Correction: Mobile App Intervention of a Randomized Controlled Trial for Patients With Obesity and Those Who Are Overweight in General Practice: User Engagement Analysis Quantitative Study.

[This corrects the article DOI: 10.2196/45942.].

Identifying risk factors for developing obesity: a record linkage longitudinal study in metropolitan Sydney using the 45 and Up Study.

BACKGROUND: Primary care clinicians have key responsibilities in obesity prevention and weight management. AIMS: We aimed to identify risk factors for developing obesity among people aged ≥45 years. METHODS: We conducted a record linkage longitudinal study of residents of metropolitan Sydney, Australia using data from the: (1) 45 and Up Study at baseline (2005-2009) and first follow-up (2012-2015); (2) Medicare claims; (3) Pharmaceutical Benefits Scheme; and (4) deaths registry. We examined risk factors for developing obesity (body mass index [BMI]: 30-40) at follow-up, separately for people within the: (1) healthy weight range (BMI 18.5-<25) and (2) overweight range (BMI 25-<30) at baseline. Covariates included demographics, modifiable behaviours, health status, allied health use, and medication use. Crude and adjusted relative risks were estimated using Poisson regression modelling. RESULTS: At follow-up, 1.1% (180/16,205) of those in the healthy weight range group, and 12.7% (1,939/15,266) of those in the overweight range group developed obesity. In both groups, the following were associated with developing obesity: current smoking at baseline, physical functioning limitations, and allied health service use through team care planning, while any alcohol consumption and adequate physical activity were found to be associated with a lower risk of developing obesity. In the healthy weight group, high psychological distress and the use of antiepileptics were associated with developing obesity. In the overweight group, female sex and full-time work were associated with developing obesity, while older age was found to be associated with a lower risk of developing obesity. CONCLUSIONS: These findings may inform the targeting of preventive interventions for obesity in clinical practice and broader public health programs.

Early intervention to prevent weight gain requires a targeted multidisciplinary team-based approach to improve diet, increase physical activity, and change behaviour. However, the capacity to provide this within primary care is limited and there is little funding for consultations with allied health professionals. There is a need to identify priority at-risk groups to help primary care clinicians target interventions to those in most need. We have identified, using a longitudinal study of residents of metropolitan Sydney, key characteristics of older adults who are at risk of gaining weight and developing obesity, including risk behaviours (smoking and physical inactivity), and chronic conditions or their treatment (physical function, psychological distress, and use of anti-epileptic medications). These findings may help alert clinicians to the need for preventive interventions in selected cases, as well as informing the targeting of public health programs.

Mobile App Intervention of a Randomized Controlled Trial for Patients With Obesity and Those Who Are Overweight in General Practice: User Engagement Analysis Quantitative Study.

BACKGROUND: The Health eLiteracy for Prevention in General Practice trial is a primary health care-based behavior change intervention for weight loss in Australians who are overweight and those with obesity from lower socioeconomic areas. Individuals from these areas are known to have low levels of health literacy and are particularly at risk for chronic conditions, including diabetes and cardiovascular disease. The intervention comprised health check visits with a practice nurse, a purpose-built patient-facing mobile app (mysnapp), and a referral to telephone coaching. OBJECTIVE: This study aimed to assess mysnapp app use, its user profiles, the duration and frequency of use within the Health eLiteracy for Prevention in General Practice trial, its association with other intervention components, and its association with study outcomes (health literacy and diet) to determine whether they have significantly improved at 6 months. METHODS: In 2018, a total of 22 general practices from 2 Australian states were recruited and randomized by cluster to the intervention or usual care. Patients who met the main eligibility criteria (ie, BMI>28 in the previous 12 months and aged 40-74 years) were identified through the clinical software. The practice staff then provided the patients with details about this study. The intervention consisted of a health check with a practice nurse and a lifestyle app, a telephone coaching program, or both depending on the participants' choice. Data were collected directly through the app and combined with data from the 6-week health check with the practice nurses, the telephone coaching, and the participants' questionnaires at baseline and 6-month follow-up. The analyses comprised descriptive and inferential statistics. RESULTS: Of the 120 participants who received the intervention, 62 (52%) chose to use the app. The app and nonapp user groups did not differ significantly in demographics or prior recent hospital admissions. The median time between first and last app use was 52 (IQR 4-95) days, with a median of 5 (IQR 2-10) active days. App users were significantly more likely to attend the 6-week health check (2-sided Fisher exact test; P<.001) and participate in the telephone coaching (2-sided Fisher exact test; P=.007) than nonapp users. There was no association between app use and study outcomes shown to have significantly improved (health literacy and diet) at 6 months. CONCLUSIONS: Recruitment and engagement were difficult for this study in disadvantaged populations with low health literacy. However, app users were more likely to attend the 6-week health check and participate in telephone coaching, suggesting that participants who opted for several intervention components felt more committed to this study. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12617001508369; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=373505. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2018-023239.

