Background: Many validated patient-reported outcome measures (PROMs) evaluate activation, behaviors, and attitudes towards managing one’s own health and care. However, their association with utilization of diabetes management technology and glycemic control has not been well studied. Methods: People participating in Livongo’s RDMP for 12-18 months were surveyed using 3 validated PROMs that assessed activation (PAM), empowerment (DES), and distress (DDS). Pearson correlation was used to assess the relationship between PROMs and multivariable logistic regression was used to evaluate associations of PROM scores on RDMP usage and A1c improvement overall and by insulin use. Results: PAM and DES scores were most correlated with each other (r=0.48); neither were associated with A1c improvement. People with higher DES scores were 20% more likely to have high RDMP usage (p<0.05) and people with high RDMP usage were 4.4 times more likely to have A1c improvement. High distress (DDS17>=3) was negatively associated with A1c improvement. Conclusions: In this study, PAM was not associated with activation, behaviors, and attitudes influencing RDMP usage and outcomes, while the DES and DDS17 were associated with RDMP. Because of the impact of distress on outcomes, it should be monitored and RDMP interventions should focus on distress reduction. Disclosure M. Perez-Nieves: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. W. Lu: Employee; Self; Livongo Health. J. Poon: Employee; Self; Eli Lilly and Company. L. Fan: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. R. James: Advisory Panel; Self; AstraZeneca. Employee; Self; Livongo Health. B. Shah: Employee; Self; Livongo Health.
Background: Remote monitoring platforms (RMP) with personalized coaching continue to scale and have shown to be an effective tool for self-monitoring blood glucose (BG) and blood pressure (BP) for individuals with diabetes (DM) and hypertension (HTN). With HTN being a common risk factor for individuals with DM, it’s important to understand how RMPs can support individuals with both DM and HTN. Methods: Members were enrolled in a multi-chronic condition RMP with access to both DM and HTN programs. Using Propensity Score Matching to ensure no statistical differences in age, gender, race, DM type, insulin dependence, self-reported HbA1c, median zip code income, and zip code, two sample populations were constructed of those who were enrolled in DM only and DM+HTN between 1/1/2019 and 6/30/2019 and active on the platform for at least 1 year. For robust results, Bootstrapped Propensity Score Matching was used with 2,000 iterations. Paired t-test was used to compare reduction in estimated a1c at 6- and 9-months. Due to COVID-19, outcomes at 12-months were not evaluated. Results: No statistical differences between DM only (n=888) and DM+HTN (n=888) groups in age (56 vs. 56), gender (50% vs. 48% female), race (59% vs. 62% Caucasian), diabetes type (95% vs. 95% T2DM), insulin dependent (24% vs. 27%), started uncontrolled (49% vs. 50%), and self-reported HbA1c at time of registration (7.3% vs. 7.3%). Average estimated HbA1c reductions at 6 months were -0.46% for DM only and -0.65% for DM+HTN (p = 0.011). Reduction was similar at 9-month though no longer statistically different with values of -0.42% vs. -0.57%, respectively. Conclusions: Members enrolled in both DM+HTN versus DM only had improved glucose control within 6-months indicating RMPs that support multiple chronic conditions may have better outcomes. Future study is required to better understand the drivers of the clinical improvement for multiple conditions. Disclosure S. Liu: Employee; Self; Livongo, Teladoc Health. S. L. Painter: Employee; Self; Livongo. R. James: Employee; Self; Livongo, Teladoc Health, Stock/Shareholder; Self; Livongo, Teladoc Health. T. Kompala: Consultant; Self; Eli Lilly and Company, Employee; Self; Livongo. B. Shah: Employee; Self; Teladoc Health, Stock/Shareholder; Self; Teladoc Health.
