Impact of Use Frequency of a Mobile Diabetes Management App on Blood Glucose Control: Evaluation Study

Evaluation of Mobile Applications for Patients with Diabetes Mellitus: A Scoping Review

There has been increasing interest in mobile healthcare for diabetes management. However, there remains limited evidence regarding the effectiveness of these mobile applications (apps). This scoping review aimed to evaluate the clinical effectiveness of mobile diabetes management apps. We used the following search terms: "mobile app", "mobile application", and "diabetes". We included only articles written in English and published between January 2016 and August 2021. We identified two, six, and four articles focused on type 1 diabetes, type 2 diabetes, and both diabetes types, respectively. Five, four, and three of these studies reported on the apps' functionality, usability, and both, respectively. Our findings indicated that diabetes mobile apps allowed for convenient user experience and improved blood sugar levels in patients with diabetes. Considering these findings, usability must be comprehensively evaluated by using definitions such as the ISO9241-11 usability definition or the mobile application rating scale (MARS) when developing diabetes-related apps. For the feasibility of diabetes mobile apps, we recommend that HbA1C and self-management be included as evaluation variables. Given the increasing importance of continuous management for patients with diabetes, interventions using mobile apps are bound to become effective tools for patient-led self-management.

Validity and Reliability of a Vibration-Based Cell Phone in Detecting Peripheral Neuropathy among Patients with a Risk of Diabetic Foot Ulcer

The aim of this study was to determine the validity and reliability of a vibration-based cell phone in detecting peripheral neuropathy among individuals with a risk of diabetic foot ulcer (DFU). The current study consisted of 3 phases: a pilot study, concurrent validity study, and interreliability study. A 128 Hz tuning fork and vibration-based cell phone using the Vibrations-Test app was compared in the primary care setting. The pilot study confirmed that the vibration-based cell phone has an adequate reliability (r > 0.70, P < .001). The concurrent validity test involved 96 participants (aged 55.78 ± 8.32 years). The Pearson correlation test found the highest correlation in the fifth metatarsal heads (r = 0.741, right feet; 0.772, left feet; P < .001). In the 4 primary care settings, the interrater reliability between nurses of the vibration-based cell phone were k = 1.000, 1.000, 1.000, and 0.720. The concurrent validity test found moderate to strong correlation results and that the interrater reliability had a strong agreement that was almost perfect. Thus, vibration-based cell phone applications can be used as screening tools for detecting neuropathy among individuals with a risk of DFU.

Determinants of American Adults' Use of Digital Health and Willingness to Share Health Data to Providers, Family, and Social Media: A Cross-sectional Study

With the global pandemic driving the adoption of digital health, understanding the predictors or determinants of digital health usage and information sharing gives an opportunity to advocate for broader adoption. We examined the prevalence and predictors of digital health usage and information-sharing behaviors among American adults. Data were from the Health Information National Trends Survey 5 Cycle 4. More than two-thirds used a digital resource for health-related activities (eg, to check test results). About 81% were willing to share their digital data with their provider, 75% with family, and 58% with friends. Only 14% shared health information on social media. Gender, education, device types, and performance expectancy of digital health were common factors associated with both digital health usage and information-sharing behaviors. Other predictors included rurality, patient portal access, income, and having a chronic disease. Of note, we found that Asian American Pacific Islanders, compared with Whites, were less likely to share information with providers. Performance expectancy was a significant determinant of information sharing. Those diagnosed with diabetes were 4% less likely to share information with their providers. With the growing digital divide, there is a need to advocate for more usable and accessible digital health to assist with person-centered care.

The effect of an mHealth intervention for adolescents with Type 1 diabetes and their parents

BACKGROUND

Past research has demonstrated that adolescents with Type 1 diabetes (T1D) typically have a decline in health outcomes as they begin to assume more self-management activities. Mobile app interventions have been suggested as one possible way to improve this behavior.

PURPOSE

The primary aim of this study was to address declines in health outcomes by pilot testing the satisfaction, use and feasibility of an app-based family communication intervention aimed to assist in adolescent self-management of T1D.

METHODS

Thirty-three adolescent-parent pairs were enrolled in and completed the 12-week pilot study. Participants were randomized 2:1 to intervention (app use) or control group. Pre/post quantitative and qualitative data were collected, including HbA1c and surveys. Paired-sample t-tests and ANOVA statistics were conducted.

