Vol. 16 nº 2 - Apr/May/Jun de 2022
Original Article Pages 171 to 180

Retest effects in a diverse sample: sociodemographic predictors and possible correction approaches
Efeito de reteste em uma amostra diversa: preditores sociodemográficos e possíveis abordagens para a correção

Authors: Laiss Bertola1; Isabela Judith Martins Bense√Īor2,3; Andre Russowsky Brunoni2,3,4; Paulo Caramelli5; Sandhi Maria Barreto6; Arlinda Barbosa Moreno7; Rosane Harter Griep8; Maria Carmen Viana9; Paulo Andrade Lotufo2,3; Claudia Kimie Suemoto10


Descriptors: Reproducibility of Results; Aged; Longitudinal Studies; Psychometrics.
Reprodutibilidade dos Testes; Idoso; Estudos Longitudinais; Psicometria.

Repeated cognitive assessment in longitudinal studies favors the occurrence of retest effects, usually increasing the scores obtained at the follow-up assessments when compared to baseline. Therefore, retest effects can compromise the evaluation of cognitive decline in older adults.
OBJECTIVES: We aimed to verify the occurrence of the retest effect and the impact of sociodemographic characteristics on the follow-up scores in a sample of 5,592 participants with a diverse sociodemographic profile, who were assessed twice during 4 years of follow-up.
METHODS: We tested two possible approaches to correct the retest effect and calculated the Reliable Change Index.
RESULTS: We observed increased scores at the follow-up assessment after 4 years, but the results indicate a modest occurrence of retest effects. The regression difference correction successfully generated follow-up corrected scores, while the mean difference did not provide effective corrections. Sociodemographic characteristics had a minor impact on the retest.
CONCLUSIONS: We recommend the regression difference correction for retest effects. The absence of this methodological approach might lead to biased results using longitudinal cognitive scores.

Avalia√ß√Ķes cognitivas repetidas em estudos longitudinais favorecem a ocorr√™ncia de efeitos de retestagem ou de pr√°tica, geralmente aumentando os escores obtidos nas avalia√ß√Ķes de acompanhamento quando comparados aos da primeira avalia√ß√£o. Sendo assim, os efeitos do retestagem podem comprometer a verifica√ß√£o do decl√≠nio cognitivo em idosos.
OBJETIVOS: Objetivamos verificar a ocorrência do efeito de prática e o impacto das características sociodemográficas nos escores de seguimento em uma amostra de 5.592 participantes com perfil sociodemográfico diverso, avaliada duas vezes durante quatro anos de seguimento.
M√%TODOS: Testamos duas abordagens poss√≠veis para corrigir o efeito de pr√°tica e calculamos o √≠ndice de mudan√ßa confi√°vel.
RESULTADOS: Observamos escores sutilmente maiores na avalia√ß√£o de seguimento ap√≥s quatro anos, o que sugere a ocorr√™ncia de efeitos de retestagem. A corre√ß√£o pela diferen√ßa da regress√£o gerou escores corrigidos de acompanhamento satisfat√≥rios, enquanto a corre√ß√£o pela diferen√ßa m√©dia n√£o forneceu corre√ß√Ķes eficazes. As caracter√≠sticas sociodemogr√°ficas tiveram impacto m√≠nimo no efeito de pr√°tica.
CONCLUS√ES: Recomendamos a forma de corre√ß√£o pela diferen√ßa da regress√£o para efeitos de retestagem. A¬†aus√™ncia dessa abordagem metodol√≥gica, quando utilizamos escores cognitivos longitudinais, pode levar a resultados enviesados.


Longitudinal cognitive studies should consider the occurrence of practice or retest effects with repeated neuropsychological assessments. Repeated assessments with the same tests increase the occurrence of retest effects, usually increasing the score obtained at the follow-up assessment when compared to the first evaluation. Previous studies have shown that the second assessment shows the largest retest effects1. After three or more repeated cognitive assessments, there is a plateau in the retest effects2,3. Therefore, from the third assessment onward, the cognitive scores became more reliable due to the more stable retest effect1,4,5. The increase in the second assessment score might be due to several causes, including increased comfort in being tested, reduced anxiety at the follow-up visits for knowing what to expect, learning the test paradigm more than the items themselves, or even remembering test items. Besides, regression to the mean could be present since subjects with very low scores on the first assessment might increase their performance in subsequent evaluations2,6. These possible explanations can lead to increased cognitive scores at the second visit or they might even have caused slightly reduced performance at the first visit.

