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Negative perceptions of aging modify the association between frailty and cognitive function in older adults
PAID-07198; No of Pages 6 Personality and Individual Differences xxx (2015) xxx–xxx
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Negative perceptions of aging modify the association between frailty and cognitive function in older adults Deirdre A. Robertson a,⁎, Rose Anne Kenny a,b a b
TILDA (The Irish Longitudinal Study on Aging), Department of Medical Gerontology, Trinity College, Dublin, Ireland Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
a r t i c l e
i n f o
Article history: Received 30 June 2015 Received in revised form 28 October 2015 Accepted 2 December 2015 Available online xxxx Keywords: Self-perceptions Aging Cognition Frailty
a b s t r a c t Introduction: Older adults who are physically frail have poorer cognition compared to their robust peers. The mechanisms behind this association have yet to be established. Recent research has suggested that selfperceptions of aging are important predictors of physical and cognitive function in later life. This paper investigated whether self-perceptions of aging modify the relationship between frailty and cognitive function. Methods: 4135 participants from the Irish Longitudinal Study on Aging (TILDA) completed the Brief Aging Perceptions Questionnaire (B-APQ), a cognitive battery, and frailty measures. Results: Frailty was associated with poorer cognition in participants with negative perceptions of aging but not in those without. There was a significant interaction between negative perceptions of aging and frailty in predicting global cognition (B = −0.11, SE = .04) executive function (B = −0.09, SE = .04) and attention (B = 0.13, SE = .04) but not memory (B = −0.03, SE = .04). Conclusion: Negative perceptions of aging may modify the association between frailty and frontal cognitive domains in older adults. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction Older adults who are frail are more likely to be cognitively impaired. This association has been demonstrated consistently in longitudinal studies of aging (Robertson, Savva, & Kenny, 2013). Yet while the association appears to be well-established the mechanisms underpinning the relationship have yet to be explained. Frailty is an age-related syndrome characterised by a collection of symptoms including slow walking speed, weakness, sedentariness, unintentional weight loss and exhaustion (Fried et al., 2001). It is associated with adverse outcomes including disability and mortality. In gerontological research frailty is considered to be a physiological syndrome and thus the relationship between frailty and cognition is thought to be bound by a common physiological mediator (Fried et al., 2001). A number of physiological factors are common to both cognitive impairment and frailty yet the current empirical evidence for these as mechanisms remains relatively weak (e.g. Robertson et al., 2013). As frailty is a cluster of symptoms it is probable that no single biomarker will perfectly predict the syndrome. Instead, a combination of factors likely contributes to frailty. Indeed, some suggest that a definition of frailty should include nutrition, physical ability, senses, mood, coping, social relations and social support (Gobbens, Luijkx, Wijnen-Sponselee, & Schols, 2010; Rodriguez-Manas et al., 2013). Yet ⁎ Corresponding author. E-mail address: [email protected] (D.A. Robertson).
the latter four are rarely, if ever, included despite increasing evidence to suggest that psychological factors such as depression play a role in the development and maintenance of the syndrome (e.g. Paulson & Lichtenberg, 2012). As psychological factors such as depression are also associated with cognitive decline (Ownby, Crocco, Acevedo, John, & Loewenstein, 2006) there may be a common psychological mechanism which underpins frailty and cognitive impairment. Recent research has identified another psychological factor which predicts physical and cognitive function more strongly than, and independent of, depression. Older adults' self-perceptions of aging predict declines in cognition and walking speed – a key component of frailty – over two years independent of health changes and depression (Robertson, King-Kallimanis, & Kenny, 2015a; Robertson, Savva, King-Kallimanis, & Kenny, 2015b). Perceptions of aging encompass beliefs about aging including expectations, feelings of control and emotional responses to getting older. When perceptions are negative they cause declines in self-esteem, life satisfaction, self-rated health and objective cognitive and physical function (Robertson et al., 2015a; Weiss & Lang, 2012; Wurm & Benyamini, 2014; Wurm, Warner, Ziegelmann, Wolff, & Schuz, 2013). Physiological pathways including increased cardiovascular reactivity and risk of cardiovascular disease are also affected (Levy, Hausdorff, Hencke, & Wei, 2000; Levy, Zonderman, Slade, & Ferrucci, 2009). One theory has touched on perceptions as a contributory factor in the development of frailty. Lang, Michel, and Zekry (2009) suggest that frailty is a cycle of decline in which physiological changes interact
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Please cite this article as: Robertson, D.A., & Kenny, R.A., Negative perceptions of aging modify the association between frailty and cognitive function in older adults, Personality and Individual Differences (2015), http://dx.doi.org/10.1016/j.paid.2015.12.010
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with psychological responses to these changes. They suggest that when some older adults experience a change – e.g. normal decline in muscle mass – they have a resultant increase in their perception of the effort involved in being physically active. This perception of effort leads to a decline in physical activity which puts them at risk of further muscle mass depletion and thus frailty. This theory could be expanded to include cognitive function as physical activity is also associated with risk of cognitive decline through both the harmful effects of sedentariness itself and a potential resulting decrease in social engagement (Robertson et al., 2013). Adults with negative perceptions of aging may therefore respond to age-related changes by withdrawing from activities which would otherwise protect against greater physical and cognitive decline. Following from these theories we propose that older adults with negative perceptions of aging will be more likely to enter a cycle of frailty and cognitive impairment. Data from The Irish Longitudinal Study on Aging (TILDA) has previously shown that frailty is associated with poorer global cognition, executive function, memory and attention (Robertson, Savva, Coen, & Kenny, 2014). We thus sought to re-examine this relationship with the hypothesis that the association between frailty and cognitive function will only exist in adults with negative perceptions of aging. 2. Methods 2.1. Population Data was taken from Wave 1 of TILDA, a prospective, population representative sample of community-dwelling adults aged 50+. Ethical approval was obtained and all participants provided written informed consent. Participants with a doctor's diagnosis of dementia or who were unable to personally consent to participation due to cognitive impairment were not included in the study. The study has been described in detail elsewhere (Kearney et al., 2011) but in brief 8175 participants aged 50+ were interviewed in their own homes followed by a nurseadministered health assessment.
Folstein, & McHugh, 1975; Nasreddine et al., 2005). MMSE and MoCA are 30-point tests of global cognition. The MMSE includes questions assessing orientation, language, attention and memory. The MoCA has an additional assessment of executive function. There are ongoing debates about the strength of each (e.g. Aggarwal & Kean, 2010) and thus we included both. Executive function was assessed using the visual reasoning subtest of the CAMDEX, Color Trails Test 2 and verbal fluency. For the visual reasoning test participants are asked to identify which of six objects would complete a pattern of three similar objects. In the verbal fluency test participants have 1 min to name as many words beginning with F as they can. The Color Trails Test part 1 requires participants to draw a line connecting circles numbered 1–25 in consecutive order. Part 2 requires participants to complete the same task while alternating between pink and yellow circles. Part 1 assesses visual scanning and attention while the mental flexibility and switching of part 2 involves executive function (D'Elia, 1996; Nasreddine et al., 2005; Roth et al., 1986). Memory was assessed through the Picture Recognition and Recall subtests of the CAMDEX. Participants are shown 6 consecutive pictures of everyday objects (e.g. a lamp) and then asked to recall them. They are then shown a second series of pictures in which they have to pick the picture they previously saw from a series of similar objects (e.g. 3 different lamps). Participants were also given a list of 10 words and asked to repeat them back immediately and then five minutes later. This provided a measure of immediate and delayed memory (Roth et al., 1986; Wallace & Herzog, 1995). Attention was assessed through the Color Trails Test 1 and the Sustained Attention to Response Task (SART) (D'Elia, 1996; Robertson, Manly, Andrade, Baddeley, & Yiend, 1997). In the fixed SART task participants were shown a computer screen which repeated a sequence of numbers from 1 to 9 for 4 min. Numbers appeared every 300 ms and participants were asked to click in response to each number except 3. Composite scores for each domain were calculated by obtaining z-scores and combining them to create total scores for global cognition, executive function, memory and attention.
