Skip to main content Accessibility help
×
Home
Hostname: page-component-cf9d5c678-p4zth Total loading time: 0.442 Render date: 2021-07-27T10:21:15.229Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": true, "newCiteModal": false, "newCitedByModal": true, "newEcommerce": true, "newUsageEvents": true }

Cross-sectional association between objective cognitive performance and perceived age-related gains and losses in cognition

Published online by Cambridge University Press:  14 April 2021

Serena Sabatini
Affiliation:
Medical School, College of Medicine and Health, University of Exeter, Exeter, UK
Obioha C. Ukoumunne
Affiliation:
Medical School, NIHR ARC South West Peninsula (PenARC), University of Exeter, Exeter, UK
Clive Ballard
Affiliation:
Medical School, College of Medicine and Health, University of Exeter, Exeter, UK
Rachel Collins
Affiliation:
Medical School, College of Medicine and Health, University of Exeter, Exeter, UK
Kaarin J. Anstey
Affiliation:
Medical School, Ageing Futures Institute, University of New South Wales, Sydney, and Neuroscience Research Australia, Sydney, Australia
Manfred Diehl
Affiliation:
Department of Human Development and Family Studies, Colorado State University, Fort Collins, CO, USA
Allyson Brothers
Affiliation:
Department of Human Development and Family Studies, Colorado State University, Fort Collins, CO, USA
Hans-Werner Wahl
Affiliation:
Medical School, Institute of Psychology, Heidelberg University, Heidelberg, Germany
Anne Corbett
Affiliation:
Medical School, College of Medicine and Health, University of Exeter, Exeter, UK
Adam Hampshire
Affiliation:
Department of Brain Sciences, Imperial College London, London, UK
Helen Brooker
Affiliation:
Medical School, College of Medicine and Health, University of Exeter, Exeter, UK Ecog Pro Ltd., Bristol, UK
Linda Clare
Affiliation:
Medical School, College of Medicine and Health, University of Exeter, Exeter, UK Medical School, NIHR ARC South West Peninsula (PenARC), University of Exeter, Exeter, UK
Corresponding
E-mail address:
Rights & Permissions[Opens in a new window]

Abstract

Objectives:

Evidence linking subjective concerns about cognition with poorer objective cognitive performance is limited by reliance on unidimensional measures of self-perceptions of aging (SPA). We used the awareness of age-related change (AARC) construct to assess self-perception of both positive and negative age-related changes (AARC gains and losses). We tested whether AARC has greater utility in linking self-perceptions to objective cognition compared to well-established measures of self-perceptions of cognition and aging. We examined the associations of AARC with objective cognition, several psychological variables, and engagement in cognitive training.

Design:

Cross-sectional observational study.

Participants:

The sample comprised 6056 cognitively healthy participants (mean [SD] age = 66.0 [7.0] years); divided into subgroups representing middle, early old, and advanced old age.

Measurements:

We used an online cognitive battery and measures of global AARC, AARC specific to the cognitive domain, subjective cognitive change, attitudes toward own aging (ATOA), subjective age (SA), depression, anxiety, self-rated health (SRH).

Results:

Scores on the AARC measures showed stronger associations with objective cognition compared to other measures of self-perceptions of cognition and aging. Higher AARC gains were associated with poorer cognition in middle and early old age. Higher AARC losses and poorer cognition were associated across all subgroups. Higher AARC losses were associated with greater depression and anxiety, more negative SPA, poorer SRH, but not with engagement in cognitive training.

Conclusions:

Assessing both positive and negative self-perceptions of cognition and aging is important when linking self-perceptions to cognitive functioning. Objective cognition is one of the many variables – alongside psychological variables – related to perceived cognitive losses.

Type
Original Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© International Psychogeriatric Association 2021

Global estimations suggest that 50 million people are living with dementia (World Health Organization, 2020); a condition that creates a high social and economic burden. It is, therefore, important to identify individuals with poorer cognition as they may benefit the most from timely interventions aimed at preventing pathological cognitive decline. Self-perceptions of cognition and, more generally, self-perceptions of aging (SPA) may help to identify such individuals. Importantly, evidence on the association of self-reported cognitive limitations with objective cognition is inconclusive. Whereas, some studies found that self-reported cognitive limitations are correlated with poorer cognition and often precede pathological cognitive decline (Amariglio et al., Reference Amariglio2012; Jessen et al., Reference Jessen2014), others found these associations to be either small or statistically irrelevant (Burmester et al., Reference Burmester, Leathem and Merrick2016; Crumley et al., Reference Crumley, Stetler and Horhota2014; Hertzog et al., Reference Hertzog, Hülür, Gerstorf and Pearman2018; Jonker et al., Reference Jonker, Geerlings and Schmand2000).

These mixed results may be due to a lack of a recognized conceptualization of self-reported cognitive limitations (Rabin et al., Reference Rabin2015; Tandetnik et al., Reference Tandetnik2015). Among the many existing concepts of self-reported cognitive limitations, some (e.g. subjective memory decline) are used to refer to those people thought to have an early manifestation of dementia, whereas others (e.g. functional memory decline) are used to refer to memory complaints thought to be potentially reversible due to their associations with negative psychosocial factors (Blackburn et al., Reference Blackburn, Harkness, Reuber, Venneri, Shanks and Wakefield2014). Moreover, some concepts (e.g. subjective cognitive decline) capture perceived decline in several domains (Slot et al., Reference Slot2018), whereas others (e.g. subjective memory decline) focus solely on one domain (Hertzog et al., Reference Hertzog, Hülür, Gerstorf and Pearman2018). In addition, some studies assessed complaints about current cognitive difficulties, whereas others assessed perceived cognitive deterioration that occurred over time (Rabin et al., Reference Rabin2015). Another reason for the inconsistent evidence may be that individuals with specific psychological characteristics, such as depression and anxiety, can report cognitive decline that is not objectively measurable (Hill et al., Reference Hill2016; Siebert et al., Reference Siebert, Braun and Wahl2020). So far, most studies on the topic have focused on subjective memory decline (e.g. Hertzog et al., Reference Hertzog, Hülür, Gerstorf and Pearman2018), but individuals can experience a decline in several cognitive domains (Smart and Krawitz, Reference Smart and Krawitz2015). It would, therefore, be sensible to use measures that cover subjective difficulties across several cognitive domains.