Retest effects produce unique repercussions in aging studies, compromising the expected observation of cognitive decline in older adults7. This phenomenon occurs because the average score gains in the presence of retest are often higher than the real cognitive change that happens during the follow-up period2.

It is also known that frequent assessments may obscure the real cognitive decline5 and that cognitive tests have distinct practice effects1,8,9. Previous studies have suggested the use of parallel tests to reduce the retest effects7. However, this solution depends on well-matched equivalent test forms to avoid measurement errors that can be erroneously interpreted as cognitive improvement or decline10.

Literature diverges about whether sociodemographic characteristics are related to the retest effects. Effects¬†were reported to be higher in younger participants (18‚?"53 years old compared to 54‚?"97 years old)4, while other studies found that age and other sociodemographic variables (e.g., sex, education, and race/ethnicity) were not related to retest effects8,11. Although¬†education was not previously related to retest effects, we hypothesized that individuals with low education are more prone to underperform in their first assessment due to unfamiliarity with testing situations.

Therefore, we assume that, if not considered in the analyses, retest effects can lead to biased cognitive results in longitudinal studies. Therefore, the aims of this study were to (1) verify the occurrence of retest effects in a longitudinal study, (2) verify whether sociodemographic characteristics are related to this effect, and (3) address how to take retest effects into account when using a data set with two visits.



The ELSA-Brasil sample is composed of 15,105 active or retired employees from public institutions from six large Brazilian cities (e.g., Belo Horizonte, Porto Alegre, Rio de Janeiro, Salvador, S√£o Paulo, and Vit√≥ria), of both sexes, aged between 35 and 74 years at baseline (2008‚?"2010)12,13. The ELSA-Brasil is a longitudinal study investigating the incidence and evolution of chronic diseases, especially cardiovascular diseases and diabetes, among middle-aged and older adults. The exclusion criteria of this study were the presence of clinically observed severe cognitive or communication impairment, intention to quit work at the institution shortly for reasons not related to retirement, and, if retired, living outside the corresponding metropolitan area. Women currently or recently pregnant were rescheduled so that the first interview could take place at least 4 months after delivery. All participants were Brazilian-Portuguese speakers.

The baseline assessment in the study included sociodemographic information, clinical history, cognitive and mental health evaluation, lifestyle factors, occupational history, and family history of major diseases. Cognitive¬†function was reassessed only in participants aged 55 years or older (7,066 eligible participants) at the second visit (2012‚?"2014), after 4-year interval. The local institutional review board approved the study that was conducted following the ethical rules for human experimentation stated in the Declaration of Helsinki, and all participants signed an informed consent.

For this study, participants were excluded if they reported diagnoses of neurological diseases at the baseline (e.g., stroke, concussion, brain tumor, multiple sclerosis, Parkinson‚?Ts disease, dementia, and epilepsy), if they were using any medication with psychoactive effects (e.g., benzodiazepines, neuroleptics, antiparkinsonian agents, anticonvulsants, sedating antihistamines, lithium, α-adrenergic agonists, and tricyclic antidepressants), and those who had psychiatric symptoms based on mental health evaluation (Figure 1). We also excluded participants with missing cognitive test scores at baseline or follow-up evaluations. Among 7,066 eligible participants who were 55 years old at the second visit, 5,592 were considered the final sample (Figure 1).

Figure 1. Sample selection flowchart.

