skip to main content
10.1145/3340496.3342758acmconferencesArticle/Chapter ViewAbstractPublication PagesfseConference Proceedingsconference-collections
research-article

Healthcare Android apps: a tale of the customers’ perspective

Published:27 August 2019Publication History

ABSTRACT

Healthcare mobile apps are becoming a reality for users interested in keeping their daily activities under control. In the last years, several researchers have investigated the effect of healthcare mobile apps on the life of their users as well as the positive/negative impact they have on the quality of life. Nonetheless, it remains still unclear how users approach and interact with the developers of those apps. Understanding whether healthcare mobile app users request different features with respect to other applications is important to estimate the alignment between the development process of healthcare apps and the requests of their users. In this study, we perform an empirical analysis aimed at (i) classifying the user reviews of healthcare open-source apps and (ii) analyzing the sentiment with which users write down user reviews of those apps. In doing so, we define a manual process that enables the creation of an extended taxonomy of healthcare users' requests. The results of our study show that users of healthcare apps are more likely to request new features and support for other hardware than users of different types of apps. Moreover, they tend to be less critical of the defects of the application and better support developers when debugging.

References

  1. {n.d.}. App Download and Usage Statistics. http://www.businessofapps.com/ data/appstatistics/. {Online; accessed 04-February-2019}.Google ScholarGoogle Scholar
  2. {n.d.}. World Health Organization. http://www.who.int/mediacentre/factsheets/ fs312/en/. {Online; accessed 28-March-2018}.Google ScholarGoogle Scholar
  3. {n.d.}. World Health Organization. http://www.who.int/publications/10yearreview/dgletter/en/. {Online; accessed 28-March-2018}.Google ScholarGoogle Scholar
  4. Rana Alkadhi, Manuel Nonnenmacher, Emitza Guzman, and Bernd Bruegge. {n.d.}. How Do Developers Discuss Rationale? ({n. d.}).Google ScholarGoogle Scholar
  5. Kevin Anderson, Oksana Burford, and Lynne Emmerton. 2016. Mobile health apps to facilitate self-care: a qualitative study of user experiences. PLoS One (2016).Google ScholarGoogle Scholar
  6. Michelle Annett and Grzegorz Kondrak. 2008. A comparison of sentiment analysis techniques: Polarizing movie blogs. In Conference of the Canadian Society for Computational Studies of Intelligence. Springer, 25–35. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Arnold Elvin Aronson. 1990.Google ScholarGoogle Scholar
  8. Clinical voice disorders: An interdisciplinary approach. Thieme New York.Google ScholarGoogle Scholar
  9. Alberto Bacchelli, Michele Lanza, and Romain Robbes. 2010.Google ScholarGoogle Scholar
  10. Linking e-mails and source code artifacts. In Proceedings of the 32Nd ACM/IEEE International Conference on Software Engineering-Volume 1. ACM, 375–384. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Jeffrey Boase and Rich Ling. 2013. Measuring mobile phone use: Self-report versus log data. Journal of Computer-Mediated Communication 18, 4 (2013), 508–519.Google ScholarGoogle ScholarCross RefCross Ref
  12. Jennifer K Carroll, Anne Moorhead, Raymond Bond, William G LeBlanc, Robert J Petrella, and Kevin Fiscella. 2017. Who uses mobile phone health apps and does use matter? A secondary data analytics approach. Journal of medical Internet research 19, 4 (2017).Google ScholarGoogle ScholarCross RefCross Ref
  13. Ning Chen, Jialiu Lin, Steven CH Hoi, Xiaokui Xiao, and Boshen Zhang. 2014. ARminer: mining informative reviews for developers from mobile app marketplace. In Proceedings of the 36th International Conference on Software Engineering. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Seryung Choo, Ju Young Kim, Se Young Jung, Sarah Kim, Jeong Eun Kim, Jong Soo Han, Sohye Kim, Jeong Hyun Kim, Jeehye Kim, Yongseok Kim, et al. 2016. Development of a weight loss mobile app linked with an accelerometer for use in the clinic: usability, acceptability, and early testing of its impact on the patient-doctor relationship. JMIR mHealth and uHealth 4, 1 (2016).Google ScholarGoogle Scholar
  15. Joseph F Faber. 1982. Life tables for the United States: 1900-2050. (1982).Google ScholarGoogle Scholar
  16. Bin Fu, Jialiu Lin, Lei Li, Christos Faloutsos, Jason Hong, and Norman Sadeh. 2013.Google ScholarGoogle Scholar