Community health navigator-assisted transition of care from hospital to community: protocol for a randomised controlled trial.

INTRODUCTION: The objective of this parallel group, randomised controlled trial is to evaluate a community health navigator (CHN) intervention provided to patients aged over 40 years and living with chronic health conditions to transition from hospital inpatient care to their homes. Unplanned hospital readmissions are costly for the health system and negatively impact patients. METHODS AND ANALYSIS: Patients are randomised post hospital discharge to the CHN intervention or usual care. A comparison of outcomes between intervention and control groups will use multivariate regression techniques that adjust for age, sex and any independent variables that are significantly different between the two groups, using multiple imputation for missing values. Time-to-event analysis will examine the relationship between seeing a CHN following discharge from the index hospitalisation and reduced rehospitalisations in the subsequent 60 days and 6 months. Secondary outcomes include medication adherence, health literacy, quality of life, experience of healthcare and health service use (including the cost of care). We will also conduct a qualitative assessment of the implementation of the navigator role from the viewpoint of stakeholders including patients, health professionals and the navigators themselves. ETHICS APPROVAL: Ethics approval was obtained from the Research Ethics and Governance Office, Sydney Local Health District, on 21 January 2022 (Protocol no. X21-0438 and 2021/ETH12171). The findings of the trial will be disseminated through peer-reviewed journals and national and international conference presentations. Data will be deposited in an institutional data repository at the end of the trial. This is subject to Ethics Committee approval, and the metadata will be made available on request. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN 12622000659707). ARTICLE SUMMARY: The objective of this trial is to evaluate a CHN intervention provided to patients aged over 40 years and living with chronic health conditions to transition from hospital inpatient care to their homes.

Impact of multimorbidity and complex multimorbidity on healthcare utilisation in older Australian adults aged 45 years or more: a large population-based cross-sectional data linkage study.

OBJECTIVES: As life expectancy increases, older people are living longer with multimorbidity (MM, co-occurrence of ≥2 chronic health conditions) and complex multimorbidity (CMM, ≥3 chronic conditions affecting ≥3 different body systems). We assessed the impacts of MM and CMM on healthcare service use in Australia, as little was known about this. DESIGN: Population-based cross-sectional data linkage study. SETTING: New South Wales, Australia. PARTICIPANTS: 248 496 people aged ≥45 years who completed the Sax Institute's 45 and Up Study baseline questionnaire. PRIMARY OUTCOME: High average annual healthcare service use (≥2 hospital admissions, ≥11 general practice visits and ≥2 emergency department (ED) visits) during the 3-year baseline period (year before, year of and year after recruitment). METHODS: Baseline questionnaire data were linked with hospital, Medicare claims and ED datasets. Poisson regression models were used to estimate adjusted and unadjusted prevalence ratios for high service use with 95% CIs. Using a count of chronic conditions (disease count) as an alternative morbidity metric was requested during peer review. RESULTS: Prevalence of MM and CMM was 43.8% and 15.5%, respectively, and prevalence increased with age. Across three healthcare settings, MM was associated with a 2.02-fold to 2.26-fold, and CMM was associated with a 1.83-fold to 2.08-fold, increased risk of high service use. The association was higher in the youngest group (45-59 years) versus the oldest group (≥75 years), which was confirmed when disease count was used as the morbidity metric in sensitivity analysis.When comparing impact using three categories with no overlap (no MM/CMM, MM with no CMM, and CMM), CMM had greater impact than MM across all settings. CONCLUSION: Increased healthcare service use among older adults with MM and CMM impacts on the demand for primary care and hospital services. Which of MM or CMM has greater impact on risk of high healthcare service use depends on the analytic method used. Ageing populations living longer with increasing burdens of MM and CMM will require increased Medicare funding and provision of integrated care across the healthcare system to meet their complex needs.

