A Randomized Trial on Home Telemonitoring for the Management of Metabolic and Cardiovascular Risk in Patients with Type 2 Diabetes

Abstract

Background:

This study evaluated whether a home telehealth (HT) system enabling the patient to monitor body weight, blood glucose values, and blood pressure values, associated with remote educational support and feedback to the general practitioner, can improve metabolic control and overall cardiovascular risk in individuals with type 2 diabetes mellitus, compared with usual practice.

Materials and Methods:

This was a randomized, parallel-group (1:1), open-label, multicenter study conducted in general practice. Follow-up was for 12 months.

Results:

Overall, 29 general practitioners enrolled 302 patients (153 assigned to the HT group and 149 to the control group). Use of the HT system was associated with a statistically significant reduction in glycated hemoglobin (HbA1c) levels compared with the control group (estimated mean difference, 0.33±0.1; P=0.001). No difference emerged as for body weight, blood pressure, and lipid profile. The proportion of patients reaching the target of HbA1c <7.0% was higher in the HT group than in the control group after 6 months (33.0% vs. 18.7%; P=0.009) and 12 months (28.1% vs. 18.5%; P=0.07). As for quality of life (evaluated with the 36-item Short Form health survey), significant differences in favor of the HT group were detected as for physical functioning (P=0.01), role limitations due to emotional problems (P=0.02), mental health (P=0.005), and mental component summary (P=0.03) scores. A lower number of specialist visits was reported in the telemedicine group (incidence rate ratio, 0.72; 95% confidence interval, 0.51–1.01; P=0.06).

Conclusions:

Use of the HT system was associated with better metabolic control and quality of life; a marginally nonsignificant lower resource utilization was also documented. No impact was documented on blood pressure, lipid profile, and body weight.

Introduction

The growing prevalence of diabetes, particularly among elderly people, represents a major challenge in all countries, and healthcare systems are facing a profound reorganization to incorporate the principles of the chronic care models. These models emphasize the centrality of the person with diabetes and his or her active involvement and responsibility in managing the disease, made possible by adequate education and supported by telemedicine, enabling a continuous exchange of information between patient and provider, while reducing the need for office visits.^1
–3

The effectiveness of different telemedicine systems in the management of chronic diseases has been clearly documented. In particular, the implementation of home monitoring systems within studies involving patients with diabetes has led to substantial reductions in costs, made possible by a decrease in the number of emergency room visits, hospital admissions, and office visits.^4

–7 In addition to the positive impact on resource consumption, an improvement in metabolic control has also been documented.^8

–12 In individuals with diabetes, telemedicine can also facilitate the transmission of data regarding self-monitoring of blood glucose, with real-time feedback from the healthcare professional. This aspect is of particular importance in individuals with type 2 diabetes mellitus not treated with insulin, in whom the role of self-monitoring of blood glucose is still a matter of debate,¹³ because it puts the patient in the position of acting in response to particularly elevated or low blood glucose values, by modifying food intake, level of physical activity, or therapy.

The opportunity to utilize telemedicine systems combining home monitoring with remote educational interventions promoting self-care and treatment compliance thus represents a particularly appealing innovation. Furthermore, although home monitoring systems evaluated so far addressed one specific problem (i.e., diabetes, heart failure, chronic obstruction pulmonary disease), more comprehensive interventions including the monitoring of multiple parameters for the global management of cardiovascular risk would represent a very important, but seldom-evaluated, approach.⁸ In addition, existing evidence largely derives from studies performed in academic medical centers, and data demonstrating the effectiveness of diabetes interventions in general practice, where barriers to quality care may differ from the academic context,^14
–16 are still lacking. An intervention proven to simultaneously impact diabetes and hypertension outcomes in the community setting could be widely adopted and produce important results in terms of morbidity and resource utilization.

The main purpose of this randomized study is to evaluate whether the use of a home telemedicine system, which allows patients to monitor weight, blood glucose, and blood pressure, associated with a support system and with a remote educational telehealth system accessible by the family doctor, may improve glycemic control (glycated hemoglobin [HbA1c]) and cardiovascular risk profile in patients with type 2 diabetes, compared with the usual care provided by the general practitioner.

