Real-World Evidence of User Engagement With Mobile Health for Diabetes Management: Longitudinal Observational Study

Behavioral Engagement and Activation Model Study (BEAMS): A latent class analysis of adopters and non-adopters of digital health technologies among people with Type 2 diabetes

Many people with Type 2 diabetes (T2D) who could benefit from digital health technologies (DHTs) are either not using DHTs or do use them, but not for long enough to reach their behavioral or metabolic goals. We aimed to identify subgroups within DHT adopters and non-adopters and describe their unique profiles to better understand the type of tailored support needed to promote effective and sustained DHT use across a diverse T2D population. We conducted latent class analysis of a sample of adults with T2D who responded to an internet survey between December 2021 and March 2022. We describe the clinical and psychological characteristics of DHT adopters and non-adopters, and their attitudes toward DHTs. A total of 633 individuals were characterized as either DHT "Adopters" (n = 376 reporting any use of DHT) or "Non-Adopters" (n = 257 reporting never using any DHT). Within Adopters, three subgroups were identified: 21% (79/376) were "Self-managing Adopters," who reported high health activation and self-efficacy for diabetes management, 42% (158/376) were "Activated Adopters with dropout risk," and 37% (139/376) were "Non-Activated Adopters with dropout risk." The latter two subgroups reported barriers to using DHTs and lower rates of intended future use. Within Non-Adopters, two subgroups were identified: 31% (79/257) were "Activated Non-Adopters," and 69% (178/257) were "Non-Adopters with barriers," and were similarly distinguished by health activation and barriers to using DHTs. Beyond demographic characteristics, psychological, and clinical factors may help identify different subgroups of Adopters and Non-Adopters.

Development of "DiabetesSutra" a mobile application for lifestyle management of Type 2 Diabetes in India

Background

Multiple mhealth (mobile health) interventions and mobile applications have been developed to support diabetes self-management. However, most of the apps are developed without the need for assessment and evaluation by experts in the field. This study aimed to design and develop a mobile application (app) supporting diabetes self-management for people with Type 2 Diabetes Mellitus (T2D) using a systematic approach.

Methods

In this study mixed method design was used to develop the mobile application. The mhealth intervention was designed and developed in five steps: i) Extensive literature search, ii) Needs assessment of patients with T2D with the help of healthcare providers and patients (Interviews with 15 healthcare providers like clinicians, dietitians, and diabetes educators, and 2 focus group discussions with patients) iii) Ideation and content development of app based on outcomes of needs assessment; iv) content validation (by 10 healthcare providers) and v) App development on a hybrid platform. Evaluation of the app by users i.e., type 2 diabetes patients was done using the users' Mobile App rating scale (uMARS). The app was evaluated by 40 patients and rated on the uMARS questionnaire.

Results

A patient-centric mobile app was developed for the nutritional management of diabetes with three modules: The patient module, the Evaluation module, and the Healthcare provider module. The patient module was the app that was provided to the patients with features like diet, physical activity, blood glucose log, education, etc., in addition to, a symptom checker, Stress meter blog, and FAQ. The evaluation module was integrated with the app it works when a user enters any log, it evaluates the entry against the standard cutoffs and flash prompts on the screen. The Healthcare provider module interacts with the server to provide them with patient data, comments, and feedback.

Conclusions

The users found the app to be satisfactory. Incorporating additional features to enhance the user interface and streamline navigation could potentially enhance user engagement, thereby aiding in the management of T2D.

Weight management personas of breast cancer patients undergoing chemotherapy in China: a multi-method study

BACKGROUND

Mobile health (mHealth) may be an ideal solution for breast cancer (BC) patients in China to access weight management interventions. User retention and engagement are the main challenges faced by mHealth applications. A user persona, which is a user-centered design process, can lead to the development of mHealth that is more acceptable to the needs of target users. This study aimed to investigate the variety of experiences in weight management and the behavioral preferences of BC patients receiving chemotherapy to develop users' personal information and persona development for the design and implementation of mHealth interventions.

METHODS

Sixteen individual semi-structured in-depth interviews were conducted with BC patients receiving chemotherapy. We employed the thematic analysis method to analyze the interview transcripts in NVivo 11 software. The themes obtained from the analysis were used as the subdomains of personas. A proforma was designed to extract each participant's experience in each subdomain. Patients who exhibited similar experience in subdomains were grouped into a persona using affinity diagrams. The personas were named according to their prominent features. A questionnaire survey was conducted to validate the personas and to test whether the personas that were generated from the qualitative interview data were applicable to the Chinese population with BC.

RESULTS

Four themes were identified as subdomains of weight management personas: the perception of weight management while undergoing chemotherapy, symptoms and emotional disturbance, changes in diet and exercise, and health literacy and information seeking. Five personas were ultimately obtained: (1) positive weight controllers, (2) patients who were inactive due to fatigue, (3) young patients who avoided communication, (4) overweight patients with treatment priority, and (5) patients who engaged in irregular exercise. Finally, the quantitative study showed that 51.58% of patients chose one of these five personas to represent themselves in weight management. None of the patient reported selecting options that were not explicitly outlined in the questionnaire and provided personalized descriptions of their weight management characteristics.

CONCLUSIONS

The selected personas were developed from in-depth interviews on biopsychosocial areas. They highlight different weight management patterns in Chinese BC patients and provide implications for both the design of mHealth systems and traditional interventions.

Which behaviour change techniques work best for diabetes self-management mobile apps? Results from a systematic review and meta-analysis of randomised controlled trials

BACKGROUND

Self-management is pivotal in addressing noncommunicable diseases, such as diabetes. The increased availability of digital behaviour change interventions (DBCIs) delivered through mobile health apps offers unprecedented opportunities to enhance self-management and improve health outcomes. However, little is known about the characteristics of DBCIs for diabetes that significantly impact glycaemic control. Therefore, our systematic review with meta-analysis aimed to summarize characteristics and behaviour change components in DBCIs for diabetes self-management and explore potential associations with metabolic outcomes.

METHODS

A systematic search was conducted in PubMed, Embase, the Cochrane Central Register of Controlled Trials, and Scopus to identify randomized controlled trials published until November 2023. The main outcome variable was the change in the mean difference of HbA1c levels between baseline and follow-up across intervention and control groups. Random-effects meta-regression was used to explore variation in glycaemic control as a function of prespecified characteristics of study designs and app interventions.

FINDINGS

A total of 57 studies was included in the analysis, showing a statistically significant percentage point reduction in HbA1c for the intervention group compared to the control arm (-0.36, 95% CI = -0.46 to -0.26, p < 0.001). The inclusion of "self-monitoring of behaviour" as a behaviour change technique (β = -0.22, p = 0.04) and "taking medication" as a target behaviour (β = -0.20, p = 0.05) was associated with improved metabolic outcomes.

