Obstructive sleep apnea (OSA) is a prevalent and serious medical condition characterized by repeated complete or partial obstructions of the upper airway during sleep and is prevalent in 2% to 4% of working middle-aged adults. Nasal continuous positive airway pressure (CPAP) is the gold-standard treatment for OSA. Because compliance rates with CPAP therapy are disappointingly low, effective interventions are needed to improve CPAP compliance among patients diagnosed with OSA.
The aim was to determine whether wireless telemonitoring of CPAP compliance and efficacy data, compared to usual clinical care, results in higher CPAP compliance and improved OSA outcomes.
45 patients newly diagnosed with OSA were randomized to either telemonitored clinical care or usual clinical care and were followed for their first 2 months of treatment with CPAP therapy. CPAP therapists were not blinded to the participants’ treatment group.
20 participants in each group received the designated intervention. Patients randomized to telemonitored clinical care used CPAP an average of 4.1 ± 1.8 hours per night, while the usual clinical care patients averaged 2.8 ± 2.2 hours per night (
Telemonitoring of CPAP compliance and efficacy data and rapid use of those data by the clinical sleep team to guide the collaborative (ie, patient and provider) management of CPAP treatment is as effective as usual care in improving compliance rates and outcomes in new CPAP users. This study was designed as a pilot—larger, well-powered studies are necessary to fully evaluate the clinical and economic efficacy of telemonitoring for this population.
Sleep-disordered breathing is a generic diagnostic term broadly used to describe apnea (cessation of airflow), hypopnea (reduction in airflow), and other breathing irregularities that occur during sleep. It is a common, albeit underdiagnosed, chronic condition in the adult population, with up to 4% of females and 9% of males experiencing at least 15 episodes of apnea and/or hypopnea per hour of sleep [
Nasal application of continuous positive airway pressure (CPAP) [
Over the past decade, a variety of psychoeducational and technological interventions designed to improve CPAP compliance have been developed and tested. To date, the scope of psychoeducational interventions includes telephone follow-ups with dissemination of OSA-CPAP literature to patients [
This pilot intervention was informed and shaped by three major trends in the findings of prior studies, each predicated on a departure from usual care for OSA-CPAP patients. Usual care entails initial patient-specific titration of the CPAP device to the critical pressure needed to keep the upper airway open during sleep, a follow-up telephone call from the CPAP provider or sleep physician’s office to check on the patient’s comfort and usage within one week of CPAP initiation in the home environment, and subsequent in-office visits starting several weeks after CPAP initiation and continuing thereafter as needed. The first salient trend in prior findings is that patients’ self-reported difficulties with CPAP as well as their subjective compliance do not provide sufficient or reliable information to guide appropriate clinical management of OSA under usual care conditions. For example, engaged in a novel therapy, a patient may not be aware of excessive mask leakage during the initial week of CPAP and so will fail to report the problem during the one-week follow-up call. Uncorrected mask leakage, by hampering critical pressure delivery, can have a seriously deleterious impact on compliance. The second trend is that objective compliance and efficacy data are typically not obtained in a timely manner. There are, in general, three standard patient-dependent modes by which a clinical sleep team obtains the objective compliance data recorded as machine-on time by the CPAP unit: (1) the patient brings the CPAP unit into the office and the sleep team physically downloads the data, (2) the patient transmits the data from home via a telephone line, and (3) the patient removes a memory card from the CPAP unit and either mails it in or brings it into the office during a follow-up visit. Much as compliance with medical regimens can be problematic [
Given the very limited number of prior CPAP compliance- and efficacy-related studies within the telemedicine arena, the present study breaks new ground by attempting to counter the known shortcomings of usual care in both telemonitoring design and to answer the overarching question: Does more quickly delivered advice and counsel from providers to patients about developing objectively measured in-home compliance patterns and problems translate to enhanced treatment compliance levels sufficient for improvements in patient health status and outcomes?
This was a randomized controlled pilot study approved by the local Institutional Review Board. Newly diagnosed OSA patients who met inclusion criteria were asked to participate. Patients were randomized to either the usual clinical care (UCC) group or the telemonitored clinical care (TCC) group. Both groups of patients received the monitoring device and were followed for an intervention period of 2 months.
