BACKGROUND:People with schizophrenia spectrum disorders (SSD) experience high rates of type 2 diabetes (T2D), mainly due to antipsychotic medication side-effects and lifestyle factors (e.g. suboptimal nutrition and physical inactivity). Digital technologies may reduce T2D risk by complementing face-to-face and pharmacological treatments, through the provision of flexible and personalised psychoeducation and behavioural prompts tailored to end-users. AIMS:This study tested the preliminary efficacy of the Schizophrenia and diabetes Mobile-Assisted Remote Trainer (SMART), a co-designed text message-facilitated intervention, designed to reduce the risk and/or improve self-management of T2D, along with its acceptability and feasibility. METHOD:Using an uncontrolled pre-post design, 29 out-patients of an endocrinology mental health clinic and two community-based rehabilitation mental health facilities used SMART for 12 weeks. The primary outcome was patient activation, measured using the Patient Activation Measure. Secondary outcomes were combined objective cardiometabolic and self-reported health and mental health indicators. Pre-post changes were analysed with a linear mixed model, accounting for within-participant variation. RESULTS:Significant improvements (p < 0.05) were detected in patient activation, confidence in diabetes self-management and general health management, health literacy and mental health recovery. High levels of acceptability and feasibility were confirmed, with recruitment, retention and adherence rates of 67.4, 92.9 and 93.0%, respectively. CONCLUSIONS:SMART is a world-first digital intervention aimed at improving metabolic health in individuals with SSD. This study provides evidence of its preliminary efficacy in self-management of metabolic health while confirming its high acceptability and feasibility, supporting expansion towards a sufficiently powered controlled trial to assess its clinical effectiveness.

Introduction:Compared with the general population, people with schizophrenia and schizophrenia-related disorders (SSD) have a higher prevalence of type 2 diabetes (T2D) and T2D risk factors such as poor diet and sedentary lifestyle. Antipsychotic drugs significantly contribute to this risk through metabolic adverse effects, including weight gain and insulin resistance. Prevention and self-management of T2D is challenging in this population due to inherent motivational and cognitive challenges associated with schizophrenia. The objective of this study was to describe the co-design and test the feasibility, acceptability, and usability of a novel digital health intervention, Schizophrenia and diabetes Mobile-Assisted Remote Trainer (SMART), for prevention and self-management of T2D in people with SSD. Methods:SMART was developed through an iterative process including review of relevant literature (eg, disease-specific guidelines), stakeholder involvement, and user testing. A pre-post mixed-methods design was used to assess the acceptability and feasibility of SMART over 4 weeks among five outpatients with schizophrenia/schizoaffective disorder and pre-diabetes/T2D. Results:The co-design process resulted in a digital intervention, which consisted of personalised, interactive text messages, providing psychoeducation and strengthening motivation for self-care behaviours that promote effective diabetes self-management (ie, nutrition, physical activity, weight management, and stress coping). The pilot study demonstrated good acceptability of SMART (response rates 75-95%). Trends towards improved clinical outcomes were observed in well-being, depression, anxiety, and mental health recovery. Barriers to usability included lack of mobile/internet data, precluding the ability to reply to text messages, and a preference for more hyperlinks and additional interactive features. Conclusion:The comprehensive co-design process resulted in the development of a novel digital intervention for prevention and self-management of T2D tailored to unique needs and preferences of people with SSD. The pilot study findings indicate that SMART is acceptable and potentially usable for this population. Results will inform further adaptation and a future feasibility study to examine preliminary effectiveness of SMART.

