Gestión de la Calidad

Initial Cluster Centres

PRMQ Total T-Score 74.00 16.00

PRMQ PM T-Score 74.00 19.00

PRMQ RM T-score 71.00 17.00

Final Cluster Centres (n=48) (n=49)

PRMQ Total T-Score 61.83 42.59

PRMQ PM T-Score 59.96 41.43

PRMQ RM T-score 59.85 43.41

PRMQ: Prospective and Retrospective Memory Questionnaire; PM: Prospective memory; RM: Retrospective Memory

The resulting subgroups differed significantly in terms of anxiety (U = 629.50, p <.001) and depression (U = 740.50, p = .001) as measured by the HADS. Anxiety and Depression were, however, low in both groups [Anxiety: High self-reporter group mean = 5.93, Median = 5.00, SD = 3.58; Low self-reporter group mean = 3.77, Median = 3.00, SD = 3.47; Depression: High self-reporter group mean = 3.20, Median = 2.00, SD = 2.19; Low self-reporter group mean = 2.07, Median = 2.00, SD = 2.37]. Neither group can, therefore, be considered significantly anxious or depressed.

A series of Mann Whitney U tests showed that high (Median = 30) and low self-reporters (Median = 30) did not differ significantly on MMSE (U = 1091.50 , p = .523), Mini-Cog (high reporters Median = 5; low reporters Median = 5) (U = 1144.50 , p = .816), GPCOG (high reporters Median = 9; low reporters Median

= 9) (U = 1142.00, p = .810) or CAMPROMPT – either on the original six CAMPROMPT performance bands, Fisher’s exact (5)= 2.097, p = .916; or on the collapsed categories of “poor”, “average” and

“good” CAMPROMPT performance, X² = .213, p = .899. For this latter analysis, the original performance bands of the CAMPROMPT were collapsed to form three categories (poor, average and good). The

“poor” category contained performers originally classified as impaired, borderline or poor; the

“average” category remained unchanged, containing those originally classified as average performers, and the “good” category contained performers originally classified as ‘above average’ or ‘very good’.

The relationship between PRMQ T-scores (subjective memory ratings) and performance on the objective cognitive tests was also explored by means of correlational analyses (Kendall’s Tau-b correlations). These nonparametric correlations are presented in Table 5.11 below and no correlations

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were significant. Note that for these correlational analyses, the six original CAMPROMPT performance categories (Impaired, Borderline, Poor, Average, Above average and Very good) were utilised.

Table 5.11: Correlations* between the PRMQ T-scores and total scores of cognitive tests (n=97) PRMQ

PM T-score RM T-score Total T-score

MMSE .032 .067 .055

Mini-Cog -.043 .015 -.017

GPCOG -.009 .046 .018

CAMPROMPT .044 .063 .054

*Kendall’s tau

A direct logistic-regression analysis was also performed with subjective memory status (poorer memory -v- good self-reported memory) as outcome and the predictors MMSE total score, Mini-Cog total score, GPCOG total score and the collapsed “good” (comprising Above Average and Very Good performers) and “poor” (comprising Impaired, Borderline and Poor performers) performance categories of the CAMPROMPT. A test of the full model with all of these predictors against a constant-only model was not statistically significant, X² ( 5, N = 97) = 1.005 , p = .962, indicating that these predictors (objective cognitive test scores) did not significantly distinguish between high reporters and low self-reporters of memory lapses. Accordingly, the variance in subjective memory status accounted for was very small, with Nagelkerke’s R square = .014. Classification was unimpressive, with just 56% of high self-reporters (Cluster 1: poorer memory) classified correctly, and just 51.1% of low self-reporters (Cluster 2: good memory) classified correctly, with an overall success rate of 58% (compared to a null model of 51.5%).

5.4.7 Objective test performance: influence of demographic and other variables

5.4.7.1 Influence of age: Spearman correlations were carried out to investigate relations between age and scores on three of the cognitive tests (MMSE, Mini-Cog and GPCOG). A significant effect of age was found for the MMSE, Spearman’s rho = -.176, p = .042 (one-tailed) but not for either the Mini-Cog, Spearman’s rho = -.118, p = .126 or the GPCOG, Spearman’s rho = -.082, p = .213. A significant effect of age was also found across the categories of the CAMPROMPT collapsed into “poor” , “average” and

“good” classification categories, as demonstrated by Kruskal-Wallis test; H(2) = 6.370, p = .041, with average performers reporting higher age than the other CAMPOMPT groups.

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5.4.7.2 Influence of gender: To explore the potential effect of gender on the objective cognitive test scores, Mann-Whitney U tests were carried out. No significant differences were found in MMSE total scores between males (Mdn = 30.00) and females (Mdn = 30.00), U = -1126.50, p = .970. Similarly, no evidence of a significant gender difference was found on the Mini-Cog [males: Mdn = 5.00; females:

Mdn = 5.00, U = -1091.00, p = .726] or on the GPCOG [males:Mdn = 5.00; females: Mdn = 5.00, U = 1040.50, p = .402].

Since the CAMPROMPT performance bands are considered categories, Fisher’s exact test was carried out to examine whether the distribution of CAMPROMPT performance classifications differed significantly across males and females. Results showed that, in this sample, there was no significant difference in CAMPROMPT performance between males and females, p = .491.

5.4.7.3 Influence of education: The influence of level of education on cognitive test performance was investigated with a series of Kruskal Wallis tests. These analyses revealed no significant effect of level of education for MMSE total scores, H(8) = 5.605, p = .691, Mini-Cog total scores H(8) = 6.581, p = .582 or GPCOG total scores H(8) = 6.797, p = .559.

Due to the small counts in some cells of the CAMPROMPT performance bands and the highest levels of education obtained, CAMPROMPT performance bands were further collapsed into good performance (comprising Very Good, Above Average, and Average performance bands) and poor performance (comprising Impaired, Borderline or Poor performance bands). A Chi-square test revealed no evidence that CAMPROMPT performance was dependent on level of education, X2(16) = 13.484, p = .637.

5.4.7.4 Influence of mood state: The potential influence of self-reported anxiety and depression on performance on each of the objective cognitive tests was assessed via non-parametric correlational analyses. As shown by Table 5.12 below, correlational analyses revealed that levels of anxiety and depression, as measured by HADS, were not significantly related to total scores on each of the objective cognitive tests. Because QQ-plots, normality plots and skewness and kurtosis statistics showed HADS data to be positively skewed and kurtotic and Shapiro-Wilk tests showed the distribution of cognitive test scores departed from normality, Spearman’s rho correlations were computed.

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Table 5.12: Correlations between Cognitive Test Scores and Mood State: Spearman’s rho MMSE

5.4.7.5 Influence of sleep: Differences in performance on the objective cognitive tests between those who reported problems with sleep and those who did not were investigated with Mann-Whitney U tests and Fisher’s Exact test.

Difficulty falling asleep: On the MMSE, those who reported difficulty falling asleep (Mdn = 30) did not perform more poorly than did those who did not report difficulty falling asleep (Mdn = 30); U = 689.00, p = .283. Similarly, there was no group difference in Mini-Cog performance between those who reported difficulty falling asleep (Mdn = 9) and those who did not (Mdn = 9), U = 699.00, p = .302 and CAMPROMPT performance was not dependent on group membership X²(2) = 3.261, p = .196. However, there was a significant difference in GPCOG performance between those who reported difficulty falling asleep (Mdn = 8) and those who did not (Mdn = 9), U = 589.00, p = .021, with those who had difficulty falling asleep performing more poorly.

Waking during the night: Performance on the MMSE did not differ significantly between those who reported waking during the night (Mdn = 30) and those who did not (Mdn = 30), U = 620.00, p = .760.

Similarly, performance on the Mini-Cog did not differ significantly between those who reported waking during the night and those who did not, U = 624.00, p = .781, not did performance on the GPCOG, U = 618.50, p = .718 or CAMPROMPT, X²(5) = 3.390 p = .640.

Waking earlier than intended: MMSE performance did not differ significantly between those who reported waking earlier than intended and those who did not, U = 1129.50, p = .879. Similarly, Mini-Cog performance did not differ significantly between those who reported waking earlier than intended (Mdn = 5) than did those who did not (Mdn = 5), U = 1116.00, p = .781. Neither did GPCPG performance differ significantly between those who reported waking earlier than intended (Mdn = 9) and those who

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did not (Mdn = 9) , U = 1130.00, p = .869. Finally, performance on CAMPROMPT did not depend on group membership, X²(2), = .198, p = .906.

Takes naps during the day: Mann-Whitney U tests revealed no significant differences between those who nap during the day and those who do not. On the MMSE, performance did not differ between nappers (Mdn = 30) and non-nappers (Mdn = 30), U = 1021.50, p = .329. Similarly, on Mini-Cog, performance did not differ between nappers (Mdn = 5 ) and non-nappers (Mdn = 5), U = 998.00, p = .329 and a similar finding was observed on GPCOG scores [nappers: Mdn = 9; non-nappers: Mdn = 9, U

= 1123.50, p = .879]. Finally, there was no significant difference in the spread of CAMPROMPT performance bands across nappers and non-nappers, X²(5) = 3.462, p = .629.

Average number of hours of sleep: Interestingly, the average number of hours of sleep reported by participants related significantly to some cognitive tests and not to others. The relationship between the average number of hours of sleep and MMSE performance was significant, Spearman’s r = .174, p

= .044, such that more hours of sleep was associated with better MMSE performance. Spearman’s r correlation between the average number of hours of sleep and Mini-Cog performance was also significant; Spearman’s r = .383, p = .000. In contrast, the relationship between the average number of hours sleep and GPCOG performance was not significant; Spearman’s r = .099, p = .168 and there was no significant relationship between the average number of hours of sleep and performance on the CAMPROMPT, r = .143, p = .082. Separately, there was no significant difference in the average number of hours of sleep obtained by those who reported they take naps (M = 6.76, SD = 1.49) during the day and those who do not (M = 6.71, SD = 1.1.3), t(95) = .196, p = .845.

5.4.7.6 Influence of physical health (multimorbidity): A series of Mann-Whitney U tests was conducted to investigate whether levels of memory failures, as denoted by scores of the MMSE, Mini-Cog and GPCOG, differed significantly as a function of multimorbidity whilst the impact of multimorbidity on CAMPROMPT performance was examined by means of a chi-square test. No significant difference was found in the distribution of MMSE scores between those with multimorbidity (Mdn = 30.00) and those without (Mdn =30.00); U =769.00, p = .992. Likewise, Mini-Cog scores did not differ significantly between those with (Mdn= 5) and without multimorbidity (Mdn= 5); U = 686.00, p = .373 nor did GPCOG scores [with multimorbidity: Mdn= 9; without multimorbidity: Mdn= 9; U = 677.50, p = .299]. In contrast, CAMPROMPT performance (as denoted by the three collapsed CAMPROMPT categories of

“good”, “average” and “poor”) was dependent upon multimorbidity status; X²(2) = 8.301, p = .016.

5.4.8 Predictors of Subjective Memory Status: Binary Logistic Regression

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In order to examine whether self-reporter status could be predicted by cognitive test performance, a series of binary logistic regression analyses were performed with self-reporter status as the outcome variable and the respective cognitive test as the sole predictor in each model. Results showed that none of these models represented a significant improvement above the null model and none of the cognitive tests were significant predictors of self-reporter status. Accordingly, a number of binary logistic regression analyses were performed to investigate whether objective cognitive test performance, in combination with a small number of covariates selected based on the univariate analyses and psychological theory, resulted in improved prediction of self-reported status. In each model, 97 cases were analysed. The findings from these analyses are summarised below in Table 5.13.

In each of the models, classification rates represented an improvement above the null-model classification rate (53.6%) but in no model did the objective cognitive test contribute significantly to the final classification rate. Confirming other results, the key predictor variables were typically anxiety, and the presence of multimorbidity.