Participants were patients at the Veterans Affairs San Diego Healthcare System (VASDHS) who were referred to the sleep clinic by their physician for suspicion of OSA. All patients had their sleep recorded with the Stardust sleep recording system (Respironics, Pittsburgh, PA), which monitors heart rate, oximetry, nasal airflow, chest wall movement, and body position. Patients diagnosed with OSA based on the results of their sleep study and clinical history were recruited, consented, and enrolled if they met all of the following criteria: diagnosis of moderate-to-severe OSA, defined as an Apnea-Hypopnea Index (AHI) ≥ 15 events per hour; naive to CPAP therapy; stable sleep environment (operationally defined as a permanent address, requisite for wireless monitoring); and at least 18 years of age. An AHI of ≥15 was chosen in an effort to be consistent with current OSA guidelines and practice parameters [
Patients were excluded from the study if they met any one of the following criteria: allergies or sensitivity to the mask or mask material; previous use of any other PAP device (eg, bi-level PAP, auto-adjusting PAP); current use of prescribed supplemental oxygen; or significant comorbid medical conditions that would prevent the patient from completing the protocol. Significant comorbidities were defined as any medical or mental health condition that could interfere with the daily use of CPAP. Additionally, patients were excluded if they lived in a geographically unsuitable region (ie, outside of the wireless network coverage area). A total of 91 patients at the VASDHS Sleep Clinic either signed or gave verbal consent to be contacted so they could learn more about the study. From these 91 patients, 46 were either were not interested in study participation or did not satisfy the inclusion and exclusion criteria.
The remaining 45 patients signed consent forms and were enrolled into the study. The study took place from October 2004 to August 2006.
Participant flow and randomization chart
Each participant was provided with an AutoSet Spirit flow generator unit (ResMed Corp, Poway, CA) set to fixed-mode pressure, which was equipped with the HumidAire 2i heated humidifier (ResMed Corp, Poway, CA). Each participant was provided a compatible nasal or full-face mask; nasal pillows were not used in this study.
Model of wireless telemonitoring of CPAP compliance and efficacy.
All CPAP flow generators used in the study were outfitted with a ResTraxx wireless transmitter (ResMed Corp, Poway, CA) (
Only research and clinical staff had secured access to the data via a standard browser and entry into the ResTraxx Data Center (RDC), the patient and data management website designed for 24/7 access to telemonitored compliance and efficacy data. Per each 24-hour cycle of data transmission, the ResTraxx Data Center website displays the data in a calendar format that reveals daily and weekly data trends. The Multimedia Appendix provides four screenshots of the ResTraxx Data Center website, including patient demographics, prescription, monitoring, and compliance (the latter screen is also shown in
Screenshot of the ResTraxx Data Center website, showing the compliance tab with sample compliance and efficacy data for one month of CPAP use for a hypothetical patient. The thresholds specified on another tab allow for color-coding of efficacy and compliance data — this color-coding allows for quick review of how well any one patient is doing on CPAP. Specific data values are also provided, which can aid clinical management. The symbol legend at the bottom of the tab explains the meaning of each symbol used in this tab (see also Table 2).
Regardless of group assignment, all patients who participated in this study had identical CPAP setups. The initial set-up visit consisted of sleep apnea education, sleep study review, and CPAP instruction and mask-fitting. During this visit, a study staff member educated the patient about sleep apnea and explained the patient’s baseline sleep study results. Each patient was then instructed on how to use CPAP and was fitted for a mask. The patient wore the mask while the CPAP unit was set at the prescribed pressure for approximately 10 minutes for mask adjustment and assessment of pressure tolerance. Both groups were shown how to attach the ResTraxx wireless device to their CPAP units and were provided with written instructions on CPAP use.
Patients randomized to UCC were treated according to the prevailing standard of care for OSA patients at the VASDHS CPAP Clinic. Usual care consisted of a 1-week telephone call after CPAP initiation and a 1-month in-office follow-up visit by CPAP clinic staff. Patients were encouraged to call the clinic any time they had a problem or concern. CPAP compliance and efficacy data were downloaded at the 1-month time point to help direct clinical management.
The essence of the TCC intervention is the ability to telemonitor compliance and efficacy data for each patient on a daily basis from the first day of treatment and to act on those data collaboratively, and in partnership, with the patient. Collaborative management refers to the joint decision making and partnership between provider and patient and is characterized by communication, negotiation, and consideration of important patient factors and preferences. Patients in this group had their objective flow generator data monitored as frequently as needed per specified clinical pathways throughout the active 2-month treatment period. The frequency and nature of the clinical interactions depended on both the objectively measured nightly data values and subjective patient reports.
Telemonitoring included review of both compliance and efficacy data. Compliance data encompassed details on how many total hours the PAP unit was used each night at therapeutic pressure. Efficacy data consisted of the amount of mask leakeage (L/s) and the AHI (total number of apneas and hypopneas per hour of sleep). Thresholds for each parameter were set by the study team using the password-secured interactive ResTraxx Data Center website (
Specifications of thresholds for each parameter
Parameter | Threshold |
CPAP compliance | 4 hours/night |
Apnea-Hypopnea Index | 10 events/hour of sleep |
Mask leak | 0.4 L/s |
The ResTraxx Data Center calendar display (see also
Color-coding scheme summarizing compliance and efficacy measures on the ResTraxx Data Center website
Code | Description | Interpretation | Action | ||
green/... |
Compliance ≥ 4 hours/night | Compliance at or above threshold | Compliance within limits, no intervention necessary | ||
red/... |
Compliance < 4 hours/night | Compliance below threshold | Consider intervening to increase compliance | ||
.../green |
AHI < 10 | and | Leak < 0.4 L/s | Leak and AHI below threshold | Efficacy within limits, no intervention necessary |
.../yellow |
AHI < 10 | and | Leak ≥ 0.4 L/s | Either leak or AHI above threshold | Identify which is high and intervene as necessary |
|
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AHI ≥ 10 | and | Leak < 0.4 L/s |
Note: The only color code not shown is red/red, which indicates that CPAP was not used on the particular night of monitoring.
For the purposes of this study, we defined clinical pathways for the interventionists to follow. The pathways specified how frequently the clinical team would check the CPAP compliance and efficacy data values on the ResTraxx Data Center website for each patient. Human monitoring is designed to be more intensive in the earlier stages of treatment, with a gradual tapering off over time if patterns of CPAP compliance are established. A green/green pathway (ie, when all three parameters of compliance, AHI, and mask leak are within normal limits) specified this gradually attenuated monitoring schedule. A red/yellow pathway specified the general course of action required when one or more parameters were not within the normal range of values. In each case, the clinician and the patient collaboratively assessed the source of the problem, considered alternative solutions, and selected a corrective measure. The clinical team then continued close monitoring until each parameter was back within normal range. The red/yellow pathway referred the clinician to an intervention matrix of corrective actions to consider for each problem (
CPAP clinician intervention management matrix for TCC group (adapted from [
Symptoms | Cause | Correction | |
Dry eyes | Leak | Verify mask fit/size | |
Irritated skin | Head gear too tight | Readjust head gear | |
Pressure sores | Incorrect mask fit | ||
Dry nose, mouth, throat | Lack of humidified air | Use heated humidifier | |
Nasal congestion | Mouth breathing | Try chin strap | |
Feeling of need for |
Incorrect pressure (too low or high) | Verify prescribed pressure | |
Chest discomfort | Claustrophobia | Add short periods of use during the day | |
CPAP noise | Blocked air intake | Check air filter | |
Bed partner disturbance | Multiple factors | Move unit further from bed | |
Still snoring and/or sleepy | Pressure too low | Consider pressure increase |
Questionnaire data were collected at baseline and postintervention. The Measures section below describes each assessment tool and provides operational definitions. Flow generator data from all patients were both wirelessly transmitted to the secure ResTraxx Data Center server and manually downloaded by research staff at the end of the 2-month monitoring period. Data obtained wirelessly were compared to data obtained via manual download to verify accuracy in transmission and confirm the 100% accuracy rate reported previously in the literature for wireless transmission of CPAP data [
This study assessed a number of measures both at baseline and postintervention from a number of domains, including sleep study data, CPAP-related data, OSA symptoms, health-related quality of life, and psychological factors (ie, depressive symptoms). Sleep study data were obtained from the baseline Stardust sleep recording system measures that established the diagnosis of OSA and included the AHI. Sleep apnea symptom scales were based on previously published scales of sleep apnea symptom frequency that are reliable, valid, and highly predictive of OSA [
A fundamental methodological advantage in studying CPAP compliance is that compliance is measured objectively via a device-internal clock counter. The primary measure used in this study was the number of hours per night that the unit was used at the prescribed pressure. For CPAP device efficacy, three facets of PAP efficacy were measured by the AutoSet Spirit flow generator: mask leak (L/s), pressure (cm H20), and AHI. AHI measurements by AutoSet Spirit have been shown to be highly correlated with the measures recorded by polysomnography [
OSA-specific health-related quality of life was assessed using the Functional Outcomes of Sleep Questionnaire (FOSQ). This is a 32-item self-report measure that assesses the impact of disorders of excessive sleepiness on multiple activities of daily living [
Clinician satisfaction and patient satisfaction were assessed by questionnaires that include both Likert-type items and open-ended questions. Participants were asked to rate their overall satisfaction with care (1 = poor; 5 = excellent), their likelihood of continuing to use CPAP (1 = not likely; 5 = highly likely), and their concern about being monitored wirelessly (1 = not concerned; 5 = highly concerned).
CPAP self-efficacy refers to OSA patients’ confidence or belief that they can adhere to the regimen necessary to manage their OSA with CPAP. The CPAP self-efficacy scale is a 5-item self-report scale that was developed and validated by the primary author [
Communication with the health care team was assessed by a 3-item self-report measure that measures the frequency with which patients use certain communication behaviors, including preparing a list of questions, asking questions about things they want to know and things they don’t understand about their treatment, and discussing any personal problems that may be related to their illness [
For comparison between usual and telemonitored groups at postintervention, an unpaired
We based the power analysis on the effect size from one study that compared intensive clinical support with standard clinical support [
The one variable that is difficult to accurately estimate prospectively is the standard deviation (SD) for the treatment compliance; to the extent that SD is high relative to the mean difference, power is lower.
The participants who completed this study were primarily male (98%), older, and overweight and had moderate-to-severe OSA.
Baseline patient characteristics and treatment period, by study arm
Total Group | TCC | UCC | |||||
Characteristic | Mean ± SD | Range | Mean ± SD | Range | Mean ± SD | Range |
|
Age (years) | 59 ± 14.3 | 23-80 | 60 ± 10.8 | 42-80 | 58 ± 13.7 | 23-74 | .50 |
Body mass index (kg/m2) | 32.8 ± 5.7 | 26-45 | 33.3 ± 4.9 | 25-46 | 30.5 ± 5.1 | 25-42 | .22 |
Apnea-Hypopnea Index | 39 ± 16.8 | 21-94.7 | 44.8 ± 17.9 | 23.3-93.7 | 37.6 ± 14.3 | 21-62.5 | .29 |
CPAP pressure (cm H20) | 10.3 ± 1.6 | 8-13 | 10.4 ± 1.3 | 8-13 | 9.7 ± 1.4 | 8-13 | .29 |
Sleep apnea symptoms: night | 3.1 ± 0.7 | 1.8-4.7 | 3.2 ± 0.8 | 1.8-4.7 | 2.9 ± 0.6 | 1.9-3.9 | .30 |
Sleep apnea symptoms: day | 2.6 ± 0.8 | 0.9-4.7 | 2.6 ± 0.9 | 0.9-4.7 | 2.7 ± 0.8 | 1.4-4.6 | .75 |
Epworth Sleepiness Scale | 12.6 ± 5.8 | 4-23 | 13.7 ± 5.8 | 4-23 | 11.7 ± 4.4 | 2-20 | .25 |
Sleepiness visual analog scale | 5.8 ± 2.6 | 0-10 | 5.6 ± 3.0 | 0-10 | 6.1 ± 2.3 | 2-10 | .77 |
Functional Outcomes of Sleep Questionnaire | 13.8 ± 3.8 | 6.2-19.3 | 13 ± 4.5 | 5.3-19.3 | 14.2 ± 3.4 | 3.9-20 | .54 |
Treatment period (number days monitored) | 59.6 ± 4.0 | 43-63 | 60 ± 3.0 | 51-63 | 60.2 ± 2.8 | 55-63 | .26 |
*
Participants were followed for an average treatment period of 60 ± 4.0 days. The primary outcome measure of the study was level of CPAP treatment compliance: the TCC group used CPAP 4.1 ± 1.8 hours per night, while the UCC control group used CPAP 2.8 ± 2.2 hours per night (
CPAP compliance and efficacy data summary, postintervention
Total Group | TCC | UCC | |||||
Characteristic | Mean ± SD | Range | Mean ± SD | Range | Mean ± SD | Range |
|
|
|||||||
Use (hours/night) (all days) | 3.5 ± 2.1 | 0.2-6.8 | 4.1 ± 1.8 | 0.12-6.8 | 2.8 ± 2.2 | 0.2-6.2 | .07 |
Use (hours/night) (days used) | 4.4 ± 2.2 | 0.4-8.7 | 5.0 ± 1.8 | 0.5-7.7 | 3.8 ± 2.3 | 0.4-7.8 | .10 |
% nights with CPAP use > 0 hours of use | 65 ± 31 | 0-100 | 78 ± 22 | 24-98 | 60 ± 32 | 5-100 | .07 |
% nights with CPAP use > 4 hours of use | 44 ± 32 | 0-93 | 52 ± 27 | 0-93 | 37 ± 34 | 0-89 | .16 |
|
|||||||
Apnea-Hypopnea Index (events/hour) | 6.8 ± 5.3 | 0.1-25.6 | 7.9 ± 4.1 | 3.4-19.1 | 5.0 ± 4.0 | 0.1-13 | .04 |
Arousal Index (events/hour) | 1.5 ± 2.2 | 0.1-11.6 | 1.4 ± 1.3 | 0.2-4.9 | 1.2 ± 1.4 | 0.02-4.0 | .64 |
Apnea-Hypopnea Index change | 35.6 ± 16.4 | 12.5-89.3 | 38.1 ± 18.4 | 18.6-89.3 | 32.2 ± 14.8 | 12.5-58.8 | .32 |
Mask leak, median (L/s) | 0.18 ± .36 | 0-2.1 | 0.12 ± 0.11 | 0.01-0.44 | 0.26 ± 0.51 | 0-2.1 | .29 |
Mask leak, 95th percentile (L/s) | 0.44 ± .36 | 0.1-2.2 | 0.38 ± 0.18 | 0.17-0.82 | 0.50 ± 0.47 | 0.1-2.2 | .31 |
Mask leak, maximum (L/s) | 0.71 ± .41 | 0.1-2.2 | 0.68 ± 0.25 | 0.26-1.4 | 0.75 ± 0.54 | 0.1-2.2 | .62 |
*
The two classes of CPAP efficacy measures were effect of CPAP on disease severity (AHI) and effect of CPAP on reduction of mask leak. The mean AHI across the 2-month intervention period was significantly different between the groups (TCC: 7.9 ± 4.1; UCC: 5.0 ± 4.0;
With respect to the effect of CPAP on mask leakage, no statistically significant differences in mask leak level were found between the groups on three difference measures: median leak, 95th percentile leak, and maximum leak. See
No statistically significant differences were found for any of the outcome measures, as summarized in
Outcome measures, postintervention
Total Group | TCC | UCC | |||||
Characteristic | Mean ± SD | Range | Mean ± SD | Range | Mean ± SD | Range |
|
Sleep apnea symptoms: night | 2.4 ± 0.62 | 1.4-4.0 | 2.4 ± 0.66 | 1.4-3.7 | 2.3 ± 0.61 | 1.4-4.0 | .96 |
Sleep apnea symptoms: day | 2.1 ± 0.89 | 0.6-4.1 | 2.0 ± 1.1 | 0.57-4.1 | 2.1 ± 0.65 | 0.92-3.21 | .57 |
Epworth Sleepiness Scale | 9.6 ± 5.9 | 2.0-21.0 | 9.2 ± 6.6 | 2.0-21.0 | 9.9 ± 5.2 | 3.0-20.0 | .72 |
Sleepiness visual analog scale | 4.3 ± 2.9 | 0.0-10.0 | 3.8 ± 3.4 | 0.0-10.0 | 4.8 ± 2.2 | 2.0-9.0 | .35 |
Functional Outcomes of Sleep Questionnaire | 14.8 ± 4.6 | 1.8-19.8 | 15.2 ± 5.0 | 1.8-19.8 | 14.4 ± 4.2 | 1.8-19.9 | .61 |
Center for Epidemiological Studies Depression Scale | 8.5 ± 6.4 | 0.0-23.0 | 8.6 ± 7.0 | 0.0-23.0 | 8.3 ± 5.8 | 0.0-19.0 | .91 |
CPAP self-efficacy | 3.8 ± 1.1 | 1.0-5.0 | 4.0 ±1.2 | 1.0-5.0 | 3.5 ± 0.83 | 2.0-5.0 | .21 |
Communication with health care team | 2.9 ± 1.2 | 0.7-5.0 | 3.3 ± 1.2 | 0.7-5.0 | 2.5 ± 1.2 | 0.7-5.0 | .07 |
*
Patients in the TCC group rated their likelihood to continue using CPAP significantly higher than the UCC group (4.8 vs 4.3;
The results of this pilot study suggest that use of telemonitored CPAP compliance and efficacy data to guide the collaborative clinical management of CPAP treatment appears to be as good as usual care in its effect on compliance rates and outcomes in new CPAP users. Given that CPAP use patterns are established early in the treatment initialization process, the monitoring of compliance and efficacy data early in this process, updated in continuous 24-hour periods, to oversee and, if necessary, intervene in treatment is one method clinicians have available to them.
While the usual care compliance rate of 2.8 hours per night is low compared to other published CPAP compliance intervention studies [
This study did not find a statistically significant effect at the .05 alpha level for the TCC group on the primary outcome measure of CPAP compliance. The initial power analysis, described above in the Methods section, underestimated the residual variance. With the actual variance, a sample of the size that we collected had a low power, implying that a “failure to reject” at the .05 alpha level results in an unacceptably high type two error rate. The obtained effect size would be clinically significant if it could be established to be reliable, and the only way to do that is to perform a study with a larger sample, as we propose.
The present study confirmed the 100% accuracy rate of wireless data transmission in comparison to manually downloaded data. There was some concern at study outset over the potential for data loss via wireless transmission; however, we found the loss was negligible. While some data may not have come through on the website (either due to residence location with intermittent coverage or to initial patient problems in attaching the units), the data were always available on the flow generator unit itself. It should be noted that once the wireless unit was properly attached, data from previous nights that are stored on the flow generator device can be re-transmitted and obtained wirelessly. As a further safeguard, data can always be manually downloaded directly from the flow generator device during a patient visit. Our experience was that the data could be obtained within a few days and did not negatively impact clinical management. Future generations of flow generator devices may benefit from the pre-installation of an internal wireless device, thereby avoiding potential patient problems with attaching the wireless unit, especially for older adult patients with less technical experience or limited manual dexterity.
The study design dictated that both groups have ResTraxx wireless devices attached to their flow generator units. Prior research has suggested that the mere use of medical devices or components can result in a placebo effect [
Mask leak was not significantly different between the groups. This result is likely owing to the fact that both groups had median mask leak levels that were within normal limits. Likewise, the AHI measured by the CPAP unit across the intervention period was not significantly different between the two groups. This may be a function of measurement (though there is evidence that the AHI measured by the flow generator device used in this study is quite comparable to the AHI measured by polysomnography [
TCC appeared to have no effect on outcomes relative to UCC. Only communication with the health care team was close to being statistically significant at the .05 alpha level. This measure assesses the frequency with which patients use certain communication behaviors, including preparing a list of questions, asking questions about things they want to know and things they don’t understand about their treatment, and discussing any personal problems that may be related to their illness. Focusing on the content and process of patient-sleep provider interactions in future CPAP compliance studies may prove to be a fruitful area of research, particularly in looking at the differences between face-to-face interactions compared to telephone calls.
It can be difficult to reliably measure outcomes in OSA patients given the heterogeneity of the clinical presentation. For example, it is well documented that measures of sleepiness and the current gold-standard measure of disease severity, the AHI, correlate only modestly in the 0.30 range [
The present study is most comparable to three prior CPAP telemedicine studies. One study utilized a daily computer-based telephone system to monitor patients’ self-reported compliance behavior and provide automated counseling through a structured dialogue [
This study was designed as a pilot exploration of the telemedicine intervention in comparison to the standard of care for OSA. Clearly, larger well-powered studies are necessary to follow up on the trends found in the data and to evaluate fully the effect of telemonitoring on compliance, efficacy, and key outcomes in this population.
This work was supported in part by ResMed Corp (Poway, CA), VA MREP #02-266, VA IIR #02-275, the Research Service of the VA San Diego Healthcare System, and the Veterans Medical Research Foundation.
None declared.
Screenshots of the ResTraxx Data Center website
Apnea-Hypopnea Index
continuous positive airway pressure
obstructive sleep apnea
positive airway pressure
telemonitored clinical care
usual clinical care
Veterans Affairs San Diego Healthcare System