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Appl Clin Inform 2022; 13(03): 532-540
DOI: 10.1055/s-0042-1748857
Research Article

Veteran and Staff Experience from a Pilot Program of Health Care System–Distributed Wearable Devices and Data Sharing

Jason J. Saleem
1   Department of Industrial Engineering, J.B. Speed School of Engineering, University of Louisville, Louisville, Kentucky, United States
2   Center for Human Systems Engineering, University of Louisville, Louisville, Kentucky, United States
,
Nancy R. Wilck
3   Department of Veterans Affairs (VA), Office of Connected Care, Patient Care Services, Veterans Health Administration, Washington, District of Columbia, United States
,
John J. Murphy
3   Department of Veterans Affairs (VA), Office of Connected Care, Patient Care Services, Veterans Health Administration, Washington, District of Columbia, United States
,
Jennifer Herout
3   Department of Veterans Affairs (VA), Office of Connected Care, Patient Care Services, Veterans Health Administration, Washington, District of Columbia, United States
› Institutsangaben Funding This study was supported by the U.S. Department of Veterans Affairs, Veterans Health Administration, Office of Connected Care (IPA PO# 133C16012).
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Abstract

Objective The growing trend to use wearable devices to track activity and health data has the potential to positively impact the patient experience with their health care at home and with their care team. As part of a pilot program, the U.S. Department of Veterans Affairs (VA) distributed Fitbits to Veterans through four VA medical centers. Our objective was to assess the program from both Veterans' and clinicians' viewpoints. Specifically, we aimed to understand barriers to Fitbit setup and use for Veterans, including syncing devices with a VA mobile application (app) to share data, and assess the perceived value of the device functions and ability to share information from the Fitbit with their care team. In addition, we explored the clinicians' perspective, including how they expected to use the patient-generated health data (PGHD).

Methods We performed semi-structured interviews with 26 Veterans and 16 VA clinicians to assess the program. Responses to each question were summarized in order of frequency of occurrence across participants and audited by an independent analyst for accuracy.

Results Our findings reveal that despite setup challenges, there is support for the use of Fitbits to engage Veterans and help manage their health. Clinicians believed there were benefits for having Veterans use the Fitbits and expected to use the PGHD in a variety of ways as part of the Veterans' care plans, including monitoring progress toward health behavior goals. Veterans were overwhelmingly enthusiastic about using the Fitbits; this enthusiasm seems to extend beyond the 3 month “novelty period.”

Conclusion The pilot program for distributing Fitbits to Veterans appears to be successful from both Veterans' and clinicians' perspectives and suggests that expanded use of wearable devices should be considered. Future studies will need to carefully consider how to incorporate the PGHD into the electronic health record and clinical workflow.

Keywords

remote monitoring - wearable device - patient-generated health data - virtual care

Protection of Human and Animal Subjects

The findings reported in this publication were not derived, in whole or in part, from activities constituting research as described by VHA policy (VHA Office of Research & Development Program Guide 1200.21). Since this project was designed for VHA internal purposes only and was not intended to produce generalizable knowledge, this project does not constitute research activities that are subject to a variety of requirements and oversight by VA Office of Research Oversight and Office of Research and Development including institutional review board (IRB) approval. Although IRB approval was not required or sought, publication of the findings reported in this article has been authorized by the VHA. Privacy and confidentiality of data was maintained for all Veterans and clinicians interviewed for this project.




Publikationsverlauf

Eingereicht: 31. Dezember 2021

Angenommen: 23. März 2022

Artikel online veröffentlicht:
25. Mai 2022

© 2022. Thieme. All rights reserved.

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  • References

  • 1 Guk K, Han G, Lim J. et al. Evolution of wearable devices with real-time disease monitoring for personalized healthcare. Nanomaterials (Basel) 2019; 9 (06) E813
    CrossrefPubMedGoogle Scholar
  • 2 Kamišalić A, Fister Jr I, Turkanović M, Karakatič S. Sensors and functionalities of non-invasive wrist-wearable devices: a review. Sensors (Basel) 2018; 18 (06) E1714
    CrossrefPubMedGoogle Scholar
  • 3 Li C, Chen X, Bi X. Wearable activity trackers for promoting physical activity: a systematic meta-analytic review. Int J Med Inform 2021; 152: 104487
    CrossrefPubMedGoogle Scholar
  • 4 Ringeval M, Wagner G, Denford J, Paré G, Kitsiou S. Fitbit-based interventions for healthy lifestyle outcomes: systematic review and meta-analysis. J Med Internet Res 2020; 22 (10) e23954
    CrossrefPubMedGoogle Scholar
  • 5 Xie Z, Jo A, Hong Y-R. Electronic wearable device and physical activity among US adults: an analysis of 2019 HINTS data. Int J Med Inform 2020; 144: 104297
    CrossrefPubMedGoogle Scholar
  • 6 Huh J, Le T, Reeder B, Thompson HJ, Demiris G. Perspectives on wellness self-monitoring tools for older adults. Int J Med Inform 2013; 82 (11) 1092-1103
    CrossrefPubMedGoogle Scholar
  • 7 Ng A, Kornfield R, Schueller SM, Zalta AK, Brennan M, Reddy M. Provider perspectives on integrating sensor-captured patient-generated data in mental health care. Proc ACM Hum Comput Interact 2019; 3 (CSCW): 115
    PubMedGoogle Scholar
  • 8 Omoloja A, Vundavalli S. Patient generated health data: benefits and challenges. Curr Probl Pediatr Adolesc Health Care 2021; 51 (11) 101103
    CrossrefPubMedGoogle Scholar
  • 9 Natarajan A, Su HW, Heneghan C. Assessment of physiological signs associated with COVID-19 measured using wearable devices. NPJ Digit Med 2020; 3 (01) 156
    CrossrefPubMedGoogle Scholar
  • 10 Das SR, Kinsinger LS, Yancy Jr WS. et al. Obesity prevalence among veterans at Veterans Affairs medical facilities. Am J Prev Med 2005; 28 (03) 291-294
    CrossrefPubMedGoogle Scholar
  • 11 Nelson KM. The burden of obesity among a national probability sample of veterans. J Gen Intern Med 2006; 21 (09) 915-919
    CrossrefPubMedGoogle Scholar
  • 12 Balog A, Băjenaru L, Cristescu I. Analyzing the factors affecting the quality of IoT-based smart wearable devices using the DANP method. Stud Inform Control 2019; 28 (04) 431-442
    CrossrefPubMedGoogle Scholar
  • 13 Canhoto AI, Arp S. Exploring the factors that support adoption and sustained use of health and fitness wearables. J Mark Manage 2017; 33 (1–2): 32-60
    CrossrefPubMedGoogle Scholar
  • 14 Dargazany AR, Stegagno P, Mankodiya K. WearableDL: wearable internet-of-things and deep learning for big data analytics—concept, literature, and future. Mobile Information Systems 2018; 2018: 1-20 DOI: 10.1155/2018/8125126.
    CrossrefPubMedGoogle Scholar
  • 15 Kao Y-S, Nawata K, Huang C-Y. An exploration and confirmation of the factors influencing adoption of IoT-based wearable fitness trackers. Int J Environ Res Public Health 2019; 16 (18) 3227
    CrossrefPubMedGoogle Scholar
  • 16 Ng A, Reddy M, Zalta AK, Schueller SM. Veterans' perspectives on fitbit use in treatment for post-traumatic stress disorder: an interview study. JMIR Ment Health 2018; 5 (02) e10415
    CrossrefPubMedGoogle Scholar
  • 17 Teyhen DS, Aldag M, Centola D. et al. Incentives to create and sustain healthy behaviors: technology solutions and research needs. Mil Med 2014; 179 (12) 1419-1431
    CrossrefPubMedGoogle Scholar
  • 18 Teyhen DS, Aldag M, Centola D. et al. Key enablers to facilitate healthy behavior change: workshop summary. J Orthop Sports Phys Ther 2014; 44 (05) 378-387
    CrossrefPubMedGoogle Scholar
  • 19 Kallio H, Pietilä AM, Johnson M, Kangasniemi M. Systematic methodological review: developing a framework for a qualitative semi-structured interview guide. J Adv Nurs 2016; 72 (12) 2954-2965
