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Subtle mistakes in self‐report surveys predict future transition to dementia
dc.contributor.authorSchneider, Stefan
dc.contributor.authorJunghaenel, Doerte U.
dc.contributor.authorZelinski, Elizabeth M.
dc.contributor.authorMeijer, Erik
dc.contributor.authorStone, Arthur A.
dc.contributor.authorLanga, Kenneth M.
dc.contributor.authorKapteyn, Arie
dc.date.accessioned2022-01-06T15:50:06Z
dc.date.available2022-02-06 10:50:05en
dc.date.available2022-01-06T15:50:06Z
dc.date.issued2021
dc.identifier.citationSchneider, Stefan; Junghaenel, Doerte U.; Zelinski, Elizabeth M.; Meijer, Erik; Stone, Arthur A.; Langa, Kenneth M.; Kapteyn, Arie (2021). "Subtle mistakes in self‐report surveys predict future transition to dementia." Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring 13(1): n/a-n/a.
dc.identifier.issn2352-8729
dc.identifier.issn2352-8729
dc.identifier.urihttps://hdl.handle.net/2027.42/171196
dc.description.abstractIntroductionWe investigate whether indices of subtle reporting mistakes derived from responses in self‐report surveys are associated with dementia risk.MethodsWe examined 13,831 participants without dementia from the prospective, population‐based Health and Retirement Study (mean age 69 ± 10 years, 59% women). Participants’ response patterns in 21 questionnaires were analyzed to identify implausible responses (multivariate outliers), incompatible responses (Guttman errors), acquiescent responses, random errors, and the proportion of skipped questions. Subsequent incident dementia was determined over up to 10 years of follow‐up.ResultsDuring follow‐up, 2074 participants developed dementia and 3717 died. Each of the survey response indices was associated with future dementia risk controlling for confounders and accounting for death as a competing risk. Stronger associations were evident for participants who were younger and cognitively normal at baseline.DiscussionMistakes in the completion of self‐report surveys in longitudinal studies may be early indicators of dementia among middle‐aged and older adults.
dc.publisherWiley Periodicals, Inc.
dc.publisherCambridge University Press
dc.subject.othersurvey response behaviors
dc.subject.othercognitive impairment
dc.subject.otherdementia
dc.subject.otherearly detection
dc.subject.otherepidemiology
dc.subject.otherfunctional abilities
dc.subject.otherlongitudinal
dc.subject.otherpopulation‐based
dc.subject.otherprospective
dc.subject.otherself‐report surveys
dc.titleSubtle mistakes in self‐report surveys predict future transition to dementia
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelNeurology and Neurosciences
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/171196/1/dad212252.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/171196/2/dad212252_am.pdf
dc.identifier.doi10.1002/dad2.12252
dc.identifier.sourceAlzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
dc.identifier.citedreferenceMcCammon RJ, Fisher GG, Hassan H, Faul JD, Rogers W, Weir DR. Health and Retirement Study Imputation of Cognitive Functioning Measures: 1992‐2016. Ann Arbor: University of Michigan; 2019.
dc.identifier.citedreferenceStringer G, Couth S, Brown L, et al. Can you detect early dementia from an email? A proof of principle study of daily computer use to detect cognitive and functional decline. Int J Geriatr Psychiatry. 2018; 33: 867 ‐ 874.
dc.identifier.citedreferenceSeelye A, Mattek N, Howieson DB, et al. Embedded online questionnaire measures are sensitive to identifying mild cognitive impairment. Alzheimer Dis Assoc Disord. 2016; 30: 152.
dc.identifier.citedreferenceTourangeau R, Rips LJ, Rasinski K. The Psychology of Survey Response. Cambridge, UK: Cambridge University Press; 2000.
dc.identifier.citedreferenceSchwarz N, Knäuper B. Cognition, aging, and self‐reports. In: Park D, Schwarz N, eds. Cognitive Aging: A Primer. Philadelphia, PA: Psychology Press; 2012: 233 ‐ 252.
dc.identifier.citedreferenceColsher PL, Wallace RB. Data quality and age: health and psychobehavioral correlates of item nonresponse and inconsistent responses. J Gerontol. 1989; 44: P45 ‐ P52.
dc.identifier.citedreferenceSchneider S. Extracting Response style bias from measures of positive and negative affect in aging research. J Gerontol B Psychol Sci Soc Sci. 2018; 73: 64 ‐ 74.
dc.identifier.citedreferenceConijn JM, van der Ark LA, Spinhoven P. Satisficing in mental health care patients: the effect of cognitive symptoms on self‐report data quality. Assessment. 2020; 27: 178 ‐ 193.
dc.identifier.citedreferenceKutschar P, Weichbold M, Osterbrink J. Effects of age and cognitive function on data quality of standardized surveys in nursing home populations. BMC Geriatr. 2019; 19: 1 ‐ 10.
