Factors relating to sustainability and scalability of the 'Food, Move, Sleep (FOMOS) for Postnatal Mental Health' program: Qualitative perspectives from key stakeholders across Australia.

ISSUE ADDRESSED: Supporting healthy behaviours (quality diet, physical activity, sleep) through home-based interventions is feasible to improve postnatal mental health. Involving stakeholders in developing interventions is essential for maximising accessibility, implementation and scale-up. This study aimed to identify factors affecting the sustainable implementation and scalability of the Food, Move, Sleep (FOMOS) for Postnatal Mental Health program, including strategies to enhance research-practice translation. METHODS: Stakeholders (n = 13) involved in promoting physical activity, healthy eating, postnatal and mental health, public health and/or policy participated in semi-structured interviews. Interviews, based on PRACTIS Guide recommendations for implementation and scale-up, explored perceptions of program design, implementation and scalability. Reflexive thematic analysis was undertaken. Identified implementation and scale-up strategies were mapped against the Expert Recommendations for Implementing Change compendium and PRACTIS Guide. RESULTS: Individual-level: Targeting multiple systems (primary, tertiary, community-based care) and entry points (early, mid-postpartum) for uptake was important. For equity, screening women in public hospitals, engaging with community agencies and targeting most at-risk women, was suggested. Provider-level: Stakeholders identified strategies to enhance future roll-out (organisations assisting with recruitment). Factors impacting sustainability included high demand for the FOMOS program, and governance around screening and funding; online delivery, connecting with partners and providers and integration into existing services may enhance sustainability. Systems-level: Political support and community champions were perceived important for program dissemination. Nine strategies addressing program uptake, reach, implementation, potential scalability and sustainability were identified. CONCLUSIONS: For sustainable implementation and potential scalability of a home-based multi-behaviour postnatal intervention, multi-level implementation and scale-up strategies, aligned with existing health systems, policies and initiatives to support postnatal mental health should be considered. SO WHAT?: This paper provides a comprehensive list of strategies that can be used to enhance sustainable implementation and scalability of healthy behaviour programs targeting postnatal mental health. Further, the interview schedule, systematically developed and aligned with the PRACTIS Guide, may serve as a useful resource for researchers conducting similar studies in future.

Bidirectional associations of sleep and discretionary screen time in adults: Longitudinal analysis of the UK biobank.

The direction of the association between discretionary screen time (DST) and sleep in the adult population is largely unknown. We examined the bidirectional associations of DST and sleep patterns in a longitudinal sample of adults in the general population. A total of 31,361 UK Biobank study participants (52% female, 56.1 ± 7.5 years) had two repeated measurements of discretionary screen time (TV viewing and leisure-time computer use) and self-reported sleep patterns (five sleep health characteristics) between 2012 and 2018 (follow-up period of 6.9 ± 2.2 years). We categorised daily DST into three groups (low, <3 h/day; medium, 3-4 h/day; and high, >4 h/day), and calculated a sleep pattern composite score comprising morning chronotype, adequate sleep duration (7-8 h/day), never or rare insomnia, never or rare snoring, and infrequent daytime sleepiness. The overall sleep pattern was categorised into three groups (healthy: ≥ 4; intermediate: 2-3; and poor: ≤ 1 healthy sleep characteristic). Multiple logistic regression analyses were applied to assess associations between DST and sleep with adjustments for potential confounders. Participants with either an intermediate (OR: 1.40; 95% CI: 1.15, 1.71) or a poor (OR: 1.16; 95% CI: 1.10, 1.24) sleep pattern at baseline showed higher odds for high DST at follow-up, compared with those with a healthy baseline sleep pattern. Participants with medium (OR: 1.40; 95% CI: 1.14, 1.71) or high DST (OR: 1.62; 95% CI: 1.30, 2.00) at baseline showed higher odds for poor sleep at follow-up, compared with participants with a low DST. In conclusion, our findings provide consistent evidence that a high DST at baseline is associated with poor sleep over a nearly 7 year follow-up period, and vice versa.