2.2. Frailty 2.4. Perceptions of aging We used the Fried definition of frailty which is composed of five criteria: weakness, slowness, sedentariness, unintentional weight loss and exhaustion (Fried et al., 2001). Dominant hand grip strength was assessed using a dynamometer. Weak grip strength was defined as any score below the age, gender and BMI adjusted 20th percentile. Gait speed was measured as time taken to complete the Timed Up and Go task. Participants rose from a chair (seat height 46 cm), walked 3 m at a normal pace, turned, walked back to the chair and sat down (Podsiadlo & Richardson, 1991). Participants below the gender and height adjusted 20th percentile were considered to have slow gait. Physical activity was measured using the short form of the International Physical Activity Questionnaire (Hagstromer, Oja, & Sjostrom, 2006). Unintentional weight loss was assessed by the question ‘In the past year have you lost 10 lb (4.5 kg) or more in weight when you were not trying to?’ Exhaustion was assessed using 2 questions from the 20-item Centre for Epidemiological Studies Depression (CES-D) scale: “I could not get going” and “I felt that everything I did was an effort”. A response of ‘sometimes’ or ‘often’ to either question was classified as exhaustion (Orme, Reis, & Herz, 1986). Participants with ≥3 indicators were defined as frail, ≥ 1 as pre-frail and 0 as robust (Fried et al., 2001). 2.3. Cognitive function The cognitive battery included tests of global cognition, executive function, memory and attention. Global cognition was assessed using the Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) (Folstein,
The Brief Aging Perceptions Questionnaire (B-APQ) measure comprises Likert-scaled statements about participants' perceptions of aging (Sexton, King-Kallimanis, Morgan, & McGee, 2014). The full questionnaire has five subscales but previous work suggests that one – the negative control and consequences subscale – is associated with both physical and cognitive function (Robertson et al., 2015a). Participants rated their agreement with 5 statements such as ‘As I get older I do not cope as well with problems that arise.’ Each statement is scored on a scale of 1 to 5. The mean score across all five statements was calculated for each participant to give a total perceptions score. Higher scores indicated more negative perceptions of aging. Cronbach's alpha in this sample showed good internal reliability (α = .79). 2.5. Covariates Age, gender and education were self-reported with education categorized as primary, secondary or third level. Chronic conditions were ascertained by self-report of a doctor's diagnosis and were included as number of conditions. These included: joint problems, cataracts, glaucoma, age-related macular degeneration, lung disease, asthma, arthritis, osteoporosis, cancer, ulcers, liver disease, alcohol or substance abuse and chronic pain. Participants were asked to record all medications taken on a regular basis. Self-rated health was assessed with the question: “Compared to other people your age, would you say your health is…excellent, very good, good, fair or poor?” Higher scores were indicative of worse self-rated health. Depressed mood was assessed using 18 items from the 20-item CES-D scale excluding the 2
Please cite this article as: Robertson, D.A., & Kenny, R.A., Negative perceptions of aging modify the association between frailty and cognitive function in older adults, Personality and Individual Differences (2015), http://dx.doi.org/10.1016/j.paid.2015.12.010
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items used to calculated exhaustion for the frailty score (Orme et al., 1986). Cronbach's alpha indicated good internal reliability (α = .83).
2.6. Statistical analyses We ascertained descriptive statistics on all variables for the total sample, for those below the median on the aging perceptions subscale (median = 2.8) and for those above the median. Due to small numbers of frail participants with complete data (N = 75) and to the further reduction once participants were split by perceptions of aging we collapsed the pre-frailty and frailty groups. Linear regression models estimated the association between frailty/ pre-frailty and cognitive function across the four cognitive domains in those with high negative perceptions (at or above the median) and those with low (below median). We tested the interaction between pre-frailty/frailty and the continuous perceptions of aging subscale using a second set of linear regression models adjusted for covariates. We estimated and plotted marginal means of each cognitive domain for the two frailty groups (robust and pre-frail/frail) across five points of the negative perceptions subscale (1, 2, 3, 4 and 5). As there was a greater proportion of frail participants with negative perceptions of aging, and as we had collapsed the pre-frail and frail groups into one, we considered that the higher proportion of frail participants in the latter group may be driving the effect. We re-analysed the data after removing all frail participants. Due to missing data in the aging perceptions subscale we also imputed missing scores using 10 multiple chained imputations. The imputed scores were calculated based on non-missing APQ questions, age, gender, education, chronic conditions and self-rated health as these had no or minimal missing data. Analyses were performed using STATA, version 12.1 (College Station, TX: StataCorp LP).
3. Results Of 8175 participants 5895 participants completed a health or home assessment and 5355 of these (91%) returned a self-completion questionnaire. Consistent with the definition of frailty we excluded participants with Parkinson's Disease, a history of stroke, an MMSE b 18 and those taking anti-depressants, dementia medication or Sinemet (Fried et al., 2001). The final sample size was 4135 (Fig. 1). Table 1 gives descriptive statistics for the total sample, for those with high negative perceptions of aging and those with low negative perceptions. In linear regression analyses adjusted for all covariates frailty was statistically significantly associated with all cognitive domains in the high negative perceptions group but the effect was not found in the low negative perceptions group (see Fig. 2). Linear regression analyses tested the interaction between negative aging perceptions and frailty on different domains of cognitive function. After full adjustment there was a significant interaction in predicting global cognition, executive function, and attention but not memory (see Table 2). Fig. 3 shows the marginal mean interaction effects. As agreement with the negative perceptions subscale increased cognitive function decreased in both robust and pre-frail/frail groups. There was no difference between the robust and pre-frail/frail participants in those with low negative perceptions of aging (average scores 1–2) but an increasingly large difference between groups with high negative perceptions of aging (scores 3–5). We reanalysed the data after excluding frail participants (≥ 3 indicators) and again using imputed APQ scores however the results were not changed and thus are not presented.
Fig. 1. Flow chart of sample.
4. Discussion In this sample of adults aged 50+ frail participants have poorer cognition but this association only exists in the added presence of negative perceptions of aging. The effect persisted after adjusting for depression and health factors which are known to be associated with physical and cognitive functions. This suggests that perceptions of aging may play a role in initiating or maintaining the cycle of decline involving frailty and cognitive function. However as this is cross-sectional data we cannot determine causality. Physical decline may lead to negative perceptions of aging. Nevertheless, previous work exploring this suggests that aging perceptions predict change in physical function rather than vice versa (Sargent-Cox, Anstey, & Luszcz, 2012). Perceptions of aging are associated with adaptation and coping in older age. As people get older, age-related changes and ill health can
Please cite this article as: Robertson, D.A., & Kenny, R.A., Negative perceptions of aging modify the association between frailty and cognitive function in older adults, Personality and Individual Differences (2015), http://dx.doi.org/10.1016/j.paid.2015.12.010
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Table 1 Descriptive statistics for sample. Measure (range) N = 4135
Frailtya Robust Pre-frail Frail Negative perceptions (1–5) General cognition MMSE (0–30) MoCA (0–30) Executive function Visual reasoning (0–6) Color Trails B (seconds) Verbal fluency Memory Immediate memory(0–10) Delayed memory (0–10) Picture recall (0–6) Picture recognition (0–6) Attention SART Color Trail A (seconds) Covariates Age Sex (female) Education (third level) Chronic conditions Medications Self-rated healthb (1–5) Depressive symptoms
Mean (SD) or % Total sample
Low negative perceptions
High negative perceptions
70.7% (N = 2895) 27.5% (N = 1125) 1.8% (N = 75) 2.8 (0.73)
78.1% (N = 1567)⁎ 21.2% (N = 426)⁎⁎ 0.7% (N = 14) 2.15 (0.41)
63.6% (N = 1328) 33.5% (N = 699) 2.9% (N = 61) 3.36 (0.47)⁎⁎⁎
28.7 (1.6) 25.3 (3.1)
28.9 (1.4) 25.9 (2.8)
28.4 (1.7)⁎⁎⁎ 24.7 (3.3)⁎⁎⁎
3.2 (1.3) 107.9 (38.8) 12.2 (5.0)
3.4 (1.3) 98.5 (32.0) 12.9 (5.0)
2.9 (1.3)⁎⁎⁎ 117.0 (42.5)⁎⁎⁎ 11.5 (5.1)⁎⁎⁎
6.0 (1.6) 6.3 (2.2) 3.3 (1.1) 5.6 (0.7)
6.3 (1.5) 6.7 (2.1) 3.4 (1.0) 5.7 (0.6)
5.8 (1.6)⁎⁎⁎ 5.8 (2.2)⁎⁎⁎ 3.2 (1.1)⁎⁎⁎ 5.5 (0.7)⁎⁎⁎
0.3 (0.2) 53.8 (23.0)
0.29 (0.16) 49.1 (19.5)
0.33 (0.16)⁎⁎⁎ 58.4 (25.0)⁎⁎⁎
62.0 (8.7) 53.4% (N = 2209) 37.0% (N = 1530) 1.2 (1.2) 2.3 (2.5) 2.2 (1.0) 4.6 (5.5)
59.8 (7.3) 56.4% (N = 1138) 41.9% (N = 845) 0.9 (1.1) 1.8 (2.1) 2.0 (0.9) 3.5 (4.5)
64.2 (9.4)⁎⁎⁎ 50.6% (N = 1071)⁎⁎⁎ 32.4% (N = 685)⁎⁎⁎ 1.4 (1.4)⁎⁎⁎ 2.7 (2.6)⁎⁎⁎ 2.5 (1.0)⁎⁎⁎ 5.5 (6.2)⁎⁎⁎
T-tests for continuous and chi-square tests for categorical variables. a Frailty N = 4095. Missing values in the 3-level variable were recouped in the dichotomous variable (robust vs pre-frail/frail) as participants with 1 indicator, regardless of other missing data, could be included. b Higher self-rated health scores correspond to worse self-rated health. ⁎⁎⁎ p b .001. ⁎⁎ p b .01. ⁎ p b .05.
