Abstract
Introduction:
Although therapeutic inertia is a known driver of suboptimal type 2 diabetes control, little is known about how to combat this phenomenon. We analyzed randomized trial data to determine whether a comprehensive telehealth intervention was more effective than a less structured telehealth approach (telemonitoring and care coordination) at promoting treatment intensification in poorly controlled diabetes.
Methods:
Patients with poorly controlled type 2 diabetes were randomized 1:1 to telemonitoring/care coordination or a comprehensive telehealth intervention, which included an active, study provider-guided medication management component. Prospectively collected medication lists were used to determine whether treatment intensification occurred for each patient during 3-month intervals throughout the study period. To examine between-arm differences in treatment intensification over time, we fit a generalized estimation equation model. In each arm, hemoglobin A1c levels at the beginning and end of each 3-month interval were used to distinguish between therapeutic inertia and potentially appropriate nonintensification of treatment.
Results:
The mean, model-estimated likelihood of treatment intensification during 3-month intervals was 61.3% in the comprehensive telehealth group versus 48.6% for telemonitoring/care coordination (odds ratio 1.7, 95% confidence interval 1.2–2.2; p = 0.0007), with no evidence that treatment effect varied over time (p = 0.54). Treatment intervals with observed therapeutic inertia were more common in the telemonitoring/care coordination arm than the comprehensive telehealth arm (116/300, 39% vs. 57/275, 21%).
Conclusions:
A comprehensive telehealth approach that integrated protocol-guided medication management increased treatment intensification and reduced therapeutic inertia compared with a less structured telehealth approach. The studied approaches may serve as examples of how systems might use telehealth to combat therapeutic inertia.
Clinical Trial Registration: ClinicalTrials.gov NCT03520413.
Keywords: diabetes, telehealth, treatment, inertia, telemonitoring
Introduction
Despite evidence that achieving earlier control of type 2 diabetes is associated with improved diabetes-related morbidity and mortality, 50–70% of people with type 2 diabetes do not meet glycemic goals.1–3 Multiple factors determine achievement of glycemic targets, but failure to intensify therapy for patients who do not meet treatment goals (i.e., therapeutic inertia) is a key contributor.4 Although guidelines recommend intensifying treatment when glycemic targets are not met, less than half of patients with diabetes receive treatment intensification within 1 year of an above-target hemoglobin A1c (HbA1c).4–8 To reduce therapeutic inertia, the American Diabetes Association (ADA) announced a 3-year initiative titled “Addressing Therapeutic Inertia in 2020 and Beyond.”2 However, while the clinical importance of therapeutic inertia is widely recognized, relatively little is known about how best to promote treatment intensification in clinical practice. Pragmatic, evidence-based solutions to counter therapeutic inertia are urgently needed.2,9
Given rising utilization of telehealth during and after the COVID-19 pandemic, telehealth represents a potentially important strategy for facilitating timely treatment intensification.10–13 Some telehealth interventions that utilize telemonitoring or care coordination have shown promise in combatting therapeutic inertia, but there have been no studies to date that have directly compared intensive telehealth interventions with simpler telehealth approaches.14–16 A recently published randomized clinical trial (RCT) compared two telehealth interventions for patients with persistently poorly controlled type 2 diabetes mellitus (PPDM), and demonstrated that a comprehensive telehealth intervention combining telemonitoring, self-management support, diet/activity support, depression support, and protocol-guided medication management reduced HbA1c versus a less structured telehealth intervention (telemonitoring and care coordination).17 Because the comprehensive telehealth intervention included protocol-guided medication management led by study personnel, it may have reduced therapeutic inertia relative to the simpler telehealth approach.
In these secondary analyses, we sought to determine whether the comprehensive telehealth intervention increased the frequency of timely treatment intensification and decreased therapeutic inertia in comparison to telemonitoring and care coordination.
