Allsop, SJ, et al
The Medical journal of Australia. 2022;(3):156-157
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Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review.
Thornton, L, Osman, B, Champion, K, Green, O, Wescott, AB, Gardner, LA, Stewart, C, Visontay, R, Whife, J, Parmenter, B, et al
JMIR mHealth and uHealth. 2022;(2):e27337
Abstract
BACKGROUND Poor diet, alcohol use, and tobacco smoking have been identified as strong determinants of chronic diseases, such as cardiovascular disease, diabetes, and cancer. Smartphones have the potential to provide a real-time, pervasive, unobtrusive, and cost-effective way to measure these health behaviors and deliver instant feedback to users. Despite this, the validity of using smartphones to measure these behaviors is largely unknown. OBJECTIVE The aim of our review is to identify existing smartphone-based approaches to measure these health behaviors and critically appraise the quality of their measurement properties. METHODS We conducted a systematic search of the Ovid MEDLINE, Embase (Elsevier), Cochrane Library (Wiley), PsycINFO (EBSCOhost), CINAHL (EBSCOHost), Web of Science (Clarivate), SPORTDiscus (EBSCOhost), and IEEE Xplore Digital Library databases in March 2020. Articles that were written in English; reported measuring diet, alcohol use, or tobacco use via a smartphone; and reported on at least one measurement property (eg, validity, reliability, and responsiveness) were eligible. The methodological quality of the included studies was assessed using the Consensus-Based Standards for the Selection of Health Measurement Instruments Risk of Bias checklist. Outcomes were summarized in a narrative synthesis. This systematic review was registered with PROSPERO, identifier CRD42019122242. RESULTS Of 12,261 records, 72 studies describing the measurement properties of smartphone-based approaches to measure diet (48/72, 67%), alcohol use (16/72, 22%), and tobacco use (8/72, 11%) were identified and included in this review. Across the health behaviors, 18 different measurement techniques were used in smartphones. The measurement properties most commonly examined were construct validity, measurement error, and criterion validity. The results varied by behavior and measurement approach, and the methodological quality of the studies varied widely. Most studies investigating the measurement of diet and alcohol received very good or adequate methodological quality ratings, that is, 73% (35/48) and 69% (11/16), respectively, whereas only 13% (1/8) investigating the measurement of tobacco use received a very good or adequate rating. CONCLUSIONS This review is the first to provide evidence regarding the different types of smartphone-based approaches currently used to measure key behavioral risk factors for chronic diseases (diet, alcohol use, and tobacco use) and the quality of their measurement properties. A total of 19 measurement techniques were identified, most of which assessed dietary behaviors (48/72, 67%). Some evidence exists to support the reliability and validity of using smartphones to assess these behaviors; however, the results varied by behavior and measurement approach. The methodological quality of the included studies also varied. Overall, more high-quality studies validating smartphone-based approaches against criterion measures are needed. Further research investigating the use of smartphones to assess alcohol and tobacco use and objective measurement approaches is also needed. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-https://doi.org/10.1186/s13643-020-01375-w.
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Parent-based interventions to improve multiple lifestyle risk behaviors among adolescents: A systematic review and meta-analysis.
Champion, KE, Gardner, LA, McCann, K, Hunter, E, Parmenter, B, Aitken, T, Chapman, C, Spring, B, Thornton, L, Slade, T, et al
Preventive medicine. 2022;:107247
Abstract
Lifestyle risk behaviors often co-occur and are prevalent among adolescents. Parent-based interventions addressing risk behaviors concurrently have the potential to improve youth and parent outcomes. This systematic review evaluated the efficacy of parent-based interventions targeting multiple lifestyle risk behaviors among adolescents and parents. MEDLINE (Ovid), Embase (Ovid), PsycInfo (Ovid), Scopus, CINAHL, the Cochrane Database of Systematic Reviews (CDSR) and Cochrane Central Register of Controlled Trials (CENTRAL) were searched from 2010-May 2021. Eligible studies were randomised controlled trials (RCTs) of parent-based interventions addressing 2+ risk behaviors: alcohol use, smoking, poor diet, physical inactivity, sedentary behaviors, and poor sleep. Studies directly targeting parents, and that assessed adolescent outcomes (11-18 years) were eligible. Where possible, random-effects meta-analysis was conducted. From 11,975 identified records, 46 publications of 36 RCTs (n = 28,322 youth, n = 7385 parents) were eligible. Parent-based interventions were associated with improved adolescent moderate-to-vigorous physical activity (MVPA) [Odds Ratio (OR) = 1.82, 95% CI = 1.18, 2.81; p = 0.007], and reduced screen time (SMD = -0.39, 95% CI = -0.62, -0.16, p = 0.0009) and discretionary food intake (SMD = -0.18; 95% CI = -0.30, -0.06; p = 0.002) compared to controls. However, there was some evidence that interventions increased the odds of ever using tobacco in the medium-term (OR = 1.47, 95% CI = 0.99, 2.18, p = 0.06) and of past month tobacco use in the long-term (OR = 1.46, 95% CI = 1.12, 1.90; p = 0.005). Overall, the quality of evidence was moderate. Parent-based interventions targeting multiple risk behaviors improved adolescent MVPA, and reduced screen time discretionary food intake. Further research is needed to address sleep problems and increase intervention efficacy, particularly for alcohol and tobacco use.
