Professor Mitch Duncan

Wearable activity trackers and smartphone apps have been shown to increase physical activity in children and adults. However, interventions using activity trackers and apps have rarely been tested in whole families. This study examined the experience and satisfaction with an activity tracker and app intervention (Step it Up Family) to increase physical activity in whole families. Telephone interviews were conducted with Queensland-based families (n = 19) who participated in the Step it Up Family intervention (N = 40, single-arm, pre/post feasibility study) in 2017/2018. Using commercial activity trackers combined with apps, the intervention included an introductory session, individual and family-level goal setting, self-monitoring, family step challenges, and weekly motivational text messages. Qualitative content analysis was conducted to identify themes, categories and sub-categories. In summary, parents reported that children were engaged with the activity tracker and app features to reach their daily step goals. Some technical difficulties were experienced with app navigation, syncing of activity tracker data, and tracker band discomfort. Although families liked that the weekly text messages reminded them to be active, they did not find them very motivating. Using text messages for physical activity motivation in families requires further testing. Overall, the intervention was well-received by families for increasing physical activity motivation.

Objective: Sleep restriction and prolonged sitting are both associated with reduced cognitive performance which is detrimental to safe driving. Regular bouts of walking to break up prolonged sitting have been shown to improve cognitive performance. It is unknown if breaking up sitting with walking may improve cognitive performance related to driving, particularly when drivers are sleep restricted. Methods: Healthy participants (n = 81, age 23.6 ± 4.5 years, 49% female) participated in a 7-day laboratory study (1 Adaptation day, 5 Experimental days, 1 Recovery day). Participants were randomly allocated to a condition: sitting 9 h sleep (Sit9), breaking up sitting 9 h sleep (Break9), sitting 5 h sleep (Sit5) and breaking up sitting 5 h sleep (Break5). Participants allocated to the Break9 and Break5 conditions completed 3-min of light-intensity walking on a treadmill every 30 min between 09:00¿17:00 h, while participants in Sit9 and Sit5 groups remained seated during these times. Each participant completed a 20-min monotonous simulated driving commute at 08:10 h and 17:30 h on each of the 5 Experimental days. Results: There was a significant main effect of condition with decreased speed variability for Break9 compared to Sit9, Sit5 and Break5 (p < 0.0001) for both commutes. Break5 had significantly higher lane variability compared to Sit9 and Break9, and Sit5 (p = <0.05) for both commutes. Break5 reported greater subjective sleepiness before each commute compared to Sit9, Break9 and Sit5 (p < 0.001). Cox Proportional Hazards model showed a significant increase in time to first crash for Sit5 and Break5 compared to Sit9 (p= <0.005). There were no significant two- or three-way interaction effects by condition, day or commute time for any of the outcome measures. Conclusion: The findings from this study highlight that breaking up sitting with light-intensity walking may improve driving performance in areas such as speed maintenance, but may not extend to lane keeping performance. The findings also show that the benefits of breaking up sitting with light-intensity walking may not be sufficient to overcome detrimental impacts of sleep restriction for all areas of driving performance. Future research should focus on increased intensity of activity as a possible means to extend the benefits more comprehensively across additional areas of driving performance.

Background: Physical inactivity and insomnia symptoms are independently associated with increased risk of depression and anxiety; however, few studies jointly examine these risk factors. This study aimed to prospectively examine the joint association of physical activity (PA) and insomnia symptoms with onset of poor mental health in adults. Methods: Participants from the 2013 to 2018 annual waves of the Household Income and Labour Dynamics in Australia panel study who had good mental health (Mental Health Inventory-5 >54) in 2013, and who completed at least 1 follow-up survey (2014¿2018), were included (n = 10,977). Poor mental health (Mental Health Inventory-5 = 54) was assessed annually. Baseline (2013) PA was classified as high/moderate/low, and insomnia symptoms (i.e., trouble sleeping) were classified as no insomnia symptoms/insomnia symptoms, with 6 mutually exclusive PA-insomnia symptom groups derived. Associations of PA¿insomnia symptom groups with onset of poor mental health were examined using discrete-time proportional-hazards logit-hazard models. Results: There were 2322 new cases of poor mental health (21.2%). Relative to the high PA/no insomnia symptoms group, there were higher odds (odds ratio and 95% confidence interval (95%CI)) of poor mental health among the high PA/insomnia symptoms (OR = 1.87, 95%CI: 1.57¿2.23), moderate PA/insomnia symptoms (OR = 1.93, 95%CI: 1.61¿2.31), low PA/insomnia symptoms (OR = 2.33, 95%CI: 1.96¿2.78), and low PA/no insomnia symptoms (OR = 1.14, 95%CI: 1.01¿1.29) groups. Any level of PA combined with insomnia symptoms was associated with increased odds of poor mental health, with the odds increasing as PA decreased. Conclusion: These findings highlight the potential benefit of interventions targeting both PA and insomnia symptoms for promoting mental health.

Background: Preliminary evidence suggests that web-based physical activity interventions with tailored advice and Fitbit integration are effective and may be well suited to older adults. Therefore, this study aimed to examine the engagement, acceptability, usability, and satisfaction with ¿Active for Life,¿ a web-based physical activity intervention providing computer-tailored physical activity advice to older adults. Methods: Inactive older adults (n = 243) were randomly assigned into 3 groups: 1) tailoring + Fitbit, 2) tailoring only, or 3) a wait-list control. The tailoring + Fitbit group and the tailoring-only group received 6 modules of computer-tailored physical activity advice over 12¿weeks. The advice was informed by objective Fitbit data in the tailoring + Fitbit group and self-reported physical activity in the tailoring-only group. This study examined the engagement, acceptability, usability, and satisfaction of Active for Life in intervention participants (tailoring + Fitbit n = 78, tailoring only n = 96). Wait-list participants were not included. Engagement (Module completion, time on site) were objectively recorded through the intervention website. Acceptability (7-point Likert scale), usability (System Usability Scale), and satisfaction (open-ended questions) were assessed using an online survey at post intervention. ANOVA and Chi square analyses were conducted to compare outcomes between intervention groups and content analysis was used to analyse program satisfaction. Results: At post-intervention (week 12), study attrition was 28% (22/78) in the Fitbit + tailoring group and 39% (37/96) in the tailoring-only group. Engagement and acceptability were good in both groups, however there were no group differences (module completions: tailoring + Fitbit: 4.72 ± 2.04, Tailoring-only: 4.23 ± 2.25 out of 6 modules, p =.14, time on site: tailoring + Fitbit: 103.46 ± 70.63, Tailoring-only: 96.90 ± 76.37¿min in total, p =.56, and acceptability of the advice: tailoring + Fitbit: 5.62 ± 0.89, Tailoring-only: 5.75 ± 0.75 out of 7, p =.41). Intervention usability was modest but significantly higher in the tailoring + Fitbit group (tailoring + Fitbit: 64.55 ± 13.59, Tailoring-only: 57.04 ± 2.58 out of 100, p =.003). Participants reported that Active for Life helped motivate them, held them accountable, improved their awareness of how active they were and helped them to become more active. Conversely, many participants felt as though they would prefer personal contact, more detailed tailoring and more survey response options. Conclusions: This study supports web-based physical activity interventions with computer-tailored advice and Fitbit integration as engaging and acceptable in older adults. Trial registration: Australian and New Zealand Clinical Trials Registry: ACTRN12618000646246. Registered April 23 2018, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374901

Introduction: Many university students have poor mental health, and co-occurring health risk behaviors. Targeting health behavior change in this population may improve mental health outcomes. This scoping review describes the extent and range of randomized controlled trials (RCT) evaluating interventions targeting health risk behaviors and measuring a mental health outcome, among university students. Methods: Six electronic databases were searched for RCTs published until the 18th May 2021. Eligible RCTs included university students, evaluated interventions that promoted health behavior change (i.e., dietary intake, physical activity, sedentary behavior, alcohol and drug use, smoking, and sleep), and measured a mental health-related outcome. Results: Fifty-nine RCTs met the inclusion criteria that were published from 2000 to 2021, and over half (n = 33) were conducted in the United States. Interventions evaluated within the RCTs (n = 92) predominantly targeted changes to dietary intake (n = 41 interventions), physical activity (n = 39), or alcohol intake (n = 35). Most interventions targeted one (n = 51) or two (n = 27) health behaviors only. Included RCTs considered mental ill health outcomes (n = 24), psychological wellbeing outcomes (n = 20), or both (n = 15). Discussion: This scoping review identified a moderate volume of experimental research investigating the impact of health behavior interventions on university students' mental health. There is scope for further research examining health behavior interventions targeting university students, particularly interventions taking a multi-behavioral approach.

Background: Outdoor gym installations have become a popular initiative by local government agencies globally to increase community-based physical activity. Despite their rapid spread, usage rates and user characteristics are not well described, particularly the mode/type of activity performed. This study aims to investigate the proportion and timing of outdoor gym use, and demographic characteristics of gym users across seven locations. Method: The current study employed a cross-sectional research design with observational measures to examine outdoor gym use across seven locations in a large regional area in New South Wales, Australia. An outdoor gym audit including the number of pieces and names of the equipment, and type of outdoor gym was undertaken in the month prior to data collection. Results: A total of 56¿h of observation periods were conducted, during which 2950 individuals were observed moving through the observation target areas. Across all parks, 3.8% of individuals (n = 112) were observed using the outdoor gyms for exercise purposes. Males (65%) were more likely to be observed using the equipment than females (35%) and there were more young-to-middle aged adult (83.9%) users than seniors (6.3%), adolescents (4.5%) and children (3.6%). Conclusion: Outdoor gyms have the potential to influence community-based physical inactivity rates as they appear to be a popular strategy by many government jurisdictions globally. To increase usage, local government agencies would benefit from working collaboratively with researchers to determine effective education and promotion strategies and in developing interventions to increase usage of outdoor gym equipment, particularly among women and older adults.

