Skip to main content

The Need for Mobile Apps for Maternal and Child Health Care in Center and East Europe

  • Conference paper
  • First Online:
Mobile Web and Intelligent Information Systems (MobiWIS 2019)

Abstract

Mobile health services are booming in the field of maternal and child health in Europe, due to extensions in the area of electronic health and the introduction of the European Policies to increase fertility rate. There are many applications (apps) related to mother and child health in computer stores, but the exact number of mobile apps, their download volume, and the functionality of these applications are not known. the reason of this research was to investigate on the use of mobile health apps (mHealth) in Android and IOS application stores and to describe the key features of the most popular applications that provide information on maternal health and baby. The researchers searched the most popular Android app stores and the iTunes App Store in Center and East Europe. All applications related to family planning (contraception and pregnancy preparedness), pregnancy and perinatal care, neonatal care and health, as well as the development of children under six, were included in the initial analysis. Mobile maternal and child health applications with prominent features in product marketing, children’s songs, animation, and games were excluded from the study.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Infant mortality, males - European Health Information Gateway. https://gateway.euro.who.int/en/indicators/h2020_37-infant-mortality-males/visualizations/#id=26720

  2. Nicholson, W.K., et al.: The gestational diabetes management system (GooDMomS): development, feasibility and lessons learned from a patient-informed, web-based pregnancy and postpartum lifestyle intervention. BMC Pregnancy Childbirth 16 (2016). https://doi.org/10.1186/s12884-016-1064-z

  3. Pindeh, N., Suki, N.M., Suki, N.M.: User acceptance on mobile apps as an effective medium to learn Kadazandusun language. Procedia Econ. Finance 37, 372–378 (2016). https://doi.org/10.1016/S2212-5671(16)30139-3

    Article  Google Scholar 

  4. Park, B.-W., Lee, K.C.: A pilot study to analyze the effects of user experience and device characteristics on the customer satisfaction of smartphone users. In: Kim, T.-h., Adeli, H., Robles, R.J., Balitanas, M. (eds.) UCMA 2011. CCIS, vol. 151, pp. 421–427. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20998-7_50

    Chapter  Google Scholar 

  5. Cheng, L.K., et al.: Usability prioritization using performance metrics and hierarchical agglomerative clustering in MAR-learning application. In: Fujita, H., Selamat, A., Omatu, S. (eds.) New Trends in Intelligent Software Methodologies, Tools and Techniques, pp. 731–744. Ios Press, Amsterdam (2017)

    Google Scholar 

  6. Photo gallery - World Statistics Day 2016. http://www.euro.who.int/en/health-topics/Life-stages/maternal-and-newborn-health/data-and-statistics/photo-gallery-world-statistics-day-2016

  7. Infant mortality rate by country - Thematic Map – Europe. https://www.indexmundi.com/map/?t=0&v=29&r=eu&l=en

  8. The Effect of Mobile App Interventions on Influencing Healthy Maternal Behavior and Improving Perinatal Health Outcomes: Systematic Review. - PubMed – NCBI. https://www.ncbi.nlm.nih.gov/pubmed/30093368

  9. The Rise of mHealth Apps: A Market Snapshot - Liquid State. https://liquid-state.com/mhealth-apps-market-snapshot/

  10. Pavlas, J., Krejcar, O., Maresova, P., Selamat, A.: Prototypes of user interfaces for mobile applications for patients with diabetes. Computers 8 (2019). https://doi.org/10.3390/computers8010001

    Article  Google Scholar 

  11. Chan, K.L., Chen, M.: Effects of social media and mobile health apps on pregnancy care: meta-analysis. JMIR Mhealth Uhealth. 7, e11836 (2019). https://doi.org/10.2196/11836

    Article  Google Scholar 

  12. Dalton, J.A., et al.: The Health-e Babies App for antenatal education: feasibility for socially disadvantaged women. PLoS One 13, e0194337 (2018). https://doi.org/10.1371/journal.pone.0194337

    Article  Google Scholar 

  13. Mambou, S., Krejcar, O., Maresova, P., Selamat, A., Kuca, K.: Novel four stages classification of breast cancer using infrared thermal imaging and a deep learning model. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds.) IWBBIO 2019. LNCS, vol. 11466, pp. 63–74. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17935-9_7

    Chapter  Google Scholar 

  14. Mambou, S.J., Maresova, P., Krejcar, O., Selamat, A., Kuca, K.: Breast cancer detection using infrared thermal imaging and a deep learning model. Sensors (Basel) 18 (2018). https://doi.org/10.3390/s18092799

    Article  Google Scholar 

  15. Mambou, S., Maresova, P., Krejcar, O., Selamat, A., Kuca, K.: Breast cancer detection using modern visual IT techniques. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q.T. (eds.) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol. 769, pp. 397–407. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76081-0_34

    Chapter  Google Scholar 

  16. Hirst, J.E., et al.: Acceptability and user satisfaction of a smartphone-based, interactive blood glucose management system in women with gestational diabetes mellitus. J Diabetes Sci. Technol. 9, 111–115 (2015). https://doi.org/10.1177/1932296814556506

    Article  Google Scholar 

  17. Maresova, P., Klimova, B., Kuca, K.: Legislation, regulation and policies issues of orphan drugs in developed countries from 2010 to 2016. J. Appl. Biomed. 16, 175–179 (2018). https://doi.org/10.1016/j.jab.2018.04.002

    Article  Google Scholar 

  18. Zairina, E., et al.: Telehealth to improve asthma control in pregnancy: a randomized controlled trial. Respirology 21, 867–874 (2016). https://doi.org/10.1111/resp.12773