BackgroundDigital health interventions (DHIs) have rapidly evolved and significantly revolutionized the health care system. The quadruple aims of health care (improving population health, enhancing consumer experience, enhancing health care provider [HCP] experience, and decreasing health costs) serve as a strategic guiding framework for DHIs. It is unknown how DHIs can impact the burden of type 2 diabetes mellitus (T2DM), as measured by the quadruple aims. ObjectiveThis study aimed to systematically review the effects of DHIs on improving the burden of T2DM, as measured by the quadruple aims. MethodsPubMed, Embase, CINAHL, and Web of Science were searched for studies published from January 2014 to March 2024. Primary outcomes were the development of T2DM, hemoglobin A1c (HbA1c) change, and blood glucose change (dysglycemia changes). Secondary outcomes were consumer experience, HCP experience, and health care costs. Outcomes were mapped to the quadruple aims. DHIs were categorized using the World Health Organization’s DHI classification. For each study, DHI categories were assessed for their effects on each outcome, categorizing the effects as positive, negative, or neutral. The overall effects of each DHI category were determined by synthesizing all reported positive, neutral, or negative effects regardless of the number of studies supporting each effect. The Cochrane risk-of-bias version 2 (RoB 2) tool for randomized trials was used to assess the quality of randomized controlled trials (RCTs), while the ROBINS-I (risk of bias in nonrandomized studies of interventions) tool was applied for nonrandomized studies. ResultsIn total, 53 papers were included. For the T2DM development outcome, the effects of DHIs were positive in 1 (1.9%) study and neutral in 9 (17%) studies, and there were insufficient data to assess in 4 (7.5%) studies. For the dysglycemia outcome, the effects were positive in 23 (43.4%) studies and neutral in 24 (45.3%) studies, and there were insufficient data in 6 (11.3%) studies. There were mixed effects on consumer experience (n=13, 24.5%) and a lack of studies reporting HCP experience (n=1, 1.9%) and health care costs (n=3, 5.7%). All studies that reported positive population health outcomes used a minimum of 2 distinct categories of DHIs. Among these successful studies, the one that reported delaying the development of T2DM and 16 (69.6%) of those reporting improvements in dysglycemia involved HCP interaction. Targeted communication with persons (TCP), personal health tracking (PHT), and telemedicine (TM) showed some evidence as a potentially useful tool for T2DM prevention and dysglycemia. ConclusionsThe effects of DHIs on T2DM prevention, as measured by the quadruple aims, have not been comprehensively assessed, with proven benefits for population health, mixed results for consumer experience, and insufficient studies on HCP experience and health care costs. To maximize their effectiveness in preventing T2DM and managing dysglycemia, DHIs should be used in combination and strategically integrated with in-person or remote HCP interaction. Trial RegistrationPROSPERO CRD42024512690; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024512690

Objective Mobile Health (mHealth), a subset of digital health, requires people to own smartphones, but ownership barriers overlap with social factors linked to type 2 diabetes (T2D) burden. We describe the prevalence of smartphone ownership, app use and mobile internet access and factors around uptake and utilisation among people with T2D accessing care in a community setting. Methods A cross-sectional survey was performed with people with diabetes attending a community-based general practitioner-led diabetes clinic located in Inala, a multiculturally diverse but socioeconomically marginalised suburban region of Brisbane, Queensland. The survey was read aloud to participants, with interpreters if required. Results There were 104 participants, the median age was 63years, 47.1% were female and 44.2% spoke language(s) other than English (LOTE) at home. Smartphone ownership was high (85.6%), and average self-rated confidence with advanced feature use was between 'somewhat confident' and 'confident'. Older adults were significantly less likely to own smartphones, less confident with advanced features and less likely to use apps regularly, but many knew someone who could support uptake. LOTE spoken at home was not associated with ownership, mobile internet access, app use or self-rated confidence with advanced feature use, suggesting smartphone technology is already part of daily life. Conclusions Smartphone ownership and utilisation does not appear to be a major barrier to mHealth uptake in our context. Older adults need tailored supports and education to encourage mHealth uptake.

