Hostname: page-component-cb9f654ff-hqlzj Total loading time: 0 Render date: 2025-08-30T22:44:20.766Z Has data issue: false hasContentIssue false
  • English
  • Français

The Everyday Compensation (EComp) Questionnaire: Construct Validity and Associations with Diagnosis and Longitudinal Change in Cognition and Everyday Function in Older Adults

Published online by Cambridge University Press:  31 October 2019

Sarah Tomaszewski Farias ,
Jason Gravano ,
Alyssa Weakley Open the ORCID record for Alyssa Weakley [Opens in a new window] ,
Maureen Schmitter-Edgecombe ,
Danielle Harvey ,
Dan Mungas ,
Michelle Chan  and
Tania Giovannetti Open the ORCID record for Tania Giovannetti [Opens in a new window]
Sarah Tomaszewski Farias
Affiliation:
Neurology, University of California Davis, Sacramento, CA, USA
Jason Gravano
Affiliation:
Neurology, University of California, San Francisco, Fresno, CA, USA
Alyssa Weakley*
Affiliation:
Neurology, University of California Davis, Sacramento, CA, USA
Maureen Schmitter-Edgecombe
Affiliation:
Psychology, Washington State University, Pullman, WA, USA
Danielle Harvey
Affiliation:
Division of Biostatistics, Public Health Sciences, University of California Davis, Sacramento, CA, USA
Dan Mungas
Affiliation:
Neurology, University of California Davis, Sacramento, CA, USA
Michelle Chan
Affiliation:
Neurology, University of California Davis, Sacramento, CA, USA
Tania Giovannetti
Affiliation:
Psychology, Temple University, Philadelphia, PA, USA
*
*Correspondence and reprint requests to: Alyssa Weakley, University of California, Davis, 4860 Y St., Suite 3900 Sacramento, CA 95187, USA. Email: aweakley@ucdavis.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective:

The Everyday Compensation scale (EComp) is an informant-rated questionnaire designed to measure cognitively based compensatory strategies that support both everyday memory and executive function in the context of completing instrumental activities of daily living (IADLs). Although previous findings provided early support for the usefulness of the initial version of EComp, the current paper further describes the development, refinement, and validation of EComp as a new assessment tool of compensation for IADLs.

Method:

Confirmatory factor analysis (CFA) was used to examine its factor structure. Convergent and predictive validity was evaluated by examining the relationship between EComp and markers of disease, including diagnosis, cognitive change, and trajectories of functional abilities.

Results:

CFA supported a general compensation factor after accounting for variance attributable to IADL domain-specific engagement. The clinical groups differed in compensatory strategy use, with those with dementia using significantly fewer compensatory strategies as compared to individuals with normal cognition or mild cognitive impairment. Greater levels of compensation were related to better cognitive functions (memory and executive function) and functional abilities, as well as slower rates of cognitive and functional decline over time. Importantly, higher levels of compensation were associated with less functional difficulties and subsequently slower rate of functional decline independent of the level of cognitive impairment.

Conclusions:

Engagement in compensatory strategies among older adults has important implications for prolonging functional independence, even in those with declining cognitive functioning. Results suggest that the revised EComp is likely to be useful in measuring cognitively based compensation in older adults.

Keywords

Activities of daily livingDementiaMild cognitive impairmentExecutive functionFactor analysisStatisticalSurveys and questionnaires

Information

Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 26 , Issue 3 , March 2020 , pp. 303 - 313
Copyright
Copyright © INS. Published by Cambridge University Press, 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Alzheimer’s Association. (2018). 2018 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 14(3), 367–429.CrossRefGoogle Scholar
Aronov, A., Rabin, L.A., Fogel, J., Chi, S.Y., Kann, S.J., Abdelhak, N., & Zimmerman, M.E. (2015). Relationship of cognitive strategy use to prospective memory performance in a diverse sample of nondemented older adults with varying degrees of cognitive complaints and impairment. Aging, Neuropsychology, and Cognition, 22(4), 486–501.CrossRefGoogle Scholar
Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246.CrossRefGoogle ScholarPubMed
Bollen, K.A. (1989). Structural equations with latent variables. New York: Wiley.CrossRefGoogle Scholar
Chudoba, L., Sawaqdeh, A., Dahmen, J., Brown, K., & Schmitter-Edgecombe, M. (In Press). The development of a Digital Memory Notebook intervention with case illustrations. Neuropsychological Rehabilitation.Google Scholar
Chudoba, L., Weakley, A., & Schmitter-Edgecombe, M. (2018). Who Knows Best?: Reliability of Informant- and Self-Reported Compensatory Strategies for Every Day Medication Use in Community-Dwelling Older Adults. Poster presented at the thirty-eight National Academy of Neuropsychology meeting, New Orleans, LA, October 18.Google Scholar
Cicerone, K. D., Dahlberg, C., Malec, J. F., Langenbahn, D. M., Felicetti, T., Kneipp, S., Ellmo, W., Kalmar, K., Giacino, J.T., Harley, J.P., & Laatsch, L. (2005). Evidence-based cognitive rehabilitation: updated review of the literature from 1998 through 2002. Archives of physical medicine and rehabilitation, 86(8), 1681–1692.CrossRefGoogle ScholarPubMed
Cudeck, R. & Browne, M.W. (1983). Cross-validation of covariance structures. Multivariate Behavioral Research, 18, 147–167.CrossRefGoogle ScholarPubMed
Dassel, K.B. & Schmitt, F.A. (2008). The impact of caregiver executive skills on reports of patient functioning. The Gerontologist, 48, 781–792.CrossRefGoogle ScholarPubMed
de Frias, C.M. & Dixon, R.A. (2005). Confirmatory factor structure and measurement invariance of the Memory Compensation Questionnaire. Psychological Assessment, 17(2), 168.CrossRefGoogle ScholarPubMed
Denny, K.G. & Tomaszewski-Farias, S. (2017). A multi-modal intervention to enhance cognitive compensation strategies and promote brain health activities. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 13(7), P824.CrossRefGoogle Scholar
Denny, K.G., Barbra, C., & Tomaszewski-Farias, S. (2017). Long-term change associated with a multi-modal intervention to enhance cognitive compensation strategies and promote brain health activities. Poster presented at the International Neuropsychological Society meeting. New Orleans, LA, February 3.Google Scholar
Dixon, R.A., de Frias, C.M., & Bäckman, L. (2001). Characteristics of self-reported memory compensation in older adults. Journal of Clinical and Experimental Neuropsychology, 23(5), 650–661.CrossRefGoogle ScholarPubMed
Early, D.R., Widaman, K.F., Harvey, D., Beckett, L., Park, L.Q., Farias, S.T., Reed, BR., DeCarli, C., & Mungas, D. (2013). Demographic predictors of cognitive change in ethnically diverse older persons. Psychology and Aging, 28(3), 633.CrossRefGoogle ScholarPubMed
Freedman, V.A., Martin, L.G., & Schoeni, R.F. (2002). Recent trends in disability and functioning among older adults in the United States: A systematic review. JAMA, 288(24), 3137–3146.CrossRefGoogle ScholarPubMed
Greenaway, M.C., Duncan, N.L., & Smith, G.E. (2013). The memory support system for mild cognitive impairment: Randomized trial of a cognitive rehabilitation intervention. International Journal of Geriatric Psychiatry, 28(4), 402–409.CrossRefGoogle ScholarPubMed
Hinton, L., Carter, K., Reed, B.R., Beckett, L., Lara, E., DeCarli, C., & Mungas, D. (2010). Recruitment of a community-based cohort for research on diversity and risk of dementia. Alzheimer disease and associated disorders, 24(3), 234.Google ScholarPubMed
Holzinger, K.J. & Swineford, S. (1937). The bi-factor method. Psychometrika, 47, 41–54.CrossRefGoogle Scholar
Hu, L.T. & Bentler, P.M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological methods, 3(4), 424–453.CrossRefGoogle Scholar
Hutchens, R.L., Kinsella, G.J., Ong, B., Pike, K.E., Parsons, S., Storey, E., Ames, D., Saling, M.M., Mullaly, E., Rand, E. & Clare, L. (2012). Knowledge and use of memory strategies in amnestic mild cognitive impairment. Psychology and Aging, 27(3), 768.CrossRefGoogle ScholarPubMed
Jennrich, R.I. & Bentler, P.M. (2011). Exploratory bi-factor analysis. Psychometrika, 76, 537–549.CrossRefGoogle ScholarPubMed
Lau, K.M., Parikh, M., Harvey, D.J., Huang, C.J., & Farias, S.T. (2015). Early cognitively based functional limitations predict loss of independence in instrumental activities of daily living in older adults. Journal of the International Neuropsychological Society, 21(9), 688–698.CrossRefGoogle ScholarPubMed
Lawton, M.P. & Brody, E.M. (1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. The gerontologist, 9(3_Part_1), 179–186.CrossRefGoogle ScholarPubMed
Marshall, G.A., Rentz, D.M., Frey, M.T., Locascio, J.J., Johnson, K.A., & Sperling, R.A. (2011). Executive function and instrumental activities of daily living in mild cognitive impairment and Alzheimer’s disease. Alzheimer’s & Dementia, 7, 300–308.CrossRefGoogle ScholarPubMed
McAlister, C. & Schmitter-Edgecombe, M. (2016). Everyday functioning and cognitive correlates in healthy older adults with subjective cognitive concerns. The Clinical Neuropsychologist, 30, 1087–1103.CrossRefGoogle ScholarPubMed
McAlister, C., Schmitter-Edgecombe, M., & Lamb, R. (2016). Examination of variables that may affect the relationship between cognition and functional status in individuals with mild cognitive impairment: a meta-analysis. Archives of Clinical Neuropsychology, 31, 123–147.Google ScholarPubMed
McDonald, R.P. (1999) Test theory: A unified treatment. Mahwah, NJ: Erlbaum.Google Scholar
Morris, J.C., Weintraub, S., Chui, H.C., Cummings, J., DeCarli, C., Ferris, S., Foster, N.L., Galasko, D., Graff-Radford, N., Peskind, E.R., & Beekly, D. (2006). The Uniform Data Set (UDS): clinical and cognitive variables and descriptive data from Alzheimer Disease Centers. Alzheimer Disease & Associated Disorders, 20(4), 210–216.CrossRefGoogle ScholarPubMed
Mungas, D., Beckett, L., Harvey, D., Tomaszewski Farias, S., Reed, B., Carmichael, O., Olichney, J., Miller, J., & DeCarli, C. (2010). Heterogeneity of cognitive trajectories in diverse older persons. Psychology and aging, 25(3), 606.CrossRefGoogle ScholarPubMed
Mungas, D., Reed, B.R., Crane, P.K., Haan, M.N., & González, H. (2004). Spanish and English Neuropsychological Assessment Scales (SENAS): Further development and psychometric characteristics. Psychological assessment, 16(4), 347.CrossRefGoogle ScholarPubMed
Mungas, D., Reed, B.R., Haan, M.N., & González, H. (2005). Spanish and English Neuropsychological Assessment Scales: Relationship to demographics, language, cognition, and independent function. Neuropsychology, 19(4), 466.CrossRefGoogle ScholarPubMed
Muthén, B.O., In Bollen, K.A. & Long, J.S. (1993). Goodness of fit with categorical and other non-normal variables (Eds.), Testing structural equation models, (pp. 205–243). Newbury Park, CA: Sage.Google Scholar
Muthén, L.K. & Muthén, B.O. (2007). Mplus User’s Guide (6th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
Pereira, F.S., Yassuda, M.S., Oliveira, A.M., & Forlenza, O.V. (2008). Executive dysfunction correlates with impaired functional status in older adults with varying degrees of cognitive impairment. International Psychogeriatrics, 20(6), 1104–1115.CrossRefGoogle ScholarPubMed
Richardson, E.D., Nadler, J.D., & Malloy, P.F. (1995). Neuropsychologic prediction of performance measures of daily living skills in geriatric patients. Neuropsychology, 9, 565–572.CrossRefGoogle Scholar
Rog, L.A., Park, L.Q., Harvey, D.J., Huang, C.J., Mackin, S., & Farias, S.T. (2014). The independent contributions of cognitive impairment and neuropsychiatric symptoms to everyday function in older adults. The Clinical Neuropsychologist, 28(2), 215–236.CrossRefGoogle ScholarPubMed
Royall, D.R., Lauterbach, E.C., Kaufer, D., Malloy, P., Coburn, K.L., & Black, K.J. (2007). The cognitive correlates of functional status: A review from the Committee on Research of the American Neuropsychiatric Association. The Journal of neuropsychiatry and clinical neurosciences, 19(3), 249–265.CrossRefGoogle Scholar
Schmitter-Edgecombe, M. & Dyck, D.G. (2014). Cognitive rehabilitation multi-family group intervention for individuals with mild cognitive impairment and their care-partners. Journal of the International Neuropsychological Society, 20(09), 897–908.CrossRefGoogle ScholarPubMed
Schmitter–Edgecombe, M., McAlister, C., & Weakley, A. (2012). Naturalistic assessment of everyday functioning in individuals with mild cognitive impairment: The day-out task. Neuropsychology, 26, 631–641.CrossRefGoogle ScholarPubMed
Schmitter-Edgecombe, M. & Parsey, C. (2014). Assessment of functional change and cognitive correlates in the progression from normal aging to dementia. Neuropsychology, 28, 881–893.CrossRefGoogle Scholar
Schmitter-Edgecombe, M., Parsey, C., & Lamb, R. (2014). Development and psychometric properties of the instrumental activities of daily living – compensation scale (IADL-C). Archives of Clinical Neuropsychology, 29, 776–792.CrossRefGoogle Scholar
Tomaszewski Farias, S., Cahn-Weiner, D.A., Harvey, D.J., Reed, B.R., Mungas, D., Kramer, J.H., & Chui, H. (2009). Longitudinal changes in memory and executive functioning are associated with longitudinal change in instrumental activities of daily living in older adults. The Clinical Neuropsychologist, 23(3), 446–461.CrossRefGoogle ScholarPubMed
Tomaszewski Farias, S., Chou, E, Harvey, D.J, Mungas, D, Reed, B.R., & DeCarli, C. (2013). Longitudinal trajectories of everyday function by diagnostic status. Psychology and Aging, 28(4), 1070–1075.CrossRefGoogle Scholar
Tomaszewski Farias, S., Lau, K., Harvey, D., Denny, K.G., Barba, C., & Mefford, A.N. (2017). Early functional limitations in cognitively normal older adults predict diagnostic conversion to mild cognitive impairment. Journal of the American Geriatrics Society, 65(6), 1152–1158.CrossRefGoogle Scholar
Tomaszewski Farias, S., Mungas, D., Reed, B.R., Cahn-Weiner, D., Jagust, W., Baynes, K., & DeCarli, C. (2008). The measurement of everyday cognition (ECog): Scale development and psychometric properties. Neuropsychology, 22(4), 531–544.CrossRefGoogle Scholar
Tomaszewski Farias, S., Mungas, D., Reed, B.R., Harvey, D., Cahn-Weiner, D., & DeCarli, C. (2006). MCI is associated with deficits in everyday functioning. Alzheimer Disease and Associated Disorders, 20, 217–223.CrossRefGoogle Scholar
Tomaszewski Farias, S., Schmitter-Edgecombe, M., Weakley, A., Harvey, D., Denny, K.G., Barba, C., Gravano, J.T., Giovannetti, T., & Willis, S. (2018). Compensation Strategies in Older Adults: Association With Cognition and Everyday Function. American Journal of Alzheimer’s Disease & Other Dementias, 33(3), 184–191.CrossRefGoogle ScholarPubMed
Troyer, A.K., Murphy, K.J., Anderson, N.D., Moscovitch, M., & Craik, F.I. (2008). Changing everyday memory behaviour in amnestic mild cognitive impairment: A randomised controlled trial. Neuropsychological Rehabilitation, 18(1), 65–88.CrossRefGoogle ScholarPubMed
Troyer, A.K. & Rich, J.B. (2002). Psychometric properties of a new metamemory questionnaire for older adults. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 57(1), P19–P27.CrossRefGoogle ScholarPubMed
Tucker, L.R. & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10.CrossRefGoogle Scholar
Yu, C.Y. (2002). Evaluation of model fit indices for latent variable models with categorical and continuous outcomes. Paper presented at the annual conference of the American Educational Research Association, New Orleans, April 4, 2002.Google Scholar
Zissimopoulos, J., Crimmins, E., & Clair, P.S. (2015). The value of delaying Alzheimer’s disease onset. Forum for Health Economics and Policy, 18(1), 25–39.CrossRefGoogle Scholar
Supplementary material: File

Tomaszewski Farias et al. supplementary material

Table S1

Download Tomaszewski Farias et al. supplementary material(File)
File 13.1 KB