    CrossrefPubMedGoogle Scholar
  • 20 Low J. Unstructured and semi-structured interviews in health research. In: Saks M, Allsop J. eds. Researching Health: Qualitative, Quantitative and Mixed Methods. 2nd ed.. London: Sage; 2013: 87-105
    Google Scholar
  • 21 Cook A, Herout J. Developing a usability ranking system for findings in health information technology products. Paper presented at: Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care. New Delhi, India: SAGE Publications Sage India; 2015: 23-28
    Google Scholar
  • 22 Read JM, Weiler DT, Satterly T, Soares C, Saleem JJ. Provider preference in exam room layout design and computing. Appl Clin Inform 2019; 10 (05) 972-980
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 23 Saleem JJ, Russ AL, Justice CF. et al. Exploring the persistence of paper with the electronic health record. Int J Med Inform 2009; 78 (09) 618-628
    CrossrefPubMedGoogle Scholar
  • 24 Saleem JJ, Savoy A, Etherton G, Herout J. Investigating the need for clinicians to use tablet computers with a newly envisioned electronic health record. Int J Med Inform 2018; 110: 25-30
    CrossrefPubMedGoogle Scholar
  • 25 Holden RJ. Physicians' beliefs about using EMR and CPOE: in pursuit of a contextualized understanding of health IT use behavior. Int J Med Inform 2010; 79 (02) 71-80
    CrossrefPubMedGoogle Scholar
  • 26 Saleem JJ, Read JM, Loehr BM. et al. Veterans' response to an automated text messaging protocol during the COVID-19 pandemic. J Am Med Inform Assoc 2020; 27 (08) 1300-1305
    CrossrefPubMedGoogle Scholar
  • 27 Shapiro M, Johnston D, Wald J, Mon D. Patient-generated health data. RTI International, April 2012
    PubMedGoogle Scholar
  • 28 Nielsen J. Enhancing the explanatory power of usability heuristics. Paper presented at: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 1994: 152-158
    PubMedGoogle Scholar
  • 29 Shin G, Feng Y, Jarrahi MH, Gafinowitz N. Beyond novelty effect: a mixed-methods exploration into the motivation for long-term activity tracker use. JAMIA Open 2018; 2 (01) 62-72
    CrossrefPubMedGoogle Scholar
  • 30 Haun JN, Chavez M, Nazi K. et al. Veterans' preferences for exchanging information using Veterans Affairs Health Information Technologies: Focus Group Results and Modeling Simulations. J Med Internet Res 2017; 19 (10) e359
    CrossrefPubMedGoogle Scholar
  • 31 Klein DM, Fix GM, Hogan TP, Simon SR, Nazi KM, Turvey CL. Use of the blue button online tool for sharing health information: qualitative interviews with patients and providers. J Med Internet Res 2015; 17 (08) e199
    CrossrefPubMedGoogle Scholar
  • 32 Turvey C, Klein D, Fix G. et al. Blue Button use by patients to access and share health record information using the Department of Veterans Affairs' online patient portal. J Am Med Inform Assoc 2014; 21 (04) 657-663
    CrossrefPubMedGoogle Scholar
  • 33 Saleem JJ, Moon B, Bross E. et al. Understanding Veteran Attitudes, Interests, and Needs around Virtual Care Applications. Paper presented at: Proceedings of the 2020 HFES 64th International Annual Meeting; 2020: 731-735
    PubMedGoogle Scholar
  • 34 Vesselkov A, Hämmäinen H, Töyli J. Design and governance of mHealth data sharing. Comm Assoc Inform Syst 2019; 45 (01) 18
    PubMedGoogle Scholar
  • 35 Hilty DM, Armstrong CM, Edwards-Stewart A, Gentry MT, Luxton DD, Krupinski EA. Sensor, wearable, and remote patient monitoring competencies for clinical care and training: scoping review. J Technol Behav Sci 2021; 6 (02) 252-277
    CrossrefPubMedGoogle Scholar
  • 36 Hilty DM, Armstrong CM, Luxton DD, Gentry MT, Krupinski EA. A scoping review of sensors, wearables, and remote monitoring for behavioral health: uses, outcomes, clinical competencies, and research directions. J Technol Behav Sci 2021; 6: 278-313
    CrossrefPubMedGoogle Scholar