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. Author manuscript; available in PMC: 2022 Sep 1.
Published in final edited form as: J Nurs Scholarsh. 2021 Apr 29;53(5):643–652. doi: 10.1111/jnu.12667

Perceptions of Using Multiple Mobile Health Devices to Support Self-Management among Adults with Type 2 Diabetes: A Qualitative Descriptive Study

Allison A Lewinski 1, Jacqueline Vaughn 2, Anna Diane 3, Angel Barnes 4, Matthew J Crowley 5, Dori Steinberg 6, Janee Stevenson 7, Qing Yang 8, Allison A Vorderstrasse 9, Daniel Hatch 10, Meilin Jiang 11, Ryan J Shaw 12
PMCID: PMC8448945  NIHMSID: NIHMS1716344  PMID: 33928755

Abstract

Purpose.

This study identified facilitators and barriers pertaining to the use of multiple mobile health (mHealth) devices (Fitbit Alta® fitness tracker, iHealth® glucometer, BodyTrace® scale) that support self-management behaviors in individuals with type 2 diabetes mellitus (T2DM).

Design.

This qualitative descriptive study presents study participants’ perceptions of using multiple mobile devices to support T2DM self-management. Additionally, this study assessed whether participants found visualizations, generated from each participant’s health data as obtained from the three separate devices, useful and easy to interpret.

Methods.

Semistructured interviews were completed with a convenience sample of participants (n=20) from a larger randomized control trial on T2DM self-management. Interview questions focused on participants’ use of three devices to support T2DM self-management. A study team member created data visualizations of each interview participant’s health data using RStudio®.

Results.

We identified two themes from descriptions of study participants: feasibility and usability. We identified one theme about visualizations created from data obtained from the mobile devices. Despite some challenges, individuals with T2DM found it feasible to use multiple mobile devices to facilitate engagement in T2DM self-management behaviors.

Discussion.

As mHealth devices become increasingly popular for diabetes self-management and are integrated into care delivery, we must address issues associated with the use of multiple mHealth devices and the use of aggregate data to support T2DM self-management.

Clinical relevance.

Real-time patient-generated health data that are easily accessible and readily available can assist T2DM self-management and catalyze conversations, leading to better self-management. Our findings lay an important groundwork for understanding how individuals with T2DM can use multiple mHealth devices simultaneously to support self-management.

Keywords: patient-generated health data, diabetes mellitus, type 2, qualitative analysis

INTRODUCTION

The Centers for Disease Control (CDC) estimates that 9.3% of the United States population has been diagnosed with diabetes (CDC, 2018). Consistent engagement in self-management is crucial to maintaining good health in adults with type 2 diabetes mellitus (T2DM) (CDC, 2018), yet self-management for this population remains suboptimal. Diligent self-management is essential to delay the onset of comorbidities such as heart disease and renal failure and to maintain optimal health (CDC, 2018).

Diabetes self-management consists of a complex set of daily behaviors, including regular blood glucose monitoring, maintaining a healthy weight, being physically active, and eating healthy (CDC, 2018; ADA, 2019), in addition to routine activities of daily living. The amount, frequency, and type of self-management behaviors are person-specific, and the majority of T2DM self-management is completed by the individual in their home setting (Funnell & Anderson, 2002). Critically, individuals with T2DM must also maintain an accurate record of behavioral (e.g., physical activity, food log) and clinical (e.g., blood glucose values, weight) data to help guide treatment changes (Hartz, Yingling, & Powell-Wiley, 2016).