INTRODUCTION:Moderately increased (micro) albuminuria serves as a critical early indicator of Diabetic Kidney Disease (DKD). However, traditional screening methods that rely on laboratory-based analyses face significant challenges in enabling timely and continuous monitoring. This study addresses these limitations by introducing a non-invasive approach for albuminuria risk detection, allowing real-time estimation of mild albuminuria increases using vital signs and body measurements. METHODS:We developed a non-invasive model for albuminuria risk detection using vital signs and body measurements. Data were drawn from the NHANES cohort (USA) and a Bangladeshi cohort of people with diabetes (PwD). Feature selection identified four non-laboratory predictors - estimated cardiac output (eCO), body mass index, waist circumference, and diabetes duration - as the most informative inputs. The proposed models were benchmarked against baseline machine learning approaches and existing methods developed over the past decade, with model interpretability assessed via SHapely Additive exPlanation (SHAP) contributions. RESULTS:Our best model, an XGBoost classifier, achieved an AUC of 0.75 [0.67-0.84], an accuracy of 0.70, and a macro F1 score of 0.68, outperforming other non-invasive risk scores (0.58) and machine learning baselines. Validation against an external reference risk score confirmed superior precision-recall balance for both positive (microalbuminuria) and negative classes. CONCLUSION:This study demonstrates that a fine-tuned, non-invasive XGBoost model using simple clinical measures can support albuminuria monitoring and early DKD detection without laboratory tests. While the selected predictors may not represent the definitive or optimal feature set, their strong performance highlights the potential of leveraging easily obtainable, clinically relevant measures. In particular, the contribution of eCO underscores a promising direction for exploring heart-kidney-metabolism interactions in DKD risk assessment. Together, these findings highlight a scalable, non-invasive tool for resource-limited settings, an interpretable framework for clinical trust, and a pathway to refining feature sets for both accuracy and biological insight.

BackgroundThe prevalence of type 2 diabetes is rising in Australia, particularly in regional areas where access to specialist care is limited. To address this, Queensland Health (Queensland, Australia) established a telehealth network, including the Diabetes Telehealth Service (DTS) at the Princess Alexandra Hospital (PAH). The service facilitates video consultations between city-based endocrinologists and regional health centres, with local clinicians providing in-person support. While telehealth interventions have been evaluated in short-term studies, there is a need for longitudinal data to assess their long-term effectiveness in routine diabetes care. This study aims to describe the clinical characteristics and outcomes of patients with type 2 diabetes accessing care from the PAH DTS.MethodsThis retrospective cohort study used data from the PAH DTS to follow adults with type 2 diabetes over 24 months. Data was collected as part of routine care and analysed to assess changes in glycated haemoglobin (HbA1c) levels and cardiovascular risk factors. Statistical analyses included descriptive analysis, t-tests, Chi-squared tests, and fixed effects regression models.ResultsThe study included 374 patients with type 2 diabetes, with a mean age of 57.9 years and a mean duration of diabetes at enrolment of 11.6 years. Baseline HbA1c levels were available for 86% of the patients, with a median HbA1c of 8.4%. The median number of appointments in the 24-month period was 2, and the average time between a person's first and last visit was 72 weeks. The average change in HbA1c between these visits was -0.8%. Statistically significant changes were also seen in cholesterol levels, weight, body mass index, and diastolic blood pressure. A linear regression analysis revealed that the greatest decrease in HbA1c levels occurred within the first 3 months since the initial clinic visit. HbA1c levels continued to decrease over the 24-month follow-up period, but the rate of decrease slowed after the first 3 months.ConclusionThis study provides valuable insights into the telehealth model of care for tertiary diabetes in regional, rural, and remote settings. It demonstrates the effectiveness of this model in improving glycaemic control, particularly in the initial months, while also highlighting areas for improvement.

OBJECTIVES:Diabetes care in Australia is often fragmented and provider-centred, resulting in suboptimal care. Innovative solutions are needed to bridge the evidence-practice gap, and technology can facilitate the redesign of type 2 diabetes care. We used participatory design to increase the chances of fulfilling stakeholders' needs. Using this method, we explored solutions aimed at redesigning diabetes care, focussing on the previously identified needs. METHODS:The participatory design project was guided by stakeholders' contributions. Stakeholders of this project included people with type 2 diabetes, health-care professionals, technology developers, and researchers. Information uncovered at each step influenced the next: 1) identification of needs, 2) generation of solutions, and 3) testing of solutions. Here, we present steps 2 and 3. In step 2, we presented previously identified issues and elicited creative solutions. In step 3, we obtained stakeholders' feedback on the solutions from step 2, presented as care pathways. RESULTS:Suggested solutions included a multidisciplinary wellness centre, a mobile app, increased access to education, improved care coordination, increased support for general practitioners, and a better funding model. The revised care pathways featured accessible community resources, a tailored self-management and educational app, a care coordinator, a digital dashboard, and specialized support for primary care to deal with complex cases. CONCLUSIONS:Using a participatory design, we successfully identified multiple innovative solutions with the potential to improve person-centred and integrated type 2 diabetes care in Australia. These solutions will inform the implementation and evaluation of a redesigned care model by our team.

