From evidence-based research to practice-based evidence : disseminating a web-based computer-tailored workplace sitting intervention through a health promotion organisation

Prolonged sitting has been linked to adverse health outcomes; therefore, we developed and examined a web-based, computer-tailored workplace sitting intervention. As we had previously shown good effectiveness, the next stage was to conduct a dissemination study. This study reports on the dissemination efforts of a health promotion organisation, associated costs, reach achieved, and attributes of the website users. The organisation systematically registered all the time and resources invested to promote the intervention. Website usage statistics (reach) and descriptive statistics (website users' attributes) were also assessed. Online strategies (promotion on their homepage; sending e-mails, newsletters, Twitter, Facebook and LinkedIn posts to professional partners) were the main dissemination methods. The total time investment was 25.6 h, which cost approximately 845 EUR in salaries. After sixteen months, 1599 adults had visited the website and 1500 (93.8%) completed the survey to receive personalized sitting advice. This sample was 38.3 +/- 11.0 years, mainly female (76.9%), college/university educated (89.0%), highly sedentary (88.5% sat >8 h/day) and intending to change (93.0%) their sitting. Given the small time and money investment, these outcomes are positive and indicate the potential for wide-scale dissemination. However, more efforts are needed to reach men, non-college/university educated employees, and those not intending behavioural change

Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour : a systematic review

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 standalone 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

Computer-tailored dietary behaviour change interventions: a systematic review

Improving dietary behaviours such as increasing fruit and vegetable consumption and reducing saturated fat intake are important in the promotion of better health. Computer tailoring has shown promise as a strategy to promote such behaviours. A narrative systematic review was conducted to describe the available evidence on ‘second’-generation computer-tailored primary prevention interventions for dietary behaviour change and to determine their effectiveness and key characteristics of success. Systematic literature searches were conducted through five databases: Medline, Embase, PsycINFO, CINAHL and All EBM Reviews and by examining the reference lists of relevant articles to identify studies published in English from January 1996 to 2008. Randomized controlled trials or quasi-experimental designs with pre-test and post-test behavioural outcome data were included. A total of 13 articles were reviewed, describing the evaluation of 12 interventions, seven of which found significant positive effects of the computer-tailored interventions for dietary behaviour outcomes, one also for weight reduction outcomes. Although the evidence of short-term efficacy for computer-tailored dietary behaviour change interventions is fairly strong, the uncertainty lies in whether the reported effects are generalizable and sustained long term. Further research is required to address these limitations of the evidence

Designing engaging online behaviour change interventions: a proposed model of user engagement

Camille E. Short, Amanda L. Rebar, Ronald C. Plotnikoff, Corneel Vandelanott

Automatic evaluation stimuli - the most frequently used words to describe physical activity and the pleasantness of physical activity

Physical activity is partially regulated by non-conscious processes including automatic evaluations - the spontaneous affective reactions we have to physical activity that lead us to approach or avoid physical activity opportunities. A sound understanding of which words best represent the concepts of physical activity and pleasantness (as associated with physical activity) is needed to improve the measurement of automatic evaluations and related constructs (e.g., automatic self-schemas, attentional biases). The first aim of this study was to establish population-level evidence of the most common word stimuli for physical activity and pleasantness. Given that response latency measures have been applied to assess automatic evaluations of physical activity and exercise, the second aim was to determine whether people use the same behavior and pleasant descriptors for physical activity and exercise. Australian adults (N = 1,318; 54.3% women; 48.9% aged 55 years or older) were randomly assigned to one of two groups, through a computer-generated 1:1 ratio allocation, to be asked to list either five behaviors and pleasant descriptors of physical activity (n = 686) or of exercise (n = 632). The words were independently coded twice as to whether they were novel words or the same as another (i.e., same stem or same meaning). Intercoder reliability varied between moderate and strong (agreement = 50.1 to 97.8%; κ = 0.48 to 0.82). A list of the 20 most common behavior and pleasantness words were established based on how many people reported them, weighted by the ranking (1-5) people gave them. The words people described as physical activity were mostly the same as those people used to describe exercise. The most common behavior words were 'walking,' 'running,' 'swimming,' 'bike riding,' and 'gardening'; and the most common pleasant descriptor words were 'relaxing,' 'happiness,' 'enjoyment,' 'exhilarating,' 'exhausting,' and 'good.' These sets of stimuli can be utilized as resources for response latency measurement tasks of automatic evaluations and for tools to enhance automatic evaluations of physical activity in evaluative conditioning tasks.Amanda L. Rebar, Stephanie Schoeppe, Stephanie J. Alley, Camille E. Short, James A. Dimmock, Ben Jackson, David E. Conroy, Ryan E. Rhodes and Corneel Vandelanott

