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A Systematic Review of Healthcare Applications for Smartphones
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Background: Advanced mobile communications and portable computation are now combined in handheld devices called “smartphones”, which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category. Methods: In April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches. Results: A total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students. Conclusions: Many medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.
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![Figure 3 Smartphone U.S. Market Share Feb-2010 to May-2011[3-5]. This figure presents the market share of five major smartphone platforms (i.e. Palm Web OS, Windows Phone, BlackBerry, iOS, Android) in the United States during the period of 2010 – 2011. In the middle of 2011, Android has become the leader in smartphone market share while all other platforms have shown decreasing trend except iOS. The market share of iOS was almost consistent during this period.](/paper-figures/876942c884d24b05a911471358665106/a-systematic-review-of-healthcare-applications-for-smartphones-figure-3.jpg)
![Figure 4 Smartphone Worldwide Market Share Forecast 2015[133]. This histogram presents the worldwide market share forecast data from IDC for six smartphone OS platforms in 2015. Android is predicted to be the global market leader in smartphones acquiring almost half of the market share by 2015.](/paper-figures/22114fd036e94f0eaa87d386e5ea6a2c/a-systematic-review-of-healthcare-applications-for-smartphones-figure-4.jpg)
![Figure 5 Smartphone Worldwide Market Share Forecast 2010–2015[134]. This figure presents the market share forecast data from Gartner for five smartphone OS platforms up to 2015. Android is predicted to be the leader in smartphone market by 2015 acquiring almost half of the market share. Symbian OS will lose almost all of the market share since its vendor Nokia announced in February, 2011 to shift from Symbian OS to Windows Phone 7 [153], thus global market share for Windows Phone is forecast to gain by 2015, placing the platform in second position.](/paper-figures/09c0d68d483a4f9898878613568d2a33/a-systematic-review-of-healthcare-applications-for-smartphones-figure-5.jpg)
![Figure 6 Number of Applications in Apple’s App Store and Google’s Android Market (July’08 – Nov’11)[141-156]. This figure presents the growth rate of two major smartphone application stores: Apples’s App Store and Google’s Android Market, during the period of July, 2008 to November 2011. Both of the stores are growing very fast. According to the most recent updates, the total number of applications in Apple’s App Store is more than 425,000 as of July, 2011 [141] and in Android Market is more than 352,800 as of November, 2011 [152]. Overall, the Apple’s App Store is currently leading in terms of the application store size; however, the growth rate is much slower than Android Market.](/paper-figures/2f444a7cb69747a7b85719d9048b89a9/a-systematic-review-of-healthcare-applications-for-smartphones-figure-6.jpg)
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“Generally, health care apps have been exhaustively discussed and reviewed in the literature [58]; however, the number of papers that focus on reviewing and evaluating tinnitus-related smartphone apps is underwhelming.”
From Smartphone and mobile health apps for tinnitus: Systematic identification, analysis, and assessment (2020) · cites this paper as [58]
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Cites
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859
Studies in Health Technology and Informatics
Aphinyanaphongs, Y., & Aliferis, C. (2007) · Cited by 1,247
Text messaging as a tool for behavior change in disease prevention and management
Cole-Lewis, H., & Kershaw, T. (2010) · Epidemiologic Reviews · doi:10.1093/epirev/mxq004 · Cited by 1,190
Boulos, M. N. K., Wheeler, S., Tavares, C., & Jones, R. B. (2011) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-10-24 · Cited by 1,123
Healthcare via Cell Phones: A Systematic Review
Krishna, S., Boren, S. A., & Balas, E. A. (2009) · Telemedicine and e-Health · doi:10.1089/tmj.2008.0099 · Cited by 882
Adolphe Quetelet (1796 1874) the average man and indices of obesity
Eknoyan, G. (2007) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfm517 · Cited by 746
Recent advances: Consumer health informatics
Eysenbach, G. (2000) · BMJ · doi:10.1136/bmj.320.7251.1713 · Cited by 670
Liang, X., Wang, Q., Yang, X., Cao, J., Chen, J., Mo, X., Huang, J., Wang, L., & Gu, D. (2011) · Diabetic Medicine · doi:10.1111/j.1464-5491.2010.03180.x · Cited by 515
Wireless World: Social and Interactional Aspects of the Mobile Age
Brown, B. A. T., & Green, N. H. (2001) · Cited by 367
Research works printed but not linked: 10
- US Mobile Year in Review. [http://www2.comscore.com/l/1552/ileYearinReview2010Webinar-pdf/QOZR3]
- Knowledge on call: Physicians increasingly are discovering smartphones serve a purpose beyond being a convenient communication gadget. [http://www.ama-assn.org/amednews/2009/01/05/plus/bisa0105.pdf]
- M B: Proceedings of Ume°a’s 14th Student Conference in Computing Science. Opportunities and Challenges when Applying Mobile Technology in Health Care. 2010, UME°A, SWEDEN: UME°A UNIVERSITY, 1-12.
