Opportunities and Challenges of a Self-Management App to Support People With Spinal Cord Injury in the Prevention of Pressure Injuries: Qualitative Study
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Abstract
Background: Mobile health applications can offer tailored self-management support to individuals living with chronic health conditions. However, there are several challenges to the adoption of these technologies in practice. Co-design is a promising approach to overcoming some of these challenges by enabling the development of solutions that meet the actual needs and preferences of the relevant stakeholder groups. Objective: Taking spinal cord injury as a case in point, the overall objectives of this study were to identify the perceived benefits of a co-designed self-management app that could promote its uptake and to explore the factors that may impede adoption. Methods: We adopted a qualitative research approach guided by the Technology Acceptance Model. Data were collected through semistructured interviews with individuals with spinal cord injury (n=15) and two focus groups with health care professionals specialized in spinal cord injury (n=7, n=5). Prior to the interviews and focus groups, study participants were given time to explore the app prototype. All interviews were transcribed verbatim and analyzed using inductive thematic analysis. Results: Findings of our analysis indicate that study participants perceived the app prototype as potentially useful for supporting individuals with spinal cord injury in preventing pressure injuries. In particular, we identified three concrete use cases highlighting the benefits of the app for different audiences: (1) a companion for newly injured individuals, (2) an emergency kit and motivational support, and 3) a guide for informal caregivers and family members. We also uncovered several challenges that might impede the adoption of the self-management app in practice, including (1) challenges in motivating individuals to use the app, (2) concerns about the misuse and abuse of the app, and (3) organizational and maintenance challenges. Conclusions: This study adds to a growing body of research that investigates individuals’ adoption and nonadoption behavior regarding mobile health solutions. Building on earlier work, we make recommendations on how to address the barriers to the adoption of mobile health solutions identified by this study. In particular, there is a need to foster trust in mobile health among prospective users, including both patients and health care professionals. Moreover, increasing personal relevance of mobile health solutions through personalization may be a promising approach to promote uptake. Last but not least, organizational support also plays an instrumental role in mobile health adoption. We conclude that even though co-design is promoted as a promising approach to develop self-management tools, co-design does not guarantee adoption. More research is needed to identify the most promising strategies to promote the adoption of evidence-based mobile health solutions in practice.
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Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414 · see citing works
A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066 · see citing works
Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A'Court, C., Hinder, S., Fahy, N., Procter, R., & Shaw, S. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.8775 · Cited by 2,887 · cited 2× · see citing works
“Drawing on our findings and Greenhalgh et al’s nonadoption, abandonment, scale-up, spread, and sustainability framework [34], we propose three promising strategies to address the barriers to adoption identified by this study (Figure 2).”
A behavior model for persuasive design
Fogg, B. (2009) · Proceedings of the 4th International Conference on Persuasive Technology · doi:10.1145/1541948.1541999 · Cited by 2,328 · see citing works
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018) · Annals of Behavioral Medicine · doi:10.1007/s12160-016-9830-8 · Cited by 2,171 · see citing works
A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies
van Gemert-Pijnen, J. E., Nijland, N., van Limburg, M., Ossebaard, H. C., Kelders, S. M., Eysenbach, G., & Seydel, E. R. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1672 · Cited by 1,126 · see citing works
The emerging field of mobile health
Steinhubl, S. R., Muse, E. D., & Topol, E. J. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa3487 · Cited by 822 · see citing works
