Internet Use and Cognitive Functioning in Later Life: Focus on Asymmetric Effects and Contextual Factors

Yijung K. Kim1 & Sae Hwang Han2

1Texas Aging & Longevity Center, The University of Texas at Austin

2Department of Human Development and Family Sciences, The Univeristy of Texas at Austin

Published in The Gerontologist


  • To examine how changes in Internet use influence older adults' cognitive decline over time, we analyzed the effects of Internet use onset and Internet use cessation on older adults' cognitive functioning over time (2002-2019) using the Health and Retirement Study.

  • We also examined the contextual effect of birth cohorts and living arrangement.

  • Transitioning into Internet use (i.e., Internet use onset) was associated with a concurrent increase in the level of cognitive functioning as well as a slower rate of cognitive decline.

  • Transtioning out of Internet use (i.e., Internet use cessation) was associated with a concurrent decrease in the level of cognitive functioning as well as a faster rate of cognitive decline.

  • The detrimental effects of ceasing Internet use was worse for older adults born in 1941 or before.

  • The cognitive benefits of starting Internet use were greater for those older adults who live alone.


Despite emerging literature linking Internet usage and cognitive functioning in later life, research seldom takes changes in older adults’ Internet use into account. How changes in Internet use influence older adults’ cognitive decline over time, particularly in the context of sociodemographic factors that shape Information and Communication Technology (ICT) use, remains an open question.Using 9 waves of panel data from the Health and Retirement Study (2002–2018), we examined within-person asymmetric effects of transitioning into and out of Internet use on cognitive functioning, and whether the associations vary across birth cohorts and by living arrangement.The findings highlight the interplay between technology, social environment, and cognitive functioning in later life. The salubrious effects of using the Internet, as well as the deleterious effects of ceasing to use such technology, underscore the importance of promoting digital literacy and access to ICT among the older adult population.


Internet as an antecedent of cognitive health, based on the idea that the Internet offers a rich array of activities that may be cognitively stimulating for older adults1,2. However, we are still at an early stage in our understanding of the topic. Little is known about how changes in Internet use and nonuse across time influence cognitive functioning, and how such linkages may vary depending upon the sociodemographic context in which the Internet is used across individuals. These represent important gaps in the literature because the pattern of engagement with the Internet changes over time, and older adults are not a homogeneous group in their reasons for use/nonuse of the Internet3-5. Such complexities, in turn, are likely to influence the cognitive benefits derived from using the Internet in later life. By examining how patterns of technology use influence the health and well-being of U.S. older adults and identifying the effects of contextual factors, this study aims to expand our understanding of the consequences of digital inequalities in later life.

  1. This study investigated within-person associations between changes in Internet use and cognitive functioning in later life.

  2. We further investigated how such Internet–cognition nexus varies by birth cohorts and living arrangement, which are known to influence older adults’ engagement with the Internet.


the Health and Retirement Study is a nationally representative panel survey of individuals older than age 51 and their spouses (of any age) in the United States. The initial HRS cohort (b. 1931–1941) was interviewed in 1991, which was later merged with a subsequent study known as Asset and Health Dynamics Among the Oldest Old (b. 1890–1923). Two new cohorts—the Children of the Depression (b. 1924–1930) and the War Babies (b. 1942–1947)—were further recruited in 1998. The HRS replenishes the sample every 6 years; Early Baby Boomers (b. 1948–1953) and Mid Baby Boomers (b. 1954–1959) were added in 2004 and 2010, respectively. Blacks and Hispanic participants were oversampled in the HRS.

We analyzed nine waves of data from 2002 to 2018 because information on Internet use was not available before the 2002 wave. Between 2002 and 2018, 34,318 nonproxy respondents who were part of the targeted HRS cohorts were interviewed. Among 34,318 nonproxy respondents, we restricted our sample to 18,646 individuals who were 65 or older and had completed the full cognitive battery at a given wave; accordingly, “baseline” in this study refers to the first wave in which an individual was at least 65 years old during the study period. Of 18,646 respondents, we further excluded 54 individuals (0.3%) who were missing information on the key study variables. The final study included 18,592 respondents (respondent-wave observation N = 78,289).

We adopted the recently proposed asymmetrical fixed effects approach6 that separates individuals’ record of Internet use into a positive (i.e., changing from nonuse to use; transitioning into Internet use) and a negative (i.e., change from use to nonuse, transitioning out of Internet use) component. Given that the fixed-effects models do not produce estimates for time-invariant characteristics, the asymmetrical effects approach was incorporated into the within–between random effects (REWB) modeling framework. REWB models decompose all time-varying predictors to a between-person (BP; Level 2) component, calculated as the person-specific mean across the observation period, and a within-person component (WP; Level 1), calculated as the deviation from one’s person-specific mean at each occasion7,8 .

The abbreviated multilevel equation for predicting person i’s cognitive functioning at time t (i.e., Cogit) was as follows:

where γ00 and γ10 represent the fixed intercept and the effect of time (elapsed from baseline). The within-person effects of transitioning into and out of Internet use were captured with γ20 and γ30, respectively. The within-person effects of time-varying covariates (TVC), which included marital status, household income, depressive symptoms, health conditions, and activities of daily living limitations, are represented by γ40. Following Curran and Bauer (2011)9, γ01 (i.e., person-mean of time) was also added to the model to adjust for panel observations that were unbalanced with respect to time. The between-person effects of transitioning into and out of Internet use are given by γ02 and γ03; the between person effects of TVCs are given by γ04. The effects of time-invariant covariates (TIC), which included baseline age, gender, race/ethnic status, and education, are given by γ05. Random intercept, random effect of time, and residual are represented by u0i, u0i, and eti, respectively.


Sample Characteristics

At baseline, study participants on average were 71 years old and reported about two health conditions: almost no difficulty with activities of daily living and a little more than one depressive symptom. The majority of participants were non-Hispanic White, female, married, had completed high school, and about 33% reported using the Internet at the initial observation. Approximately 13% of the participants (n = 2,376) reported transitioning into Internet use, and about 14% (n = 2,525) reported transitioning out of Internet use during the 16-year period (not shown in tables). About 2% of participants transitioned into (n = 375) or transitioned out of (n = 324) Internet use 2 or 3 times. In total, there were 5,647 transitions around Internet use over the study period.

Associations Between Internet Use/Nonuse and Cognitive Functioning

As expected, transitions into Internet use were associated with an increase in the level of cognitive functioning at a given wave (b = 0.40, p < .001), while transitions out of Internet use were associated with a decrease in the level of cognitive functioning at a given wave (b = −0.37, p < .001). The transition into Internet use mitigated the rate of cognitive decline (b = 0.16, p < .001), and the transition out of Internet use worsened the rate of cognitive decline over time (b = −0.25, p < .001).

For the earlier-born cohort, transitioning into Internet use was associated with an increase in the level of cognitive functioning at a given wave (b = 0.43, p < .001), and transitioning out of Internet use was associated with a decrease in the level of cognitive functioning at a given wave (b = −0.43, p < .001). Moreover, transitioning in and out of Internet use mitigated (b = 0.18, p < .001) and exacerbated (b = −0.24, p < .001) the rate of cognitive decline for the earlier-born cohort, respectively. In contrast, transitions around Internet use were unrelated to the level of cognitive functioning among the later-born cohort.

Regardless of living arrangements, transitions into Internet use attenuated the rate of cognitive decline (living with others b = 0.15, p < .001; living alone b = 0.21, p < .001), while transitions out of Internet use accelerated the rate of cognitive decline (living with others b = −0.26, p < .001; living alone b = 0.23, p < .001). A difference between living arrangement types emerged regarding the effect of transitioning into Internet use; the concurrent cognitive benefits associated with the transition into Internet use were greater when individuals were living alone as opposed to living with others (p = .044).

Figure 1. A graphical representation of the effect of transitioning into Internet use (i.e., Internet use onset) on the rate of cognitive decline over a 16-year period.


The Internet has become an essential, if not an indispensable, aspect of daily living for most Americans. In addition, the recent coronavirus disease 2019 pandemic highlighted the significance of digital participation for older adults when physical distance became the recommended practice. While evidence often supports the benefit of using new technologies (e.g., Internet, smartphones) in later life, research seldom explores the potential detriments of use cessation. To the best of our knowledge, this is the first study that uncovered cognitive detriments of Internet use cessation in addition to the cognitive benefits of starting Internet use. Identifying.

Furthermore, more nuanced findings emerged across birth cohorts and living arrangements. In particular, older adults born in 1941 or before reported cognitive decline when they stopped using the Internet, and individuals living alone reaped cognitive benefits when they started using the Internet, thus underscoring how sociodemographic characteristics contextualize the Internet–cognition nexus. These findings strongly support policy endeavors and intervention efforts aimed at promoting digital engagement and technology use in later life, especially for sectors of the older population deemed to be on the “wrong” side of the digital divide.


The one-item measure of Internet use in this study is less than optimal to capture the wide variation in older adults’ online engagement. In this study, we were not able to explicate the impact of accumulated socioeconomic disadvantages in relation to Internet use, in part because the data do not allow us to understand in what capacity older adults used the internet. Relatedly, the present approach does not model cognitive outcomes as a function of the duration of continued Internet use/nonuse, and the question of how the duration of dis(use) affects cognitive functioning over time remains to be answered in a future study. We further note that the age–period–cohort effects represent important and unique forces that could shape how Internet use may influence cognitive functioning. The current research design did not account for period effects, in part due to the identification problem associated with the linear relationship between age, period, and cohort. As such, this study assumed that period effects of sociohistoric events that occurred during the observation period (i.e., a wider Internet availability) manifested through cohort succession, rather than influencing Internet use behaviors of different cohorts in an identical manner10. Finally, we are unable to claim causality in our findings in part because Internet use may also be influenced by one’s cognitive capacities.


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