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Little is known about the feasibility of mobile health (mHealth) support among people with type 1 diabetes (T1D) using advanced diabetes technologies including continuous glucose monitoring (CGM) systems and hybrid closed-loop insulin pumps (HCLs).
This study aims to evaluate patient access and openness to receiving mHealth diabetes support in people with T1D using CGM systems or HCLs.
We conducted a cross-sectional survey among patients with T1D using CGM systems or HCLs managed in an academic medical center. Participants reported information regarding their mobile device use; cellular call, SMS text message, or internet connectivity; and openness to various channels of mHealth communication (smartphone apps, SMS text messages, and interactive voice response [IVR] calls). Participants’ demographic characteristics and CGM data were collected from medical records. The analyses focused on differences in openness to mHealth and mHealth communication channels across groups defined by demographic variables and measures of glycemic control.
Among all participants (N=310; female: n=198, 63.9%; mean age 45, SD 16 years), 98.1% (n=304) reported active cellphone use and 80% (n=248) were receptive to receiving mHealth support to improve glucose control. Among participants receptive to mHealth support, 98% (243/248) were willing to share CGM glucose data for mHealth diabetes self-care assistance. Most (176/248, 71%) were open to receiving messages via apps, 56% (139/248) were open to SMS text messages, and 12.1% (30/248) were open to IVR calls. Older participants were more likely to prefer SMS text messages (
Most people with T1D who use advanced diabetes technologies have access to cell phones and are receptive to receiving mHealth support to improve diabetes control.
About 1.6 million people in the United States have type 1 diabetes (T1D) [
Technologies, such as continuous glucose monitoring (CGM) systems and hybrid closed-loop insulin pumps (HCLs), can provide patients with T1D with real-time glucose information and algorithm-based insulin delivery [
More than 85% of the US [
Given that CGM systems provide data about glucose levels in real time, opportunities exist for the development of T1D mHealth support programs that retrieve data continuously and use that information to deliver timely and personalized patient feedback [
To address these gaps in knowledge, we conducted a survey among a large sample of individuals with T1D using CGM systems and receiving diabetes care in an academic medical center. Here we report the findings from that survey including information about participants’ access to mobile technology; receptivity to mHealth interventions that require sharing their CGM data; and openness to communication via stand-alone apps, SMS text messaging, or IVR calls.
The survey was conducted between January and April 2021 after receiving approval from the University of Michigan Institutional Review Board (HUM00189672). The sampling frame for the survey was the population of adults with T1D receiving care through outpatient clinics associated with the University of Michigan Health System.
The University of Michigan Health is a tertiary health center that provides health care to the surrounding communities, with more than 1 million people living in southeastern Michigan, and regularly supports diabetes care for about 3000 adults with T1D. A total of 1024 adults with diagnoses of T1D and ongoing CGM use were identified from the electronic medical record (EMR) system and invited via emails sent through REDCap. Candidates with missing or invalid email addresses were contacted via postal letters and telephone calls. The investigators avoided directly contacting their own patients for recruitment to prevent possible coercion or sampling biases. Survey participants provided written informed consent for linkage of their surveys with demographic data from the EMR and glucose data from their CGM systems. All people determined to be aged ≥18 years, have T1D, and use CGM systems based on EMRs were included in the study and analyses. Participants without 4-week CGM data within the past 3 months were excluded from the analyses involving CGM data.
The survey assessed participants’ durations of diabetes, CGM type and use duration, and insulin pump use information. Cellphone use, including the frequency of the participant carrying the cellphone (“How often do you have your cellphone with you?”) and cellular connectivity for calls and SMS text messages (“How often does your cellphone have good reception for text messages or phone calls?”), and internet access (“How often does your cellphone have access to the internet?”) at home, at work, and outside of home and work were assessed. Items developed for the study asked about participants’ receptivity to mHealth diabetes interventions and openness to different mHealth communication channels. Specifically, we asked “Cellphones could be used for receiving on-site, real-time support as we often carry them around...If you could get additional support at the time of high or low glucose levels to help you with your glucose control, which method(s) would you prefer?” (The response options were apps, SMS text messages, IVR calls, and “do not want diabetes support delivered through cellphone.”) Participants could select more than one communication channel option as their response. Surveys also assessed participants’ willingness to share real-time CGM information for glucose control support. Participants were encouraged to complete the survey directly via REDCap. Study team members conducted telephone surveys for participants without immediate access to the internet.
Participants’ age, sex, race, ethnicity, and hemoglobin A1c (HbA1c) levels were abstracted from the EMR. Recent CGM data [
Using the Cochran formula, we calculated that a sample of 280 respondents was needed to determine the prevalence of people receptive to mHealth diabetes interventions at a 95% confidence level with 5% precision for a pool of 1024 potential respondents. We conducted descriptive analyses of participants’ demographics characteristics and CGM glucose data. The Mann-Whitney
A total of 310 eligible participants completed the survey (
Participant demographics (N=310).
Characteristics | Participants | ||
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Female | 198 (63.9) | |
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Male | 112 (36.1) | |
Age (years), mean (SD) | 45 (16) | ||
Age (years), median (IQR) | 43 (31-58) | ||
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White or Caucasian | 289 (93.2) | |
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Black or African American | 10 (3.2) | |
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Asian | 3 (1.0) | |
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Refused to answer/unknown | 1 (0.3) | |
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Other | 7 (2.3) | |
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Non-Hispanic | 295 (95.2) | |
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Hispanic | 9 (2.9) | |
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Refused to answer/unknown | 6 (1.9) | |
Duration of diabetes (years), median (IQR) | 23 (14-32) | ||
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0-3 months | 9 (2.9) | |
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4-6 months | 13 (4.2) | |
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7-12 months | 23 (7.4) | |
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1 year to 3 years | 131 (42.3) | |
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4-6 years | 80 (25.8) | |
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>6 years | 54 (17.4) | |
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Dexcom G5 | 4 (1.3) | |
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Dexcom G6 | 277 (89.4) | |
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Medtronic Guardian Sensor 3 | 29 (9.4) | |
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245 (79.0) | ||
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With auto-suspension features | 164 (52.9) | |
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With closed-loop features | 149 (48.1) | |
Last HbA1cb level (%), median (IQR) | 7.2 (6.5-7.8) | ||
Time of CGM use (%), median (IQR) | 97 (88-99) | ||
CGM average glucose level (mg/dL), median (IQR) | 159 (143-178) | ||
TARc on CGM (%), median (IQR) | 32 (20-44) | ||
TBRd on CGM (%), median (IQR) | 1.4 (0.6-3.0) |
aCGM: continuous glucose monitoring.
bHBA1c: hemoglobin A1c
cTAR: time above range.
dTBR: time below range.
Patient participation flowchart. CGM: continuous glucose monitoring.
Of all 310 participants, 304 (98.1%) reported using cellphones. All these individuals reported using a smartphone, with 68.1% (207/304) using an iPhone and 29.9% (91/304) using an Android phone. About 90.1% (274/304) of participants reported carrying their mobile devices with them all or most of the time and that their mobile devices have connectivity for phone calls, SMS text messages, and the internet all or most of the time (
Accessibility to mobile health support (N=310).
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Having cellphone accompanied, n (%) | Good reception for phone calls or SMS text messages, n (%) | Having access to the internet, n (%) | |||
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All the time | 185 (59.7) | 187 (60.3) | 226 (72.9) | ||
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Most of the time | 105 (33.9) | 109 (35.2) | 68 (21.9) | ||
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About half of the time | 12 (3.9) | 9 (2.9) | 9 (2.9) | ||
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Less than half of the time | 4 (1.3) | 3 (1.0) | 4 (1.3) | ||
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Rarely | 4 (1.3) | 2 (0.6) | 3 (1.0) | ||
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All the time | 195 (62.9) | 170 (54.8) | 217 (70.0) | ||
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Most of the time | 81 (26.1) | 116 (37.4) | 68 (21.9) | ||
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About half of the time | 8 (2.6) | 16 (5.2) | 15 (4.8) | ||
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Less than half of the time | 9 (2.9) | 2 (0.6) | 1 (0.3) | ||
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Rarely | 17 (5.5) | 6 (1.9) | 9 (2.9) | ||
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All the time | 225 (72.6) | 114 (36.8) | 121 (39.0) | ||
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Most of the time | 73 (23.5) | 183 (59.0) | 145 (46.8) | ||
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About half of the time | 8 (2.6) | 9 (2.9) | 22 (7.1) | ||
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Less than half of the time | 4 (1.3) | 3 (1.0) | 15 (4.8) | ||
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Rarely | 0 (0.0) | 1 (0.3) | 7 (2.3) |
Of the 310 participants, 248 (80%) were receptive to receiving diabetes self-care support through their phones with the goal of improving their glucose control. There were no significant differences in sex, age, diabetes duration, average glucose level, TAR, TBR, and the percent of time spent with glucose levels above 250 mg/dL and below 54 mg/dL between those who were versus were not receptive to receiving mHealth support. Among participants receptive to mHealth support, 98% (243/248) responded that they would “very much” or “probably” be willing to share real-time glucose level data to receive tailored support for diabetes management.
Among those who were receptive to mHealth support, 71% (176/248) were open to receiving support via apps, 56% (139/248) were open to SMS text messages, and 12.1% (30/248) were open to IVR calls. Participants open to apps but not IVR calls were younger than those open to IVR calls but not apps (
Patient demographics and glycemic characteristics grouped by openness to mobile health communication channels.
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Apps, median (IQR) | SMS text messages, median (IQR) | IVRa calls, median (IQR) | |||
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Apps vs SMS text messagesc | Apps vs IVR callsc | SMS text messages vs IVR callsc |
Age (years) | 40 (28-54) | 44 (32-58) | 53 (36-64) | .009 | .03 | .12 |
Duration of diabetes (years) | 24 (14-32) | 23 (12-32) | 21 (15-40) | .45 | .98 | .05 |
Average glucose level (mg/dL) | 158 (143-175) | 157 (141-176) | 153 (145-182) | .88 | .57 | .99 |
TARd (%) | 30 (19-42) | 31 (18-43) | 30 (20-45) | .99 | .50 | .79 |
Time with glucose level >250 mg/dL (%) | 7 (2-13) | 7 (2-13) | 5 (2-14) | .98 | .95 | .69 |
TBRe (%) | 1.4 (0.5-3.0) | 1.5 (0.7-3.0) | 2.4 (0.8-3.8) | .51 | .90 | .05 |
Time with glucose level <54 mg/dL (%) | 0.2 (0-0.6) | 0.2 (0-0.5) | 0.2 (0-0.7) | .58 | .13 | .44 |
aIVR: interactive voice response.
bStatistical analysis conducted with the Mann-Whitney
cParticipants who selected both communication channels were excluded from the analysis.
dTAR: time above range.
eTBR: time below range.
In this survey of a large sample of people with T1D who used CGM systems and HCLs, nearly all participants used smartphones, and nearly all reported the ability to make phone calls, receive SMS text messages, and connect to the internet most of the time. Participants were receptive to receiving support for diabetes care, including being willing to share CGM data automatically so that mHealth support could be personalized based on their clinical needs. When asked about their openness to various communication channels for receiving mHealth support, the majority were open to apps or SMS text messaging, and only a smaller proportion of individuals indicated openness to receiving IVR calls. Older participants preferred to receive mHealth support through SMS text messaging or IVR calls over apps.
Prior studies have shown that adolescents with T1D are receptive to self-management assistance via mHealth tools [
This study demonstrates that most advanced diabetes technology users are receptive to receiving mHealth support that could enhance their ability and motivation for effective self-care behaviors beyond the simple alarms for hypo- and hyperglycemia currently available via CGM systems and HCLs. Alarm fatigue can lead to turning off the hypo/hyperglycemia alarms or simply ignoring them [
We found that the majority of T1D advanced diabetes technology users were open to smartphone apps. However, a significant proportion also favored other communication channels such as SMS text messages and IVR calls, particularly those who were older. This finding underscores the significance of maintaining a diversity of mHealth approaches to promote intervention engagement in heterogeneous diabetes populations [
This study is one of the first to report information related to the feasibility and potential interest in mHealth support among people with T1D using advanced diabetes technologies. Comparisons of characteristics of respondents to nonrespondents identified only a relatively small difference in the sex distribution, and analysis of survey data did not suggest that sex was related to any of the outcomes of interest. Glycemic indexes, including CGM glucose information, confirmed that both patient populations with and without controlled diabetes were receptive to receiving mHealth support.
Several limitations of this study should be considered. Participants were recruited from a population receiving care in a single tertiary academic health center. However, this health care system also has outreach clinics and medical services providing care to >1 million people in surrounding communities. The distribution of participants across racial/ethnicity groups and the proportion reporting use of an insulin pump were similar to the 2016-2018 T1D Exchange national report [
We found that people with T1D using advanced diabetes technologies have access to mobile technologies and are receptive to receiving mHealth support for improving diabetes control. The majority of people in this population are open to smartphone apps or SMS text messages, and older individuals may favor SMS text messages or IVR calls for mHealth support.
artificial intelligence
continuous glucose monitoring
electronic medical record
hemoglobin A1c
hybrid closed-loop insulin pump
interactive voice response
mobile health
type 1 diabetes
time above range
time below range
The study was supported by the Michigan Center for Clinical and Translational Research Pilot and Feasibility Grant (P30DK092926). YKL was supported by K23DK129724; JP was a VA’s Health Services Research and Development Service research career scientist; REDCap was supported by NCATS UL1TR00240.
We appreciate all the assistance from the University of Michigan faculty, Adult Diabetes Education Program and Data Office for Clinical and Translational Research, the Michigan Institute for Clinical and Health Research, and the Michigan Center for Diabetes Translational Research. We also thank all the study participants, without whom this study would not have been possible.
The data sets generated and analyzed during the study are available from the corresponding author on reasonable request.
YKL and JP proposed the study. YKL, CR, RPB, GP, and JP designed the study and study instruments. YKL and ID collected study data. YKL, CR, RPB, GP, and JP conducted the analysis and interpreted the results. All authors contributed to the manuscript and reviewed the manuscript before submission.
None declared.