Brief Digital Solutions in Behavior Change Interventions for Type 2 Diabetes Mellitus: A Literature Review

Which behaviour change techniques work best for diabetes self-management mobile apps? Results from a systematic review and meta-analysis of randomised controlled trials

BACKGROUND

Self-management is pivotal in addressing noncommunicable diseases, such as diabetes. The increased availability of digital behaviour change interventions (DBCIs) delivered through mobile health apps offers unprecedented opportunities to enhance self-management and improve health outcomes. However, little is known about the characteristics of DBCIs for diabetes that significantly impact glycaemic control. Therefore, our systematic review with meta-analysis aimed to summarize characteristics and behaviour change components in DBCIs for diabetes self-management and explore potential associations with metabolic outcomes.

METHODS

A systematic search was conducted in PubMed, Embase, the Cochrane Central Register of Controlled Trials, and Scopus to identify randomized controlled trials published until November 2023. The main outcome variable was the change in the mean difference of HbA1c levels between baseline and follow-up across intervention and control groups. Random-effects meta-regression was used to explore variation in glycaemic control as a function of prespecified characteristics of study designs and app interventions.

FINDINGS

A total of 57 studies was included in the analysis, showing a statistically significant percentage point reduction in HbA1c for the intervention group compared to the control arm (-0.36, 95% CI = -0.46 to -0.26, p < 0.001). The inclusion of "self-monitoring of behaviour" as a behaviour change technique (β = -0.22, p = 0.04) and "taking medication" as a target behaviour (β = -0.20, p = 0.05) was associated with improved metabolic outcomes.

INTERPRETATION

Our analyses endorse the use of diabetes self-management apps, highlighting characteristics statistically associated with intervention effectiveness and guiding the design of more effective DBCIs.

FUNDING

This project received funding from the European Union's Horizon 2020 programme.

[Diabetes control and visits to the family doctor during the COVID-19 pandemic: a multicenter study in primary care]

OBJECTIVES

To determine whether in patients with type 2 diabetes (DM2) the changes in their relationship with family doctors during the COVID-19 pandemic, in-person (iPC) and telematic (TC) consultations, were associated with control of their disease.

DESIGN

Multicentric study of retrospective follow-up.

SETTING

Seven health centers in Tenerife, Spain.

PARTICIPANTS

3543 patients with DM2.

MAIN MEASUREMENTS

Sex, age, iPC, TC and DM2 control using glycosylated hemoglobin (A1c) during the period 2019-2021. Logistic regression models were fitted with DM2 control as an effect, and with the other measurements as independent variables.

RESULTS

50% were women. 38% were less than 65 years old. A1c was measured in 84% of patients in 2019, 68% in 2020, and 77% in 2021. 58.4% had good control in 2019, 46.1% in 2020, and 50.3% in 2021. Median iPC were 7 in 2019, 4 in 2020 and 5 in 2021 (p<0.001). The OR(95%CI) of good control in 2019 were 1.04(1.04-1.05) per year of age and 1.03(1.01-1.04) for each iPC; In 2020 they were 1.04 (1.03-1.05) per year of age, 1.05 (1.04-1.07) for each iPC and 1.04 (1.02-1.07) for each TC; in 2021 they were 1.04 (1.04-1.05) per year of age, 1.05 (1.03-1.06) for each iPC and 1.02 (1.00-1.04) for each TC.

CONCLUSIONS

The control of patients with DM2 during the period 2019-2021 had a direct relationship with the change in the frequency of consultations at the health center, with differences depending on the type of consultation and the age of the patient.

Understanding how self-management education and support programmes for type 2 diabetes are expected to change behaviour: A document analysis of two programmes

AIMS

Attendance at diabetes self-management education and support (DSMES) programmes for type 2 diabetes is associated with positive outcomes, but the impact on some outcomes is inconsistent and tends to decline over time. Understanding the active ingredients of effective programmes is essential to optimise their effectiveness. This study aimed to (1) retrospectively identify behaviour change techniques (BCTs), mechanisms of action (MoAs) and intervention functions in two DSMES programmes, the Community-Oriented Diabetes Education and the Diabetes Education and Self-Management for Ongoing and Newly Diagnosed and (2) examine variation in content between programmes.

