Cognitive Function and Self-Management Behaviors in Older Adults With Type 2 Diabetes.

BACKGROUND: Type 2 diabetes (T2D) is strongly associated with cognitive impairment. Decreased cognitive function could affect daily self-management behaviors critical for people with T2D. Executive function is significant for daily self-management, and decreased subjective cognitive function could be an early indicator of poor daily self-management. However, little is known about whether executive or subjective cognitive function affects daily self-management behaviors in older adults. OBJECTIVES: We investigated the effect of executive function or subjective cognitive function on daily self-management behaviors (diet, glucose management, physical activity, and physician contact) in older adults with T2D. METHODS: We used a cross-sectional, observational design with convenience sampling of 84 adults aged ≥60 years with T2D. Telephone-administered cognitive function tests measured participants' overall cognitive and executive function levels. Subjective cognitive function, diabetes self-management, and covariates, including demographic information (age, gender, race/ethnicity, and level of education), body mass index, depressive symptoms, and diabetes duration, were assessed using online surveys. Data were analyzed using bivariate correlation and backward stepwise regression. RESULTS: The mean age of the sample was 68.46 ± 5.41 years. Participants were predominantly female and White, and the majority had normal cognitive function. Controlling for demographics, body mass index, depressive symptoms, and diabetes duration, a decrease in executive function indicated by a greater number of errors made during the telephone-administered Oral Trail Making Test Part B relative to the sample was associated with poorer adherence to physician contact behaviors. Subjective cognitive function was not associated with any self-management behaviors. DISCUSSION: A reduction in executive function was associated with poorer adherence to physician contact behaviors in older adults with T2D and normal cognitive function; lack of adherence to physician contact behaviors could be an early indicator of declining cognitive function. Difficulties or changes in routine diabetes self-management behaviors should be closely monitored in older adults. Cognitive assessment should be followed when needed.

Impact of worries associated with COVID-19 on diabetes-related psychological symptoms in older adults with Type 2 diabetes.

This study examined the associations between worries associated with COVID-19, diabetes-specific distress, and depressive symptoms in older adults with type 2 diabetes (T2D), who are particularly vulnerable to COVID-19 and its psychological impacts. A cross-sectional online survey was conducted with 84 older adults with T2D from June to December 2020. Participants had little to moderate worries associated with COVID-19, with the greatest worries about the economy recession, followed by a family member catching COVID-19, lifestyle disruptions, and overwhelmed local hospitals. Bivariate correlation and tobit regression revealed that increases in worries associated with COVID-19 were associated with increased diabetes distress and depressive symptoms. Specifically, worries associated with COVID-19 increased diabetes-specific emotional burden and physician-related and regimen-related distress. Increased diabetes distress and depressive symptoms worsened by COVID-19 may ultimately lead to poor glucose control. Additional assessment by mental health experts should be considered for older adults with T2D during and after infectious disease pandemic.

Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems.

Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.

Online Glucose Prediction Using Computationally Efficient Sparse Kernel Filtering Algorithms in Type-1 Diabetes.

Streaming data from continuous glucose monitoring (CGM) systems enable the recursive identification of models to improve estimation accuracy for effective predictive glycemic control in patients with type-1 diabetes. A drawback of conventional recursive identification techniques is the increase in computational requirements, which is a concern for online and real-time applications such as the artificial pancreas systems implemented on handheld devices and smartphones where computational resources and memory are limited. To improve predictions in such computationally constrained hardware settings, efficient adaptive kernel filtering algorithms are developed in this paper to characterize the nonlinear glycemic variability by employing a sparsification criterion based on the information theory to reduce the computation time and complexity of the kernel filters without adversely deteriorating the predictive performance. Furthermore, the adaptive kernel filtering algorithms are designed to be insensitive to abnormal CGM measurements, thus compensating for measurement noise and disturbances. As such, the sparsification-based real-time model update framework can adapt the prediction models to accurately characterize the time-varying and nonlinear dynamics of glycemic measurements. The proposed recursive kernel filtering algorithms leveraging sparsity for improved computational efficiency are applied to both in-silico and clinical subjects, and the results demonstrate the effectiveness of the proposed methods.

Sleep disturbance and next-day physical activity in COPD patients.

Physical inactivity and sleep disturbance are more problematic in patients with chronic obstructive pulmonary disease (COPD) than in healthy individuals. The purpose of the study was to identify impacts of nighttime sleep on next-day physical activity in COPD patients. The study included 52 COPD patients reporting disturbed sleep. Sleep and physical activity were measured using an accelerometer for 5 days. Increased sleep latency was associated with less next-day physical activity during the afternoon (4-6 p.m.). Greater waking duration/times were associated with less next-morning (6-8 a.m.) physical activity. Greater total sleep time was associated with less next-morning (12-9 a.m.) physical activity, and greater sleep efficiency was associated with less next-morning (1-3 a.m.) and more next-evening (6-7 p.m.) physical activity. Results suggest that sleep disturbance had varying influences on next-day hourly physical activity. These results support the potential value of sleep management in promoting physical activity in COPD patients.

Heart Rate Variability and Cardiorespiratory Fitness in Non-Hispanic Black Versus Non-Hispanic White Adolescents With Type 1 Diabetes.

BACKGROUND: Evidence indicates that fewer non-Hispanic black versus non-Hispanic white youths with type 1 diabetes are meeting treatment goals for optimal glycemic outcomes, predisposing them to risks for cardiovascular (CV) morbidity and mortality in adulthood. PURPOSE: We sought to assess the association of sex and race with heart rate variability (HRV) and cardiorespiratory fitness in adolescents with type 1 diabetes. The association between the HRV and cardiorespiratory outcomes with glucose control was also examined. METHODS: A secondary data analysis of 95 adolescents with type 1 diabetes (n = 66 non-Hispanic white n = 29 non-Hispanic black) was used. Using 24-hour Holter recordings, spectral and time domain measures of HRV were obtained. Cardiorespiratory fitness using a graded exercise test was completed. Descriptive statistics and Pearson correlation coefficients were used to assess associations between glucose control and study outcomes, and general linear models were applied to explore and quantify associations of sex and race with HRV and cardiorespiratory fitness. RESULTS: Body mass index (mean [standard deviation]) was similar between non-Hispanic black (23.5 [3.9]) and non-Hispanic white (22.7 [3.8]) adolescents. Females and non-Hispanic black adolescents had significantly lower HRV and cardiorespiratory fitness levels. Moderate associations were found between lower HRV and poorer glycemic control (HbA1c). Recent HbA1c was significantly higher in non-Hispanic black (9.7 [1.8]) than non-Hispanic white (8.2 [1.2]). CONCLUSION: Findings support the importance of early identification of CV health risks in adolescents with type 1 diabetes, particularly for non-Hispanic black adolescents. Interventions focused on overall improvement in glycemic control for adolescents with type 1 diabetes are a priority for minimizing future CV complications.

Simulation Software for Assessment of Nonlinear and Adaptive Multivariable Control Algorithms: Glucose - Insulin Dynamics in Type 1 Diabetes.

A simulator for testing automatic control algorithms for nonlinear systems with time-varying parameters, variable time delays, and uncertainties is developed. It is based on simulation of virtual patients with Type 1 diabetes (T1D). Nonlinear models are developed to describe glucose concentration (GC) variations based on user-defined scenarios for meal consumption, insulin administration, and physical activity. They compute GC values and physiological variables, such as heart rate, skin temperature, accelerometer, and energy expenditure, that are indicative of physical activities affecting GC dynamics. This is the first simulator designed for assessment of multivariable controllers that consider supplemental physiological variables in addition to GC measurements to improve glycemic control. Virtual patients are generated from distributions of identified model parameters using clinical data. The simulator will enable testing and evaluation of new control algorithms proposed for automated insulin delivery as well as various control algorithms for nonlinear systems with uncertainties, time-varying parameters and delays.

Pathways of the relationships among eating behavior, stress, and coping in adults with type 2 diabetes: A cross-sectional study.

Stress is related to eating behavior, and eating behavior is important in diabetes treatment. The aim of this study was to examine the relationships among perceived stress, diabetes-related stress, coping strategies, and eating behaviors in adults living with type 2 diabetes (T2DM). Adults with type 2 diabetes (n = 183) were recruited from a large metropolitan area in the Midwest United States. Stress factors and coping strategies associated with eating behaviors were measured using validated questionnaires. Structural equation modeling was used to investigate the relationships among perceived stress, diabetes-related stress, coping strategies, and eating behaviors. The final model showed that emotion-oriented coping partially mediated the effect of stress on eating behaviors. Specifically, emotion-oriented coping partially mediated the effect of diabetes-related stress on restrained eating behavior (r = 0.318, p < .001) and emotional eating behavior (r = 0.399, p < .001); emotion-oriented coping partially mediated the effect of perceived stress on external eating behavior (r = 0.276, p < .001). Emotion-oriented coping was found to be a partial mediator in the path model between stress and eating behaviors in people with type 2 diabetes. Knowledge of the association of stress with eating behaviors may prove important for health care providers in treatment and care of people with type 2 diabetes.

Endothelial Dysfunction is Related to Glycemic Variability and Quality and Duration of Sleep in Adults With Type 1 Diabetes.

BACKGROUND: Elevated cardiovascular disease risk in people with type 1 diabetes (T1DM) is incompletely understood. Glycemic control, glycemic variability, and sleep quality and duration may relate to cardiovascular disease risk in this population via endothelial dysfunction. OBJECTIVE: The aim of this study was to examine relationships among glycemic control, glycemic variability, sleep quality and duration, and endothelial function in adults with T1DM. METHODS: Endothelial function was measured using flow-mediated dilation. Glycemic control and glycemic variability were measured using A1C and a continuous glucose monitor, respectively; sleep quality and duration were measured with the Pittsburgh Sleep Quality Index. RESULTS: Twenty subjects were recruited. Reduced flow-mediated dilation and higher glucose levels were associated with poorer sleep quality (r = -0.51, P = .01; r = 0.52, P = .03). Subjects with shorter sleep duration had greater glycemic variability. CONCLUSIONS: Endothelial dysfunction (a precursor to cardiovascular disease) relates to glycemic control, glycemic variability, and sleep quality in T1DM.

Relationship and variation of diabetes related symptoms, sleep disturbance and sleep-related impairment in adults with type 2 diabetes.

AIM: The objective of this study was to examine whether diabetes-related symptoms (e.g. fatigue, neuropathic pain, diabetes distress and depressive symptoms) were related to sleep disturbance and sleep-related impairment in adults with type 2 diabetes while controlling for potential covariates. BACKGROUND: In people with type 2 diabetes, sleep disturbance and sleep-related impairment are common and likely associated with diabetes-related symptoms. However, limited research has investigated the predictive ability of diabetes-related symptoms on sleep. DESIGN: A correlational, cross-sectional design was used. METHODS: Data were collected at a large university in the Midwestern United States from September 2013-March 2014. Multiple linear regression analyses were used to examine the relationship of diabetes-related symptoms (fatigue, neuropathic pain, distress and depressive symptoms) to sleep disturbance and sleep-related impairment. The instruments included Patient-Reported Outcomes Measurement Information System instruments, Diabetes Symptom Checklist and Diabetes Distress Scale. FINDINGS: In this study of adults with type 2 diabetes (N = 90; 52.2% female, mean age 57.4 years), gender, A1C, neuropathic pain and fatigue were significantly related to sleep disturbance when age, diabetes duration, depressive symptoms and distress were controlled. Those variables collectively explained 52% of the variation in sleep disturbance. Fatigue was significantly associated with sleep-related impairment when the same covariates were controlled. CONCLUSION: Findings suggested that diabetes-related symptoms, including neuropathic pain and fatigue, are strongly related to sleep disturbance and sleep-related impairment in adults with type 2 diabetes, underscoring the need to include detailed assessments of neuropathic pain and fatigue when evaluating sleep.

