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Barriers to Self-Management in Depressed Women With Type 2 Diabetes.

ABSTRACT: Purpose To describe the impact of family functioning on the self-management of type 2 diabetes (T2DM) and depression in a subsample of women who completed a randomized clinical trial using vitamin D3 (5000 or 50,000 IUs weekly) for depression treatment. Background Women are at higher risk for increased severity of T2DM when experiencing depression. Methods Narrative inquiry was used. A semi-structured interview was conducted to understand helpful strategies and barriers in managing T2DM and depression. In addition, women were asked their meaning of family quality of life (FQOL). Results Twenty-one women participated after completion of the six-month final visit in the randomized clinical trial. The mean age was 55.2 years. Participants were 24% Hispanic, 48% African-American, and 52% Caucasian. The major themes generated related to family issues that impacted their self-management, yet participants did not want to "bring fault" to their families. Three themes emerged: (a) experience of family hardships-"it's been hard for me," (b) lack of disclosure to family about being depressed-"no point in talking to them," and (c) the need for connectedness with family and others-"the way it used to be… close as a family." Conclusion Family-centered approaches could address barriers to self-management. A "family lens" for practice and research may improve health outcomes.

SUBMITTER: Ridosh MM

PROVIDER: S-EPMC6107345 | biostudies-other | 2017 Dec

REPOSITORIES: biostudies-other

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Barriers to Self-Management in Depressed Women With Type 2 Diabetes.

Ridosh Monique M MM Roux Gayle G Meehan Meghan M Penckofer Sue S

The Canadian journal of nursing research = Revue canadienne de recherche en sciences infirmieres 20171016 4

Purpose To describe the impact of family functioning on the self-management of type 2 diabetes (T2DM) and depression in a subsample of women who completed a randomized clinical trial using vitamin D3 (5000 or 50,000 IUs weekly) for depression treatment. Background Women are at higher risk for increased severity of T2DM when experiencing depression. Methods Narrative inquiry was used. A semi-structured interview was conducted to understand helpful strategies and barriers in managing T2DM and depr ...[more]

PMID: 29037063

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Project description:ImportanceA patient’s perceived barriers to diabetes self-management (DSM) may affect his or her risk of diabetic retinopathy (DR); however, few studies have examined this association.ObjectiveTo examine the association between perceived barriers to DSM and the severity spectrum of DR in Asian patients with type 2 diabetes.Design, setting, and participantsA cross-sectional clinic-based study, the Singapore Diabetes Management Project, was conducted from December 28, 2010, to March 20, 2013, at the Singapore National Eye Centre, a tertiary eye care institute. After excluding patients with type 1 diabetes and ungradable fundus images, 361 participants were included in the analyses. Statistical analysis was conducted from July 20 to September 8, 2017.ExposureThe degree of perceived barriers to DSM was assessed using a 23-item questionnaire comprising items about knowledge of DSM, access to care, and confidence in health care professionals. Rasch analysis was used to optimize the scale’s psychometric properties, with lower scores indicating a higher degree of self-perceived barriers.Main outcomes and measuresDiabetic retinopathy was graded from 2-field retinal images into categories of no DR (Early Treatment Diabetic Retinopathy Study levels 10-15; n = 154), mild to moderate DR (levels 20-43; n = 112), and severe DR (levels ≥53 and/or presence of clinically significant macular edema; n = 95) using the modified Airlie House classification system of DR. Multinomial logistic regression models were used to assess the association between perceived barriers and severity of DR in the worse-affected eye.ResultsAmong the 361 participants (105 women and 256 men; mean [SD] age, 57 [8] years), a greater magnitude of perceived barriers to DSM was independently associated with higher odds of having any DR (odds ratio, 1.32; 95% CI, 1.06-1.66), mild to moderate DR (odds ratio, 1.30; 95% CI, 1.01-1.68), and severe DR (odds ratio, 1.36; 95% CI, 1.03-1.79). This association was independent of diabetes control (hemoglobin A1c, blood pressure, and lipid levels), presenting visual acuity, and socioeconomic indicators.Conclusions and relevanceThese results suggest that greater perceived barriers to DSM are independently associated with severity of DR. Although longitudinal data are needed, these findings suggest that evidence-based interventions to reduce patient-, practitioner-, and system-related barriers to diabetes care may help reduce the risk of DR.

Project description:BackgroundFor adolescents living with type 1 diabetes (T1D), completion of multiple daily self-management tasks, such as monitoring blood glucose and administering insulin, can be challenging because of psychosocial and contextual barriers. These barriers are hard to assess accurately and specifically by using traditional retrospective recall. Ecological momentary assessment (EMA) uses mobile technologies to assess the contexts, subjective experiences, and psychosocial processes that surround self-management decision-making in daily life. However, the rich data generated via EMA have not been frequently examined in T1D or integrated with machine learning analytic approaches.ObjectiveThe goal of this study is to develop a machine learning algorithm to predict the risk of missed self-management in young adults with T1D. To achieve this goal, we train and compare a number of machine learning models through a learned filtering architecture to explore the extent to which EMA data were associated with the completion of two self-management behaviors: mealtime self-monitoring of blood glucose (SMBG) and insulin administration.MethodsWe analyzed data from a randomized controlled pilot study using machine learning-based filtering architecture to investigate whether novel information related to contextual, psychosocial, and time-related factors (ie, time of day) relate to self-management. We combined EMA-collected contextual and insulin variables via the MyDay mobile app with Bluetooth blood glucose data to construct machine learning classifiers that predicted the 2 self-management behaviors of interest.ResultsWith 1231 day-level SMBG frequency counts for 45 participants, demographic variables and time-related variables were able to predict whether daily SMBG was below the clinical threshold of 4 times a day. Using the 1869 data points derived from app-based EMA data of 31 participants, our learned filtering architecture method was able to infer nonadherence events with high accuracy and precision. Although the recall score is low, there is high confidence that the nonadherence events identified by the model are truly nonadherent.ConclusionsCombining EMA data with machine learning methods showed promise in the relationship with risk for nonadherence. The next steps include collecting larger data sets that would more effectively power a classifier that can be deployed to infer individual behavior. Improvements in individual self-management insights, behavioral risk predictions, enhanced clinical decision-making, and just-in-time patient support in diabetes could result from this type of approach.

Project description:ObjectiveThe study aimed to identify the common gaps in skills and self-efficacy for diabetes self-management and explore other factors which serve as enablers of, and barriers to, achieving optimal diabetes self-management. The information gathered could provide health professionals with valuable insights to achieving better health outcomes with self-management education and support for diabetes patients.MethodsInternational online survey and telephone interviews were conducted on adults who have type 1 or type 2 diabetes. The survey inquired about their skills and self-efficacy in diabetes self-management, while the interviews assessed other enablers of, and barriers to, diabetes self-management. Surveys were analysed using descriptive and inferential statistics. Interviews were analysed using inductive thematic analysis.ResultsSurvey participants (N = 217) had type 1 diabetes (38.2%) or type 2 diabetes (61.8%), with a mean age of 44.56 SD 11.51 and were from 4 continents (Europe, Australia, Asia, America). Identified gaps in diabetes self-management skills included the ability to: recognize and manage the impact of stress on diabetes, exercise planning to avoid hypoglycemia and interpreting blood glucose pattern levels. Self-efficacy for healthy coping with stress and adjusting medications or food intake to reach ideal blood glucose levels were minimal. Sixteen participants were interviewed. Common enablers of diabetes self-management included: (i) the will to prevent the development of diabetes complications and (ii) the use of technological devices. Issues regarding: (i) frustration due to dynamic and chronic nature of diabetes (ii) financial constraints (iii) unrealistic expectations and (iv) work and environment-related factors limited patients' effective self-management of diabetes.ConclusionsEducational reinforcement using technological devices such as mobile application has been highlighted as an enabler of diabetes self-management and it could be employed as an intervention to alleviate identified gaps in diabetes self-management. Furthermore, improved approaches that address financial burden, work and environment-related factors as well as diabetes distress are essential for enhancing diabetes self-management.

Project description:BackgroundIt has been established that careful diabetes self-management is essential in avoiding chronic complications that compromise health. Disciplined diet control and regular exercise are the keys for the type 2 diabetes self-management. An ability to maintain one's blood glucose at a relatively flat level, not fluctuating wildly with meals and hypoglycemic medical intervention, would be the goal for self-management. Hemoglobin A1c (HbA1c or simply A1c) is a measure of a long-term blood plasma glucose average, a reliable index to reflect one's diabetic condition. A simple regimen that could reduce the elevated A1c levels without altering much of type 2 diabetic patients' daily routine denotes a successful self-management strategy.MethodsA relatively simple model that relates the food impact on blood glucose excursions for type 2 diabetes was studied. Meal is treated as a bolus injection of glucose. Medical intervention of hypoglycaemic drug or injection, if any, is lumped with secreted insulin as a damping factor. Lunch was used for test meals. The recovery period of a blood glucose excursion returning to the pre-prandial level, the maximal reach, and the area under the excursion curve were used to characterize one's ability to regulate glucose metabolism. A case study is presented here to illustrate the possibility of devising an individual-based self-management regimen.ResultsResults of the lunch study for a type 2 diabetic subject indicate that the recovery time of the post-prandial blood glucose level can be adjusted to 4 hours, which is comparable to the typical time interval for non-diabetics: 3 to 4 hours. A moderate lifestyle adjustment of light supper coupled with morning swimming of 20 laps in a 25 m pool for 40 minutes enabled the subject to reduce his A1c level from 6.7 to 6.0 in six months and to maintain this level for the subsequent six months.ConclusionsThe preliminary result of this case study is encouraging. An individual life-style adjustment can be structured from the extracted characteristics of the post-prandial blood glucose excursions. Additional studies are certainly required to draw general applicable guidelines for lifestyle adjustments of type 2 diabetic patients.

Dataset Information

Barriers to Self-Management in Depressed Women With Type 2 Diabetes.

Publications

Barriers to Self-Management in Depressed Women With Type 2 Diabetes.

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