dc.identifier.citedreferenceLechner CM, Rammstedt B. Cognitive ability, acquiescence, and the structure of personality in a sample of older adults. Psychol Assessment. 2015; 27: 1301 ‐ 1311.
dc.identifier.citedreferenceIenca M, Vayena E, Blasimme A. Big data and dementia: charting the route ahead for research, ethics, and policy. Front Med. 2018; 5: 13.
dc.identifier.citedreferenceSnowdon DA, Kemper SJ, Mortimer JA, Greiner LH, Wekstein DR, Markesbery WR. Linguistic ability in early life and cognitive function and Alzheimer’s disease in late life: findings from the Nun Study. JAMA. 1996; 275: 528 ‐ 532.
dc.identifier.citedreferenceWhalley L, Starr J, Athawes R, Hunter D, Pattie A, Deary I. Childhood mental ability and dementia. Neurology. 2000; 55: 1455 ‐ 1459.
dc.identifier.citedreferenceSmith J, Fisher GG, Ryan L, Clarke P, House J, Weir D. Health and Retirement Study Psychosocial and Lifestyle Questionnaire 2006–2010. Ann Arbor, MI: University of Michigan; 2013.
dc.identifier.citedreferenceCrimmins EM, Kim JK, Langa KM, Weir DR. Assessment of cognition using surveys and neuropsychological assessment: the health and retirement study and the aging, demographics, and memory study. J Gerontol B Psychol Sci Soc Sci. 2011; 66: i162 ‐ i71.
dc.identifier.citedreferenceLanga KM, Larson EB, Crimmins EM, et al. A comparison of the prevalence of dementia in the United States in 2000 and 2012. JAMA Intern Med. 2017; 177: 51 ‐ 58.
dc.identifier.citedreferenceLanga K, Weir DR, Kabeto MU, Sonnega A. Langa‐Weir Classification of Cognitive Function (1995 Onward). Anne Arbor: University of Michigan; 2020.
dc.identifier.citedreferenceVanderWeele TJ. Principles of confounder selection. Eur J Epidemiol. 2019; 34: 211 ‐ 219.
dc.identifier.citedreferenceFine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999; 94: 496 ‐ 509.
dc.identifier.citedreferenceSchaie KW. A general model for the study of developmental problems. Psychol Bull. 1965; 64: 92 ‐ 107.
dc.identifier.citedreferenceFeveile H, Olsen O, Hogh A. A randomized trial of mailed questionnaires versus telephone interviews: response patterns in a survey. BMC Med Res Methodol. 2007; 7: 1 ‐ 7.
dc.identifier.citedreferenceHeagerty PJ, Lumley T, Pepe MS. Time‐dependent ROC curves for censored survival data and a diagnostic marker. Biometrics. 2000; 56: 337 ‐ 344.
dc.identifier.citedreferenceSaha P, Heagerty P. Time‐dependent predictive accuracy in the presence of competing risks. Biometrics. 2010; 66: 999 ‐ 1011.
dc.identifier.citedreferenceBlanche P, Dartigues JF. Jacqmin‐Gadda H. Estimating and comparing time‐dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med. 2013; 32: 5381 ‐ 5397.
dc.identifier.citedreferenceClark TG, Altman DG, De Stavola BL. Quantification of the completeness of follow‐up. Lancet. 2002; 359: 1309 ‐ 1310.
dc.identifier.citedreferenceCohen J. Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Erlbaum Associates; 1988.
dc.identifier.citedreferenceVan Vaerenbergh Y, Thomas TD. Response styles in survey research: a literature review of antecedents, consequences, and remedies. Int J Public Opin Res. 2013; 25: 195 ‐ 217.
dc.identifier.citedreferenceHayes‐Larson E, Ackley SF, Zimmerman SC, et al. The competing risk of death and selective survival cannot fully explain the inverse cancer‐dementia association. Alzheimer’s Dement. 2020; 16: 1696 ‐ 1703.
dc.identifier.citedreferenceSeligman SC, Giovannetti T, Sestito J, Libon DJ. A new approach to the characterization of subtle errors in everyday action: implications for mild cognitive impairment. Clin Neuropsychol. 2014; 28: 97 ‐ 115.
dc.identifier.citedreferenceKozauer N, Katz R. Regulatory innovation and drug development for early‐stage Alzheimer’s disease. N Engl J Med. 2013; 368: 1169 ‐ 1171.
dc.identifier.citedreferenceGamaldo AA, An Y, Allaire JC, Kitner‐Triolo MH, Zonderman AB. Variability in performance: identifying early signs of future cognitive impairment. Neuropsychology. 2012; 26: 534 ‐ 540.
dc.identifier.citedreferenceMeijer RR. The number of Guttman errors as a simple and powerful person‐fit statistic. Appl Psychol Meas. 1994; 18: 311 ‐ 314.
