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. Author manuscript; available in PMC: 2024 Mar 1.
Published in final edited form as: Arch Gerontol Geriatr. 2022 Nov 24;106:104877. doi: 10.1016/j.archger.2022.104877

Technology Use and Subjective Cognitive Concerns in Older Adults

Jared F Benge 1,2, Andrew M Kiselica 3, Alyssa Aguirre 1,2,4, Robin C Hilsabeck 1,2, Michael Douglas 5, David Paydarfar 1,2, Michael K Scullin 6
PMCID: PMC9868079  NIHMSID: NIHMS1854091  PMID: 36459914

Abstract

Objectives:

How technology impacts the day to day cognitive functioning of older adults is a matter of some debate. On the one hand, the use of technologies such as smartphones and social media, may lead to more subjective cognitive concerns (SCC) by promoting distractibility and reliance on devices to perform memory tasks. However, continued digital engagement in older adults may also be related to better cognitive functioning. Given these competing viewpoints, our study evaluated if frequency of digital device use was associated with greater or less subjective cognitive concerns.

Method:

Participants were 219 adults over the age of 65 (mean age =75 years) who had internet access. Measures assessing frequency of digital device use along with SCC were administered. Hierarchical multiple regression was used to gauge association between frequency of device use and SCC, controlling for relevant demographic and lifestyle factors.

Results:

Increased frequency of digital device use was associated with less SCC, over and above the influence of demographic factors, across cognitive (but especially in executive) domains. This effect was observed for general device usage, with no statistically significant associations were observed between texting/video call, social media use and SCC.

Discussion:

Results were broadly consistent with the technological reserve hypothesis in that digital engagement was associated with better experienced cognitive functioning in older adults. While device use may contribute to distractibility in certain cases, the current results add to a burgeoning literature that digital engagement may be a protective factor for cognitive changes with age.

Keywords: Internet, Social Media, Memory, Cognition, Executive Functioning, Text Messaging

1. Introduction

Increasingly, cognitive scientists have raised concerns about the potential negative impact of digital technologies (such as social media, internet, and smartphone use) on cognitive functioning (Schacter, 2022; Small et al., 2020). This digital distraction hypothesis predicts that increased technological engagement might lead to negative cognitive outcomes either in the form of a) executive dysfunction marked by increased distractibility, shallower cognitive processing, and difficulties with task organization/completion and/or b) increased forgetfulness, owing to reliance on technologies undercutting innate memory systems for tasks, such as route finding or recall of personal information like phone numbers (Small et al., 2020; see also Ng, Lim, Niti, & Collinson, 2012).

In older adults, there is empirical support for increased subjective cognitive concerns (SCC) among individuals who report more frequent use of certain technologies, such as social media (Sharifian & Zahodne, 2020) or texting in specific high cognitive demand situations (Sanjani et al., 2020). Older adults are already at increased risk for increased SCC due to a host of factors, including age related cognitive changes, incipient neurodegenerative disease, and other neuropsychiatric disorders (Rabin et al., 2017). Thus, the digital distraction hypothesis would assert that for older adults crossing the ―digital divide‖ (Charness & Boot, 2022)—that is, incorporating more technology use into everyday life— greater technology use could increase SCC.

While digital distraction may occur at the level of specific situations, a competing hypothesis is also emerging in the literature, termed the technological reserve hypothesis (Benge & Scullin, 2020; Wolff et al., 2021). According to the technological reserve hypothesis, the potentially cognitively stimulating and compensatory(e.g., automated reminders) benefits of engaging with technology may confer a protective effect on for older adults marked by better cognitive performance and less risk of developing dementia (Jones, Benge & Scullin, 2021; Krell-Roesch et al., 2017; Wilson et al., 2022). A corollary of the technological reserve hypothesis would be that that increased use of technologies should lead to less SCC among older adults.

Whether help or hindrance, technology use may have a differential impact on specific aspects of subjective cognitive functioning, particularly memory and executive functioning. As noted above, distractibility and dependence on devices for memory has been hypothesized as potential cognitive downsides of digital engagement. Further, better memory and executive functioning are most strongly associated with efficient use of technology-based healthcare tools (Czaja et al., 2013), efficient use of everyday technological devices (Slegers, Van Boxtel, & Jolles, 2009), and in a large longitudinal study (Choi, Wisniewski, and Zelinksi, 2021) and clinical study (Wu, Lewis, & Rigaud, 2019) greater technology use was associated with better executive functioning and memory performance at follow up. Thus, current evidence suggests that technology use may be most strongly associated with everyday performance in the memory and executive domains.

In light of these two competing theories and the need for more research on technology and SCC in older adults we investigated whether digital technology use was associated with better or worse SCC in older adults. Within this broader aim we had three specific questions:

  1. For older adults who have crossed the digital divide, is technology use associated with more less SCC?

  2. Do differing domains of SCC (e.g. memory concerns vs. executive functioning concerns) demonstrate differential associations with technology use?

  3. Are there particular facets of technology use that are associated with SCC? In other words, do different uses of technology (overall device use, interpersonal uses like texting, social media use) lead to different SCC outcomes in older adults?

2. Methods

2.1. Sample and Data Collection

Data were obtained from a community based online survey conducted from April to July 2021 that queried questions related to the impact of technology use in older adults during the COVID-19 pandemic as well as technology use in activities of daily living (for details see Benge et al, 2022). Two hundred and nineteen participants over the age of 65 completed surveys. Given the ongoing COVID-19 pandemic at the time of data collection, recruitment was primarily done via dissemination of an IRB approved flyer through several community organizations email list servs, most notably the Georgetown Neuroscience Foundation (www.georgetownneurosciencefoundation.org). Respondents were primarily from the suburbs of the Austin, Texas region of the United States of America.

To meet inclusion criteria of digital exposures enough to study the primary aims, participants included only individuals who had internet access at home and who answered no to questions regarding having received a diagnosis of mild cognitive impairment or dementia. Additional descriptive data for the sample are reported in Table 1. All study procedures were approved by the local institutional review board.

Table 1:

Descriptive Data for the Sample (n=219)

Variable Mean SD %
Demographics
Age 75.01 5.21
Education (years) 16.72 2.01
% with college degree 38.4
% with graduate degree 41.5
Gender % Female 37.9
% Male 62.1
Race and Ethnicity % Non-Hispanic White 97.2
% Other 2.8
Employment Status % Retired 91.3
Clinical Variables
Current reported depression T-score (M=50, SD=10) 42.72 5.87
Everyday Cognition Scale-Total Score (1= no change; 4=much worse) 1.37 0.26
Memory Score (1= no change; 4=much worse) 1.67 0.44
Executive Composite Score (1= no change; 4=much worse) 1.25 0.27
Technology Use and Ownership % Own or Use
Smartphone (0=Never; 6=Many Times a Day) 5.51 1.47 94.1
Tablet (0=Never; 6=Many Times a Day) 3.16 2.69 65.3
Desktop or Laptop Computer (0=Never; 6=Many Times a Day) 5.11 1.58 96.8
Sum of Device Use Frequency 13.77 3.28
Text Messaging (0=Never; 6=Many Times a Day) 4.82 1.66 94.1
Video Calls (0=Never; 6=Many Times a Day) 2.16 1.68 72.6
Sum of Interpersonal Activity Use Frequency 6.97 2.76
Facebook Use (0=Never; 6=Many Times a Day) 2.83 2.52 62.1
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2.2. Measures

2.2.1. Everyday Cognition Scale.

The Everyday Cognition Scale (ECOG) is a 39 item scale empirically designed to evaluate SCC relative to an individual’s baseline (Farias et al., 2008). Though originally created as an informant report, the self-report form has been validated as a measure of SCC and is used in major aging and neurodegenerative related projects, such as the Alzheimer’s Disease Neuroimaging Initiative (Swinford et al., 2018). On the ECOG, individuals report their current difficulty with a given cognitive task from a scale of 1 (no change relative to 10 years prior) to 4 (much worse now than 10 years ago). Items cover several domains of cognition, including planning, organization, divided attention (grouped together for the current study as an executive composite score), episodic memory, language, and visuospatial functioning. Across these domains scales, higher scores indicate worsening perceived cognitive functioning.

