Association Between Weight Loss and Glycemic Outcomes: A Post Hoc Analysis of a Remote Patient Monitoring Program for Diabetes Management.

Background: Evidence-based guidelines for the management of type 2 diabetes (T2D) consist of blood glucose monitoring, medication adherence, and lifestyle modifications that may particularly benefit from reminders, consultation, education, and behavioral reinforcements through remote patient monitoring (RPM). Objectives: To identify predictors of weight loss and to examine the association between weight loss and hemoglobin A1C (HbA1C) outcomes for T2D patients who were enrolled in an RPM program for diabetes management. Materials and Methods: The study applied logistic and ordinary least-squares regression models to examine the relationship between baseline characteristics and the likelihood of weight loss during the RPM, and how the magnitude of weight loss was related to changes in HbA1C outcomes for 1,103 T2D patients who went through 3 months of RPM from 2014 to 2017. Results: Older patients were 3% more likely to have weight loss (odds ratio [OR], 1.03; 95% confidence interval [CI], 1.02-1.05), whereas patients with higher baseline HbA1C had 9% reduced odds (OR, 0.91; 95% CI, 0.85-0.97) of experiencing weight loss. For every pound of weight lost, there was a 0.02-point (95% CI, 0.01-0.03) reduction on the HbA1C measured at the end of the RPM. Moreover, compared with those who had weight loss of ≤3%, participants who had lost 5-7%, or >7% of their baseline weight had a 0.37- and 0.58-point reduction in HbA1C, respectively. Conclusions: This study revealed a notable relationship between weight loss and positive HbA1C outcomes for T2D patients in an RPM-facilitated diabetes management program, which pointed to the potential of integrating evidence-based lifestyle modification programs into future telemedicine programs to improve diabetes management outcomes.

Diabetes Management Through Remote Patient Monitoring: The Importance of Patient Activation and Engagement with the Technology.

Background: The documented efficacy and promise of telemedicine in diabetes management does not necessarily mean that it can be easily translated into clinical practice. An important barrier concerns patient activation and engagement with telemedicine technology. Objective: To assess the importance of patient activation and engagement with remote patient monitoring technology in diabetes management among patients with type 2 diabetes. Methods: Ordinary least squares and logistic regression analyses were used to examine how patient activation and engagement with remote patient monitoring technology were related to changes in hemoglobin A1c (HbA1c) for 1,354 patients with type 2 diabetes monitored remotely for 3 months between 2015 and 2017. Results: Patients with more frequent and regular participation in remote monitoring had lower HbA1c levels at the end of the program. Compared to patients who uploaded their biometric data every 2 days or less frequently, patients who maintained an average frequency of one upload per day were less likely to have a postmonitoring HbA1c > 9% after adjusting for selected covariates on baseline demographics and health conditions. Conclusions: Higher levels of patient activation and engagement with remote patient monitoring technology were associated with better glycemic control outcomes. Developing targeted interventions for different groups of patients to promote their activation and engagement levels would be important to improve the effectiveness of remote patient monitoring in diabetes management.

Effects of a digital diabetes prevention program on cardiovascular risk among individuals with prediabetes.

OBJECTIVE: To examine changes in cardiovascular disease (CVD) risk outcomes of overweight/obese adults with prediabetes. METHODS: Using data from a randomized control trial of digital diabetes prevention program (d-DPP) with 599 participants. We applied the atherosclerotic CVD (ASCVD) risk calculator to predict 10-year CVD risk for d-DPP and small education (comparison) groups. Between-group risk changes at 4 and 12 months were compared using a repeated measures linear mixed-effect model. We examined within-group differences in proportion of participants over time for specific CVD risk factors using generalized estimating equations. RESULTS: We found no differences between baseline 10-year ASCVD risk. Relative to the comparison group, the d-DPP group experienced greater reductions in predicted 10-year ASCVD risk at each follow-up visit and a significant group difference at 4 months (-0.96%; 95% confidence interval: -1.58%, -0.34%) (but not at 12 months). Additionally, we observed that the d-DPP group experienced a decreased proportion of individuals with hyperlipidemia (18% and 16% from baseline to 4 and 12 months), high-risk total cholesterol (8% from baseline to 12 months), and being insufficiently active (26% and 22% from baseline to 4 and 12 months at follow-up time points. CONCLUSIONS: Our findings suggest that a digitally adapted DPP may promote the prevention of cardiometabolic disease among overweight/obese individuals with prediabetes. However, given the lack of maintenance of effect on ASCVD risk at 12 months, there may also be a need for additional interventions to sustain the effect detected at 4 months.

