pii: sp- 00254-15
http://dx.doi.org/10.5665/sleep.5514
SLEEP DISORDERED BREATHING
Remote Ambulatory Management of Veterans with Obstructive Sleep Apnea Barry G. Fields, MD, MSEd1,2; Pratima Pathak Behari, MD3,4,5; Susan McCloskey, CRNP3; Gala True, PhD3; Diane Richardson, PhD3; Arwin Thomasson, PhD3,6; Danijela Korom-Djakovic, PhD3,7; Keith Davies, BSEE3; Samuel T. Kuna, MD3,4,5 1 Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Atlanta, GA; 2Atlanta Veterans Affairs Medical Center, Decatur, GA; 3Philadelphia Veterans Affairs Medical Center, Philadelphia, PA; 4Division of Sleep Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; 5Center for Sleep and Circadian Neurobiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; 6Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; 7Independent Researcher, Philadelphia, PA
Study Objectives: Despite significant medical sequelae of obstructive sleep apnea (OSA), the condition remains undiagnosed and untreated in many affected individuals. We explored the feasibility of a comprehensive, telemedicine-based OSA management pathway in a community-based Veteran cohort. Methods: This prospective, parallel-group randomized pilot study assessed feasibility of a telemedicine-based pathway for OSA evaluation and management in comparison to a more traditional, in-person care model. The study included 60 Veterans at the Philadelphia Veterans Affairs Medical Center and two affiliated community-based outpatient clinics. Telemedicine pathway feasibility, acceptability, and outcomes were assessed through a variety of quantitative (Functional Outcomes of Sleep Questionnaire, dropout rates, positive airway pressure [PAP] adherence rates, participant satisfaction ratings) and qualitative (verbal feedback) metrics. Results: There was no significant difference in functional outcome changes, patient satisfaction, dropout rates, or objectively measured PAP adherence between groups after 3 months of treatment. Telemedicine participants showed greater improvement in mental health scores, and their feedback was overwhelmingly positive. Conclusions: Our pilot study suggests that telemedicine-based management of OSA patients is feasible in terms of patient functional outcomes and overall satisfaction with care. Future studies should include larger populations to further elucidate these findings while assessing provider- and patient-related cost effectiveness. Keywords: obstructive sleep apnea, telemedicine, home sleep testing, auto-titrating positive airway pressure Citation: Fields BG, Behari PP, McCloskey S, True G, Richardson D, Thomasson A, Korom-Djakovic D, Davies K, Kuna ST. Remote ambulatory management of veterans with obstructive sleep apnea. SLEEP 2016;39(3):501–509. Significance Limited access to sleep medicine care is a growing concern within the Veterans Affairs (VA) healthcare system and beyond. Although various telemedicine-based modalities show promise in addressing this need, technological advancement has outpaced evidence-based implementation. Our pilot study is the first to compare prospectively a novel, telemedicine-based obstructive sleep apnea (OSA) management pathway with in-person care. Through quantitative and qualitative indices, we demonstrated no significant inter-pathway difference in participant functional outcomes or satisfaction rates following three months of treatment. These findings lay the groundwork for future sleep telemedicine research and clinical pathway development as this nascent field evolves.
INTRODUCTION Obstructive sleep apnea (OSA) is a common disorder resulting from repetitive pharyngeal airway closure during sleep. It has been linked to major cardiovascular disorders such as coronary artery disease, congestive heart failure, hypertension, and stroke.1–3 The prevalence of moderate to severe OSA has increased to 3% and 10% among 30- to 49-y-old women and men, respectively; between ages 50 and 70 y, 9% of women and 17% of men have the disorder.4–6 Because the condition remains undiagnosed in a majority of these individuals,7 improved access to testing is essential. Veterans are particularly vulnerable to diagnostic access limitations; about one-half of them live more than 25 miles from a Veterans Affairs Medical Center (VAMC).8 The Veterans Health Administration (VHA) Philadelphia VAMC (PVAMC) utilizes various modalities to address this problem: real-time video teleconferencing (clinical video telehealth; CVT), home sleep testing (HST), modem-enabled positive airway pressure (PAP) units, and automatically-adjusting PAP (APAP) machines. A similar strategy has been employed at the Michael E. DeBakey VAMC in Houston, Texas.9 Previous studies have shown HST and APAP-based strategies are not clinically inferior to laboratory-based polysomnography (PSG) in terms of cost, APAP adherence, and symptomatic SLEEP, Vol. 39, No. 3, 2016
improvement with treatment.10–13 Parikh et al.14 showed no difference in participant satisfaction (prospectively) or PAP adherence rates (retrospectively) in patients having either an in-person sleep physician visit or a CVT-based visit at their initial evaluation. Nevertheless, there have been no prospective, patient-oriented outcome studies in community-based populations that combine HST, APAP, and CVT into a comprehensive OSA management pathway. METHODS We conducted a prospective, parallel-group randomized pilot study assessing feasibility of a telemedicine-based pathway (TELE) for OSA evaluation and management in comparison to a more traditional, in-person care model (INP). The study’s primary aim was to compare functional outcomes between the 2 groups after 3 mos of APAP treatment utilizing the Functional Outcomes of Sleep Questionnaire (FOSQ)15,16 as the primary outcome. Secondary outcome measures included the Epworth Sleepiness Scale (ESS),17 Center for Epidemiological Studies Depression Scale (CES-D),18 and the Health Outcomes Short Form-12 (SF-12),19 as well as the Working Alliance Inventory-Short Form (WAI-SF)20 and Client Satisfaction Questionnaire-8 (CSQ-8).21 Objective APAP adherence was also compared between the two groups. Additionally, we conducted 501
Remote Management of Veterans with OSA—Fields et al.
Figure 1—Cohort diagram of participants randomized to in-person versus telemedicine-based pathways. APAP, automatically-adjusting positive airway pressure; FU, follow-up; HST, home sleep testing; INP, in-person; OSA, obstructive sleep apnea; TELE, telemedicine.
participants were compensated for their time completing questionnaires and providing telephone feedback, if applicable.
a formative evaluation of the TELE pathway through other quantitative indices, qualitative feedback, and attrition rates. Finally, we compared HST failure rates and recording quality between pathways. In doing so, we sought to refine the TELE pathway for improved clinical care and use in future comparative effectiveness trials.
Procedures After providing informed consent, all participants were scheduled for an initial sleep consultation at their local CBOC. Prior to this visit, each person completed the FOSQ, ESS, SF-12, and CES-D questionnaires.15,16 After the visit, they completed the WAI-SF and CSQ-8. The WAI-SF is a 12-item survey assessing patients’ perceived level of collaboration with their provider. It has been utilized to demonstrate patient-provider alliance in a previous CVT-based study.22 The CSQ-8 is an 8-item survey that elicits patient satisfaction with service provision. It has been used extensively within multiple specialties, including sleep telemedicine.23 Participants in both arms were provided an HST device for use that night. Individuals in whom OSA was diagnosed and treated completed the same questionnaires at follow-up encounters 1 and 3 mo after commencing PAP therapy (Figure 1).
Participants We recruited 30 patients from each of two community-based outpatient centers (CBOCs) affiliated with the PVAMC, for a total of 60 study patients. The Victor J. Saracini CBOC (Horsham, Pennsylvania) and Joint Base McGuire-Dix, New Jersey CBOC are located 33 and 46 miles away from the PVAMC, respectively. All participants were at least age 18 y, received primary care at the CBOC, and were fluent in English. Patients were excluded from the study if they were unable or unwilling to provide informed consent, stated an inability to return for follow-up sessions, or had a previous diagnosis of sleep disordered breathing (OSA, central sleep apnea, Cheyne-Stokes respiration, obesity hypoventilation syndrome) or narcolepsy. Verbal consent was obtained via telephone to avoid their having to make an additional visit to the Philadelphia VAMC or a CBOC. Participants were then randomized to the TELE (experimental) or INP (standard care) pathway using a computer-generated, blocked randomization scheme. They were not informed of pathway allocation until they arrived for their initial clinic visit. The complete study protocol was approved by the Institutional Review Board at the Philadelphia VAMC, and SLEEP, Vol. 39, No. 3, 2016
Telemedicine Arm
For their initial evaluation, TELE participants were interviewed at their local CBOC via real-time CVT by a sleep provider (BGF, PPB, or SM) in Philadelphia. A telemedicine technician at the CBOC seated participants in a private examination room facing a telemedicine cart; the unit consisted of a high-definition camera and 17-inch video monitor. A CoderDecoder (CODEC) compressed the two-way data streams, enabling their transmission over high bandwidth (high speed)
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connections. This audiovisual data was secured using Health Insurance Portability and Accountability Act (HIPAA)-compliant, Federal Information Processing Standards 140-2 certified encryption modules. All equipment, security procedures, and contributions from a telemedicine technician were utilized for usual clinical care and were not unique to the study protocol. All visits were completed using a standardized patient encounter template designed to last approximately 40 min. Immediately after their initial visit, participants were provided with an HST device for use that night. An instructional DVD and brochure were provided to show how to perform the study, but there was no in-person set-up instruction. The recordings were downloaded and then scored and interpreted by an experienced clinician blinded to pathway allocation (STK). APAP was ordered for an apnea-hypopnea index (AHI) ≥ 15 events/h or 5 ≤ AHI < 15 events/h with clinical symptoms. Sleep providers phoned TELE participants 1 w and 1 and 3 mo after commencing APAP therapy. Week 1 encounters were brief opportunities (10 min or less) to ensure the patient had started using the device, provide encouragement, and answer questions. Month 1 and 3 phone calls utilized standardized interview templates designed to last approximately 20 min. These encounters included PAP unit data review, assessed for common PAP-related concerns (e.g. mask leaks, claustrophobia), and provided another opportunity for patients’ questions. Individuals who completed the study protocol were invited to participate in a telephone feedback session with a qualitative researcher (DKD).
hypopneas. The AHI was calculated as the mean number of apneas and hypopneas per hour of recording. Quality of each study was rated as the percentage of time that each signal was scoreable. A technically acceptable study required a total of at least 3 h of oxygen saturation signal coinciding with at least one respiratory signal. Technically unacceptable studies were repeated once. Auto-Titrating Positive Airway Pressure Treatment
Participants in whom OSA was diagnosed (AHI ≥ 5 events/h) were initiated on APAP treatment. An APAP (Philips-Respironics System One, Murrysville, PA) unit was delivered to the participant’s home by a sleep therapist from a regional home healthcare company contracted by the PVAMC. The therapist fitted the participant with an appropriate mask interface and explained how to operate and care for the equipment. APAP machines were set at a pressure range of 4 to 20 cm H20 with heated in-line humidification. Wireless modem technology was used for secure transmission of objective data on machine usage (i.e., daily mask-on time), efficacy in controlling OSA (i.e. apnea and hypopnea detection), and air leak from the circuit. All modem data were transmitted daily from participants’ APAP units to EncoreAnywhere (Philips-Respironics), a HIPAA-compliant, password-protected internet database that can be accessed by the patient’s clinician and home health care company. One- and 3-mo data summaries were downloaded from the EncoreAnywhere website, with adherence determined as mean daily minutes of PAP use over 3 mo. Days without data were considered to be days when the participant did not use the treatment, i.e., 0 min of use.
In-Person Arm
INP participants’ initial visits were conducted face-to-face by the sleep clinician (BGF) travelling to their CBOC. Patient encounter templates were identical to those used in the TELE arm. Participants were scheduled to return 1 to 2 w later for in-person HST instruction from experienced sleep therapists before using the unit that same night. No instructional DVD was provided. HST results were analyzed as in the TELE arm and participants were prescribed APAP as indicated. The sleep provider phoned INP participants 1 w after commencing APAP therapy (same brief encounter as the TELE arm), and conducted in-person follow-up appointments (same standardized templates as the TELE arm) at the participants’ CBOCs 1 and 3 mo after commencing PAP therapy.
End-of-Study Phone Calls
A qualitative researcher (DKD) conducted individual phone interviews with TELE participants to (1) assess their perceptions about the use of telemedicine to diagnose and treat sleep apnea, and (2) gather feedback about barriers and facilitators to participation in OSA diagnosis and care via telemedicine to inform future research and translation to clinical practice. A semistructured interview protocol included questions on the individuals’ experiences with study participation, their thoughts about the differences between in-person and telemedicine visits, and their assessment of the effectiveness of care received. Verbal informed consent was obtained prior to starting the interviews, which ranged from 10 to 25 min. The qualitative researcher had no other interaction with the participant during the protocol.
Home Sleep Testing
Participants performed unattended HST using a Type 3 portable monitor (Embletta Gold; Embla, Inc., Broomfield, CO).24 This monitor has been used in large randomized controlled trials comparing in-laboratory polysomnography (PSG) to HST.10,25 The following signals were recorded: airflow (nasal pressure), rib cage and abdominal movement, snoring, body position, heart rate, and oxygen saturation. The studies were scored by a sleep specialist (STK) who was blinded to participant randomization. Apneas were scored for a > 90% reduction of airflow from baseline for at least 10 sec, with the respiratory effort channels used to distinguish obstructive versus central apneas. Reductions in respiration for at least 10 sec associated with at least a 4% oxygen desaturation were scored as SLEEP, Vol. 39, No. 3, 2016
Data Analyses Sample Size
The pilot’s sample size of 60 patients was selected based on nonstatistical considerations, in order to provide an adequate number of participants to assess the feasibility of the intervention. We determined that with this sample size, and assuming 15% loss to follow up over 3 mo, we would have 70% power to detect a clinically significant effect based on an assumed improvement in FOSQ comparable to published values of 1.74 (standard deviation [SD] 2.5)10 with a two-sided test at α = 0.05. 503
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Table 1—Participant characteristics at baseline. Variable Age, y n (%) males n (%) African American n (%) Latino n (%) OSA-positive Body mass index, kg/m2 Charlson Index AHI FOSQ total CES-D ESS SF-12 physical SF-12 mental WAI-SF CSQ-8
Total Cohort (n = 60) 50.8 ± 14.8 55 (91.7) 15 (25.0) 1 (1.7) 37 (61.7) 32.4 ± 5.5 0.7 ± 1.5 22.7 ± 28.5 a 13.4 ± 3.8 d 22.4 ± 13.4 d 12.7 ± 5.4 d 40.9 ± 9.5 d 41.3 ± 13.1 d 74.5 ± 8.0 d 20.1 ± 2.5 d
In-Person Care (n = 28) 53.9 ± 15.8 27 (96.4) 5 (17.9) 0 19 (67.9) 33.3 ± 4.71 0.82 ± 1.7 25.5 ± 28.7 b 13.5 ± 4.0 c 20.7 ± 13.9 c 13.4 ± 5.5 c 39.3 ± 8.3 c 45.3 ± 11.9 c 76.1 ± 6.4 c 20.2 ± 3.1 c
Telemedicine Care (n = 32) 48.0 ± 13.4 28 (87.5) 10 (31.3) 1 (3.1) 18 (56.3) 31.6 ± 6.1 0.63 ± 1.4 20.3 ± 28.7 c 13.3 ± 3.8 e 24.1 ± 13.0 e 12.0 ± 5.3 e 42.4 ± 10.4 e 37.5 ± 13.3 e 73.0 ± 9.1 e 20.0 ± 1.8 e
P 0.100 0.359 0.368 > 0.999 0.332 0.115 0.674 0.190 0.888 0.270 0.390 0.296 0.034 0.248 0.825
Values presented as mean ± standard deviation or n (%). a n = 47. b n = 22. c n = 25. d n = 51. e n = 26. AHI, apnea-hypopnea Index; CES-D, Center for Epidemiologic Studies Depression Scale; CSQ-8, Client Satisfaction Questionnaire; ESS, Epworth Sleepiness Scale; FOSQ, Functional Outcomes of Sleep Questionnaire; OSA, obstructive sleep apnea; SF-12, Short Form 12; WAI-SF, Working Alliance Inventory-Short Form.
Statistical Analysis for Functional Outcomes
analysis software (QSR International, Burlington, MA). We used content analysis30 and thematic coding31 to generate categories of codes that captured participants’ responses. When all interviews were coded, we reorganized the codes into categories corresponding to the main aims of our analysis: to assess perceptions and acceptability of telemedicine, and identify potential barriers and facilitators to TELE-based delivery of OSA care.
Descriptive statistics for patient characteristics, including the Charlson comorbidity index,26 were used to characterize the full patient sample and by treatment group. Patient characteristics were compared between groups to confirm that the randomization resulted in no clinically significant differences at baseline. These comparisons were conducted using the Fisher exact test for categorical variables and the Wilcoxon-MannWhitney test for continuous variables, without adjusting for multiple testing. For the primary analysis, primary and secondary study outcomes were analyzed based on a per protocol population (OSA-positive participants who were followed for 3 mo on APAP and who completed at least the baseline FOSQ). Descriptive statistics were calculated, and between-group comparisons were performed as previously described for patient characteristics and baseline outcome measures in this population. We used linear mixed models to estimate the mean change from baseline for each outcome at the end of follow-up, and the between-group difference in mean change from baseline.27,28 In these models, we included the baseline outcome measure, indicators for visit number and intervention group, and an interaction term for visit number with intervention group, all as fixed effects. We used the interaction term coefficient to test for the difference in pre-post outcomes between groups. We accounted for within-patient clustering of outcomes with a banded (Toeplitz) covariance-correlation structure. Results are presented as the mean change from baseline for each study outcome at 3 mos, within groups, and as between-group mean differences in change from baseline, assessed at 3 mos.
RESULTS We randomized 60 participants, 28 to the INP pathway and 32 to TELE-based care. Figure 1 summarizes the flow of participants and the loss to follow up before and after HST. All randomized participants’ data were included in the descriptive analysis in Table 1 of patient baseline characteristics, except where baseline questionnaire data and/or HST results were not obtained. At baseline, INP and TELE participants did not differ in terms of age, sex, race, ethnicity, body mass index (BMI), or comorbidities (Table 1). The 60 participants had a mean (SD) age of 50.8 (14.8) y and BMI of 32.4 (5.5) kg/m 2. Their mean (SD) Charlson Comorbidity Index was 0.7 (1.5). Fifteen individuals (25.0%) were African-American and 1 (1.7%) was Latino. TELE participants demonstrated lower baseline SF-12 mental health component scores; that is, they reported lower levels of mental health. None of the other baseline questionnaire scores differed significantly between groups (Table 1). Thirteen participants (21.7%) withdrew from the study—4 in the INP arm and 9 in the TELE arm (Figure 1). Four of the TELE participant withdrawals occurred before participants were aware of treatment arm allocation. Forty-one participants (75.9%) who completed an HST were found to have OSA (AHI ≥ 5 events/h). The overall mean (SD) AHI was 22.7 (28.5) events/h, and OSA severity did not differ between groups
Qualitative Analyses
End-of-study telephone interviews were digitally recorded and transcribed by a professional transcriptionist. The interview transcripts were analyzed using NVivo 1029 qualitative data SLEEP, Vol. 39, No. 3, 2016
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Table 2—Baseline participant characteristics for individuals treated with automatically-adjusting positive airway pressure treatment. Variable Age, y n (%) males n (%) African American n (%) Latino Body mass index, kg/m2 Charlson Index AHI FOSQ total CES-D ESS SF-12 physical SF-12 mental WAI-SF CSQ-8
Total Cohort (n = 34) 53.2 ± 14.8 32 (94) 11 (32.4) 0 33.0 ± 4.8 0.6 ± 1.6 31.0 ± 31.3 13.2 ± 3.6 21.8 ± 13.5 14.0 ± 4.7 40.6 ± 9.2 41.1 ± 13.2 74.1 ± 8.2 20.1 ± 2.8
In-Person Care (n = 20) 58.2 ± 14.4 18 (94.7) 4 (21.1) 0 32.9 ± 3.8 1.0 ± 2.0 30.1 ± 30.0 13.3 ± 3.7 20.5 ± 14.7 13.4 ± 4.6 38.8 ± 8.3 45.2 ± 12.9 77.2 ± 5.3 20.3 ± 3.5
Telemedicine Care (n = 14) 46.7 ± 13.1 14 (93.3) 7 (46.7) 0 33.2 ± 6.0 0.2 ± 0.4 32.0 ± 33.9 13.1 ± 3.7 23.5 ± 11.9 14.7 ± 4.8 42.9 ± 10.0 35.9 ± 11.9 70.1 ± 9.6 19.8 ± 1.8
P 0.022 > 0.999 0.163 – 0.900 0.324 0.755 0.917 0.376 0.356 0.238 0.056 0.023 > 0.999
Values presented as mean ± standard deviation or n (%). AHI, apnea-hypopnea Index; CES-D, Center for Epidemiologic Studies Depression Scale; CSQ8, Client Satisfaction Questionnaire; ESS, Epworth Sleepiness Scale; FOSQ, Functional Outcomes of Sleep Questionnaire; SF-12, Short Form 12; WAISF, Working Alliance Inventory-Short Form.
Table 3—Unadjusted 3-mo mean changes in primary (Functional Outcomes of Sleep Questionnaire) and secondary outcome measures in participants after 3 mo of automatically-adjusting positive airway pressure treatment. Measure (Total Scores) FOSQ CES-D ESS SF-12 physical activity SF-12 mental health WAI-SF CSQ-8
Total Cohort (n = 30) 1.41 ± 2.73 −5.57 ± 8.83 −3.80 ± 5.82 1.48 ± 7.28 4.79 ± 9.31 3.67 ± 7.61 −0.45 ± 2.90
In-Person (n = 18) 0.91 ± 2.58 −4.61 ± 8.62 −3.61 ± 5.16 1.90 ± 9.03 1.38 ± 6.92 1.83 ± 5.19 −0.50 ± 3.52
Telemedicine (n = 12) 2.18 ± 2.87 −7.00 ± 9.32 −4.08 ± 6.92 0.87 ± 3.60 9.90 ± 10.33 6.42 ± 9.86 −0.36 ± 1.57
P 0.228 0.800 > 0.999 0.920 0.012 0.210 0.784
Values presented as unadjusted mean ± standard deviation change in score. Includes only participants (n = 30) not missing baseline and 3-mo follow-up FOSQ scores. CES-D, Center for Epidemiologic Studies Depression Scale; CSQ-8, Client Satisfaction Questionnaire; ESS, Epworth Sleepiness Scale; FOSQ, Functional Outcomes of Sleep Questionnaire; SF-12, Short Form 12; WAI-SF, Working Alliance Inventory-Short Form.
(Table 1). Only participants in whom OSA was diagnosed were eligible to be treated with APAP; as a result, participants determined to be OSA-negative (n = 4 in INP and n = 9 in TELE) after the review of HST results were not included in the final per protocol analyses of study outcomes. Table 2 reports baseline characteristics and primary and secondary outcome data of participants used in the per-protocol analysis set (see “Data Analyses” in “Methods” section; not all randomized participants in Table 1 completed baseline assessments because randomization occurred prior to visit 1, and some who completed those assessments did not have OSA). One participant in the INP arm in whom OSA was diagnosed chose not to proceed with treatment and was therefore not included in per-protocol analyses. Mean (SD) AHI was 31.0 (31.3) events/h and did not differ significantly between groups. Baseline SF-12 mental health score was lower in the TELE group, but the difference did not reach statistical significance (P = 0.056). Baseline WAI-SF scores were significantly higher in the INP group than the TELE group (P = 0.023), and TELE SLEEP, Vol. 39, No. 3, 2016
participants were significantly younger than INP participants in the per-protocol group (P = 0.022). After 3 mo, the per-protocol population demonstrated greater improvement in SF-12 mental health score among the TELE group than among the INP group in both unadjusted (P = 0.012; Table 3) and adjusted analyses (P = 0.003; Table 4). A similar trend of greater improvement was noted for FOSQ scores (Figure 2) and WAI-SF scores. However, the difference in improvement between groups for these two scores did not reach statistical significance at the α = 0.05 level. There were no significant differences in changes from baseline CES-D, ESS, SF-12 physical health, WAI-SF, or CSQ-8 scores (Tables 3 and 4). APAP Adherence Adherence to APAP treatment over the 3-mo treatment period did not differ between groups. INP participants used the device 54 ± 8% of possible days, compared to 65 ± 8% of possible days among TELE participants (P = 0.493; Table 5). Usage 505
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Table 4—Adjusted 3-mo mean changes and adjusted differences in mean changes in primary (Functional Outcomes of Sleep Questionnaire) and secondary outcome measures in participants after 3 mo of automatically-adjusting positive airway pressure treatment. Measure (Total Scores) FOSQ CES-D ESS SF-12 physical activity SF-12 mental health WAI-SF CSQ-8
Change in Score In-Person (n = 20) Telemedicine (n = 14) 0.89 ± 0.59 2.57 ± 0.69 −4.31 ± 1.73 −6.51 ± 2.03 −3.56 ± 1.13 −4.22 ± 1.31 2.08 ± 1.54 0.86 ± 1.83 0.73 ± 1.78 9.26 ± 2.09 1.70 ± 1.50 5.93 ± 1.77 0.013 ± 0.48 −0.31 ± 0.57
Difference in Changes 1.69 ± 0.91 −2.19 ± 2.66 −0.67 ± 1.73 −1.22 ± 2.40 8.53 ± 2.75 4.23 ± 2.32 −0.32 ± 0.74
P 0.067 0.413 0.702 0.611 0.003 0.074 0.665
Values presented as adjusted mean ± standard error. Includes all observations with one or more follow-up FOSQ measure, site, age, and body mass index. Estimated using mixed models and adjusted for baseline score. CES-D, Center for Epidemiologic Studies Depression Scale; CSQ-8, Client Satisfaction Questionnaire; ESS, Epworth Sleepiness Scale; FOSQ, Functional Outcomes of Sleep Questionnaire; SF-12, Short Form 12; WAI-SF, Working Alliance Inventory-Short Form.
Table 5—Adherence to automatically-adjusting positive airway pressure 3 mo after its initiation. In-Person Telemedicine Variable Care (n = 20) Care (n = 14) % days with device usage 54 ± 8 65 ± 8 % days ≥ 4 h 39 ± 8 47 ± 9 Use, min (all days) 175.6 ± 36.8 220.8 ± 37.5 Use, min (days used) 268.9 ± 32.1 305.7 ± 29.9
P 0.493 0.493 0.301 0.426
Values presented as mean ± standard error.
Table 6—Percentage of time that signals on technically adequate home sleep testing were able to be scored. Signal Pulse oximetry Nasal pressure Thoracic excursion Abdominal excursion
In-Person Care (n = 25) 90.6 ± 5.2 87.5 ± 5.5 86.6 ± 6.0 94.4 ± 4.0
Telemedicine Care (n = 30) 86.7 ± 4.4 87.8 ± 4.0 92.3 ± 5.7 95.8 ± 3.3
Figure 2—Box plots illustrating 3-mo adjusted mean changes in FOSQ score by study arm per protocol. Horizontal line within boxes is the median change. Upper and lower box boundaries are 75th and 25th percentile respectively. Whiskers represent distance to minimum and maximum values. FOSQ, Functional Outcomes of Sleep Questionnaire.
Values presented as mean percent of time scoreable ± standard error.
more than 4 h per day occurred in 39 ± 8% of possible days in INP participants and 47 ± 9% of possible days in TELE participants (P = 0.493). INP participants used the machine an average of 269 min (4 h, 29 min) on nights used, and TELE participants recorded 306 min (5 h, 6 min) of use on nights used (P = 0.426).
Qualitative Feedback Of 22 eligible TELE participants (completed the TELE protocol whether OSA positive or negative), 15 (68%) completed the phone interviews; two individuals declined to participate and we were unable to contact 5 individuals. Fourteen participants were male and 11 participants received a diagnosis of OSA.
HST Quality Four INP participants and 9 TELE participants repeated their first night of HST due to inadequate technical quality for scoring. One INP participant failed a second attempt at HST. Overall, 83.3% of INP studies were scoreable on their first attempt, compared to 65.4% of TELE studies. Respiratory signals were of adequate quality for at least 86% of the time during scoreable studies in both groups (Table 6).
Prior to this study, only 1 participant had previous exposure to telemedicine; the type was not specified. The most frequently mentioned advantages were decreased travel burden and the convenience. All but 1 participant noted that it was more convenient for them to drive to their local CBOC than to the VAMC for an appointment. Six participants were highly satisfied with the encounters due to the quality of the interaction
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Perceptions of Telemedicine
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they established with their providers. These individuals described their providers as “very pleasant to talk to,” “very caring,” “understanding,” “knowledgeable,” and helpful in providing information and answering the participants’ questions thoroughly. One participant stated, “[provider] wanted to help me and there was a sense that [provider] actually cared. You know, I wasn’t just a number.” In comparing study-related CVT encounters to other inperson clinical visits, TELE participants thought that the 2 types of visits were very similar. One participant noted: Well, I don’t think it was really any different. I mean, like I said, he was right there. We were talking faceto-face over the video. The only difference between talking over the video versus talking to a doctor actually in the office is, you know, they are there, in the office. TELE visits had no effect on participants’ perception of the quality of interactions with the provider, comfort levels during the visit, or the quality of information and the treatment received. Participants noted that they felt comfortable asking questions and discussing any sleep and health issues with their providers. They believed they learned as much about OSA from talking to their provider over the video as they would have had they met in person. Several participants noted that the high audio/visual quality of the visits—with the doctor “on a big flat screen TV”—made the appointment more personal and made them feel “just like being in the room with the doctor.” Two participants emphasized their excitement at seeing technology being used in medicine to provide more convenience to patients. One participant suggested that telemedicine could be used in the treatment of other conditions beyond OSA. Although TELE participants had positive experiences and agreed that their OSA care was good quality, 2 participants expressed a strong preference for in-person visits. One participant described himself as a “one-on-one type person” who “would have been more comfortable actually being in a room with the doctor,” but he also commented that the “video visit was OK, too.” The other participant thought that “more things may have come to mind” to ask the doctor had the visits been in person. However, in examining the advantages and disadvantages of the 2 types of visits, this individual noted that “being it [TELE visit] was more convenient, it was worth that little bit I might have missed discussing…” Only 1 participant expressed significant concern about confidentiality during telemedicine visits. During other in-person visits, he said he had a clear sense of who was present for the exchange of information, while during TELE visits he was never completely sure “who [was] getting the information.”
the wireless router. And you wouldn’t have to go to the clinic, you know, every six months, once a year, they can do it right from the house there and I think it’s great. In addition, several participants pointed out that “the study is simple,” involvement in the study “doesn’t take a lot of time,” the treatment for sleep apnea “doesn’t hurt even a little bit,” and “it works.” Participants did not have any suggestions for improving the quality, interactions, and information shared in TELE visits. They stated that all the information they received about OSA from their providers was very useful. Although 10 participants did not have any suggestions for improving the protocol, 5 participants shared specific requests and recommendations. They described either having difficulty using the PAP machine (e.g., not having a mask interface that was fitting properly) or getting a machine that did not work well due to technical problems. DISCUSSION Our pilot study demonstrated feasibility of a telemedicinebased OSA evaluation, diagnosis, and management pathway in a VA community-based population. To our knowledge, it is the first to examine this comprehensive care model prospectively. Although it was not designed as an efficacy or superiority trial, TELE participants may have experienced greater functional and mental health improvement than INP counterparts. However, we did not adjust for multiple comparisons and it is possible that these differences are due to chance. APAP adherence did not differ between groups after 3 mos of treatment. TELE participants were just as satisfied with their care as INP participants (CSQ-8), and felt just as much of a working alliance with their provider (WAI-SF). Overall, loss to follow-up was greater among TELE participants than INP participants. Both TELE and INP attrition rates were comparable to other studies exploring HST and APAP-based OSA management models.10,25 Furthermore, 4 of the 9 TELE withdrawals occurred before participants were informed of, or had experienced, their pathway allocation (Figure 1). The other 5 TELE withdrawals all occurred after completion of the HST but before the first follow-up visit. This pattern of attrition was not replicated on the INP arm. Conclusions are difficult to draw based on (1) limited sample size and (2) identical study arm PAP initiation protocols. Therefore, larger studies are needed to evaluate these attrition patterns and explore potential interactions with the telemedicine-based pathway components. Higher first-attempt HST success rates in the INP arm (83.3%) than the TELE arm (65.4%) are also difficult to interpret. INP participants may have benefited from in-person HST set-up instruction even if it occurred 1 to 2 w after their initial evaluation. TELE participants had not received that instruction; they left their initial visit with an HST machine, a DVD describing how to use it, and an informational set-up sheet. Of the 15 TELE participants interviewed at the end of the study, 5 claimed not to have used the DVD and 3 found it too long and confusing. Although these findings do not implicate inperson HST instruction as the only effective strategy, they do suggest that (1) instructional materials must be as high quality
Feedback on the Study Protocol
All participants expressed satisfaction with all parts of the study, including initial and follow-up visits with their providers. When asked what they would tell other Veterans, participants made comments such as: I think it’s just a great idea and a great study all the way around. With the fact that they can, you know, monitor your sleep patterns remotely with SLEEP, Vol. 39, No. 3, 2016
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as possible and (2) patient preference in instructional modality could be elicited when ordering HST. Though not specifically tracked in this pilot, it is possible that increased need for retesting in the TELE arm could have driven up cost over time. Future studies utilizing similar HST-distribution protocols might further explore this possibility. Prior research indicates that telemedicine has been wellreceived by patients with OSA,14,32 as well as by patients with other medical conditions.33,34 Participants in our study reported similarly high satisfaction with the TELE pathway, with all of them agreeing that the quality and content of their telemedicine visits were comparable to in-person visits. As other studies found,35–37 the greatest advantages of telemedicine identified were decreased travel burden and more convenience. Our study did not identify any major barriers to receptiveness of telemedicine. One common concern among TELE participants was lack of proper mask fitting at time of machine delivery. Although this finding was not attributable to the telemedicine intervention (TELE and INP arm APAP delivery protocols were identical), it does indicate a particularly vulnerable point in any home-based OSA management protocol. Extra attention to initial mask fitting and machine set-up appears essential and may affect future adherence to PAP.38 Indeed, patients’ first week with therapy can predict future adherence.39 In our pilot, PAP adherence—determined as both daily use time and as a percentage of nights with use more than 4 h—did not differ significantly between groups. Previous studies have shown improvement in daytime functioning, sleepiness, and memory to be optimized with at least 6 h of nightly use.40,41 Although mean adherence rates fell short of this threshold in both groups, they were similar to ranges observed in previous studies.10,38 In addition to the indices reported, any telemedicine pathway’s feasibility must also be assessed in light of local resources. Many VAMCs provide the hardware, personnel, information security measures, and clinical space necessary to create viable sleep telemedicine programs.42 Other centers must consider similar initial and ongoing costs when developing their programs. Additionally, there is significant variability in telemedicine encounter reimbursement among and within states; potential telemedicine practitioners should familiarize themselves with current insurance coverage policies.43 There were several limitations to our pilot study. First, our sample size was relatively small and only involved one VA institution, curtailing our ability to generalize findings, conduct subgroup analyses, and evaluate noninferiority between the two pathways. Second, we conducted telephone-based feedback interviews between 2 and 6 mo after the participants completed the study. This time lag may have affected their recollection of study details and diminished the potential richness of their accounts. Third, logistical considerations forced a lack of provider-site homogeneity. That is, all providers (BGF, PP, SM) could not visit both sites or participate in both study arms. This protocol asymmetry could have inadvertently influenced the final results. Fourth, among participants who completed a baseline FOSQ, TELE participants were significantly younger than INP participants. It is possible that this younger population is inherently more receptive to TELE care. SLEEP, Vol. 39, No. 3, 2016
Despite these limitations, this study provides valuable insight into a comprehensive, telemedicine-based model for OSA care. At the end of 3 mos, TELE participants’ functional improvement, satisfaction, and PAP adherence were at least comparable to the INP group. They had positive perceptions of sleep telemedicine and viewed it as a viable alternative to inperson visits. We identified a potential area for improvement in the TELE pathway based on HST failure rates (modifying our patient set-up instruction protocol) and participant feedback (devising better mask-fitting paradigms). Larger, multicenter trials are needed to ensure these findings are replicable across institutions. Future studies could also expand on this pilot by examining within-group differences over time, exploring the interaction of age with telemedicine care models, and conducting provider time and cost analyses. REFERENCES 1. Shahar E, Whitney CW, Redline S, et al. Sleep-disordered breathing and cardiovascular disease: cross-sectional results of the Sleep Heart Health Study. Am J Respir Crit Care Med 2001;163:19–25. 2. Marin JM, Carrizo SJ, Vicente E, Agusti AG. Long-term cardiovascular outcomes in men with obstructive sleep apnoeahypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet 2005;365:1046–53. 3. Yaggi HK, Concato J, Kernan WN, Lichtman JH, Brass LM, Mohsenin V. Obstructive sleep apnea as a risk factor for stroke and death. N Engl J Med 2005;353:2034–41. 4. Young T, Finn L, Peppard PE, et al. Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. Sleep 2008;31:1071–8. 5. Young T, Skatrud J, Peppard PE. Risk factors for obstructive sleep apnea in adults. JAMA 2004;291:2013–6. 6. Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM. Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol 2013;177:1006–14. 7. Kapur V, Strohl KP, Redline S, Iber C, O’Connor G, Nieto J. Underdiagnosis of sleep apnea syndrome in U.S. communities. Sleep Breath 2002;6:49–54. 8. VA Health Care: Improving Veterans’ Access Poses Financial and Mission-Related Challenges (GAO/HEHS-97-7). Accessed March 11, 2015. Available at http://www.gao.gov/assets/230/223354.pdf. 9. Hirshkowitz M, Sharafkhaneh A. A telemedicine program for diagnosis and management of sleep-disordered breathing: the fasttrack for sleep apnea tele-sleep program. Semin Respir Crit Care Med 2014;35:560–70. 10. Kuna ST, Gurubhagavatula I, Maislin G, et al. Noninferiority of functional outcome in ambulatory management of obstructive sleep apnea. Am J Respir Crit Care Med 2011;183:1238–44. 11. Antic NA, Buchan C, Esterman A, et al. A randomized controlled trial of nurse-led care for symptomatic moderate-severe obstructive sleep apnea. Am J Respir Crit Care Med 2009;179:501–8. 12. Berry RB, Hill G, Thompson L, McLaurin V. Portable monitoring and autotitration versus polysomnography for the diagnosis and treatment of sleep apnea. Sleep 2008;31:1423–31. 13. Mulgrew AT, Fox N, Ayas NT, Ryan CF. Diagnosis and initial management of obstructive sleep apnea without polysomnography: a randomized validation study. Ann Intern Med 2007;146:157–66. 14. Parikh R, Touvelle MN, Wang H, Zallek SN. Sleep telemedicine: patient satisfaction and treatment adherence. Telemed J E-Health 2011;17:609–14. 15. Weaver TE, Laizner AM, Evans LK, et al. An instrument to measure functional status outcomes for disorders of excessive sleepiness. Sleep 1997;20:835–43.
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16. Weaver TE, Mancini C, Maislin G, et al. Continuous positive airway pressure treatment of sleepy patients with milder obstructive sleep apnea: results of the CPAP Apnea Trial North American Program (CATNAP) randomized clinical trial. Am J Respir Crit Care Med 2012;186:677–83. 17. Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep 1991;14:540–5. 18. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. App Psychol Meas 1977;1:385–401. 19. Ware J, Jr., Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care 1996;34:220–33. 20. Munder T, Wilmers F, Leonhart R, Linster HW, Barth J. Working Alliance Inventory-Short Revised (WAI-SR): psychometric properties in outpatients and inpatients. Clin Psychol Psychother 2010;17:231–9. 21. Larsen DL, Attkisson CC, Hargreaves WA, Nguyen TD. Assessment of client/patient satisfaction: development of a general scale. Eval Program Plann 1979;2:197–207. 22. Himle JA, Fischer DJ, Muroff JR, et al. Videoconferencing-based cognitive-behavioral therapy for obsessive-compulsive disorder. Behav Res Ther 2006;44:1821–9. 23. Witmans MB, Dick B, Good J, et al. Delivery of pediatric sleep services via telehealth: the Alberta experience and lessons learned. Behav Sleep Med 2008;6:207–19. 24. Centers for Medicare and Medicade Services Decision Memo for Continuous Positive Airway Pressure (CPAP) Therapy for Obstructive Sleep Apnea (OSA). 2010. Accessed July 15, 2015. Available at https:// www.cms.gov/medicare-coverage-database/details/nca-decisionmemo.aspx?NCAId=19&ver=7&NcaName=Continuous+Positive+Air way+Pressure+(CPAP)+Therapy+for+Obstructive+Sleep+Apnea+(OS A)&TAId=50&bc=AAAAAAAAEAAA&. 25. Rosen CL, Auckley D, Benca R, et al. A multisite randomized trial of portable sleep studies and positive airway pressure autotitration versus laboratory-based polysomnography for the diagnosis and treatment of obstructive sleep apnea: the HomePAP study. Sleep 2012;35:757–67. 26. Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol 1994;47:1245–51. 27. Olsen MK, Stechuchak KM, Edinger JD, Ulmer CS, Woolson RF. Move over LOCF: principled methods for handling missing data in sleep disorder trials. Sleep Med 2012;13:123–32. 28. Moher D, Hopewell S, Schulz KF, et al. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. J Clin Epidemiol 2010;63:e1–37. 29. NVivo. QSR International Pty Ltd. Version 10, 2012. 30. Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res 2005;15:1277–88. 31. Boyatzis RE. Transforming qualitative information: thematic analysis and code development. Thousand Oaks, CA: SAGE Publications, 1998. 32. Smith CE, Dauz ER, Clements F, et al. Telehealth services to improve nonadherence: a placebo-controlled study. Telemed J E-Health 2006;12:289–96. 33. Harrison R, Macfarlane A, Murray E, Wallace P. Patients’ perceptions of joint teleconsultations: a qualitative evaluation. Health Expect 2006;9:81–90.
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34. Azad N, Amos S, Milne K, Power B. Telemedicine in a rural memory disorder clinic-remote management of patients with dementia. Can Geriatr J 2012;15:96–100. 35. Venter A, Burns R, Hefford M, Ehrenberg N. Results of a telehealthenabled chronic care management service to support people with longterm conditions at home. J Telemed Telecare 2012;18:172–5. 36. Dorsey ER, Deuel LM, Voss TS, et al. Increasing access to specialty care: a pilot, randomized controlled trial of telemedicine for Parkinson’s disease. Mov Disord 2010;25:1652–9. 37. Murphie P, Paton R, Scholefield C, McIntosh N, Little S. Telesleep medicine review - patient and clinician experience. ERJ 2014;44:P3283. 38. Sawyer AM, Gooneratne NS, Marcus CL, Ofer D, Richards KC, Weaver TE. A systematic review of CPAP adherence across age groups: clinical and empiric insights for developing CPAP adherence interventions. Sleep Med Rev 2011;15:343–56. 39. Aloia MS, Arnedt JT, Stanchina M, Millman RP. How early in treatment is PAP adherence established? Revisiting night-to-night variability. Behav Sleep Med 2007;5:229–40. 40. Weaver TE, Maislin G, Dinges DF, et al. Relationship between hours of CPAP use and achieving normal levels of sleepiness and daily functioning. Sleep 2007;30:711–9. 41. Zimmerman ME, Arnedt JT, Stanchina M, Millman RP, Aloia MS. Normalization of memory performance and positive airway pressure adherence in memory-impaired patients with obstructive sleep apnea. Chest 2006;130:1772–8. 42. Sarmiento K, Rossettie J, Stepnowsky C, Atwood C, Calvitti A, Network VAS. The state of Veterans Affairs sleep medicine programs: 2012 inventory results. Sleep Breath 2015 April 30. [Epub ahead of print]. 43. State Policy Resource Center. Accessed July 25, 2015. Available at http://www.americantelemed.org/policy/state-policy-resource-center#. VbQye_lViko.
ACKNOWLEDGMENTS The authors acknowledge the following individuals for their assistance with protocol planning and implementation: Tatiana Blackshear, Ellen Fritch, Ellen Hoover-McGee, David Ingram, LaShauna McCoy, Codey Semder, Kimberly Simmons, and Carl Wesley.
SUBMISSION & CORRESPONDENCE INFORMATION Submitted for publication May, 2015 Submitted in final revised form August, 2015 Accepted for publication September, 2015 Address correspondence to: Barry G. Fields MD, MSEd, Atlanta Veterans Affairs Medical Center, Sleep Medicine Center, 250 N. Arcadia Ave., Decatur, GA 30030; Tel: (404) 321-3610; Fax: (404) 417-2903; Email: barry. [email protected]
DISCLOSURE STATEMENT This was not an industry supported study. Financial support was provided by VISN 4 Competitive Pilot Project Fund. The authors have indicated no financial conflicts of interest.
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http://dx.doi.org/10.5665/sleep.5514
SLEEP DISORDERED BREATHING
Remote Ambulatory Management of Veterans with Obstructive Sleep Apnea Barry G. Fields, MD, MSEd1,2; Pratima Pathak Behari, MD3,4,5; Susan McCloskey, CRNP3; Gala True, PhD3; Diane Richardson, PhD3; Arwin Thomasson, PhD3,6; Danijela Korom-Djakovic, PhD3,7; Keith Davies, BSEE3; Samuel T. Kuna, MD3,4,5 1 Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Atlanta, GA; 2Atlanta Veterans Affairs Medical Center, Decatur, GA; 3Philadelphia Veterans Affairs Medical Center, Philadelphia, PA; 4Division of Sleep Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; 5Center for Sleep and Circadian Neurobiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; 6Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; 7Independent Researcher, Philadelphia, PA
Study Objectives: Despite significant medical sequelae of obstructive sleep apnea (OSA), the condition remains undiagnosed and untreated in many affected individuals. We explored the feasibility of a comprehensive, telemedicine-based OSA management pathway in a community-based Veteran cohort. Methods: This prospective, parallel-group randomized pilot study assessed feasibility of a telemedicine-based pathway for OSA evaluation and management in comparison to a more traditional, in-person care model. The study included 60 Veterans at the Philadelphia Veterans Affairs Medical Center and two affiliated community-based outpatient clinics. Telemedicine pathway feasibility, acceptability, and outcomes were assessed through a variety of quantitative (Functional Outcomes of Sleep Questionnaire, dropout rates, positive airway pressure [PAP] adherence rates, participant satisfaction ratings) and qualitative (verbal feedback) metrics. Results: There was no significant difference in functional outcome changes, patient satisfaction, dropout rates, or objectively measured PAP adherence between groups after 3 months of treatment. Telemedicine participants showed greater improvement in mental health scores, and their feedback was overwhelmingly positive. Conclusions: Our pilot study suggests that telemedicine-based management of OSA patients is feasible in terms of patient functional outcomes and overall satisfaction with care. Future studies should include larger populations to further elucidate these findings while assessing provider- and patient-related cost effectiveness. Keywords: obstructive sleep apnea, telemedicine, home sleep testing, auto-titrating positive airway pressure Citation: Fields BG, Behari PP, McCloskey S, True G, Richardson D, Thomasson A, Korom-Djakovic D, Davies K, Kuna ST. Remote ambulatory management of veterans with obstructive sleep apnea. SLEEP 2016;39(3):501–509. Significance Limited access to sleep medicine care is a growing concern within the Veterans Affairs (VA) healthcare system and beyond. Although various telemedicine-based modalities show promise in addressing this need, technological advancement has outpaced evidence-based implementation. Our pilot study is the first to compare prospectively a novel, telemedicine-based obstructive sleep apnea (OSA) management pathway with in-person care. Through quantitative and qualitative indices, we demonstrated no significant inter-pathway difference in participant functional outcomes or satisfaction rates following three months of treatment. These findings lay the groundwork for future sleep telemedicine research and clinical pathway development as this nascent field evolves.
INTRODUCTION Obstructive sleep apnea (OSA) is a common disorder resulting from repetitive pharyngeal airway closure during sleep. It has been linked to major cardiovascular disorders such as coronary artery disease, congestive heart failure, hypertension, and stroke.1–3 The prevalence of moderate to severe OSA has increased to 3% and 10% among 30- to 49-y-old women and men, respectively; between ages 50 and 70 y, 9% of women and 17% of men have the disorder.4–6 Because the condition remains undiagnosed in a majority of these individuals,7 improved access to testing is essential. Veterans are particularly vulnerable to diagnostic access limitations; about one-half of them live more than 25 miles from a Veterans Affairs Medical Center (VAMC).8 The Veterans Health Administration (VHA) Philadelphia VAMC (PVAMC) utilizes various modalities to address this problem: real-time video teleconferencing (clinical video telehealth; CVT), home sleep testing (HST), modem-enabled positive airway pressure (PAP) units, and automatically-adjusting PAP (APAP) machines. A similar strategy has been employed at the Michael E. DeBakey VAMC in Houston, Texas.9 Previous studies have shown HST and APAP-based strategies are not clinically inferior to laboratory-based polysomnography (PSG) in terms of cost, APAP adherence, and symptomatic SLEEP, Vol. 39, No. 3, 2016
improvement with treatment.10–13 Parikh et al.14 showed no difference in participant satisfaction (prospectively) or PAP adherence rates (retrospectively) in patients having either an in-person sleep physician visit or a CVT-based visit at their initial evaluation. Nevertheless, there have been no prospective, patient-oriented outcome studies in community-based populations that combine HST, APAP, and CVT into a comprehensive OSA management pathway. METHODS We conducted a prospective, parallel-group randomized pilot study assessing feasibility of a telemedicine-based pathway (TELE) for OSA evaluation and management in comparison to a more traditional, in-person care model (INP). The study’s primary aim was to compare functional outcomes between the 2 groups after 3 mos of APAP treatment utilizing the Functional Outcomes of Sleep Questionnaire (FOSQ)15,16 as the primary outcome. Secondary outcome measures included the Epworth Sleepiness Scale (ESS),17 Center for Epidemiological Studies Depression Scale (CES-D),18 and the Health Outcomes Short Form-12 (SF-12),19 as well as the Working Alliance Inventory-Short Form (WAI-SF)20 and Client Satisfaction Questionnaire-8 (CSQ-8).21 Objective APAP adherence was also compared between the two groups. Additionally, we conducted 501
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Figure 1—Cohort diagram of participants randomized to in-person versus telemedicine-based pathways. APAP, automatically-adjusting positive airway pressure; FU, follow-up; HST, home sleep testing; INP, in-person; OSA, obstructive sleep apnea; TELE, telemedicine.
participants were compensated for their time completing questionnaires and providing telephone feedback, if applicable.
a formative evaluation of the TELE pathway through other quantitative indices, qualitative feedback, and attrition rates. Finally, we compared HST failure rates and recording quality between pathways. In doing so, we sought to refine the TELE pathway for improved clinical care and use in future comparative effectiveness trials.
Procedures After providing informed consent, all participants were scheduled for an initial sleep consultation at their local CBOC. Prior to this visit, each person completed the FOSQ, ESS, SF-12, and CES-D questionnaires.15,16 After the visit, they completed the WAI-SF and CSQ-8. The WAI-SF is a 12-item survey assessing patients’ perceived level of collaboration with their provider. It has been utilized to demonstrate patient-provider alliance in a previous CVT-based study.22 The CSQ-8 is an 8-item survey that elicits patient satisfaction with service provision. It has been used extensively within multiple specialties, including sleep telemedicine.23 Participants in both arms were provided an HST device for use that night. Individuals in whom OSA was diagnosed and treated completed the same questionnaires at follow-up encounters 1 and 3 mo after commencing PAP therapy (Figure 1).
Participants We recruited 30 patients from each of two community-based outpatient centers (CBOCs) affiliated with the PVAMC, for a total of 60 study patients. The Victor J. Saracini CBOC (Horsham, Pennsylvania) and Joint Base McGuire-Dix, New Jersey CBOC are located 33 and 46 miles away from the PVAMC, respectively. All participants were at least age 18 y, received primary care at the CBOC, and were fluent in English. Patients were excluded from the study if they were unable or unwilling to provide informed consent, stated an inability to return for follow-up sessions, or had a previous diagnosis of sleep disordered breathing (OSA, central sleep apnea, Cheyne-Stokes respiration, obesity hypoventilation syndrome) or narcolepsy. Verbal consent was obtained via telephone to avoid their having to make an additional visit to the Philadelphia VAMC or a CBOC. Participants were then randomized to the TELE (experimental) or INP (standard care) pathway using a computer-generated, blocked randomization scheme. They were not informed of pathway allocation until they arrived for their initial clinic visit. The complete study protocol was approved by the Institutional Review Board at the Philadelphia VAMC, and SLEEP, Vol. 39, No. 3, 2016
Telemedicine Arm
For their initial evaluation, TELE participants were interviewed at their local CBOC via real-time CVT by a sleep provider (BGF, PPB, or SM) in Philadelphia. A telemedicine technician at the CBOC seated participants in a private examination room facing a telemedicine cart; the unit consisted of a high-definition camera and 17-inch video monitor. A CoderDecoder (CODEC) compressed the two-way data streams, enabling their transmission over high bandwidth (high speed)
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connections. This audiovisual data was secured using Health Insurance Portability and Accountability Act (HIPAA)-compliant, Federal Information Processing Standards 140-2 certified encryption modules. All equipment, security procedures, and contributions from a telemedicine technician were utilized for usual clinical care and were not unique to the study protocol. All visits were completed using a standardized patient encounter template designed to last approximately 40 min. Immediately after their initial visit, participants were provided with an HST device for use that night. An instructional DVD and brochure were provided to show how to perform the study, but there was no in-person set-up instruction. The recordings were downloaded and then scored and interpreted by an experienced clinician blinded to pathway allocation (STK). APAP was ordered for an apnea-hypopnea index (AHI) ≥ 15 events/h or 5 ≤ AHI < 15 events/h with clinical symptoms. Sleep providers phoned TELE participants 1 w and 1 and 3 mo after commencing APAP therapy. Week 1 encounters were brief opportunities (10 min or less) to ensure the patient had started using the device, provide encouragement, and answer questions. Month 1 and 3 phone calls utilized standardized interview templates designed to last approximately 20 min. These encounters included PAP unit data review, assessed for common PAP-related concerns (e.g. mask leaks, claustrophobia), and provided another opportunity for patients’ questions. Individuals who completed the study protocol were invited to participate in a telephone feedback session with a qualitative researcher (DKD).
hypopneas. The AHI was calculated as the mean number of apneas and hypopneas per hour of recording. Quality of each study was rated as the percentage of time that each signal was scoreable. A technically acceptable study required a total of at least 3 h of oxygen saturation signal coinciding with at least one respiratory signal. Technically unacceptable studies were repeated once. Auto-Titrating Positive Airway Pressure Treatment
Participants in whom OSA was diagnosed (AHI ≥ 5 events/h) were initiated on APAP treatment. An APAP (Philips-Respironics System One, Murrysville, PA) unit was delivered to the participant’s home by a sleep therapist from a regional home healthcare company contracted by the PVAMC. The therapist fitted the participant with an appropriate mask interface and explained how to operate and care for the equipment. APAP machines were set at a pressure range of 4 to 20 cm H20 with heated in-line humidification. Wireless modem technology was used for secure transmission of objective data on machine usage (i.e., daily mask-on time), efficacy in controlling OSA (i.e. apnea and hypopnea detection), and air leak from the circuit. All modem data were transmitted daily from participants’ APAP units to EncoreAnywhere (Philips-Respironics), a HIPAA-compliant, password-protected internet database that can be accessed by the patient’s clinician and home health care company. One- and 3-mo data summaries were downloaded from the EncoreAnywhere website, with adherence determined as mean daily minutes of PAP use over 3 mo. Days without data were considered to be days when the participant did not use the treatment, i.e., 0 min of use.
In-Person Arm
INP participants’ initial visits were conducted face-to-face by the sleep clinician (BGF) travelling to their CBOC. Patient encounter templates were identical to those used in the TELE arm. Participants were scheduled to return 1 to 2 w later for in-person HST instruction from experienced sleep therapists before using the unit that same night. No instructional DVD was provided. HST results were analyzed as in the TELE arm and participants were prescribed APAP as indicated. The sleep provider phoned INP participants 1 w after commencing APAP therapy (same brief encounter as the TELE arm), and conducted in-person follow-up appointments (same standardized templates as the TELE arm) at the participants’ CBOCs 1 and 3 mo after commencing PAP therapy.
End-of-Study Phone Calls
A qualitative researcher (DKD) conducted individual phone interviews with TELE participants to (1) assess their perceptions about the use of telemedicine to diagnose and treat sleep apnea, and (2) gather feedback about barriers and facilitators to participation in OSA diagnosis and care via telemedicine to inform future research and translation to clinical practice. A semistructured interview protocol included questions on the individuals’ experiences with study participation, their thoughts about the differences between in-person and telemedicine visits, and their assessment of the effectiveness of care received. Verbal informed consent was obtained prior to starting the interviews, which ranged from 10 to 25 min. The qualitative researcher had no other interaction with the participant during the protocol.
Home Sleep Testing
Participants performed unattended HST using a Type 3 portable monitor (Embletta Gold; Embla, Inc., Broomfield, CO).24 This monitor has been used in large randomized controlled trials comparing in-laboratory polysomnography (PSG) to HST.10,25 The following signals were recorded: airflow (nasal pressure), rib cage and abdominal movement, snoring, body position, heart rate, and oxygen saturation. The studies were scored by a sleep specialist (STK) who was blinded to participant randomization. Apneas were scored for a > 90% reduction of airflow from baseline for at least 10 sec, with the respiratory effort channels used to distinguish obstructive versus central apneas. Reductions in respiration for at least 10 sec associated with at least a 4% oxygen desaturation were scored as SLEEP, Vol. 39, No. 3, 2016
Data Analyses Sample Size
The pilot’s sample size of 60 patients was selected based on nonstatistical considerations, in order to provide an adequate number of participants to assess the feasibility of the intervention. We determined that with this sample size, and assuming 15% loss to follow up over 3 mo, we would have 70% power to detect a clinically significant effect based on an assumed improvement in FOSQ comparable to published values of 1.74 (standard deviation [SD] 2.5)10 with a two-sided test at α = 0.05. 503
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Table 1—Participant characteristics at baseline. Variable Age, y n (%) males n (%) African American n (%) Latino n (%) OSA-positive Body mass index, kg/m2 Charlson Index AHI FOSQ total CES-D ESS SF-12 physical SF-12 mental WAI-SF CSQ-8
Total Cohort (n = 60) 50.8 ± 14.8 55 (91.7) 15 (25.0) 1 (1.7) 37 (61.7) 32.4 ± 5.5 0.7 ± 1.5 22.7 ± 28.5 a 13.4 ± 3.8 d 22.4 ± 13.4 d 12.7 ± 5.4 d 40.9 ± 9.5 d 41.3 ± 13.1 d 74.5 ± 8.0 d 20.1 ± 2.5 d
In-Person Care (n = 28) 53.9 ± 15.8 27 (96.4) 5 (17.9) 0 19 (67.9) 33.3 ± 4.71 0.82 ± 1.7 25.5 ± 28.7 b 13.5 ± 4.0 c 20.7 ± 13.9 c 13.4 ± 5.5 c 39.3 ± 8.3 c 45.3 ± 11.9 c 76.1 ± 6.4 c 20.2 ± 3.1 c
Telemedicine Care (n = 32) 48.0 ± 13.4 28 (87.5) 10 (31.3) 1 (3.1) 18 (56.3) 31.6 ± 6.1 0.63 ± 1.4 20.3 ± 28.7 c 13.3 ± 3.8 e 24.1 ± 13.0 e 12.0 ± 5.3 e 42.4 ± 10.4 e 37.5 ± 13.3 e 73.0 ± 9.1 e 20.0 ± 1.8 e
P 0.100 0.359 0.368 > 0.999 0.332 0.115 0.674 0.190 0.888 0.270 0.390 0.296 0.034 0.248 0.825
Values presented as mean ± standard deviation or n (%). a n = 47. b n = 22. c n = 25. d n = 51. e n = 26. AHI, apnea-hypopnea Index; CES-D, Center for Epidemiologic Studies Depression Scale; CSQ-8, Client Satisfaction Questionnaire; ESS, Epworth Sleepiness Scale; FOSQ, Functional Outcomes of Sleep Questionnaire; OSA, obstructive sleep apnea; SF-12, Short Form 12; WAI-SF, Working Alliance Inventory-Short Form.
Statistical Analysis for Functional Outcomes
analysis software (QSR International, Burlington, MA). We used content analysis30 and thematic coding31 to generate categories of codes that captured participants’ responses. When all interviews were coded, we reorganized the codes into categories corresponding to the main aims of our analysis: to assess perceptions and acceptability of telemedicine, and identify potential barriers and facilitators to TELE-based delivery of OSA care.
Descriptive statistics for patient characteristics, including the Charlson comorbidity index,26 were used to characterize the full patient sample and by treatment group. Patient characteristics were compared between groups to confirm that the randomization resulted in no clinically significant differences at baseline. These comparisons were conducted using the Fisher exact test for categorical variables and the Wilcoxon-MannWhitney test for continuous variables, without adjusting for multiple testing. For the primary analysis, primary and secondary study outcomes were analyzed based on a per protocol population (OSA-positive participants who were followed for 3 mo on APAP and who completed at least the baseline FOSQ). Descriptive statistics were calculated, and between-group comparisons were performed as previously described for patient characteristics and baseline outcome measures in this population. We used linear mixed models to estimate the mean change from baseline for each outcome at the end of follow-up, and the between-group difference in mean change from baseline.27,28 In these models, we included the baseline outcome measure, indicators for visit number and intervention group, and an interaction term for visit number with intervention group, all as fixed effects. We used the interaction term coefficient to test for the difference in pre-post outcomes between groups. We accounted for within-patient clustering of outcomes with a banded (Toeplitz) covariance-correlation structure. Results are presented as the mean change from baseline for each study outcome at 3 mos, within groups, and as between-group mean differences in change from baseline, assessed at 3 mos.