SPA, such as attitudes toward own aging (ATOA) and subjective age (SA), are also related to cognition. ATOA is commonly assessed with the ATOA scale (Lawton, Reference Lawton1975), which captures affective and cognitive components of self-related aging attitude. SA is frequently measured with a single-item question asking individuals to report the age they feel they are (Barrett, Reference Barrett2003). Generally, more positive ATOA and/or feeling younger than one’s chronological age are associated with better cognition (Seidler and Wolff, Reference Seidler and Wolff2017), and consequently, with a lower risk of dementia (Siebert et al., Reference Siebert, Wahl, Degen and Schröder2018). This may be due to individuals with more positive SPA being more engaged in preventive behaviors and experiencing better mental and physical health (Bryant et al., Reference Bryant, Bei, Gilson, Komiti, Jackson and Judd2012; Hess, Reference Hess2006). These specific factors are protective against cognitive decline (Anstey, Reference Anstey2013).

Despite research supporting connections between more positive SPA and better cognition, existing evidence relies on unidimensional measures that treat positive and negative self-perceptions as two ends of the same spectrum (e.g. Barrett, Reference Barrett2003; Jorm and Jacomb, Reference Jorm and Jacomb1989; Lawton, Reference Lawton1975). These may provide an overly simplified picture. On one hand, with aging individuals often experience complex comorbidities, chronic health conditions, decline in some cognitive abilities (e.g. memory), decreased functional ability, and as a consequence of these losses, mild depressive, and anxiety symptoms (Palsson et al., Reference Palsson, Ostling and Skoog2001; Weyerer et al., Reference Weyerer2013). On the other hand, aging also involves gains including valuable social relations, increased leisure time, and accumulated knowledge and life experience (Carstensen et al., Reference Carstensen2011; Christensen, Reference Christensen2001; Steverink et al., Reference Steverink, Westerhof, Bode and Dittmann-Kohli2001). Both positive and negative changes can impact on SPA. Moreover, many available measures (e.g. Barrett, Reference Barrett2003; Lawton, Reference Lawton1975) capture only global SPA, but not perceived age-related changes in cognition. Therefore, when linking self-perceptions to objective cognitive performance, it may be important to assess the coexistence of perceived age-related gains and losses in cognition.

The construct of awareness of age-related change (AARC) captures individuals’ awareness that their behavior, performance, and/or life experiences have changed due to their increased age (Diehl and Wahl, Reference Diehl and Wahl2010). AARC is the first concept assessing the coexistence of perceived gains and losses across five life domains: health and physical functioning; cognition; interpersonal relationships; socio–cognitive and socio–emotional functioning; lifestyle. AARC is assessed via self-administered questionnaires; items capturing perceived gains and losses were derived from qualitative interviews and focus groups in which middle-aged and older individuals reported positive and negative aspects of aging (Brothers et al., Reference Brothers, Gabrian, Wahl and Diehl2019; Miche et al., Reference Miche, Wahl, Diehl, Oswald, Kaspar and Kolb2014; Wahl et al., Reference Wahl, Konieczny and Diehl2013). From both the 50-item (Brothers et al., Reference Brothers, Gabrian, Wahl and Diehl2019) and shorter 10-item (AARC-10 SF; Kaspar et al., Reference Kaspar, Gabrian, Brothers, Wahl and Diehl2019) versions of the AARC questionnaire, it is possible to obtain two global scores representing AARC gains and losses across life domains. In addition, the 50-item version of the questionnaire makes it possible to obtain 10 domain-specific scores; 1 for gains and 1 for losses in each of the 5 AARC life domains including the cognitive domain (AARC-50 cognitive functioning subscale; Brothers et al., Reference Brothers, Gabrian, Wahl and Diehl2019).

In contrast to some conceptualizations of subjective cognitive decline that attribute cognitive limitations to the development of brain pathology (Blackburn et al., Reference Blackburn, Harkness, Reuber, Venneri, Shanks and Wakefield2014), AARC losses more broadly capture any self-reported cognitive limitation that individuals attribute to aging. Moreover, differently from unidimensional measures of self-reported cognitive limitations, AARC assumes that perceived cognitive losses coexist with cognitive gains. This is possible as AARC gains (in this paper referred to as social cognitive gains) and losses relate to different aspects of cognition: gains capture perceived improvements in knowledge, wisdom, and/or reflexivity, whereas losses capture perceived limitations in processing speed, memory, and/or mental capacity. Perceptions of social cognitive gains are quite independent from perceptions of cognitive losses (Sabatini et al., Reference Sabatini2020b). As AARC captures self-perceptions of cognition across several cognitive domains (e.g. memory and processing speed), it makes it possible to link self-perceptions of cognition to a wide range of objectively assessed cognitive abilities.

Although the current study relies on cross-sectional data, it adds important facets to previous research as it explores for the first time the relation between AARC and objective cognitive performance. First, as the measures used in this study make it possible to obtain both a global assessment of AARC and an assessment of AARC specific to the cognitive domain, this study tests whether an assessment of social cognitive gains and AARC losses specific to the cognitive domain is more strongly related to objective cognitive functioning, compared to a global assessment of AARC and to three well-established unidimensional measures of self-perceptions of cognition and aging (subjective cognitive changes, ATOA, SA). Second, given its large sample, this study robustly examines whether the associations of perceived social cognitive gains and cognitive losses with cognitive functioning vary among midlife, early old age, and advanced old age. Third, this study explores whether depression, anxiety, ATOA, SA, self-rated health (SRH), or level of engagement in computerized cognitive training explain variability in levels of AARC in cognition across age subgroups. Psychological variables, such as poor psychological health, are associated with negative SPA (Sabatini et al., Reference Sabatini2020a; Siebert et al., Reference Siebert, Braun and Wahl2020) and engagement in online cognitive training is linked to more positive self-perceptions of cognition (Sullivan et al., Reference Sullivan, Law, Loyola, Knoll, Shub and French2020).

We hypothesize that the assessment of perceived social cognitive gains and cognitive losses will be more strongly associated with cognitive functioning compared to a global assessment of AARC gains and losses, subjective cognitive change, ATOA, and SA. Second, we hypothesize that more AARC gains and fewer losses are associated with better cognitive performance across all age-based subgroups. Third, we hypothesize that more severe depression and/or anxiety, negative ATOA, an older SA, poorer SRH, or less engagement in computerized cognitive training are associated with fewer social cognitive gains and more AARC losses in cognition. We expect these associations to become stronger in older age as older individuals tend to be more accurate when self-evaluating their cognitive performance (Wang et al., Reference Wang2004). Moreover, due to negative age stereotypes becoming increasingly salient with aging, it may be that when individuals in advanced old age experience cognitive decline, they are more likely to perceive themselves in a negative way compared to younger individuals (Meisner, Reference Meisner2012).

Method

Study design and participants

This study used secondary data collected from the ongoing PROTECT study (https://www.protectstudy.org.uk) in 2019 and 2020. Participants were 6056 cognitively healthy UK individuals aged 51.4–95.9 (age, M = 66.0 years, SD = 7.0 years); of which 76.2% were women and 98.6% were White. Among study participants, 3111 were middle aged (51–65 years); 2473 were in early old age (66–75 years), and 472 were in advanced old age (≥76 years). Further information on study design and participants is provided in Supplementary text 1.

Measures

Indicators of self-perceptions of aging and cognition

To assess perceived social cognitive gains and cognitive losses, we used the AARC-50 cognitive functioning subscale taken from the 50-item version of the questionnaire (Brothers et al., Reference Brothers, Gabrian, Wahl and Diehl2019). Gains items capture social cognition and wisdom, whereas losses items capture perceived cognitive decline (memory, processing speed, etc.). To assess global perceptions of gains and losses across the five different AARC life domains, the AARC-10 SF (Kaspar et al., Reference Kaspar, Gabrian, Brothers, Wahl and Diehl2019) was used. For both the AARC-50 cognitive functioning subscale and the AARC-10 SF, participants rate how much each of the 10 items (reported in Table 1) applies to them on a 5-point scale (1 = not at all; 5 = very much). Scores can be obtained for gains and losses by summing the five items falling into the respective subscale. Higher scores indicate higher gains and losses (range: 5–25).

Table 1. Items included in the AARC-50 cognitive functioning subscale and the AARC-10 SF

PHY = Health and physical functioning; COG = Cognitive functioning; INT = Interpersonal relations; SCSE = Social–cognitive and social–emotional functioning; LIFE = Lifestyle and engagement.

The Informant Questionnaire on Cognitive Decline in the Elderly short form was used (IQCODE-Self; Jorm and Jacomb, Reference Jorm and Jacomb1989) to assess subjective cognitive change over the last 10 years. It includes 16 items that are answered on a 5-point scale (1 = much improved; 5 = much worse). The final score is the mean of the item scores; higher scores indicate subjective cognitive decline, whereas lower scores indicate subjective cognitive improvement.

In order to test whether objective cognition is more strongly associated with AARC gains and losses compared to other measures of SPA, we assessed ATOA and SA. We used the ATOA scale (taken from the Philadelphia Geriatric Center Morale Scale; Lawton, Reference Lawton1975) to assess ATOA. Lower scores indicate more negative ATOA, whereas higher scores indicate more positive ATOA (range: 0–5). To assess SA, participants were asked to write the age (in years) they feel most of the time (Barrett, Reference Barrett2003). A positive value indicates a younger SA, whereas a negative value indicates an older SA. To assess SRH, participants were asked to rate their health on a 4-point scale (4 = Excellent; 1 = Poor) (Ware and Sherbourne, Reference Ware and Sherbourne1992).

Cognitive functioning

To assess cognitive functioning, we used the PROTECT Cognitive Test Battery (Corbett et al., Reference Corbett2015), which was self-administered online and included four tests: (1) Self-Ordered Search (SOS) assesses spatial working memory (range: 0–20); (2) Grammatical Reasoning (GR) assesses verbal reasoning (range: from 0-no upper limit); (3) Paired Associate Learning (PAL) assesses visual episodic memory (range: 0–16); (4) Digit Span (DS) assesses verbal working memory (0–20). For each test, a score is obtained by subtracting the number of errors from the number of correct answers; a higher score indicates better performance on the test. For GR, the score has no upper limit as the number of trials within the allocated time for the test varies depending on how rapidly participants respond during the test.

Through the PROTECT platform, participants have access to 12 online brain training games (validated by Owen et al., Reference Owen2010) covering reasoning, problem-solving, mathematics, attention, and memory. The number of times participants played any brain training game between 2015 and 2019 was used as an indicator of the frequency of engagement in cognitive training. Further information on the assessment of cognitive functioning is presented in Supplementary text 2.

Mental health

The Patient Health Questionnaire – 9 (Kroenke et al., Reference Kroenke, Spitzer and Williams2001) was used to assess depression; higher scores indicate greater depression (range: 9–36). The Generalized Anxiety Disorder – 7 (Spitzer et al., Reference Spitzer, Kroenke, Williams and Lowe2006) was used to assess anxiety; higher scores indicate more severe anxiety (range: 7–28).

Information on the reliability of study measures is presented in Supplementary text 3.

Demographic information

Demographic information included sex, employment status (employed; not employed), and education level (secondary education; postsecondary education; vocational qualification; undergraduate degree; postgraduate degree; doctorate).

Data analysis

For SA, a proportional discrepancy score was calculated by subtracting participants’ SA from their chronological age and dividing this difference score by participants’ chronological age. We fitted path analysis models to estimate the associations of participants’ scores on the four objective cognitive tests (outcomes) with their scores on an assessment of perceived social cognitive gains and cognitive losses, a global assessment of AARC gains and losses, subjective cognitive change, ATOA, and SA. Path analysis made it possible to explore within one model the extent to which the coexistence of gains and losses (predictors) explains variability in the cognitive tests (outcomes). In the path analysis models, scores on objective cognitive tests were allowed to correlate. Sex, education, employment status, depression, anxiety, and frequency of cognitive training were included in the path analysis models as covariates. We treated depression, anxiety, and frequency of cognitive training as covariates as they likely impact on AARC and on objective cognition (Anstey, Reference Anstey2013). As the directions of all these associations have not been empirically investigated, we also tested a model excluding depression, anxiety, and frequency of cognitive training from covariates.

Partial coefficients of determination were not reported in the output from the path analysis models. However, as regression coefficients obtained with multiple linear regressions having gains and losses as predictors of scores on the cognitive tests led to similar results to those obtained with path analysis models, we reported results for the multiple regressions including information about the coefficients of determination in Supplementary Tables 35. To examine whether the strength of the associations of perceived social cognitive gains and cognitive losses (predictors) with cognitive performance varies among age subgroups, we estimated Pearson’s r correlation coefficients and three separate path analysis models for individuals in middle age, early old age, and advanced old age. The Comparative Fit Index (CFI), the Tucker–Lewis index (TLI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR) were used to assess model fit. Values considered acceptable were CFI and TLI >.90, RMSEA <.08 (90% CI: 0; .08), and SRMR <.06 (Byrne, Reference Byrne2012).

To test whether more depression and/or anxiety, more negative ATOA, an older SA, poorer SRH, and frequency of cognitive training (predictors) are associated with fewer perceived social cognitive gains and more cognitive losses across age subgroups, we fitted simple and multiple regressions. Standardized coefficients are reported to quantify effects. Associations ≤.09 were considered negligible, .10–.29 small, .30–.49 moderate, and ≥.50 large (Cohen, Reference Cohen1988).