Impact of Fruit and Fruit Juice on Death and Disease Incidence: A Sex-Specific Longitudinal Analysis of 18 603 Adults.

BACKGROUND: The health benefits of fruits are well established, but fruit juice has been more controversial. Fruit and juice are often ingested with other foods, which prompted our investigation to determine whether fruit consumed as juice may negate the beneficial effects of consuming whole fruit in people with cardiovascular disease. METHODS AND RESULTS: We retrospectively analyzed data from a population-based study in Australia (the 45 and Up Study) linked with hospitalization and mortality data up to September 2018. Kaplan-Meier survival estimates and Cox proportional hazards models were used to examine effects of fruit, fruit juice, and the combination of fruit and fruit juice in relation to death and disease incidence among men and women living with cardiovascular disease. A total of 7308 deaths occurred among 18 603 participants diagnosed with cardiovascular disease over a 13-year follow-up. After multivariable adjustment, inadequate fruit intake (hazard ratio [HR], 1.12 [95% CI, 1.01-1.24]) and high fruit juice intake (HR, 1.26 [95% CI, 1.12-1.41]) predicted all-cause mortality in women. Also, high fruit juice intake plus either adequate fruit intake (HR, 1.18 [95% CI, 1.02-1.37]) or inadequate fruit intake (HR, 1.43 [95% CI, 1.21-1.69]) predicted mortality in women. No relationships were found in men after multivariable adjustments. Also, we found no prognostic value for fruit and fruit juice intake on disease incidence. CONCLUSIONS: In adults with cardiovascular disease, we found that fruit juice (in combination with adequate or inadequate fruit intake) predicted mortality in women but not in men. These effects became less clear when focusing on disease incidence.

Preventing chronic disease in overweight and obese patients with low health literacy using eHealth and teamwork in primary healthcare (HeLP-GP): a cluster randomised controlled trial.

OBJECTIVES: To evaluate a multifaceted intervention on diet, physical activity and health literacy of overweight and obese patients attending primary care. DESIGN: A pragmatic two-arm cluster randomised controlled trial. SETTING: Urban general practices in lower socioeconomic areas in Sydney and Adelaide. PARTICIPANTS: We aimed to recruit 800 patients in each arm. Baseline assessment was completed by 215 patients (120 intervention and 95 control). INTERVENTION: A practice nurse-led preventive health check, a mobile application and telephone coaching. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes were measured at baseline, 6 and 12 months, and included patient health and eHealth literacy, weight, waist circumference and blood pressure. Secondary outcomes included changes in diet and physical activity, preventive advice and referral, blood lipids, quality of life and costs. Univariate and multivariate analyses of difference-in-differences (DiD) estimates for each outcome were conducted. RESULTS: At 6 months, the intervention group, compared with the control group, demonstrated a greater increase in Health Literacy Questionnaire domain 8 score (ability to find good health information; mean DiD 0.22; 95% CI 0.01 to 0.44). There were similar differences for domain 9 score (understanding health information well enough to know what to do) among patients below the median at baseline. Differences were reduced and non-statistically significant at 12 months. There was a small improvement in diet scores at 6 months (DiD 0.78 (0.10 to 1.47); p=0.026) but not at 12 months. There were no differences in eHealth literacy, physical activity scores, body mass index, weight, waist circumference or blood pressure. CONCLUSIONS: Targeted recruitment and engagement were challenging in this population. While the intervention was associated with some improvements in health literacy and diet, substantial differences in other outcomes were not observed. More intensive interventions and using codesign strategies to engage the practices earlier may produce a different result. Codesign may also be valuable when targeting lower socioeconomic populations. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN 12617001508369) (http://www.ANZCTR.org.au/ACTRN12617001508369.aspx). TRIAL PROTOCOL: The protocol for this trial has been published (open access; https://bmjopen.bmj.com/content/8/6/e023239).

