Abstract
Background:
Non-Hispanic Black (NHB) and Hispanic/Latinx (H/L) patients bear a disproportionate burden of type 2 diabetes and associated complications. Regular visits to a primary care doctor or diabetes specialist are warranted to maintain glycemic control, but for a myriad of reasons disparity populations may have difficulties receiving diabetes care. We seek to determine the feasibility of telehealth added to care as usual and secondarily to improve health outcomes (hemoglobin A1c [HbA1c]) in NHB and H/L with uncontrolled type 2 diabetes managed with two or three noninsulin agents.
Methods:
Twenty-nine patients were randomized to monthly phone calls or weekly to biweekly telehealth visits. Feasibility outcomes were summarized descriptively for the telehealth arm. Differences scores for A1C level and surveys were computed between baseline and three months and compared across arms using a two-sample t test or Mann-Whitney U test.
Results:
Patients in the telehealth arm completed a median of eight visits (IQR: 5, 8), and 53% of those in the telephone arm completed 100% of their calls. Change in HbA1c was greater for those in the telephone arm (−2.57 vs −2.07%, P = .70) but the mean baseline HbA1c was higher in the telephone group (11.1% vs 10.3%). Although the change in HbA1c was not statistically different across arms, it was clinically significant.
Conclusions:
Augmenting care as usual with telehealth provided by telephone or tablet can be of benefit in improving glycemic control in NHB and H/L with type 2 diabetes. Larger studies need to explore this further.
Keywords: diabetes, disparities, outpatient, telehealth
Introduction
In 2020, the prevalence of type 2 diabetes (T2D) is 34.2 million or 10.5% of the US population, with roughly 20% of persons being undiagnosed.1 Diabetes disproportionately affects non-Hispanic Blacks (NHB: 16.4%) and Hispanic/Latinx (H/L: 14.7%) when compared to non-Hispanic Whites (NHW: 11.9%).1 These increased rates of diabetes also correlate with higher rates of retinopathy, end-stage renal disease (ESRD), and limb amputation in NHB and H/L patients as compared to NHW.2 This occurs for a myriad of biological reasons, including genetic predisposition and variance in fat distribution.3 Furthermore, psychosocial variables contribute to poor diabetes care, which in part arise from limited financial resources,4 lower health literacy rates,5 diet,6 language barriers,7 poor self-management practices,8 and limited access to care.9
One way to circumvent limited access to care is by using telehealth to augment care as usual. Telehealth is defined as “the use of electronic information and communications technologies to provide and support health care when distance separates the participants.”10 Telehealth offers flexibility to underserved disparity patients who may have difficulty accessing traditional health care services and possibly reduce cost of disease burden and hospitalizations while increasing convenience and access to care. Systematic reviews of telehealth use in diabetes have been associated with improvements in glycemic control, blood pressure, and/or foot care.11-15 Telephone calls, videoconferencing, online portals, and mobile applications can potentially be used for diabetes-related health services such as diabetes self-management education and support, retinal examination, blood glucose pattern management, and participation in motivational support groups.11
Despite the successes of telehealth, there have also been challenges, including: equipment cost,16 connectivity issues,17 privacy concerns,18 lack of perceived benefit by the patient,19 and the depersonalization of the experience.20 Another limitation is evidence of efficacy in disparity populations: NHB and H/L patients tend to be underrepresented in telehealth trials despite their higher rates of diabetes.21 The largest and longest trial of diabetes telehealth in a diverse sample is The Informatics for Diabetes Education and Telemedicine (IDEATel) Project, which evaluated the long-term health outcomes of telemedicine provided by a registered nurse compared to care as usual over a five-year period to 1655 NHB, H/L, and NHW patients.22 Although the telehealth intervention was successful in facilitating glycemic control, with significant hemoglobin A1c (HbA1C) improvements in the intervention arm compared to care as usual at years four and five,23 it also found that NHB and H/L patients had significantly fewer glucose uploads than NHW. In addition, the cost of the equipment at $622 per person per month made it prohibitively expensive. In the decade since the IDEATel trial was published, the cost of technology has decreased and the technology itself has become more accessible; it is therefore important to revisit the issue of usability in disparity populations.
