Crafting Usable Quantified Self-wearable Technologies for Older Adult

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 795)


Commercially off-the-shelf (COTS) quantified self-wearable technologies (QSWT) have enabled younger individuals to adopt a measurable living style [49] through the collection of “quantifiable data”. However, the adoption of wearables remains lowest among the older adult, and the question of what is holding adoption back remains. The purpose of this study is to: (i) explore and present the device characteristics of smartwatches and pedometers that affect the adoption of wearables across different cultures; (ii) study country-specific older adult’s importance weights on identified issues; and (iii) provide informal usability guidelines for manufacturers, researchers, and application developers. The results revealed that the usability issues such as screen size, tapping detection, device size, interaction techniques, navigation, and typography were some of the reasons for the low adoption of wearables among the older adult. Further, device and screen size were significantly more essential for the Finnish compared to US older adult participants, demonstrating that culture might influence the perception of some device characteristics.


Wearables Usability Culturability Older adult Framework User interface Elderly Quantified self-technologies Smartwatches Pedometers 



First author would like to thank Miina Sillanpää Foundation, LUT Research Platform on Smart Services for Digitalisation (DIGI-USER) and Second author would like to thank Ulla Tuominen Foundation for their generous support of research.


  1. 1.
    Abdi, H.: The Bonferonni and Šidák corrections for multiple comparisons. In: Salkind, N. (ed.) Encyclopedia of Measurement and Statistics. SAGE Publications Inc., Thousand Oaks (2007)Google Scholar
  2. 2.
    Ally, M., Gardiner, M.: Application and device characteristics as drivers for smart mobile device adoption and productivity. Int. J. Organ. Behav. 17(4), 35–47 (2012)Google Scholar
  3. 3.
    Angelini, L., et al.: Designing a desirable smart bracelet for older adults. In: Proceedings of the 2013 ACM Conference Pervasive and Ubiquitous Computing Adjunct Publication, pp. 425–434 (2013)Google Scholar
  4. 4.
    Bababekova, Y., et al.: Font size and viewing distance of handheld smart phones. Optom. Vis. Sci. 88(7), 795–797 (2011)CrossRefGoogle Scholar
  5. 5.
    Barber, W., Badre, A.: Culturability: the merging of culture and usability. In: Proceedings of the 4th Conference on Human Factors and the Web, pp. 1–14 (1998)Google Scholar
  6. 6.
    Batsis, J.A., et al.: Use of a wearable activity device in rural older obese adults. Gerontol. Geriatr. Med. 2, 233372141667807 (2016)CrossRefGoogle Scholar
  7. 7.
    Bergmann, R., Ludbrook, J.: Different outcomes of the Wilcoxon—Mann—Whitney test from different statistics packages. Am. Stat. 54(1), 72–77 (2000)Google Scholar
  8. 8.
    Carmien, S., Manzanares, A.G.: Elders using smartphones – a set of research based heuristic guidelines for designers. In: Proceedings of the 8th International Conference, UAHCI 2014 Held as Part of HCI International 2014 Heraklion, Crete, Greece, 22–27 June 2014, Part II, pp. 26–37 (2014)Google Scholar
  9. 9.
    Chen, K., et al.: Kansei design with cross cultural perspectives. In: Usability and Internationalization. HCI and Culture, pp. 47–56 Springer, Heidelberg (2007)Google Scholar
  10. 10.
    Chen, S.Y., Fu, Y.C.: Leisure participation and enjoyment among the elderly: individual characteristics and sociability. Educ. Gerontol. 34(10), 871–889 (2008)CrossRefGoogle Scholar
  11. 11.
    Chiu, C.-J., Liu, C.-W.: Understanding older adult’s technology adoption and withdrawal for elderly care and education: mixed method analysis from national survey. J. Med. Internet Res. 19(11), e374 (2017)CrossRefGoogle Scholar
  12. 12.
    Cockburn, A., et al.: The effects of interaction sequencing on user experience and preference. Int. J. Hum. Comput Stud. 108, 89–104 (2017)CrossRefGoogle Scholar
  13. 13.
    Fausset, C.B., et al.: Older adults’ use of and attitudes toward activity monitoring technologies. In: Proceedings of the Human Factors Ergonomics Society Annual Meeting, vol. 57, no. 1, pp. 1683–1687 (2013)CrossRefGoogle Scholar
  14. 14.
    Gudur, R.R., et al.: Ageing, technology anxiety and intuitive use of complex interfaces. In: Lecture Notes in Computer Science (including Subseries, Lecture Notes in Artificial Intelligence Notes Bioinformatics), vol. 8119, Part 3, pp. 564–581 (2013)CrossRefGoogle Scholar
  15. 15.
    Hekkert, P.: Design aesthetics: principles of pleasure in design. Psychol. Sci. 48(2), 157–172 (2006)Google Scholar
  16. 16.
    Holzinger, A., et al.: Perceived usefulness among elderly people: experiences and lessons learned during the evaluation of a wrist device. In: Proceedings of the 4th International ICST Conference Pervasive Computing Technologies for Healthcare, pp. 1–5 (2010)Google Scholar
  17. 17.
    Huang, D.L., et al.: Effects of font size, display resolution and task type on reading Chinese fonts from mobile devices. Int. J. Ind. Ergon. 39(1), 81–89 (2009)CrossRefGoogle Scholar
  18. 18.
    Ivory, M.Y., Hearst, M.A.: The state of the art in automating usability evaluation of user interfaces. ACM Comput. Surv. 33(4), 470–516 (2001)CrossRefGoogle Scholar
  19. 19.
    Jeong, H., et al.: Smartwatch wearing behavior analysis. In: Proceedings of the ACM Interactive, Mobile, Wearable Ubiquitous Technologies, vol. 1, no. 3, pp. 1–31 (2017)CrossRefGoogle Scholar
  20. 20.
    Kalantari, M.: Consumers’ adoption of wearable technologies: literature review, synthesis, and future research agenda. Int. J. Technol. Mark. 12(3), 274–307 (2017)CrossRefGoogle Scholar
  21. 21.
    Kanis, H.: Usage centred research for everyday product design. Appl. Ergon. 29(1), 75–82 (1998)CrossRefGoogle Scholar
  22. 22.
    Khakurel, J. et al.: Living with smartwatches and pedometers: the intergenerational gap in internal and external contexts. In: GOODTECHS Conference Proceedings, Lecture Notes of ICST (LNICST), Pisa, Italy, pp. 31–41. Springer, Heidelberg (2018)CrossRefGoogle Scholar
  23. 23.
    Khaslavsky, J.: Integrating culture into interface design. In: CHI 98 Conference Summary on Human Factors in Computing Systems, CHI 1998, pp. 365–366 (1998)Google Scholar
  24. 24.
    Kim, K.J.: Shape and size matter for smartwatches: effects of screen shape, screen size, and presentation mode in wearable communication. J. Comput. Commun. 22(3), 124–140 (2017)Google Scholar
  25. 25.
    Liu, C.C., Wu, D.W., Jou, M., Tsai, S.J.: Development of a sensor network system for industrial technology education. In: Lytras, M.D., Ordonez De Pablos, P., Ziderman, A., Roulstone, A., Maurer, H., Imber, J.B. (eds.) WSKS 2010. CCIS, vol. 111, pp. 369–374. Springer, Heidelberg (2010). Scholar
  26. 26.
    Macefield, R.: How to specify the participant group size for usability studies: a practitioner’s guide. J. Usability Stud. 5(1), 34–45 (2009)Google Scholar
  27. 27.
    Mallenius, S., et al.: Factors affecting the adoption and use of mobile devices and services by elderly people–results from a pilot study. In: 6th Annual Global Mobility Roundtable, vol. 31, p. 12 (2007)Google Scholar
  28. 28.
    Marino, M., et al.: Measuring sleep: accuracy, sensitivity, and specificity of wrist actigraphy compared to polysomnography. Sleep 36(11), 1747–1755 (2013)CrossRefGoogle Scholar
  29. 29.
    Morrison, L.G., et al.: The effect of timing and frequency of push notifications on usage of a smartphone-based stress management intervention: an exploratory trial. PLoS ONE 12, 1 (2017)Google Scholar
  30. 30.
    Ni, H., et al.: Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment. Front. Comput. Sci. 9(6), 966–979 (2015)CrossRefGoogle Scholar
  31. 31.