Mobile health (mHealth) devices are one way to support self-management behaviors in individuals with T2DM (Greenwood, Gee, Fatkins, & Peeples, 2017; Hartz, Yingling, & Powell-Wiley, 2016). Devices such as wearables (e.g., Fitbit® Apple Watch™), smartphone applications, and connected devices simplify the recording, tracking, and monitoring of daily self-management tasks, including blood glucose checks and exercise (Greenwood et al., 2017; Hartz et al., 2016; Sieverdes, Treiber, Jenkins, & Hermayer, 2013). mHealth devices enable the in situ collection of large quantities of longitudinal patient-generated behavioral and clinical data, which facilitate evaluation of the individual’s health status and/or responses to T2DM medical care (Sieverdes et al., 2013). Although in a recent study by Shaw et al. (2020) adults with T2DM found it feasible to use multiple mobile devices to support their self-management of T2DM, individuals with T2DM may feel challenged or overwhelmed by behavioral and clinical data collected from multiple mHealth devices (Caban & Gotz, 2015). Data overload and fatigue, and lack of understanding of the data collected, can lead overwhelmed individuals to discontinue their use of devices altogether (Caban & Gotz, 2015).

Visualizations of data collected from mHealth devices can promote understanding and adherence. Data visualizations present large amounts of health data in an efficient, accessible, and meaningful format (West, Borland, & Hammond, 2015). Formats for data visualizations include diagrams, graphs, and charts that descriptively illuminate associations, provide insight into trends (West et al., 2015), and illustrate disease status and progression (Caban & Gotz, 2015). Succinct data visualizations help individuals review self-management and clinical data, facilitate communication about self-management with health care providers or family, and inform decisions about self-management behaviors. A better understanding of how individuals with T2DM perceive and interpret data visualizations may help researchers and designers to improve and optimize the use of mHealth data (Lor et al., 2019).

The primary purpose of this study was to describe the feasibility and acceptability of using multiple mobile devices to support T2DM self-management; a secondary purpose was to obtain and use data from the mHealth devices to assess whether participants found data visualizations useful and easy to interpret.

METHODS

We chose a qualitative descriptive study design to examine qualitative data collected as part of the Diabetes Mobile Care study; protocol details and findings (Shaw et al., 2019; Shaw et al., 2020) were published elsewhere. All research occurred at an academic medical center in the southeastern United States. The University’s Institutional Review Board approved all study activities (IRB # Pro00071569).

Describing Feasibility and Acceptability

Qualitative descriptive studies, by nature, are descriptive and less interpretive than other qualitative methods such as ethnography, grounded theory, phenomenology, narrative study, or case studies (Kim et al., 2017; Sandelowski, 2000; Sandelowski, 2010; Neergaard et al., 2009). In a qualitative descriptive study, data analysis stays ‘data near’ (i.e., closer to the collected interview data), which results in a straight-forward description of the concept or phenomenon (Kim et al., 2017; Sandelowski, 2000; Sandelowski, 2010; Neergaard et al., 2009); therefore, we operationalized concepts of feasibility and acceptability based on salient literature (Burford, Park, & Dawda, 2019; Choe, Lee, Lee, Pratt, & Kientz, 2014; Zarghom, Di Fonzo, & Leung, 2013) and our expertise conducting mHealth research (Lewinski et al., 2018; Lewinski et al., 2019; Shaw et al., 2019; Shaw et al., 2020) to examine the feasibility and acceptability of using multiple mobile devices to support T2DM self-management and the usefulness of data visualizations. These concepts informed our understanding of challenges related to the use of mobile health devices in T2DM self-management (Table). Overall, we chose qualitative description due to our desire for an overarching summary and actionable findings to apply in subsequent studies of participants’ experiences using multiple mobile devices to support self-management.

Table: Operationalization of concepts for this qualitative analysis.

