Long-term television viewing patterns and gray matter brain volume in midlife

Dougherty, Ryan J.; Hoang, Tina D.; Launer, Lenore J.; Jacobs, David R.; Sidney, Stephen; Yaffe, Kristine

doi:10.1007/s11682-021-00534-4

Original Research
Published: 06 September 2021

Long-term television viewing patterns and gray matter brain volume in midlife

Ryan J. Dougherty¹,
Tina D. Hoang²,
Lenore J. Launer³,
David R. Jacobs⁴,
Stephen Sidney⁵ &
Kristine Yaffe^2,6

Brain Imaging and Behavior volume 16, pages 637–644 (2022)Cite this article

1319 Accesses
119 Altmetric
Metrics details

Abstract

The purpose of this study was to investigate whether long-term television viewing patterns, a common sedentary behavior, in early to mid-adulthood is associated with gray matter brain volume in midlife and if this is independent of physical activity. We evaluated 599 participants (51% female, 44% black, mean age 30.3 ± 3.5 at baseline and 50.2 ± 3.5 years at follow-up and MRI) from the prospective Coronary Artery Risk Development in Young Adults (CARDIA) study. We assessed television patterns with repeated interviewer-administered questionnaire spanning 20 years. Structural MRI (3T) measures of frontal cortex, entorhinal cortex, hippocampal, and total gray matter volumes were assessed at midlife. Over the 20 years, participants reported viewing an average of 2.5 ± 1.7 h of television per day (range: 0–10 h). After multivariable adjustment, greater television viewing was negatively associated with gray matter volume in the frontal (β = − 0.77; p = 0.01) and entorhinal cortex (β = − 23.83; p = 0.05) as well as total gray matter (β = − 2.09; p = 0.003) but not hippocampus. These results remained unchanged after additional adjustment for physical activity. For each one standard deviation increase in television viewing, the difference in gray matter volume z-score was approximately 0.06 less for each of the three regions (p < 0.05). Among middle-aged adults, greater television viewing in early to mid-adulthood was associated with lower gray matter volume. Sedentariness or other facets of television viewing may be important for brain aging even in middle age.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Availability of data

Anonymized data are available from the CARDIA Coordinating Center (cardia.dopm.uab.edu/contact-cardia). A description of the National Heart, Lung, and Blood Institute policies governing the data and describing access to the data can be found online (cardia.dopm.uab.edu/study-information/nhlbi-data-repository-data).