Results

Descriptive data

On average, participants perceived “a little bit” of social cognitive gains and cognitive losses; “quite a bit” of gains and “a little bit” of losses in the global assessment of AARC; reported subjective cognitive decline and mixed ATOA; felt 17% younger than their chronological age; and had minimal levels of depression and anxiety. Frequency of engagement in cognitive training varied greatly among participants. A high proportion of participants perceived their health as good (54.6%) or excellent (30.7%). Characteristics for the overall study sample, subsamples, and participants excluded from analyses are reported in Table 2.

Table 2. Descriptive statistics of demographic variables and main study variables for the study sample, study subsamples, and participants not included in the study

Indicators of self-perceptions of aging and cognition as predictors of cognitive functioning

The associations of perceived social cognitive gains and cognitive losses, global levels of AARC gains and losses, subjective cognitive change, ATOA, and SA with cognitive performance are reported in Table 3 and Supplementary Table 1. Overall gains and losses in cognition and global levels of AARC gains and losses showed stronger associations with cognitive performance compared to subjective cognitive change, ATOA, and SA. However, the associations of cognitive performance with perceived social cognitive gains and cognitive losses and global levels of AARC gains and losses were either small or negligible. The global assessment of AARC gains and losses explained slightly more variance in cognitive functioning than the assessment of perceived social cognitive gains and cognitive losses.

Table 3. Path analysis model exploring AARC gains and losses in cognition, AARC gains and losses across life domains, subjective cognitive change, ATOA, and SA as predictors of cognition in the overall study sample while controlling for sex, education, employment status, depression, anxiety, and frequency of cognitive training

RMSEA = Root mean square error of approximation; CFI = Comparative fit index; TLI = Tucker–Lewis index; SRMR = Standard root mean square residual; R2 = R-squared/coefficient of determination.

Associations of perceived social cognitive gains and cognitive losses with cognitive performance across age subgroups

Estimates for correlations, path analysis models, and multiple linear regressions exploring the associations of perceived social cognitive gains and cognitive losses with scores on cognitive tasks across age subgroups are reported in Table 4 and Supplementary Tables 25. Overall, higher social cognitive gains were associated with poorer scores on cognitive tests among participants in middle age and early old age. Although higher social cognitive gains were also related to poorer scores on cognitive tasks in advanced old age; these associations were not statistically significant. Higher AARC losses in cognition were associated with poorer performance on most cognitive tasks, especially on GR. These associations were consistent across all age subgroups but strongest in size in early old and advanced old age. For both perceived social cognitive gains and cognitive losses associations with scores on the cognitive tasks were either negligible or small; even though associations were slightly stronger in size for AARC losses.

Table 4. Path analysis model exploring AARC gains and losses in cognition as predictors of cognition in the three age subgroups while controlling for sex, education, employment status, depression, anxiety, and frequency of cognitive training

RMSEA = Root mean square error of approximation; CFI = Comparative fit index; TLI = Tucker–Lewis index; SRMR = Standard root mean square residual; R2 = R-squared/coefficient of determination.

Associations of psychological variables and frequency of cognitive training with perceived social cognitive gains and cognitive losses across age subgroups

Fewer social cognitive gains were associated with a younger SA in early old age, whereas the associations of social cognitive gains with depression, anxiety, ATOA, and SRH were either negligible or nonsignificant; see Table 5. Among participants in middle and early old age, more severe depression and anxiety, more negative ATOA, an older SA, and poorer SRH were associated with more AARC losses in cognition. Among participants in advanced old age, more severe depression and anxiety, an older SA, and poorer SRH showed small to moderate associations with more AARC losses in cognition (see Table 5). Higher engagement in computerized cognitive training was associated with higher social cognitive gains in middle age only and was not associated with AARC losses in any subgroup.

Table 5. Associations of psychological variables and frequency of cognitive training with AARC gains and losses in cognition across three age subgroups

ß = Standardized regression coefficient; Partial R2 = Partial R-squared/coefficient of determination.

Discussion

This was the first study to examine whether the AARC-50 cognitive functioning subscale has greater utility in linking SPA to objective cognition compared to the AARC-10 SF and other well-established measures of self-perceptions of cognition and aging. This study was also the first to explore whether the coexistence of perceived social cognitive gains and cognitive losses is associated with objective cognitive functioning, a range of psychological variables, or frequency of engagement in cognitive training. Compared to unidimensional measures of SPA, measures capturing the coexistence of positive and negative age-related changes were more strongly associated with cognitive performance. However, the global assessment of AARC – encompassing perceptions of age-related changes across several life domains – is more strongly associated with objective cognition than a domain-specific assessment of AARC in cognition. Unexpectedly, both perceived social cognitive gains and cognitive losses were associated with poorer cognitive performance. Higher AARC losses in cognition, but not social cognitive gains, were related to greater depression and anxiety, more negative SPA, and poorer SRH suggesting that poorer cognitive functioning may be one of the many variables related to perceptions of losses in cognition. AARC losses in cognition and social cognitive gains respectively showed nonsignificant and negligible associations with engagement in cognitive training.

Our findings support the importance of assessing the coexistence of perceived gains and losses when relating self-perceptions of cognition and/or aging to objective cognitive functioning. However, in contrast to our hypothesis, a global assessment of AARC gains and losses may be more informative of objective cognition compared to a domain-specific assessment of AARC in cognition. This may be due to the global assessment of AARC capturing individuals’ perceptions of declines in their mental, physical, and social functioning, alongside cognition, and these are all domains related to objective cognitive functioning (Anstey, Reference Anstey2013). Hence, study results question the previously suggested additional value of domain-specific measures of SPA in predicting matched outcomes (Levy and Leifheit-Limson, Reference Levy and Leifheit-Limson2009).

The higher social cognitive gains reported by those with poorer objective cognition may be due to these individuals making less accurate appraisals of their cognitive performance. This result is in contrast to research showing that cognitively healthy people are generally accurate appraisers of their performance on cognitive tests (Clare et al., Reference Clare, Whitaker and Nelis2010). In our study, participants were asked to report perceived cognitive abilities in general, rather than evaluating their performance before and/or after having completed a specific cognitive test; this may explain the difference between our findings and existing literature. However, as we found that more AARC losses in cognition were associated with worse performance on all cognitive tests, this suggests that participants are at least somewhat accurate in their perceptions of their cognitive abilities.

