Objective To determine the effectiveness of a web-based self-management programme for people with type 2 diabetes in improving glycaemic control and reducing diabetes-related distress. Methods and design Individually randomised two-arm controlled trial. Setting 21 general practices in England. Participants Adults aged 18 or over with a diagnosis of type 2 diabetes registered with participating general practices. Intervention and comparator Usual care plus either Healthy Living for People with Diabetes (HeLP-Diabetes), an interactive, theoretically informed, web-based self-management programme or a simple, text-based website containing basic information only. Outcomes and data collection Joint primary outcomes were glycated haemoglobin (HbA1c) and diabetes-related distress, measured by the Problem Areas in Diabetes (PAID) scale, collected at 3 and 12 months after randomisation, with 12 months the primary outcome point. Research nurses, blind to allocation collected clinical data; participants completed self-report questionnaires online. Analysis The analysis compared groups as randomised (intention to treat) using a linear mixed effects model, adjusted for baseline data with multiple imputation of missing values. Results Of the 374 participants randomised between September 2013 and December 2014, 185 were allocated to the intervention and 189 to the control. Final (12 month) follow-up data for HbA1c were available for 318 (85%) and for PAID 337 (90%) of participants. Of these, 291 (78%) and 321 (86%) responses were recorded within the predefined window of 10–14 months. Participants in the intervention group had lower HbA1c than those in the control (mean difference −0.24%; 95% CI −0.44 to −0.049; p=0.014). There was no significant overall difference between groups in the mean PAID score (p=0.21), but prespecified subgroup analysis of participants who had been more recently diagnosed with diabetes showed a beneficial impact of the intervention in this group (p = 0.004). There were no reported harms. Conclusions Access to HeLP-Diabetes improved glycaemic control over 12 months. Trial registration number ISRCTN02123133.
Digital health interventions have potential to contribute to better health outcomes, better healthcare and lower costs. However, evidence for their effectiveness is variable. The development and content of digital health interventions are often not described in enough detail to enable others to replicate the research or improve on previous interventions. This has led to a call for transparent reporting of intervention content and development.To describe the development process and content of a digital self-management intervention for people with type 2 diabetes (HeLP-Diabetes) that has been found to achieve its target clinical outcome, the reduction of HbA1c, a measure of glycaemic control.We synthesised theory, data from existing research evidence and international guidelines, and new qualitative data from target users to identify the determinants of self-management and the content to be included in HeLP-Diabetes. Using an ongoing iterative participatory design approach the content of the intervention was written, produced, reviewed and changed.It is possible to develop and transparently report self-management programmes for long-term conditions, which reflect current best evidence, theoretical underpinning and user involvement. We intend that reporting the development process and content will inform future digital intervention development.
effectiveness of facilitated access to a self-management website (HeLP-Diabetes) compared to usual care for patients with Type 2 Diabetes:a randomised-control trial.
Evidence on how to implement new interventions into complex healthcare environments is often poorly reported and indexed, reducing its potential to inform initiatives to improve healthcare services. Using the implementation of a digital intervention within routine National Health Service (NHS) practice, we provide an example of how to develop a theoretically based implementation plan and how to report it transparently. In doing so we also highlight some of the challenges to implementation in routine healthcare.The implemented intervention was HeLP-Diabetes, a digital self-management programme for people with Type 2 Diabetes, which was effective in improving diabetes control. The target setting for the implementation was an inner city London Clinical Commissioning Group in the NHS comprised of 34 general practices. HeLP-Diabetes was designed to be offered to patients as part of routine diabetes care across England. Evidence synthesis, engagement of local stakeholders, a theory of implementation (Normalization Process Theory), feedback, qualitative interviews and usage data were used to develop an implementation plan.A new implementation plan was developed to implement HeLP-Diabetes within routine practice. Individual component strategies were selected and developed informed by Normalization Process Theory. These strategies included: engagement of local opinion leaders, provision of educational materials, educational visits, educational meetings, audit and feedback and reminders. Additional strategies were introduced iteratively to address barriers that arose during the implementation. Barriers largely related to difficulties in allocating resources to implement the intervention within routine care.This paper provides a worked example of implementing a digital health intervention. The learning from this work can inform others undertaking the work of planning and executing implementation activities in routine healthcare. Of particular importance is: the selection of appropriate theory to guide the implementation process and selection of strategies; ensuring that enough attention is paid to planning implementation; and a flexible approach that allows response to emerging barriers.
