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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.

Fly-in fly-out (FIFO) work involves commuting long distances and living in provided accommodation for 1–4 weeks while on shift. Little is known about couple communication and relationship satisfaction of this population. Separate cohorts of FIFO workers and partners of FIFO workers completed daily surveys to self-report time spent communicating with their partner (in person, over the computer, and by phone) and relationship satisfaction for up to 7 days during on-shift and 7 days during off-shift periods (806 observations from N = 106 with 19 couples). For FIFO workers, but not partners of FIFO workers, relationship satisfaction was lower during on-shift days than off-shift days. This difference was completely mediated by time spent communicating with romantic partner, such that after accounting for the impact of lower time spent communicating on relationship satisfaction, there remained no significant difference in relationship satisfaction on-shift vs off-shift. Communication between FIFO workers and their romantic partners is essential to ensure the relationship does not suffer while the worker is away from home. FIFO organisations need to investigate ways to ensure quality communication between romantic partners can be maintained while the worker is on-site and partner spend long periods of time away from one another.

People with knowledge of the benefits of physical activity tend to be more active; however, such knowledge is typically operationalized as a basic understanding that physical activity is 'good' for health. Therefore, the aim of this study was to investigate whether there are differences in how detailed a person's knowledge is about the benefits of physical activity. Participants (N = 615) completed an online survey to measure their current physical activity behaviour, as well as their level of knowledge of the benefits and risks of physical (in)activity. The majority of participants (99.6%) strongly agreed that physical activity is good for health, however on average, participants only identified 13.8 out of 22 diseases associated with physical inactivity and over half of participants (55.6%) could not identify how much physical activity is recommended for health benefits. Furthermore, 45% of the participants overestimated, 9% underestimated and 27% did not know the increased risk of disease resulting from inactivity as indicated by the Australian Department of Health. Participants were significantly more active when they correctly identified more diseases associated with physical inactivity and when they overestimated the risks associated with inactivity. Therefore, health promotion initiatives should increase knowledge of the types of diseases associated with inactivity. Low knowledge of physical activity guidelines suggest they should be promoted more, as this knowledge provides guidance on frequency, types and duration of physical activity needed for health.

Background Studies of the physical activity intention-behavior gap, and factors that may moderate the gap (e.g., habit, perceived behavioral control), can inform physical activity promotion efforts. Yet, these studies typically apply linear modeling procedures, and so conclusions rely on linearity and homoscedasticity assumptions, which may not hold. Methods We modelled and plotted physical activity intention-behavior associations and the moderation effects of habit using simulated data based on (a) normal distributions with no shared variance, (b) correlated parameters with normal distribution, and (c) realistically correlated and non-normally distributed parameters. Results In the uncorrelated and correlated normal distribution datasets, no violations were unmet, and the moderation effects applied across the entire data range. However, because in the realistic dataset, few people who engaged in physical activity behavior had low intention scores, the intention-behavior association was non-linear, resulting in inflated linear moderation estimations of habit. This finding was replicated when tested with intention-behavior moderation of perceived behavioral control. Conclusions Comparisons of the three scenarios illustrated how an identical correlation coefficient may mask different types of intention-behavior association and moderation effects. These findings highlight the risk of misinterpreting tests of the intention-behavior gap and its moderators for physical activity due to unfounded statistical assumptions. The previously well-documented moderating effects of habit, whereby the impact of intention on behavior weakens as habit strength increases, may be based on statistical byproducts of unmet model assumptions.

Background Growing evidence suggests that prolonged uninterrupted sitting can be detrimental to health. Much sedentary behaviour research is reliant on self-reports of sitting time, and sitting-reduction interventions often focus on reducing motivation to sit. These approaches assume that people are consciously aware of their sitting time. Drawing on Action Identification Theory, this paper argues that people rarely identify the act of sitting as ‘sitting’ per se, and instead view it as an incidental component of more meaningful and purposeful typically-seated activities. Methods Studies 1 and 2 explored whether people mentioned sitting in written descriptions of actions. Studies 3–5 compared preferences for labelling a typically desk-based activity as ‘sitting’ versus alternative action identities. Studies 6 and 7 used card-sort tasks to indirectly assess the prioritisation of ‘sitting’ relative to other action descriptions when identifying similar actions. Results Participants rarely spontaneously mentioned sitting when describing actions (Studies 1–2), and when assigning action labels to a seated activity, tended to offer descriptions based on higher-order goals and consequences of action, rather than sitting or other procedural elements (Studies 3–5). Participants primarily identified similarities in actions based not on sitting, but on activities performed while seated (e.g. reading; Studies 6–7). Conclusion ‘Sitting’ is a less accessible cognitive representation of seated activities than are representations based on the purpose and implications of seated action. Findings suggest that self-report measures should focus on time spent in seated activities, rather than attempting to measure sitting time via direct recall. From an intervention perspective, findings speak to the importance of targeting behaviours that entail sitting, and of raising awareness of sitting as a potential precursor to attempting to reduce sitting time.

Background Physical activity is important for healthy ageing, however most older adults are inactive. Numerous reviews with a range of inclusion criteria have been conducted on digital interventions to promote physical activity in older adults, and a synthesis of these is needed. Therefore, the objective of this study is to conduct an umbrella review and meta-meta-analysis on the effectiveness of digital interventions to promote physical activity in older adults. Methods Nine databases were searched from January 2010 to December 2023. Systematic reviews and meta-analyses of primary studies using digital physical activity interventions to target healthy older adults or clinical populations of older adults with a self-reported or device measured physical activity outcome were eligible for inclusion. Results In total, 22 systematic reviews and meta-analyses covering 185 primary research papers were eligible for inclusion. The total number of participants across all primary studies was 28,198. Most (21, 95%) reviews and meta-analyses were rated as having a low or critically low AMSTAR-2 confidence rating. Of the 22 included systematic reviews, 13 (59%) conducted a meta-analysis and 10 (45%) conducted a narrative synthesis. Most systematic reviews with a narrative synthesis found strong evidence for a positive effect or moderate evidence for a positive effect for physical activity outcomes (7/9, 78%) and steps (3/3, 100%). The meta-meta-analysis of primary papers included in meta-analyses demonstrated a significant moderate effect for steps and a significant small effect for total PA and MVPA. The strength of effect did not vary by intervention components (activity tracker, app-based, SMS/phone, web-based, and face-to-face), population (primary or secondary prevention), control group (none, other digital intervention, or non-digital intervention), or outcome measurement (self-reported or device measured). Only 3 (14%) reviews included longer term follow up outcomes after the end of the intervention, with mixed results. Conclusions Evidence from 22 reviews and meta-analyses suggests that digital physical activity interventions are effective at increasing physical activity in older adults. Further primary research is needed in adults 65 years and over exclusively, and with longer-term follow up of physical activity outcomes. Future reviews should include a published protocol and interpret results according to risk-of-bias.

To compare the frequency of and trends in healthy lifestyle factors between singles and couples. 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. 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). 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.

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.

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.

People act in ways that align with the values and roles that constitute their identity. However, the consistency of identity’s influence across different behaviours, and whether identity influences behaviours directly or indirectly via intention, self-determined motivation, or habit, remains uncertain. Participants (N = 98; Mage = 30.4 years, SD = 11.7 years) completed up to 12 weekly surveys, self-reporting engagement in physical activity, student, and support-seeking behaviours, and behaviour-associated identity, intention strength, self-determined motivation, and habit. Stepwise multilevel models tested the between- and within-person associations of identity with behaviour, and whether the relationships remained after accounting for intention, self-determined motivation, and habit. Results suggested identity as stable, with the most variability at the between-person level. Identity was associated with behaviour at both within- and between-person levels, with the exception that support seeking and identity were only associated between-person. For student behaviour and physical activity, the identity–behaviour relationship at the within-person level waned and became non-significant after accounting for intention, but not self-determined motivation or habit. These findings highlight that identity may be difficult to change. However, as identity is associated with a range of behaviours, a person’s identification with a particular behaviour may be valuable for tailoring behaviour change interventions, specifically through or in the same way as behavioural intentions.