Methods
Study design
These analyses utilized data from a multisite, parallel-arm, Institutional Review Board (IRB)-approved RCT (IRB protocol No. 2130 [Durham, NC, USA: Veterans Affairs Medical Center] and 2398 [Richmond, VA, USA: Veterans Affairs Medical Center]) that compared the glycemic effects of two 12-month telehealth interventions among patients with PPDM. The study protocol and primary RCT results are described in detail elsewhere.17,18 Briefly, patients from two Veterans Health Administration (VHA) medical centers were randomized to receive either a comprehensive telehealth intervention or a less structured telehealth approach, telemonitoring and care coordination; telemonitoring/care coordination was chosen as the active comparator for this RCT, because the study population (patients with PPDM) is by definition refractory to usual diabetes care.
The comprehensive telehealth intervention comprised five components: telemonitoring, self-management support, diet/activity support, depression support, and protocol-guided medication management. Importantly, both interventions leveraged existing clinical staffing and infrastructure, so were explicitly designed for practical clinical delivery within VHA.
Study population
We analyzed the randomized study population, which consisted of N = 200 patients with PPDM recruited from VHA medical centers in Richmond, VA, USA and Durham, NC, USA. PPDM was defined based on diagnosed type 2 diabetes; the presence of at least two HbA1c values ≥8.5% during the prior year, with no readings <8.5%; and receipt of clinic-based diabetes management during the prior year from Primary Care with or without diabetes specialty care (e.g., Endocrinology, Clinical Pharmacy). Full inclusion and exclusion criteria may be found elsewhere.18 Of the 200 participants, 101 were randomized to receive comprehensive telehealth and 99 were randomized to receive telemonitoring and care coordination.
Telehealth interventions
Both 12-month interventions were delivered by clinical nurses rather than research staff.17,18 These intervention nurses worked with the VHA Home Telehealth (HT) program, which is available at VHA medical centers nationwide; all study participants entered the HT program upon enrollment. As per HT requirements, patients transmitted self-monitored blood glucose (SMBG) data to their intervention nurse using a telehealth device (Medtronic®, Minneapolis, MN, USA), blood glucose meter (Abbott®, Alameda, CA, USA), and connector cable that connects the glucose meter to the telehealth device. Although experienced with telehealth-based disease care, the study intervention nurses had no specialized diabetes training. Individual nurses delivered only one study intervention with no crossover.
For the telemonitoring and care coordination intervention, patients were asked to transmit SMBG data daily using the equipment supplied by HT. HT nurses proactively called patients who did not transmit SMBG data for 3 days; after 7 days a letter was mailed, and 30 days without a response or transmission of data triggered a discontinuation of HT services. Patients received nurse calls for alert SMBG values and could reach nurses as needed for any concerns but did not complete encounter calls on a scheduled basis. Participants also received care coordination, which involved HT nurses notifying primary providers of acute needs, communication about upcoming appointments, and preappointment compilation of SMBG data for primary provider review. Medication adjustments were at the discretion of existing clinical providers during or outside of scheduled encounters. The comprehensive telehealth intervention's five components were telemonitoring, self-management support, diet/activity support, medication management, and depression support.
Of these components, two were explicitly designed to facilitate treatment intensification (telemonitoring and medication management). The telemonitoring component (as in the telemonitoring/care coordination arm) involved prompting patients daily to transmit their SMBG data.
In addition, intervention nurses completed telephone-based encounter calls with patients every 2 weeks, during which they reviewed interim SMBG data, reconciled medications, and assessed self-reported medication adherence. This information was then summarized in the electronic health record (EHR)-based report. This intervention component was intended to facilitate treatment intensification by addressing uncertainty regarding glycemic control and medication adherence. For the medication management intervention component, a study medication manager with expertise in diabetes management (a Nurse Practitioner, Clinical Pharmacy Specialist, or Physician) reviewed the intervention nurse's report within 24–48 h and determined whether medication changes were indicated. The medication manager entered any necessary orders and relayed any recommendations back to the nurse through an addendum to the EHR-based report. The nurse then implemented any medication changes with the patient. This process repeated every 2 weeks.