Objective Sustained engagement with Internet-based behavioural interventions is crucial to achieve successful behaviour change outcomes. As this has been problematic in many interventions, a lot of research has focused on participants with little or no engagement. However, few studies have attempted to understand users with continuous long-term engagement, the so called ¿super engaged users¿, and why they keep on using programs when everybody else has long stopped. Therefore, the aim of this research was to qualitatively examine characteristics, usage profile and motivations of super engaged users in the 10,000 Steps program. Methods Twenty 10,000 Steps users (10 with more than 1 year of engagement, and 10 with more than 10 years of engagement) participated in semi-structured interviews, that were transcribed and thematically analysed. Results Participants were aged 60 years on average, with more than half being overweight/obese and/or suffering from chronic disease despite logging high step counts (219 million steps per participant on average) on the 10,000 Steps platform. Participants indicated that the reasons for sustained use were that engaging the program had become a habit, that the program kept them motivated, and that it was easy to use. Few participants had suggestions for improvement or expressed there were program elements they did not like. Uptake of program innovations (e.g., app-version, use of advanced activity tracker instead of pedometer) was modest among the super engaged users. Conclusion The findings from this study emphasise the need for digital health programs to incorporate features that will support the development of habits as soon as participants start to engage with the program. While a program¿s usability, user-friendliness and acceptability are important to engage and retain new users, habit formation may be more important for sustained long-term engagement with the behaviour and the program.

Type 2 diabetes mellitus (T2DM) accounts for approximately 90% of diabetes cases globally. Regular physical activity is regarded as one of the key components in T2DM management. Aerobic exercise was traditionally recommended; however, there is a growing body of research examining the independent effect of resistance training (RT) on glycemic control. This systematic review and meta-analysis aimed to conduct an update on the effects of RT on glycosylated hemoglobin (HbA1c) in adults with T2DM and examine the moderating effects of training effect (ie, muscular strength improvements), risk of bias and intervention duration. Peer-reviewed articles published in English were searched across MEDLINE, Embase, CINAHL, Scopus and SPORTDiscus from database inception until January 19, 2021. Each online database was systematically searched for randomized controlled trials reporting on the effects of RT on HbA1c in individuals with T2DM. Twenty studies (n=1172) were included in the meta-analysis. RT significantly reduced HbA1c compared with controls (weighted mean difference=-0.39, 95% CI -0.60 to -0.18, p<0.001, I 2 =69.20). Training effect significantly (p<0.05) moderated the results, with larger improvements in muscular strength leading to greater reductions in HbA1c (ß=-0.99, CI -1.97 to -0.01). Intervention duration and risk of bias did not significantly moderate the effects. As a secondary analysis, this study found no significant differences in HbA1c when comparing RT and aerobic training (p=0.42). This study demonstrates that RT is an effective strategy to decrease HbA1c in individuals with T2DM. Importantly, RT interventions that had a larger training effect appeared more effective in reducing HbA1c, compared with interventions producing medium and small effects. PROSPERO registration number CRD42020134046.

Aim: To examine the influence of redeveloping two parks based on consultation with local community and stakeholders on park visitation rates and activity levels of park users. Subject and methods: Pre¿post design. Two local neighbourhood parks located in low socioeconomic areas in the regional city of Bundaberg, Australia. Number of park visitors and park users¿ activity levels were assessed using SOPARC pre (April¿August 2013) and post (April¿May 2014) park redevelopment. Park data were pooled for analysis. Pre¿post differences in the park visitors and park user activity levels were examined overall and also by sex and age group (children, teen, adults, seniors) using a negative binomial generalised linear model (Stata MP 15.1, a = 0.05). Results: Average daily visitors (baseline: 5.08; follow-up: 45.09; p < 0.001) and the number of child, teen, adult, and senior visitors increased from baseline to follow-up. The daily number of park users engaged in sedentary (baseline: 1.56; follow-up: 8.30; p < 0.001), moderate-intensity (baseline: 2.61; follow-up: 26.23; p < 0.001) and vigorous activity (baseline: 0.91; follow-up: 10.56; p < 0.001) also increased. Conclusion: Redeveloping local parks, informed by community, local government and researcher stakeholder consultations increased the number of and activity levels of park users.

Objective: This study examined the feasibility of an activity tracker and app intervention to increase physical activity in whole families. Methods: This was a single-arm feasibility study with pre¿post-intervention measures. Between 2017 and 2018, 40 families (58 children aged 6¿10 years, 39 mothers, 33 fathers) participated in the 6-week Step it Up Family programme in Queensland, Australia. It was delivered using Garmin activity trackers and apps, weekly motivational text messages and an introductory session. Online surveys and semi-structured interviews conducted with parents assessed intervention usage, acceptability, usability, perceived usefulness, use of physical activity self-management strategies and programme influence. Analyses included descriptive statistics, Wilcoxon signed-rank test and qualitative content analysis. Results: Overall, 38 families completed the post-intervention survey (95% family retention; 90% children, 95% mothers, 88% fathers). Garmin activity tracker usage was high (i.e. nearly 24/7 during the 6 weeks intervention). Families also used the Garmin apps regularly (i.e. mostly 2¿6 times per week). Further, 80% of mothers and 52% of fathers read the motivational mobile text messages. Usability and perceived usefulness of the Garmin activity trackers and apps were rated high. Both parents significantly increased their use of physical activity self-management strategies from pre to post-intervention. Parents expressed that the intervention had increased awareness of physical (in)activity in the family, encouraged to be active and promoted outdoor family activities. Conclusions: Using activity trackers and apps to increase physical activity in the whole family was well received by children and parents which suggests that this intervention approach is feasible. However, further testing is needed amongst more diverse family populations. These early findings support the implementation of a randomised controlled trial to examine intervention efficacy.

Objectives Some online, personally tailored, text-based physical activity interventions have proven effective. However, people tend to ¿ skim' and ¿ scan' web-based text rather than thoroughly read their contents. In contrast, online videos are more engaging and popular. We examined whether web-based personally tailored physical activity videos were more effective in promoting physical activity than personally tailored text and generic information. Methods 501 adults were randomised into a video-tailored intervention, text-tailored intervention or control. Over a 3-month period, intervention groups received access to eight sessions of web-based personally tailored physical activity advice. Only the delivery method differed between intervention groups: tailored video versus tailored text. The primary outcome was 7-day ActiGraph-GT3X+ measured moderate-to-vigorous physical activity (MVPA) assessed at 0, 3 and 9 months. Secondary outcomes included self-reported MVPA and website engagement. Differences were examined using generalised linear mixed models with intention-to-treat and multiple imputation. Results Accelerometer-assessed MVPA increased 23% in the control (1.23 (1.06, 1.43)), 12% in the text-tailored (1.12 (0.95, 1.32)) and 28% in the video-tailored (1.28 (1.06, 1.53)) groups at the 3-month follow-up only, though there were no significant between-group differences. Both text-tailored (1.77 (1.37, 2.28]) and video-tailored (1.37 (1.04, 1.79)) groups significantly increased self-reported MVPA more than the control group at 3 months only, but there were no differences between video-tailored and text-tailored groups. The video-tailored group spent significantly more time on the website compared with text-tailored participants (90 vs 77 min, p=0.02). Conclusions The personally tailored videos were not more effective than personally tailored text in increasing MVPA. The findings from this study conflict with pilot study outcomes and previous literature. Process evaluation and mediation analyses will provide further insights. Trial registration number ACTRN12615000057583

Understanding for whom behaviour change interventions work is important, however there is a lack of studies examining potential moderators in such interventions. This study investigated potential moderators on the effectiveness of a computer-tailored intervention to increase physical activity among Australian adults. People who had <150 min of moderate-vigorous physical activity (MVPA) a week, able to speak and read English, aged =18 years, lived in Australia, and had internet access were eligible to participate. Participants recruited through social media, emails, and third-party databases, were randomly assigned to either the control (n = 167) or intervention groups (n = 334). Physical activity was measured objectively by ActiGraph GT3X and also by self-report at baseline and three months. Three-way interaction terms were tested to identify moderators (i.e., demographic characteristics, BMI, and perceived neighbourhood walkability). The results showed that the three-way interaction was marginally significant for sex on accelerometer measured MVPA/week (p = 0.061) and steps/day (p = 0.047). The intervention appeared to be more effective for women compared to men. No significant three-way interactions were found for the other potential moderators. Strategies to improve levels of personalisation may be needed so that physical activity interventions can be better tailored to different subgroups, especially sex, and therefore improve intervention effectiveness.

Background: To examine if a composite activity-sleep behaviour index (ASI) mediates the effects of a combined physical activity and sleep intervention on symptoms of depression, anxiety, or stress, quality of life (QOL), energy and fatigue in adults. Methods: This analysis used data pooled from two studies: Synergy and Refresh. Synergy: Physically inactive adults (18¿65 years) who reported poor sleep quality were recruited for a two-arm Randomised Controlled Trial (RCT) (Physical Activity and Sleep Health (PAS; n = 80), or Wait-list Control (CON; n = 80) groups). Refresh: Physically inactive adults (40¿65 years) who reported poor sleep quality were recruited for a three-arm RCT (PAS (n = 110), Sleep Health-Only (SO; n = 110) or CON (n = 55) groups). The SO group was omitted from this study. The PAS groups received a pedometer, and accessed a smartphone/tablet ¿app¿ using behaviour change strategies (e.g., self-monitoring, goal setting, action planning), with additional email/SMS support. The ASI score comprised self-reported moderate-to-vigorous-intensity physical activity, resistance training, sitting time, sleep duration, efficiency, quality and timing. Outcomes were assessed using DASS-21 (depression, anxiety, stress), SF-12 (QOL-physical, QOL-mental) and SF-36 (Energy & Fatigue). Assessments were conducted at baseline, 3 months (primary time-point), and 6 months. Mediation effects were examined using Structural Equation Modelling and the product of coefficients approach (AB), with significance set at 0.05. Results: At 3 months there were no direct intervention effects on mental health, QOL or energy and fatigue (all p > 0.05), and the intervention significantly improved the ASI (all p < 0.05). A more favourable ASI score was associated with improved symptoms of depression, anxiety, stress, QOL-mental and of energy and fatigue (all p < 0.05). The intervention effects on symptoms of depression ([AB; 95%CI] -0.31; - 0.60,-0.11), anxiety (- 0.11; - 0.27,-0.01), stress (- 0.37; - 0.65,-0.174), QOL-mental (0.53; 0.22, 1.01) and ratings of energy and fatigue (0.85; 0.33, 1.63) were mediated by ASI. At 6 months the magnitude of association was larger although the overall pattern of results remained similar. Conclusions: Improvements in the overall physical activity and sleep behaviours of adults partially mediated the intervention effects on mental health and quality of life outcomes. This highlights the potential benefit of improving the overall pattern of physical activity and sleep on these outcomes. Trial registration: Australian New Zealand Clinical Trial Registry: ACTRN12617000680369; ACTRN12617000376347. Universal Trial number: U1111¿1194-2680; U1111¿1186-6588. Human Research Ethics Committee Approval: H-2016-0267; H-2016¿0181.