INTERPRETATION

Our analyses endorse the use of diabetes self-management apps, highlighting characteristics statistically associated with intervention effectiveness and guiding the design of more effective DBCIs.

FUNDING

This project received funding from the European Union's Horizon 2020 programme.

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial

BACKGROUND

Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change.

OBJECTIVE

The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately 12 months.

METHODS

Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes, and symptom outcomes of the different clusters that were discovered.

RESULTS

Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group. Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors influencing engagement were whether the same device was used to submit different health and well-being parameters, the number of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher levels of engagement may improve the participants' outcomes (eg, reduce symptom exacerbation and increase physical activity).

CONCLUSIONS

The findings indicate potential factors that influence older adult engagement with digital health technologies for home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of home-based digital health technologies may improve symptom management and physical activity outcomes for older adults self-managing multimorbidity.

Periodic mobile application (eMOM) with self-tracking of glucose and lifestyle improves treatment of diet-controlled gestational diabetes without human guidance: a randomized controlled trial

BACKGROUND

Digitalization with minimal human resources could support self-management among women with gestational diabetes and improve maternal and neonatal outcomes.

OBJECTIVE

This study aimed to investigate if a periodic mobile application (eMOM) with wearable sensors improves maternal and neonatal outcomes among women with diet-controlled gestational diabetes without additional guidance from healthcare personnel.

STUDY DESIGN

Women with gestational diabetes were randomly assigned in a 1:1 ratio at 24 to 28 weeks' gestation to the intervention or the control arm. The intervention arm received standard care in combination with use of the periodic eMOM, whereas the control arm received only standard care. The intervention arm used eMOM with a continuous glucose monitor, an activity tracker, and a food diary 1 week/month until delivery. The primary outcome was the change in fasting plasma glucose from baseline to 35 to 37 weeks' gestation. Secondary outcomes included capillary glucose, weight gain, nutrition, physical activity, pregnancy complications, and neonatal outcomes, such as macrosomia.

RESULTS

In total, 148 women (76 in the intervention arm, 72 in the control arm; average age, 34.1±4.0 years; body mass index, 27.1±5.0 kg/m2) were randomized. The intervention arm showed a lower mean change in fasting plasma glucose than the control arm (difference, -0.15 mmol/L vs -2.7 mg/mL; P=.022) and lower capillary fasting glucose levels (difference, -0.04 mmol/L vs -0.7 mg/mL; P=.002). The intervention arm also increased their intake of vegetables (difference, 11.8 g/MJ; P=.043), decreased their sedentary behavior (difference, -27.3 min/d; P=.043), and increased light physical activity (difference, 22.8 min/d; P=.009) when compared with the control arm. In addition, gestational weight gain was lower (difference, -1.3 kg; P=.015), and there were less newborns with macrosomia in the intervention arm (difference, -13.1 %; P=.036). Adherence to eMOM was high (daily use >90%), and the usage correlated with lower maternal fasting (P=.0006) and postprandial glucose levels (P=.017), weight gain (P=.028), intake of energy (P=.021) and carbohydrates (P=.003), and longer duration of the daily physical activity (P=.0006). There were no significant between-arm differences in terms of pregnancy complications.

CONCLUSION

Self-tracking of lifestyle factors and glucose levels without additional guidance improves self-management and the treatment of gestational diabetes, which also benefits newborns. The results of this study support the use of digital self-management and education tools in maternity care.

Fostering Patient-Clinician Communication to Promote Rapid HIV, Hepatitis B Virus, and Hepatitis C Virus Diagnostic Testing: Conceptual Development of a Multilingual App

BACKGROUND

Migrants are disproportionately affected by HIV, hepatitis B virus (HBV), and hepatitis C virus (HCV). Clinicians, at times, fail to offer rapid diagnostic testing (RDT) for these viruses when a language barrier exists in the patient-clinician relationship, therefore creating missed testing opportunities. Although their effectiveness has been demonstrated elsewhere, conventional, in-person interpreters are costly and underused in practice. Furthermore, clinicians often call upon ad hoc interpreters, which introduces complexities in the clinical relationship. Digital solutions exist to diminish the burden of language barriers; however, the challenges of developing a multilingual and multicultural app have yet to be documented with respect to RDT in the nonfrancophone migrant population in France.

OBJECTIVE

Our goal was to design a multilingual app to overcome language barriers, health literacy barriers, and fears related to being tested to promote RDT of HIV, HBV, and HCV in the nonfrancophone migrant population in France.

METHODS

A combination of qualitative methods, agile development, and user-centered design was used. We conducted 2 focus groups (FGs) with 12 participants, including physicians, nurses, and social workers conducting RDT, as well as 1 modified Delphi survey with 68 participants including physicians and nurses. FGs explored the content (risk factors and medical history), functions (cultural adaptation and instant translation), and interface ergonomics (graphics and font) needed in the app. The Delphi presented 95 content items that the researchers sought to include in the app.

RESULTS

Using FGs to inform the Delphi survey, we scientifically determined the app's content consisting of 95 items using expert consensus, developed a mock-up, and conducted initial user testing. We created an app that contains both migrant and clinician interfaces and includes a sociodemographic, risk assessment, health literacy, and testing barrier questionnaires available in 11 languages. Educational content is related to HIV, HBV, and HCV, along with the ability to understand whether the migrant agrees to be tested.

CONCLUSIONS

This study allowed us to conceptualize a multilingual app that aims to increase the acceptance of RDT for HIV, HBV, and HCV. The specific features of the Assistant intelligent au dépistage des allophones app were designed to overcome the testing barriers in the nonfrancophone migrant population. The next phase will be an implementation study, as we intend to validate our app.

Supporting the Management of Gestational Diabetes Mellitus With Comprehensive Self-Tracking: Mixed Methods Study of Wearable Sensors

BACKGROUND

Gestational diabetes mellitus (GDM) is an increasing health risk for pregnant women as well as their children. Telehealth interventions targeted at the management of GDM have been shown to be effective, but they still require health care professionals for providing guidance and feedback. Feedback from wearable sensors has been suggested to support the self-management of GDM, but it is unknown how self-tracking should be designed in clinical care.

OBJECTIVE

This study aimed to investigate how to support the self-management of GDM with self-tracking of continuous blood glucose and lifestyle factors without help from health care personnel. We examined comprehensive self-tracking from self-discovery (ie, learning associations between glucose levels and lifestyle) and user experience perspectives.

