Care Partner-Assisted Diabetes Self-Management Through Linking Continuous Glucose Monitoring With Mobile Health: Improving Outcomes for Older Adults With Mild Cognitive Impairment (CP-CGMH)

Care Partner-Assisted Diabetes Self-Management Through Linking Continuous Glucose Monitoring With Mobile Health: Improving Outcomes for Older Adults With Mild Cognitive Impairment

CP-CGMH: Care Partner-Assisted Diabetes Self-Management Through Linking Continuous Glucose Monitoring With Mobile Health: Improving Outcomes for Older Adults With Mild Cognitive Impairment

Eligible older adults with Type 2 Diabetes-Mild Cognitive Impairment (T2D-MCI) will be provided a Continuous Glucose Monitoring (CGM) device and asked to share CGM data with their care partners for daily decision-making for diabetes self-management. After 2 weeks, individual interviews will be conducted in 20 participants (10 dyads). Older adults with T2D-MCI (n=10) and their care partners (n=10) will be interviewed separately to identify key features of the Care Partner-Assisted Intervention through linking continuous glucose monitoring and Mobile Health (CP-CGMH) app.

CGM sensors, measuring glucose levels every 1-5 minutes, can identify hyper- or hypoglycemic episodes that might not be captured by the routine glucose monitoring using finger stick. CGM also enables sharing real-time glucose values and visual trends with care partners as well as provides alerts and alarms when glucose levels are extremely high or low. These visual and alert cues can be used to inform and empower self-management decision making as well as motivate and engage self-management behavior changes (e.g., adjust insulin dose, reduce high-carbohydrate food intake, and increase exercise), which may address the unique issue of diabetes management caused by cognitive impairment for older adults with T2D-MCI. The investigators will use FDA cleared FreeStyle Libre Glucose Monitoring System (hereon referred to as the 'FreeStyle Libre System or 'System'), which is an integrated continuous glucose monitoring system (iCGM) that provides continuous glucose measurements every minute to provide glucose levels, trends and alerts. The use of this device provides ambulatory glucose profiles, giving graphic and quantitative information on 24-hour glucose patterns. It does not require finger-prick testing for calibration. The system consists of a reader and a sensor (35 mm x 5 mm). The sensor is applied to the back of a person's arm. The sensor automatically measures interstitial glucose at 1-minute intervals during daily activities like work, sleep, eating, and exercise. It is able to store blocks of glucose data for 14 days. Unlike previous versions, with this device, patients are not required to perform finger-stick blood glucose monitoring for calibration. Patients can use the reader or their personal smartphone to scan the CGM sensor to get the real-time glucose values. After 2- week wearing, the participants will return to the research lab (final assessment) to download the CGM report from the LibreView. The data can be viewed as graphical data and summary statistics, or as numerical data. The data report will be de-identified by the study's appointed trained RA before the data are used for analysis. The manufacturer of the device was not involved in the funding, the design or the interpretation of the study.

CP-CGMH: Care Partner-Assisted Intervention through linking continuous glucose monitoring and Mobile Health. Participants will receive a CGM device and asked to share CGM data with their care partners for daily decision-making for diabetes self-management for two weeks. The LibreLinkup mHealth app will be used to share data.

FDA-cleared FreeStyle Libre Glucose Monitoring System manufactured by Abbott. Integrated continuous glucose monitoring system that provides continuous glucose measurements every minute to provide glucose levels, trends and alerts.

13-item questionnaire assessing participants' acceptance of the CGM technology. Items ranked on 7-point Likert scale ranging from 1-7. The total score is the sum of responses and ranges from 13 to 91; lower scores indicate greater overall acceptance.

Patients can use the reader or their personal smartphone to scan the CGM sensor to get the real-time glucose values.

Inclusion Criteria: Older adults must meet all of the following criteria: be ≥ 65 years old; have had a diagnosis of T2D for at least one year; have MCI, defined as score 19-25 on the Montreal Cognitive Assessment (MoCA);60,61 have a care partner (e.g., spouse or adult children); be willing to use CGM; be fluent in English. Eligible care partners must be: ≥ 18 years old; living with older adults with T2D-MCI; willing to be involved in patients' daily care; fluent in English. Exclusion Criteria: Older adults who meet any of the following criteria will be excluded from participation in this study: Refuse or are unable to provide informed consent; Have serious physical illness (e.g., end-stage renal disease); Presence of mental illness (e.g., schizophrenia or bipolar disorder) that would preclude participation. The care partners will be excluded if they: Refuse or are unable to provide informed consent; Have MCI.

The de-identified participant data from the final research dataset used in the published manuscript will be shared upon reasonable request beginning 9 months and ending 36 months following article publication or as required by a condition of awards and agreements supporting the research provided the investigator who proposes to use the data executes a data use agreement with NYU Langone Health. Requests may be directed to: Yaguang.zheng@nyu.edu. The protocol and statistical analysis plan will be made available on Clinicaltrials.gov only as required by federal regulation or as a condition of awards and agreements supporting the research.

Beginning 9 months and ending 36 months following article publication or as required by a condition of awards and agreements supporting the research.

The investigator who proposed to use the data will have access to the data upon reasonable request. Requests should be directed to Yaguang.zheng@nyu.edu. To gain access, data requestors will need to sign a data access agreement.