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

BACKGROUND: Preliminary evidence suggests that web-based physical activity interventions with tailored advice and Fitbit integration are effective and may be well suited to older adults. Therefore, this study aimed to examine the engagement, acceptability, usability, and satisfaction with 'Active for Life,' a web-based physical activity intervention providing computer-tailored physical activity advice to older adults. METHODS: Inactive older adults (n = 243) were randomly assigned into 3 groups: 1) tailoring + Fitbit, 2) tailoring only, or 3) a wait-list control. The tailoring + Fitbit group and the tailoring-only group received 6 modules of computer-tailored physical activity advice over 12 weeks. The advice was informed by objective Fitbit data in the tailoring + Fitbit group and self-reported physical activity in the tailoring-only group. This study examined the engagement, acceptability, usability, and satisfaction of Active for Life in intervention participants (tailoring + Fitbit n = 78, tailoring only n = 96). Wait-list participants were not included. Engagement (Module completion, time on site) were objectively recorded through the intervention website. Acceptability (7-point Likert scale), usability (System Usability Scale), and satisfaction (open-ended questions) were assessed using an online survey at post intervention. ANOVA and Chi square analyses were conducted to compare outcomes between intervention groups and content analysis was used to analyse program satisfaction. RESULTS: At post-intervention (week 12), study attrition was 28% (22/78) in the Fitbit + tailoring group and 39% (37/96) in the tailoring-only group. Engagement and acceptability were good in both groups, however there were no group differences (module completions: tailoring + Fitbit: 4.72 ± 2.04, Tailoring-only: 4.23 ± 2.25 out of 6 modules, p = .14, time on site: tailoring + Fitbit: 103.46 ± 70.63, Tailoring-only: 96.90 ± 76.37 min in total, p = .56, and acceptability of the advice: tailoring + Fitbit: 5.62 ± 0.89, Tailoring-only: 5.75 ± 0.75 out of 7, p = .41). Intervention usability was modest but significantly higher in the tailoring + Fitbit group (tailoring + Fitbit: 64.55 ± 13.59, Tailoring-only: 57.04 ± 2.58 out of 100, p = .003). Participants reported that Active for Life helped motivate them, held them accountable, improved their awareness of how active they were and helped them to become more active. Conversely, many participants felt as though they would prefer personal contact, more detailed tailoring and more survey response options. CONCLUSIONS: This study supports web-based physical activity interventions with computer-tailored advice and Fitbit integration as engaging and acceptable in older adults. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry: ACTRN12618000646246. Registered April 23 2018, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374901.

Mediating effects of sleep on mental health in older adolescents: Findings from the Burn 2 Learn randomized controlled trial.

PURPOSE: Our study explored the mediating effect of sleep-related variables on older adolescents' mental health in the context of a school-based physical activity intervention. METHODS: We evaluated the Burn 2 Learn (B2L) intervention using a cluster randomized controlled trial, which included two cohorts. Participants for this sub-study were from the second cohort, which included 292 older adolescents (16.0 ± 0.5 years) from 10 secondary schools in New South Wales, Australia. Teachers at intervention schools delivered two high-intensity activity breaks (approximately 10 mins) per week to students during academic lessons. Participants completed measures of mental health (i.e., perceived stress and internalizing problems) and hypothesized mediators (i.e., sleep duration, sleep latency, awakenings, and daytime sleepiness) at baseline (February-April 2019) and post-intervention (August-September 2019). Single mediation analyses were conducted to explore the potential mediating effects of sleep variables on mental health outcomes using a product-of-coefficient test. RESULTS: We observed a small statistically significant effect for perceived stress (ß = -0.11, SE = 0.034, p = 0.002), but not for internalizing problems (ß = 0.02, SE = 0.051, p = 0.760). There were no significant intervention effects for sleep-related variables. Several sleep-related variables were associated with mental health outcomes but no mediated effects were found. CONCLUSION: The B2L intervention had a small beneficial effect on perceived stress, however our mediation analyses suggest this was not explained by changes in sleep-related variables. Markers of sleep were associated with mental health constructs, highlighting the importance of sleep for good psychological health. However, in the context of a physical activity intervention, effects on mental health may be driven by other behavioral, neurobiological, or psychosocial mechanisms.

Exploring the Interplay Between Message Format, Need for Cognition and Personal Relevance on Processing Messages About Physical Activity: a Two-Arm Randomized Experimental Trial.

BACKGROUND: According to the Elaboration Likelihood Model, persuasion can occur via two different routes (the central route and peripheral route), with the route utilized dependent on factors associated with motivation and ability. This study aimed to explore the moderating role of need for cognition (NFC) and perceived relevance on the processing of physical activity messages designed to persuade via either the central route or the peripheral route. METHOD: Participants (N = 50) were randomized to receive messages optimized for central route processing or messages optimized for peripheral route processing. Eye-tracking devices were used to assess attention, which was the primary outcome. Message perceptions and the extent of persuasion (changes in physical activity determinants) were also assessed via self-report as secondary outcomes. Moderator effects were examined using interaction terms within mixed effects models and linear regression models. RESULTS: There were no detected interactions between condition and NFC for any of the study outcomes (all ps > .05). Main effects of personal relevance were observed for some self-report outcomes, with increased relevance associated with better processing outcomes. An interaction between need for cognition and personal relevance was observed for perceived behavioral control (p = 0.002); greater relevance was associated with greater perceived behavioral control for those with a higher need for cognition. CONCLUSION: Matching physical activity messages based on NFC may not increase intervention efficacy. Relevance of materials is associated with greater change in physical activity determinants and may be more so among those with a higher NFC.

Rehabilitation Support via Postcard (RSVP): A Randomised Controlled Trial of a Postcard to Promote Uptake of Cardiac Rehabilitation.

PURPOSE: The aim was to increase cardiac rehabilitation (CR) uptake using a novel intervention, Rehabilitation Support Via Postcard (RSVP), among patients with acute myocardial infarction discharged from two major hospitals in Hunter New England Local Health District (HNELHD), New South Wales, Australia. METHODS: The RSVP trial was evaluated using a two-armed randomised controlled trial design. Participants (N=430) were recruited from the two main hospitals in HNELHD, and enrolled and randomised to either the intervention (n=216) or control (n=214) group over a six-month period. All participants received usual care; however, the intervention group received postcards promoting CR attendance between January and July 2020. The postcard was ostensibly written as an invitation from the patient's admitting medical officer to promote timely and early uptake of CR. The primary outcome was CR attendance at outpatient HNELHD CR services in the 30-days post-discharge. RESULTS: Fifty-four percent (54%) of participants who received RSVP attended CR, compared to 46% in the control group; however this difference was not statistically significant (odds ratio [OR]=1.4, 95% confidence interval [CI]=0.9-2.0, p=0.11). Exploratory post-hoc analysis among four sub-groups (i.e., Indigeneity, gender, age and rurality), found that the intervention significantly increased attendance in males (OR=1.6, 95%CI=1.0-2.6, p=0.03) but had no significant impact on attendance for other sub-groups. CONCLUSIONS: While not statistically significant, postcards increased overall CR attendance by 8%. This strategy may be useful to increase attendance, particularly in men. Alternative strategies are necessary to increase CR uptake among women, Indigenous people, older people and people from regional and remote locations.

Sleep Health in Male-dominated Workplaces: A Qualitative Study Examining the Perspectives of Male Employees.