Mobile Health Use by Older Individuals at Risk of Cardiovascular Disease and Type 2 Diabetes Mellitus in an Australian Cohort: Cross-sectional Survey Study.

BACKGROUND: The digital transformation has the potential to change health care toward more consumers' involvement, for example, in the form of health-related apps which are already widely available through app stores. These could be useful in helping people understand their risk of chronic conditions and helping them to live more healthily. OBJECTIVE: With this study, we assessed mobile health app use among older Australians in general and among those who were at risk of cardiovascular disease or type 2 diabetes mellitus. METHODS: In this cross-sectional analysis, we used data from the second follow-up wave of the 45 and Up Study. It is a cohort study from New South Wales, Australia, with 267,153 participants aged 45 years and older that is based on a random sample from the Services Australia (formerly the Australian Government Department of Human Services) Medicare enrollment database. The 2019 follow-up questionnaire contained questions about technology and mobile health use. We further used data on prescribed drugs and hospitalizations to identify participants who already had cardiovascular disease or diabetes or who were at risk of these conditions. Our primary outcome measure was mobile health use, defined as having used a mobile health app before. We used descriptive statistics and multivariate logistic regression to answer the research questions. RESULTS: Overall, 31,946 individuals with a median age of 69 (IQR 63-76) years had completed the follow-up questionnaire in 2019. We classified half (16,422/31,946, 51.41%) of these as being at risk of cardiovascular disease or type 2 diabetes mellitus and 38.04% (12,152/31,946) as having cardiovascular disease or type 1 or type 2 diabetes mellitus. The proportion of mobile health app users among the at-risk group was 31.46% (5166/16,422) compared to 29.16% (9314/31,946) in the total sample. Those who used mobile health apps were more likely to be female, younger, without physical disability, and with a higher income. People at risk of cardiovascular disease or type 2 diabetes mellitus were not statistically significantly more likely to use mobile health than were people without risk (odds ratio 1.06, 95% CI 0.97-1.16; P=.18; adjusted for age, sex, income, and physical disability). CONCLUSIONS: People at risk of cardiovascular disease or type 2 diabetes mellitus were not more likely to use mobile health apps than were people without risk. Those who used mobile health apps were less likely to be male, older, with a physical disability, and with a lower income. From the results, we concluded that aspects of equity must be considered when implementing a mobile health intervention to reach all those that can potentially benefit from it.

Remotely Conducted App-Based Intervention for Cardiovascular Disease and Diabetes Risk Awareness and Prevention: Single-Group Feasibility Trial.