Neuropsychological assessment

Baseline assessment used the standardized memory tests from the Consortium to Establish a Registry for Alzheimer‚?Ts Disease (CERAD)14 validated for the Brazilian population15 to assess learning, delayed word recall, and recognition (CERAD Word List Test [WLT]). The recognition score is the number of corrected classified words that belonged to the list (0‚?"10 points) with penalization for including distractors (the number of correctly identified words minus false-positive errors ‚?" distractors words identified as part of the list). The¬†baseline assessment also included the semantic verbal fluency (SVF) and phonemic verbal fluency (PVF) tests (animals and letter F, respectively)16,17 and the Trail Making Test B (TMT-B)18. All tests were performed using the Brazilian-Portuguese version. Follow-up assessment used the same cognitive measures, except in the case of the verbal fluency tasks. Letter A replaced the PVF of letter F, and the SVF of animals was replaced by vegetables in order to reduce learning effects. However, we used previously test equated scores19. Equated scores aim to guarantee that the distinct versions of the verbal fluency tests measure the construct with the same difficulty level, by transforming one test score into the same metric and range of values from another test. Trained examiners administered the tests in a fixed order during one single session, and all psychometric environment requirements were met (a quiet, lighted, and free of distractors environment)20.

Statistical analysis

We evaluated the retest effects using three approaches to clarify if there is a real increase in cognitive performance, and we tested distinct possibilities to correct retest scores to be used in clinical studies. Two approaches were inspired on the study by Racine et al.21 The comparative approach was no retest correction, using the raw cognitive scores at follow-up. The first approach was the mean difference correction21. This approach first subtracts the observed baseline score from the follow-up score and then the mean of the difference of the sample is considered the retest effect. Then, the mean retest effect was subtracted from the follow-up value to obtain the mean difference corrected score for follow-up. The second approach was the predicted difference correction21. This regression-based approach first uses the baseline score to predict a retest score (follow-up).
Then, the regression predicted retest score is subtracted from the observed score at follow-up to obtain the retest effect. Finally, the retest effect was added to the observed baseline score at baseline to obtain the predicted difference corrected score for follow-up. All assumptions required to perform the linear regression models were met. Considering that the regular method for these corrections is to use a control sample to first extract the retest effect and subsequently apply the correction to the entire sample, we used a subsample of participants that previously built a robust normative data, based on the absence of risk factors and objective cognitive decline (for the complete description, see Bertola et al.)22. Briefly, this robust subsample of the ELSA-Brasil was composed of 3,888 participants who, after exclusion criteria (e.g., baseline and follow-up self-reported stroke, use of psychoactive medications, missing cognitive scores, and Reliable Change Index [RCI]>-1.96), were considered not having possible cognitive decline after 4-year interval. This subsample offers the mean retest effect at the second approach, so it could be subtracted from the follow-up value for the entire sample. Similarly, this subsample provided the regression coefficients needed to predict the retest score (follow-up) for the entire sample.

We calculated the within-subject t-test to compare the baseline score with no correction, mean predicted difference correction, and predicted difference correction.

The third approach is an RCI23. Considering that there are distinct options to compute the RCI, we decided to use the Crawford and Howell‚?Ts method once their mathematical expression corrects for practice and regression to the mean in the predicted score and individualizes error term based on the initial test score24. Basically, the individual‚?Ts predicted retest score is subtracted from their actual retest score and then divided by a standard error (the complete formula is published and can be accessed from Hinton-Bayre)23. This approach extracted the correlation value, baseline and follow-up mean, standard deviation, and variance values from the same robust normative subsample. The¬†regression coefficient to obtain the predicted score was derived using a weighted least square model to account for heteroscedasticity. This approach does not produce a corrected score, but rather indicates if the observed change in scores from baseline to the follow-up visit is a meaningful score change or a change that might be attributable to retest effect and/or the test reliability. RCI score between -1.64 and 1.64 suggests cognitive stability, score below -1.64 suggests cognitive decline, and score above 1.64 suggests cognitive improvement with a 90% confidence interval.

Retest effects and sociodemographic characteristics

To verify if sociodemographic characteristics can distinctly affect the occurrence of retest effects, we performed linear regression analysis for each task retest effect from the predicted difference correction method. Age, education, and sex were added as predictors of the retest effect.


Table 1 shows the characteristics of the sample (n=5,592). Overall, 12% of our participants had only elementary school levels (up to 10 years of schooling), 56% were white, and 55% were women. The raw mean cognitive scores on baseline and follow-up revealed a small increase after the 4-year interval (Tables 2), with exception of PVF task, revealing retest effects after within-subject t-test (Table 3). The approaches of mean difference correction and the predicted difference correction showed scores slightly lower than the baseline ones (Tables 2 and 3).