METHODS

We conducted a mixed methods study where women with GDM (N=10) used a continuous glucose monitor (CGM; Medtronic Guardian) and 3 physical activity sensors: activity bracelet (Garmin Vivosmart 3), hip-worn sensor (UKK Exsed), and electrocardiography sensor (Firstbeat 2) for a week. We collected data from the sensors, and after use, participants took part in semistructured interviews about the wearable sensors. Acceptability of the wearable sensors was evaluated with the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. Moreover, maternal nutrition data were collected with a 3-day food diary, and self-reported physical activity data were collected with a logbook.

RESULTS

We found that the CGM was the most useful sensor for the self-discovery process, especially when learning associations between glucose and nutrition intake. We identified new challenges for using data from the CGM and physical activity sensors in supporting self-discovery in GDM. These challenges included (1) dispersion of glucose and physical activity data in separate applications, (2) absence of important trackable features like amount of light physical activity and physical activities other than walking, (3) discrepancy in the data between different wearable physical activity sensors and between CGMs and capillary glucose meters, and (4) discrepancy in perceived and measured quantification of physical activity. We found the body placement of sensors to be a key factor in measurement quality and preference, and ultimately a challenge for collecting data. For example, a wrist-worn sensor was used for longer compared with a hip-worn sensor. In general, there was a high acceptance for wearable sensors.

CONCLUSIONS

A mobile app that combines glucose, nutrition, and physical activity data in a single view is needed to support self-discovery. The design should support tracking features that are important for women with GDM (such as light physical activity), and data for each feature should originate from a single sensor to avoid discrepancy and redundancy. Future work with a larger sample should involve evaluation of the effects of such a mobile app on clinical outcomes.

TRIAL REGISTRATION

Clinicaltrials.gov NCT03941652; https://clinicaltrials.gov/study/NCT03941652.

Understanding Whether and How a Digital Health Intervention Improves Transition Care for Emerging Adults Living With Type 1 Diabetes: Protocol for a Mixed Methods Realist Evaluation

BACKGROUND

Emerging adults living with type 1 diabetes (T1D) face a series of challenges with self-management and decreased health system engagement, leading to an increased risk of acute complications and hospital admissions. Effective and scalable strategies are needed to support this population to transfer seamlessly from pediatric to adult care with sufficient self-management capability. While digital health interventions for T1D self-management are a promising strategy, it remains unclear which elements work, how, and for which groups of individuals.

OBJECTIVE

This study aims to evaluate the design and implementation of a multicomponent SMS text message-based digital health intervention to support emerging adults living with T1D in real-world settings. The objectives are to identify the intervention components and associated mechanisms that support user engagement and T1D health care transition experiences and determine the individual characteristics that influence the implementation process.

METHODS

We used a realist evaluation embedded alongside a randomized controlled trial, which uses a sequential mixed methods design to analyze data from multiple sources, including intervention usage data, patient-reported outcomes, and realist interviews. In step 1, we conducted a document analysis to develop a program theory that outlines the hypothesized relationships among "individual-level contextual factors, intervention components and features, mechanisms, and outcomes," with special attention paid to user engagement. Among them, intervention components and features depict 10 core characteristics such as transition support information, problem-solving information, and real-time interactivity. The proximal outcomes of interest include user engagement, self-efficacy, and negative emotions, whereas the distal outcomes of interest include transition readiness, self-blood glucose monitoring behaviors, and blood glucose. In step 2, we plan to conduct semistructured realist interviews with the randomized controlled trial's intervention-arm participants to test the hypothesized "context-intervention-mechanism-outcome" configurations. In step 3, we plan to triangulate all sources of data using a coincidence analysis to identify the necessary combinations of factors that determine whether and how the desired outcomes are achieved and use these insights to consolidate the program theory.

RESULTS

For step 1 analysis, we have developed the initial program theory and the corresponding data collection plan. For step 2 analysis, participant enrollment for the randomized controlled trial started in January 2023. Participant enrollment for this realist evaluation was anticipated to start in July 2023 and continue until we reached thematic saturation or achieved informational power.

CONCLUSIONS

Beyond contributing to knowledge on the multiple pathways that lead to successful engagement with a digital health intervention as well as target outcomes in T1D care transitions, embedding the realist evaluation alongside the trial may inform real-time intervention refinement to improve user engagement and transition experiences. The knowledge gained from this study may inform the design, implementation, and evaluation of future digital health interventions that aim to improve transition experiences.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/46115.

Human-to-Computer Interactivity Features Incorporated Into Behavioral Health mHealth Apps: Systematic Search

BACKGROUND

While there are thousands of behavioral health apps available to consumers, users often quickly discontinue their use, which limits their therapeutic value. By varying the types and number of ways that users can interact with behavioral health mobile health apps, developers may be able to support greater therapeutic engagement and increase app stickiness.

OBJECTIVE

The main objective of this analysis was to systematically characterize the types of user interactions that are available in behavioral health apps and then examine if greater interactivity was associated with greater user satisfaction, as measured by app metrics.

METHODS

Using a modified PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) methodology, we searched several different app clearinghouse websites and identified 76 behavioral health apps that included some type of interactivity. We then filtered the results to ensure we were examining behavioral health apps and further refined our search to include apps that identified one or more of the following terms: peer or therapist forum, discussion, feedback, professional, licensed, buddy, friend, artificial intelligence, chatbot, counselor, therapist, provider, mentor, bot, coach, message, comment, chat room, community, games, care team, connect, share, and support in the app descriptions. In the final group of 34 apps, we examined the presence of 6 types of human-machine interactivities: human-to-human with peers, human-to-human with providers, human-to-artificial intelligence, human-to-algorithms, human-to-data, and novel interactive smartphone modalities. We also downloaded information on app user ratings and visibility, as well as reviewed other key app features.

RESULTS

We found that on average, the 34 apps reviewed included 2.53 (SD 1.05; range 1-5) features of interactivity. The most common types of interactivities were human-to-data (n=34, 100%), followed by human-to-algorithm (n=15, 44.2%). The least common type of interactivity was human-artificial intelligence (n=7, 20.5%). There were no significant associations between the total number of app interactivity features and user ratings or app visibility. We found that a full range of therapeutic interactivity features were not used in behavioral health apps.

CONCLUSIONS

Ideally, app developers would do well to include more interactivity features in behavioral health apps in order to fully use the capabilities of smartphone technologies and increase app stickiness. Theoretically, increased user engagement would occur by using multiple types of user interactivity, thereby maximizing the benefits that a person would receive when using a mobile health app.

Engagement With mHealth COVID-19 Digital Biomarker Measurements in a Longitudinal Cohort Study: Mixed Methods Evaluation

BACKGROUND

The COVID-19 pandemic accelerated the interest in implementing mobile health (mHealth) in population-based health studies, but evidence is lacking on engagement and adherence in studies. We conducted a fully remote study for ≥6 months tracking COVID-19 digital biomarkers and symptoms using a smartphone app nested within an existing cohort of adults.

