Awareness of having hypertension, diabetes and dyslipidaemia among US adults: The 2011-2018 NHANES data.

AIM: This study aimed to investigate awareness of having hypertension, diabetes and dyslipidaemia and their associated factors among US adults. METHODS: Data from the National Health and Nutrition Examination Survey, including 21,399 adults aged ⩾20 years (pregnant women excluded) collected between 2011 and 2018, were used. Blood pressure was measured using a Baumanometer calibrated mercury true gravity wall model sphygmomanometer. Serum total cholesterol levels were measured using enzymatic assays. The percentage of haemoglobin A1C (HbA1c), which reflects long-term blood glucose levels, was measured and used to identify diabetes. Participants self-reported whether they were told by a doctor that they have hypertension, dyslipidaemia and diabetes. Awareness was defined as alignment between objective and self-reported measures for having the conditions. Sampling weights and the Taylor series linearisation variance estimation method were used in the analyses. RESULTS: The findings showed that 64.06% of people with hypertension, 54.71% of those with dyslipidaemia and 78.40% of those with diabetes were aware of having the respective condition. Age, sex and health insurance were associated with awareness of having all three conditions, but marital status was not associated with any outcome. Weight status was associated with awareness of having hypertension and dyslipidaemia, whereas ethnicity was associated with awareness of having hypertension and diabetes. Relative family income was only associated with awareness of having hypertension. CONCLUSIONS: Large proportions of US adults with hypertension, dyslipidaemia and diabetes are not aware of having the conditions. Interventions targeting groups at higher risk of being unaware of these conditions are needed.

Engagement, acceptability, usability and satisfaction with Active for Life, a computer-tailored web-based physical activity intervention using Fitbits in older adults.

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.

A mixed-methods exploration of attitudes towards pregnant Facebook fitness influencers.

BACKGROUND: Exercise during pregnancy is associated with various health benefits for both mother and child. Despite these benefits, most pregnant women do not meet physical activity recommendations. A known barrier to engaging in exercise during pregnancy is a lack of knowledge about appropriate and safe exercise. In our current era of social media, many pregnant women are turning to online information sources for guidance, including social media influencers. Little is known about attitudes towards pregnancy exercise information provided by influencers on social media platforms. This study aimed to explore attitudes towards exercise during pregnancy depicted by social media influencers on Facebook, and user engagement with posted content. METHODS: A mixed-methods approach was used to analyse data from 10 Facebook video posts of social media influencers exercising during pregnancy. Quantitative descriptive analyses were used to report the number of views, shares, comments and emotive reactions. Qualitative analysis of user comments was achieved using an inductive thematic approach. RESULTS: The 10 video posts analysed were viewed a total of 12,117,200 times, shared on 11,181 occasions, included 13,455 user comments and 128,804 emotive icon reactions, with the most frequently used icon being 'like' (81.48%). The thematic analysis identified three themes associated with attitudes including [1] exercise during pregnancy [2] influencers and [3] type of exercise. A fourth theme of community was also identified. Most user comments were associated with positive attitudes towards exercise during pregnancy and the influencer. However, attitudes towards the types of exercise the influencer performed were mixed (aerobic and body weight exercises were positive; resistance-based exercise with weights were negative). Finally, the online community perceived by users was mostly positive and recognised for offering social support and guidance. CONCLUSIONS: User comments imply resistance-based exercise with weights as unsafe and unnecessary when pregnant, a perception that does not align with current best practice guidelines. Collectively, the findings from this study highlight the need for continued education regarding exercise during pregnancy and the potential for social media influencers to disseminate evidence-based material to pregnant women who are highly receptive to, and in need of reliable health information.

The Effectiveness of a Computer-Tailored Web-Based Physical Activity Intervention Using Fitbit Activity Trackers in Older Adults (Active for Life): Randomized Controlled Trial.

