Sleep Breath (2016) 20:777–785
DOI 10.1007/s11325-015-1298-4

SLEEP BREATHING PHYSIOLOGY AND DISORDERS • ORIGINAL ARTICLE

A telehealth program for CPAP adherence reduces labor
and yields similar adherence and efficacy when compared
to standard of care
Dominic Munafo 1 & William Hevener 1 & Maureen Crocker 2 & Leslee Willes 3 &Sarah Sridasome 1 & Ma’an Muhsin 1

Received: 14 October 2015 / Revised: 24 November 2015 / Accepted: 15 December 2015 / Published online: 11 January 2016
# The Author(s) 2016. This article is published with open access at Springerlink.com

Abstract
Purpose This study evaluated the effectiveness and coachinglabor requirements of a web-based automated telehealth (TH)messaging program compared with standard of care (SOC) in
newly diagnosed patients with obstructive sleep apnea (OSA).
Methods In this non-blinded, multicenter, prospective study,
all patients were started on continuous positive airway pressure (CPAP) with heated humidification and a wireless modem. They all received standardized CPAP education and setup. Patients in the TH group (n = 58) received an automatedseries of text messages and/or e-mails that were triggered bypreset conditions. Patients in the SOC group (n = 64) received
scheduled calls on days 1, 7, 14, and 30. Additional contacts
were allowed for patients in both groups as deemed clinicallynecessary. Coaching labor was calculated by totaling the number and type of patient contacts and assigning historical timevalues.
Results One hundred twenty-two patients were included in
the final analysis. There were no statistically significant differences between the TH and SOC groups for Medicare adherence (83 vs. 73 %), daily CPAP usage (5.1 ± 1.9 h vs.
4.7 ± 2.1 h), CPAP efficacy (mean residual apnea-hypopnea
index (3.0 ± 4.1/h vs. 2.8 ± 3.8/h), or change in Epworth
Sleepiness Scale score (−5.8 ± 5.5 vs. –5.1 ± 5.9), although* Dominic Munafo
dmunafo@sleepdata.com

1

Sleep Data, 4420 Hotel Ct #240, San Diego, CA 92108, USA

2

ResMed Science Center, ResMed Corp, 9001 Spectrum Center
Blvd., San Diego, CA, USA

3

Willes Consulting Group, Inc., 1327 Walnutview Drive, Encinitas,
San Diego, CA 92024, USA

all trends favored the TH group. There was, however, a significant reduction in the number of minutes coaching required
per patient in the TH vs. SOC group (23.9 ± 26 vs. 58.3 ± 25,
59 % reduction; p < 0.0001).
Conclusions Use of a web-based telehealth program for
CPAP adherence coaching significantly reduced the coachinglabor requirement compared with SOC, while maintaining
similar adherence and effectiveness.
Keywords Obstructive sleep apnea . Telehealth . Continuous
positive airway pressure . Adherence . Compliance coaching .
Labor

Introduction
Obstructive sleep apnea (OSA) is the most common form of
sleep-disordered breathing (SDB) and is typified by repetitive
complete or partial collapse of the upper airway during sleep.
The prevalence of OSA has been increasing steadily, in partdue to the rise in obesity, with recent estimates suggesting that
it affects ≥13 % of men and 6 % of women [1, 2]. This represents a considerable growth rate of 14–55 % over the past two
decades [2]. OSA has been associated with numerous comorbidities including hypertension, congestive heart failure,stroke, diabetes mellitus, and an increased risk of motor vehicle accidents [3–10]. OSA places a substantial financial burden on the healthcare system, with the cost of untreated OSA
estimated to be US$67–165 billion [11].
First-line therapy for OSA is continuous positive airway
pressure (CPAP), which improves symptoms including excessive daytime sleepiness, fatigue, memory impairment, anddepressed mood [12, 13]. Long-term adherence with CPAP
therapy has also been shown to improve blood pressure andcardiovascular morbidity and mortality [14]. Conversely,

