scholarly journals Caregiver’s Opinions on the Design of the Screens of a Future Gamified Mobile Application for Self-Management of Type 1 Diabetes in Children in Saudi Arabia

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Demah M. Alsalman ◽  
Zahra Bu Ali ◽  
Zainab Alnosaier ◽  
Norah Alotaibi ◽  
Turki M. Alanzi
Keyword(s):  
Type 1 Diabetes ◽  
Saudi Arabia ◽  
Blood Glucose ◽  
Mobile Application ◽  
Eating Habits ◽  
Stable State ◽  
Insulin Injection ◽  
Self Management ◽  
Practical Implementation

The objective of this study was to design the screens of a future gamified mobile application for self-management of type 1 diabetes in children based on the opinion of caregivers at the King Fahad Hospital Diabetes Center, Saudi Arabia. To achieve this objective, a questionnaire was designed and distributed among 100 caregivers through face-to-face communication and social media using a Google Forms link. 65% of the participants met the inclusion criteria. The main result of this study was the design of 13 screens of a gamified application for self-management of type 1 diabetes in children from Saudi Arabia. The key features of the screens were caring for a character; using a challenging friend; inclusion of points, level, and leaderboard as rewarding principles; use of reminders and notifications for doctor’s appointments, insulin injection times, blood glucose readings; and tips for improving medication adherence, increasing blood glucose readings, supporting physical activities, and adopting healthy eating habits. It can be concluded that the practical implementation of the screens in a future mobile application can motivate children with type 1 diabetes to improve eating habits, physical exercise, and cognitive, emotional, and social behaviors to maintain a stable state of health. Also, the content of the designed screens can help to monitor blood glucose readings and comply with medication treatment. The designed screens are adapted to the Arab culture.

The second Moroccan mobile application for type 1 diabetes self-care : towards a new pedagogical dynamism in T1DM self-management

2021 ◽  
Author(s):  
Nada Derkaoui ◽  
Yakhlef Salma Ben ◽  
Rami Imane ◽  
Ouafae Elmehraoui ◽  
Messaoudi Najoua ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Mobile Application ◽  
Self Care ◽  
Self Management

scholarly journals Hybrid Closed-Loop Insulin Delivery Systems for People With Type 1 Diabetes

2021 ◽  
Vol 1 (3) ◽  
Author(s):  
CADTH Health Technology Assessment Service
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Closed Loop ◽  
Insulin Pump ◽  
Insulin Delivery ◽  
Blood Glucose Monitoring ◽  
Self Management ◽  
Publicly Funded ◽  
Delivery Methods

Blood glucose monitoring and insulin delivery are essential parts of the management of type 1 diabetes. Hybrid closed-loop insulin delivery (HCL) systems are a treatment option for people with type 1 diabetes and consist of an insulin pump, a continuous glucose monitor (CGM), and a computer program (algorithm) that allows the devices to communicate with each other and calculates insulin needs. CADTH conducted a Health Technology Assessment (HTA) of the use of HCL systems compared to other insulin delivery methods in people with type 1 diabetes to inform decisions regarding whether HCL systems have a place in the management of type 1 diabetes. HCL therapy generally improved the amount of time a person spent in target blood glucose ranges. Additionally, people who used HCL systems had improved average blood glucose levels (glycated hemoglobin [A1C]) over the preceding 2 or 3 months. However, the effectiveness or safety of HCL systems based on age, sex, race, glucose management, or other clinical features (e.g., those who are pregnant or planning pregnancy, or who have hypoglycemia unawareness or a history of severe hypoglycemia) is unknown. HCL systems were generally as safe as other insulin delivery methods. Additional studies with longer follow-up periods and more participants are needed to confirm the clinical effectiveness and safety of HCL systems. From a pan-Canadian, publicly funded health care system perspective, the cost of covering HCL systems for individuals with type 1 diabetes who are eligible for insulin pumps in their jurisdictions was estimated to be an additional $822,635,045 over 3 years compared with diabetes supplies that are currently covered. If HCL systems are covered for all individuals with type 1 diabetes, regardless of their current insulin-pump eligibility, the budget impact will be higher. HCL systems can help provide distance from demanding self-management and monitoring tasks for people living with type 1 diabetes; however, in order to do this, people using these systems must navigate complex relationships built on trust and collaboration. Given that type 1 diabetes self-management to date has required considerable attention to blood glucose numbers and technical tasks, developing these relationships of trust and collaboration will require a shift in understanding what it means to care for someone who has — or to self-manage — type 1 diabetes. It is not possible to conclude whether HCL systems will improve overall population health over the longer-term because the data for this are not available. It is also unclear which people with type 1 diabetes would benefit most from HCL systems. Eligibility criteria for the existing public insulin-pump program may be useful in making coverage decisions; trial periods may be considered to ensure HCL systems are working well for new users. Education and support are needed for people living with type 1 diabetes when they start to use HCL systems. Clinicians noted the need for interactions between diabetes educators and HCL system pump users. User-friendly devices and understandable reports are key to effective use. Eligibility for access through any publicly funded program for HCL systems should be based on evidence. The criteria for coverage should be consistent with broader public health goals and should not contribute to existing inequities in diabetes management.

