Abstract
As technology is more than ever part of everyday life and activities, their benefits and potential have to be optimized. Currently, this is not happening and technology adherence and continued use is very low. We need to have simple but clear means to understand why that is so and what needs to be done to improve it close to the technology itself and its users. This work introduces AnyMApp, an anonymous digital twin human-app interactions framework to provide online anonymous testing of mock-up applications. These applications may or may not exist and even be in different stages of their deployment. The main goal of AnyMApp is to provide an easy, online way to collect data from users’ interactions with the application and complement these with questions to the user regarding contextual, demographic and domain specific. Collected data will be used to quickly detect usability and interactional problems but can also be used to explore relations between humans and technology, and identify experiences and behavioural patterns of the target population.
R. Chilro—Independent Researcher.
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References
Stephanidis, C., et al.: Seven HCI grand challenges. Int. J. Hum. Comput. Interact. 35(14), 1229–1269 (2019). https://doi.org/10.1080/10447318.2019.1619259
Chiaramida, V., Pinci, F., Buy, U., Gjomemo, R.: AppSeer: discovering flawed interactions among Android components. In: Proceedings of the 1st International Workshop on Advances in Mobile App Analysis (A-Mobile 2018), pp. 29–34. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3243218.3243225
The Human Factor: Technology Changes Faster Than Humans. The State of Security. Tripwire Guest Authors. https://www.tripwire.com/state-of-security/off-topic/human-factor-technology-changes-faster-humans/. Accessed 16 Feb 2021
Sigg, S., Lagerspetz, E., Peltonen, E., Nurmi, P., Tarkoma, S.: Exploiting usage to predict instantaneous app popularity: trend filters and retention rates. ACM Trans. Web 13(2), Article no. 13, 25 p., April 2019. https://doi.org/10.1145/3199677
Mennig, P., Scherr, S.A., Elberzhager, F.: Supporting rapid product changes through emotional tracking. In: 2019 IEEE/ACM 4th International Workshop on Emotion Awareness in Software Engineering (SEmotion), Montreal, QC, Canada, pp. 8–12 (2019). https://doi.org/10.1109/SEmotion.2019.00009
Donker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M.R., Christensen, H.: Smartphones forsmarter delivery of mental health programs: a systematic review. J. Med. Internet Res. 15(11), e247 (2013 15). https://doi.org/10.2196/jmir.2791. PMID: 24240579; PMCID: PMC3841358
Boateng, G., Batsis, J.A., Halter, R., Kotz, D.: ActivityAware: an app for real-time daily activity level monitoring on the Amulet wrist-worn device. In: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerComWorkshops), Kona, HI, pp. 431–435 (2017). https://doi.org/10.1109/PERCOMW.2017.7917601
Ferre, X., Villalba, E., Julio, H., Zhu, H.: Extending mobile app analytics for usability test logging. In: Bernhaupt, R., Dalvi, G., Joshi, A., K. Balkrishan, D., O’Neill, J., Winckler, M. (eds.) INTERACT 2017. LNCS, vol. 10515, pp. 114–131. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67687-6_9
Turkington, R., Mulvenna, M., Bond, R., O’Neill, S., Armour, C.: The application of user event log data for mental health and wellbeing analysis. In: Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI 2018), Swindon, GBR, Article no. 4, pp. 1–14. BCS Learning & Development Ltd. (2018). https://doi.org/10.14236/ewic/HCI2018.4
Böhm, A.K., Jensen, M.L., Sørensen, M.R., Stargardt, T.: Real-world evidence of user engagement with mobile health for diabetes management: longitudinal observational study. JMIR Mhealth Uhealth. 8(11), e22212 (2020). https://doi.org/10.2196/22212. PMID:32975198; PMCID: PMC7679206
Deng, T., et al.: Measuring smartphone usage and task switching with log tracking and self-reports. Mobile Media Commun. 7, 23–33 (2019)
Boase, J., Ling, R.: Measuring mobile phone use: self-report versus log data. J. Comput. Med. Commun. 18(4), 508–519 (2013). https://doi.org/10.1111/jcc4.12021
Herselman, M.: A scoping review of the use of data analytics for the evaluation of mhealth applications (2020). sun.ac.za
Ferreira, A., Muchagata, J., Vieira-Marques, P., Abrantes, D., Teles, S.: Perceptions of security and privacy in mHealth. In: Moallem, A. (eds.) HCII 2021. LNCS, vol. 12788, pp. 297–309. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77392-2_19
Moura, P., Fazendeiro, P., Inácio, P.R.M., Vieira-Marques, P., Ferreira, A.: Assessing access control risk for mHealth: a Delphi study to categorize security of health data and provide risk assessment for mobile apps. J. Healthc. Eng., Article no. 5601068, 14 p. (2020). https://doi.org/10.1155/2020/5601068
Ferreira, A., Muchagata, J.: TagUBig - taming your big data. In: 2018 International Carnahan Conference on Security Technology (ICCST), Montreal, QC, Canada, pp. 1–5 (2018). https://doi.org/10.1109/CCST.2018.8585539
Billmann, M., Böhm, M., Krcmar, H.: Use of workplace health promotion apps: analysis of employee log data. Health Policy Technol. 9(3), 285–293 (2020). ISSN 2211-8837. https://doi.org/10.1016/j.hlpt.2020.06.003
Tian, Y., Zhou, K., Lalmas, M., Liu, Y., Pelleg, D.: Cohort modeling based app category usage prediction. In: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2020), pp. 248–256. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3340631.3394849
Ferreira, A., Vieira-Marques, P., Almeida, R., Fernandes, J., Fonseca, J.: How inspiring is your app: a usability take on an app for asthma medication adherence. In: 11th International Conference on e-Health, pp. 225–229 (2019)
Aliannejadi, M., Harvey, M., Costa, L., Pointon, M., Crestani, F.: Understanding mobile search task relevance and user behaviour in context. In: Proceedings of the 2019 Conference on Human Information Interaction and Retrieval (CHIIR 2019), pp. 143–151. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3295750.3298923
McCallum, C., Rooksby, J., Gray, C.M.: Evaluating the impact of physical activity apps and wearables: interdisciplinary review. JMIR Mhealth Uhealth 6(3), e58 (2018). https://doi.org/10.2196/mhealth.9054. PMID: 29572200; PMCID: PMC5889496
General Data Protection Regulation (EU) 2016/679 of the European Parliament and of the Council L 119. Official Journal of the European Union
Qin, Z., et al.: Demographic information prediction based on smartphone application usage. In: 2014 International Conference on Smart Computing, Hong Kong, China, pp. 183–190 (2014). https://doi.org/10.1109/SMARTCOMP.2014.7043857
Olson, J.S., Kellogg, W.A.: Ways of Knowing in HCI, Springer, New York (2014). https://doi.org/10.1007/978-1-4939-0378-8
Stragier, J., et al.: Data mining in the development of mobile health apps: assessing in-app navigation through Markov chain analysis. J. Med. Internet Res. 21(6), e11934 (2019)
Qiu, L., Zhang, Z., Shen, Z., Sun, G.: AppTrace: dynamic trace on android devices. In: 2015 IEEE International Conference on Communications (ICC), London, UK, pp. 7145–7150 (2015). https://doi.org/10.1109/ICC.2015.7249466
De Nadai, M., Cardoso, A., Lima, A., et al.: Strategies and limitations in app usage and human mobility. Sci. Rep. 9, 10935 (2019). https://doi.org/10.1038/s41598-019-47493-x
Gruschka, N., Mavroeidis, V., Vishi, K., Jensen, M.: Privacy issues and data protection in big data: a case study analysis under GDPR. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5027–5033 (2018). https://doi.org/10.1109/BigData.2018.8622621
Rocher, L., Hendrickx, J.M., de Montjoye, Y.A.: Estimating the success of re-identifications in incomplete datasets using generative models. Nat. Commun. 10, 3069 (2019). https://doi.org/10.1038/s41467-019-10933-3
De-Identification tools. Privacy Engineering Program. NIST – Information Technology Laboratory/Applied Sybersecurity Division. https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/focus-areas/de-id/tools. Accessed 25 May 2022
Valli Kumari, V., Varma, N.S., Sri Krishna, A., Ramana, K.V., Raju, K.V.S.V.N.: Checking anonymity levels for anonymized data. In: Natarajan, R., Ojo, A. (eds.) ICDCIT 2011. LNCS, vol. 6536, pp. 278–289. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19056-8_21
Gordon, M.L., Gatys, L., Guestrin, C., Bigham, J.P., Trister, A., Patel, K.: App usage predicts cognitive ability in older adults. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI 2019), paper 168, pp. 1–12. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3290605.3300398
Acknowledgements
This work is financed by project AnyMApp - Anonymous Digital Twin for Human-App Interactions (EXPL/CCI-COM/0052/2021) (FCT – Fundação para a Ciência e Tecnologia).
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Ferreira, A., Chilro, R., Cruz-Correia, R. (2022). AnyMApp Framework: Anonymous Digital Twin Human-App Interactions. In: Kurosu, M., et al. HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction. HCII 2022. Lecture Notes in Computer Science, vol 13516. Springer, Cham. https://doi.org/10.1007/978-3-031-17615-9_15
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