Materials and Methods

End points

The primary end point is represented by HbA1c levels after 6 and 12 months from randomization.

Secondary efficacy outcomes include:

• Percentage of patients with HbA1c at target (HbA1c <7.0%)

• Percentage of patients with blood pressure at target (<130/80 mm Hg)

• Percentage of patients with low-density lipoprotein (LDL) cholesterol at target (LDL cholesterol <100 mg/dL)

• Changes in body weight

• Changes in blood pressure

• Changes in levels of blood lipids (total cholesterol, LDL cholesterol, and triglycerides)

• Number of emergency room visits and hospital admissions

• Number of office visits and home visits

• Changes in the use of medications for diabetes, hypertension, and dyslipidemia

• Changes in the 36-item Short Form (SF-36) health survey scores.

Safety end points include the number of patients with severe hypoglycemic episodes, the total number of severe hypoglycemic episodes, and adverse events profile.

Study design

This is a randomized, parallel-group (1:1), open-label, multicenter study conducted in the area of general practice.

Inclusion and exclusion criteria

Patients were considered eligible for the study if they met the following criteria:

• Subjects with type 2 diabetes, defined according to the criteria of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus¹⁷

• Both sexes

• Age >45 years

• In treatment with sulfonylureas, alone or in association with other oral hypoglycemic agents, or treated with basal insulin, alone or in association with oral hypoglycemic agents

• Able to perform blood glucose self-monitoring

• HbA1c between 7.5% and 10%

• Blood pressure >130/80 mm Hg regardless of the presence of antihypertensive treatment

• Signature of the specific informed consent for the study.

Patients were excluded from enrollment if they met one or more of the following criteria:

• Diabetes mellitus treated only with lifestyle intervention, or with monotherapy with metformin, glitazones, dipeptidyl peptidase-4 inhibitors, or glucagon-like peptide-1 analogs

• Multiple injections of insulin

• Mental conditions, depression, or high anxiety such as to render the subject incapable of understanding the nature, purpose, and possible consequences of the study

• Inability to use the telemedicine system

• Pregnancy

• Major cardiovascular event in the last 6 months (heart attack, stroke, intervention of coronary, carotid, or peripheral vascular reperfusion/revascularization)

• Any serious health condition that substantially reduces life expectancy

• Any disease or condition, including abuse of drugs or alcohol, that in the opinion of the investigator could interfere with the completion of the study

• Nonadherence to the protocol (e.g., unreliability, inability to attend follow-up visits, and unlikely to complete the study procedures).

Randomization

Upon verification of inclusion and exclusion criteria, patients were randomized in a 1:1 ratio to the home telemonitoring system or to usual care. Group allocation was based on centralized telephone randomization, stratified by participating physician and by treatment (oral agents, insulin). Permuted blocks randomization was used.

Study intervention

Home telehealth (HT) is a system that applies information and communication technologies to bring care in the household. In line with the new requirements for primary care organization and the adoption of “chronic care models,” HT can represent an important tool for the delivery of home care. In fact, HT

• Facilitates the exchange of information between patient and provider

• Provides the doctor with remote diagnostic tools and allows remote monitoring of parameters and vital signs (weight, blood pressure, blood sugar, etc.)

• Allows access to patient data from any site

• Allows the monitoring of patient compliance with therapy

• Represents a source for patient education and decision support.

The telemedicine system consists of three main elements: the patient, the doctor, and a central system of information and interface with both the patient and the doctor (telehealth center).

Patients randomized to the telemedicine system received a weight scale (model uc321 Precision; A&D Medical, San Jose, CA), a glucometer (model G31; Fora Care, St. Gallen, Switzerland), and a sphygmomanometer (model ua 767 Plus; A&D Medical) and were instructed on their proper use. The tools are ready to use (plug-and-play) and are connected via Bluetooth^® (Bluetooth SIG, Kirkland, WA) to a hub (Hermes; H&S, Milan, Italy) that transmits in real time the information to the central system, where all data are stored and made available to the general practitioner and the patient. The instruments have internal memory, to allow their use in any place, with data downloaded at the subsequent connection with the telehealth center. The patient was also given a “call me” button through which he or she could ask to be called by the telehealth center at any time, 24 h/day. In case of emergencies, the staff of the telehealth center could contact the doctor or the patient's family, according to a prespecified protocol.

The general practitioner, who remains the sole professional responsible for any medical decision, can connect periodically to the central system and get all the information about the patients managed with the telemedicine system.

The telehealth center is the place where all information originating from the patient is stored in a protected database. The data may be read by the patient him- or herself and the family and by the general practitioner, or by other specialists upon prior authorization from the patient. The system is able to generate a series of reports that include the following:

• Basic statistics (e.g., number of measurements made, percentage of measurements below, within or above a predetermined range, etc.)

• Graphical representation of trends over time for the measured parameters

• Automatically filled-in logbooks that show the individual measurements according to the hours of the day and indicate whether the value is above, below, or within the standard range

• Detailed lists of the patient's historical readings, also viewable by meal slot, and for a certain time frame.

In agreement with the doctor, it is possible to generate remainders, notifications, or warning messages for the physician and/or the patient. These messages can be used to alert the doctor when a patient has entered his or her measurements, to warn if the patient has not entered predefined measurements, or if alarm values are recorded (e.g., blood glucose level of less than 60 mg/dL). The central system can also generate remainders for the patient, to encourage adherence to therapy. All types of messages can be sent by text message, e-mail, or telephone.

In the telehealth center are trained nurses who are responsible for contacting the patients once a month by phone, to discuss with them the results of self-monitoring and to identify possible barriers to compliance or possible causes of inadequate metabolic control or pressure. The phone calls are made using a standardized form.

Study procedures

The study was conducted according to the Declaration of Helsinki. The study protocol was approved by the local ethics committees of the participating physicians. A written informed consent was signed by all eligible patients before randomization. Patients allocated to the telemedicine group received all the study material and were instructed to send the data to the telehealth center twice every month. Instructions on the use of the telemedicine system were provided through a 30-min telephone education session conducted by the trained nurses operating in the telehealth center. Patients allocated to the control group continued to be followed by their general practitioner as usual. Clinical information was collected at baseline, after 6 months, and after 12 months, using an ad hoc clinical record form. Blood samples were collected on the same occasions, and HbA1c levels and lipid profiles were measured in a centralized laboratory. During the visits, patients were requested to fill in the SF-36 health survey. Patients were recruited between October 2011 and September 2012.

SF-36 health survey

The SF-36 is one of the most widely used measures of health-related quality of life (QoL) and consists of 36 items covering eight dimensions: physical functioning, role limitations caused by physical health problems, bodily pain, general health perception, vitality, social functioning, role limitations caused by emotional health problems, and mental health.¹⁸ These eight domains may be further aggregated into two summary measures: the Physical Component Summary measure and the Mental Component Summary measure.¹⁸ These aggregated scores are transformed to norm-based scores (mean, 50; SD, 10), with higher scores indicating more favorable physical functioning/psychological well-being.

Statistical methods

Descriptive data are reported as mean±SD values or percentages. Baseline characteristics were compared between groups by using Student's t test (continuous, normally distributed variables), the Mann–Whitney U test (continuous, non-normally distributed variables), or the χ² test (categorical variables).

Continuous outcome measures were compared between groups using general linear models for repeated measures, adjusted for baseline value. Between-group differences are expressed as mean±SE values.

Resource utilization is expressed as number of hospital/emergency room admissions, home visits, and specialist visits per person-year. Between-group comparison was performed using Poisson regression via generalized linear models, and results were expressed as incidence rate ratios with 95% confidence intervals.

