Chronic obstructive pulmonary disease (COPD) has an estimated global prevalence of 11.7% and is the third leading cause of death worldwide.1 Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with significant rates of hospitalization and accelerated disease progression, leading to greater patient disability.2,3
Many interventions aim to reduce exacerbation frequency and hospitalizations.4 These include pharmacologic and nonpharmacologic therapies.5 If identified and treated early, COPD exacerbations can often be managed on an outpatient basis with the use of bronchodilators, antibiotics, and oral steroids.6,7 Nonpharmacologic therapies include long-term oxygen therapy,8 pulmonary rehabilitation,9,10 and home non-invasive ventilation.11 Identifying AECOPDs early, however, requires overcoming multiple challenges including a heightened awareness of symptoms and a demonstrated ability to coordinate provider examination and care, including pharmacologic intervention. Experience shows that patients are often slow or loathe to self-report symptoms that indicate an emerging AECOPD.12,13
To pre-identify exacerbations, physiologic and spirometric measures have been used including changes in pulse rate, peripheral oxygen saturation, blood pressure, body temperature, forced expiratory volume in the first second (FEV1), and respiratory rate.14 However, devices currently available and commonly used by COPD patients, such as spirometers and pulse oximeters, may be inadequate to accurately pre-identify AECOPDs.15 While research has demonstrated the potential for remote patient monitoring (RPM) to pre-identify exacerbations,16 there is a lack of real-world evidence showing that this method can improve outcomes. The objective of this study was to determine whether RPM, with sustained use and a protocol that allows for timely medical intervention in respiratory subjects, would improve healthcare resource utilization in a COPD cohort.
Study Design and Participants
This study included a retrospective analysis of data collected from subjects at a large outpatient pulmonology practice in a mid-Atlantic metropolitan city of the United States between May 2019 and February 2022. At this clinic, patients who were candidates for RPM had been offered voluntary participation in a service tailored to chronic respiratory disease patients (Spire Health, San Francisco, CA, USA). They continued regular follow-ups with their pulmonologists and received usual care according to the direction of their primary pulmonary physician for their COPD. The study compared chart data from each patient’s initial 12 months of the intervention to their own chart data 12 months prior, for a total of 24 months of chart data per patient.
Inclusion criteria for the present study included a clinical diagnosis of COPD (COPD, chronic bronchitis, obstructive lung/airways disease, or emphysema), subscribed to RPM for at least 12 months as of February 28, 2022, full electronic medical records (EMR) at the site in question for one year before and one year after the start of RPM, and at least one all-cause ER visit or unplanned hospitalization in the year prior to enrollment. All such patients were included in the analysis. Patients with less than 12 months of exposure to the intervention were not included.
The primary endpoint was unplanned, all-cause hospitalizations per subject. Secondary endpoints included unplanned cardiopulmonary hospitalizations, respective lengths of stay, ER visits, outpatient pulmonary visits, systemic corticosteroid use, adherence to RPM, and time-to-visit (RPM escalation to provider visit).
Ethical standards for patient data confidentiality were performed in compliance with the principles stated in the Declaration of Helsinki. The study was approved by the Western Institutional Review Board (00000533) and registered on ClinicalTrials.gov (NCT05518981). A waiver of informed consent was granted due to the retrospective nature of the data retrieval. All subjects separately consented to RPM as part of their enrollment.
The RPM service includes an FDA-cleared remote physiologic monitor validated for clinical accuracy17 and adherence18 among COPD patients, and currently in active use at practices around the United States. It is tailored to chronic respiratory disease patients, utilizing a system that includes three components: undergarment-adhered cardiorespiratory sensors (Figure 1), an in-home hub (Figure 2), and web-based clinical dashboard (not shown). The RPM service’s physiologic monitors are a set of six proprietary, skin-safe devices that include sensors19 for intermittent photoplethysmography for pulse, continuous respiratory force, and tri-axis accelerometers for activity. Each set lasts over 12 months and does not require recharging. They are designed to remain adhered to the inner waistband of undergarments through the washer/dryer. The proprietary respiration sensor senses the entire respiratory waveform continuously, providing granular coverage of respiratory behavior when undergarments are worn.