Physical activity screening to recruit inactive randomized controlled trial participants: how much is too much?

Citation: Vandelanotte, C., Stanton, R., Rebar, A. L., Van Itallie, A. K., Caperchione, C. M., Duncan, M. J., . . . Kolt, G. S. (2015). Physical activity screening to recruit inactive randomized controlled trial participants: how much is too much? Trials, 16, 3. doi:10.1186/s13063-015-0976-7Screening physical activity levels is common in trials to increase physical activity in inactive populations. Commonly applied single-item screening tools might not always be effective in identifying those who are inactive. We applied the more extensive Active Australia Survey to identify inactive people among those who had initially been misclassified as too active using a single-item measure. Those enrolled after the Active Australia Survey screening had significantly higher physical activity levels at subsequent baseline assessment. Thus, more extensive screening measures might result in the inclusion of participants who would otherwise be excluded, possibly introducing unwanted bias

Optimising web-based computer-tailored physical activity interventions for prostate cancer survivors: a randomised controlled trial examining the impact of website architecture on user engagement

Background: Web-based computer-tailored interventions can assist prostate cancer survivors to become more physically active by providing personally relevant behaviour change support. This study aimed to explore how changing the website architecture (free choice vs. tunnelled) impacted engagement within a physical activity computer-tailored intervention targeting prostate cancer survivors. Methods: On a 2:2:1 ratio, 71 Australian prostate cancer survivors with local or locally advanced disease (mean age: 66.6 years ± 9.66) were randomised into either a free-choice (N = 27), tunnelled (N = 27) or minimal intervention control arm (N =17). The primary outcome was differences in usage of the physical activity self-monitoring and feedback modules between the two intervention arms. Differences in usage of other website components between the two intervention groups were explored as secondary outcomes. Further, secondary outcomes involving comparisons between all study groups (including the control) included usability, personal relevance, and behaviour change. Results: The average number of physical activity self-monitoring and feedback modules accessed was higher in the tunnelled arm (M 2.6 SD 1.3) compared to the free-choice arm (M 1.5 SD 1.4), p = 0.01. However, free-choice participants were significantly more likely to have engaged with the social support (p = 0.008) and habit formation (p = 0.003) 'once-off' modules compared to the standard tunnelled arm. There were no other between-group differences found for any other study outcomes. Conclusion: This study indicated that website architecture influences behavioural engagement. Further research is needed to examine the impact of differential usage on mechanisms of action and behaviour change.Amy Finlay, Holly Evans, Andrew Vincent, Gary Wittert, Corneel Vandelanotte and Camille E Shor

How is adults' screen time behaviour influencing their views on screen time restrictions for children? A cross-sectional study

Published: 1 March 2016High screen time in children and its detrimental health effects is a major public health problem. How much screen time adults think is appropriate for children remains little explored, as well as whether adults' screen time behaviour would determine their views on screen time restrictions for children. This study aimed to investigate how adults' screen time behaviour influences their views on screen time restrictions for children, including differences by gender and parental status.In 2013, 2034 Australian adults participated in an online survey conducted by the Population Research Laboratory at Central Queensland University, Rockhampton. Adult screen time behaviour was assessed using the Workforce Sitting Questionnaire. Adults reported the maximum time children aged between 5-12 years should be allowed to spend watching TV and using a computer. Ordinal logistic regression was used to compare adult screen time behaviour with views on screen time restrictions for children.Most adults (68 %) held the view that children should be allowed no more than 2 h of TV viewing and computer use on school days, whilst fewer adults (44 %) thought this screen time limit is needed on weekend days. Women would impose higher screen time restrictions for children than men (p  2 h on watching TV and using the computer at home on work days (66 %) and non-work days (88 %). Adults spending ≤ 2 h/day in leisure-related screen time were less likely to permit children > 2 h/day of screen time. These associations did not differ by adult gender and parental status.Most adults think it is appropriate to limit children's screen time to the recommended ≤ 2 h/day but few adults themselves adhere to this screen time limit. Adults with lower screen use may be more inclined to limit children's screen time. Strategies to reduce screen time in children may also need to target adult screen use.Stephanie Schoeppe, Amanda L. Rebar, Camille E. Short, Stephanie Alley, Wendy Van Lippevelde and Corneel Vandelanott

