Self-management interventions for Type 2 Diabetes: systematic review protocol focusing on patient workload and capacity support

Identification of challenges and leveraging mHealth technology, with need-based solutions to empower self-management in type 2 diabetes: a qualitative study

INTRODUCTION

Effective diabetes management relies mainly on an individual's ability to perform self-care tasks. However, this process is influenced by a complex interplay of factors. This study explores the multifaceted influences on Diabetes Self-Management (DSM), examining both factors influencing and affecting DSM. Understanding these influences is crucial for developing targeted Digital Health Interventions that empower individuals with diabetes to achieve successful self-management.

OBJECTIVES

To identify problems faced by Type 2 Diabetes Mellitus (T2DM) individuals in self-managing diabetes and leveraging mHealth technology, with need-based solutions to Empower Self-Management in T2DM.

METHODOLOGY

In-depth semi-structured interviews were conducted among ten patients with T2DM visiting the outpatient department of a tertiary care hospital in coastal Karnataka. Additionally, six healthcare professionals (HCPs) working closely with T2DM patients were interviewed to understand their perspectives on using mHealth to manage T2DM effectively. The themes for the solutions described were analyzed using ATLAS-TI software.

RESULTS

Our research examined certain factors that might have influenced effective diabetes self-management and investigated patient perspectives on using digital health solutions in diabetes self-management. This study found that technology skills, duration of diabetes, knowledge, and personal beliefs were all significant factors affecting self-management in participants with T2DM. Additionally, socioeconomic factors were also seen to influence effective diabetes self-management. The Google search engine was used by 50% of the participants interviewed to learn about T2DM. Diet management through Google searches was used by a minority (30%) of the patients. None of the participants had previously used any mobile health applications (mHealth apps) to manage T2DM. 20% of the participants expressed limited knowledge about using smartphones or wearables to track health parameters. The study also identified potential non-technological barriers to mHealth adoption. To address these concerns, researchers used an empathy map to develop solutions that promote mHealth use.

CONCLUSION

Several challenges and need-based mHealth solutions were identified to empower diabetes self-management education among T2DM patients. Implementing need-based mHealth solutions such as data tracking, personalized feedback, and access to educational resources can lead to better disease control and a higher quality of life for those with T2DM. Further research and development in mHealth interventions, and collaborative efforts among healthcare providers, patients, and technology developers, hold a promising future for the healthcare sector in providing efficient, effective, and accessible care.

Comparative effectiveness of ehealth self-management interventions for patients with heart failure: A Bayesian network meta-analysis

OBJECTIVE

This study evaluated the effectiveness of electronic self-management support interventions in reducing all-cause mortality, cardiovascular mortality, readmission rates, and HF-related readmission in heart failure patients.

METHODS

Following the PRISMA-P guidelines and PRISMS taxonomy, we searched Pubmed, Cochrane Library, and Embase for RCTs and trials of electronic health technologies for heart failure interventions. Develop support programs in advance for education, monitoring, reminders, or a combination of these to screen and categorize studies. The Cochrane ROB2 tool was used to assess the risk of bias.

RESULTS

The monitoring interventions may improve all-cause mortality (OR 0.77, 95% CI 0.63 to 0.93) and cardiovascular mortality (OR 0.75, 95% CI 0.61 to 0.93) compared to usual care. Reminder interventions were associated with significantly reducing readmission rates (OR 0.07, 95% CI 0.00 to 0.94). Mixed interventions were most effective in reducing HF-related readmission rates (OR 0.75, 95% CI 0.56 to 0.99).

CONCLUSION

Electronic self-management interventions, particularly monitoring and reminders, can potentially improve outcomes of heart failure patients, including reducing all-cause mortality, cardiovascular mortality, and readmission rates.

PRACTICE IMPLICATIONS

The eHealth model and the combination of self-management are significant for long-term intervention in patients with HF to improve their quality of life and prognosis.

Supporting patients' workload through wearable devices and mobile health applications, a systematic literature review

Patients face a challenging workload in their course of care. In this study, we investigate the impact of using mobile health technologies in supporting this workload and identify the system challenges of its application through a systematic review of the literature published in the last two decades following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Reviews and Meta-Analysis guidelines PRISMA guidelines. Twenty-two studies that satisfied the inclusion criteria were included. The review revealed various mobile health and wearable devices used to support mental demand, physical demand, frustration, and performance. Better outcomes were related to mobile health use in healthcare for patients in different settings. There were no applications of health that supported the temporal demand of patients. Some populations, such as cancer patients, need more than only physical demand. Mhealth devices are important in supporting the patients' workload in their daily activities and clinical settings.Practitioner summary: This review study shows the importance of mHealth and wearables in supporting patients' workload (physical, mental, emotional) but not the temporal load.

The effect evaluation of continuous nursing intervention in patients with type 2 diabetic retinopathy

INTRODUCTION

To evaluate the application effect of continuous nursing intervention in type 2 diabetic retinopathy (DR).

METHODS

Patients with type 2 DR were selected and divided into intervention group and control group by random. The control group received routine nursing intervention, and the intervention group received continuous nursing intervention on the basis of the control group. The clinical effects of the two groups were compared.

RESULTS

After 1 and 2 years of intervention, the intervention group compared to the control group. The rate of visual acuity decrease was significantly lower (p < 0.05). Fasting blood glucose, 2 h postprandial blood glucose, and glycosylated hemoglobin were significantly lower (p < 0.05). The self-management ability and satisfaction were significantly higher, and the readmission rate was significantly lower (p < 0.05).

CONCLUSION

The continuous nursing intervention model has a good clinical effect on the visual acuity of patients with type 2 DR.

Self-management interventions for Type 2 Diabetes: systematic review protocol focusing on patient workload and capacity support

INTRODUCTION

People living with type 2 diabetes undertake a range of tasks to manage their condition, collectively referred to as self-management. Interventions designed to support self-management vary in their content, and efficacy. This systematic review will analyse self-management interventions for type 2 diabetes drawing on theoretical models of patient workload and capacity.

METHODS AND ANALYSIS

Five electronic databases (Medline, Embase, CENTRAL, CINAHL and PsycINFO) will be searched from inception to 27th April 2021, supplemented by citation searching and hand-searching of reference lists. Two reviewers will independently review titles, abstracts and full texts. Inclusion criteria include Population: Adults with type 2 diabetes mellitus; Intervention: Randomised controlled trials of self-management support interventions; Comparison: Usual care; Outcomes: HbA1c (primary outcome) health-related quality of life (QOL), medication adherence, self-efficacy, treatment burden, healthcare utilization (e.g. number of appointment, hospital admissions), complications of type 2 diabetes (e.g. nephropathy, retinopathy, neuropathy, macrovascular disease) and mortality; Setting: Community. Study quality will be assessed using the Effective Practice and Organisation of Care (EPOC) risk of bias tool. Interventions will be classified according to the EPOC taxonomy and the PRISMS self-management taxonomy and grouped into similar interventions for analysis. Clinical and methodological heterogeneity will be assessed within subgroups, and random effects meta-analyses performed if appropriate. Otherwise, a narrative synthesis will be performed. Interventions will be graded on their likely impact on patient workload and support for patient capacity. The impact of these theoretical constructs on study outcomes will be explored using meta-regression. Conclusion This review will provide a broad overview of self-management interventions, analysed within the cumulative complexity model theoretical framework. Analyses will explore how the workload associated with self-management, and support for patient capacity, impact on outcomes of self-management interventions.

REGISTRATION NUMBER

PROSPERO CRD42021236980.