2.2.2. Technological Activities Survey.

The technological activities survey asked participants to rate their frequency of use from 0 (Never) to 6 (many times a day) for a range of devices and internet services. For the current paper, the focus was on a)general device use; b) interpersonal activity use, and c) social media use. For this study, general device use was defined as frequency of uses of technologies across smartphone, computer, and tablets. For interpersonal activity use, frequency of texting and video phone calls was measured. For social media use, we queried about a range of services and found Facebook being the most frequently used by 62.1% of participants. Frequency of use of other networks without Facebook was relatively rare, with only 6 participants using Instagram only, 1 Reddit only, and 3 using Twitter only; thus Facebook use was employed as our marker for social media use.

2.2.3. Demographics and Depression.

Participants reported health information including answering questions as to whether they had ever received a prior diagnoses of dementia or mild cognitive impairment. Participants also completed the Neuro QOL 1.0 Depression Short Form Questionnaire (Cella et al, 2011) as an index of current depressive symptoms. The Neuro-QOL 1.0 Depression short form measure is an 8 item measure of depressive symptoms. Scores for this measure are converted via item response theory scoring into T-scores, such that an average score (50T) represents no elevation in depression relative to the United State general population, and higher T-scores indicate more depressive features. These normative sample consisted of English-speaking respondents recruited to be comparable to the United States population demographically (n=2113; for additional details see Cella et al, 2011).

2.3. Analysis and Sample Size Justification

Pearson correlations were used to determine the relationship between SCC and technology use. Next, hierarchical multiple regression was used to determine the relationship of technology use with SCC over and above factors known to influence SCC such as age, education, and depression (Rabin et al., 2017). For research question 1, total score on the ECOG was used as the dependent variable, with control variables entered on step 1, and general device use, interpersonal technology use, and social media use entered on steps 2–4. This analytic plan was repeated with ECOG memory concerns and then ECOG executive composite score as the dependent variables to address research question 2. This method also allowed us to compare the impact of general device use, interpersonal technology use, and social media use on SCC (research question 3). Post-hoc power estimates suggested the available sample size (n=219) was sufficient for power >0.8 to detect small to medium effect sizes (Soper et al 2022).

3. Results

3.1. Association of Technology Use with Overall Cognitive Concerns

General device use demonstrated an inverse correlation with global SCC (r=−.216, p= .001), indicating that more frequent device use was associated with less SCC (consistent with the technological reserve hypothesis). Table 2 indicates that the strongest individual correlate of global SCC was tablet use.

Table 2:

Correlations of Technology Use with Domains of Cognitive Concerns

Technology Variable Total Cognition Concerns Memory Concerns Executive Concerns
Total Device Use −.216** −.199** −.248**
Smartphone Use −0.12 −.164* −0.09
Tablet Use −.146* −0.124 −.210**
Computer Use −0.089 −0.049 −0.074
Interpersonal Computer Use −0.045 −0.023 −0.044
Texting −0.09 −0.05 −0.106
Video Calling 0.014 0.011 0.033
Social Media (Facebook) Use 0.014 0.033 −0.005
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**

Correlation is significant at the 0.01 level (2-tailed).

*

Correlation is significant at the 0.05 level (2-tailed).

Hierarchical multiple regression was used to determine the relative contribution of technology use to SCC, over and above demographic factors and depression (Table 3). Age, education and depression emerged as significant predictors of global SCC, explaining 16.6% of the variance. Total device usage (Table 3, model 2) resulted in a statistically significant increase in R2 (an additional 2.5% of the variance explained). Interpersonal device use and social media (Facebook) usage did not increase the predictive utility of the model (Table 3, models 3 and 4).