Background: Physical activity is an important health behavior, due to its association with many physical and mental health conditions. During distressing events, such as the COVID-19 pandemic, there is a concern that physical activity levels may be negatively impacted. However, recent studies have shown inconsistent results. Additionally, there is a lack of studies in Australia on this topic. Objective: The aim of this study is to investigate changes in physical activity reported through the 10,000 Steps program and changes in engagement with the program during the COVID-19 pandemic. Methods: Data between January 1, 2018, and June 30, 2020, from registered members of the 10,000 Steps program, which included 3,548,825 days with step data, were used. The number of daily steps were logged manually by the members or synced automatically from their activity trackers connected to the program. Measures on program usage were the number of new registered members per day, the number of newly registered organizations per day, the number of steps logged per day, and the number of step entries per day. Key dates used for comparison were as follows: The first case with symptoms in Wuhan, China; the first case reported in Australia; the implementation of a 14-day ban for noncitizens arriving in Australia from China; the start of the lockdown in Australia; and the relaxing of restrictions by the Australian Government. Wilcoxon signed-rank tests were used to test for significant differences in number of steps between subgroups, between engagement measures in 2019 versus 2020, and before and after an event. Results: A decrease in steps was observed after the first case in Australia was reported (1.5%; P=.02) and after the start of the lockdown (3.4%; P<.001). At the time that the relaxing of restrictions started, the steps had already recovered from the lockdown. Additionally, the trends were consistent across genders and age groups. New South Wales, Australian Capital Territory, and Victoria had the greatest step reductions, with decreases of 7.0% (P<.001), 6.2% (P=.02), and 4.7% (P<.001), respectively. During the lockdown, the use of the program increased steeply. On the peak day, there were more than 9000 step entries per day, with nearly 100 million steps logged per day; in addition, more than 450 new users and more than 15 new organizations registered per day, although the numbers decreased quickly when restrictions were relaxed. On average per day, there were about 55 new registered users (P<.001), 2 new organizations (P<.001), 25.6 million steps (P<.001), and 2672 log entries (P<.001) more in 2020 compared to the same period in 2019. Conclusions: The pandemic has had negative effects on steps among Australians across age groups and genders. However, the effect was relatively small, with steps recovering quickly after the lockdown. There was a large increase in program usage during the pandemic, which might help minimize the health impact of the lockdown and confirms the important role of physical activity programs during times of distress and lockdowns.

Introduction: Inadequate sleep is a major public health concern, with large economic, health, and operational costs to Australia. Despite the implementation of public sleep health campaigns, approximately 40% of Australian adults do not obtain the recommended 7¿9¿hours of sleep. Thus, while people may know how much sleep is required, this knowledge may not be adequately translated to actual sleep behavior. Consequently, this study aims to examine the discrepancy between knowledge of sleep recommendations and self-reported sleep behaviors. Methods: A sample of 1265 Australian adults (54% female, aged 18¿65) completed a phone interview as part of the 2017 National Social Survey and were asked questions about their knowledge of sleep guidelines and their actual sleep behavior. Binary logistic regression was used to determine the factors associated with awareness of sleep recommendations and whether this corresponded with reported sleep duration. Results: The final sample size was 998. Although 94% of the sample were aware of current sleep recommendations, 23% of participants did not self-report regularly obtaining 7¿9¿h sleep per night. These participants were less likely to want to obtain more sleep, less likely to view sleep as a priority before stressful events, and less likely to self-report good health. Conclusion: Although a majority of the sample were aware of sleep recommendations, almost a quarter of the participants¿ behavior did not align with their knowledge. Future sleep health campaigns should consider options beyond education, including emphasis on practical strategies and modifiable lifestyle factors to assist individuals to obtain the recommended amount of sleep.

Objectives: Evidence demonstrates that physical activity is favorably associated with indicators of sleep health. However, population-based studies rarely examine the relationship between different physical activity modalities (ie, aerobic exercise vs muscle-strengthening exercise) with components of sleep health. Methods: Cross-sectional analyses were conducted on the US 2017 Behavioral Risk Factor Surveillance System. Validated items assessed self-reported moderate-to-vigorous-intensity aerobic physical activity (MVPA), muscle-strengthening exercise (MSE), sleep duration, difficulties and disorders. Poisson regression assessed prevalence ratios (PR) of 5 components of detrimental sleep health (short sleep; long sleep; poor quality sleep; observed snoring; and observed breathing stoppage) separately for adults 18-64 years and =65 years, across 4 categories of physical activity guideline adherence (met neither [reference]; MSE only; MVPA only; met both). Results: The sample comprised 47,564 adults (mean age: 48.4 years; ±1.4; 51.6% female). Among those 18-64 years, with the exception of short sleep (4-6 hours), for all other detrimental sleep health components, the lowest PRs were observed among those meeting both MVPA-MSE guidelines. Among those aged =65 years, for all 5 detrimental sleep health components, compared to the other physical activity categories, the lowest PRs were observed among those meeting both MVPA-MSE guidelines. All associations remained after adjusting for potential confounders (sex, education, income, smoking, alcohol, depression, hypertension, diabetes). Conclusion: A physical activity routine that includes both MVPA and MSE is likely to be beneficial for better sleep health. Longitudinal studies are needed to establish the temporal relationships between MVPA/MSE guideline adherence and sleep health.

Introduction: Little is known about the joint effects of physical activity and sleep difficulties on hypertension. The aim of this study was to examine the joint associations of physical activity and sleep difficulties with the incidence of hypertension in mid-aged women. Study Design: Prospective cohort study. Main Outcome Measures: Mid-aged participants (n = 5,300) in the Australian Longitudinal Study on Women's Health completed four triennial surveys starting in 2004, when they had a mean age of 55 years. The presence of hypertension, physical activity and the number of sleep difficulties (range 0-4) were reported at each survey. Total MET.min/week of physical activity was assessed, and dichotomised as inactive (<500 MET.min/wk) or active (=500 MET.min/wk). Joint categories of physical activity and sleep difficulties were created using six mutually exclusive groups. Associations of joint physical activity and sleep difficulty groups with incident hypertension were examined via discrete-time survival analysis using logit-hazard models. Results: There were 1,175 cases of incident hypertension (22.2%). Compared with the Active and No Difficulties group, women in the Inactive and 1 Difficulty (Odds Ratio (95% confidence interval) (1.31 (1.06, 1.62)) and Inactive and 2-4 Difficulties (1.44 (1.16, 1.78)) groups were more likely to develop hypertension. Sleep difficulties were not associated with hypertension among active women. Conclusions: Mid-aged inactive women with sleep difficulties were more likely to develop hypertension. Physical activity appeared to protect against the increased risk of hypertension in women with sleeping difficulties.

Objectives: Examining mediators of intervention efficacy in an m-health intervention targeting physical activity and sleep in 160 Australian adults. Design: Nationwide randomised controlled trial. Main outcome measures: Moderate- and vigorous-intensity physical activity (MVPA), assessed using the Active Australia Questionnaire; sleep quality (Pittsburgh Sleep Quality Index); and sleep hygiene practices (Sleep Hygiene Index). Hypothesised psychosocial (e.g. self-efficacy) and behavioural (i.e. MVPA, sleep quality, sleep hygiene) mediators were tested on primary endpoint data at 3 months using bias-corrected bootstrapping (PROCESS 2 for SPSS). All outcomes and mediators were assessed using self-report. Results: At three months, the intervention had significantly improved sleep quality (d = 0.48, 95% CI: -2.26, -0.33, p = 0.009) and sleep hygiene (d = 0.40, 95% CI: -3.10, -0.19, p = 0.027). Differences in MVPA were not significant (d = 0.24, 95% CI: -35.53, 254.67, p = 0.139). Changes in MVPA were mediated by self-efficacy, perceived capability, environment, social support, intentions and planning, some of which showed inconsistent mediation (suppression). None of the hypothesised psychosocial factors mediated sleep outcomes. Changes in sleep hygiene mediated changes in sleep quality. Conclusions: Several psychosocial factors mediated changes in physical activity but not in sleep outcomes. Mediation effects of sleep hygiene on sleep quality highlight the importance of providing evidence-based strategies to improve sleep quality.

Background Poor sleep health is highly prevalent. Physical activity is known to improve sleep quality but not specifically targeted in sleep interventions. Purpose To compare the efficacy of a combined physical activity and sleep intervention with a sleep-only intervention and a wait-list control, for improving sleep quality in middle-aged adults without a diagnosed sleep disorder. Methods Three-arm randomized controlled trial (Physical Activity and Sleep Health (PAS), Sleep Health Only (SO), Wait-list Control (CON) groups; 3-month primary time-point, 6-month follow-up) of 275 (PAS = 110, SO = 110, CON = 55) inactive adults (40¿65 years) reporting poor sleep quality. The main intervention component was a smartphone/tablet ¿app¿ to aid goal setting and self-monitoring physical activity and/or sleep hygiene behaviors (including stress management), and a pedometer for PAS group. Primary outcome was Pittsburgh Sleep Quality Index (PSQI) global score. Secondary outcomes included several self-reported physical activity measures and PSQI subcomponents. Group differences were examined stepwise, first between pooled intervention (PI = PAS + SO) and CON groups, then between PAS and SO groups. Results Compared with CON, PI groups significantly improved PSQI global and subcomponents scores at 3 and 6 months. There were no differences in sleep quality between PAS and SO groups. The PAS group reported significantly less daily sitting time at 3 months and was significantly more likely to report =2 days/week resistance training and meeting physical activity guidelines at 6 months than the SO group. Conclusions PIs had statistically significantly improved sleep quality among middle-aged adults with poor sleep quality without a diagnosed sleep disorder. The adjunctive physical activity intervention did not additionally improve sleep quality.