(Burford et al., 2019; Choe et al., 2014; Zarghom et al., 2013)

Concept Operationalization in this study
Usability How or to what extent the devices were used to monitor the participant’s weight, daily blood glucose, and activity.
Presentation of visualized data Perception (helpful/unhelpful, understanding) of the data visualized from the mobile devices
Accuracy Participant’s perception of the accuracy of the mobile device
Facilitation of conversation Description of how the participant used the mobile device to facilitate a conversation with others about diabetes self-management
Familiarity with technology Expression of prior experience with mobile devices
Accountability How the mobile devices helped support the participant in T2DM self-management
Feasibility How easy or difficult the mobile device was to use to support T2DM self-management during the study period
Satisfaction Satisfaction or dissatisfaction with the mobile devices in T2DM self-management
Data Accessibility How the participants could access the raw data from the mobile devices and use these data when engaging in T2DM self-management
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Design and Sample

Diabetes Mobile Care was a six-month longitudinal trial that tested the feasibility of using multiple mobile devices to support T2DM self-management. Upon enrollment, study participants were provided with three mobile devices (Fitbit Alta® fitness tracker, iHealth® Glucometer [model BG5], and BodyTrace® Scale) with which to monitor their behavioral and clinical data (Shaw et al., 2019; Shaw et al., 2020). Participants found the use of multiple mobile devices to manage T2DM feasible (Shaw et al., 2020). A qualitative descriptive study was conducted to examine the feasibility of using multiple mobile devices to support T2DM self-management based on a convenience subsample of participants who had completed the parent study. At the conclusion of the parent study, research staff invited 36 participants to complete semistructured interviews, and n=20 agreed. The research team created and sent (via email or postal mail) data visualizations of each participant’s data prior to the interview. Interviews were conducted via the telephone, lasted between 25-30 minutes, and were recorded.

Measures

Demographic and clinical data.

As part of enrollment in the parent study, a research assistant collected demographic data (e.g., age, gender, race, duration of T2DM), clinical data (HbA1c), and data pertaining to familiarity with, and use of mobile devices at baseline.

Data Visualizations.

A study team member created six different data visualizations of each participant’s health data using RStudio® (Version 3.5.0— © 2009-2018). Details on data aggregation and visualization were previously published (Wood et al., 2019). The interviewer was given a copy of all six participant data visualizations (bar graph, line graphs, gauges) to use during each interview, and study participants were asked to describe how these visualizations might influence their behaviors and communications with clinicians about T2DM.

Questions and probes.

Interview questions referenced the participant’s use of the three mobile devices and their perceptions of the data visualizations. Questions regarding use of the devices focused on overall experience, challenges and problems, problem-solving, perceptions of support, and discussions with others (i.e., family, friends, clinicians). Questions about the visualizations concerned overall impressions, perception of missing data and displays, and suggestions for improvements. Standardized probes (e.g., tell me more, what do you mean by that?) clarified responses and elicited greater detail. All interviews were transcribed and de-identified by a professional transcription service.

Analytic strategy.

We used directed content analysis (Hsieh & Shannon, 2005) to analyze these data and ATLAS.ti version 8 (Berlin, Germany) to support coding and analysis. A coding team comprised of 3 authors developed a priori codes based on the operationalizations of feasibility and acceptability (Table) (Hsieh & Shannon, 2005). Coding team members independently read and coded each transcript, and during meetings they discussed codes and coded sections, reconciled differences, and grouped and detailed themes to ensure reliability and validity (Morse, 2015; Whittemore, Chase, & Mandle, 2001). No limits were placed on the number of codes assigned to a coded section in order to obtain a comprehensive description of using multiple mobile devices. Developed themes were discussed with the entire study team biweekly throughout the analysis.

RESULTS

Semistructured interview participants (n=20) were 75% female, 55% Black, and 45% White, and mean age was 57.2 ± 10.3. At baseline, these participants had mean duration of T2DM diagnosis of 9.5 years (range, 2 to 23 years), and a mean glycosylated hemoglobin of 7.9 ± 1.15. Study participants described the feasibility and acceptability of using multiple mobile devices to support their diabetes self-management during the course of the parent study. Additionally, these participants stated their perceptions of the data visualizations created from the mobile health devices during the course of the study. We identified three themes: feasibility, usability, and thoughts about multiple mobile devices and data visualizations (Figure).

Figure:

Figure:

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Identified themes to describe use of multiple mobile devices to support T2DM self-management

Theme 1: Feasibility of multiple mobile devices to support T2DM self-management.

Feasibility addressed participant’s perception of ease or difficulty in using the mobile devices to support self-management of T2DM. First, we requested participant’s thoughts about feasibility of using multiple mobile devices together (as more effort is required to use three devices than a single device). Most participants reported minimal challenges in using multiple study-provided mobile devices. Participants said they quickly learned how to use each device following instruction by the study coordinator. One participant stated, “I loved it. … it was new to me, but I learned really quick, I mean it was just wonderful. I’m a pretty simple, basic person. Doing all this on mobile and collecting my data, I liked that.” This sentiment was echoed by other participants who found the devices easy to use and did not encounter technical difficulties. Overall, participants described that using multiple devices simultaneously, as well as using each device individually, was feasible during daily self-management.

Ease of use.

We asked participants about the feasibility of using each device provided for the study. When asked about the glucometer, many participants verbalized not having experienced challenges or technical issues. One participant stated, “It was very easy to use and you could take it with you. … the information is recorded right there so when I went to the doctor, all I had to do is pull out my phone.” When asked about the fitness tracker, several participants described having no challenges using the tracker over the six months. One participant commented on how the fitness tracker was helpful: “It was just real handy and it made me feel better about the little things I was doing. It was comfortable, it was easy to charge, and it just made me feel good about myself.” When we asked about the scale, several participants stated that they had experienced no challenges.

Challenges of use.

Participants described experiencing some challenges with the devices. Several participants reported challenges with the glucometer, such as (a) manually logging blood glucose readings into the glucometer, (b) connecting the glucometer to Bluetooth, (c) updating the glucometer, or (d) scanning the glucose testing strips. One participant noted, “I had problems with the glucose meter hooking to the Bluetooth, but I mean, you can always enter it manually. Yes, a lot of times it just doesn’t connect for some reason.” Similarly, several participants reported challenges such as technical difficulties or problems with the fitness tracker band or charger, “I think it’s a pattern that happens with it, one side breaks off. It comes loose.” Some participants described challenges such as inconsistent cellular connectivity of the scale, previous weights not recorded in the scale, or receiving a general error message. One participant stated,

As soon as I stepped off the scale, the data got sent and that was the last I knew of it. I mean unless I had gone to the trouble of trying to put a piece of paper and a pencil next to the scale where I could write it down on a daily basis [I could not remember the reading].

Theme 2: Usability of the multiple mobile devices.

Usability described the manner and/or extent of device use to monitor the participant’s weight, daily blood glucose, and activity. Participants described use of and also their impressions of the mobile devices. First, we invited participants’ thoughts on the use of the three devices together. Several participants found that using the devices together helped them to develop a routine. One participant described,

I really enjoyed having the devices to use, and it really made me a lot more conscious of doing the readings and all of that on a daily basis… it became a routine, …It was a good experience. I mean it was simple.

Additionally, we noted descriptions of (a) ways in which the devices motivated participants to self-manage their T2DM, (b) how participants spoke to others about the devices, (c) the degree to which the participants trusted the devices, and (d) their ability to obtain their data.

Responses to questions about the usability of each specific device demonstrated participants used the glucometer to (a) monitor blood glucose in response to specific behaviors (i.e., eating certain foods, not eating), (b) adjust dietary behaviors, (c) track blood glucose trends, and (d) track trends to share and discuss with their health care provider. One participant stated,

With this app on your phone, if your doctor says, well ‘what was your blood sugar yesterday?’ You could go to your phone, pull up your app, go to the app and it will show you what your blood sugars were the day before.

When asked about the glucometer use, most participants expressed satisfaction, including that it was easy to use, easily paired with a phone, and enabled the participant to track their blood glucose readings over time. Similarly, participants were satisfied with the fitness tracker because it (a) contained reminders and step recording to help them schedule and track exercise goals and sleep regimens, (b) provided positive messages, and (c) supported self-management behaviors such as medication compliance, exercise, and sleep. One participant stated, “the fitness tracker…makes you want to get more steps in, and then it helped me as far as the diabetes.” Two participants used the fitness tracker to share activity data with a family member or health care provider. Participants also described using the scale to monitor weight between health care visits and track weight trends. One participant stated, “I wouldn’t say it helped me manage my diabetes, not the scale. It helped me being able to weigh myself every day on a nice scale. … it did help me manage weight loss.” Several participants expressed that their use of the scale had been satisfactory and without problems.

Reminder to self-manage.

We defined reminder to self-manage as the means by which the mobile device prompted the participant to engage in T2DM self-management. Participants described how participation in the study and use of the mobile devices to track their health data prompted them to develop and engage in a T2DM self-management routine. One participant stated, “[The devices made] me aware of me: What I was doing, what I was eating, what my blood sugar was doing depending on what I ate, when I took my medicine, all of that.” Participants commented on how specific devices helped them to remain compliant with T2DM self-management behaviors. One participant remarked that the log of past blood glucose data prompted them to use the glucometer more frequently: “I ended up doing testing with [the study-provided glucometer and test strips] to keep accountable.” Participants stated that the fitness tracker prompted them to set self-management schedules and goals for physical activity, sleep, and medication compliance, and to improve overall health. Participants expressed that the scale prompted them to become aware of the need to monitor their weight.

Facilitation of conversations with others.

We described how the use of the mobile devices facilitated conversations about T2DM self-management. Many participants discussed their involvement in the study and shared the health care data from the mobile devices with their health care provider during a medical visit. One participant stated, “I showed it [the glucometer] to [my provider], and they looked to see where the highs are, and the lows, and any spikes or anything like that … we just talked about [the readings].” Participants also discussed study participation and the health care data from the devices with family members. One participant described having shared the glucometer data with her daughter: “She’s concerned about my health, so she looked at [the glucometer] a lot.”

Accuracy.

Some participants commented on their perception of the accuracy of the devices used in the study. One participant stated, “I wore [the fitness tracker]. … my watch does steps, and they were pretty accurate … right there together.” Participants noted that the study-provided glucometer produced different blood glucose readings than their regular glucometer. One participant noted,

It did not, the comparison between what I was getting by my insulin pump and line feeder, I sometimes use a meter at work just to check it out. It was way off. It depended what the glucose was at the time, it was fairly close, I would say within the standing to fifteen percent on the lower end. But once you got over one thirty, it was way off.

Regarding the scale, participants found the scale to be accurate.

Accessibility.

Participants commented positively on having convenient and immediate access to blood glucose data to help them meet self-management goals. Several participants commented positively on the interoperability of the mobile devices because they could access and share their data across multiple devices. One participant stated,

Everything had an app that they gave me, so the scale had its own part, so when you weigh yourself it records it and then if you go into it and just play around with it, it’s options for you to set goals for yourself.

Theme 3: Thoughts about the data visualizations

We queried participants on the style and format of the visualizations created from the data collected from their multiple mobile health devices. Participants’ perceptions of the meaning and use of the data visualizations were varied, and they provided suggestions for improvements.

Useful to see data.

Most participants could verbalize the relationship between the data points in the visualization and their health behaviors at the time the data were collected. One participant commented, “[The gauge] looks like a gas dial or a speed dial on your car, you can see what’s going on. [The gauge] let me know my blood sugars were often in the target range.”

The data visualizations assisted several participants in their understanding of the link between generated health data and health behaviors (e.g., physical activity, blood glucose monitoring). One participant stated, “This [graph] lets me know that I was always in my target range except for maybe the one dot in April.” Some participants, however, while able to verbalize the trajectory of their blood glucose values and see that variation occurred, did not link these data to their health behaviors. For instance, one participant stated, “What do these dots mean? … [Interviewer describes the visualization]. I don’t know if that’s good for it to be in the normal range. Yeah, I really don’t understand how this works.”