Mohr, D. C., Schueller, S. M., Montague, E., Burns, M. N., & Rashidi, P. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3077 · Cited by 510 · see citing works
Availability and quality of mobile health app privacy policies
Sunyaev, A., Dehling, T., Taylor, P. L., & Mandl, K. D. (2015) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002605 · Cited by 360 · see citing works
The privacy-personalization paradox in mHealth services acceptance of different age groups
Guo, X.-T., Zhang, X., & Sun, Y.-Q. (2016) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2015.11.001 · Cited by 318 · see citing works
Medical apps for smartphones: lack of evidence undermines quality and safety
Buijink, A. W. G., Visser, B. J., & Marshall, L. (2013) · Evidence Based Medicine · doi:10.1136/eb-2012-100885 · Cited by 303 · see citing works
Gagnon, M. P., Orruño, E., Asua, J., Abdeljelil, A. B., & Emparanza, J. I. (2011) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2011.0066 · Cited by 301 · see citing works
Kruger, E. A., Pires, M., Ngann, Y., et al. (2013) · Journal of Spinal Cord Medicine · doi:10.1179/2045772313y.0000000093 · Cited by 221 · see citing works
Reviewing the data security and privacy policies of mobile apps for depression
O'Loughlin, K., Neary, M., Adkins, E. C., & Schueller, S. M. (2019) · Internet Interventions · doi:10.1016/j.invent.2018.12.001 · Cited by 179 · see citing works
Byambasuren, O., Beller, E., & Glasziou, P. (2019) · JMIR mhealth and uhealth · doi:10.2196/13199 · Cited by 177 · see citing works
Issues in mHealth: Findings From Key Informant Interviews
Whittaker, R. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1989 · Cited by 173 · see citing works
Abelein, U., & Paech, B. (2013) · Empirical Software Engineering · doi:10.1007/s10664-013-9278-4 · Cited by 136 · see citing works
Crosby, L. E., Ware, R. E., Goldstein‐Leever, A., Walton, A. E., Joffe, N. E., Vogel, C., & Britto, M. T. (2016) · Pediatric Blood & Cancer · doi:10.1002/pbc.26177 · Cited by 111 · see citing works
Mobile Healthcare and People with Disabilities: Current State and Future Needs
Jones, M. L., Morris, J., & DeRuyter, F. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15030515 · Cited by 109 · see citing works
Creating Gameful Design in mHealth: A Participatory Co-Design Approach
Jessen, S., Mirković, J., & Ruland, C. M. (2018) · JMIR mhealth and uhealth · doi:10.2196/11579 · Cited by 102 · see citing works
Scheel‐Sailer, A., Wyss, A., Boldt, C., Post, M. W. M., & Lay, V. (2013) · Spinal Cord · doi:10.1038/sc.2013.91 · Cited by 96 · see citing works
Self-citationDevelopment of the mHealth App Trustworthiness checklist
van Haasteren, A., Gille, F., Fadda, M., & Vayena, E. (2019) · Digital Health · doi:10.1177/2055207619886463 · Cited by 95 · see citing works
Peters, D., Davis, S., Calvo, R. A., Sawyer, S. M., Smith, L., & Foster, J. M. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6994 · Cited by 92 · see citing works
Transparency of Health-Apps for Trust and Decision Making
Albrecht, U. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2981 · Cited by 90 · see citing works
Marin, J., Nixon, J., & Gorecki, C. (2013) · Spinal Cord · doi:10.1038/sc.2013.29 · Cited by 88 · see citing works
Brienza, D. M., Krishnan, S., Karg, P. E., Sowa, G., & Allegretti, A. (2017) · Spinal Cord · doi:10.1038/sc.2017.96 · Cited by 85 · see citing works
Marent, B., Henwood, F., Darking, M., & EmERGE Consortium (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.9856 · Cited by 84 · see citing works
Zahia, S., García-Zapirain, B., Sevillano, X., González, A., Kim, P. J., & Elmaghraby, A. S. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101742 · Cited by 84 · see citing works
Gabbe, B. J., & Nunn, A. K. (2016) · Injury · doi:10.1016/j.injury.2016.06.012 · Cited by 80 · see citing works
The fitness of apps: a theory-based examination of mobile fitness app usage over 5 months
Herrmann, L. K., & Kim, J. (2017) · mHealth · doi:10.21037/mhealth.2017.01.03 · Cited by 73 · see citing works
Losing Control to Data-Hungry Apps: A Mixed-Methods Approach to Mobile App Privacy
Brandtzæg, P. B., Pultier, A., & Moen, G. M. (2018) · Social Science Computer Review · doi:10.1177/0894439318777706 · Cited by 60 · see citing works
Understanding the perception towards using mHealth applications in practice
Sezgın, E., Özkan, S., & Yıldırım, S. (2016) · Information Development · doi:10.1177/0266666916684180 · Cited by 59 · see citing works
Apps and Mobile Health Technology in Rehabilitation
Ramey, L. N., Osborne, C. L., Kasitinon, D., & Juengst, S. B. (2019) · Physical Medicine and Rehabilitation Clinics of North America · doi:10.1016/j.pmr.2018.12.001 · Cited by 54 · see citing works
Revenäs, Å., Opava, C. H., Martin, C., Demmelmaier, I., Keller, C., & Åsenlöf, P. (2015) · JMIR Research Protocols · doi:10.2196/resprot.3795 · Cited by 53 · see citing works
Setiawan, I. M. A., Zhou, L., Alfikri, Z. F., Saptono, A., Fairman, A. D., Dicianno, B. E., & Parmanto, B. (2019) · JMIR Formative Research · doi:10.2196/12982 · Cited by 50 · see citing works
Amann, J., Fiordelli, M., Brach, M., Bertschy, S., Scheel-Sailer, A., & Rubinelli, S. (2020) · JMIR mHealth and uHealth · doi:10.2196/18018 · Cited by 44 · see citing works
Self-citationYu, J., Kuhn, E. R., Miller, K. E., & Taylor, K. L. (2018) · Translational Behavioral Medicine · doi:10.1093/tbm/iby014 · Cited by 43 · see citing works
Tong, H. L., Coiera, E. W., Tong, W., Wang, Y., Quiroz, J. C., Martin, P. B., & Laranjo, L. (2019) · JMIR mhealth and uhealth · doi:10.2196/12181 · Cited by 39 · see citing works
Carlson, M. E., Vigen, C. L. P., Rubayi, S., Blanche, E. I., Blanchard, J., Atkins, M. S., Bates‐Jensen, B. M., Garber, S. L., Pyatak, E. A., Díaz, J., et al. (2017) · Journal of Spinal Cord Medicine · doi:10.1080/10790268.2017.1313931 · Cited by 39 · see citing works
Development of a Self-Management App for People with Spinal Cord Injury
Mortenson, W. B., Singh, G., MacGillivray, M. K., Sadeghi, M., Mills, P. B., Adams, J. R., & Sawatzky, B. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1273-x · Cited by 36 · see citing works
Hubli, M., Zemp, R., Albisser, U., et al. (2020) · Spinal Cord · doi:10.1038/s41393-020-0522-7 · Cited by 33 · see citing works
MacGillivray, M. K., Sadeghi, M., Mills, P. B., Adams, J. R., Sawatzky, B., & Mortenson, W. B. (2019) · Journal of Spinal Cord Medicine · doi:10.1080/10790268.2019.1614343 · Cited by 25 · see citing works
Taki, S., Russell, C. G., Lymer, S., Laws, R., Campbell, K., Appleton, J., Ong, K.-L., & Denney-Wilson, E. (2019) · Frontiers in Endocrinology · doi:10.3389/fendo.2019.00397 · Cited by 24 · see citing works
Tremblay, M., Latulippe, K., Giguere, A. M. C., Provencher, V., Poulin, V., Dubé, V., Guay, M., Éthier, S., Sévigny, A., Carignan, M., et al. (2019) · JMIR Aging · doi:10.2196/12327 · Cited by 18 · see citing works
Optimizing mHealth Interventions with a Bandit
Rabbi, M., Klasnja, P., Choudhury, T., Tewari, A., & Murphy, S. A. (2019) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-31620-4_18 · Cited by 17 · see citing works
Shirai, T., Bulandres, P., Choi, J.-A., D’Ortenzio, D., Moon, N., Musselman, K. E., & Gabison, S. (2020) · Disability and Rehabilitation · doi:10.1080/09638288.2020.1771780 · Cited by 14 · see citing works
Fiordelli, M., Zanini, C., Amann, J., Scheel‐Sailer, A., Brach, M., Stucki, G., & Rubinelli, S. (2020) · JMIR mhealth and uhealth · doi:10.2196/15818 · Cited by 13 · see citing works
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Methods for Human-Centered eHealth Development: Narrative Scoping Review
Kip, H., Keizer, J., da Silva, M. C., Beerlage-de Jong, N., Köhle, N., & Kelders, S. M. (2022) · Journal of Medical Internet Research · doi:10.2196/31858 · Cited by 67 · see citing works
Methods for Human-Centered eHealth Development: Narrative Scoping Review (Preprint)
Kip, H., Keizer, J., da Silva, M. C., Beerlage-de Jong, N., Köhle, N., & Kelders, S. M. (2021) · doi:10.2196/preprints.31858 · Cited by 67 · see citing works
- Authors
- Amann, J., Fiordelli, M., Scheel-Sailer, A., Brach, M., & Rubinelli, S.
- Affiliations
- ETH Zurich (CH)
- Swiss Paraplegic Research (CH)
- University of Lucerne (CH)
- Corresponding author
- Amann, Julia
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2020-12-09
- Volume
- 8
- Issue
- 12
- Pages
- e22452
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC7758166
- OpenAlex
- W3112469528
- Cited by
- 28
- References
- 47
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 2 · 2023: 6 · 2022: 6 · 2021: 2
- FWCI
- 3.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Demography › Technology Use by Older Adults
- Keywords
- spinal cord injury, pressure ulcer prevention, self-management apps, mobile health, co-design, technology adoption, technology acceptance model, barriers to adoption, informal caregivers, family caregivers, patient motivation, personalization, organizational support
- MeSH terms
- Self-Management, Pressure Ulcer, Female, Humans, Male, Prospective Studies, Spinal Cord Injuries, Qualitative Research, Mobile Applications
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