Outcomes of a Home Telehealth Intervention for Patients with Diabetes and Hypertension

Abstract

Background: Home telehealth programs often focus on a single disease, yet many patients who need monitoring have multiple conditions. This study evaluated secondary outcomes from a clinical trial evaluating the efficacy of home telehealth to improve outcomes of patients with co-morbid diabetes and hypertension. Subjects and Methods: A single-center randomized controlled clinical trial compared two remote monitoring intensity levels (low and high) and usual care in patients with type 2 diabetes and hypertension being treated in primary care. Secondary outcomes assessed were knowledge (diabetes, hypertension, medications), self-efficacy, adherence (diabetes, medications), and patient perceptions of the intervention mode. Results: Knowledge scores improved in the high-intensity intervention group participants, but upon further analysis, we found the intervention effect was not mediated by gain in knowledge. No significant differences were found across the groups in self-efficacy, adherence, or patient perceptions of the intervention mode. Conclusions: Home telehealth can enhance detection of key clinical symptoms that occur between regular physician visits. While our intervention improved glycemic and blood pressure control, the mechanism of the effect for this improvement was not clear.

Introduction

Diabetes mellitus (DM) and hypertension (HTN) are prevalent and costly illnesses. Self-management support can be provided by using home-based technologies (i.e., remote monitoring) to enable healthcare professionals to monitor patients more routinely than is possible through face-to-face office visits. Prior work has evaluated the use of remote monitoring to manage DM,^1,2 but most projects have focused on single-disease populations. The data reported in this article are drawn from a randomized controlled clinical trial that evaluated the efficacy of a low- and high-intensity remote monitoring intervention compared with usual care for improving outcomes of patients with co-morbid DM and HTN.³ This article reports on patient outcomes of knowledge, self-efficacy, and adherence and patient perceptions of the technology.

Subjects and Methods

Details of the clinical trial are reported elsewhere.³ In brief, the parent study was a single-center randomized controlled clinical trial design comparing three groups, two remote monitoring intensity levels and usual care, with respect to improvement in hemoglobin A1c (A1c) and systolic blood pressure (SBP) in participants with co-morbid DM and HTN. The local institutional review board approved the study prior to data collection.

The study was conducted at the Iowa City VA Medical Center, which provides primary, secondary, and tertiary medical, surgical, psychiatric, and neurological care to more than 36,000 veterans residing in eastern Iowa and western Illinois. The target population comprised patients with Type 2 DM and HTN being treated by a VA primary care provider. Patients were mailed a study invitation letter in advance of a scheduled primary care appointment that included a description of the study, a copy of the consent form, and contact information for study personnel; letters were followed up with a phone call from study staff. Study nurses met with patients who expressed interest at a scheduled Primary Care Clinic appointment to review the study and obtain written informed consent. Three hundred two subjects were randomized to three groups: 107 to usual care, 102 to the low-intensity group, and 93 to the high-intensity group. Of these, 85% (n=257) completed 6-month data collection, and 81% (n=246) completed 12-month data collection.

The intervention combined close surveillance via a home telehealth device and nurse care management over a 6-month time period. The home telehealth device (Viterion; Bayer/Panasonic) used a standard telephone line to enable data transmission between the patient's home and the study center. Using the device, intervention participants in both groups manually entered blood pressure (BP) and blood glucose (BG) measurements. In addition to uploading BG and BP, each day the high-intensity group received health information tips and questions using a branching algorithm programmed into the device that focused on diet, exercise, smoking cessation, foot care, advice for sick days, medications, weight management, preventive care, and behavior modification and lifestyle adjustments. The low-intensity group uploaded BG and BP and responded to two daily questions but did not receive the informational tips and questions from the algorithm. Participants received appropriate automated responses depending on how they answered the device prompt, individualized messages from the study nurses, or follow-up telephone or mailed information as needed. Usual-care subjects were followed in the Primary Care Clinic per standard protocol and had access to a nurse care manager employed by the Medical Center.

Data were collected at baseline, 6 months (end of intervention period), and 12 months (to determine maintenance of outcomes following completion of the intervention).

Measures

Baseline demographic information included age, gender, race, education, marital status, and body mass index. We also asked whether participants received assistance in managing their illness (for example, preparing meals, setting up medications, measuring BG/BP, transportation assistance).

