Download PDF
Appl Clin Inform 2018; 09(04): 772-781
DOI: 10.1055/s-0038-1672138
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Factors Influencing Sustained Engagement with ECG Self-Monitoring: Perspectives from Patients and Health Care Providers

Meghan Reading
1   Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medicine, Cornell University, New York, New York, United States
,
Dawon Baik
2   School of Nursing, Columbia University, New York, New York, United States
,
Melissa Beauchemin
2   School of Nursing, Columbia University, New York, New York, United States
,
Kathleen T. Hickey
2   School of Nursing, Columbia University, New York, New York, United States
,
Jacqueline A. Merrill
2   School of Nursing, Columbia University, New York, New York, United States
3   Department of Biomedical Informatics, Columbia University, New York, New York, United States
› Author Affiliations Funding M.R. is supported by the National Institute of Nursing Research (NINR; F31NR017313) and Jonas Center for Nursing Excellence. D.B. and M.B. are supported by NINR (T32NR007969). K.T.H. is supported by NINR (R01NR014853).
Further Information

Publication History

09 May 2018

19 August 2018

Publication Date:
10 October 2018 (online)

   Permissions and Reprints

Abstract

Background Patient-generated health data (PGHD) collected digitally with mobile health (mHealth) technology has garnered recent excitement for its potential to improve precision management of chronic conditions such as atrial fibrillation (AF), a common cardiac arrhythmia. However, sustained engagement is a major barrier to collection of PGHD. Little is known about barriers to sustained engagement or strategies to intervene upon engagement through application design.

Objective This article investigates individual patient differences in sustained engagement among individuals with a history of AF who are self-monitoring using mHealth technology.

Methods This qualitative study involved patients, health care providers, and research coordinators previously involved in a randomized, controlled trial involving electrocardiogram (ECG) self-monitoring of AF. Patients were adults with a history of AF randomized to the intervention arm of this trial who self-monitored using ECG mHealth technology for 6 months. Semistructured interviews and focus groups were conducted separately with health care providers and research coordinators, engaged patients, and unengaged patients. A validated model of sustained engagement, an adapted unified theory of acceptance and use of technology (UTAUT), guided data collection, and analysis through directed content analysis.

Results We interviewed 13 patients (7 engaged, 6 unengaged), 6 providers, and 2 research coordinators. In addition to finding differences between engaged and unengaged patients within each predictor in the adapted UTAUT model (perceived ease of use, perceived usefulness, facilitating conditions), four additional factors were identified as being related to sustained engagement in this population. These are: (1) internal motivation to manage health, (2) relationship with health care provider, (3) supportive environments, and (4) feedback and guidance.

Conclusion Although it required some modification, the adapted UTAUT model was useful in understanding of the parameters of sustained engagement. The findings of this study provide initial requirement specifications for the design of applications that engage patients in this unique population of adults with AF.

Keywords

patient engagement - patient-provider communication - workflows and human interactions - smart phone - mobile platforms - general information systems and technologies in clinics - consumer health informatics

Protection of Human and Animal Subjects

This study received approval from the Institutional Review Board at Columbia University Medical Center.


Supplementary Material

 