BACKGROUND: Physical activity is an integral part of healthy aging; yet, most adults aged ≥65 years are not sufficiently active. Preliminary evidence suggests that web-based interventions with computer-tailored advice and Fitbit activity trackers may be well suited for older adults. OBJECTIVE: The aim of this study was to examine the effectiveness of Active for Life, a 12-week web-based physical activity intervention with 6 web-based modules of computer-tailored advice to increase physical activity in older Australians. METHODS: Participants were recruited both through the web and offline and were randomly assigned to 1 of 3 trial arms: tailoring+Fitbit, tailoring only, or a wait-list control. The computer-tailored advice was based on either participants' Fitbit data (tailoring+Fitbit participants) or self-reported physical activity (tailoring-only participants). The main outcome was change in wrist-worn accelerometer (ActiGraph GT9X)-measured moderate to vigorous physical activity (MVPA) from baseline to after the intervention (week 12). The secondary outcomes were change in self-reported physical activity measured by means of the Active Australia Survey at the midintervention point (6 weeks), after the intervention (week 12), and at follow-up (week 24). Participants had a face-to-face meeting at baseline for a demonstration of the intervention and at baseline and week 12 to return the accelerometers. Generalized linear mixed model analyses were conducted with a γ distribution and log link to compare MVPA and self-reported physical activity changes over time within each trial arm and between each of the trial arms. RESULTS: A total of 243 participants were randomly assigned to tailoring+Fitbit (n=78, 32.1%), tailoring only (n=96, 39.5%), and wait-list control (n=69, 28.4%). Attrition was 28.8% (70/243) at 6 weeks, 31.7% (77/243) at 12 weeks, and 35.4% (86/243) at 24 weeks. No significant overall time by group interaction was observed for MVPA (P=.05). There were no significant within-group changes for MVPA over time in the tailoring+Fitbit group (+3%, 95% CI -24% to 40%) or the tailoring-only group (-4%, 95% CI -24% to 30%); however, a significant decline was seen in the control group (-35%, 95% CI -52% to -11%). The tailoring+Fitbit group participants increased their MVPA 59% (95% CI 6%-138%) more than those in the control group. A significant time by group interaction was observed for self-reported physical activity (P=.02). All groups increased their self-reported physical activity from baseline to week 6, week 12, and week 24, and this increase was greater in the tailoring+Fitbit group than in the control group at 6 weeks (+61%, 95% CI 11%-133%). CONCLUSIONS: A computer-tailored physical activity intervention with Fitbit integration resulted in improved MVPA outcomes in comparison with a control group in older adults. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618000646246; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12618000646246.

Are web-based personally tailored physical activity videos more effective than personally tailored text-based interventions? Results from the three-arm randomised controlled TaylorActive trial.

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.

Sedentary behaviour research in adults: A scoping review of systematic reviews and meta-analyses.

Sedentary behaviour research is rapidly growing. Scoping reviews are important to inform policy and practice.The aim of this scoping a review was to map the available evidence from systematic reviews and meta-analyses of sedentary behaviour research on adults (≥18 years), within the phases of the behavioural epidemiology framework, and to identify bibliometric parameters of studies included in this review. Nine bibliographic databases were searched. Studies were screened and relevant information (e.g., general information, inclusion criteria, findings and reporting quality) was extracted independently by two authors. In total, 108 systematic reviews and/or meta-analyses of sedentary behaviour research in adults (≥18 years) were included. Most papers (91.7%) were published between 2010 and 2020. Studies on the relationship of sedentary behaviour with health (53.7%) and interventions to reduce sedentary behaviour (25.9%) were most common. Forty-five (41.7%) studies reported quality assessment with categorization, and 887 out of 1268 (70%) included primary studies were categorized having moderate-to-high quality. Sedentary behaviour research on adults (≥18 years) has grown exponentially in the last decade and demonstrates strength in several stages of the behavioural epidemiology framework. However, more research should focus on the measurement, prevalence/epidemiology and determinants of sedentary behaviour, to better inform policy development.

Should I sit or stand: likelihood of adherence to messages about reducing sitting time.

BACKGROUND: High population levels of sitting is contributing to high rates of chronic health problems. Therefore, the aim of this study was to identify the sitting time messages with the greatest potential to reduce sitting behaviour, as well as identify how this may differ according to demographic, behavioural and psychosocial characteristics. METHODS: Australian adults (N = 1460) were asked to report the likelihood that they would adhere to seven messages promoting reduced sitting time and two messages promoting increased physical activity (from 'not at all likely' to 'very likely'). Ordinal regression models were used to compare messages on the likelihood of adherence and whether likelihood of adherence differed as a function of demographic, psychosocial and behavioural characteristics. RESULTS: Likelihood of adherence was highest for the messages, 'Stand and take a break from sitting as frequently as you can' (83% respectively) and 'Avoid sitting for more than 10 hours during the entire day' (82%) and was significantly lower for the message, 'Sit as little as possible on all days of the week' (46%) compared to all other messages. CONCLUSIONS: To increase likelihood of adherence messages should be specific, achievable and promote healthy alternatives to sitting (e.g. standing). Messages promoting standing as a healthy alternative to sitting may be more likely to engage people with high sitting behaviour and messages promoting physical activity may be more likely to engage males and retired adults.

