×
Home Current Archive Editorial board
News Contact
Original Clinical Research

Remote Patient Monitoring Effectively Assures Continuity of Care in Asthma Patients During the COVID-19 Pandemic

By
Christopher March, BS Orcid logo ,
Christopher March, BS
Kimberly Gandy, MD, PhD ,
Kimberly Gandy, MD, PhD
Jos Domen, PhD Orcid logo ,
Jos Domen, PhD
Sayyed Hamidi, MD, MBA, MPH ,
Sayyed Hamidi, MD, MBA, MPH
Ryan Chen, BS ,
Ryan Chen, BS
Paul Barach, MD, MPH Orcid logo ,
Paul Barach, MD, MPH
Anthony Szema, MD Orcid logo
Anthony Szema, MD

Abstract

Background: Digital health tools to bridge gaps in managing infectious pandemics was a proposition grounded until recently more in the hypothetical than in reality. The last two years have exposed the extraordinary global need for robust digital solutions. Objective: The objective of this study was to determine the ability of remote patient monitoring (RPM) during the COVID-19 pandemic to improve clinical outcomes and assure continuity of care in patients with asthma. Methods and Findings: Design: The intervention combined health coaching telephone calls and remote telemonitoring. Participants: 102 patients with asthma were enrolled in a telemonitoring protocol at the beginning of the COVID-19 pandemic in the United States. Setting: A private, university affiliated, outpatient clinical adult and pediatric allergy/immunology and pulmonary practice. Intervention: Patients were enrolled with the primary rationale of maintaining continuity of care in the face of uncertain clinical care options. Enrollment and data collection proceeded in a fashion to allow detailed retrospective analysis. Telemonitoring included a pulse oximeter linked to a smart phone using the software platform Plan-it Med (PIM)®. A healthcare professional monitored data daily, and patients were contacted by providers due to vital sign abnormalities and treatment plan alterations.  Patients were encouraged to remain on the platform daily during the first three months of the pandemic. After respiratory and or clinical stability was achieved and clinic visit opportunities were resumed, patients were encouraged to maintain engagement with the platform but were not expected to use the platform daily. Main Outcome measures: Asthma Control Test (ACT) scores were recorded before and after 6 months. Paired Wilcoxon signed-rank tests (dependent groups, before vs. after) and Wilcoxon rank-sum (Mann-Whitney) tests were performed for unpaired results (independent groups, RPM vs. Control).  Results: 19 of 102 patients had physiological abnormalities detected (18.6%). Eight of these 19 patients had actionable changes in prescription regimens based on RPM findings (42.1%). In patients utilizing RPM, there was a reported decrease in shortness of breath episodes and a decreased need for rescue inhalers/nebulizer medications (P=0.005). Daily engagement in the first three months of the protocol was 61%. In a subset analysis, 48 study participants (47.1%) chose to continue to actively use the program for at least 14 months. 54 RPM patients were 99.1% compliant with RPM after 110 patient months. Of the patients that chose to discontinue the RPM program the reasons included: (1) symptom alleviation (41.7%); (2) out-of-pocket costs to patients (38.9%), and (3) difficulty using the RPM program (16.7%). Conclusions:  A novel RPM technology positively impacted continuity of care, asthma outcomes, quality of life, and self-care.