Sample size estimation

The study was designed to have a 90% statistical power to detect a between-group difference of 0.3 in the levels of HbA1c during a follow-up period of 12 months. The initial sample size estimation was based on the assumption that the SD of the HbA1c measure at baseline be 1.0%, leading to a sample size estimate of 468 patients. From the analysis of baseline data relative to the first 300 patients enrolled, it emerged that the observed SD was 0.76, thus requiring 136 patients per group to ensure a statistical power of 90% (α=0.05) to detect a difference of 0.3. Therefore enrollment was terminated prematurely because adequate statistical power was ensured by the sample available.

Results

Overall, 29 general practitioners from two health districts enrolled 302 patients, of whom 153 were assigned to the telemedicine group and 149 to the control group. Shortly after randomization, 39 subjects in the telemedicine group and 14 subjects in the control group withdrew their consent. Consequently, 82.5% of the total sample completed the 12-month assessment. Study disposition is shown in Supplementary Figure S1 (Supplementary Data are available online at www.liebertonline.com/dia). Patients who dropped out were slightly older than those who completed the study (62±9 vs. 58±10 years; P=0.01) and were more likely to be female (54.7% vs. 34.9%; P=0.007) but did not differ in terms of HbA1c (P=0.39) or body mass index (P=0.84) levels. Patient characteristics according to study arm are reported in Table 1. Patients in the control group had slightly higher diastolic blood pressure values and were more likely to have dyslipidemia. The exclusion of patients who dropped out prematurely did not create any imbalance between the two groups, which remained comparable for all the characteristics but dyslipidemia (Supplementary Table S1).

Table 1.

Patient Characteristics According to Study Arm

Characteristic	Telemedicine	Control	P
Number of subjects	153	149
Age (years)	59.1±10.3	57.8±8.9	0.25
Males (%)	61.4	61.7	0.96
BMI (kg/m²)	28.7±4.6	29.0±5.0	0.65
Diabetes duration (years)	8.3±6.2	8.7±6.2	0.54
Smoking	9.2	7.4	0.58
HbA1c (%)	7.9±0.7	8.0±0.8	0.78
Blood pressure (mm Hg)
Systolic	142.3±7.5	143.0±9.5	0.50
Diastolic	86.2±4.0	87.2±4.6	0.05
Lipids (mg/dL)
Total cholesterol	188±40	182±38	0.18
HDL cholesterol	52±13	52±14	0.97
LDL cholesterol	124±34	119±33	0.16
Triglycerides	140±70	149±77	0.62
Hypertension (%)	76.5	77.9	0.78
Dyslipidemia (%)	36.6	49.7	0.02
Diabetes complications
Retinopathy	4.6	6.7	0.42
Neuropathy	6.5	6.0	0.86
Nephropathy	2.0	5.4	0.11
Foot ulcer	0.7	0.7	0.97
Cardiovascular complications
Myocardial infarction	3.9	8.1	0.12
Coronary heart disease	7.2	8.7	0.62
Congestive heart failure	4.6	3.4	0.59
Stroke/TIA	1.3	3.4	0.28
Peripheral vascular disease	3.9	6.0	0.40
Diabetes treatment
Metformin	92.2	94.0	0.54
Secretagogues	40.5	46.3	0.31
Pioglitazone	13.1	14.1	0.80
DPP-4 inhibitors	10.5	9.4	0.76
GLP-1 agonists	2.0	2.7	0.72
Basal insulin	7.8	9.4	0.63
Diabetes treatment scheme			0.64
Monotherapy with
Metformin	40.5	36.9
Secretagogues	5.2	2.0
Other monotherapy	0.7	1.3
Dual oral	37.3	37.6
Triple oral	6.5	10.1
GLP-1 agonists+oral	2.0	2.7
Oral+basal insulin	7.8	9.4

BMI, body mass index; DPP-4, dipeptidyl peptidase-4; GLP-1, glucagon-like peptide-1; HbA1c, glycated hemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TIA, transient ischemic attack.

Use of the telemedicine system

Overall, 2,986 contacts from the patients to the telehealth center were recorded (average of 2.2 contacts per patient-month), whereas 11,214 contacts from the telehealth center to the patients were made (average of 8.2 contacts per patient-month).