A Test of How Australian Adults Allocate Time for Physical Activity.

The most common reported barrier to physical activity is a lack of sufficient time. Just like most resources in economics are finite, so too is time within a day. We utilized a time-utility model to better understand how people are allocating time for physical activity. Additionally, we tested whether the allocation of physical activity time impacts people's perception of "lack of time" as a barrier for physical activity or their likelihood of being sufficiently physical active. Australian adults (N = 725 participants, 54% men) reported their time use throughout their day, perceived lack of time as a barrier to activity, and physical activity. Cluster analysis and χ2-tests were used to test the study research questions. People tended to either be entirely inactive (29%) or active while doing either leisure (18%), occupation (18%), transport (14%), or household (22%) activities. Those who were active during their leisure or transport time were most likely to be sufficiently active. There were no significant differences among clusters in how much people perceived that lack of time was a physical activity barrier. The commonly reported barrier of not having enough time to be active might be a fallacy. Although a lack of time is a commonly reported barrier of physical activity, these findings bring to light that increasing physical activity behavior is not as simple as adding more time to the day.

Physical Activity Attitudes, Preferences, and Experiences of Regionally-Based Australia Adults Aged 65 Years and Older.

An understanding of physical activity attitudes, preferences, and experiences in older adults is important for informing interventions. Focus groups were conducted with 46 regionally-based Australian adults aged 65 years and older, who were not currently meeting activity recommendations. Content analysis revealed that participants mainly engaged in incidental activities such as gardening and household chores rather than planned exercise; however, leisure-time walking was also mentioned frequently. Although participants valued the physical and mental health benefits of physical activity, they reported being restricted by poor physical health, extreme weather, and fear of injury. Participants were interested in exercise groups and physical activity programs tailored to their existing physical health. The majority of participants reported preferring to be active with others. The findings from this study are useful in for informing future interventions specifically tailored to the needs of older adults in Australia.

Age differences in physical activity intentions and implementation intention preferences.

This study aimed to examine older adults' physical activity intentions and preferred implementation intentions, and how intentions and preferred implementation intentions differ between older, middle aged and younger adults. A cross-sectional Australian wide telephone survey of 1217 respondents was conducted in 2016. Multiple and ordinal regression analyses were conducted to compare intentions and preferred implementation intentions between older (65 +), middle aged (45-64) and younger adults (< 45). A higher percentage of older adults had no intentions to engage in regular physical activity within the next 6 months (60%) compared to younger adults (25%). Older adults' most popular preferences included being active at least once a day and for 30 min or less and were more likely to prefer more frequent and shorter sessions compared to younger adults. Both older and middle aged adults were more likely to prefer slower paced physical activity compared to younger adults who preferred fast paced physical activity. Physical activity interventions for older adults should address the high percentage of older adults with no intentions and public health campaigns for older adults should promote 30 min daily sessions of slow paced activity.

The Effectiveness of a Web-Based Computer-Tailored Physical Activity Intervention Using Fitbit Activity Trackers: Randomized Trial.

BACKGROUND: Web-based interventions that provide personalized physical activity advice have demonstrated good effectiveness but rely on self-reported measures of physical activity, which are prone to overreporting, potentially reducing the accuracy and effectiveness of the advice provided. OBJECTIVE: This study aimed to examine whether the effectiveness of a Web-based computer-tailored intervention could be improved by integrating Fitbit activity trackers. METHODS: Participants received the 3-month TaylorActive intervention, which included 8 modules of theory-based, personally tailored physical activity advice and action planning. Participants were randomized to receive the same intervention either with or without Fitbit tracker integration. All intervention materials were delivered on the Web, and there was no face-to-face contact at any time point. Changes in physical activity (Active Australia Survey), sitting time (Workforce Sitting Questionnaire), and body mass index (BMI) were assessed 1 and 3 months post baseline. Advice acceptability, website usability, and module completion were also assessed. RESULTS: A total of 243 Australian adults participated. Linear mixed model analyses showed a significant increase in total weekly physical activity (adjusted mean increase=163.2; 95% CI 52.0-274.5; P=.004) and moderate-to-vigorous physical activity (adjusted mean increase=78.6; 95% CI 24.4-131.9; P=.004) in the Fitbit group compared with the non-Fitbit group at the 3-month follow-up. The sitting time and BMI decreased more in the Fitbit group, but no significant group × time interaction effects were found. The physical activity advice acceptability and the website usability were consistently rated higher by participants in the Fitbit group. Non-Fitbit group participants completed 2.9 (SD 2.5) modules, and Fitbit group participants completed 4.4 (SD 3.1) modules. CONCLUSIONS: Integrating physical activity trackers into a Web-based computer-tailored intervention significantly increased intervention effectiveness. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12616001555448; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=371793 (Archived by WebCite at http://www.webcitation.org/73ioTxQX2).

