Evidence Summary
Older Adults’ Internet Use Is Varied, Suggesting the Need for Targeted
Rather Than Broadly Focused Outreach
A Review of:
van Boekel, L.C., Peek, S. T., & Luijkx, K.G. (2017). Diversity in older adults’ use of the
Internet: Identifying subgroups through latent class analysis. Journal of Medical Internet Research, 19(5:e180),
1-10. doi: 10.2196/jmir.6853
Reviewed by:
Ann Glusker
Research & Data Coordinator
National Network of Libraries of Medicine, Pacific
Northwest Region
University of Washington Health Sciences Library
Seattle, Washington, United States of America
Email: glusker@uw.edu
Received: 28 Feb. 2018 Accepted: 24 Apr. 2018
2018 Glusker.
This is an Open Access article distributed under the terms of the Creative
Commons‐Attribution‐Noncommercial‐Share Alike License 4.0
International (http://creativecommons.org/licenses/by-nc-sa/4.0/),
which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly attributed, not used for commercial
purposes, and, if transformed, the resulting work is redistributed under the
same or similar license to this one.
Abstract
Objective –
To determine the amount and types of variation in Internet use among
older adults, and to test its relationship to social and health factors.
Design – Representative
longitudinal survey panel of households
Setting – The
Netherlands
Subjects – A panel
with 1,418 members who were over 65 years of age had answered the survey
questionnaire that included Internet use questions, and who reported access to
and use of the Internet.
Methods – Using
information about the Internet activities the respondents reported, the authors
conducted latent class analysis and extracted a best-fitting model including
four clusters of respondent Internet use types.
The four groups were analyzed using descriptive statistics and compared
using ANOVA and chi-square tests.
Analysis and comparisons were conducted both between groups, and on the
relationship of the groups with a range of social and health variables.
Main Results – The four clusters identified included: 1)
practical users using the Internet for practical purposes such as financial
transactions; 2) social users using the Internet for activities such as social
media and gaming; 3) minimizers, who spent the least
time on the Internet and were the oldest group; and 4) maximizers, who used the
Internet for the widest range of purposes, for the most time, and who were the
youngest group. Once the clusters were
delineated, social and health factors were examined (specifically social and
emotional loneliness, psychological well-being, and two activities of daily
living (ADL) measures). There were
significant differences between groups, but the effect sizes were small. Practical users had higher psychological well-being,
whereas minimizers had the lowest scores related to ADLs and overall health
(however, they were also the oldest group).
Conclusions
– The establishment of four clusters of Internet use types demonstrates
that older adults are not homogeneous in their Internet practices. However, there were no marked findings
showing differences between the clusters in social and health-related variables
(the minimizers reported lower health status, but they were also the oldest
group). Nevertheless, the finding of
Internet use heterogeneity is an important one for those who wish to connect
with older adults through Internet-based programming. The different patterns evidenced in each
cluster will require differing outreach strategies. It also highlights the need
for ongoing longitudinal research, to determine whether those who are currently
younger and more technologically savvy will age into similar patterns that
these authors found, or whether a new set of older adult Internet use profiles
will emerge as younger generations with more Internet experience and affinity
become older.
Commentary
– For this evidence summary, methodologies were systematically assessed
using Glynn’s critical appraisal checklist (2006). The authors used secondary data from a large
randomized sample, collected in a rigorous manner. Along with their appropriate use of
methodologies and proportionate statements of findings relative to effect
sizes, there are few concerns about this study’s data quality.
Nevertheless, there are limitations of note, several of which (mostly
technical) were mentioned by the authors.
Among those they did not mention was the question of whether the
researchers only including respondents who had Internet access and who also had
completed the “social integration and leisure” questionnaire may have
introduced bias. Also, while they note a survey drawn from Dutch citizens is
“considered to be comparable to other Western populations in terms of Internet
use”, they cite information that Internet use in the United States is 14
percentage points lower than that in the Netherlands, and some Internet
activities among older adults are higher in the Netherlands than in the rest of
Europe, leaving some question of the representativeness of the population
studied. There is also no mention of the
potentially lower percentages of Internet use in households with low income and
disabled older adults (Choi & DiNitto, 2013).
Most importantly, however, the authors note that there was no
information available about the supports available to the respondents for using
technology in general and the Internet in particular. Information about whether older adults were
living alone (which relates to lower percentage uptake of the Internet (van Deursen & Helsper, 2015)),
whether they had ever had jobs requiring Internet use (Chang et al., 2014; Hargittai & Dobransky, 2017),
and what their cognitive status was (Freese et al., 2006), could be very
illuminating to their results.
At the same time, the potential for future studies on the topic of
heterogeneity of older adult Internet use is vast and fascinating, since the
topic is such an important one for those who wish to engage older adults in
order to promote programs and activities such as those related to eHealth. The authors call for a longitudinally-focused
replication of their study, which would demonstrate whether currently younger
adults will age into a similar profile to that now seen, or into a more
Internet-intensive use profile, given their deeper Internet experience. Additionally the authors suggest research on
how older adults overcome physical and mental (specifically, cognitive)
barriers to Internet use, whether there are any direct associations between
declining health and Internet use, and how older adults expand and contract
their choices of activities in general. Hargittai and Dobransky (2017)
also suggest that research include the Internet use skill levels of older
adults: “understanding how the Internet and online services work is something
that can be taught and is thus open to intervention, [so] it is an important
factor to examine in work on digital inequality”(p. 208).
Finally, the phenomenon of eHealth is referred to
repeatedly in this article, and it would help the reader to have a clearer
definition of how that is experienced in the Dutch context.
References
Chang,
J., McAllister,C., &
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N.G. & DiNitto, D.M. (2013). The digital divide
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