METHODS

A multiple case study approach, including a documentary analysis of the programme materials, was conducted. Materials were coded using the BCT Taxonomy v1, the Mode of Delivery Ontology v1 and the Intervention Source Ontology v1. The Behaviour Change Wheel guidance and the Theory and Techniques tool were used to identify intervention functions and MoAs. Programme stakeholders provided feedback on the findings.

RESULTS

Thirty-four BCTs were identified across the programmes, with 22 common to both. The identified BCTs were frequently related to 'goals and planning', 'feedback and monitoring' and 'natural consequences'. BCTs were linked with 15 MoAs, predominantly related to reflective motivation ('beliefs about capabilities' and 'beliefs about consequences') and psychological capability ('knowledge'). BCTs served six intervention functions, most frequently 'education', 'enablement' and 'persuasion'.

CONCLUSIONS

Although both programmes included several BCTs, some BCTs were rarely or never used. Additional BCTs could be considered to potentially enhance effectiveness by addressing a wider range of barriers.

An overview of environmental risk factors for type 2 diabetes research using network science tools

Objective

Current studies lack a comprehensive understanding of the environmental factors influencing type 2 diabetes, hindering an in-depth grasp of the overall etiology. To address this gap, we utilized network science tools to highlight research trends, knowledge structures, and intricate relationships among factors, offering a new perspective for a profound understanding of the etiology.

Methods

The Web of Science database was employed to retrieve documents relevant to environmental risk factors in type 2 diabetes from 2012 to 2024. Bibliometric analysis using Microsoft Excel and OriginPro provided a detailed scientific production profile, including articles, journals, countries, and authors. Co-occurrence analysis was employed to determine the collaboration state and knowledge structures, utilizing social network tools such as Gephi, Tableau, and R Studio. Additionally, theme evolutionary analysis was conducted using SciMAT to offer insights into research trends.

Results

The publications and themes related to environmental factors in type 2 diabetes have consistently risen, shaping a well-established research domain. Lifestyle environmental factors, particularly diet and nutrition, stand out as the most represented and rapidly growing topics. Key focal hotspots include sedentary and digital behavior, PM2.5, ethnicity and socioeconomic status, traffic and greenspace, and depression. The theme evolutionary analysis revealed three distinct paths: (1) oxidative stress-air pollutants-PM2.5-air pollutants; (2) calcium-metabolic syndrome-cardiovascular disease; and (3) polychlorinated biphenyls (PCBs)-persistent organic pollutants (POPs)-obesity.

Conclusions

Digital behavior signifies a novel approach for preventing and managing type 2 diabetes. The influence of PM2.5 and calcium on oxidative stress and abnormal vascular contraction is intricately linked to microvascular diabetes complications. The transition from PCBs and POPs to obesity underscores the disruption of endocrine function by chemicals, elevating the risk of diabetes. Future studies should explore the connections between environmental factors, microvascular complications, and long-term outcomes in diabetes.

Implementation of digital health in rural populations with chronic musculoskeletal conditions: A scoping review protocol

