Outcomes of a smartphone-based application with live health-coaching post-percutaneous coronary intervention

Kaavya Paruchuri, Phoebe Finneran, Nicholas A Marston, Emma W Healy, John Andreo Jr, Ryan Lynch, Alexander J Blood, Maeve Jones-O'Connor, Bradley Lander, Noreen Kelly, Maria T Vivaldi, Kate Traynor, Stephen Wiviott, Pradeep Natarajan, Kaavya Paruchuri, Phoebe Finneran, Nicholas A Marston, Emma W Healy, John Andreo Jr, Ryan Lynch, Alexander J Blood, Maeve Jones-O'Connor, Bradley Lander, Noreen Kelly, Maria T Vivaldi, Kate Traynor, Stephen Wiviott, Pradeep Natarajan

Abstract

Background: The interval between inpatient hospitalization for symptomatic coronary artery disease (CAD) and post-discharge office consultation is a vulnerable period for adverse events.

Methods: Content was customized on a smartphone app-based platform for hospitalized patients receiving percutaneous coronary intervention (PCI) which included education, tracking, reminders and live health coaches. We conducted a single-arm open-label pilot study of the app at two academic medical centers in a single health system, with subjects enrolled 02/2018-05/2019 and 1:3 propensity-matched historical controls from 01/2015-12/2017. To evaluate feasibility and efficacy, we assessed 30-day hospital readmission (primary), outpatient cardiovascular follow-up, and cardiac rehabilitation (CR) enrollment as recorded in the health system. Outcomes were assessed by Cox Proportional Hazards model.

Findings: 118 of 324 eligible (36·4%) 21-85 year-old patients who underwent PCI for symptomatic CAD who owned a smartphone or tablet enrolled. Mean age was 62.5 (9·7) years, 87 (73·7%) were male, 40 of 118 (33·9%) had type 2 diabetes mellitus, 68 (57·6%) enrolled underwent PCI for MI and 59 (50·0%) had previously known CAD; demographics were similar among matched historical controls. No significant difference existed in all-cause readmission within 30 days (8·5% app vs 9·6% control, ARR -1.1% absolute difference, 95% CI -7·1-4·8, p = 0·699) or 90 days (16·1% app vs 19·5% control, p = 0.394). Rates of both 90-day CR enrollment (HR 1·99, 95% CI 1·30-3·06) and 1-month cardiovascular follow up (HR 1·83, 95% CI 1·43-2·34) were greater with the app. Weekly engagement at 30- and 90-days, as measured by percentage of weeks with at least one day of completion of tasks, was mean (SD) 73·5% (33·9%) and 63·5% (40·3%). Spearman correlation analyses indicated similar engagement across age, sex, and cardiovascular risk factors.

Interpretations: A post-PCI smartphone app with live health coaches yielded similarly high engagement across demographics and safely increased attendance in cardiac rehabilitation. Larger prospective randomized controlled trials are necessary to test whether this app improves cardiovascular outcomes following PCI.

Funding: National Institutes of Health, Boston Scientific.

Clinical trial registration: NCT03416920 (https://ichgcp.net/clinical-trials-registry/NCT03416920).

Keywords: Apps; Digital health; Outcomes; Smartphone.

Conflict of interest statement

Declaration of Competing Interest The study was supported by an investigator-initiated grant to P.N. from Boston Scientific. The sponsor had no role in the design of this study, and did not have a role in the execution, analyses, interpretation of the data, or decision to submit results for publication; however, company representatives were permitted to review and comment on the manuscript prior to submission. P.N. also reports investigator-initiated grants from Apple, Amgen, AstraZeneca, and Novartis; he is a scientific advisor to Apple, Genentech, Novartis, AstraZeneca, Forsite Labs and Blackstone Life Sciences, and he reports spousal employment and equity in Vertex. S.W. reports grants from Amgen, Arena, AstraZeneca, Bristol Myers Squibb, Daiichi Sankyo, Eisai, Eli Lilly, Janssen, Merck and Sanofi-Aventis and consulting fees from ARENA, AstraZeneca, Aegerion, Allergan, Angelmed, Boehringer-Ingelheim, Boston Clinical Research Institute, Bristol Myers Squibb, Daiichi Sankyo, Eisai, Eli Lilly, Icon Clinical, Janssen, Lexicon, Merck, Servier, St Jude Medical, and Xoma. S.W. also reports personal membership in the TIMI Study Group which receives institutional research grant support through Brigham and Women's Hospital as well as spousal employment in Merck.

Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.

Figures

Fig. 1
Fig. 1
Weekly app engagement. Patients’ interaction with the app decreased gradually over time but the overall rate was stable amongst patients who engaged early (within first 30 days). Engagement peaked at 4.35 days in the first week and gradually decreased to 2.73 days by the last week of the study period. (Engagement = a day with completion of at least one task; Week = 7-day period starting from enrollment).
Fig. 2
Fig. 2
A smartphone app and longitudinal cardiovascular care. (a) Two-fold increase in attendance of cardiac rehabilitation intake and (b) two-fold increase in 1-month outpatient cardiovascular follow up in the intervention group. Error bars represent confidence intervals. (MGB = Mass General Brigham).
Fig. 3
Fig. 3
Use of a smartphone app and readmission rates post-PCI. No significant difference in (a) all-cause readmissions or (b) cardiovascular readmission to MGB facilities at either 30- or 90-days post-PCI. Error bars represent confidence interval.

References

    1. Lozano R., Naghavi M., Foreman K. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2012;380(9859):2095–2128. doi: 10.1016/S0140-6736(12)61728-0.
    1. S. Virani Salim, Alvaro Alonso, Aparicio Hugo J. Heart disease and stroke statistics—2021 update: a report from the American Heart Association. Circulation. 2021;143(8):e254–e743. doi: 10.1161/CIR.0000000000000950.
    1. O'Gara Patrick T., Kushner Frederick G., Ascheim Deborah D. 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: executive summary. Circulation. 2013;127(4):529–555. doi: 10.1161/CIR.0b013e3182742c84.
    1. Amsterdam E.A., Wenger N.K., Brindis R.G. 2014 AHA/ACC guideline for the management of patients with non–ST-elevation acute coronary syndromes: a report of the American College of Cardiology/American Heart Association task force on practice guidelines. Circulation. 2014;130(25) doi: 10.1161/CIR.0000000000000134.
    1. Heran B.S., Chen J.M., Ebrahim S. Exercise-based cardiac rehabilitation for coronary heart disease. Cochrane Database Syst Rev. 2011;(7) doi: 10.1002/14651858.CD001800.pub2.
    1. Lawler P.R., Filion K.B., Eisenberg M.J. Efficacy of exercise-based cardiac rehabilitation post–myocardial infarction: a systematic review and meta-analysis of randomized controlled trials. Am Heart J. 2011;162(4):571–584. doi: 10.1016/j.ahj.2011.07.017. e2.
    1. Anderson L., Oldridge N., Thompson D.R. Exercise-based cardiac rehabilitation for coronary heart disease: cochrane systematic review and meta-analysis. J Am Coll Cardiol. 2016;67(1):1–12. doi: 10.1016/j.jacc.2015.10.044.
    1. Johnson N., Fisher J., Nagle A., Inder K., Wiggers J. Factors associated with referral to outpatient cardiac rehabilitation services. J Cardpulm Rehabil. 2004;24(3):165–170. doi: 10.1097/00008483-200405000-00005.
    1. Aragam K.G., Dai D., Neely M.L. Gaps in referral to cardiac rehabilitation of patients undergoing percutaneous coronary intervention in the United States. J Am Coll Cardiol. 2015;65(19):2079–2088. doi: 10.1016/j.jacc.2015.02.063.
    1. Sukul D., Seth M., Barnes G.D. Cardiac Rehabilitation use after percutaneous coronary intervention. J Am Coll Cardiol. 2019;73(24):3148–3152. doi: 10.1016/j.jacc.2019.03.515.
    1. Sérvio T.C., Britto R.R., de Melo Ghisi G.L. Barriers to cardiac rehabilitation delivery in a low-resource setting from the perspective of healthcare administrators, rehabilitation providers, and cardiac patients. BMC Health Serv Res. 2019;19 doi: 10.1186/s12913-019-4463-9.
    1. Dahhan A., Maddox W.R., Sharma G.K. The gaps in cardiac rehabilitation referral: the elephant in the room. J Am Coll Cardiol. 2015;66(22):2574. doi: 10.1016/j.jacc.2015.06.1359.
    1. Scherrenberg M., Wilhelm M., Hansen D. The future is now: a call for action for cardiac tele-rehabilitation in the COVID-19 pandemic from the secondary prevention and rehabilitation section of the European Association of Preventive Cardiology. Eur J Prev Cardiol. 2020 doi: 10.1177/2047487320939671. Published online July 2.
    1. Keesara S., Jonas A., Schulman K. Covid-19 and Health Care's Digital Revolution. N Engl J Med. 2020;382(23):e82. doi: 10.1056/NEJMp2005835.
    1. Aspry K., Wu W.-.C., Salmoirago-Blotcher E. Cardiac rehabilitation in patients with established atherosclerotic vascular disease: new directions in the era of value-based healthcare. Curr Atheroscler Rep. 2016;18(2):10. doi: 10.1007/s11883-016-0561-x.
    1. Dalal H.M., Zawada A., Jolly K., Moxham T., Taylor R.S. Home based versus centre based cardiac rehabilitation: cochrane systematic review and meta-analysis. BMJ. 2010:340. doi: 10.1136/bmj.b5631.
    1. Frederix I., Caiani E.G., Dendale P. ESC e-cardiology working group position paper: overcoming challenges in digital health implementation in cardiovascular medicine. Eur J Prev Cardiol. 2019;26(11):1166–1177. doi: 10.1177/2047487319832394.
    1. Schmid J.-.P. Telehealth during COVID-19 pandemic: will the future last? Eur J Prev Cardiol. 2020:zwaa016. doi: 10.1093/eurjpc/zwaa016. Published online September 27.
    1. Hamilton S.J., Mills B., Birch E.M., Thompson S.C. Smartphones in the secondary prevention of cardiovascular disease: a systematic review. BMC Cardiovasc Disord. 2018;18(1):25. doi: 10.1186/s12872-018-0764-x.
    1. Xu L., Li F., Zhou C., Li J., Hong C., Tong Q. The effect of mobile applications for improving adherence in cardiac rehabilitation: a systematic review and meta-analysis. BMC Cardiovasc Disord. 2019;19 doi: 10.1186/s12872-019-1149-5.
    1. Su J.J., Yu D.S.F., Paguio J.T. Effect of eHealth cardiac rehabilitation on health outcomes of coronary heart disease patients: a systematic review and meta-analysis. J Adv Nurs. 2020;76(3):754–772. doi: 10.1111/jan.14272.
    1. Lunde P., Bye A., Bergland A., Nilsson B.B. Effects of individualized follow-up with a smartphone-application after cardiac rehabilitation: protocol of a randomized controlled trial. BMC Sports Sci Med Rehabil. 2019;11 doi: 10.1186/s13102-019-0148-2.
    1. Yudi M.B., Clark D.J., Tsang D. SMARTphone-based, early cardiac REHABilitation in patients with acute coronary syndromes [SMART-REHAB Trial]: a randomized controlled trial protocol. BMC Cardiovasc Disord. 2016;16(1) doi: 10.1186/s12872-016-0356-6.
    1. Gonzalez M., Sjölin I., Bäck M. Effect of a lifestyle-focused electronic patient support application for improving risk factor management, self-rated health, and prognosis in post-myocardial infarction patients: study protocol for a multi-center randomized controlled trial. Trials. 2019;20 doi: 10.1186/s13063-018-3118-1.
    1. Pirruccello James P., Traynor Kathleen, Aragam Krishna G. “Road Map” to Improving enrollment in cardiac rehabilitation: identifying barriers and evaluating alternatives. J Am Heart Assoc. 2017;6(10) doi: 10.1161/JAHA.117.007468.
    1. Ho D.E., Imai K., King G., Stuart E.A. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal. 2007;15(3):199–236. doi: 10.1093/pan/mpl013.
    1. Zhang Z., Kim H.J., Lonjon G., Zhu Y. Balance diagnostics after propensity score matching. Ann Transl Med. 2019;7(1) doi: 10.21037/atm.2018.12.10.
    1. Forman D.E., LaFond K., Panch T., Allsup K., Manning K., Sattelmair J. Utility and efficacy of a smartphone application to enhance the learning and behavior goals of traditional cardiac rehabilitation: a feasibility study. J Cardiopulm Rehabil Prev. 2014;34(5):327–334. doi: 10.1097/HCR.0000000000000058.
    1. Varnfield M., Karunanithi M., Lee C.-.K. Smartphone-based home care model improved use of cardiac rehabilitation in postmyocardial infarction patients: results from a randomized controlled trial. Heart. 2014;100(22):1770–1779. doi: 10.1136/heartjnl-2014-305783.
    1. Fuller T.E., Pong D.D., Piniella N. Interactive digital health tools to engage patients and caregivers in discharge preparation: implementation study. J Med Internet Res. 2020;22(4) doi: 10.2196/15573.
    1. Williamson C., Kelly P., Niven A., Graham Baker P, Mutrie N. Get the message? A scoping review of physical activity messaging. Int J Behav Nutr Phys Act. 2020;17 doi: 10.1186/s12966-020-00954-3.
    1. Salvi D., Ottaviano M., Muuraiskangas S. An m-Health system for education and motivation in cardiac rehabilitation: the experience of HeartCycle guided exercise. J Telemed Telecare. 2018;24(4):303–316. doi: 10.1177/1357633X17697501.
    1. DeSmet A., De Bourdeaudhuij I., Chastin S., Crombez G., Maddison R., Cardon G. Adults’ preferences for behavior change techniques and engagement features in a mobile app to promote 24-hour movement behaviors: cross-sectional survey study. JMIR MHealth UHealth. 2019;7(12):e15707. doi: 10.2196/15707.
    1. Harzand A., Witbrodt B., Davis-Watts M.L. Feasibility of a smartphone-enabled cardiac rehabilitation program in male veterans with previous clinical evidence of coronary heart disease. Am J Cardiol. 2018;122(9):1471–1476. doi: 10.1016/j.amjcard.2018.07.028.
    1. McConnell M.V., Shcherbina A., Pavlovic A. Feasibility of Obtaining Measures of Lifestyle From a Smartphone App: the MyHeart Counts Cardiovascular Health Study. JAMA Cardiol. 2017;2(1):67–76. doi: 10.1001/jamacardio.2016.4395.
    1. Shcherbina A., Hershman S.G., Lazzeroni L. The effect of digital physical activity interventions on daily step count: a randomized controlled crossover sub-study of the MyHeart counts cardiovascular health study. Lancet Digit Health. 2019;1(7):e344–e352. doi: 10.1016/S2589-7500(19)30129-3.
    1. Bostrom J., Sweeney G., Whiteson J., Dodson J.A. Mobile health and cardiac rehabilitation in older adults. Clin Cardiol. 2019;43(2):118–126. doi: 10.1002/clc.23306.
    1. Kumar K.R., Pina I.L. Cardiac rehabilitation in older adults: new options. Clin Cardiol. 2019;43(2):163–170. doi: 10.1002/clc.23296.
    1. Jaana M., Paré G. Comparison of Mobile Health Technology Use for Self-Tracking Between Older Adults and the General Adult Population in Canada: cross-Sectional Survey. JMIR MHealth UHealth. 2020;8(11):e24718. doi: 10.2196/24718.
    1. Richard E., Moll van Charante E.P., Hoevenaar-Blom M.P. Healthy ageing through internet counselling in the elderly (HATICE): a multinational, randomised controlled trial. Lancet Digit Health. 2019;1(8):e424–e434. doi: 10.1016/S2589-7500(19)30153-0.
    1. Chow C.K., Redfern J., Hillis G.S. Effect of lifestyle-focused text messaging on risk factor modification in patients with coronary heart disease: a randomized clinical trial. JAMA. 2015;314(12):1255–1263. doi: 10.1001/jama.2015.10945.
    1. Quinn C.C., Shardell M.D., Terrin M.L., Barr E.A., Ballew S.H., Gruber-Baldini A.L. Cluster-randomized trial of a mobile phone personalized behavioral intervention for blood glucose control. Diabetes Care. 2011;34(9):1934–42. doi:10.2337/dc11-0366
    1. Gremaud Allene L., Carr Lucas J., Simmering Jacob E. Gamifying accelerometer use increases physical activity levels of sedentary office workers. J Am Heart Assoc. 2018;7(13) doi: 10.1161/JAHA.117.007735.
    1. Eberly L.A., Khatana S.A.M., Nathan A.S. Telemedicine outpatient cardiovascular care during the COVID-19 pandemic: bridging or opening the digital divide? Circulation. 2020;142(5):510–512. doi: 10.1161/CIRCULATIONAHA.120.048185.
    1. Narla A., Paruchuri K., Natarajan P. Digital health for primary prevention of cardiovascular disease: promise to practice. Cardiovasc Digit Health J. 2020;1(2):59–61. doi: 10.1016/j.cvdhj.2020.09.002.

Source: PubMed

3
Abonnere