  17. Why people hate your app: Making sense of user feedback in a mobile app store. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1276–1284. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Franz-Xaver Geiger, Ivano Malavolta, Luca Pascarella, Fabio Palomba, Dario Di Nucci, and Alberto Bacchelli. 2018. A Graph-based Dataset of Commit History of Real-World Android apps. In Proceedings of the 15th International Conference on Mining Software Repositories, MSR. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Claudia Iacob and Rachel Harrison. 2013.Google ScholarGoogle Scholar
  20. Retrieving and analyzing mobile apps feature requests from online reviews. In Proceedings of the 10th Working Conference on Mining Software Repositories. IEEE Press, 41–44. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. RG Jahns and P Houck. 2013.Google ScholarGoogle Scholar
  22. Mobile Health Market Report 2013-2017.Google ScholarGoogle Scholar
  23. Research2Guidance (2013).Google ScholarGoogle Scholar
  24. Robbert Jongeling, Proshanta Sarkar, Subhajit Datta, and Alexander Serebrenik. 2017.Google ScholarGoogle Scholar
  25. On negative results when using sentiment analysis tools for software engineering research. Empirical Software Engineering 22, 5 (2017), 2543–2584. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Hammad Khalid, Emad Shihab, Meiyappan Nagappan, and Ahmed E Hassan. 2015. What do mobile app users complain about? IEEE Software (2015).Google ScholarGoogle Scholar
  27. Paul Krebs and Dustin T Duncan. 2015. Health app use among US mobile phone owners: a national survey. JMIR mHealth and uHealth 3, 4 (2015).Google ScholarGoogle Scholar
  28. Klaus Krippendorff. 2011. Computing Krippendorff ’s alpha-reliability. (2011).Google ScholarGoogle Scholar
  29. Huoran Li, Xuan Lu, Xuanzhe Liu, Tao Xie, Kaigui Bian, Felix Xiaozhu Lin, Qiaozhu Mei, and Feng Feng. 2015. Characterizing smartphone usage patterns from millions of android users. In Proceedings of the 2015 Internet Measurement Conference. ACM, 459–472. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. William Lidwell, Kritina Holden, and Jill Butler. 2010.Google ScholarGoogle Scholar
  31. Universal Principles of Design. Rockport Publishers.Google ScholarGoogle Scholar
  32. Bin Lin, Fiorella Zampetti, Gabriele Bavota, Massimiliano Di Penta, Michele Lanza, and Rocco Oliveto. 2018. Sentiment Analysis for Software Engineering: How Far Can We Go?. In Conference on Software Engineering. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Mario Linares-Vásquez, Gabriele Bavota, Massimiliano Di Penta, Rocco Oliveto, and Denys Poshyvanyk. 2014. How do api changes trigger stack overflow discussions? a study on the android sdk. In proceedings of the 22nd International Conference on Program Comprehension. ACM, 83–94. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. WR Macdonell. 1913.Google ScholarGoogle Scholar
  35. On the expectation of life in ancient Rome, and in the provinces of Hispania and Lusitania, and Africa. Biometrika 9, 3/4 (1913), 366–380.Google ScholarGoogle Scholar
  36. Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014. The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations. 55–60.Google ScholarGoogle ScholarCross RefCross Ref
  37. Vivien Marx. 2013. Biology: The big challenges of big data.Google ScholarGoogle Scholar
  38. Anita Mehta. 2012.Google ScholarGoogle Scholar
  39. Granular Matter: an interdisciplinary approach. Springer Science & Business Media.Google ScholarGoogle Scholar
  40. Chilukuri K Mohan and Dayaprasad Kulkarni. 2016.Google ScholarGoogle Scholar
  41. The role of health informatics in volunteer supported healthcare for underserved populations. In Global Humanitarian Technology Conference (GHTC), 2016. IEEE, 660–665.Google ScholarGoogle Scholar
  42. Vivian Obiodu and Emeka Obiodu. 2012.Google ScholarGoogle Scholar
  43. An empirical review of the top 500 medical apps in a European Android market. Journal of Mobile Technology in Medicine 1, 4 (2012), 22–37.Google ScholarGoogle Scholar
  44. S Jay Olshansky, Douglas J Passaro, Ronald C Hershow, Jennifer Layden, Bruce A Carnes, Jacob Brody, Leonard Hayflick, Robert N Butler, David B Allison, and David S Ludwig. 2005. A potential decline in life expectancy in the United States in the 21st century. New England Journal of Medicine 352, 11 (2005), 1138–1145.Google ScholarGoogle ScholarCross RefCross Ref
  45. Dennis Pagano and Walid Maalej. 2013.Google ScholarGoogle Scholar
  46. User feedback in the appstore: An empirical study. In Requirements Engineering Conference (RE), 2013 21st IEEE International. IEEE, 125–134.Google ScholarGoogle Scholar
  47. Fabio Palomba, Mario Linares-Vasquez, Gabriele Bavota, Rocco Oliveto, Massimiliano Di Penta, Denys Poshyvanyk, and Andrea De Lucia. 2015. User reviews matter! tracking crowdsourced reviews to support evolution of successful apps. In Software Maintenance and Evolution (ICSME), 2015 IEEE International Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Fabio Palomba, Mario Linares-Vásquez, Gabriele Bavota, Rocco Oliveto, Massimiliano Di Penta, Denys Poshyvanyk, and Andrea De Lucia. 2018. Crowdsourcing user reviews to support the evolution of mobile apps. Journal of Systems and Software 137 (2018), 143–162.Google ScholarGoogle ScholarCross RefCross Ref
  49. Fabio Palomba, Pasquale Salza, Adelina Ciurumelea, Sebastiano Panichella, Harald Gall, Filomena Ferrucci, and Andrea De Lucia. 2017.Google ScholarGoogle Scholar
  50. Recommending and localizing change requests for mobile apps based on user reviews. In Proceedings of the 39th international conference on software engineering. IEEE Press, 106–117. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Bo Pang, Lillian Lee, et al. 2008. Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval 2, 1–2 (2008), 1–135. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Sebastiano Panichella, Andrea Di Sorbo, Emitza Guzman, Corrado A Visaggio, Gerardo Canfora, and Harald C Gall. 2015. How can i improve my app? classifying user reviews for software maintenance and evolution. In Software maintenance and evolution (ICSME), 2015 IEEE international conference on. IEEE, 281–290. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Chris Parnin, Christoph Treude, Lars Grammel, and Margaret-Anne Storey. 2012.Google ScholarGoogle Scholar
  54. Crowd documentation: Exploring the coverage and the dynamics of API discussions on Stack Overflow. Georgia Institute of Technology, Tech. Rep (2012).Google ScholarGoogle Scholar
  55. Luca Pascarella. 2018. Classifying code comments in Java mobile applications. In 2018 IEEE/ACM 5th International Conference on Mobile Software Engineering and Systems (MOBILESoft). IEEE, 39–40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Luca Pascarella, Magiel Bruntink, and Alberto Bacchelli. 2019. Classifying code comments in Java software systems. Empirical Software Engineering (2019), 1–39. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Luca Pascarella, Franz-Xaver Geiger, Fabio Palomba, Dario Di Nucci, Ivano Malavolta, and Alberto Bacchelli. 2018. Self-Reported Activities of Android Developers. In International Conference on Mobile Software Engineering and Systems. Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Luca Pascarella, Davide Spadini, Fabio Palomba, Magiel Bruntink, and Alberto Bacchelli. 2018. Information needs in contemporary code review. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 135. Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Steve Pieper, Bill Lorensen, Will Schroeder, and Ron Kikinis. 2006. The NA-MIC Kit: ITK, VTK, pipelines, grids and 3D slicer as an open platform for the medical image computing community. In Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on. IEEE, 698–701.Google ScholarGoogle Scholar
  60. Preethi R Sama, Zubin J Eapen, Kevin P Weinfurt, Bimal R Shah, and Kevin A Schulman. 2014. An evaluation of mobile health application tools. JMIR mHealth and uHealth 2, 2 (2014).Google ScholarGoogle Scholar
  61. Hinrich Schütze, Christopher D Manning, and Prabhakar Raghavan. 2008.Google ScholarGoogle Scholar
  62. Introduction to information retrieval. Vol. 39. Cambridge University Press.Google ScholarGoogle Scholar
  63. Bruno MC Silva, Joel JPC Rodrigues, Isabel de la Torre Díez, Miguel López-Coronado, and Kashif Saleem. 2015. Mobile-health: A review of current state in 2015.Google ScholarGoogle Scholar
  64. Journal of biomedical informatics 56 (2015), 265–272.Google ScholarGoogle Scholar
  65. Shripad Tuljapurkar, Nan Li, and Carl Boe. 2000. A universal pattern of mortality decline in the G7 countries. Nature 405, 6788 (2000), 789.Google ScholarGoogle Scholar
  66. SM Vohra and JB Teraiya. 2013.Google ScholarGoogle Scholar
  67. A comparative study of sentiment analysis techniques. Journal JIKRCE 2, 2 (2013), 313–317.Google ScholarGoogle Scholar
  68. Lisa Whitehead and Philippa Seaton. 2016. The effectiveness of self-management mobile phone and tablet apps in long-term condition management: a systematic review. Journal of medical Internet research 18, 5 (2016).Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Healthcare Android apps: a tale of the customers’ perspective

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      WAMA 2019: Proceedings of the 3rd ACM SIGSOFT International Workshop on App Market Analytics
      August 2019
      46 pages
      ISBN:9781450368582
      DOI:10.1145/3340496

      Copyright © 2019 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 27 August 2019

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Upcoming Conference

      FSE '24

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader