scholarly journals Information Visualization for Diabetes Management: A Literature Review

2020 ◽  
Author(s):  
Yixuan Zhang ◽  
Andrea G. Parker ◽  
Cody Dunne

Type 1 diabetes is a chronic illness that affects millions of people. People with type 1 diabetes regularly collect multidimensional data which they use to improve their well-being. Such data often includes blood glucose levels, insulin administration, diet, and physical activity. Monitoring and analysis tools for diabetes care often include information visualizations to help people make sense of this complex data. However, we have only an incomplete understanding of the visualization design approaches used or any justifications for the final design. To address this gap, we surveyed 21 diabetes data analysis tools which use visualization. From this, we derived a design space that consists of data, views, and strategies. We also provide design considerations for future researchers, tool designers, and developers.

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A338-A338
Author(s):  
Jamie Calma ◽  
Sabrina Sangha ◽  
Marina Basina

Abstract Introduction: Data on the impact of the COVID-19 lockdown on glycemic control and psychological well-being in individuals with Type 1 Diabetes Mellitus (T1DM) showed mixed results. Some studies showed improvement in glycemic control attributed to more time for self-care and a more regular lifestyle schedule during the lockdown. However, most published data reflects a short duration of 3–5 months. The impact of long-term social isolation and transition to telemedicine on the health of T1DM patients remains unknown. Our study analyzes patient perception surrounding the impact of an 11-month lockdown on glycemic control, well-being, and self-reported depression symptoms. Methods: PHQ-9 was integrated into a 55-question survey created using RedCap, a secure portal for managing surveys. The survey was sent to 160 T1DM patients over the age of 18 to gauge their current diabetes management and overall well-being prior to, and during the pandemic. The survey also inquired about patients’ perceived effectiveness of telemedicine visits. PHQ9 scores were collected and analyzed along with survey responses. Results: Data collection is still ongoing. From the 47 responders, the PHQ9 screening showed 51% were in the minimal depression score, 34% in the range of mild depression, 11% in moderate depression, and 4% scored in moderate to severe depression. No patients scored within severe depression. In a regular week during the pandemic, 40% of patients experienced difficulty with their motivation and diabetes management and 60% reported no concern, as compared to 36% and 64% respectively before the pandemic. Among the 47 of patient respondents, 30 reported both A1c levels prior and during the pandemic of which 46% showed an improved A1c amid the pandemic, 10% had no change, and 44% reported a worsened A1c level. For the telehealth part of the survey, 90% of patients reported feeling “comfortable with the level of care” they receive via telemedicine, whereas the other 10% were not. Whilst 54% of patients preferred in-person visits and 46% indicated a preference for telehealth visits. Conclusion: T1DM management is challenging. The pandemic adds to the complexity and burden to both self-management and healthcare delivery. Staying locked down for a prolonged period of time imposes economical, psychological, and medical constraints to diabetes care, as nearly half of the patients reported worsening of glycemic control. Our comprehensive survey reports the longest duration reported up to date of how the COVID-19 lockdown impacts patient’s perceived changes in their mental health and diabetes management. It helps clinicians understand the connection between mental and physical health during the pandemic and improve time-restricted telehealth visits by understanding patient concerns. Additional larger scale studies are imperative to expand the knowledge in this field.


2019 ◽  
Vol 37 (1) ◽  
pp. 345-354 ◽  
Author(s):  
Juwon Lee ◽  
Vicki S. Helgeson ◽  
Meredith Van Vleet ◽  
Eunjin L. Tracy ◽  
Robert G. Kent de Grey ◽  
...  

We-talk (first-person plural pronoun usage) is frequently used to represent the degree to which a person views an illness as shared within a couple. There is evidence that we-talk is related to good relationship and health. However, research has failed to examine the implications of we-talk for spouses and the interpersonal mechanisms that underlie relational and health benefits. To address these limitations, we investigated the association of we-talk to relationship and health among 199 couples in which one person had type 1 diabetes. We-talk was assessed in the context of a brief coping interview with patients and spouses separately. Patients reported their perceptions of their spouse’s behavior over the past month. Actor–partner interdependence, regression, and bootstrap models showed that patient we-talk was unrelated to patient and spouse well-being, but greater spouse we-talk was associated with higher patient relationship satisfaction, higher patient self-efficacy, and better patient self-care behavior. For spouses, greater spouse we-talk also was associated with higher relationship satisfaction, lower stress, and fewer depressive symptoms. Mediational analyses showed that patients’ perceptions of spouses’ greater emotional support and fewer critical behaviors partially accounted for these associations. Spouse we-talk may be more important than patient we-talk because it signifies that spouses are involved in helping with diabetes management, namely by providing emotional support and refraining from criticizing the patient.


2021 ◽  
pp. 104973232110018
Author(s):  
Mathilde Overgaard ◽  
Ulla Christensen ◽  
Mette A. Nexø

Well-being at work is important to quality of life. However, reconciling work and diabetes management is often challenging; failing to do so threatens the well-being of people with type 1 diabetes (T1D). We explored the mechanisms underlying diabetes-specific challenges at work using theories of logics, involvement, and action space. Thematic analyses of two data sets, consisting of interviews with adults with T1D ( n = 22) showed that people with T1D experience a conflict between two logics linked to diabetes and work, owing to the contradictory demands of work life and diabetes management. Individuals’ ability to lower the priority of work tasks—shifting them from their main to a side involvement so as to properly manage T1D—helps resolve the conflict, as does being able to create an enabling action space for diabetes management at work. These insights can inform interventions targeting the well-being of workers with T1D.