Background: Emerging evidence links a poor diet with mental ill-health although the direction of this association is unclear. The aim was to examine the bidirectional prospective relationships between core (and non-core food consumption, and symptoms of depression. Methods: Depressive symptoms (Mental Health Index-5, MHI-5), current/prior depression and consumption of core (recommended food groups) and non-core (discretionary) foods were assessed in the population-based 2013 and 2017 Household Income and Labour Dynamics in Australia cohort study. Three cross-lagged linear models assessed associations between all three baseline variables in 2013, alternating 2017 variables as outcomes. Results: In the population (n = 10,003; 48.3% women; 48.5[15.7] years), core food score in 2013 was associated with MHI-5 (ß:0.102, 95%CI: 0.010,0.193) in 2017, while the non-core food score was not (ß:-0.030, 95%CI:-0.099,0.160). Depressive symptom score in 2013 was not associated with either food score in 2017. Current/prior diagnosis of depression in 2013 was associated with core (ß:-0.198, 95%CI:-0.329,-0.067) but not non-core (ß:-0.036, 95%CI: -0.151,0.080) food score in 2017. Limitations: Results may not be generalizable to the whole population due to some selection bias, self-report depression diagnosis may have led to misclassification of previous mental illness, and core and non-core food scores are not validated measures of diet quality. Conclusions: There is a prospective association between core food consumption and depressive symptoms. This association is of small magnitude and we cannot discount insufficient core food consumption reflecting an effect of prior mental illness. Our results suggest that, for depression, public health focus should be on improving core food intake.

Sleep health is an important aspect of wellbeing and merits incorporation into workplace health promotion programs for employees. Men are a unique population with whom many traditional workplace health promotion programs have had limited success. This systematic review posed the question do workplace health promotion programs improve sleep among men, and what program design features contribute to improving sleep among working men? Databases searched were MEDLINE, EMBASE, the Cochrane Library, CINHAL, Academic Search Complete and Health Source: Nursing/Academic Edition and Google Scholar. Empirical research reporting non-pharmacological behavioral sleep programs and/or interventions for working men were eligible for review. 1049 articles were identified; 15 intervention studies were included: 13 interventions were delivered through workplaces, and two recruited workers to programs delivered outside of work. Interventions incorporated health education, stress reduction/relaxation, and/or physical activity components. Eleven studies reported positive findings for sleep health outcome(s) in men. A moderate level of evidence exists for sleep health programs with physical activity and stress management components. Evidence for the effectiveness of sleep health education programs was mixed. That only one study included a gender-sensitized intervention, where men's preferences shaped the content of a stress-reduction program which resulted in improved sleep quality, attests to the insufficient evidence and lack of gender-specific content and analyses. Next research steps should include considering cultural constructions of masculinity in program design in order to strengthen the appeal and engagement of men, and optimize health benefits for working men.

Understanding the associations between types of alcoholic drinks and adiposity has public health relevance, considering that adult overweight and obesity prevalence are increasing worldwide. We aimed to evaluate the association between overall alcohol consumption and types of alcohol drinks with markers of adiposity from the UK Biobank baseline data (n = 280,183, 48.3% female). Generalized linear models were used to examine the associations between alcohol consumption with body mass index (BMI) and body fat percentage. Those drinking within the public health guidelines had a lower BMI by 1.34 kg/m2 (95% CI 1.42, 1.26 kg/m2) compared to never drinkers. Association between alcohol consumption and body fat percentage were not statistically significant. Compared to those who never drink wines (red wine, champagne and fortified wine), drinkers of these alcoholic beverages had lower BMI (difference of -0.75 kg/m2, 95% CI -0.78, -0.72 kg/m2; -0.48 kg/m2, 95% CI -0.52, -0.45 kg/m2; and -0.24 kg/m2, 95% CI -0.29, -0.18 kg/m2, respectively). Beer and spirits drinkers had higher BMI compared to never drinkers of beer and spirits (difference of 0.18 kg/m2, 95% CI 0.14, 0.22 kg/m2 and 0.64 kg/m2, 95% CI 0.61, 0.68 kg/m2, respectively). Our data did not find a link between alcohol drinking and higher risk of obesity.

Objectives: Supporting healthy ageing is a key priority worldwide. Physical activity, diet quality and sleep are all associated with health outcomes, but few studies have explored their independent associations with all-cause mortality in an older population in the same model. The study aim was to examine associations between step-count, self-reported diet quality, restless sleep, and all-cause mortality in adults aged 55¿85 years. Design: A prospective cohort study of adults in Newcastle, New South Wales, Australia. Method: Data were from 1697 participants (49.3% women; baseline mean age 65.4 ± 7.1 years). Daily steps (measured by pedometer), diet quality (from a modified Australian Recommended Food Score), and frequency of restless sleep (by self-report) were assessed in relation to all-cause mortality using Cox proportional hazard regression with adjustment for sex, age, household income and smoking. Baseline data were collected between January 2005 and April 2008, and last follow-up was in March 2017 (median follow-up 9.6 years). Results: Higher step count (HR: 0.93, 95%CI: 0.88¿0.98 per 1000-step increment) and higher diet quality (HR: 0.86, 95%CI: 0.74¿0.99 per 8-point increment in diet quality score) were associated with reduced mortality risk. Restless sleep for =3 nights/week was not associated with mortality risk (HR: 1.03, 95%CI: 0.78¿1.39). Sensitivity analyses, adjusting for chronic disease and excluding deaths <1 year after baseline, did not change these estimates. Conclusions: Increased daily steps and consumption of a greater variety of nutrient-dense foods every week would result in substantial health benefits for older people. Future research should include a greater variety of sleep measures.

This study used a bibliometric analysis through the Scopus database to examine papers that combined physical activity and sleep, published between 1979 and 2018. Bibliometric indicators of productivity included publication volume and citation distribution, top 10 authors, average authors per paper, single- and multicountry collaboration, collaborative index, top 10 countries, leading journals, highly cited papers and network visualization for coauthorship, international collaboration, and co-occurrence of author keywords. The initial search identified 1,509 papers, of which 607 passed through comprehensive screening and were included in the final analysis. Most of the papers were research articles (90.8%) and published in English (90.8%). Most papers (81.4%) were published within the past decade, 2009¿2018. The mean number of papers published per year was 15.2, the mean number of citations per paper was 257.3, and the mean number of authors per paper was 5.5. International collaboration was evident for 21.6% of the papers, and 95.6% of papers were multiauthored. The most prolific publishing institutions and authors were from the United States, Canada, Australia, Switzerland, and Brazil. Keyword analysis suggested that almost all age groups and study designs were covered, but most papers focus on noncommunicable diseases. Although there has been a rise in scientific production on combined physical activity and sleep research in recent years, future work in this area should include researchers from developing countries.

Background: Recent research has demonstrated an association between dietary intake and sleep health that can influence chronic disease risk factors. A scoping review of research studies investigating dietary intake and sleep was undertaken to determine the extent and scope of research in laboratory-based, free-living and mixed settings. Additionally, this review determines how well subpopulations and geographical locations are represented and the methodologies used to assess outcome measures. Methods: Five online databases were used to identify papers published between 1970 and 2017. Included studies were those conducted in adults and reported both outcomes of interest: (i) sleep health, including sleep restriction and sleep hygiene and (ii) dietary outcomes, including altered nutrients, dietary patterns and supplements. Results: In total, 129 publications were included with the majority¿being dietary interventions investigating sleep outcomes (n¿=¿109) with fewer¿being sleep interventions investigating and reporting dietary outcomes (n¿=¿20). Dietary interventions were most often carried out in free-living environments, in contrast to sleep interventions that were most often carried out in laboratory-based environments. The majority of dietary interventions investigated use of a supplement (n¿=¿66 studies), which was predominantly caffeine (n¿=¿49). Sleep interventions investigated sleep duration only, with the majority (n¿=¿17) investigating the effect of partial sleep restriction under 5.5¿h per night on dietary intake,¿while three studies investigating total sleep deprivation. Conclusions: Investigating broader aspects of dietary such as overall diet quality and dietary patterns and other components of sleep health such as quality, timing and sleep¿hygiene are important aspects for future research.

The 10,000 Steps program originated from a landmark whole-of-community multi-strategy intervention to increase physical activity (PA) in Rockhampton, Australia in 2001-2003. It used a social ecological framework to promote physical activity at the individual, population, environmental and policy level. Two of the fundamental aspects of the original program were goal setting (10,000 steps per day) and self-monitoring (use of a pedometer for daily step counts). A project website (www.10000steps.org.au) allowed registered participants to record their physical activity. Over time the program morphed into an e- & mHealth intervention without face-to-face elements. The program is now delivered via website and smartphone apps and employs activity trackers (pedometers, Fitbit, Garmin). To date the project has signed-up over 425,000 members who have logged 221 billion steps (?43 million a day) on the website or app. More than 14,000 workplaces and community organisations have been involved with the program. A central element of the program, the 'Workplace Challenge' has been used by ?65% of 10,000 Steps members, which on average increases physical activity by 159 min/week for those who participate in it. In 2011, the Queensland Government designated the 10,000 Steps program as their key physical activity workplace health promotion strategy. Multiple factors underpin the success of the program. The message is simple and clear: the project name, with its distinctive logo and tagline ('Every Step Counts') provides a clear and prescriptive target for the physical activity 'dose'. Using effective behaviour change techniques: goal setting (the 10,000 Steps concept), self-monitoring (steps are tracked), social support (participants organise as 'teams' to reach certain step goals) and gamification (teams competing against each other creating 'friendly competition'). Ongoing redevelopment: since inception, there have been three complete redesigns of the website (including a branding redesign), and new smartphone apps. More recently, the website was modified to allow syncing of steps using popular activity trackers. Resources to support implementation: the program provides resources (e.g. 'Active Workplace Guide') and has dedicated staff to respond to queries from workplaces and individuals to help overcome implementation barriers. Project staff continuously promote the program via media interviews, attendance at events, social media and marketing, advertising, and networking and collaboration. Ongoing evaluation has contributed to continuous funding: to ensure the program remains successful in a fast-changing technology environment, continuous evaluation has been necessary. These evaluation strategies, the success of the original project and the strong partnership with the program funder (Queensland Health) have all contributed to the long-term (19 years) support for the project.

Health behaviour change programs that utilise IT-based delivery have great potential to improve health. Whilst more static Web 1.0 technologies have been somewhat effective, they often failed to promote longer-term user engagement required for greater health promotion impact. With Web 2.0 technologies, however, there is potential for greater engagement and retention, through allowing individuals to determine how information is generated, modified, and shared collaboratively. The WALK 2.0 study utilised a Web 2.0-based platform to engage participants in health behaviour change aimed at increasing physical activity levels. The program included two trials: (1) a three-arm randomised controlled trial (RCT) that compared the effectiveness of Web 2.0, Web 1.0, and paper-based logbook interventions; and (2) a real-world randomised ecological trial (RET) that compared a Web 2.0 and Web 1.0 intervention. The aim of this paper is not to focus on the research trial results per se, but rather the success factors and challenges in both the RCT and RET. Both the RCT and RET demonstrated successful outcomes, with greater improvements in physical activity for the Web 2.0 groups. A range of challenges, however, were identified in designing, implementing, and evaluating such interventions. These include IT-based intervention development within a research context, the ability to establish a self-sustaining online community, the rapid pace of change in web-based technology and implications for trial design, the selection of best outcome measures for ecological trials, and managing engagement, non-usage and study attrition in real-world trials. Future research and developments in this area must look to broader research designs that allow for the ever-changing IT-user landscape and behaviour, and greater reliance on development and testing in real-world settings.

Objective: To identify the patterns of activity, sitting and sleep that adults engage in, the demographic and biological correlates of activity-sleep patterns and the relationship between identified patterns and self-rated health. Design and Setting: Online panel of randomly selected Australian adults (n = 2034) completing a cross-sectional survey in October-November 2013. Participants: Panel members who provided complete data on all variables were included (n = 1532). Measurements: Participants self-reported their demographic characteristics, height, weight, self-rated health, duration of physical activity, frequency of resistance training, sitting time, sleep duration, sleep quality, and variability in bed and wake times. Activity-sleep patterns were determined using latent class analysis. Latent class regression was used to examine the relationships between identified patterns, demographic and biological characteristics, and self-rated health. Results: A 4-class model fit the data best, characterized by very active good sleepers, inactive good sleepers, inactive poor sleepers, moderately active good sleepers, representing 38.2%, 22.2%, 21.2%, and 18.4% of the sample, respectively. Relative to the very active good sleepers, the inactive poor sleepers, and inactive good sleepers were more likely to report being female, lower education, higher body mass index, and lower self-rated health, the moderately active good sleepers were more likely to be older, report lower education, higher body mass index and lower self-rated health. Associations between activity-sleep pattern and self-rated health were the largest in the inactive poor sleepers. Conclusions: The 4 activity-sleep patterns identified had distinct behavioral profiles, sociodemographic correlates, and relationships with self-rated health. Many adults could benefit from behavioral interventions targeting improvements in physical activity and sleep.

Aims Sedentary behaviour (particularly television (TV) viewing) is thought to be a risk factor for cardiovascular disease. We employed a negative control outcome to explore whether the association between TV viewing and heart disease mortality is explained by confounding. Methods The sample was drawn from the UK Biobank study and comprised 479 658 participants (aged 56.5±8.0 years; 45.7% men) followed up over a mean of 10.4 years. TV viewing was measured from self-report. Results There were 1437 ischaemic heart disease (IHD) deaths, and 214 accidental deaths (employed as the negative control outcome). TV viewing was related to the following confounding variables: age, smoking, alcohol, diet, obesity, physical inactivity, cardiovascular disease and education. The confounding structures were similar for both outcomes. TV viewing (per hour/d) was associated with IHD (hazard ratio (HR)=1.30, 95% CI, 1.27 to 1.33) and accidental death (HR=1.15, 95% CI, 1.07 to 1.24) in unadjusted models. Associations were attenuated for both outcomes and were considerably converged after adjustment for confounders; IHD (HR=1.09, 95% CI, 1.06 to 1.12) and accidental death (HR=1.06, 95% CI, 0.98 to 1.15). Conclusion The pattern of results for TV with an implausible outcome mirrored that of IHD, suggesting that observed associations between TV and heart disease are likely to be driven by confounding.

Diet quality, physical activity, alcohol use, smoking, sleep and sitting-time are behaviors known to influence health. The aims of this study were to identify how these behaviors co-occur to form distinct health-behavior patterns, and to investigate the relationship between these patterns, and mental and self-rated health. Members of the Australian 10,000 Steps project were invited to participate in an online survey in November¿December 2011. The participants self-reported demographic and behavioral characteristics (fruit and vegetable intake, fast food, soft drink and alcohol consumption, smoking, physical activity, sitting-time and sleep), frequency of mental distress and self-rated health. Latent Class Analysis was used to identify health-behavior patterns. Latent class regression was used to examine relationships between behavior patterns, mental and self-rated health, and socio-demographic and economic factors. Data were analyzed in October 2017. Complete datasets were obtained from 10,638 participants. Four latent classes were identified, characterized by ¿Low-Risk Behavior¿, ¿Poor Sleep, Low-Risk Daytime Behavior¿, 'sound Sleep, High-Risk Daytime Behavior¿ and ¿High-Risk Behavior¿. The latter two classes, both characterized by high-risk daytime behaviors, were associated with poor self-rated health. Participants in classes with high-risk daytime behaviors were more likely to be younger, non-partnered, non-university educated, from lower income households and work longer hours. Classes characterized by poor sleep quality were associated with higher frequency of mental distress. Findings suggest that experiencing poor sleep is partly independent of daytime behaviors, demographic and socioeconomic factors, but has a strong association with mental health.

This study investigated if object control relates to children¿s game play competence, and examined these competencies as correlates of physical activity and cardiorespiratory fitness. Game play (Game Performance Assessment Instrument), object control (The Test Gross Motor Development-3), moderate-to-vigorous physical activity (Accelerometry), and cardiorespiratory fitness (20-metre shuttle) assessments were completed for 107 children (57% Female, 43% Male) aged 9¿12¿years (M 10.53, SD 0.65). Two-level regression of object control on game play competence, and object control and game play competence on physical activity and cardiorespiratory fitness assessed associations. Object control competence was positively associated with game play competence (Std. B¿=¿0.25, t (104.77)¿=¿2.38, p¿=¿0.001). Game play competence (Std. B¿=¿0.33, t (99.81)¿=¿5.21, p¿<¿0.000) was more strongly associated with moderate-to-vigorous physical activity than object control competence (Std. B¿=¿0.20, t (106.93)¿=¿2.96, p¿=¿0.003). Likewise, game competence (Std. B¿=¿0.39, t (104.41)¿=¿4.36, p¿<¿0.000) was more strongly associated with cardiorespiratory fitness than object control competence (Std. B¿=¿0.22, t (106.69)¿=¿2.63, p¿=¿0.002). Object control and game competence are both important as correlates of physical activity and cardiorespiratory fitness in children.

Objectives: While outdoor gyms are being rapidly installed around the globe, little is known about their implications on physical activity, and fitness and other health-related outcomes. The primary objective of this systematic review was to examine the effects of outdoor gyms on physical activity, fitness and other health-related outcomes. Secondary objectives were to describe outdoor gym characteristics, user characteristics, and outdoor gym usage. Design: Systematic review. Methods: Peer-reviewed papers published in English were obtained through online database searches of the following databases; EBSCO, SPORTdiscus, Medline, PsycINFO, Web of Science, Scopus and Informit. Searches covered the periods from database inception to January 2019. Studies that reported on the efficacy of outdoor gym use for physical activity, health-related outcomes or descriptive aspects of outdoor gyms and their users were eligible for inclusion. Results: There was some support that outdoor gyms may improve physical activity, fitness and other health-related outcomes, however few experimental studies have been conducted. There was no consistency between outdoor gyms in terms types and number of installed equipment for each facility. Further, this review found discrepancies of whom were the most likely users of outdoor gyms and when they were mainly used. Conclusions: This review contributes to the limited evidence on the impact and characteristics of outdoor gyms on physical activity and fitness and health-related outcomes.

Objective: Many young adults obtain less than the recommended sleep duration for healthy and safe functioning. Behavior change interventions have had only moderate success in increasing sleep duration for this cohort. This may be because the way young adults think about sleep, including their willingness and ability to change sleep behavior, is unknown. The purpose of the present study was to determine what changes, if any, young adults are willing to make to their sleep behavior, and to identify factors that may enable or prevent these changes. Participants: Fifty-seven young adults (16¿25 years; 57% female) took part in focus groups addressing (a) willingness to change, (b) desired outcomes of change, and (c) barriers to change in regards to sleep behavior. Methods: An inductive approach to data analysis was employed, involving data immersion, coding, categorization, and theme generation. Results: Participants were willing to change sleep behavior, and had previously employed strategies including advancing bedtime and minimizing phone use, with limited success. Desired changes were improved waking function, advanced sleep onset, optimized sleep periods, and improved sleep habits. Barriers to making these changes included time demands, technology use, difficulty switching off, and unpredictable habits. Young adults want to improve sleep behavior and waking function; this is an important first step in modifying behavior. Notably, participants wanted more efficient and better quality sleep, rather than increasing sleep duration. Conclusion: The reported barriers to sleep, particularly using technology for social purposes, will require innovative and specialized strategies if they are to be overcome.

This study examined sitting time, knowledge, and intentions to change sitting time in older adults. An online survey was completed by 494 Australians aged 65+. Average daily sitting was high (9.0 hr). Daily sitting time was the highest during TV (3.3 hr), computer (2.1 hr), and leisure (1.7 hr). A regression analysis demonstrated that women were more knowledgeable about the health risks of sitting compared to men. The percentage of older adults intending to sit less were the highest for TV (24%), leisure (24%), and computer (19%) sitting time. Regression analyses demonstrated that intentions varied by gender (for TV sitting), education (leisure and work sitting), body mass index (computer, leisure, and transport sitting), and physical activity (TV, computer, and leisure sitting). Interventions should target older adults' TV, computer, and leisure time sitting, with a focus on intentions in older males and older adults with low education, those who are active, and those with a normal weight.

