Type 1 diabetes (T1D) is a significant public health problem, with increasing numbers of adults now living into late adulthood [1]. T1D self-management requires a number of daily diabetes tasks including checking blood glucose (either through a blood glucose meter or a continuous glucose monitor [CGM]) and administering insulin to account for changes in food intake and exercise. Older adults with T1D are at increased risk for hypoglycemia, hyperglycemia, and glucose variability that may result in seizures, falls, and myocardial infarctions [2,3]. These risks are due to a number of factors including reduced awareness of hypoglycemic warning symptoms; reduced hormonal counterregulatory response; and changes in dexterity, visual acuity, cognitive function, depression, and anxiety that may prevent affected individuals from taking corrective actions [3,4].

Care partners (CPs; eg, spouse, adult child, romantic partner, and friend) can serve as important resources for older adults living with T1D in reducing these risks to self-management [5-9]. Over half of the adults with diabetes have an unpaid CP who regularly assists with diabetes management [8], and age-related changes often increase persons with diabetes’ need for CP assistance. One tool that is available to persons with diabetes and their CPs to reduce harmful glucose levels is real-time CGM. A CGM transmits glucose trend data to the smartphone of persons with diabetes and provides predictive alarms before problematic and potentially dangerous hypoglycemia and hyperglycemia levels [10]. Recommendations for standards of care in diabetes [11] now include the use of CGM with older adults with T1D to address increased risks of hypoglycemia. Persons with diabetes are given glucose goals of 70-180 mg/dL, and the percentage of time spent within this range is called time-in-range (TIR), an important metric for evaluating glucose targets [12,13]. In older adults, CGM is effective at decreasing glucose variability, with older adults highly adherent to wearing CGMs [14,15]. The data provided by the CGM can now be shared with a CP’s smartphone to facilitate diabetes support for the person with diabetes [16]. Yet, few older adults and CPs are sharing data with CGMs [17].

There is great potential for CGM data sharing to increase CP involvement in a way that improves glucose targets and reduces distress for persons with diabetes. For instance, the free data-sharing app, Dexcom Follow [18], allows CGM readings to be displayed on the smartphone or smartwatch of persons with diabetes and their selected CPs. Follow also allows the user to see CGM glucose levels and receive predictive hypoglycemia and hyperglycemia alerts. Additional apps such as the free Dexcom Clarity app [19] use the data from CGM to create glucose data reports to allow persons with diabetes and their selected CPs to identify glucose patterns and trends and has also been associated with a greater quality of life (QoL) when reports are viewed with a CP [14]. However, few older adults share their CGM data or review clarity reports with a CP. Data from the Wireless Innovation for Seniors with Diabetes Mellitus trial showed that only 9.2% of older adults with T1D used data sharing, and only 32% used their smartphones to monitor their CGM data [15]. In our preliminary studies, older adults with diabetes reported an increased perceived need to have others monitor their glucose levels for safety purposes [20], but few persons with diabetes are adopting this technology.

This intervention was designed to maximize the use of data sharing by persons with diabetes and their CPs. Our prior work identified several barriers to using data sharing involving dyadic communication that involved patients’ and partners’ different expectations regarding family involvement [20]. Persons with diabetes frequently regarded diabetes as “their own illness,” whereas spouses viewed the illness as more shared [21,22]. Improving collaboration and communication among those with type 2 diabetes [23] was associated with lower distress in persons with diabetes and their partners, higher satisfaction, and improved glycemic levels (among those with moderately elevated hemoglobin A_1c [HbA_1c]).

The intervention is guided by the Dyadic Coping Model (Figure 1) [24,25], a widely used framework for how individuals with chronic illness and CPs can be involved in chronic illness management. As applied to diabetes, dyadic coping involves two components: appraisal of the illness and coping efforts. Appraisal refers to whether the persons with diabetes and their CPs perceive diabetes as “our” problem versus “my” or “your” problem. Shared appraisal can initiate and facilitate coping strategies that are more collaborative to address diabetes stressors that are also perceived as shared [26]. When a person with diabetes appraises diabetes as an illness that is shared with a CP, persons with diabetes report more collaborative involvement from their spouse, greater relationship satisfaction, and less regimen distress [22]. Challenges can arise when one person in a dyad (eg, the person with diabetes) appraises diabetes as their own issue to deal with and the CP engages in high levels of involvement (eg, wishes to be very involved in data sharing). That is, collaborative involvement of the CP may be detrimental when the person with diabetes views diabetes as only their illness to deal with and does not consider its effects on the CP. The Dyadic Coping Model supports the value of a CP’s collaborative involvement in a person with diabetes’ glucose monitoring via CGM.