References

Ainsworth, B. E., Haskell, W. L., Whitt, M. C., Irwin, M. L., Swartz, A. M., Strath, S. J., & Leon, A. S. (2000). Compendium of physical activities: An update of activity codes and MET intensities. Medicine and Science in Sports and Exercise, 32(9 Suppl), S498-504. https://doi.org/10.1097/00005768-200009001-00009
CAS Article PubMed Google Scholar
Allison, S. L., Koscik, R. L., Cary, R. P., Jonaitis, E. M., Rowley, H. A., Chin, N. A., & Johnson, S. C. (2019). Comparison of different MRI-based morphometric estimates for defining neurodegeneration across the Alzheimer’s disease continuum. Neuroimage Clinical, 23, 101895. https://doi.org/10.1016/j.nicl.2019.101895
Article PubMed PubMed Central Google Scholar
Arnardottir, N. Y., Koster, A., Domelen, D. R. V., Brychta, R. J., Caserotti, P., Eiriksdottir, G., & Sveinsson, T. (2016). Association of change in brain structure to objectively measured physical activity and sedentary behavior in older adults: Age, Gene/Environment Susceptibility-Reykjavik Study. Behavioural Brain Research, 296, 118–124. https://doi.org/10.1016/j.bbr.2015.09.005
Article PubMed Google Scholar
Barnes, D. E., & Yaffe, K. (2011). The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurology, 10(9), 819–828. https://doi.org/10.1016/S1474-4422(11)70072-2
Article PubMed PubMed Central Google Scholar
Barnes, J. N. (2015). Exercise, cognitive function, and aging. Advances in Physiology Education, 39(2), 55–62. https://doi.org/10.1152/advan.00101.2014
Article PubMed PubMed Central Google Scholar
Burgess, N., Maguire, E. A., & O’Keefe, J. (2002). The human hippocampus and spatial and episodic memory. Neuron, 35(4), 625–641. https://doi.org/10.1016/s0896-6273(02)00830-9
CAS Article PubMed Google Scholar
Clark, B. K., Sugiyama, T., Healy, G. N., Salmon, J., Dunstan, D. W., & Owen, N. (2009). Validity and reliability of measures of television viewing time and other non-occupational sedentary behaviour of adults: A review. Obesity Reviews, 10(1), 7–16. https://doi.org/10.1111/j.1467-789X.2008.00508.x
Article PubMed Google Scholar
Doshi, J., Erus, G., Ou, Y., Gaonkar, B., & Davatzikos, C. (2013). Multi-atlas skull-stripping. Academic Radiology, 20(12), 1566–1576. https://doi.org/10.1016/j.acra.2013.09.010
Article PubMed Google Scholar
Doshi, J., Erus, G., Ou, Y., Resnick, S. M., Gur, R. C., & Gur, R. E. (2016). MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection. NeuroImage, 127, 186–195. https://doi.org/10.1016/j.neuroimage.2015.11.073
Article PubMed Google Scholar
Dougherty, R. J., Ellingson, L. D., Schultz, S. A., Boots, E. A., Meyer, J. D., Lindheimer, J. B., & Cook, D. B. (2016). Meeting physical activity recommendations may be protective against temporal lobe atrophy in older adults at risk for Alzheimer’s disease. Alzheimers Dement (amst), 4, 14–17. https://doi.org/10.1016/j.dadm.2016.03.005
Article Google Scholar
Dougherty, R. J., Jonaitis, E. M., Gaitán, J. M., Lose, S. R., Mergen, B. M., Johnson, S. C., & Cook, D. B. (2021). Cardiorespiratory fitness mitigates brain atrophy and cognitive decline in adults at risk for Alzheimer’s disease. Alzheimers Dement (amst), 13(1), e12212. https://doi.org/10.1002/dad2.12212
Article Google Scholar
Dougherty, R. J., Schultz, S. A., Boots, E. A., Ellingson, L. D., Meyer, J. D., Van Riper, S., & Cook, D. B. (2017). Relationships between cardiorespiratory fitness, hippocampal volume, and episodic memory in a population at risk for Alzheimer’s disease. Brain and Behavior: A Cognitive Neuroscience Perspective, 7(3), e00625. https://doi.org/10.1002/brb3.625
Article Google Scholar
Driscoll, I., Davatzikos, C., An, Y., Wu, X., Shen, D., Kraut, M., & Resnick, S. M. (2009). Longitudinal pattern of regional brain volume change differentiates normal aging from MCI. Neurology, 72(22), 1906–1913. https://doi.org/10.1212/WNL.0b013e3181a82634
CAS Article PubMed PubMed Central Google Scholar
Dunstan, D. W., Howard, B., Healy, G. N., & Owen, N. (2012). Too much sitting–a health hazard. Diabetes Research and Clinical Practice, 97(3), 368–376. https://doi.org/10.1016/j.diabres.2012.05.020
Article PubMed Google Scholar
Ekelund, U., Steene-Johannessen, J., Brown, W. J., Fagerland, M. W., Owen, N., & Powell, K. E. (2016). Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. Lancet, 388(10051), 1302–1310. https://doi.org/10.1016/S0140-6736(16)30370-1
Article PubMed Google Scholar
Erickson, K. I., Leckie, R. L., & Weinstein, A. M. (2014). Physical activity, fitness, and gray matter volume. Neurobiology of Aging, 35(Suppl 2), S20-28. https://doi.org/10.1016/j.neurobiolaging.2014.03.034
Article PubMed Google Scholar
Falck, R. S., Davis, J. C., & Liu-Ambrose, T. (2017). What is the association between sedentary behaviour and cognitive function? A systematic review. British Journal of Sports Medicine, 51(10), 800–811. https://doi.org/10.1136/bjsports-2015-095551
Article PubMed Google Scholar