Musculoskeletal conditions are a major source of disability worldwide, and its burden have been rising in the last decades. Rural areas, in particular, are associated with higher prevalence of these conditions as well as higher levels of disability, which is likely related to other determinants that affect these communities. Although digital health has been identified as a potential solution to mitigate the impact of these determinants, it is also known that these populations may face barriers that limit the implementation of these interventions. Therefore, the aim of this scoping review is to comprehensively map the evidence regarding the implementation of digital health interventions in rural populations with chronic musculoskeletal conditions. We will include studies published from the year 2000; that report the use of digital interventions that promote prevention, treatment or monitoring of any chronic musculoskeletal condition or chronic pain from musculoskeletal origin, in patients that live in rural areas. This protocol follows the methodological framework for scoping reviews proposed by Arksey and O'Malley, as well as the Joana Briggs Institute (JBI) approach. We will conduct the search on Medline (PubMed), EMBASE, Web of Science and Scopus, as well as grey literature databases. Two independent reviewers will screen titles and abstracts followed by a full-text review to assess the eligibility of the articles. Data extracted will include the identification of the digital interventions used, barriers and enablers identified by the patients or healthcare providers, the patient-level outcomes measured, and the implementation strategies and outcomes reported. By mapping the evidence on the implementation of digital health interventions in rural communities with musculoskeletal conditions, this scoping review will enhance our understanding of their applicability in real-world settings.

The effect of personalized intelligent digital systems for self-care training on type II diabetes: a systematic review and meta-analysis of clinical trials

AIMS

Type 2 diabetes (T2D) is rising worldwide. Self-care prevents diabetic complications. Lack of knowledge is one reason patients fail at self-care. Intelligent digital health (IDH) solutions have a promising role in training self-care behaviors based on patients' needs. This study reviews the effects of RCTs offering individualized self-care training systems for T2D patients.

METHODS

PubMed, Web of Science, Scopus, Cochrane Library, and Science Direct databases were searched. The included RCTs provided data-driven, individualized self-care training advice for T2D patients. Due to the repeated studies measurements, an all-time-points meta-analysis was conducted to analyze the trends over time. The revised Cochrane risk-of-bias tool (RoB 2.0) was used for quality assessment.

RESULTS

In total, 22 trials met the inclusion criteria, and 19 studies with 3071 participants were included in the meta-analysis. IDH interventions led to a significant reduction of HbA1c level in the intervention group at short-term (in the third month: SMD = - 0.224 with 95% CI - 0.319 to - 0.129, p value < 0.0; in the sixth month: SMD = - 0.548 with 95% CI - 0.860 to - 0.237, p value < 0.05). The difference in HbA1c reduction between groups varied based on patients' age and technological forms of IDH services delivery. The descriptive results confirmed the impact of M-Health technologies in improving HbA1c levels.

CONCLUSIONS

IDH systems had significant and small effects on HbA1c reduction in T2D patients. IDH interventions' impact needs long-term RCTs. This review will help diabetic clinicians, self-care training system developers, and researchers interested in using IDH solutions to empower T2D patients.

Functions and mechanisms of protein lysine butyrylation (Kbu): Therapeutic implications in human diseases

Post-translational modifications (PTM) are covalent modifications of proteins or peptides caused by proteolytic cleavage or the attachment of moieties to one or more amino acids. PTMs play essential roles in biological function and regulation and have been linked with several diseases. Modifications of protein acylation (Kac), a type of PTM, are known to induce epigenetic regulatory processes that promote various diseases. Thus, an increasing number of studies focusing on acylation modifications are being undertaken. Butyrylation (Kbu) is a new acylation process found in animals and plants. Kbu has been recently linked to the onset and progression of several diseases, such as cancer, cardiovascular diseases, diabetes, and vascular dementia. Moreover, the mode of action of certain drugs used in the treatment of lymphoma and colon cancer is based on the regulation of butyrylation levels, suggesting that butyrylation may play a therapeutic role in these diseases. In addition, butyrylation is also commonly involved in rice gene expression and thus plays an important role in the growth, development, and metabolism of rice. The tools and analytical methods that could be utilized for the prediction and detection of lysine butyrylation have also been investigated. This study reviews the potential role of histone Kbu, as well as the mechanisms underlying this process. It also summarizes various enzymes and analytical methods associated with Kbu, with the goal of providing new insights into the role of Kbu in gene regulation and diseases.

A Randomised Control Trial to Explore the Impact and Efficacy of the Healum Collaborative Care Planning Software and App on Condition Management in the Type 2 Diabetes Mellitus Population in NHS Primary Care

INTRODUCTION

Effective and scalable solutions to support management of Type 2 Diabetes (T2D) at a distance are a priority for health systems worldwide. The use of personalised care planning has been shown to be effective at improving the health outcomes and the experience of care amongst people with T2D and other long-term health conditions. Here we describe a specific example of such an intervention.