Research into associations between lifestyle behaviours and health has largely focused on morbidity, mortality and disease prevention. Greater focus is needed to examine relationships between lifestyle behaviours and positive health outcomes such as well-being. This study aims to examine the associations between multiple lifestyle behaviours and excellent well-being. Participants (n = 6788) were adults in the member database of the 10,000 Steps Australia project who were asked to participate in an online survey in November¿December 2016. Well-being (WHO-5) Smoking, dietary behaviour, alcohol consumption, physical activity, sitting time, sleep duration, and sleep quality were assessed by self-report. Logistic regression analyses were used to examine relationships between excellent well-being (top quintile) and the individual lifestyle behaviours and also a lifestyle behaviour index (the number of lower-risk behaviours performed). Lower-risk dietary behaviour (OR = 1.29, 95% CI: 1.10¿1.51), physical activity (OR = 1.90, 95% CI: 1.48¿2.42), sitting time (OR = 1.46, 95% CI: 1.26¿1.69), sleep duration (OR = 1.52, 95% CI: 1.32¿1.75) and higher sleep quality (OR = 2.98, 95% CI: 2.55¿3.48) were positively associated with excellent well-being, after adjusting for socio-demographics, chronic disease, depression, anxiety and all other lifestyle behaviours. Engaging in a higher number of lower risk lifestyle behaviours was positively associated with excellent well-being. These results highlight the need for multiple lifestyle behaviour interventions to improve and maintain higher well-being.

Background: Physical activity (PA) and diet are 2 lifestyle factors that affect cardiometabolic risk. However, data on how a high-fat high-carbohydrate (HFHC) diet influences the effect of different intensities of PA on cardiometabolic health and cardiovascular function in a controlled setting are yet to be fully established. This study investigated the effect of sedentary behavior, light-intensity training (LIT), and high-intensity interval training (HIIT) on cardiometabolic markers and vascular and cardiac function in HFHC-fed adult rats. Methods: Twelve-week-old Wistar rats were randomly allocated to 4 groups (12 rats/group): control (CTL), sedentary (SED), LIT, and HIIT. Biometric indices, glucose and lipid control, inflammatory and oxidative stress markers, vascular reactivity, and cardiac electrophysiology of the experimental groups were examined after 12 weeks of HFHC-diet feeding and PA interventions. Results: The SED group had slower cardiac conduction (p = 0.0426) and greater thoracic aortic contractile responses (p < 0.05) compared with the CTL group. The LIT group showed improved cardiac conduction compared with the SED group (p = 0.0003), and the HIIT group showed decreased mesenteric artery contractile responses compared with all other groups and improved endothelium-dependent mesenteric artery relaxation compared with the LIT group (both p < 0.05). The LIT and HIIT groups had lower visceral (p = 0.0057 for LIT, p = 0.0120 for HIIT) and epididymal fat (p < 0.0001 for LIT, p = 0.0002 for HIIT) compared with the CTL group. Conclusion: LIT induced positive adaptations on fat accumulation and cardiac conduction, and HIIT induced a positive effect on fat accumulation, mesenteric artery contraction, and endothelium-dependent relaxation. No other differences were observed between groups. These findings suggest that few positive health effects can be achieved through LIT and HIIT when consuming a chronic and sustained HFHC diet.

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.

Objective The present study examined the effect of a school-based multicomponent physical activity intervention on aggression in adolescent males from low-income areas, and explored potential mediators of change in aggression. Methods Participants were adolescent males (N = 361; 12.7 ± 0.5 years) enrolled in the ¿Active Teen Leaders Avoiding Screen-time¿ cluster RCT. Self-report measures for aggression, perceived strength and recreational screen-time were collected at baseline and 8-months. The effect of the intervention on aggression was tested using multi-level linear regression and potential mediators (i.e., screen-time and perceived strength) were explored using a product-of-coefficients test. Results There was no significant intervention effects for aggression (C [SE] = -0.038 [0.044], p =.384) or perceived strength (A [SE] = -0.0 [0.0], p =.884). However, a statistically significant effect was found for screen-time (A [SE] = -0.160 [0.04], p= <0.01). Changes in screen-time significantly mediated changes in aggression at post-test (AB [SE] = -0.021 [0.009], 95% CI = -0.042 to -0.005). Conclusion Limiting recreational screen-time may help to reduce aggression in adolescent boys. Interventions targeting adolescents' recreational screen-time should examine their ¿flow-on¿ effects on externalising behaviours in adolescent populations.

This cross-sectional study of 504 community-dwelling Australian adults (328 females, 176 males, mean age 50.8¿±¿13.0¿years) sought to examine the reliability and validity of measurement scales for physical activity (PA) self-efficacy and outcome expectations. Participants completed demographic and anthropometric measurements, and a 23-item psychosocial questionnaire pertinent to an intervention target of 10,000 steps per day. Exploratory (n¿=¿252) and confirmatory (n¿=¿252) factor analyses were conducted to determine psychometric properties of the measures. Based on theory and goodness-of-fit indices, six factors were extracted from the questionnaire: PA self-efficacy; PA barriers self-efficacy (including general, personal, and conflict); and physical and mental outcome expectations. From confirmatory factor analysis, the model demonstrated good data fit in four out of five indices: CFI¿=¿0.99; TLI¿=¿0.99; SRMR¿=¿0.03; RMSEA¿=¿0.03, 90%CI¿=¿0.01¿0.05, ¿2¿=¿113.14 (88), p¿=¿0.04; including good fit by sex, age, weight status, education, and birth country. PA interventions can employ our psychometrically sound social cognitive measures.

Background/Aim Web 2.0 internet technology has great potential in promoting physical activity. This trial investigated the effectiveness of a Web 2.0-based intervention on physical activity behaviour, and the impact on website usage and engagement. Methods 504 (328 women, 126 men) insufficiently active adult participants were randomly allocated to one f two web-based interventions or a paper-based Logbook group. The Web 1.0 group participated in the existing 10 000 Steps programme, while the Web 2.0 group participated in a Web 2.0-enabled physical activity intervention including user-to-user interaction through social networking capabilities. ActiGraph GT3X activity monitors were used to assess physical activity at four points across the intervention (0, 3, 12 and 18 months), and usage and engagement were assessed continuously through website usage statistics. Results Treatment groups differed significantly in trajectories of minutes/day of physical activity (p=0.0198), through a greater change at 3 months fo Web 2.0 than Web 1.0 (7.3 min/day, 95% CI 2.4 to 12.3). In the Web 2.0 group, physical activity increased at 3 (mean change 6.8 min/day, 95% CI 3.9 to 9.6) and 12 months (3.8 min/day, 95% CI 0.5 to 7.0), but not 18 months. The Logbook group also increased physical activity at 3 (4.8 min/day, 95% CI 1.8 to 7.7) and 12 months (4.9 min/day, 95% CI 0.7 to 9.1), but not 18 months. The Web 1.0 group increased physical activity at 12 months only (4.9 min/day, 95% CI 0.5 to 9.3). The Web 2.0 group demonstrated higher levels of website engagement (p=0.3964).Conclusions In comparison to a Web 1.0 intervention, a more interactive Web 2.0 intervention, as well as the paper-based Logbook intervention, improved physicalactivity in the short term, but that effect reduced over time, despite higher levels of engagement of the Web 2.0 group. Trial registration number ACTRN12611000157976.

Issue addressed Sedentary behaviours, in particular sitting, increases the risk of cardiovascular disease, type 2 diabetes, obesity and poorer mental health status. In Australia, 70% of adults sit for more than 8h per day. The use of social media applications (e.g. Facebook, Twitter, and Instagram) is on the rise; however, no studies have explored the association of social media use with sitting time and body mass index (BMI). Methods Cross-sectional self-report data on demographics, BMI and sitting time were collected from 1140 participants in the 2013 Queensland Social Survey. Generalised linear models were used to estimate associations of a social media score calculated from social media use, perceived importance of social media, and number of social media contacts with sitting time and BMI. Results Participants with a high social media score had significantly greater sitting times while using a computer in leisure time and significantly greater total sitting time on non-workdays. However, no associations were found between social media score and sitting to view TV, use motorised transport, work or participate in other leisure activities; or total workday, total sitting time or BMI. Conclusions These results indicate that social media use is associated with increased sitting time while using a computer, and total sitting time on non-workdays. So what? The rise in social media use may have a negative impact on health by contributing to computer sitting and total sitting time on non-workdays. Future longitudinal research with a representative sample and objective sitting measures is needed to confirm findings.

The current review clarifies the cardiometabolic health effects of high-intensity interval training (HIIT) in adults. A systematic search (PubMed) examining HIIT and cardiometabolic health markers was completed on 15 October 2015. Sixty-five intervention studies were included for review and the methodological quality of included studies was assessed using the Downs and Black score. Studies were classified by intervention duration and body mass index classification. Outcomes with at least 5 effect sizes were synthesised using a random-effects meta-analysis of the standardised mean difference (SMD) in cardiometabolic health markers (baseline to postintervention) using Review Manager 5.3. Short-term (ST) HIIT (<12 weeks) significantly improved maximal oxygen uptake (VO2 max; SMD 0.74, 95% CI 0.36 to 1.12; p<0.001), diastolic blood pressure (DBP; SMD -0.52, 95% CI -0.89 to -0.16; p<0.01) and fasting glucose (SMD -0.35, 95% CI -0.62 to -0.09; p<0.01) in overweight/obese populations. Long-term (LT) HIIT (=12 weeks) significantly improved waist circumference (SMD -0.20, 95% CI -0.38 to -0.01; p<0.05), % body fat (SMD -0.40, 95% CI -0.74 to -0.06; p<0.05), VO2 max (SMD 1.20, 95% CI 0.57 to 1.83; p<0.001), resting heart rate (SMD -0.33, 95% CI -0.56 to -0.09; p<0.01), systolic blood pressure (SMD -0.35, 95% CI -0.60 to -0.09; p<0.01) and DBP (SMD -0.38, 95% CI -0.65 to -0.10; p<0.01) in overweight/obese populations. HIIT demonstrated no effect on insulin, lipid profile, C reactive protein or interleukin 6 in overweight/obese populations. In normal weight populations, ST-HIIT and LT-HIIT significantly improved VO2 max, but no other significant effects were observed. Current evidence suggests that ST-HIIT and LT-HIIT can increase VO2 max and improve some cardiometabolic risk factors in overweight/obese populations.