Table 3:

Hierarchical Regression Predicting Total Cognitive Subjective Cognitive Concerns

Everyday Cognition Total Score
Model 1 Model 2 Model 3 Model 4
Variable B β B β B β B β
Constant 0.317 0.529 0.502 0.467
Age 0.014 0.265** 0.012 0.241** 0.013 0.25** 0.013 0.259**
Gender −0.006 −0.011 0.002 0.003 −0.005 −0.01 −0.003 −0.006
Education −0.023 −0.173* −0.019 −0.146* −0.02 −0.149* −0.019 −0.146*
Depression 0.01 0.215* 0.01 0.21* 0.009 0.208* 0.009 0.201*
Frequency of Device Usage −0.013 −0.163* −0.015 −0.182* −0.015 −0.192*
Frequency of Interpersonal Tech Usage 0.005 0.055 0.004 0.044
Frequency of Social Media Usage 0.007 0.007
R2 0.166 0.191 0.193 0.197
F 9.099** 8.593** 7.223** 6.316**
ΔR2 0.025 0.002 0.004
ΔF 5.642* 0.495 0.894
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Note:

*

p<.05,

**

p<.001

3.2. Association of Technology Use with Memory Concerns

Total device use was inversely related to memory concerns (r=−.199, p<.01; Table 2), indicating that more frequent device usage was associated with less reported memory concerns. Interestingly, smartphone usage was the strongest individual predictor of decreased memory concerns (Benge et al., 2020).

In hierarchical multiple regressions, (see Table 4), the overall pattern of results was similar to global SCC, with age, education, and depression explaining 16.1% of the variance in subjective memory concerns (model 1). Adding total device usage to the model (Table 4 model 2) resulted in a statistically significant increase in R2 (an additional 1.9 % of the variance explained). Interpersonal device usage and social media (Facebook) usage did not increase the predictive ability of the model (Table 4, models 3 and 4).

Table 4:

Hierarchical Regression Predicting Memory Concerns

Everyday Cognition Memory Score
Model 1 Model 2 Model 3 Model 4
Variable B β B β B β B β
Constant 0.559 0.871 0.787 0.722
Age 0.019 0.216* 0.017 0.194* 0.018 0.211* 0.019 0.222*
Gender −0.022 −0.024 −0.012 −0.012 −0.033 −0.036 −0.03 −0.032
Education −0.053 −0.239** −0.048 −0.216* −0.049 −0.222* −0.048 −0.219*
Depression 0.015 0.201* 0.015 0.192* 0.014 0.188* 0.014 0.181*
Frequency of Device Usage −0.019 −0.143* −0.024 −0.177* −0.025 −0.188*
Frequency of Interpersonal Tech Usage 0.016 0.101 0.014 0.089
Frequency of Social Media Usage 0.013 0.072
R2 0.161 0.18 0.188 0.193
F 8.802** 8.016** 6.986** 6.138**
ΔR2 0.019 0.008 0.005
ΔF 4.249* 1.682 1.044
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Note:

*

p<.05,

**

p<.001

3.3. Association of Technology Use with Executive Concerns

The strongest, although still small to medium in size, inverse correlation of total device use was observed with executive concerns (r=−.248, p<.01; see Table 2), indicating that greater device usage was associated with less executive concerns. Greater usage of tablets, but not smartphones, was associated with fewer executive concerns (r=−.210, p< .01).

Table 5 displays the regression models for ECOG executive composite scores. The results for executive concerns were different than the results for global and memory scores in that age was the only demographic factor to significantly contribute to subjective executive scores (8.8% of the variance; Table 5, model 1). The addition of device usage (Table 5 model 2) resulted in 6% of additional variance explained. Interpersonal and social media usage did not contribute significantly to executive Concerns (models 3 and 4).

Table 5:

Hierarchical Regression Predicting Executive Concerns

Everyday Cognition Executive Composite
Model 1 Model 2 Model 3 Model 4
Variable B β B β B β B β
Constant 0.19 0.513 0.478 0.459
Age 0.012 0.245** 0.01 0.206* 0.011 0.219* 0.011 0.224*
Gender −0.005 −0.01 0.006 0.011 −0.003 −0.006 −0.002 −0.004
Education −0.007 −0.052 −0.001 −0.01 −0.002 −0.015 −0.002 −0.014
Depression 0.006 0.132 0.006 0.126 0.005 0.123 0.005 0.119
Frequency of Device Usage −0.02 −0.254** −0.022 −0.28** −0.022 −0.285**
Frequency of Interpersonal Tech Usage 0.007 0.074 0.006 0.068
Frequency of Social Media Usage 0.004 0.037
R2 0.088 0.149 0.153 0.154
F 4.418* 6.362** 5.442** 4.683**
ΔR2 0.061 0.004 0.001
ΔF 12.981** 0.865 0.261
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Note:

*

p<.05,

**

p<.001

4. Discussion

Older adults are increasingly crossing the digital divide (Charness & Boot, 2022) with critical implications to daily life and research. Digital technologies are rapidly becoming core means of performing daily activities, such as navigating in the community and performing financial tasks (Benge et al, 2020) even amongst older adults. Internet enabled communication increasingly serves as a foundational skill for improving social engagement and connection with age (Dodge et al, 2015). Mobile based cognitive assessment and technology based cognitive interventions are becoming increasingly entwined in clinical research and practice with older adults (Scullin et al, 2021; Thompson et al, 2022). Thus, it becomes critical to consider how the pervasive use of computer and smart phone technologies might also be contributing to SCC (digital distraction hypothesis). However, in our study we found small, but statistically significant inverse relationships between technology use and SCC, suggesting that increased use was associated with fewer SCC.

We believe that this finding provides additional support for the technological reserve hypothesis (Benge & Scullin, 2020). While our results were not experimental and cannot prove a causal relationship between technology use and fewer subjective cognitive concerns, the results are grounded against the backdrop of studies that suggest such a causal relationship may exist. For example, when older adults were trained to use a tablet (Chan et al., 2016) cognition was better with increased use. Further, in a large longitudinal representative sample, even after controlling for a number of confounds, technology use was associated with better long term cognitive outcomes (Choi, Wisniewski, & Zelinski, 2021). Detailed epidemiological studies also suggest such an association between technology use and lower risk of developing MCI and dementia (Krell-Roesch et al., 2017).

However, we also acknowledge that there are compelling alternative explanations for our observed association as well. Decreasing technology use might be an indicator of the earliest stages of cognitive decline (Pieau et al., 2019) and so continued device use might reflect the inverse: be a marker of cognitive health. Both phenomena may be true as well suggestive of a bidirectional relationship (Choi, Wisniewski, & Zelinski, 2021): declines in technology use might presage cognitive decline in older adults but continued attempts at engagement may provide some cognitive stimulation or other benefit. As older adults increasingly engage with digital technologies, teasing apart these complex relationships will likely be an important goal to understand cognitive and digital health.

4.1. Is the Association of Technology Domain Specific?

While the effect sizes technology use for general cognitive concerns and memory concerns were statistically significant, the associations were relatively small, explaining approximately 2% of incremental variance, with factors such as age and depression contributing more strongly to SCC. However, for executive concerns, the magnitude of effect was roughly three times the size (6% of variance explained), a magnitude similar to that of age. This suggests that device usage is associated with fewer executive concerns in particular.

The relatively stronger relationship between executive concerns and daily technology use has been observed in some clinical studies as well (Schmidt & Wahl, 2019). Questions of causation again are present here, with some evidence to suggest those who have the least executive dysfunction may be the ones most likely to continue using digital technologies with age (e.g., Sommerlad et al., 2020; Wu et al., 2019). However, although findings have been mixed (see Zhang et al., 2021), training in digital technologies, such as gaming, has also been shown to have a particular beneficial effect on speed of processing and divided attention (Wang et al., 2021).

Our findings also parallel results from Choi and colleagues (2021). In this study which utilized a large scale representative longitudinal study of older adults, the use of internet communication technologies predicted both objective memory and executive functioning over time. However, controlling for baseline cognitive functioning, the impact of technology use was greater for executive functions than memory performance, suggesting an objective beneficial impact for technology use on executive tasks in particular. In contrast, declines in memory predicted later declines in technology use; executive functioning did not show such a pattern. In sum, our current results add to an emerging literature which suggests that technology use may have differential impacts on different domains of cognitive functioning. In particular technology use may be particularly beneficial for executive skills while the impacts on other domains of cognition may be more modest.