OBJECTIVE

We aimed to investigate participant characteristics associated with initial and sustained engagement in digital biomarker collection from a bespoke smartphone app and if engagement changed over time or because of COVID-19 factors and explore participants' reasons for consenting to the smartphone substudy and experiences related to initial and continued engagement.

METHODS

Participants in the Fenland COVID-19 study were invited to the app substudy from August 2020 to October 2020 until study closure (April 30, 2021). Participants were asked to complete digital biomarker modules (oxygen saturation, body temperature, and resting heart rate [RHR]) and possible COVID-19 symptoms in the app 3 times per week. Participants manually entered the measurements, except RHR that was measured using the smartphone camera. Engagement was categorized by median weekly frequency of completing the 3 digital biomarker modules (categories: 0, 1-2, and ≥3 times per week). Sociodemographic and health characteristics of those who did or did not consent to the substudy and by engagement category were explored. Semistructured interviews were conducted with 35 participants who were purposively sampled by sex, age, educational attainment, and engagement category, and data were analyzed thematically; 63% (22/35) of the participants consented to the app substudy, and 37% (13/35) of the participants did not consent.

RESULTS

A total of 62.61% (2524/4031) of Fenland COVID-19 study participants consented to the app substudy. Of those, 90.21% (2277/2524) completed the app onboarding process. Median time in the app substudy was 34.5 weeks (IQR 34-37) with no change in engagement from 0 to 3 months or 3 to 6 months. Completion rates (≥1 per week) across the study between digital biomarkers were similar (RHR: 56,517/77,664, 72.77%; temperature: 56,742/77,664, 73.06%; oxygen saturation: 57,088/77,664, 73.51%). Older age groups and lower managerial and intermediate occupations were associated with higher engagement, whereas working, being a current smoker, being overweight or obese, and high perceived stress were associated with lower engagement. Continued engagement was facilitated through routine and personal motivation, and poor engagement was caused by user error and app or equipment malfunctions preventing data input. From these results, we developed key recommendations to improve engagement in population-based mHealth studies.

CONCLUSIONS

This mixed methods study demonstrated both high initial and sustained engagement in a large mHealth COVID-19 study over a ≥6-month period. Being nested in a known cohort study enabled the identification of participant characteristics and factors associated with engagement to inform future applications in population-based health research.

Analyzing the Effectiveness of mHealth to Manage Diabetes Mellitus Among Adults Over 50: A Systematic Literature Review

Purpose

A total of 537 million suffered from diabetes mellitus in 2021, and the aging of the population will not abate this number in the future. Diabetes predisposes people to ailments and doubles the risk of COVID-19 mortality. mHealth has shown promise to help manage diabetes. The aim of this review is to objectively analyze research from the last 2.5 years to assess effectiveness where mHealth has been used as an intervention to help manage diabetes in older patients. We also analyzed patient satisfaction, quality, and barriers to adoption of mHealth to manage diabetes.

Patients and Methods

No human subjects were involved in this review. We queried four research databases for mHealth to manage diabetes in older adults. We conducted the review based on the Kruse Protocol for writing as systematic review and we reported our findings in accordance with PRISMA (2020).

Results

Thirty research articles from 11 countries were analyzed. Five interventions of mHealth were identified. Of these mHealth Short Message service (SMS) helped change behavior and encouraged self-care. mHealth SMS coupled with telemedicine for coaching showed positive effects on weight loss, BMI, diet, exercise, HbA1C, disease awareness, blood pressure, cholesterol, medication adherence, and foot care.

Conclusion

mHealth SMS coupled with telemedicine for coaching shows the greatest promise for educating, changing behavior, and realizing positive outcomes across a broad spectrum of health factors. The largest drawback is the cost of acquiring equipment and training users.

Can digital health apps provide patients with support to promote structured diabetes education and ongoing self-management? A real-world evaluation of myDiabetes usage

Objective

Structured diabetes education has evidenced benefits yet reported uptake rates for those referred to traditional in-person programmes within 12 months of diagnosis were suboptimal. Digital health interventions provide a potential solution to improve diabetes education delivery at population scale, overcoming barriers identified with traditional approaches. myDiabetes is a cloud-based interactive digital health self-management app. This evaluation analysed usage data for people with type 2 diabetes focusing on digital structured diabetes education.

Methods

Descriptive quantitative analyses were conducted on existing anonymised user data over 12 months (November 2019-2020) to evaluate whether digital health can provide additional support to deliver diabetes education. Data was divided into two equal 6-month periods. As this overlapped the onset of COVID-19, analyses of its effect on usage were included as a secondary outcome. All data was reported via myDiabetes. Users were prescribed myDiabetes by National Health Service healthcare primary care teams. Those who registered for app use within the study period (n = 2783) were assessed for eligibility (n = 2512) and included if activated.

Results

Within the study period, n = 1245/2512 (49.6%) registered users activated myDiabetes. No statistically significant differences were observed between gender (p = 0.721), or age (p = 0.072) for those who activated (59.2 years, SD 12.93) and those who did not activate myDiabetes (57.6 years, SD 13.77). Activated users (n = 1119/1245 (89.8%)) viewed 11,572 education videos. No statistically significant differences were observed in education video views across age groups (p = 0.384), gender (p = 0.400), diabetes treatment type (p = 0.839) or smoking status (p = 0.655). Comparison of usage pre-COVID-19 and post-COVID-19 showed statistically significant increases in app activity (p ≤0.001).

Conclusion

Digital health is rapidly evolving in its role of supporting patients to self-manage. Since COVID-19 the benefits of digital technology have become increasingly recognised. There is potential for increasing diabetes education rates by offering patients a digital option in combination with traditional service delivery which should be substantiated through future research.

A Mobile Application to Improve Diabetes Self-Management Using Rapid Prototyping: Iterative Co-Design Approach in Asian Settings

BACKGROUND

Diabetes is a global public health issue, causing burden on healthcare system and increasing risk of mortality. Mobile applications (apps) can be a promising approach to facilitate diabetes self-management. An increasingly utilized approach to facilitate engagement with mobile health (mHealth) technology is to involve potential users in the creation of the technology.

OBJECTIVE

The aim of this study was to use co-design for type 2 diabetes mellitus (T2DM) self-management mHealth development.

METHODS

Three rounds of iterative rapid prototyping panel sessions were conducted with a total of 9 T2DM participants in an Asian setting between Oct 2020 and April 2021. The participants were recruited through convenience sampling. For each round, feedback was gathered through qualitative interviews, and the feedback was used as a reference by the development team to develop and test a more refined version of the app in the next round. Transcribed semi-structured interview data was analyzed thematically using an inductive approach.