    Article  Google Scholar 

  19. Stockwell, M.S., et al.: Influenza vaccine text message reminders for urban, low-income pregnant women: a randomized controlled trial. Am. J. Public Health 104(Suppl. 1), e7–12 (2014). https://doi.org/10.2105/AJPH.2013.301620

    Article  Google Scholar 

  20. Jordan, E.T., Bushar, J.A., Kendrick, J.S., Johnson, P., Wang, J.: Encouraging influenza vaccination among Text4baby pregnant women and mothers. Am. J. Prev. Med. 49, 563–572 (2015). https://doi.org/10.1016/j.amepre.2015.04.029

    Article  Google Scholar 

  21. Yudin, M.H., et al.: Text messages for influenza vaccination among pregnant women: a randomized controlled trial. Vaccine 35, 842–848 (2017). https://doi.org/10.1016/j.vaccine.2016.12.002

    Article  Google Scholar 

  22. Nes, A.A.G., et al.: The development and feasibility of a web-based intervention with diaries and situational feedback via smartphone to support self-management in patients with diabetes type 2. Diabetes Res. Clin. Pract. 97, 385–393 (2012). https://doi.org/10.1016/j.diabres.2012.04.019

    Article  Google Scholar 

  23. Hayashi, A., et al.: Testing the feasibility and usability of a novel smartphone-based self-management support system for dialysis patients: a pilot study. JMIR Res. Protoc. 6, e63 (2017). https://doi.org/10.2196/resprot.7105

    Article  Google Scholar 

  24. Bush, J., Barlow, D.E., Echols, J., Wilkerson, J., Bellevin, K.: Impact of a mobile health application on user engagement and pregnancy outcomes among wyoming medicaid members. Telemed. J. E Health 23, 891–898 (2017). https://doi.org/10.1089/tmj.2016.0242

    Article  Google Scholar 

  25. Rehman, H., Kamal, A.K., Sayani, S., Morris, P.B., Merchant, A.T., Virani, S.S.: Using mobile health (mHealth) technology in the management of diabetes mellitus, physical inactivity, and smoking. Curr. Atherosclerosis Rep. 19, 16 (2017). https://doi.org/10.1007/s11883-017-0650-5

    Article  Google Scholar 

  26. Homko, C.J., et al.: Use of an internet-based telemedicine system to manage underserved women with gestational diabetes mellitus. Diabetes Technol. Ther. 9, 297–306 (2007). https://doi.org/10.1089/dia.2006.0034

    Article  Google Scholar 

  27. The Outcomes of Gestational Diabetes Mellitus after a Telecare Approach Are Not Inferior to Traditional Outpatient Clinic Visits. https://www.hindawi.com/journals/ije/2010/386941/

  28. Homko, C.J., et al.: Impact of a telemedicine system with automated reminders on outcomes in women with gestational diabetes mellitus. Diabetes Technol. Ther. 14, 624–629 (2012). https://doi.org/10.1089/dia.2012.0010

    Article  Google Scholar 

  29. Klímová, B., Marešová, P.: Economic methods used in health technology assessment. E a M: Ekonomie a Management 21, 116–126 (2018). https://doi.org/10.15240/tul/001/2018-1-008

    Article  Google Scholar 

  30. The EU exodus: When doctors and nurses follow the money – POLITICO. https://www.politico.eu/article/doctors-nurses-migration-health-care-crisis-workers-follow-the-money-european-commission-data/

  31. Physicians employed in Europe in 2016. Statistic. https://www.statista.com/statistics/554938/practising-physicians-employed-in-europe/

  32. Mambou, S., Krejcar, O., Kuca, K., Selamat, A.: Novel human action recognition in RGB-D videos based on powerful view invariant features technique. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q.T. (eds.) Modern Approaches for Intelligent Information and Database Systems, pp. 343–353. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76081-0_29

    Chapter  Google Scholar 

  33. Mambou, S., Krejcar, O., Kuca, K., Selamat, A.: Novel cross-view human action model recognition based on the powerful view-invariant features technique. Future Internet 10, 89 (2018). https://doi.org/10.3390/fi10090089

    Article  Google Scholar 

  34. Mambou, S., Krejcar, O., Selamat, A.: Approximate outputs of accelerated turing machines closest to their halting point. In: Nguyen, N.T., Gaol, F.L., Hong, T.-P., Trawiński, B. (eds.) ACIIDS 2019. LNCS (LNAI), vol. 11431, pp. 702–713. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14799-0_60

    Chapter  Google Scholar 

  35. Jirka, J., Prauzek, M., Krejcar, O., Kuca, K.: Automatic epilepsy detection using fractal dimensions segmentation and GP–SVM classification. Neuropsychiatric Dis. Treat. 14, 2439–2449 (2018). https://doi.org/10.2147/NDT.S167841

    Article  Google Scholar 

Download references

Acknowledgement

The work was supported by the SPEV project “Smart Solutions in Ubiquitous Computing Environments”, 2019, University of Hradec Kralove, FIM, Czech Republic.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ondrej Krejcar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mambou, S., Krejcar, O., Maresova, P., Selamat, A., Kuca, K. (2019). The Need for Mobile Apps for Maternal and Child Health Care in Center and East Europe. In: Awan, I., Younas, M., Ăśnal, P., Aleksy, M. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2019. Lecture Notes in Computer Science(), vol 11673. Springer, Cham. https://doi.org/10.1007/978-3-030-27192-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-27192-3_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27191-6

  • Online ISBN: 978-3-030-27192-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics