Search

Background: The problem of sedentary behavior among primary school children is alarming, with numbers gradually increasing worldwide, including Sri Lanka. Physical activity interventions within classroom settings have been acknowledged as a critical strategy to increase students' movement behaviors while enhancing their academic achievement and health. Yet, the busy curriculum and challenging educational demands encourage more sedentary classroom behavior. Hence, this study aims to develop and evaluate an in-classroom physical activity breaks (IcPAB) intervention among fifth graders in Sri Lanka., Methods: The study will adopt a randomized controlled trial (RCT), comprising an in-classroom physical activity breaks program group and a control group to evaluate the effects of IcPAB on academic achievement, movement behaviors and health outcomes. The intervention design is based on the capability (C), opportunity (O) and motivation (M) behavior (B) (COM-B) model. A least 198 fifth graders will be recruited from two schools in Uva province, Sri Lanka. The recruitment process will start in late 2022. Class teachers of the intervention group will implement 5-min activity breaks at least three times a day after completing a training session. The primary variables include mathematics and reading achievement. The secondary variables include physical activity levels, steps count, sedentary behavior, body mass index, aerobic fitness, and perceived stress. Data collection will be implemented at pre-test and post-test, respectively. Intervention fidelity and the process will also be evaluated., Discussion: The IcPAB is designed to prevent pure educational time loss by introducing curriculum-integrated short bouts of physical active breaks into the classroom routine. If the IcPAB is effective, it can (1) improve the mathematics and reading achievement of fifth-grade girls and boys, which is a significant factor determining the performance at the Grade Five National Scholarship Examination in Sri Lanka; (2) improve movement behaviors as well as physical and mental health outcomes among primary school students. Sequentially, the IcPAB will enrich school-based physical activity intervention approaches which can in turn bring academic and health benefits to primary school children in Sri Lanka., Trial Registration: The first version of the trial was registered with the ISRCTN registry (Ref: ISRCTN52180050) on 20/07/2022., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Peiris, Duan, Vandelanotte, Liang and Baker.)

Issue Addressed: Interventions targeting health care professionals' behaviours are assumed to support them in learning how to give behavioural advice to patients, but such assumptions are rarely examined. This study investigated whether key assumptions were held regarding the design and delivery of physical activity interventions among health care professionals in applied health care settings. This study was part of the 'Physical Activity Tailored intervention in Hospital Staff' randomised controlled trial of three variants of a web-based intervention., Methods: We used data-prompted interviews to explore whether the interventions were delivered and operated as intended in health care professionals working in four hospitals in Western Australia (N = 25). Data were analysed using codebook thematic analysis., Results: Five themes were constructed: (1) health care professionals' perceived role in changing patients' health behaviours; (2) work-related barriers to physical activity intervention adherence; (3) health care professionals' use of behaviour change techniques; (4) contamination between groups; and (5) perceptions of intervention tailoring., Conclusions: The intervention was not experienced by participants, nor did they implement the intervention guidance, in the way we expected. For example, not all health care professionals felt responsible for providing behaviour change advice, time and shift constraints were key barriers to intervention participation, and contamination effects were difficult to avoid. SO WHAT?: Our study challenges assumptions about how health care professionals respond to behaviour change advice and possible knock-on benefits for patients. Applying our learnings may improve the implementation of health promotion interventions in health care settings., (© 2023 The Authors. Health Promotion Journal of Australia published by John Wiley & Sons Australia, Ltd on behalf of Australian Health Promotion Association.)

This study aims to compare the effectiveness, engagement, usability, and acceptability of a web-based, computer-tailored physical activity intervention (provided as video or text) between participants who were matched or mismatched to their self-reported learning style (visual and auditory delivery through video or text-based information). Generalised linear mixed models were conducted to compare time (baseline, 3 months) by group (matched, mismatched) on ActiGraph-GT3X+measured moderate-to-vigorous physical activity (MVPA) and steps. Generalised linear models were used to compare group (matched and mismatched) on session completion, time-on-site, usability, and acceptability. MVPA and steps improved from baseline to 3-months, however this did not differ between participants whose learning styles were matched or mismatched to the intervention they received. Session completion, time-on-site, usability, and acceptability did not differ between matched and mismatched participants. Therefore, aligning intervention delivery format to learning style is unlikely to influence intervention effectiveness or engagement.

Objective: Physical inactivity is a leading modifiable cause of death and disease worldwide. Population-based interventions to increase physical activity are needed. Existing automated expert systems (e.g., computer-tailored interventions) have significant limitations that result in low long-term effectiveness. Therefore, innovative approaches are needed. This special communication aims to describe and discuss a novel mHealth intervention approach that proactively offers participants with hyper-personalised intervention content adjusted in real-time., Methods: Using machine learning approaches, we propose a novel physical activity intervention approach that can learn and adapt in real-time to achieve high levels of personalisation and user engagement, underpinned by a likeable digital assistant. It will consist of three major components: (1) conversations: to increase user's knowledge on a wide range of activity-related topics underpinned by Natural Language Processing; (2) nudge engine: to provide users with hyper-personalised cues to action underpinned by reinforcement learning (i.e., contextual bandit) and integrating real-time data from activity tracking, GPS, GIS, weather, and user provided data; (3) Q&A: to facilitate users asking any physical activity related questions underpinned by generative AI (e.g., ChatGPT, Bard) for content generation., Results: The detailed concept of the proposed physical activity intervention platform demonstrates the practical application of a just-in-time adaptive intervention applying various machine learning techniques to deliver a hyper-personalised physical activity intervention in an engaging way. Compared to traditional interventions, the novel platform is expected to show potential for increased user engagement and long-term effectiveness due to: (1) using new variables to personalise content (e.g., GPS, weather), (2) providing behavioural support at the right time in real-time, (3) implementing an engaging digital assistant and (4) improving the relevance of content through applying machine learning algorithms., Conclusion: The use of machine learning is on the rise in every aspect of today's society, however few attempts have been undertaken to harness its potential to achieve health behaviour change. By sharing our intervention concept, we contribute to the ongoing dialogue on creating effective methods for promoting health and well-being in the informatics research community. Future research should focus on refining these techniques and evaluating their effectiveness in controlled and real-world circumstances., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Classroom-based physical activity interventions have demonstrated positive effects in reducing sedentary behaviour among school children. However, this is an understudied area, especially in low- and middle-income countries such as Sri Lanka. This study aims to explore teachers' opportunity, capability and motivation relating to the implementation of an in-classroom physical activity breaks programme., Methods: Twenty-seven teachers were recruited through snowball sampling and participated in semi-structured telephone interviews from early-January to the mid-June 2022. The Capability, Opportunity, and Motivation Behaviour (COM-B) model was used to guide and deductively thematic analyse the interviews., Results: 21 out of the recruited teachers responded to the full study. The mean age of respondents was 39.24 years old ranging from 27 years to 53 years. Teaching experience of the respondents ranged from three to 37 years, and 57% were female. Three teachers had a degree with a teacher training diploma, while others were having General Certificate of Education in Advanced Level with a teacher training diploma as the highest education qualification. Capability factors such as age, dress code, mask wearing, knowledge, skills and workload of the teachers were identified as important factors in implementing a physical activity breaks intervention in a Sri Lankan classroom setting. Classroom space, facilities, student backgrounds and safety were identified as opportunity factors. Obtaining policy level decisions to implement the activity breaks and managing the time of the activities to reduce time lost in education time were identified as motivational factors., Conclusion: During the intervention development phase, implementation facilitators and barriers must be considered carefully. Behaviour change techniques can be utilised to address the identified COM-B factors to ensure a good implementation of the intervention., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Peiris et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background and Purpose: With an aging population, falls have become an increasing public health concern. While face-to-face exercise programs have demonstrated efficacy in reducing falls, their effectiveness is hampered by low participation and adherence. Digital technologies are a novel and potentially effective method for delivering tailored fall prevention exercise programs to older adults. In addition, they may increase the reach, uptake, and sustainability of fall prevention programs. Therefore, understanding older adults' experiences of using technology-driven methods is essential. This study explored the user experience of StandingTall , a home-based fall prevention program delivered through a tablet computer., Methods: Fifty participants were recruited using purposive sampling, from a larger randomized controlled trial. Participants were selected to ensure maximum variability with respect to age, gender, experience with technology, and adherence to the program. Participants undertook a one-on-one structured interview. We followed an iterative approach to develop themes., Results and Discussion: Eight themes were identified. These fall under 2 categories: user experience and program design. Participants found StandingTall enjoyable, and while its flexible delivery facilitated exercise, some participants found the technology challenging. Some participants expressed frustration with technological literacy, but most demonstrated an ability to overcome these challenges, and learn a new skill. Older adults who engaged in a technology-driven fall prevention program found it enjoyable, with the flexibility provided by the online delivery central to this experience. While the overall experience was positive, participants expressed mixed feelings about key design features. The embedded behavior change strategies were not considered motivating by most participants. Furthermore, some older adults associated the illustrated characters with gender-based stereotypes and negative views of aging, which can impact on motivation and preventive behavior., Conclusion: This study found digital technologies are an effective and enjoyable method for delivering a fall prevention program. This study highlights that older adults are interested in learning how to engage successfully with novel technologies., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 APTA Geriatrics, An Academy of the American Physical Therapy Association.)

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., (© 2023. The Author(s).)

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., Competing Interests: The authors have declared that no competing interests exist.

In-Classroom physical activity breaks (IcPAB) are a promising way to promote children’s health behaviors, while contributing to the development of their academic and cognitive ability and health outcomes. Yet the effect of the activity breaks, which are exclusive to classroom settings, are still mixed and unclear. Hence, this review was conducted to identify the characteristics and the effects of IcPAB among primary school children. The review protocol was registered on PROSPERO (CRD42021234192). Following the Cochrane guidelines, PubMed, PsycINFO (ProQuest), MEDLINE (EBSCOhost), Embase/Ovid, SportDISCUS (EBSCOhost), Web of Science, Scopus and Academic Search Premier (EBSCOhost) databases were searched to collect data on randomised control trials without a time restriction. The final database search was conducted on the 8 November 2021. Random effects models were used to calculate the effect sizes. The systematic review identified ten eligible studies, nine of which were also included in the meta-analysis. Few studies used the theoretical frameworks and process evaluations. IcPAB showed mixed effectiveness on academic outcomes: i.e., IcPAB had effects on spelling performance (p < 0.05) and foreign language learning (p < 0.01) but not on mathematics and reading performance. Health behaviors such as moderate-to-vigorous physical activity levels were improved (p < 0.01), but IcPAB did not have an impact on cognition outcomes and health outcomes. Given these mixed results, further research is needed underpinned by strong methodological quality, theoretical underpinnings and reliable process evaluation methods.

Background: Behavioral eHealth and mobile health interventions have been moderately successful in increasing physical activity, although opportunities for further improvement remain to be discussed. Chatbots equipped with natural language processing can interact and engage with users and help continuously monitor physical activity by using data from wearable sensors and smartphones. However, a limited number of studies have evaluated the effectiveness of chatbot interventions on physical activity., Objective: This study aims to investigate the feasibility, usability, and effectiveness of a machine learning-based physical activity chatbot., Methods: A quasi-experimental design without a control group was conducted with outcomes evaluated at baseline and 6 weeks. Participants wore a Fitbit Flex 1 (Fitbit LLC) and connected to the chatbot via the Messenger app. The chatbot provided daily updates on the physical activity level for self-monitoring, sent out daily motivational messages in relation to goal achievement, and automatically adjusted the daily goals based on physical activity levels in the last 7 days. When requested by the participants, the chatbot also provided sources of information on the benefits of physical activity, sent general motivational messages, and checked participants' activity history (ie, the step counts/min that were achieved on any day). Information about usability and acceptability was self-reported. The main outcomes were daily step counts recorded by the Fitbit and self-reported physical activity., Results: Among 116 participants, 95 (81.9%) were female, 85 (73.3%) were in a relationship, 101 (87.1%) were White, and 82 (70.7%) were full-time workers. Their average age was 49.1 (SD 9.3) years with an average BMI of 32.5 (SD 8.0) kg/m2. Most experienced technical issues were due to an unexpected change in Facebook policy (93/113, 82.3%). Most of the participants scored the usability of the chatbot (101/113, 89.4%) and the Fitbit (99/113, 87.6%) as at least "OK." About one-third (40/113, 35.4%) would continue to use the chatbot in the future, and 53.1% (60/113) agreed that the chatbot helped them become more active. On average, 6.7 (SD 7.0) messages/week were sent to the chatbot and 5.1 (SD 7.4) min/day were spent using the chatbot. At follow-up, participants recorded more steps (increase of 627, 95% CI 219-1035 steps/day) and total physical activity (increase of 154.2 min/week; 3.58 times higher at follow-up; 95% CI 2.28-5.63). Participants were also more likely to meet the physical activity guidelines (odds ratio 6.37, 95% CI 3.31-12.27) at follow-up., Conclusions: The machine learning-based physical activity chatbot was able to significantly increase participants' physical activity and was moderately accepted by the participants. However, the Facebook policy change undermined the chatbot functionality and indicated the need to use independent platforms for chatbot deployment to ensure successful delivery of this type of intervention., (©Quyen G To, Chelsea Green, Corneel Vandelanotte. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 26.11.2021.)

Background: To date, no citation analysis has been conducted in the physical activity field, which can contribute to assess the impact of this research field and identify knowledge gaps. Therefore, this study aimed to identify the 500 most cited physical activity publications and report their bibliometric characteristics., Methods: The Web of Science database (all database indexes) was searched, and bibliometric characteristics were imported and calculated., Results: A total of 520 publications were ranked as the top 500. The sum of the citations was 326,258, and the average citation density was 41.0 (45.1) citations per year. Original research articles constituted the major portion of included publications (53.7%; 170,774 citations). Papers reporting relationship of physical activity with health were the most prevalent type of publication included (43.7%; 141,027 citations). Journal impact factor had a weak but significant positive correlation with citation density (r = .12; P = .006). The United States was ranked first in terms of the contributions from institutions and authors contributing to the most cited physical activity papers., Conclusions: Top physical activity publications are well cited compared with other health behavior fields. Original research reporting on the associations between physical activity and health has a higher citation impact compared with other types of original research within the physical activity field. The physical activity research field continues to expand rapidly as newer publications attract more citations in a shorter time span compared with older publications.

Technology-based physical activity programs are a novel solution to the major public health issue of physical inactivity. However, to be successful, there must be a large and population-appropriate uptake, which depends heavily on promotion. This study evaluates the effectiveness of an advertising campaign to disseminate a physical activity smartphone app. The experiment used a 3 × 3 × 3 full-factorial design, examining platforms (Facebook; Facebook Messenger; Instagram), selling-techniques (hard-sell-sending viewers directly to (a) Apple Store or (b) Google Play, and soft-sell-sending viewers from an ad to a (c) landing-page, then to an app store) and themes (Health and Wellbeing; Body and Self-Confidence; Social Enjoyment). Outcomes were reach, click-through, and app downloads. Advertisements reached 1,373,273 people, achieving 2,989 clicks and 667 downloads. Instagram and Facebook Messenger had higher reach compared to Facebook (F[2,27] = 27.17, p < .001), whilst Facebook and Facebook Messenger both produced higher click-through (F[2,27] = 8.98, p < .001) and downloads (F[2,27] = 4.649, p = .018). Selling-technique differed, with soft-selling ads producing greater reach (F[2,27] = 4,616.077, p < .001); however, both hard-selling ads (Apple Store and Google Play) had greater click-through (F[2,27] = 10.77, p < .001) and downloads (F[2,27] = 3.791, p < .001). Advertising theme varied, with Social Enjoyment themes producing less click-through (F[2,27] = 5.709, p = .009) and downloads (F[2,27] = 5.480, p = .010). We recommend future studies to consider Facebook and Facebook Messenger, using hard-selling techniques, with themes relating to Health and Wellbeing and Body and Self-Confidence., (© Society of Behavioral Medicine 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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.

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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

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., (©Quyen G To, Mitch J Duncan, Anetta Van Itallie, Corneel Vandelanotte. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.01.2021.)

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., (Copyright © 2020 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

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.

(1) Background: Interventions using activity trackers and smartphone apps have demonstrated their ability to increase physical activity in children and adults. However, they have not been tested in whole families. Further, few family-centered interventions have actively involved both parents and assessed physical activity effects separately for children, mothers and fathers. Objective: To examine the feasibility and short-term effects of an activity tracker and app intervention to increase physical activity in the whole family (children, mothers and fathers). (2) Methods: This was a single-arm feasibility study with pre-post intervention measures. Between 2017-2018, 40 families (58 children aged 6-10 years, 39 mothers, 33 fathers) participated in the 6-week Step it Up Family program in Queensland, Australia. Using commercial activity trackers combined with apps (Garmin Vivofit Jr for children, Vivofit 3 for adults; Garmin Australasia Pty Ltd., Sydney, Australia), the intervention included individual and family-level goal-setting, self-monitoring, performance feedback, family step challenges, family social support and modelling, weekly motivational text messages and an introductory session. Parent surveys were used to assess physical activity effects measured as pre-post intervention changes in moderate-to-vigorous physical activity (MVPA) in children, mothers and fathers. Objective Garmin activity tracker data was recorded to assess physical activity levels (steps, active minutes) during the intervention. (3) Results: Thirty-eight families completed the post intervention survey (95% retention). At post intervention, MVPA had increased in children by 58 min/day (boys: 54 min/day, girls: 62 min/day; all p < 0.001). In mothers, MVPA increased by 27 min/day ( p < 0.001) and in fathers, it increased by 31 min/day ( p < 0.001). The percentage of children meeting Australia's physical activity guidelines for children (≥60 MVPA min/day) increased from 34% to 89% ( p < 0.001). The percentage of mothers and fathers meeting Australia's physical activity guidelines for adults (≥150 MVPA min/week) increased from 8% to 57% ( p < 0.001) in mothers and from 21% to 68% ( p < 0.001) in fathers. The percentage of families with 'at least one child and both parents' meeting the physical activity guidelines increased from 0% to 41% ( p < 0.001). Objective activity tracker data recorded during the intervention showed that the mean ( SD ) number of active minutes per day in children was 82.1 (17.1). Further, the mean ( SD ) steps per day was 9590.7 (2425.3) in children, 7397.5 (1954.2) in mothers and 8161.7 (3370.3) in fathers. (4) Conclusions: Acknowledging the uncontrolled study design, the large pre-post changes in MVPA and rather high step counts recorded during the intervention suggest that an activity tracker and app intervention can increase physical activity in whole families. The Step it Up Family program warrants further efficacy testing in a larger, randomized controlled trial.

Objective: To explore older adults' perceptions and preferences for web-based physical activity interventions., Participants: Adults 65+ years were recruited via telephoning randomly selected households in Central Queensland, Australia., Methods: Six focus groups were conducted with a total of 46 adults 65+ years. Data were analyzed by qualitative content analysis., Results: This group of older adults liked websites that have links to information and included instructional videos and disliked websites that were hard to navigate. Many participants did not express an initial interest in web-based physical activity programs. The most common reason was that they did not have a computer or adequate internet connection. Some participants said they would be interested if it included a structured exercise program. When asked about preferences for web-based physical activity programs, this group preferred them to be simple and not cluttered, to include personalized advice, to include reminder check-ins and the ability to review goals after illness or injury. The most common preference for personalized advice in web-based interventions was that the information needs to be tailored to their existing injuries and illnesses., Conclusion: The findings from this study will inform the design of future web-based interventions specifically tailored to the needs of older people.

Background: This study compared the efficacy of two multi-component m-health interventions with a wait-list control group on body weight (primary outcome), and secondary outcomes of cardiovascular risk factors, lifestyle behaviours, and mental health., Methods: Three-arm randomised controlled trial (Enhanced: physical activity, diet, sleep, Traditional: physical activity, diet, Control) with assessments conducted at baseline, 6 and 12 months. Participants ( n = 116) were overweight or obese adults aged 19-65 (M = 44.5 [SD = 10.5]). The 6-month intervention was delivered via a smartphone app providing educational materials, goal-setting, self-monitoring and feedback, and also included one face-to-face dietary consultation, a Fitbit and scales. The trial was prospectively registered and conducted between May 2017 and September 2018. Group differences on primary and secondary outcomes were examined between the Pooled Intervention groups (Pooled Intervention = Enhanced and Traditional) and Control groups, and then between Enhanced and Traditional groups., Results: Nineteen participants (16.4%) formally withdrew from the trial. Compared with the Control group, average body weight of the Pooled Intervention group did not differ at 6 (between-group difference = -0.92, (95% CI -3.33, 1.48)) or 12 months (0.00, (95% CI -2.62, 2.62)). Compared with the Control group, the Pooled Intervention group significantly increased resistance training (OR = 7.83, (95% CI 1.08, 56.63)) and reduced energy intake at 6 months (-1037.03, (-2028.84, -45.22)), and improved insomnia symptoms at 12 months (-2.59, (-4.79, -0.39)). Compared with the Traditional group, the Enhanced group had increased waist circumferences (2.69, (0.20, 5.18)) and sedentary time at 6 months (105.66, (30.83, 180.48)), and improved bed time variability at 12 months (-1.08, (-1.86, -0.29)). No other significant differences were observed between groups., Conclusions: Relative to Controls, the Pooled Intervention groups did not differ on body weight but improved resistance training, and reduced energy intake and insomnia symptom severity. No additional weight loss was apparent when targeting improvements in physical activity, diet and sleep in combination compared with physical activity and diet.

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., Clinical Trial Information: Australian New Zealand Clinical Trial Registry: ACTRN12617000680369; Universal Trial number: U1111-1194-2680; Human Research Ethics Committee, Blinded by request of journal: H-2016-0267., (© Society of Behavioral Medicine 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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.

Introduction: Appealing approaches to increasing physical activity levels are needed. This study evaluated whether a social and gamified smartphone app (Active Team) could be one such approach., Study Design: A 3-group cluster RCT compared the efficacy of Active Team with a basic self-monitoring app and waitlist control group., Setting/participants: Australian adults (N=444, mean age of 41 years, 74% female) were recruited in teams (n=121) and randomly assigned (1:1:1) to the Active Team (n=141, 39 teams), self-monitoring app (n=160, 42 teams), or waitlist group (n=143, 40 teams). Data were collected in 2016-2017, and analysis was conducted in 2018-2019., Intervention: Active Team is a 100-day app-based, gamified, online social networking physical activity intervention., Main Outcome Measures: The primary outcome was change in objective physical activity from baseline to 3-month follow-up. Secondary outcomes included objective physical activity at 9 months and self-reported physical activity, quality of life, depression, anxiety and stress, well-being, and engagement., Results: Mixed models indicated no significant differences in objective physical activity between groups at 3 (F=0.17, p=0.84; Cohen's d=0.03, 95% CI= -0.21, 0.26) or 9 months (F=0.23, p=0.92; d=0.06, 95% CI= -0.17, 0.29) and no significant differences for secondary outcomes of quality of life, depression, anxiety and stress, or well-being. Self-reported moderate-to-vigorous physical activity was significantly higher in the Active Team group at the 9-month follow-up (F=3.05, p=0.02; d=0.50, 95% CI=0.26, 0.73). Engagement was high; the Active Team group logged steps on an average of 72 (SD=35) days and used the social and gamified features an average of 89 (SD=118) times., Conclusions: A gamified, online social networking physical activity intervention did not change objective moderate-to-vigorous physical activity, though it did increase self-reported moderate-to-vigorous physical activity and achieve high levels of engagement. Future work is needed to understand if gamification, online social networks, and app-based approaches can be leveraged to achieve positive behavior change., Trial Registration: This study is registered at Australian and New Zealand Clinical Trial Registry (protocol: ANZCTR12617000113358)., (Copyright © 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Background: Substantial evidence links activity domains with health and well-being; however, research has typically examined time-use behaviors independently, rather than considering daily activity as a 24-hour time-use composition. This study used compositional data analysis to estimate the difference in physical and mental well-being associated with reallocating time between behaviors., Methods: Participants (n = 430; 74% female; 41 [12] y) wore an accelerometer for 7 days and reported their body mass index; health-related quality of life (QoL); and symptoms of depression, anxiety, and stress. Regression models determined whether time-use composition, comprising sleep, sedentary behavior, light physical activity (LPA), and moderate to vigorous physical activity (MVPA), was associated with well-being. Compositional isotemporal substitution models estimated the difference in well-being associated with reallocating time between behaviors., Results: Time-use composition was associated with body mass index and physical health-related QoL. Reallocating time to MVPA from sleep, sedentary behavior, and LPA showed favorable associations with body mass index and physical health-related QoL, whereas reallocations from MVPA to other behaviors showed unfavorable associations. Reallocations from LPA to sedentary behavior were associated with better physical health-related QoL and vice versa., Conclusion: Results reinforce the importance of MVPA for physical health but do not suggest that replacing sedentary behavior with LPA is beneficial for health and well-being.

Background: This study examined the criterion validity of the online Active Australia Survey, using accelerometry as the criterion, and whether self-report bias was related to level of activity, age, sex, education, body mass index and health-related quality of life., Methods: The online Active Australia Survey was validated against the GENEActiv accelerometer as a direct measure of activity. Participants (n = 344) wore an accelerometer for 7 days, completed the Active Australia Survey, and reported their health and demographic characteristics. A Spearman's rank coefficient examined the association between minutes of moderate-to-vigorous physical activity recorded on the Active Australia Survey and GENEActiv accelerometer. A Bland-Altman plot illustrated self-report bias (the difference between methods). Linear mixed effects modelling was used to examine whether participant factors predicted self-report bias., Results: The association between moderate-to-vigorous physical activity reported on the online Active Australia Survey and accelerometer was significant (r s  = .27, p < .001). Participants reported 4 fewer minutes per day on the Active Australia Survey than was recorded by accelerometry (95% limits of agreement -104 - 96 min) but the difference was not significant (t(343) = -1.40, p = .16). Self-report bias was negatively associated with minutes of accelerometer-recorded moderate-to-vigorous physical activity and positively associated with mental health-related quality of life., Conclusions: The online Active Australia Survey showed limited criterion validity against accelerometry. Self-report bias was related to activity level and mental health-related quality of life. Caution is recommended when interpreting studies using the online Active Australia Survey.

Introduction: Physical activity is an integral part of healthy ageing, yet the majority of older adults 65+ years are not sufficiently active. Web-based physical activity interventions hold much promise to reach older adults. Preliminary evidence suggests that web-based interventions with tailored advice and Fitbits may be well suited for older adults., Methods and Analysis: This study aims to test the effectiveness of 'Active for Life', a 12-week computer-tailored web-based physical activity intervention using Fitbits for older adults. We will recruit 300 participants who will be randomly assigned to one of three trial arms: (1) web-based physical activity intervention with tailored advice only, (2) web-based physical activity intervention with tailored advice and Fitbit or (3) a wait-list control. The primary outcome, objective moderate to vigorous physical activity (MVPA) and secondary outcomes of objective sedentary behaviour, objective sleep, quality of life, social support, physical function and satisfaction with life will be assessed at baseline and week 12. The secondary outcomes of self-reported physical activity, sitting time and sleep will be assessed at baseline, week 6, 12 and 24. Website usability and participant satisfaction will be assessed at week 12 and website usage and intervention fidelity will be assessed from week 1 to 24. Intention-to-treat linear mixed model analyses will be used to test for group (tailoring only, tailoring +Fitbit, control) differences on changes in the main outcome, MVPA and secondary outcomes. Generalised linear models will be used to compare intervention groups (tailoring only, tailoring +Fitbit) on website usability, participant satisfaction, website usage and intervention fidelity., Ethics and Dissemination: The study has received ethics approval from the Central Queensland University Human Research Ethics Committee (H16/12-321). Study outcomes will be disseminated through peer-reviewed publications and academic conferences and used to inform improvements and dissemination of a tailored, web-based physical activity intervention for adults 65+ years., Trial Registration Number: Australian and New Zealand Clinical Trials Registry Number: ACTRN12618000646246., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Social media campaigns provide broad-reach and convenience for promoting freely-available health programs. However, their effectiveness and subsequent engagement of new users is unknown. This study aimed to assess the reach and new member registration rates resulting from a dedicated 10,000 Steps social media campaign (SMC) and to compare program engagement and time to non-usage attrition of new users from the SMC with other users. SMC reach (using Facebook, Instagram, and display advertisements engagement metrics), new-user numbers, engagement (usage of the website and its features), and time to non-usage attrition were assessed using generalized linear regression, binary logistic regression, and Cox proportion hazards regression models. During the SMC, Instagram and display advertisement impressions, Facebook reach and new daily registrations were significantly higher compared with six weeks and one year prior. There were no between-group differences in the average usage of most website/program features. Risk of non-usage attrition was higher among new users from the SMC than new users from one year prior. The SMC was effective in promoting awareness of the 10,000 Steps program. Further research to identify long-term engagement strategies and the most effective combination of social media platforms for promotion of, and recruitment to, health programs is warranted.

Competing Interests: Competing interests: CV (ID 100427) and MJD (ID 100029) are supported by a Future Leader Fellowship from the National Heart Foundation of Australia.

Background: The success of a mobile phone app in changing health behavior is thought to be contingent on engagement, commonly operationalized as frequency of use., Objective: This subgroup analysis of the 2 intervention arms from a 3-group randomized controlled trial aimed to examine user engagement with a 100-day physical activity intervention delivered via an app. Rates of engagement, associations between user characteristics and engagement, and whether engagement was related to intervention efficacy were examined., Methods: Engagement was captured in a real-time log of interactions by users randomized to either a gamified (n=141) or nongamified version of the same app (n=160). Physical activity was assessed via accelerometry and self-report at baseline and 3-month follow-up. Survival analysis was used to assess time to nonuse attrition. Mixed models examined associations between user characteristics and engagement (total app use). Characteristics of super users (top quartile of users) and regular users (lowest 3 quartiles) were compared using t tests and a chi-square analysis. Linear mixed models were used to assess whether being a super user was related to change in physical activity over time., Results: Engagement was high. Attrition (30 days of nonuse) occurred in 32% and 39% of the gamified and basic groups, respectively, with no significant between-group differences in time to attrition (P=.17). Users with a body mass index (BMI) in the healthy range had higher total app use (mean 230.5, 95% CI 190.6-270.5; F 2 =8.67; P<.001), compared with users whose BMI was overweight or obese (mean 170.6, 95% CI 139.5-201.6; mean 132.9, 95% CI 104.8-161.0). Older users had higher total app use (mean 200.4, 95% CI 171.9-228.9; F 1 =6.385; P=.01) than younger users (mean 155.6, 95% CI 128.5-182.6). Super users were 4.6 years older (t 297 =3.6; P<.001) and less likely to have a BMI in the obese range (χ 2 2 =15.1; P<.001). At the 3-month follow-up, super users were completing 28.2 (95% CI 9.4-46.9) more minutes of objectively measured physical activity than regular users (F 1,272 =4.76; P=.03)., Conclusions: Total app use was high across the 100-day intervention period, and the inclusion of gamified features enhanced engagement. Participants who engaged the most saw significantly greater increases to their objectively measured physical activity over time, supporting the theory that intervention exposure is linked to efficacy. Further research is needed to determine whether these findings are replicated in other app-based interventions, including those experimentally evaluating engagement and those conducted in real-world settings., Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12617000113358; https://www.anzctr.org.au/ACTRN12617000113358.aspx., (©Sarah Martine Edney, Jillian C Ryan, Tim Olds, Courtney Monroe, François Fraysse, Corneel Vandelanotte, Ronald Plotnikoff, Rachel Curtis, Carol Maher. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 27.11.2019.)

Introduction: Interventions that improve both physical activity and sleep quality may be more effective in improving overall health. The purpose of the Synergy Study is to test the efficacy of a mobile health combined behavior intervention targeting physical activity and sleep quality., Study Design: Randomized, waitlist-controlled trial., Setting/participants: This study had an app-based delivery mode, Australia-wide. The participants were 160 adults who reported insufficient physical activity and poor sleep quality in an eligibility survey., Intervention: The intervention was a mobile app providing educational resources, goal setting, self-monitoring, and feedback strategies. It included 12 weeks of personalized support including weekly reports, tool sheets, and prompts., Main Outcome Measures: Outcomes were assessed at baseline, 3 months (primary), and 6 months (secondary endpoint). Self-reported minutes of moderate-to-vigorous intensity physical activity and sleep quality were co-primary outcomes. Resistance training; sitting time; sleep hygiene; sleep timing variability; insomnia severity; daytime sleepiness; quality of life; and depression, anxiety, and stress symptoms were secondary outcomes. Data were collected between June 2017 and February 2018 and analyzed in August 2018., Results: At 3 months, between-group differences in moderate-to-vigorous intensity physical activity were not statistically significant (p=0.139). Significantly more participants in the intervention group engaged in ≥2 days/week (p=0.004) of resistance training. The intervention group reported better overall sleep quality (p=0.009), subjective sleep quality (p=0.017), sleep onset latency (p=0.013), waketime variability (p=0.018), sleep hygiene (p=0.027), insomnia severity (p=0.002), and lower stress symptoms (p=0.032) relative to waitlist controls. At 6 months, group differences were maintained for sleep hygiene (p=0.048), insomnia severity (p=0.002), and stress symptoms (p=0.006). Differences were observed for bedtime variability (p=0.023), sleepiness (p<0.001), daytime dysfunction (p=0.039), and anxiety symptoms (p=0.003) at 6 months, but not 3 months., Conclusions: This remotely delivered intervention did not produce statistically significant between-group differences in minutes of moderate-to-vigorous intensity physical activity. Significant short-term differences in resistance training and short- and medium-term differences in sleep health in favor of the intervention were observed., Trial Registration: This study is registered at anzctr.org.au ACTRN12617000376347., (Copyright © 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Background: Homophily is the tendency to associate with friends similar to ourselves. This study explored the effects of homophily on team formation in a physical activity challenge in which "captains" signed up their Facebook friends to form teams., Methods: This study assessed whether participants (n = 430) were more similar to their teammates than to nonteammates with regard to age, sex, education level, body mass index, self-reported and objectively measured physical activity, and negative emotional states; and whether captains were more similar to their own teammates than to nonteammates. Variability indices were calculated for each team, and a hypothetical variability index, representing that which would result from randomly assembled teams, was also calculated., Results: Within-team variability was less than that for random teams for all outcomes except education level and depression, with differences (SDs) ranging from +0.15 (self-reported physical activity) to +0.47 (age) (P < .001 to P = .001). Captains were similar to their teammates except in regard to age, with captains being 2.6 years younger (P = .003)., Conclusions: Results support hypotheses that self-selected teams are likely to contain individuals with similar characteristics, highlighting potential to leverage team-based health interventions to target specific populations by instructing individuals with risk characteristics to form teams to help change behavior.

Objective: This study examines the reach, effectiveness, adoption, implementation, and maintenance of the 10,000 Steps Pedometer Microgrant Scheme using the RE-AIM framework., Methods: The study used a mixed methods pre-post design. RE-AIM indicators were examined using employee surveys and workplace reports of microgrant implementation, adoption, and maintenance., Results: A total of 259 microgrants and 21,211 pedometers were awarded (reach). Significant increases in physical activity were observed (P < 0.05) (effectiveness). Many (78%) workplaces reported using at least one challenge resource (adoption). Barriers were higher (26.5%) or lower (20.5%) than anticipated participation rates (implementation). Fifty percent of workplaces would continue to promote physical activity (maintenance)., Conclusions: The microgrant reached a large number of employees and workplaces, increased physical activity, and achieved good levels of adoption and implementation. Employee and workplace levels of maintenance were mixed and need to be improved.

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.

Our understanding of how multiple health-behaviours co-occur is in its infancy. This study aimed to: (1) identify patterns of physical activity, diet, sitting, and sleep; and (2) examine the association between sociodemographic and health-risk indicators. Pooled data from annual cross-sectional telephone surveys of Australian adults (2015-2017, n = 3374, 51.4% women) were used. Participants self-reported physical activity, diet, sitting-time, sleep/rest insufficiency, sociodemographic characteristics, smoking, alcohol use, height and weight to calculate body mass index (BMI), and mental distress frequency. Latent class analysis identified health-behaviour classes. Latent class regression determined the associations between health-behaviour patterns, sociodemographic, and health-risk indicators. Three latent classes were identified. Relative to a 'moderate lifestyle' pattern (men: 43.2%, women: 38.1%), a 'poor lifestyle' pattern (men: 19.9%, women: 30.5%) was associated with increased odds of a younger age, smoking, BMI ≥ 30.0 kg/m2, frequent mental distress (men and women), non-partnered status (men only), a lower Socioeconomic Index for Areas centile, primary/secondary education only, and BMI = 25.0-29.9 kg/m 2 (women only). An 'active poor sleeper' pattern (men: 37.0%, women: 31.4%) was associated with increased odds of a younger age (men and women), working and frequent mental distress (women only), relative to a 'moderate lifestyle' pattern. Better understanding of how health-behaviour patterns influence future health status is needed. Targeted interventions jointly addressing these behaviours are a public health priority.

Background: To date, many online health behavior programs developed by researchers have not been translated at scale. To inform translational efforts, health researchers must work with marketing experts to design cost-effective marketing campaigns. It is important to understand the characteristics of end users of a given health promotion program and identify key market segments., Objective: This study aimed to describe the characteristics of the adopters of Active Team, a gamified online social networking physical activity app, and identify potential market segments to inform future research translation efforts., Methods: Participants (N=545) were Australian adults aged 18 to 65 years who responded to general advertisements to join a randomized controlled trial (RCT) evaluating the Active Team app. At baseline they provided demographic (age, sex, education, marital status, body mass index, location of residence, and country of birth), behavioral (sleep, assessed by the Pittsburgh Quality Sleep Index) and physical activity (assessed by the Active Australia Survey), psychographic information (health and well-being, assessed by the PERMA [Positive Emotion, Engagement, Relationships, Meaning, Achievement] Profile; depression, anxiety and stress, assessed by the Depression, Anxiety, and Stress Scale [DASS-21]; and quality of life, assessed by the 12-Item Short Form Health Survey [SF-12]). Descriptive analyses and a k-medoids cluster analysis were performed using the software R 3.3.0 (The R Foundation) to identify key characteristics of the sample., Results: Cluster analyses revealed four clusters: (1) younger inactive women with poor well-being (218/545), characterized by a higher score on the DASS-21, low mental component summary score on the SF-12, and relatively young age; (2) older, active women (153/545), characterized by a lower score on DASS-21, a higher overall score on the SF-12, and relatively older age; (3) young, active but stressed men (58/545) with a higher score on DASS-21 and higher activity levels; and (4) older, low active and obese men (30/545), characterized by a high body mass index and lower activity levels., Conclusions: Understanding the characteristics of population segments attracted to a health promotion program will guide the development of cost-effective research translation campaigns., Trial Registration: Australian New Zealand Clinical Trial Registry ACTRN12617000113358; https://www.anzctr.org .au/Trial/Registration/TrialReview.aspx?id=371463., International Registered Report Identifier (irrid): RR2-10.1186/s12889-017-4882-7., (©Ilea Sanders, Camille E Short, Svetlana Bogomolova, Tyman Stanford, Ronald Plotnikoff, Corneel Vandelanotte, Tim Olds, Sarah Edney, Jillian Ryan, Rachel G Curtis, Carol Maher. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 03.06.2019.)

Background: This study aimed to investigate the validity of the Active Australia Survey across different subgroups and its responsiveness to change, as few previous studies have examined this., Methods: The Active Australia Survey was validated against the ActiGraph as an objective measure of physical activity. Participants (n = 465) wore the ActiGraph for 7 days and subsequently completed the Active Australia Survey. Moderate activity, vigorous activity and total moderate and vigorous physical activity were compared using Spearman rank-order correlations. Changes in physical activity between baseline and 3-month assessments were correlated to examine responsiveness to change. The data were stratified to assess outcomes according to different subgroups (e.g., gender, age, weight, activity levels)., Results: With regards to the validity, a significant correlation of ρ = 0.19 was found for moderate physical activity, ρ = 0.33 for vigorous physical activity and ρ = 0.23 for moderate and vigorous physical activity combined. For vigorous physical activity correlations were higher than 0.3 for most subgroups, whereas they were only higher than 0.3 in those with a healthy weight for the other activity outcomes. With regards to responsiveness to change, a correlation of ρ = 0.32 was found for moderate physical activity, ρ = 0.19 for vigorous physical activity and ρ = 0.35 for moderate and vigorous physical activity combined. For moderate and vigorous activity combined correlations were higher than 0.4 for several subgroups, but never for vigorous physical activity., Conclusions: Little evidence for the validity of Active Australia Survey was found, although the responsiveness to change was acceptable for several subgroups. Findings from studies using the Active Australia Survey should be interpreted with caution., Trial Registration: World Health Organisation Universal Trial Number: U111-1119-1755. Australian New Zealand Clinical Trials Registry, ACTRN12611000157976 . Registration date: 8 March 2011.

Objectives: Insufficient sleep is being increasingly recognized as a public health issue. There is a need to identify correlates of insufficient sleep to guide future preventative health interventions. This study aims to determine the sociodemographic and behavioral correlates of frequent perceived insufficient sleep in the Australian population., Design: Pooled analyses of two cross-sectional, self-report national telephone surveys were conducted in 2015 (July-August) and 2016 (June-August)., Setting: Adults living in Australia., Participants: Data from participants (age 18 years and over) of both surveys were pooled for analysis (2015 n = 1041; 2016 n = 1170), with 2211 participants being included in the current study., Measurements: Participants self-reported their age, gender, education and employment level, language spoken at home, urbanization, chronic disease, and height and weight to calculate BMI. Self-reported physical activity, sitting time, smoking, and consumption of fruit, vegetables, fast food, alcohol and frequency of perceived insufficient sleep were also assessed. Binary logistic regression analysis examined the relationship between insufficient sleep (≥14 days out of 30), sociodemographic and behavioral variables., Results: The overall prevalence of insufficient sleep was 24%. Female gender, obesity, >8 h/d sitting time, smoking, and frequent consumption of fast food were positively associated with frequent insufficient sleep (P < .05). Higher levels of physical activity and being aged 51 years or older were negatively associated with frequent insufficient sleep (P < .05)., Conclusions: The sociodemographic and behavioral characteristics associated with frequent perceived insufficient sleep can be used to guide the development of future interventions to reduce sleep insufficiency., (Copyright © 2018 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

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 χ 2 -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.

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)., (©Corneel Vandelanotte, Mitch J Duncan, Carol A Maher, Stephanie Schoeppe, Amanda L Rebar, Deborah A Power, Camille E Short, Christopher M Doran, Melanie J Hayman, Stephanie J Alley. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 18.12.2018.)

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., Competing Interests: The authors have declared that no competing interests exist.

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., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Introduction: Poor sleep health is common and has a substantial negative health impact. Physical activity has been shown to improve sleep health. Many sleep interventions do not explicitly target physical activity, potentially limiting changes in activity and also sleep. Few intervention target those with poor sleep health but without a diagnosed disorder. This study aims to examine the efficacy of a combined physical activity and sleep intervention to improve sleep quality in middle-aged adults and its effect on physical activity, depression and quality of life., Methods: A three-arm randomised trial with a three-month primary time-point, will be conducted. Adults (N = 275) aged 40-65 years, who report physical inactivity and poor sleep quality, will be randomly allocated to either a combined Physical Activity and Sleep Health, a Sleep Health-Only or a Wait List Control group. The multi-component m-health intervention will be delivered using a smartphone/tablet "app", supplemented with email and SMS. Participants will use the app to access educational material, set goals, self-monitor and receive feedback about behaviours. Assessments will be conducted at baseline, three-month primary time-point and six-month follow-up. Generalized linear models using an ANCOVA (baseline-adjusted) approach, will be used to identify between-group differences in sleep quality, following an intention-to-treat principle., Discussion: This study will determine whether the addition of a physical activity intervention enhances the effectiveness of a sleep intervention to improve sleep quality, relative to a sleep-only intervention, in physically inactive middle-aged adults who report poor sleep health, but without a sleep disorder., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Objective: Despite the benefits associated with workplace health programs, many organizations are unable to offer them due to financial constraints. To address this barrier, the existing 10,000 Steps program trialed the 10,000 Steps Pedometer Microgrant Scheme. This study assessed the feasibility and acceptability of the Microgrant Scheme., Methods: Semi-structured interviews with employee representatives (n = 19) were used to explore perceptions of the Microgrant Scheme. Thematic inductive analysis was conducted., Results: Three main themes emerged: 1) the need for workplace initiatives to address health promotion issues (The Need); 2) the factors associated with the application and implementation process (The Process); and 3) employee and employer benefits associated with the Microgrant Scheme (The Outcomes)., Conclusion: These findings highlight the potential utility of a Microgrant Scheme to extend the reach and long-term sustainability of workplace health promotion activities.

