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Does internet use enhance the cognitive abilities of middle-aged and older adults? Dual evidence from regression analysis and generalized random forests

Published online by Cambridge University Press:  05 March 2026

Jia Tang Open the ORCID record for Jia Tang [Opens in a new window] ,
Baokui Shao ,
Xiaosen Zang  and
Zhenglong Cong Open the ORCID record for Zhenglong Cong [Opens in a new window]
Jia Tang
Affiliation:
School of Population and Health, Renmin University of China, Beijing, China Bigdata and Responsible Artificial Intelligence for National Governance, Renmin University of China, Beijing, China
Baokui Shao
Affiliation:
Wenlan School of Business, Zhongnan University of Economics and Law, Wuhan, China
Xiaosen Zang
Affiliation:
School of Economics, University of Chinese Academy of Social Sciences, Beijing, China
Zhenglong Cong*
Affiliation:
School of Social Research, Renmin University of China, Beijing, China
*
Corresponding author: Zhenglong Cong; Email: congzhenglong@ruc.edu.cn
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Abstract

Rapid population ageing and the digitalization of daily life have created a dual challenge: ensuring cognitive health while preventing digital exclusion among older adults. While the ‘use it or lose it’ hypothesis suggests that environmental stimulation protects against cognitive decline, international evidence regarding the causal impact of internet use on cognition remains mixed, particularly in developing contexts where digital adoption is uneven. This study addresses this gap by investigating the causal impact of internet use on cognitive abilities among middle-aged and older adults in China, while also exploring the heterogeneity of effects and potential underlying mechanisms. Utilizing three waves of panel data from the China Health and Retirement Longitudinal Study (CHARLS) with 40,438 observations, we employ instrumental variable (IV) estimation to address endogeneity and generalized random forests (GRFs) to estimate heterogeneous treatment effects. The results demonstrate that internet use significantly enhances cognitive abilities, with the IV estimates confirming a strong causal link. Notably, the GRF analysis reveals that these cognitive benefits are larger for rural residents, individuals with lower education levels and those who adopt the internet in middle age. Mechanism analyses indicate that these benefits are primarily driven by increased social interaction and reduced stress levels rather than changes in health behaviours. We conclude that digital inclusion serves as a critical non-medical intervention for healthy ageing, particularly in developing countries where it can help bridge cognitive disparities caused by socio-economic inequalities.

Keywords

internet usecognitive abilityinstrumental variablemachine learningI12I18O33

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Article
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Ageing & Society , Volume 46 , 2026 , e38
Copyright
© The Author(s), 2026. Published by Cambridge University Press.

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Footnotes

The authors contribute equally to this work. All errors are our own.

References

Abaluck, J and Gruber, J (2011) Choice inconsistencies among the elderly: evidence from plan choice in the medicare part D program. American Economic Review 101, article 4. https://doi.org/10.1257/aer.101.4.1180.Google ScholarPubMed
Anstey, KJ, von Sanden, C, Salim, A and O’Kearney, R (2007) Smoking as a risk factor for dementia and cognitive decline: A meta-analysis of prospective studies. American Journal of Epidemiology 166, article 4. https://doi.org/10.1093/aje/kwm116.CrossRefGoogle ScholarPubMed
Athey, S and Imbens, G (2016) Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences 113, article 27. https://doi.org/10.1073/pnas.1510489113.CrossRefGoogle ScholarPubMed
Athey, S, Tibshirani, J and Wager, S (2019) Generalized random forests. The Annals of Statistics 47. https://doi.org/10.1214/18-AOS1709.CrossRefGoogle Scholar
Athey, S and Wager, S (2019) Estimating treatment effects with causal forests: An application. Observational Studies 5, article 2. https://doi.org/10.1353/obs.2019.0001.CrossRefGoogle Scholar
Berkman, LF (2000) Which influences cognitive function: Living alone or being alone? The Lancet 355, article 9212. https://doi.org/10.1016/S0140-6736(00)02107-3.CrossRefGoogle ScholarPubMed
Berner, J, Comijs, H, Elmståhl, S, Welmer, A-K, Berglund, JS, Anderberg, P and Deeg, D (2019) Maintaining cognitive function with internet use: A two-country, six-year longitudinal study. International Psychogeriatrics 31, article 7. https://doi.org/10.1017/S1041610219000668.CrossRefGoogle Scholar
Bielak, AA (2010) How can we not ‘lose it’ if we still don’t understand how to ‘use it’? Unanswered questions about the influence of activity participation on cognitive performance in older age – A mini-review. Gerontology 56, article 5. https://doi.org/10.1159/000264918.CrossRefGoogle Scholar
Cai, S (2022) Does social participation improve cognitive abilities of the elderly? Journal of Population Economics 35, article 2. https://doi.org/10.1007/s00148-020-00817-y.CrossRefGoogle Scholar
Chan, MY, Haber, S, Drew, LM and Park, DC (2016) Training older adults to use tablet computers: Does it enhance cognitive function? The Gerontologist 56, article 3. https://doi.org/10.1093/geront/gnu057.CrossRefGoogle ScholarPubMed
Chernozhukov, V, Demirer, M, Duflo, E and Fernandez-Val, I (2018). Generic machine learning inference on heterogeneous treatment effects in randomized experiments, with an application to immunization in India. National Bureau of Economic Research. www.nber.org/papers/w24678.10.3386/w24678CrossRefGoogle Scholar
Cotten, SR, Anderson, WA and McCullough, BM (2013) Impact of internet use on loneliness and contact with others among older adults: Cross-sectional analysis. Journal of Medical Internet Research 15, e2306. https://doi.org/10.2196/jmir.2306.CrossRefGoogle ScholarPubMed
Czaja, SJ, Charness, N, Fisk, AD, Hertzog, C, Nair, SN, Rogers, WA and Sharit, J (2006) Factors predicting the use of technology: Findings from the Center for Research and Education on Ageing and Technology Enhancement (CREATE). Psychology and Ageing 21, article 2. https://doi.org/10.1037/0882-7974.21.2.333.CrossRefGoogle Scholar
Dell’Ariccia, G and Pence, KM (2009) The age of reason: Financial decisions over the life cycle and implications for regulation – Comments and discussion. Brookings Papers on Economic Activity 2009, 102–117. https://doi.org/10.1353/eca.0.0070.Google Scholar
Devine, D, Ogletree, AM, Shah, P and Katz, B (2022) Internet addiction, cognitive, and dispositional factors among US adults. Computers in Human Behavior Reports 6, 100180. https://doi.org/10.1016/j.chbr.2022.100180.CrossRefGoogle Scholar
Ejlskov, L, Wulff, J, Bøggild, H, Kuh, D and Stafford, M (2018) Assessing the relative importance of correlates of loneliness in later life. Gaining insight using recursive partitioning. Ageing and Mental Health 22, article 11. https://doi.org/10.1080/13607863.2017.1370690.CrossRefGoogle ScholarPubMed
Elliot, AJ, Mooney, CJ, Douthit, KZ and Lynch, MF (2014) Predictors of older adults’ technology use and its relationship to depressive symptoms and well-being. Journals of Gerontology Series B: Psychological Sciences and Social Sciences 69, article 5. https://doi.org/10.1093/geronb/gbt109.CrossRefGoogle ScholarPubMed
Finke, MS, Howe, JS and Huston, SJ (2017) Old age and the decline in financial literacy. Management Science 63, article 1. https://doi.org/10.1287/mnsc.2015.2293.CrossRefGoogle Scholar
Firth, J, Torous, J, Stubbs, B, Firth, JA, Steiner, GZ, Smith, L, Alvarez-Jimenez, M, Gleeson, J, Vancampfort, D, Armitage, CJ and Sarris, J (2019) The ‘online brain’: How the internet may be changing our cognition. World Psychiatry: Official Journal of the World Psychiatric Association (WPA) 18, article 2. https://doi.org/10.1002/wps.20617.CrossRefGoogle ScholarPubMed
Geyer, S, Kuhlmann, BG, Beller, J and Grasshoff, J (2023) The role of school education in time-dependent changes of cognitive abilities in cohorts from midlife to old age. Ageing and Mental Health 27, article 4. https://doi.org/10.1080/13607863.2022.2068132.CrossRefGoogle ScholarPubMed
Green, CP, Mao, L and O’Sullivan, V (2021) Internet usage and the cognitive function of retirees. Journal of Economic Behavior and Organization 190, 747–767. https://doi.org/10.1016/j.jebo.2021.08.013.CrossRefGoogle Scholar
Hakiki, B, Pancani, S, Portaccio, E, Molino-Lova, R, Sofi, F, Macchi, C and Cecchi, F (2021) Impact of occupational complexity on cognitive decline in the oldest-old. Ageing and Mental Health 25, article 9. https://doi.org/10.1080/13607863.2020.1746739.CrossRefGoogle ScholarPubMed
Hertzog, C, Kramer, AF, Wilson, RS and Lindenberger, U (2008) Enrichment effects on adult cognitive development: Can the functional capacity of older adults be preserved and enhanced? Psychological Science in the Public Interest 9, article 1. https://doi.org/10.1111/j.1539-6053.2009.01034.x.CrossRefGoogle ScholarPubMed
Hunsaker, A and Hargittai, E (2018) A review of internet use among older adults. New Media and Society 20, article 10. https://doi.org/10.1177/1461444818787348.CrossRefGoogle Scholar
Hyun, J, Sliwinski, MJ, Almeida, DM, Smyth, JM and Scott, SB (2018) The moderating effects of ageing and cognitive abilities on the association between work stress and negative affect. Ageing and Mental Health 22, article 5. https://doi.org/10.1080/13607863.2017.1299688.CrossRefGoogle ScholarPubMed
Jackson, JJ, Hill, PL, Payne, BR, Parisi, JM and Stine-Morrow, EAL (2020) Linking openness to cognitive ability in older adulthood: The role of activity diversity. Ageing and Mental Health 24, article 7. https://doi.org/10.1080/13607863.2019.1655705.CrossRefGoogle ScholarPubMed
Jiao, H, Guo, Z, Sun, J, Wang, K and Yang, J (2025) Exploring the impact of internet use on cognitive abilities in the older adults: Evidence from the CHARLS 2020 database. Frontiers in Public Health 13, 1510418. https://doi.org/10.3389/fpubh.2025.1510418.CrossRefGoogle ScholarPubMed
Kamin, ST and Lang, FR (2020) Internet use and cognitive functioning in late adulthood: Longitudinal findings from the Survey of Health, Ageing and Retirement in Europe (SHARE). Journals of Gerontology: Series B 75, article 3. https://doi.org/10.1093/geronb/gby123Google ScholarPubMed
Knittel, CR and Stolper, S (2019). Using machine learning to target treatment: The case of household energy use. National Bureau of Economic Research. www.nber.org/papers/w26531.10.3386/w26531CrossRefGoogle Scholar
König, R, Seifert, A and Doh, M (2018) Internet use among older Europeans: An analysis based on SHARE data. Universal Access in the Information Society 17, article 3. https://doi.org/10.1007/s10209-018-0609-5.CrossRefGoogle Scholar
Lawton, MP (2013) Behavior-relevant ecological factors. In Schaie, KW and Schooler, C (eds), Social Structure and Aging: Psychological Processes. New York: Psychology Press, pp. 57–78.Google Scholar
Li, L, Luo, J, Reangsing, C and Schneider, JK (2022) Effects of exercise on depressive symptoms among nursing home residents: A meta-analysis. Ageing and Mental Health 26, article 8. https://doi.org/10.1080/13607863.2021.1951658.CrossRefGoogle ScholarPubMed
Lim, HM, Ng, CJ, Abdullah, A, Danee, M, Raubenheimer, J and Dunn, AG (2024) Online health information behaviour and its association with statin adherence in patients with high cardiovascular risk: A prospective cohort study. Digital Health 10, 20552076241241250. https://doi.org/10.1177/20552076241241250.CrossRefGoogle ScholarPubMed
Lin, S, Wu, Y, He, L and Fang, Y (2023) Prediction of depressive symptoms onset and long-term trajectories in home-based older adults using machine learning techniques. Ageing and Mental Health 27, article 1. https://doi.org/10.1080/13607863.2022.2031868.CrossRefGoogle ScholarPubMed
Lu, H and Kandilov, IT (2021) Does mobile internet use affect the subjective well-being of older Chinese adults? An instrumental variable quantile analysis. Journal of Happiness Studies 22, article 7. https://doi.org/10.1007/s10902-021-00365-6.CrossRefGoogle Scholar
Lu, X, Yao, Y and Jin, Y (2022) Digital exclusion and functional dependence in older people: Findings from five longitudinal cohort studies. EClinicalMedicine 54, 101708. https://doi.org/10.1016/j.eclinm.2022.101708.CrossRefGoogle ScholarPubMed
Maher, CA, Lewis, LK, Ferrar, K, Marshall, S, De Bourdeaudhuij, I and Vandelanotte, C (2014) Are health behavior change interventions that use online social networks effective? A systematic review. Journal of Medical Internet Research 16, article 2. https://doi.org/10.2196/jmir.2952.CrossRefGoogle ScholarPubMed
Myhre, JW, Mehl, MR and Glisky, EL (2017) Cognitive benefits of online social networking for healthy older adults. Journals of Gerontology Series B: Psychological Sciences and Social Sciences 72, article 5.Google ScholarPubMed
Nilsen, C, Andel, R, Fors, S, Meinow, B, Darin Mattsson, A and Kåreholt, I (2014) Associations between work-related stress in late midlife, educational attainment, and serious health problems in old age: A longitudinal study with over 20 years of follow-up. BMC (BioMed Central) Public Health 14, article 1. https://doi.org/10.1186/1471-2458-14-878.Google Scholar
Salthouse, TA (2006) Mental exercise and mental ageing: Evaluating the validity of the ‘use it or lose it’ hypothesis. Perspectives on Psychological Science 1, article 1. https://doi.org/10.1111/j.1745-6916.2006.00005.x.CrossRefGoogle ScholarPubMed
Sandi, C (2013) Stress and cognition. WIREs Cognitive Science 4, article 3. https://doi.org/10.1002/wcs.1222.CrossRefGoogle ScholarPubMed
Schlomann, A, Seifert, A, Zank, S, Woopen, C and Rietz, C (2020) Use of information and communication technology (ICT) devices among the oldest-old: loneliness, anomie, and autonomy. Innovation in Ageing 4, article 2. https://doi.org/10.1093/geroni/igz050.Google ScholarPubMed
Schulz, R, Wahl, H-W, Matthews, JT, De Vito Dabbs, A, Beach, SR and Czaja, SJ (2015) Advancing the ageing and technology agenda in gerontology. The Gerontologist 55, article 5. https://doi.org/10.1093/geront/gnu071.CrossRefGoogle ScholarPubMed
Seifert, A, Cotten, SR and Xie, B (2021) A double burden of exclusion? Digital and social exclusion of older adults in times of Covid-19. Journals of Gerontology Series B: Psychological Sciences and Social Sciences 76, article 3. https://doi.org/10.1093/geronb/gbaa098.CrossRefGoogle ScholarPubMed
Stern, Y (2002) What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society 8, article 3. https://doi.org/10.1017/S1355617702813248.CrossRefGoogle ScholarPubMed
Suzman, R, Beard, JR, Boerma, T and Chatterji, S (2015) Health in an ageing world – What do we know? The Lancet 385, article 9967. https://doi.org/10.1016/S0140-6736(14)61597-X.CrossRefGoogle Scholar
Topiwala, A, Allan, CL, Valkanova, V, Zsoldos, E, Filippini, N, Sexton, C, Mahmood, A, Fooks, P, Singh-Manoux, A and Mackay, CE (2017) Moderate alcohol consumption as risk factor for adverse brain outcomes and cognitive decline: Longitudinal cohort study. BMJ (British Medical Journal) 357, www.bmj.com/content/357/BMJ.j2353.abstract.Google ScholarPubMed
Tun, PA and Lachman, ME (2010) The association between computer use and cognition across adulthood: Use it so you won’t lose it? Psychology and Ageing 25, 560. https://doi.org/10.1037/a0019543.CrossRefGoogle Scholar
Turner, AD, James, BD, Capuano, AW, Aggarwal, NT and Barnes, LL (2017) Perceived stress and cognitive decline in different cognitive domains in a cohort of older african Americans. American Journal of Geriatric Psychiatry 25, article 1. https://doi.org/10.1016/j.jagp.2016.10.003.CrossRefGoogle Scholar
Van Praag, H, Kempermann, G and Gage, FH (2000) Neural consequences of enviromental enrichment. Nature Reviews, Neuroscience 1, article 3. https://doi.org/10.1038/35044558.CrossRefGoogle Scholar
Vaportzis, E, Martin, M and Gow, AJ (2017) A tablet for healthy ageing: The effect of a tablet computer training intervention on cognitive abilities in older adults. American Journal of Geriatric Psychiatry: Official Journal of the American Association for Geriatric Psychiatry 25, article 8. https://doi.org/10.1016/j.jagp.2016.11.015.CrossRefGoogle ScholarPubMed
Wager, S and Athey, S (2018) Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association 113, article 523. https://doi.org/10.1080/01621459.2017.1319839.CrossRefGoogle Scholar
Wang, X, Shi, J and Kong, H (2021) Online health information seeking: A review and meta-analysis. Health Communication 36, 1163–1175. https://doi.org/10.1080/10410236.2020.1748829.CrossRefGoogle ScholarPubMed
Wang, Y, Wu, Z, Duan, L, Liu, S, Chen, R, Sun, T, Wang, J, Zhou, J, Wang, H and Huang, P (2024) Digital exclusion and cognitive impairment in older people: Findings from five longitudinal studies. BMC (BioMed Central) Geriatrics 24, article 1. https://doi.org/10.1186/s12877-024-05026-w.Google ScholarPubMed
Wu, Y-H, Lewis, M and Rigaud, A-S (2019) Cognitive function and digital device use in older adults attending a memory clinic. Gerontology and Geriatric Medicine 5, 2333721419844886. https://doi.org/10.1177/2333721419844886.CrossRefGoogle ScholarPubMed
Xu, Y, Zeng, K, Dong, L, Zheng, X and Si, Y (2023) Understanding older adults’ smartphone addiction in the digital age: Empirical evidence from China. Frontiers in Public Health 11, 1136494. https://doi.org/10.3389/fpubh.2023.1136494.CrossRefGoogle ScholarPubMed
Yu, D and Fiebig, DG (2020) Internet use and cognition among middle-aged and older adults in China: A cross-lagged panel analysis. Journal of the Economics of Ageing 17, 100262. https://doi.org/10.1016/j.jeoa.2020.100262.CrossRefGoogle Scholar
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