Several participants provided additional details about the usefulness of seeing their data in the form of a visualization. Comments included: “I think being able … to visualize the pluses [and] the minuses is of value,” and “[The different fitness tracker readings] definitely make sense. I like the average steps per day because my days are very different … so it does help for me to see this.” Additionally, seeing data in pictorial form caused some participants to note a discrepancy when viewing the data visualizations as the visualizations did not appear to reflect their personal data or actions. For instance, one participant shared, “Some of these that were above one seventy-three may not have been me. I had two boys that lived with me and they may not have realized the scale was part of that study.”

Design improvements.

Participants provided suggestions on how to improve the data visualizations related to information incorporation, design preferences, and access challenges. Some participants requested additional information be incorporated, such as granular information pertaining to recorded blood glucose values (e.g., time stamps), average blood glucose values over time, longer timelines with more data points, and predictive HbA1c values based on current blood glucose values. One participant stated,

Is there a way that it could project what my A1c would be? …Every time I go to the doctor, I’m sitting there waiting for my A1c… This ain’t about you just getting a number, it’s about me being able to track and know, even with me changing my diet, I know what I’m reading every day and all of that, but in accumulation, what does that mean?

Participants suggested altering the design of the data visualizations to (a) make them easier to read and understand, (b) represent and clarify demarcations between months/weeks/days/time of day, (c) eliminate medical jargon; it was also suggested that a legend for interpreting symbols/colors used in the visualizations be included. One participant commented, “I can see exactly my highs and lows [for the fitness tracker]. And, [with the blood sugar] … it doesn’t say, like, AM or PM, what time of the day.” Regarding jargon, one participant commented,

I don’t know what [name brand of scale] means. Is this my weight? Is that the name of the scale? I didn’t know whether that was some kind of medical term but it’s just a brand.

Several participants suggested providing choice of time frame (i.e., monthly, weekly) as well as access to the data visualizations in real-time and in different devices (e.g., mobile phone, email).

DISCUSSION

Mobile health devices can facilitate improved self-management among adults with T2DM (Arsand, Muzny, Bradway, Muzik, & Hartvigsen, 2015; Goyal et al., 2016; Greenwood et al., 2017; Hartz et al., 2016). As these consumer devices become increasingly popular for diabetes self-management and are integrated into care delivery, it is important to understand (a) the benefits and potential challenges of using multiple mHealth devices simultaneously, and (b) how their aggregate data can support T2DM self-management. To address this need, we examined perceptions of adults who used multiple mHealth devices simultaneously, as well as of visualizations from patient-generated health care data. We found: (1) despite some challenges, individuals with T2DM found it feasible to use multiple mobile devices to facilitate engagement in T2DM self-management behaviors; and (2) while visualizations can help individuals describe their self-management behaviors, the design of these visualizations must be user-centered.

Mobile health devices hold potential to improve T2DM self-management because these devices enable individuals to monitor daily physiologic, behavioral, and lifestyle-related elements of the disease; however, the devices alone do not manage the disease. Understanding of the benefits and challenges of using mobile health devices is necessary for users to sustain engagement with these mobile devices (Holubova, Vlaskova, Muzik, & Broz, 2019; Nelson, Coston, Cherrington, & Osborn, 2016; Shan, Sarkar, & Martin, 2019). Our findings showed that many study participants found it feasible, helpful, and acceptable to use a wrist-worn activity tracker, a wireless glucometer, and a cellular scale to monitor and track their patient-generated health data over a 6-month period. These findings were similar to those obtained in a study by Arsand et al. (2015) in which participants with diabetes provided positive responses to the functionality and usefulness of an Apple® Watch and mobile app to manage T2DM. Participants in our study stated that the mobile health devices (i.e., fitness tracker) provided prompts that served as encouragement and positively reinforced self-management behaviors. The use of mobile technology facilitated conversations among the participants and their health care providers, family members, and friends. Overall, our findings highlight that use of mobile health tools for self-management is person-specific and relies on individual perceptions and abilities.