- Sarasohn-Kahn J: How Smartphones Are Changing Health Care for Consumers and Providers. 2010, Oakland, CA: California HealthCare Foundation,http://posterous.com/getfile/files.posterous.com/mobihealth/Pqvz1Ff7GcGuEwfNYj1WFMiuBfqUe0h2NY7fqAisAPu0okpu0fmuCGAytEI0/HowSmartPhonesChngHealthcare.pdf,
- Zhang T, Wang J, Liu P, Hou J: Fall detection by embedding an accelerometer in cellphone and using KFD algorithm. International Journal of Computer Science and Network Security: IJCSNS. 2006, 6: 277-284.
- de Buenaga M, Gachet D, Maña MJ, de la Villa M, Mata J: SIGIR 2008 Workshop on Mobile Information Retrieval. Clustering and Summarizing Medical Documents to Improve Mobile Retrieval. 2008, Singapore, 54-57.
- Mayo Clinic Proceedings. Tri JL, Severson RP, Hyberger LK, Hayes DL. 2007, Mayo Clinic, 282-285. 82nd · DOI
- Bertman J: Tech 101: a new generation of smartphones. MDNG Neurology. 2009, 11: 26-
- Hoyt RE, Cruz RW: Chapter 11: Mobile Technology. In Medical Informatics. Practical Guide for Healthcare and Information Technology Professionals. Edited by: Hoyt RE. 2010, Lulu Enterprises Inc, 199-217. 4th
- Introducing the T-Mobile G2 with Google — the First Smartphone Delivering 4G Speeds on T-Mobile’s Super-Fast HSPA+ Network. [http://www.htc.com/us/press/introducing-the-t-mobile-g2-with-google--the-first-smartphone-delivering-4g-speeds-on-t-mobiles-super-fast-hspa-network/19]
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Islam, S. M. R., Kwak, D., Kabir, M. H., Hossain, M., & Kwak, K.-S. (2015) · IEEE Access · doi:10.1109/access.2015.2437951 · Cited by 3,199
Mobile devices and apps for health care professionals: uses and benefits.
Ventola, C. L. (2014) · PubMed Central · doi:10.57945/manara.23966214 · Cited by 847
Analyzing older users’ home telehealth services acceptance behavior—applying an Extended UTAUT model
Cimperman, M., Brenčić, M. M., & Trkman, P. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.03.002 · Cited by 634
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Burke, L. E., Ma, J., Azar, K. M. J., Bennett, G. G., Peterson, E. D., Zheng, Y., Riley, W., Stephens, J., Shah, S. H., Suffoletto, B., et al. (2015) · Circulation · doi:10.1161/cir.0000000000000232 · Cited by 516
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Lehne, M., Sass, J., Essenwanger, A., Schepers, J., & Thun, S. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0158-1 · Cited by 501
Photonic crystals: emerging biosensors and their promise for point-of-care applications
Inan, H., Poyraz, M., İnci, F., Lifson, M. A., Baday, M., Cunningham, B. T., & Demirci, U. (2016) · Chemical Society Reviews · doi:10.1039/c6cs00206d · Cited by 490
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Kim, H., & Xie, B. (2017) · Patient Education and Counseling · doi:10.1016/j.pec.2017.01.015 · Cited by 496
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Ming, L. C., Hameed, M. A., Lee, D. D., Apidi, N. A., Lai, P. S. M., Hadi, M. A., Al‐Worafi, Y. M., & Khan, T. M. (2016) · Therapeutic Innovation & Regulatory Science · doi:10.1177/2168479015624732 · Cited by 412
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Junaid, S. B., Imam, A. A., Balogun, A. O., Silva, L. C. D., Surakat, Y. A., Kumar, G., Abdulkarim, M., Shuaibu, A. N., Garba, A., Sahalu, Y., et al. (2022) · Healthcare · doi:10.3390/healthcare10101940 · Cited by 398
Shull, P. B., Jirattigalachote, W., Hunt, M. A., Cutkosky, M. R., & Delp, S. L. (2014) · Gait & Posture · doi:10.1016/j.gaitpost.2014.03.189 · Cited by 384
Matthew‐Maich, N., Harris, L., Ploeg, J., Markle‐Reid, M., Valaitis, R. K., Ibrahim, S., Gafni, A., & Isaacs, S. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.5127 · Cited by 409
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Fox, G., & Connolly, R. (2018) · Information Systems Journal · doi:10.1111/isj.12179 · Cited by 344
Elazhary, H. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.10.021 · Cited by 328
Smartphones and Health Promotion: A Review of the Evidence
Bert, F., Giacometti, M., Gualano, M. R., & Siliquini, R. (2013) · Journal of Medical Systems · doi:10.1007/s10916-013-9995-7 · Cited by 314
Smartphone and tablet self management apps for asthma
Marcano Belisario, J. S., Huckvale, K., Greenfield, G., Car, J., & Gunn, L. H. (2013) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd010013.pub2 · Cited by 288
Torous, J., Friedman, R., & Keshavan, M. (2014) · JMIR mHealth and uHealth · doi:10.2196/mhealth.2994 · Cited by 287
Boruff, J. T., & Storie, D. (2014) · Journal of the Medical Library Association JMLA · doi:10.3163/1536-5050.102.1.006 · Cited by 295
Medical students: what educational resources are they using?
