scholarly journals Identification of Type 2 Diabetes Management Mobile App Features and Engagement Strategies: Modified Delphi Approach

10.2196/17083 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e17083
Author(s):  
Hanan A Alenazi ◽  
Amr Jamal ◽  
Mohammed A Batais
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Diabetes Management ◽  
Mobile App ◽  
Patient Specific ◽  
Moderate Degree ◽  
Modified Delphi ◽  
Engagement Strategies ◽  
Delphi Approach

Background Diabetes is a significant public health issue. Saudi Arabia has the highest prevalence of type 2 diabetes mellitus (T2DM) in the Arab world. Currently, it affects 31.6% of the general population, and the prevalence of T2DM is predicted to rise to 45.36% by 2030. Mobile health (mHealth) offers improved and cost-effective care to people with T2DM. However, the efficiency of engagement strategies and features of this technology need to be reviewed and standardized according to stakeholder and expert perspectives. Objective The main objective of this study was to identify the most agreed-upon features for T2DM self-management mobile apps; the secondary objective was to identify the most agreed-upon strategies that prompt users to use these apps. Methods In this study, a 4-round modified Delphi method was applied by experts in the domain of diabetes care. Results In total, 11 experts with a mean age of 47.09 years (SD 11.70) consented to participate in the study. Overall, 36 app features were generated. The group of experts displayed weak agreement in their ranking of intervention components (Kendall W=0.275; P<.001). The top 5 features included insulin dose adjustment according to carbohydrate counting and blood glucose readings (5.36), alerting a caregiver of abnormal or critical readings (6.09), nutrition education (12.45), contacts for guidance if required (12.64), and offering patient-specific education tailored to the user’s goals, needs, and blood glucose readings (12.90). In total, 21 engagement strategies were generated. Overall, the experts showed a moderate degree of consensus in their strategy rankings (Kendall W=0.454; P<.001). The top 5 engagement strategies included a user-friendly design (educational and age-appropriate design; 2.82), a free app (3.73), allowing the user to communicate or send information/data to a health care provider (HCP; 5.36), HCPs prescribing the mobile app in the clinic and asking about patients’ app use compliance during clinical visits (6.91), and flexibility and customization (7.91). Conclusions This is the first study in the region consisting of a local panel of experts from the diabetes field gathering together. We used an iterative process to combine the experts’ opinions into a group consensus. The results of this study could thus be useful for health app developers and HCPs and inform future decision making on the topic.

scholarly journals Identification of Type 2 Diabetes Management Mobile App Features and Engagement Strategies: Modified Delphi Approach (Preprint)

2019 ◽  
Author(s):  
Hanan A Alenazi ◽  
Amr Jamal ◽  
Mohammed A Batais
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Diabetes Management ◽  
Mobile App ◽  
Patient Specific ◽  
Moderate Degree ◽  
Modified Delphi ◽  
Engagement Strategies ◽  
Delphi Approach

BACKGROUND Diabetes is a significant public health issue. Saudi Arabia has the highest prevalence of type 2 diabetes mellitus (T2DM) in the Arab world. Currently, it affects 31.6% of the general population, and the prevalence of T2DM is predicted to rise to 45.36% by 2030. Mobile health (mHealth) offers improved and cost-effective care to people with T2DM. However, the efficiency of engagement strategies and features of this technology need to be reviewed and standardized according to stakeholder and expert perspectives. OBJECTIVE The main objective of this study was to identify the most agreed-upon features for T2DM self-management mobile apps; the secondary objective was to identify the most agreed-upon strategies that prompt users to use these apps. METHODS In this study, a 4-round modified Delphi method was applied by experts in the domain of diabetes care. RESULTS In total, 11 experts with a mean age of 47.09 years (SD 11.70) consented to participate in the study. Overall, 36 app features were generated. The group of experts displayed weak agreement in their ranking of intervention components (Kendall W=0.275; <i>P</i>&lt;.001). The top 5 features included insulin dose adjustment according to carbohydrate counting and blood glucose readings (5.36), alerting a caregiver of abnormal or critical readings (6.09), nutrition education (12.45), contacts for guidance if required (12.64), and offering patient-specific education tailored to the user’s goals, needs, and blood glucose readings (12.90). In total, 21 engagement strategies were generated. Overall, the experts showed a moderate degree of consensus in their strategy rankings (Kendall W=0.454; <i>P</i>&lt;.001). The top 5 engagement strategies included a user-friendly design (educational and age-appropriate design; 2.82), a free app (3.73), allowing the user to communicate or send information/data to a health care provider (HCP; 5.36), HCPs prescribing the mobile app in the clinic and asking about patients’ app use compliance during clinical visits (6.91), and flexibility and customization (7.91). CONCLUSIONS This is the first study in the region consisting of a local panel of experts from the diabetes field gathering together. We used an iterative process to combine the experts’ opinions into a group consensus. The results of this study could thus be useful for health app developers and HCPs and inform future decision making on the topic.

scholarly journals A Review of Emerging Technologies in Diabetes Management for Multiple-Dose Insulin-Injecting Patients With Type 2 Diabetes Who Self-monitor Blood Glucose

2018 ◽  
Vol 35 (2) ◽  
pp. 69-81
Author(s):  
Malgorzata Slugocki ◽  
Damian Bialonczyk ◽  
Ayşe Elif Özdener
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Multiple Dose ◽  
English Language ◽  
Diabetes Management ◽  
Data Extraction ◽  
Glucose Monitoring ◽  
Patient Specific ◽  
Smart Devices

Objective: The management of diabetes mellitus requires a precise interpretation of blood glucose (BG) data by patients and providers and is increasingly associated with a need for medical technologies that aid in achieving patient-specific outcomes while making the process convenient. This review aims to summarize the current landscape in diabetes management technology, focusing specifically on devices that assist with pattern management in patients with type 2 diabetes (T2DM) who are on multiple-dose insulin regimens. Data Sources: The authors searched MEDLINE to identify articles from 2007 to 2018 that evaluated technologies for BG pattern management and diabetes monitoring. Additional references were generated through review of identified literature citations. Article selection was based on mutual agreement for inclusion. Data Selection and Data Extraction: Relevant articles were defined as English-language articles, describing technologies that assist with diabetes management in insulin-injecting patients with T2DM. Articles that focused exclusively on type 1 diabetes were excluded. Data Synthesis: The literature search yielded 334 articles, of which 21 were included for synthesis. The current BG monitoring practices emphasize the benefit of the structured self-monitoring of BG approach. Several randomized controlled trials conclude that the available technology aids in comprehensive data collection and facilitates communication between patients and providers. Digitally enabled “smart” devices are valuable tools that may help improve outcomes while providing a flexible, personalized approach. Conclusions: Integration of digital technology with diabetes management allows for accurate collection and analysis of data. Emergence of digital tools promotes a comprehensive, precise, and objective approach to glucose monitoring and encourages patient-provider collaborations.

Can Blood Glucose Monitoring be Simplified/Modified in Order to Achieve Better Diabetes Management—Perspectives for the Future

2010 ◽  
Vol 06 (01) ◽  
pp. 48
Author(s):  
Robert M Cuddihy ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Diabetes Management ◽  
Healthcare Providers ◽  
Glucose Monitoring ◽  
Blood Glucose Monitoring ◽  
Clear Understanding ◽  
Major Advance ◽  
Oral Agents