Fig. 2. The association between frailty and cognitive function in those with and without negative perceptions of aging. ⁎ = p b .05, ⁎⁎ = p b .01, ⁎⁎⁎ = p b .001. Beta regression coefficients and 95% confidence intervals. Robust compared to pre-frail/frail. Note: Higher attention scores usually indicate worse attention but for the sake of comparability in this graph a reciprocal transformation reversed the direction.
Please cite this article as: Robertson, D.A., & Kenny, R.A., Negative perceptions of aging modify the association between frailty and cognitive function in older adults, Personality and Individual Differences (2015), http://dx.doi.org/10.1016/j.paid.2015.12.010
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Table 2 Linear regression analyses assessing the interaction effect of frailty and perceptions of aging on four cognitive domains. Global cognition
Pre-frail/frail Negative perceptions Frailty ∗ negative perceptions Age Gender Education Secondary Third/higher Chronic conditions Medications Self-rated health Depression scores Observations
Executive function
Attentiona
Memory
B (SE)
T
B (SE)
T
B (SE)
T
B (SE)
T
0.15 (0.11) −0.09 (0.02) −0.11 (0.04) −0.02 (0.002) 0.03 (0.02)
1.44 −4.45⁎⁎⁎ −2.85⁎⁎ −9.77⁎⁎⁎ 1.23
0.16 (0.11) −0.12 (0.02) −0.09 (0.04) −0.03 (0.002) 0.02 (0.03)
1.41 −5.18⁎⁎⁎ −2.33⁎ −15.62⁎⁎⁎ 0.94
0.03 (0.12) −0.11 (0.02) −0.03 (0.04) −0.03 (0.002) 0.26 (0.03)
0.26 −4.40⁎⁎⁎ −0.80 −16.07⁎⁎⁎ 9.77⁎⁎⁎
−0.21 (0.12) 0.03 (0.02) 0.13 (0.04) 0.04 (0.002) −0.02 (0.03)
−1.79 1.17 3.22⁎⁎ 20.88⁎⁎⁎ −0.77
0.50 (0.04) 0.80 (0.04) 0.01 (0.01) 0.01 (0.01) −0.04 (0.01) −.001 (.002) 4135
12.70⁎⁎⁎ 21.28⁎⁎⁎ 1.12 1.33 −3.00⁎⁎ −0.42
0.57 (0.03) 1.02 (0.04) 0.02 (0.01) .004 (0.01) −0.07 (0.02) 0.0001 (0.002) 4135
16.42⁎⁎⁎ 28.34⁎⁎⁎ 2.00⁎ 0.7 −4.29⁎⁎⁎ 0.02
0.21 (0.04) 0.48 (0.04) 0.02 (0.01) −0.01 (0.01) −0.06 (0.02) −0.01 (.003) 4135
5.69⁎⁎⁎ 12.12⁎⁎⁎ 1.66 −1.51 −3.53⁎⁎⁎ −1.81
−0.34 (0.04) −0.47 (0.04) −0.01 (0.01) −0.003 (0.01) 0.08 (0.02) −0.0003 (0.002) 4135
−8.76⁎⁎⁎ −12.05⁎⁎⁎ −0.88 −0.47 4.93 −0.15
Model statistics: global cognition (R2 = .24, F(11,4123) = 87.25, p b .001); executive function (R2 = .30, F(11,4123) = 181.66, p b .001); memory (R2 = .22, F(11,4123) = 92.61, p b .001); attention (R2 = .25. F(11, 4123) = 105.15, p b .001). a Higher attention scores = worse attention. ⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.
limit the achievability of some goals. It is how people respond to these changes, however, that predicts how they will be affected. Older adults who face ill health but who respond by using SOC strategies (selection, optimisation and compensation) do not experience negative effects of ill health to the same extent as older adults who do not use these strategies (Wurm et al., 2013). SOC strategies involve selection of appropriate goals, optimisation of current functions to reach goals and compensation for losses by using alternative means to reach a goal. Interestingly only older adults with positive aging perceptions engaged in these strategies (Wurm et al., 2013). This may be one explanation as to why only older adults with negative aging perceptions experience both frailty and cognitive decline: they fail to engage in coping strategies to deal with age-related changes, and through consequent reductions in physical and social activities put themselves at risk of actual physical and cognitive decline. There is no work that we are aware of examining coping and adaptation in frailty. Another potentially important factor is stress and associated physiological processes including cortisol production and decreased inflammatory suppression. If older adults constantly expect negative
consequences they are likely also to experience greater stress. Psychological stress is associated with overproduction of cortisol, which is damaging for muscle mass and cognitive function, and a decreased ability to suppress inflammatory cytokine IL-6 (Lupien, McEwen, Gunnar, & Heim, 2009; Miller, Chen, & Zhou, 2007; Peeters, Van Schoor, Van Rossum, Visser, & Lips, 2008). Both frailty and cognitive decline are associated with over-active inflammatory cytokines including IL-6 (Mulero, Zafrilla, & Martinez-Cacha, 2011). Negative aging perceptions encompass explicit expectations for adverse events. As previous research shows that expectation of a negative event, as much as or more than the event itself, causes an increase in cortisol (Lupien et al., 1997) this may explain why negative perceptions may predict physical and cognitive function more powerfully than depressed mood. Increased stress as a result of negative perceptions may therefore contribute to the pathway between frailty and poor cognitive function. We did not detect an interaction effect in predicting memory. This is consistent with previous work showing that negative aging perceptions are associated with executive function but not memory (Robertson et al., 2015a). However it may merely be due to the nature of the
Fig. 3. Interaction effect between frailty and aging perceptions in predicting four domains of cognitive function. Note a: X-axis indicates 5 exemplar points on the aging perceptions scale. Higher scores = more negative perceptions. Note b: Higher scores reflect worse attentional abilities.
Please cite this article as: Robertson, D.A., & Kenny, R.A., Negative perceptions of aging modify the association between frailty and cognitive function in older adults, Personality and Individual Differences (2015), http://dx.doi.org/10.1016/j.paid.2015.12.010
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memory measures which had smaller ranges (0–6 and 0–10), and therefore smaller variance, compared to other measures leading to a lower likelihood of picking up small between-person differences. There are no studies to our knowledge assessing the effect of aging perceptions on different domains of cognition with which to compare results. It may also be that negative perceptions affect only cognitive domains that are dependent on cognitive control and top–down processing such as attention and executive function. In support of this, priming older adults with aging stereotypes – a relative of aging perceptions – affects performance in controlled cognitive processes but not automatic ones (Mazerolle, Régner, Morisset, Rigalleau, & Huguet, 2012). In addition, older adults with negative perceptions of aging may be at increased risk of frailty and cognitive impairment due to declines in physical and social engagement. Other work has found, albeit cross-sectionally, that decreased social engagement is associated with poorer executive function but not memory (Andrew, Fisk, & Rockwood, 2011). These findings may therefore reflect a true difference in the impact of negative perceptions of aging on domains of cognitive function. This study has a number of strengths and limitations. One strength is the population representative sample and nurse-led health assessment which allows for in-depth collection of objective data. We also had a large cognitive battery allowing for assessment of different domains. A limitation is the cross-sectional data which does not allow us to determine causality. Declines in cognitive function may affect both negative aging perceptions and frailty. Thirdly, the frailty measure allows for categorisation into three groups but does not account for frailty severity within categories. Participants with high negative perceptions may have been frailer overall. We attempted to control for this by adding selfrated health as a covariate under the assumption that those in the pre-frail/frail group who were frailer than others in the same group would also report worse overall health. As the interaction effect persisted after including self-rated health we are more confident that this is not driving the result but longitudinal data including change in perceptions is needed. We will be able to further explore these pathways using wave 3 of TILDA. Future waves will also result in a larger frail sample. 5. Conclusions The relationship between frailty and cognition may be bound by a psychological construct: self-perceptions of aging. Aging perceptions are known to contribute to both physical and cognitive decline suggesting that this could be a targetable factor to delay impairment in some older adults. Promisingly, recent work found implicitly improving older adults' aging perceptions over 4-weeks led to improvements in physical function which surpassed even the effects of a 6-month exercise intervention (Levy, Pilver, Chung, & Slade, 2014). Aging perceptions are only one pathway through which adults may experience frailty and cognitive decline but, should interventions be developed, we may be able to delay a cycle of decline that leads to the development of two debilitating conditions simultaneously. References Aggarwal, A., & Kean, E. (2010). Comparison of the Folstein Mini Mental State Examination (MMSE) to the Montreal Cognitive Assessment (MoCA) as a cognitive screening tool in an inpatient rehabilitation setting. Neuroscience & Medicine, 1(02), 39. Andrew, M. K., Fisk, J. D., & Rockwood, K. (2011). Social vulnerability and prefrontal cortical function in elderly people: A report from the Canadian Study of Health and Aging. International Psychogeriatrics, 23(3), 450–458. http://dx.doi.org/10.1017/ s1041610210001195. D'Elia, L. (1996). Color trails test: Professional manual. Psychological Assessment Resources. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-Mental State: A practical method for grading the cognitive state of patients for the clinician. Pergamon Press. Fried, L. P., Tangen, C. M., Walston, J., Newman, A. B., Hirsch, C., Gottdiener, J., ... McBurnie, M. A. (2001). Frailty in older adults: Evidence for a phenotype. Journals of Gerontology. Series A: Biological Sciences and Medical Sciences, 56(3), M146–M156. Gobbens, R. J., Luijkx, K. G., Wijnen-Sponselee, M. T., & Schols, J. M. (2010). Towards an integral conceptual model of frailty. The Journal of Nutrition, Health & Aging, 14(3), 175–181.
