UPBEAT-UK: a programme of research into the relationship between coronary heart disease and depression in primary care patients

Explainable Early Prediction of Gestational Diabetes Biomarkers by Combining Medical Background and Wearable Devices: A Pilot Study With a Cohort Group in South Africa

This study aims to explore the potential of Internet of Things (IoT) devices and explainable Artificial Intelligence (AI) techniques in predicting biomarker values associated with GDM when measured 13-16 weeks prior to diagnosis. We developed a system that forecasts biomarkers such as LDL, HDL, triglycerides, cholesterol, HbA1c, and results from the Oral Glucose Tolerance Test (OGTT) including fasting glucose, 1-hour, and 2-hour post-load glucose values. These biomarker values are predicted based on sensory measurements collected around week 12 of pregnancy, including continuous glucose levels, short physical movement recordings, and medical background information. To the best of our knowledge, this is the first study to forecast GDM-associated biomarker values 13 to 16 weeks prior to the GDM screening test, using continuous glucose monitoring devices, a wristband for activity detection, and medical background data. We applied machine learning models, specifically Decision Tree and Random Forest Regressors, along with Coupled-Matrix Tensor Factorisation (CMTF) and Elastic Net techniques, examining all possible combinations of these methods across different data modalities. The results demonstrated good performance for most biomarkers. On average, the models achieved Mean Squared Error (MSE) between 0.29 and 0.42 and Mean Absolute Error (MAE) between 0.23 and 0.45 for biomarkers like HDL, LDL, cholesterol, and HbA1c. For the OGTT glucose values, the average MSE ranged from 0.95 to 2.44, and the average MAE ranged from 0.72 to 0.91. Additionally, the utilisation of CMTF with Alternating Least Squares technique yielded slightly better results (0.16 MSE and 0.07 MAE on average) compared to the well-known Elastic Net feature selection technique. While our study was conducted with a limited cohort in South Africa, our findings offer promising indications regarding the potential for predicting biomarker values in pregnant women through the integration of wearable devices and medical background data in the analysis. Nevertheless, further validation on a larger, more diverse cohort is imperative to substantiate these encouraging results.

Patients' perspectives of barriers and facilitators to accessing support through primary care for common mental health problems in England: A systematic review

BACKGROUND

Common mental disorders (CMDs) are prevalent throughout the population. Psychological therapy is often sought via primary care; however, equitable access is not commonplace. This review aims to investigate the barriers and facilitators adults experiencing CMDs perceive when accessing evidence-based psychological treatment in England.

METHODS

A qualitative systematic review with meta-synthesis was conducted (PROSPERO CRD42020227039). Seven electronic databases were searched for papers from 2008 to October 2022.

RESULTS

Searches identified 30 studies from which three themes were developed with seven subthemes. Stigma and patients' perceptions and understandings of CMDs impacted their help-seeking decision-making and engagement with services. This meant that services were not used as a first resort for help-seeking. Upon reaching services, patients appeared to perceive primary care as not prioritising mental health problems, nor as being the place where they would be supported, particularly as healthcare professionals did not appear to know about CMDs and therapy was seen as difficult to access. The interaction between healthcare professional and patients was seen as pivotal to whether patients accessed support or not.

LIMITATIONS

The review is limited to research conducted within England. Additionally, it only explores access barriers prior to treatment experiences.

CONCLUSION

Knowledge, attitudinal, systemic and relational barriers and facilitators were identified. Future research should focus on developing stigma reduction initiatives. Clinical implications include provision of standardised training across primary care HCP (healthcare professionals).

Integrated self-management support provided by primary care nurses to persons with chronic diseases and common mental disorders: a scoping review

AIM

To map integrated and non-integrated self-management support interventions provided by primary care nurses to persons with chronic diseases and common mental disorders and describe their characteristics.

DESIGN

A scoping review.

DATA SOURCES

In April 2020, we conducted searches in several databases (Academic Research Complete, AMED, CINAHL, ERIC, MEDLINE, PsycINFO, Scopus, Emcare, HealthSTAR, Proquest Central) using self-management support, nurse, primary care and their related terms. Of the resulting 4241 articles, 30 were included into the analysis.

REVIEW METHODS

We used the Rainbow Model of Integrated Care to identify integrated self-management interventions and to analyze the data and the PRISMS taxonomy for the description of interventions. Study selection and data synthesis were performed by the team. Self-management support interventions were considered integrated if they were consistent with the Rainbow model's definition of clinical integration and person-focused care.

RESULTS

The 30 selected articles related to 10 self-management support interventions. Among these, five interventions were considered integrated. The delivery of the interventions showed variability. Strategies used were education, problem-solving therapies, action planning, and goal setting. Integrated self-management support intervention characteristics were nurse-person relationship, engagement, and biopsychosocial approach. A framework for integrated self-management was proposed. The main characteristics of the non-integrated self-management support were disease-specific approach, protocol-driven, and lack of adaptability.

CONCLUSION

Our review synthesizes integrated and non-integrated self-management support interventions and their characteristics. We propose recommendations to improve its clinical integration. However, further theoretical clarification and qualitative research are needed.

IMPLICATION FOR NURSING

Self-management support is an important activity for primary care nurses and persons with chronic diseases and common mental disorders, who are increasingly present in primary care, and require an integrated approach.

IMPACT

This review addresses the paucity of details surrounding integrated self-management support for persons with chronic diseases and common mental disorders and provides a framework to better describe its characteristics. The findings could be used to design future research and improve the clinical integration of this activity by nurses.

Quality of life, anxiety and depressive disorders in patients with extrasystolic arrhythmia

Introduction

The aim of this study was to perform a comparative and economic analysis of the degree of development of anxiety-depressive disorder in patients with different types of extrasystolic arrhythmia using different assessment scales.

Material and methods

The study was conducted in 2018-2019 at the premises of clinic No. 4, involving 450 patients (Moscow, Russia). Patients were divided into three groups: with coronary heart disease (CHD) (147 patients), with myocardiodystrophy (MCD) (113) and with cardiopsychoneurosis (CPN) (190). Everyone underwent round-the-clock electrocardiography (ECG) and echocardiography. If symptoms of depressive disorder are detected in a timely manner, the risk of adverse cardiovascular diseases may be reduced.

Results

Depression and anxiety indicators on all three scales differed significantly (p ≤ 0.05) in patients with supraventricular extrasystole (more than 40 points on the Zung scale, 14 points on the Montgomery-Asberg depression scale, more than 38 points on Zung and 3 points on Covi anxiety scales). For patients with ventricular extrasystole, a significant difference was established between groups 1 and 2 only in terms of the level of depression on the Zung scale. Factors of psychogenic origin determine the development of cardiac rhythm disturbances.

Conclusions

The study showed that for supraventricular extrasystoles, these factors determine the overall health of the patient. The differences between the three groups are significant on all scales of depression and anxiety (p ≤ 0.05). The most susceptible to depression and anxiety are patients with extrasystolic arrhythmias diagnosed with CHD, as well as MCD.