778

OSA symptoms return when CPAP therapy is discontinued[15]. Unfortunately, CPAP therapy can take some time to getused to, and long-term adherence with therapy can be challenging. Historically, CPAP adherence rates have been ≈50 %and when applying Medicare’s definition of adherence (≥4 h
CPAP usage each night for 70 % of nights during a 30
consecutive-day period anytime during the first 90 days of
initial usage), the rate has been reported to be as low as
46 % [14, 16, 17]. However, more recent reports have documented adherence rates of 60–85 % when more intensive
follow-up and educational protocols are used [18].
Lack of adherence with long-term regimens is an ongoingchallenge for both patients and healthcare providers. It has been
estimated that non-adherence results in annual costs of $290 billion in the USA alone [19]. World Health Organization (WHO)data indicate that patients with chronic diseases only adhere to
their doctor’s long-term recommended therapy 50 % of the time[19]. Such data led to the conclusion that “interventions aimed at
improving adherence would provide a significant positive returnon investment through primary prevention (of risk factors) andsecondary prevention of adverse health outcomes” [19].
Telehealth (TH) has been defined as the delivery of healthrelated services and information via telecommunications technologies. The development and increasing availability of TH
technology offers the opportunity to provide quality carewhile effectively managing costs. Chronic diseases requirelong-term commitment of resources for a successful therapy
program and, as such, are an excellent area to apply TH
technology.
U-Sleep (ResMed Corp) is a TH program that uses a multimedia approach to contact patients about their CPAP use.
When usage falls, e-mail and text messages are sent to encourage patients to use CPAP more regularly. Electronic messages
are also sent to the patient’s clinician, with triggered alerts tofurther assist in CPAP management. Clinicians also remotely
monitor their patients’ progress using U-Sleep technology via
metrics including daily usage, apnea-hypopnea index (AHI)
values, and leak. There are currently no published clinicalresearch studies on use of U-Sleep technology as a tool tofacilitate management of OSA patients.
This study compared the U-Sleep program with a standardof-care (SOC) program in patients with OSA receiving CPAP
therapy for the first time. The primary objective was to compare adherence rates and healthcare professional (HCP) time
(resource use) in the two groups.

Methods
This randomized, prospective, non-blinded study was conducted by Sleep Data Holdings, LLC, a Joint Commission
on Accreditation of Healthcare Organizations-accredited
CPAP durable medical equipment provider in Southern

Sleep Breath (2016) 20:777–785

California, USA. The study protocol, informed consent form,and Health Insurance Portability and Accountability Act(HIPAA) form were approved by the Chesapeake
Institutional Review Board. Informed consent was obtained
from all individual participants included in the study.
Subjects
Patients were recruited and evaluated at two Sleep Data locations (San Diego County) in March–August 2013.
Participants were consecutive patients referred to receive
CPAP for OSA. Inclusion criteria were age 18–80 years,
CPAP-naïve, confirmed OSA (AHI 5–70/h) diagnosis basedon polysomnography (PSG) or home sleep test. In addition,
patients were required to have access to and be able to utilize,
communication technology (text messaging, e-mail).
Exclusion criteria were prominent central apnea (>20 %),claustrophobia, current use of mandibular repositioning device, or other OSA therapy. A simple randomization scheme
was used to allocate patients to CPAP treatment plus SOC or
TH.
CPAP therapy
At baseline, all patients had a 1-h education session with arespiratory therapist (RRT) about OSA and its consequences,proper use and maintenance of the CPAP device and mask,
and therapy expectations. All patients were provided with afixed or auto CPAP device, heated humidifier, modem, andmask interface (S9 Elite, S9 AutoSet, H5i heated humidifier;
ResMed Corp.). Patients saw an RRT at all clinic visits; telephone follow-up was performed by registered PSG technicians (RPSGT).
Adherence monitoring
Patients randomized to SOC were dispensed a CPAP device
on Day 0, then contacted via phone on Days 1, 7, 14, 30, and90 (Fig. 1). CPAP usage and efficacy data were tracked via thewireless modem attached to the CPAP machine. Modem data
were accessed via ResMed’s EasyCare Online (ECO) platform. Sleep Data SOC procedures include frequent phone
calls and return clinic visits as necessary.
Patients in the TH group were dispensed a CPAP device
on Day 0, along with a pamphlet about U-Sleep, which wasused to monitor adherence. U-Sleep is a secure, HIPAA-compliant, web-based application that is designed to receive
CPAP device data and message patients and providers via text
and/or e-mail based on a customizable set of rules. At the timeof set up, patients were encouraged to log-in to the U-Sleep
website from home so that they could follow their therapy.
Sleep Data study staff were trained to set up and use thesoftware, which was provided to patients at no charge. Initial779

