Additional file 2: of The effectiveness of e-& mHealth interventions to promote physical activity and healthy diets in developing countries: A systematic review

Risk of bias assessment using the CONSORT checklist. (DOCX 32 kb

8-year trends in physical activity, nutrition, TV viewing time, smoking, alcohol and BMI: a comparison of younger and older Queensland adults

Lifestyle behaviours significantly contribute to high levels of chronic disease in older adults. The aims of the study were to compare the prevalence and the prevalence trends of health behaviours (physical activity, fruit and vegetable consumption, fast food consumption, TV viewing, smoking and alcohol consumption), BMI and a summary health behaviour indicator score in older (65+ years) versus younger adults (18–65 years). The self-report outcomes were assessed through the Queensland Social Survey annually between 2007–2014 (n = 12,552). Regression analyses were conducted to compare the proportion of older versus younger adults engaging in health behaviours and of healthy weight in all years combined and examine trends in the proportion of younger and older adults engaging in health behaviours and of healthy weight over time. Older adults were more likely to meet recommended intakes of fruit and vegetable (OR = 1.43, 95%CI = 1.23–1.67), not consume fast food (OR = 2.54, 95%CI = 2.25–2.86) and be non-smokers (OR = 3.02, 95%CI = 2.53–3.60) in comparison to younger adults. Conversely, older adults were less likely to meet the physical activity recommendations (OR = 0.86, 95%CI = 0.78–0.95) and watch less than 14 hours of TV per week (OR = 0.65, 95%CI = 0.58–0.74). Overall, older adults were more likely to report engaging in 3, or at least 4 out of 5 healthy behaviours. The proportion of both older and younger adults meeting the physical activity recommendations (OR = 0.97, 95%CI = 0.95–0.98 and OR = 0.94, 95%CI = 0.91–0.97 respectively), watching less than 14 hours of TV per week (OR = 0.96, 95%CI = 0.94–0.99 and OR = 0.94, 95%CI = 0.90–0.99 respectively) and who were a healthy weight (OR = 0.95, 95%CI = 0.92–0.99 and OR = 0.96, 95%CI = 0.94–0.98 respectively) decreased over time. The proportion of older adults meeting the fruit and vegetable recommendations (OR = 0.90, 95%CI = 0.84–0.96) and not consuming fast food (OR = 0.94, 95%CI = 0.88–0.99) decreased over time. Although older adults meet more health behaviours than younger adults, the decreasing prevalence of healthy nutrition behaviours in this age group needs to be addressed

Interest and preferences for using advanced physical activity tracking devices: results of a national cross-sectional survey

Objectives: Pedometers are an effective selfmonitoring tool to increase users' physical activity. However, a range of advanced trackers that measure physical activity 24 hours per day have emerged (eg, Fitbit). The current study aims to determine people's current use, interest and preferences for advanced trackers. Design and participants: A cross-sectional national telephone survey was conducted in Australia with 1349 respondents. Outcome measures: Regression analyses were used to determine whether tracker interest and use, and use of advanced trackers over pedometers is a function of demographics. Preferences for tracker features and reasons for not wanting to wear a tracker are also presented. Results: Over one-third of participants (35%) had used a tracker, and 16% are interested in using one. Multinomial regression (n=1257) revealed that the use of trackers was lower in males (OR=0.48, 95% CI 0.36 to 0.65), non-working participants (OR=0.43, 95% CI 0.30 to 0.61), participants with lower education (OR=0.52, 95% CI 0.38 to 0.72) and inactive participants (OR=0.52, 95% CI 0.39 to 0.70). Interest in using a tracker was higher in younger participants (OR=1.73, 95% CI 1.15 to 2.58). The most frequently used tracker was a pedometer (59%). Logistic regression (n=445) revealed that use of advanced trackers compared with pedometers was higher in males (OR=1.67, 95% CI 1.01 to 2.79) and younger participants (OR=2.96, 95% CI 1.71 to 5.13), and lower in inactive participants (OR=0.35, 95% CI 0.19 to 0.63). Over half of current or interested tracker users (53%) prefer to wear it on their wrist, 31% considered counting steps the most important function and 30% regarded accuracy as the most important characteristic. The main reasons for not wanting to use a tracker were, 'I don't think it would help me' (39%), and 'I don't want to increase my activity' (47%). Conclusions: Activity trackers are a promising tool to engage people in self-monitoring a physical activity. Trackers used in physical activity interventions should align with the preferences of target groups, and should be able to be worn on the wrist, measure steps and be accurate

Sample demographics by year.

Sample demographics by year.</p

Odds ratios of health behaviours and BMI by year and age group.

<p>Odds ratios of health behaviours and BMI by year and age group.</p

Percentage of Queensland adults engaging in healthy behaviours and BMI by year.

<p>Percentage of Queensland adults engaging in healthy behaviours and BMI by year.</p

Do singles or couples live healthier lifestyles? Trends in Queensland between 2005-2014

Objectives: To compare the frequency of and trends in healthy lifestyle factors between singles and couples. Methods Cross-sectional data from annual surveys conducted from 2005-2014 were used. The pooled sample included 15,001 Australian adults (mean age: 52.9 years, 50% male, 74% couples) who participated in the annual Queensland Social Survey via computer-assisted telephone interviews. Relationship status was dichotomised into single and couple. Binary logistic regression was used to assess associations between relationship status, and the frequency of and trends in healthy lifestyle factors. Results Compared to singles, couples were significantly more likely to be a non-smoker (OR = 1.82), and meet recommendations for limited fast food (OR = 1.12), alcohol consumption (OR = 1.27) and fruit and vegetable intake (OR = 1.24). Fruit and vegetable intake was not significantly associated with relationship status after adjusting for the other healthy lifestyle factors. Conversely, couples were significantly less likely to be within a normal weight range (OR = 0.81). In both singles and couples, the trend data revealed significant declines in the rates of normal weight (singles: OR = 0.97, couples: OR = 0.97) and viewing TV for less than 14 hours per week (singles: OR = 0.85, couples: OR = 0.84), whilst non-smoking rates significantly increased (singles: OR = 1.12, couples: OR = 1.03). The BMI trend was no longer significant when adjusting for health behaviours. Further, in couples, rates of meeting recommendations for physical activity and fruit/vegetable consumption significantly decreased (OR = 0.97 and OR = 0.95, respectively), as did rates of eating no fast food (OR = 0.96). These trends were not significant when adjusting for the other healthy lifestyle factors. In singles, rates of meeting alcohol recommendations significantly increased (OR = 1.08). Conclusions Health behaviour interventions are needed in both singles and couples, but relationship status needs to be considered in interventions targeting alcohol, fast food, smoking and BMI. Further research is needed to understand why health behaviours differ by relationship status in order to further improve interventions

Additional file 1: of Should I sit or stand: likelihood of adherence to messages about reducing sitting time

How likelihood of adherence differs as a function of demographic, psychosocial and behavioural characteristics. (DOCX 18 kb

Additional file 2: of Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review

Search strategy used in the databases. (DOCX 32 kb

Trends in healthy lifestyle factors in couples.

Trends in healthy lifestyle factors in couples.</p