Aims: Intestinal microbiota modulates the development of clinical conditions, including metabolic syndrome and obesity. Many of these conditions are influenced by nutritional and exercise behaviours. This study aimed to investigate the ability of exercise to re-shape the intestinal microbiota and the influence of the diet on the process. Methods and Results: A rat model was used to examine the intestinal microbiota responses to four activity conditions, including: high-intensity interval training (HIIT), light-intensity training (LIT), sedentary and normal control, each containing two nutritional conditions: high-fat high-fructose diet (HF) and standard chow (SC) diet. No significant differences in microbiota were apparent between activity conditions in rats fed a HF diet but changes in the presence/absence of phylotypes were observed in the LIT and HIIT groups. In rats fed SC, significant differences in intestinal microbiota were evident between exercised and nonexercised rats. Both LIT and HIIT induced significant differences in intestinal microbiota in SC-fed rats compared to their respective SC-fed controls. Characterization of the exercise-induced bacterial phylotypes indicated an increase in bacteria likely capable of degrading resistant polysaccharides and an increase in short chain fatty acid producers. Conclusions: While a significant effect of exercise on microbiota composition occurred in SC-fed rats, the HF-fed rats microbiota showed little response. These data suggest that a HF diet prevented microbiota differentiation in response to exercise. Significance and Impact of the Study: The importance of diet¿exercise interaction is extended to the level of intestinal bacteria and gut health.

The purpose of this study was to investigate adult attitudes on distances children should be allowed to travel to places and play in outdoor areas without adult supervision, with consideration of differences in adult attitudes by socio-demographics and urbanisation. In 2013, Australian adults (N = 1293) were asked what distances children aged 8¿12 years should be allowed to walk/cycle to places, and play outdoors without adults. Descriptive analyses and chi-square tests were conducted to assess adult attitudes on children's independent mobility range. Overall, 62% of adults would restrict children's independent travel to places <500 m from home, and 74% would restrict independent outdoor play <500 m from home. Women and adults with lower education were more likely to report restrictive attitudes than men and adults with higher education, respectively. The promotion of active travel and outdoor play in children may require increasing adults¿ acceptance of greater independent mobility range.

Background: Health and fitness applications (apps) have gained popularity in interventions to improve diet, physical activity and sedentary behaviours but their efficacy is unclear. This systematic review examined the efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour in children and adults. Methods: Systematic literature searches were conducted in five databases to identify papers published between 2006 and 2016. Studies were included if they used a smartphone app in an intervention to improve diet, physical activity and/or sedentary behaviour for prevention. Interventions could be stand-alone interventions using an app only, or multi-component interventions including an app as one of several intervention components. Outcomes measured were changes in the health behaviours and related health outcomes (i.e., fitness, body weight, blood pressure, glucose, cholesterol, quality of life). Study inclusion and methodological quality were independently assessed by two reviewers. Results: Twenty-seven studies were included, most were randomised controlled trials (n = 19; 70%). Twenty-three studies targeted adults (17 showed significant health improvements) and four studies targeted children (two demonstrated significant health improvements). Twenty-one studies targeted physical activity (14 showed significant health improvements), 13 studies targeted diet (seven showed significant health improvements) and five studies targeted sedentary behaviour (two showed significant health improvements). More studies (n = 12; 63%) of those reporting significant effects detected between-group improvements in the health behaviour or related health outcomes, whilst fewer studies (n = 8; 42%) reported significant within-group improvements. A larger proportion of multi-component interventions (8 out of 13; 62%) showed significant between-group improvements compared to stand-alone app interventions (5 out of 14; 36%). Eleven studies reported app usage statistics, and three of them demonstrated that higher app usage was associated with improved health outcomes. Conclusions: This review provided modest evidence that app-based interventions to improve diet, physical activity and sedentary behaviours can be effective. Multi-component interventions appear to be more effective than stand-alone app interventions, however, this remains to be confirmed in controlled trials. Future research is needed on the optimal number and combination of app features, behaviour change techniques, and level of participant contact needed to maximise user engagement and intervention efficacy.

This study investigated changes in Australian children's independent mobility levels between1991 and 2012. Data from five cross-sectional studies conducted in 1991, 1993, 2010, 2011 and 2012 were analysed. Parent and child surveys were used to assess parental licences for independent mobility and actual independent mobility behaviour in children aged 8¿13 years. Findings show declines in the proportion of young children (=10 years of age) being allowed to travel home from school alone (1991: 68%, 1993: 50%, 2010: 43%, 2011: 45%, 2012: 31%) and travel on buses alone (1991: 31%, 1993: 15%, 2010: 8%, 2011: 6%, 2012: 9%). Furthermore, the proportion of children travelling independently to school decreased (1991: 61%, 1993: 42%, 2010: 31%, 2011: 32%, 2012: 32%). Significantly fewer girls than boys travelled independently to school at each time point (p =.001). Overall, the findings suggest that Australian children's independent mobility levels declined between 1991 and 2012.

Aim: This study examines associations between the variability in bed/rise times, usual bed/rise time and dietary quality, physical activity, alcohol consumption, sitting time, sleep insufficiency and a composite index of behaviors. Subject and methods: A random sample of Australian adults drawn from an online Panel cohort in 2013 completed a cross-sectional online survey. A total of 1,317 participants, median age 57 (IQR = 20) completed the survey. Bed- and wake times, variability in bed- and wake-times, dietary quality, physical activity, alcohol consumption, sitting time, sleep insufficiency and socio-demographics were assessed using a questionnaire. Associations were examined with generalized linear models. Results: Having bed - times that varied by >30¿min were associated with lower dietary quality, higher alcohol consumption, higher sitting time, more frequent insufficient sleep and poorer overall pattern of lifestyle behaviors. Greater variability in wake times, usual bed times and usual wake times were inconsistently associated with lifestyle behaviours. Conclusions: Greater bed-time variability is associated with a less healthy pattern of lifestyle behaviors. Greater consistency in sleep timing may contribute to, or be reflective of, a healthier lifestyle.

Physical activity is an effective primary or adjunctive treatment to reduce depressive symptoms. Computer-tailored and interactive web-based physical activity interventions are potentially effective and accessible means for promoting physical activity, but little evidence exists regarding their efficacy in reducing depressive symptoms. We conducted a 2-arm randomised trial to compare the efficacy of these web-based interventions for increasing physical activity and reducing depressive symptoms. Participants (18 years or older and had no health condition limiting physical activity) were randomised to have access to a web-based physical activity intervention program with either computer-tailored advice (MyPAA) or interactive features (Walk 2.0). Only half of participants accessed the website at least once (MyPAA: allocated n¿=¿252, accessed program n¿=¿154, 61.1%; Walk 2.0: allocated n¿=¿262, accessed program n¿=¿120, 45.8%). Participants and the research team were blinded to group assignment. There were no significant between-group differences in change of self-reported physical activity or depressive symptoms. Physical activity significantly increased from baseline to one month (g¿=¿0.21) and three months (g¿=¿0.20), and depressive symptoms significantly decreased from baseline to one month (g¿=¿0.36) and three months (g¿=¿0.42). People who visited the website more and for longer had larger changes in physical activity and depressive symptoms than those who visited less. Web-based interventions with computer-tailoring and interactive features show promise as a method for increasing physical activity and reducing depressive symptoms, particularly for those who engage with the program. Trial registration Australian New Zealand Clinical Trials Registry ACTRN12613001215718.

Objective To describe in detail the recruitment methods and enrollment rates, the screening methods, and the baseline characteristics of a sample of adults participating in the Walk 2.0 Study, an 18 month, 3-arm randomized controlled trial of a Web 2.0 based physical activity intervention. Methods A two-fold recruitment plan was developed and implemented, including a direct mail-out to an extract from the Australian Electoral Commission electoral roll, and other supplementary methods including email and telephone. Physical activity screening involved two steps: a validated single-item self-report instrument and the follow-up Active Australia Questionnaire. Readiness for physical activity participation was also based on a two-step process of administering the Physical Activity Readiness Questionnaire and, where needed, further clearance from a medical practitioner. Results Across all recruitment methods, a total of 1244 participants expressed interest in participating, of which 656 were deemed eligible. Of these, 504 were later enrolled in the Walk 2.0 trial (77% enrollment rate) and randomized to the Walk 1.0 group (n = 165), the Walk 2.0 group (n = 168), or the Logbook group (n = 171). Mean age of the total sample was 50.8 years, with 65.2% female and 79.1% born in Australia. Conclusion The results of this recruitment process demonstrate the successful use of multiple strategies to obtain a diverse sample of adults eligible to take part in a web-based physical activity promotion intervention. The use of dual screening processes ensured safe participation in the intervention. This approach to recruitment and physical activity screening can be used as a model for further trials in this area.

Background: Growing evidence points to the benefits of addressing multiple health behaviors rather than single behaviors. Purpose: This review evaluates the relative effectiveness of simultaneous and sequentially delivered multiple health behavior change (MHBC) interventions. Secondary aims were to identify: a) the most effective spacing of sequentially delivered components; b) differences in efficacy of MHBC interventions for adoption/cessation behaviors and lifestyle/addictive behaviors, and c) differences in trial retention between simultaneously and sequentially delivered interventions. Methods: MHBC intervention trials published up to October 2015 were identified through a systematic search. Eligible trials were randomised controlled trials that directly compared simultaneous and sequential delivery of a MHBC intervention. A narrative synthesis was undertaken. Results: Six trials met the inclusion criteria and across these trials the behaviors targeted were smoking, diet, physical activity, and alcohol consumption. Three trials reported a difference in intervention effect between a sequential and simultaneous approach in at least one behavioral outcome. Of these, two trials favoured a sequential approach on smoking. One trial favoured a simultaneous approach on fat intake. There was no difference in retention between sequential and simultaneous approaches. Conclusions: There is limited evidence regarding the relative effectiveness of sequential and simultaneous approaches. Given only three of the six trials observed a difference in intervention effectiveness for one health behavior outcome, and the relatively consistent finding that the sequential and simultaneous approaches were more effective than a usual/minimal care control condition, it appears that both approaches should be considered equally efficacious. PROSPERO registration number: CRD42015027876.