TelehealthStandard of Care

OSA
Diagnosis

OSA
Diagnosis

CPAP Set-Up Ordered

CPAP Set-Up Ordered

CPAP Set-up

CPAP Set-up

CPAP Therapy
Education

CPAP Therapy
Educationcommunication with patient; 9 min), and clinic visits (e.g.,mask refitting and education performed by an RRT; 30 min).
Times assigned to each contact were determined based on asystematic review of Sleep Data’s clinical experience.
Study endpoints
The primary endpoints were CPAP adherence (use for ≥4 h/night on 70 % of nights during a 30 consecutive-day period
anytime during the first 90 days of initial usage) and resourceuse. The adherence rate was the percentage of patients whoachieved Medicare adherence. Whether patient adherencewith CPAP in the TH group was superior to the publishedaverage of 50 % [13] was also determined.

Follow-up #1
Day 1 Phone Call

Day 90 Phone Call

Follow-up #2
Day 7 Phone Call

Follow-up #3
Day 14 Phone Call

Follow-up #4
Day 30 Phone Call

Sample size
Potential ad hoc contact with patient

U-Sleep Notification(s) toPatient and/or Provider
via text/email

Potential ad hoc contact with patient

Study Period - OSA Management

Pre-Study

Sleep Breath (2016) 20:777–785

Follow-up #5
Day 90 Phone Call

Fig. 1 Study design. CPAP continuous positive airway pressure, OSA
obstructive sleep apnea

patient contacts were triggered by ≥1 of five intervention
points based on metrics (AHI, leak, therapy hours) (Table 1).
After initial contact, subsequent contacts were in response toan automated message or based on clinical judgment. All TH
patients received a final phone call on day 90. All patientswere contacted at day 90 and asked to rate how well thefollow-up program had met their expectations (on a scale from
1 to 5).
In both groups, all phone contacts were performed by an
RPSGT from Sleep Data using a standardized script.
Additional telephone contacts or clinic visits were scheduled
as deemed clinically necessary by the RPSGT for evaluationof issues such as high mask leak, appropriate pressure settings,
AHI elevation, and daily usage. Tasks undertaken at clinic
visits included mask refit, patient re-education and training
with an RRT, provision of a copy of CPAP therapy data downloads, and machine performance evaluations.
Resource use
The total number of contacts was recorded. Contacts werecategorized as contact attempt (e.g., staff left a phone message
with patient to follow up; 4 min), phone contact (RPSGT

Equivalence in CPAP adherence was defined as a <20 % difference in rates between the SOC and TH groups. Assuming
an expected adherence rate of 70 % (based on historical Sleep
Data rates), with a power of ≥80 % (one-tailed alpha 0.05) todetect a ≥20 % difference, the enrolment target was 69
patients/group [20]. For comparing adherence in the TH groupto the global average, a sample size of 37 patients/groupwould have ≥80 % power (one-tailed alpha 0.05) to detect a
difference of ≥20 %, assuming an expected adherence rate for
the TH group of 70 % and a published average of 50 %.
Statistical analysis
Primary endpoint analyses were generated for the intention-totreat (ITT) and completed cases (CC) populations. The ITT
population included all randomized patients except two who
withdrew consent. Patients with no compliance data, and one
patient who never enrolled in the U-Sleep program, were considered non-adherent to CPAP; for those lost-to-follow-up,
adherence results for the last available assessment were used.
The CC population included patients who completed the study
according to the protocol. Additional analyses were conductedin the CC population without imputation for missing values.
Equivalence in CPAP adherence between the two treatmentgroups was tested using a two-sided 95 % confidence interval(CI) for the difference in proportions. The null hypothesis isrejected and equivalence established if the upper limit of the
CI is ≤20 %. The superiority hypothesis (TH adherence vs.
published average adherence) was tested using a two-sided
exact binomial test of the TH adherence proportion compared
to typical published average adherence; a two-sided 95 % CI
was calculated for the TH adherence proportion.
Baseline characteristics and 90-day evaluations were compared between treatment groups using Student’s t test for continuous variables and chi-square or Fisher’s exact tests for
categorical variables, as appropriate. Wilcoxon rank sum tests