Health and well-being benefits of e-bike commuting for inactive, overweight people living in regional Australia

Introduction: Pedal-assisted electric-bikes (e-bikes) are bicycles fitted with electric motors. Motorised functions on e-bikes only operate when the user pedals, allowing riders a moderate amount of physical activity. This study aimed to explore the mental and physical health and well-being impacts related to ebike usage for inactive overweight or obese individuals living in regional Australia. Methods: Twenty inactive, overweight/obese people who seldom cycled were provided with an ebike over a 12-week period. Individual semi-structured interviews conducted at the end of the trial generated data about participants’ experiences of using ebikes. Inductive thematic analysis of interview data using Thomas (2006) data analysis framework and NVivo 12 software was undertaken. Results: Data analysis revealed that e-cycling improved participants’ mental and physical well-being and that they felt happier when riding an e-bike. Conclusions: Riding an e-bike can improve mental and physical health, happiness and overall sense of well-being. Greater uptake of e-bikes would have positive health implications for the wider community. Results from this study can be used to inform active transport policy. So what?: Our study demonstrated that encouraging active transport in the form of e-cycling can improve the overall health and well-being of overweight and obese Australians. More specifically, e-cycling demonstrated a positive impact on mental health well-being

Associations between quality of life and duration and frequency of physical activity and sedentary behaviour: Baseline findings from the WALK 2.0 randomised controlled trial.

While physical and mental health benefits of regular physical activity are well known, increasing evidence suggests that limiting sedentary behaviour is also important for health. Evidence shows associations of physical activity and sedentary behaviour with health-related quality of life (HRQoL), however, these findings are based predominantly on duration measures of physical activity and sedentary behaviour (e.g., minutes/week), with less attention on frequency measures (e.g., number of bouts). We examined the association of HRQoL with physical activity and sedentary behaviour, using both continuous duration (average daily minutes) and frequency (average daily bouts≥10 min) measures. Baseline data from the WALK 2.0 trial were analysed. WALK 2.0 is a randomised controlled trial investigating the effects of Web 2.0 applications on engagement, retention, and subsequent physical activity change. Daily physical activity and sedentary behaviour (duration = average minutes, frequency = average number of bouts ≥10 minutes) were measured (ActiGraph GT3X) across one week, and HRQoL was assessed with the 'general health' subscale of the RAND 36-Item Health Survey. Structural equation modelling was used to evaluate associations. Participants (N = 504) were 50.8±13.1 (mean±SD) years old with a BMI of 29.3±6.0. The 465 participants with valid accelerometer data engaged in an average of 24.0±18.3 minutes and 0.64±0.74 bouts of moderate-vigorous physical activity per day, 535.2±83.8 minutes and 17.0±3.4 bouts of sedentary behaviour per day, and reported moderate-high general HRQoL (64.5±20.0). After adjusting for covariates, the duration measures of physical activity (path correlation = 0.294, p<0.05) and sedentary behaviour were related to general HRQoL (path coefficient = -0.217, p<0.05). The frequency measure of physical activity was also significant (path coefficient = -0.226, p<0.05) but the frequency of sedentary behaviour was not significantly associated with general HRQoL. Higher duration levels of physical activity in fewer bouts, and lower duration of sedentary behaviour are associated with better general HRQoL. Further prospective studies are required to investigate these associations in different population groups over time