In this pilot, we seek to explore the usability of an in-home telehealth program with endocrine consultation for NHB and H/L patients with T2D to evaluate its effects on health outcomes and quality of life measures. Based on previous studies, our hypothesis is that patients in the telehealth intervention arm will have statistically greater improvements in adherence and health outcomes relative to the telephone arm, as well as greater improvements in quality of life measures. Positivity of these outcomes would suggest that telehealth is a feasible method of diabetes control.
Methods
Design
Written consent was obtained and patients were told that their data would be used anonymously for future publication. Subjects who consented to the study were randomized by a 1:1 ratio to either the telephone or intervention (telehealth/video call) arms using REDCap software. The total participation time for each subject was three months.
Participants
This pilot study sought to enroll a total of 30 participants with T2D who are either Black or Hispanic, 22 of which were included in the modified intention-to-treat (mITT) analysis (Table 1). Enrollment began in October 2016 and ended June 2019. Patients were recruited from six general internal medicine practices located in Queens and Nassau—New York counties that are affiliated with a large health system located in the New York metropolitan area. Eligible participants were defined as adult male and female 18 years or older who identified as Black/African American or Hispanic/Latino, English speaking, active diagnosis of T2D, last known HbA1c level of ≥9%, currently prescribed two or more anti-diabetes, noninsulin agents, and had access to a phone. Patients who had a history of type 1 diabetes (T1D), active pregnancy or history of cognitive impairment were excluded from participation. IRB approval and patient consent were acquired prior to the start of any research-related activities. Potential subjects were identified through the Allscripts® Electronic Medical Record (EMR), weekly data reports, and provider referrals.
Table 1.
Patient Demographics (N = 22) of Those Included in the mITT.
| Telehealth N = 9 |
Telephone N = 13 |
|
|---|---|---|
| Gender, n (%) | ||
| Male | 5 (50.00) | 5 (50.00) |
| Female | 4 (33.33) | 8 (66.67) |
| Age (years) | ||
| Mean (SD) | 56.56 (7.97) | 58.69 (11.80) |
| Race/ethnicity, n (%) | ||
| Black/African American | 8 (47.06) | 9 (52.94) |
| Hispanic/Latino | 1 (20.00) | 4 (80.00) |
Intervention
Patients who were randomized to the intervention group received a telehealth unit from a third party vendor, Health Recovery Solutions (HRS). The telehealth unit consisted of a Samsung® Tablet with 4G LTE connectivity provided by AT&T as well as Bluetooth wireless peripherals: blood pressure monitor, scale, and pulse oximeter (Figure 1). Subjects were trained by the research coordinator and also received handouts with instructions. HRS also provided 24/7 technical support to subjects. Subjects received a total of eight video calls with an endocrinology attending or fellow (once a week for month one and then biweekly for months two and three). They were asked the same scripted questions (Table 2). Subjects were asked to upload their blood pressure, weight, pulse oximetry, blood glucose, physical activity, and complete a brief risk survey (Figure 2) on a daily basis. They also had a daily medication reminder. Using the tablet, subjects were able to contact the research team through audio calls, video calls, and text-messaging. The research team would then review the data and interface with subjects using the HRS ClinicianConnect™ desktop software. A subject’s primary care provider (PCP) was contacted on a need-only basis if there were any significant complaints or changes that needed to be made (ie, modification to medication regimen).
Figure 1.
Telehealth tablet and peripherals.
Table 2.
Telehealth group phone script.
|
Figure 2.
Check-in survey on the tablet.
Care as Usual
Those who were randomized to the telephone arm received a phone call once a month (three total) from an attending endocrinologist or endocrinology Fellow. Subjects were asked about their current medications, diet, and lifestyle (Table 3), and recommendations were made as necessary.
Table 3.
Telephone arm phone script.
|
Clinical End Points
The primary goal of this study was to assess the feasibility of using telemedicine to aid patients from disparity communities in managing their diabetes. Feasibility was determined by assessing the number of scheduled telemedicine visits, number of glucose uploads per week, and the Telehealth Satisfaction and Usefulness Questionnaire (TSUQ). Secondary goals for the study were to compare clinical outcomes (HbA1c and hypoglycemia) for patients receiving telemedicine versus telephone call at baseline and three months, and to compare patient satisfaction and quality of life in patients receiving telemedicine versus telephone calls. Both groups were also asked to complete a series of self-reported survey measures at baseline and three months. Measures included the Patient Health Questionnaire-9 (PHQ-9), Short-Form Patient Satisfaction Questionnaire (PSQ-18), Problem Areas in Diabetes Questionnaire (PAID), Diabetes Quality of Life (DQOL), and Diabetes Distress Scale (DDS).
