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ObjectivesAlthough 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.Methods380 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.ResultsAfter 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.ConclusionThe 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 current review clarifies the cardiometabolic health effects of high-intensity interval training (HIIT) in adults. A systematic search (PubMed) examining HIIT and cardiometabolic health markers was completed on 15 October 2015. Sixty-five intervention studies were included for review and the methodological quality of included studies was assessed using the Downs and Black score. Studies were classified by intervention duration and body mass index classification. Outcomes with at least 5 effect sizes were synthesised using a random-effects meta-analysis of the standardised mean difference (SMD) in cardiometabolic health markers (baseline to postintervention) using Review Manager 5.3. Short-term (ST) HIIT (<12 weeks) significantly improved maximal oxygen uptake (VO2 max; SMD 0.74, 95% CI 0.36 to 1.12; p<0.001), diastolic blood pressure (DBP; SMD −0.52, 95% CI −0.89 to −0.16; p<0.01) and fasting glucose (SMD −0.35, 95% CI −0.62 to −0.09; p<0.01) in overweight/obese populations. Long-term (LT) HIIT (≥12 weeks) significantly improved waist circumference (SMD −0.20, 95% CI −0.38 to −0.01; p<0.05), % body fat (SMD −0.40, 95% CI −0.74 to −0.06; p<0.05), VO2 max (SMD 1.20, 95% CI 0.57 to 1.83; p<0.001), resting heart rate (SMD −0.33, 95% CI −0.56 to −0.09; p<0.01), systolic blood pressure (SMD −0.35, 95% CI −0.60 to −0.09; p<0.01) and DBP (SMD −0.38, 95% CI −0.65 to −0.10; p<0.01) in overweight/obese populations. HIIT demonstrated no effect on insulin, lipid profile, C reactive protein or interleukin 6 in overweight/obese populations. In normal weight populations, ST-HIIT and LT-HIIT significantly improved VO2 max, but no other significant effects were observed. Current evidence suggests that ST-HIIT and LT-HIIT can increase VO2 max and improve some cardiometabolic risk factors in overweight/obese populations.

Persistently poor glycemic control in adult type 1 diabetes patients is a common, complex, and serious problem initiating significant damage to the cardiovascular, renal, neural, and visual systems. Currently, there is a plethora of low-cost and free diabetes self-management smartphone applications available in online stores. The aim of this study was to examine the effectiveness of a freely available smartphone application combined with text-message feedback from a certified diabetes educator to improve glycemic control and other diabetes-related outcomes in adult patients with type 1 diabetes in a two-group randomized controlled trial. Patients were recruited through an online type 1 diabetes support group and letters mailed to adults with type 1 diabetes throughout Australia. In a 6-month intervention, followed by a three-month follow-up, patients (n=72) were randomized to usual care (control group) or usual care and the use of a smartphone application (Glucose Buddy) with weekly text-message feedback from a Certified Diabetes Educator (intervention group). All outcome measures were collected at baseline and every three months over the study period. Patients' glycosylated hemoglobin levels (HbA1c) were measured with a blood test and diabetes-related self-efficacy, self-care activities, and quality of life were measured with online questionnaires. The mean age of patients was 35.20 years (SD 10.43) (28 male, 44 female), 39% (28/72) were male, and patients had been diagnosed with type 1 diabetes for a mean of 18.94 years (SD 9.66). Of the initial 72 patients, 53 completed the study (25 intervention, 28 control group). The intervention group significantly improved glycemic control (HbA1c) from baseline (mean 9.08%, SD 1.18) to 9-month follow-up (mean 7.80%, SD 0.75), compared to the control group (baseline: mean 8.47%, SD 0.86, follow-up: mean 8.58%, SD 1.16). No significant change over time was found in either group in relation to self-efficacy, self-care activities, and quality of life. In adjunct to usual care, the use of a diabetes-related smartphone application combined with weekly text-message support from a health care professional can significantly improve glycemic control in adults with type 1 diabetes. Australian New Zealand Clinical Trials Registry: ACTRN12612000132842; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12612000132842 (Archived by WebCite at http://www.webcitation.org/6Kl4jqn5u).