Fancourt, D., & Steptoe, A. (2019). Television viewing and cognitive decline in older age: Findings from the English Longitudinal Study of Ageing. Science and Reports, 9(1), 2851. https://doi.org/10.1038/s41598-019-39354-4
CAS Article Google Scholar
Flayelle, M., Canale, N., Vögele, C., Karila, L., Maurage, P., & Billieux, J. (2019). Assessing binge-watching behaviors: Development and validation of the “Watching TV Series Motives” and “Binge-Watching Engagement and Symptoms” questionnaires. Computers in Human Behavior, 90, 26–36.
Article Google Scholar
Friedman, G. D., Cutter, G. R., Donahue, R. P., Hughes, G. H., Hulley, S. B., Jacobs, D. R., & Savage, P. J. (1988). CARDIA: Study design, recruitment, and some characteristics of the examined subjects. Journal of Clinical Epidemiology, 41(11), 1105–1116. https://doi.org/10.1016/0895-4356(88)90080-7
CAS Article PubMed Google Scholar
Fuster, J. M. (2001). The prefrontal cortex–an update: Time is of the essence. Neuron, 30(2), 319–333. https://doi.org/10.1016/s0896-6273(01)00285-9
CAS Article PubMed Google Scholar
Goldszal, A. F., Davatzikos, C., Pham, D. L., Yan, M. X., Bryan, R. N., & Resnick, S. M. (1998). An image-processing system for qualitative and quantitative volumetric analysis of brain images. Journal of Computer Assisted Tomography, 22(5), 827–837. https://doi.org/10.1097/00004728-199809000-00030
CAS Article PubMed Google Scholar
Hamer, M., & Chida, Y. (2009). Physical activity and risk of neurodegenerative disease: A systematic review of prospective evidence. Psychological Medicine, 39(1), 3–11. https://doi.org/10.1017/S0033291708003681
CAS Article PubMed Google Scholar
Hoang, T. D., Reis, J., Zhu, N., Jacobs, D. R., Launer, L. J., Whitmer, R. A., & Yaffe, K. (2016). Effect of early adult patterns of physical activity and television viewing on midlife cognitive function. JAMA Psychiatry, 73(1), 73–79. https://doi.org/10.1001/jamapsychiatry.2015.2468
Article PubMed PubMed Central Google Scholar
Hughes, G. H., Cutter, G., Donahue, R., Friedman, G. D., Hulley, S., Hunkeler, E., & Pirie, P. (1987). Recruitment in the coronary artery disease risk development in young adults (cardia) study. Controlled Clinical Trials, 8(4 Suppl), 68S-73S. https://doi.org/10.1016/0197-2456(87)90008-0
CAS Article PubMed Google Scholar
Iturria-Medina, Y., Sotero, R. C., Toussaint, P. J., Mateos-Pérez, J. M., & Evans, A. C. (2016). Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis. Nature Communications, 7, 11934. https://doi.org/10.1038/ncomms11934
CAS Article PubMed PubMed Central Google Scholar
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., & Haeberlein, S. B. (2018). NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia, 14(4), 535–562. https://doi.org/10.1016/j.jalz.2018.02.018
Article Google Scholar
Jacobs, D. R., Hahn, L. P., Haskell, W. L., Pirie, P., & Sidney, S. (1989). Validity and reliability of short physical activity history: cardia and the minnesota heart health program. Journal of Cardiopulmonary Rehabilitation, 9(11), 448–459. https://doi.org/10.1097/00008483-198911000-00003
Article PubMed PubMed Central Google Scholar
Katzmarzyk, P. T., Church, T. S., Craig, C. L., & Bouchard, C. (2009). Sitting time and mortality from all causes, cardiovascular disease, and cancer. Medicine and Science in Sports and Exercise, 41(5), 998–1005. https://doi.org/10.1249/MSS.0b013e3181930355
Article PubMed Google Scholar
Launer, L. J., Lewis, C. E., Schreiner, P. J., Sidney, S., Battapady, H., Jacobs, D. R., & Bryan, R. N. (2015). Vascular factors and multiple measures of early brain health: CARDIA brain MRI study. PLoS ONE, 10(3), e0122138. https://doi.org/10.1371/journal.pone.0122138
CAS Article PubMed PubMed Central Google Scholar
Lindstrom, H. A., Fritsch, T., Petot, G., Smyth, K. A., Chen, C. H., Debanne, S. M., & Friedland, R. P. (2005). The relationships between television viewing in midlife and the development of Alzheimer’s disease in a case-control study. Brain and Cognition, 58(2), 157–165. https://doi.org/10.1016/j.bandc.2004.09.020
Article PubMed Google Scholar
Nualnim, N., Barnes, J. N., Tarumi, T., Renzi, C. P., & Tanaka, H. (2011). Comparison of central artery elasticity in swimmers, runners, and the sedentary. American Journal of Cardiology, 107(5), 783–787. https://doi.org/10.1016/j.amjcard.2010.10.062
Article Google Scholar
Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401.
Article Google Scholar
Raichlen, D. A., Klimentidis, Y. C., Bharadwaj, P. K., & Alexander, G. E. (2019). Differential associations of engagement in physical activity and estimated cardiorespiratory fitness with brain volume in middle-aged to older adults. Brain Imaging and Behavior. https://doi.org/10.1007/s11682-019-00148-x
Article Google Scholar
Raz, N., Lindenberger, U., Rodrigue, K. M., Kennedy, K. M., Head, D., Williamson, A., & Acker, J. D. (2005). Regional brain changes in aging healthy adults: General trends, individual differences and modifiers. Cerebral Cortex, 15(11), 1676–1689. https://doi.org/10.1093/cercor/bhi044