The implementation of mobile health interventions is key to the future management of diabetes. A new model of digital care for type 2 diabetes using a Bluetooth-enabled blood glucose monitoring system has been implemented at the Princess Alexandra Hospital. The perceptions of patients, healthcare professionals, and executives involved in the implementation of this model have been assessed to establish the key facilitators and barriers to digital health intervention adoption.

Background: Type 2 diabetes (T2D) significantly burdens both people who develop it and the health system. For people with T2D (PwD), fragmented care leads to suboptimal health care outcomes. Mobile health systems can help deliver better integrated care, more personalised support and assist with self-management. Such systems can be tailored for individual needs and address health care access gaps; particularly for populations that are multiculturally diverse or experiencing socioeconomic marginalisation.We describe our study methods that aim to understand the technology needs of stakeholders who deliver or receive care in primary and secondary care settings.Methods: Study setting: a secondary care diabetes clinic co-located with a general practice clinic in Inala, Brisbane. Inala is both multiculturally diverse and has a high level of socioeconomic disadvantage.Needs assessment (Stage 1): A survey will be performed to ascertain smartphone utilisation rates and confidence among PwD attending the clinic. Then, semi-structured interviews with PwD and staff who form the care team, including community general practitioners. Interviews will elicit information on barriers and facilitators they experience when trying to achieve optimal integrated care and T2D management in the local context and what relevant technologies they have access to or need. Finally, we will observe staff workflows to help us identify further needs and contextualise our findings. We will analyse interview data using a thematic framework method.Co-design (Stage 2): Co-design workshops will be conducted. These aim to adapt an integrated care model for PwD using digital health addressing the needs identified earlier. Participants will be PwD, their carers or relatives, health care staff, including community general practitioners and health service managers and digital health developers.Conclusion: Our findings will inform a future adaptation and implementation of a digital health platform that optimises integrated care for PwD.

Background: There is plenty of evidence supporting the clinical benefits of mHealth interventions for type 2 diabetes, but despite often being promoted as cost-effective or cost-saving, there is still limited research to support such claims. The objective of this review was to summarize and critically analyze the current body of economic evaluation (EE) studies for mHealth interventions for type 2 diabetes. Methods: Using a comprehensive search strategy, five databases were searched for full and partial EE studies for mHealth interventions for type 2 diabetes from January 2007 to March 2022. “mHealth” was defined as any intervention that used a mobile device with cellular technology to collect and/or provide data or information for the management of type 2 diabetes. The CHEERS 2022 checklist was used to appraise the reporting of the full EEs. Results: Twelve studies were included in the review; nine full and three partial evaluations. Text messages smartphone applications were the most common mHealth features. The majority of interventions also included a Bluetooth-connected medical device, eg, glucose or blood pressure monitors. All studies reported their intervention to be cost-effective or cost-saving, however, most studies’ reporting were of moderate quality with a median CHEERS score of 59%. Conclusion: The current literature indicates that mHealth interventions for type 2 diabetes can be cost-saving or cost-effective, however, the quality of the reporting can be substantially improved. Heterogeneity makes it difficult to compare study outcomes, and the failure to report on key items leaves insufficient information for decision-makers to consider.

AIM:Globally, type 2 diabetes care is often fragmented and still organised in a provider-centred way, resulting in suboptimal care for many individuals. As healthcare systems seek to implement digital care innovations, it is timely to reassess stakeholders' priorities to guide the redesign of diabetes care. This study aimed to identify the needs and wishes of people with type 2 diabetes, and specialist and primary care teams regarding optimal diabetes care to explore how to better support people with diabetes in a metropolitan healthcare service in Australia. METHODS:Our project was guided by a Participatory Design approach and this paper reports part of the first step, identification of needs. We conducted four focus groups and 16 interviews (November 2019-January 2020) with 17 adults with type 2 diabetes and seven specialist clinicians from a diabetes outpatient clinic in Brisbane, Australia, and seven primary care professionals from different clinics in Brisbane. Data were analysed using reflexive thematic analysis, building on the Capability, Opportunity, Motivation and Behaviour model. RESULTS:People with diabetes expressed the wish to be equipped, supported and recognised for their efforts in a holistic way, receive personalised care at the right time and improved access to connected services. Healthcare professionals agreed and expressed their own burden regarding their challenging work. Overall, both groups desired holistic, personalised, supportive, proactive and coordinated care pathways. CONCLUSIONS:We conclude that there is an alignment of the perceived needs and wishes for improved diabetes care among key stakeholders, however, important gaps remain in the healthcare system.

