» Articles » PMID: 22560523

Computer Activities, Physical Exercise, Aging, and Mild Cognitive Impairment: a Population-based Study

Overview
Journal Mayo Clin Proc
Specialty General Medicine
Date 2012 May 8
PMID 22560523
Citations 25
Authors
Affiliations
Soon will be listed here.
Abstract

Objective: To examine the association between computer use, physical exercise, aging, and mild cognitive impairment (MCI).

Patients And Methods: The Mayo Clinic Study of Aging is a population-based study of aging and MCI in Olmsted County, Minnesota. The study sample consists of a random sample of 926 nondemented individuals aged 70 to 93 years who completed self-reported questionnaires on physical exercise, computer use, and caloric intake within 1 year of the date of interview. The study was conducted from April 1, 2006, through November 30, 2008. An expert consensus panel classified each study participant as cognitively normal or having MCI on the basis of published criteria.

Results: Using a multivariable logistic regression model, we examined the impact of the presence during the study period of 2 lifestyle factors (physical exercise and computer use) after adjusting for a third lifestyle factor (caloric intake) on aging and MCI. We also adjusted for age, sex, education, medical comorbidity, and depression. The median daily caloric intake was significantly higher in participants with MCI than in controls (odds ratio, 1.04; 95% confidence interval, 1.02-1.06; P=.001). Participants who engaged in both moderate physical exercise and computer use had significantly decreased odds of having MCI (odds ratio [95% confidence interval], 0.36 [0.20-0.68]) compared with the reference group. In the interaction analyses, there was an additive interaction (P=.012) but not multiplicative interaction (P=.780).

Conclusion: In this population-based sample, the presence of both physical exercise and computer use as assessed via survey was associated with decreased odds of having MCI, after adjustment for caloric intake and traditional confounders.

Citing Articles

Fine Tuning of an Advanced Planner for Cognitive Training of Older Adults.

Gaspari M, Mioni G, Signorello D, Stablum F, Zuppiroli S Eur J Investig Health Psychol Educ. 2025; 15(1).

PMID: 39852187 PMC: 11765506. DOI: 10.3390/ejihpe15010004.


Cognitive dispersion is related to subtle objective daily functioning changes in older adults with and without cognitive impairment.

De Vito A, Ju C, Lee S, Cohen A, Trofimova A, Liu Y Alzheimers Dement (Amst). 2024; 16(1):e12539.

PMID: 38312515 PMC: 10835082. DOI: 10.1002/dad2.12539.


Brain Health Indicators Following Acute Neuro-Exergaming: Biomarker and Cognition in Mild Cognitive Impairment (MCI) after Pedal-n-Play (iPACES).

Nath K, Ferguson I, Puleio A, Wall K, Stark J, Clark S Brain Sci. 2023; 13(6).

PMID: 37371324 PMC: 10296731. DOI: 10.3390/brainsci13060844.


Geroprotective interventions in the 3xTg mouse model of Alzheimer's disease.

Sonsalla M, Lamming D Geroscience. 2023; 45(3):1343-1381.

PMID: 37022634 PMC: 10400530. DOI: 10.1007/s11357-023-00782-w.


Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people.

Wang S, Wang W, Li X, Liu Y, Wei J, Zheng J Front Aging Neurosci. 2022; 14:977034.

PMID: 36034140 PMC: 9407018. DOI: 10.3389/fnagi.2022.977034.


References
1.
Wilson R, Bennett D, Beckett L, Morris M, Gilley D, Bienias J . Cognitive activity in older persons from a geographically defined population. J Gerontol B Psychol Sci Soc Sci. 1999; 54(3):P155-60. DOI: 10.1093/geronb/54b.3.p155. View

2.
Geda Y, Topazian H, Roberts L, Lewis R, Roberts R, Knopman D . Engaging in cognitive activities, aging, and mild cognitive impairment: a population-based study. J Neuropsychiatry Clin Neurosci. 2011; 23(2):149-54. PMC: 3204924. DOI: 10.1176/jnp.23.2.jnp149. View

3.
Petersen R, Smith G, Waring S, Ivnik R, Tangalos E, Kokmen E . Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999; 56(3):303-8. DOI: 10.1001/archneur.56.3.303. View

4.
Beck A, Steer R, Ball R, Ranieri W . Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. J Pers Assess. 1996; 67(3):588-97. DOI: 10.1207/s15327752jpa6703_13. View

5.
Charlson M, Pompei P, Ales K, MacKenzie C . A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987; 40(5):373-83. DOI: 10.1016/0021-9681(87)90171-8. View