Hagstromer, M., Oja, P., & Sjostrom, M. (2006). The International Physical Activity Questionnaire (IPAQ): A study of concurrent and construct validity. Public Health Nutrition, 9(6), 755–762. Kearney, P. M., Cronin, H., O'Regan, C., Kamiya, Y., Savva, G. M., Whelan, B., & Kenny, R. (2011). Cohort profile: The Irish Longitudinal Study on Ageing. International Journal of Epidemiology, 40(4), 877–884. http://dx.doi.org/10.1093/ije/dyr116. Lang, P. O., Michel, J. P., & Zekry, D. (2009). Frailty syndrome: A transitional state in a dynamic process. Gerontology, 55(5), 539–549. http://dx.doi.org/10.1159/000211949. Levy, B. R., Hausdorff, J. M., Hencke, R., & Wei, J. Y. (2000). Reducing cardiovascular stress with positive self-stereotypes of aging. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 55(4), 205–213. Levy, B. R., Pilver, C., Chung, P. H., & Slade, M. D. (2014). Subliminal strengthening improving older individuals' physical function over time with an implicit-age-stereotype intervention. Psychological Science, 25(12), 2127–2135. Levy, B. R., Zonderman, A. B., Slade, M. D., & Ferrucci, L. (2009). Age stereotypes held earlier in life predict cardiovascular events in later life. Psychological Science, 20(3), 296–298. http://dx.doi.org/10.1111/j.1467-9280.2009.02298.x. Lupien, S. J., McEwen, B. S., Gunnar, M. R., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience, 10(6), 434–445. Lupien, S., Gaudreau, S., Tchiteya, B., Maheu, F., Sharma, S., Nair, N., ... Meaney, M. (1997). Stress-induced declarative memory impairment in healthy elderly subjects: Relationship to cortisol reactivity 1. The Journal of Clinical Endocrinology & Metabolism, 82(7), 2070–2075. Mazerolle, M., Régner, I., Morisset, P., Rigalleau, F., & Huguet, P. (2012). Stereotype threat strengthens automatic recall and undermines controlled processes in older adults. Psychological Science, 23(7), 723–727. Miller, G. E., Chen, E., & Zhou, E. S. (2007). If it goes up, must it come down? Chronic stress and the hypothalamic–pituitary–adrenocortical axis in humans. Psychological Bulletin, 133(1), 25. Mulero, J., Zafrilla, P., & Martinez-Cacha, A. (2011). Oxidative stress, frailty and cognitive decline. The Journal of Nutrition, Health & Aging, 15(9), 756–760. Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., ... Chertkow, H. (2005). The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53(4), 695–699. Orme, J. G., Reis, J., & Herz, E. J. (1986). Factorial and discriminant validity of the Center for Epidemiological Studies Depression (CES-D) Scale. Journal of Clinical Psychology, 42(1), 28–33. Ownby, R. L., Crocco, E., Acevedo, A., John, V., & Loewenstein, D. (2006). Depression and risk for Alzheimer disease: Systematic review, meta-analysis, and metaregression analysis. Archives of General Psychiatry, 63(5), 530–538. Paulson, D., & Lichtenberg, P. A. (2012). Vascular depression: An early warning sign of frailty. Aging & Mental Health. http://dx.doi.org/10.1080/13607863.2012.692767. Peeters, G., Van Schoor, N., Van Rossum, E., Visser, M., & Lips, P. (2008). The relationship between cortisol, muscle mass and muscle strength in older persons and the role of genetic variations in the glucocorticoid receptor. Clinical Endocrinology, 69(4), 673–682. Podsiadlo, D., & Richardson, S. (1991). The timed “Up & Go”: A test of basic functional mobility for frail elderly persons. Journal of the American Geriatrics Society, 39(2), 142–148. Robertson, D. A., Savva, G. M., Coen, R. F., & Kenny, R. A. (2014). Cognitive function in the prefrailty and frailty syndrome. Journal of the American Geriatrics Society, 62(11), 2118–2124. Robertson, D. A., Savva, G. M., & Kenny, R. A. (2013). Frailty and cognitive impairment—A review of the evidence and causal mechanisms. Ageing Research Reviews, 12(4), 840–851. http://dx.doi.org/10.1016/j.arr.2013.06.004. Robertson, D., King-Kallimanis, B., & Kenny, R. (2015a). Negative perceptions of aging predict longitudinal decline in cognitive function. Psychology and Aging, 30(4). Robertson, D., Savva, G., King-Kallimanis, B., & Kenny, R. (2015b). Negative perceptions of aging and decline in walking speed: A self-fulfilling prophecy. PLoS One, 10(4), e0123260. Robertson, I. H., Manly, T., Andrade, J., Baddeley, B. T., & Yiend, J. (1997). “Oops!”: Performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia, 35(6), 747–758. Rodriguez-Manas, L., Feart, C., Mann, G., Vina, J., Chatterji, S., Chodzko-Zajko, W., ... Vega, E. (2013). Searching for an operational definition of frailty: A Delphi method based consensus statement: The frailty operative definition-consensus conference project. Journals of Gerontology. Series A: Biological Sciences and Medical Sciences, 68(1), 62–67. http://dx.doi.org/10.1093/gerona/gls119. Roth, M., Tym, E., Mountjoy, C., Huppert, F. A., Hendrie, H., Verma, S., & Goddard, R. (1986). CAMDEX. A standardised instrument for the diagnosis of mental disorder in the elderly with special reference to the early detection of dementia. The British Journal of Psychiatry, 149(6), 698–709. Sargent-Cox, K. A., Anstey, K. J., & Luszcz, M. A. (2012). The relationship between change in self-perceptions of aging and physical functioning in older adults. Psychology and Aging, 27(3), 750. Sexton, E., King-Kallimanis, B. L., Morgan, K., & McGee, H. (2014). Development of the Brief Ageing Perceptions Questionnaire (B-APQ): A confirmatory factor analysis approach to item reduction. BMC Geriatrics, 14(1), 44. http://dx.doi.org/10.1186/ 1471–2318-14-44. Wallace, R. B., & Herzog, A. R. (1995). Overview of the health measures in the Health and Retirement Study. Journal of Human Resources, 84–107. Weiss, D., & Lang, F. R. (2012). “They” are old but “I” feel younger: Age-group dissociation as a self-protective strategy in old age. Psychology and Aging, 27(1), 153–163. http:// dx.doi.org/10.1037/a0024887. Wurm, S., & Benyamini, Y. (2014). Optimism buffers the detrimental effect of negative self-perceptions of ageing on physical and mental health. Psychology & Health, 29(7), 832–848. http://dx.doi.org/10.1080/08870446.2014.891737. Wurm, S., Warner, L. M., Ziegelmann, J. P., Wolff, J. K., & Schuz, B. (2013). How do negative self-perceptions of aging become a self-fulfilling prophecy? Psychology and Aging, 28(4), 1088–1097. http://dx.doi.org/10.1037/a0032845.