The RCI analysis (Supplementary Table 1) suggests that the majority of the sample did not have an actual change in the cognitive performance after considering the effect for practice and regression to the mean in the predicted score and individualized error term based on the initial test score. The majority of participants (95‚?"99%) obtained RCI scores between -1.64 and 1.64.

Education, age, and sex demonstrated to be significant predictors of retest effects for most of the cognitive scores. However, the models revealed small explained variance and small effect sizes (Table 4), indicating a minor impact of sociodemographic characteristics on the retest effects. Being older, having lower education, and being male were indicatives of marginally larger effect sizes at follow-up, but these results should be interpreted carefully. Sex was not a predictor for PVF and TMT-B.

Figure 2 illustrates the retest effects as a function of age (<65 years or ≥65 years) and education group (elementary or high school [HS]+college or more), the most consistent predictors. Retest effects were more prevalent among older participants (≥65 years) with lower education (E), but younger participants (<65¬†years) with lower educational attainment (E) also revealed pronounced retest effects. The WLT Recognition trial (Figure 2C) was the only score with minimal or absence of retest effects and maintenance of ceiling effects, except for the participants with lower education attainment.

Figure 2. Retest effects boxplot by age (<65 years and 65 years or older) and education (elementary or high school and college or more) and groups comparisons (corrected for multiple comparisons). (A) WLT Learning: Word Learning Test ‚?" Learning trial. (B) WLT Recall: Word Learning Test ‚?" Recall Trial. (C) WLT Recognition: Word Learning Test ‚?" Recognition Trial. (D) SVF: semantic verbal fluency. (E) PVF: phonemic verbal fluency. (F) TMT-B: Trail Making Test Part B. Whiskers represent the standard deviation. ns: p>0.05, *p≤0.05, **p≤0.01, ***p≤0.001, ****p≤0.0001.

Considering that the educational group division resulted in uneven sample sizes, we performed additional comparisons of the retest effect among further educational groups (Supplementary Table 2). Retest effect reduced when educational attainment increase in participants younger than 65 years, except for the WLT Recognition trial. For participants aged 65 years or older, retest effect is similar to participants with elementary and HS levels, suggesting that the retest effect only reduces after a higher educational level (college or more). When educational level was kept constant and participants were compared across age, younger and older participants with elementary level did not differ in their retest effect, except for the TMT-B. Participants with HS and college levels differed, among the age groups, in WLT Learning, WLT Recall, and TMT-B.

We also performed analysis comparing the retest effect of participants with the lowest level of education (<5 years of schooling) with participants who completed the elementary school (8 years), HS (11 years), and college or more (15-16 years) (Supplementary Table 3). This additional analysis aimed to clarify the impact of the second assessment, considering that very low educated subjects underwent fewer situations of performance assessment during life. Younger participants (<65 years old) with less than 5 years of education had higher levels of retest effect only when compared with participants with HS or more (except for WLT Recognition and TMT-B). Older participants (65 years or older) with less than 5 years of education have higher levels of retest effect when compared to participants with college or more (except for WLT Recognition).


Retest effects are common in longitudinal studies with recurrent cognitive assessments and a source of bias when not taken into account to verify cognitive change across time. We aimed to verify the occurrence of retest effects, possible approaches to correct for it, and the sociodemographic predictors of its occurrence. We found that modest retest effects occurred in the tests used at the ELSA-Brasil study (except on PVF), with some tests revealing higher effect and others revealing lower effect, especially those with the limitation of showing ceiling effects (WLT Recognition). Our results revealed smaller retest effects than usually observed in numerous studies that observed marked by improvement in test scores on the second assessment1,2,4-8,25,26.

Although most cited studies have a smaller follow-up interval than the ELSA-Brasil (4 years), the longitudinal increase has been reported even after a 7-year interval27. Additionally, a 3-year interval was associated with a mean increase of 0.30 standard deviation in scores due to retest effects26, a similar mean value found by our study with 4-year interval.

Our results suggest that age, education, and sex might be the potential predictors of the retest effects. However, the small effect sizes indicated that the influence of sociodemographic variables might be minimal. Gross et¬†al.8 found no sociodemographic predictors in a sample of older adults, while Salthouse4 found that young adults revealed a higher effect. This last study compared adults aged 18‚?"53 years with older adults aged 54‚?"97 years that might had a true cognitive decline commonly seen in advanced ages. Middle-aged adults and young older adults might not demonstrate meaningful differences in retest effects, once age effect is not always shown. Nevertheless, we found that older adults aged 55‚?"64 years with lower educational levels revealed higher retest effects than their more educated counterparts. Also, we found that among participants with HS or college education, adults aged 65 years or older revealed higher retest effects than their younger counterparts (aged 55‚?"64 years).

Educational experience usually exposes the subject to recurrent schooling assessments. Higher educational levels increase the performance and knowledge about evaluation procedures, and this might contribute to less anxiety in the face of a first formal cognitive assessment. Subjects with lower education might face assessments with more anxiety symptoms for not being used to have their performance evaluated28. This experience might be similar to previous controlled exposures that reduce retests effects7.

Considering that this effect might be more prominent in lower educated subjects and that these subjects are at higher risk for presenting cognitive decline or dementia29, longitudinal studies from low- and middle-income countries should be extremely aware of follow-up scores correction. These subjects are a considerable proportion of older adults in these countries30, and higher practice effects might cover a true cognitive decline.

Once the correction of follow-up scores is needed, there are two main options to avoid biased cognitive scores: the mean difference and the predicted difference corrections. Nonetheless, considering the possible impact of sociodemographic predictors on this effect in this sample, we recommend that further studies choose the predicted difference correction. This approach allows the inclusion of relevant predictors in the regression analysis to improve the correction of retest effects for each research question asked and additionally account for the effect of regression to the mean21,31.

The RCI results also highlighted that the majority of the participants did not increase their cognitive performance after 4 years. Most of the small differences in scores from baseline to follow-up might be due to test reliability and practice effect susceptibility. The RCI did not revealed higher proportion of lower educated (elementary level) participants with significant decreased or increased scores on the second assessment when compared to HS and college education, except for the TMT-B (20% revealed an improvement). Stein and colleagues studied the CERAD battery and found that the RCI analysis revealed that changes in the test battery after 3 years can be interpreted with uncertainty due to possible measurement errors, practice effects, and even normal age-related cognitive decline32. The RCI is a limited approach that only allows for the comparison of two evaluation at a time and is not suitable for longitudinal studies with multiple cognitive assessments, in which regression approaches are more recommended33.

Previous to baseline or in-between waves exposure to external cognitive assessment might increase or decrease the retest effects. The absence of this information in the ELSA-Brasil questionnaire is a limitation to our comprehension of additional factors that might affect the retest effects. Given that we only have available data for two waves, we could not apply a model-based correction21. Further studies with this approach are recommended, including the interaction terms with time when future follow-up data become available. There are other approaches (e.g., indicator of the first cognitive visit, number of prior testing occasions, and square root of the number of prior testing occasions) to account for practice effects in the face of multiple follow-ups, and how the effects are specified can lead to considerable differences in estimated rates of cognitive change34.

Our study has some limitations. We do not have information if the participant has been exposed to other out-of-the study cognitive assessment previously to the baseline assessment. We could not control for other sources that might have contributed to the increase in follow-up scores. However, it is highly unlikely that participants were exposed to a cognitive assessment or rehabilitation outside the ELSA-Brasil during the study period. The absence of a test validity assessment on the battery also contributes to our limited interpretation of why the low educated participants revealed a higher practice effect. However, considering the sample selection, it is unlikely that the participants were not sufficiently engaged to perform the cognitive battery to consider the scores unreliable. Finally, the tests have reliability studies inside the ELSA-Brasil study and validity studies in other Brazilian samples, and thus the complete absence of bias cannot be guaranteed.

Our study addressed and contributed to the understanding of predictors of retest effects using a diverse socioeconomic sample. Moreover, we identified and recommended the best retest correction for an extensive data set with the potential to explore factors associated with cognitive decline in a low- to middle-income country. Future studies with the ELSA-Brasil data set will contribute to increasing the knowledge about protective and risk factors for health and pathological aging, through unbiased cognitive change scores.