RESULTS

The parents and adolescents reported high satisfaction with the app, and that it was easy to use. Results showed HbA1c stability in the intervention group and significant worsening in the control group. There were also significant improvements in adherence to diabetes management and quality of life for the parents in the intervention group. The adolescents did not show any improvement in quality-of-life measures.

CONCLUSION

This study suggests that the app intervention is acceptable, shows promise for improving health outcomes for adolescents with T1D, and may improve family communication. The public health implications of this work are that app interventions have a potential role in positively influencing chronic disease outcomes. Additional research with a more extensive and diverse sample is needed to determine generalizability.

High engagement in mobile peer support is associated with better glycemic control in type 1 diabetes: a real-world study

AIMS

Peer support for diabetes has become convenient and interactive following the emergence of mobile health (mHealth). We aimed to evaluate the association between engagement in peer support via mHealth app and glycemic control in type 1 diabetes (T1D).

METHODS

This retrospective study included adults with T1D who had joined the mobile community "TangTangQuan (TTQ)" since May 2018 for at least one year. "Like", "comment" and "share" were the major interaction indicators of mobile community and were used to assess engagement in peer support. The patients were divided into four engagement groups by quartile. The primary outcome was the change in HbA_1c , mean fasting blood glucose (FBG) and postprandial blood glucose (PBG) from baseline to the 12th month. Other outcomes included the change of self-monitoring of blood glucose frequency, hypoglycemia frequency, and the proportion of reaching optimal glycemic control.

RESULTS

Among the 693 individuals, the HbA_1c , mean FBG and PBG improved in the 12th month. Multiple regression analysis showed that higher engagement in peer support was associated with a greater reduction of HbA_1c (β=-0.45, P<0.001) and mean FBG (β=-0.82, P<0.001). In the subgroup of poor glycemic control, the association between engagement in peer support and glycemic improvement still remained (HbA_1c : β=-0.86, P=0.002; FBG: β=-1.36, P=0.001). The engagement in mobile peer support was positively correlated with educational level (OR=1.42, P=0.042), household income (OR=1.43, P=0.013), and the use of continuous subcutaneous insulin infusion (OR=1.73, P=0.009).

CONCLUSION

High engagement in mobile peer support was associated with better glycemic control in adults with T1D.

New digital health technologies for insulin initiation and optimization for people with type 2 diabetes

OBJECTIVE

The health and economic burden of type 2 diabetes (T2D) is of global significance. Many people with T2D eventually need insulin to help reduce their risk of serious associated complications. However, barriers in initiating and/or optimizing insulin expose people with diabetes to sustained hyperglycemia. In this review, we investigate how new and future technologies may provide opportunities to help overcome barriers to insulin initiation and/or optimization.

METHODS

A focused literature search of PubMed and key scientific congresses was conducted. Software tools and devices developed to support insulin initiation and/or optimization were identified by manually filtering over 300 publications and conference abstracts.

RESULTS

Most software tools have been developed for a smartphone platform. At present, published data suggest that use of these technologies is associated with equivalent or improved glycemic outcomes compared with standard care with additional benefits such as reduced healthcare provider (HCP) time burden and improved diabetes knowledge. However, there remains a paucity of good quality evidence. Most new devices to support insulin therapy help track the dose and timing of insulin use.

CONCLUSIONS

New digital health tools may help to reduce barriers to optimal insulin therapy. An integrated solution that connects glucose monitoring, dose recording, titration advice, and records comorbidities and lifestyle factors has the potential to reduce the complexity and burden of treatment and may improve titration and treatment adherence, resulting in better outcomes for people with diabetes.

Mobile applications for the sport and exercise nutritionist: a narrative review

Mobile technology is widespread in modern society, and the applications (apps) that they run can serve various purposes. Features such as portability, ease of communication, storage, and relative low cost may make such technology attractive to practitioners in several fields. This review provides a critical narrative on the existing literature for apps relevant to the field of sport and exercise nutrition. Three main areas are discussed: (1) dietary analysis of athletes, (2) nutrition education for athletes, (3) estimating body composition. The key purpose of the review was to identify what literature is available, in what areas apps may have a benefit over traditional methods, and considerations that practitioners should make before they implement apps into their practice or recommend their use to coaches and athletes.

A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin

Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D under MDI therapy, providing a decision-support system and improving confidence toward self-management of the disease considering the dataset used by Bertachi et al. Different machine learning (ML) algorithms, data sources, optimization metrics and mitigation measures to predict and avoid NH events have been studied. In addition, we have designed population and personalized models and studied the generalizability of the models and the influence of physical activity (PA) on them. Obtaining 30 g of rescue carbohydrates (CHO) is the optimal value for preventing NH, so it can be asserted that this is the value with which the time under 70 mg/dL decreases the most, with almost a 35% reduction, while increasing the time in the target range by 1.3%. This study supports the feasibility of using ML techniques to address the prediction of NH in patients with T1D under MDI therapy, using continuous glucose monitoring (CGM) and a PA tracker. The results obtained prove that BG predictions can not only be critical in achieving safer diabetes management, but also assist physicians and patients to make better and safer decisions regarding insulin therapy and their day-to-day lives.

Mobile Health, Disease Knowledge, and Self-Care Behavior in Chronic Kidney Disease: A Prospective Cohort Study

Mobile health (mHealth) management is an emerging strategy of care for patients with chronic diseases. However, the effect of mHealth management on clinical outcomes of patients with chronic kidney disease (CKD) has not been well-studied. The aim of this study was to investigate the additional influence of mHealth on disease knowledge and self-care behavior in CKD patients who had received traditional education. We designed and developed a new healthcare mobile application, called iCKD, which has several major features, including home-based physiological signal monitoring, disease health education, nutrition analysis, medication reminder, and alarms and a warning system. Trained nurses interviewed patients with CKD using structured questionnaires of disease knowledge and self-care behavior. After propensity score matching, we analyzed 107 patients who used iCKD and traditional education, and 107 who received traditional education. The patients who used iCKD had higher disease knowledge scores than those who received traditional education. In multivariate analysis, iCKD was significantly and positively associated with disease knowledge scores. Patients with high education levels could have greater disease knowledge through using mHealth. There was no significant difference in total scores of self-care behavior between the two groups. In conclusion, mHealth can significantly increase disease knowledge in patients with CKD.

User Engagement With Smartphone Apps and Cardiovascular Disease Risk Factor Outcomes: Systematic Review

Background

The use of mobile health (mHealth) interventions, including smartphone apps, for the prevention of cardiovascular disease (CVD) has demonstrated mixed results for obesity, hypercholesterolemia, diabetes, and hypertension management. A major factor attributing to the variation in mHealth study results may be mHealth user engagement.

Objective

This systematic review aims to determine if user engagement with smartphone apps for the prevention and management of CVD is associated with improved CVD health behavior change and risk factor outcomes.

Methods

We conducted a comprehensive search of PubMed, CINAHL, and Embase databases from 2007 to 2020. Studies were eligible if they assessed whether user engagement with a smartphone app used by an individual to manage his or her CVD risk factors was associated with the CVD health behavior change or risk factor outcomes. For eligible studies, data were extracted on study and sample characteristics, intervention description, app user engagement measures, and the relationship between app user engagement and the CVD risk factor outcomes. App user engagement was operationalized as general usage (eg, number of log-ins or usage days per week) or self-monitoring within the app (eg, total number of entries made in the app). The quality of the studies was assessed.

Results

Of the 24 included studies, 17 used a randomized controlled trial design, 4 used a retrospective analysis, and 3 used a single-arm pre- and posttest design. Sample sizes ranged from 55 to 324,649 adults, with 19 studies recruiting participants from a community setting. Most of the studies assessed weight loss interventions, with 6 addressing additional CVD risk factors, including diabetes, sleep, stress, and alcohol consumption. Most of the studies that assessed the relationship between user engagement and reduction in weight (9/13, 69%), BMI (3/4, 75%), body fat percentage (1/2, 50%), waist circumference (2/3, 67%), and hemoglobin A_1c (3/5, 60%) found statistically significant results, indicating that greater app user engagement was associated with better outcomes. Of 5 studies, 3 (60%) found a statistically significant relationship between higher user engagement and an increase in objectively measured physical activity. The studies assessing the relationship between user engagement and dietary and diabetes self-care behaviors, blood pressure, and lipid panel components did not find statistically significant results.

Conclusions

Increased app user engagement for prevention and management of CVD may be associated with improved weight and BMI; however, only a few studies assessed other outcomes, limiting the evidence beyond this. Additional studies are needed to assess user engagement with smartphone apps targeting other important CVD risk factors, including dietary behaviors, hypercholesterolemia, diabetes, and hypertension. Further research is needed to assess mHealth user engagement in both inpatient and outpatient settings to determine the effect of integrating mHealth interventions into the existing clinical workflow and on CVD outcomes.