Knowledge was assessed in two ways. First, we used a 16-item multiple choice test focused on DM and HTN. The test contained 13 items specific to DM developed by a co-investigator (M.A.) for a clinical diabetes education program; we added 3 items on hypertension. The test was administered at baseline and at 6 and 12 months. One point was assigned for each correct answer; thus scores could range from 0 to 16. Second, we asked participants about medications, an important variable affecting control of DM and HTN. At baseline, 6 months, and 12 months participants were asked, “Do you understand what your medications are for and their side effects?” This question was scored as percentage correct responses.

Self-efficacy was measured using the Self-Efficacy to Manage Disease in General scale.⁴ This scale contains 5 items that rate the patient's confidence in managing a chronic illness using Likert-type scale responses. Developed by the Stanford Patient Education Research Center, initial scale items were constructed using items from existing measures, investigator experience, and focus groups. The scale was tested using multitrait scaling in a large sample of health maintenance organization clinic patients (n=1,130). All item-scale correlations (internal consistency reliability) exceeded 0.50. Scores range from 1 to 10, with higher scores indicating greater confidence in managing disease.

Adherence was measured in two ways. First, we used a validated DM regimen adherence scale⁵ that addresses medication, diet, exercise, and BG testing. This scale was validated in a sample of 181 patients (95% white, 46% male, 60% type 1 DM). All 6 items were combined into one scale score with moderate reliability (alpha=0.73). Each item is measured using a scale that ranges from 1=never to 5=always adherent. Mean scores were calculated for each participant; higher scores indicate greater adherence with recommendations. Because the Edwards Regimen Adherence Scale⁵ only addressed DM and because medications are an important therapy for HTN, at baseline, 6 months, and 12 months participants were asked, “Are you taking your medications as prescribed?” Adherence scores represent the proportion of medications for which the responses agreed with the directions for use on the VA Pharmacy medication profile. This approach has been successfully used in prior studies and was found to be comparable to examining the timeliness of medication refills.⁶

Perceptions of the technology were assessed using the Telemedicine Patient Questionnaire (TMPQ).⁷ Participants in the two intervention groups completed this home telehealth-specific survey at the end of the 6-month intervention period. The survey is a 17-item instrument with Likert-type scales. Each item was rated on a scale from 1 (strongly disagree) to 5 (strongly agree). In preliminary testing on 32 patients, a 20-item form of the instrument had acceptable reliability (Cronbach's alpha of 0.8), but three items were redundant. The revised 17-item instrument tested on 10 patients had high test–retest reliability (r=0.98).

Statistical Analysis

Data were double-entered, and discrepancies were reconciled. Descriptive statistics were calculated for continuous variables, and the three groups were compared using analysis of variance with respect to demographics. Data for the three groups were compared using Kruskal–Wallis tests for receipt of assistance, knowledge, self-efficacy, and adherence. TMPQ scores between the low- and high-intensity groups were compared using a t test.

Results

Study subjects were mostly male (98%), white (96%), and married (66%) with a mean age of 68 years (standard deviation=10 years; range, 40–89 years). Most had a high school education or higher (89%). Body mass index was high across all participants at baseline (33.9, 33.1, and 33.2 kg/m² for control and low- and high-intensity groups, respectively). There were no statistically significant differences across the three groups for any baseline measure.

Intervention subjects (low and high intensity) had significantly decreased A1c during the 6-month intervention period compared with the control group, but 6 months after the intervention was withdrawn the intervention groups were comparable with the control group. For SBP the high-intensity subjects had a significant decrease in SBP compared with the other groups at 6 months, and this pattern was maintained at 12 months.

Regarding questions on assistance received, in response to the question, “Do you have someone who helps take care of your DM and/or HTN,” 131 participants (45%) responded yes; of those, most helpers were spouses (n=112). Of all 302 respondents, the percentages of those receiving help were as follows: preparing meals, 33% (n=101); setting up medications, 15% (n=45); help with transportation, 15% (n=44); measuring BP, 11% (n=33); and measuring BG, 7% (n=20), indicating a relatively independent group of participants. There were no significant differences across groups for these tasks.

