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Long-term Effects of Telemonitoring on Healthcare Usage in Patients with
Heart Failure or COPD
Jorien M.M. van der Burg, N. Ahmad Aziz, Maurits C. Kaptein, Martine J.M.
Breteler, Joris H. Janssen, Lisa van Vliet, Daniel Winkeler, Anneke van
Anken, Marise J. Kasteleyn, Niels H. Chavannes
PII: S2588-9141(20)30007-1
DOI: https://doi.org/10.1016/j.ceh.2020.05.001
Reference: CEH 18
To appear in: Clinical eHealth
Received Date: 5 November 2019
Revised Date: 29 April 2020
Accepted Date: 11 May 2020
Please cite this article as: J.M.M. van der Burg, N. Ahmad Aziz, M.C. Kaptein, M.J.M. Breteler, J.H. Janssen, L.
van Vliet, D. Winkeler, A. van Anken, M.J. Kasteleyn, N.H. Chavannes, Long-term Effects of Telemonitoring on
Healthcare Usage in Patients with Heart Failure or COPD, Clinical eHealth (2020), doi: https://doi.org/10.1016/
j.ceh.2020.05.001
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Long-term Effects of Telemonitoring on Healthcare Usage in Patients
with
Heart Failure or COPD
Jorien M.M. van der Burg1, M.D. Ph.D., N. Ahmad Aziz2,3, M.D. Ph.D., Maurits C. Kaptein4,
PhD, Martine J.M. Breteler5,6, M.Sc., Joris H. Janssen5, Ph.D., Lisa van Vliet1, M.Sc., Daniel
Winkeler7, M.Sc., Anneke van Anken8, M.Sc., Marise J. Kasteleyn1,9, Ph.D., Niels H.
Chavannes1, M.D. Ph.D.
1. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden,
The Netherlands.
2. Department of Neurology, University of Bonn, Bonn, Germany.
3. Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE),
Bonn, Germany.
4. Jheronimus Academy of Data Science, Den Bosch, The Netherlands & Department of
Statistics and Research Methods, Tilburg University, Tilburg, The Netherlands.
5. FocusCura, Driebergen-Rijssenburg, The Netherlands.
6. Department of Anesthesiology, University Medical Center Utrecht, Utrecht University,
Utrecht, The Netherlands.
7. Room To, De Meern, The Netherlands.
8. Department of Cardiology, Slingeland Hospital, Doetinchem, The Netherlands.
9. Department of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands.
Corresponding author: Jorien van der Burg, M.D. Ph.D., Department of Public Health and
Primary Care, Leiden University Medical Center, Leiden, The Netherlands.
0031(0)715268486, jorienvanderburg@gmail.com.
In collaboration with the Slingeland Hospital in Doetinchem, The Netherlands, and Stichting
Sensire in Varsseveld (‘InBeeld’ program), The Netherlands.
Keywords: Heart failure; Chronic Obstructive Pulmonary Disease (COPD); telemonitoring;
remote patient monitoring (RPM); home monitoring; home telemonitoring; telemedicine;
eHealth.
Credit Author Statement
Jorien M.M. van der Burg1, M.D. Ph.D.: Conceptualization, Data curation, Formal
analysis, Methodology, Writing (original draft), Writing (review and editing).
N. Ahmad Aziz2,3, M.D. Ph.D.: Conceptualization, Data curation, Formal analysis,
Validation, Writing (review and editing)
Maurits C. Kaptein4, Ph.D.: Formal analysis, Validation, Writing (review and editing)
Martine J.M. Breteler5,6, M.Sc.: Software, Methodology, Writing (review and editing)
Joris H. Janssen5, Ph.D.: Software, Methodology, Writing (review and editing)
Lisa van Vliet1, M.Sc.: Investigation, Writing (original draft)
Daniel Winkeler7, M.Sc.: Investigation, Resources, Methodology, Project
administration, Writing (review and editing)
Anneke van Anken8, M.Sc.: Investigation, Resources, Writing (review and editing)
Marise J. Kasteleyn1,9, Ph.D.: Conceptualization, Visualization, Writing (review and
editing)
Niels H. Chavannes1, M.D. Ph.D.: Supervision, Project administration, Visualization,
Writing (review and editing).
Long-term Effects of Telemonitoring on Healthcare Usage in Patients
with
Heart Failure or COPD
Jorien M.M. van der Burg1, M.D. Ph.D., N. Ahmad Aziz2,3, M.D. Ph.D., Maurits C. Kaptein4,
Ph.D., Martine J.M. Breteler5,6, M.Sc., Joris H. Janssen5, Ph.D., Lisa van Vliet1, M.Sc., Daniel
Winkeler7, M.Sc., Anneke van Anken8, M.Sc., Marise J. Kasteleyn1,9, Ph.D., Niels H.
Chavannes1, M.D. Ph.D.
1. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden,
The Netherlands.
2. Department of Neurology, University of Bonn, Bonn, Germany.
3. Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE),
Bonn, Germany.
4. Jheronimus Academy of Data Science, Den Bosch, The Netherlands & Department of
Statistics and Research Methods, Tilburg University, Tilburg, The Netherlands.
5. FocusCura, Driebergen-Rijssenburg, The Netherlands.
6. Department of Anesthesiology, University Medical Center Utrecht, Utrecht University,
Utrecht, The Netherlands.
7. Room To, De Meern, The Netherlands.
8. Department of Cardiology, Slingeland Hospital, Doetinchem, The Netherlands.
9. Department of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands.
Corresponding author: Jorien van der Burg, M.D. Ph.D., Department of Public Health and
Primary Care, Leiden University Medical Center, Leiden, The Netherlands.
0031(0)715268486, jorienvanderburg@gmail.com.
In collaboration with the Slingeland Hospital in Doetinchem, The Netherlands, and Stichting
Sensire in Varsseveld (‘InBeeld’ program), The Netherlands.
Keywords: Heart failure; Chronic Obstructive Pulmonary Disease (COPD); telemonitoring;
remote patient monitoring (RPM); home monitoring; home telemonitoring; telemedicine;
eHealth.
Abstract
Background
Heart failure and chronic obstructive pulmonary disease (COPD) are leading causes of
disability and lead to substantial healthcare costs. The aim of this study was to evaluate the
effectiveness of home telemonitoring in reducing healthcare usage and costs in patients
with heart failure or COPD.
Methods
The study was a retrospective observational study with a follow-up duration of up to 3 years
in which for all participants data before and after enrollment in the telemonitoring program
was compared. Hundred seventy-seven patients with heart failure (NYHA functional class 3
or 4) and 83 patients with COPD (GOLD stage 3 or 4) enrolled in a home telemonitoring
program in addition to receiving usual hospital care. The primary outcome was the number
of hospitalisations; the secondary outcomes were total number of hospitalisation days and
healthcare costs during the follow-up period. Generalized Estimating Equations were applied
to account for repeated measurements, adjusting for sex, age and length of follow-up.
Results
In heart failure patients, after initiation of home telemonitoring both the number of
hospitalisations and the total number of hospitalisation days significantly decreased
(incidence rate ratio of 0.35 (95% CI: 0.26-0.48) and 0.35 (95% CI: 0.24-0.51), respectively),
as did the total healthcare costs (exp(B) = 0.11 (95% CI: 0.08-0.17)), all p<0.001. In COPD
patients neither the number of hospitalisations nor the number of hospitalisation days
changed compared to the pre-intervention period. However, the healthcare costs were
about 54% lower in COPD patients after the start of the telemonitoring intervention (exp(B)
= 0.46, 95% CI 0.25-0.84, p=0.011).
Conclusions
Integrated home telemonitoring significantly reduced the number of hospital admissions
and days spent in hospital in patients with heart failure, but not in patients with COPD.
Importantly, in both patients with heart failure and COPD the intervention substantially
reduced the total healthcare costs.
1. Introduction
Heart failure and chronic obstructive pulmonary disease (COPD) are in the top ten of the
most common chronic disorders worldwide [1-3]. They are a leading cause of death and
disability and a major burden to society due to substantial healthcare costs involved. As the
number of people over 60 years of age is expected to almost double in the next 35 years, the
healthcare costs related to chronic diseases are also expected to rise [4]. Thus, developing
strategies to reduce (re-)admissions of patients with heart failure and COPD is important for
both improving quality of life of the patients and reducing healthcare costs.
Telemonitoring is a promising strategy for improving outcomes and reducing
healthcare costs in patients with heart failure, COPD and other chronic diseases [5-10].
Home telemonitoring is a technology to monitor patients at home, for example by daily
measurements of body weight or blood pressure. This enables early detection of
deterioration and allows early intervention which could potentially reduce the number of
hospital visits, number of hospitalisations, duration of hospital stays and mortality.
Therefore, it is not surprising that telemonitoring has attracted great interest, especially
among policy makers, as a potential solution to the global challenge of providing care for the
growing number of patients with chronic diseases.
Despite the potential benefits of telemonitoring, robust and unequivocal evidence is
still lacking which precludes drawing firm conclusions about its clinical efficacy or cost-
effectiveness [7]. Some research suggests that telemonitoring can have a positive effect on
patients with chronic diseases, such as improved quality of life [11], and reduced use of
secondary healthcare, including emergency hospital admissions [12-18]. Yet, other studies
found either no effect or a negative effect [19-24]. The heterogeneity of telemonitoring
interventions in these studies varied widely from ‘simple’ telephone follow-up to daily
monitoring of physiological symptoms with substantive clinical support, which contributes to
the difficulty in interpreting the different outcomes. In addition, different studies focused on
different patient groups: some studied the effects of telemonitoring only in subjects with
COPD, while others focused on subjects with heart failure or diabetes. Moreover, most
studies had a relatively short follow-up duration of several months to a year. As patients
with most chronic diseases are hospitalised on average only once to twice per year, such a
short follow-up duration could make it difficult to detect an effect on the number of
hospitalisations or healthcare costs. This heterogeneity among studies has led to calls for
further research to clarify the effectiveness of telemonitoring in chronic diseases [6], such as
heart failure and COPD.
To evaluate the effectiveness of home telemonitoring in reducing healthcare
utilisation and costs in patients with heart failure or COPD, we conducted a retrospective
observational study with a follow-up duration of up to 3 years. To our knowledge, this is the
first time that the effect of telemonitoring is studied conjointly in two of the most common
chronic diseases with such a relatively long follow-up duration.
2. Methods
2.1. Study design
In 2012 the Slingeland Hospital (a large, general, non-academic hospital in Doetinchem, The
Netherlands) started a telemonitoring program for patients with COPD or heart failure as
part of their usual care. Data were collected between January 2012 and December 2016. The
study was approved by the local medical ethical committee of the hospital and all patients
provided written informed consent before participation. We conducted a retrospective
observational study applying a pre-post research design in which data for all participants
before and after enrollment in the program was compared.
2.2. Eligibility and enrollment of patients
Patients were eligible for the telemonitoring program if they had an advanced disease stage
(New York Heart Association (NYHA) functional class 3 or 4; COPD Global Initiative for
Chronic Obstructieve Lung Disease (GOLD) stage 3 or 4), received treatment for this
condition by a cardiologist or pulmonary specialist at the Slingeland Hospital, were proficient
in Dutch and capable of providing informed consent (Figure 1). Exclusion criteria were
absence of the cognitive or physical capacity or access to resources required to fully
participate in the intervention, lack of an internet connection at home, being admitted to a
skilled nursing facility, unwillingness to participate in the study, or being unable to provide
informed consent.
2.3. Usual care
The usual care for heart failure and COPD patients at the Slingeland Hospital consisted of
routine monitoring through regular check-ups with their specialist at the hospital, either a
cardiac or pulmonary specialist. The frequency of check-ups for heart failure patients with
severe morbidity is every three months, whereas the frequency of check-ups in COPD
patients with severe morbidity is at least every six months, including a general assessment
every year. These regular check-ups are not completely structured. In brief, these
encounters consist of a review of the complaints, exacerbations and/or hospitalisations since
the last contact with the healthcare provider, an evaluation of the current clinical and
functional situation, an update of the (pharmacological) treatment if necessary, promoting
therapy compliance, reviewing psychological complaints, and a reminder of some
recommendations for healthy habits. In addition, in COPD, a patient’s inhalation technique
might be observed and evaluated.
In between these regular check-ups by a cardiac or pulmonary specialist, the general
practioner was responsible for the daily medical care of the patients. This daily medical care
was not structured, was designed by the general practitioner and therefore differed among
general practitioners. During a hospitalisation, the care for the patient was the full
responsibility of the medical specialist. After a hospitalisation, the daily care for the patient
was taken over again by the general practitioner and the check-ups were resumed by the
medical specialist according to the original schedule.
2.4. Telemonitoring Intervention
The integrated home telemonitoring intervention consisted of at home registration of
different measurements via a specially designed application (FocusCura cVitals, CE medical
device class I) on a leased electronic touch screen (iPad, Apple Inc. Cupertino, CA, USA).
Patients received this care in addition to the usual care from the hospital. A schematic
overview of the home telemonitoring intervention is presented in Figure 2. Upon
enrollment, patients were visited at home by a member of staff to provide an explanation
and to setup the measurement devices. Phone or video support was available in case of
technical issues.
In patients with heart failure the health status was evaluated by daily weight
measurements on a scale (iHealth Lite, iHealth Labs Inc. Mountain View, CA, US), and weekly
by measurements of blood pressure and heart rate (Omron MIT-5, Omron Healthcare
Europe, Hoofddorp, the Netherlands), as well as completion of a symptom questionnaire
(Supplementary File 1). The provided scale and blood pressure monitor automatically sent
measurements to the application, preventing errors that can occur when patients would
have to enter the measurements manually. For all patients, a trained nurse established a
personal bandwidth for the different parameters, based on the stage and stability of the
disease, and use of medication. This personal bandwidth was determined after a ‘measuring
week’, where patients measured their parameters every day. Bandwidths could be set for
both minimum and maximum absolute values as well as for the difference with the previous
measurement. These bandwidths could be altered by the nurse at all times. Patients were
able to see their own measurements and threshold limits over time within the application.
The integrated telemonitoring intervention for COPD consisted of twice weekly
registration of symptoms via the Clinical COPD Questionnaire (CCQ). The CCQ is a simple,
validated health-related quality of life questionnaire, consisting of 10 questions [25]. It is
widely used worldwide and has been demonstrated to predict health status and mortality in
COPD patients [26, 27]. The outcome of the CCQ is an overall clinical control score ranging
from 0 (very good control) to 6 (very poor control).
Home registrations were sent to a Medical Service Center (MSC; NAAST, Varsseveld)
for diagnostic interpretation and monitoring (Figure 2). Patients who failed to provide their
data in time were called by the study nurse of the MSC to ensure continued compliance.
Entered data was evaluated automatically based on the bandwidths set by the nurse, and
the Medical Service Center was available 24/7 for patients. An alert was generated if either
the personal bandwidth or the difference with a previous measurement was exceeded.
Consequently, a trained nurse at the MSC contacted the patient within 4 hours through a
video chat call. In addition, weight and CCQ-scores were also considered abnormal when the
difference with the previous measurement exceeded a preset value. A weight increase of
more than 1.5 kg and 0.4 points increase in CCQ score were always, despite the preset
bandwidth, considered abnormal. In case of abnormal results, the nurse at the MSC could
contact a specialised nurse from either the pulmonology or cardiology department in the
hospital (within office hours), or the medical specialist (outside office hours). The specialised
nurse or medical specialist could further assess the situation, contact the patient, evaluate
and adjust medication, and observe medication administration.
2.5. Data collection
Patient data from 1 January 2012 until 31 December 2016 was obtained from the electronic
patient files from the Slingeland Hospital. Only data from patient files with diagnostic codes
concerning heart failure or COPD were included. All data was pseudonymised before
statistical analysis. Differences in healthcare utilisation were measured by the number of
hospitalisations per year related to COPD or heart failure, and the number of hospitalisation
days in case of hospitalisation. Only hospitalisations with a duration of at least 1 day were
included; thus patients who visited the Emergency Department and were discharged on the
same day were not counted as a hospitalisation. All patients who met our inclusion criteria
were approached for enrollment in the study, including those who had a follow-up duration
of ≤ 1 month. However, in case a patient enrolled the study after a recent hospitalisation,
the initial hospitalisation was not counted as part of the pre-intervention period. Differences
in total healthcare costs were compared before and after the start of the intervention and
consisted of all activity based costs incurred in the hospital, including consultations,
hospitalisations, blood tests, spirometry, x-rays, electrocardiography, Holter monitoring, and
more. The estimation of total costs was independent of whether a patient was hospitalised
or visited the Emergency Department or the outpatient clinic. For duration of follow-up
before start of the intervention the time period between the first registered date per patient
after January 1st, 2012, and start of the intervention was calculated. For duration of follow-
up after the start of the intervention the time period between the start of the intervention
and 31 December 2016, or earlier if a patient for some reason (e.g. death or no longer being
able to fulfill the requirements of participation) had to quit the program, was calculated.
Thus, our analyses were based on those individuals who had follow-up data available
both before and after the start of the telemonitoring intervention. This way, the
prepared dataset contained two data points per patient per outcome variable (i.e. number
of hospitalisations, number of hospitalisation days and total healthcare costs): One data
point before the start of the intervention period and one data point after the start of the
intervention.
2.6. Statistical analysis
Data are presented as means and confidence intervals (CIs) or medians and interquartile
ranges (for non-normally distributed variables) unless otherwise specified. Patients who
were diagnosed with both COPD and heart failure (N=7) were included in the telemonitoring
programs for both conditions and analysed as such. To account for the repeated intra-
individual measurements before and after the initiation of the telemonitoring program, we
used Generalized Estimating Equation (GEE) with an independent covariance matrix.
Our primary outcome was the total number of hospital admissions, whereas our
secondary outcomes were the total number of hospitalisation days as well as the total
healthcare costs during the follow-up period, comparing the period before and after the
start of the intervention (i.e., length of follow-up before the start of the intervention, and
length of follow-up during the intervention). For the analysis of the effect of the intervention
on the number of hospital admissions and the number of hospital admission days, which can
be conceptualized as count data, we used GEE models with a negative binomial log link-
function to account for potential overdispersion. Total healthcare costs were strongly right-
skewed, and therefore, were first log-transformed and subsequently analysed using GEE
models with a normal link-function. As explanatory variables we used length of follow-up
(log-transformed), sex, age at baseline and telemonitoring intervention (coded as 0 or 1 for
the pre- and post-intervention period, respectively). Furthermore, we performed a
sensitivity analysis by including follow-up time as an offset variable, instead of an
explanatory variable, to assess whether different specifications of time would affect
our results. All GEE covariance estimates were based on robust estimators (i.e. Huber-
White robust sandwich estimators) which provide consistent estimates even when the
correlation matrix is misspecified (in contrast to generalized linear mixed effect models
which are more sensitive to covariance structure misspecification). To visualize the results
for each outcome variable we plotted the model predictions against follow-up time applying
spline regression to depict trends over time. A p-value of less than 0.05 was considered
statistically significant. All analyses were performed using SPSS version 25 (Released 2017,
Armonk, NY: IBM Corp.).
3. Results
3.1. Baseline characteristics
One hundred seventy-seven patients with heart failure and 83 patients with COPD were
included in the study. The characteristics of the participants with heart failure or COPD
before and after the start of the telemonitoring program are displayed in tables 1 and 2. The
duration of participation differed between patients. Of the patients with heart failure, 85
patients (48.0%) were included in the telemonitoring program for at least 12 months. Fifty-
four patients with COPD (65.1%) were included in the telemonitoring program for at least 12
months.
In the cohort with heart failure patients, the median age was 70.3 years (IQR 63.5-
78.5 years). Thirty-three percent (32.8%) of the participants was female (Table 1). The
median [IQR] follow-up duration of the cardiologist before start of the telemonitoring
intervention was 174.0 [32.0-719.0] days. The median duration of inclusion was 563.0 days
(IQR 281.0-758.0). Twenty-nine patients (16.4%) died during the telemonitoring
intervention period, while 37 patients (20.9%) were lost to follow-up for other
reasons before the pre-defined end-date of the intervention.
In the cohort with COPD patients, the median [IQR] age was 65.5 [60.7-71.4] years,
and 56.6% of the participants was female (Table 1). The median [IQR] follow-up by the
pulmonary specialist before start of the telemonitoring intervention was 918.0 [IQR 623.0-
1205.0] days. As shown in Table 2, the median [IQR] duration of inclusion in the program
was 563.0 [281.0-758.0] days. Fifteen patients (18.3%) died during the telemonitoring
intervention period, while 4 patients (4.8%) were lost to follow-up for other reasons
before the pre-defined end-date of the intervention.
3.2. Effects of telemonitoring in patients with heart failure
In patients with heart failure, after the start of the telemonitoring intervention, the rate of
hospital admission decreased by approximately 65% (incidence Rate Ration (IRR) = 0.35, 95%
CI: 0.26 to 0.48, p < 0.001), Figure 3 A. The number of hospital admission days also
significantly decreased in the period after the telemonitoring intervention was started (IRR =
0.35, 95% CI: 0.24 to 0.51, p < 0.001), Figure 3 B. Similarly, the total incurred costs were
almost 90% lower in the period of telemonitoring as compared to the period preceding the
commencement of the intervention (exp(B) = 0.1, 95% CI: 0.08 to 0.17, p < 0.001) (Figure 3
C). The rate of hospital admission, number of hospital admission days as well as total costs
increased with every year of follow up, but neither age at baseline nor gender were
significantly associated with any of the outcome measures (both p ≥ 0.178) (Table 3). We
also fitted a model including the Intervention Follow-Up interaction but this did not
improve model fit so it is not reported further. The effects of telemonitoring became even
more pronounced in a sensitivity analysis in which we included follow-up time as an offset
variable (Supplementary Table 1).
3.3. Effects of telemonitoring in patients with COPD
In patients with COPD, the telemonitoring intervention was not significantly associated with
either the number of hospital admissions (IRR = 1.09, 95% CI: 0.72-1.64, p = 0.684) or the
number of hospital admission days (IRR = 1.04, 95% CI: 0.63-1.71, p = 0.879) (Figure 4 A & B).
However, the total healthcare costs in the period after the initiation of the telemonitoring
program were significantly lower as compared to the period preceding the intervention
(exp(B) = 0.46, 95% CI: 0.25 to 0.84, p = 0.011) (Figure 4 C). The effect of follow-up time on
the number of admissions, the number of admission days as well as total costs was much
more pronounced in patient with COPD as compared to patients with heart failure, with risk
of being admitted increasing by almost 60% per year (Table 4). In parallel to the findings in
patients with heart failure, neither age nor gender was significantly associated with any of
the predefined outcome measures in patients with COPD (Table 4). We also fitted a model
including the Intervention Follow-Up interaction but this did not improve model fit so it is
not reported further. However, the sensitivity analysis with follow-up time as the offset
variable, suggested a borderline significant effect on the number of admissions, with a
slightly higher number of admissions per year during the intervention period.
Moreover, the effect on total costs became non-significant, although still indicating a
lower amount of costs per year during the intervention period (Supplementary Table
2). Therefore, the effect of telemonitoring in COPD patients is likely to be more
complex, possibly because of a strong confounding effect of time, i.e. disease
progression.
4. Discussion
4.1. Principal findings of the study
We reported the results of a home telemonitoring intervention with a follow-up duration of
up to three years in a group of patients with heart failure and a group of COPD patients in a
before-after study design. To our knowledge, this is the first time that the effect of
telemonitoring is studied conjointly in two of the most common chronic diseases with such a
relatively long follow-up duration. Our most important finding is that telemonitoring can
significantly decrease both the number of hospitalisations and the duration of
hospitalisation in patients with heart failure, but we did not observe this difference in
patients with COPD. These effects remained significant after adjusting for baseline
characteristics. These findings are in line with previous studies that found a positive effect of
home telemonitoring in patients with heart failure [9, 28], but not in patients with COPD [8,
20, 21, 23].
There are several explanations why home telemonitoring may have a significant
effect in heart failure patients, but not in patients with COPD. First, this could result from
differences in disease characteristics, making one disease more receptive to a
telemonitoring intervention than the other disease. It is known that in patients with chronic
heart failure deterioration in symptoms, including weight gain, are usually present 8 to 12
days before admission to a hospital [29, 30]. Rapid up-titration of diuretics or other
adjustments of medication could reverse these symptoms and potentially prevent
hospitalisation [29]. Therefore, heart failure patients may highly benefit from early detection
of deterioration. Conversely, it could well be that in COPD, due to its different
characteristics, there are not such clear cut predictors of an upcoming acute exacerbation as
weigh gain in heart failure. Moreover, it may be that rapid adjustment of medication has less
effect in preventing an exacerbation in patients with COPD compared to patients with heart
failure.
Second, it might be that telemonitoring has an effect in heart failure patients, but not
in COPD patients, because of the lack of sensitive and specific at home predictors of an
imminent exacerbation in COPD patients [31]. Levels of CRP and (pro)calcitonin in blood of
COPD patients have been found to be reliable predictors of an upcoming exacerbation, but
these are not easily available at home on a regular basis. Other potential biomarkers of an
impending exacerbation, such as decreases in oxygen saturation levels and increases in heart
rate and respiratory rate, have not yet been investigated thoroughly in the home setting.
However, interestingly a recent study found that home measurements of vital signs acquired
from a pulse oximeter (pulse rate and oxygen saturation) are predictive of an impending
COPD exacerbation, with oxygen saturation being the most predictive [32]. Such findings
could possibly explain why some studies indeed found a positive effect of telemonitoring in
COPD: these studies perhaps used better predictors, such as oxygen saturation, to detect an
upcoming exacerbation.
In addition to the number of hospitalisations and duration of hospitalisation, we also
analysed the effects of telemonitoring on healthcare costs. There is mixed evidence as to
whether home telemonitoring can reduce total costs of care. Some studies have found that
telemonitoring could reduce the total healthcare costs [33], while others found no effect
[11, 34, 35] or only found a reduction in healthcare costs when the costs of the intervention
were excluded [36]. Interestingly, we observed a significant decrease in healthcare costs
both in heart failure and in COPD patients after the telemonitoring intervention had started.
The costs that we have compared before and after the intervention include all costs incurred
in the hospital, including consultations, hospitalisations, blood tests, spirometry. However,
the costs for the intervention could not be included in the analyses. The reason for this is
that the costs of the intervention were absolute costs, whereas the costs incurred in the
hospital are not absolute, but indirect costs. This is because the Dutch healthcare system is
characterized by a payment system called ‘Diagnosis-Treatment Combinations (DBCs)’.
According to this system, the health insurance company pays a predetermined price for a
package of ‘care activities’ that comes with a certain diagnosis. As such, hospitals themselves
can determine the price of each care activity within a DBC. This price is indirectly based on
real costs, is negotiable and may vary from hospital to hospital. As our analyses concerned
patients from only one hospital who all received comparable ‘Diagnosis-Treatment
Combinations’, however, this could not have affected our findings of decreased total
healthcare costs.
The decrease in costs we found after the start of the intervention is spectacular and
although these are not absolute costs, we believe that telemonitoring, if applied to the right
target group and under the right circumstances, can lead to a significant reduction in
healthcare costs. From our study it has not become clear which factors define the ‘right
target group’ or the ‘right circumstances’ and this is certainly a point of attention for our
follow-up studies.
Interestingly, total costs of care activities in COPD decreased despite the lack of a
reduction in hospitalisations and hospitalisation duration. From our dataset it was
impossible to further analyse why the total healthcare costs declined in the COPD group. We
hypothesize that since the start of the telemonitoring intervention COPD patients needed
less consultations, blood tests, spirometry, x-rays, sputum cultures and other investigations.
These findings are in line with findings of the ‘Whole Systems Demonstrator telehealth
questionnaire study’ that found a slight decrease in total cost for health and social care of a
12 months telemonitoring intervention when the costs of the intervention were excluded
[36]. We are planning to further investigate the cost effectiveness of telemonitoring,
especially the observed decrease in costs in COPD, more thoroughly in a future research
project. Future studies should also focus on the differences in patient ‘types’ (e.g., ‘active’,
‘passive’), individual disease progression trajectories and the consequences in
responsiveness to home telemonitoring and patient outcomes.
4.2. Strengths and weaknesses of the study
One of the most important strengths of our study is the relatively long follow-up duration of
up to three years. In addition, the patients turned out to be very motivated to continue
taking part in the study and they consistently continued to send their data through the app
on a daily and weekly basis. The relatively high attrition rate that we found is remarkable
because attrition is often a problem in such studies [37]. Another strength of our study is the
integration of the program into the usual care. This blended care is important, because such
programs often do not run well due to the lack of organizational structure.
However, as we used a pre-post research design, we lacked a parallel control group.
Therefore, the results of this study have to be interpreted with caution. The lack of a control
group makes it more difficult to attribute observed changes to the intervention. One of the
effects that particularly could play a role in such a study design, is the so called Hawthorne-
effect, an often positive effect of an intervention that results solely from the fact of
participating in a study because of the feeling of being observed. However, as both the heart
failure and COPD group consisted of a relatively large number of participants, who were
followed for several years, it is likely that similar results would have been found as in a
matched-control study design as the Hawthorne-effect is expected to disappear within a few
months.
Further, it is important to note that due to the restrictive selection criteria, only a
relatively small proportion of the patients that were treated for COPD or heart failure at the
Slingeland hospital were able to participate in the study. Although it is possible that this
subset of patients may not be representative of the entire group of patients seen in this
regional hospital, this does not affect our finding with regard to the effects of the eHealth
intervention per se. This is because we applied a pre-post-intervention design which enabled
us to compare the effect of the intervention within every individual separately. Only patients
who were willing to use telemonitoring were included. These patients may have been more
motivated to change their behaviour, which might have affected our results regarding
hospitalisations and costs. However, if we would have also conducted the study on patients
who were not motivated to participate or who were unable to participate, that would have
been a far cry from the daily practice, especially with regard to these kinds of interventions
that require high patient compliance and commitment. Just like any drug therapy, eHealth
will not be suitable for everyone and will not be effective for everyone. Therefore, our
results are not generalisable to all heart failure and COPD patients. Further studies are
needed to find out which characteristics make a patient suitable for participation and in
whom to expect a beneficial effect from eHealth monitoring.
Moreover, approximately 21% of the patients were lost to follow-up. Among the
reasons for disenrollment was the perception that the intervention was too intensive. It
could be that patients who disenrolled, often had a high burden of disease and thus would
have had high costs if they had remained in the analysis.
Finally, the health care system and overall healthcare costs differ from country to
country. Further studies are necessary to investigate whether the telemonitoring
intervention as performed in the Slingeland hospital is also cost-effective in other hospitals
and in other countries.
4.3. Conclusions
Our results suggest that home telemonitoring significantly decreases the number of
hospitalisations as well as the total days of hospitalisation in patients with heart failure, but
not in patients with COPD. Nevertheless, we observed a strong and significant decrease in
total healthcare costs in both heart failure and COPD after the initiation of the home
telemonitoring intervention. The mechanism behind these effects of home telemonitoring is
not yet clear. It is likely that home telemonitoring helps patients to manage their conditions
better and leads to earlier detection of deterioration, thereby reducing the incidence of
acute exacerbations that necessitate emergency admission. Moreover, telemonitoring could
change people’s perception of when they need to seek additional support, as well as
professionals’ decisions about whether to refer or admit patients. Further research,
preferably multi-center randomized controlled studies in which different at home
measurements are compared (including body weight, heart rate, blood pressure, oxygen
saturation, breathing rate and questionnaires) are needed to unravel the exact mechanisms
by which home telemonitoring can lead to reductions in hospital care and healthcare related
costs.
Conflicts of interest
We have no conflicts of interest.
Acknowledgments
We would like to thank all participants, as well as the staff and employees of the Slingeland
Hospital in Doetinchem, The Netherlands, that contributed to this study. We also thank the
employees of the Medical Service Center (MSC) in Varsseveld, The Netherlands, for their
contribution.
Figures:
Figure 1. Flowchart of the study population.
Figure 2. Schematic representation of the telemonitoring intervention.
At home registration of different measurements were sent via a specially designed
application on an iPad to a Medical Service Center (MSC) for diagnostic interpretation and
monitoring. In case of abnormal results, the specialised nurse at the MSC would contact, if
necessary, both the patient and the doctor or specialised nurse in the hospital and action
would be taken (red arrows). Patients could also contact the nurse at the MSC themselves at
all times (green arrow).
Table 1. Patient characteristics before the intervention
Variable
COPD (n=83)
Heart failure (n=177)
Age (years)+
Median 65.5 (IQR 60.7-
71.4)
Median 70.3 (IQR 63.5-
78.5)
Sex (% female)
56.6
32.8
Duration of specialist treatment (days)
Median 918.0 (IQR 623.0-
1205.0)
Median 174.0 (IQR 32.0-
719.0)
- Duration of follow-up <1 month, N (%)
3 (3.61)
46 (26.0)
- Duration of follow-up >1 year, N (%)
73 (88.0)
69 (39.0)
+At start of the telemonitoring intervention
Table 2. Outcomes after intervention
Variable
COPD (n=83)
Heart failure (n=177)
Duration of inclusion in the home
telemonitoring program (days)
Median 563.0 (IQR 281.0-
758.0)
Median 345.0 (IQR 142.5-
628.0)
-Duration of inclusion < 1 month N (%)
4 (4.8)
12 (6.8)
-Duration of inclusion > 1 year N (%)
54 (65.1)
85 (48.0)
Deaths N (%)
Lost to follow-up N (%)*
15 (18.3)
4 (4.8)
29 (16.4)
37 (20.9)
* Data on the lost to follow-up reasons were not systematically collected, but included relocation to other areas
within the Netherlands and revocation of the informed consent by some participants primarily due to the
perceived intensity of the intervention.
Figure 3. Effects of telemonitoring on healthcare usage in patients with heart failure.
Effects of telemonitoring on the number of hospital admissions (A), hospitalisation days (B)
and hospital related costs (C) before (red) and after (blue) the home telemonitoring
intervention in patients with heart failure. The solid lines represent the model predicted
mean values, which can be interpreted as the cumulative effect over time, with the
associated 95% confidence intervals of the mean indicated by the dashed lines.
Table 3. Effect of telemonitoring on heart failure.
Legend: The estimates are based on the outcomes of the Generalized Estimating Equations
models in which follow-up time, age at baseline, gender and intervention were included as
covariates (see methods section for more details). Abbreviations: CI, confidence interval;
IRR, incidence rate ratio.
No. hospital admissions
No. of admission days
Total costs
IRR (95% CI)
p-value
IRR (95% CI)
p-value
exp(B) (95%
CI)
p-
value
Follow-up time (per
year)
1.15 (1.08-
1.24)
<0.001
1.15 (1.05-
1.26)
0.002
1.33 (1.17-
1.51)
<0.001
Age at baseline (per
year)
1.01 (0.99-
1.02)
0.334
1.01 (0.99-
1.03)
0.282
1.00 (0.98-
1.01)
0.829
Gender (female)
0.81 (0.59-
1.10)
0.178
1.08 (0.72-
1.62)
0.708
0.91 (0.56-
1.48)
0.702
Intervention
(yes/no)
0.35 (0.26-
0.48)
<0.001
0.35 (0.24-
0.51)
<0.001
0.11 (0.08-
0.17)
<0.001
Figure 4. Effects of telemonitoring on healthcare usage in patients with COPD.
Effects of telemonitoring on the number of hospital admissions (A), hospitalisation days (B)
and hospital related costs (C) before (red) and after (blue) the home telemonitoring
intervention in patients with COPD. The solid lines represent the model predicted mean
values, which can be interpreted as the cumulative effect over time, with the associated 95%
confidence intervals of the mean indicated by the dashed lines.
Table 4. Effect of telemonitoring on COPD.
Legend: The estimates are based on the outcomes of the Generalized Estimating Equations
models in which follow-up time, age at baseline, gender and intervention were included as
covariates (see methods section for more details). Abbreviations: CI, confidence interval;
IRR, incidence rate ratio.
No. hospital admissions
No. of admission days
Total costs
IRR (95% CI)
p-value
IRR (95% CI)
p-value
exp(B) (95% CI)
p-value
Follow-up time (per
year)
1.57 (1.23-1.99)
<0.001
1.59 (1.22-2.06)
0.001
3.12 (2.15-4.73)
<0.001
Age at baseline (per
year)
1.02 (0.99-1.05)
0.257
1.01 (0.99-1.04)
0.353
0.99 (0.95-1.04)
0.807
Gender (female)
1.21 (0.75-1.94)
0.433
1.31 (0.77-2.26)
0.322
1.58 (0.81-3.10)
0.179
Intervention (yes/no)
1.09 (0.72-1.64)
0.684
1.04 (0.63-1.71)
0.879
0.46 (0.25-0.84)
0.011
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Supplementary File 1. Heart Failure Questionnaire
Question 1.
Heart failure affects people's lives in different ways. Some suffer from shortness of breath,
while others are tired. Could you indicate to what extent you feel limited by your heart failure
in your daily activities (for example when dressing, showering, climbing stairs or taking a
walk)?
º Not limited
º A bit limited
º Fairly limited
º Very limited
º Extremely limited
Question 2.
Do you currently suffer from one (or several) of the symptoms below?
º Shortness of breath
º Fatigue
º Edema (swelling) of the ankles
Question 3.
How often has shortness of breath limited you in what you wanted to do?
º Less than once a week
º 1 to 2 times a week
º More than 3 times a week, but not daily
º At least once a day
º Multiple times a day
º Constantly
Question 4.
How do you experience your shortness of breath?
º Not annoying
º A bit annoying
º Fairly annoying
º Very annoying
º Really annoying
Question 5.
How often has fatigue limited you in what you wanted to do?
º Less than once a week
º 1 to 2 times a week
º More than 3 times a week, but not daily
º At least once a day
º Multiple times a day
º Constantly
Question 6.
How do you experience your fatigue?
º Not annoying
º A bit annoying
º Fairly annoying
º Very annoying
º Really annoying
Question 7.
How often have you experienced edema (swelling) of your feet, ankles or legs when you got
up in the morning?
º Less than once a week
º 1 to 2 times a week
º More than 3 times a week, but not daily
º Every day
Question 8.
How do you experience the edema (swelling) of your feet, ankles or legs?
º Not annoying
º A bit annoying
º Fairly annoying
º Very annoying
º Really annoying
Question 9.
Do you feel depressed or depressed at the moment due to your heart failure?
º Not depressed
º A bit depressed
º Fairly depressed
º Very depressed
º Extremely depressed
Question 10.
Would you like to tell us more about the completed answers? (This question is not
mandatory.)
Enter your explanation here (maximum 300 characters)
Supplementary Table 1. Effect of telemonitoring on heart failure.
Legend: The estimates are based on the outcomes of the Generalized Estimating Equations
models in which age at baseline, gender and intervention were included as covariates, while
follow-up time (years) was included as an offset variable. For count outcomes (i.e. no. of
hospital admissions and no. of hospital admission days), log-transformed follow-up time was
used as the offset. Abbreviations: CI, confidence interval; IRR, incidence rate ratio.
No. hospital admissions
No. of admission days
Total costs
IRR (95% CI)
p-value
IRR (95% CI)
p-
value
exp(B) (95%
CI)
p-
value
Age at baseline
(per year)
1.00 (0.99-
1.02)
0.671
1.01 (0.99-
1.03)
0.507
1.04 (0.99-
1.08)
0.133
Gender (female)
0.69 (0.46-
1.02)
0.065
1.22 (0.64-
2.30)
0.545
0.74 (0.26-
3.36)
0.652
Intervention
(yes/no)
0.18 (0.13-
0.26)
<0.001
0.09 (0.06-
0.16)
<0.001
0.02 (0.01-
0.08)
<0.001
Supplementary Table 2. Effect of telemonitoring on COPD.
Legend: The estimates are based on the outcomes of the Generalized Estimating Equations
models in which age at baseline, gender and intervention were included as covariates, while
follow-up time (years) was included as an offset variable. For count outcomes (i.e. no. of
hospital admissions and no. of hospital admission days), log-transformed follow-up time was
used as the offset. Abbreviations: CI, confidence interval; IRR, incidence rate ratio.
No. hospital admissions
No. of admission days
Total costs
IRR (95% CI)
p-value
IRR (95% CI)
p-
value
exp(B) (95%
CI)
p-
value
Age at baseline
(per year)
1.02 (0.99-
1.05)
0.292
1.00 (0.96-
1.31)
0.861
1.10 (0.87-
1.38)
0.436
Gender (female)
1.20 (0.74-
1.93)
0.463
1.18 (0.63-
2.22)
0.597
10.10 (0.13-
763)
0.295
Intervention
(yes/no)
1.44 (1.02-
2.03)
0.038
0.99 (0.53-
1.84)
0.969
0.74 (0.03-
20.7)
0.859
