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Mobile Health from Developers’ Perspective

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Abstract

This paper extends an initial investigation of eHealth from the developers’ perspective. In this extension, our focus is on mobile health data. Despite the significant potential of this development area, few studies try to understand the challenges faced by these professionals. This perspective is relevant to identify the most used technologies and future perspectives for research investigation. Using a KDD-based process, this work analyzed eHealth and mHealth discussions from Stack Overflow (SO) to comprehend this developers’ community. We got and processed 6082 eHealth and 1832 mHealth questions. The most discussed topics include manipulating medical images, electronic health records with the HL7 standard, and frameworks to support mobile health (mHealth) development. Concerning the challenges faced by these developers, there is a lack of understanding of the DICOM and HL7 standards, the absence of data repositories for testing, and the monitoring of health data in the background using mobile and wearable devices. Our results also indicate that discussions have grown mainly on mHealth, primarily due to monitoring health data through wearables and about how to optimize resource consumption during health-monitoring.

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Availability of data and material

All data used are available on the internet to ensure the study reproducibility and future in-depth analysis. Queries: data.stackexchange.com/users/32389 First dataset: bit.ly/3cHPEPL Second dataset: bit.ly/3MmAX64LDA and N-gram notebook: github.com/pedroalmir/trends-ehealth Higher resolution images: bit.ly/3lhKhvP.

Notes

  1. We are social Report: https://datareportal.com/reports/digital-2022-global-overview-report.

  2. Stack Overflow website: https://stackoverflow.com/company.

  3. Stack Exchange website: http://data.stackexchange.com.

  4. Stack Overflow Survey: https://insights.stackoverflow.com/survey/2019.

  5. MeSH: ncbi.nlm.nih.gov/mesh.

  6. SO tag search system: https://stackoverflow.com/tags.

  7. Tableau website: https://www.tableau.com.

  8. LDAvis repository: https://github.com/cpsievert/LDAvis.

  9. Flutter website: https://flutter.dev/.

References

  1. Eysenbach G. What is e-health? J Med Internet Res. 2001;3(2):20.

    Article  Google Scholar 

  2. Black AD, Car J, Pagliari C, Anandan C, Cresswell K, Bokun T, McKinstry B, Procter R, Majeed A, Sheikh A. The impact of ehealth on the quality and safety of health care: a systematic overview. PLoS Med. 2011;8(1).

  3. Andrade R, Carvalho R, de Araújo I, Oliveira K, Maia M. What changes from ubiquitous computing to internet of things in interaction evaluation? In: International Conference on Distributed, Ambient, and Pervasive Interactions, 2017;3–21. Springer.

  4. Masood A, Sheng B, Li P, Hou X, Wei X, Qin J, Feng D. Computer-assisted decision support system in pulmonary cancer detection and stage classification on ct images. J Biomed Inform. 2018;79:117–28.

    Article  Google Scholar 

  5. Peterson KJ, Jiang G, Liu H. A corpus-driven standardization framework for encoding clinical problems with hl7 fhir. Jo Biomed Inform. 2020;103541.

  6. Almeida RL, Macedo AA, de Araújo ÍL, Aguilar PA, Andrade RM:.Watchalert: Uma evolução do aplicativo falert para detecção de quedas em smartwatches. In: Anais Estendidos do XXII Simpósio Brasileiro de Sistemas Multimídia e Web, 2016;124–127. SBC

  7. de Araujo FHD, Santana AM, dos Santos N. PdA: Evaluation of classifiers based on decision tree for learning medical claim process. IEEE Latin Am Transa. 2015;13(1):299–306.

    Article  Google Scholar 

  8. Araújo FH, Santana AM. Neto, PdAS: using machine learning to support healthcare professionals in making preauthorisation decisions. Int J Med Inform. 2016;94:1–7.

    Article  Google Scholar 

  9. Gaddi A, Capello F, Manca M. eHealth. Care and Quality of Life. London: Springer; 2013.

    Google Scholar 

  10. Ponzanelli L, Bacchelli A, Lanza M. Seahawk: Stack overflow in the ide. In: 35th Int. Conf. on Soft. Engineering (ICSE). 2013;1295–1298.

  11. Silva R, Roy C, Rahman M, Schneider K, Paixao K, Maia M. Recommending comprehensive solutions for programming tasks by mining crowd knowledge. In: 2019 IEEE/ACM 27th Int. Conf. on Program Comprehension, 2019;358–368. IEEE

  12. Barua A, Thomas SW, Hassan AE. What are developers talking about? An analysis of topics and trends in stack overflow. Emp Softw Eng. 2014;19(3):619–54.

    Article  Google Scholar 

  13. Kitchenham BA, Budgen D, Brereton P. Evidence-based Software Engineering and Systematic Reviews vol. 4. CRC press 2015.

  14. Treude C, Barzilay O, Storey M-A. How do programmers ask and answer questions on the web?: Nier track. In: 2011 33rd International Conference on Software Engineering (ICSE), 2011;804–807. IEEE.

  15. Beyer S, Macho C, Di Penta M, Pinzger M. What kind of questions do developers ask on stack overflow? a comparison of automated approaches to classify posts into question categories. Emp Softw Eng. 2019;1–44.

  16. Pérez-López R, Blanco G, Fdez-Riverola F, Lourenço A. The activity of bioinformatics developers and users in stack overflow. In: International Conference on Practical Applications of Computational Biology & Bioinformatics, 2020;23–31. Springer.

  17. Chen H, Coogle J, Damevski K. Modeling stack overflow tags and topics as a hierarchy of concepts. J Syst Softw. 2019;156:283–99.

    Article  Google Scholar 

  18. Ragkhitwetsagul C, Krinke J, Paixao M, Bianco G, Oliveto R. Toxic code snippets on stack overflow. IEEE Trans Softw Eng. 2019.

  19. Wu Y, Wang S, Bezemer C-P, Inoue K. How do developers utilize source code from stack overflow? Emp Softw Eng. 2019;24(2):637–73.

    Article  Google Scholar 

  20. Culotta A. Towards detecting influenza epidemics by analyzing twitter messages. In: Proceedings of the First Workshop on Social Media Analytics, 2010;115–122. ACM.

  21. Paul MJ, Dredze M. Discovering health topics in social media using topic models. PloS One. 2014;9(8): 103408.

    Article  Google Scholar 

  22. Nguyen T, Nguyen DT, Larsen ME, O’Dea B, Yearwood J, Phung D, Venkatesh S, Christensen H. Prediction of population health indices from social media using kernel-based textual and temporal features. In: Proceedings of the 26th International Conference on World Wide Web Companion, 2017;99–107. International World Wide Web Conferences Steering Committee.

  23. Kwon J, Grady C, Feliciano JT, Fodeh SJ. Defining facets of social distancing during the covid-19 pandemic: Twitter analysis. medRxiv 2020.

  24. Chiarini G, Ray P, Akter S, Masella C, Ganz A. mhealth technologies for chronic diseases and elders: a systematic review. IEEE J Select Areas Commun. 2013;31(9):6–18.

    Article  Google Scholar 

  25. Gagnon M-P, Ngangue P, Payne-Gagnon J, Desmartis M. m-health adoption by healthcare professionals: a systematic review. J Am Med Inform Assoc. 2015;23(1):212–20.

    Article  Google Scholar 

  26. Robbins TD, Keung SNLC, Arvanitis TN. E-health for active ageing; a systematic review. Maturitas. 2018;114:34–40.

    Article  Google Scholar 

  27. Paiva JO, Andrade RM, de Oliveira PAM, Duarte P, Santos IS. Evangelista, ALdP, Theophilo, RL, de Andrade, LOM, Barreto, ICdH: Mobile applications for elderly healthcare: A systematic mapping. PloS One. 2020;15(7):0236091.

    Article  Google Scholar 

  28. Oliveira P, Costa Junior E, Santos IDS, Andrade R, Santos Neto PdA. Ten years of ehealth discussions on stack overflow. In: International Conference on Health Informatics (HEALTHINF1’22) 2022.

  29. Ullah M, Fiedler M, Wac K. On the ambiguity of quality of service and quality of experience requirements for ehealth services. In: 2012 6th International Symposium on Medical Information and Communication Technology (ISMICT), 2012;1–4. IEEE

  30. Sahama T, Simpson L, Lane B. Security and privacy in ehealth: is it possible? In: 15th International Conference on e-Health Networking, Applications and Services (Healthcom), 2013;249–253. IEEE.

  31. Farahani B, Firouzi F, Chang V, Badaroglu M, Constant N, Mankodiya K. Towards fog-driven iot ehealth: promises and challenges of iot in medicine and healthcare. Fut Gen Comput Syst. 2018;78:659–76.

    Article  Google Scholar 

  32. Bostrom J, Sweeney G, Whiteson J, Dodson JA. Mobile health and cardiac rehabilitation in older adults. Clini Cardiol. 2020;43(2):118–26.

    Article  Google Scholar 

  33. Qudah B, Luetsch K. The influence of mobile health applications on patient-healthcare provider relationships: a systematic, narrative review. Patient Educ Counsel. 2019;102(6):1080–9.

    Article  Google Scholar 

  34. Curran K, Nichols E, Xie E, Harper R. An intensive insulinotherapy mobile phone application built on artificial intelligence techniques. J Diabet Sci Technol. 2010;4(1):209–20.

    Article  Google Scholar 

  35. Ribu L, Holmen H, Torbjørnsen A, Wahl AK, Grøttland A, Småstuen MC, Elind E, Bergmo TS, Breivik E, Årsand E, et al. Low-intensity self-management intervention for persons with type 2 diabetes using a mobile phone-based diabetes diary, with and without health counseling and motivational interviewing: protocol for a randomized controlled trial. JMIR Res Protocols. 2013;2(2):2768.

    Article  Google Scholar 

  36. Toro-Ramos T, Kim Y, Wood M, Rajda J, Niejadlik K, Honcz JY, Marrero D, Fawer A, Michaelides A. Efficacy of a mobile hypertension prevention delivery platform with human coaching. J Hum Hyperten. 2017;31(12):795–800.

    Article  Google Scholar 

  37. Toro-Ramos T, Lee D-H, Kim Y, Michaelides A, Oh TJ, Kim KM, Jang HC, Lim S. Effectiveness of a smartphone application for the management of metabolic syndrome components focusing on weight loss: a preliminary study. Metab Synd Relat Disord. 2017;15(9):465–73.

    Article  Google Scholar 

  38. Portz JD, Vehovec A, Dolansky MA, Levin JB, Bull S, Boxer R. The development and acceptability of a mobile application for tracking symptoms of heart failure among older adults. Telemed e-Health. 2018;24(2):161–5.

    Article  Google Scholar 

  39. Elias P, Rajan NO, McArthur K, Dacso CC. Inspire to promote lung assessment in youth: evolving the self-management paradigms of young people with asthma. Medicine 2.0 2013;2(1).

  40. Ribeiro N, Moreira L, Almeida AMP, Santos-Silva F. Pilot study of a smartphone-based intervention to promote cancer prevention behaviours. Int J Med Inform. 2017;108:125–33.

    Article  Google Scholar 

  41. Klasnja P, Consolvo S, McDonald DW, Landay JA, Pratt W. Using mobile and personal sensing technologies to support health behavior change in everyday life: lessons learned. In: AMIA Annual Symposium Proceedings, 2009;2009:338. American Medical Informatics Association.

  42. Gabrielli S, Dianti M, Maimone R, Betta M, Filippi L, Ghezzi M, Forti S, et al. Design of a mobile app for nutrition education (trec-lifestyle) and formative evaluation with families of overweight children. JMIR mHealth and uHealth. 2017;5(4):7080.

    Article  Google Scholar 

  43. Ming LC, Untong N, Aliudin NA, Osili N, Kifli N, Tan CS, Goh KW, Ng PW, Al-Worafi YM, Lee KS, et al. Mobile health apps on covid-19 launched in the early days of the pandemic: content analysis and review. JMIR mHealth and uHealth. 2020;8(9):19796.

    Article  Google Scholar 

  44. Organization WH, et al. Clinical management of severe acute respiratory infection (sari) when covid-19 disease is suspected: interim guidance, 13 March 2020. In: Technical report: World Health Organization; 2020.

  45. Organization WH. et al. Classification of digital health interventions v1.0: a shared language to describe the uses of digital technology for health. Technical report, World Health Organization. 2018.

  46. Hensher M, Cooper P, Dona SWA, Angeles MR, Nguyen D, Heynsbergh N, Chatterton ML, Peeters A. Scoping review: development and assessment of evaluation frameworks of mobile health apps for recommendations to consumers. J Am Med Inform Assoc. 2021;28(6):1318–29.

    Article  Google Scholar 

  47. Nadal C, Sas C, Doherty G, et al. Technology acceptance in mobile health: scoping review of definitions, models, and measurement. J Med Internet Res. 2020;22(7):17256.

    Article  Google Scholar 

  48. Mumtaz S, Rodriguez C, Benatallah B. Expert2vec: Experts representation in community question answering for question routing. In: International Conference on Advanced Information Systems Engineering, 2019;213–229. Springer.

  49. Bandeira A, Medeiros CA, Paixao M, Maia PH. We need to talk about microservices: an analysis from the discussions on stackoverflow. In: 16th International Conference on Mining Software Repositories, 2019;255–259. IEEE Press

  50. Mei Q, Cai D, Zhang D, Zhai C. Topic modeling with network regularization. In: Proceedings of the 17th International Conference on World Wide Web, 2008;101–110.

  51. Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res. 2003;3(Jan):993–1022.

    Google Scholar 

  52. Sukhija N, Tatineni M, Brown N, Van Moer M, Rodriguez P, Callicott S. Topic modeling and visualization for big data in social sciences. In: 2016 Intl IEEE Conferences on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress, 2016;1198–1205. IEEE.

  53. Drosatos G, Kavvadias SE, Kaldoudi E. Topics and trends analysis in ehealth literature. In: EMBEC & NBC 2017, 2017;563–566. Springer, London.

  54. Sievert C, Shirley K. Ldavis: A method for visualizing and interpreting topics. In: Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, 2014;63–70.

  55. Gardner MJ, Lutes J, Lund J, Hansen J, Walker D, Ringger E, Seppi K. The topic browser: An interactive tool for browsing topic models. In: Nips Workshop on Challenges of Data Visualization, 2010;2. Whistler Canada.

  56. Thomas SW, Hassan AE, Blostein D. Mining unstructured software repositories. In: Evolving Software Systems. Springer, Berlin. 2014.

  57. Bespalov D, Bai B, Qi Y, Shokoufandeh A. Sentiment classification based on supervised latent n-gram analysis. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, 2011;375–382.

  58. Ahmed H, Traore I, Saad S. Detection of online fake news using n-gram analysis and machine learning techniques. In: International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, 2017;127–138. Springer.

  59. Nessa S, Abedin M, Wong WE, Khan L, Qi Y. Software fault localization using n-gram analysis. In: International Conference on Wireless Algorithms, Systems, and Applications, 2008;548–559. Springer.

  60. Fiordelli M, Diviani N, Schulz PJ, et al. Mapping mhealth research: a decade of evolution. J Med Internet Res. 2013;15(5):2430.

    Article  Google Scholar 

  61. Fayyad U, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery in databases. AI Mag. 1996;17(3):37–37.

    Google Scholar 

  62. Wohlin C. Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: 18th Int. Conf. on Evaluation and Assessment in Software Engineering, 2014;1–10.

  63. Kavaler D, Posnett D, Gibler C, Chen H, Devanbu P, Filkov V. Using and asking: Apis used in the android market and asked about in stackoverflow. In: International Conference on Social Informatics, 2013;405–418. Springer.

  64. Stol K-J, Ralph P, Fitzgerald B. Grounded theory in software engineering research: a critical review and guidelines. In: Proceedings of the 38th Int. Conf. on Software Engineering, 2016;120–131.

  65. Puurula A. Cumulative progress in language models for information retrieval. In: Proc. Australasian Language Technology Association Work. 2013 (ALTA 2013), Brisbane, Australia, 2013;96–100. www.aclweb.org/anthology/U13-1013.

  66. McCallum AK. Mallet: A machine learning for language toolkit. 2002. http://mallet.cs.umass.edu.

  67. Van Rossum G, Drake FL. The Python Language Reference Manual. London: Network Theory Ltd.; 2011.

    Google Scholar 

  68. Hejlsberg A, Wiltamuth S, Golde P. The C# Programming Language. New Jersey: Adobe Press; 2006.

    Google Scholar 

  69. Herniter ME. Programming in MATLAB. London: Brooks/Cole Publishing Co.; 2000.

    Google Scholar 

  70. Arnold K, Gosling J, Holmes D, Holmes D. The Java Programming Language, vol. 2. Reading, London: Addison-Wesley; 2000.

    Google Scholar 

  71. Stroustrup B. The c++ programming language: reference manual. Bell Lab: Technical report; 1984.

  72. Flanagan D, Matilainen P. JavaScript. London: Anaya Multimedia; 2007.

  73. Knaster S, Dalrymple M. Learn Objective-C on the Mac. London: Springer; 2009.

    Google Scholar 

  74. Van der Walt S, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T. scikit-image: image processing in python. PeerJ. 2014;2:453.

    Article  Google Scholar 

  75. Mildenberger P, Eichelberg M, Martin E. Introduction to the dicom standard. Eur Radiol. 2002;12(4):920–7.

    Article  Google Scholar 

  76. Bender D, Sartipi K. Hl7 fhir: an agile and restful approach to healthcare information exchange. In: Proceedings of the 26th IEEE Int. Symposium on Computer-based Medical Systems, 2013;326–331.

  77. Kalra D, Beale T, Heard S. The openehr foundation. Stud Health Tech Inform. 2005;115:153–73.

    Google Scholar 

  78. Zapata BC, Fernández-Alemán JL, Idri A, Toval A. Empirical studies on usability of mhealth apps: a systematic literature review. J Med Syst. 2015;39(2):1.

    Article  Google Scholar 

  79. Varshney U. Mobile health: four emerging themes of research. Decis Sup Syst. 2014;66:20–35.

    Article  Google Scholar 

  80. Ghosh S, Löchner J, Mitra B, De P. Your smartphone knows you better than you may think: Emotional assessment ‘on the go’via tapsense. In: Quantifying Quality of Life, 2022;209–267. Springer, London.

  81. Appari A, Johnson ME. Information security and privacy in healthcare: current state of research. Int J Internet Enterp Manag. 2010;6(4):279–314.

    Article  Google Scholar 

  82. dos Santos EB, Andrade RM, de Sousa Santos I. Runtime testing of context-aware variability in adaptive systems. Inform Softw Technol. 2021;131: 106482.

    Article  Google Scholar 

  83. Lynch BM, Matthews CE, Wijndaele K. New mesh for sedentary behavior. J Phys Act Health. 2019;16(5):305–305.

    Article  Google Scholar 

  84. Salvador-Oliván JA, Marco-Cuenca G, Arquero-Avilés R. Errors in search strategies used in systematic reviews and their effects on information retrieval. J Med Lib Assoc JMLA. 2019;107(2):210.

    Google Scholar 

  85. Georgiou K, Mittas N, Chatzigeorgiou A, Angelis L. An empirical study of covid-19 related posts on stack overflow: topics and technologies. J Syst Softw. 2021;182: 111089.

    Article  Google Scholar 

  86. Drosatos G, Kaldoudi E. A probabilistic semantic analysis of ehealth scientific literature. J Telemed Telecare. 2020;26(7–8):414–32.

    Article  Google Scholar 

  87. Ahmed B, Dannhauser T, Philip N. A systematic review of reviews to identify key research opportunities within the field of ehealth implementation. J Telemed Telecare. 2019;25(5):276–85.

    Article  Google Scholar 

  88. Rahmani AM, Szu-Han W, Yu-Hsuan K, Haghparast M. The internet of things for applications in wearable technology. IEEE Access. 2022;10:123579–94.

    Article  Google Scholar 

  89. Aljedaani B, Ahmad A, Zahedi M, Babar MA. An empirical study on developing secure mobile health apps: The developers’ perspective. In: 2020 27th Asia-Pacific Software Engineering Conference (APSEC), 2020;208–217. IEEE.

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Acknowledgements

The authors would like to thank CNPQ for the Productivity Scholarship of Rossana M. C. Andrade DT-1 (\(N^{o}\) 306362 / 2021-0), for the Productivity Scholarship of Pedro de A. dos Santos Neto DT-2 (\(N^{o}\) 315198/2018-4), and CAPES that provided to the Evilasio C. Junior a Ph.D. scholarship.

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Author notes

  1. Evilasio Costa Junior, Rossana M. C. Andrade, Ismayle S. Santos and Pedro A. Santos Neto have contributed equally to this work.

Authors and Affiliations

  1. Group of Computer Networks, Software Engineering and Systems (GREat), Federal University of Ceará, Ceará, Brazil

    Pedro Almir M. Oliveira, Evilasio Costa Junior, Rossana M. C. Andrade & Ismayle S. Santos

  2. Federal Institute of Maranhão (IFMA), Maranhão, Brazil

    Pedro Almir M. Oliveira

  3. Laboratory of Software Optimization and Testing (LOST), Federal University of Piauí, Piauí, Brazil

    Pedro A. Santos Neto

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Correspondence to Pedro Almir M. Oliveira.

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Oliveira, P.A.M., Junior, E.C., Andrade, R.M.C. et al. Mobile Health from Developers’ Perspective. SN COMPUT. SCI. 5, 132 (2024). https://doi.org/10.1007/s42979-023-02455-z

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