Reach Outcomes and Costs of Different Physician Referral Strategies for a Weight Management Program Among Rural Primary Care Patients: Type 3 Hybrid Effectiveness-Implementation Trial.

BACKGROUND: Rural residents are at high risk for obesity; however, little resources exist to address this disproportional burden of disease. Primary care may provide an opportunity to connect primary care patients with overweight and obesity to effective weight management programming. OBJECTIVE: The purpose of this study is to examine the utility of different physician referral and engagement processes for improving the reach of an evidence-based and technology-delivered weight management program with counseling support for rural primary care patients. METHODS: A total of 5 rural primary care physicians were randomly assigned a sequence of four referral strategies: point-of-care (POC) referral with active telephone follow-up (ATF); POC referral, no ATF; a population health registry-derived letter referral with ATF; and letter referral, no ATF. For registry-derived referrals, physicians screened a list of patients with BMI ≥25 and approved patients for participation to receive a personalized referral letter via mail. RESULTS: Out of a potential 991 referrals, 573 (57.8%) referrals were made over 16 weeks, and 98 (9.9%) patients were enrolled in the program (58/98, 59.2% female). Differences based on letter (485/991, 48.9%) versus POC (506/991, 51.1%) referrals were identified for completion (100% vs 7%; P<.001) and for proportion screened (36% vs 12%; P<.001) but not for proportion enrolled (12% vs 8%; P=.10). Patients receiving ATF were more likely to be screened (47% vs 7%; P<.001) and enrolled (15% vs 7%; P<.001) than those not receiving ATF. On the basis of the number of referrals made in each condition, we found variations in the proportion and number of enrollees (POC with ATF: 27/190, 50%; POC no ATF: 14/316, 41%; letter ATF: 30/199; 15.1%; letter no ATF: 27/286, 9.4%). Across all conditions, participants were representative of the racial and ethnic characteristics of the region (60% female, P=.15; 94% White individuals, P=.60; 94% non-Hispanic, P=.19). Recruitment costs totaled US $6192, and the overall recruitment cost per enrolled participant was US $63. Cost per enrolled participant ranged from POC with ATF (US $47), registry-derived letter without ATF (US $52), and POC without ATF (US $56) to registry-derived letter with ATF (US $91). CONCLUSIONS: Letter referral with ATF appears to be the best option for enrolling a large number of patients in a digitally delivered weight management program; however, POC with ATF and letters without ATF yielded similar numbers at a lower cost. The best referral option is likely dependent on the best fit with clinical resources. TRIAL REGISTRATION: ClinicalTrials.gov NCT03690557; http://clinicaltrials.gov/ct2/show/NCT03690557.

Using a population health management approach to enroll participants in a diabetes prevention trial: reach outcomes from the PREDICTS randomized clinical trial.

Population health management (PHM) strategies to address diabetes prevention have the potential to engage large numbers of at-risk individuals in a short duration. We examined a PHM approach to recruit participants to a diabetes prevention clinical trial in a metropolitan health system. We examined reach and representativeness and assessed differences from active and passive respondents to recruitment outreach, and participants enrolled through two clinical screening protocols. The PHM approach included an electronic health record (EHR) query, physician review of identified patients, letter invitation, and telephone follow-up. Data describe the reach and representativeness of potential participants at multiple stages during the recruitment process. Subgroup analyses examined proportional reach, participant differences based on passive versus active recruitment response, and clinical screening method used to determine diabetes risk status. The PHM approach identified 10,177 potential participants to receive a physician letter invitation, 60% were contacted by telephone, 2,796 (46%) completed telephone screening, 1,961 were eligible from telephone screen, and 599 were enrolled in 15 months. Accrual was unaffected by shifting clinical screening protocols despite the increase in participant burden. Relative to census data, study participants were more likely to be obese, female, older, and Caucasian. Relative to the patient population, enrolled participants were less likely to be Black and were older. Active respondents were more likely to have a higher income than passive responders. PHM strategies have the potential to reach a large number of participants in a relatively short period, though concerted efforts are needed to increase participant diversity.

Program completion and glycemic control in a remote patient monitoring program for diabetes management: Does gender matter?