scholarly journals Self-Reported Compensation Techniques for Carbohydrate, Exercise, and Alcohol Behaviors in Patients With Type 1 Diabetes on Insulin Pump Therapy

2017 ◽  
Vol 12 (2) ◽  
pp. 412-414 ◽  
Author(s):  
Danielle Groat ◽  
Hiral Soni ◽  
Maria Adela Grando ◽  
Bithika Thompson ◽  
Curtiss B. Cook
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Insulin Pump ◽  
Insulin Pump Therapy ◽  
Self Management ◽  
Pump Therapy ◽  
Electronic Survey ◽  
Alcohol Behaviors ◽  
Management Behaviors

Studies have found variability in self-care behaviors in patients with type 1 diabetes, particularly when incorporating exercise and alcohol consumption. The objective of this study was to provide results from a survey to understand (1) insulin pump behaviors, (2) reported self-management behaviors for exercise and alcohol, and (3) perceptions of the effects of exercise and alcohol on blood glucose (BG) control. Fourteen participants from an outpatient endocrinology practice were recruited and administered an electronic survey. Compensation techniques for exercise and alcohol, along with reasons for employing the techniques were identified. Also identified were factors that participants said affected BG control with regard to exercise and alcohol. These results confirm the considerable inconsistency patients have about incorporating exercise and alcohol into decisions about self-management behaviors.

Habits and Routines during Transitions among Emerging Adults with Type 1 Diabetes

2019 ◽  
Vol 42 (6) ◽  
pp. 446-453
Author(s):  
Kathleen M. Hanna ◽  
Jed R. Hansen
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Emerging Adults ◽  
Diabetes Management ◽  
Insulin Dose ◽  
A Priori ◽  
Self Management ◽  
Typical Day ◽  
Insight Into

To provide insight into poorly understood diabetes self-management among emerging adults with type 1 diabetes (TID) experiencing transitions, this study described their diabetes self-management-related habits, routines, and disruptions as well as explored relationships among habits and routines. A qualitative study, guided by critical incidence technique, was conducted. Participants were asked to describe situations when they did and did not check blood glucose, administer insulin, eat meals, and exercise as planned. They were also asked to describe activities in a typical day and in association with diabetes self-management. Content analysis with a priori definitions of habits and routines was performed. Participants described diabetes self-management-related transitional disruption as forgetting and disorder. They described habits associated with checking a blood glucose, giving an insulin dose, eating a meal, and initiating exercise. They described routines in association with meals, exercise, and overall diabetes management. These findings provide information on variables to target in intervention research.

scholarly journals A mobile app identifies momentary psychosocial and contextual factors related to mealtime self-management in adolescents with type 1 diabetes

2019 ◽  
Vol 26 (12) ◽  
pp. 1627-1631 ◽  
Author(s):  
Shelagh A Mulvaney ◽  
Sarah E Vaala ◽  
Rachel B Carroll ◽  
Laura K Williams ◽  
Cindy K Lybarger ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Problem Solving ◽  
Ecological Momentary Assessment ◽  
Mobile App ◽  
Self Management ◽  
Within Subjects ◽  
Ecological Momentary ◽  
Momentary Assessment

Abstract Effective diabetes problem solving requires identification of risk factors for inadequate mealtime self-management. Ecological momentary assessment was used to enhance identification of factors hypothesized to impact self-management. Adolescents with type 1 diabetes participated in a feasibility trial for a mobile app called MyDay. Meals, mealtime insulin, self-monitored blood glucose, and psychosocial and contextual data were obtained for 30 days. Using 1472 assessments, mixed-effects between-subjects analyses showed that social context, location, and mealtime were associated with missed self-monitored blood glucose. Stress, energy, mood, and fatigue were associated with missed insulin. Within-subjects analyses indicated that all factors were associated with both self-management tasks. Intraclass correlations showed within-subjects accounted for the majority of variance. The ecological momentary assessment method provided specific targets for improving self-management problem solving, phenotyping, or integration within just-in-time adaptive interventions.

Use of a Self-Management Mobile Application by Adolescents with Type 1 Diabetes

2016 ◽  
Vol 40 (5) ◽  
pp. S65-S66
Author(s):  
Caitlin Nunn ◽  
Michael Rotondi ◽  
Shivani Goyal ◽  
Sally Reiser ◽  
Angelo Simone ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Mobile Application ◽  
Self Management

scholarly journals Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes (Preprint)

2018 ◽  
Author(s):  
Ashenafi Zebene Woldaregay ◽  
Eirik Årsand ◽  
Taxiarchis Botsis ◽  
David Albers ◽  
Lena Mamykina ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
State Of The Art ◽  
The State ◽  
Self Management ◽  
Machine Learning Applications ◽  
Anomaly Classification

BACKGROUND Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes. OBJECTIVE This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management. METHODS A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. RESULTS The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others. CONCLUSIONS Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual’s GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the difference among patients and also track its temporal change over time. Moreover, studies should also give more emphasis on the types of inputs used and their associated time lag. Generally, we foresee that these developments might encourage researchers to further develop and test these systems on a large-scale basis.

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