Knowledge scores using the multiple-choice test were comparable at baseline. There were significant differences at 6 months, with the high-intensity group scoring higher compared with the other two groups (see Knowledge scores in Table 1); however, these differences were not maintained at 12 months. In our previous work we found that change-score means for A1c at 6 months and for SBP at 6 and 12 months statistically differed between the intervention groups (p=0.03, 0.004, and 0.02, respectively).³ Motivated by the significant associations between knowledge score outcomes and intervention group, we investigated if the significant outcome–intervention associations might be mediated by knowledge. Specifically, we investigated if the effect of the intervention on change in SBP from baseline to 6 months was mediated by the change in composite knowledge between baseline and 6 months, and whether the effect of the intervention on change in SBP from baseline to 12 months was mediated by the change in composite knowledge from baseline to either 6 or 12 months.

Table 1.

Scores for Knowledge, Self-Efficacy, Adherence, and Patient Perceptions at Baseline, 6 Months, and 12 Months

	CONTROL (N=107)	LOW-INTENSITY (N=102)	HIGH-INTENSITY (N=93)	P VALUE
Knowledge scores^a
Baseline	13.5 (2.1)	13.4 (2.1)	13.5 (1.8)	0.64
6 months	13.9 (1.5)	13.7 (1.7)	14.4 (1.7)	0.02
12 months	13.9 (1.8)	13.7 (1.8)	14.1 (1.7)	0.41
Medication knowledge^b
Baseline	87.6 (18.0)	87.2 (18.0)	88.3 (16.8)	0.93
6 months	87.3 (18.7)	87.1 (17.0)	89.1 (14.8)	0.81
12 months	89.9 (18.0)	88.7 (16.3)	89.4 (18.0)	0.77
Self-efficacy scores^c
Baseline	8.0 (2.1)	8.1 (1.8)	7.6 (1.9)	0.09
6 months	8.1 (1.8)	8.1 (1.9)	7.7 (2.0)	0.19
12 months	8.3 (1.9)	8.3 (2.0)	7.8 (1.9)	0.53
Adherence on Edward's scale^d
Baseline	3.2 (0.6)	3.4 (0.5)	3.2 (0.5)	0.20
6 months	3.3 (0.5)	3.4 (0.4)	3.2 (0.5)	0.21
12 months	3.3 (0.5)	3.4 (0.6)	3.3 (0.4)	0.09
Medication-taking adherence^e
Baseline	99.5 (4.4)	99.7 (1.6)	99.7 (2.7)	0.65
6 months	99.6 (2.2)	99.8 (1.4)	99.6 (2.0)	0.79
12 months	98.9 (6.0)	99.7 (1.4)	100 (0)	0.20

Data are mean (standard deviation) values.

Maximum possible score=16.

Percentage of medications' purpose and side effects correctly reported.

Maximum possible score=10, with higher scores indicating greater self-efficacy.

Score range 1–5, where higher scores indicating greater adherence.

Percentage of medications reported taking according to directions.

We tested for mediation by estimating three linear regression models: (1) composite knowledge change score on intervention; (2) SBP change score on intervention; and (3) SBP change score on intervention and composite knowledge change score. According to Baron and Kenny,⁸ composite knowledge change score is a mediator if there are significant associations from the first two regressions and if the association between SBP and intervention in the third regression, adjusted for composite knowledge, is noticeably decreased in comparison with the (unadjusted) association between SPB and intervention in the second model.

From estimating the first regression model we found that 6-month and 12-month composite knowledge change-score variables were significantly associated with the 12-month SBP change-score variable, but 6-month composite knowledge change was not associated with 6-month SBP change. From our previous work we had already estimated the second regression model and found both 6- and 12-month SBP change to be significantly associated with intervention. Based on these results we need only consider if 6- and 12-month knowledge change is a mediator for 12-month SBP change for the third regression model. We regressed 12-month SBP on intervention and 6-month composite knowledge change, and we regressed 12-month SBP on intervention and 12-month composite knowledge change. For both of the estimated models, intervention remained significantly associated with 12-month SBP (p=0.007 and 0.02, respectively, for the intervention effect, corresponding to having 6- and 12-month knowledge change in the model). Because the intervention p values for Model 3 are similar to those for Model 2, we conclude that the intervention effect was not mediated by gain in knowledge.

Regarding medication knowledge, on average, participants in all three groups could correctly list all of their current medications (data not shown), but slightly fewer than 90% of participants in each group correctly reported the purpose and side effects of their medications (see Medication knowledge in Table 1). However, there were no significant differences across the three groups in medication knowledge at any time point.

Self-efficacy scores were higher in the control and low-intensity groups compared with the high-intensity group at all three measurement points (baseline, 6 months, and 12 months (Table 1). However, none of these differences was significant.