Wynter, L. A., Burgess, A. W., Kalman, E., Heron, J. E., & Bleasel, J. F. (2019) · BMC Medical Education · doi:10.1186/s12909-019-1462-9 · Cited by 274
Benefits of gamification in medical education
Krishnamurthy, K., Selvaraj, N., Gupta, P., Cyriac, B., Dhurairaj, P., Abdullah, A., Krishnapillai, A. D. S., Lugova, H., Haque, M., Xie, S., et al. (2022) · Clinical Anatomy · doi:10.1002/ca.23916 · Cited by 269
Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
Ahad, A., Tahir, M., Sheikh, M. A., Ahmed, K. I., Mughees, A., & Numani, A. (2020) · Sensors · doi:10.3390/s20144047 · Cited by 247
Vashist, S. K., Schneider, E. M., & Luong, J. H. T. (2014) · Diagnostics · doi:10.3390/diagnostics4030104 · Cited by 247
Dehling, T., Gao, F., Schneider, S., & Sunyaev, A. (2015) · JMIR mHealth and uHealth · doi:10.2196/mhealth.3672 · Cited by 243
Mobile healthcare applications: system design review, critical issues and challenges
Baig, M. M., GholamHosseini, H., & Connolly, M. J. (2014) · Australasian Physical & Engineering Sciences in Medicine · doi:10.1007/s13246-014-0315-4 · Cited by 250
Applications of Smartphone-Based Sensors in Agriculture: A Systematic Review of Research
Pongnumkul, S., Chaovalit, P., & Surasvadi, N. (2015) · Journal of Sensors · doi:10.1155/2015/195308 · Cited by 222
Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review
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Pandey, A., Hasan, S., Dubey, D., & Sarangi, S. (2013) · Journal of Cancer Education · doi:10.1007/s13187-012-0446-9 · Cited by 213
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Bhuyan, S. S., Lu, N., Chandak, A., Kim, H., Wyant, D., Bhatt, J., Kedia, S., & Chang, C. F. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0492-7 · Cited by 210
Mindfulness-Based Mobile Applications: Literature Review and Analysis of Current Features
Plaza, I., Demarzo, M., Herrera‐Mercadal, P., & García‐Campayo, J. (2013) · JMIR mhealth and uhealth · doi:10.2196/mhealth.2733 · Cited by 224
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Dennis, T. A., & O’Toole, L. J. (2014) · Clinical Psychological Science · doi:10.1177/2167702614522228 · Cited by 197
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Kyriakoulis, K. G., Patelarou, A. E., Laliotis, A., Wan, A. C. T., Matalliotakis, M., Tsiou, C., & Patelarou, E. (2016) · Journal of Educational Evaluation for Health Professions · doi:10.3352/jeehp.2016.13.34 · Cited by 190
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Charani, E., Castro‐Sánchez, E., Moore, L. S. P., & Holmes, A. H. (2014) · BMC Medicine · doi:10.1186/1741-7015-12-29 · Cited by 120
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Dörr, M., Lesmes, L. A., Lu, Z., & Bex, P. J. (2013) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.13-11743 · Cited by 109
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He, D., Naveed, M., Gunter, C. A., & Nahrstedt, K. (2014) · PubMed Central · Cited by 101
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Nguyen, L. H., Tran, B. X., Rocha, L. E. C., Nguyen, H. L. T., Yang, P., Latkin, C. A., Thorson, A. E., & Strömdahl, S. K. (2019) · AIDS and Behavior · doi:10.1007/s10461-019-02626-1 · Cited by 101
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Lordon, R. J., Mikles, S. P., Kneale, L., Evans, H. L., Munson, S. A., Backonja, U., & Lober, W. B. (2020) · Health Informatics Journal · doi:10.1177/1460458220928184 · Cited by 93
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Metadata
- Authors
- Mosa, A. S. M., Yoo, I., & Sheets, L.
- Affiliations
- University of Missouri (US)
- Corresponding author
- Yoo, Illhoi
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2012-07-10
- Volume
- 12
- Issue
- 1
- Pages
- 67-67
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC3534499
- OpenAlex
- W2168217134
- Cited by
- 1,184
- References
- 122
- Citations by year
- 2026: 23 · 2025: 55 · 2024: 69 · 2023: 75 · 2022: 88 · 2021: 102 · 2020: 108 · 2019: 92 · 2018: 110 · 2017: 132 · 2016: 122 · 2015: 95 · 2014: 69 · 2013: 41 · 2012: 3
- FWCI
- 10.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Food and Drug Administration
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
- Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
- Keywords
- mobile application, mobile health, healthcare workers, medical students, nursing students, disease diagnosis, drug reference, medical calculators, clinical communication, hospital information systems, medical education, patient self-management, chronic disease, patient education, remote patient monitoring, evidence-based medicine
- MeSH terms
- Medical Informatics Applications, Cell Phone
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