Please cite this article as: Robertson, D.A., & Kenny, R.A., Negative perceptions of aging modify the association between frailty and cognitive function in older adults, Personality and Individual Differences (2015), http://dx.doi.org/10.1016/j.paid.2015.12.010
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Negative perceptions of aging modify the association between frailty and cognitive function in older adults Deirdre A. Robertson a,⁎, Rose Anne Kenny a,b a b
TILDA (The Irish Longitudinal Study on Aging), Department of Medical Gerontology, Trinity College, Dublin, Ireland Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
a r t i c l e
i n f o
Article history: Received 30 June 2015 Received in revised form 28 October 2015 Accepted 2 December 2015 Available online xxxx Keywords: Self-perceptions Aging Cognition Frailty
a b s t r a c t Introduction: Older adults who are physically frail have poorer cognition compared to their robust peers. The mechanisms behind this association have yet to be established. Recent research has suggested that selfperceptions of aging are important predictors of physical and cognitive function in later life. This paper investigated whether self-perceptions of aging modify the relationship between frailty and cognitive function. Methods: 4135 participants from the Irish Longitudinal Study on Aging (TILDA) completed the Brief Aging Perceptions Questionnaire (B-APQ), a cognitive battery, and frailty measures. Results: Frailty was associated with poorer cognition in participants with negative perceptions of aging but not in those without. There was a significant interaction between negative perceptions of aging and frailty in predicting global cognition (B = −0.11, SE = .04) executive function (B = −0.09, SE = .04) and attention (B = 0.13, SE = .04) but not memory (B = −0.03, SE = .04). Conclusion: Negative perceptions of aging may modify the association between frailty and frontal cognitive domains in older adults. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction Older adults who are frail are more likely to be cognitively impaired. This association has been demonstrated consistently in longitudinal studies of aging (Robertson, Savva, & Kenny, 2013). Yet while the association appears to be well-established the mechanisms underpinning the relationship have yet to be explained. Frailty is an age-related syndrome characterised by a collection of symptoms including slow walking speed, weakness, sedentariness, unintentional weight loss and exhaustion (Fried et al., 2001). It is associated with adverse outcomes including disability and mortality. In gerontological research frailty is considered to be a physiological syndrome and thus the relationship between frailty and cognition is thought to be bound by a common physiological mediator (Fried et al., 2001). A number of physiological factors are common to both cognitive impairment and frailty yet the current empirical evidence for these as mechanisms remains relatively weak (e.g. Robertson et al., 2013). As frailty is a cluster of symptoms it is probable that no single biomarker will perfectly predict the syndrome. Instead, a combination of factors likely contributes to frailty. Indeed, some suggest that a definition of frailty should include nutrition, physical ability, senses, mood, coping, social relations and social support (Gobbens, Luijkx, Wijnen-Sponselee, & Schols, 2010; Rodriguez-Manas et al., 2013). Yet ⁎ Corresponding author. E-mail address: [email protected] (D.A. Robertson).
the latter four are rarely, if ever, included despite increasing evidence to suggest that psychological factors such as depression play a role in the development and maintenance of the syndrome (e.g. Paulson & Lichtenberg, 2012). As psychological factors such as depression are also associated with cognitive decline (Ownby, Crocco, Acevedo, John, & Loewenstein, 2006) there may be a common psychological mechanism which underpins frailty and cognitive impairment. Recent research has identified another psychological factor which predicts physical and cognitive function more strongly than, and independent of, depression. Older adults' self-perceptions of aging predict declines in cognition and walking speed – a key component of frailty – over two years independent of health changes and depression (Robertson, King-Kallimanis, & Kenny, 2015a; Robertson, Savva, King-Kallimanis, & Kenny, 2015b). Perceptions of aging encompass beliefs about aging including expectations, feelings of control and emotional responses to getting older. When perceptions are negative they cause declines in self-esteem, life satisfaction, self-rated health and objective cognitive and physical function (Robertson et al., 2015a; Weiss & Lang, 2012; Wurm & Benyamini, 2014; Wurm, Warner, Ziegelmann, Wolff, & Schuz, 2013). Physiological pathways including increased cardiovascular reactivity and risk of cardiovascular disease are also affected (Levy, Hausdorff, Hencke, & Wei, 2000; Levy, Zonderman, Slade, & Ferrucci, 2009). One theory has touched on perceptions as a contributory factor in the development of frailty. Lang, Michel, and Zekry (2009) suggest that frailty is a cycle of decline in which physiological changes interact
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Please cite this article as: Robertson, D.A., & Kenny, R.A., Negative perceptions of aging modify the association between frailty and cognitive function in older adults, Personality and Individual Differences (2015), http://dx.doi.org/10.1016/j.paid.2015.12.010
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with psychological responses to these changes. They suggest that when some older adults experience a change – e.g. normal decline in muscle mass – they have a resultant increase in their perception of the effort involved in being physically active. This perception of effort leads to a decline in physical activity which puts them at risk of further muscle mass depletion and thus frailty. This theory could be expanded to include cognitive function as physical activity is also associated with risk of cognitive decline through both the harmful effects of sedentariness itself and a potential resulting decrease in social engagement (Robertson et al., 2013). Adults with negative perceptions of aging may therefore respond to age-related changes by withdrawing from activities which would otherwise protect against greater physical and cognitive decline. Following from these theories we propose that older adults with negative perceptions of aging will be more likely to enter a cycle of frailty and cognitive impairment. Data from The Irish Longitudinal Study on Aging (TILDA) has previously shown that frailty is associated with poorer global cognition, executive function, memory and attention (Robertson, Savva, Coen, & Kenny, 2014). We thus sought to re-examine this relationship with the hypothesis that the association between frailty and cognitive function will only exist in adults with negative perceptions of aging. 2. Methods 2.1. Population Data was taken from Wave 1 of TILDA, a prospective, population representative sample of community-dwelling adults aged 50+. Ethical approval was obtained and all participants provided written informed consent. Participants with a doctor's diagnosis of dementia or who were unable to personally consent to participation due to cognitive impairment were not included in the study. The study has been described in detail elsewhere (Kearney et al., 2011) but in brief 8175 participants aged 50+ were interviewed in their own homes followed by a nurseadministered health assessment.
Folstein, & McHugh, 1975; Nasreddine et al., 2005). MMSE and MoCA are 30-point tests of global cognition. The MMSE includes questions assessing orientation, language, attention and memory. The MoCA has an additional assessment of executive function. There are ongoing debates about the strength of each (e.g. Aggarwal & Kean, 2010) and thus we included both. Executive function was assessed using the visual reasoning subtest of the CAMDEX, Color Trails Test 2 and verbal fluency. For the visual reasoning test participants are asked to identify which of six objects would complete a pattern of three similar objects. In the verbal fluency test participants have 1 min to name as many words beginning with F as they can. The Color Trails Test part 1 requires participants to draw a line connecting circles numbered 1–25 in consecutive order. Part 2 requires participants to complete the same task while alternating between pink and yellow circles. Part 1 assesses visual scanning and attention while the mental flexibility and switching of part 2 involves executive function (D'Elia, 1996; Nasreddine et al., 2005; Roth et al., 1986). Memory was assessed through the Picture Recognition and Recall subtests of the CAMDEX. Participants are shown 6 consecutive pictures of everyday objects (e.g. a lamp) and then asked to recall them. They are then shown a second series of pictures in which they have to pick the picture they previously saw from a series of similar objects (e.g. 3 different lamps). Participants were also given a list of 10 words and asked to repeat them back immediately and then five minutes later. This provided a measure of immediate and delayed memory (Roth et al., 1986; Wallace & Herzog, 1995). Attention was assessed through the Color Trails Test 1 and the Sustained Attention to Response Task (SART) (D'Elia, 1996; Robertson, Manly, Andrade, Baddeley, & Yiend, 1997). In the fixed SART task participants were shown a computer screen which repeated a sequence of numbers from 1 to 9 for 4 min. Numbers appeared every 300 ms and participants were asked to click in response to each number except 3. Composite scores for each domain were calculated by obtaining z-scores and combining them to create total scores for global cognition, executive function, memory and attention.
2.2. Frailty 2.4. Perceptions of aging We used the Fried definition of frailty which is composed of five criteria: weakness, slowness, sedentariness, unintentional weight loss and exhaustion (Fried et al., 2001). Dominant hand grip strength was assessed using a dynamometer. Weak grip strength was defined as any score below the age, gender and BMI adjusted 20th percentile. Gait speed was measured as time taken to complete the Timed Up and Go task. Participants rose from a chair (seat height 46 cm), walked 3 m at a normal pace, turned, walked back to the chair and sat down (Podsiadlo & Richardson, 1991). Participants below the gender and height adjusted 20th percentile were considered to have slow gait. Physical activity was measured using the short form of the International Physical Activity Questionnaire (Hagstromer, Oja, & Sjostrom, 2006). Unintentional weight loss was assessed by the question ‘In the past year have you lost 10 lb (4.5 kg) or more in weight when you were not trying to?’ Exhaustion was assessed using 2 questions from the 20-item Centre for Epidemiological Studies Depression (CES-D) scale: “I could not get going” and “I felt that everything I did was an effort”. A response of ‘sometimes’ or ‘often’ to either question was classified as exhaustion (Orme, Reis, & Herz, 1986). Participants with ≥3 indicators were defined as frail, ≥ 1 as pre-frail and 0 as robust (Fried et al., 2001). 2.3. Cognitive function The cognitive battery included tests of global cognition, executive function, memory and attention. Global cognition was assessed using the Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) (Folstein,
The Brief Aging Perceptions Questionnaire (B-APQ) measure comprises Likert-scaled statements about participants' perceptions of aging (Sexton, King-Kallimanis, Morgan, & McGee, 2014). The full questionnaire has five subscales but previous work suggests that one – the negative control and consequences subscale – is associated with both physical and cognitive function (Robertson et al., 2015a). Participants rated their agreement with 5 statements such as ‘As I get older I do not cope as well with problems that arise.’ Each statement is scored on a scale of 1 to 5. The mean score across all five statements was calculated for each participant to give a total perceptions score. Higher scores indicated more negative perceptions of aging. Cronbach's alpha in this sample showed good internal reliability (α = .79). 2.5. Covariates Age, gender and education were self-reported with education categorized as primary, secondary or third level. Chronic conditions were ascertained by self-report of a doctor's diagnosis and were included as number of conditions. These included: joint problems, cataracts, glaucoma, age-related macular degeneration, lung disease, asthma, arthritis, osteoporosis, cancer, ulcers, liver disease, alcohol or substance abuse and chronic pain. Participants were asked to record all medications taken on a regular basis. Self-rated health was assessed with the question: “Compared to other people your age, would you say your health is…excellent, very good, good, fair or poor?” Higher scores were indicative of worse self-rated health. Depressed mood was assessed using 18 items from the 20-item CES-D scale excluding the 2
Please cite this article as: Robertson, D.A., & Kenny, R.A., Negative perceptions of aging modify the association between frailty and cognitive function in older adults, Personality and Individual Differences (2015), http://dx.doi.org/10.1016/j.paid.2015.12.010
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items used to calculated exhaustion for the frailty score (Orme et al., 1986). Cronbach's alpha indicated good internal reliability (α = .83).
2.6. Statistical analyses We ascertained descriptive statistics on all variables for the total sample, for those below the median on the aging perceptions subscale (median = 2.8) and for those above the median. Due to small numbers of frail participants with complete data (N = 75) and to the further reduction once participants were split by perceptions of aging we collapsed the pre-frailty and frailty groups. Linear regression models estimated the association between frailty/ pre-frailty and cognitive function across the four cognitive domains in those with high negative perceptions (at or above the median) and those with low (below median). We tested the interaction between pre-frailty/frailty and the continuous perceptions of aging subscale using a second set of linear regression models adjusted for covariates. We estimated and plotted marginal means of each cognitive domain for the two frailty groups (robust and pre-frail/frail) across five points of the negative perceptions subscale (1, 2, 3, 4 and 5). As there was a greater proportion of frail participants with negative perceptions of aging, and as we had collapsed the pre-frail and frail groups into one, we considered that the higher proportion of frail participants in the latter group may be driving the effect. We re-analysed the data after removing all frail participants. Due to missing data in the aging perceptions subscale we also imputed missing scores using 10 multiple chained imputations. The imputed scores were calculated based on non-missing APQ questions, age, gender, education, chronic conditions and self-rated health as these had no or minimal missing data. Analyses were performed using STATA, version 12.1 (College Station, TX: StataCorp LP).
3. Results Of 8175 participants 5895 participants completed a health or home assessment and 5355 of these (91%) returned a self-completion questionnaire. Consistent with the definition of frailty we excluded participants with Parkinson's Disease, a history of stroke, an MMSE b 18 and those taking anti-depressants, dementia medication or Sinemet (Fried et al., 2001). The final sample size was 4135 (Fig. 1). Table 1 gives descriptive statistics for the total sample, for those with high negative perceptions of aging and those with low negative perceptions. In linear regression analyses adjusted for all covariates frailty was statistically significantly associated with all cognitive domains in the high negative perceptions group but the effect was not found in the low negative perceptions group (see Fig. 2). Linear regression analyses tested the interaction between negative aging perceptions and frailty on different domains of cognitive function. After full adjustment there was a significant interaction in predicting global cognition, executive function, and attention but not memory (see Table 2). Fig. 3 shows the marginal mean interaction effects. As agreement with the negative perceptions subscale increased cognitive function decreased in both robust and pre-frail/frail groups. There was no difference between the robust and pre-frail/frail participants in those with low negative perceptions of aging (average scores 1–2) but an increasingly large difference between groups with high negative perceptions of aging (scores 3–5). We reanalysed the data after excluding frail participants (≥ 3 indicators) and again using imputed APQ scores however the results were not changed and thus are not presented.
Fig. 1. Flow chart of sample.
4. Discussion In this sample of adults aged 50+ frail participants have poorer cognition but this association only exists in the added presence of negative perceptions of aging. The effect persisted after adjusting for depression and health factors which are known to be associated with physical and cognitive functions. This suggests that perceptions of aging may play a role in initiating or maintaining the cycle of decline involving frailty and cognitive function. However as this is cross-sectional data we cannot determine causality. Physical decline may lead to negative perceptions of aging. Nevertheless, previous work exploring this suggests that aging perceptions predict change in physical function rather than vice versa (Sargent-Cox, Anstey, & Luszcz, 2012). Perceptions of aging are associated with adaptation and coping in older age. As people get older, age-related changes and ill health can
Please cite this article as: Robertson, D.A., & Kenny, R.A., Negative perceptions of aging modify the association between frailty and cognitive function in older adults, Personality and Individual Differences (2015), http://dx.doi.org/10.1016/j.paid.2015.12.010
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Table 1 Descriptive statistics for sample. Measure (range) N = 4135
Frailtya Robust Pre-frail Frail Negative perceptions (1–5) General cognition MMSE (0–30) MoCA (0–30) Executive function Visual reasoning (0–6) Color Trails B (seconds) Verbal fluency Memory Immediate memory(0–10) Delayed memory (0–10) Picture recall (0–6) Picture recognition (0–6) Attention SART Color Trail A (seconds) Covariates Age Sex (female) Education (third level) Chronic conditions Medications Self-rated healthb (1–5) Depressive symptoms
Mean (SD) or % Total sample
Low negative perceptions
High negative perceptions
70.7% (N = 2895) 27.5% (N = 1125) 1.8% (N = 75) 2.8 (0.73)
78.1% (N = 1567)⁎ 21.2% (N = 426)⁎⁎ 0.7% (N = 14) 2.15 (0.41)
63.6% (N = 1328) 33.5% (N = 699) 2.9% (N = 61) 3.36 (0.47)⁎⁎⁎
28.7 (1.6) 25.3 (3.1)
28.9 (1.4) 25.9 (2.8)
28.4 (1.7)⁎⁎⁎ 24.7 (3.3)⁎⁎⁎
3.2 (1.3) 107.9 (38.8) 12.2 (5.0)
3.4 (1.3) 98.5 (32.0) 12.9 (5.0)
2.9 (1.3)⁎⁎⁎ 117.0 (42.5)⁎⁎⁎ 11.5 (5.1)⁎⁎⁎
6.0 (1.6) 6.3 (2.2) 3.3 (1.1) 5.6 (0.7)
6.3 (1.5) 6.7 (2.1) 3.4 (1.0) 5.7 (0.6)
5.8 (1.6)⁎⁎⁎ 5.8 (2.2)⁎⁎⁎ 3.2 (1.1)⁎⁎⁎ 5.5 (0.7)⁎⁎⁎
0.3 (0.2) 53.8 (23.0)
0.29 (0.16) 49.1 (19.5)
0.33 (0.16)⁎⁎⁎ 58.4 (25.0)⁎⁎⁎
62.0 (8.7) 53.4% (N = 2209) 37.0% (N = 1530) 1.2 (1.2) 2.3 (2.5) 2.2 (1.0) 4.6 (5.5)
59.8 (7.3) 56.4% (N = 1138) 41.9% (N = 845) 0.9 (1.1) 1.8 (2.1) 2.0 (0.9) 3.5 (4.5)
64.2 (9.4)⁎⁎⁎ 50.6% (N = 1071)⁎⁎⁎ 32.4% (N = 685)⁎⁎⁎ 1.4 (1.4)⁎⁎⁎ 2.7 (2.6)⁎⁎⁎ 2.5 (1.0)⁎⁎⁎ 5.5 (6.2)⁎⁎⁎
T-tests for continuous and chi-square tests for categorical variables. a Frailty N = 4095. Missing values in the 3-level variable were recouped in the dichotomous variable (robust vs pre-frail/frail) as participants with 1 indicator, regardless of other missing data, could be included. b Higher self-rated health scores correspond to worse self-rated health. ⁎⁎⁎ p b .001. ⁎⁎ p b .01. ⁎ p b .05.
Fig. 2. The association between frailty and cognitive function in those with and without negative perceptions of aging. ⁎ = p b .05, ⁎⁎ = p b .01, ⁎⁎⁎ = p b .001. Beta regression coefficients and 95% confidence intervals. Robust compared to pre-frail/frail. Note: Higher attention scores usually indicate worse attention but for the sake of comparability in this graph a reciprocal transformation reversed the direction.
Please cite this article as: Robertson, D.A., & Kenny, R.A., Negative perceptions of aging modify the association between frailty and cognitive function in older adults, Personality and Individual Differences (2015), http://dx.doi.org/10.1016/j.paid.2015.12.010
D.A. Robertson, R.A. Kenny / Personality and Individual Differences xxx (2015) xxx–xxx
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Table 2 Linear regression analyses assessing the interaction effect of frailty and perceptions of aging on four cognitive domains. Global cognition
Pre-frail/frail Negative perceptions Frailty ∗ negative perceptions Age Gender Education Secondary Third/higher Chronic conditions Medications Self-rated health Depression scores Observations
Executive function
Attentiona
Memory
B (SE)
T
B (SE)
T
B (SE)
T
B (SE)
T
0.15 (0.11) −0.09 (0.02) −0.11 (0.04) −0.02 (0.002) 0.03 (0.02)
1.44 −4.45⁎⁎⁎ −2.85⁎⁎ −9.77⁎⁎⁎ 1.23
0.16 (0.11) −0.12 (0.02) −0.09 (0.04) −0.03 (0.002) 0.02 (0.03)
1.41 −5.18⁎⁎⁎ −2.33⁎ −15.62⁎⁎⁎ 0.94
0.03 (0.12) −0.11 (0.02) −0.03 (0.04) −0.03 (0.002) 0.26 (0.03)
0.26 −4.40⁎⁎⁎ −0.80 −16.07⁎⁎⁎ 9.77⁎⁎⁎
−0.21 (0.12) 0.03 (0.02) 0.13 (0.04) 0.04 (0.002) −0.02 (0.03)
−1.79 1.17 3.22⁎⁎ 20.88⁎⁎⁎ −0.77
0.50 (0.04) 0.80 (0.04) 0.01 (0.01) 0.01 (0.01) −0.04 (0.01) −.001 (.002) 4135
12.70⁎⁎⁎ 21.28⁎⁎⁎ 1.12 1.33 −3.00⁎⁎ −0.42
0.57 (0.03) 1.02 (0.04) 0.02 (0.01) .004 (0.01) −0.07 (0.02) 0.0001 (0.002) 4135
16.42⁎⁎⁎ 28.34⁎⁎⁎ 2.00⁎ 0.7 −4.29⁎⁎⁎ 0.02
0.21 (0.04) 0.48 (0.04) 0.02 (0.01) −0.01 (0.01) −0.06 (0.02) −0.01 (.003) 4135
5.69⁎⁎⁎ 12.12⁎⁎⁎ 1.66 −1.51 −3.53⁎⁎⁎ −1.81
−0.34 (0.04) −0.47 (0.04) −0.01 (0.01) −0.003 (0.01) 0.08 (0.02) −0.0003 (0.002) 4135
−8.76⁎⁎⁎ −12.05⁎⁎⁎ −0.88 −0.47 4.93 −0.15
Model statistics: global cognition (R2 = .24, F(11,4123) = 87.25, p b .001); executive function (R2 = .30, F(11,4123) = 181.66, p b .001); memory (R2 = .22, F(11,4123) = 92.61, p b .001); attention (R2 = .25. F(11, 4123) = 105.15, p b .001). a Higher attention scores = worse attention. ⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.
limit the achievability of some goals. It is how people respond to these changes, however, that predicts how they will be affected. Older adults who face ill health but who respond by using SOC strategies (selection, optimisation and compensation) do not experience negative effects of ill health to the same extent as older adults who do not use these strategies (Wurm et al., 2013). SOC strategies involve selection of appropriate goals, optimisation of current functions to reach goals and compensation for losses by using alternative means to reach a goal. Interestingly only older adults with positive aging perceptions engaged in these strategies (Wurm et al., 2013). This may be one explanation as to why only older adults with negative aging perceptions experience both frailty and cognitive decline: they fail to engage in coping strategies to deal with age-related changes, and through consequent reductions in physical and social activities put themselves at risk of actual physical and cognitive decline. There is no work that we are aware of examining coping and adaptation in frailty. Another potentially important factor is stress and associated physiological processes including cortisol production and decreased inflammatory suppression. If older adults constantly expect negative
consequences they are likely also to experience greater stress. Psychological stress is associated with overproduction of cortisol, which is damaging for muscle mass and cognitive function, and a decreased ability to suppress inflammatory cytokine IL-6 (Lupien, McEwen, Gunnar, & Heim, 2009; Miller, Chen, & Zhou, 2007; Peeters, Van Schoor, Van Rossum, Visser, & Lips, 2008). Both frailty and cognitive decline are associated with over-active inflammatory cytokines including IL-6 (Mulero, Zafrilla, & Martinez-Cacha, 2011). Negative aging perceptions encompass explicit expectations for adverse events. As previous research shows that expectation of a negative event, as much as or more than the event itself, causes an increase in cortisol (Lupien et al., 1997) this may explain why negative perceptions may predict physical and cognitive function more powerfully than depressed mood. Increased stress as a result of negative perceptions may therefore contribute to the pathway between frailty and poor cognitive function. We did not detect an interaction effect in predicting memory. This is consistent with previous work showing that negative aging perceptions are associated with executive function but not memory (Robertson et al., 2015a). However it may merely be due to the nature of the
Fig. 3. Interaction effect between frailty and aging perceptions in predicting four domains of cognitive function. Note a: X-axis indicates 5 exemplar points on the aging perceptions scale. Higher scores = more negative perceptions. Note b: Higher scores reflect worse attentional abilities.
Please cite this article as: Robertson, D.A., & Kenny, R.A., Negative perceptions of aging modify the association between frailty and cognitive function in older adults, Personality and Individual Differences (2015), http://dx.doi.org/10.1016/j.paid.2015.12.010
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D.A. Robertson, R.A. Kenny / Personality and Individual Differences xxx (2015) xxx–xxx
memory measures which had smaller ranges (0–6 and 0–10), and therefore smaller variance, compared to other measures leading to a lower likelihood of picking up small between-person differences. There are no studies to our knowledge assessing the effect of aging perceptions on different domains of cognition with which to compare results. It may also be that negative perceptions affect only cognitive domains that are dependent on cognitive control and top–down processing such as attention and executive function. In support of this, priming older adults with aging stereotypes – a relative of aging perceptions – affects performance in controlled cognitive processes but not automatic ones (Mazerolle, Régner, Morisset, Rigalleau, & Huguet, 2012). In addition, older adults with negative perceptions of aging may be at increased risk of frailty and cognitive impairment due to declines in physical and social engagement. Other work has found, albeit cross-sectionally, that decreased social engagement is associated with poorer executive function but not memory (Andrew, Fisk, & Rockwood, 2011). These findings may therefore reflect a true difference in the impact of negative perceptions of aging on domains of cognitive function. This study has a number of strengths and limitations. One strength is the population representative sample and nurse-led health assessment which allows for in-depth collection of objective data. We also had a large cognitive battery allowing for assessment of different domains. A limitation is the cross-sectional data which does not allow us to determine causality. Declines in cognitive function may affect both negative aging perceptions and frailty. Thirdly, the frailty measure allows for categorisation into three groups but does not account for frailty severity within categories. Participants with high negative perceptions may have been frailer overall. We attempted to control for this by adding selfrated health as a covariate under the assumption that those in the pre-frail/frail group who were frailer than others in the same group would also report worse overall health. As the interaction effect persisted after including self-rated health we are more confident that this is not driving the result but longitudinal data including change in perceptions is needed. We will be able to further explore these pathways using wave 3 of TILDA. Future waves will also result in a larger frail sample. 5. Conclusions The relationship between frailty and cognition may be bound by a psychological construct: self-perceptions of aging. Aging perceptions are known to contribute to both physical and cognitive decline suggesting that this could be a targetable factor to delay impairment in some older adults. Promisingly, recent work found implicitly improving older adults' aging perceptions over 4-weeks led to improvements in physical function which surpassed even the effects of a 6-month exercise intervention (Levy, Pilver, Chung, & Slade, 2014). Aging perceptions are only one pathway through which adults may experience frailty and cognitive decline but, should interventions be developed, we may be able to delay a cycle of decline that leads to the development of two debilitating conditions simultaneously. References Aggarwal, A., & Kean, E. (2010). Comparison of the Folstein Mini Mental State Examination (MMSE) to the Montreal Cognitive Assessment (MoCA) as a cognitive screening tool in an inpatient rehabilitation setting. Neuroscience & Medicine, 1(02), 39. Andrew, M. K., Fisk, J. D., & Rockwood, K. (2011). Social vulnerability and prefrontal cortical function in elderly people: A report from the Canadian Study of Health and Aging. International Psychogeriatrics, 23(3), 450–458. http://dx.doi.org/10.1017/ s1041610210001195. D'Elia, L. (1996). Color trails test: Professional manual. Psychological Assessment Resources. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-Mental State: A practical method for grading the cognitive state of patients for the clinician. Pergamon Press. Fried, L. P., Tangen, C. M., Walston, J., Newman, A. B., Hirsch, C., Gottdiener, J., ... McBurnie, M. A. (2001). Frailty in older adults: Evidence for a phenotype. Journals of Gerontology. Series A: Biological Sciences and Medical Sciences, 56(3), M146–M156. Gobbens, R. J., Luijkx, K. G., Wijnen-Sponselee, M. T., & Schols, J. M. (2010). Towards an integral conceptual model of frailty. The Journal of Nutrition, Health & Aging, 14(3), 175–181.
Hagstromer, M., Oja, P., & Sjostrom, M. (2006). The International Physical Activity Questionnaire (IPAQ): A study of concurrent and construct validity. Public Health Nutrition, 9(6), 755–762. Kearney, P. M., Cronin, H., O'Regan, C., Kamiya, Y., Savva, G. M., Whelan, B., & Kenny, R. (2011). Cohort profile: The Irish Longitudinal Study on Ageing. International Journal of Epidemiology, 40(4), 877–884. http://dx.doi.org/10.1093/ije/dyr116. Lang, P. O., Michel, J. P., & Zekry, D. (2009). Frailty syndrome: A transitional state in a dynamic process. Gerontology, 55(5), 539–549. http://dx.doi.org/10.1159/000211949. Levy, B. R., Hausdorff, J. M., Hencke, R., & Wei, J. Y. (2000). Reducing cardiovascular stress with positive self-stereotypes of aging. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 55(4), 205–213. Levy, B. R., Pilver, C., Chung, P. H., & Slade, M. D. (2014). Subliminal strengthening improving older individuals' physical function over time with an implicit-age-stereotype intervention. Psychological Science, 25(12), 2127–2135. Levy, B. R., Zonderman, A. B., Slade, M. D., & Ferrucci, L. (2009). Age stereotypes held earlier in life predict cardiovascular events in later life. Psychological Science, 20(3), 296–298. http://dx.doi.org/10.1111/j.1467-9280.2009.02298.x. Lupien, S. J., McEwen, B. S., Gunnar, M. R., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience, 10(6), 434–445. Lupien, S., Gaudreau, S., Tchiteya, B., Maheu, F., Sharma, S., Nair, N., ... Meaney, M. (1997). Stress-induced declarative memory impairment in healthy elderly subjects: Relationship to cortisol reactivity 1. The Journal of Clinical Endocrinology & Metabolism, 82(7), 2070–2075. Mazerolle, M., Régner, I., Morisset, P., Rigalleau, F., & Huguet, P. (2012). Stereotype threat strengthens automatic recall and undermines controlled processes in older adults. Psychological Science, 23(7), 723–727. Miller, G. E., Chen, E., & Zhou, E. S. (2007). If it goes up, must it come down? Chronic stress and the hypothalamic–pituitary–adrenocortical axis in humans. Psychological Bulletin, 133(1), 25. Mulero, J., Zafrilla, P., & Martinez-Cacha, A. (2011). Oxidative stress, frailty and cognitive decline. The Journal of Nutrition, Health & Aging, 15(9), 756–760. Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., ... Chertkow, H. (2005). The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53(4), 695–699. Orme, J. G., Reis, J., & Herz, E. J. (1986). Factorial and discriminant validity of the Center for Epidemiological Studies Depression (CES-D) Scale. Journal of Clinical Psychology, 42(1), 28–33. Ownby, R. L., Crocco, E., Acevedo, A., John, V., & Loewenstein, D. (2006). Depression and risk for Alzheimer disease: Systematic review, meta-analysis, and metaregression analysis. Archives of General Psychiatry, 63(5), 530–538. Paulson, D., & Lichtenberg, P. A. (2012). Vascular depression: An early warning sign of frailty. Aging & Mental Health. http://dx.doi.org/10.1080/13607863.2012.692767. Peeters, G., Van Schoor, N., Van Rossum, E., Visser, M., & Lips, P. (2008). The relationship between cortisol, muscle mass and muscle strength in older persons and the role of genetic variations in the glucocorticoid receptor. Clinical Endocrinology, 69(4), 673–682. Podsiadlo, D., & Richardson, S. (1991). The timed “Up & Go”: A test of basic functional mobility for frail elderly persons. Journal of the American Geriatrics Society, 39(2), 142–148. Robertson, D. A., Savva, G. M., Coen, R. F., & Kenny, R. A. (2014). Cognitive function in the prefrailty and frailty syndrome. Journal of the American Geriatrics Society, 62(11), 2118–2124. Robertson, D. A., Savva, G. M., & Kenny, R. A. (2013). Frailty and cognitive impairment—A review of the evidence and causal mechanisms. Ageing Research Reviews, 12(4), 840–851. http://dx.doi.org/10.1016/j.arr.2013.06.004. Robertson, D., King-Kallimanis, B., & Kenny, R. (2015a). Negative perceptions of aging predict longitudinal decline in cognitive function. Psychology and Aging, 30(4). Robertson, D., Savva, G., King-Kallimanis, B., & Kenny, R. (2015b). Negative perceptions of aging and decline in walking speed: A self-fulfilling prophecy. PLoS One, 10(4), e0123260. Robertson, I. H., Manly, T., Andrade, J., Baddeley, B. T., & Yiend, J. (1997). “Oops!”: Performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia, 35(6), 747–758. Rodriguez-Manas, L., Feart, C., Mann, G., Vina, J., Chatterji, S., Chodzko-Zajko, W., ... Vega, E. (2013). Searching for an operational definition of frailty: A Delphi method based consensus statement: The frailty operative definition-consensus conference project. Journals of Gerontology. Series A: Biological Sciences and Medical Sciences, 68(1), 62–67. http://dx.doi.org/10.1093/gerona/gls119. Roth, M., Tym, E., Mountjoy, C., Huppert, F. A., Hendrie, H., Verma, S., & Goddard, R. (1986). CAMDEX. A standardised instrument for the diagnosis of mental disorder in the elderly with special reference to the early detection of dementia. The British Journal of Psychiatry, 149(6), 698–709. Sargent-Cox, K. A., Anstey, K. J., & Luszcz, M. A. (2012). The relationship between change in self-perceptions of aging and physical functioning in older adults. Psychology and Aging, 27(3), 750. Sexton, E., King-Kallimanis, B. L., Morgan, K., & McGee, H. (2014). Development of the Brief Ageing Perceptions Questionnaire (B-APQ): A confirmatory factor analysis approach to item reduction. BMC Geriatrics, 14(1), 44. http://dx.doi.org/10.1186/ 1471–2318-14-44. Wallace, R. B., & Herzog, A. R. (1995). Overview of the health measures in the Health and Retirement Study. Journal of Human Resources, 84–107. Weiss, D., & Lang, F. R. (2012). “They” are old but “I” feel younger: Age-group dissociation as a self-protective strategy in old age. Psychology and Aging, 27(1), 153–163. http:// dx.doi.org/10.1037/a0024887. Wurm, S., & Benyamini, Y. (2014). Optimism buffers the detrimental effect of negative self-perceptions of ageing on physical and mental health. Psychology & Health, 29(7), 832–848. http://dx.doi.org/10.1080/08870446.2014.891737. Wurm, S., Warner, L. M., Ziegelmann, J. P., Wolff, J. K., & Schuz, B. (2013). How do negative self-perceptions of aging become a self-fulfilling prophecy? Psychology and Aging, 28(4), 1088–1097. http://dx.doi.org/10.1037/a0032845.
Please cite this article as: Robertson, D.A., & Kenny, R.A., Negative perceptions of aging modify the association between frailty and cognitive function in older adults, Personality and Individual Differences (2015), http://dx.doi.org/10.1016/j.paid.2015.12.010