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Family members or friends (care partners [CPs]) of older adults with type 1 diabetes (T1DM) regularly become part of the diabetes care team, but they often lack knowledge about how to become involved to prevent hypo- and hyperglycemia. Continuous glucose monitoring (CGM) allows a person with diabetes to see their glucose levels continuously and to receive predictive alerts. A smartphone data-sharing app called the Follow app allows the person with diabetes to share continuous glucose numbers with others and to receive predictive alerts of impending hypo- and hyperglycemia. However, there are barriers to sharing this continuous glucose level data with CPs.
This study aimed to address the barriers to sharing CGM data. Our objective was to examine the feasibility of using CGM with the Follow app and a data-sharing intervention called SHARE
Older adults with T1DM (n=20) and their CPs (n=20) received the SHARE
The SHARE
Older adults with T1DM and their CPs identified having someone else aware of glucose levels and working together with a partner on diabetes self-management as positive aspects of the use of the SHARE
An estimated 1.59 million individuals have type 1 diabetes (T1DM) in the United States [
Since Medicare began covering continuous glucose monitoring (CGM), access to CGM has increased among older adults with T1DM and it has shown some efficacy at reducing risk of hypo- and hyperglycemia in these individuals. The Diamond and WISDM (Wireless Intervention for Seniors with Diabetes Mellitus) trials [
Several CGM systems have apps that allow a CP to see CGM glucose levels continuously and receive alerts and a hypoglycemia alarm. Dexcom has a mobile app called Follow that allows CPs to access glucose data for people with diabetes [
Our prior research with adults and CGM reveals several barriers to the use of Follow among adults, namely the need for knowledge on smartphone technology, difficulties setting up the sharing features, and challenges in dyadic communication that reflect people with diabetes and partners’ different expectations regarding family involvement [
To address the current gaps in CGM data sharing among older adults with T1DM, this study examined the feasibility of a CGM with a Follow app intervention, SHARE
A 1-group experimental design was chosen to determine if there was interest and adherence to the intervention and to identify the components of the intervention that need refinement. This study was approved by the University of Utah Institutional Review Board (00114642). Participants signed an institutional review board informed consent.
Participants were recruited from an academic endocrinology clinic and an internal medicine/diabetologist office in Utah and included people with diabetes and their CPs (spouse, adult child, friend; henceforth called dyads when both are referenced). People with diabetes were included if they were ≥60 years, were diagnosed as having T1DM, had normal or mild cognitive impairment (MCI; Montreal Cognitive Assessment [MoCA] score 18-26) [
From the pool of potential participants (N=123), 20 (16.2%) people with diabetes and their CPs (20, 16.2%) met the recruitment criteria. The remaining participants did not meet the recruitment criteria for the following reasons: could not be reached by phone or letter (65/123, 52.8%), had no time or interest in research (16/123, 13%), had no CP (6/123, 4.8%), had an incompatible phone (7/123, 5.7%), had cancer (2/123, 1.6%), had Parkinson disease (1/123, 0.8%), had moderate or severe dementia (3/123, 2.4%), and exhibited delusional behavior (2/123, 1.6%).
Following a screening visit and participant enrollment, data were collected at baseline and at 3 months. After baseline data were collected, people with diabetes and CPs received basic CGM training using technical manuals and components of a Dexcom G6 training video that was adapted for use in older adults with CPs by a trained research assistant. At that same visit, people with diabetes and their CPs received the SHARE
Dyads participated in an interactive CGM with data sharing intervention (
1. Communication strategies
Communication strategies around using real-time continuous glucose monitoring with the Follow app. People with diabetes were asked about their willingness to talk about glucose numbers (hypo- and hyperglycemia). The objective of this discussion was to determine what glucose information the person with diabetes was comfortable sharing.
2. Problem-solving strategies
Barriers to sharing glucose levels were identified and discussed (eg, glucose levels are private, people with diabetes do not want to be judged).
Problem-solving around expectations and length of waiting time before the care partner should contact the person with diabetes for a concerning glucose level and the preferred mode that the care partner uses to contact the person with diabetes (eg, phone call, SMS text messaging, email) were identified. Dyads engaged in a discussion and problem solving around alarms for the Follow app on the person with diabetes and care partner’s smartphone to determine an agreeable strategy. The objective of this step was to guide the dyad on how to manage real-time continuous glucose monitoring expectations and how to incorporate SHARE into their lives.
People with diabetes identified how they wanted the care partner involved (when and how to respond, troubleshooting). Care partners were asked how they feel about this type of communication and if it is acceptable. The objective of this discussion was to explore supportive and unsupportive conversation strategies between dyads.
3. Action plan
Communication plan in writing that includes how to give feedback, length of waiting time, communication mode.
Set alarms with people with diabetes and care partners (each can have different alarms).
Written responsibility and frequency of monitoring glucose levels for people with diabetes and care partners.
Actions to take for severe low blood sugar, chest pain and symptoms of heart attack or stroke, etc.
The following data were examined: retention, reasons for study discontinuation, feasibility (appointment attendance, length of all sessions, number of unscheduled appointments for extra assistance, number of telephone calls for the person with diabetes or CP support), and implementation (percentage of protocol completion, barriers).
Demographics and cognitive status (MoCA) [
DQOL using CGM was measured at 12 weeks using a 15-item instrument with 3 subscales: perceived control (α=.88), hypoglycemia safety (α=.84), and interpersonal support (α=.75) [
Dyads were asked 4 questions on what they liked and did not like about sharing CGM data with their CP, what recommendations they have for others who share CGM data, and what recommendations they have for intervention improvements.
Data were analyzed using descriptive statistics with means and SDs for continuous variables (summary scores) and frequency counts and percentages for categorical data. A content analysis was conducted on the open-ended satisfaction questions. The satisfaction responses were read word for word and then coded. Next, the coded data were categorized and summarized.
People with diabetes (n=20) had a mean age of 70 (SD 5) years and diabetes duration of 31 (SD 18.30) years, and the majority were married (13/20, 65%), White individuals (18/20, 90%), and male (11/20, 55%). The majority wore an insulin pump (11/20, 55%) and had previously used CGM but were naïve to using the Follow app (11/20, 55%), while 9/20 (45%) had never used CGM. There were 8/20 (40%) participants that required extra assistance or support with initial CGM use; 4 (50%) of these participants were naïve to wearing CGM and 4 (50%) had previous experience with CGM. The people with diabetes had a variety of comorbid conditions (
Demographics for people with diabetes and their care partners.
Demographics | RT-CGMa user (n=20) | CPb (n=20) | ||||||||||||
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Mean (SD) | 70.45 (4.90) | 56.6 (16.75) | |||||||||||
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Median (IQR) | 69 (66-73.8) | 62.5 (41.5-69.8) | |||||||||||
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Male | 11 (55) | 7 (35) | |||||||||||
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Female | 9 (45) | 13 (65) | |||||||||||
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Hispanic or Latino | 2 (10) | 1 (5) | |||||||||||
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Not Hispanic or Latino | 18 (90) | 19 (95) | |||||||||||
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White | 17 (85) | 19 (95) | |||||||||||
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African American | 0 (0) | 0 (0) | |||||||||||
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Native American/Alaskan/Pacific Native | 2 (10) | 0 (0) | |||||||||||
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Other | 1 (5) | 1 (5) | |||||||||||
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Single | 2 (10) | 5 (25) | |||||||||||
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Married | 13 (65) | 13 (65) | |||||||||||
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Divorced | 5 (25) | 2 (10) | |||||||||||
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High school or less | 1 (5) | 2 (10) | |||||||||||
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Technical/associate/some college | 6 (30) | 7 (35) | |||||||||||
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Bachelor’s degree | 6 (30) | 7 (35) | |||||||||||
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Master’s degree | 4 (20) | 3 (15) | |||||||||||
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Doctoral degree | 3 (15) | 1 (5) | |||||||||||
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Full-time | 6 (30) | 9 (45) | |||||||||||
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Part-time | 2 (10) | 3 (15) | |||||||||||
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Retired | 11 (55) | 6 (30) | |||||||||||
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Unemployed | 0 (0) | 2 (10) | |||||||||||