OBJECTIVE: The purpose of this qualitative study was to explore working men's perspectives about sleep health and the intersecting influences of gender and work, describing participant's views on current and potential programming and organizational support to promote sleep health. METHODS: Twenty men employed in male-dominated industries in the north-central region of Alberta, Canada, participated in 4 consultation group discussions addressing motivators, facilitators and barriers to sleep health. RESULTS: Participants reported sleeping an average of 6.36 (SD ±1.1) hours per night, and the majority worked more than 40 hours per week. Data were analyzed using an inductive approach. The findings provided important insights. In normalizing sleep deprivation and prioritizing the need to "just keep going" on six or less hours of sleep, the men subscribed to masculine ideals related to workplace perseverance, stamina and resilience. Workplace cultures and practices were implicated including normative dimensions of overtime and high productivity and output, amid masculine cultures constraining emotions and conversations about sleep, the sum of which muted avenues for discussing, let alone promoting sleep. Challenges to good sleep were primarily constructed around time constraints, and worry about meeting work and home responsibilities. Men's preferences for workplace support included providing and incentivizing the use of sleep health resources, designing work for sleep health (e.g., shift schedules, overtime policies) and getting advice from experienced coworkers and experts external to the workplace organization. CONCLUSION: These findings hold potential for informing future gender-sensitive programming and organizational practices to support sleep health among working men.

Sleep and physical activity in relation to all-cause, cardiovascular disease and cancer mortality risk.

OBJECTIVES: Although both physical inactivity and poor sleep are deleteriously associated with mortality, the joint effects of these two behaviours remain unknown. This study aimed to investigate the joint association of physical activity (PA) and sleep with all-cause and cause-specific mortality risks. METHODS: 380 055 participants aged 55.9 (8.1) years (55% women) from the UK Biobank were included. Baseline PA levels were categorised as high, medium, low and no moderate-to-vigorous PA (MVPA) based on current public health guidelines. We categorised sleep into healthy, intermediate and poor with an established composited sleep score of chronotype, sleep duration, insomnia, snoring and daytime sleepiness. We derived 12 PA-sleep combinations, accordingly. Mortality risks were ascertained to May 2020 for all-cause, total cardiovascular disease (CVD), CVD subtypes (coronary heart disease, haemorrhagic stroke, ischaemic stroke), as well as total cancer and lung cancer. RESULTS: After an average follow-up of 11.1 years, sleep scores showed dose-response associations with all-cause, total CVD and ischaemic stroke mortality. Compared with high PA-healthy sleep group (reference), the no MVPA-poor sleep group had the highest mortality risks for all-cause (HR (95% CIs), (1.57 (1.35 to 1.82)), total CVD (1.67 (1.27 to 2.19)), total cancer (1.45 (1.18 to 1.77)) and lung cancer (1.91 (1.30 to 2.81))). The deleterious associations of poor sleep with all outcomes, except for stroke, was amplified with lower PA. CONCLUSION: The detrimental associations of poor sleep with all-cause and cause-specific mortality risks are exacerbated by low PA, suggesting likely synergistic effects. Our study supports the need to target both behaviours in research and clinical practice.

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

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

Effect of a physical activity and sleep m-health intervention on a composite activity-sleep behaviour score and mental health: a mediation analysis of two randomised controlled trials.

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.

The bidirectional association between sleep and physical activity: A 6.9 years longitudinal analysis of 38,601 UK Biobank participants.

Although physical activity and sleep may influence each other, little is known about the bidirectional association of these two behaviors. The present analyses included 38,601 UK Biobank participants (51% female, 55.7 ± 7.6 years old, 6.9 ± 2.2 years of follow-up). Physical activity was categorized by the weekly metabolic equivalent of task minutes (highly active: ≥ 1200; active: 600 to <1200; inactive: < 600), and sleep patterns were determined using a composite score of healthy sleep characteristics: morning chronotype, adequate sleep duration (7-8 h/d), never or rare insomnia, never or rare snoring, and infrequent daytime sleepiness. We categorized the sleep score into three patterns (healthy: ≥ 4; intermediate: 2-3; poor: ≤ 1). Multiple logistic regressions examined the association of baseline (or the temporal changes in) sleep/physical activity with physical inactivity/poor sleep at follow-up. Participants with an intermediate or poor sleep pattern at baseline had higher odds (adjusted odds ratio: 1.24 [1.17, 1.32] and 1.65 [1.45, 1.88], respectively) for physical inactivity at follow-up, compared to those with healthy sleep, while shifting to a healthy sleep pattern over time attenuated these adverse associations. Compared to individuals highly active at both time points, being physically inactive at baseline and reducing physical activity over time were both associated with higher odds for poor sleep at follow-up. In conclusion, sleep improvements over time benefitted physical activity at follow-up, while reduced physical activity had a detrimental effect on sleep patterns at follow-up. Our results provide scope for interventions to concurrently target physical activity and sleep.