Amidst strong efforts to promote the therapeutic benefits of physical activity for reducing depression and anxiety in clinical populations, little focus has been directed towards the mental health benefits of activity for non-clinical populations. The objective of this meta-meta-analysis was to systematically aggregate and quantify high-quality meta-analytic findings of the effects of physical activity on depression and anxiety for non-clinical populations. A systematic search identified eight meta-analytic outcomes of randomised trials that investigated the effects of physical activity on depression or anxiety. The subsequent meta-meta-analyses were based on a total of 92 studies with 4310 participants for the effect of physical activity on depression and 306 study effects with 10,755 participants for the effect of physical activity on anxiety. Physical activity reduced depression by a medium effect [standardised mean difference (SMD) = -0.50; 95% CI: -0.93 to -0.06] and anxiety by a small effect (SMD = -0.38; 95% CI: -0.66 to -0.11). Neither effect showed significant heterogeneity across meta-analyses. These findings represent a comprehensive body of high-quality evidence that physical activity reduces depression and anxiety in non-clinical populations.

Background: Physical inactivity levels are unacceptably high and effective interventions that can increase physical activity in large populations at low cost are urgently needed. Web-based interventions that use computer-tailoring have shown to be effective, though people tend to 'skim' and 'scan' text on the Internet rather than thoroughly read it. The use of online videos is, however, popular and engaging. Therefore, the aim of this 3-group randomised controlled trial is to examine whether a web-based physical activity intervention that provides personally-tailored videos is more effective when compared with traditional personally-tailored text-based intervention and a control group. Methods/design: In total 510 Australians will be recruited through social media advertisements, e-mail and third party databases. Participants will be randomised to one of three groups: text-tailored, video-tailored, or control. All groups will gain access to the same web-based platform and a library containing brief physical activity articles. The text-tailored group will additionally have access to 8 sessions of personalised physical activity advice that is instantaneously generated based on responses to brief online surveys. The theory-based advice will be provided over a period of 3 months and address constructs such as self-efficacy, motivation, goal setting, intentions, social support, attitudes, barriers, outcome expectancies, relapse prevention and feedback on performance. Text-tailored participants will also be able to complete 7 action plans to help them plan what, when, where, who with, and how they will become more active. Participants in the video-tailored group will gain access to the same intervention content as those in the text-tailored group, however all sessions will be provided as personalised videos rather than text on a webpage. The control group will only gain access to the library with generic physical activity articles. The primary outcome is objectively measured physical activity. Secondary outcomes include website engagement and retention, quality of life, depression, anxiety, stress, sitting time, sleep and psychosocial correlates of physical activity. Outcomes will be measured at baseline, 3, and 9 months. Discussion: This study presents an ideal opportunity to study the effectiveness of an isolated feature within a web-based physical activity intervention and the knowledge generated from this study will help to increase intervention effectiveness. Trial registration: Australian New-Zealand Clinical Trial Registry: ACTRN12615000057583. Registered 22 January 2015.

Background: Physical activity (PA) offers numerous benefits to health and well-being, but most adults are not sufficiently physically active to afford such benefits. The 10,000 steps campaign has been a popular and effective approach to promote PA. The Transtheoretical Model posits that individuals have varying levels of readiness for health behavior change, known as Stages of Change (Precontemplation, Contemplation, Preparation, Action, and Maintenance). Few validated assessment instruments are available for determining Stages of Change in relation to the PA goal of 10,000 steps per day. The purpose of this study was to assess the criterion-related validity of the SoC-Step, a brief 10,000 steps per day Stages of Change instrument. Methods: Participants were 504 Australian adults (176 males, 328 females, mean age = 50.8 ± 13.0 years) from the baseline sample of the Walk 2.0 randomized controlled trial. Measures included 7-day accelerometry (Actigraph GT3X), height, weight, and self-reported intention, self-efficacy, and SoC-Step: Stages of Change relative to achieving 10,000 steps per day. Kruskal-Wallis H tests with pairwise comparisons were used to determine whether participants differed by stage, according to steps per day, general health, body mass index, intention, and self-efficacy to achieve 10,000 steps per day. Binary logistic regression was used to test the hypothesis that participants in Maintenance or Action stages would have greater likelihood of meeting the 10,000 steps goal, in comparison to participants in the other three stages. Results: Consistent with study hypotheses, participants in Precontemplation had significantly lower intention scores than those in Contemplation (p = 0.003) or Preparation (p < 0.001). Participants in Action or Maintenance stages were more likely to achieve =10,000 steps per day (OR = 3.11; 95 % CI = 1.66,5.83) compared to those in Precontemplation, Contemplation, or Preparation. Intention (p < 0.001) and self-efficacy (p < 0.001) to achieve 10,000 steps daily differed by stage, and participants in the Maintenance stage had higher general health status and lower body mass index than those in Precontemplation, Contemplation and Preparation stages (p < 0.05). Conclusions: This brief SoC-Step instrument appears to have good criterion-related validity for determining Stages of Change related to the public health goal of 10,000 steps per day. Trial registration: Australian New Zealand Clinical Trials Registry reference: ACTRN12611000157976 World Health Organization Universal Trial Number: U111-1119-1755.

Office workers are commonly targeted in interventions to modify their sitting behaviour, yet there is limited evidence of the correlates of breaks in sitting to inform intervention development. This study identifies the individual, workplace and spatial configuration correlates of the frequency of breaks in sitting (number/hour) in office workers (n = 5531) stratified by office type (private-enclosed, shared, open plan). All behaviours and potential correlates were measured via self-report using an online cross-sectional survey. Regression analyses revealed age was the only socio-demographic characteristic associated with frequency of breaks in sitting in all office types. Greater job autonomy and local connectivity were positively associated with frequency of breaks in sitting in shared and open-plan offices. In open-plan offices co-worker proximity was negatively associated with frequency of breaks in sitting. Co-worker visibility was positively associated with frequency of breaks in sitting in all office types. This study demonstrates that individual, workplace and spatial configuration factors are all associated with the frequency of breaks in sitting and that these relationships differ by office type. These observations extend prior studies that have only examined correlates at a single level (e.g. the individual). This evidence could be useful to guide future interventions in the design of workplaces to increase breaks in sitting and workers physical activity.

Background: Both obesity and a lack of physical activity have been associated with an elevated risk of cardiovascular disease (CVD). The incidence of obesity is increasing, especially in juvenile-adolescents. While there is limited research examining the chronic effects of obesity in adolescent humans and animal models of this condition, little is also known concerning how moderate physical activity might prevent or attenuate secondary cardiovascular complications induced by obesity during adolescence. We investigated the effects of diet-induced obesity (consisting of a high-fat, high-carbohydrate diet (HFHC)) on biometric indices, vascular and airway function, cardiovascular function, systemic oxidative stress and markers of inflammation in a juvenile-adolescent rodent model. Four groups were used: control (CON), physical activity (PA) treated, HFHC and HFHC + PA (n = 16 per group). HFHC feeding started at 4 weeks of age for a period of 12 weeks. Physical activity treatment was initiated (PA and HFHC + PA groups) when the animals were 8 weeks of age, for 8 weeks. Results: Physical activity in juvenile-adolescent healthy rats showed no change in comparison to the CON group in all experimental parameters except for increases in lipid peroxidation, decreases in inflammatory cytokines, improvements in vascular reactivity and decreased atrial responses to positive chronotropic agents. The HFHC animals were mildly hyperglycemic, hypertensive, displayed renal hypertrophy and showed increased retroperitoneal fat pad deposition compared to the CON group. HFHC + PA rats were also hypertensive, however showed improvements in cardiac electrophysiology, body weight, fat pad deposition and inflammatory signaling, in comparison to the HFHC fed rats and CON animals. Conclusion: In conclusion, in a juvenile-adolescent animal model of diet-induced obesity engagement in physical activity is beneficial in reducing the inflammatory effects of obesity.

This paper presents a new visual analytics framework for analyzing health-related physical activity data. Existing techniques mostly rely on node-links visualizations to represent the usage patterns as social networks. This work takes a different approach that provides interactive scatter-plot visualizations on classified and time-based data. By providing a flexible visualization that can provide different angles on the multidimensional and classified data, the analyst could have better understanding and insight on web user behavior compared to the traditional social network methods. The effectiveness of our method has been demonstrated with a case study on an online portal system for tracking passive physical activity, called Walk 2.0.

Introduction Low levels of healthenhancing physical activity require novel approaches that have the potential to reach broad populations. Webbased interventions are a popular approach for behaviour change given their wide reach and accessibility. However, challenges with participant engagement and retention reduce the longterm maintenance of behaviour change. Web 2.0 features present a new and innovative online environment supporting greater interactivity, with the potential to increase engagement and retention. In order to understand the applicability of these innovative interventions for the broader population, 'realworld' interventions implemented under 'everyday conditions' are required. The aim of this study is to investigate the difference in physical activity behaviour between individuals using a traditional Web 1.0 website with those using a novel Web 2.0 website.

The prevalence of chronic diseases among middle aged males outweigh their female counterparts in developed countries. To prevent this, delivery of health promotion programs targeting lifestyle modifications of physical activity and nutrition in middle-aged males has been essential, but often difficult. ManUp health promotion program was a recent initiative that uses current advances in information and communication technology (ICT) to reach the middle-aged males. One of the key components of the ICT approach was the development of smartphone application to enable middle-aged men to uptake the program with their own mobile phone. The smart phone application was aimed at providing varied level of challenges towards physical activity and healthy eating behavior, with interactive and motivational feedback SMS messages. The ManUp program was recently implemented and trialed in a randomized control trial in Gladstone and Rockhampton, Queens. This paper describes the components of the smart phone application integrated within the ManUp health promotion program.