  • References

  • 1 Bhavnani SP, Narula J, Sengupta PP. Mobile technology and the digitization of healthcare. Eur Heart J 2016; 37 (18) 1428-1438
    CrossrefPubMedGoogle Scholar
  • 2 HIMSS. Definitions of mHealth. 2012. Available at: https://www.himss.org/definitions-mhealth . Accessed August 12, 2018
    PubMedGoogle Scholar
  • 3 Silva BM, Rodrigues JJ, de la Torre Díez I, López-Coronado M, Saleem K. Mobile-health: a review of current state in 2015. J Biomed Inform 2015; 56: 265-272
    CrossrefPubMedGoogle Scholar
  • 4 Lai AM, Hsueh PS, Choi YK, Austin RR. Present and future trends in consumer health informatics and patient-generated health data. Yearb Med Inform 2017; 26 (01) 152-159
    Article in Thieme ConnectPubMedGoogle Scholar
  • 5 Woods SS, Evans NC, Frisbee KL. Integrating patient voices into health information for self-care and patient-clinician partnerships: Veterans Affairs design recommendations for patient-generated data applications. J Am Med Inform Assoc 2016; 23 (03) 491-495
    PubMedGoogle Scholar
  • 6 Lavallee DC, Chenok KE, Love RM. , et al. Incorporating patient-reported outcomes into health care to engage patients and enhance care. Health Aff (Millwood) 2016; 35 (04) 575-582
    CrossrefPubMedGoogle Scholar
  • 7 Arsoniadis EG, Tambyraja R, Khairat S. , et al. Characterizing patient-generated clinical data and associated implications for electronic health records. Stud Health Technol Inform 2015; 216: 158-162
    PubMedGoogle Scholar
  • 8 Howie L, Hirsch B, Locklear T, Abernethy AP. Assessing the value of patient-generated data to comparative effectiveness research. Health Aff (Millwood) 2014; 33 (07) 1220-1228
    CrossrefPubMedGoogle Scholar
  • 9 Shapiro M. , et al. Patient-Generated Health Data: White Paper. 2012. Office of Policy and Planning, Office of the National Coordinator for Health Information Technology, Research Triangle Park, NC
    PubMedGoogle Scholar
  • 10 Sanger PC, Hartzler A, Lordon RJ. , et al. A patient-centered system in a provider-centered world: challenges of incorporating post-discharge wound data into practice. J Am Med Inform Assoc 2016; 23 (03) 514-525
    CrossrefPubMedGoogle Scholar
  • 11 Antman EM, Loscalzo J. Precision medicine in cardiology. Nat Rev Cardiol 2016; 13 (10) 591-602
    CrossrefPubMedGoogle Scholar
  • 12 Hull S. Patient-generated health data foundation for personalized collaborative care. Comput Inform Nurs 2015; 33 (05) 177-180
    CrossrefPubMedGoogle Scholar
  • 13 CDC. Atrial Fibrillation Fact Sheet; 2015 . Available at: http://www.cdc.gov/dhdsp/data_statistics/fact_sheets/fs_atrial_fibrillation.htm . Accessed September 3, 2018
    PubMed
  • 14 Kirchhof P, Benussi S, Kotecha D. , et al. 2016 ESC guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Rev Esp Cardiol (Engl Ed) 2017; 70 (01) 50
    PubMedGoogle Scholar
  • 15 Verdino RJ. Untreated atrial fibrillation in the United States of America: understanding the barriers and treatment options. J Saudi Heart Assoc 2015; 27 (01) 44-49
    PubMedGoogle Scholar
  • 16 Simantirakis EN, Papakonstantinou PE, Chlouverakis GI. , et al. Asymptomatic versus symptomatic episodes in patients with paroxysmal atrial fibrillation via long-term monitoring with implantable loop recorders. Int J Cardiol 2017; 231: 125-130
    CrossrefPubMedGoogle Scholar
  • 17 Go AS, Hylek EM, Phillips KA. , et al. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA 2001; 285 (18) 2370-2375
    CrossrefPubMedGoogle Scholar
  • 18 Huxley RR, Lopez FL, Folsom AR. , et al. Absolute and attributable risks of atrial fibrillation in relation to optimal and borderline risk factors: the Atherosclerosis Risk in Communities (ARIC) study. Circulation 2011; 123 (14) 1501-1508
    CrossrefPubMedGoogle Scholar
  • 19 Turakhia MP, Kaiser DW. Transforming the care of atrial fibrillation with mobile health. J Interv Card Electrophysiol 2016; 47 (01) 45-50
    CrossrefPubMedGoogle Scholar