RESULTS

Participants' ages ranged from 40 to 69 years. Data saturation was reached, with no new themes emerging from the data. During the sessions, the participants expressed a variety of concerns and feedback on T2DM self-management using EMPOWER app and raised suggestions on the features of ideal T2DM self-management app. Important features include 1) reminders and notifications for medications, 2) Bluetooth integration with glucometers and blood pressure machines to minimize manual entry, 3) enlarged local food database including information on sugar content and recommendations for healthier options, 4) one touch for logging of routine medications and favorite foods, 5) export function for data sharing with physicians. Overall inputs concerned aspects such as user-friendliness of the app, customization possibilities, and educational content for the features in the mobile app.

CONCLUSION

In this study, we explored users' opinions on a T2DM self-management mobile app using co-design approach. This study adds to the growing body of literature on co-designing behavioral mHealth interventions and can potentially guide researchers in mobile app design for other chronic conditions.

Predictors of sustained use of mobile health applications: Content analysis of user perspectives from a fever management app

Objectives

Mobile health applications could be means of educating and changing behaviours of their users. Their features and qualities determine the sustainability of use. The FeverApp with two main features of information and documentation is a research-based app. In this observational cohort study, to evaluate the influential predictors of use, users' feedback on the FeverApp, were analyzed.

Methods

Feedback is given using a structured questionnaire, four Likert items and two open questions regarding positive and negative impressions, available via app menu. Conventional content analysis (inductive approach) on the two open questions was performed. Comments were grouped into 12 codes. These codes were grouped hierarchically in an iterative process into nine subcategories and lastly into two main categories 'format' and 'content'. Descriptive and quantitative analysis were performed.

Results

Out of 8243 users, 1804 of them answered the feedback questionnaire. The features of the app (N  =  344), followed by the information aspect (N  =  330) were most frequently mentioned. Documentation process (N  =  226), request for new features or improvement of the current ones (N  =  193), and functioning (N  =  132) were also highlighted in users' feedback. App's ease of use, design and being informative were important for the users. The first impression of the app seems important as the majority of feedback were given during the first month of using the app.

Conclusion

In-app feedback function could highlight shortcomings and strengths of mobile health apps. Taking users' feedback into consideration could increase the chance of sustained use. Besides ease of use and clear, likeable designs, users want apps that serve their needs while saving time.

Who stop telemonitoring disease activity and who adhere: a prospective cohort study of patients with inflammatory arthritis

BACKGROUND

The use of frequent electronic patient reported outcome measures (ePRO's) enables monitoring disease activity at a distance (telemonitoring) in patients with inflammatory arthritis. However, telemonitoring studies report declining long-term adherence to reporting ePRO's, which may oppose the benefits of telemonitoring. Therefore, the objective was to investigate what factors are associated with (non-)adherence to telemonitoring with a weekly ePRO in patients with inflammatory arthritis (IA).

METHODS

We performed a prospective cohort study in patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA) and ankylosing spondylitis (AS) at Reade Amsterdam, The Netherlands. Patients telemonitored their disease activity weekly for 6 months with a modified Multidimensional Health Assessment Questionnaire completed in a smartphone application. The primary outcome was time to dropout, defined as ≥ 4 weeks of consecutively nonresponse. Based on literature and through expert meetings, a predefined set of 13 baseline factors were selected to assess the association with time to dropout through a multivariable Cox-regression analysis.

RESULTS

A total of 220 consecutive patients were included (mean age 54, SD 12; 55% females; 99 RA, 81 PsA, and 40 AS). A total of 141 patients (64%) dropped out, with a median time to dropout of 17 weeks (IQR 9-26). Women had a significant higher chance to dropout over 6 months compared to men (HR 1.58, 95% CI 1.06-2.36).

CONCLUSION

In the set of investigated factors, women stopped reporting the weekly ePRO sooner than men. Future focus group discussions will be performed to investigate the reasons for dropout, and in specific why women dropped out sooner. Trial registration This trials was prospectively registered at www.trialregister.nl (NL8414).

Comprehensive self-tracking of blood glucose and lifestyle with a mobile application in the management of gestational diabetes: a study protocol for a randomised controlled trial (eMOM GDM study)

INTRODUCTION

Gestational diabetes (GDM) causes various adverse short-term and long-term consequences for the mother and child, and its incidence is increasing globally. So far, the most promising digital health interventions for GDM management have involved healthcare professionals to provide guidance and feedback. The principal aim of this study is to evaluate the effects of comprehensive and real-time self-tracking with eMOM GDM mobile application (app) on glucose levels in women with GDM, and more broadly, on different other maternal and neonatal outcomes.

METHODS AND ANALYSIS

This randomised controlled trial is carried out in Helsinki metropolitan area. We randomise 200 pregnant women with GDM into the intervention and the control group at gestational week (GW) 24-28 (baseline, BL). The intervention group receives standard antenatal care and the eMOM GDM app, while the control group will receive only standard care. Participants in the intervention group use the eMOM GDM app with continuous glucose metre (CGM) and activity bracelet for 1 week every month until delivery and an electronic 3-day food record every month until delivery. The follow-up visit after intervention takes place 3 months post partum for both groups. Data are collected by laboratory blood tests, clinical measurements, capillary glucose measures, wearable sensors, air displacement plethysmography and digital questionnaires. The primary outcome is fasting plasma glucose change from BL to GW 35-37. Secondary outcomes include, for example, self-tracked capillary fasting and postprandial glucose measures, change in gestational weight gain, change in nutrition quality, change in physical activity, medication use due to GDM, birth weight and fat percentage of the child.

ETHICS AND DISSEMINATION

The study has been approved by Ethics Committee of the Helsinki and Uusimaa Hospital District. The results will be presented in peer-reviewed journals and at conferences.

TRIAL REGISTRATION NUMBER

NCT04714762.

Association Between Mobile Health App Engagement and Weight Loss and Glycemic Control in Adults With Type 2 Diabetes and Prediabetes (D'LITE Study): Prospective Cohort Study

Background

Mobile health apps are increasingly used as early intervention to support behavior change for diabetes prevention and control, with the overarching goal of lowering the overall disease burden.

Objective

This prospective cohort study conducted in Singapore aimed to investigate app engagement features and their association with weight loss and improved glycemic control among adults with diabetes and prediabetes from the intervention arm of the Diabetes Lifestyle Intervention using Technology Empowerment randomized controlled trial.

Methods

Diabetes and prediabetes participants (N=171) with a median age of 52 years, BMI of 29.3 kg/m², and glycated hemoglobin (HbA_1c) level of 6.5% and who were being assigned the Nutritionist Buddy Diabetes app were included. Body weight and HbA_1c were measured at baseline, 3 months, and 6 months. A total of 476,300 data points on daily app engagement were tracked via the backend dashboard and developer’s report. The app engagement data were analyzed by quartiles and weekly means expressed in days per week. Linear mixed model analysis was used to determine the associations between the app engagements with percentage weight and HbA_1c change.