Authors‚?T contributions. LB: conceptualization, data curation, formal analysis, visualization, and writing ‚?" original draft preparation. IMB, SMB, ABM, RG, MCV, and PAL: funding acquisition, investigation, project administration, resources, and writing ‚?" review & editing. ARB and PC: writing ‚?" review & editing. CKS: conceptualization, supervision, validation, and writing ‚?" original draft preparation.


1. Calamia M, Markon K, Tranel D. Scoring higher the second time around: meta-analyses of practice effects in neuropsychological assessment. Clin Neuropsychol. 2012;26(4):543-70. https://doi.org/10.1080/13854046.2012.680913

2. Bartels C, Wegrzyn M, Wiedl A, Ackermann V, Ehrenreich H. Practice effects in healthy adults: A longitudinal study on frequent repetitive cognitive testing. BMC Neurosci. 2010;11:118. https://doi.org/10.1186/1471-2202-11-118.

3. Lievens F, Reeve CL, Heggestad ED. An examination of psychometric bias due to retesting on cognitive ability tests in selection settings. J Appl Psychol. 2007;92(6):1672-82. https://doi.org/10.1037/0021-9010.92.6.1672

4. Salthouse TA. Influence of age on practice effects in longitudinal neurocognitive change. Neuropsychology. 2010;24(5):563-72. https://doi.org/10.1037/a0019026

5. Salthouse TA. Frequent assessments may obscure cognitive decline. Psychol Assess. 2014;26(4):1063-9. https://doi.org/10.1037/pas0000007

6. Scharfen J, Peters JM, Holling H. Retest effects in cognitive ability tests: a meta-analysis. Intelligence. 2018;67:44-66. https://doi.org/10.1016/j.intell.2018.01.003

7. Goldberg TE, Harvey PD, Wesnes KA, Snyder PJ, Schneider LS. Practice effects due to serial cognitive assessment: implications for preclinical Alzheimer‚?Ts disease randomized controlled trials. Alzheimers Dement (Amst). 2015;1(1):103-11. https://doi.org/10.1016/j.dadm.2014.11.003

8. Gross AL, Benitez A, Shih R, Bangen KJ, Glymour MM, Sachs B, et al. Predictors of retest effects in a longitudinal study of cognitive aging in a diverse community-based sample. J Int Neuropsychol Soc. 2015;21(7):506-18. https://doi.org/10.1017/S1355617715000508

9. Strauss E, Sherman EM, Spreen O. A compendium of neuropsychological tests: administration, norms, and commentary. 3rd ed. New York: Oxford University Press; 2006.

10. Gross AL, Inouye SK, Rebok GW, Brandt J, Crane PK, Parisi JM, et al. Parallel but not equivalent: challenges and solutions for repeated assessment of cognition over time. J Clin Exp Neuropsychol. 2012;34(7):758-72. https://doi.org/10.1080/13803395.2012.681628

11. Wilson RS, Li Y, Bienias L, Bennett DA. Cognitive decline in old age: Separating retest effects from the effects of growing older. Psychol Aging. 2006;21(4):774-89. https://doi.org/10.1037/0882-7974.21.4.774

12. Aquino EM, Barreto SM, Bensenor IM, Carvalho MS, Chor D, Duncan BB, et al. Brazilian Longitudinal Study of Adult health (ELSA-Brasil): Objectives and design. Am J Epidemiol. 2012;175(4):315-24. https://doi.org/10.1093/aje/kwr294

13. Schmidt MI, Duncan BB, Mill JG, Lotufo PA, Chor D, Barreto SM, et al. Cohort profile: Longitudinal Study of Adult Health (ELSA-Brasil). Int J Epidemiol. 2014;44(1):68-75. https://doi.org/10.1093/ije/dyu027

14. Morris JC, Heyman A, Mohs RC, Hughes JP, van Belle G, Fillenbaum G, et¬†al. The Consortium to Establish a Registry for Alzheimer‚?Ts Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer‚?Ts disease. Neurology. 1989;39(9):1159-65. https://doi.org/10.1212/wnl.39.9.1159

15. Bertolucci PH, Okamoto IH, Brucki SM, Siviero MO, Neto JT, Ramos LR. Applicability of the CERAD neuropsychological battery to Brazilian elderly. Arq Neuro-Psiquiatr. 2001;59(3A):532-6. https://doi.org/10.1590/S0004-282X2001000400009