Self-monitoring of blood glucose (SMBG) with reflectance meters was heralded as a major advance in the management of diabetes and has been available to individuals with diabetes for home use since the late 1970s. This tool was put to use in the landmark Diabetes Control and Complications Trial (DCCT), which revolutionized care for individuals with type 1 diabetes, enabling these individuals to intensify their glucose control. SMBG has similar benefit in individuals with type 2 diabetes requiring insulin therapy. Its use in other individuals with type 2 diabetes treated with oral agents or non-insulin therapies is less clear. While SMBG is a potentially powerful tool to aid in the daily management of diabetes, to be used effectively, SMBG must be optimized to ensure the information derived from it can be acted on to modify physical activity, dietary intake, or medications to improve glycemic control. Recently, studies looking at this population have called into question the utility of SMBG in the management of individuals with type 2 diabetes treated with non-insulin therapies. However, these studies are lacking in the specifics of how such information was used to modify therapies. In addition to this, the lack of a universally accepted output for SMBG data significantly impedes its uptake and appropriate use by healthcare providers and patients. To maximize the effectiveness of SMBG, both patients and providers need to have a clear understanding of when and how to use SMBG data and, most importantly, act upon the data to effect a change in their diabetes management.

scholarly journals Pilot Study of a Prototype Integrated Diabetes Management System and the Relationship of Use to Diabetes Management Behaviors and HbA1c Among Type 2 Diabetes Patients

Iproceedings ◽  
10.2196/16298 ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. e16298
Author(s):  
Connor Devoe ◽  
Nils Fischer ◽  
Tim Hale ◽  
Neda Derakhshani ◽  
Mursal Atif ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Pilot Study ◽  
Basal Insulin ◽  
Diabetes Management ◽  
The United States ◽  
Step Count ◽  
Management Behaviors ◽  
Hba1c Levels

Background Type 2 diabetes (T2D) is the seventh leading cause of death (2017) in the United States, and by 2030 it is estimated that it will affect 439 million globally. Effective glycemic control can be challenging for patients. A tool to guide patients’ in their self-management behaviors and share this data with their physician may improve insulin adherence leading to lower HbA1c. We examined an integrated diabetes management (IDM) system that utilizes a Bluetooth-enabled insulin event capture device, a Bluetooth-enabled glucometer, and an Android smartphone app. IDM data can be viewed by clinicians in the electronic medical record (EMR). Objective The primary aim of this study is to describe how app use is related to insulin adherence, blood glucose measurements, meal snapshots, and step count. Secondarily, we assessed the impact on HbA1c levels over a 3- and 6-month period. Methods Thirty-five participants were enrolled from Boston-area hospitals in this single-arm pilot study. Use of the IDM system was defined as the number of days per week participants logged into the app and moved past the home screen. Three app use groups were created: low app use (0.33-2.46 days per week), medium app use (2.54-5.08 days per week), and high app use (>5.4 days per week). Adherence to insulin, blood glucose measurements, and meal snapshots were defined as a ratio of actual weekly events recorded by participants’ app use divided by their physician’s recommendation. Step count was defined as the total weekly steps for each participant. Daily app-generated data on app use and indicators of diabetes management were collected. HbA1c levels were assessed via blood test at enrollment, 3-months, and 6-months. Using a hierarchical linear mixed model, we examined changes in outcome measures while accounting for random intercepts and slopes to control for variation in individual outcomes over the study. Results Overall app use (average unique days using the app per week) declined from 6.19 days to 3.00 days (at 1 and 24 weeks, respectively). Participants with high app use had significant improvement in bolus and basal insulin adherence per week (0.009 P=.041 [95% CI 0.0004 to 0.018] and 0.016 P<.001 [95% CI 0.0079 to 0.023], respectively), but participants had no significant improvements in blood glucose and meal snapshot adherence or absolute step count. HbA1c significantly decreased per week (coefficient –0.025 [95% CI –0.044 to –0.007], P=.007) with an overall change of 0.6. Participants with high app use significantly improved their HbA1c per week (–0.037 P=.016 [–0.066 to –0.0067]) compared to participants with medium and low app use, yielding a total improvement of 0.88 over 24 weeks. Conclusions Results show that bolus and basal insulin may have increased with higher app use. HbA1c significantly improved over the course of the study, along with significantly greater improvement in HbA1c among participants with higher app use compared to participants in the middle or low app use groups. This study is not designed or intended to evaluate efficacy but provides results to guide the future design and development of this prototype IDM system.

scholarly journals Effectiveness of Mobile App-Assisted Self-Care Interventions for Improving Patient Outcomes in Type 2 Diabetes and/or Hypertension: Systematic Review and Meta-Analysis of Randomized Controlled Trials (Preprint)

2019 ◽  
Author(s):  
Kaifeng Liu ◽  
Zhenzhen Xie ◽  
Calvin Kalun Or
Keyword(s):  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Patient Outcomes ◽  
Meta Analysis ◽  
Self Care ◽  
Mobile App ◽  
Randomized Controlled ◽  
Secondary Outcomes

BACKGROUND Mobile app-assisted self-care interventions are emerging promising tools to support self-care of patients with chronic diseases such as type 2 diabetes and hypertension. The effectiveness of such interventions requires further exploration for more supporting evidence. OBJECTIVE A systematic review and meta-analysis of randomized controlled trials (RCTs) were conducted to examine the effectiveness of mobile app-assisted self-care interventions developed for type 2 diabetes and/or hypertension in improving patient outcomes. METHODS We followed the Cochrane Collaboration guidelines and searched MEDLINE, Cochrane Library, EMBASE, and CINAHL Plus for relevant studies published between January 2007 and January 2019. Primary outcomes included changes in hemoglobin A<sub>1c</sub> (HbA<sub>1c</sub>) levels, systolic blood pressure (SBP), and diastolic blood pressure (DBP). Changes in other clinical-, behavioral-, knowledge-, and psychosocial-related outcomes were included as secondary outcomes. Primary outcomes and objective secondary outcomes that were reported in at least two trials were meta-analyzed; otherwise, a narrative synthesis was used for data analysis. RESULTS A total of 27 trials were identified and analyzed. For primary outcomes, the use of mobile app-assisted self-care interventions was associated with significant reductions in HbA<sub>1c</sub> levels (standardized mean difference [SMD] −0.44, 95% CI −0.59 to −0.29; <i>P</i>&lt;.001), SBP (SMD −0.17, 95% CI −0.31 to −0.03, <i>P</i>=.02), and DBP (SMD −0.17, 95% CI −0.30 to −0.03, <i>P</i>=.02). Subgroup analyses for primary outcomes showed that several intervention features were supportive of self-management, including blood glucose, blood pressure, and medication monitoring, communication with health care providers, automated feedback, personalized goal setting, reminders, education materials, and data visualization. In addition, 8 objective secondary outcomes were meta-analyzed, which showed that the interventions had significant lowering effects on fasting blood glucose levels and waist circumference. A total of 42 secondary outcomes were narratively synthesized, and mixed results were found. CONCLUSIONS Mobile app-assisted self-care interventions can be effective tools for managing blood glucose and blood pressure, likely because their use facilitates remote management of health issues and data, provision of personalized self-care recommendations, patient–care provider communication, and decision making. More studies are required to further determine which combinations of intervention features are most effective in improving the control of the diseases. Moreover, evidence regarding the effects of these interventions on the behavioral, knowledge, and psychosocial outcomes of patients is still scarce, which warrants further examination.

scholarly journals Overcoming obstacles: new management options

2004 ◽  
pp. T23-T27 ◽  
Author(s):  
L Heinemann
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Diabetes Management ◽  
Treatment Options ◽  
Glucose Monitoring ◽  
Regular Insulin ◽  
Insulin Analogues ◽  
Management Options ◽  
Insulin Replacement

The outlook for patients with type 2 diabetes looks set to improve with the availability of new diabetes management options that provide more comprehensive control of blood glucose levels and/or encourage better patient compliance than previous alternatives. New insulin analogues, such as insulin lispro, aspart and glargine, allow more physiological insulin replacement and greater freedom in the timing and content of meals, compared with regular insulin preparations. The development of novel non-invasive routes of insulin administration promises to further improve diabetes management. Many barriers to initiating insulin relate to the need for frequent insulin injections, fears that insulin injections will be painful and difficult to administer, and concerns about hypoglycaemia and weight gain. Thus, each measure that reduces these barriers will help to prevent inappropriate delays in starting insulin therapy as well as to promote better compliance with therapy. The output from continuous glucose monitoring devices will assist accurate insulin replacement, which is difficult using point-estimates of blood glucose. Such devices will hopefully also circumvent the need for finger stick tests. There are several novel therapies in development that will further expand the portfolio of treatment options for patients with type 2 diabetes. Improved quality and choice of diabetes management options will provide doctors with the tools they require to develop tailored treatment plans, increase the probability that treatment goals are achieved and thereby reduce the risk of patients developing late-stage diabetes-related complications.