Poor sleep quality is associated with nocturnal glycaemic variability and fear of hypoglycaemia in adults with type 1 diabetes.

AIMS: To examine sleep quality and its associations with glycaemic control, glycaemic variability (GV), and fear of hypoglycaemia (FOH) in adults with type 1 diabetes. BACKGROUND: Poor sleep quality has negative health consequences and is a frequent complaint among adults with type 1 diabetes. Sleep quality in adults with type 1 diabetes is likely affected by glucose levels as well as stressors associated with managing a chronic condition. DESIGN: A retrospective secondary analysis of pooled data from two previous cross-sectional studies was conducted. METHODS: We examined subjective sleep quality, FOH; objective measures of glycaemic control (HbA1c); and GV (3-day continuous glucose monitoring) in 48 men and women aged 18-45 years with type 1 diabetes. The data were collected over 3 years in 2013-2016. RESULTS/FINDINGS: Poor sleep quality was reported by 46% of patients. Those with poor sleep quality had significantly greater nocturnal GV and FOH. Nocturnal GV and FOH were significantly associated with poor sleep quality. The interaction effect of GV and FOH was significant. CONCLUSION: These findings suggest that glycaemic control and FOH are targets for intervention to improve sleep quality in those with type 1 diabetes.

Sleep disturbance in people with diabetes: A concept analysis.

AIMS AND OBJECTIVES: To clarify the meaning of sleep disturbance in people with diabetes and examine its antecedents, attributes and consequences through concept analysis. BACKGROUND: Sleep is crucial for health, and people with diabetes are frequently beset with disturbances in their sleep. The concept of sleep disturbance in people with diabetes has not been clearly defined. The inconsistent use of sleep disturbance has created confusion and impeded our understanding of the sleep in people with diabetes. This analysis will provide a conceptual foundation of sleep disturbance in diabetes, thereby facilitating more effective means for assessment and treatment. DESIGN: Concept analysis. METHODS: A systematic search without time restriction on the publication year was carried out using PubMed, CINAHL, PsycINFO, Web of Science and ProQuest Dissertations and Theses. Rodgers's method of evolutionary concept analysis guided the analysis. Inductive thematic analysis was conducted to identify the attributes, antecedents and consequences. RESULTS: Based on the 26 eligible studies, two major attributes are that sleep disturbance is a symptom and is characterised by impaired sleep quality and/or abnormal sleep duration. Two antecedents are diabetes-related physiological change and psychological well-being. Sleep disturbance can result in impaired daytime functioning, glucose regulation and quality of life. CONCLUSIONS: Defining the concept of sleep disturbance in people with diabetes facilitates consistent use and effective communication in both practice and research. Sleep disturbance in people with diabetes is a complex symptom that includes impaired sleep quality and/or abnormal sleep duration. This paper contributes to the current knowledge of sleep in people with diabetes. Future research on antecedents and consequences of sleep disturbance is necessary for further clarifications. RELEVANCE TO CLINICAL PRACTICE: Findings from this paper underscore the need for nursing education, clinical assessment and effective management of sleep disturbance in people with diabetes.

Model-Fusion-Based Online Glucose Concentration Predictions in People with Type 1 Diabetes.

Accurate predictions of glucose concentrations are necessary to develop an artificial pancreas (AP) system for people with type 1 diabetes (T1D). In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements. To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories. The filter fusion mechanism is developed based on various prediction performance indexes to guide the overall output of the forecasting paradigm. The efficiency of the proposed model fusion based forecasting method is evaluated using simulated and clinical datasets, and the results demonstrate the capability and prediction accuracy of the data-based fusion filters, especially in the case of limited data availability. The model fusion framework may be used in the development of an AP system for glucose regulation in patients with T1D.