VO2peak and 24-hour sleep, sedentary behavior, and physical activity in Australian truck drivers.

This study assessed cardiorespiratory fitness (CRF) and 24-h sleep, sedentary behavior (SB), and physical activity (PA) in Australian truck drivers. Participants (n = 37 men) attended clinic sessions for an exercise test to exhaustion to establish CRF and wore an accelerometer (Actigraph GT3X+) to estimate sleep, SB, and PA. Relative to normative values, CRF was "below percentile 25" in 51% of drivers (mean [SD] VO2peak=30 .1 [7.6] mL.kg-1.min-1). Accelerometer data indicated that total vigorous-intensity (3.5-4.5 min/day) and sustained moderate- to vigorous-intensity PA (>5 min; 0.5-1.6 bouts/day) were limited. The findings show there is a need to design and test PA interventions that can improve poor levels of CRF in truck drivers.

The Discrepancy between Knowledge of Sleep Recommendations and the Actual Sleep Behaviour of Australian Adults.

Introduction: Inadequate sleep is a major public health concern, with large economic, health, and operational costs to Australia. Despite the implementation of public sleep health campaigns, approximately 40% of Australian adults do not obtain the recommended 7-9 hours of sleep. Thus, while people may know how much sleep is required, this knowledge may not be adequately translated to actual sleep behavior. Consequently, this study aims to examine the discrepancy between knowledge of sleep recommendations and self-reported sleep behaviors.Methods: A sample of 1265 Australian adults (54% female, aged 18-65) completed a phone interview as part of the 2017 National Social Survey and were asked questions about their knowledge of sleep guidelines and their actual sleep behavior. Binary logistic regression was used to determine the factors associated with awareness of sleep recommendations and whether this corresponded with reported sleep duration.Results: The final sample size was 998. Although 94% of the sample were aware of current sleep recommendations, 23% of participants did not self-report regularly obtaining 7-9 h sleep per night. These participants were less likely to want to obtain more sleep, less likely to view sleep as a priority before stressful events, and less likely to self-report good health.Conclusion: Although a majority of the sample were aware of sleep recommendations, almost a quarter of the participants' behavior did not align with their knowledge. Future sleep health campaigns should consider options beyond education, including emphasis on practical strategies and modifiable lifestyle factors to assist individuals to obtain the recommended amount of sleep.

Behavioural mediators of reduced energy intake in a physical activity, diet, and sleep behaviour weight loss intervention in adults.

Reduced energy intake is a major driver of weight loss and evidence suggests that physical activity, dietary, and sleep behaviours interact to influence energy intake. Energy restriction can be challenging to sustain. Therefore to improve intervention efficacy, evaluation of how changes in physical activity, diet, and sleep behaviours mediate reduced energy intake in adults with overweight/obesity who participated in a six-month multiple-behaviour-change weight loss intervention was undertaken. This was a secondary analysis of a 3-arm randomised controlled trial. Adults with body mass index (BMI) 25-40 kg/m2 were randomised to either: a physical activity and diet intervention; physical activity, diet, and sleep intervention; or wait-list control. Physical activity, dietary intake, and sleep was measured at baseline and six-months using validated measures. The two intervention groups were pooled and compared to the control. Structural equation modelling was used to estimate the mediated effects (AB Coefficient) of the intervention on total energy intake. One hundred and sixteen adults (70% female, 44.5y, BMI 31.7 kg/m2) were enrolled and 70% (n = 81) completed the six-month assessment. The significant intervention effect on energy intake at six-months (-1011 kJ/day, 95% CI -1922, -101) was partially mediated by reduced fat intake (AB = -761.12, 95% CI -1564.25, -53.74) and reduced consumption of energy-dense, nutrient-poor foods (AB = -576.19, 95% CI -1189.23, -97.26). In this study, reducing fat intake and consumption of energy-dense, nutrient-poor foods was an effective strategy for reducing daily energy intake in adults with overweight/obesity at six-months. These strategies should be explicitly targeted in future weight loss interventions.