Sitting Time in Adults 65 Years and Over: Behavior, Knowledge, and Intentions to Change.

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.

Apps to improve diet, physical activity and sedentary behaviour in children and adolescents: a review of quality, features and behaviour change techniques.

BACKGROUND: The number of commercial apps to improve health behaviours in children is growing rapidly. While this provides opportunities for promoting health, the content and quality of apps targeting children and adolescents is largely unexplored. This review systematically evaluated the content and quality of apps to improve diet, physical activity and sedentary behaviour in children and adolescents, and examined relationships of app quality ratings with number of app features and behaviour change techniques (BCTs) used. METHODS: Systematic literature searches were conducted in iTunes and Google Play stores between May-November 2016. Apps were included if they targeted children or adolescents, focused on improving diet, physical activity and/or sedentary behaviour, had a user rating of at least 4+ based on at least 20 ratings, and were available in English. App inclusion, downloading and user-testing for quality assessment and content analysis were conducted independently by two reviewers. Spearman correlations were used to examine relationships between app quality, and number of technical app features and BCTs included. RESULTS: Twenty-five apps were included targeting diet (n = 12), physical activity (n = 18) and sedentary behaviour (n = 7). On a 5-point Mobile App Rating Scale (MARS), overall app quality was moderate (total MARS score: 3.6). Functionality was the highest scoring domain (mean: 4.1, SD: 0.6), followed by aesthetics (mean: 3.8, SD: 0.8), and lower scoring for engagement (mean: 3.6, SD: 0.7) and information quality (mean: 2.8, SD: 0.8). On average, 6 BCTs were identified per app (range: 1-14); the most frequently used BCTs were providing 'instructions' (n = 19), 'general encouragement' (n = 18), 'contingent rewards' (n = 17), and 'feedback on performance' (n = 13). App quality ratings correlated positively with numbers of technical app features (rho = 0.42, p < 0.05) and BCTs included (rho = 0.54, p < 0.01). CONCLUSIONS: Popular commercial apps to improve diet, physical activity and sedentary behaviour in children and adolescents had moderate quality overall, scored higher in terms of functionality. Most apps incorporated some BCTs and higher quality apps included more app features and BCTs. Future app development should identify factors that promote users' app engagement, be tailored to specific population groups, and be informed by health behaviour theories.

Impact of increasing social media use on sitting time and body mass index.

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

BACKGROUND: Promoting physical activity and healthy eating is important to combat the unprecedented rise in NCDs in many developing countries. Using modern information-and communication technologies to deliver physical activity and diet interventions is particularly promising considering the increased proliferation of such technologies in many developing countries. The objective of this systematic review is to investigate the effectiveness of e-& mHealth interventions to promote physical activity and healthy diets in developing countries. METHODS: Major databases and grey literature sources were searched to retrieve studies that quantitatively examined the effectiveness of e-& mHealth interventions on physical activity and diet outcomes in developing countries. Additional studies were retrieved through citation alerts and scientific social media allowing study inclusion until August 2016. The CONSORT checklist was used to assess the risk of bias of the included studies. RESULTS: A total of 15 studies conducted in 13 developing countries in Europe, Africa, Latin-and South America and Asia were included in the review. The majority of studies enrolled adults who were healthy or at risk of diabetes or hypertension. The average intervention length was 6.4 months, and text messages and the Internet were the most frequently used intervention delivery channels. Risk of bias across the studies was moderate (55.7 % of the criteria fulfilled). Eleven studies reported significant positive effects of an e-& mHealth intervention on physical activity and/or diet behaviour. Respectively, 50 % and 70 % of the interventions were effective in promoting physical activity and healthy diets. CONCLUSIONS: The majority of studies demonstrated that e-& mHealth interventions were effective in promoting physical activity and healthy diets in developing countries. Future interventions should use more rigorous study designs, investigate the cost-effectiveness and reach of interventions, and focus on emerging technologies, such as smart phone apps and wearable activity trackers. TRIAL REGISTRATION: The review protocol can be retrieved from the PROSPERO database (Registration ID: CRD42015029240 ).

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 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.

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

BACKGROUND: High 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. METHODS: 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. RESULTS: 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 < 0.01). Most adults themselves spent > 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. CONCLUSIONS: 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.

Is preference for mHealth intervention delivery platform associated with delivery platform familiarity?

BACKGROUND: The aim of this paper was to ascertain whether greater familiarity with a smartphone or tablet was associated with participants' preferred mobile delivery modality for eHealth interventions. METHODS: Data from 1865 people who participated in the Australian Health and Social Science panel study were included into two multinomial logistic regression analyses in which preference for smartphone and tablet delivery for general or personalised eHealth interventions were regressed onto device familiarity and the covariates of sex, age and education. RESULTS: People were more likely to prefer both general and personalised eHealth interventions presented on tablets if they reported high or moderate tablet familiarity (compared to low familiarity) and people were more likely to prefer both general and personalised eHealth interventions presented on smartphones if they reported high or moderate smartphone familiarity, were younger, and had university education (compared to completing high school or less). CONCLUSION: People prefer receiving eHealth interventions on the mobile devices they are most familiar with. These findings have important implications that should be considered when developing eHealth interventions, and demonstrates that eHealth interventions should be delivered using multiple platforms simultaneously to optimally cater for as many people as possible.

Mothers and Fathers Both Matter: The Positive Influence of Parental Physical Activity Modelling on Children's Leisure-Time Physical Activity.

PURPOSE: To investigate associations between maternal and paternal sport participation, and children's leisure-time physical activity, and to explore differences by child gender. METHOD: The sample comprised 737 year five students (mean age: 11.0 ± 0.6 years, 52% male) recruited through the Fit for Pisa Project which was conducted in 2008 at 6 secondary schools in Goettingen, Germany. Maternal and paternal sport participation were assessed through child reports of mothers' and fathers' weekly participation in sport. Children's leisure-time physical activity was measured as minutes/week that children engaged in organized and nonorganized sport. Multiple linear regression was used to assess associations between maternal and paternal sport participation, and children's leisure-time physical activity. RESULTS: Both maternal and paternal sport participation were positively associated with children's leisure-time physical activity (maternal: b = 34.20, p < .001; paternal: b = 25.32, p < .05). When stratifying analyses by child gender, maternal sport participation remained significantly associated with leisure-time physical activity in girls (b = 60.64, p < .001). In contrast, paternal sport participation remained significantly associated with leisure-time physical activity in boys (b = 43.88, p < .01). CONCLUSION: Both maternal and paternal modeling positively influence children's leisure-time physical activity.

Maternal and paternal support for physical activity and healthy eating in preschool children: a cross-sectional study.

BACKGROUND: Parental support is a key influence on children's health behaviours; however, no previous investigation has simultaneously explored the influence of mothers' and fathers' social support on eating and physical activity in preschool-aged children. This study evaluated the singular and combined effects of maternal and paternal support for physical activity (PA) and fruit and vegetable consumption (FV) on preschoolers' PA and FV. METHODS: A random sample comprising 173 parent-child dyads completed validated scales assessing maternal and paternal instrumental support and child PA and FV behaviour. Pearson correlations, controlling for child age, parental age, and parental education, were used to evaluate relationships between maternal and paternal support and child PA and FV. K-means cluster analysis was used to identify families with distinct patterns of maternal and paternal support for PA and FV, and one-way ANOVA examined the impact of cluster membership on child PA and FV. RESULTS: Maternal and paternal support for PA were positively associated with child PA (r = 0.37 and r = 0.36, respectively; P < 0.001). Maternal but not paternal support for FV was positively associated with child FV (r = 0.35; P < 0.001). Five clusters characterised groups of families with distinct configurations of maternal and paternal support for PA and FV: 1) above average maternal and paternal support for PA and FV, 2) below average maternal and paternal support for PA and FV, 3) above average maternal and paternal support for PA but below average maternal and paternal support for FV, 4) above average maternal and paternal support for FV but below average maternal and paternal support for PA, and 5) above average maternal support but below average paternal support for PA and FV. Children from families with above average maternal and paternal support for both health behaviours had higher PA and FV levels than children from families with above average support for just one health behaviour, or below average support for both behaviours. CONCLUSIONS: The level and consistency of instrumental support from mothers and fathers for PA and FV may be an important target for obesity prevention in preschool-aged children.