Abstract

Older adults tend to under-utilize digital technology and online services that can yield substantial benefits to their health and wellbeing. Addressing this problem requires determining robust and consistent predictors of older adults' technology use. Also, few studies have examined older adults who are elite users of digital technology, who may provide insights into how individuals can prepare to become competent users of future technologies as they age. To address these gaps in the technology and aging literature, this dissertation offers (1) large-scale machine learning analyses, (2) longitudinal perspectives, (3) age group comparisons across the adult life span, (4) the novel recruitment of elite, older users of digital technology, and (5) the development and validation of a technology use scale focused on current innovations. In Study 1, data from the Health and Retirement Study were used. Machine learning classified Internet users versus non-users with an accuracy of ~80%. Across a 14-year span, results largely supported current models of aging and technology use. Age, cognition, and socioeconomics emerged as the most robust and consistent predictors of Internet use from competition with hundreds of variables. In Study 2, the outcome variable was expanded to include nine domains of technology use. Elite, older users exhibited many markers of successful aging, including higher levels of cognition, socioeconomics, and self-efficacy. Across studies, results suggested that skills needed to engage with technology at a basic level differ slightly from those needed to reach higher levels of technology use. Specifically, poor episodic long-term memory may pose a barrier to basic technology use among older adults (e.g., assessing the Internet), while better short-term memory is required to achieve elite-level technology use. These results highlight the potential value of exposure to new technology at a younger age – when there are fewer barriers of entry (e.g., cognitive limitations) and a foundation of technology use principles can be developed and built upon across adulthood.

Notes

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Graduation Date

2022

Semester

Spring

Advisor

Lighthall, Nichole

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

Psychology

Degree Program

Psychology; Human Factors Cognitive Psychology

Format

application/pdf

Identifier

CFE0009082; DP0026415

URL

https://purls.library.ucf.edu/go/DP0026415

Language

English

Release Date

May 2022

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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