Glycemic Monitoring in Diabetics with Sickle Cell Plus β-Thalassemia Hemoglobinopathy

2005 ◽  
Vol 39 (9) ◽  
pp. 1557-1560 ◽  
Author(s):  
Sheri M Kosecki ◽  
Philip T Rodgers ◽  
Martha B Adams
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Sickle Cell Disease ◽  
Glycemic Control ◽  
Sickle Cell ◽  
Diabetes Management ◽  
Glycosylated Hemoglobin ◽  
Fasting Blood Glucose ◽  
Cell Disease

OBJECTIVE: To report a case of diabetes management in a patient with a hemoglobinopathy that caused her clinician to seek a different measure of glycemic control, fructosamine, rather than glycosylated hemoglobin (HbA1c). CASE SUMMARY: A 53-year-old African American woman presented with a past medical history of type 2 diabetes, hypertension, seizure disorder, rheumatoid arthritis, and sickle cell disease plus β-thalassemia. She reported fasting blood glucose values ranging broadly from 50 to 320 mg/dL, yet her HbA1c result remained steady in a low range of >6%. A measure of fructosamine returned elevated at 340 μmol/L (reference range 200–300%). DISCUSSION: We believe that this patient's hemoglobinopathy resulted in falsely low levels of HbA1c, and we substantiate this interpretation with the patient's self-monitored blood glucose values from home that appeared higher and inconsistent with the HbA1c results. Although few reports on using the measure of fructosamine appear in the literature, this patient's high fructosamine result supports fructosamine as the more appropriate measure of glycemic control. CONCLUSIONS: Serum fructosamine levels may be considered as an appropriate laboratory measurement when monitoring long-term glycemic control in patients with type 2 diabetes mellitus and sickle cell disease.

scholarly journals Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yixiang Deng ◽  
Lu Lu ◽  
Laura Aponte ◽  
Angeliki M. Angelidi ◽  
Vera Novak ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Deep Learning ◽  
Blood Glucose ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Generative Models ◽  
Patient Specific ◽  
Learning Methods ◽  
Glucose Levels

AbstractAccurate prediction of blood glucose variations in type 2 diabetes (T2D) will facilitate better glycemic control and decrease the occurrence of hypoglycemic episodes as well as the morbidity and mortality associated with T2D, hence increasing the quality of life of patients. Owing to the complexity of the blood glucose dynamics, it is difficult to design accurate predictive models in every circumstance, i.e., hypo/normo/hyperglycemic events. We developed deep-learning methods to predict patient-specific blood glucose during various time horizons in the immediate future using patient-specific every 30-min long glucose measurements by the continuous glucose monitoring (CGM) to predict future glucose levels in 5 min to 1 h. In general, the major challenges to address are (1) the dataset of each patient is often too small to train a patient-specific deep-learning model, and (2) the dataset is usually highly imbalanced given that hypo- and hyperglycemic episodes are usually much less common than normoglycemia. We tackle these two challenges using transfer learning and data augmentation, respectively. We systematically examined three neural network architectures, different loss functions, four transfer-learning strategies, and four data augmentation techniques, including mixup and generative models. Taken together, utilizing these methodologies we achieved over 95% prediction accuracy and 90% sensitivity for a time period within the clinically useful 1 h prediction horizon that would allow a patient to react and correct either hypoglycemia and/or hyperglycemia. We have also demonstrated that the same network architecture and transfer-learning methods perform well for the type 1 diabetes OhioT1DM public dataset.

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