Does Cognitive Function Predict Self-Reported Ability to Use Computers?

The world is rapidly becoming digital. But what about those who, for whatever reason, find it hard to access digital services? In order to understand how best to support this group of people, we need to first of all seek to find out who they are.

In this study, we used computer self-efficacy (people’s self-reported ability to use computers) as a proxy marker – those who feel less able to cope with computers are more likely to feel “digitally left behind”. We leveraged the large, well-curated SHARE data set, a comprehensive survey of health, ageing, and retirement which includes data on socioeconomic circumstances as well as health, IT use, and cognition. We were particularly interested in the extent to which lower cognitive abilities (assessed using memory and semantic fluency) might be associated with feeling “left behind”.

However, in our analysis, we found that the single most important predictor of feeling digitally left behind, feeling unable to use technology, was prior experience with using computers in their job before retirement. We also saw effects of gender (women feeling more left behind than men), location (people in rural areas feeling more left behind than people in urban areas), and ability to make ends meet (poorer people feeling more left behind).

As these findings suggest, there are no simple causal connections here. First of all, this is self-rated ability, which always diverges from actual ability, secondly, well-educated male city dwellers who are well off are more likely to have had white collar jobs that require IT use, and finally, the model we used for our analysis did not include any interactions.

Our next steps are to refine the model, and to construct country-specific models. Initial investigations by Yifeng Gao in his Master’s Thesis suggest that the contributions of each of the factors may vary quite substantially between countries.

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References

R1: digitally left behind:

Bailey, Dan, Perks, M., & Winter, Chris. (2018). Supporting the Digitally Left Behind. Ingenia, 76.

Cotten, S. R., Francis, J., Kadylak, T., Rikard, R. V., Huang, T., Ball, C., & DeCook, J. (2016). A Tale of Two Divides: Technology Experiences Among Racially and Socioeconomically Diverse Older Adults. In J. Zhou & G. Salvendy (Eds.), Human Aspects of IT for the Aged Population. Design for Aging (pp. 167–177). Springer International Publishing.

Quan-Haase, A., Martin, K., & Schreurs, K. (2016). Interviews with digital seniors: ICT use in the context of everyday life. Information, Communication & Society, 19(5), 691–707. https://doi.org/10.1080/1369118X.2016.1140217

R2: trend towards digital

Smith, A. C., Thomas, E., Snoswell, C. L., Haydon, H., Mehrotra, A., Clemensen, J., & Caffery, L. J. (2020). Telehealth for global emergencies: Implications for coronavirus disease 2019  (COVID-19). Journal of Telemedicine and Telecare, 26(5), 309–313. https://doi.org/10.1177/1357633X20916567

R3: reasons for problems with using technology

Gilleard, C., & Higgs, P. (2008). Internet use and the digital divide in the English longitudinal study of ageing. European Journal of Ageing, 5(3), 233. https://doi.org/10.1007/s10433-008-0083-7

R4: SHARE

Börsch-Supan, A., J. Bristle, K. Andersen-Ranberg, A. Brugiavini, F. Jusot, H. Litwin, G. Weber (Eds.) (2019). Health and Socio-Economic Status over the Life Course. First Results from SHARE Waves 6 and 7. Berlin: De Gruyter

Bergmann, M., A. Scherpenzeel and A. Börsch-Supan (Eds.) (2019). SHARE Wave 7 Methodology: Panel Innovations and Life Histories. Munich: Munich Center for the Economics of Aging (MEA).

König, R., & Seifert, A. (2020). From Online to Offline and Vice Versa: Change in Internet Use in Later Life Across Europe. Frontiers in Sociology, 5, 4. https://doi.org/10.3389/fsoc.2020.00004

König, R., Seifert, A., & Doh, M. (2018). Internet use among older Europeans: An analysis based on SHARE data. Universal Access in the Information Society, 17(3), 621–633. https://doi.org/10.1007/s10209-018-0609-5

Tavares, A. I. (2020). Self-assessed health among older people in Europe and internet use. International Journal of Medical Informatics, 141, 104240. https://doi.org/10.1016/j.ijmedinf.2020.104240

R5: normalisation method

Gelman, A. (2008). Scaling regression inputs by dividing by two standard deviations. Stat Med, 27(15), 2865–2873. https://doi.org/10.1002/sim.3107

R6: people may not realise how well they are doing

Choi, J. S., Betz, J., Deal, J., Contrera, K. J., Genther, D. J., Chen, D. S., Gispen, F. E., & Lin, F. R. (2015). A Comparison of Self-Report and Audiometric Measures of Hearing and Their Associations With Functional Outcomes in Older Adults. Journal of Aging and Health, 28(5), 890–910. https://doi.org/10.1177/0898264315614006