dc.identifier.citedreferenceKnowles ES, Condon CA. Why people say" yes": a dual‐process theory of acquiescence. J Pers Soc Psychol. 1999; 77: 379 ‐ 386.
dc.identifier.citedreferenceSperling RA, Aisen PS, Beckett LA, et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging‐Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 2011; 7: 280 ‐ 292.
dc.identifier.citedreferenceCummings J, Aisen PS, DuBois B, et al. Drug development in Alzheimer’s disease: the path to 2025. Alzheimer’s Res Ther. 2016; 8: 39.
dc.identifier.citedreferenceFiandaca MS, Mapstone ME, Cheema AK, Federoff HJ. The critical need for defining preclinical biomarkers in Alzheimer’s disease. Alzheimer’s Dement. 2014; 10: S196 ‐ S212.
dc.identifier.citedreferenceEdmonds EC, Delano‐Wood L, Galasko DR, Salmon DP, Bondi MW. Subtle cognitive decline and biomarker staging in preclinical Alzheimer’s disease. J Alzheimer’s Dis. 2015; 47: 231 ‐ 242.
dc.identifier.citedreferenceGomar JJ, Bobes‐Bascaran MT, Conejero‐Goldberg C, Davies P, Goldberg TE, AsDN Initiative. Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer’s disease neuroimaging initiative. Arch Gen Psychiatry. 2011; 68: 961 ‐ 969.
dc.identifier.citedreferenceTarnanas I, Tsolaki A, Wiederhold M, Wiederhold B, Tsolaki M. Five‐year biomarker progression variability for Alzheimer’s disease dementia prediction: can a complex instrumental activities of daily living marker fill in the gaps?. Alzheimer’s Dement. 2015; 1: 521 ‐ 532.
dc.identifier.citedreferenceVerlinden VJ, van der Geest JN, de Bruijn RF, Hofman A, Koudstaal PJ, Ikram MA. Trajectories of decline in cognition and daily functioning in preclinical dementia. Alzheimer’s Dement. 2016; 12: 144 ‐ 153.
dc.identifier.citedreferencePérès K, Helmer C, Amieva H, et al. Natural history of decline in instrumental activities of daily living performance over the 10 years preceding the clinical diagnosis of dementia: a prospective population‐based study. J Am Geriatr Soc. 2008; 56: 37 ‐ 44.
dc.identifier.citedreferenceFauth EB, Schwartz S, Tschanz JT, Østbye T, Corcoran C, Norton MC. Baseline disability in activities of daily living predicts dementia risk even after controlling for baseline global cognitive ability and depressive symptoms. Int J Geriatr Psychiatry. 2013; 28: 597 ‐ 606.
dc.identifier.citedreferenceOkonkwo OC, Wadley VG, Griffith HR, et al. Awareness of deficits in financial abilities in patients with mild cognitive impairment: going beyond self‐informant discrepancy. Am J Geriatr Psychiatry. 2008; 16: 650 ‐ 659.
dc.identifier.citedreferenceDebettignies BH, Mahurin RK, Pirozzolo FJ. Insight for impairment in independent living skills in Alzheimer’s disease and multi‐infarct dementia. J Clin Exp Neuropsychol. 1990; 12: 355 ‐ 363.
dc.identifier.citedreferenceLoewenstein DA, Argüelles S, Bravo M, et al. Caregivers’ judgments of the functional abilities of the Alzheimer’s disease patient: a comparison of proxy reports and objective measures. J Gerontol B Psychol Sci Soc Sci. 2001; 56: P78 ‐ P84.
dc.identifier.citedreferenceJekel K, Damian M, Wattmo C, et al. Mild cognitive impairment and deficits in instrumental activities of daily living: a systematic review. Alzheimer’s Res Ther. 2015; 7: 17.
dc.identifier.citedreferenceLindbergh CA, Dishman RK, Miller LS. Functional disability in mild cognitive impairment: a systematic review and meta‐analysis. Neuropsychol Rev. 2016; 26: 129 ‐ 159.
dc.identifier.citedreferenceKaye J, Mattek N, Dodge HH, et al. Unobtrusive measurement of daily computer use to detect mild cognitive impairment. Alzheimer’s Dement. 2014; 10: 10 ‐ 17.
dc.identifier.citedreferenceSeelye A, Mattek N, Sharma N, et al. Weekly observations of online survey metadata obtained through home computer use allow for detection of changes in everyday cognition before transition to mild cognitive impairment. Alzheimer’s Dement. 2018; 14: 187 ‐ 194.
dc.identifier.citedreferenceSeelye A, Hagler S, Mattek N, et al. Computer mouse movement patterns: a potential marker of mild cognitive impairment. Alzheimer’s Dement. 2015; 1: 472 ‐ 480.
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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