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.

Background: Electronic health (eHealth) and mobile health (mHealth) approaches to address low physical activity levels, sedentary behavior, and unhealthy diets have received significant research attention. However, attempts to systematically map the entirety of the research field are lacking. This gap can be filled with a bibliometric study, where publication-specific data such as citations, journals, authors, and keywords are used to provide a systematic overview of a specific field. Such analyses will help researchers better position their work., Objective: The objective of this review was to use bibliometric data to provide an overview of the eHealth and mHealth research field related to physical activity, sedentary behavior, and diet., Methods: The Web of Science (WoS) Core Collection was searched to retrieve all existing and highly cited (as defined by WoS) physical activity, sedentary behavior, and diet related eHealth and mHealth research papers published in English between January 1, 2000 and December 31, 2016. Retrieved titles were screened for eligibility, using the abstract and full-text where needed. We described publication trends over time, which included journals, authors, and countries of eligible papers, as well as their keywords and subject categories. Citations of eligible papers were compared with those expected based on published data. Additionally, we described highly-cited papers of the field (ie, top ranked 1%)., Results: The search identified 4805 hits, of which 1712 (including 42 highly-cited papers) were included in the analyses. Publication output increased on an average of 26% per year since 2000, with 49.00% (839/1712) of papers being published between 2014 and 2016. Overall and throughout the years, eHealth and mHealth papers related to physical activity, sedentary behavior, and diet received more citations than expected compared with papers in the same WoS subject categories. The Journal of Medical Internet Research published most papers in the field (9.58%, 164/1712). Most papers originated from high-income countries (96.90%, 1659/1717), in particular the United States (48.83%, 836/1712). Most papers were trials and studied physical activity. Beginning in 2013, research on Generation 2 technologies (eg, smartphones, wearables) sharply increased, while research on Generation 1 (eg, text messages) technologies increased at a reduced pace. Reviews accounted for 20 of the 42 highly-cited papers (n=19 systematic reviews). Social media, smartphone apps, and wearable activity trackers used to encourage physical activity, less sedentary behavior, and/or healthy eating were the focus of 14 highly-cited papers., Conclusions: This study highlighted the rapid growth of the eHealth and mHealth physical activity, sedentary behavior, and diet research field, emphasized the sizeable contribution of research from high-income countries, and pointed to the increased research interest in Generation 2 technologies. It is expected that the field will grow and diversify further and that reviews and research on most recent technologies will continue to strongly impact the field., (©Andre Matthias Müller, Carol A Maher, Corneel Vandelanotte, Melanie Hingle, Anouk Middelweerd, Michael L Lopez, Ann DeSmet, Camille E Short, Nicole Nathan, Melinda J Hutchesson, Louise Poppe, Catherine B Woods, Susan L Williams, Petra A Wark. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 18.04.2018.)

Introduction: There is a need to reduce physical inactivity and poor sleep health in the adult population to decrease chronic disease rates and the associated burden. Given the high prevalence of these risk behaviours, effective interventions with potential for wide reach are warranted., Methods and Analysis: The aim of this two-arm RCT will be to test the effect of a three month personalised mobile app intervention on two main outcomes: minutes of moderate-to-vigorous-intensity physical activity and overall sleep quality. In addition, between-group changes in health-related quality of life and mental health status will be assessed as secondary outcomes. The pre-specified mediators and moderators include social cognitive factors, the neighbourhood environment, health (BMI, depression, anxiety, stress), sociodemographic factors (age, gender, education) and app usage. Assessments will be conducted after three months (primary endpoint) and six months (follow-up). The intervention will provide access to a specifically developed mobile app, through which participants can set goals for active minutes, daily step counts, resistance training, sleep times and sleep hygiene practice. The app also allows participants to log their behaviours daily and view progress bars as well as instant feedback in relation to goals. The personalised support system will consist of weekly summary reports, educational and instructional materials, prompts on disengagement and weekly facts., Ethics and Dissemination: The Human Research Ethics Committee of The University of Newcastle, Australia granted full approval: H-2016-0181. This study will assess the efficacy of a combined behaviour intervention, mechanisms of behaviour change and gather high-quality process data, all of which will help refine future trials. Dissemination of findings will include publication in a peer-reviewed journal and presentation at national or international conferences. Participants will receive a plain English summary report of results., Trial Registration Number: ACTRN12617000376347; Pre-results., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Background: Interactive web-based physical activity interventions using Web 2.0 features (e.g., social networking) have the potential to improve engagement and effectiveness compared to static Web 1.0 interventions. However, older adults may engage with Web 2.0 interventions differently than younger adults. The aims of this study were to determine whether an interaction between intervention (Web 2.0 and Web 1.0) and age group (<55y and ≥55y) exists for website usage and to determine whether an interaction between intervention (Web 2.0, Web 1.0 and logbook) and age group (<55y and ≥55y) exists for intervention effectiveness (changes in physical activity)., Methods: As part of the WALK 2.0 trial, 504 Australian adults were randomly assigned to receive either a paper logbook (n = 171), a Web 1.0 (n = 165) or a Web 2.0 (n = 168) physical activity intervention. Moderate to vigorous physical activity was measured using ActiGraph monitors at baseline 3, 12 and 18 months. Website usage statistics including time on site, number of log-ins and number of step entries were also recorded. Generalised linear and intention-to-treat linear mixed models were used to test interactions between intervention and age groups (<55y and ≥55y) for website usage and moderate to vigorous physical activity changes., Results: Time on site was higher for the Web 2.0 compared to the Web 1.0 intervention from baseline to 3 months, and this difference was significantly greater in the older group (OR = 1.47, 95%CI = 1.01-2.14, p = .047). Participants in the Web 2.0 group increased their activity more than the logbook group at 3 months, and this difference was significantly greater in the older group (moderate to vigorous physical activity adjusted mean difference = 13.74, 95%CI = 1.08-26.40 min per day, p = .03). No intervention by age interactions were observed for Web 1.0 and logbook groups., Conclusions: Results partially support the use of Web 2.0 features to improve adults over 55 s' engagement in and behaviour changes from web-based physical activity interventions., Trial Registration: ACTRN ACTRN12611000157976 , Registered 7 March 2011.