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Study protocol of the Health4Life initiative: a cluster randomised controlled trial of an eHealth school-based program targeting multiple lifestyle risk behaviours among young Australians.
Teesson, M, Champion, KE, Newton, NC, Kay-Lambkin, F, Chapman, C, Thornton, L, Slade, T, Sunderland, M, Mills, K, Gardner, LA, et al
BMJ open. 2020;(7):e035662
Abstract
INTRODUCTION Lifestyle risk behaviours, including alcohol use, smoking, poor diet, physical inactivity, poor sleep (duration and/or quality) and sedentary recreational screen time ('the Big 6'), are strong determinants of chronic disease. These behaviours often emerge during adolescence and co-occur. School-based interventions have the potential to address risk factors prior to the onset of disease, yet few eHealth school-based interventions target multiple behaviours concurrently. This paper describes the protocol of the Health4Life Initiative, an eHealth school-based intervention that concurrently addresses the Big 6 risk behaviours among secondary school students. METHODS AND ANALYSIS A multisite cluster randomised controlled trial will be conducted among year 7 students (11-13 years old) from 72 Australian schools. Stratified block randomisation will be used to assign schools to either the Health4Life intervention or an active control (health education as usual). Health4Life consists of (1) six web-based cartoon modules and accompanying activities delivered during health education (once per week for 6 weeks), and a smartphone application (universal prevention), and (2) additional app content, for students engaging in two or more risk behaviours when they are in years 8 and 9 (selective prevention). Students will complete online self-report questionnaires at baseline, post intervention, and 12, 24 and 36 months after baseline. Primary outcomes are consumption of sugar-sweetened beverages, moderate-to-vigorous physical activity, sleep duration, sedentary recreational screen time and uptake of alcohol and tobacco use. ETHICS AND DISSEMINATION This study has been approved by the University of Sydney (2018/882), NSW Department of Education (SERAP no. 2019006), University of Queensland (2019000037), Curtin University (HRE2019-0083) and relevant Catholic school committees. Results will be presented to schools and findings disseminated via peer-reviewed journals and scientific conferences. This will be the first evaluation of an eHealth intervention, spanning both universal and selective prevention, to simultaneously target six key lifestyle risk factors among adolescents. TRIAL REGISTRATION NUMBER Australian New Zealand Clinical Trials Registry (ACTRN12619000431123), 18 March 2019.
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Vitamin D status: multifactorial contribution of environment, genes and other factors in healthy Australian adults across a latitude gradient.
Lucas, RM, Ponsonby, AL, Dear, K, Valery, PC, Taylor, B, van der Mei, I, McMichael, AJ, Pender, MP, Chapman, C, Coulthard, A, et al
The Journal of steroid biochemistry and molecular biology. 2013;:300-8
Abstract
Vitamin D deficiency is common and implicated in risk of several human diseases. Evidence on the relative quantitative contribution of environmental, genetic and phenotypic factors to vitamin D status (assessed by the serum concentration of 25-hydroxyvitamin D, 25(OH)D) in free-living populations is sparse. We conducted a cross-sectional study of 494 Caucasian adults aged 18-61years, randomly selected from the Australian Electoral Roll according to groups defined by age, sex and region (spanning 27°-43°South). Data collected included personal characteristics, sun exposure behaviour, biomarkers of skin type and past sun exposure, serum 25(OH)D concentration and candidate single nucleotide polymorphisms. Ambient ultraviolet radiation (UVR) levels in the month six weeks before blood sampling best predicted vitamin D status. Serum 25(OH)D concentration increased by 10nmol/L as reported time in the sun doubled. Overall, 54% of the variation in serum 25(OH)D concentration could be accounted for: 36% of the variation was explained by sun exposure-related factors; 14% by genetic factors (including epistasis) and 3.5% by direct measures of skin phenotype. Novel findings from this study are demonstration of gene epistasis, and quantification of the relative contribution of a wide range of environmental, constitutional and genetic factors to vitamin D status. Ambient UVR levels and time in the sun were of prime importance but it is nonetheless important to include the contribution of genetic factors when considering sun exposure effects. This article is part of a Special Issue entitled 'Vitamin D Workshop'.