AIMS: To examine gender differences in program completion and glycemic outcomes for patients with type 2 diabetes (T2D) in a remote patient monitoring (RPM) program for diabetes management. METHODS: Based on data from an RPM program that enrolled post-discharge T2D patients (n = 1645) in 2014-2017, logistic regression models were estimated to assess gender difference in the likelihood of completing the three-month RPM program; whereas ordinary least squares (OLS) regression models were used to examine gender difference in post-RPM hemoglobin A1c (HbA1c), controlling for demographics, baseline health status, including HbA1c, patient activation scores, and physiological data upload frequency for patients who had completed the program. RESULTS: Among enrolled participants, men had lower odds of completing the three-month RPM program than women (adjusted odds ratio, 0.61; 95% confidence interval [CI], 0.39-0.95). However, among those who completed the program, men had lower post-RPM HbA1c than women (-0.18; 95% CI, -0.33, -0.03) after controlling for baseline HbA1c and other covariates. CONCLUSIONS: While female patients with T2D were more likely to complete the RPM program, they showed a higher glycemic level at the end of the program compared to male patients. To close gender disparities in health, interventions through telemedicine tailored towards women's diabetes outcomes and men's engagement level are warranted.

Preventing diabetes with digital health and coaching for translation and scalability (PREDICTS): A type 1 hybrid effectiveness-implementation trial protocol.

BACKGROUND: Diabetes prevention remains a top public health priority; digital approaches are potential solutions to existing scalability and accessibility challenges. There remains a gap in our understanding of the relationship between effectiveness, costs, and potential for sustained implementation of digital diabetes prevention strategies within typical healthcare settings. PURPOSE: To describe the methods and design of a type 1 hybrid effectiveness-implementation trial of a digital diabetes prevention program (DPP) using the iPARIHS and RE-AIM frameworks. METHODS: The trial will contrast the effects of two DPP interventions: (1) small group, in-person class, and (2) a digital DPP consisting of small group support, personalized health coaching, digital tracking tools, and weekly behavior change curriculum. Each intervention includes personal action planning with a focus on key elements of the lifestyle intervention from the CDC National DPP. Adults at risk for diabetes (BMI ≥25 and 5.7% ≤ HbA1c ≤ 6.4) will be randomly assigned to either the intervention group (n = 241) or the small group (n = 241). Assessment of primary (HbA1c) and secondary (weight loss, costs, cardiovascular risk factors) outcomes will occur at baseline, 4, and 12 months. Additionally, the trial will explore the potential for future adoption, implementation, and sustainability of the digitally-based intervention within a regional healthcare system based on key informant interviews and assessments of organizational administrators and primary care physicians. CONCLUSION: This trial of a digital DPP will allow the research team to determine the relationships between reach, effectiveness, implementation, and costs.

Remote Patient Monitoring and Clinical Outcomes for Postdischarge Patients with Type 2 Diabetes.

The objective of this study was to evaluate changes in clinical outcomes for patients with type 2 diabetes (T2D) after a 3-month remote patient monitoring (RPM) program, and examine the relationship between hemoglobin A1c (HbA1c) outcomes and participant characteristics. The study sample included 955 patients with T2D who were admitted to an urban Midwestern medical center for any reason from 2014 to 2017, and used RPM for 3 months after discharge. Clinical outcomes included HbA1c, weight, body mass index (BMI), and patient activation scores. Logistic regression was used to estimate the likelihood of having a postintervention HbA1c <9% by patient characteristics, among those who had baseline HbA1c >9%. Most patients experienced decreases in HbA1c (67%) and BMI (58%), and increases in patient activation scores (67%) (P < 0.001 in all 3 cases) at the end of RPM. Logistic regression analyses revealed that among patients who had HbA1c >9% at baseline, men (odds ratio [OR] = 3.72; 95% confidence interval [CI], 1.43-9.64), those who had increased patient activation scores after intervention (OR = 1.05; 95% CI, 1.01-1.09), those who had higher baseline patient activation scores, and those who had a greater number of biometric data uploads during the intervention (OR = 1.02; 95% CI, 1.00-1.04) were more likely to have reduced their HbA1c to <9% at the end of RPM. RPM for postdischarge patients with T2D might be a promising approach for HbA1c control with increased patient engagement. Future studies with study designs that include a control group should provide more robust evidence.