Health-Related Stigma in Young Adults With Type 1 Diabetes Mellitus.

Young adults with type 1 diabetes mellitus (T1DM) often exhibit poor treatment adherence and suboptimal glucose control. Health-related stigma may adversely influence diabetes self-management behavior. The aim of the current study was to explore health-related stigma among young adults with T1DM using qualitative descriptive methods in focus groups. Data were collected from a purposive sample of 14 young adults ages 20 to 34 with T1DM. Using content analysis, transcripts from two focus groups were analyzed, and five main themes were identified: (a) Desire to be Seen as a Person, Not a Disease; (b) Wanting to be "Normal"; (c) Feeling Ashamed Managing Diabetes in Public; (d) Struggling to Overcome Anger and Distress; and (e) Feeling Distrusted by Others to Manage Their Condition. Young adults with T1DM reported experiencing a high degree of stigma in their daily lives, which negatively influenced their self-management and psychological well-being. Stigma led to delaying blood glucose monitoring and insulin dosing and feelings of anger and distress. Young adults with T1DM require strategies to help cope with stigma and the anger and distress they experience. [Journal of Psychosocial Nursing and Mental Health Services, 56(10), 44-51.].

Circulating soluble RAGE isoforms are attenuated in obese, impaired-glucose-tolerant individuals and are associated with the development of type 2 diabetes.

The soluble receptor for advanced glycation end products (sRAGE) may be protective against inflammation associated with obesity and type 2 diabetes (T2DM). The aim of this study was to determine the distribution of sRAGE isoforms and whether sRAGE isoforms are associated with risk of T2DM development in subjects spanning the glucose tolerance continuum. In this retrospective analysis, circulating total sRAGE and endogenous secretory RAGE (esRAGE) were quantified via ELISA, and cleaved RAGE (cRAGE) was calculated in 274 individuals stratified by glucose tolerance status (GTS) and obesity. Group differences were probed by ANOVA, and multivariate ordinal logistic regression was used to test the association between sRAGE isoform concentrations and the proportional odds of developing diabetes, vs. normal glucose tolerance (NGT) or impaired glucose tolerance (IGT). When stratified by GTS, total sRAGE, cRAGE, and esRAGE were all lower with IGT and T2DM, while the ratio of cRAGE to esRAGE (cRAGE:esRAGE) was only lower (P < 0.01) with T2DM compared with NGT. When stratified by GTS and obesity, cRAGE:esRAGE was higher with obesity and lower with IGT (P < 0.0001) compared with lean, NGT. In ordinal logistic regression models, greater total sRAGE (odds ratio, 0.91; P < 0.01) and cRAGE (odds ratio, 0.84; P < 0.01) were associated with lower proportional odds of developing T2DM. Reduced values of sRAGE isoforms observed with both obesity and IGT are independently associated with greater proportional odds of developing T2DM. The mechanisms by which each respective isoform contributes to obesity and insulin resistance may reveal novel treatment strategies for diabetes.

Use of Wearable Sensors and Biometric Variables in an Artificial Pancreas System.