Alternatively, the higher social cognitive gains reported by those with poorer objective cognition may be due to these individuals paying more attention to their cognitive gains as this may facilitate acceptance of negative changes and re-establishment of self-efficacy and positive emotional states (Loidl and Leipold, Reference Loidl and Leipold2019). However, more social cognitive gains were fairly independent from more positive SPA, mental, and physical health. Hence, individuals perceiving more social cognitive gains may not have a general tendency to be more positive in their self-evaluations and may only show this tendency when rating their cognition. Finally, the counterintuitive association of higher social cognitive gains with worse cognitive functioning may be due to the nature of items used to assess AARC in the cognitive domain (Sabatini et al., Reference Sabatini2020b). Whereas, the losses items capture perceived decline in cognitive domains, such as memory and processing speed that can be compared to objective performance in cognitive tasks assessing the same domains, the gains items capture social cognition and wisdom, which may not be suitable for comparison with performance on objective cognitive tasks.

This study found that individuals perceiving high cognitive losses may be experiencing poorer cognition across several domains. In line with international evidence on the associations of AARC with mental and physical health (Sabatini et al., Reference Sabatini2020a, Reference Sabatini2020c), associations of objective cognition with AARC losses were stronger than associations with gains. Among cognitive tests, AARC losses were most strongly associated with GR. This finding was consistent across all age subgroups but strongest in advanced old age; supporting the greater accuracy of older individuals in reporting cognitive difficulties (Jessen et al., Reference Jessen2014). A recent study examining daily within-person variability in AARC and cognitive performance showed that AARC losses predict within-person decreases in inductive reasoning on the same day and decreases from day 1 to the next (Zhu and Neupert, Reference Zhu and Neupert2020). Despite the methodological differences between this study and ours, both found that among several cognitive domains AARC is most strongly associated with reasoning. This may be due to reasoning being vulnerable to age-related decline (Christensen, Reference Christensen2001).

The small size of the associations of higher AARC losses in cognition with poorer scores on objective cognitive tasks may be due to perceived cognitive losses reflecting individuals’ experience of a trajectory of subtle cognitive decline that is not captured with the cross-sectional assessment of objective cognition (Caselli et al., Reference Caselli2014). Research shows that, although SRH generally does not match with objective measures of health, it can be a better predictor of future levels of health than objective measures of health (Idler and Benyamini, Reference Idler and Benyamini1997). Similarly, AARC losses in cognition may be more strongly associated with objective cognition at the longitudinal level than at the cross-sectional level.

Studies exploring the association of self-perceptions of cognition with objective cognition report mixed results (Burmester et al., Reference Burmester, Leathem and Merrick2016; Crumley et al., Reference Crumley, Stetler and Horhota2014; Jessen et al., Reference Jessen2014; Jonker et al., Reference Jonker, Geerlings and Schmand2000). Our results are in line with those studies reporting a statistically significant, but small association between more negative self-perceptions of cognition and poorer objective cognition (Amariglio et al., Reference Amariglio2012). This may be due to self-perceptions of cognition being influenced by many psychosocial factors including depressive symptoms and negative ATOA (Segel-Karpas and Palgi, Reference Segel-Karpas and Palgi2019; Siebert et al., Reference Siebert, Braun and Wahl2020). Indeed, in our study, higher levels of AARC losses in cognition are associated with more severe depression and anxiety, more negative ATOA, an older SA, and poorer SRH.

We found that with increasing age depression and anxiety are more strongly associated with AARC losses in cognition, whereas poorer SRH and negative ATOA are most strongly related to AARC losses in middle age. These findings are aligned with literature documenting the co-occurrence of depression, anxiety, poorer cognitive, and physical health in older age (Anstey, Reference Anstey2013; Roehr et al., Reference Roehr2017), but are inconsistent with research supporting the greater self-relevance of ATOA in older age (Kornadt and Rothermund, Reference Kornadt and Rothermund2012). The association of more AARC losses in cognition with and older SA is in line with research reporting that those individuals with an older SA pay more attention to age-related losses in memory compared to those who feel their age or younger than their age (Segel-Karpas and Palgi, Reference Segel-Karpas and Palgi2019).

Overall, the small associations of AARC losses in cognition with objective cognition and the small to moderate associations of AARC losses with more negative scores on psychological variables suggest that perceived cognitive losses may be somewhat influenced not only by individuals’ objective cognitive ability, but also by their interpretation of the cognitive changes they experience. The way in which older individuals interpret their cognitive changes may be shaped by their beliefs about age-related changes in cognition and their current emotional state (Brothers et al., Reference Brothers, Kornadt, Nehrkorn-Bailey, Wahl and Diehl2020; Weiss and Kornadt, Reference Weiss and Kornadt2018). Our findings suggest that even though individuals perceiving many cognitive losses may benefit from cognitive interventions (e.g. compensatory cognitive training; Burton et al., Reference Burton, Vella and Twamley2011), they may benefit more substantially from interventions promoting psychological health (Siebert et al., Reference Siebert, Braun and Wahl2020).

Strengths and limitations

This study has several limitations. First, participants were aged 51 and over, so they may not have been old enough to perceive many cognitive losses. Second, analyses are based on a selective group of participants. Indeed, out of the 14,882 participants that took part in the PROTECT annual assessment between January and March 2019, 8826 were excluded from study analyses as they did not complete the AARC questionnaire, the objective cognitive tasks, or they may have had mild cognitive impairment or dementia. Even though there may be a systematic bias in those who did not complete study measures (e.g. less enthusiastic to take part in the study), participants excluded from study analyses had similar demographic profiles and mental and physical health to the study sample.

Third, similarly to most available studies on AARC (Sabatini et al., Reference Sabatini2020a), the study sample included a majority of participants who were women, well-educated, and who rated their health as good or excellent, hence extrapolation of results to a broader population should be considered with caution. Fourth, analyses were based on cross-sectional data; hence, causality for the associations of perceived cognitive gains and losses with cognitive functioning and psychological variables cannot be inferred. Fifth, even though the AARC questionnaire captures awareness of changes, we explored it in association with current objective cognition instead of cognitive change. Nonetheless, we deemed the AARC questionnaires suitable to assess current self-perceptions of cognition as it is reasonable to assume that current self-perceptions of cognition are on average more positive for those who perceive more social cognitive gains and more negative for those who perceive more AARC losses.