Article PubMed Google Scholar
Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., & Davatzikos, C. (2003). Longitudinal magnetic resonance imaging studies of older adults: A shrinking brain. Journal of Neuroscience, 23(8), 3295–3301.
CAS Article PubMed Google Scholar
Schultz, S. A., Larson, J., Oh, J., Koscik, R., Dowling, M. N., Gallagher, C. L., & Okonkwo, O. C. (2015). Participation in cognitively-stimulating activities is associated with brain structure and cognitive function in preclinical Alzheimer’s disease. Brain Imaging and Behavior, 9(4), 729–736. https://doi.org/10.1007/s11682-014-9329-5
Article PubMed PubMed Central Google Scholar
Seals, D. R., Desouza, C. A., Donato, A. J., & Tanaka, H. (2008). Habitual exercise and arterial aging. Journal of Applied Physiology, 105(4), 1323–1332. https://doi.org/10.1152/japplphysiol.90553.2008
Article PubMed PubMed Central Google Scholar
Seals, D. R., Hagberg, J. M., Hurley, B. F., Ehsani, A. A., & Holloszy, J. O. (1984). Endurance training in older men and women: I: Cardiovascular responses to exercise. Journal of Applied Physiology: Respiratory, Environmental and Exercise Physiology, 57(4), 1024–1029. https://doi.org/10.1152/jappl.1984.57.4.1024
CAS Article Google Scholar
Shen, D., & Davatzikos, C. (2002). HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Transactions on Medical Imaging, 21(11), 1421–1439. https://doi.org/10.1109/TMI.2002.803111
Article PubMed Google Scholar
Siddarth, P., Burggren, A. C., Eyre, H. A., Small, G. W., & Merrill, D. A. (2018). Sedentary behavior associated with reduced medial temporal lobe thickness in middle-aged and older adults. PLoS ONE, 13(4), e0195549. https://doi.org/10.1371/journal.pone.0195549
CAS Article PubMed PubMed Central Google Scholar
Sidney, S., Jacobs, D. R., Haskell, W. L., Armstrong, M. A., Dimicco, A., Oberman, A., & Van Horn, L. (1991). Comparison of two methods of assessing physical activity in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. American Journal of Epidemiology, 133(12), 1231–1245. https://doi.org/10.1093/oxfordjournals.aje.a115835
CAS Article PubMed Google Scholar
Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging, 29(6), 1310–1320. https://doi.org/10.1109/TMI.2010.2046908
Article PubMed PubMed Central Google Scholar
Verghese, J., LeValley, A., Derby, C., Kuslansky, G., Katz, M., Hall, C., & Lipton, R. B. (2006). Leisure activities and the risk of amnestic mild cognitive impairment in the elderly. Neurology, 66(6), 821–827. https://doi.org/10.1212/01.wnl.0000202520.68987.48
CAS Article PubMed Google Scholar
Verghese, J., Lipton, R. B., Katz, M. J., Hall, C. B., Derby, C. A., Kuslansky, G., & Buschke, H. (2003). Leisure activities and the risk of dementia in the elderly. New England Journal of Medicine, 348(25), 2508–2516. https://doi.org/10.1056/NEJMoa022252
Article Google Scholar
Villemagne, V. L., Burnham, S., Bourgeat, P., Brown, B., Ellis, K. A., & Salvado, O. (2013). Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet Neurology, 12(4), 357–367. https://doi.org/10.1016/S1474-4422(13)70044-9
CAS Article PubMed Google Scholar
Voss, M. W., Carr, L. J., Clark, R., & Weng, T. (2014). Revenge of the “sit” II: Does lifestyle impact neuronal and cognitive health through distinct mechanisms associated with sedentary behavior and physical activity? Mental Health and Physical Activity, 7(1), 9–24.
Article Google Scholar
Wheeler, M. J., Dempsey, P. C., Grace, M. S., Ellis, K. A., Gardiner, P. A., Green, D. J., & Dunstan, D. W. (2017). Sedentary behavior as a risk factor for cognitive decline? A focus on the influence of glycemic control in brain health. Alzheimers Dement (n Y), 3(3), 291–300. https://doi.org/10.1016/j.trci.2017.04.001
Article Google Scholar
Yaffe, K., Barnes, D., Nevitt, M., Lui, L. Y., & Covinsky, K. (2001). A prospective study of physical activity and cognitive decline in elderly women: Women who walk. Archives of Internal Medicine, 161(14), 1703–1708. https://doi.org/10.1001/archinte.161.14.1703
CAS Article PubMed Google Scholar
Yang, L., Cao, C., Kantor, E. D., Nguyen, L. H., Zheng, X., Park, Y., & Cao, Y. (2019). Trends in sedentary behavior among the US population, 2001–2016. JAMA, 321(16), 1587–1597. https://doi.org/10.1001/jama.2019.3636
Article PubMed PubMed Central Google Scholar
Zacharaki, E. I., Kanterakis, S., Bryan, R. N., & Davatzikos, C. (2008). Measuring brain lesion progression with a supervised tissue classification system. Med Image Comput Comput Assist Interv, 11(Pt 1), 620–627. https://doi.org/10.1007/978-3-540-85988-8_74
Article PubMed Google Scholar
Zlatar, Z. Z., Hays, C. C., Mestre, Z., Campbell, L. M., Meloy, M. J., Bangen, K. J., & Wierenga, C. E. (2019). Dose-dependent association of accelerometer-measured physical activity and sedentary time with brain perfusion in aging. Experimental Gerontology, 125, 110679. https://doi.org/10.1016/j.exger.2019.110679
Article PubMed PubMed Central Google Scholar

Download references

Acknowledgements

The authors gratefully acknowledge the support of the CARDIA staff for their assistance in recruitment and data collection. Above all, the authors thank their dedicated volunteers for their participation in this research.

Funding

The Coronary Artery Risk Development in Young Adults Study (CARDIA) is supported by Contracts HHSN268201800003I, HHSN268201800004I, HHSN268201800005I, HHSN268201800006I, and HHSN268201800007I from the National Heart, Lung, and Blood Institute (NHLBI). CARDIA was also partially supported by the Intramural Research Program of the National Institute on Aging (NIA) and an intra-agency agreement between NIA and NHLBI (AG0005). Ryan J Dougherty was supported by an NIA training Grant (T32AG027668). Kristine Yaffe was supported in part by NIA Grants K24AG031155 and R01AG063887.

Author information

Authors and Affiliations

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 2024 E. Monument St., Suite 2-700, Baltimore, MD, 21205, USA
Ryan J. Dougherty
San Francisco VA Health Care System, San Francisco, CA, USA
Tina D. Hoang & Kristine Yaffe
National Institute on Aging, Bethesda, MD, USA
Lenore J. Launer
University of Minnesota, Minneapolis, MN, USA
David R. Jacobs
Kaiser Permanente Division of Research, Oakland, CA, USA
Stephen Sidney
University of California, San Francisco, San Francisco, CA, USA
Kristine Yaffe

Authors

Ryan J. Dougherty
View author publications
You can also search for this author in PubMed Google Scholar
Tina D. Hoang
View author publications
You can also search for this author in PubMed Google Scholar
Lenore J. Launer
View author publications
You can also search for this author in PubMed Google Scholar
David R. Jacobs
View author publications
You can also search for this author in PubMed Google Scholar
Stephen Sidney
View author publications
You can also search for this author in PubMed Google Scholar
Kristine Yaffe
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

Drafting/revising the manuscript for content: RJD, TDH, LJL, DRJ, SS, KY. Study concept or design: RJD, TDH, KY. Analysis or interpretation of data: RJD, TDH, LJL, DRJ, SS, KY.

Corresponding author

Correspondence to Ryan J. Dougherty.

Ethics declarations

Conflict of interest

Ryan J. Dougherty, Tina D. Hoang, Lenore J. Launer, David R. Jacobs, Jr, and Stephen Sidney declare no conflicts of interest. Kristine Yaffe serves on DSMBs for Eli Lilly and several National Institute on Aging–sponsored studies and is also a member of the Beeson Scientific Advisory Board and the Global Council on Brain Health.

Ethical approval

The CARDIA study and brain MRI ancillary study were approved by the Institutional Review Board (IRB) of each of the participating sites as well as the University of Pennsylvania and University of California, San Francisco. All procedures followed were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. At each examination participants were provided and signed a separate written informed consent for the CARDIA study and the brain MRI ancillary study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Dougherty, R.J., Hoang, T.D., Launer, L.J. et al. Long-term television viewing patterns and gray matter brain volume in midlife. Brain Imaging and Behavior 16, 637–644 (2022). https://doi.org/10.1007/s11682-021-00534-4

Download citation

Accepted: 02 August 2021
Published: 06 September 2021
Issue Date: April 2022
DOI: https://doi.org/10.1007/s11682-021-00534-4

Keywords

Lifestyle factors
Sitting time
Volumetric MRI
Epidemiology
Cohort studies