Results

The median overall app engagement rate was maintained above 90% at 6 months. Participants who were actively engaged in ≥5 app features were associated with the greatest overall weight reduction of 10.6% from baseline (mean difference −6, 95% CI −8.9 to −3.2; P<.001) at 6 months. Adhering to the carbohydrate limit of >5.9 days per week and choosing healthier food options for >4.3 days per week had the most impact, eliciting weight loss of 9.1% (mean difference −5.2, 95% CI −8.2 to −2.2; P=.001) and 8.8% (mean difference −4.2, 95% CI −7.1 to −1.3; P=.005), respectively. Among the participants with diabetes, those who had a complete meal log for >5.1 days per week or kept within their carbohydrate limit for >5.9 days per week each achieved greater HbA_1c reductions of 1.2% (SD 1.3%; SD 1.5%), as compared with 0.2% (SD 1%; SD 0.6%). in the reference groups who used the features <1.1 or ≤2.5 days per week, respectively.

Conclusions

Higher app engagement led to greater weight loss and HbA_1c reduction among adults with overweight or obesity with type 2 diabetes or prediabetes.

Trial Registration

Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12617001112358; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12617001112358

Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review

Background

Mobile health (mHealth) apps show vast potential in supporting patients and health care systems with the increasing prevalence and economic costs of noncommunicable diseases (NCDs) worldwide. However, despite the availability of evidence-based mHealth apps, a substantial proportion of users do not adhere to them as intended and may consequently not receive treatment. Therefore, understanding the factors that act as barriers to or facilitators of adherence is a fundamental concern in preventing intervention dropouts and increasing the effectiveness of digital health interventions.

Objective

This review aimed to help stakeholders develop more effective digital health interventions by identifying factors influencing the continued use of mHealth apps targeting NCDs. We further derived quantified adherence scores for various health domains to validate the qualitative findings and explore adherence benchmarks.

Methods

A comprehensive systematic literature search (January 2007 to December 2020) was conducted on MEDLINE, Embase, Web of Science, Scopus, and ACM Digital Library. Data on intended use, actual use, and factors influencing adherence were extracted. Intervention-related and patient-related factors with a positive or negative influence on adherence are presented separately for the health domains of NCD self-management, mental health, substance use, nutrition, physical activity, weight loss, multicomponent lifestyle interventions, mindfulness, and other NCDs. Quantified adherence measures, calculated as the ratio between the estimated intended use and actual use, were derived for each study and compared with the qualitative findings.

Results

The literature search yielded 2862 potentially relevant articles, of which 99 (3.46%) were included as part of the inclusion criteria. A total of 4 intervention-related factors indicated positive effects on adherence across all health domains: personalization or tailoring of the content of mHealth apps to the individual needs of the user, reminders in the form of individualized push notifications, user-friendly and technically stable app design, and personal support complementary to the digital intervention. Social and gamification features were also identified as drivers of app adherence across several health domains. A wide variety of patient-related factors such as user characteristics or recruitment channels further affects adherence. The derived adherence scores of the included mHealth apps averaged 56.0% (SD 24.4%).

Conclusions

This study contributes to the scarce scientific evidence on factors that positively or negatively influence adherence to mHealth apps and is the first to quantitatively compare adherence relative to the intended use of various health domains. As underlying studies mostly have a pilot character with short study durations, research on factors influencing adherence to mHealth apps is still limited. To facilitate future research on mHealth app adherence, researchers should clearly outline and justify the app’s intended use; report objective data on actual use relative to the intended use; and, ideally, provide long-term use and retention data.

Using mHealth applications for self-care - An integrative review on perceptions among adults with type 1 diabetes

BACKGROUND

Individually designed interventions delivered through mobile health applications (mHealth apps) may be able to effectively support diabetes self-care. Our aim was to review and synthesize available evidence in the literature regarding perception of adults with type 1 diabetes on the features of mHealth apps that help promote diabetes self-care, as well as facilitators and barriers to their use. An additional aim was to review literature on changes in patient reported outcome measures (PROMs) in the same population while using mHealth apps for diabetes self-care.

METHODS

Quantitative and qualitative studies focusing on adults aged 18 years and over with type 1 diabetes in any context were included. A systematic literature search using selected databases was conducted. Data was synthesised using narrative synthesis.

RESULTS

We found that features of mHealth apps designed to help promote and maintain diabetes self-care could be categorized into self-care data monitoring, app display, feedback & reminders, data entry, data sharing, and additional features. Factors affecting the use of mHealth apps reported in the literature were personal factors, app design or usability factors, privacy and safety factors, or socioeconomic factors. Quality of life and diabetes distress were the most commonly reported PROMs in the included studies.

CONCLUSION

We are unable to reach a conclusive result due to the heterogeneity of the included studies as well as the limited number of studies reporting on these areas among adults with type 1 diabetes. We therefore recommend further large-scale studies looking into these areas that can ultimately improve mHealth app use in type 1 diabetes self-care.

SYSTEMATIC REVIEW REGISTRATION

Prospero CRD42020157620 .

A Mobile App for Children With Asthma to Monitor Indoor Air Quality (AirBuddy): Development and Usability Study

Background

Indoor air quality is an important environmental factor that triggers and exacerbates asthma, the most common chronic disease in children. A mobile app to monitor indoor air quality could help occupants keep their indoor air quality clean. However, no app is available that allows children to monitor and improve their indoor air quality.

Objective

Previously, we conducted a series of user-centered design studies to identify user needs and design requirements toward creating a mobile app that helps children with asthma to engage in monitoring and improving indoor air quality as part of their asthma management. Based on the findings from these studies, we created AirBuddy, a child-friendly app that visualizes air quality indoors and outdoors.

Methods

This paper reports on the findings from a field deployment with 7 pediatric asthma patients, where we evaluated AirBuddy’s usability and usefulness in real-world settings by conducting weekly semistructured interviews for 8 weeks.

Results

All participants positively responded to the usefulness and usability of AirBuddy, which we believe is thanks to the iterative, user-centered design approach that allowed us to identify and address potential usability issues early on and throughout the design process.

Conclusions

This project contributes to the field of mHealth app design for children by demonstrating how a user-centered design process can lead to the development of digital devices that are more acceptable and relevant to target users’ needs.