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Table 1 U-Sleep notification
triggers

Sleep Breath (2016) 20:777–785

Trigger

To whom

MethodNo CPAP data for 2 consecutive days

Patient and provider

E-mail and/or text

CPAP usage <4 h for 3 consecutive nights

Patient and provider

E-mail and/or text

Median mask leak >24 L/min for 2 consecutive days
AHI >15/h for 5 consecutive days

ProviderProviderE-mailE-mailCPAP usage met Medicare criteria for adherence

Patient and provider

E-mail and/or text

were employed when a non-parametric test of continuous variables was warranted. P values of <0.05 were considered statistically significant. Analyses were generated using SAS software, version 9.2 or greater.

meeting predefined equivalence criteria. The Medicare adherence rate in the TH group was 69.6 % (95 % CI 57.3–80.1 %)compared with the published average of 50 % (p = 0.002).

Primary endpoint (CC)

Results
All approached patients consented to participate (n = 140); 18
patients did not complete the 90-day study (16 lost to followup, 2 withdrew consent) (Fig. 2). The ITT and CC populations
comprised 138 and 122 patients, respectively. Baseline characteristics were similar in the TH and SOC groups (Table 2).
CPAP therapy had similar effectiveness in both treatmentgroups, based on residual AHI and Epworth Sleepiness scalescore.
Primary endpoint (ITT)
Medicare adherence rates were 69.6 % in the TH group and68.1 % in the SOC group (p = 0.85). The upper 95 % confidence limit of the difference between groups was 14.0 %,Adherence rates were excellent and similar in both groups(p = 0.22) (Table 3). The Medicare adherence rate in the TH
group was 82.8 % (95 % CI 70.6–91.4 %), significantly higher
than the published average of 50 % (p < 0.0001). The
Medicare adherence rate in the SOC group was 73.4 %.
The upper 95 % confidence limit for the difference in
Medicare adherence rates was 5.2 %, again meeting equivalence criteria. The majority of patients in both groups achieved
Medicare adherence within the first 30 days of CPAP initiation, and time to reach target adherence was similar in the twogroups (Table 3 and Fig. 3). Mean CPAP usage and the percentage of days that patients used CPAP for ≥4 h were slightly,
but not significantly, higher in the TH versus SOC group.
Multiple logistic regression analysis evaluating the effectof baseline characteristics on adherence rates showed that age
and AHI at baseline were significantly associated with

Fig. 2 Patient flow through thestudy. TH telehealth, SOC
standard of careRandomized
n=140

TH Group
n=70

Lost to F/u: n=10
Voluntary withdraw: n=1
Other: n=1

SOC Group
n=70

Lost to F/u: n=5
Voluntary withdraw: n=1

Final TH Group
n=58

Final SOC Group
n=64

Sleep Breath (2016) 20:777–785
Table 2 Baseline patientcharacteristics

781

Characteristic

TH group (N = 69)

SOC group (N = 69)

p value

Age, years (mean±SDa)
Male sex, n (%)
Body mass index, kg/m2 (mean ± SD)
Ethnicity, n (%)
Caucasian52.3 ± 10.6

50.0 ± 11.7

0.23

50/69 (72.5)
33.5 ± 8.2

45/69 (65.2)
32.9 ± 7.1

0.36
0.63

(68)
49/68 (72.1)(61)
50/61 (82.0)

0.34

Hispanic

11/68 (16.2)

4/61 (6.6)

African-AmericanAsian

3/68 (4.4)
5/68 (7.4)

2/61 (3.3)
4/61 (6.6)

Other

0 (0)
(n = 66)

1/61 (1.6)(n = 68)

10.9 ± 4.7
11.0(n = 69)

10.2 ± 5.7
10.0(n = 69)

0.42

33.4 ± 24.5
27.2

27.4 ± 18.0
23.2

0.23

59/69 (85.5)
10/69 (14.5)

58/69 (84.1)
11/69 (15.9)

0.81

20/68 (29.4)
39/68 (57.4)
9/68 (13.2)(n = 58)

20/69 (29.0)
38/69 (55.1)
11/69 (15.9)(n = 58)

0.90

10.6 ± 2.5
10.0

10.5 ± 2.0
10.0

0.81

ESSb score
Mean ± SD
MedianAHIc/hMean ± SD
MedianPAP device, n (%)
S9 Elite
AutoSetMask type, n (%)
FFMd
Nasal mask
Nasal pillow
Device therapy setting, cmH2O
Mean ± SD (n)MedianaStandard deviationb

Epworth Sleepiness Scalec

Apnea-hypopnea indexdFull face mask

adherence. Adherent patients were significantly older than
non-adherent patients and had a higher baseline AHI(Table 4).
Program acceptance
The majority of TH patients (92.8 % [52/56]) stated that the
new approach to monitoring met or exceeded their expectations compared with 94.5 % (52/55) in the SOC group(p = 0.82).
Resource use
There was a significant reduction in the mean aggregate timerequired to coach a patient in the TH versus SOC group(Table 5). The number of follow-up clinic visits was similarin the two groups; only 17/58 patients (19 visits) in the TH
group and 18/64 patients in the SOC group (20 visits) had aclinic visit. The mean total number of contacts was

significantly lower in the TH group (2.2 ± 2.6/patient) compared with the SOC group (7.8 ± 4.1/patient; p < 0.0001),
corresponding to a 59 % reduction in labor with the TH monitoring program. In CPAP-adherent patients, labor time wassignificantly lower in the TH versus SOC group (Table 5).

Discussion
In this study, TH monitoring of CPAP adherence using the USleep platform required 59 % less coaching labor than an SOC
model, with equivalent adherence and efficacy. Adherence
was achieved quickly, particularly in the TH group, nearly
two thirds of whom had achieved Medicare-defined CPAP
adherence within 30 days.
Previous studies examining the use of TH strategies for
managing OSA patients have had varying results [13, 14,
17, 19, 21]. Few have explored resource utilization. The
TELEPAP study was a randomized, controlled clinical study

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Table 3Sleep Breath (2016) 20:777–785
Adherence results in the completed cases population
THSOC

p value

Medicare adherence rates

N = 58N = 64

Patients achieving adherence in first 30 days, n (%)

36 (62.1)

34 (53.1)

Patients achieving adherence within 31–60 days, n (%)
Patients achieving adherence within 61–90 days, n (%)

10 (17.2)
2 (3.5)8 (12.5)5 (7.8)

Patients not achieving adherence during the study, n (%)
Total patients achieving adherence at any time, n (%)

10 (17.2)
48 (82.8)

17 (26.6)
47 (73.4)

0.22

Average time to achieve adherence, days
Mean±SDa (range)(n = 48)30.0 ± 12.2 (21–73)(n = 47)
34.0 ± 18.3 (21–83)

0.21

Usage
Daily usage, h

N = 58N = 645.1 ± 1.9 (0.4–9.1)
5.5

4.7 ± 2.1 (0.1–9.0)
4.9

0.24

70.2 ± 26.7 (3.3–100)
77.8

63.3 ± 28.5 (0–98.9)
71.1

0.17

Mean ± SD (range)
Median0.40

Days CPAP used for >4 h, % patients
Mean ± SD (range)
MedianaStandard deviation

Fig. 3 Average time spent onfollow-up care by time frominitiation of CPAP

Avg Resource Time for Follow-Up Care (min)in 51 patients with newly diagnosed OSA [16]. The results did
not show an advantage in adherence or efficacy fortelemonitoring. With respect to labor requirements, it was assumed that standard clinical care was the lower cost option.
To our knowledge, there is only one other study documenting
reduced labor requirements with TH [21]. A significant reduction
in nursing time per patient in CPAP users managed using awireless, remote monitoring system (ResTraxx Online
System®; ResMed) compared with control was documented.
DeMolles and colleagues used motivational interviewing techniques administered via an automated telephone-linked communication system to promote CPAP use [22]. CPAP use significantly increased in the telemedicine group, by 1 hour at 6 months
and 2 hours at 12 months; labor costs were not assessed. In a 2month pilot study in 45 newly diagnosed OSA patients,
telemonitoring was as effective as usual care in achieving
CPAP adherence and efficacy, but economic efficiency and labor