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With a disability | 1 (5) | 0 (0) | |||||||||||
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$34,999 or less | 3 (15) | 4 (20) | |||||||||||
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$35,000 to $49,999 | 2 (10) | 3 (15) | |||||||||||
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$50,000 to $99,999 | 5 (25) | 10 (50) | |||||||||||
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$100,000 to $149,999 | 6 (30) | 3 (15) | |||||||||||
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Declined to state income | 4 (20) | 0 (0) | |||||||||||
Diabetes duration, mean (SD) | 30.9 (18.27) |
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Hypothyroidism | 10 (50) | N/Ac | |||||||||||
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Hypertension | 7 (35) | N/A | |||||||||||
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Dyslipidemia | 5 (25) | N/A | |||||||||||
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Gastrointestinal disease | 5 (25) | N/A | |||||||||||
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Retinopathy | 4 (20) | N/A | |||||||||||
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Neuropathy | 4 (20) | N/A | |||||||||||
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Depression | 4 (20) | N/A | |||||||||||
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Stroke | 3 (15) | N/A | |||||||||||
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Myocardial infarction | 2 (10) | N/A | |||||||||||
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Nephropathy | 2 (10) | N/A |
aRT-CGM: real-time continuous glucose monitoring.
bCP: care partner.
cN/A: not applicable.
The MoCA screening test showed that 45% (9/20) of people with diabetes had a MoCA score <26 (range 20-29), indicating MCI. Those with MCI had a mean age of 71.4 (SD 6) years and median diabetes duration of 30 (IQR 7-45) years, and the majority were White individuals (8/9, 89%) and male (6/9, 67%). Those without MCI had a mean age of 69.6 (SD 4) and median diabetes duration of 30 (IQR 20-45) years, and the majority were White individuals (9/11, 81%) and female (6/11, 56%). The majority of people with diabetes without MCI wore an insulin pump (8/11, 73%), had previously used CGM but were naïve to using the Follow app (8/11, 73%), and had Medicare insurance coverage (8/11, 73%). There were no differences in CGM glycemic data or the results of the satisfaction survey between those who completed the assessment and had a MoCA score in the MCI range and those who did not have a score in this range.
The initial SHARE
All participants were willing to share their hypoglycemia data, but only 55% (11/20) were willing to share their hyperglycemia data (
SHARE plus intervention data.
SHARE intervention components | Responses, n (%) | |
Agreement to share hypoglycemia data (yes) | 20 (100) | |
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Yes | 11 (55) |
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Maybe | 1 (0.5) |
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65-69 | 4 (20) |
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70-74 | 4 (20) |
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75-79 | 2 (10) |
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80-84 | 2 (10) |
People with diabetes willing to talk about glucose numbers with a CPa | 14 (70) | |
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“Are you okay” | 11 (55) |
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“Your sugar is low, what do you need” | 4 (20) |
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“What can I do to help” | 2 (10) |
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“Your blood sugar is low, you need to eat” | 3 (15) |
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Same alarms for CPs and people with diabetes | 6 (30) |
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Higher alarms for CPs | 5 (25) |
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CP turns alarms off for highs | 9 (45) |
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0 (immediately) | 6 (30) |
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5 | 8 (40) |
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10 | 2 (10) |
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15 | 2 (10) |
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20 | 1 (0.5) |
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Phone call | 12 (60) |
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SMS text messaging | 6 (30) |
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Phone call and SMS text messaging | 2 (10) |
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No data | 1 (0.5) |
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Call friend/family | 10 (50) |
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Come to the home of the person with diabetes | 6 (30) |
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Call emergency medical services | 4 (20) |
aCP: care partner.
People with diabetes spent the majority (median 62%) of their time in range (70-180 mg/dL) and had minimal time spent in the hypoglycemic range (median <1%). The SHARE
At the end of the third month, DQOL using CGM was measured. Broad improvement was noted for the perceived control domain (77% people with diabetes, 75% CP), hypoglycemia safety (74% people with diabetes, 63% CP), and interpersonal support (63% people with diabetes, 63% CP).
People with diabetes and their CPs reported high satisfaction with SHARE
Satisfaction survey responses for people with diabetes.
Question and theme for people with diabetes | Value, n (%) |
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Having someone else aware of glucose levels | 8 (40) |
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Having a partner work together | 4 (20) |
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Receiving help from care partner | 4 (20) |
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Partner can notice challenges | 2 (10) |
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Nothing | 10 (50) |
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Partner nagging or overreacting | 3 (15) |
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Highly recommend | 11 (55) |
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Take time to understand diabetes | 1 (5) |
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Nothing | 9 (45) |
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More education |
4 (20) |
aRT-CGM: real-time continuous glucose monitoring.
Satisfaction survey responses for care partners.
Question and theme for care partner | Value, n (%) | |
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Constantly being able to see the glucose numbers | 13 (65) | |
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Peace of mind knowing partner is alright | 7 (35) |
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Work as a team | 3 (15) |
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Nothing | 12 (60) |
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Not always accurate | 2 (10) |
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Scared with seeing lows | 2 (10) |
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Highly recommend | 10 (50) |
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Important to have a good relationship | 3 (15) |
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Have good communication established | 2 (10) |
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Nothing | 13 (65) |
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More education |
6 (30) |
aRT-CGM: real-time continuous glucose monitoring.
Exemplar satisfaction quotes from people with diabetes and their care partners.
Question | Exemplar quotes from people with diabetes | Exemplar quotes from care partners |
Likes about RT-CGMa data sharing |
“That if I am having a low blood sugar someone else is aware and can help if I need it” “Made us both aware of my situation and allowed us to work together on my progress and challenges” “He saved me by calling when I had a very low blood sugar” “She sees how challenging it is to maintain good control” |
“I liked being able to have instant access to her numbers” “It was comforting to know where his blood sugars were” “It is very helpful and allows us all as a family to suggest treatment decisions” |
Dislikes about RT CGM data sharing |
“I usually knew what was going on, was a little irritating to have him remind me” “They over-reacted” |
“Some inconsistencies between [meter] and CGM and variable times losing contact with sensor data” “I got scared a few times when he had lows and maybe I worried about him more than when I didn’t know” |
Recommendations for other people like you for RT-CGM with data sharing |
“I felt freedom and constant knowledge of glucose. Do it! Do it! Freedom” “Be patient and just know that your partner is looking out for you” “Diabetes is a roller coaster experience, it will take time to learn how to deal with it!” |
“Highly recommend the CGM and shared data, has helped the family dynamics (i.e., reducing anxiety and constant stress of asking ___ to check his blood sugars” “My husband is exceptional with no temper. It might be hard for some people if they didn’t have the right kind of relationship” “As long as there is already good communication and the [person with diabetes] is willing to take responsibility rather than making you their ‘blood sugar police,’ I think it can be great” |
Recommended |
“I need to know more about adjusting alarm sounds for highs” “A bit more training on the computer program that stores the results?” “The clarity apps are helpful, and produce a big picture of the complications of the disease, but they do not help much when I want to know how many units of insulin I need to drop or increase the reading by ‘x’ units” |
“Follow up every week” “….Talk about the [CGM] data over time” “Remember older people might forget certain things over time like” calibrating” CGM with [meter] blood glucose readings” “Could have used additional written instructions on how to install a new transmitter to the phone” |
aRT-CGM: real-time continuous glucose monitoring.
The majority of people with diabetes liked the CP support they received from data sharing. However, 3/20 (15%) individuals reported that CPs nagged or overreacted. The CPs liked the ability to see the data, which gave them peace of mind. A total of 2/20 (10%) CPs reported concerns about CGM accuracy. With regard to education, 4/20 (20%) people with diabetes and 6/20 (30%) CPs wanted more education. The majority of dyads recommended CGM with data sharing, and a few CPs cited the importance of having a good relationship and good communication skills.