Data visualizations allow for individualized, personal self-management profiles. Presenting patient-generated health data in understandable and meaningful ways to individuals with T2DM may promote increased engagement in self-management behaviors (Burford et al., 2019). Results from similar studies on data visualizations and T2DM self-management indicate the importance of obtaining user perceptions of the design of the visualizations, how adults will use the visualizations while engaging in self-management, and the usability of the visualizations (Burford et al., 2019; Martinez et al., 2018). Our findings are similar to those of previous studies in that our participants acknowledged the value of “seeing” their data at the completion of the six-month study. Some participants were able to relate fluctuations in data (e.g., blood glucose values, amount of exercise, weight change) to changes in activity (e.g., seasonal activities, workplace demands); however, our findings indicated a lack of consensus on how to present data optimally to individuals with T2DM. These findings imply that data visualizations may need to be person-specific so that individuals choose the display types based on their preferences. Thus, future research on developing personalized approaches to using multiple mobile devices and visualizing data to support T2DM self-management is warranted.

Limitations

Several limitations to this study should be acknowledged concerning the generalizability of our findings. First, we used a small convenience sample of demographically similar individuals engaged in the same study at an academic medical center who, as evidenced by the mean glycosylated hemoglobin in our study, were fairly well-controlled diabetes. Sampling a wider range of individuals who participated in the parent study may provide different responses and perspectives. Second, the sample was primarily female with a mean age of 57, thus the viewpoints from our participants may not be representative of individuals who are male or younger. Third, the data visualizations were provided to participants at study completion. Future research should identify methods to provide data visualizations throughout the study period either real-time or periodically. Despite these limitations, our study adds to knowledge of using multiple mobile devices to self-manage T2DM and real-time patient-generated health care data.

CONCLUSION

Patient-generated health data that are accessible and readily available to individuals with T2DM can improve self-management strategies. Our findings lay an important groundwork for understanding how individuals with T2DM can use multiple mHealth devices simultaneously to support self-management. Additionally, our study provides insight into the use of data visualizations developed using mHealth devices. Future research should focus on sustained use of multiple mHealth devices, as well as on strategies to enhance the development and usability of data visualizations displaying patient-generated health care data from these devices, to support T2DM self-management.

Clinical Resources.

  • Lor M, Koleck TA, Bakken S. Information visualizations of symptom information for patients and providers: a systematic review. J Am Med Inform Assoc. 2019;26(2):162-171. https://pubmed.ncbi.nlm.nih.gov/30535152/

  • Nelson LA, Coston TD, Cherrington AL, Osborn CY. Patterns of User Engagement with Mobile- and Web-Delivered Self-Care Interventions for Adults with T2DM: A Review of the Literature. Current diabetes reports. 2016;16(7):1-20.

  • Shan R, Sarkar S, Martin SS. Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia. 2019;62(6):877-887.

Acknowledgements:

This work was supported by the National Institutes of Health, National Institute of Nursing Research Grant No. 1R15NR015890 (to RJS); Duke University Data+ (to RJS); US Department of Veterans Affairs Office of Academic Affiliations grant No. TPH 21-000 (to AAL); Durham Center of Innovation to Accelerate Discovery and Practice Transformation Grant No. CIN 13-410 (to AAL, MJC); and National Institutes of Health, National Institute of Nursing Research Grants Nos. 1F31NR018100 (to JV) and 1F31NR019213 (to AD). The content is solely the responsibility of the authors and does not necessarily reflect the position or policy of Duke University, the U.S. Department of Veterans Affairs, or the U.S. government. The authors thank Martin Streicher from the Duke Global Digital Health Science Center for programming; Donnalee Frega, Jane Shealy and Judith Hays for editorial assistance; and iHealth for their generosity in donating devices.

Footnotes

Conflicts of Interest: Dr. Lewinski reports receiving funds from PhRMA Foundation and Otsuka. The remaining authors have no competing interests to declare.

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