How to Handle Data Management of Assisting Lifelogging Technologies from a User’s Point of View
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A shift to higher proportions of older people and people in need of care requires new solutions and technologies with the potential to assist people in their everyday activities and to support them in being as independent and self-determined as possible. Lifelogging technologies have this potential by the collection, storage, and evaluation of personal data. Despite their potential, the users’ acceptance of such technologies is of great importance, in particular with regard to the technology’s handling of data security and privacy. For this reason, a quantitative study was carried out using an online questionnaire (N = 182), investigating two different application contexts of lifelogging technologies: a preventive context (frailty monitoring) and an assisting context related to patients suffering from dementia. Based on a preceding qualitative study, data access, purpose of data processing, duration as well as location of data storage were chosen as factors which were investigated, applying a conjoint analysis approach. The results revealed that the purpose of data processing and data access were the most decisive factors when users decide about the data management of lifelogging technologies and comparing the two contexts, contradicting decision patterns were found in particular for data access. Beyond these insights, user group specific decision patterns were identified for each of the application contexts. This study provides relevant insights into the users’ perspectives and requirements with regard to data management of lifelogging technologies, which should be taken into account for technology development and communication.
KeywordsData handling Data management Privacy Data security Data storage User perception Acceptance Lifelogging technology
The authors would like to thank all participants for sharing their opinions on assisting lifelogging technologies. Further, thanks go to Katharina Merkel for research support. This work resulted from the project PAAL – “Privacy Aware and Acceptable Lifelogging services for older and frail people” and was funded by the German Federal Ministry of Education and Research (16SV7955).
- 3.Haustein, T., Mischke, J., Schönfeld, F., Willand, I.: Older people in germany and the EU. Statistisches Bundesamt, Wiesbaden, Germany, February 2016Google Scholar
- 5.Bouma, H., Fozard, J.L., Bouwhuis, D.G., Taipale, V.: Gerontechnology in perspective. Gerontechnology 6, 190–216 (2007)Google Scholar
- 24.Arning, K.: Conjoint measurement. In: Matthes, J., Davis, C.S., Potter, R.F. (eds.) International Encyclopedia of Communication Research Methods, pp. 1–10. Wiley-Blackwell, Wiley (2017). https://doi.org/10.1002/9781118901731
- 26.Orme, B.: Interpreting the Results of Conjoint Analysis, Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research. pp. 77–89, Research Publications LLC, Madison (2010)Google Scholar
- 27.Sawtooth Software: The CBC System for Choice-Based Conjoint Analysis. Technical Paper Series (2009). https://www.sawtoothsoftware.com/download/techpap/cbctech.pdf
- 28.Sawtooth Software. Survey Software & Conjoint Analysis—CBC Latent Class Technical Paper. Sawtooth Technical Paper Series (2014). https://www.sawtoothsoftwarecom/support/technical-papers/sawtooth-software-products/cbc-latent-class-technical-paper-2004
- 30.Sawtooth Software: Testing the CBC Design. Technical Paper Series (2019). http://www.sawtooth-software.com/help/lighthouse-studio/manual/index.html?hidweb_cbc_designs_6.html
- 34.Ajzen, I., Fishbein, M.: Understanding Attitudes and Predicting Social Behavior, 1st edn. Prentice-Hall, Englewood Cliffs (1980)Google Scholar