RESULTS We randomized 60 participants, 28 to the INP pathway and 32 to TELE-based care. Figure 1 summarizes the flow of participants and the loss to follow up before and after HST. All randomized participants’ data were included in the descriptive analysis in Table 1 of patient baseline characteristics, except where baseline questionnaire data and/or HST results were not obtained. At baseline, INP and TELE participants did not differ in terms of age, sex, race, ethnicity, body mass index (BMI), or comorbidities (Table 1). The 60 participants had a mean (SD) age of 50.8 (14.8) y and BMI of 32.4 (5.5) kg/m 2. Their mean (SD) Charlson Comorbidity Index was 0.7 (1.5). Fifteen individuals (25.0%) were African-American and 1 (1.7%) was Latino. TELE participants demonstrated lower baseline SF-12 mental health component scores; that is, they reported lower levels of mental health. None of the other baseline questionnaire scores differed significantly between groups (Table 1). Thirteen participants (21.7%) withdrew from the study—4 in the INP arm and 9 in the TELE arm (Figure 1). Four of the TELE participant withdrawals occurred before participants were aware of treatment arm allocation. Forty-one participants (75.9%) who completed an HST were found to have OSA (AHI ≥ 5 events/h). The overall mean (SD) AHI was 22.7 (28.5) events/h, and OSA severity did not differ between groups
Qualitative Analyses
End-of-study telephone interviews were digitally recorded and transcribed by a professional transcriptionist. The interview transcripts were analyzed using NVivo 1029 qualitative data SLEEP, Vol. 39, No. 3, 2016
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Table 2—Baseline participant characteristics for individuals treated with automatically-adjusting positive airway pressure treatment. Variable Age, y n (%) males n (%) African American n (%) Latino Body mass index, kg/m2 Charlson Index AHI FOSQ total CES-D ESS SF-12 physical SF-12 mental WAI-SF CSQ-8
Total Cohort (n = 34) 53.2 ± 14.8 32 (94) 11 (32.4) 0 33.0 ± 4.8 0.6 ± 1.6 31.0 ± 31.3 13.2 ± 3.6 21.8 ± 13.5 14.0 ± 4.7 40.6 ± 9.2 41.1 ± 13.2 74.1 ± 8.2 20.1 ± 2.8
In-Person Care (n = 20) 58.2 ± 14.4 18 (94.7) 4 (21.1) 0 32.9 ± 3.8 1.0 ± 2.0 30.1 ± 30.0 13.3 ± 3.7 20.5 ± 14.7 13.4 ± 4.6 38.8 ± 8.3 45.2 ± 12.9 77.2 ± 5.3 20.3 ± 3.5
Telemedicine Care (n = 14) 46.7 ± 13.1 14 (93.3) 7 (46.7) 0 33.2 ± 6.0 0.2 ± 0.4 32.0 ± 33.9 13.1 ± 3.7 23.5 ± 11.9 14.7 ± 4.8 42.9 ± 10.0 35.9 ± 11.9 70.1 ± 9.6 19.8 ± 1.8
P 0.022 > 0.999 0.163 – 0.900 0.324 0.755 0.917 0.376 0.356 0.238 0.056 0.023 > 0.999
Values presented as mean ± standard deviation or n (%). AHI, apnea-hypopnea Index; CES-D, Center for Epidemiologic Studies Depression Scale; CSQ8, Client Satisfaction Questionnaire; ESS, Epworth Sleepiness Scale; FOSQ, Functional Outcomes of Sleep Questionnaire; SF-12, Short Form 12; WAISF, Working Alliance Inventory-Short Form.
Table 3—Unadjusted 3-mo mean changes in primary (Functional Outcomes of Sleep Questionnaire) and secondary outcome measures in participants after 3 mo of automatically-adjusting positive airway pressure treatment. Measure (Total Scores) FOSQ CES-D ESS SF-12 physical activity SF-12 mental health WAI-SF CSQ-8
Total Cohort (n = 30) 1.41 ± 2.73 −5.57 ± 8.83 −3.80 ± 5.82 1.48 ± 7.28 4.79 ± 9.31 3.67 ± 7.61 −0.45 ± 2.90
In-Person (n = 18) 0.91 ± 2.58 −4.61 ± 8.62 −3.61 ± 5.16 1.90 ± 9.03 1.38 ± 6.92 1.83 ± 5.19 −0.50 ± 3.52
Telemedicine (n = 12) 2.18 ± 2.87 −7.00 ± 9.32 −4.08 ± 6.92 0.87 ± 3.60 9.90 ± 10.33 6.42 ± 9.86 −0.36 ± 1.57
P 0.228 0.800 > 0.999 0.920 0.012 0.210 0.784
Values presented as unadjusted mean ± standard deviation change in score. Includes only participants (n = 30) not missing baseline and 3-mo follow-up FOSQ scores. CES-D, Center for Epidemiologic Studies Depression Scale; CSQ-8, Client Satisfaction Questionnaire; ESS, Epworth Sleepiness Scale; FOSQ, Functional Outcomes of Sleep Questionnaire; SF-12, Short Form 12; WAI-SF, Working Alliance Inventory-Short Form.
(Table 1). Only participants in whom OSA was diagnosed were eligible to be treated with APAP; as a result, participants determined to be OSA-negative (n = 4 in INP and n = 9 in TELE) after the review of HST results were not included in the final per protocol analyses of study outcomes. Table 2 reports baseline characteristics and primary and secondary outcome data of participants used in the per-protocol analysis set (see “Data Analyses” in “Methods” section; not all randomized participants in Table 1 completed baseline assessments because randomization occurred prior to visit 1, and some who completed those assessments did not have OSA). One participant in the INP arm in whom OSA was diagnosed chose not to proceed with treatment and was therefore not included in per-protocol analyses. Mean (SD) AHI was 31.0 (31.3) events/h and did not differ significantly between groups. Baseline SF-12 mental health score was lower in the TELE group, but the difference did not reach statistical significance (P = 0.056). Baseline WAI-SF scores were significantly higher in the INP group than the TELE group (P = 0.023), and TELE SLEEP, Vol. 39, No. 3, 2016
participants were significantly younger than INP participants in the per-protocol group (P = 0.022). After 3 mo, the per-protocol population demonstrated greater improvement in SF-12 mental health score among the TELE group than among the INP group in both unadjusted (P = 0.012; Table 3) and adjusted analyses (P = 0.003; Table 4). A similar trend of greater improvement was noted for FOSQ scores (Figure 2) and WAI-SF scores. However, the difference in improvement between groups for these two scores did not reach statistical significance at the α = 0.05 level. There were no significant differences in changes from baseline CES-D, ESS, SF-12 physical health, WAI-SF, or CSQ-8 scores (Tables 3 and 4). APAP Adherence Adherence to APAP treatment over the 3-mo treatment period did not differ between groups. INP participants used the device 54 ± 8% of possible days, compared to 65 ± 8% of possible days among TELE participants (P = 0.493; Table 5). Usage 505
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Table 4—Adjusted 3-mo mean changes and adjusted differences in mean changes in primary (Functional Outcomes of Sleep Questionnaire) and secondary outcome measures in participants after 3 mo of automatically-adjusting positive airway pressure treatment. Measure (Total Scores) FOSQ CES-D ESS SF-12 physical activity SF-12 mental health WAI-SF CSQ-8
Change in Score In-Person (n = 20) Telemedicine (n = 14) 0.89 ± 0.59 2.57 ± 0.69 −4.31 ± 1.73 −6.51 ± 2.03 −3.56 ± 1.13 −4.22 ± 1.31 2.08 ± 1.54 0.86 ± 1.83 0.73 ± 1.78 9.26 ± 2.09 1.70 ± 1.50 5.93 ± 1.77 0.013 ± 0.48 −0.31 ± 0.57
Difference in Changes 1.69 ± 0.91 −2.19 ± 2.66 −0.67 ± 1.73 −1.22 ± 2.40 8.53 ± 2.75 4.23 ± 2.32 −0.32 ± 0.74
P 0.067 0.413 0.702 0.611 0.003 0.074 0.665
Values presented as adjusted mean ± standard error. Includes all observations with one or more follow-up FOSQ measure, site, age, and body mass index. Estimated using mixed models and adjusted for baseline score. CES-D, Center for Epidemiologic Studies Depression Scale; CSQ-8, Client Satisfaction Questionnaire; ESS, Epworth Sleepiness Scale; FOSQ, Functional Outcomes of Sleep Questionnaire; SF-12, Short Form 12; WAI-SF, Working Alliance Inventory-Short Form.
Table 5—Adherence to automatically-adjusting positive airway pressure 3 mo after its initiation. In-Person Telemedicine Variable Care (n = 20) Care (n = 14) % days with device usage 54 ± 8 65 ± 8 % days ≥ 4 h 39 ± 8 47 ± 9 Use, min (all days) 175.6 ± 36.8 220.8 ± 37.5 Use, min (days used) 268.9 ± 32.1 305.7 ± 29.9
P 0.493 0.493 0.301 0.426
Values presented as mean ± standard error.
Table 6—Percentage of time that signals on technically adequate home sleep testing were able to be scored. Signal Pulse oximetry Nasal pressure Thoracic excursion Abdominal excursion
In-Person Care (n = 25) 90.6 ± 5.2 87.5 ± 5.5 86.6 ± 6.0 94.4 ± 4.0
Telemedicine Care (n = 30) 86.7 ± 4.4 87.8 ± 4.0 92.3 ± 5.7 95.8 ± 3.3
Figure 2—Box plots illustrating 3-mo adjusted mean changes in FOSQ score by study arm per protocol. Horizontal line within boxes is the median change. Upper and lower box boundaries are 75th and 25th percentile respectively. Whiskers represent distance to minimum and maximum values. FOSQ, Functional Outcomes of Sleep Questionnaire.
Values presented as mean percent of time scoreable ± standard error.
more than 4 h per day occurred in 39 ± 8% of possible days in INP participants and 47 ± 9% of possible days in TELE participants (P = 0.493). INP participants used the machine an average of 269 min (4 h, 29 min) on nights used, and TELE participants recorded 306 min (5 h, 6 min) of use on nights used (P = 0.426).
Qualitative Feedback Of 22 eligible TELE participants (completed the TELE protocol whether OSA positive or negative), 15 (68%) completed the phone interviews; two individuals declined to participate and we were unable to contact 5 individuals. Fourteen participants were male and 11 participants received a diagnosis of OSA.
HST Quality Four INP participants and 9 TELE participants repeated their first night of HST due to inadequate technical quality for scoring. One INP participant failed a second attempt at HST. Overall, 83.3% of INP studies were scoreable on their first attempt, compared to 65.4% of TELE studies. Respiratory signals were of adequate quality for at least 86% of the time during scoreable studies in both groups (Table 6).
Prior to this study, only 1 participant had previous exposure to telemedicine; the type was not specified. The most frequently mentioned advantages were decreased travel burden and the convenience. All but 1 participant noted that it was more convenient for them to drive to their local CBOC than to the VAMC for an appointment. Six participants were highly satisfied with the encounters due to the quality of the interaction
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Perceptions of Telemedicine
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they established with their providers. These individuals described their providers as “very pleasant to talk to,” “very caring,” “understanding,” “knowledgeable,” and helpful in providing information and answering the participants’ questions thoroughly. One participant stated, “[provider] wanted to help me and there was a sense that [provider] actually cared. You know, I wasn’t just a number.” In comparing study-related CVT encounters to other inperson clinical visits, TELE participants thought that the 2 types of visits were very similar. One participant noted: Well, I don’t think it was really any different. I mean, like I said, he was right there. We were talking faceto-face over the video. The only difference between talking over the video versus talking to a doctor actually in the office is, you know, they are there, in the office. TELE visits had no effect on participants’ perception of the quality of interactions with the provider, comfort levels during the visit, or the quality of information and the treatment received. Participants noted that they felt comfortable asking questions and discussing any sleep and health issues with their providers. They believed they learned as much about OSA from talking to their provider over the video as they would have had they met in person. Several participants noted that the high audio/visual quality of the visits—with the doctor “on a big flat screen TV”—made the appointment more personal and made them feel “just like being in the room with the doctor.” Two participants emphasized their excitement at seeing technology being used in medicine to provide more convenience to patients. One participant suggested that telemedicine could be used in the treatment of other conditions beyond OSA. Although TELE participants had positive experiences and agreed that their OSA care was good quality, 2 participants expressed a strong preference for in-person visits. One participant described himself as a “one-on-one type person” who “would have been more comfortable actually being in a room with the doctor,” but he also commented that the “video visit was OK, too.” The other participant thought that “more things may have come to mind” to ask the doctor had the visits been in person. However, in examining the advantages and disadvantages of the 2 types of visits, this individual noted that “being it [TELE visit] was more convenient, it was worth that little bit I might have missed discussing…” Only 1 participant expressed significant concern about confidentiality during telemedicine visits. During other in-person visits, he said he had a clear sense of who was present for the exchange of information, while during TELE visits he was never completely sure “who [was] getting the information.”
the wireless router. And you wouldn’t have to go to the clinic, you know, every six months, once a year, they can do it right from the house there and I think it’s great. In addition, several participants pointed out that “the study is simple,” involvement in the study “doesn’t take a lot of time,” the treatment for sleep apnea “doesn’t hurt even a little bit,” and “it works.” Participants did not have any suggestions for improving the quality, interactions, and information shared in TELE visits. They stated that all the information they received about OSA from their providers was very useful. Although 10 participants did not have any suggestions for improving the protocol, 5 participants shared specific requests and recommendations. They described either having difficulty using the PAP machine (e.g., not having a mask interface that was fitting properly) or getting a machine that did not work well due to technical problems. DISCUSSION Our pilot study demonstrated feasibility of a telemedicinebased OSA evaluation, diagnosis, and management pathway in a VA community-based population. To our knowledge, it is the first to examine this comprehensive care model prospectively. Although it was not designed as an efficacy or superiority trial, TELE participants may have experienced greater functional and mental health improvement than INP counterparts. However, we did not adjust for multiple comparisons and it is possible that these differences are due to chance. APAP adherence did not differ between groups after 3 mos of treatment. TELE participants were just as satisfied with their care as INP participants (CSQ-8), and felt just as much of a working alliance with their provider (WAI-SF). Overall, loss to follow-up was greater among TELE participants than INP participants. Both TELE and INP attrition rates were comparable to other studies exploring HST and APAP-based OSA management models.10,25 Furthermore, 4 of the 9 TELE withdrawals occurred before participants were informed of, or had experienced, their pathway allocation (Figure 1). The other 5 TELE withdrawals all occurred after completion of the HST but before the first follow-up visit. This pattern of attrition was not replicated on the INP arm. Conclusions are difficult to draw based on (1) limited sample size and (2) identical study arm PAP initiation protocols. Therefore, larger studies are needed to evaluate these attrition patterns and explore potential interactions with the telemedicine-based pathway components. Higher first-attempt HST success rates in the INP arm (83.3%) than the TELE arm (65.4%) are also difficult to interpret. INP participants may have benefited from in-person HST set-up instruction even if it occurred 1 to 2 w after their initial evaluation. TELE participants had not received that instruction; they left their initial visit with an HST machine, a DVD describing how to use it, and an informational set-up sheet. Of the 15 TELE participants interviewed at the end of the study, 5 claimed not to have used the DVD and 3 found it too long and confusing. Although these findings do not implicate inperson HST instruction as the only effective strategy, they do suggest that (1) instructional materials must be as high quality
Feedback on the Study Protocol
All participants expressed satisfaction with all parts of the study, including initial and follow-up visits with their providers. When asked what they would tell other Veterans, participants made comments such as: I think it’s just a great idea and a great study all the way around. With the fact that they can, you know, monitor your sleep patterns remotely with SLEEP, Vol. 39, No. 3, 2016
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as possible and (2) patient preference in instructional modality could be elicited when ordering HST. Though not specifically tracked in this pilot, it is possible that increased need for retesting in the TELE arm could have driven up cost over time. Future studies utilizing similar HST-distribution protocols might further explore this possibility. Prior research indicates that telemedicine has been wellreceived by patients with OSA,14,32 as well as by patients with other medical conditions.33,34 Participants in our study reported similarly high satisfaction with the TELE pathway, with all of them agreeing that the quality and content of their telemedicine visits were comparable to in-person visits. As other studies found,35–37 the greatest advantages of telemedicine identified were decreased travel burden and more convenience. Our study did not identify any major barriers to receptiveness of telemedicine. One common concern among TELE participants was lack of proper mask fitting at time of machine delivery. Although this finding was not attributable to the telemedicine intervention (TELE and INP arm APAP delivery protocols were identical), it does indicate a particularly vulnerable point in any home-based OSA management protocol. Extra attention to initial mask fitting and machine set-up appears essential and may affect future adherence to PAP.38 Indeed, patients’ first week with therapy can predict future adherence.39 In our pilot, PAP adherence—determined as both daily use time and as a percentage of nights with use more than 4 h—did not differ significantly between groups. Previous studies have shown improvement in daytime functioning, sleepiness, and memory to be optimized with at least 6 h of nightly use.40,41 Although mean adherence rates fell short of this threshold in both groups, they were similar to ranges observed in previous studies.10,38 In addition to the indices reported, any telemedicine pathway’s feasibility must also be assessed in light of local resources. Many VAMCs provide the hardware, personnel, information security measures, and clinical space necessary to create viable sleep telemedicine programs.42 Other centers must consider similar initial and ongoing costs when developing their programs. Additionally, there is significant variability in telemedicine encounter reimbursement among and within states; potential telemedicine practitioners should familiarize themselves with current insurance coverage policies.43 There were several limitations to our pilot study. First, our sample size was relatively small and only involved one VA institution, curtailing our ability to generalize findings, conduct subgroup analyses, and evaluate noninferiority between the two pathways. Second, we conducted telephone-based feedback interviews between 2 and 6 mo after the participants completed the study. This time lag may have affected their recollection of study details and diminished the potential richness of their accounts. Third, logistical considerations forced a lack of provider-site homogeneity. That is, all providers (BGF, PP, SM) could not visit both sites or participate in both study arms. This protocol asymmetry could have inadvertently influenced the final results. Fourth, among participants who completed a baseline FOSQ, TELE participants were significantly younger than INP participants. It is possible that this younger population is inherently more receptive to TELE care. SLEEP, Vol. 39, No. 3, 2016
Despite these limitations, this study provides valuable insight into a comprehensive, telemedicine-based model for OSA care. At the end of 3 mos, TELE participants’ functional improvement, satisfaction, and PAP adherence were at least comparable to the INP group. They had positive perceptions of sleep telemedicine and viewed it as a viable alternative to inperson visits. We identified a potential area for improvement in the TELE pathway based on HST failure rates (modifying our patient set-up instruction protocol) and participant feedback (devising better mask-fitting paradigms). Larger, multicenter trials are needed to ensure these findings are replicable across institutions. Future studies could also expand on this pilot by examining within-group differences over time, exploring the interaction of age with telemedicine care models, and conducting provider time and cost analyses. REFERENCES 1. Shahar E, Whitney CW, Redline S, et al. Sleep-disordered breathing and cardiovascular disease: cross-sectional results of the Sleep Heart Health Study. Am J Respir Crit Care Med 2001;163:19–25. 2. Marin JM, Carrizo SJ, Vicente E, Agusti AG. Long-term cardiovascular outcomes in men with obstructive sleep apnoeahypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet 2005;365:1046–53. 3. Yaggi HK, Concato J, Kernan WN, Lichtman JH, Brass LM, Mohsenin V. Obstructive sleep apnea as a risk factor for stroke and death. N Engl J Med 2005;353:2034–41. 4. Young T, Finn L, Peppard PE, et al. Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. Sleep 2008;31:1071–8. 5. Young T, Skatrud J, Peppard PE. Risk factors for obstructive sleep apnea in adults. JAMA 2004;291:2013–6. 6. Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM. Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol 2013;177:1006–14. 7. Kapur V, Strohl KP, Redline S, Iber C, O’Connor G, Nieto J. Underdiagnosis of sleep apnea syndrome in U.S. communities. Sleep Breath 2002;6:49–54. 8. VA Health Care: Improving Veterans’ Access Poses Financial and Mission-Related Challenges (GAO/HEHS-97-7). Accessed March 11, 2015. Available at http://www.gao.gov/assets/230/223354.pdf. 9. Hirshkowitz M, Sharafkhaneh A. A telemedicine program for diagnosis and management of sleep-disordered breathing: the fasttrack for sleep apnea tele-sleep program. Semin Respir Crit Care Med 2014;35:560–70. 10. Kuna ST, Gurubhagavatula I, Maislin G, et al. Noninferiority of functional outcome in ambulatory management of obstructive sleep apnea. Am J Respir Crit Care Med 2011;183:1238–44. 11. Antic NA, Buchan C, Esterman A, et al. A randomized controlled trial of nurse-led care for symptomatic moderate-severe obstructive sleep apnea. Am J Respir Crit Care Med 2009;179:501–8. 12. Berry RB, Hill G, Thompson L, McLaurin V. Portable monitoring and autotitration versus polysomnography for the diagnosis and treatment of sleep apnea. Sleep 2008;31:1423–31. 13. Mulgrew AT, Fox N, Ayas NT, Ryan CF. Diagnosis and initial management of obstructive sleep apnea without polysomnography: a randomized validation study. Ann Intern Med 2007;146:157–66. 14. Parikh R, Touvelle MN, Wang H, Zallek SN. Sleep telemedicine: patient satisfaction and treatment adherence. Telemed J E-Health 2011;17:609–14. 15. Weaver TE, Laizner AM, Evans LK, et al. An instrument to measure functional status outcomes for disorders of excessive sleepiness. Sleep 1997;20:835–43.
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ACKNOWLEDGMENTS The authors acknowledge the following individuals for their assistance with protocol planning and implementation: Tatiana Blackshear, Ellen Fritch, Ellen Hoover-McGee, David Ingram, LaShauna McCoy, Codey Semder, Kimberly Simmons, and Carl Wesley.
SUBMISSION & CORRESPONDENCE INFORMATION Submitted for publication May, 2015 Submitted in final revised form August, 2015 Accepted for publication September, 2015 Address correspondence to: Barry G. Fields MD, MSEd, Atlanta Veterans Affairs Medical Center, Sleep Medicine Center, 250 N. Arcadia Ave., Decatur, GA 30030; Tel: (404) 321-3610; Fax: (404) 417-2903; Email: barry. [email protected]
DISCLOSURE STATEMENT This was not an industry supported study. Financial support was provided by VISN 4 Competitive Pilot Project Fund. The authors have indicated no financial conflicts of interest.
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Remote Management of Veterans with OSA—Fields et al.