Managing Diabetes Using Mobiab: Long Term Case Study of the Impact of a Mobile App on Self-Management (Preprint)

Background

This paper describes the development of a mobile app for diabetes mellitus (DM) control and self-management and presents the results of long-term usage of this system in the Czech Republic. DM is a chronic disease affecting large numbers of people worldwide, and this number is continuously increasing. There is massive potential to increase adherence to self-management of DM with the use of smartphones and digital therapeutics interventions.

Objective

This study aims to describe the process of development of a mobile app, called Mobiab, for DM management and to investigate how individual features are used and how the whole system benefits its long-term users. Using at least 1 year of daily records from users, we analyzed the impact of the app on self-management of DM.

Methods

We have developed a mobile app that serves as an alternative form to the classic paper-based protocol or diary. The development was based on cooperation with both clinicians and people with DM. The app consists of independent individual modules. Therefore, the user has the possibility to use only selected features that they find useful. Mobiab was available free of charge on Google Play Store from mid-2014 until 2019. No targeted recruitment was performed to attract users.

Results

More than 500 users from the Czech Republic downloaded and signed up for the mobile app. Approximately 80% of the users used Mobiab for less than 1 week. The rest of the users used it for a longer time and 8 of the users produced data that were suitable for long-term analysis. Additionally, one of the 8 users provided their medical records, which were compared with the gathered data, and the improvements in their glucose levels and overall metabolic stability were consistent with the way in which the mobile app was used.

Conclusions

The results of this study showed that the usability of a DM-centered self-management smartphone mobile app and server-based systems could be satisfactory and promising. Nonetheless, some better ways of motivating people with diabetes toward participation in self-management are needed. Further studies involving a larger number of participants are warranted to assess the effect on long-term diabetes management.

Home-based digital health technologies for older adults to self-manage multiple chronic conditions: A data-informed analysis of user engagement from a longitudinal trial

Background

Ageing populations are resulting in higher prevalence of people with multiple chronic conditions (multimorbidity). Digital health platforms have great potential to support self-management of multimorbidity, increasing a person's awareness of their health and well-being, supporting a better understanding of diseases and encouraging behaviour change. However, little research has explored the long-term engagement of older adults with such digital interventions.

Methods

The aim of this study is to analyse how 60 older adults with multimorbidity engaged with digital symptom and well-being monitoring through a digital health platform over a period of approximately 12 months. Data analysis focused on user retention, frequency of monitoring, intervals in monitoring and patterns of daily engagement.

Results

Our findings show that the overall engagement with the digital health platform was high, with more than 80% of participants using the technology devices for over 200 days. The submission frequency for symptom parameters (e.g. blood glucose (BG), blood pressure (BP), etc.) was between three and four times per week which was higher than that of self-report (2.24) and weight (2.84). Submissions of exercise (6.12) and sleep (5.67) were more frequent. The majority of interactions happened in the morning time. The most common time of submission for symptom parameters was 10 am, whereas 8 am was the most common time for weight measurements.

Conclusions

The findings indicate the patterns of engagement of older adults with complex chronic diseases with digital home-based self-management systems.

A Digital Platform to Support Self-management of Multiple Chronic Conditions (ProACT): Findings in Relation to Engagement During a One-Year Proof-of-Concept Trial

Background

Populations globally are ageing, resulting in higher incidence rates of chronic diseases. Digital health platforms, designed to support those with chronic conditions to self-manage at home, offer a promising solution to help people monitor their conditions and lifestyle, maintain good health, and reduce unscheduled clinical visits. However, despite high prevalence rates of multimorbidity or multiple chronic conditions, most platforms tend to focus on a single disease. A further challenge is that despite the importance of users actively engaging with such systems, little research has explored engagement.

Objective

The objectives of this study are to design and develop a digital health platform, ProACT, for facilitating older adults self-managing multimorbidity, with support from their care network, and evaluate end user engagement and experiences with this platform through a 12-month trial.

Methods

The ProACT digital health platform is presented in this paper. The platform was evaluated in a year-long proof-of-concept action research trial with 120 older persons with multimorbidity in Ireland and Belgium. Alongside the technology, participants had access to a clinical triage service responding to symptom alerts and a technical helpdesk. Interactions with the platform during the trial were logged to determine engagement. Semistructured interviews were conducted with participants and analyzed using inductive thematic analysis, whereas usability and user burden were examined using validated questionnaires.

Results

This paper presents the ProACT platform and its components, along with findings on engagement with the platform and its usability. Of the 120 participants who participated, 24 (20%) withdrew before the end of the study, whereas 3 (2.5%) died. The remaining 93 participants actively used the platform until the end of the trial, on average, taking 2 or 3 health readings daily over the course of the trial in Ireland and Belgium, respectively. The participants reported ProACT to be usable and of low burden. Findings from interviews revealed that participants experienced multiple benefits as a result of using ProACT, including improved self-management, health, and well-being and support from the triage service. For those who withdrew, barriers to engagement were poor health and frustration when technology, in particular sensing devices, did not work as expected.

Conclusions

This is the first study to present findings from a longitudinal study of older adults using digital health technology to self-manage multimorbidity. Our findings show that older adults sustained engagement with the technology and found it usable. Potential reasons for these results include a strong focus on user-centered design and engagement throughout the project lifecycle, resulting in a platform that meets user needs, as well as the integration of behavior change techniques and personal analytics into the platform. The provision of triage and technical support services alongside the platform during the trial were also important facilitators of engagement.

International Registered Report Identifier (IRRID)

RR2-10.2196/22125

An Electronic Patient-Reported Outcome Mobile App for Data Collection in Type A Hemophilia: Design and Usability Study

Background

There is currently limited evidence on the level and intensity of physical activity in individuals with hemophilia A. Mobile technologies can offer a rigorous and reliable alternative to support data collection processes but they are often associated with poor user retention. The lack of longitudinal continuity in their use can be partly attributed to the insufficient consideration of stakeholder inputs in the development process of mobile apps. Several user-centered models have been proposed to guarantee that a thorough knowledge of the end user needs is considered in the development process of mobile apps.

Objective

The aim of this study is to design and validate an electronic patient-reported outcome mobile app that requires sustained active input by individuals during POWER, an observational study that aims at evaluating the relationship between physical activity levels and bleeding in patients with hemophilia A.

Methods

We adopted a user-centered design and engaged several stakeholders in the development and usability testing of this mobile app. During the concept generation and ideation phase, we organized a need-assessment focus group (FG) with patient representatives to elicit specific design requirements for the end users. We then conducted 2 exploratory FGs to seek additional inputs for the app’s improvement and 2 confirmatory FGs to validate the app and test its usability in the field through the mobile health app usability questionnaire.