AIMS:To identify the views of people with Type 2 diabetes (PWD) and healthcare professionals (HCP) about diabetes care. METHODS:A systematic review of qualitative studies reporting both groups' views using thematic synthesis frameworked by the eHealth Enhanced Chronic Care Model was conducted. RESULTS:We searched six electronic databases between 2010 and 2020, identified 6999 studies and included 21. Thirty themes were identified with in general complementary views between PWD and HCP. PWD and HCP find lifestyle changes challenging and get frustrated when PWD struggle to achieve it. Good self-management requires a trustful PWD-HCP relationship. Diabetes causes distress and often HCP focus on clinical aspects. They value diabetes education. PWD require broader, tailored, consistent and ongoing information, but HCPs do not have enough time for providing it. There is need for diabetes training for primary HCP. Shared decision making can mitigate PWD's fears. Different sources of social support can influence PWD's ability to self-manage and PWD/HCP suggest online peer groups. PWD/HCP indicate lack of communication and collaboration between HCP. PWD's and HCP's views about quality in diabetes care differ. They believe that comprehensive, multidisciplinary and locally provided care can help to achieve better outcomes. They recognise digital health benefits, with room for personal interaction (PWD) and eHealth literacy improvements (HCP). Evidence-based guidelines are important but can detract from personalised care. CONCLUSION:We hypothesise that including PWD's and HCP's complementary views, multidisciplinary teams and digital tools in the redesign of Type 2 diabetes care can help with overcoming some of the challenges and achieving common goals.

Objective Intensification of diabetes therapy with insulin is often delayed for people with suboptimal glycaemic control. This paper reports on the feasibility of using an innovative mobile health (mHealth) programme to assist a diabetes insulin dose adjustment (IDA) service. Methods Twenty adults with diabetes referred to a tertiary hospital IDA service were recruited. They were provided with a cloud-based mobile remote monitoring system—the mobile diabetes management system (MDMS). The credentialled diabetes educator (CDE) recorded the time taken to perform IDA utilising the MDMS versus the conventional method—which is a weekly adjustment of insulin doses by a CDE through telephone contact based on three or more daily blood glucose readings. Participants and staff completed a feedback questionnaire. Results The CDE spent 55% less time performing IDA using MDMS than using the conventional method. The participants were satisfied with MDMS use and the CDEs reported improved efficiency. Conclusion Incorporating a mHealth programme for an IDA service has the potential to improve service delivery efficiencies while simultaneously improving the patient experience.

Conventional outpatient services are unlikely to meet burgeoning demand for diabetes services given increasing prevalence of diabetes, and resultant impact on the healthcare workforce and healthcare costs. Disruptive technologies (such as smartphone and wireless sensors) create an opportunity to redesign outpatient services. In collaboration, the Department of Diabetes and Endocrinology at Brisbane Princess Alexandra Hospital, the University of Queensland Centre for Health Services Research and the Australian e-Health Research Centre developed a mobile diabetes management system (MDMS) to support the management of complex outpatient type 2 diabetes mellitus (T2DM) adults. The system comprises of a mobile App, an automated text-messaging feedback and a clinician portal. Blood glucose levels (BGL) data are automatically transferred by Bluetooth-enabled glucose meter to the clinician portal via the mobile App. The primary aim of the study described here is to examine improvement in glycaemic control of a new model of care employing MDMS for patients with complex T2DM attending a tertiary level outpatient service. A two-group, 12-month, pilot pragmatic randomised control trial will recruit 44 T2DM patients. The control group will receive routine care. The intervention group will be supported by the MDMS enabling the participants to potentially better self-manage their diabetes, and the endocrinologists to remotely monitor BGL and to interact with patients through a variety of eHealth modalities. Intervention participants will be encouraged to complete relevant pathology tests, and report on current diabetes management through an online questionnaire. Using this information, the endocrinologist may choose to reschedule the appointment or substitute it with a telephone or video-consultation. This pilot study will guide the conduct of a large-scale study regarding the capacity for a new model of care. This model utilises multimodal eHealth strategies via the MDMS to primarily improve glycaemic control with secondary aims to improve patient experience, reduce reliance on physical clinics, and decrease service delivery cost.

Background: Insulin initiation and/or titration for type 2 diabetes (T2DM) is often delayed as it is a resource-intensive process, often requiring frequent exchange of information between a patient and their diabetes healthcare professional, such as a credentialed diabetes educator (CDE) for insulin dose adjustment (IDA). Existing models of IDA are unlikely to meet the increasing service demand unless efficiencies are increased. Mobile health (mHealth), a subset of Ehealth, has been shown to improve glycaemic control through enhanced self-management and feedback leading to improved patient satisfaction and could simultaneously reduce costs. Considering the potential benefits of mHealth, we have developed an innovative mHealth-based care model to support patients and clinicians in diabetes specialist community outreach and telehealth clinics, that is, REthinking Model of Outpatient Diabetes care utilizing EheaLth – Insulin Dose Adjustment (REMODEL-IDA). This model primarily aims to improve the glycaemic management of patients with T2DM on insulin, with the secondary aims of improving healthcare service delivery efficiency and the patients’ experience. Methods/Design: A two-arm pilot randomized controlled trial (RCT) will be conducted for 3 months with 44 participants, randomized at a 1:1 ratio to receive either the mHealth-based model of care (intervention) or routine care (control), in diabetes specialist community outreach and telehealth clinics. The intervention arm will exchange information related to blood glucose levels via the Mobile Diabetes Management System developed for outpatients with T2DM. They will receive advice on insulin titration from the CDE via the mobile-app and receive automated text-message prompts for better self-management based on their blood glucose levels and frequency of blood glucose testing. The routine care arm will be followed up via telephone calls by the CDE as per usual practice. The primary outcome is change in glycated haemoglobin, a marker of glycaemic management, at 3 months. Patient and healthcare provider satisfaction, and time required to perform IDA by healthcare providers in both arms will be collected. This pilot study will guide the conduct of a large-scale pragmatic RCT in regional Australia.

Diabetes care is undergoing a remarkable transformation by the advancements in information and communications technology (ICT). The aim of this review is to provide a general overview of various ICT-based interventions for diabetes care, challenges of their adoption, and consider future directions.

BACKGROUND:Many patients with diabetes require insulin therapy to achieve optimal glycemic control. Initiation and titration of insulin often require an insulin dose adjustment (IDA) program, involving frequent exchange of blood glucose levels (BGLs) and insulin prescription advice between the patient and healthcare team. This process is time consuming with logistical barriers.OBJECTIVE:To develop an innovative mobile health (m-Health) mobile-based IDA program (mIDA) and evaluate the user adherence and experience through a proof-of-concept trial.METHODS:In the program, an m-Health system was designed to be integrated within a clinical IDA service, comprising a Bluetooth-enabled glucose meter, smartphone application, and clinician portal. Insulin-requiring patients with type-2 diabetes mellitus and stable BGL were recruited to use the m-Health system to record and exchange BGL entries, insulin dosages, and clinical messages for 2 weeks. The user experience was evaluated by a Likert scale questionnaire.RESULTS:Nine participants, aged 58 ± 14 years (mean ± SD), completed the trial with average daily records of 3.1 BGL entries and 1.2 insulin dosage entries. The participants recognized the potential value of the clinical messages. They felt confident about managing their diabetes and were positive regarding ease of use and family support of the system, but disagreed that there were no technical issues. Finally, they were satisfied with the program and would continue to use it if possible.CONCLUSIONS:The m-Health system for IDA showed promising levels of adherence, usability, perception of usefulness, and satisfaction. Further research is required to assess the feasibility and cost-effectiveness of using this system in outpatient settings.