Over a period of 12 months, the use of telemedicine was associated with a statistically significant reduction in average HbA1c levels compared with the control group (estimated mean difference of 0.33±0.1) (Table 2). No difference emerged as for body weight, blood pressure, and lipid profile (Table 2). The proportion of patients reaching the target of HbA1c <7.0% was higher in the telemedicine group after 6 months (33.0% vs. 18.7%; P=0.009) and 12 months (28.1% vs. 18.5%; P=0.07). No significant differences emerged between groups in the percentage of patients reaching a blood pressure target of <130/80 mm Hg and an LDL cholesterol target of <100 mg/dL (Supplementary Table S2).

Table 2.

Change in Clinical Measures in Control and Telemedicine Subjects After 6 and 12 Months

	Telemedicine			Control			Estimated mean
Parameter	Baseline	6 months	12 months	Baseline	6 months	12 months	difference ^a	P
HbA1c (%)	7.94±0.8	7.32±0.8	7.44±1.0	7.99±0.8	7.70±0.9	7.78±1.1	−0.33±0.1	0.001
Body weight (kg)	82.9±15.6	82.0±15.3	82.2±15.4	82.6±15.6	81.7±14.8	81.3±14.3	0.23±0.51	0.66
Blood pressure (mm Hg)
Systolic	142.4±7.3	136.1±10.3	135.7±9.6	143.2±9.7	135.6±13.0	135.9±12.4	0.67±1.1	0.58
Diastolic	86.6±3.7	79.4±7.6	79.6±7.2	87.2±4.8	79.7±6.3	79.2±7.0	0.37±0.73	0.62
Lipids (mg/dL)
Total cholesterol	188±40	184±41	184±40	183±38	183±40	179±35	−0.28±3.38	0.94
HDL cholesterol	52.0±12.9	50.1±12.8	51.3±12.9	51.7±13.8	48.7±14.0	50.1±11.9	1.1±0.8	0.21
LDL cholesterol	125±35	123±32	118±33	120±33	119±34	114±30	0.7±2.7	0.77
Triglycerides	149±73	135±55	141±80	150±79	157±117	148±89	−13.8±8.8	0.12

Telemedicine – control.

HbA1c, glycated hemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein.

The analysis of therapy changes during the study did not reveal between-group differences for any of the drug classes investigated, with the only exception being a larger use of fibrates in the telemedicine group (Supplementary Table S3). Of note is that the proportion of patients treated with basal insulin almost doubled in the control group, whereas it did not change in the telemedicine group.

In terms of QoL, all SF-36 scores improved in the telemedicine group but not in the control group (Table 3). In particular, a statistically significant improvement compared with baseline was detected at 6 months for physical functioning, role limitations due to physical problems or to emotional problems, mental health, Physical Component Summary score, and Mental Component Summary score. At 12 months, a significant improvement versus baseline was still present for mental health; a marginally nonsignificant difference also persisted for the Mental Component Summary score. Between-group comparison during the 12 months showed significant differences in favor of the telemedicine group for physical functioning, role limitations due to emotional problems, mental health, and Mental Component Summary score (Table 3).

Table 3.

Change in 36-Item Short Form Scores in Control and Telemedicine Subjects After 6 and 12 Months

	Control			Telemedicine			Estimated mean
Scale	Baseline	6 months	12 months	Baseline	6 months	12 months	difference ^a	P
PF	75.7±22.8	73.1±26.3	73.5±25.4	74.9±24.0	78.9±21.9	79.4±24.3	8.2±3.2	0.01
P vs. baseline	—	0.21	0.39		0.02	0.09
RP	71.6±37.5	71.6±36.8	64.0±40.4	62.5±42.1	77.5±36.4	75.2±36.0	9.1±5.2	0.09
P vs. baseline		0.48	0.09		<0.0001	0.11
BP	67.0±25.1	69.4±27.7	70.1±28.3	69.0±29.0	71.4±25.6	70.1±26.9	−0.6±3.6	0.86
P vs. baseline		0.14	0.09		0.33	0.55
GH	53.5±19.9	53.7±18.8	52.6±20.8	53.9±20.3	58.7±21.2	59.1±16.6	3.9±2.6	0.14
P vs. baseline		0.63	0.43		0.26	0.29
VT	57.5±19.3	58.9±18.9	56.1±20.1	58.4±20.2	63.8±21.3	62.1±20.1	4.2±2.7	0.13
P vs. baseline		0.11	0.72		0.23	0.40
SF	83.3±22.1	84.3±18.3	82.7±23.3	80.6±24.5	82.8±24.1	85.4±22.3	2.3±3.1	0.46
P vs. baseline		0.80	0.91		0.64	0.27
RE	71.1±36.8	71.7±37.6	64.0±41.0	72.5±37.8	79.6±36.2	79.5±36.0	12.6±5.1	0.02
P vs. baseline		0.46	0.11		0.04	0.27
MH	67.7±19.1	66.6±18.7	64.8±19.6	67.5±18.4	73.1±20.5	71.4±21.0	7.4±2.6	0.005
P vs. baseline		0.96	0.48		0.01	0.006
PCS	45.3±8.7	45.5±9.3	45.4±10.2	44.3±10.2	46.4±8.7	46.9±8.8	0.6±1.4	0.66
P vs. baseline		0.68	0.90		0.04	0.50
MCS	48.0±10.1	48.2±9.7	46.7±10.2	48.6±10.0	50.4±9.9	50.0±10.8	3.4±1.5	0.03
P vs. baseline		0.82	0.48		0.02	0.07

Telemedicine – control.

BP, Bodily Pain; GH, General Health; MCS, Mental Component Summary score; MH, Mental Health; PCS, Physical Component Summary score; PF, Physical Functioning; RE, Role Emotional; RP, Role Physical; SF, Social Functioning; VT, Energy/Vitality.

As for resource utilization, the two groups did not significantly differ in terms of hospital admissions and home visits, although a marginally nonsignificant lower number of office visits was reported in the telemedicine group (Table 4). In particular, individuals in the control group were 39% more likely to require a specialist visit than individuals in the telemedicine group.

Table 4.

Resource Usage During a 12-Month Interval by Control and Telemedicine Subjects

	Control		Telemedicine
	n	IR	n	IR	IRR (95% CI)	P
Hospitalization/ER	14	0.04	11	0.04	1.08 (0.49–2.37)	0.86
Home visits	9	0.02	8	0.03	0.95 (0.37–2.46)	0.92
Specialist visits	87	0.24	53	0.17	1.39 (0.99–1.95)	0.06
Total	110	0.30	72	0.23	1.29 (0.96–1.74)	0.09

CI, confidence interval; ER, emergency room; IR, incidence rate per person/year; IRR, incidence rate ratio.

No safety problems were detected during the study. In particular, no episode of severe hypoglycemia was recorded.

Discussion

Key findings

This randomized trial, conducted in the primary care setting, shows that a telemedicine system combining home monitoring with remote educational interventions promoting self-care is effective in improving metabolic control over a 12-month interval in individuals with type 2 diabetes. Compared with usual care, a larger proportion of patients in the telemedicine group reached the HbA1c target after 6 and 12 months, without significant differences in terms of drugs used and without safety concerns. Considering the very small changes in therapy during the follow-up, we can hypothesize that the intervention was primarily effective as an educational tool and a support to the patient. The use of telemedicine was associated with improved QoL and a marginally nonsignificant lower number of office visits. No major changes in body weight and lipid profile were detected during the study, although systolic and diastolic blood pressure values improved substantially in both groups. We can speculate that improvement in emotional well-being, perhaps associated with a better adherence to treatment and lifestyle recommendations, is responsible for the better metabolic control in the experimental group, despite the lack of substantial therapy changes.

Comparison with other studies

Our results are in line with a recent systematic review and meta-analysis,¹² showing that the use of telemedicine was associated with improved glycemic control in patients with diabetes, with no significant effects on blood pressure, LDL cholesterol, and body weight. The benefits of telemedicine documented in our study (−0.33% vs. usual care) fall in the range of those documented in this meta-analysis (−0.44; 95% confidence interval, −0.26, −0.61).

One systematic review has investigated the effect of telehealth on health-related QoL in diabetes.¹⁹ This review included two patient outcomes with different meanings: health-related QoL and patient satisfaction. Of only five studies that actually measured health-related QoL, three found no difference between telephone support and usual care,^20
–22 one failed to report differences between telehealth and usual care,²³ and one pre-/postintervention study of telehealth found significant improvements on only three of eight SF-36 subscales (Role-Physical, Bodily Pain, and Social Functioning).⁷ More recently, a nested analysis of a large cluster randomized trial, involving 448 patients with diabetes, was unable to show any benefit of telemedicine compared with usual care in terms of QoL improvement.²⁴ Our study represents the strongest evidence that the use of telemedicine can be associated with a marked positive effect on different domains of QoL, first of all, psychological well-being. The chance to have a privileged channel of communication with the carer likely provides reassurance to the patient, thus leading to improved emotional well-being. The improvements in metabolic control may have also had a positive impact on psychological outcomes.

Strengths and weaknesses

The major strength of our study is represented by the evaluation of the telemedicine system in the primary care setting, under routine clinical practice conditions, thus providing information on the effectiveness of telemedicine in the real world. Furthermore, the effectiveness of the system was evaluated by taking into consideration a wide array of different outcomes including clinical, QoL, and resource utilization measures.

The study also has limitations. Some of the patients withdrew their consent shortly after randomization, mainly for the complexity of the study design or the difficulty in using the telemedicine tools. Nevertheless, this did not create any imbalance between the two groups. A few patients also abandoned the study because of emotional problems caused by using the system; this may have led to an overestimation of the positive effects of telemedicine on QoL. These findings suggest that a careful assessment of the attitudes, beliefs, and skills of the individual patient is fundamental before proposing the use of telemedicine.

As a second limitation, we could not assess the types of interactions and to what degree primary care providers looked at patient data and took actions in response to the results of monitoring.

Finally, a 12-month observation is a fairly limited amount of time to detect significant impact on hospitalizations and emergency room access. Nevertheless, from a clinical perspective the study showed a marginally nonsignificant lower number of office visits in the telemedicine group; the lower resource utilization needs to be confirmed in a future extensive research program.

Conclusions

The aging of the population and the growing number of individuals with chronic diseases call for innovative, effective models of care delivery, able to ensure the continuity of care while making a rational use of limited resources. Telemedicine can represent a valid solution, by facilitating the monitoring and management of long-term conditions, while improving patient knowledge and self-care. Additional research will help to better understand the role of telemedicine in elderly individuals with multiple chronic conditions. A clearer identification of subgroups of patients more likely to benefit from telemedicine is also needed.

Footnotes

Acknowledgments

This study was supported by a research grant to A.N. from MSD Italia.

Author Disclosure Statement

S.C. is an employee of MSD Italia. A.N., A.C., F.M., and G.G declare no competing financial interests exist.

Appendix

References

Bodenheimer

, Wagner

, Grumbach

: Improving primary care for patients with chronic illness. JAMA, 2002; 288:1775–1779.

Bodenheimer

, Wagner

, Grumbach

: Improving primary care for patients with chronic illness: the Chronic Care Model, Part 2. JAMA, 2002; 288:1909–1914.

Hebert

, Korabek

, Scott

: Moving research into practice: A decision framework for integrating home telehealth into chronic illness. Int J Med Inform, 2006; 75:786–794.

Jia

, Feng

, Wang

, et al.: A longitudinal study of health service utilization for diabetes patients in a care coordination home-telehealth programme. J Telemed Telecare, 2011; 17:123–126.

Jia

, Chuang

, Wu

, et al.: Long-term effect of home telehealth services on preventable hospitalization use. J Rehabil Res Dev, 2009; 46:557–566.

Barnett

, Chumbler

, Vogel

, et al.: The effectiveness of a care coordination home telehealth program for veterans with diabetes mellitus: a 2-year follow-up. Am J Manag Care, 2006; 12:467–474.

Chumbler

, Neugaard

, Kobb

, et al.: Evaluation of a care coordination/home-telehealth program for veterans with diabetes: health services utilization and health-related quality of life. Eval Health Prof, 2005; 28:464–478.

Dang

, Sanchez

, Oropesa

, et al.: Telehealth-assisted care coordination of older veterans with type 2 diabetes lowers coronary heart disease risk despite clinical inertia. Diabetes Technol Ther, 2010; 12:995–1001.

Stone

, Rao

, Sevick

, et al.: Active care management supported by home telemonitoring in veterans with type 2 diabetes: the DiaTel randomized controlled trial. Diabetes Care, 2010; 33:478–484.

10.

Wakefield

, Holman

, Ray

, et al.: Effectiveness of home telehealth in comorbid diabetes and hypertension: a randomized, controlled trial. Telemed J E Health, 2011; 17:254–261.

11.

Davis

, Hitch

, Salaam

, et al.: TeleHealth improves diabetes self-management in an underserved community: diabetes TeleCare. Diabetes Care, 2010; 33:1712–1717.

12.

Marcolino

, Maia

, Alkmim

, et al.: Telemedicine application in the care of diabetes patients: systematic review and meta-analysis. PLoS One, 2013; 8:e79246.

13.

Klonoff

, Blonde

, Cembrowski

, et al.: Consensus report: the current role of self-monitoring of blood glucose in non-insulin-treated type 2 diabetes. J Diabetes Sci Technol, 2011; 5:1529–1548.

14.

Kirkman

, Williams

, Caffrey

, et al.: Impact of a program to improve adherence to diabetes guidelines by primary care physicians. Diabetes Care, 2002; 25:1946–1951.

15.

Crowley

, Bosworth

, Coffman

, et al.: Tailored Case Management for Diabetes and Hypertension (TEACH-DM) in a community population: study design and baseline sample characteristics. Contemp Clin Trials, 2013; 36:298–306.

16.

Kennedy

, Bower

, Reeves

, et al.: Implementation of self management support for long term conditions in routine primary care settings: cluster randomised controlled trial. BMJ, 2013; 346:f2882.

17.

Expert Committee on the Diagnosis and Classification of Diabetes Mellitus: Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care, 2003; 26(Suppl 1):S5–S20.

18.

Ware

Jr , Sherbourne

: The MOS 36-item Short-form Health Survey (SF-36). I. Conceptual framework and item selection. Med Care, 1992; 30:473e83.

19.

Polisena

, Tran

, Cimon

, et al.: Home telehealth for diabetes management: a systematic review and meta-analysis. Diabetes Obes Metab, 2009; 11:913–930.

20.

Maljanian

, Grey

, Staff

, et al.: Intensive telephone follow-up to a hospital-based disease management model for patients with diabetes mellitus. Dis Manag, 2005; 8:15–25.

21.

Piette

, Weinberger

, McPhee

: The effect of automated calls with telephone nurse follow-up on patient-centered outcomes of diabetes care: a randomized, controlled trial. Med Care, 2000; 38:218–230.

22.

Whitlock

, Brown

, Moore

, et al.: Telemedicine improved diabetic management. Mil Med, 2000; 165:579–584.

23.

Jansa

, Vidal

, Viaplana

, et al.: Telecare in a structured therapeutic education programme addressed to patients with type 1 diabetes and poor metabolic control. Diabetes Res Clin Pract, 2006; 74:26–32.

24.

Cartwright

, Hirani

, Rixon

, et al.: Effect of telehealth on quality of life and psychological outcomes over 12 months (Whole Systems Demonstrator telehealth questionnaire study): nested study of patient reported outcomes in a pragmatic, cluster randomised controlled trial. BMJ, 2013; 346:f653.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.08 MB

0.02 MB