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

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

Challenging assumptions underlying physical activity promotion for health care professionals in Australia: A data-prompted interview study.

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.

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.

Increasing physical activity using an just-in-time adaptive digital assistant supported by machine learning: A novel approach for hyper-personalised mHealth interventions.

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.

Symptoms of sleep problems and adherence to dietary guidelines in older women: evidence from the Australian Longitudinal Study on Women's Health.

OBJECTIVE: This study examined adherence to dietary guidelines and symptoms of sleep problems (e.g. taking a long time to fall sleep or waking up early) and their associations in a sample of older Australian women (68-73 years of age). DESIGN: This was a population-based cross-sectional study. Adherence to the dietary guidelines was measured using a validated FFQ and reported as a diet quality score. Symptoms of sleep problems were measured using five questions and a total score was derived. Multivariate linear regression was used to investigate the association between these outcomes, adjusted for the potential confounding influence of demographic (i.e. age and marital status) and lifestyle (i.e. physical activity, stress, alcohol intake, sleep medication use) variables. SETTING: Respondents from the 1946-1951 cohort of the Australian Longitudinal Study on Women's Health who completed Survey 9 were included. PARTICIPANTS: Data from n 7956 older women (mean age ± sd: 70·8 ± 1·5) were included. RESULTS: 70·2 % reported having at least one symptom and 20·5 % had between 3 and 5 symptoms of sleep problems (mean score ± sd: 1·4 ± 1·4, range 0-5). Adherence to dietary guidelines was poor with an average diet quality score of 56·9 ± 10·7 (range 0-100). Better adherence to dietary guidelines was associated with fewer sleep problem symptoms (ß: -0·065, 95 % CI: -0·012, -0·005) and remained significant after adjusting for confounding influences. CONCLUSIONS: These findings support the evidence that adherence to dietary guidelines is associated with symptoms of sleep problems in older women.

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.

Can occupational health professionals successfully apply the Goldilocks Work Paradigm in a simulated work redesign?

This study aimed to assess occupational health professionals' application of the Goldilocks Work Paradigm in redesigning jobs for healthier physical behaviours while maintaining productivity. During a group simulation exercise, participants (n = 16) created job descriptions for four different occupation cases (factory worker, office worker, teacher, train driver) and then redesigned the jobs using the Paradigm. Substantial changes in the time spent in sitting (9-30%), standing (8-42%), walking (6-14%), and high-intensity (0-24%) physical behaviours were achieved, which if implemented would likely result in enhanced health for workers. Overall, occupational health professionals were able to successfully redesign fictitious jobs aligned with the Goldilocks Work Paradigm. The simulation task used in this study may be useful to train professionals and assist workplaces to understand and implement the Goldilocks Work Paradigm into practice.Practitioner summary: This study assessed whether occupational health professionals could be trained in the Goldilocks Work Paradigm through a job redesign simulation task. Participants were able to redesign jobs to achieve a healthier 'just right' balance of physical behaviours. Simulations may help workplaces understand and implement a Goldilocks Work approach into practice.

Economic evaluation of the e-Health StandingTall balance exercise programme for fall prevention in people aged 70 years and over.

BACKGROUND: globally, falls and fall-related injuries are the leading cause of injury-related morbidity and mortality in older people. In our ageing society healthcare costs are increasing, therefore programmes that reduce falls and are considered value for money are needed. OBJECTIVE: to complete an economic evaluation of an e-Health balance exercise programme that reduced falls and injurious falls in community-dwelling older people compared to usual care from a health and community-care funder perspective. DESIGN: a within-trial economic evaluation of an assessor-blinded randomised controlled trial with 2 years of follow-up. SETTING: StandingTall was delivered via tablet-computer at home to older community-dwelling people in Sydney, Australia. PARTICIPANTS: five hundred and three individuals aged 70+ years who were independent in activities of daily living, without cognitive impairment, progressive neurological disease or any other unstable or acute medical condition precluding exercise. MAIN OUTCOME MEASURES: cost-effectiveness was measured as the incremental cost per fall and per injurious fall prevented. Cost-utility was measured as the incremental cost per quality-adjusted life year (QALY) gained. MAIN RESULTS: the total average cost per patient for programme delivery and care resource cost was $8,321 (standard deviation [SD] 18,958) for intervention participants and $6,829 (SD 15,019) for control participants. The incremental cost per fall prevented was $4,785 and per injurious fall prevented was $6,585. The incremental cost per QALY gained was $58,039 (EQ5D-5L) and $110,698 (AQoL-6D). CONCLUSION: this evaluation found that StandingTall has the potential to be cost-effective in specific subpopulations of older people, but not necessarily the whole older population. TRIAL REGISTRATION: ACTRN12615000138583.

Associations between depression, domain-specific physical activity, and BMI among US adults: NHANES 2011-2014 cross-sectional data.

BACKGROUND: Physical activity is associated with depression. However, benefits of physical activity on depression may differ for specific domains of physical activity (i.e., leisure-time, work, and travel). Moreover, the relationship between physical activity and depression could also differ for people in different Body Mass Index (BMI) categories. This study investigated the relationship between domain-specific physical activity and BMI with depression, and the moderation effects of BMI on the relationship between domain physical activity and depression. METHODS: Complex survey data from the NHANES 2011-2014 was used (N=10,047). Depression was measured using the Patient Health Questionnaire (PHQ-9). Participants reported physical activity minutes in each domain using the Global Physical Activity Questionnaire. Demographic characteristics were self-reported. Weight and height were objectively measured and used for calculating BMI. Survey procedures were used to account for complex survey design. As two survey cycles were used, sampling weights were re-calculated and used for analyses. Taylor series linearisation was chosen as a variance estimation method. RESULTS: Participants who engaged in ≥150 minutes/week of total moderate-vigorous physical activity (MVPA) (adjusted B = 0.83, 95% CI [0.50, 1.16]) and leisure-time MVPA (adjusted B = 0.84, 95% CI [0.57, 1.11]) experienced lower levels of depression compared to those engaging in <150 MVPA minutes/week. Work and travel-related physical activity were not associated with depression. Overweight (adjusted B = -0.40, 95% CI [-0.76, -0.04]) and underweight/normal weight participants (adjusted B = -0.60, 95%CI [-0.96, -0.25]) experienced less depressive symptoms compared to obese participants. BMI did not moderate the relationship between domain-specific physical activity and depression. CONCLUSIONS: Interventions that focus on leisure-time physical activity appear to be best suited to improve depression, however, this needs to be confirmed in purposefully designed intervention studies. Future studies may also examine ways to improve the effectiveness of work and travel physical activity for reducing depression.

The association of resilience with depression, anxiety, stress and physical activity during the COVID-19 pandemic.

BACKGROUND: COVID-19 has resulted in substantial global upheaval. Resilience is important in protecting wellbeing, however few studies have investigated changes in resilience over time, and associations between resilience with depression, anxiety, stress, and physical activity during the COVID-19 pandemic. METHODS: Online surveys were conducted to collect both longitudinal and cross-sectional data at three time points during 2020. Australian adults aged 18 years and over were invited to complete the online surveys. Measures include the six-item Brief Resilience Scale, the 21-item Depression, Anxiety and Stress Scale, and the Active Australia Survey which have eight items identifying the duration and frequency of walking, and moderate and vigorous physical activities (MVPA), over the past 7 days. General linear mixed models and general linear models were used in the analysis. RESULTS: In the longitudinal sample, adjusted differences (aDif) in resilience scores did not significantly change over time (time 2 vs. time 1 [aDif = - 0.02, 95% CI = - 0.08, 0.03], and time 3 vs. time 1 [aDif = < 0.01, 95% CI = - 0.07, 0.06]). On average, those engaging in at least 150 min of MVPA per week (aDif = 0.10, 95% CI = 0.04, 0.16), and having depression (aDif = 0.40, 95% CI = 0.33), anxiety (aDif = 0.34, 95% CI = 0.26, 0.41), and stress scores (aDif = 0.30, 95% CI = 0.23, 0.37) within the normal range had significantly higher resilience scores. The association between resilience and physical activity was independent of depression, anxiety, and stress levels. All results were similar for the cross-sectional sample. CONCLUSIONS: Resilience scores did not change significantly during the COVID-19 pandemic. However, there were significant associations between resilience with physical activity and psychological distress. This research helps inform future interventions to enhance or nurture resilience, particularly targeted at people identified as at risk of psychological distress.

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.

Health and well-being benefits of e-bike commuting for inactive, overweight people living in regional Australia.

INTRODUCTION: Pedal-assisted electric-bikes (e-bikes) are bicycles fitted with electric motors. Motorised functions on e-bikes only operate when the user pedals, allowing riders a moderate amount of physical activity. This study aimed to explore the mental and physical health and well-being impacts related to ebike usage for inactive overweight or obese individuals living in regional Australia. METHODS: Twenty inactive, overweight/obese people who seldom cycled were provided with an ebike over a 12-week period. Individual semi-structured interviews conducted at the end of the trial generated data about participants' experiences of using ebikes. Inductive thematic analysis of interview data using Thomas (2006) data analysis framework and NVivo 12 software was undertaken. RESULTS: Data analysis revealed that e-cycling improved participants' mental and physical well-being and that they felt happier when riding an e-bike. CONCLUSIONS: Riding an e-bike can improve mental and physical health, happiness and overall sense of well-being. Greater uptake of e-bikes would have positive health implications for the wider community. Results from this study can be used to inform active transport policy. SO WHAT?: Our study demonstrated that encouraging active transport in the form of e-cycling can improve the overall health and well-being of overweight and obese Australians. More specifically, e-cycling demonstrated a positive impact on mental health well-being.

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 Physical Activity Messaging Framework (PAMF) and Checklist (PAMC): International consensus statement and user guide.

Effective physical activity messaging plays an important role in the pathway towards changing physical activity behaviour at a population level. The Physical Activity Messaging Framework (PAMF) and Checklist (PAMC) are outputs from a recent modified Delphi study. This sought consensus from an international expert panel on how to aid the creation and evaluation of physical activity messages. In this paper, we (1) present an overview of the various concepts within the PAMF and PAMC, (2) discuss in detail how the PAMF and PAMC can be used to create physical activity messages, plan evaluation of messages, and aid understanding and categorisation of existing messages, and (3) highlight areas for future development and research. If adopted, we propose that the PAMF and PAMC could improve physical activity messaging practice by encouraging evidence-based and target population-focused messages with clearly stated aims and consideration of potential working pathways. They could also enhance the physical activity messaging research base by harmonising key messaging terminologies, improving quality of reporting, and aiding collation and synthesis of the evidence.

Examining social-cognitive theory constructs as mediators of behaviour change in the active team smartphone physical activity program: a mediation analysis.

BACKGROUND: Regular engagement in physical activity has well-established physical and psychological health benefits. Despite this, over a quarter of the global adult population is insufficiently physically active. Physical activity interventions grounded in behaviour change theory, such as the social-cognitive theory, are widely considered to be more effective than non-theoretical approaches. Such interventions set out to intervene on the ultimate outcome (physical activity), but also influence intermediate factors (social-cognitive theory constructs) which in turn, are believed to influence physical activity behaviour. The primary aim of the study was to use mediation analysis to examine whether changes in the social-cognitive theory and related constructs, in particular self-efficacy, outcome expectations, intentions, barriers and goal setting, mediated the effects of a smartphone-based social networking physical activity intervention. METHODS: Mediation analyses were conducted using the PROCESS Macro in SPSS to (i) calculate the regression coefficients for the effect of the independent variable (group allocation) on the hypothesised mediators (social-cognitive theory constructs), (ii) calculate the regression coefficient for the effect of the hypothesised mediators (social-cognitive theory constructs) on the dependent variable (objectively measured physical activity or self-report physical activity), independent of group assignment and (iii) determine the total, direct and indirect intervention effects. RESULTS: Data from 243 participants were included in the mediation analysis. There was no evidence of mediation for change in objectively measured MVPA or self-reported MVPA. CONCLUSIONS: There was no conclusive evidence that any of the social-cognitive theory constructs mediated the relationship between an app-based intervention and change in physical activity. Ongoing efforts to develop and understand components that make physical activity app-based interventions effective are recommended. TRIAL REGISTRATION: This trial was registered with the Australian and New Zealand Clinical Trial Registry ( ACTRN12617000113358 , date of registration 23 January, 2017).

Examining the Priorities, Needs and Preferences of Men with Metastatic Prostate Cancer in Designing a Personalised eHealth Exercise Intervention.

BACKGROUND: Few individuals with metastatic prostate cancer have access to prostate cancer-specific exercise support, despite demonstrated benefits. eHealth tools, such as websites, may be viable options for increasing access. To be effective and acceptable, future eHealth websites need to consider end-users' perspectives, capacity and needs. We aim to provide insight into these factors by exploring daily priorities, activities and health literacy of individuals with metastatic prostate cancer and their perspectives towards exercise and exercise-based web-based eHealth interventions. METHODS: Semi-structured interviews explored participant's experiences and understanding of their disease, exercise levels, advice received from health care providers, as well as acceptability of and suggested content for an eHealth tool. A thematic analysis was undertaken. RESULTS: Interviews were conducted with eighteen Australians (55-83 years; M = 71.5, SD = 8.9) living with metastatic prostate cancer. Needing to perform daily responsibilities was a key priority. Participants had limited understanding of the benefits of prostate cancer-specific exercise, and less than half discussed exercise with their health team. Fourteen men felt they could report metastases location, but only four could provide detailed information, which has clinical implications for exercise prescription. A potential web-based intervention was considered acceptable by seventeen men for reasons such as affordability, accessibility and convenience. User-friendly design and practitioner support were important. CONCLUSIONS: Results identified key aspects useful for person-centred design of exercise programs. Participants were positive towards the proposed web-based tool and expressed the need for individualised, user-friendly and reliable information with support from a professional embedded. Lastly, not all participants could accurately report metastasis locations.

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.