An artificial pancreas (AP) computes the optimal insulin dose to be infused through an insulin pump in people with Type 1 Diabetes (T1D) based on information received from a continuous glucose monitoring (CGM) sensor. It has been recognized that exercise is a major challenge in the development of an AP system. The use of biometric physiological variables in an AP system may be beneficial for prevention of exercise-induced challenges and better glucose regulation. The goal of the present study is to find a correlation between biometric variables such as heart rate (HR), heat flux (HF), skin temperature (ST), near-body temperature (NBT), galvanic skin response (GSR), and energy expenditure (EE), 2D acceleration-mean of absolute difference (MAD) and changes in glucose concentrations during exercise via partial least squares (PLS) regression and variable importance in projection (VIP) in order to determine which variables would be most useful to include in a future artificial pancreas. PLS and VIP analyses were performed on data sets that included seven different types of exercises. Data were collected from 26 clinical experiments. Clinical results indicate ST to be the most consistently important (important for six out of seven tested exercises) variable over all different exercises tested. EE and HR are also found to be important variables over several types of exercise. We also found that the importance of GSR and NBT observed in our experiments might be related to stress and the effect of changes in environmental temperature on glucose concentrations. The use of the biometric measurements in an AP system may provide better control of glucose concentration.

Update on Prevention of Cardiovascular Disease in Adults With Type 2 Diabetes Mellitus in Light of Recent Evidence: A Scientific Statement From the American Heart Association and the American Diabetes Association.

Cardiovascular disease risk factor control as primary prevention in patients with type 2 diabetes mellitus has changed substantially in the past few years. The purpose of this scientific statement is to review the current literature and key clinical trials pertaining to blood pressure and blood glucose control, cholesterol management, aspirin therapy, and lifestyle modification. We present a synthesis of the recent literature, new guidelines, and clinical targets, including screening for kidney and subclinical cardiovascular disease for the contemporary management of patients with type 2 diabetes mellitus.

Glucose control and fatigue in type 2 diabetes: the mediating roles of diabetes symptoms and distress.

AIM: The purpose of this study was to examine the mediating influence of diabetes health characteristics (diabetes distress, depression symptoms and diabetes symptoms) on the relationship between glucose control and fatigue in adults with type 2 diabetes. BACKGROUND: In patients with type 2 diabetes, fatigue is common and can affect diabetes self-management behaviours. Although long thought to result from hyperglycaemia, little evidence supports a relationship between fatigue and glucose control. DESIGN: A cross-sectional, descriptive study design was used. METHOD: Data were combined from two studies conducted at a large urban university in the Midwestern United States, resulting in a total sample of 155 urban-dwelling adults with type 2 diabetes. Data were collected over the course of 6 days from 2013-March 2014. Fatigue and related biological and psychological phenomena were measured to perform path analyses using structural equation modelling methods. The STATA software was used to analyse the data. FINDINGS: In patients with A1C less than or equal to 7%, fatigue was related to diabetes distress and diabetes symptoms, but not to A1C directly or indirectly. In the group with A1C greater than 7%, fatigue was indirectly related to A1C; this relationship was mediated through diabetes symptoms, depression and diabetes distress. CONCLUSION: Our findings suggest that fatigue is indirectly related to glucose control, but only in patients who have elevated A1C levels. In those with adequate glucose control, fatigue is mainly influenced by the presence of diabetes symptoms and distress. In both groups, the number and severity of diabetes symptoms were the strongest predictors of fatigue, regardless of blood glucose control.

Development and psychometric evaluation of the Diabetes Self-Efficacy Scale.

BACKGROUND AND PURPOSE: No scales measure self-efficacy in women with Type 2 diabetes. A scale was developed and tested. METHODS: Items generated, content validity index (CVI) assessed by experts, the 2-part Diabetes Self-Efficacy Scale (DSLF-I and DSLF-II) was piloted with 62 women, administered to 208 women, and then readministered to 30 women to determine initial reliability. Factor analysis was conducted for construct validity. Discriminant, convergent, and predictive validity was examined. RESULTS: The CVI index was 98%. Cronbach's alphas were 0.88 (DSLF-I) and 0.82 (DSLF-II; pilot) and 0.87 and 0.86, respectively (main study); test-retest correlation was .60 (DSLF-I) and .69 (DSLF-II). There were 3 factors that emerged: diabetes knowledge of self-care activity, diabetes diet self-care, and diabetes medication self-care. CONCLUSIONS: The Diabetes Self-Efficacy Scale demonstrates good initial reliability and validity.