Mobile applications for emerging adults transitioning to independent diabetes monitoring

Access to high-quality mhealth tools for diabetes management is critical. The purpose was to systematically review mobile apps for features relevant to helping emerging adults manage their diabetes as they transition to independent diabetes monitoring. Mobile apps were reviewed for relevance to emerging adults, aged 18-25, living with diabetes. The GooglePlay store was systematically searched to identify diabetes management mobile tools. Of the 29 apps, only one app had any features relevant to emerging adults. In total, 20 apps had a feature to share a copy of diet or blood sugar logs with a family member or provider. Only 9 apps had any interactivity other than tracking. While most apps had graphics, only 5 were deemed high quality. Just one app met all three included Mobile Application Rating Scale (MARS) criteria. This review serves as a starting point to guide educators and patients, especially to aid continuity of care when in-person support is not feasible. Ongoing review of new apps with improved functionality and effectiveness studies of the apps' impact on emerging adults' diabetes management is imperative.

An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning

(1) Background: People living with type 1 diabetes (T1D) require self-management to maintain blood glucose (BG) levels in a therapeutic range through the delivery of exogenous insulin. However, due to the various variability, uncertainty and complex glucose dynamics, optimizing the doses of insulin delivery to minimize the risk of hyperglycemia and hypoglycemia is still an open problem. (2) Methods: In this work, we propose a novel insulin bolus advisor which uses deep reinforcement learning (DRL) and continuous glucose monitoring to optimize insulin dosing at mealtime. In particular, an actor-critic model based on deep deterministic policy gradient is designed to compute mealtime insulin doses. The proposed system architecture uses a two-step learning framework, in which a population model is first obtained and then personalized by subject-specific data. Prioritized memory replay is adopted to accelerate the training process in clinical practice. To validate the algorithm, we employ a customized version of the FDA-accepted UVA/Padova T1D simulator to perform in silico trials on 10 adult subjects and 10 adolescent subjects. (3) Results: Compared to a standard bolus calculator as the baseline, the DRL insulin bolus advisor significantly improved the average percentage time in target range (70-180 mg/dL) from 74.1%±8.4% to 80.9%±6.9% (p<0.01) and 54.9%±12.4% to 61.6%±14.1% (p<0.01) in the the adult and adolescent cohorts, respectively, while reducing hypoglycemia. (4) Conclusions: The proposed algorithm has the potential to improve mealtime bolus insulin delivery in people with T1D and is a feasible candidate for future clinical validation.

User Retention and Engagement With a Mobile App Intervention to Support Self-Management in Australians With Type 1 or Type 2 Diabetes (My Care Hub): Mixed Methods Study

Background

Mobile health apps are commonly used to support diabetes self-management (DSM). However, there is limited research assessing whether such apps are able to meet the basic requirements of retaining and engaging users.

Objective

This study aimed to evaluate participants’ retention and engagement with My Care Hub, a mobile app for DSM.

Methods

The study employed an explanatory mixed methods design. Participants were people with type 1 or type 2 diabetes who used the health app intervention for 3 weeks. Retention was measured by completion of the postintervention survey. Engagement was measured using system log indices and interviews. Retention and system log indices were presented using descriptive statistics. Transcripts were analyzed using content analysis to develop themes interpreted according to the behavioral intervention technology theory.

Results

Of the 50 individuals enrolled, 42 (84%) adhered to the study protocol. System usage data showed multiple and frequent interactions with the app by most of the enrolled participants (42/50, 84%). Two-thirds of participants who inputted data during the first week returned to use the app after week 1 (36/42, 85%) and week 2 (30/42, 71%) of installation. Most daily used features were tracking of blood glucose (BG; 28/42, 68%) and accessing educational information (6/42, 13%). The interview results revealed the app’s potential as a behavior change intervention tool, particularly because it eased participants’ self-care efforts and improved their engagement with DSM activities such as BG monitoring, physical exercise, and healthy eating. Participants suggested additional functionalities such as extended access to historical analytic data, automated data transmission from the BG meter, and periodic update of meals and corresponding nutrients to further enhance engagement with the app.

Conclusions

The findings of this short-term intervention study suggested acceptable levels of participant retention and engagement with My Care Hub, indicating that it may be a promising tool for extending DSM support and education beyond the confines of a physical clinic.