16. Machado TH, Fichman HC, Santos E, Carvalho V. Normative data for healthy elderly on the phonemic verbal fluency task ‚?" FAS. Dement Neuropsychol. 2009;3(1):55-60. https://doi.org/10.1590/S1980-57642009DN30100011

17. Fichman HC, Fernandes CS, Nitrini R, Lourenço RA, Paradela EM, Carthery-Goulart MT, et al. Age and educational level effects on the performance of normal elderly on category verbal fluency tasks. Dement Neuropsychol. 2009;3(1):49-54 https://doi.org/10.1590/S1980-57642009DN30100010

18. Hamdan AC, Hamdan EM. Effects of age and education level on the Trail Making Test in a healthy Brazilian sample. Psychol Neurosci. 2009;2(2):199-203. https://doi.org/10.3922/j.psns.2009.2.012

19. Bertola L, Bense√Īor I, Gross A, Caramelli P, Barreto S, Moreno A, et¬†al. Longitudinal measurement invariance of neuropsychological tests in a diverse sample from the ELSA-Brasil study. Braz J Psychiatry. 2021;43(3):254-61. https://doi.org/10.1590/1516-4446-2020-0978

20. Passos VM, Caramelli P, Bense√Īor I, Giatti L, Maria Barreto S. Methods of cognitive function investigation in the Longitudinal Study on Adult Health (ELSA-Brasil). Sao Paulo Med J. 2014;132(3):170-7. https://doi.org/10.1590/1516-3180.2014.1323646

21. Racine AM, Gou Y, Fong TG, Marcantonio ER, Schmitt EM, Travison TG, et al. Correction for retest effects across repeated measures of cognitive functioning: a longitudinal cohort study of postoperative delirium. BMC Med Res Methodol. 2018;18(1):69. https://doi.org/10.1186/s12874-018-0530-x

22. Bertola L, Bense√Īor I, Goulart A, Brunoni A, Caramelli P, Barreto S, et¬†al. Normative data for the ELSA-Brasil neuropsychological assessment and operationalized criterion for cognitive impairment for middle-aged and older adults. J Int Neuropsychol Soc. 2021;27(3):293-303. https://doi.org/10.1017/S1355617720000880

23. Hinton-Bayre AD. Deriving reliable change statistics from test-retest normative data: Comparison of models and mathematical expressions. Arch Clin Neuropsychol. 2010;25(3):244-56. https://doi.org/10.1093/arclin/acq008

24. Crawford JR, Howell DC. Regression equations in clinical neuropsychology: An evaluation of statistical methods for comparing predicted and obtained scores. J Clin Exp Neuropsychol. 1998;20(5):755-62. https://doi.org/10.1076/jcen.20.5.755.1132

25. Rijnen SJ, van der Linden SD, Emons WH, Sitskoorn MM, Gehring K. Test-retest reliability and practice effects of a computerized neuropsychological battery: A solution-oriented approach. Psychol Assess. 2018;30(12):1652-62. https://doi.org/10.1037/pas0000618

26. Salthouse TA, Schroeder DH, Ferrer E. Estimating retest effects in longitudinal assessments of cognitive functioning in adults between 18 and 60 years of age. Dev Psychol. 2004;40(5):813-22. https://doi.org/10.1037/0012-1649.40.5.813

27. Hofer SM, Sliwinski MJ. Two - design and analysis of longitudinal studies on aging. In: Birren JE, Schaie KW, Abeles RP, Gatz M, Salthouse TA, editors. Handbook of the Psychology of Aging. 6th ed. Burlington: Academic Press; 2006. p. 15-37. https://doi.org/10.1016/B978-012101264-9/50005-7

28. Kosmidis MH. Challenges in the neuropsychological assessment of illiterate older adults. Lang Cogn Neurosci. 2018;33(3):373-86. https://doi.org/10.1080/23273798.2017.1379605

29. Farfel JM, Nitrini R, Suemoto CK, Grinberg LT, Ferretti RE, Leite RE, et al. Very low levels of education and cognitive reserve: a clinicopathologic study. Neurology. 2013;81(7):650-7. https://doi.org/10.1212/WNL.0b013e3182a08f1b