Results

The findings from the thematic analysis of the need-assessment FG revealed that there was a demand for sense making, for simplification of app functionalities, for maximizing integration, and for minimizing the feeling of external control. Participants involved in the later stages of the design refinement contributed to improving the design further by upgrading the app’s layout and making the experience with the app more efficient through functions such as chatbots and visual feedback on the number of hours a wearable device had been worn, to ensure that the observed data were actually registered. The end users rated the app highly during the quantitative assessment, with an average mobile health app usability questionnaire score of 5.32 (SD 0.66; range 4.44-6.23) and 6.20 (SD 0.43; range 5.72-6.88) out of 7 in the 2 iterative usability testing cycles.

Conclusions

The results of the usability test indicated a high, growing satisfaction with the electronic patient-reported outcome app. The adoption of a thorough user-centered design process using several types of FGs helped maximize the likelihood of sustained retention of the app’s users and made it fit for data collection of relevant outcomes in the observational POWER study. The continuous use of the app and the actual level of engagement will be evaluated during the ongoing trial.

Trial Registration

ClinicalTrials.gov NCT04165135; https://clinicaltrials.gov/ct2/show/NCT04165135

Early Glycemic Control Assessment Based on Consensus CGM Metrics. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:1269-1275. [PMID: 34891517 DOI: 10.1109/embc46164.2021.9631015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Continuous glucose monitoring (CGM) has revolutionized the world of diabetes and transformed the approach to diabetes care. In this context, an expert panel has reached consensus on clinical targets for CGM data interpretation based on eight CGM metrics. At least 70% of 14 consecutive CGM days (referred to as a period) are recommended to assess glycemic control based on the metrics. In clinical practice less CGM data may be available. Therefore, the primary aim of this study is to explore the ability to recover the consensus metrics utilizing less than 14 days of CGM data (intra-period). As a secondary aim, we investigate the recovery considering two consecutive periods (inter-period). The analyses are based on real-world CGM data from 484 diabetes users (4726 periods) acquired from the Cornerstones4Care® Powered by Glooko app. Using up to 14 accumulated days, the consensus metrics are calculated for each user and period, and compared to the fully 14 accumulated intra- and inter-period days. Relatively low deviations were observed for time in range (TIR) and average based metrics when using less than 14 days, however, we observed large deviations in metrics characterizing infrequent events such as time below range (TBR). Furthermore, the consensus metrics obtained in two consecutive 14 day periods have clear discrepancies (inter-period). Recovering consensus metrics using less than 14 days might still be valuable in terms of interpreting CGM data in certain clinical contexts. However, caution should be taken if treatment decisions would be made with less than 14 days of data on critical metrics such as TBR, since the metrics characterizing infrequent events deviate substantially when less data are available. Substantial deviation is also seen when comparing across two consecutive periods, which means that care should be taken not to over-generalize consensus metric based glycemic control conclusions from one period to subsequent periods.

Patterns of eHealth Website User Engagement Based on Cross-site Clickstream Data: Correlational Study

BACKGROUND

User engagement is a key performance variable for eHealth websites. However, most existing studies on user engagement either focus on a single website or depend on survey data. To date, we still lack an overview of user engagement on multiple eHealth websites derived from objective data. Therefore, it is relevant to provide a holistic view of user engagement on multiple eHealth websites based on cross-site clickstream data.

OBJECTIVE

This study aims to describe the patterns of user engagement on eHealth websites and investigate how platforms, channels, sex, and income influence user engagement on eHealth websites.

METHODS

The data used in this study were the clickstream data of 1095 mobile users, which were obtained from a large telecom company in Shanghai, China. The observation period covered 8 months (January 2017 to August 2017). Descriptive statistics, two-tailed t tests, and an analysis of variance were used for data analysis.

RESULTS

The medical category accounted for most of the market share of eHealth website visits (134,009/184,826, 72.51%), followed by the lifestyle category (46,870/184,826, 25.36%). The e-pharmacy category had the smallest market share, accounting for only 2.14% (3947/184,826) of the total visits. eHealth websites were characterized by very low visit penetration and relatively high user penetration. The distribution of engagement intensity followed a power law distribution. Visits to eHealth websites were highly concentrated. User engagement was generally high on weekdays but low on weekends. Furthermore, user engagement gradually increased from morning to noon. After noon, user engagement declined until it reached its lowest level at midnight. Lifestyle websites, followed by medical websites, had the highest customer loyalty. e-Pharmacy websites had the lowest customer loyalty. Popular eHealth websites, such as medical websites, can effectively provide referral traffic for lifestyle and e-pharmacy websites. However, the opposite is also true. Android users were more engaged in eHealth websites than iOS users. The engagement volume of app users was 4.85 times that of browser users, and the engagement intensity of app users was 4.22 times that of browser users. Male users had a higher engagement intensity than female users. Income negatively moderated the influence that platforms (Android vs iOS) had on user engagement. Low-income Android users were the most engaged in eHealth websites. Conversely, low-income iOS users were the least engaged in eHealth websites.

CONCLUSIONS

Clickstream data provide a new way to derive an overview of user engagement patterns on eHealth websites and investigate the influence that various factors (eg, platform, channel, sex, and income) have on engagement behavior. Compared with self-reported data from a questionnaire, cross-site clickstream data are more objective, accurate, and appropriate for pattern discovery. Many user engagement patterns and findings regarding the influential factors revealed by cross-site clickstream data have not been previously reported.

Real-World Evidence and Glycemic Improvement Using Dexcom G6 Features

Background: Optional features of continuous glucose monitoring (CGM) systems empower patients and caregivers to understand and manage diabetes in new ways. We examined associations between use of optional features, demographics, and glycemic outcomes. Methods: Retrospective cohort studies were performed with data from US-based users of the G6 CGM System (Dexcom, Inc.). For all cohorts, data included sensor glucose values (SGVs). In separate cohorts, use of alert features (for hyperglycemia, existing hypoglycemia, and impending hypoglycemia), remote data sharing feature (Share), software for retrospective pattern analysis (CLARITY), "virtual assistant" feature that announces the current SGV and trend in response to a spoken request were assessed. Descriptive statistics were used to summarize feature set utilization patterns and relate them to glycemic outcomes. Results: Most individual features were consistently adopted by high proportions of G6 users. Threshold SGVs chosen for activating hyperglycemia and hypoglycemia alerts varied with age and were higher among the youngest and oldest patients. Use of the Share feature was more common among young patients and those with type 1 diabetes. Individuals who used more of the alert and notification features had more favorable glycemic outcomes, including time in range (TIR), than those who used fewer. More extensive engagement with CLARITY notifications was associated with higher TIR. Frequent use of the virtual assistant feature was associated with higher TIR and lower mean SGV. Conclusions: Optional features of the G6 CGM system are acceptable to and appear to benefit patients who use them. Different levels of engagement suggest that demographics and personal circumstances play a role in how patients and caregivers use CGM features to help manage diabetes.