Statistical Methods
TSUQ,24 PHQ-9,25 DDS,26 PAID,27 DQOL,28 and PSQ29 questionnaires were summarized according to prior literature. Difference scores between study initiation and study end were computed for A1C level and PHQ-9, DDS, PAID, and DQOL scores (difference = end-initiation). Analyses were based on a modified mITT population. The mITT population was defined as at least one visit. For the treatment arm, this was one telehealth visit and for the telephone arm this was one phone call.
Baseline characteristics (age, sex, race, HbA1C) were summarized descriptively across arms for the mITT population. To assess feasibility, the primary goal, feasibility outcomes (number of telemedicine visits, number of glucose uploads), and TSUQ scores were summarized using mean, standard deviation, min, and max for the telehealth arm. To assess clinical outcomes, the secondary goal, difference scores for A1C level, and PHQ-9, DDS, PAID, and DQOL scores were summarized using mean and standard deviation and compared across arms using the two-sample t test or Wilcoxon Rank Sum test. All analyses were performed using SAS Studio version 3.8.
Results
Feasibility
Of the 25 patients randomized, one participant in the telehealth arm and two participants in the telephone arm did not complete any calls and therefore did not meet the mITT criteria and were excluded from analysis (10% and 13%, respectively). Of patients who completed at least one call in the telehealth arm, the median number of telemedicine calls completed was eight of eight possible calls (IQR: 5, 8).75% (six of eight) of those who completed the TSUQ indicated a high level of satisfaction (Table 4). The median number of calls completed in the telephone arm was three of three possible calls (IQR: 2, 3). Eight of those in the telephone arm completed all three of their calls, and another three completed two of three calls.
Table 4.
Telehealth Feasibility/TSUQ Outcomes (Telehealth Arm Only).
| Median (IQR) | Mean (SD) | |
|---|---|---|
| Number of telemedicine visits (8 possible) (n = 9) | 8.00 (5.00, 8.00) | 6.56 (2.24) |
| Number of glucose uploads (n = 7) | 61.00 (2.00, 68.00) | 42.14 (33.48) |
| TSUQ satisfaction score (n = 6) | 4.24 (3.76, 4.90) | 4.25 (0.65) |
| TSUQ usefulness score (n = 6) | 5.00 (4.67, 5.00) | 4.72 (0.53) |
Abbreviation: TSUQ, Telehealth Satisfaction and Usefulness Questionnaire.
Clinical Outcomes
The mean HbA1c change at three months was greater for the telephone arm (−2.57%) than that in the intervention arm (−2.07%), but the difference was not statistically significant. It does, however, have clinical significance, as the telephone arm on average had a higher baseline HbA1c (10.3% vs 11.1%). Men had a greater mean change in HbA1c at three months when compared to women (−2.84% vs −1.58%), which—although clinically significant—was not statistically significant. The sample was too small to assess a difference between NHB and H/L.
Hypoglycemia was only experienced by one patient in the telehealth arm who was taking sulfonylurea and metformin, so he was advised to take a half tablet and not go more than six hours without eating.
Quality of Life Measures
There were no differences in the average scores for depression as measured by the PHQ-9, in diabetes distress as measured by the DDS and PAID, or in the QOL scales.
Discussion
NHB and H/L have largely been underrepresented in studies with regard to telehealth and T2D.21 This study not only adds to this limited literature but was unique in that it included NHB and H/L persons who were both American and foreign-born.
Feasibility/Attrition
Overall, 30 patients were consented to participate in the study and 29 were randomized. Of the remaining 29 patients, seven patients did not meet mITT criteria. The median number of visits in the telehealth arm was eight (out of eight), which is higher than seen in other studies.30 Also in some studies, the number of telehealth visits were not reported and instead only health outcomes were given.31
Fourteen of those consented either declined to participate, prematurely terminated their participation in the study, or demonstrated poor adherence to the study protocol. These patients were invited to participate in semi-structured interviews about the study. Eight of these agreed to engage in semi-structured interviews about the study. As documented in our previous study, the most common reasons for not completing the study were: disinterest, inconvenience, lack of perceived benefit, lack of awareness of T2D diagnosis, and perceived lack of ability to do the study19 (Table 5). Other studies have shown the same results as well as other causes for dropping out including: technology issues, irrelevant and incomprehensible content, or preferring face-to-face visits.31
Table 5.
Patient-Perceived Barriers or Benefits of Telehealth.
| Benefits | Barriers |
|---|---|
| Greater access to their provider | Connectivity |
| Seeing their provider visually | Too much effort |
| Appointment flexibility | Having to carry the tablet to work or on vacation |
| The tablet stores all values | Asymptomatic disease |
| Being technologically advanced | Cost/Insurance reimbursement |
Tablet/Peripherals
The tablet was delivered to subjects with several peripherals: blood pressure cuff, scale, and pulse oximeter. Patients used their own meter and uploaded their levels. Participants had mixed reviews about the peripherals. One noted that her long nails prevented her from using the pulse oximetry device. Another asked if she could purchase the blood pressure cuff whereas two did not like how it felt.
One of the disadvantages of using a tablet is that if patients left their tablet at home, they were not able to keep up with the daily uploads. One participant who dropped out after three visits noted that he travelled frequently and would leave his tablet at home. In addition, he did not have a meter so he never provided any blood sugar uploads. Connectivity issues were also problematic. One patient would take his tablet with him to work and due to the poor reception in his building the connection was often poor. Three others had issues at home. A couple of participants had problems with the tablet shutting off; for one participant this occurred as he was not charging his tablet daily.
Satisfaction
Those in the telehealth arm were overall satisfied with the tablet. One patient noted: “It made me more knowledgeable. Checking sugar helped a lot.” Another participant noted: “I love it. I am going to miss it. And to get a live person even once a week.” Six of the 10 in the telehealth arm completed the TSUQ. Overall, the satisfaction and usefulness of telehealth was high. On a scale of one to five with five being the most favorable, there was a median score of five for satisfaction and 4.24 for usefulness, respectively. The telehealth group gave a higher score for satisfaction in response to time with the doctor (Table 6).
Table 6.
Baseline to Three-Month Change in Outcomes by Arm (Extended Secondary Outcomes).
| Variable
(difference) (n telehealth, n telephone) |
Telehealth arm mean (SD) |
Telephone arm mean (SD) |
|---|---|---|
| DDS subscores | ||
| Change in DDS emotional
burden (n = 6, n = 4) |
−0.47 (1.06) | 0.05 (0.66) |
| Change in DDS physician-related
distress (n = 6, n = 4) |
−0.08 (0.26) | 0.13 (0.25) |
| Change in DDS regimen-related
distress (n = 6, n = 4) |
−0.23 (0.83) | −0.35 (0.66) |
| Change in DDS interpersonal
distress (n = 6, n = 4) |
−0.11 (0.66) | −0.42 (0.63) |
| DQOL subscores | ||
| Change in DQOL satisfaction (n = 5, n = 3) |
−0.05 (0.57) | −0.44 (0.57) |
| Change in DQOL impact (n = 6, n = 2) |
−0.10 (0.29) | −0.15 (0.28) |
| Change in DQOL worry
(social/vocational) (n = 6, n = 2) |
−0.45 (0.55) | −0.21 (0.10) |
| Change in DQOL worry (diabetes
related) (n = 6, n = 4) |
−0.04 (0.84) | 0.25 (0.54) |
| PSQ subscores | ||
| Change in PSQ general
satisfaction (n = 6, n = 4) |
−0.25 (0.42) | 0.13 (0.48) |
| Change in PSQ technical
quality (n = 6, n = 4) |
0.04 (0.37) | 0.13 (0.32) |
| Change in PSQ interpersonal
manner (n = 6, n = 4) |
0.17 (0.26) | 0.25 (0.29) |
| Change in PSQ communication (n = 6, n = 4) |
0.42 (0.80) | 0.63 (0.95) |
| Change in PSQ financial
aspects (n = 6, n = 4) |
0.50 (1.26) | −0.13 (0.25) |
| Change in PSQ time with
doctor (n = 6, n = 4) |
0.58 (0.80) | −0.13 (0.63) |
| Change in PSQ
accessibility/convenience (n = 6, n = 4) |
−0.42 (0.26) | −0.06 (0.38) |
Abbreviations: DDS, Diabetes Distress Scale; DQOL, Diabetes Quality of Life; PSQ, Patient Satisfaction Questionnaire.
Health Outcomes
Surprisingly, the telephone arm had a 0.50% greater reduction in HbA1c (2.07% vs 2.57%, P = .70) than the telehealth group. Although this finding was not statistically significant, it would be considered clinically significant, as the Food and Drug Administration (FDA) required a 0.3%-0.4% non-inferiority margin in drug trials for diabetes.32 It is important to note that those in the telephone arm had a higher HbA1c at baseline than those in the telehealth group: 11.1% versus 10.3%. Also, ethnic minorities are noted to have falsely elevated HbA1c due to variations in hemoglobin.33
Overall, patients in both arms had minimal depressive or diabetes distress symptoms. Interestingly, there was a discrepancy between the two surveys used for diabetes distress in the telephone arm, as noted by their average decrease on the DDS but average increase on the PAID. There were significant differences in the changes of these items from baseline to three months (Table 7).
Table 7.
Outcomes by Telehealth Versus Telephone Arm.
| Variable (n telehealth, n telephone) |
Telehealth arm mean (SD) |
Telephone arm mean (SD) |
P value |
|---|---|---|---|
| Primary | |||
| Change in A1C (n = 6, n = 6) |
−2.07 (1.99) | −2.57 (2.44) | .7051 |
| Secondary | |||
| Hypoglycemic event, n
(%) (n = 8, n = 8) |
1 (12.50) | 0 (0.00) | 1.0000 |
| Follow-up with PCP, n
(%) (n = 8, n = 8) |
6 (75.00) | 6 (75.00) | 1.0000 |
| Change in PHQ-9 total score (n = 6, n = 3) |
0.00 (5.29) | −0.33 (3.51) | .9253 |
| Change in DDS total score (n = 6, n = 4) |
−0.25 (0.63) | −0.13 (0.51) | .7743 |
| Change in PAID total score (n = 6, n = 4) |
−1.67 (24.78) | 5.63 (5.54) | .5142 |
| Change in DQOL total score (n = 5, n = 2) |
−0.10 (0.29) | −0.21 (0.32) | .6848 |
Abbreviations: DDS, Diabetes Distress Scale; DQOL, Diabetes Quality of Life; PAID, Problem Areas in Diabetes Questionnaire; PCP, primary care provider; PHQ-9, Patient Health Questionnaire-9.
Cost
Cost has been an ongoing issue in telehealth studies. The cost of having telehealth equipment and the use of a nurse cost up to 117% more than those who had outpatient care as usual.34 The per person expense of having the telehealth device was up to $8000/year.34 In this study, the HRS device cost $112.50 per month, which included the use of hardware, software, wireless peripherals, 24/7 service help, and data storage. Those in the telephone arm bore no expense, as they were called from the hospital landline or the providers’ cell phone. Due to the impact of SARS-CoV-2 (COVID-19), our health system (and many others across the country) has since implemented the use of its own telehealth technology so future studies in this area will not be of such a high cost.
Conclusion
Patients in the telehealth arm were generally satisfied with telehealth but their glycemic control improved less than the telephone arm, suggesting that tablets are not the only means of use to obtain glycemic control in those with uncontrolled diabetes. This feasibility study, although limited by a small number of participants shed light on some of the strengths and pitfalls of using telehealth in a group of persons not commonly studied in diabetes telehealth studies. It is imperative to include NHB and H/L in such studies given a disproportionately high burden of diabetes in these patients. The use of telehealth has grown to be even more necessary in the setting of the recent COVID-19 pandemic. The data collected from this study can be used to devise a larger study powered to show the benefit of telehealth use in NHB and H/L persons with diabetes.
Footnotes
Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the Empire Clinical Research Investigator Program (ECRIP).
ORCID iD: Kerry Meyers 
https://orcid.org/0000-0002-4999-067X
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