    Piwek, L., et al.: The rise of consumer health wearables: promises and barriers. PLoS Med. 13, 2 (2016)CrossRefGoogle Scholar
  32. 32.
    Preusse, K.C., et al.: Activity monitoring technologies and older adult users. In: Proceedings of the International Symposium Human Factors Ergonomics Health Care, vol. 3, no. 1, 23–27 (2014)CrossRefGoogle Scholar
  33. 33.
    Pulli, P., et al.: User interaction in smart ambient environment targeted for senior citizen. Med. Biol. Eng. Comput. 50(11), 1119–1126 (2012)CrossRefGoogle Scholar
  34. 34.
    R Core Team. R: A Language and Environment for Statistical Computing (2017).
  35. 35.
    Rasche, P., et al.: Activity tracker and elderly. In: 2015 IEEE International Conference on IS - SN - VO - VL, Computing Information Technology Ubiquitous Computing Communications Dependable, Autonomic Secure Computing Pervasive Intelligence Computing, pp. 1411–1416 (2015)Google Scholar
  36. 36.
    Revelle, W.: psych: Procedures for Psychological, Psychometric, and Personality Research (2017).
  37. 37.
    Rieman, J.: The diary study: a workplace-oriented research tool to guide laboratory efforts. In: Proceedings of the SIGCHI Conference Human Factors Computing System, CHI 1993, pp. 321–326 (1993)Google Scholar
  38. 38.
    Rosales, A., et al.: Older people and smartwatches, initial experiences. El Prof. la Inf. 26(3), 457 (2017)Google Scholar
  39. 39.
    Schlomann, A., et al.: Means of motivation or of stress? The use of fitness trackers for self-monitoring by older adults. HeilberufeScience 7(3), 111–116 (2016)CrossRefGoogle Scholar
  40. 40.
    Shi, Q.: Cultural usability: the effects of culture on usability testing. In: Human-Computer Interaction, INTERACT 2007, Pt 2, Proceedings, pp. 611–616 (2007)Google Scholar
  41. 41.
    Sin, A.K., et al.: A wearable device for the elderly: a case study in Malaysia. In: Proceedings of the 6th International Conference on Information Technology and Multimedia, pp. 318–323. IEEE (2014)Google Scholar
  42. 42.
    Smarr, B.L., et al.: A wearable sensor system with circadian rhythm stability estimation for prototyping biomedical studies. IEEE Trans. Affect. Comput. 7(3), 220–230 (2016)CrossRefGoogle Scholar
  43. 43.
    Stanton, N.: Ecological Ergonomics: Understanding Human Action in Context. Taylor & Francis, Abingdon-on-Thames (1994)Google Scholar
  44. 44.
    Tractinsky, N.: Aesthetics and apparent usability: empirically assessing cultural and methodological issues. In: Proceedings of the Conference Human Factors in Computing System, pp. 115–122 (1997)Google Scholar
  45. 45.
    Wallace, S., et al.: Culture and the importance of usability attributes. Inf. Technol. People. 26(1), 77–93 (2013)CrossRefGoogle Scholar
  46. 46.
    Wallace, S., Yu, H.-C.: The effect of culture on usability: comparing the perceptions and performance of Taiwanese and North American MP3 player users. J. Usability Stud. 4(3), 136–146 (2009)Google Scholar
  47. 47.
    Wirz-Justice, A.: How to measure circadian rhythms in humans. Medicographia 29, 84–90 (2007)Google Scholar
  48. 48.
    Wohlin, C., et al.: Experimentation in Software Engineering. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  49. 49.
    Wu, Q., et al.: How fitness trackers facilitate health behavior change. In: Proceedings of the Human Factors Ergonomics Society Annual Meeting, vol. 60, no. 1, 1068–1072 (2016)CrossRefGoogle Scholar
  50. 50.
    Yang, C.-C., Hsu, Y.-L.: A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 10(8), 7772–7788 (2010)CrossRefGoogle Scholar
  51. 51.
    Zhao, M., et al.: Learning sleep stages from radio signals: a conditional adversarial architecture. ICML 70, 4100–4109 (2017)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Lapppeenranta University of TechnologyLappeenrantaFinland
  2. 2.Lero, The Irish Software Research CentreGlasnevinIreland
  3. 3.California State UniversityLong BeachUSA

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