No abstract
Background: Predictive models to identify people with diabetes mellitus (DM) at high-risk for future ED visits and inpatient admissions (IA) are an area of clinical interest. The models do not include self-monitoring blood glucose (SMBG) levels. Livongo, the leader in Applied Health Signals, includes cellular-enabled BG meter that allows instantaneous uploading of SMBG values into the cloud with millions of values in people with DM. Methods: Leveraging data collected via the Livongo program with medical and pharmacy claims, machine learning techniques were used to identify the 25 most predictive variables of severe hypo- and hyperglycemia (BG ≤54 mg/dL and ≥400 mg/dL, respectively) resulting in an ED or IA encounter within three days. Four models were constructed for DM (type 1 and type 2) and encounter (ED and IA). Participants had to be enrolled for at least 12 months with continuous medical benefits eligibility for 24 months. Variable categories included in modeling were demographics, comorbidities based on ICD-10 codes, prior HCU, new and current medications, 30-days SMBG patterns with mean BG levels, and months on program. Area under the curve (AUC) was used to assess model performance. Results: There were 7,633 people selected. They had a mean age of 54 years, 48% were female, and 11% had type 1 DM. In this group, 924 and 1,518 severe hypo- and hyperglycemic with ED or IA encounters occurred. Random forest models had the highest AUC with values greater than 98% and sensitivity and specificity above 93% and 99%, respectively. HCU variables were the most predictive variables in all 4 models. Mean 7-day BG level, 30-day count of BG checks, and before-breakfast checking were also highly predictive. Conclusions: SMBG variables are independent predictors of hypo- and hyperglycemia with ED and IA encounters. Real-time BG remote monitoring programs have the capability to identify people at high-risk of costly HCU and develop interventions to improve care. Disclosure W. Lu: Employee; Self; Livongo Health. R. James: Advisory Panel; Self; AstraZeneca. Employee; Self; Livongo Health. S.L. Painter: Employee; Self; Livongo Health. B. Shah: Employee; Self; Livongo Health.
Background: Diabetes is thought to have a bi-directional relationship with mental health, including stress. Therefore, it is increasingly important to understand stress of patients with diabetes in real-time. Despite extensive use and success of RDMPs, there is limited psychometric research on the development of real-time measures of stress in the context of RDMPs. The objective of this study is to examine the validity of a stress tag among participants in an RDMP. Methods: Participants in the study (ages 8-92) were enrolled in Livongo for Diabetes, an RDMP offering a glucometer that prompts user to endorse a feeling-tag to provide behavioral context to BG readings. Participants also received smartphone surveys at baseline, 6 months, and 12 months containing a two-item Diabetes Distress Scale SF. Repeated BG and stress data from the first 12 months of the program were aggregated to 3-month windows surrounding the smartphone surveys. Two-part generalized linear mixed models were used to test association between stress tags and DDS-SF. Results: Participants (N=470) were, on average, 59.69 years old, 50% female, and 80% White, with 91% reporting type 2 diabetes and 36% reporting insulin usage. Two-part models revealed that each additional point on the DDS-SF scale was associated with a 46% increased likelihood (95% CI: 1.23-1.75) of any stress tag at that time point (z=4.25, p<0.001). When stress tags were endorsed, each additional point on the DDS-SF scale was associated with a 14% (95% CI: 1.04-1.26) increase in the proportion of stress tags for that time period (z=2.72, p<0.01). Conclusions: The study found that a stress tag during glucometer use was associated with DDS-SF. In the context of an RDMP, a stress indicator allows for real-time data analysis, and thus a better understanding of a patient's experience with their disease. Future studies may seek to understand the extent to which such stress measures can be used to predict lapses in medication adherence. Disclosure E. Dzubur: Employee; Self; Livongo, Teladoc Health, Stock/Shareholder; Self; Livongo, Teladoc Health. L. Fan: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company, Stock/Shareholder; Spouse/Partner; Eli Lilly and Company. R. James: Employee; Self; Livongo, Teladoc Health, Stock/Shareholder; Self; Livongo, Teladoc Health. W. Lu: Employee; Self; Teladoc Health. E. Meadows: Employee; Self; Eli Lilly and Company, Employee; Spouse/Partner; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company, Stock/Shareholder; Spouse/Partner; Eli Lilly and Company. M. Perez-nieves: Employee; Self; Eli Lilly and Company. B. Shah: Employee; Self; Teladoc Health, Stock/Shareholder; Self; Teladoc Health.
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