Background University students incur significantly elevated levels of stress compared to the general population and their non-student counterparts. Health risk behaviours are important modifiable determinants for the onset and aggravation of various mental health disorders, in which, university students generally exhibit poor engagement. Thus, this study aims to determine the efficacy of health behaviour interventions in relation to change in health behaviour and mental health outcomes, the impact of interventions (i.e., penetration, fidelity, and implementation), intervention characteristics associated with improved outcomes (efficacy) and the economic evaluation of interventions. Methods Six electronic databases were searched for randomised controlled trials (RCT) published from the 1st January 2012 to 11th July 2023. Eligible RCTs included university students, evaluated behavioural interventions targeting health behaviours (i.e. dietary intake, physical activity, sedentary behaviour, alcohol use, substance use, smoking, and sleep) and reported a change in both health behaviour and mental health outcomes. Results Twenty-two RCTs met the study inclusion criteria. Overall, only seven studies were effective in improving both health behaviour and mental health outcomes, with most ( n  = 4) focused on improving sleep behaviours. Insufficient evidence was found regarding intervention impact, intervention characteristics associated with improved outcomes and the economic evaluation of interventions to guide future implementation of health behaviour interventions in universities due to inadequate reporting of outcomes. Conclusions There is limited evidence regarding the efficacy of health behaviour interventions in improving both health behaviour and mental health outcomes. There is also insufficient evidence regarding intervention impact, intervention characteristics associated with improved outcomes and economic evaluation to guide the implementation of these interventions in the university setting.

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. 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. 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.

The independent and combined influence of smoking, alcohol consumption, physical activity, diet, sitting time, and sleep duration and quality on health status is not routinely examined. This study investigates the relationships between these lifestyle behaviors, independently and in combination, and health-related quality of life (HRQOL). Adult members of the 10,000 Steps project (n = 159,699) were invited to participate in an online survey in November-December 2011. Participant socio-demographics, lifestyle behaviors, and HRQOL (poor self-rated health; frequent unhealthy days) were assessed by self-report. The combined influence of poor lifestyle behaviors were examined, independently and also as part of two lifestyle behavior indices, one excluding sleep quality (Index 1) and one including sleep quality (Index 2). Adjusted Cox proportional hazard models were used to examine relationships between lifestyle behaviors and HRQOL. A total of 10,478 participants provided complete data for the current study. For Index 1, the Prevalence Ratio (p value) of poor self-rated health was 1.54 (p = 0.001), 2.07 (p≤0.001), 3.00 (p≤0.001), 3.61 (p≤0.001) and 3.89 (p≤0.001) for people reporting two, three, four, five and six poor lifestyle behaviors, compared to people with 0-1 poor lifestyle behaviors. For Index 2, the Prevalence Ratio (p value) of poor self-rated health was 2.26 (p = 0.007), 3.29 (p≤0.001), 4.68 (p≤0.001), 6.48 (p≤0.001), 7.91 (p≤0.001) and 8.55 (p≤0.001) for people reporting two, three, four, five, six and seven poor lifestyle behaviors, compared to people with 0-1 poor lifestyle behaviors. Associations between the combined lifestyle behavior index and frequent unhealthy days were statistically significant and similar to those observed for poor self-rated health. Engaging in a greater number of poor lifestyle behaviors was associated with a higher prevalence of poor HRQOL. This association was exacerbated when sleep quality was included in the index.