Figure 1 The sensors included in the RPM service are adhered to undergarments to address the burden involved in maintaining a clip-on or wrist-worn sensor, without requiring skin-adhesion.
Figure 2 The hub passively captures sensor data and securely transmits it to the cloud. It provides limited feedback to patients.
The sensors communicate wirelessly and passively with a dedicated data transmission hub, offering patients minimal feedback except for their current respiratory rate, pulse rate, duration worn, and total steps taken for the day. The hub sends data securely via Wi-Fi or a cellular connection to a cloud-based dashboard used by the clinical team.
The clinical dashboard is monitored seven days a week by clinical liaisons (CL), which vary in accreditation but in this study were comprised of respiratory therapists and registered nurses with clinical experience in pulmonary disease. The dashboard displays notifications based on sustained elevation of respiratory and pulse rates starting at 10% and 20% over rolling baselines, respectively. Notifications are also triggered when rates exceed the absolute thresholds of 35 brpm (breaths per minute) and 135 bpm (beats per minute). Each patient’s thresholds are adjusted based on input from the clinical team. Low quality physiologic data are automatically excluded by the notification algorithms.
The RPM clinical monitoring protocol includes a “risk assessment” (Table 1) phone call 24–48 hours after notification of physiologic deviation(s). Patients who fail the risk assessment were escalated to the practice for an in-person or virtual visit. Where possible, CLs scheduled patients directly by using the site’s practice management system. In months where no risk assessment calls were necessary, CLs performed a generic “monthly check-in” call to address any operational issue with the RPM service, changes in insurance, and to elicit patient-reported events.
Table 1 The Risk Assessment Script
EMR data of the period 12 months prior to and 12 months post-initiation of the intervention were coded and analyzed. Hospital notes and discharge summaries from the local hospital network were routinely imported into the EMR system as part of the practice’s care. Thus, data collection included both clinic and all available hospital notes as source documents.
EMR records were reviewed and labeled by a rater with the goal of identifying reliable information about subject, encounters, and hospitalizations. The rater was trained with the research team for over 10 hours with an additional 110 hours of practice alongside weekly review by the site pulmonologist. The rater reviewed each of the subject’s encounter records, subject correspondence notes, and physician notes chronologically to search for references to hospitalizations and ER visits.
“Hospitalizations” included all acute, unplanned hospital admissions. “Cardiopulmonary” events were determined based on the treating provider’s documentation referencing a respiratory or cardiovascular finding such as chest pain, shortness of breath, or pneumonia. Adherence to remote monitoring, captured automatically by the RPM platform, was calculated as the proportion of days during the intervention period where physiological sensors were worn for at least 8 hours.
To compare differences between pre- and post-initiation periods for the discrete variables, a paired Wilcoxon Signed Rank test (non-normal) was used. The Kolmogorov–Smirnov (KS) statistical test was used to test normality (see Appendix A for results). Because length of stay data was not available for all hospitalizations depending on the data source, sample sizes were unequal, and the Wilcoxon Rank Sum test was used to compare length of stay data. A two-sided P-value of <0.05 was considered statistically significant. Statistical analysis was conducted using the SciPy Python package (version 1.8.1, 2022).
126 subjects were identified who fit the inclusion criteria. At the time of analysis, all patients were included who met the inclusion criteria. No patients were excluded from the analysis. The baseline characteristics of the study participants are summarized in Table 2.
Table 2 Baseline Characteristics of Subjects
Healthcare Resource Utilization
The number of all-cause hospitalizations in the cohort decreased 65.0%, from 137 to 48 (see Figure 3). On a per-patient basis, there was a significant difference pre-initiation to post-initiation (1.09 ± 0.07 to 0.38 ± 0.06, P<0.001). On average, the length of stay during all-cause hospitalizations was 0.60 days shorter post-initiation but the difference was not statistically significant (5.77 ± 0.86 to 5.06 ± 1.80 days, P=0.612) (Table 3).
Table 3 Differences in Healthcare Utilization per Patient Pre- and Post-Initiation of RPM
Figure 3 Healthcare resource utilization outcomes pre- to post-initiation of RPM. Each bar is labeled with the absolute number of events.
Cardiopulmonary hospitalizations decreased 63.6%, from 88 to 32. On a per-patient basis, there was a significant difference pre-initiation to post-initiation (0.70 ± 0.08 to 0.25 ± 0.05, P<0.001). On average, the length of stay during cardiopulmonary hospitalizations was 1.28 days shorter post-initiation of RPM but the difference was not statistically significant (6.74 ± 1.40 to 5.46 ± 1.63, P=0.097). Table 4 presents greater detail on the hospitalization endpoints.
Table 4 Descriptive Statistics of Hospitalizations, All-Cause and Cardiopulmonary
All-cause ER visits decreased 44.3%, from 61 to 34. On a per-patient basis, there was a significant difference pre-initiation to post-initiation (0.48 ± 0.07 to 0.27 ± 0.09, P<0.001). Cardiopulmonary ER visits decreased 44.4%, from 36 to 20. On a per-patient basis, there was a significant difference pre-initiation to post-initiation (0.29 ± 0.05 to 0.16 ± 0.06, P=0.002).
Total outpatient pulmonary provider visits increased 13.2%, from 532 to 602. On a per-patient basis, there was a significant difference pre-initiation to post-initiation (4.22 ± 0.24 to 4.78 ± 0.28, P=0.038). The total number of prescribed steroid courses increased 3.4%, from 116 to 120. On a per-patient basis, there was a non-significant difference pre-initiation to post-initiation (0.92 ± 0.13 to 0.95 ± 0.14, P=0.589). We also observed a significant difference pre-initiation to post-initiation in the average number of steroid courses prescribed per office visit (0.17 ± 0.02 to 0.14 ± 0.02, P=0.026).
Among subjects who had at least 1 hospital admission pre-initiation of RPM, 79.4% experienced a reduction in hospitalizations, 10.8% had the same number, and 9.8% experienced an increase (Figure 4). All patients with frequent prior admissions (ie, ≥3 in the 12 months prior to enrollment) experienced a reduction after RPM was started.
Adherence, where an adherent day was one where the sensors were worn ≥8 hours per 24 hours, among patients remained high (Figure 5) with most patients being adherent ≥90% of the 12-month post-initiation period. Adherence waned over time (Figure 6) but the overall adherence per patient was 88.6% ± 1.1% of days.
Figure 5 The mean RPM adherence values of the individual participants in the cohort across all months post-initiation of RPM.
Figure 6 Boxplots showing the distribution of adherence rates to RPM for each month post-initiation. For each month, adherence is calculated as the proportion of days in which sensors were worn ≥8 hours.
Pre-initiation of RPM, there were 532 outpatient office visits to the pulmonology practice in the cohort. There were 70 more office visits during the post-initiation period; 52 office visits occurred as a direct result of an RPM escalation. Of these, the mean time-to-visit (escalation to office visit) was 2.8 ± 0.3 days.
While the study design and timing introduced limitations and biases, the study setting, magnitude of the effect, intervention novelty, and support for a proposed mechanism motivates future research. To our knowledge, this is one of the first real-world studies which assesses the impact of a disease management program leveraging longitudinal and passive cardiorespiratory RPM on healthcare resource utilization in a COPD cohort. Both hospitalization and ER visits were significantly lower during the intervention period (both all-cause and cardiopulmonary). It is also possible to see utilization shifts (ie, from hospital to office visits) that point to a justifiable mechanism of change. The study duration was adequate, at 24 months, to account for seasonal variation and to assess longer-term outcomes. An indication that the program was well-received is the sustained device adherence rate during the intervention period. By having subjects serve as their own control group in a pair-wise analysis, the pre- and post- groups were automatically well-matched. For these reasons, the reduction in hospitalization demonstrated in the intervention group appears to be clinically significant.
The study’s main finding was a significant reduction in hospitalizations associated with the initiation of RPM. The reason for this effect is likely multi-faceted. We hypothesize that RPM may have led to subjects being treated earlier for exacerbations than they may have otherwise, which has been shown to improve outcomes.7 Proactive engagement with subjects due to the identification of physiologic changes as well as frequent phone contact with subjects likely helped identify medical problems, COPD or otherwise, earlier and encouraged compliance with best practices, such as medication adherence. Finally, the contact and knowledge that they were being monitored likely helped subjects feel more connected to their provider, reducing anxiety20 and easing care-seeking in the time of clinical deterioration. All these factors, individually or together, have the potential to reduce acute care visits.
Adherence of wearable RPM devices has been a concern, particularly in chronic diseases that require long-term monitoring. Yet, the participants of this study had high adherence; despite receiving no incentive other than the prospect of improved medical care, adherence to the garment-adhered devices was high, averaging ~90% per month. We attribute this in part to the device form factor, which aims to reduce burden compared to traditional wearable devices that require regular charging and maintenance.
The majority of prior research investigating remote management of COPD falls into the larger umbrella of telemedicine and telemonitoring.21–23 These remote monitoring solutions often place a high technological burden on an aging patient population with potential barriers to care such as technology anxiety and difficulty in collecting data manually.24,25 Furthermore, the efficacy of telehealth solutions in COPD populations has yet to be fully demonstrated and has yielded mixed results.26 The reduction in hospitalizations we observed indicates that passive physiologic sensing may improve upon prior methods and we attribute this to its low patient burden, high adherence rates, and to the benefits of continuous monitoring of parameters directly relevant to AECOPDs (ie, respiration), which may allow for earlier detection of exacerbations.
However, the intervention at hand is not remote monitoring alone – it included low-burden and continuous physiologic monitoring, clinical monitoring, and escalation followed by timely response by providers. In support of this mechanism, we observed a significant increase in the number of pulmonary outpatient visits but not courses of steroids. On average, the duration from RPM escalation to provider visit was less than 3 days. This key point supports the conclusion that healthcare resource utilization was shifted from higher acuity (ie, hospital and ER) to lower acuity areas (ie, outpatient visits).
This study has several limitations. First, the behavioral changes exhibited by COPD patients during the COVID-19 pandemic may influence the number of events in both arms and in unknown directions and magnitudes. It has been reported that at a population level, fewer hospitalizations for AECOPD were observed during the initial phase of the pandemic.27,28 In this study, the treatment period began after COVID-19 “lockdown” periods, with the earliest participants starting May 15, 2020 and local, state-mandated lockdowns ending May 8, 2020. Second, subjects served as their own controls and were not randomized into the intervention. This may present a selection bias of subjects who were referred and subsequently elected to enroll and remain in the intervention. Third, as a commercially available RPM service, there were insurance coverage and expense factors that may have impacted their decision to enroll, potentially introducing further selection bias. Fourth, survivorship bias29 may have influenced the results in that only patients who remained alive and continued RPM for at least a year were included. Finally, it is possible the difference in events observed in this study could be affected in part by a regression to the mean;30 without a randomized and contemporaneous control group, the true expected counterfactual rate of acute events in the post period is unknown.
Another limitation is that the study was conducted at a single large pulmonary practice, one with the capacity to do both in-person and telemedicine visits. Additionally, the RPM program used was well-integrated into the EHR and the practice management system. These factors allowed subjects to have rapid access to the providers and outpatient visits. It is possible other clinics would be unable to match this rapidity of service. However, given the importance of early management of AECOPDs, such early medical intervention is a primary goal of care.
The results from this study are preliminary evidence of improved clinical outcomes. The shift observed to less acute care also suggests support for earlier intervention as a proposed mechanism. This research would benefit from further validation using study designs that allow for causal inference, such as a randomized controlled trial.
All-cause and cardiopulmonary hospitalization rates were significantly lower in subjects started on RPM for COPD during the year after initiation when compared to the year prior. This improvement was paralleled by reductions in all-cause and cardiopulmonary ER visits. At the same time, the number of office visits increased significantly, suggesting that RPM was redirecting care to less expensive and less burdensome settings. These results support the potential of RPM to improve the long-term management of COPD and reduce acute healthcare utilization. While the study design and timing carry limitations, including the impact of the COVID-19 pandemic, the results justify further investigation into the potential benefits of RPM for the management of COPD.
COPD, chronic obstructive pulmonary disease; AECOPD, acute exacerbation of COPD; FEV1, forced expiratory volume in the first second; RPM, remote patient monitoring; EMR, electronic medical record; CL, clinical liaison; brpm, breaths per minute; bpm, beats per minute; KS, Kolmogorov–Smirnov.
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation, or in all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
The study was funded by Spire Health.
Neema Moraveji, Ashley Hendricks, Richard Murray, and Robert K Teresi are employees of Spire Health. In addition, Neema Moraveji has a patent (system for physiological monitoring) issued to Spire Health. Richard Murray is currently the Board Chair of the Asthma and Allergy Foundation of America (AAFA). Michael Polsky receives financial support from Spire Health. Diego J Maselli reports personal fees from GSK, AstraZeneca, Sanofi/Regeneron, and Amgen, outside the submitted work. The authors have no other conflicts of interest to disclose for this work.
1. Global Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis, management and prevention of chronic obstructive lung disease; 2022. Available from: goldcopd.org/wp-content/uploads/2021/11/GOLD-REPORT-2022-v1.0-12Nov2021_WMV.pdf.
2. Anzueto A, Leimer I, Kesten S. Impact of frequency of COPD exacerbations on pulmonary function, health status and clinical outcomes. Int J Chron Obstruct Pulmon Dis. 2009;4:245–251. doi:10.2147/copd.s4862
3. Halpin DM, Decramer M, Celli B, Kesten S, Liu D, Tashkin DP. Exacerbation frequency and course of COPD. Int J Chron Obstruct Pulmon Dis. 2012;7:653–661. doi:10.2147/COPD.S34186
4. Criner GJ, Bourbeau J, Diekemper RL, et al. Prevention of acute exacerbations of COPD: American college of chest physicians and Canadian thoracic society guideline. Chest. 2015;147(4):894–942. doi:10.1378/chest.14-1676
5. Wedzicha JA, Singh R, Mackay AJ. Acute COPD exacerbations. Clin Chest Med. 2014;35(1):157–163. doi:10.1016/j.ccm.2013.11.001
6. Patel N, Kinmond K, Jones P, Birks P, Spiteri MA. Validation of COPDPredict™: unique Combination Of Remote Monitoring And Exacerbation Prediction To Support Preventative Management of COPD exacerbations. Int J Chron Obstruct Pulmon Dis. 2021;16:1887–1899. doi:10.2147/COPD.S309372
7. Wilkinson TM, Donaldson GC, Hurst JR, Seemungal TA, Wedzicha JA. Early therapy improves outcomes of exacerbations of chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2004;169(12):1298–1303. doi:10.1164/rccm.200310-1443OC
8. Ringbaek TJ, Viskum K, Lange P. Does long-term oxygen therapy reduce hospitalisation in hypoxaemic chronic obstructive pulmonary disease? Eur Respir J. 2002;20(1):38–42. doi:10.1183/09031936.02.00284202
9. Cecins N, Geelhoed E, Jenkins SC. Reduction in hospitalisation following pulmonary rehabilitation in patients with COPD. Aust Health Rev. 2008;32(3):415–422. doi:10.1071/ah080415
10. Stefan MS, Pekow PS, Priya A, et al. Association between initiation of pulmonary rehabilitation and rehospitalizations in patients hospitalized with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2021;204(9):1015–1023. doi:10.1164/rccm.202012-4389OC
11. Köhnlein T, Windisch W, Köhler D, et al. Non-invasive positive pressure ventilation for the treatment of severe stable chronic obstructive pulmonary disease: a prospective, multicentre, randomised, controlled clinical trial. Lancet Respir Med. 2014;2(9):698–705. doi:10.1016/S2213-2600(14
12. Adams R, Chavannes N, Jones K, Ostergaard MS, Price D. Exacerbations of chronic obstructive pulmonary disease--A patients’ perspective. Prim Care Respir J. 2006;15(2):102–109. doi:10.1016/j.pcrj.2006.01.003
13. Locke ER, Young JP, Battaglia C, et al. Care-seeking and delay of care during COPD exacerbations. NPJ Prim Care Respir Med. 2022;32(1):7. doi:10.1038/s41533-022-00269-9
14. Al Rajeh AM, Hurst JR. Monitoring of physiological parameters to predict exacerbations of chronic obstructive pulmonary disease (COPD): a systematic review. J Clin Med. 2016;5(12):108. doi:10.3390/jcm5120108
15. Fan KG, Mandel J, Agnihotri P, Tai-Seale M. Remote patient monitoring technologies for predicting chronic obstructive pulmonary disease exacerbations: review and comparison. JMIR Mhealth Uhealth. 2020;8(5):e16147. doi:10.2196/16147
16. Pépin J-L, Degano B, Tamisier R, Viglino D. Remote monitoring for prediction and management of acute exacerbations in chronic obstructive pulmonary disease (AECOPD). Life. 2022;12(4):499. doi:10.3390/life12040499
17. Holt M, Yule B, Jackson D, Zhu M, Moraveji N. Ambulatory monitoring of respiratory effort using a clothing-adhered biosensor.
18. Moraveji N, Golz P, Hollenbach J, Holt M, Murray R. Long-term, ambulatory respiratory monitoring of COPD patients using garment-adhered sensors.
19. Moraveji N, Holt M, Hollenbach J, Murray R, White H, Crocker M. Adherence to a garment-adhered respiratory force monitor in patients with advanced COPD.
20. Yohannes AM, Kaplan A, Hanania NA. Anxiety and depression in chronic obstructive pulmonary disease: recognition and management. Cleve Clin J Med. 2018;85(2):S11–S18. doi:10.3949/ccjm.85.s1.03
21. De San Miguel K, Smith J, Lewin G. Telehealth remote monitoring for community-dwelling older adults with chronic obstructive pulmonary disease. Telemed J E Health. 2013;19(9):652–657. doi:10.1089/tmj.2012.0244
22. Gokalp H, de Folter J, Verma V, Fursse J, Jones R, Clarke M. Integrated telehealth and telecare for monitoring frail elderly with chronic disease. Telemed J E Health. 2018;24(12):940–957. doi:10.1089/tmj.2017.0322
23. Soriano JB, García-Río F, Vázquez-Espinosa E, et al. A multicentre, randomized controlled trial of telehealth for the management of COPD. Respir Med. 2018;144:74–81. doi:10.1016/j.rmed.2018.10.008
24. Gorst SL, Armitage CJ, Brownsell S, Hawley MS. Home telehealth uptake and continued use among heart failure and chronic obstructive pulmonary disease patients: a systematic review. Ann Behav Med. 2014;48(3):323–336. doi:10.1007/s12160-014-9607-x
25. Witry M, Comellas A, Simmering J, Polgreen P. The association between technology use and health status in a chronic obstructive pulmonary disease cohort: multi-method study. J Med Internet Res. 2018;20(4):e125. doi:10.2196/jmir.9382
26. Li X, Xie Y, Zhao H, Zhang H, Yu X, Li J. Telemonitoring interventions in COPD patients: overview of Systematic Reviews. Biomed Res Int. 2020;1–9. doi:10.1155/2020/5040521
27. Alqahtani JS, Oyelade T, Aldhahir AM, et al. Reduction in hospitalised COPD exacerbations during COVID-19: a systematic review and meta-analysis. PLoS One. 2021;16:8. doi:10.1371/journal.pone.0255659
28. Trujillo C, Garnet B, Zadeh AV, Urdaneta G, Campos M. Decrease in exacerbations during the coronavirus disease 2019 pandemic in a Cohort of veterans with COPD. Int J Chron Obstruct Pulmon Di. 2021;8:572–579. doi:10.15326/jcopdf.2021.0223
29. Brown SJ, Goetzmann W, Ibbotson RG, Ross SA. Survivorship bias in performance studies. Rev Financ Stud. 1992;5(4):553–580. doi:10.1093/rfs/5.4.553
30. Stigler SM. Regression towards the mean, historically considered. Stat Methods Med Res. 1997;6(2):103–114. doi:10.1177/096228029700600202