Retrospective descriptive study of CPAP adherence associatedwith use of the ResMed myAir application
S Lynch, BID, RN; A Blase, BS; A Wimms MScMed; L Erikli, RN; A Benjafield, PhD; C Kelly, PhD; L Willes, MS.
ResMed Science Center, ResMed Ltd, Sydney, Australia

Abstract
Digital health technologies are gaining increased recognition as having the potential to improve adherence to continuous
positive airway pressure (CPAP) therapy. This retrospective analysis found that 83.9% of brand new CPAP users who
received standard care and who used the ResMed myAir self-management application, which incorporates empiricallytested behavioural science strategies, achieved Medicare adherence in 90 days and 75.4% achieved adherence in 30 days.
The median number of days to achieve adherence was 23 (95% confidence interval, 23-23). The adherence rates achievedin this study suggest that myAir has a positive effect on CPAP adherence.

Introduction
Nocturnal continuous positive airway pressure (CPAP) is
considered the gold standard treatment for sleep apnea.
However, adherence to CPAP is essential for the healthbenefits of therapy to be realised. The US Medicare policy
for continued coverage of a CPAP device requires objective
evidence of CPAP use for ≥4 hours per night on at least
70% of nights during a consecutive 30-day period anytime
during the first 3 months of initial usage [1]. Yet there is
no globally-accepted definition of good CPAP adherence interms of the level of usage required to achieve and maintaintherapeutic effectiveness.
There appears to be a direct relationship between theeffectiveness of CPAP therapy and the hours of use acrossa wide range of measures, from daytime sleepinessand neurocognitive function to reductions in BP andcardiovascular events [2-14]. However, a “one size fitsall” approach to defining optimal CPAP usage may not beappropriate given that the threshold level for daily CPAP
use appears to depend on the outcome measure used
and may vary significantly between patients [15]. Given
this variation, a better and more detailed understanding ofadherence to CPAP therapy for individual patients may help
to optimize delivery of CPAP therapy and related outcomes.
Available data suggest that the pattern of usage isestablished early in CPAP treatment and that early usage
predicts later adherence [16, 17]. Understanding the patternof CPAP usage over time is interesting because it may
guide more effective interventions.
Many adherence studies focus on the first three monthsof CPAP treatment and this is a critical time over which tosupport adherence. However, CPAP usage beyond 3 months
is also important because sustained benefits will only beachieved with consistent and ongoing adherence to CPAP

therapy. It is not uncommon for health-promoting behaviors
to lapse over time, but the cost of supporting adherence
over the long term can be prohibitive for many services.
The importance of long-term interventions that help sustain
health behaviors is also highlighted by study data showing
significant treatment-by-compliance interactions withsleepiness [2, 13], scores on the Multiple Sleep Latency
Test (MSLT) [13] and Functional Outcomes of Sleep
Questionnaire (FOSQ) [2, 13], and the vitality subscale of
the Short Form-36 (SF-36) [2] and cardiovascular outcomes.
The American Thoracic Society recommends that CPAP
adherence be monitored consistently over the long term[18]. However, there is a relative lack of knowledge aboutadherence to CPAP treatment after 90 days of therapy, andabout potential cost-effective strategies that could be used
to ensure adherence over longer time periods.
A number of techniques have been used to try andimprove adherence with CPAP therapy, including patient
education, supportive and behavioural interventions [1921], peer support [20, 22-24] and clinical support [21-23,25]. However, these can be labor intensive and therefore
costly, and are difficult to sustain for patients requiring longterm therapy. Another approach that is gaining increased
recognition is the use of digital health solutions such as
telemedicine [26-30]. However, there is a limited amountof data on the effectiveness of digital health solutions forsupporting adherence.
myAir is a theory-based, Health Insurance Portability andAccountability Act (HIPAA)-compliant mobile responsive
cloud-based application that is an available option forpatients using the ResMed AirSense 10 CPAP series(AirSense 10 CPAP, AirSense 10 AutoSet, AirSense 10
Elite, AirSense 10 Autoset for Her) and AirCurve 10(AirCurve 10 ASV, AirCurve 10 VAuto, AirCurve 10 S,
AirCurve 10 ST) devices. The myAir program integrates
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aspects of Social Cognitive Theory (SCT) [31, 32], theFogg Behavior Model (FBM) [33], the spirit of Motivational
Interviewing [34] and the Chronic Care Model (CCM)[35-38]. Additional strategies employed by myAir tofacilitate behavior change include simplifying complex
tasks and information, positive reinforcement, providing
encouragement, tailoring information, intervening at theright time, digital storytelling, language and self-monitoring.
Mobility and connectivity also provide an opportunity forpatients to access myAir and to receive messages at an
opportune moment, during periods of downtime (e.g. while
the patient is standing in a line) and when it would not be
possible or convenient for clinicians to contact them.
This study used data from myAir to characterize CPAP
usage in a large real-world population of CPAP recipients
who activated the myAir component of their Airsense 10
series devices.

Methods
The myAir database was queried for records createdbetween October 2014 and March 2015. During the myAir
setup procedure patients can optionally enter the monthand year that they started CPAP treatment. This allowed
patients to be divided into different user groups based on
their experience using CPAP prior to activation of myAir.
Records for accounts that had been activated, had at least
90 days of data and had a realistic treatment start monthand year specified were included in the analysis (patientswith treatment start dates before 1990 were excluded).
Two different categorizations of user experience wereused in the analyses: (1) brand new users (CPAP use for0-30 days) vs. others (CPAP use for >30 days); (2) novice(CPAP use for 0-90 days), intermediate (CPAP use for >90
to 180 days), experienced (CPAP use for >180 days) andstrugglers (Figure 1). Strugglers were defined users who
had 0 to 30 days experience using CPAP treatment prior toactivating their myAir account and <2 hours average usage
in the first 14 days on myAir. Medicare calculations werecompleted only on the brand new users to minimize theconfounding effect of experience. Days where there was
no CPAP usage and days when usage was <20 minutes
were considered as non-used days. Non-used days were
included in calculations.

To determine user groupings, the myAir activation date wasused as the CPAP treatment start date if the therapy start
month and year matched the activation date. Otherwise,
the therapy start date was assumed to be the 15th day ofthe month and year recorded.
The proportion of patients achieving the Medicare definitionof CPAP [1] was calculated for brand new users. The
earliest time that it was possible to satisfy these criteria
was after 21 days of CPAP therapy. Once participants were
defined as satisfying the Medicare criteria they remained in
the compliant group even if CPAP usage subsequently fell
below the compliance threshold at later times in treatment.
Average usage hours, intervals and thresholds, and theaverage number of days per week CPAP was used overthe 90-day assessment period in novice, intermediate,experienced and all CPAP users were also determinedbased on myAir data.
Data are presented using descriptive statistics, including
percentage of patients, mean and 95% confidence
interval (CI), median, range, standard deviation (SD)and standard error (SE) where applicable. Comparisons
between patient groups were made using t-tests, ANOVA,
Chi-square tests, or Fisher’s exact tests, as appropriate.
SAS Version 9.3 was used for most analyses.
As a sensitivity analysis, two missing data procedures
were used to evaluate the effect of blank data fields. The
primary method was a conservative imputation of themissing usage hours: if CPAP usage hours were missing fora particular night, they were imputed as zero. The second
method was to treat the missing values as missing; in thiscase the missing values were not included in calculationsof means, SD, SE values and proportions.

Results
There were 2343 patients who met the inclusion criteria;
70% of participants were male, 29% were female and 1%
did not disclose their gender. Novice (n=1731), intermediate(n=37) and experienced (n=575) users had 16.2±21.7 days,
121.1±21.0 days and 7.9±4.6 years of experience using
CPAP treatment prior to using myAir, respectively. A totalof 1067 patients were classified as brand new users and 86
patients were classified as strugglers.

Medicare adherence (brand new users)

Figure 1: Study flow diagram

2

The proportion of brand new users who achieved thecriteria for Medicare adherence in 90 days was 83.9%, and75.4% achieved Medicare adherence within 30 days. The
median time to achieve Medicare adherence was 23 days
(Table 1); this was similar in males and females. Eightysix brand new users were classified as strugglers. Of
these, 19.8% went on to achieve Medicare adherence in
90 days (Table 2). Female strugglers took longer than malestrugglers to achieve Medicare adherence.

Adherence and device usage byuser group.

In addition, participants in the experienced user group used
CPAP more regularly than both novice and intermediateusers. This was reflected in higher usage hours inexperienced compared with novice users, with significant
differences between user groups; experienced users had
significantly higher mean usage than both novice andintermediate users (Table 3). Only 19.5% and 32.2% of
novice and experienced patients respectively used CPAP
for >20 minutes every day.

The proportion of patients using CPAP for ≥4 hours for atleast 70% of days over a 90 day period was significantly
higher in the experienced user group compared with novice(p<0.0001) and intermediate users (p=0.02) (Table 3).

Table 1: Medicare adherence in brand new users
GenderAll Subjects (N=1067)

Male (N=708)
Number of days to achieve Medicare adherence

Female (N=349)

Mean (SE)*Median (95% CI)

36.3 (0.96)
23.0 (23.0, 23.0)

36.0 (1.33)
23.0 (23.0, 24.0)

36.3 (0.78)
23.0 (23.0, 23.0)

533 (75.3)
40 (5.6)
20 (2.8)
115 (16.2)
593 (83.8)

262 (75.1)
20 ( 5.7)
11 (3.2)
56 (16.0)
293 (84.0)

804 (75.4)
60 (5.6)
31 (2.9)172 (16.1)
895 (83.9)

Subjects with adherence, n (%)
21 to 30 days
>30 to 60 days
>60 to 90 days
Medicare non-adherent
Medicare adherent

CI, confidence interval; SE, standard error.
* The mean survival time and its SE were underestimated because the largest observation was censored and the estimation was restricted to thelargest event time (90 days).

Table 2: Medicare adherence in brand new users defined as strugglers
GenderMale (N=53)
Number of days to achieve Medicare adherence
Mean (SE)*Median#All Subjects (N=86)
Female (N=32)

68.8 (1.38)>90

82.5 (2.80)>90

81.4 (1.65)>90

9 (17.0)
2 (3.8)42 (79.2)
11 (20.8)

3 ( 9.4)
3 (9.4)26 (81.3)
6 (18.8)

12 (14.0)
5 (5.8)69 (80.2)
17 (19.8)

Subjects with adherence, n (%)>30 to 60 days
>60 to 90 days
Medicare non-adherent
Medicare adherent

Struggers were defined as having average CPAP use of <2 h/night in the first 14 days on myAir.
* The mean survival time and its SE were underestimated because the largest observation was censored and the estimation was restricted to thelargest event time (90 days).

Table 3: CPAP compliance and usage by category of user experience
User group
Intermediate (N=37)

Experienced (N=575)

1228 (70.9)

25 (67.6)475 (82.6)ab28 (1.6)
180 (10.4)
1185 (68.5)
338 (19.5)

3 (8.1)5 (13.5)
19 (51.4)
10 (27.0)

9 (1.6)32 (5.6)
349 (60.7)
185 (32.2)

5.6±2.21

5.4±2.58

6.5±2.04cd

Novice (N=1731)
Average usage ≥4 hours for at least 70% of nights, n (%)*
Average weekly usage, n (%)
0 days>0 to <4 days
≥4 to <7 days
Every dayAverage nightly usage, hours (mean±SD)
SD, standard deviation.
* Calculated over 90 days; this differs from Medicare adherence because the criteria needs to be satisfied for the entire 90 day period rather thanduring any consecutive 30 day period during the first 3 months of initial usage.ap<0.0001 vs novice users; bp=0.02 vs intermediate users; cp<0.0001
vs novice users; dp=0.01 vs intermediate users.

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Overall, 57.1% of CPAP recipients used their device for≥6 hours/night. The proportion of patients using CPAP
for longer time periods was higher for experienced users(Table 4). Exclusion of missing usage data had only aminimal effect on these values.

Discussion
The main finding of this study was that a high proportion(83.9%) of brand new CPAP users who used standard
care and the ResMed myAir self-management applicationachieved Medicare adherence in the first 90 days of CPAP
treatment. The median time to achieve Medicare adherence
was 23 days. This suggests that the myAir digital healthsolution may have a positive effect on adherence to CPAP
therapy.
This study also found that experienced users are generallyusing treatment for longer and more frequently comparedwith novice and intermediate users, suggesting that thesefactors may be associated with long-term adherence forparticular cohorts of CPAP users. One possible mechanismis that patients using treatment for longer and more often
are more likely to notice the benefits of treatment whichhelps motivate continued use. Another possible reason isthat with time, persistence and practice, patients become
more skilled at using CPAP treatment, also supporting
longer and regular usage. Also, some patients will quit
CPAP treatment over time meaning that the experienced
user group is less likely to include those struggling on
CPAP treatment, thus increasing average usage hours in
the experienced group. For these reasons it is likely thatthe usage profile of experienced, habituated and probably
motivated users in this study represents a best-casescenario achieved with a broad range of US-based standard
care.
There have been few studies looking at supporting CPAP
usage over the longer term. One of these compared
the effects of high-touch intensive and standard followup on CPAP adherence over a 24-month period in 3100
Mediterranean patients with newly-diagnosed moderateto-severe sleep apnea [39]. The standard care approach inthis study was relatively comprehensive by world standards,
and included a 15- to 30-min follow-up visit at the sleep
clinic during which compliance data were downloaded
from the device and discussed with the patient, and side

effects that could lead to suboptimal compliance wereaddressed. Any uncertainty about a patient’s complianceor willingness to continue on CPAP therapy was discussedwith a physician and steps undertaken to resolve barriers
to compliance. In addition to these measures, the hightouch intensive intervention group also received multiple
simultaneous strategies such as education, role models,authority figures, spousal involvement, monitoring, regularfollow-up, trouble shooting, home visits, telephone calls,
lectures, counselling to reduce perceived barriers to CPAP
use, sleep diaries, and access to a 24-hour consultation
telephone line. Use of these labour-intensive approaches
to maintaining or improving adherence resulted in 79.8%
of patients in the standard group and 92.8% of those in theintensive group using CPAP regularly for ≥4 hours per nighton at least 70% of days. In the current study, the additionof the low-touch myAir digital health solution resulted in
70.9% of novice CPAP users adhering to CPAP for ≥4hours per night on 70% of days; the corresponding rate
for experienced users was 82.6%. In the Mediterranean
study [39], mean±SD hours of CPAP use calculated over a
24-month period were 5.2±2.2 hours in the control group
and 6.9±1.8 hours in the intervention group. This myAir
study found that the mean±SD hours of CPAP use overa 3-month period in novice users was 5.6±2.21 hours andin experienced users it was 6.5±2.04 hours. These rates
compare favorably with those achieved in the intensive
intervention trial, and were obtained with markedly lower
time and resource requirements. This suggests that myAir
may be a cost effective and sustainable addition to standard
care that can improve adherence to CPAP therapy.
In this study, 53.2% of novice users and 69.2% ofexperienced users were sleeping on CPAP for ≥6 hours/
night. There was a difference of 16% between experienced
and novice users, and these CPAP usage rates aresubstantially higher than those reported in previous
studies where 41.7% and 45.1% of participants used
CPAP for ≥6 hours/night [2, 13]. Given the dose-response
relationship between CPAP usage and normalization ofneurocognitive and cardiovascular outcomes it would
appear that a high proportion of patients receiving CPAP
treatment in the short to longer term may continue to haveexcessive daytime sleepiness, poor Functional Outcomesof Sleep Questionnaire (FOSQ) and vitality scores, and also
potentially elevated cardiovascular risk because they are

Table 4: Nightly CPAP usage by user group
User Group≤ 2 hours
2–4 hours
4–5 hours
5–6 hours
6–7 hours≥7 hours

4

All Subjects (N=2343)

Novice (N=1731

Intermediate (N=37)

Experienced (N=575)

166 (9.6%)
185 (10.7%)
181 (10.5%)278 (16.1%)
388 (22.4%)
533 (30.8%)

6 (16.2%)
4 (10.8%)
1 (2.7%)7 (18.9%)
6 (16.2%)
13 (35.1%)

28 (4.9%)34 (5.9%)
41 (7.1%)
74 (12.9%)
120 (20.9%)278 (48.3%)

200 (8.5%)
223 (9.5%)
223 (9.5%)359 (15.3%)
514 (21.9%)
824 (35.2%)

not using CPAP enough. This reinforces the importance ofshort- to long-term cost-effective interventions that enable
early intervention and help to facilitate and sustain positive
health behaviors.
The current study has a number of strengths. It includes
a large sample overall and the number of blank data fields
was very low (<0.2% of usage) and did not significantly
influence the results of the analysis. The low rate of missing
data was probably due to the reliable connectivity of the
AirSense 10 series devices and increases the accuracyof the study findings. Even when taking a conservative
approach and replacing blank data fields with ‘0’, adherence
remained good.
There are also a number of limitations that need to betaken into account when interpreting the results of thisstudy. Patients using myAir are likely to be more engaged
and motivated to treat their sleep apnea, and higher CPAP
usage would be expected in a more motivated group.
Another factor that might have influenced the study findings
is that Day 1 on myAir may not correspond to Day 1 on
CPAP treatment. Patients registering for myAir after a few
weeks of CPAP treatment may have had the opportunity towork through major treatment issues with their clinicians
and this could account for the high level of usage seen.
The reliance on patients to enter data accurately into
myAir (e.g. CPAP start month) is another factor that might
introduce bias into the study findings.
This study showed that patients using myAir during CPAP
therapy showed good adherence over the first 90 daysof treatment. Adherence to CPAP therapy was better inexperienced users than in novice users. Overall, the results
suggest that myAir is a simple, easy-to-use and sustainableoption that may lead to improved compliance in patients
receiving CPAP.

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