Comparing a Social and Communication App, Telephone Intervention, and Usual Care for Diabetes Self-Management: 3-Arm Quasiexperimental Evaluation Study

Background

Many technology-assisted innovations have been used to manage disease. However, most of these innovations are not broadly used by older adults due to their cost. Additionally, disease management through technology-assisted innovations has not been compared with other interventions.

Objective

In this study, we tested the employment of a free and widely used social and communication app to help older adults with diabetes manage their distress and glycemic control. We also compared the effectiveness of the app with 2 other methods, namely telephone and conventional health education, and determined which subgroup experiences the most effects within each intervention.

Methods

Adults aged ≥50 years with type 2 diabetes were recruited from Southern Taiwan (N=231) and were allocated to different 3-month interventions. Informed consent was obtained at the Ministry of Science and Technology and approved by the National Cheng Kung University Hospital Institutional Review Board (No. A-ER-102-425).

Results

Participants in the mobile-based group had significant reductions in hemoglobin A1c compared with the telephone-based and usual care groups (mean changes of –0.4%, 0.1%, and 0.03%, respectively; P=.02). Diabetes-specific distress decreased to a greater extent in the mobile-based group compared to the other 2 groups (mean changes of –5.16, –3.49, and –2.44, respectively, P=.02). Subgroup analyses further revealed that the effects on reducing blood glucose levels in the social and communication app groups were especially evident in patients with lower distress scores, and diabetes-related distress was especially evident in participants who were younger than 60 years or had higher educational levels.

Conclusions

The findings of this study inform more flexible use of social and communication apps with in-person diabetes education and counselling.

Effectiveness of Lilly Connected Care Program (LCCP) App-Based Diabetes Education for Patients With Type 2 Diabetes Treated With Insulin: Retrospective Real-World Study

Background

Diabetes poses heavy economic and social burdens worldwide. Mobile apps show great potential for diabetes self-management education. However, there is limited evidence for the effectiveness of providing general diabetes education through mobile apps.

Objective

The aim of this study was to clarify the effectiveness of Lilly Connected Care Program (LCCP) app-based diabetes education for glycemic control.

Methods

This retrospective cohort study included patients with diabetes recruited to the LCCP platform from September 1, 2018, to May 31, 2019. Each patient was followed for 12 weeks. According to the number of diabetes education courses they had completed, the patients were divided into the following three groups: group A (0-4 courses), group B (5-29 courses), and group C (≥30 courses). The main outcomes were the change in blood glucose at the 12th week compared with baseline and the differences in blood glucose at the 12th week among the three groups. The associations of the number of diabetes education courses completed with the average blood glucose and frequency of self-monitoring of blood glucose (SMBG) at the 12th week were assessed by multivariate linear regression analyses controlling for other confounding covariates. Univariate and multivariate linear regression analyses were used to assess factors influencing patients’ engagement in the diabetes education courses.

Results

A total of 5011 participants were enrolled. Their mean fasting blood glucose (FBG) and postprandial blood glucose (PBG) were significantly lower at the 12th week than at baseline (FBG, 7.46 [standard deviation (SD) 1.95] vs 7.79 [SD 2.18] mmol/L, P<.001; PBG, 8.94 [SD 2.74] vs 9.53 [SD 2.81] mmol/L, P<.001). The groups that completed more diabetes education courses had lower FBG (group B, β=−0.14, 95% CI −0.26 to −0.03; group C, β=−0.29, 95% CI −0.41 to −0.16; P for trend <.001) and PBG (group B, β=−0.29, 95% CI −0.46 to −0.11; group C, β=−0.47, 95% CI −0.66 to −0.28; P for trend <.001) and a higher frequency of SMBG at the 12th week (group B, β=1.17, 95% CI 0.81-1.53; group C, β=4.21, 95% CI 3.81-4.62; P for trend <.001) when compared with the findings in group A. Age and education were related to patients’ engagement in the diabetes education courses. Middle-aged patients (35-59 years old) and elderly patients (≥60 years old) completed more diabetes education courses (middle-aged group, β=2.22, P=.01; elderly group, β=2.42, P=.02) than young patients (18-34 years old).

Conclusions

LCCP app-based diabetes education is effective for glycemic control and SMBG behavior improvement in patients with type 2 diabetes receiving insulin therapy. Young patients’ engagement in the education courses was relatively low. We need to conduct in-depth interviews with users to further improve the curriculum.