30. Mukadam N, Sommerlad A, Huntley J, Livingston G. Population attributable fractions for risk factors for dementia in low-income and middle-income countries: an analysis using cross-sectional survey data. Lancet Glob Heal. 2019;7(5):e596-603. https://doi.org/10.1016/S2214-109X(19)30074-9

31. Chelune GJ, Duff K. The assessment of change: serial assessments in dementia evaluations. In: Ravdin LK, editor. Handbook on the Neuropsychology of Aging and Dementia Clinical Handbooks in Neuropsychology. New York, NY: Springer; 2013. https://doi.org/10.1007/978-1-4614-3106-0_4

32. Stein J, Luppa M, Luck T, Maier W, Wagner M, Daerr M, et al. The Assessment of Changes in Cognitive Functioning: Age-, Education-, and Gender-Specific Reliable Change Indices for Older Adults Tested on the CERAD-NP Battery: Results of the German Study on Ageing, Cognition, and Dementia in Primary Care Patients (AgeCoDe). Am J Geriatr Psychiatry. 2012;20(1):84-97. https://doi.org/10.1097/JGP.0b013e318209dd08

33. Crawford JR, Garthwaite PH, Denham AK, Chelune GJ. Using regression equations built from summary data in the psychological assessment of the individual case: extension to multiple regression. Psychol Assess. 2012;24(4):801-14. https://doi.org/10.1037/a0027699

34. Vivot A, Power MC, Glymour MM, Mayeda ER, Benitez A, Spiro 3rd A, et al. Jump, hop, or skip: modeling practice effects in studies of determinants of cognitive change in older adults. Am J Epidemiol. 2016;183(4):302-14. https://doi.org/10.1093/aje/kwv212

This study was conducted by the Faculdade de Medicina, Universidade de S√£o Paulo, S√£o Paulo, SP, Brazil

1. Universidade de S√£o Paulo, Faculdade de Medicina, S√£o Paulo SP, Brazil
2. Universidade de São Paulo, Hospital Universitário, Centro de Pesquisa Epidemiológica e Clínica, São Paulo SP, Brazil
3. Universidade de S√£o Paulo, Faculdade de Medicina, Departamento de Medicina Interna, S√£o Paulo SP, Brazil
4. Universidade de São Paulo, Faculdade de Medicina, Departamento e Instituto de Psiquiatria, Laboratório de Neurociências, São Paulo SP, Brazil
5. Universidade Federal de Minas Gerais, Faculdade de Medicina, Departamento de Clínica Médica, Belo Horizonte MG, Brazil
6. Universidade Federal de Minas Gerais, Faculdade de Medicina, Hospital das Clínicas, Belo Horizonte MG, Brazil
7. Funda√ß√£o Oswaldo Cruz, Escola Nacional de Sa√ļde P√ļblica S√©rgio Arouca, Departamento de Epidemiologia e M√©todos Quantitativos em Sa√ļde, Rio de Janeiro RJ, Brazil
8. Funda√ß√£o Oswaldo Cruz, Istituto Oswaldo Cruz, Laborat√≥rio de Educa√ß√£o em Sa√ļde e Meio Ambiente, Rio de Janeiro RJ, Brazil
9. Universidade Federal do Espírito Santo, Departamento de Medicina Social, Vitória ES, Brazil
10. Universidade de S√£o Paulo, Faculdade de Medicina, Divis√£o de Geriatria, S√£o Paulo SP, Brazil


Laiss Bertola
Email: laissbertola@gmail.com

Received on March 05, 2021
Accepted in final form on October 13, 2021

Disclosure: The authors report no conflicts of interest

Funding: The ELSA-Brasil baseline study was supported by the Brazilian Ministry of Health (Science and Technology Department) and the Brazilian Ministry of Science and Technology (FINEP [Financiadora de Estudos e Projetos] and CNPq, National Research Council) (grants 01 06 0010.00 to RS, 01 06 0212.00 to BA, 01 06 0300.00 to ES, 01 06 0278.00 to MG, 01 06 0115.00 to SP, and 01 06 0071.00 to RJ). S.M.B. and R.H.G. are research fellows of the National Research Council (CNPq, grant numbers 300159/99-4 and 301807/2016-7, respectively). P.C. receives support from CNPq Brazil (research productivity fellow)


Home Contact