Sixth, as cognitive tests were self-administered online, those participants who are less familiar with technology may perform more poorly compared to when assessed by a researcher. However, in PROTECT, all participants were familiar with the online cognitive tests from previous assessments. As participants level of engagement while undertaking the cognitive tests was not assessed, some critics suggest that it is possible participants could have someone else undertake the cognitive tasks. However, this seems unlikely given the motivation of participants involved in the study. Seventh, even though PROTECT participants are invited to repeat the completion of the cognitive tests in three sessions within a week and then the average score is calculated, numerous participants did not complete the tests over three sessions. In order to optimize the use of data across the cohort, we used only data from the first session. Eighth, the cognitive tests were completed on a separate day (within 2 months) to that on which participants answered the AARC questionnaires. This is a limitation as levels of perceived cognitive gains and losses can vary on a daily basis (Zhu and Neupert, Reference Zhu and Neupert2020). However, cognitive functioning among individuals without dementia is generally stable over 2 months (e.g. Lövdén et al., Reference Lövdén, Rönnlund, Wahlin, Bäckman, Nyberg and Nilsson2004). Ninth, items assessing perceived cognitive losses may overlap with symptoms of depression and anxiety (Jessen et al., Reference Jessen2007) and those individuals who are more introspective may score high on perceived cognitive losses, depression, and anxiety (Roberts et al., Reference Roberts, Clare and Woods2009). Tenth, this study only considered frequency of engagement in computerized online cognitive training available as part of the PROTECT study; this is a limitation as individuals could have been cognitively engaged in many other ways not recorded in this study.

The large sample size and the use of valid measures assessing the coexistence of perceived gains and losses across several domains made it possible to advance knowledge on self-perceptions of cognition by showing that across three age groups more perceived gains and losses both in cognition and across life domains may be associated with poorer cognitive performance.

Conclusions

This study adds several contributions to existing research. First, when examining the association of SPA with objective cognition, it is important to assess the coexistence of positive and negative SPA across several life domains. Second, both higher levels of perceived social cognitive gains and cognitive losses may be indicative of poorer cognitive functioning, even though associations are either negligible or small and the reasons underlying the association of higher perceived social cognitive gains and poorer cognitive functioning need to be investigated with future research. Third, whereas perceived social cognitive gains are minimally related to psychological variables, AARC losses in cognition are associated with more severe depression and anxiety, more negative ATOA, older SA, and poorer SRH. Overall, poorer cognitive functioning may be only one of the many variables related to AARC losses in cognition.

Conflict of interest

None.

Source of funding

This work was supported by the University of Exeter College of Life and Environmental Sciences (School of Psychology), University of Exeter College of Medicine and Health, and the National Health and Medical Research Council Centre for Research Excellence in Cognitive Health (#1100579 to Kaarin Anstey).

Description of authors’ roles

SS served as principal investigator of the research, designed the study, conducted data analyses, and took the lead in writing the manuscript. LC contributed to the design of the study, analysis of data, and writing the manuscript. OU contributed to analyses of data, and provided feedback on the draft of the manuscript. AC, HB, AH, and CB contributed to data collection and design of the PROTECT study, and provided feedback on the draft of the manuscript. The remaining co-authors provided feedback on the draft of the manuscript.

Acknowledgments

We are grateful to the University of Exeter for funding a PhD scholarship for Serena Sabatini to carry out this work. This paper represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. Obioha Ukoumunne was supported by the National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) South West Peninsula. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care.

Availability of data and materials

This study was conducted using secondary data collected as part of the UK version of the PROTECT ongoing study. PROTECT data are available to investigators outside the PROTECT team after request and approval by the PROTECT Steering Committee. Data for the AARC questionnaires will be available from May 2022.

Supplementary material

To view supplementary material for this article, please visit https://doi.org/10.1017/S1041610221000375.