  • 20 Olgun Kucuk H, Kucuk U, Yalcin M, Isilak Z. Time to use mobile health devices to diagnose paroxysmal atrial fibrillation. Int J Cardiol 2016; 222: 1061
    CrossrefPubMedGoogle Scholar
  • 21 ONC. Conceptualizing a Data Infrastructure for the Capture, Use, and Sharing of Patient-Generated Health Data in Care Delivery and Research through 2024: Draft White Paper for a PGHD Policy Framework; 2016
    PubMed
  • 22 Glasgow RE, Christiansen SM, Kurz D. , et al. Engagement in a diabetes self-management website: usage patterns and generalizability of program use. J Med Internet Res 2011; 13 (01) e9
    CrossrefPubMedGoogle Scholar
  • 23 Mattila E, Orsama AL, Ahtinen A, Hopsu L, Leino T, Korhonen I. Personal health technologies in employee health promotion: usage activity, usefulness, and health-related outcomes in a 1-year randomized controlled trial. JMIR Mhealth Uhealth 2013; 1 (02) e16
    CrossrefPubMedGoogle Scholar
  • 24 Ford II JH, Alagoz E, Dinauer S, Johnson KA, Pe-Romashko K, Gustafson DH. Successful organizational strategies to sustain use of A-CHESS: a mobile intervention for individuals with alcohol use disorders. J Med Internet Res 2015; 17 (08) e201
    CrossrefPubMedGoogle Scholar
  • 25 Lasorsa I, D Antrassi, P, Ajčević M. , et al. Personalized support for chronic conditions. A novel approach for enhancing self-management and improving lifestyle. Appl Clin Inform 2016; 7 (03) 633-645
    Article in Thieme ConnectPubMedGoogle Scholar
  • 26 Khaneghah PA, Miguel-Cruz A, Bentley P, Liu L, Stroulia E, Ferguson-Pell M. Users' attitudes towards personal health records: a cross-sectional pilot study. Appl Clin Inform 2016; 7 (02) 573-586
    Article in Thieme ConnectPubMedGoogle Scholar
  • 27 Shimada SL, Allison JJ, Rosen AK, Feng H, Houston TK. Sustained use of patient portal features and improvements in diabetes physiological measures. J Med Internet Res 2016; 18 (07) e179
    CrossrefPubMedGoogle Scholar
  • 28 King AC, Hekler EB, Grieco LA. , et al. Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults. PLoS One 2013; 8 (04) e62613
    CrossrefPubMedGoogle Scholar
  • 29 Venkatesh V, Morris MG, David GB, Davis FD. User acceptance of information technology: toward a unified view. Manage Inf Syst Q 2003; 27 (03) 425-478
    CrossrefPubMedGoogle Scholar
  • 30 Kim S, Lee KH, Hwang H, Yoo S. Analysis of the factors influencing healthcare professionals' adoption of mobile electronic medical record (EMR) using the unified theory of acceptance and use of technology (UTAUT) in a tertiary hospital. BMC Med Inform Decis Mak 2016; 16: 12
    PubMedGoogle Scholar
  • 31 Lin B-S, Wong AM, Tseng KC. Community-based ECG monitoring system for patients with cardiovascular diseases. J Med Syst 2016; 40 (04) 80
    CrossrefPubMedGoogle Scholar
  • 32 Ma Q, Chan AH, Chen K. Personal and other factors affecting acceptance of smartphone technology by older Chinese adults. Appl Ergon 2016; 54: 62-71
    CrossrefPubMedGoogle Scholar
  • 33 Jiang Y, Sereika SM, Dabbs AD, Handler SM, Schlenk EA. Acceptance and use of mobile technology for health self-monitoring in lung transplant recipients during the first year post-transplantation. Appl Clin Inform 2016; 7 (02) 430-445
    Article in Thieme ConnectPubMedGoogle Scholar
  • 34 Sandelowski M. Whatever happened to qualitative description?. Res Nurs Health 2000; 23 (04) 334-340
    CrossrefPubMedGoogle Scholar
  • 35 Hickey KT, Hauser NR, Valente LE. , et al. A single-center randomized, controlled trial investigating the efficacy of a mHealth ECG technology intervention to improve the detection of atrial fibrillation: the iHEART study protocol. BMC Cardiovasc Disord 2016; 16: 152
    CrossrefPubMedGoogle Scholar
  • 36 Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res 2005; 15 (09) 1277-1288
    CrossrefPubMedGoogle Scholar
  • 37 Graneheim UH, Lundman B. Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Educ Today 2004; 24 (02) 105-112
    CrossrefPubMedGoogle Scholar