Scores on the Edwards Regimen Adherence Scale were not significantly different across groups at any of the three time points (Table 1). Reported medication adherence was high across the groups at all three times points, but there were no significant differences across the three groups (see Medication-taking adherence in Table 1).

Comparing only the low- and high-intensity intervention participants, there were no significant differences between the two groups in TMPQ scores (low-intensity mean=3.8; high-intensity mean=3.7).

Discussion

Although the intervention subjects had improved A1c at 6 months and the high-intensity subjects had a significant decrease in SBP at both 6 and 12 months, there were few differences across the groups in knowledge, and knowledge did not act as a mediator in explaining the intervention effect. There were no differences in self-efficacy or adherence across the groups at any time point.

The high-intensity group received daily educational tips and a greater number of DM and HTN questions daily, and knowledge scores in this group improved significantly at 6 months. Although we did not directly ask participants if they incorporated information from the daily information and questions to make changes in their disease management, the high-intensity group improved in both A1c and SBP at 6 months and maintained improvement in SBP at 12 months. DM education is beneficial and cost-effective⁹ but is generally not sufficient alone to change health behaviors. Interventions using regular reinforcement, such as this study where participants sent data on a daily basis and received feedback, may be more effective in improving glycemic control.¹⁰

Self-efficacy did not improve significantly during the study, even though study nurses used self-efficacy enhancement strategies during the contacts with the intervention participants. Self-efficacy scores in our sample were relatively high at baseline (ranging from 7.6 to 8.1 on a 10-point scale); thus there was little room for improvement. Other studies in veterans have found an association between higher self-efficacy and better adherence to diabetes management behaviors such as medications, exercise, and a low fat diet.¹¹ Thus we believe that interventions focused on enhancing self-efficacy warrant further attention.

Studies have shown an association between adherence and patient–provider communication.¹² Prior work in a similar non-veteran population found that adherence with antihypertensive medication was associated with patient–provider communication (i.e., nonadherent participants reported discomfort asking questions and were more likely to report that healthcare visits were stressful).¹³ Another study, conducted in the veteran population, found that collaborative and proactive communication between patients and providers was associated with improved HTN control in patients with diabetes, independent of medication adherence.¹⁴ Studies assessing the relationship between outcomes such as adherence and communication with clinicians have almost exclusively focused on physicians. Healthcare is increasingly adopting alternatives to the traditional office visit such as disease-focused collaboratives¹⁵ and the patient-centered medical home where the roles of nonphysician providers such as nurses and pharmacists are being expanded to include chronic disease management. Our participants reported relatively high adherence with their self-management regimen and especially with medication-taking behavior throughout the study period, but the role of nurse–patient communication in enhancing adherence in this sample is not clear. Given the expanding role of nurses and pharmacists in chronic disease management approaches such as the patient-centered medical home, future studies should focus on how communication with providers other than physicians affect adherence in patients with chronic disease.

Although the high-intensity group received more information and questions daily on the home telehealth device, there were no differences between the two intervention groups in perceptions of the intervention using TMPQ scores. Intervention participants rated the program positively with a few criticisms and suggestions for improvement mostly related to the technology.¹⁶ These findings are consistent with perceptions of participants enrolled in the VA remote monitoring program.¹⁷

In conclusion, participants in both intervention groups improved glycemic control, and the high-intensity group also improved BP control. Although knowledge scores improved in the high-intensity group, it is unlikely that improved knowledge was the sole reason for improvement in clinical outcomes, and our mediation analysis supports that conclusion. Home telehealth can facilitate detection of key clinical symptoms that occur between regular physician visits. Several studies have evaluated the effectiveness of home telehealth in chronic disease management, and results have been mixed.^1,2 Thus, the mechanism of effect for improvement in our study is not clear. Future studies should provide detailed descriptions of intervention components in order to determine how and why these interventions work.

Footnotes

Acknowledgments

The research reported here was supported by the Department of Veterans Affairs, Veterans Health Administration, the Health Services Research and Development (VA HSR&D) Service (number NRI 03-312) and a VA HSR&D Career Development Award to B.W., and the VA HSR&D Center for Comprehensive Access & Delivery Research and Evaluation at the Iowa City VA Medical Center, Iowa City, IA.

Disclosure Statement

No competing financial interests exist.

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