Work-related experiences of consultant psychiatrists during the COVID-19 response: qualitative analysis.

BACKGROUND: Research has begun to draw attention to the challenges mental health professionals faced in delivering services during the COVID-19 pandemic response. However, few studies have examined the specific experiences of consultant psychiatrists. AIMS: To examine the work-related experiences and psychosocial needs of consultant psychiatrists situated in the Republic of Ireland arising from the COVID-19 response. METHOD: We interviewed 18 consultant psychiatrists and analysed data using inductive thematic analysis. RESULTS: Work-related experience of participants was characterised by increased workload associated with assumption of guardianship of physical and mental health of vulnerable patients. Unintended consequences of public health restrictions increased case complexity, limited availability of alternative supports and hindered the practice of psychiatry, including inhibiting peer support systems for psychiatrists. Participants perceived available psychological supports as generally unsuitable for their needs given their specialty. Long-standing under-resourcing, mistrust in management and high levels of burnout exacerbated the psychological burden of the COVID-19 response. CONCLUSIONS: The challenges of leading mental health services were evident in the increased complexity involved in caring for vulnerable patients during the pandemic, contributing to uncertainty, loss of control and moral distress among participants. These dynamics worked synergistically with pre-existing system-level failures, eroding capacity to mount an effective response. The longer-term psychological well-being of consultant psychiatrists - as well as the pandemic preparedness of healthcare systems - is contingent on implementation of policies addressing long-standing under-investment in the services vulnerable populations rely on, not least community mental health services.

Psychosocial Outcomes among Users and Nonusers of Open-Source Automated Insulin Delivery Systems: Findings from an International Survey of Adults with Type 1 Diabetes

Background: Use of open-source automated insulin delivery (AID) is associated with improved psychosocial outcomes among people with type 1 diabetes (T1D) . However, research to date has been qualitative or used study-specific single items. There is a need for quantitative research using validated measures in larger samples. Method: We conducted an international online survey to examine the psychosocial outcomes of open-source AID users and non-users. Validated questionnaires assessed diabetes-specific quality of life (QoL) , impact of the COVID-pandemic on diabetes-specific QoL, diabetes specific-positive well-being, diabetes treatment satisfaction, diabetes distress, fear of hypoglycaemia, general emotional well-being, and subjective sleep quality. Results: 587 participants completed at least one questionnaire, including 447 adults using open-source AID (mean age 43, 42% women) and 140 non-users (mean age 40, 64% women) . Table 1 shows significant between-group differences for all questionnaire scores. Discussion: Adults with T1D using open-source AID report significantly better psychosocial outcomes than non-users. Due to the cross-sectional design of this study, we cannot make any causal inferences about the use of these devices. Further research is needed to examine the reasons for these differences.

Open-source Web Portal for Managing Self-reported Data and Real-world Data Donation in Diabetes Research: Platform Feasibility Study.

BACKGROUND: People with diabetes and their support networks have developed open-source automated insulin delivery systems to help manage their diabetes therapy, as well as to improve their quality of life and glycemic outcomes. Under the hashtag #WeAreNotWaiting, a wealth of knowledge and real-world data have been generated by users of these systems but have been left largely untapped by research; opportunities for such multimodal studies remain open. OBJECTIVE: We aimed to evaluate the feasibility of several aspects of open-source automated insulin delivery systems including challenges related to data management and security across multiple disparate web-based platforms and challenges related to implementing follow-up studies. METHODS: We developed a mixed methods study to collect questionnaire responses and anonymized diabetes data donated by participants-which included adults and children with diabetes and their partners or caregivers recruited through multiple diabetes online communities. We managed both front-end participant interactions and back-end data management with our web portal (called the Gateway). Participant questionnaire data from electronic data capture (REDCap) and personal device data aggregation (Open Humans) platforms were pseudonymously and securely linked and stored within a custom-built database that used both open-source and commercial software. Participants were later given the option to include their health care providers in the study to validate their questionnaire responses; the database architecture was designed specifically with this kind of extensibility in mind. RESULTS: Of 1052 visitors to the study landing page, 930 participated and completed at least one questionnaire. After the implementation of health care professional validation of self-reported clinical outcomes to the study, an additional 164 individuals visited the landing page, with 142 completing at least one questionnaire. Of the optional study elements, 7 participant-health care professional dyads participated in the survey, and 97 participants who completed the survey donated their anonymized medical device data. CONCLUSIONS: The platform was accessible to participants while maintaining compliance with data regulations. The Gateway formalized a system of automated data matching between multiple data sets, which was a major benefit to researchers. Scalability of the platform was demonstrated with the later addition of self-reported data validation. This study demonstrated the feasibility of custom software solutions in addressing complex study designs. The Gateway portal code has been made available open-source and can be leveraged by other research groups.

Costs and underuse of insulin and diabetes supplies: Findings from the 2020 T1International cross-sectional web-based survey.

AIMS: To investigate self-reported out-of-pocket expenses (OoPE) associated with insulin and diabetes supplies for people living with type 1 diabetes (T1D) worldwide. METHODS: A web-based, cross-sectional survey was conducted from August to December 2020. The analysis included comparisons between responses from countries with no, partial, and full healthcare coverage. RESULTS: 1,066 participants from 64 countries took part in the study. ~25% of respondents reported having underused insulin at least once within the last year due to perceived cost. A significant correlation was observed between OoPEs and reported household income for respondents with partial healthcare coverage. 63.2% of participants reported disruption of insulin supplies and 25.3% reported an increase of prices related to the COVID-19 pandemic. CONCLUSIONS: This study confirms previous reports of ~25% of people in the United States with T1D using less insulin and/or fewer supplies at least once in the last year due to cost, a trend associated with the extent of healthcare coverage. Similar trends were observed in some middle/low income countries. Moreover, patients reported an increase in insulin prices and disruption of supplies during the COVID-19 pandemic. This study highlights the importance of self-reported OoPEs and its association with underuse/rationing of insulin.

Exploring Associations Between Children's Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard.

BACKGROUND: Obesity is a major public health problem globally and in Europe. The prevalence of childhood obesity is also soaring. Several parameters of the living environment are contributing to this increase, such as the density of fast food retailers, and thus, preventive health policies against childhood obesity must focus on the environment to which children are exposed. Currently, there are no systems in place to objectively measure the effect of living environment parameters on obesogenic behaviors and obesity. The H2020 project "BigO: Big Data Against Childhood Obesity" aims to tackle childhood obesity by creating new sources of evidence based on big data. OBJECTIVE: This paper introduces the Obesity Prevention dashboard (OPdashboard), implemented in the context of BigO, which offers an interactive data platform for the exploration of objective obesity-related behaviors and local environments based on the data recorded using the BigO mHealth (mobile health) app. METHODS: The OPdashboard, which can be accessed on the web, allows for (1) the real-time monitoring of children's obesogenic behaviors in a city area, (2) the extraction of associations between these behaviors and the local environment, and (3) the evaluation of interventions over time. More than 3700 children from 33 schools and 2 clinics in 5 European cities have been monitored using a custom-made mobile app created to extract behavioral patterns by capturing accelerometer and geolocation data. Online databases were assessed in order to obtain a description of the environment. The dashboard's functionality was evaluated during a focus group discussion with public health experts. RESULTS: The preliminary association outcomes in 2 European cities, namely Thessaloniki, Greece, and Stockholm, Sweden, indicated a correlation between children's eating and physical activity behaviors and the availability of food-related places or sports facilities close to schools. In addition, the OPdashboard was used to assess changes to children's physical activity levels as a result of the health policies implemented to decelerate the COVID-19 outbreak. The preliminary outcomes of the analysis revealed that in urban areas the decrease in physical activity was statistically significant, while a slight increase was observed in the suburbs. These findings indicate the importance of the availability of open spaces for behavioral change in children. Discussions with public health experts outlined the dashboard's potential to aid in a better understanding of the interplay between children's obesogenic behaviors and the environment, and improvements were suggested. CONCLUSIONS: Our analyses serve as an initial investigation using the OPdashboard. Additional factors must be incorporated in order to optimize its use and obtain a clearer understanding of the results. The unique big data that are available through the OPdashboard can lead to the implementation of models that are able to predict population behavior. The OPdashboard can be considered as a tool that will increase our understanding of the underlying factors in childhood obesity and inform the design of regional interventions both for prevention and treatment.