Impact of COVID-19 on Physical Activity Among 10,000 Steps Members and Engagement With the Program in Australia: Prospective Study.

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.

The Association Between Logging Steps Using a Website, App, or Fitbit and Engaging With the 10,000 Steps Physical Activity Program: Observational Study.

BACKGROUND: Engagement is positively associated with the effectiveness of digital health interventions. It is unclear whether tracking devices that automatically synchronize data (eg, Fitbit) produce different engagement levels compared with manually entering data. OBJECTIVE: This study examines how different step logging methods in the freely available 10,000 Steps physical activity program differ according to age and gender and are associated with program engagement. METHODS: A subsample of users (n=22,142) of the free 10,000 Steps physical activity program were classified into one of the following user groups based on the step-logging method: Website Only (14,617/22,142, 66.01%), App Only (2100/22,142, 9.48%), Fitbit Only (1705/22,142, 7.7%), Web and App (2057/22,142, 9.29%), and Fitbit Combination (combination of web, app, and Fitbit; 1663/22,142, 7.51%). Generalized linear regression and binary logistic regression were used to examine differences between user groups' engagement and participation parameters. The time to nonusage attrition was assessed using Cox proportional hazards regression. RESULTS: App Only users were significantly younger and Fitbit user groups had higher proportions of women compared with other groups. The following outcomes were significant and relative to the Website Only group. The App Only group had fewer website sessions (odds ratio [OR] -6.9, 95% CI -7.6 to -6.2), whereas the Fitbit Only (OR 10.6, 95% CI 8.8-12.3), Web and App (OR 1.5, 95% CI 0.4-2.6), and Fitbit Combination (OR 8.0; 95% CI 6.2-9.7) groups had more sessions. The App Only (OR -0.7, 95% CI -0.9 to -0.4) and Fitbit Only (OR -0.5, 95% CI -0.7 to -0.2) groups spent fewer minutes on the website per session, whereas the Fitbit Combination group (OR 0.2, 95% CI 0.0-0.5) spent more minutes. All groups, except the Fitbit Combination group, viewed fewer website pages per session. The mean daily step count was lower for the App Only (OR -201.9, 95% CI -387.7 to -116.0) and Fitbit Only (OR -492.9, 95% CI -679.9 to -305.8) groups but higher for the Web and App group (OR 258.0, 95% CI 76.9-439.2). The Fitbit Only (OR 5.0, 95% CI 3.4-6.6), Web and App (OR 7.2, 95% CI 5.9-8.6), and Fitbit Combination (OR 15.6, 95% CI 13.7-17.5) groups logged a greater number of step entries. The App Only group was less likely (OR 0.65, 95% CI 0.46-0.94) and other groups were more likely to participate in Challenges. The mean time to nonusage attrition was 35 (SD 26) days and was lower than average in the Website Only and App Only groups and higher than average in the Web and App and Fitbit Combination groups. CONCLUSIONS: Using a Fitbit in combination with the 10,000 Steps app or website enhanced engagement with a real-world physical activity program. Integrating tracking devices that synchronize data automatically into real-world physical activity interventions is one strategy for improving engagement.

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

OBJECTIVES: Some online, personally tailored, text-based physical activity interventions have proven effective. However, people tend to 'skim' and 'scan' web-based text rather than thoroughly read their contents. In contrast, online videos are more engaging and popular. We examined whether web-based personally tailored physical activity videos were more effective in promoting physical activity than personally tailored text and generic information. METHODS: 501 adults were randomised into a video-tailored intervention, text-tailored intervention or control. Over a 3-month period, intervention groups received access to eight sessions of web-based personally tailored physical activity advice. Only the delivery method differed between intervention groups: tailored video versus tailored text. The primary outcome was 7-day ActiGraph-GT3X+ measured moderate-to-vigorous physical activity (MVPA) assessed at 0, 3 and 9 months. Secondary outcomes included self-reported MVPA and website engagement. Differences were examined using generalised linear mixed models with intention-to-treat and multiple imputation. RESULTS: Accelerometer-assessed MVPA increased 23% in the control (1.23 (1.06, 1.43)), 12% in the text-tailored (1.12 (0.95, 1.32)) and 28% in the video-tailored (1.28 (1.06, 1.53)) groups at the 3-month follow-up only, though there were no significant between-group differences. Both text-tailored (1.77 (1.37, 2.28]) and video-tailored (1.37 (1.04, 1.79)) groups significantly increased self-reported MVPA more than the control group at 3 months only, but there were no differences between video-tailored and text-tailored groups. The video-tailored group spent significantly more time on the website compared with text-tailored participants (90 vs 77 min, p=0.02). CONCLUSIONS: The personally tailored videos were not more effective than personally tailored text in increasing MVPA. The findings from this study conflict with pilot study outcomes and previous literature. Process evaluation and mediation analyses will provide further insights. TRIAL REGISTRATION NUMBER: ACTRN12615000057583.

Codesigning Parks for Increasing Park Visits and Physical Activity in a Low-Socioeconomic Community: The Active By Community Design Experience.

The purpose of the Active By Community Design (ABCD) project was to engage residents in the redesign of two public open spaces to increase park visits and park-based physical activity (PBPA). It was anticipated that by adding local preferences to park design considerations along with collected data and available evidence, the study would achieve increased levels of community engagement and subsequent increases in park visits and PBPA. This study summarizes two case studies outlining the community engagement and research translation process. Utilizing a pre-post evaluation design, baseline measures were completed with a series of validated tools for park use and park quality. Baseline data, evidence, community and stakeholder input were synthesized and presented to a "design subcommittee" to translate into "real-world" park redesigns. Park audits, a household survey, and park open days identified the spaces were not designed for the activities residents wanted, while park observations identified usage was low, and those observed were mostly children and adults walking. Park redesigns focused on maximizing access, infrastructure to enable physical activity (PA), PA programs and enhancements to improve aesthetics and safety concerns. The ABCD project successfully demonstrated a process of combining data and community and stakeholder engagement input to translate evidence into the creation of more supportive environments for PA. Stakeholder and resident input to the redesign processes contributed to satisfaction and ownership that residents reported on the redesigned parks, which may increase the likelihood of increased park usage and park-based physical activity.

Development and Validation of the Diet-Related Beliefs of Exercisers Scale.

Many exercise-based weight-loss interventions result in considerably less weight loss than predicted. One possible explanation could be that people have certain beliefs about the interplay of exercise and food that also influence their eating behavior, such as the belief that food is a reward for exercise. The current research outlines a systematic multiphase process to develop a psychometrically sound scale to assess these beliefs. In Study 1, regular exercisers (N = 520) completed an exploratory questionnaire on their beliefs related to diet and exercise. In Study 2 (N = 380), the factor structure of the newly developed scale was corroborated by confirmatory factor analysis. In addition, a test-retest (N = 166) was used to confirm reliability and stability. In sum, the Diet-Related Beliefs of Exercisers Scale with its four subscales ("Refrain from Eating," "Food as Reward," "Healthy Eating," and "Nutritional Replenishment") represents a valid and reliable measure of exercisers' diet-related beliefs.

Efficacy of an m-Health Physical Activity and Sleep Intervention to Improve Sleep Quality in Middle-Aged Adults: The Refresh Study Randomized Controlled Trial.

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.