There were 9/20 (45%) people with diabetes who were new to using CGM. Of these 9, 5 (56%) requested more education on insulin adjustments, changing sensors and alarms, and tracking events and alarms. Of the 11/20 (55%) people with diabetes who had previously worn the CGM device, only 3 (27%) requested more education on using Dexcom Clarity and adjusting alarm sounds for high glucose readings.
Of the 20 dyads, 18 (90%) were cohabiting. Of the remaining 2/20 (10%) CPs, 1 was a son and the other was a friend. The friend CP only had positive feedback on the satisfaction survey, but the son CP did not like “getting alarms at all hours” and wanted more education on how to “review past data and be able to do comparisons to see if things are getting worse or better.”
Our key recommendations based on these feasibility data are in
1. Overarching recommendations
Increase acceptability of data sharing
Increase willingness to share hyperglycemia and hypoglycemia data
Improve communication to decrease nagging and overreaching
Increase dyad diabetes education around diabetes self-management using CGM with Follow
Monitor SHARE plus behaviors in real time
2. Specific strategies
Coach dyads on the concept of sharing diabetes vs viewing diabetes as only the person with diabetes
Intensify the case for using the Follow app for hyperglycemia (teamwork, support, working together)
Provide 3-4 diabetes education sessions to address intensified problem-solving and communication strategies and dyadic self-management—management of hyper- and hypoglycemia
Measure SHARE plus agreement changes and how conflicts were addressed over time using ecologic momentary assessment methods
The SHARE
Similar to our study, both the WISDM and Diamond trials showed an average wear time of 6 days a week or greater during the study period [
Several key recommendations for future studies include intensifying the case for hyperglycemia data sharing (teamwork, support, working together). Additional recommendations include more diabetes education sessions that include communication and problem-solving strategies, glucose management, and the use of CGM software to track glucose trends. In another study, our team found that spouses understand how to assist with some diabetes-related recommendations, such as supporting hypoglycemia [
There are benefits and disadvantages of CP involvement in CGM data sharing. The positive aspects of CGM data sharing identified by people with diabetes included having someone else aware of glucose levels, working together with a partner on diabetes self-management, and receiving help from a partner. This type of collaboration likely occurs as the person with diabetes and their partners see T1DM as shared [
CPs often walk the line between trying to be supportive and being overbearing [
SHARE
This study is not without limitations. This study intended to examine the feasibility and therefore was not powered to detect significant changes across variables, and findings should be interpreted cautiously. A total of 16 people with diabetes declined to participate in this study because of lack of time or disinterest in research. This may be attributed to a disinterest in using CGM or involving CPs in diabetes management. However, there has been growing interest in CGM since CGM was covered by Medicare in 2017. Further exploration of people with diabetes’ disinterest in participating in a dyadic study is needed. This study lacked racial and ethnic diversity. Moreover, participants were highly educated. However, this feasibility study’s initial results suggest the need for a larger study with a more diverse sample and an assessment of technology literacy. Willingness of a person with diabetes to share hyperglycemia data or discuss glucose data was only assessed at baseline. This initial reaction may have changed either positively or negatively over time. Future studies should evaluate this willingness over time.
The results are promising in that they show that older adults with T1DM are open to sharing their glucose data with CPs and that CPs report benefits with assistance in communication and problem-solving strategies as to how to collaborate most effectively with people with diabetes. The benefits of such an intervention may become more important as older adults age and experience complications from lifelong diabetes, especially cognitive challenges that make self-management more challenging. The potential benefits of SHARE
continuous glucose monitoring
care partner
diabetes-related quality of life
mild cognitive impairment
Montreal Cognitive Assessment
type 1 diabetes
Wireless Intervention for Seniors with Diabetes Mellitus
This study was funded by the University of Utah Center on Aging. Dexcom, Inc provided some of the supplies in this study; however, Dexcom did not contribute to the study design, analysis, or results. The University of Utah Caregiver Initiative provided support for the interview transcriptions. All authors researched the data and contributed to the writing, review, and editing of the manuscript. NAA is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
MLL has received funding from Abbott for an investigator-initiated grant. There are no other potential conflicts of interest relevant to this article.