The Share plus intervention framed by the Dyadic Coping Model begins with a discussion of how persons with diabetes and CPs appraise diabetes (eg, patients alone or shared with CP) and the next communication regarding the preferred ways of involving the CP in data sharing (Figure 1). For instance, the person with diabetes is asked what words would be helpful and supportive from their CPs in response to a high or low glucose alarm and what might be viewed as critical or overprotective. Next, CPs are asked if this communication is acceptable to them and if they are willing to provide this requested support. In our preliminary studies, persons with diabetes who were initially hesitant to share their glucose data reported high satisfaction at 12 weeks [29,30]. These discussions facilitate an understanding of diabetes as a shared illness and the collaborative and supportive strategies that the CP can engage in. Moreover, these discussions match data-sharing strategies that fit with a person with diabetes’ appraisal of diabetes and are viewed as supportive rather than unsupportive. Such discussions will facilitate an action plan that will best align with the appraisal process of the person with diabetes and their CP.

This study aims to (1) evaluate the feasibility, usability, and acceptability of the Share plus intervention compared to Follow plus diabetes self-management education (DSME); (2) evaluate the effect of the Share plus intervention on TIR (primary outcome) and diabetes distress (secondary outcome); and (3) explore differences between groups in person with diabetes and CP dyadic appraisal and coping, QoL, diabetes self-care, and CP burden at 12 and 24 weeks and associations of dyadic variables on outcomes.

This study will use a 1:1 randomized controlled trial to conduct a pilot test of 80 dyads to compare the Share plus intervention to the Follow plus DSME intervention. The trial will include a 12-week active intervention to determine the change in primary (TIR) and secondary (diabetes distress) outcomes, followed by a 12-week, observation-only phase to examine maintenance effects. The SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) guidelines were used in reporting this study [31].

This clinical trial will be conducted in Utah, and participants will be recruited at the state level and the national level using telehealth.

Persons with diabetes and CPs are actively being recruited from 3 major health care systems in Utah in which we have identified champions who have assisted with recruitment in our preliminary studies. The University of Utah Enterprise Data Warehouse will be used to identify eligible persons with diabetes. An opt-out strategy will be used using letters, postcards, emails, and SMS text messages. Additionally, the Electronic Data Warehouse will be used to build a recruitment dashboard allowing the research team to reach out to contact high-volume providers to inform them of the study and to recruit participants in person before or after clinic visits.

Recruitment inside and outside of Utah will include (1) digital marketing strategies and (2) diabetes clinician and provider networks. Digital marketing strategies include posting to T1D social media groups. Professional groups, such as the Association of Diabetes Care and Education Specialists (ADCES), will also be contacted to post information and digital flyers about the study. We are committed to reaching racial or ethnic minorities at rates at least equivalent to their proportion in our catchment area population [32] as required by National Institutes of Health policy [33] and by our own strong commitment to principles of equity and social justice.

Participants with diabetes will be eligible if they (1) are aged 60 years or older, (2) have a T1D diagnosis (may add more detail here), (3) are currently using CGM, (4) have HbA_1c ≥7.5 and ≤11%, (5) are able to read and write in either English or Spanish, (6) are able to manage diabetes with respect to insulin administration and glucose monitoring (which may include assistance from a CP), and (6) are naïve to using the Follow app and are willing to use the Follow app. Participants with or without an insulin pump are eligible. Participants will be excluded if they have (1) a life expectancy estimated at <1 year, (2) extreme visual or hearing impairment that would hinder the ability to use CGM, (3) stage 4 or 5 renal disease, (4) a history of psychiatric or psychosocial issues that could limit adherence to required study tasks, or (5) a Montreal Cognitive Assessment score <19 indicating moderate to severe dementia.

CP participants will be eligible if (1) the participant with diabetes identifies them, (2) they are aged 18 years or older, (3) they are able to read and write in either English or Spanish, and (4) they are willing to participate in a data-sharing telehealth intervention study with the participant with diabetes. CPs will be excluded if they have a self-reported diagnosis of moderate or severe dementia or other medical conditions that make it inappropriate or unsafe to fulfill the role of a CP. CPs will not be directly recruited.

Persons with diabetes (n=40) and CPs (n=40) who meet the study criteria and agree to participate will be asked to sign an informed consent with a digital signature. The persons with diabetes will be randomly assigned to 1 of 2 groups (40 dyads in the Share plus intervention group and 40 dyads in the Follow plus DSME group) using a computer-generated schedule of randomly permuted blocks of sizes 2, 4, and 6 to minimize knowledge of the next participant’s assignment. Group allocation will be executed in REDCap (Research Electronic Data Capture; Vanderbilt University).

Feasibility, usability, and acceptability measures will be collected throughout the study, both quantitatively and qualitatively. Feasibility for this study is operationalized to include the ability to recruit the target population, retention of participants, participant adherence to the study protocol, CDCES fidelity in delivering the intervention, and the intervention dose provided. Our team will measure intervention fidelity as a continuous process to ensure that intervention components are delivered in a standardized way through in-depth training and standardized protocols. This will include a 10% review of Zoom intervention and control sessions conducted by 2 CDCESs. Data will be collected and reviewed in an ongoing basis for attendance, adherence to the intervention script, delivery of assigned topics, and self-report of intervention delivery and completion per study protocols. The research team will use an observation form to collect intervention fidelity data on a 4-point scale (from 0=none to 3=high level of intervention fidelity) to monitor the intervention sessions. This observation form will be used to counsel CDCESs and to monitor for any intervention delivery problems. Usability includes measures of processes to complete the intervention and a measure used to examine the reliability and validity of the telehealth and Share plus technology intervention. Process measures include appointment attendance, length of all sessions, number of unscheduled appointments for extra assistance, and number of telephone calls for support by the persons with diabetes or CPs. Engagement data include persons with diabetes and CPs’ frequency of viewing glucose data and responding to alarms; intent to continue using Follow; and a tailored System Usability Scale, to assess reliability or validity of the telehealth and Share plus technology intervention and to determine usability between groups and against System Usability Scale benchmarks. Acceptability will include persons with diabetes and CPs’ satisfaction with the Share plus intervention. Acceptability will be assessed using a 7-item Likert scale and 4 open-ended questions developed in our preliminary studies.

The 3 Share plus sessions will be conducted on HIPAA-compliant Zoom and recorded and transcribed using the meeting transcription feature. Dyadic data will be collected qualitatively in our intervention feasibility data and quantitatively. The qualitative feasibility intervention data includes collaborative involvement, cooperative actions, challenges, supportive or critical communication, problem-solving discussions regarding hyperglycemia and hypoglycemia, solutions discussed then implemented, unsuccessful and successful solutions, and barriers to protocol completion. Quantitative measures usability measures include process measures such as appointment attendance, length of all sessions, number of unscheduled appointments for extra assistance, and number of telephone calls for support by the persons with diabetes and CPs. Engagement data include persons with diabetes and CP’s reported frequency of viewing glucose data, responding to alarms, and intent to continue using the Follow app. Other quantitative measures will be available at baseline and at 12 and 24 weeks, including interpersonal processes such as diabetes appraisal [22], social support [34], relationship quality [35], diabetes self-care [36], CP burden [37], QoL [38], and percentage of protocol completion. The primary outcomes are TIR and diabetes distress [39] and partner diabetes distress [40]. CGM data will be collected from the Clarity reports for 14 days at each time point (Clarity website stores data continuously). Other descriptive measures include glycemic metrics from CGM data [41].

All data will be entered into the REDCap database, with data storage on a secure, dedicated research server. CGM data will be collected from the Clarity reports for 14 days each at baseline, 3 months, and 6 months. All individuals involved in screening and data entry will be trained to use the database. The research team and the principal investigator will review entered data for completeness and accuracy.

The study procedures were approved by the University of Utah Institutional Review Board (NIDDK Share plus: Continuous Glucose Monitoring with Data Sharing in Older Adults with T1D and Their Care Partners, 00160673). Consistent with best practices in research, informed consent will be collected from the participants, and they have the ability to opt out at any time. All data will be deidentified to protect the privacy of participants. Participants will be compensated US $50 at baseline, 12 weeks, and 24 weeks.

The expected outcome is that the Share plus intervention will have a clinically significant intervention signal (increased TIR and lower diabetes distress) indicating readiness for a fully powered trial. The Share plus intervention examines dyadic support for using CGM with data sharing in older adults with T1D. The clinical promise of CP data sharing to improve diabetes management and glycemic levels, and thus, reduce complications, will be greatly enhanced through an intervention that removes barriers and strengthens dyadic communication and support for the growing number of older adults with diabetes.

In this randomized controlled trial, we will evaluate feasibility and acceptability. By leveraging the full potential of technology and CP interventions, we may optimize the support that CPs can provide for effective glucose management in older adults with T1D.