References

1.
Gates B. Shattuck lecture innovation for pandemics. 27AD;
2.
Fineberg HV. Pandemic Preparedness and Response — Lessons from the H1N1 Influenza of 2009. New England Journal of Medicine. 2014;370(14):1335–42.
3.
Whitelaw S, Mamas MA, Topol E, Van Spall HGC. Applications of digital technology in COVID-19 pandemic planning and response. The Lancet Digital Health. 2020;2(8):e435–40.
4.
Pevnick JM, Fuller G, Duncan R, Spiegel BMR. A Large-Scale Initiative Inviting Patients to Share Personal Fitness Tracker Data with Their Providers: Initial Results. PLOS ONE. 2016;11(11):e0165908.
5.
Andreu-Perez J, Leff DR, Ip HMD, Yang GZ. From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare. IEEE Transactions on Biomedical Engineering. 2015;62(12):2750–62.
6.
Ajami S, Teimouri F. Features and application of wearable biosensors in medical care. Journal of Research in Medical Sciences. 2015;20(12):1208.
7.
Wong AH, Ahmed RA, Ray JM, Khan H, Hughes PG, McCoy CE, et al. Supporting the Quadruple Aim Using Simulation and Human Factors During COVID-19 Care. American Journal of Medical Quality. 2021;36(2):73–83.
8.
Behar JA, Liu C, Kotzen K, Tsutsui K, Corino VDA, Singh J, et al. Remote health diagnosis and monitoring in the time of COVID-19. Physiological Measurement. 2020;41(10):10TR01.
9.
Dunn P, Hazzard E. Technology approaches to digital health literacy. International Journal of Cardiology. 2019;293:294–6.
10.
Su D, Michaud TL, Estabrooks P, Schwab RJ, Eiland LA, Hansen G, et al. Diabetes Management Through Remote Patient Monitoring: The Importance of Patient Activation and Engagement with the Technology. Telemedicine and e-Health. 2019;25(10):952–9.
11.
Lee PA, Greenfield G, Pappas Y. The impact of telehealth remote patient monitoring on glycemic control in type 2 diabetes: a systematic review and meta-analysis of systematic reviews of randomised controlled trials. BMC Health Services Research. 2018;18(1).
12.
Huang Z, Tao H, Meng Q, Jing L. MANAGEMENT OF ENDOCRINE DISEASE: Effects of telecare intervention on glycemic control in type 2 diabetes: a systematic review and meta-analysis of randomized controlled trials. European Journal of Endocrinology. 2015;172(3):R93–101.
13.
Medical Advisory Secretariat. Home telemonitoring for type 2 diabetes: an evidence-based analysis. Ont Health Technol Assess Ser. 2009;(24):1–38.
14.
Zhai Y kai, Zhu W jun, Cai Y ling, Sun D xu, Zhao J. Clinical- and Cost-effectiveness of Telemedicine in Type 2 Diabetes Mellitus. Medicine. 2014;93(28):e312.
15.
Saffari M, Ghanizadeh G, Koenig HG. Health education via mobile text messaging for glycemic control in adults with type 2 diabetes: A systematic review and meta-analysis. Primary Care Diabetes. 2014;8(4):275–85.
16.
Hanlon P, Daines L, Campbell C, McKinstry B, Weller D, Pinnock H. Telehealth Interventions to Support Self-Management of Long-Term Conditions: A Systematic Metareview of Diabetes, Heart Failure, Asthma, Chronic Obstructive Pulmonary Disease, and Cancer. Journal of Medical Internet Research. 2017;19(5):e172.
17.
Hale TM, Jethwani K, Kandola MS, Saldana F, Kvedar JC. A Remote Medication Monitoring System for Chronic Heart Failure Patients to Reduce Readmissions: A Two-Arm Randomized Pilot Study. Journal of Medical Internet Research. 2016;18(4):e91.
18.
Abraham WT, Adamson PB, Bourge RC, Aaron MF, Costanzo MR, Stevenson LW, et al. Wireless pulmonary artery haemodynamic monitoring in chronic heart failure: a randomised controlled trial. The Lancet. 2011;377(9766):658–66.
19.
March C.
20.
Alotaibi S, Hernandez-Montfort J, Ali OE, El-Chilali K, Perez BA. Remote monitoring of implantable cardiac devices in heart failure patients: a systematic review and meta-analysis of randomized controlled trials. Heart Failure Reviews. 2020;25(3):469–79.
21.
Givertz MM, Stevenson LW, Costanzo MR, Bourge RC, Bauman JG, Ginn G, et al. Pulmonary Artery Pressure-Guided Management of Patients With Heart Failure and Reduced Ejection Fraction. Journal of the American College of Cardiology. 2017;70(15):1875–86.
22.
Cruz J, Brooks D, Marques A. Home telemonitoring effectiveness in COPD: a systematic review. International Journal of Clinical Practice. 2014;68(3):369–78.
23.
McLean S, Nurmatov U, Liu JL, Pagliari C, Car J, Sheikh A. Telehealthcare for chronic obstructive pulmonary disease. Cochrane Database of Systematic Reviews. 2011;2012(8).
24.
Kamei T, Yamamoto Y, Kajii F, Nakayama Y, Kawakami C. Systematic review and meta‐analysis of studies involving telehome monitoring‐based telenursing for patients with chronic obstructive pulmonary disease. Japan Journal of Nursing Science. 2012;10(2):180–92.
25.
Murphy LA, Harrington P, Taylor SJ, Teljeur C, Smith SM, Pinnock H, et al. Clinical-effectiveness of self-management interventions in chronic obstructive pulmonary disease: An overview of reviews. Chronic Respiratory Disease. 2017;14(3):276–88.
26.
Shah SA, Velardo C, Farmer A, Tarassenko L. Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System. Journal of Medical Internet Research. 2017;19(3):e69.
27.
Cooper CB, Sirichana W, Arnold MT, Neufeld EV, Taylor M, Wang X, et al. <p>Remote Patient Monitoring for the Detection of COPD Exacerbations</p> International Journal of Chronic Obstructive Pulmonary Disease. 2020;Volume 15:2005–13.
28.
Mosnaim GS, Stempel DA, Gonzalez C, Adams B, BenIsrael-Olive N, Gondalia R, et al. The Impact of Patient Self-Monitoring Via Electronic Medication Monitor and Mobile App Plus Remote Clinician Feedback on Adherence to Inhaled Corticosteroids: A Randomized Controlled Trial. The Journal of Allergy and Clinical Immunology: In Practice. 2021;9(4):1586–94.
29.
Kew KM, Cates CJ. Home telemonitoring and remote feedback between clinic visits for asthma. Cochrane Database of Systematic Reviews. 2016;2016(8).
30.
Schatz M, Sorkness CA, Li JT, Marcus P, Murray JJ, Nathan RA, et al. Asthma Control Test: Reliability, validity, and responsiveness in patients not previously followed by asthma specialists. Journal of Allergy and Clinical Immunology. 2006;117(3):549–56.
31.
Nathan RA, Sorkness CA, Kosinski M, Schatz M, Li JT, Marcus P, et al. Development of the asthma control test☆A survey for assessing asthma control. Journal of Allergy and Clinical Immunology. 2004;113(1):59–65.
32.
Piotrowicz E, Baranowski R, Bilinska M, Stepnowska M, Piotrowska M, Wójcik A, et al. A new model of home‐based telemonitored cardiac rehabilitation in patients with heart failure: effectiveness, quality of life, and adherence. European Journal of Heart Failure. 2010;12(2):164–71.
33.
Majumder S, Mondal T, Deen M. Wearable Sensors for Remote Health Monitoring. Sensors. 2017;17(1):130.
34.
Crossing the quality chasm: a new health system for the 21st century. 2001;
35.
Romani G, Mas D, Massaro F, Cobianchi M, Modenese L, Barcellini M, et al. Population health strategies to support hospital and intensive care unit resiliency during the COVID-19 pandemic: the Italian experience. Popul Health Manag. 2021;(2):174–81.
36.
Piras E, Miele F. On digital intimacy: redefining provider-patient relationships in remote monitoring. Sociol Health Illn. 2019;(Suppl 1):116–31.
37.
Parretti C, Tartaglia R, Regina L, Venneri M, Sbrana F, Mandò G, et al. Improved FMEA methods for proactive health care risk assessment of the effectiveness and efficiency of COVID-19 remote patient telemonitoring. Am J Med Qual. 2022;(6):535–44.

Citation

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 

Article metrics

Google scholar: See link

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.