2019 ◽  
Vol 26 (1) ◽  
pp. 703-718 ◽  
Author(s):  
Josep Vehí ◽  
Iván Contreras ◽  
Silvia Oviedo ◽  
Lyvia Biagi ◽  
Arthur Bertachi

Tight blood glucose control reduces the risk of microvascular and macrovascular complications in patients with type 1 diabetes. However, this is very difficult due to the large intra-individual variability and other factors that affect glycaemic control. The main limiting factor to achieve strict control of glucose levels in patients on intensive insulin therapy is the risk of severe hypoglycaemia. Therefore, hypoglycaemia is the main safety problem in the treatment of type 1 diabetes, negatively affecting the quality of life of patients suffering from this disease. Decision support tools based on machine learning methods have become a viable way to enhance patient safety by anticipating adverse glycaemic events. This study proposes the application of four machine learning algorithms to tackle the problem of safety in diabetes management: (1) grammatical evolution for the mid-term continuous prediction of blood glucose levels, (2) support vector machines to predict hypoglycaemic events during postprandial periods, (3) artificial neural networks to predict hypoglycaemic episodes overnight, and (4) data mining to profile diabetes management scenarios. The proposal consists of the combination of prediction and classification capabilities of the implemented approaches. The resulting system significantly reduces the number of episodes of hypoglycaemia, improving safety and providing patients with greater confidence in decision-making.


Author(s):  
Sheri R. Colberg ◽  
Jihan Kannane ◽  
Norou Diawara

Individuals with type 1 diabetes (T1D) are able to balance their blood glucose levels while engaging in a wide variety of physical activities and sports. However, insulin use forces them to contend with many daily training and performance challenges involved with fine-tuning medication dosing, physical activity levels, and dietary patterns to optimize their participation and performance. The aim of this study was to ascertain which variables related to the diabetes management of physically active individuals with T1D have the greatest impact on overall blood glucose levels (reported as A1C) in a real-world setting. A total of 220 individuals with T1D completed an online survey to self-report information about their glycemic management, physical activity patterns, carbohydrate and dietary intake, use of diabetes technologies, and other variables that impact diabetes management and health. In analyzing many variables affecting glycemic management, the primary significant finding was that A1C values in lower, recommended ranges (<7%) were significantly predicted by a very-low carbohydrate intake dietary pattern, whereas the use of continuous glucose monitoring (CGM) devices had the greatest predictive ability when A1C was above recommended (≥7%). Various aspects of physical activity participation (including type, weekly time, frequency, and intensity) were not significantly associated with A1C for participants in this survey. In conclusion, when individuals with T1D are already physically active, dietary changes and more frequent monitoring of glucose may be most capable of further enhancing glycemic management.


2020 ◽  
pp. 193229682097981
Author(s):  
Sarah M. McGaugh ◽  
Stephanie Edwards ◽  
Howard Wolpert ◽  
Dessi P. Zaharieva ◽  
Nany Gulati ◽  
...  

Maintaining blood glucose levels in the target range during exercise can be onerous for people with type 1 diabetes (T1D). Using evidence-based research and consensus guidelines, we developed an exercise advisor app to reduce some of the burden associated with diabetes management during exercise. The app will guide the user on carbohydrate feeding strategies and insulin management strategies before, during, and after exercise and provide targeted and individualized recommendations. As a basis for the recommendations, the decision trees for the app use various factors including the type of insulin regimen, time of activity, previous insulin boluses, and current glucose level. The app is designed to meet the various needs of people with T1D for different activities to promote safe exercise practices.


Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3303
Author(s):  
Jeremy Beauchamp ◽  
Razvan Bunescu ◽  
Cindy Marling ◽  
Zhongen Li ◽  
Chang Liu

To avoid serious diabetic complications, people with type 1 diabetes must keep their blood glucose levels (BGLs) as close to normal as possible. Insulin dosages and carbohydrate consumption are important considerations in managing BGLs. Since the 1960s, models have been developed to forecast blood glucose levels based on the history of BGLs, insulin dosages, carbohydrate intake, and other physiological and lifestyle factors. Such predictions can be used to alert people of impending unsafe BGLs or to control insulin flow in an artificial pancreas. In past work, we have introduced an LSTM-based approach to blood glucose level prediction aimed at “what-if” scenarios, in which people could enter foods they might eat or insulin amounts they might take and then see the effect on future BGLs. In this work, we invert the “what-if” scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future. Leveraging a recent state-of-the-art model for time series forecasting, we then derive a novel architecture for the same recommendation task, in which the two LSTM chain is used as a repeating block inside a deep residual architecture. Experimental evaluations using real patient data from the OhioT1DM dataset show that the new integrated architecture compares favorably with the previous LSTM-based approach, substantially outperforming the baselines. The promising results suggest that this novel approach could potentially be of practical use to people with type 1 diabetes for self-management of BGLs.


2018 ◽  
Vol 15 ◽  
pp. 77-82 ◽  
Author(s):  
Karolina Linden ◽  
Marie Berg ◽  
Annsofie Adolfsson ◽  
Carina Sparud-Lundin

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