METHODS

The sample comprised 197 participants with T2D randomised to either the active intervention group with digital health planning (App + usual care), with 115 participants, or the control group (usual care), with 82 participants. We analysed data in relation to changes in body mass index (BMI) and glycated haemoglobin (HbA1c) over a 6-month follow-up period. We also analysed responses to questionnaires sent out and held interviews with participants that were in the active treatment group and therefore had a care plan created and access to an app.

RESULTS

The active treatment group had significant reductions in HbA1c (p < 0.01) and BMI (p < 0.037) vs the control group (no significant change). The average percentage change in HbA1c for the treatment group over 6 months was - 7.4% (± SE 1.4%), compared with 1.8% (± SE 2.1%) for the control group. The average percentage change in BMI for the treatment group was - 0.7% (± SE 0.4%) and it was - 0.2% (± SE 0.5%) for the control group. A higher percentage of the active treatment group reduced their HbA1c and BMI than the control group. For HbA1c, 72.4% of the active treatment group reduced their HbA1c, compared to 41.5% of the control group. For BMI, 52.7% of the active treatment group experienced a reduction, compared to 42.9% for the control group. Self-measured quality of life (QoL) improved for patients in the active treatment group, shown by an increase in their pre-trial to post-trial EQ-5D-5L rating by an average of 0.0464 (± SE 0.0625), compared to a decrease of 0.0086 (± SE 0.0530) for the control group. The average EQ VAS score also increased pre- to post-trial for the active treatment group, on average by 8.2%, whereas it decreased by an  average of - 2.8% for the control group.

CONCLUSION

These findings point to how the provision of personalised plans of care, support and education linked to a mobile app, can result in HbA1c and BMI reduction for many individuals with T2D. The use of a patient management app as well as a personalised care plan also led to an improvement in patient self-rated QoL and engagement.

Comprehensive Network Analysis Reveals the Targets and Potential Multitarget Drugs of Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is a metabolic disease with increasing prevalence and mortality year by year. The purpose of this study was to explore new therapeutic targets and candidate drugs for multitargets by single-cell RNA expression profile analysis, network pharmacology, and molecular docking. Single-cell RNA expression profiling of islet β cell samples between T2DM patients and nondiabetic controls was conducted to identify important subpopulations and the marker genes. The potential therapeutic targets of T2DM were identified by the overlap analysis of insulin-related genes and diabetes-related genes, the construction of protein-protein interaction network, and the molecular complex detection (MCODE) algorithm. The network distance method was employed to determine the potential drugs of the target. Molecular docking and molecular dynamic simulations were carried out using AutoDock Vina and Gromacs2019, respectively. Eleven cell clusters were identified by single-cell RNA sequencing (scRNA-seq) data, and three of them (C2, C8, and C10) showed significant differences between T2DM samples and normal samples. Eight genes from differential cell clusters were found from differential cell clusters to be associated with insulin activity and T2DM. The MCODE algorithm built six key subnetworks, with five of them correlating with inflammatory pathways and immune cell infiltration. Importantly, CCR5 was a gene within the key subnetworks and was differentially expressed between normal samples and T2DM samples, with the highest area under the ROC curve (AUC) of 82.5% for the diagnosis model. A total of 49 CCR5-related genes were screened, and DB05494 was identified as the most potential drug with the shortest distance to CCR5-related genes. Molecular docking illustrated that DB05494 stably bound with CCR5 (-8.0 kcal/mol) through multiple hydrogen bonds (LYS26, TYR37, TYR89, CYS178, and GLN280) and hydrophobic bonds (TRP86, PHE112, ILE198, TRP248, and TYR251). This study identified CCR5 as a potential therapeutic target and screened DB05494 as a potential drug for T2DM treatment.