BACKGROUND: Cardiovascular disease and type 2 diabetes mellitus are two of the most prevalent chronic conditions worldwide. An unhealthy lifestyle greatly contributes to someone's risk of developing these conditions. Mobile health is an emerging technology that can help deliver health promotion interventions to the population, for example, in the form of health apps. OBJECTIVE: The aim of this study was to test the feasibility of an app-based intervention for cardiovascular and diabetes risk awareness and prevention by measuring nonusage, dropout, adherence to app use, and usability of the app over 3 months. METHODS: Participants were eligible if they were aged 45 years or older, resided in Australia, were free of cardiovascular disease and diabetes, were fluent in English, and owned a smartphone. In the beginning, participants received an email with instructions on how to install the app and a user guide. After 3 months, they received an email with an invitation to an end-of-study survey. The survey included questions about general smartphone use and the user version of the Mobile Application Rating Scale. We analyzed app-generated and survey data by using descriptive and inferential statistics as well as thematic analysis for open-text comments. RESULTS: Recruitment took place between September and October 2021. Of the 46 participants who consented to the study, 20 (44%) never used the app and 15 (33%) dropped out. The median age of the app users at baseline was 62 (IQR 56-67) years. Adherence to app use, that is, using the app at least once a week over 3 months, was 17% (8/46) of the total sample and 31% (8/26) of all app users. The mean app quality rating on the user version of the Mobile Application Rating Scale was 3.5 (SD 0.6) of 5 points. The app scored the highest for the information section and the lowest for the engagement section of the scale. CONCLUSIONS: Nonusage and dropouts were too high, and the adherence was too low to consider the intervention in its current form feasible. Potential barriers that we identified include the research team not actively engaging with participants early in the study to verify that all participants could install the app, the intervention did not involve direct contact with health care professionals, and the app did not have enough interactive features.

Impact of multimorbidity and complex multimorbidity on mortality among older Australians aged 45 years and over: a large population-based record linkage study.

OBJECTIVES: Multimorbidity (MM, co-occurrence of two or more chronic conditions) and complex multimorbidity (CMM, three or more chronic conditions affecting three or more different body systems) are used in the assessment of complex healthcare needs and their impact on health outcomes. However, little is known about the impacts of MM and CMM on mortality in Australia. DESIGN: Community-based prospective cohort study. SETTING: New South Wales, Australia. PARTICIPANTS: People aged 45 years and over who completed the baseline survey of the 45 and Up Study. MEASURES: Baseline survey data from the 45 and Up Study were linked with deaths registry data. Deaths that occurred within 8 years from the baseline survey date were the study outcome. Eleven self-reported chronic conditions (cancer, heart disease, diabetes, stroke, Parkinson's disease, depression/anxiety, asthma, allergic rhinitis, hypertension, thrombosis and musculoskeletal conditions) from the baseline survey were included in the MM and CMM classifications. Cox proportional hazard models were used to estimate adjusted and unadjusted 8-year mortality hazard ratios (HRs). RESULTS: Of 251 689 people (53% female and 54% aged ≥60 years) in the cohort, 111 084 (44.1%) were classified as having MM and 39 478 (15.7%) as having CMM. During the 8-year follow-up, there were 25 891 deaths. Cancer (34.7%) was the most prevalent chronic condition and the cardiovascular system (50.9%) was the body system most affected by a chronic condition. MM and CMM were associated with a 37% (adjusted HR 1.36, 95% CI 1.32 to 1.40) and a 22% (adjusted HR 1.22, 95% CI 1.18 to 1.25) increased risk of death, respectively. The relative impact of MM and CMM on mortality decreased as age increased. CONCLUSION: MM and CMM were common in older Australian adults; and MM was a better predictor of all-cause mortality risk than CMM. Higher mortality risk in those aged 45-59 years indicates tailored, person-centred integrated care interventions and better access to holistic healthcare are needed for this age group.

Frequent users of health services among community-based older Australians: Characteristics and association with mortality.

OBJECTIVES: To investigate characteristics of frequent users of general practice (GP; ≥21 visits in a year), medical specialist (≥10 visits), emergency department (ED; ≥2 presentations) and hospital services (≥2 overnight hospitalisations) and the association with mortality for people aged over 75 years. METHODS: The study included residents from Central and Eastern Sydney, Australia, aged over 75 years who participated in a large community-dwelling cohort study. Demographic, social and health characteristics data were extracted from the 45 and Up Study survey. Health service (GP, medical specialist, ED and hospitalisations) use and mortality data were extracted from linked administrative data. We calculated adjusted prevalence ratios to identify independent characteristics associated with frequent users of services at baseline (approx. 2008) and adjusted hazard ratios to assess the association between frequent users of services and mortality. RESULTS: Frequent users of services (GPs, medical specialists, EDs and hospitals) were more likely to be associated with ever having had heart disease and less likely to be associated with reporting good quality of life. Characteristics varied by service type. Frequent users of services were 1.5-2.0 times more likely to die within 7 years compared to those who were less frequent service users after controlling for all significant factors. CONCLUSIONS: Our analysis found that frequent service users aged over 75 years had poorer quality of life, more complex health conditions and higher mortality and so their health service use was not inappropriate. However, better management of these frequent service users may lead to better health outcomes.