Use of risk calculators for specific diseases is increasing, with an underlying assumption that they promote risk reduction as users become better informed and motivated to take preventive action. Empirical data to support this are, however, sparse and contradictory.
Aim
To explore user reactions to a cardiovascular risk calculator for people with type 2 diabetes. Objectives were to identify cognitive and emotional reactions to the presentation of risk, with a view to understanding whether and how such a calculator could help motivate users to adopt healthier behaviours and/or improve adherence to medication.
Design and setting
Qualitative study combining data from focus groups and individual user experience. Adults with type 2 diabetes were recruited through website advertisements and posters displayed at local GP practices and diabetes groups.
Method
Participants used a risk calculator that provided individualised estimates of cardiovascular risk. Estimates were based on UK Prospective Diabetes Study (UKPDS) data, supplemented with data from trials and systematic reviews. Risk information was presented using natural frequencies, visual displays, and a range of formats. Data were recorded and transcribed, then analysed by a multidisciplinary group.
Results
Thirty-six participants contributed data. Users demonstrated a range of complex cognitive and emotional responses, which might explain the lack of change in health behaviours demonstrated in the literature.
Conclusion
Cardiovascular risk calculators for people with diabetes may best be used in conjunction with health professionals who can guide the user through the calculator and help them use the resulting risk information as a source of motivation and encouragement.
We aimed to quantify the relative risk of progression from mild cognitive impairment (MCI) to dementia in people with and without diabetes, and with and without the MetS (MetS); and to identify potential modifiers of the risk of progression from MCI to dementia in people with diabetes or MetS. We searched Medline, Embase, PsycINFO, PsycArticles and Web of Science from inception through to 20th March 2018. Where possible, the results from three or more studies were pooled in a meta-analysis, while other findings have been described narratively. We included 15 articles reporting 12 studies (6865 participants). The overall unadjusted pooled odds ratio for the progression of MCI to dementia in people with diabetes/MetS was 1.67 (95% CI 1.27–2.19); the pooled odds ratio for progression in diabetes + MCI was 1.53 (95% CI 1.20–1.97) and in people with MetS + MCI was 2.95 (95% CI 1.23–7.05). There was moderate heterogeneity in the included studies (I2 < 60%). In diabetes, a longer duration of diabetes and the presence of retinopathy were associated with an increased risk of progression, while the use of statins and oral hypoglycaemic agents reduced the risk. Having multiple cardiovascular risk factors was a significant risk factor for progression from MCI to dementia in people with MetS. Diabetes and MetS were both associated with an increased incidence of dementia when co-existing with MCI. Intensive cardiovascular risk reduction and lifestyle changes for patients presenting with MCI and diabetes, prediabetes or MetS may be important in reducing incidence of dementia in this high risk population.
OBJECTIVE Structured patient education programs can reduce the risk of diabetes-related complications. However, people appear to have difficulties attending face-to-face education and alternatives are needed. This review looked at the impact of computer-based diabetes self-management interventions on health status, cardiovascular risk factors, and quality of life of adults with type 2 diabetes. RESEARCH DESIGN AND METHODS We searched The Cochrane Library, Medline, Embase, PsycINFO, Web of Science, and CINAHL for relevant trials from inception to November 2011. Reference lists from relevant published studies were screened and authors contacted for further information when required. Two authors independently extracted relevant data using standard data extraction templates. RESULTS Sixteen randomized controlled trials with 3,578 participants met the inclusion criteria. Interventions were delivered via clinics, the Internet, and mobile phones. Computer-based diabetes self-management interventions appear to have small benefits on glycemic control: the pooled effect on HbA1c was −0.2% (−2.3 mmol/mol [95% CI −0.4 to −0.1%]). A subgroup analysis on mobile phone–based interventions showed a larger effect: the pooled effect on HbA1c from three studies was −0.50% (−5.46 mmol/mol [95% CI −0.7 to −0.3%]). There was no evidence of improvement in depression, quality of life, blood pressure, serum lipids, or weight. There was no evidence of significant adverse effects. CONCLUSIONS Computer-based diabetes self-management interventions to manage type 2 diabetes appear to have a small beneficial effect on blood glucose control, and this effect was larger in the mobile phone subgroup. There was no evidence of benefit for other biological, cognitive, behavioral, or emotional outcomes.
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