  • 38 Barrett PM, Komatireddy R, Haaser S. , et al. Comparison of 24-hour Holter monitoring with 14-day novel adhesive patch electrocardiographic monitoring. Am J Med 2014; 127 (01) 95.e11-95.e17
    CrossrefPubMedGoogle Scholar
  • 39 Dekker LR, Pokushalov E, Sanders P, Lindborg KA, Maus B, Pürerfellner H. Continuous cardiac monitoring around atrial fibrillation ablation: insights on clinical classifications and end points. Pacing Clin Electrophysiol 2016; 39 (08) 805-813
    CrossrefPubMedGoogle Scholar
  • 40 Steitz B, Cronin RM, Davis SE, Yan E, Jackson GP. Long-term patterns of patient portal use for pediatric patients at an academic medical center. Appl Clin Inform 2017; 8 (03) 779-793
    Article in Thieme ConnectPubMedGoogle Scholar
  • 41 Williamson RS, Cherven BO, Gilleland Marchak J. , et al. Meaningful use of an electronic personal health record (ePHR) among pediatric cancer survivors. Appl Clin Inform 2017; 8 (01) 250-264
    Article in Thieme ConnectPubMedGoogle Scholar
  • 42 Dwivedi YK, Rana NP, Jeyaraj A, Clement M, Williams MD. , et al. Re-examining the unified theory of acceptance and use of technology (UTAUT): towards a revised theoretical model. Inf Syst Front 2017; 1-16
    PubMedGoogle Scholar
  • 43 Venkatesh V, Thong JYL, Xu X. Unified theory of acceptance and use of technology: a synthesis and the road ahead. J Assoc Inf Syst 2016; 17 (05) 328-376
    PubMedGoogle Scholar
  • 44 Hermsen S, Moons J, Kerkhof P, Wiekens C, De Groot M. Determinants for sustained use of an activity tracker: observational study. JMIR Mhealth Uhealth 2017; 5 (10) e164
    CrossrefPubMedGoogle Scholar
  • 45 Sharpe EE, Karasouli E, Meyer C. Examining factors of engagement with digital interventions for weight management: rapid review. JMIR Res Protoc 2017; 6 (10) e205
    CrossrefPubMedGoogle Scholar
  • 46 Coa K, Patrick H. Baseline motivation type as a predictor of dropout in a healthy eating text messaging program. JMIR Mhealth Uhealth 2016; 4 (03) e114
    CrossrefPubMedGoogle Scholar
  • 47 Reading MJ, Merrill JA. Converging and diverging needs between patients and providers who are collecting and using patient-generated health data: an integrative review. J Am Med Inform Assoc 2018; 25 (06) 759-771
    CrossrefPubMedGoogle Scholar
  • 48 Beeler PE, Bates DW, Hug BL. Clinical decision support systems. Swiss Med Wkly 2014; 144: w14073
    Google Scholar
  • 49 O'Sullivan D, Fraccaro P, Carson E, Weller P. Decision time for clinical decision support systems. Clin Med (Lond) 2014; 14 (04) 338-341
    PubMedGoogle Scholar
  • 50 Teixeira M, Cook DA, Heale BSE, Del Fiol G. Optimization of infobutton design and implementation: a systematic review. J Biomed Inform 2017; 74: 10-19
    CrossrefPubMedGoogle Scholar
  • 51 Long J, Hulse NC, Tao C. Infobutton usage in patient portal MyHealth. AMIA Jt Summits Transl Sci Proc 2015; 2015: 112-116
    PubMedGoogle Scholar
  • 52 Gotz D, Borland D. Data-driven healthcare: challenges and opportunities for interactive visualization. IEEE Comput Graph Appl 2016; 36 (03) 90-96
    PubMedGoogle Scholar
  • 53 Tung CE, Su D, Turakhia MP, Lansberg MG. Diagnostic yield of extended cardiac patch monitoring in patients with stroke or TIA. Front Neurol 2015; 5: 266
    PubMedGoogle Scholar
  • 54 Turakhia MP, Hoang DD, Zimetbaum P. , et al. Diagnostic utility of a novel leadless arrhythmia monitoring device. Am J Cardiol 2013; 112 (04) 520-524
    CrossrefPubMedGoogle Scholar
  • 55 Cheung CC, Kerr CR, Krahn AD. Comparing 14-day adhesive patch with 24-h Holter monitoring. Future Cardiol 2014; 10 (03) 319-322
    CrossrefPubMedGoogle Scholar
  • 56 Ancker JS, Witteman HO, Hafeez B, Provencher T, Van de Graaf M, Wei E. “You Get Reminded You're a Sick Person”: personal data tracking and patients with multiple chronic conditions. J Med Internet Res 2015; 17 (08) e202
    CrossrefPubMedGoogle Scholar
  • 57 Purtzer MA, Hermansen-Kobulnicky CJ. Optimizing the benefits of self-monitoring among patients with cancer. Oncol Nurs Forum 2016; 43 (06) E218-E225
    CrossrefPubMedGoogle Scholar