The comprehensive telehealth intervention's self-management support component utilized modules delivered by HT nurses to teach concepts such as goal setting, using SMBG, hypoglycemia self-management, and insulin self-management. The diet and activity support intervention component included development of individualized goal-based diet and activity plans, which were reviewed at study encounter visits every 2 weeks. Lastly, the depression management component was overseen by a study psychiatrist and involved regular depression screening with protocol-guided pharmacologic and nonpharmacologic treatment for patients with depression symptoms.
Assessment of treatment intensification during intervals
To facilitate nuanced assessment of treatment changes throughout the 12-month study period (Fig. 1), each patient's study participation was divided into four 3-month intervals (baseline to 3, 3–6, 6–9, and 9–12 months), which corresponded to scheduled study assessments of HbA1c and patient-reported medication lists at enrollment and every 3 months thereafter. These 3-month intervals were the unit of assessment for the present analyses; each study participant could contribute up to four 3-month intervals. Intervals with missing medication lists at the beginning or end of the 3-month period were excluded from these analyses (Fig. 1). The interval length of 3 months aligns well with guideline recommendations to consider intensifying treatment after 3–6 months of failure to reach glycemic targets.5,7
Fig. 1.
Flow chart showing the analysis of treatment intensification for all the 3-month time intervals from both study arms. After excluding intervals that were missing medication lists, time intervals were assessed for the presence of treatment intensification based on comparison of medication lists before and after the interval.
Treatment intensification, considered as a binary yes/no outcome, was the primary analytic outcome. To identify episodes of treatment intensification, we examined the medication lists at the start and end of each 3-month interval. An interval was determined to have treatment intensification when either: (1) a dose of an existing diabetes medication was increased or (2) a new diabetes medication was introduced to the medication list. If neither of these conditions were met, treatment intensification was deemed not to have occurred during that interval.
Assessment of therapeutic inertia during intervals
We next focused on intervals with an above-goal HbA1c (HbA1c ≥ 8.0%) at the start of the interval to place them into one of four categories: “treatment intensification,” “potentially appropriate non-intensification,” “unknown if true therapeutic inertia,” or “therapeutic inertia” (Supplementary Fig. S1). Intervals with missing HbA1c and/or medication lists at the beginning of the 3-month period were excluded from this analysis.
For intervals not meeting criteria for “treatment intensification,” the HbA1c value at the end of each 3-month interval was used to allocate these intervals into the following categories:
Therapeutic inertia: An interval beginning with an above-goal HbA1c (HbA1c ≥ 8.0%), during which treatment was not intensified and the HbA1c remained ≥8.0% at the end of the interval.
Potentially appropriate nonintensification: An interval beginning with an above-goal HbA1c (HbA1c ≥ 8.0%), during which treatment was not intensified but the patient's HbA1c had reached goal (HbA1c < 8.0%) by the end of the interval. These HbA1c improvements without treatment intensification were considered to potentially reflect successful nonpharmacologic interventions (e.g., improved diet or medication adherence).
Unknown if true therapeutic inertia: An interval beginning with an above-goal HbA1c (HbA1c ≥ 8.0%), during which treatment was not intensified, but the end-interval HbA1c was missing.
The HbA1c target of <8.0% for these analyses was chosen based on current guidelines indicating that an HbA1c ≥ 8.0% is above goal for most patients with PPDM; of note, patients likely to have an HbA1c target ≥8.0% (i.e., >75 years old, limited life expectancy, etc.) were excluded from the RCT.7,8
Statistical analyses
To examine differences in treatment intensification between comprehensive telehealth and telemonitoring and care coordination groups over time, we fit a generalized estimation equation (GEE) model, which accounted for repeated measures within patients and used a logit link and an unstructured correlation structure.19 Empirical standard errors were used for inference. The model included indicator variables for arm and for time of 6, 9, and 12 months as well as time indicator by arm interaction terms, and randomization stratification variables (site, prior VHA HT use, and pre-enrollment diabetes specialty care with Endocrinology or another specialist).
In sensitivity analyses of treatment intensification, we included time-varying HbA1c measurements as covariates in the GEE model to explore the association between treatment arm assignment and treatment intensification when adjusting for HbA1c values. The HbA1c measurement was the measure from the beginning of the interval, with the measure of HbA1c for the first treatment intensification 3-month interval time point being the baseline HbA1c value, HbA1c measured at 3 months is the value for medication intensification at 6 months, and so on.
To minimize possible bias introduced by adjusting for a postrandomization variable, we included the following covariates in the model in addition to the randomization stratification variables denoted above: race, sex, education level, employment status, marital status, age, number of years with diabetes diagnosis, and baseline diabetes medication adherence.
For the descriptive analyses of therapeutic inertia, we focused on 3-month time intervals with above-goal HbA1c (HbA1c ≥ 8.0%) at the beginning of interval, and compared rates of treatment intensification, potentially appropriate nonintensification, unknown if true therapeutic inertia, and therapeutic inertia overall and by treatment arms.
Results
Baseline data
Baseline characteristics are shown in Table 1. Most study participants were men (77.5%), self-identified as Black (72.0%), and had a mean age at enrollment of 57.8 years (standard deviation [SD] = 8.2). Patients had an average baseline HbA1c of 10.2% (SD = 1.3) and most patients were using insulin at study enrollment (71.5%).
Table 1.
Baseline Characteristics Stratified by Study Arm
| TELEMONITORING AND CARE COORDINATION (N = 99) | COMPREHENSIVE TELEHEALTH (N = 101) | |
|---|---|---|
| Age (years), mean (SD) | 57.8 (8.0) | 57.7 (8.3) |
| Female, n (%) | 21 (21.2) | 24 (23.8) |
| White, n (%) | 17 (17.2) | 25 (24.8) |
| Black or AA, n (%) | 76 (76.8) | 68 (67.3) |
| Hispanic/Latino Ethnicity, n (%)a | 5 (5.1) | 6 (5.9) |
| ≤High school graduate, n (%) | 28 (28.3) | 29 (28.7) |
| Some college, n (%) | 43 (43.4) | 37 (36.6) |
| ≥College graduate, n (%) | 28 (28.3) | 35 (34.7) |
| Currently married, n (%) | 46 (45.5) | 45 (46.5) |
| Employed, n (%) | 42 (42.4) | 48 (47.5) |
| Study site: Durham, n (%) | 58 (58.6) | 57 (56.4) |
| Study site: Richmond, n (%) | 41 (41.4) | 44 (43.6) |
| Years with diabetes, mean (SD) | 12.0 (7.5) | 12.1 (8.0) |
| Baseline HbA1c, mean (SD) | 10.2 (1.4) | 10.1 (1.2) |
| BMI, mean (SD) | 35.2 (7.0) | 34.5 (6.4) |
| Insulin use, n (%) | 64 (64.6) | 79 (78.2) |
| HTN, n (%) | 85 (85.9) | 81 (80.2) |
| HLD, n (%)b | 87 (87.9) | 84 (83.2) |
| Social support, n (%) | 93 (93.9) | 98 (97.0) |
| Self-reported diabetes medication nonadherence | 60 (60.6) | 61 (60.4) |
One patient responded “Don't Know” to the Hispanic/Latino Ethnicity question.
One patient in the telemonitoring and care coordination arm responded “Don't Know” to having high cholesterol.
BMI, body mass index; HbA1c, hemoglobin A1c; HLD, hyperlipidemia; HTN, hypertension; SD, standard deviation.
Treatment intensification
A total of 793 intervals across 200 patients were analyzed for the presence or absence of treatment intensification (7 intervals excluded for missing medication lists). Treatment intensification was observed in 191/394 (48%) intervals within the telemonitoring and care coordination arm, and 244/399 (61%) intervals within the comprehensive telehealth arm (Fig. 1). As per Figure 2 and Supplementary Table S1, the GEE model-estimated average rates of treatment intensification over all time intervals were 61.3% (95% confidence interval [CI] 56.1–66.2%) in the comprehensive telehealth arm and 48.6% (95% CI 43.5–53.7%) in the telemonitoring and care coordination arm (odds ratio [OR] 1.7, 95% CI 1.2–2.2; p = 0.0007). There was no evidence of changes in the between-arm difference in medication intensification over time (i.e., baseline to 3, 3–6, 6–9, and 9–12 months, p = 0.54; Supplementary Table S1).
Fig. 2.
Model estimated treatment intensification percentages with 95% confidence intervals for all time intervals from GEE models. GEE models included indicator variables for time of 6, 9, and 12 months as well as time indicator by arm interaction terms, randomization stratification variables site, prior VHA HT use, and pre-enrollment diabetes. GEE, generalized estimation equation; HT, home telehealth; VHA, Veterans Health Administration.
In sensitivity analyses of 693 intervals (n = 100 excluded due to missing HbA1c values), with inclusion of time-varying HbA1c values, the impact of the addition of HbA1c on treatment intensification (averaged over all time points) was negligible (OR = 1.1; 95% CI 0.9–1.2; p = 0.35). These results are similar to the primary analysis, with higher odds of treatment intensification in the comprehensive telehealth arm compared with the telemonitoring and care coordination arm over the duration of the study persisting after accounting for A1c levels at the beginning of the interval comprehensive telehealth arm had higher odds of treatment intensification (OR = .8; 95% CI 1.3–2.6; p = 0.0002; Supplementary Table S2).
Therapeutic inertia and potentially appropriate nonintensification
Patterns of treatment intensification for all participants across time intervals are shown in Supplementary Figure S2. After excluding intervals with missing HbA1c data (n = 100) and intervals with HbA1c < 8.0% at the beginning of interval (n = 118), we identified a total of 575 3-month intervals for analysis of therapeutic inertia, with the telehealth and care coordination arm contributing 300 intervals and the comprehensive telehealth arm 275 intervals. Overall, observed incidence of treatment intensification was 56% (324/575) within 3 months of an above-goal HbA1c. Therapeutic inertia was more common in the telehealth and care coordination arm (n = 116/300, 39%) than in the comprehensive telehealth arm (n = 57/275, 21%; Supplementary Fig. S1).
Discussion
In these secondary analyses of an RCT, a comprehensive telehealth intervention increased rates of timely treatment intensification when compared with telemonitoring and care coordination alone. This finding persisted after a sensitivity analysis accounting for changes in HbA1c levels throughout the study. Furthermore, we observed lower therapeutic inertia in the comprehensive telehealth intervention arm compared with the less structured telehealth approach (21% vs. 39%).
Therapeutic inertia is well-established as a driver of failure to achieve glycemic targets, and contributes to many of the adverse outcomes from poorly controlled diabetes.20–22 Thus, reducing therapeutic inertia is a critical objective in diabetes care.2,23 Unfortunately, there remains a lack of practical, sustainable solutions to combat therapeutic inertia.9 Telehealth may represent a viable strategy for reducing therapeutic inertia in practice; following a surge in utilization during the COVID-19 pandemic, telehealth is expected to maintain a key role in ambulatory care delivery.24 This study is the first to compare the impact of different telehealth strategies on treatment intensification and therapeutic inertia.
We observed that the overall incidence of treatment intensification was 56% within 3 months of an above-goal HbA1c. This is comparable to prior published interventions, which measure therapeutic inertia over a 3-month time interval (median 35%, range 21–68%).6,20,25–28 While interventions targeting multiple levels of care are generally regarded to be most effective for reducing therapeutic inertia,29 we believe that the active medication management component was the primary driver of the comprehensive telehealth group's superior rate of treatment intensification in this study (61%).16,23,29–31 The comprehensive telehealth intervention also supported patients in other ways (self-management support, diet/activity support, depression support), which may have increased engagement and made patients more amenable to treatment intensification.
While this study was not designed to examine how the individual intervention component(s) supported treatment intensification, understanding these individual contributions would be a valuable target for future work. Additionally, it is likely that there were more provider–patient interactions in the comprehensive telehealth arm, however this was not a focus of the current analysis.
A major strength of this study is the nuance with which treatment intensification was examined. It is known that provider decision making regarding treatment intensification is complicated, and that competing demands, concerns regarding patient adherence to current medications, decisions to focus on nonpharmacologic interventions, and patient willingness to intensify may all contribute to apparent therapeutic inertia.32,33 In our exploratory analysis, including time-varying HbA1c values, we found that differences in treatment intensification were similar to the primary analysis and that levels of HbA1c at the beginning of a 3-month interval did not explain differences between groups in treatment intensification or predict treatment intensification. By examining HbA1c levels at the start and end of each 3-month study interval, we were able to distinguish potentially appropriate nonintensification from inappropriate nonintensification, or therapeutic inertia. This detailed examination provides confidence that episodes labeled “therapeutic inertia” likely reflect a truly inappropriate decision not to intensify medications.
Additional strengths of the study included the diverse population with a demonstrated pattern of poorly controlled diabetes and the pragmatic study design, which utilized existing telehealth infrastructure.
Limitations do include this study's use of an active comparator rather than a usual care group for comparison. Since the trial included only patients with persistently poor diabetes control, the study was not conducive to a usual care arm. While this design enhances the novelty of the present work, we were unable to determine the baseline rate of therapeutic inertia in this study population. Additionally, the HbA1c is not a perfect measure and does not necessarily account for patients with periods of hypoglycemia, which could influence a provider's willingness to intensify therapy.34 The inclusion of a postrandomization covariate (HbA1c) in our exploratory analysis may induce bias in treatment comparisons; however, we included baseline covariates that could possibly relate to both treatment intensification and HbA1c to mitigate the potential bias.
The generalizability of these findings may be limited because it is a population of mostly male (77.5%) Veterans with poorly controlled diabetes, who had access to health care and were already engaged in endocrinology or primary care; however, our enrollment of a large proportion of African American patients does support the applicability of our findings to historically marginalized groups.
Lastly, the utilization of the medication manager in the comprehensive telehealth intervention, which was carried out by a provider with expertise in diabetes management, could limit dissemination to facilities without access to such experts.
In conclusion, compared with a less structured telehealth approach, a comprehensive telehealth intervention that included a medication manager was successful in increasing treatment intensification and reducing therapeutic inertia. These analyses provide insights into how health care systems might use telehealth to combat therapeutic inertia in clinical practice.
Acknowledgment
The authors acknowledge in-kind support from the Durham Center of Innovation to Accelerate Discovery and Practice Transformation (VA CIN 13–410) within the Durham VA Health Care System.
Authors' Contributions
R.A.D., M.J.C., A.-S.A., and C.J.C. were involved in the conception, design, and conduct of the study and analysis and interpretation of results. R.A.D. wrote the first draft of the article and all authors made edits to the article as subsequent drafts were composed. All authors reviewed the final draft. R.A.D. 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.
Funding and Assistance
Disclosure Statement
No competing financial interests exist.
Funding Information
This study, on which these analyses are based, was supported by a grant from Veterans Affairs Health Services Research and Development (VA IIR 16–213, Crowley PI). M.J.C. acknowledges funding from the National Institutes of Health (1R01NR019594–01), the Veterans Affairs Quality Enhancement Research Initiative (VA QUE 20–012), and the Veterans Affairs Office of Rural Health; he was supported by a Career Development Award from Veterans Affairs Health Services Research and Development (CDA 13–261) during part of the study period. A.-S.A. is supported by the Duke Clinical and Translational Science Institute (CTSI) under National Institutes of Health award No. KL2TR002554. H.B. reports research funding through his institution from BeBetter Therapeutics, Boehringer Ingelheim, Esperion, Improved Patient Outcomes, Merck, NHLBI, Novo Nordisk, Otsuka, Sanofi, Veterans Administration, Elton John Foundation, Hilton foundation, and Pfizer. H.B. also provides consulting services for Esperion, Sanofi, Webmed, and Janssen. He was also on the board of directors of Preventric Diagnostics.
Supplementary Material
References
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