Website-delivered physical activity interventions are successful in producing short-term behavior change. However, problems with engagement and retention of participants in these programs prevent long-term behavior change. New ways of accessing online content (eg, via smartphones) may enhance engagement in these interventions, which in turn may improve the effectiveness of the programs. To measure the potential of a newly developed smartphone application to improve health behaviors in existing members of a website-delivered physical activity program (10,000 Steps, Australia). The aims of the study were to (1) examine the effect of the smartphone application on self-monitoring and self-reported physical activity levels, (2) measure the perceived usefulness and usability of the application, and (3) examine the relationship between the perceived usefulness and usability of the application and its actual use. All participants were existing members of the 10,000 Steps program. We recruited the intervention group (n = 50) via email and instructed them to install the application on their smartphone and use it for 3 months. Participants in this group were able to log their steps by using either the smartphone application or the 10,000 Steps website. Following the study, the intervention group completed an online questionnaire assessing perceived usability and usefulness of the smartphone application. We selected control group participants (n = 150), matched for age, gender, level of self-monitoring, preintervention physical activity level, and length of membership in the 10,000 Steps program, after the intervention was completed. We collected website and smartphone usage statistics during the entire intervention period. Over the study period (90 days), the intervention group logged steps on an average of 62 days, compared with 41 days in the matched group. Intervention participants used the application 71.22% (2210/3103) of the time to log their steps. Logistic regression analyses revealed that use of the application was associated with an increased likelihood to log steps daily during the intervention period compared with those not using the application (odds ratio 3.56, 95% confidence interval 1.72-7.39). Additionally, use of the application was associated with an increased likelihood to log greater than 10,000 steps on each entry (odds ratio 20.64, 95% confidence interval 9.19-46.39). Linear regression analysis revealed a nonsignificant relationship between perceived usability (r = .216, P = .21) and usefulness (r = .229, P = .17) of the application and frequency of logging steps in the intervention group. Using a smartphone application as an additional delivery method to a website-delivered physical activity intervention may assist in maintaining participant engagement and behavior change. However, due to study design limitations, these outcomes should be interpreted with caution. More research, using larger samples and longer follow-up periods, is needed to replicate the findings of this study.

To compare the frequency of and trends in healthy lifestyle factors between singles and couples. Cross-sectional data from annual surveys conducted from 2005-2014 were used. The pooled sample included 15,001 Australian adults (mean age: 52.9 years, 50% male, 74% couples) who participated in the annual Queensland Social Survey via computer-assisted telephone interviews. Relationship status was dichotomised into single and couple. Binary logistic regression was used to assess associations between relationship status, and the frequency of and trends in healthy lifestyle factors. Compared to singles, couples were significantly more likely to be a non-smoker (OR = 1.82), and meet recommendations for limited fast food (OR = 1.12), alcohol consumption (OR = 1.27) and fruit and vegetable intake (OR = 1.24). Fruit and vegetable intake was not significantly associated with relationship status after adjusting for the other healthy lifestyle factors. Conversely, couples were significantly less likely to be within a normal weight range (OR = 0.81). In both singles and couples, the trend data revealed significant declines in the rates of normal weight (singles: OR = 0.97, couples: OR = 0.97) and viewing TV for less than 14 hours per week (singles: OR = 0.85, couples: OR = 0.84), whilst non-smoking rates significantly increased (singles: OR = 1.12, couples: OR = 1.03). The BMI trend was no longer significant when adjusting for health behaviours. Further, in couples, rates of meeting recommendations for physical activity and fruit/vegetable consumption significantly decreased (OR = 0.97 and OR = 0.95, respectively), as did rates of eating no fast food (OR = 0.96). These trends were not significant when adjusting for the other healthy lifestyle factors. In singles, rates of meeting alcohol recommendations significantly increased (OR = 1.08). Health behaviour interventions are needed in both singles and couples, but relationship status needs to be considered in interventions targeting alcohol, fast food, smoking and BMI. Further research is needed to understand why health behaviours differ by relationship status in order to further improve interventions.

Enhanced recovery after surgery guidelines aim to optimize perioperative care and improve recovery outcomes. The guidelines contain clinician- and patient-led recommendations for pre- and postoperative care, with patient-led recommendations, including smoking cessation, early mobilization, and early resumption of eating and drinking. While adherence to these recommendations can improve recovery outcomes, it is typically low, and many patients require support. Digital health interventions (DHIs) are increasingly accepted as useful tools in delivering individualized health care and have the potential to support adherence to enhanced recovery after surgery guidelines. Evidence suggests that intervention use is optimized when DHIs are considered acceptable to end users. RecoverEsupport is a DHI designed to support patient adherence to surgical recovery guidelines, following breast cancer surgery, intended as part of a blended approach with standard care. The study aimed to explore the surgical experiences and perceived acceptability of the RecoverEsupport DHI among former patients undergoing breast cancer surgery. This qualitative study, underpinned by a constructivist paradigm, explored the recovery experiences and acceptability of RecoverEsupport among women who had undergone mastectomy for breast cancer at a tertiary hospital in New South Wales (NSW), Australia. In total, 57 eligible patients were identified from medical records and invited to participate. Among them, 15 consented and were given access to the RecoverEsupport DHI for approximately 2 weeks. Around 11 participants then participated in a semistructured interview exploring their recovery experiences and feedback on the DHI. Interviews were transcribed and double coded prior to analysis using an inductive thematic approach. Participants reported varied experiences of breast cancer surgery and expressed a consistent need for support, with many reporting uncertainty, anxiety, and limited information and physiotherapy support. RecoverEsupport was perceived as acceptable, with strong potential to reduce anxiety, address gaps in care, and provide reassurance. Participants valued the practical content, particularly physiotherapy exercise videos, which reinforced clinician advice and promoted confidence, autonomy, and self-efficacy. The intervention was seen as empowering patients to manage recovery and support their physical and emotional needs. All participants reported that they would recommend RecoverEsupport to others undergoing breast cancer surgery, highlighting its potential as a valuable adjunct to usual care. RecoverEsupport was perceived as a valuable adjunct to standard perioperative care. Four key themes were identified. Participants reported that the program addressed key gaps in information, physiotherapy access, and emotional support, while complementing clinical care. The intervention was seen to empower patients by enhancing knowledge, confidence, and self-efficacy, enabling a more active role in their recovery. It also provided reassurance during a vulnerable period. These findings highlight the potential of DHIs to support patients within constrained health care systems and enhance recovery outcomes.

ObjectivesPedometers are an effective self-monitoring tool to increase users' physical activity. However, a range of advanced trackers that measure physical activity 24 hours per day have emerged (eg, Fitbit). The current study aims to determine people's current use, interest and preferences for advanced trackers.Design and participantsA cross-sectional national telephone survey was conducted in Australia with 1349 respondents.Outcome measuresRegression analyses were used to determine whether tracker interest and use, and use of advanced trackers over pedometers is a function of demographics. Preferences for tracker features and reasons for not wanting to wear a tracker are also presented.ResultsOver one-third of participants (35%) had used a tracker, and 16% are interested in using one. Multinomial regression (n=1257) revealed that the use of trackers was lower in males (OR=0.48, 95% CI 0.36 to 0.65), non-working participants (OR=0.43, 95% CI 0.30 to 0.61), participants with lower education (OR=0.52, 95% CI 0.38 to 0.72) and inactive participants (OR=0.52, 95% CI 0.39 to 0.70). Interest in using a tracker was higher in younger participants (OR=1.73, 95% CI 1.15 to 2.58). The most frequently used tracker was a pedometer (59%). Logistic regression (n=445) revealed that use of advanced trackers compared with pedometers was higher in males (OR=1.67, 95% CI 1.01 to 2.79) and younger participants (OR=2.96, 95% CI 1.71 to 5.13), and lower in inactive participants (OR=0.35, 95% CI 0.19 to 0.63). Over half of current or interested tracker users (53%) prefer to wear it on their wrist, 31% considered counting steps the most important function and 30% regarded accuracy as the most important characteristic. The main reasons for not wanting to use a tracker were, ‘I don't think it would help me’ (39%), and ‘I don't want to increase my activity’ (47%).ConclusionsActivity trackers are a promising tool to engage people in self-monitoring a physical activity. Trackers used in physical activity interventions should align with the preferences of target groups, and should be able to be worn on the wrist, measure steps and be accurate.