scholarly journals Smartphone apps for type 2 diabetes self-management and medication adherence : a multi-method study

2020 ◽  
Author(s):  
◽  
Zhilian Huang
Keyword(s):  
Type 2 Diabetes ◽  
Medication Adherence ◽  
Self Management ◽  
Smartphone Apps

scholarly journals SAT-637 Patient-Centered Glycemic Management of Type 2 Diabetes with Mobile Applications

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Chap-Kay K Lau ◽  
Gloria Wu ◽  
Brian Leung ◽  
Donia Momen ◽  
Shannon Luu
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Medication Adherence ◽  
Mobile Applications ◽  
American Diabetes Association ◽  
Mobile Apps ◽  
Cardiovascular Complications ◽  
Self Management ◽  
Patient Centered

Abstract BACKGROUND: Ehealth apps have 3.7 billion downloads yearly. The accessibility of diabetes mobile applications allows for patient self-management of diabetes. Renal and cardiovascular complications,1 which play a role in diabetic patient outcomes, are newly highlighted by the 2019 American Diabetes Association’s Standards of Medical Care in Diabetes.2 Purpose: To evaluate free Android mobile apps using the Diabetes Self-Management Education and Support (DSMES) and the 2019 American Diabetes Association (ADA) guidelines for renal and cardiovascular complications. Methods: Using the search term “diabetes,” Google Play store was accessed. Inclusion criteria: 1) Apps with downloads 1M-100,000; 2) free; 3) DSMES criteria; and 4) medication adherence. Exclusion criteria: 1) Purely educational factual apps on diabetes; 2) no ranking or download information. Google displayed a list of 10 free apps in 2019. The 2019 apps were analyzed for DSMES criteria, renal and cardiovascular complications (ADA 2019), and medication adherence via push notification. Results: The top 10 mobile apps in descending order were: 1) mySugr, 2) Onetouch Reveal, 3) OneDrop Diabetes Management, 4) Diabetes: M, 5) Diabetes, 6) Ontrack Diabetes, 7) Health2Sync, 8) Diabetes Connect, 9) Glucose Buddy Diabetes Tracker, 10) Blood Glucose Tracker. All of the mobile apps had the functionality of tracking blood glucose levels. 8/10 had the ability of tracking HbA1C levels. The percent of DSMES incorporation within the apps ranged from 18.2%-81.8%. None of the apps used all the DSMES guidelines or 2019 ADA. Only 1/10 of the mobile apps had the ability to track the presence of heart palpitation and retina/eye issues recommendations. None of the apps had the ability to track cardiovascular and renal complications. 7/10 of the apps had medication reminders (sound notification) and 4/10 of the apps had push notifications. Conclusion: eHealth mobile apps could be a powerful tool for patient self-management of diabetes. Currently, none of the apps incorporate all of the DSMES or ADA guidelines regarding comorbidities and complications of diabetes. Despite these shortcomings, these apps provide an introduction to the concept of patient-centered tracking of health data. We look for future improvements as more physicians use the apps and provide feedback to the app developers and eHealth commerce space. References 1. Davies MJ, D’Alessio DA, Fradkin J, et al. Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care 2018;41:2669-2701 2. American Diabetes Association. 4. Comprehensive medical evaluation and assessment of comorbidities: Standards of Medical Care in Diabetes—2019. Diabetes Care 2019;42(Suppl. 1):S34-S45

On-Demand, App-Delivered Coaching to Enable Diabetes Self-Management in Adults with Type 2 Diabetes—A Feasibility Study

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 50-LB
Author(s):  
JOHN B. HERNANDEZ ◽  
AMY ARMENTO LEE ◽  
SCOTT ROBERTSON ◽  
CARA SILVER ◽  
AMIT MAJITHIA
Keyword(s):  
Type 2 Diabetes ◽  
Feasibility Study ◽  
Self Management ◽  
On Demand

815-P: Medication Adherence by Pill Count Using Medical Reconciliation in Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 815-P
Author(s):  
MEGUMI SHIOMI ◽  
YOICHI TANAKA ◽  
MOMOKA KUROBUCHI ◽  
TESSHU TAKADA ◽  
KATSUYA OTORI
Keyword(s):  
Type 2 Diabetes ◽  
Medication Adherence ◽  
Pill Count

2180-PUB: Integration of Continuous Glucose Monitoring in Diabetes Self-Management Education for Type 2 Diabetes Improves Glycemic Control

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2180-PUB
Author(s):  
ADDIE L. FORTMANN ◽  
ALESSANDRA BASTIAN ◽  
CODY J. LENSING ◽  
SHANE HOVERSTEN ◽  
KIMBERLY LUU ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Management Education ◽  
Glucose Monitoring ◽  
Self Management

scholarly journals Optimal cut-off points for adherence measure among patients with type 2 diabetes in primary care clinics: a retrospective analysis

2021 ◽  
Vol 12 ◽  
pp. 204062232199026
Author(s):  
Ming Tsuey Lim ◽  
Norazida Ab Rahman ◽  
Xin Rou Teh ◽  
Chee Lee Chan ◽  
Shantini Thevendran ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Medication Adherence ◽  
Poor Adherence ◽  
Adherence Measure ◽  
Primary Care Clinics ◽  
Assessment Period ◽  
Hba1c Target ◽  
Optimal Adherence

Background: Medication adherence measures are often dichotomized to classify patients into those with good or poor adherence using a cut-off value ⩾80%, but this cut-off may not be universal across diseases or medication classes. This study aimed to examine the cut-off value that optimally distinguish good and poor adherence by using the medication possession ratio (MPR) and proportion of days covered (PDC) as adherence measures and glycated hemoglobin (HbA1c) as outcome measure among type 2 diabetes mellitus (T2DM) patients. Method: We used pharmacy dispensing data of 1461 eligible T2DM patients from public primary care clinics in Malaysia treated with oral antidiabetic drugs between January 2018 and May 2019. Adherence rates were calculated during the period preceding the HbA1c measurement. Adherence cut-off values for the following conditions were compared: adherence measure (MPR versus PDC), assessment period (90-day versus 180-day), and HbA1c target (⩽7.0% versus ⩽8.0%). Results: The optimal adherence cut-offs for MPR and PDC in predicting HbA1c ⩽7.0% ranged between 86.1% and 98.3% across the two assessment periods. In predicting HbA1c ⩽8.0%, the optimal adherence cut-offs ranged from 86.1% to 92.8%. The cut-off value was notably higher with PDC as the adherence measure, shorter assessment period, and a stricter HbA1c target (⩽7.0%) as outcome. Conclusion: We found that optimal adherence cut-off appeared to be slightly higher than the conventional value of 80%. The adherence thresholds may vary depending on the length of assessment period and outcome definition but a reasonably wise cut-off to distinguish good versus poor medication adherence to be clinically meaningful should be at 90%.

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