4.2. Does Type or Form of Technology Use Matter?

In our study, reported use of technological devices, particularly the use of mobile devices (smartphones/tablets) as opposed to desktop/laptop computer use was correlated with fewer SCC. We hypothesize that mobile devices may allow for more ubiquitous engagement and in turn cognitive stimulation; thus, this ability for routine use in general drives the positive relationship with subjective cognition that we observed. An alternative or additional theory is that older adults who routinely use smartphones or tablets would have had to acquire these skills in the last several years, versus computer use, which would have been available for several decades. It may be that the ability and/or desire to learn new devices over time may be driving portions both of improved subjective cognitive status and frequency of use.

While frequency of device use demonstrated statistically significant associations with SCC, no major effect was noted for interpersonal uses of device like texting/video calls or social media use (cf. Amini, Chee, Mendieta, & Parker, 2019). These findings are at odds with ecological momentary assessment studies, which have found greater SCC on days of high social media consumption in older adults (Sharifian & Zahodne, 2020). However, these discrepant findings may reflect increased ―in the moment cognitive effort‖ being expended with digital technology use and not be associated with frequency of SCC in aggregate.

4.3. Limitations

As a cross sectional and correlational study, there are limitations in interpretation that need to be considered. First, it is possible that the relationship goes the opposite direction: older adults with fewer subjective cognitive concerns may be more likely to use devices more frequently either due to cognitive capacity (e.g., Wu et al., 2019) or self-efficacy with learning to use the devices (e.g., Schmitter-Edgecombe et al., 2022). There is mixed data to support this hypothesis, with some studies suggesting that executive function declines in particular are associated with less digital technology use, while as noted above, experimental studies have suggested that training older adults to use digital devices benefits cognitive functioning. Therefore, it seems most likely that there is a bidirectional relationship between technology use and cognitive functioning in older age; declining device usage is an indicator of cognitive decline while those who can engage with and continue to use technologies over time outperform their peers (Choi et al., 2021). Future experimental and longitudinal studies will be needed to tease apart this relationship.

Second, the current results arise from a sample of older adults who have crossed the digital divide, and are older, predominantly white, able to receive and respond to a survey online, and well educated. Whether the patterns observed in our study generalize to samples de novo use of digital technologies in individuals with fewer socio-economic advantages, baseline technical familiarity, and historically less access to digital technologies warrants further study. For example, if this survey was administered via paper and pencil to individuals with fewer devices or technologies (or less familiarity with them), a participant could experience more subjective cognitive problems or distractions in the face of a learning these new technologies. We further highlight that these findings are taken from a sample that was predominantly retired. Given an increasingly aging workforce and the impact of retirement on technological engagement amongst older adults (Schuster & Cotton, 2022), exploring these associations in older adults who are employed is another direction for future studies.

A third limitation is the fact that self-reports of technology use were employed. While commonly utilized in longitudinal studies, self-report of technology use may be subject to recall bias and other distortions. As technologies emerge that allow for more accurate and objective monitoring of say internet or smartphone use, the associations reported in this paper should be revisited.

4.4. Implications and Future Directions

As the generation that gave the world the internet ages, questions emerge about how aging and technology use might be impacting cognitive processes. The current results suggest the potential for positive/protective associations between device use and cognitive outcomes, consistent with the technological reserve hypothesis.

  • Technology use might increase distractibility or forgetfulness in older adults

  • However, some evidence suggests technology use might provide cognitive benefit.

  • We evaluated cognitive concerns and technology use in 219 older adults

  • Higher use of digital devices was associated with fewer cognitive concerns

  • This impact was particularly strong for executive functioning

  • Implications for the development of technological reserve is discussed

Acknowledgements

We wish to thank the members of the Medical Advisory Board for the Georgetown Neuroscience Foundation (https://georgetownneurosciencefoundation.org/) for their gracious assistance in reviewing the surveys and distributing the opportunity to participate to their membership.

Funding

This work was supported in part by the National Institutes of Aging at the National Institutes of Health (grant number 1R01AG077017) and Alzheimer’s Association (AARG-22-924771).

Footnotes

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Conflict of Interest

The authors report no conflict of interest.

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