Qualitative Case Study: A Pilot Program to Improve the Integration of Care in a Vulnerable Inner-City Community.

Introduction: There is a strong correlation between vulnerable populations and poor health outcomes. Growing evidence suggests that person-centred interventions using 'link workers' can support communities to navigate and engage with health and community services, leading to improved health service access. We describe the initial phase and qualitative evaluation of a Healthy Living Program, supported by a link worker role. The Program aimed to improve health service access for residents of an Australian inner-city suburb. Methods: To inform future program development, semi-structured interviews were conducted with clients and stakeholders (n = 21). The interviews were analysed thematically to understand program impact, success factors, constraints and potential improvements. Results: Key themes relating to impacts were a new model of working with community, improved access to services, and responsiveness to community need. Key factors for success included being a trusted, consistent presence, having knowledge of the community and health system, and successful engagement with the community and stakeholders. The constraints included difficulty influencing health system change and lack of community input. Suggested improvements were expanding the service, enhancing health system change and increasing community involvement. Conclusion: Knowledge gained from this study will inform future integrated approaches in health districts to address health inequities in areas of need.

A Mobile App for Prevention of Cardiovascular Disease and Type 2 Diabetes Mellitus: Development and Usability Study.

BACKGROUND: Cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) are posing a huge burden on health care systems worldwide. Mobile apps can deliver behavior change interventions for chronic disease prevention on a large scale, but current evidence for their effectiveness is limited. OBJECTIVE: This paper reported on the development and user testing of a mobile app that aims at increasing risk awareness and engaging users in behavior change. It would form part of an intervention for primary prevention of CVD and T2DM. METHODS: The theoretical framework of the app design was based on the Behaviour Change Wheel, combined with the capability, opportunity, and motivation for behavior change system and the behavior change techniques from the Behavior Change Technique Taxonomy (version 1). In addition, evidence from scientific literature has guided the development process. The prototype was tested for user-friendliness via an iterative approach. We conducted semistructured interviews with individuals in the target populations, which included the System Usability Scale. We transcribed and analyzed the interviews using descriptive statistics for the System Usability Scale and thematic analysis to identify app features that improved utility and usability. RESULTS: The target population was Australians aged ≥45 years. The app included 4 core modules (risk score, goal setting, health measures, and education). In these modules, users learned about their risk for CVD and T2DM; set goals for smoking, alcohol consumption, diet, and physical activity; and tracked them. In total, we included 12 behavior change techniques. We conducted 2 rounds of usability testing, each involving 5 participants. The average age of the participants was 58 (SD 8) years. Totally, 60% (6/10) of the participants owned iPhone Operating System phones, and 40% (4/10) of them owned Android phones. In the first round, we identified a technical issue that prevented 30% (3/10) of the participants from completing the registration process. Among the 70% (7/10) of participants who were able to complete the registration process, 71% (5/7) rated the app above average, based on the System Usability Scale. During the interviews, we identified some issues related to functionality, content, and language and clarity. We used the participants' feedback to improve these aspects. CONCLUSIONS: We developed the app using behavior change theory and scientific evidence. The user testing allowed us to identify and remove technical errors and integrate additional functions into the app, which the participants had requested. Next, we will evaluate the feasibility of the revised version of the app developed through this design process and usability testing.

Different Types of Long-Term Milk Consumption and Mortality in Adults with Cardiovascular Disease: A Population-Based Study in 7236 Australian Adults over 8.4 Years.

Most studies disregard long-term dairy consumption behaviour and how it relates to mortality. We examined four different types of long-term milk consumption, namely whole milk, reduced fat milk, skim milk and soy milk, in relation to mortality among adults diagnosed with cardiovascular disease (CVD). A retrospective population-based study was conducted in Australia (the 45 and Up Study) linking baseline (2006-2009) and follow-up data (2012-2015) to hospitalisation and mortality data up to 30 September 2018. A total of 1,101 deaths occurred among 7236 participants with CVD over a mean follow-up of 8.4 years. Males (Hazard Ratio, HR = 0.69, 95% CI (0.54; 0.89)) and females (HR = 0.59 (0.38; 0.91)) with long-term reduced fat milk consumption had the lowest risk of mortality compared to counterparts with long-term whole milk consumption. Among participants with ischemic heart disease, males with a long-term reduced fat milk consumption had the lowest risk of mortality (HR = 0.63, 95% CI: 0.43; 0.92). We conclude that among males and females with CVD, those who often consume reduced fat milk over the long-term present with a 31-41% lower risk of mortality than those who often consume whole milk, supporting dairy advice from the Heart Foundation of replacing whole milk with reduced fat milk to achieve better health.

Validation of a lifestyle-based risk score for type 2 diabetes mellitus in Australian adults.

The study aimed to assess the performance of a lifestyle-based prognostic risk model (Diabetes Lifestyle Score) for the prediction of 5-year risk of type 2 diabetes mellitus. The model comprises nine self-reported predictors (sex, age, antihypertensive drugs, body mass index, family history of diabetes, physical activity, fruits, vegetables, and wholemeal/brown bread). We conducted an external validation and update of the model in an Australian cohort including 97,615 residents of New South Wales aged 45 years and older who were free of type 1 and 2 diabetes mellitus at baseline. Of all participants, 4,741 developed type 2 diabetes mellitus over 5 years. We conducted the statistical analyses in RStudio using the programming language R. The area under the receiver operating characteristic curve (AUC) of the original model was 0.726 (95% confidence interval: 0.719, 0.733). After adjusting the calibration intercept and slope, the original model performed reasonably well in the external cohort. The best performance was measured by using the numerical predictors as continuous variables and refitting all coefficients (AUC: 0.741, 95% confidence interval: 0.734, 0.748). The results of the original model after calibration were comparable to those received from the AUSDRISK score which is routinely used in Australian clinical practice. Hence, the lifestyle-based model might be a reasonable alternative for laypersons since the required information is most likely known by these. Further, the risk score may communicate the message about the importance of a healthy diet to reduce the risk of diabetes.

General practitioner follow-up after hospitalisation in Central and Eastern Sydney, Australia: access and impact on health services.

Objectives General practitioner (GP) follow-up after a hospital admission is an important indicator of integrated care. We examined the characteristics of patients who saw a GP within 2 weeks of hospital discharge in the Central and Eastern Sydney (CES) region, Australia, and the relationship between GP follow-up and subsequent hospitalisation. Methods This data linkage study used a cohort of 10240 people from the 45 and Up Study who resided in CES and experienced an overnight hospitalisation in the 5 years following recruitment (2007-14). Characteristics of participants who saw a GP within 2 weeks of discharge were compared with those who did not using generalised linear models. Time to subsequent hospitalisation was compared for the two groups using Cox proportional hazards regression models stratified by prior frequency of GP use. Results Within 2 weeks of discharge, 64.3% participants saw a GP. Seeing a GP within 2 weeks of discharge was associated with lower rates of rehospitalisation for infrequent GP users (i.e. <8 visits in year before the index hospitalisation; hazard ratio (HR) 0.83; 95% confidence interval (CI) 0.70-0.97) but not frequent GP users (i.e. ≥8 plus visits; HR 1.02; 95% CI 0.90-1.17). Conclusion The effect of seeing a GP on subsequent hospitalisation was protective but differed depending on patient care needs. What is known about the topic? There is general consensus among healthcare providers that primary care is a significant source of ongoing health care provision. What does this paper add? This study explored the relationship between GP follow-up after an uncomplicated hospitalisation and its effect on rehospitalisation. What are the implications for practitioners? Discharge planning and the transfer of care from hospital to GP through discharge arrangements have substantial benefits for both patients and the health system.

Primary Prevention of Cardiovascular Disease and Type 2 Diabetes Mellitus Using Mobile Health Technology: Systematic Review of the Literature.

BACKGROUND: Digital technology is an opportunity for public health interventions to reach a large part of the population. OBJECTIVE: This systematic literature review aimed to assess the effectiveness of mobile health-based interventions in reducing the risk of cardiovascular disease and type 2 diabetes mellitus. METHODS: We conducted the systematic search in 7 electronic databases using a predefined search strategy. We included articles published between inception of the databases and March 2019 if they reported on the effectiveness of an intervention for prevention of cardiovascular disease or type 2 diabetes via mobile technology. One researcher performed the search, study selection, data extraction, and methodological quality assessment. The steps were validated by the other members of the research team. RESULTS: The search yielded 941 articles for cardiovascular disease, of which 3 met the inclusion criteria, and 732 for type 2 diabetes, of which 6 met the inclusion criteria. The methodological quality of the studies was low, with the main issue being nonblinding of participants. Of the selected studies, 4 used SMS text messaging, 1 used WhatsApp, and the remaining ones used specific smartphone apps. Weight loss and reduction in BMI were the most reported successful outcomes (reported in 4 studies). CONCLUSIONS: Evidence on the effectiveness of mobile health-based interventions in reducing the risk for cardiovascular disease and type 2 diabetes is low due to the quality of the studies and the small effects that were measured. This highlights the need for further high-quality research to investigate the potential of mobile health interventions. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) CRD42019135405; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=135405.

The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly.

Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the first release of the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analyse the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. MGRB individuals have fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK Biobank cohorts, consistent with risk depletion. Age-related somatic changes are correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing.

Do general practice management and/or team care arrangements reduce avoidable hospitalisations in Central and Eastern Sydney, Australia?

BACKGROUND: The number of people living with chronic health conditions is increasing in Australia. The Chronic Disease Management program was introduced to Medicare Benefits Schedule (MBS) to provide a more structured approach to managing patients with chronic conditions and complex care needs. The program supports General Practitioners (GP)s claiming for up to one general practice management plan (GPMP) and one team care arrangement (TCA) every year and the patient claiming for up to five private allied health visits. We describe the profile of participants who claimed for GPMPs and/or TCAs in Central and Eastern Sydney (CES) and explore if GPMPs and/or TCAs are associated with fewer emergency hospitalisations (EH)s or potentially preventable hospitalisations (PPH)s over the following 5 years. METHODS: This research used the CES Primary and Community Health Cohort/Linkage Resource (CES-P&CH) based on the 45 and Up Study to identify a community-dwelling population in the CES region. There were 30,645 participants recruited within the CES area at baseline. The CES-P&CH includes 45 and Up Study questionnaire data linked to MBS data for the period 2006-2014. It also includes data from the Admitted Patient Data Collection, Emergency Department Data Collection and Deaths Registry linked by the NSW Centre for Health Record Linkage. RESULTS: Within a two-year health service utilisation baseline period 22% (5771) of CES participants had at least one claim for a GPMP and/or TCA. Having at least one claim for a GPMP and/or TCA was closely related to the socio-demographic and health needs of participants with higher EHs and PPHs in the 5 years that followed. However, after controlling for confounding factors such as socio-demographic need, health risk, health status and health care utilization no significant difference was found between having claimed for a GPMP and/or TCA during the two-year health service utilisation baseline period and EHs or PPHs in the subsequent 5 years. CONCLUSIONS: The use of GPMPs and/or TCAs in the CES area appears well-targeted towards those with chronic and complex care needs. There was no evidence to suggest that the use of GPMPs and /or TCAs has prevented hospitalisations in the CES region.