requirements were not addressed [20]. Fox and colleagues randomized 75 patients in a 3-month study comparing SOC with a
web-based telemonitoring application without automated messaging [23]. The telemedicine group had increased CPAP usage,
and use of telemedicine was a significant predictor of CPAP
adherence. However, on average, telemedicine patients required
an extra hour of research coordinator time to achieve the improvement. The authors postulated that the additional labor costassociated with the TH approach might be worth the investment
because of the improved adherence achieved.
In the current study, there was no significant difference inadherence between the TH and SOC groups. This may be
because the overall CPAP adherence rate in the SOC group
was at the high end of those reported in the literature. The
study was designed to detect a 20 % difference in adherence
between the SOC and TH groups, with an expected SOC
group adherence rate of 70 %. To detect a significant

45
40
35
30
25
20
15
10
5
0
0-30

31-60

61-90

Days
Telehealth/Adherent

Telehealth/Non-Adherent

SOC/Adherent

SOC/Non-Adherent

Sleep Breath (2016) 20:777–785
Table 4 Characteristics ofadherent and non-adherent
patients

783

Adherent

Non-adherent

p valuea

48.3 ± 7.1 (35–57) [n = 10]44.0 ± 6.1 (32–54) [n = 17]

0.00550.0609Age, years—mean ± SDb (range) [n]TH
SOC

52.2 ± 11.3 (20–78) [n = 48]
52.9 ± 12.2 (30–77) [n = 47]
Baseline AHIc, /h—mean ± SD (range) [n]aTH

37.8 ± 26.7 (5.3–110.0) [n = 48]

27.1 ± 17.4 (6.1–58.8) [n = 10]SOC30.4 ± 15.9 (6.4–67.6) [n = 47]

23.5 ± 23.3 (6.9–99.8) [n = 17]

Based on maximum likelihood estimates in the multiple regression modelbStandard deviationcApnea-hypopnea index

difference between the two groups, adherence in the TH groupwould need to have been 90 %. Although the SOC techniques
were clearly successful at obtaining adherence, they required
significant time investment from HCPs, and this is expensive.
We speculate that the decrease in resource use in the TH group
can largely be attributed to a marked reduction in the requirement for HCPs to manually review adherence data, identify atrisk patients, and provide coaching. Instead, automated notifications were delivered to the patient and their care provider.
The reduction in labor required in the TH group was especially evident in the savings associated with coaching patients
who reached adherence within the first 30 days (Fig. 3). The
TH program did not require any additional labor if the patient
was responding well to CPAP therapy. In contrast, the SOC
model required an intense labor investment, particularly during the first month.
The TH technology allowed personnel resources to be
diverted to patients who were non-adherent with therapy orwere having one of a defined set of clinical complications(e.g., increased AHI, high leak). The rule sets that trigger
patient and/or provider notifications can be customized basedon the specific requirements and scope of each healthcare
provider and patient population. Patient satisfaction data indicated that the TH program met patient expectations just as
effectively as the SOC program.
There are some limitations to this study. Firstly, the abilityof the study to detect a significant difference in adherence anddaily usage between the TH and SOC groups was reduced by
the exclusion of 18 patients from the final analysis and thehigh adherence rate in the SOC group. Of the 18 patients withinsufficient data, 12 were from the TH group and 6 were fromthe SOC group. Examination of the characteristics of thesepatients did not provide any explanation for the difference indropout rates between the two groups, and there is no evidence that treatment allocation played any role. Secondly,
the findings of this study may not be widely generalizablefor the following reasons. Sleep Data’s SOC program utilizes
frequent phone calls to follow and coach patients, which canbe labor-intensive. Therefore, the coaching labor time savedwith the U-Sleep program might be greater compared withless rigorous programs. In addition, the TH arm relied heavily
on access to a phone network or the internet, which, although

Table 5 Resource utilization
based on patient contacts
Total time utilized
Contact attempt, minutes (4 min each)
Follow-up contact, minutes (9 min each)
Clinic visit, minutes (30 min each)
Total time for follow-up care, minTime for follow-up care per patient, min
Mean±SDa (range)
MedianTime per patient—based on level ofadherence, min
Adherent patients, min
Mean ± SD
Non-adherent patients, min
Mean ± SD (n)aStandard deviation

TH (n = 58)

SOC (n = 64)

116
702

920
2214

570
1388

600
3734

23.9 ± 26.3 (0–107)
18.0

58.3 ± 25.0 (18–133)
52.5(n = 48)
20.7 ± 24.6
(n = 10)39.5 ± 29.6

(n = 47)53.7 ± 20.8
(n = 17)71.2 ± 31.1

p value<0.0001<0.0001
0.07

784

commonplace, is by no means universal. In particular, some of
the most challenging patients are those in lower socioeconomic groups who do not have access to these communication
tools. Thus, some patients may not have the ability to participate in a TH program. Thirdly, the time ascribed to eachfollow-up encounter was based on Sleep Data’s historical averages and not measured directly. It is possible that although
the number of phone calls made to TH patients was markedly
reduced, their duration might have been longer. Lastly, although there was close communication between Sleep Dataand the prescribing physicians, CPAP therapy for all patientswas primarily being managed by Sleep Data’s professionals;
this study did not gather detailed information about interventions that might have been performed by the prescribing physician without Sleep Data’s knowledge.
This study also has a number of strengths. The protocol
was designed to overcome some of the limitations of previous
studies involving telemedicine and OSA. This study included
more than one study center, enrolled a larger number of patients and utilized objective adherence data on which to baseconclusions. In addition, the determination of resource utilization associated with TH versus SOC has not been widely
analyzed previously. Finally, the TH system used, U-Sleep, isa HIPAA-compliant program that is currently available andprovides a standardized model that can be applied in a variety
of clinical environments to optimize treatment of OSA
patients.
As healthcare payment structures in the USA continue to
evolve, there is an increasing pressure on healthcare providers
to focus on business efficiency, especially cost management.
According to the Centers for Medicare and Medicaid Services,
total US healthcare spending was $2.8 trillion in 2012, up
3.7 % versus 2011 [17]. Given such a heavy economic burden,
more streamlined approaches to providing healthcare, such as
TH, are necessary to maintain quality healthcare while reducing costs.
In conclusion, the U-Sleep TH intervention was associatedwith excellent adherence to CPAP therapy in newly diagnosed
OSA patients. The U-Sleep program also significantly reduced the amount of HCP time that was needed to achieveadherence and efficacy comparable to the SOC model.
Whether these benefits translate into better cost effectivenessremains to be determined.

Sleep Breath (2016) 20:777–785
employee of, and has an ownership interest in Sleep Data’s managementcompany, Sleep Data Services, LLC. Maureen Crocker is an employee of
ResMed Corp. William Hevener, Leslee Willes, Sarah Sridasome and Ma’an
Muhsin have no conflicts of interest. Financial support for this study and for
database creation was provided, in part, by ResMed Corp. Medical writing
assistance from Nicola Ryan, independent medical writer, was funded by
ResMed Corp. The design and conduct of the study and manuscript preparation were performed by Dominic Munafo, MD at Sleep Data, LLC.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to theCreative Commons license, and indicate if changes were made.

References
1.

2.

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6.

7.

8.

9.

10.

Compliance with ethical standards
11.
Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of theinstitutional and/or national research committee and with the 1964
Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants
included in the study.
Conflict of interest U-Sleep is a registered product of ResMed Corp. Dr.
Dominic Munafo has received research grants from ResMed Corp and is an

12.

13.

14.

Young T, Peppard PE, Gottlieb DJ (2002) Epidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit
Care Med 165(9):1217–1239
Peppard PE, Young T, Barnet JH, et al. (2013) Increased prevalenceof sleep-disordered breathing in adults. Am J Epidemiol 177(9):1006–1014
Einhorn D, Stewart DA, Erman MK, et al. (2007) Prevalence ofsleep apnea in a population of adults with type 2 diabetes mellitus.
Endocrine Practice: Official Journal of the American College Of
Endocrinology and the American Association Of Clinical
Endocrinologists 13(4):355–362
O'Connor GT, Caffo B, Newman AB, et al. (2009) Prospective studyof sleep-disordered breathing and hypertension: the Sleep Heart
Health Study. Am J Respir Crit Care Med 179(12):1159–1164
Oldenburg O, Lamp B, Faber L, et al. (2007) Sleep-disorderedbreathing in patients with symptomatic heart failure: a contemporary study of prevalence in and characteristics of 700 patients. Eur J
Heart Fail 9(3):251–257
Reichmuth KJ, Austin D, Skatrud JB, et al. (2005) Association of
sleep apnea and type II diabetes: a population-based study. Am J
Respir Crit Care Med 172(12):1590–1595
Shahar E, Whitney CW, Redline S, et al. (2001) Sleep-disorderedbreathing and cardiovascular disease: cross-sectional results of the
Sleep Heart Health Study. Am J Respir Crit Care Med 163(1):19–25
Sorajja D, Gami AS, Somers VK, et al. (2008) Independent association between obstructive sleep apnea and subclinical coronary
artery disease. Chest 133(4):927–933
Tregear S, Reston J, Schoelles K, et al. (2009) Obstructive sleep
apnea and risk of motor vehicle crash: systematic review and metaanalysis. Journal of Clinical Sleep Medicine: JCSM: : official publication of the American Academy of Sleep Medicine 5(6):573–581
Tregear S, Reston J, Schoelles K, et al. (2010) Continuous positive
airway pressure reduces risk of motor vehicle crash among drivers
with obstructive sleep apnea: systematic review and meta-analysis.
Sleep 33(10):1373–1380
McKinsey & Company (2010). The price of fatigue: the surprising
economic costs of unmanaged sleep apnea.
Weaver TE, Grunstein RR (2008) Adherence to continuous positive
airway pressure therapy: the challenge to effective treatment. Proc
Am Thorac Soc 5(2):173–178
Weaver TE, Maislin G, Dinges DF, et al. (2007) Relationship between hours of CPAP use and achieving normal levels of sleepinessand daily functioning. Sleep 30(6):711–719
Kribbs NB, Pack AI, Kline LR, et al. (1993) Objective measurement of patterns of nasal CPAP use by patients with obstructive

Sleep Breath (2016) 20:777–785
sleep apnea. The American Review of Respiratory Disease 147(4):887–895
15. McArdle N, Devereux G, Heidarnejad H, et al. (1999) Long-term
use of CPAP therapy for sleep apnea/hypopnea syndrome. Am J
Respir Crit Care Med 159(4 Pt 1):1108–1114
16. Abreu T, Silva AM, Canhao C, et al (2013) Evaluation of wirelesstelemonitoring of CPAP therapy in obstructive sleep apnea TELEPAP study. Eur Respir J;42(Suppl. 57):406 s.
17. National Health Statistics Group US Department of Commerce
Bureau of Economic Analysis (2012) National health expendituredata.
18. Wozniak DR, Lasserson TJ, Smith I (2014) Educational, supportive and behaviroural interventions to improve usage of continuous positive airway pressure machines in adults with obstructive sleep apnoea. Cochrane Database System Review 1:
CD007736

785
19.
20.

21.

22.

23.

World Health Organisation (2003) Adherence to long-term therapies. Evidence for action.
Stepnowsky CJ, Palau JJ, Marler MR, et al (2007) Pilot randomized
trial of the effect of wireless telemonitoring on compliance andtreatment efficacy in obstructive sleep apnea. Journal of medical
Internet research;9(2):e14.
Melkko S, Hakko S, Anttalainen U, et al (2013) Telemonitoring of
CPAP therapy saves nursing time. Eur Respir J;42(Suppl.57):
P2042.
DeMolles DA, Sparrow D, Gottlieb DJ, et al. (2004) A pilot trial ofa telecommunications system in sleep apnea management. Med
Care 42(8):764–769
Fox N, Hirsch-Allen AJ, Goodfellow E, et al. (2012) The impact ofa telemedicine monitoring system on positive airway pressure adherence in patients with obstructive sleep apnea: a randomized controlled trial. Sleep 35(4):477–481