References

Amariglio, R. E. et al. (2012). Subjective cognitive complaints and amyloid burden in cognitively normal older individuals. Neuropsychologia, 50, 28802886. https://doi.org/10.1016/j.neuropsychologia.2012.08.011 CrossRefGoogle ScholarPubMed
Anstey, K. J. (2013). Optimizing cognitive development over the life course and preventing cognitive decline: introducing the Cognitive Health Environment Life Course Model (CHELM). International Journal of Behavioral Development, 38, 110. https://doi.org/10.1177/0165025413512255 CrossRefGoogle Scholar
Barrett, A. E. (2003). Socioeconomic status and age identity: the role of dimensions of health in the subjective construction of age. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 58, 101109. https://doi.org/10.1093/geronb/58.2.S101 CrossRefGoogle ScholarPubMed
Blackburn, D. J., Harkness, K., Reuber, M., Venneri, A., Shanks, M. F. and Wakefield, S. (2014). Memory difficulties are not always a sign of incipient dementia: a review of the possible causes of loss of memory efficiency. British Medical Bulletin, 112, 7181. https://doi.org/10.1093/bmb/ldu029 CrossRefGoogle Scholar
Brothers, A. F., Gabrian, M., Wahl, H.-W. and Diehl, M. K. (2019). A new multidimensional questionnaire to assess awareness of age-related change (AARC). The Gerontologist, 59, e141e151. https://doi.org/10.1093/geront/gny006 CrossRefGoogle Scholar
Brothers, A. F., Kornadt, A. E., Nehrkorn-Bailey, A., Wahl, H.-W. and Diehl, M. K. (2020). The effects of age stereotypes on physical and mental health are mediated by self-perceptions of aging. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences. https://doi.org/10.1093/geronb/gbaa176 Google Scholar
Bryant, C., Bei, B., Gilson, K., Komiti, A., Jackson, H. and Judd, F. (2012). The relationship between attitudes to aging and physical and mental health in older adults. International Psychogeriatrics, 24, 16741683. https://doi.org/10.1017/s1041610212000774 CrossRefGoogle ScholarPubMed
Burmester, B., Leathem, J. and Merrick, P. (2016). Subjective cognitive complaints and objective cognitive function in aging: a systematic review and meta-analysis of recent cross-sectional findings. Neuropsychology Review, 26, 376393. https://doi.org/10.1007/s11065-016-9332-2 CrossRefGoogle ScholarPubMed
Burton, C. Z., Vella, L. and Twamley, E. W. (2011). Clinical and cognitive insight in a compensatory cognitive training intervention. American Journal of Psychiatric Rehabilitation, 14, 307326. https://doi.org/10.1080/15487768.2011.622159 CrossRefGoogle Scholar
Byrne, B. M. (2012). Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming. New York, NY: Routledge/Taylor & Francis Group.Google Scholar
Carstensen, L. L. et al. (2011). Emotional experience improves with age: evidence based on over 10 years of experience sampling. Psychology and Aging, 26, 2133. https://doi.org/10.1037/a0021285 CrossRefGoogle ScholarPubMed
Caselli, R. J. et al. (2014). Subjective cognitive decline: self and informant comparisons. Alzheimer’s & Dementia, 10, 9398. https://doi.org/10.1016/j.jalz.2013.01.003 CrossRefGoogle ScholarPubMed
Christensen, H. (2001). What cognitive changes can be expected with normal ageing? Australian and New Zealand Journal of Psychiatry, 35, 768775. https://doi.org/10.1046/j.1440-1614.2001.00966.x CrossRefGoogle ScholarPubMed
Clare, L., Whitaker, C. J. and Nelis, S. M. (2010). Appraisal of memory functioning and memory performance in healthy ageing and early-stage Alzheimer’s Disease. Aging, Neuropsychology, and Cognition, 17, 462491. https://doi.org/10.1080/13825580903581558 CrossRefGoogle ScholarPubMed
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Earlbaum Associates.Google Scholar
Corbett, A. et al. (2015). The effect of an online cognitive training package in healthy older adults: an online randomized controlled trial. Journal of the American Medical Directors Association, 16, 990997. https://doi.org/10.1016/j.jamda.2015.06.014 CrossRefGoogle ScholarPubMed
Crumley, J. J., Stetler, C. A. and Horhota, M. (2014). Examining the relationship between subjective and objective memory performance in older adults: a meta-analysis. Psychology and Aging, 29, 250. https://doi.org/10.1037/a0035908 CrossRefGoogle ScholarPubMed
Diehl, M. K. and Wahl, H.-W. (2010). Awareness of age-related change: examination of a (mostly) unexplored concept. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 65B, S340S350. https://doi.org/10.1093/geronb/gbp110 CrossRefGoogle ScholarPubMed
Hertzog, C., Hülür, G., Gerstorf, D. and Pearman, A. (2018). Is subjective memory change in old age based on accurate monitoring of age-related memory change? Evidence from two longitudinal studies. Psychology and Aging, 33, 273287. https://doi.org/10.1037/pag0000232 CrossRefGoogle ScholarPubMed
Hess, T. M. (2006). Attitudes toward aging and their effects on behavior. In UCLA Center on Aging (Ed.) Handbook of the Psychology of Aging (pp. 379406). Los Angeles, CA: Elsevier.CrossRefGoogle Scholar
Hill, N. L. et al. (2016). Subjective cognitive impairment and affective symptoms: a systematic review. The Gerontologist, 56, e109e127. https://doi.org/10.1093/geront/gn-w091 CrossRefGoogle ScholarPubMed
Idler, E. L. and Benyamini, Y. (1997). Self-rated health and mortality: a review of twenty-seven community studies. Journal of Health and Social Behavior, 38, 2137. https://doi.org/10.2307/2955359 CrossRefGoogle ScholarPubMed
Jessen, F. et al. (2014). A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimer’s & Dementia, 10, 844852. https://doi.org/10.1016/j.jalz.2014.01.001 CrossRefGoogle ScholarPubMed
Jessen, F. et al. (2007). Patterns of subjective memory impairment in the elderly: association with memory performance. Psychological Medicine, 17531762. https://doi.org/10.1017/S0033291707001122 CrossRefGoogle ScholarPubMed
Jonker, C., Geerlings, M. I. and Schmand, B. A. (2000). Are memory complaints predictive for dementia? A review of clinical and population-based studies. International Journal of Geriatric Psychiatry, 15, 983991. https://doi.org/10.1002/1099-1166(200011)15:11 3.0.CO;2-5>CrossRefGoogle ScholarPubMed
Jorm, A. F. and Jacomb, P. A. (1989). The Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE): socio-demographic correlates, reliability, validity and some norms. Psychological Medicine, 19, 10151022. https://doi.org/10.1017/S0033291700005742 CrossRefGoogle ScholarPubMed
Kaspar, R., Gabrian, M., Brothers, A. F., Wahl, H.-W. and Diehl, M. K. (2019). Measuring awareness of age-related change: development of a 10-Item short form for use in large-scale surveys. The Gerontologist, 59, e130e140. https://doi.org/10.1093/geront/gnx213 CrossRefGoogle ScholarPubMed
Kornadt, A. E. and Rothermund, K. (2012). Internalization of age stereotypes into the self-concept via future self-views: a general model and domain-specific differences. Psychology and Aging, 27, 164172. https://doi.org/10.1037/a0025110 CrossRefGoogle ScholarPubMed
Kroenke, K., Spitzer, R. L. and Williams, J. B. (2001). The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine, 16, 606613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x CrossRefGoogle ScholarPubMed
Lawton, M. P. (1975). The Philadelphia geriatric center morale scale: a revision. Journal of Gerontology, 30, 8589.CrossRefGoogle ScholarPubMed
Levy, B. R. and Leifheit-Limson, E. (2009). The stereotype-matching effect: greater influence on functioning when age stereotypes correspond to outcomes. Psychology and Aging, 24, 230233. https://doi.org/10.1037/a0014563 CrossRefGoogle ScholarPubMed
Loidl, B. and Leipold, B. (2019). Facets of accommodative coping in adulthood. Psychology and Aging, 34, 640654. https://doi.org/10.1037/pag0000378 CrossRefGoogle ScholarPubMed
Lövdén, M., Rönnlund, M., Wahlin, Å., Bäckman, L., Nyberg, L. and Nilsson, L.-G. (2004). The extent of stability and change in episodic and semantic memory in old age: demographic predictors of level and change. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 59, P130–P134. https://doi.org/10.1093/geronb/59.3.P130 CrossRefGoogle ScholarPubMed
Meisner, B. A. (2012). A meta-analysis of positive and negative age stereotype priming effects on behavior among older adults. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 67, 1317. https://doi.org/10.1093/geronb/gbr062 CrossRefGoogle ScholarPubMed
Miche, M., Wahl, H.-W., Diehl, M. K., Oswald, F., Kaspar, R. and Kolb, M. (2014). Natural occurrence of subjective aging experiences in community-dwelling older adults. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 69, 174187. https://doi.org/10.1093/geronb/gbs164 CrossRefGoogle ScholarPubMed
Owen, A. M. et al. (2010). Putting brain training to the test. Nature, 465, 775778. https://doi.org/10.1038/nature/09042 CrossRefGoogle ScholarPubMed
Palsson, S. P., Ostling, S. and Skoog, I. (2001). The incidence of first-onset depression in a population followed from the age of 70 to 85. Psychological Medicine, 31, 11591168. https://doi.org/10.1017/S0033291701004524 CrossRefGoogle Scholar
Rabin, L. A. et al. (2015). Subjective cognitive decline in older adults: an overview of self-report measures used across 19 international research studies. Journal of Alzheimer’s Disease, 48, S63S86. https://doi.org/10.3233/JAD-150154 CrossRefGoogle Scholar
Roberts, J. L., Clare, L. and Woods, R. T. (2009). Subjective memory complaints and awareness of memory functioning in mild cognitive impairment: a systematic review. Dementia and Geriatric Cognitive Disorders, 28, 95109. https://doi.org/10.1159/000234911 CrossRefGoogle ScholarPubMed
Roehr, S. et al. (2017). Subjective cognitive decline is longitudinally associated with lower health-related quality of life. International Psychogeriatrics, 29, 19391950. https://doi.org/10.1017/S1041610217001399 CrossRefGoogle ScholarPubMed
Sabatini, S. et al. (2020a). Associations of awareness of age-related change with emotional and physical well-being: a systematic review and meta-analysis. The Gerontologist, 60, e477e490. https://doi.org/10.1093/geront/gnz101 CrossRefGoogle ScholarPubMed
Sabatini, S. et al. (2020b). International relevance of two measures of awareness of age-related change (AARC). BMC Geriatrics, 20, 359. https://doi.org/10.1186/s12877-020-01767-6 CrossRefGoogle Scholar
Sabatini, S. et al. (2020c). The cross-sectional relationship between pain and awareness of age-related changes. British Journal of Pain. https://doi.org/10.1177/2049463720961798 CrossRefGoogle Scholar
Segel-Karpas, D. and Palgi, Y. (2019). ‘It is nothing more than a senior moment’: the moderating role of subjective age in the effect of change in memory on self-rated memory. Aging & Mental Health, 23, 272276. https://doi.org/10.1080/13607863.2017.1399350 CrossRefGoogle ScholarPubMed
Seidler, A. L. and Wolff, J. K. (2017). Bidirectional associations between self-perceptions of aging and processing speed across 3 years. The Journal of Gerontopsychology and Geriatric Psychiatry, 30, 4959. https://doi.org/10.1024/1662-9647/a000165 Google Scholar
Siebert, J. S., Braun, T. and Wahl, H. W. (2020). Change in attitudes toward aging: cognitive complaints matter more than objective performance. Psychology and Aging, 35, 357368. https://doi.org/10.1037/pag0000451 CrossRefGoogle ScholarPubMed
Siebert, J. S., Wahl, H.-W., Degen, C. and Schröder, J. (2018). Attitude toward own aging as a risk factor for cognitive disorder in old age: 12-year evidence from the ILSE study. Psychology and Aging, 33, 461472. https://doi.org/10.1037/pag0000252 CrossRefGoogle ScholarPubMed
Slot, R. E. R. et al. (2018). Subjective Cognitive Impairment Cohort (SCIENCe): study design and first results. Alzheimer’s Research & Therapy, 10, 113. https://doi.org/10.1186/s13195-018-0390-y Google ScholarPubMed
Smart, C. M. and Krawitz, A. (2015). The impact of subjective cognitive decline on Iowa Gambling Task performance. Neuropsychology, 29, 971. https://doi.org/10.1037/neu0000204 CrossRefGoogle ScholarPubMed
Spitzer, R. L., Kroenke, K., Williams, J. B. and Lowe, B. (2006). A brief measure for assessing generalized anxiety disorder: the GAD-7. Archives of Internal Medicine, 166, 10921097. https://doi.org/10.1001/archinte.166.10.1092 CrossRefGoogle ScholarPubMed
Steverink, N., Westerhof, G. J., Bode, C. and Dittmann-Kohli, F. (2001). The personal experience of aging, individual resources, and subjective well-being. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 56, 364373. https://doi.org/10.1093/geronb/56.6.P364 CrossRefGoogle ScholarPubMed
Sullivan, K. W., Law, W. A., Loyola, L., Knoll, M. A., Shub, D. E. and French, L. M. (2020). A novel intervention platform for service members with subjective cognitive complaints: implementation, patient participation, and satisfaction. Military medicine, 185, 326333. https://doi.org/10.1093/milmed/usz218 CrossRefGoogle ScholarPubMed
Tandetnik, C. et al. (2015). Ascertaining subjective cognitive decline: a comparison of approaches and evidence for using an age-anchored reference group. Journal of Alzheimer’s Disease, 48, S43S55. https://doi.org/10.3233/JAD-150251 CrossRefGoogle ScholarPubMed
Wahl, H.-W., Konieczny, C. and Diehl, M. K. (2013). Experiencing age-related change in adulthood: an exploratory study based on the concept of “Awareness of Age-Related Change” (AARC). Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie, 45, 6676. https://doi.org/10.1026/0049-8637/a000081 CrossRefGoogle Scholar
Wang, L. et al. (2004). Subjective memory deterioration and future dementia in people aged 65 and older. Journal of the American Geriatrics Society, 52, 20452051. https://doi.org/10.1111/j.1532-5415.2004.52568.x CrossRefGoogle ScholarPubMed
Ware, J. E. J. and Sherbourne, C. D. (1992). The MOS 36-item short-form health survey (SF-36). Conceptual framework and item selection. Medical Care, 30, 473483. https://doi.org/10.1177/016327879702000102 CrossRefGoogle ScholarPubMed
Weiss, D. and Kornadt, A. E. (2018). Age-stereotype internalization and dissociation: contradictory processes or two sides of the same coin? Current Directions in Psychological Science, 27, 477483. https://doi.org/10.1177/0963721418777743 CrossRefGoogle Scholar
Weyerer, S. et al. (2013). Incidence and predictors of depression in non-demented primary care attenders aged 75 years and older: results from a 3-year follow-up study. Age and Ageing, 42, 173180. https://doi.org/10.1093/ageing/afs184 CrossRefGoogle ScholarPubMed
Zhu, X. and Neupert, S. D. (2020). Dynamic awareness of age-related losses predict concurrent and subsequent changes in daily inductive reasoning performance. British Journal of Developmental Psychology. https://doi.org/10.1111/bjdp.12344 Google ScholarPubMed
Figure 0

Table 1. Items included in the AARC-50 cognitive functioning subscale and the AARC-10 SF

Figure 1

Table 2. Descriptive statistics of demographic variables and main study variables for the study sample, study subsamples, and participants not included in the study

Figure 2

Table 3. Path analysis model exploring AARC gains and losses in cognition, AARC gains and losses across life domains, subjective cognitive change, ATOA, and SA as predictors of cognition in the overall study sample while controlling for sex, education, employment status, depression, anxiety, and frequency of cognitive training

Figure 3

Table 4. Path analysis model exploring AARC gains and losses in cognition as predictors of cognition in the three age subgroups while controlling for sex, education, employment status, depression, anxiety, and frequency of cognitive training

Figure 4

Table 5. Associations of psychological variables and frequency of cognitive training with AARC gains and losses in cognition across three age subgroups

Supplementary material: File

Sabatini et al. supplementary material

Sabatini et al. supplementary material

Download Sabatini et al. supplementary material(File)
File 134 KB
You have Access
Open access
1
Cited by

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Cross-sectional association between objective cognitive performance and perceived age-related gains and losses in cognition
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Cross-sectional association between objective cognitive performance and perceived age-related gains and losses in cognition
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Cross-sectional association between objective cognitive performance and perceived age-related gains and losses in cognition
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *