- Email: [email protected]
Is visual function associated with cognitive activity engagement in middle-aged and elderly individuals? A cross-sectional study
Experimental Gerontology 82 (2016) 104–111
Contents lists available at ScienceDirect
Experimental Gerontology journal homepage: www.elsevier.com/locate/expgero
Is visual function associated with cognitive activity engagement in middle-aged and elderly individuals? A cross-sectional study Sigrid Mueller-Schotte a,b,c,⁎, Yvonne T. van der Schouw a, Nienke Bleijenberg a,b, Marieke J. Schuurmans b,d a
Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, The Netherlands Department for the Chronically Ill and Elderly, University of Applied Sciences Utrecht, The Netherlands Department of Optometry and Orthoptics, University of Applied Sciences Utrecht, The Netherlands d Department of Rehabilitation, Nursing Science and Sports, UMC Utrecht, Utrecht, The Netherlands b c
a r t i c l e
i n f o
Article history: Received 27 January 2016 Received in revised form 14 June 2016 Accepted 15 June 2016 Available online 16 June 2016 Keywords: Cognition Visual acuity Contrast sensitivity Florida Cognitive Activity Scale Community living
a b s t r a c t Objective: This study investigated whether visual function is associated with cognitive activity engagement and mild cognitive impairment in middle-aged and elderly individuals. Method: This cross-sectional study was conducted on 120 individuals aged 50–89. The Florida Cognitive Activity Scale (FCAS) was used to assess cognitive activity engagement. Visual function was assessed by near visual acuity (nVA) and contrast sensitivity (CS), and both combined to obtain a visual function (VF) compound score. Multivariable linear regression models, adjusted for confounders, were used to assess the association between the determinants and FCAS. Results: After confounder adjustment, nVA was not associated with overall cognitive activity engagement. CS was significantly associated with the FCAS “Higher Cognitive Abilities” subscale score (BHC = 5.5 [95% CI 1.3; 9.7]). Adjustment for nVA attenuated the association between CS and engagement in tasks of Higher Cognitive Abilities (BHC = 4.7 [95% CI 0.1; 9.3]). In retired individuals (N = 87), the VF compound score was associated with a lower Cognitive Activity Scale score (BCA = −1.2 [95% CI −2.3; −0.1]), lower Higher Cognitive Abilities score (BHC = −0.7 [95% CI −1.3; −0.1]) and lower Frequent Cognitive Abilities score (BFA = −0.5 [95% CI −0.9; −0.1]). Conclusion: CS, but not nVA, plays a role in engagement in tasks associated with Higher Cognitive Abilities in middle-aged and elderly individuals. In retired individuals, the VF compound score is associated with lower Cognitive Activity score, lower Higher Cognitive Abilities score and lower Frequent Cognitive Abilities score. © 2016 Elsevier Inc. All rights reserved.
1. Introduction Being able to function and live independently at an older age is an important factor in maintaining good quality of life (QoL) and vitality. It requires good levels of physical function and the cognitive ability to maintain active participation in life and perform the necessary (instrumental) activities of daily living (Alexandre Tda et al., 2014; Rog et al., 2014). Cognitive function is affected by several factors, among which visual function has shown to be influential in multiple studies. Refractive errors, decreased visual acuity and contrast sensitivity are known to affect cognitive functioning (Ong et al., 2012; Ong et al., 2013; Risacher et al., 2013; See et al., 2011; Toner et al., 2012; Norton et al., 2009).
⁎ Corresponding author at: University of Applied Sciences Utrecht, Heidelberglaan 7, 3584 CS Utrecht, The Netherlands. E-mail addresses: [email protected] (S. Mueller-Schotte), [email protected] (Y.T. van der Schouw), [email protected] (N. Bleijenberg), [email protected] (M.J. Schuurmans).
http://dx.doi.org/10.1016/j.exger.2016.06.004 0531-5565/© 2016 Elsevier Inc. All rights reserved.
Poor vision has been associated with a higher risk of cognitive impairment, decreased cognitive function, and an increased risk of latelife dementia (Lin et al., 2004; Sachdev et al., 2012; Elyashiv et al., 2014; Rogers and Langa, 2010). Studies have also shown that poor visual performance in persons with normal cognitive function results in lower test outcomes of standard cognitive assessment tools (Hunt and Bassi, 2010; Wood et al., 2010; Jefferis et al., 2012). Some studies observed an improvement in the Mini-Mental State Examination (MMSE) score after cataract surgery because of improved vision (Gray et al., 2006; Ishii et al., 2008; Tamura et al., 2004), whereas other studies were unable to confirm these results (Hall et al., 2005; Grodstein et al., 2003; Anstey et al., 2006). Most research on visual function and cognition uses standard neuropsychological tests to evaluate cognitive function (Sachdev et al., 2012; Elyashiv et al., 2014; Ishii et al., 2008; Gaynes et al., 2013), many of which are at least partially vision-based assessments. In addition to evaluating several domains of cognition, it might also be important to measure actual engagement in cognitively challenging tasks because such engagement might enhance cognitive functioning
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111
but be inhibited by poor vision (Wilson et al., 2003; Bosma et al., 2002). This measurement is especially important because studies indicate a beneficial effect of cognitively stimulating activities on cognitive function (Pillai et al., 2011; Treiber et al., 2011). In addition, the level of engagement in cognitively stimulating activities might vary between those who are retired and those who are still engaged in working life, with further variability in the cognitive demands associated with specific occupations. To the best of our knowledge, no previous vision-related study has considered whether individuals actually engage in cognitively stimulating activities on a regular basis or addressed the type or frequency of the cognitively challenging tasks performed. The results are highly relevant to providing insight regarding which cognitively stimulating activities individuals actually do or cease to perform and whether visual function plays a role in that process. The aim of the present study is to investigate whether visual functioning as assessed by visual acuity, contrast sensitivity or both is associated with actual cognitive engagement among middle-aged and elderly individuals. 2. Method 2.1. Study design and population This cross-sectional population-based study on the relationship between visual function and cognitive function or activity was performed on 120 individuals aged 50–89. The present study on visual function (VF-PROFIEL) is an extension of a larger study on the Preservation of Functioning in the Elderly (PROFIEL) (den Ouden et al., 2013), a longitudinal study on 802 community-living elderly men and women (Muller et al., 2007; Lebrun et al., 2002). Details of the enrollment procedure have been described elsewhere (den Ouden et al., 2013; Muller et al., 2007; Lebrun et al., 2002; Mueller-Schotte et al., 2015). Data collection in the PROFIEL study took place between February 2010 and November 2011, whereas the VF-PROFIEL study was conducted between May 2012 and June 2013. Participants in the VF-PROFIEL study visited the research center for an extensive visual assessment. All study participants provided written informed consent prior to study enrollment. The study protocols for the PROFIEL study and the VF-PROFIEL extension were approved by the Institutional Review Board of the University Medical Center Utrecht (METC 09-248). The current cross-sectional study was based on the data of the VF-PROFIEL study. 2.2. Data collection 2.2.1. Determinants Whereas visual acuity tests the ability of the eye to discern detail, contrast sensitivity tests the ability to differentiate objects from their background. Although both measurements are taken separately in practice, both are effective simultaneously in the eye. To account for this effect, a visual function compound score combining visual acuity measurement and contrast sensitivity measurements was calculated. 2.2.1.1. Visual acuity. Using Landolt C optotypes, monocular and binocular visual acuities were assessed with presenting correction at distance (6 m) and near (40 cm). Binocular near visual acuity was used to reflect functional vision because research in the elderly indicates that vision function in daily life is better reflected by binocular assessment (Schneck et al., 2010). Near visual acuity was chosen above distance visual acuity to account for the nature of the items included in the FCAS, in which the majority of tasks are to be performed at distances of 30– 50 cm. Because visual acuity worse than 20/40 is frequently associated with difficulty in reading small print, better near visual acuity was defined as equal to or higher than 20/40 Snellen acuity (lower than or equal to 0.3 logMAR), and poorer near visual acuity was defined as worse than 20/40 (higher than 0.3 logMAR). For statistical analysis,
105
decimal visual acuity values were converted to the logarithm of the minimum angle of resolution (logMAR). Lower logMAR values indicate better performance. 2.2.1.2. Contrast sensitivity (CS). The Pelli-Robson Contrast Sensitivity Chart (Clement Clarke, Harlow, UK) was used to obtain monocular and binocular CS. At a distance of 1 m, the participant attempted to identify letters equivalent to a visual acuity of 20/100 with diminishing contrast from the upper left to the lower right corner of the chart. The letter group with the least contrast, for which at least 2 of 3 letters were correctly identified, was noted as the CS-threshold in log-units. Higher log(s) values indicate better performance. 2.2.1.3. Visual function. A compound score for near visual function was computed. This combined score of visual acuity and contrast sensitivity was calculated to discriminate between participants with worse (poor visual acuity and poor contrast sensitivity) and better (good visual acuity and good contrast sensitivity) visual function. First, individual test scores for near visual acuity and contrast sensitivity were transformed into a standardized z-score [z-score = (test score − mean test score) / SD]. Prior to calculating the compound score, the CS z-scores, where higher scores indicate better function, were re-scaled (1 - zscore) to allow for the same scaling direction as the near visual acuity scale. Second, the individual z-scores were summed to calculate the compound score. Lower compound scores correspond to individuals with both good visual acuity and good contrast sensitivity, whereas higher scores indicate poor visual acuity and poor contrast sensitivity. 2.2.2. Outcome variables 2.2.2.1. Cognitive engagement. The Florida Cognitive Activities Scale (FCAS) was used to study cognitive engagement. It is a validated 25item scale that examines the degree of active participation in a spectrum of activities varying in their cognitive demands in the elderly population, i.e., reading books or short stories or walking or driving in unfamiliar places requiring a map (Schinka et al., 2010; Dotson et al., 2008; Schinka et al., 2005). Engagement in each activity is rated on a 5-point scale ranging from 0 (never did this activity/used to do, but not in the past year) to 4 (perform the activity every day). Following the scoring directions, the overall score of the FCAS, known as the Cognitive Activity Scale score, was calculated by converting the answers for each activity to a 100-point scale, with 100 representing the highest possible activity level and 0 representing no involvement in activities. Two subscales, the Higher Cognitive Abilities score (0–40) and Frequent Cognitive Abilities score (0−32), were calculated to provide insight regarding the involvement in activities with high cognitive demands (i.e., playing chess, solving crossword puzzles, preparing meals from new recipes) and the most frequently performed activities from the 25-item Cognitive Activity scale. Furthermore, the subscale Cognitive Activity Maintenance ratio was determined by dividing the number of activities performed in the past 12 months by the number of activities ever performed. The Cognitive Activity Maintenance score (0–1) provides information on behavioral change in the year prior to data collection. The FCAS had good internal consistency in an elderly Caucasian population and an African American population, with α = 0.65 and α = 0.68, respectively (Dotson et al., 2008; Schinka et al., 2005). The external validity of the Cognitive Activity Scale with the Mini-Mental State Examination (MMSE) varied between α = 0.35 and α = 0.43 (Dotson et al., 2008; Schinka et al., 2005). 2.2.3. Other measurements 2.2.3.1. General characteristics. During the PROFIEL study visits, information on age, gender, education, smoking status, (instrumental) activities of daily living, and number of chronic diseases was collected using a questionnaire. Education was categorized as low, middle or high (including university) based on the International Standard Classification
106
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111
of Education (UNESCO. Institute of Statistics, 2011). The number of chronic diseases was based on a self-report physician diagnoses of diabetes mellitus, cardiovascular disease, stroke, chronic pulmonary disease, or psychological problems. The Dutch version of Folstein's Mini Mental State Examination (MMSE) was used as to assess global cognitive function (0 − 30) during the PROFIEL visits (Folstein et al., 1975). The MMSE is a valid and reliable measure to assess orientation to time and space, concentration, immediate and delayed memory, language and calculation (Folstein et al., 1975). In our study, a score of 27 or higher was considered normal cognitive function. The cut-off was determined prior to the analysis and based on previous studies (Mitchell, 2009; O'Bryant et al., 2008; Lebrun et al., 2005). 2.3. Data analysis The distribution of participant characteristics was described according to visual acuity levels equal to or higher than 20/40 Snellen acuity and worse than 20/40. Means and standard deviations (SDs) were calculated for continuous, normally distributed data. For non-normally distributed data, medians and interquartile ranges (IQR) are presented. Frequencies and percentages were used to describe categorical variables. Multivariable linear regression models were used to estimate the association between the determinants and the cognitive engagement outcome variables. Multivariable logistic regression models were used to assess the association between the determinants and global cognitive function. In the first model, the crude estimates and their 95%-confidence intervals (95% CI) were calculated. Confounders were selected based on prior identification in the literature or biological or clinical reasons that warranted an adjustment. The second model was adjusted for age and gender (Dotson et al., 2008; Schinka et al., 2005). The third model was additionally adjusted for education and number of chronic diseases (Dotson et al., 2008; Elliott et al., 2015; Barber, 2003; dos Santos and Andrade, 2012). The fourth model was adjusted for contrast sensitivity and visual acuity. Multicolinearity was tested using the variance inflation factor; values below 2.5 were considered to indicate the absence of collinearity.
To operationalize visual function and study the relationship between the individual and combined vision tests, we first treated the continuous variables (near visual acuity and contrast sensitivity) as single parameters to establish their individual associations with cognitive engagement adjusted for confounders as described above. Next, we evaluated the strength of the association of near visual acuity and contrast sensitivity when mutually adjusted and when combined as a compound score of visual function. Retired individuals presumably have more available time to engage in leisure and social activities than those working a regular job or engaging as volunteers. In addition, the level of cognitive demanding tasks might vary according to the occupation or type of volunteer work. To account for these issues, additional analyses were performed for those participants who were retired (not working a regular job or engaging as volunteers). All data analyses were performed using the statistical program IBM SPSS Statistics for Windows (Version 22.0; IBM Corp., Armonk, NY, USA). 3. Results The mean age of all participants (N = 120) was 74.0 (SD 9.4) years. The average binocular near visual acuity with presenting correction of all participants was 20/30 (0.2 logMAR [sd 0.2]). The median contrast sensitivity of all participants was 1.95 log-units (interquartile range 0.2). Most participants (N = 102, 85%) displayed normal cognitive function, and 18 (15%) participants had MMSE scores lower than 27. Table 1 shows the participants' characteristics stratified by near visual acuity. Contrast sensitivity was higher in those with better near visual acuity than in participants with worse near visual acuity: 2.0 log-units (interquartile range 0.0) and 1.8 log-units (interquartile range 1.0), respectively. Participants with better near visual acuity had a higher educational level and fewer chronic and ocular diseases and were more often current smokers. Table 2 shows the three most frequently performed items of cognitively stimulating activities and those most frequently ceased. All participants reported watching television or listening to the radio daily or at least 5 times or more per month. Of the participants who formerly
Table 1 Participant characteristics. Characteristic Age (years) Mean ± SD Number of females (N, %) Marital status (N, %) Married/living together Divorced/widowed Unmarried Number of chronic diseases Mean ± SD Education (N, %) Low ≤ ISCED level 3 Middle = ISCED level 4 High = ISCED level 5/6/7/8 MMSE-score ≥27 b27 FCAS - Cognitive Activity Score (0−100) Mean ± SD FCAS - Higher Cognitive Abilities (0–36) Mean ± SD FCAS - Frequent Cognitive Abilities (0–32) Mean ± SD FCAS - Cognitive Activity Maintenance (0–1) Mean ± SD Binocular near visual acuity (logMAR) with habitual correction Mean ± SD Binocular contrast sensitivity (log(s)) Median (interquartile range)
Participants with near visual acuity ≤ 0.3 logMAR (20/40 Snellen) (N = 88)
Participants with near visual acuity N 0.3 (20/40 Snellen) (N = 32)
73.3 48
9.6 (54.5)
75.9 19
8.6 (59.4)
50 28 10
(56.8) (31.8) (11.4)
17 12 3
(53.1) (37.5) (9.4)
1.7
1.4
2.1
1.9
12 42 34
(13.6) (47.7) (38.6)
10 13 9
(31.3) (40.6) (28.1)
75 13
(85.2) (14.8)
27 5
(84.4) (15.6)
41.7
8.4
39.3
9.7
12.9
4.9
11.7
5.3
23.4
3.5
23.1
4.3
0.3
0.1
0.3
0.1
0.1
0.1
0.4
0.1
2.0
0.0
1.8
0.6
Abbreviations: N = number; SD = standard deviation; MMSE = Mini-Mental State Examination; ISCED = International Standard Classification of Education.
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111
score of Higher Cognitive Abilities. Specifically, each one log-unit increase in contrast sensitivity was significantly associated with a 4.6 (95% CI 0.3; 8.9) point increase in the Higher Cognitive Abilities score according to the crude model and a 5.5 (95% CI 1.3; 9.7) point increase in the Higher Cognitive Abilities score according to the fully adjusted model. This finding indicates that better contrast sensitivity was associated with pursuing more of the higher cognitive tasks. Additional adjustment for visual acuity attenuated the strength of the association between contrast sensitivity and the Higher Cognitive Abilities score to 4.7 points (95% CI 0.1; 9.3).
Table 2 Cognitive activity items most frequently performed and most frequently ceased. Items most frequently performed⁎ 1.
Watching TV or listening to the radio
100% (N =
2.
Listening to music
120) 91.7% (N =
3.
Reading the newspaper (N = 119)
110) 90.8% (108)
Items most frequently ceased⁎⁎ 1. Playing other board games, such as checkers or Monopoly
68.1% (N =
2.
81) 53.3% (N =
3.
Playing chess, bridge, Scrabble, or Trivial Pursuit Doing art or craft kits or pattern-based activities, such as knitting, paint-by-number, or needle-work
107
3.3. Visual function compound score
64) 44.9% (N =
Fig. A.1c depicts the scores of the Florida Cognitive Activity Scale total score and the two subscales dependent on the visual function compound score (Appendix A). The visual function compound score showed a significant association between near visual function and Higher Cognitive Abilities score of −0.7 (95% CI −1.2; −0.2), indicating less engagement in higher cognitive ability tasks per log-unit decrease in visual function. No collinearity was present between variables for all visual function analyses (visual acuity, contrast sensitivity, and visual function compound score).
53)
Abbreviations: N = number; TV = television. ⁎ Items performed ‘5 times or more per month’ or ‘daily’. ⁎⁎ Items ‘used to do but not in the past year’.
engaged in playing chess, bridge, scrabble, or Trivial Pursuit, more than half (53.3%, N = 64) reported having ceased performing those activities. 3.1. Near visual acuity
3.4. Additional analysis on retirement Fig. A.1a depicts the scores of the Florida Cognitive Activity Scale total score and the two subscales dependent on visual acuity (Appendix A). After adjustment for confounders, a decrease of 1.0 logMAR in near visual acuity as a single determinant was not associated with the Cognitive Activity Scale score or with the subscales scores of Higher Cognitive Abilities, Frequent Cognitive Abilities, or Cognitive Activity Maintenance ratio (Table 3). Further adjustment for contrast sensitivity did not change these findings.
The association between near visual acuity and the Cognitive Activity Scale score and all subscales tended to be stronger in retired individuals, although this difference was not statistically significant (Table 4). The point estimates of the association between contrast sensitivity and all cognitive engagement subscales did not change in size substantially, and these differences were not statistically significant, except for the subscale score of Frequent Cognitive Abilities: 1 log-unit increase in contrast sensitivity was associated with a 3.7 point (95% CI 0.5; 7.0) higher Frequent Cognitive Ability score among retired individuals. The visual function compound score was significantly associated with the Cognitive Activity Scale score, Higher Cognitive Abilities score and Frequent Cognitive Abilities score [BCA = − 1.2 (95% CI − 2.3; − 0.1), BHC = − 0.7 (95% CI − 1.3; − 0.1) and BFA = − 0.5 (95% CI − 0.9; − 0.1), respectively], indicating less cognitive activity engagement, less engagement in higher cognitive ability tasks and less
3.2. Contrast sensitivity Fig. A.1b depicts the scores of the Florida Cognitive Activity Scale total score and the two subscales dependent on visual acuity (Appendix A). Contrast sensitivity was not significantly associated with the Cognitive Activity Scale score after adjustment for confounders (Table 3); however it was significantly associated with the subscale
Table 3 Linear regression coefficients for the association between visual functioning and cognitive engagement. Determinant
Near visual acuity
Contrast sensitivity
Compound score visual function at near
Model 1a Model 2b Model 3c Model 4d Model 1a Model 2b Model 3c Model 4e Model 1a Model 2b Model 3c
Cognitive Activity Scale Score^ (N = 120)
Higher Cognition Abilities# (N = 120)
Frequent Cognitive Abilities& (N = 120)
Activity Maintenance ratio§ (N = 120)
B coefficient
95% CIs
B coefficient
95% CIs
B coefficient
95% CIs
B coefficient
95% CIs
−6.8 −6.1 −5.0 −2.1 8.4 7.8 7.0 6.3 −1.0 −1.0 −0.8
−16.0; 2.4 −15.4; 3.3 −14.2; 4.3 −12.1; 7.9 0.8; 16.0⁎ −0.2; 15.7 −0.9; 14.9 −2.3; 14.9 −2.0; −0.1⁎ −1.9; 0.0 −1.8; 0.1
−4.3 −5.1 −4.4 −2.2 4.6 6.0 5.5 4.7 −0.6 −0.8 −0.7
−9.5; 0.9 −10.2; 0.0 −9.3; 0.6 −7.6; 3.1 0.3; 8.9⁎ 1.7; 10.3⁎ 1.3; 9.7⁎ 0.1; 9.3⁎
−1.2 −1.2 −0.5 −1.1 3.2 3.3 3.0 3.3 −0.3 −0.3 −0.2
−5.2; 2.7 −4.9; 2.6 −4.1; 3.2 −2.8; 5.0 −0.1; 6.4 0.1; 6.4⁎
0.1 0.1 0.0 0.0 −0.1 −0.1 −0.1 −0.1 0.0 0.0 0.0
0.0; 02 −0.1; 0.2 −0.1; 0.2 −0.1; 0.1 −0.2; 0.0 −0.2; 0.0 −0.2; 0.0 −0.2; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0
Abbreviations: N = number; CIs = confidence intervals. ^ Cognitive Activity Scale Score ranges from 0 to 100. # Higher Cognitive Abilities ranges from 0 to 40. & Frequent Cognitive Abilities ranges from 0 to 32. § Activity Maintenance ratio ranges from 0 to 1. a Crude model. b Adjusted for age and gender. c Adjusted for age, gender, education, and number of chronic diseases. d Adjusted for age, gender, education, number of chronic diseases, and contrast sensitivity. e Adjusted for age, gender, education, number of chronic diseases, and visual acuity. ⁎ Significant at p b 0.05.
−1.1; −0.1⁎ −1.3; −0.2⁎ −1.2; −0.2⁎
−0.1; 6.0 0.0; 6.7 −0.7; 0.1 −0.7; 0.1 −0.6; 0.1
108
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111
Table 4 Fully adjusted linear regression coefficients for the association between visual functioning and cognitive engagement based on retirement. Determinant
Near visual acuity
Contrast sensitivity
Compound score visual function at near
Cognitive Activity Scale Score^
Higher Cognitive Abilities#
Frequent Cognitive Abilities&
Activity Maintenance ratio§
B 95% coefficient CI-interval
B 95% coefficient CI-interval
B 95% coefficient CI-interval
B 95% coefficient CI-interval
All participantsa (N = 120) Retired participantsa (N =
−5.0 −10.8
−14.2; 4.3 −21.7; 0.0
−4.4 −5.7
−9.3; 0.6 −11.7; 0.2
−0.5 −3.2
−4.1; 3.2 −7.3; 1.0
0.0 0.2
−0.1; 0.2 0.0; 0.3
87) All participantsa (N = 120) Retired participantsa (N =
7.0 7.3
−0.9; 14.9 −1.3; 15.9
5.5 4.3
1.3; 9.7⁎ −0.4; 9.0
3.0 3.7
−0.1; 6.0 0.5; 7.0⁎
−0.1 −0.1
−0.2; 0.0 −0.3; 0.0
87) All participantsa (N = 120)
−0.8
−1.8; 0.1
−0.7
−1.2; −0.2⁎
−0.2
−0.6; 0.1
0.0
0.0; 0.0
Retired participantsa (N =
−1.2
−2.3; −0.1⁎
−0.7
−1.3; −0.1⁎
−0.5
−0.9; −0.1⁎
0.0
0.0; 0.0
Status of retirement
87) Abbreviations: N = number; CI = confidence interval. ^ Cognitive Activity Scale Score ranges from 0 to 100. # Higher Cognitive Abilities ranges from 0 to 40. & Frequent Cognitive Abilities ranges from 0 to 32. § Activity Maintenance ratio ranges from 0 to 1. a Adjusted for age, gender, education, and number of chronic diseases. ⁎ Statistically significant at p b 0.05.
frequent engagement in cognitive abilities per 1 log-unit decrease in visual function in retired individuals. 4. Discussion This study concerning visual function and cognitive activity engagement in middle-aged and elderly individuals revealed that contrast sensitivity (but not near visual acuity) plays a role in engagement in higher cognition tasks. Neither near visual acuity nor contrast sensitivity appear to influence overall cognitive activity engagement or activity maintenance as a single determinant. Among retired individuals, the combination of near visual acuity and contrast sensitivity (visual function score) was significantly associated with cognitive activity engagement and the frequency of activity engagement. Whereas visual acuity tests the ability of the eye to discern detail, contrast sensitivity tests the ability to differentiate objects form their background; both capabilities are necessary to master daily life. It is reasonable to assume that good contrast sensitivity is important for painting and other tasks that involve the discrimination of subtle shades of gray, which are included in the FCAS questionnaire. Contrast sensitivity has been linked to normal aging (Owsley et al., 1983; Ross et al., 1985) and several areas of cognition (See et al., 2011; Toner et al., 2012; Norton et al., 2009). We can only speculate about an underlying biological mechanism for our findings on contrast sensitivity and its relationship to cognitive activity engagement; the availability of dopamine, a neurotransmitter important for both light adaptation in the retina and cognitive function in the brain (Witkovsky, 2004; Volkow et al., 1998; Kaasinen and Rinne, 2002), might play a role. Although this possibility was not examined in the present study, it should be investigated in future research. Our results on contrast sensitivity and engagement in higher cognitive abilities are in line with those of other studies that have established an association between contrast sensitivity and cognitive function tasks regarding higher visual sensory processing demands, cognitive screening and memory measures (Risacher et al., 2013; Cronin-Golomb et al., 2007). Few studies have examined the relationship between visual acuity and mental activity engagement in older adults (Kiely et al., 2013; Tsang et al., 2013). The present study did not find an association between near visual acuity and the engagement in cognitive activities. This finding differs from that of a longitudinal study that identified low visual acuity and low mental activity as risk factors for mild cognitive impairment (Tsang et al., 2013). The same study also found that better visual acuity and engaging in higher mental activity recovered
mild cognitive impairment to normal cognitive function (Tsang et al., 2013). However, in our study, few participants had poor acuity levels, which might explain the different findings. When the activities ceased are considered, one can observe that the top two activities are those requiring two or more people to perform, which might have been ceased because of a lack of social contact. A study on preserving factors for cognitive function showed a significant negative correlation between overall loneliness and cognitive function (Fung et al., 2011). Based on our study, we can only speculate whether ceasing these activities is because of vision problems or other reasons, such as overall or visual fatigue or a change in social environment, i.e., loss of friends. We were unable to adjust for these factors. The strengths of the present study are that participants were recruited from the general population, independent of cognitive or visual status, and the good participation rate of 61.2% (120/196) in an elderly population. Furthermore, we evaluated two visual concepts both separately and together and investigated their relationship and activity engagement. In addition, a 25-item questionnaire was used to gain insight into cognitive activities performed and ceased by middle-aged and elderly individuals. To compensate for the time constraints imposed by the number of activities an individual can accomplish in any given period, a subgroup analysis was performed by restricting the analysis to those individuals who were retired, based on the assumption that they have more time available to engage in leisure activities than those still working a job or engaging as a volunteer. A potential limitation is that most participants had a good cognitive status, resulting in little differentiation in cognitive functioning. However, the ethical considerations required participants to understand the content of the informed consent. The cross-sectional study design hampered evaluating the change in cognitive activity behavior over a longer period because visual functioning and cognitive activities were not included in the baseline measurements of the longitudinal PROFIELstudy. However, the FCAS includes the Cognitive Activity Maintenance ratio, which functions as an indicator of change in health or cognitive status. In our study population, the Cognitive Activity Maintenance ratio was very low, indicating little change in cognitively challenging activities in the year prior to data collection. The results of the present study suggest that visual function should be considered when evaluating challenging cognitive tasks, especially tasks with higher processing demands. Furthermore, the results of the present study indicate that visual function tests should not be limited to the commonly used visual acuity but should be extended to measure contrast sensitivity as well. Further research is needed to investigate the
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111
influence of contrast sensitivity on cognitive activity engagement in individuals with more severe cognitive deficiencies and to evaluate whether, for example, improving contrast causes increased cognitive engagement. In addition, studies on cognitive activity engagement should consider changes in the social environment.
109
because of visual deficiencies (i.e. contrast issues) rather than cognitive abilities. Additional research is needed to evaluate whether contrast sensitivity should be included in routine visual assessments when evaluating engagement in cognitive activities.
Acknowledgments 5. Conclusions The present study found an association between contrast sensitivity and engagement in higher cognitive ability tasks among middle-aged and elderly individuals. Near visual acuity does not seem to play a role in cognitively stimulating task engagement. This result is particularly important for health care providers when evaluating test outcomes of complex cognitive activities because performance might be decreased
The PROFIEL-study was funded by ZonMw, The Netherlands' organization for health research and development, under grant number 60-61900-98-146. The funding organization played no role in the design and performance of the study, data collection, management, analysis and interpretation of the data or in the preparation, review, or approval of the manuscript. The VF-PROFIEL extension study was self-funded.
Appendix A
Fig. A.1a. Florida Cognitive Activity Scale scores as a function of near visual acuity.
Fig. A.1b. Florida Cognitive Activity Scale scores as a function of contrast sensitivity.
110
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111
Fig. A.1c. Florida Cognitive Activity Scale scores as a function of the visual function compound score.
References Alexandre Tda, S., Corona, L.P., Nunes, D.P., Santos, J.L., Duarte, Y.A., Lebrao, M.L., 2014. Disability in instrumental activities of daily living among older adults: gender differences. Rev. Saude Publica 48, 379–389. Anstey, K.J., Lord, S.R., Hennessy, M., Mitchell, P., Mill, K., von Sanden, C., 2006. The effect of cataract surgery on neuropsychological test performance: a randomized controlled trial. J. Int. Neuropsychol. Soc. 12, 632–639. Barber, A.J., 2003. A new view of diabetic retinopathy: a neurodegenerative disease of the eye. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 27, 283–290. Bosma, H., van Boxtel, M.P., Ponds, R.W., et al., 2002. Engaged lifestyle and cognitive function in middle and old-aged, non-demented persons: a reciprocal association? Z. Gerontol. Geriatr. 35, 575–581. Cronin-Golomb, A., Gilmore, G.C., Neargarder, S., Morrison, S.R., Laudate, T.M., 2007. Enhanced stimulus strength improves visual cognition in aging and Alzheimer's disease. Cortex 43, 952–966. den Ouden, M.E., Schuurmans, M.J., Mueller-Schotte, S., van der Schouw, Y.T., 2013. Identification of high-risk individuals for the development of disability in activities of daily living. A ten-year follow-up study. Exp. Gerontol. 48, 437–443. dos Santos, N.A., Andrade, S.M., 2012. Visual contrast sensitivity in patients with impairment of functional independence after stroke. BMC Neurol. 12 (90-2377-12-90). Dotson, V.M., Schinka, J.A., Brown, L.M., Mortimer, J.A., Borenstein, A.R., 2008. Characteristics of the Florida cognitive activities scale in older African Americans. Assessment 15, 72–77. Elliott, A.F., McGwin Jr., G., Kline, L.B., Owsley, C., 2015. Vision impairment among older adults residing in subsidized housing communities. The Gerontologist 55 (Suppl. 1), S108–S117. Elyashiv, S.M., Shabtai, E.L., Belkin, M., 2014. Correlation between visual acuity and cognitive functions. Br. J. Ophthalmol. 98, 129–132. 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. J. Psychiatr. Res. 12, 189–198. Fung, A.W., Leung, G.T., Lam, L.C., 2011. Modulating factors that preserve cognitive function in healthy ageing. East Asian Arch. Psychiatry 21, 152–156. Gaynes, B.I., Shah, R., Leurgans, S., Bennett, D., 2013. Neuroticism modifies the association of vision impairment and cognition among community-dwelling older adults. Neuroepidemiology 40, 142–146. Gray, C.S., Karimova, G., Hildreth, A.J., Crabtree, L., Allen, D., O'connell, J.E., 2006. Recovery of visual and functional disability following cataract surgery in older people: Sunderland cataract study. J. Cataract Refract. Surg. 32, 60–66. Grodstein, F., Chen, J., Hankinson, S.E., 2003. Cataract extraction and cognitive function in older women. Epidemiology 14, 493–497. Hall, T.A., McGwin Jr., G., Owsley, C., 2005. Effect of cataract surgery on cognitive function in older adults. J. Am. Geriatr. Soc. 53, 2140–2144. Hunt, L.A., Bassi, C.J., 2010. Near-vision acuity levels and performance on neuropsychological assessments used in occupational therapy. Am. J. Occup. Ther. 64, 105–113. Ishii, K., Kabata, T., Oshika, T., 2008. The impact of cataract surgery on cognitive impairment and depressive mental status in elderly patients. Am J. Ophthalmol. 146, 404–409. Jefferis, J.M., Collerton, J., Taylor, J.P., et al., 2012. The impact of visual impairment on MiniMental State Examination Scores in the Newcastle 85+ study. Age Ageing 41, 565–568. Kaasinen, V., Rinne, J.O., 2002. Functional imaging studies of dopamine system and cognition in normal aging and Parkinson's disease. Neurosci. Biobehav. Rev. 26, 785–793. Kiely, K.M., Anstey, K.J., Luszcz, M.A., 2013. Dual sensory loss and depressive symptoms: the importance of hearing, daily functioning, and activity engagement. Front. Hum. Neurosci. 7, 837.
Lebrun, C.E., van der Schouw, Y.T., Bak, A.A., et al., 2002. Arterial stiffness in postmenopausal women: determinants of pulse wave velocity. J. Hypertens. 20, 2165–2172. Lebrun, C.E., van der Schouw, Y.T., de Jong, F.H., Pols, H.A., Grobbee, D.E., Lamberts, S.W., 2005. Endogenous oestrogens are related to cognition in healthy elderly women. Clin. Endocrinol. 63, 50–55. Lin, M.Y., Gutierrez, P.R., Stone, K.L., et al., 2004. Vision impairment and combined vision and hearing impairment predict cognitive and functional decline in older women. J. Am. Geriatr. Soc. 52, 1996–2002. Mitchell, A.J., 2009. A meta-analysis of the accuracy of the mini-mental state examination in the detection of dementia and mild cognitive impairment. J. Psychiatr. Res. 43, 411–431. Mueller-Schotte, S., van der Schouw, Y.T., Schuurmans, M.J., 2015. Ocular straylight: a determinant of quality of life in the elderly? Gerontol. Geriatr. Med. 1. Muller, M., Grobbee, D.E., Aleman, A., Bots, M., van der Schouw, Y.T., 2007. Cardiovascular disease and cognitive performance in middle-aged and elderly men. Atherosclerosis 190, 143–149. Norton, D., McBain, R., Chen, Y., 2009. Reduced ability to detect facial configuration in middle-aged and elderly individuals: associations with spatiotemporal visual processing. J. Gerontol. B Psychol. Sci. Soc. Sci. 64, 328–334. O'Bryant, S.E., Humphreys, J.D., Smith, G.E., et al., 2008. Detecting dementia with the minimental state examination in highly educated individuals. Arch. Neurol. 65, 963–967. Ong, S.Y., Cheung, C.Y., Li, X., et al., 2012. Visual impairment, age-related eye diseases, and cognitive function: the Singapore Malay eye study. Arch. Ophthalmol. 130, 895–900. Ong, S.Y., Ikram, M.K., Haaland, B.A., et al., 2013. Myopia and cognitive dysfunction: the Singapore Malay eye study. Invest. Ophthalmol. Vis. Sci. 54, 799–803. Owsley, C., Sekuler, R., Siemsen, D., 1983. Contrast sensitivity throughout adulthood. Vis. Res. 23, 689–699. Pillai, J.A., Hall, C.B., Dickson, D.W., Buschke, H., Lipton, R.B., Verghese, J., 2011. Association of crossword puzzle participation with memory decline in persons who develop dementia. J. Int. Neuropsychol. Soc. 17, 1006–1013. Risacher, S.L., Wudunn, D., Pepin, S.M., et al., 2013. Visual contrast sensitivity in Alzheimer's disease, mild cognitive impairment, and older adults with cognitive complaints. Neurobiol. Aging 34, 1133–1144. Rog, L.A., Park, L.Q., Harvey, D.J., Huang, C.J., Mackin, S., Farias, S.T., 2014. The independent contributions of cognitive impairment and neuropsychiatric symptoms to everyday function in older adults. Clin. Neuropsychol. 28, 215–236. Rogers, M.A., Langa, K.M., 2010. Untreated poor vision: a contributing factor to late-life dementia. Am. J. Epidemiol. 171, 728–735. Ross, J.E., Clarke, D.D., Bron, A.J., 1985. Effect of age on contrast sensitivity function: uniocular and binocular findings. Br. J. Ophthalmol. 69, 51–56. Sachdev, P.S., Lipnicki, D.M., Crawford, J., et al., 2012. Risk profiles of subtypes of mild cognitive impairment: the Sydney memory and ageing study. J. Am. Geriatr. Soc. 60, 24–33. Schinka, J.A., McBride, A., Vanderploeg, R.D., Tennyson, K., Borenstein, A.R., Mortimer, J.A., 2005. Florida Cognitive Activities Scale: initial development and validation. J. Int. Neuropsychol. Soc. 11, 108–116. Schinka, J.A., Raj, A., Loewenstein, D.A., Small, B.J., Duara, R., Potter, H., 2010. Crossvalidation of the Florida Cognitive Activities Scale (FCAS) in an Alzheimer's disease research center sample. J. Geriatr. Psychiatry Neurol. 23, 9–14. Schneck, M.E., Haegerstom-Portnoy, G., Lott, L.A., Brabyn, J.A., 2010. Monocular vs. binocular measurement of spatial vision in elders. Optom. Vis. Sci. 87, 526–531. See, A.Y., Anstey, K.J., Wood, J.M., 2011. Simulated cataract and low contrast stimuli impair cognitive performance in older adults: implications for neuropsychological assessment and everyday function. Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn. 18, 1–21. Tamura, H., Tsukamoto, H., Mukai, S., et al., 2004. Improvement in cognitive impairment after cataract surgery in elderly patients. J. Cataract Refract. Surg. 30, 598–602.
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111 Toner, C.K., Reese, B.E., Neargarder, S., Riedel, T.M., Gilmore, G.C., Cronin-Golomb, A., 2012. Vision-fair neuropsychological assessment in normal aging, Parkinson's disease and Alzheimer's disease. Psychol. Aging 27, 785–790. Treiber, K.A., Carlson, M.C., Corcoran, C., et al., 2011. Cognitive stimulation and cognitive and functional decline in Alzheimer's disease: the cache county dementia progression study. J. Gerontol. B Psychol. Sci. Soc. Sci. 66, 416–425. Tsang, R.S., Sachdev, P.S., Reppermund, S., et al., 2013. Sydney memory and ageing study: an epidemiological cohort study of brain ageing and dementia. Int. Rev. Psychiatry 25, 711–725. UNESCO. Institute of Statistics, 2011. International Classification of Education ISCED. (Available at:) http://www.uis.unesco.org/Education/Documents/isced-2011-en.pdf (Accessed January 14, 2014).
111
Volkow, N.D., Gur, R.C., Wang, G.J., et al., 1998. Association between decline in brain dopamine activity with age and cognitive and motor impairment in healthy individuals. Am. J. Psychiatry 155, 344–349. Wilson, R.S., Bennett, D.A., Bienias, J.L., CF, M.d.L., MC, M., DA, E., 2003. Cognitive activity and cognitive decline in a biracial community population. Neurology 61, 812–816. Witkovsky, P., 2004. Dopamine and retinal function. Doc. Ophthalmol. 108, 17–40. Wood, J., Chaparro, A., Anstey, K., et al., 2010. Simulated visual impairment leads to cognitive slowing in older adults. Optom. Vis. Sci. 87, 1037–1043.
Contents lists available at ScienceDirect
Experimental Gerontology journal homepage: www.elsevier.com/locate/expgero
Is visual function associated with cognitive activity engagement in middle-aged and elderly individuals? A cross-sectional study Sigrid Mueller-Schotte a,b,c,⁎, Yvonne T. van der Schouw a, Nienke Bleijenberg a,b, Marieke J. Schuurmans b,d a
Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, The Netherlands Department for the Chronically Ill and Elderly, University of Applied Sciences Utrecht, The Netherlands Department of Optometry and Orthoptics, University of Applied Sciences Utrecht, The Netherlands d Department of Rehabilitation, Nursing Science and Sports, UMC Utrecht, Utrecht, The Netherlands b c
a r t i c l e
i n f o
Article history: Received 27 January 2016 Received in revised form 14 June 2016 Accepted 15 June 2016 Available online 16 June 2016 Keywords: Cognition Visual acuity Contrast sensitivity Florida Cognitive Activity Scale Community living
a b s t r a c t Objective: This study investigated whether visual function is associated with cognitive activity engagement and mild cognitive impairment in middle-aged and elderly individuals. Method: This cross-sectional study was conducted on 120 individuals aged 50–89. The Florida Cognitive Activity Scale (FCAS) was used to assess cognitive activity engagement. Visual function was assessed by near visual acuity (nVA) and contrast sensitivity (CS), and both combined to obtain a visual function (VF) compound score. Multivariable linear regression models, adjusted for confounders, were used to assess the association between the determinants and FCAS. Results: After confounder adjustment, nVA was not associated with overall cognitive activity engagement. CS was significantly associated with the FCAS “Higher Cognitive Abilities” subscale score (BHC = 5.5 [95% CI 1.3; 9.7]). Adjustment for nVA attenuated the association between CS and engagement in tasks of Higher Cognitive Abilities (BHC = 4.7 [95% CI 0.1; 9.3]). In retired individuals (N = 87), the VF compound score was associated with a lower Cognitive Activity Scale score (BCA = −1.2 [95% CI −2.3; −0.1]), lower Higher Cognitive Abilities score (BHC = −0.7 [95% CI −1.3; −0.1]) and lower Frequent Cognitive Abilities score (BFA = −0.5 [95% CI −0.9; −0.1]). Conclusion: CS, but not nVA, plays a role in engagement in tasks associated with Higher Cognitive Abilities in middle-aged and elderly individuals. In retired individuals, the VF compound score is associated with lower Cognitive Activity score, lower Higher Cognitive Abilities score and lower Frequent Cognitive Abilities score. © 2016 Elsevier Inc. All rights reserved.
1. Introduction Being able to function and live independently at an older age is an important factor in maintaining good quality of life (QoL) and vitality. It requires good levels of physical function and the cognitive ability to maintain active participation in life and perform the necessary (instrumental) activities of daily living (Alexandre Tda et al., 2014; Rog et al., 2014). Cognitive function is affected by several factors, among which visual function has shown to be influential in multiple studies. Refractive errors, decreased visual acuity and contrast sensitivity are known to affect cognitive functioning (Ong et al., 2012; Ong et al., 2013; Risacher et al., 2013; See et al., 2011; Toner et al., 2012; Norton et al., 2009).
⁎ Corresponding author at: University of Applied Sciences Utrecht, Heidelberglaan 7, 3584 CS Utrecht, The Netherlands. E-mail addresses: [email protected] (S. Mueller-Schotte), [email protected] (Y.T. van der Schouw), [email protected] (N. Bleijenberg), [email protected] (M.J. Schuurmans).
http://dx.doi.org/10.1016/j.exger.2016.06.004 0531-5565/© 2016 Elsevier Inc. All rights reserved.
Poor vision has been associated with a higher risk of cognitive impairment, decreased cognitive function, and an increased risk of latelife dementia (Lin et al., 2004; Sachdev et al., 2012; Elyashiv et al., 2014; Rogers and Langa, 2010). Studies have also shown that poor visual performance in persons with normal cognitive function results in lower test outcomes of standard cognitive assessment tools (Hunt and Bassi, 2010; Wood et al., 2010; Jefferis et al., 2012). Some studies observed an improvement in the Mini-Mental State Examination (MMSE) score after cataract surgery because of improved vision (Gray et al., 2006; Ishii et al., 2008; Tamura et al., 2004), whereas other studies were unable to confirm these results (Hall et al., 2005; Grodstein et al., 2003; Anstey et al., 2006). Most research on visual function and cognition uses standard neuropsychological tests to evaluate cognitive function (Sachdev et al., 2012; Elyashiv et al., 2014; Ishii et al., 2008; Gaynes et al., 2013), many of which are at least partially vision-based assessments. In addition to evaluating several domains of cognition, it might also be important to measure actual engagement in cognitively challenging tasks because such engagement might enhance cognitive functioning
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111
but be inhibited by poor vision (Wilson et al., 2003; Bosma et al., 2002). This measurement is especially important because studies indicate a beneficial effect of cognitively stimulating activities on cognitive function (Pillai et al., 2011; Treiber et al., 2011). In addition, the level of engagement in cognitively stimulating activities might vary between those who are retired and those who are still engaged in working life, with further variability in the cognitive demands associated with specific occupations. To the best of our knowledge, no previous vision-related study has considered whether individuals actually engage in cognitively stimulating activities on a regular basis or addressed the type or frequency of the cognitively challenging tasks performed. The results are highly relevant to providing insight regarding which cognitively stimulating activities individuals actually do or cease to perform and whether visual function plays a role in that process. The aim of the present study is to investigate whether visual functioning as assessed by visual acuity, contrast sensitivity or both is associated with actual cognitive engagement among middle-aged and elderly individuals. 2. Method 2.1. Study design and population This cross-sectional population-based study on the relationship between visual function and cognitive function or activity was performed on 120 individuals aged 50–89. The present study on visual function (VF-PROFIEL) is an extension of a larger study on the Preservation of Functioning in the Elderly (PROFIEL) (den Ouden et al., 2013), a longitudinal study on 802 community-living elderly men and women (Muller et al., 2007; Lebrun et al., 2002). Details of the enrollment procedure have been described elsewhere (den Ouden et al., 2013; Muller et al., 2007; Lebrun et al., 2002; Mueller-Schotte et al., 2015). Data collection in the PROFIEL study took place between February 2010 and November 2011, whereas the VF-PROFIEL study was conducted between May 2012 and June 2013. Participants in the VF-PROFIEL study visited the research center for an extensive visual assessment. All study participants provided written informed consent prior to study enrollment. The study protocols for the PROFIEL study and the VF-PROFIEL extension were approved by the Institutional Review Board of the University Medical Center Utrecht (METC 09-248). The current cross-sectional study was based on the data of the VF-PROFIEL study. 2.2. Data collection 2.2.1. Determinants Whereas visual acuity tests the ability of the eye to discern detail, contrast sensitivity tests the ability to differentiate objects from their background. Although both measurements are taken separately in practice, both are effective simultaneously in the eye. To account for this effect, a visual function compound score combining visual acuity measurement and contrast sensitivity measurements was calculated. 2.2.1.1. Visual acuity. Using Landolt C optotypes, monocular and binocular visual acuities were assessed with presenting correction at distance (6 m) and near (40 cm). Binocular near visual acuity was used to reflect functional vision because research in the elderly indicates that vision function in daily life is better reflected by binocular assessment (Schneck et al., 2010). Near visual acuity was chosen above distance visual acuity to account for the nature of the items included in the FCAS, in which the majority of tasks are to be performed at distances of 30– 50 cm. Because visual acuity worse than 20/40 is frequently associated with difficulty in reading small print, better near visual acuity was defined as equal to or higher than 20/40 Snellen acuity (lower than or equal to 0.3 logMAR), and poorer near visual acuity was defined as worse than 20/40 (higher than 0.3 logMAR). For statistical analysis,
105
decimal visual acuity values were converted to the logarithm of the minimum angle of resolution (logMAR). Lower logMAR values indicate better performance. 2.2.1.2. Contrast sensitivity (CS). The Pelli-Robson Contrast Sensitivity Chart (Clement Clarke, Harlow, UK) was used to obtain monocular and binocular CS. At a distance of 1 m, the participant attempted to identify letters equivalent to a visual acuity of 20/100 with diminishing contrast from the upper left to the lower right corner of the chart. The letter group with the least contrast, for which at least 2 of 3 letters were correctly identified, was noted as the CS-threshold in log-units. Higher log(s) values indicate better performance. 2.2.1.3. Visual function. A compound score for near visual function was computed. This combined score of visual acuity and contrast sensitivity was calculated to discriminate between participants with worse (poor visual acuity and poor contrast sensitivity) and better (good visual acuity and good contrast sensitivity) visual function. First, individual test scores for near visual acuity and contrast sensitivity were transformed into a standardized z-score [z-score = (test score − mean test score) / SD]. Prior to calculating the compound score, the CS z-scores, where higher scores indicate better function, were re-scaled (1 - zscore) to allow for the same scaling direction as the near visual acuity scale. Second, the individual z-scores were summed to calculate the compound score. Lower compound scores correspond to individuals with both good visual acuity and good contrast sensitivity, whereas higher scores indicate poor visual acuity and poor contrast sensitivity. 2.2.2. Outcome variables 2.2.2.1. Cognitive engagement. The Florida Cognitive Activities Scale (FCAS) was used to study cognitive engagement. It is a validated 25item scale that examines the degree of active participation in a spectrum of activities varying in their cognitive demands in the elderly population, i.e., reading books or short stories or walking or driving in unfamiliar places requiring a map (Schinka et al., 2010; Dotson et al., 2008; Schinka et al., 2005). Engagement in each activity is rated on a 5-point scale ranging from 0 (never did this activity/used to do, but not in the past year) to 4 (perform the activity every day). Following the scoring directions, the overall score of the FCAS, known as the Cognitive Activity Scale score, was calculated by converting the answers for each activity to a 100-point scale, with 100 representing the highest possible activity level and 0 representing no involvement in activities. Two subscales, the Higher Cognitive Abilities score (0–40) and Frequent Cognitive Abilities score (0−32), were calculated to provide insight regarding the involvement in activities with high cognitive demands (i.e., playing chess, solving crossword puzzles, preparing meals from new recipes) and the most frequently performed activities from the 25-item Cognitive Activity scale. Furthermore, the subscale Cognitive Activity Maintenance ratio was determined by dividing the number of activities performed in the past 12 months by the number of activities ever performed. The Cognitive Activity Maintenance score (0–1) provides information on behavioral change in the year prior to data collection. The FCAS had good internal consistency in an elderly Caucasian population and an African American population, with α = 0.65 and α = 0.68, respectively (Dotson et al., 2008; Schinka et al., 2005). The external validity of the Cognitive Activity Scale with the Mini-Mental State Examination (MMSE) varied between α = 0.35 and α = 0.43 (Dotson et al., 2008; Schinka et al., 2005). 2.2.3. Other measurements 2.2.3.1. General characteristics. During the PROFIEL study visits, information on age, gender, education, smoking status, (instrumental) activities of daily living, and number of chronic diseases was collected using a questionnaire. Education was categorized as low, middle or high (including university) based on the International Standard Classification
106
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111
of Education (UNESCO. Institute of Statistics, 2011). The number of chronic diseases was based on a self-report physician diagnoses of diabetes mellitus, cardiovascular disease, stroke, chronic pulmonary disease, or psychological problems. The Dutch version of Folstein's Mini Mental State Examination (MMSE) was used as to assess global cognitive function (0 − 30) during the PROFIEL visits (Folstein et al., 1975). The MMSE is a valid and reliable measure to assess orientation to time and space, concentration, immediate and delayed memory, language and calculation (Folstein et al., 1975). In our study, a score of 27 or higher was considered normal cognitive function. The cut-off was determined prior to the analysis and based on previous studies (Mitchell, 2009; O'Bryant et al., 2008; Lebrun et al., 2005). 2.3. Data analysis The distribution of participant characteristics was described according to visual acuity levels equal to or higher than 20/40 Snellen acuity and worse than 20/40. Means and standard deviations (SDs) were calculated for continuous, normally distributed data. For non-normally distributed data, medians and interquartile ranges (IQR) are presented. Frequencies and percentages were used to describe categorical variables. Multivariable linear regression models were used to estimate the association between the determinants and the cognitive engagement outcome variables. Multivariable logistic regression models were used to assess the association between the determinants and global cognitive function. In the first model, the crude estimates and their 95%-confidence intervals (95% CI) were calculated. Confounders were selected based on prior identification in the literature or biological or clinical reasons that warranted an adjustment. The second model was adjusted for age and gender (Dotson et al., 2008; Schinka et al., 2005). The third model was additionally adjusted for education and number of chronic diseases (Dotson et al., 2008; Elliott et al., 2015; Barber, 2003; dos Santos and Andrade, 2012). The fourth model was adjusted for contrast sensitivity and visual acuity. Multicolinearity was tested using the variance inflation factor; values below 2.5 were considered to indicate the absence of collinearity.
To operationalize visual function and study the relationship between the individual and combined vision tests, we first treated the continuous variables (near visual acuity and contrast sensitivity) as single parameters to establish their individual associations with cognitive engagement adjusted for confounders as described above. Next, we evaluated the strength of the association of near visual acuity and contrast sensitivity when mutually adjusted and when combined as a compound score of visual function. Retired individuals presumably have more available time to engage in leisure and social activities than those working a regular job or engaging as volunteers. In addition, the level of cognitive demanding tasks might vary according to the occupation or type of volunteer work. To account for these issues, additional analyses were performed for those participants who were retired (not working a regular job or engaging as volunteers). All data analyses were performed using the statistical program IBM SPSS Statistics for Windows (Version 22.0; IBM Corp., Armonk, NY, USA). 3. Results The mean age of all participants (N = 120) was 74.0 (SD 9.4) years. The average binocular near visual acuity with presenting correction of all participants was 20/30 (0.2 logMAR [sd 0.2]). The median contrast sensitivity of all participants was 1.95 log-units (interquartile range 0.2). Most participants (N = 102, 85%) displayed normal cognitive function, and 18 (15%) participants had MMSE scores lower than 27. Table 1 shows the participants' characteristics stratified by near visual acuity. Contrast sensitivity was higher in those with better near visual acuity than in participants with worse near visual acuity: 2.0 log-units (interquartile range 0.0) and 1.8 log-units (interquartile range 1.0), respectively. Participants with better near visual acuity had a higher educational level and fewer chronic and ocular diseases and were more often current smokers. Table 2 shows the three most frequently performed items of cognitively stimulating activities and those most frequently ceased. All participants reported watching television or listening to the radio daily or at least 5 times or more per month. Of the participants who formerly
Table 1 Participant characteristics. Characteristic Age (years) Mean ± SD Number of females (N, %) Marital status (N, %) Married/living together Divorced/widowed Unmarried Number of chronic diseases Mean ± SD Education (N, %) Low ≤ ISCED level 3 Middle = ISCED level 4 High = ISCED level 5/6/7/8 MMSE-score ≥27 b27 FCAS - Cognitive Activity Score (0−100) Mean ± SD FCAS - Higher Cognitive Abilities (0–36) Mean ± SD FCAS - Frequent Cognitive Abilities (0–32) Mean ± SD FCAS - Cognitive Activity Maintenance (0–1) Mean ± SD Binocular near visual acuity (logMAR) with habitual correction Mean ± SD Binocular contrast sensitivity (log(s)) Median (interquartile range)
Participants with near visual acuity ≤ 0.3 logMAR (20/40 Snellen) (N = 88)
Participants with near visual acuity N 0.3 (20/40 Snellen) (N = 32)
73.3 48
9.6 (54.5)
75.9 19
8.6 (59.4)
50 28 10
(56.8) (31.8) (11.4)
17 12 3
(53.1) (37.5) (9.4)
1.7
1.4
2.1
1.9
12 42 34
(13.6) (47.7) (38.6)
10 13 9
(31.3) (40.6) (28.1)
75 13
(85.2) (14.8)
27 5
(84.4) (15.6)
41.7
8.4
39.3
9.7
12.9
4.9
11.7
5.3
23.4
3.5
23.1
4.3
0.3
0.1
0.3
0.1
0.1
0.1
0.4
0.1
2.0
0.0
1.8
0.6
Abbreviations: N = number; SD = standard deviation; MMSE = Mini-Mental State Examination; ISCED = International Standard Classification of Education.
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111
score of Higher Cognitive Abilities. Specifically, each one log-unit increase in contrast sensitivity was significantly associated with a 4.6 (95% CI 0.3; 8.9) point increase in the Higher Cognitive Abilities score according to the crude model and a 5.5 (95% CI 1.3; 9.7) point increase in the Higher Cognitive Abilities score according to the fully adjusted model. This finding indicates that better contrast sensitivity was associated with pursuing more of the higher cognitive tasks. Additional adjustment for visual acuity attenuated the strength of the association between contrast sensitivity and the Higher Cognitive Abilities score to 4.7 points (95% CI 0.1; 9.3).
Table 2 Cognitive activity items most frequently performed and most frequently ceased. Items most frequently performed⁎ 1.
Watching TV or listening to the radio
100% (N =
2.
Listening to music
120) 91.7% (N =
3.
Reading the newspaper (N = 119)
110) 90.8% (108)
Items most frequently ceased⁎⁎ 1. Playing other board games, such as checkers or Monopoly
68.1% (N =
2.
81) 53.3% (N =
3.
Playing chess, bridge, Scrabble, or Trivial Pursuit Doing art or craft kits or pattern-based activities, such as knitting, paint-by-number, or needle-work
107
3.3. Visual function compound score
64) 44.9% (N =
Fig. A.1c depicts the scores of the Florida Cognitive Activity Scale total score and the two subscales dependent on the visual function compound score (Appendix A). The visual function compound score showed a significant association between near visual function and Higher Cognitive Abilities score of −0.7 (95% CI −1.2; −0.2), indicating less engagement in higher cognitive ability tasks per log-unit decrease in visual function. No collinearity was present between variables for all visual function analyses (visual acuity, contrast sensitivity, and visual function compound score).
53)
Abbreviations: N = number; TV = television. ⁎ Items performed ‘5 times or more per month’ or ‘daily’. ⁎⁎ Items ‘used to do but not in the past year’.
engaged in playing chess, bridge, scrabble, or Trivial Pursuit, more than half (53.3%, N = 64) reported having ceased performing those activities. 3.1. Near visual acuity
3.4. Additional analysis on retirement Fig. A.1a depicts the scores of the Florida Cognitive Activity Scale total score and the two subscales dependent on visual acuity (Appendix A). After adjustment for confounders, a decrease of 1.0 logMAR in near visual acuity as a single determinant was not associated with the Cognitive Activity Scale score or with the subscales scores of Higher Cognitive Abilities, Frequent Cognitive Abilities, or Cognitive Activity Maintenance ratio (Table 3). Further adjustment for contrast sensitivity did not change these findings.
The association between near visual acuity and the Cognitive Activity Scale score and all subscales tended to be stronger in retired individuals, although this difference was not statistically significant (Table 4). The point estimates of the association between contrast sensitivity and all cognitive engagement subscales did not change in size substantially, and these differences were not statistically significant, except for the subscale score of Frequent Cognitive Abilities: 1 log-unit increase in contrast sensitivity was associated with a 3.7 point (95% CI 0.5; 7.0) higher Frequent Cognitive Ability score among retired individuals. The visual function compound score was significantly associated with the Cognitive Activity Scale score, Higher Cognitive Abilities score and Frequent Cognitive Abilities score [BCA = − 1.2 (95% CI − 2.3; − 0.1), BHC = − 0.7 (95% CI − 1.3; − 0.1) and BFA = − 0.5 (95% CI − 0.9; − 0.1), respectively], indicating less cognitive activity engagement, less engagement in higher cognitive ability tasks and less
3.2. Contrast sensitivity Fig. A.1b depicts the scores of the Florida Cognitive Activity Scale total score and the two subscales dependent on visual acuity (Appendix A). Contrast sensitivity was not significantly associated with the Cognitive Activity Scale score after adjustment for confounders (Table 3); however it was significantly associated with the subscale
Table 3 Linear regression coefficients for the association between visual functioning and cognitive engagement. Determinant
Near visual acuity
Contrast sensitivity
Compound score visual function at near
Model 1a Model 2b Model 3c Model 4d Model 1a Model 2b Model 3c Model 4e Model 1a Model 2b Model 3c
Cognitive Activity Scale Score^ (N = 120)
Higher Cognition Abilities# (N = 120)
Frequent Cognitive Abilities& (N = 120)
Activity Maintenance ratio§ (N = 120)
B coefficient
95% CIs
B coefficient
95% CIs
B coefficient
95% CIs
B coefficient
95% CIs
−6.8 −6.1 −5.0 −2.1 8.4 7.8 7.0 6.3 −1.0 −1.0 −0.8
−16.0; 2.4 −15.4; 3.3 −14.2; 4.3 −12.1; 7.9 0.8; 16.0⁎ −0.2; 15.7 −0.9; 14.9 −2.3; 14.9 −2.0; −0.1⁎ −1.9; 0.0 −1.8; 0.1
−4.3 −5.1 −4.4 −2.2 4.6 6.0 5.5 4.7 −0.6 −0.8 −0.7
−9.5; 0.9 −10.2; 0.0 −9.3; 0.6 −7.6; 3.1 0.3; 8.9⁎ 1.7; 10.3⁎ 1.3; 9.7⁎ 0.1; 9.3⁎
−1.2 −1.2 −0.5 −1.1 3.2 3.3 3.0 3.3 −0.3 −0.3 −0.2
−5.2; 2.7 −4.9; 2.6 −4.1; 3.2 −2.8; 5.0 −0.1; 6.4 0.1; 6.4⁎
0.1 0.1 0.0 0.0 −0.1 −0.1 −0.1 −0.1 0.0 0.0 0.0
0.0; 02 −0.1; 0.2 −0.1; 0.2 −0.1; 0.1 −0.2; 0.0 −0.2; 0.0 −0.2; 0.0 −0.2; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0
Abbreviations: N = number; CIs = confidence intervals. ^ Cognitive Activity Scale Score ranges from 0 to 100. # Higher Cognitive Abilities ranges from 0 to 40. & Frequent Cognitive Abilities ranges from 0 to 32. § Activity Maintenance ratio ranges from 0 to 1. a Crude model. b Adjusted for age and gender. c Adjusted for age, gender, education, and number of chronic diseases. d Adjusted for age, gender, education, number of chronic diseases, and contrast sensitivity. e Adjusted for age, gender, education, number of chronic diseases, and visual acuity. ⁎ Significant at p b 0.05.
−1.1; −0.1⁎ −1.3; −0.2⁎ −1.2; −0.2⁎
−0.1; 6.0 0.0; 6.7 −0.7; 0.1 −0.7; 0.1 −0.6; 0.1
108
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111
Table 4 Fully adjusted linear regression coefficients for the association between visual functioning and cognitive engagement based on retirement. Determinant
Near visual acuity
Contrast sensitivity
Compound score visual function at near
Cognitive Activity Scale Score^
Higher Cognitive Abilities#
Frequent Cognitive Abilities&
Activity Maintenance ratio§
B 95% coefficient CI-interval
B 95% coefficient CI-interval
B 95% coefficient CI-interval
B 95% coefficient CI-interval
All participantsa (N = 120) Retired participantsa (N =
−5.0 −10.8
−14.2; 4.3 −21.7; 0.0
−4.4 −5.7
−9.3; 0.6 −11.7; 0.2
−0.5 −3.2
−4.1; 3.2 −7.3; 1.0
0.0 0.2
−0.1; 0.2 0.0; 0.3
87) All participantsa (N = 120) Retired participantsa (N =
7.0 7.3
−0.9; 14.9 −1.3; 15.9
5.5 4.3
1.3; 9.7⁎ −0.4; 9.0
3.0 3.7
−0.1; 6.0 0.5; 7.0⁎
−0.1 −0.1
−0.2; 0.0 −0.3; 0.0
87) All participantsa (N = 120)
−0.8
−1.8; 0.1
−0.7
−1.2; −0.2⁎
−0.2
−0.6; 0.1
0.0
0.0; 0.0
Retired participantsa (N =
−1.2
−2.3; −0.1⁎
−0.7
−1.3; −0.1⁎
−0.5
−0.9; −0.1⁎
0.0
0.0; 0.0
Status of retirement
87) Abbreviations: N = number; CI = confidence interval. ^ Cognitive Activity Scale Score ranges from 0 to 100. # Higher Cognitive Abilities ranges from 0 to 40. & Frequent Cognitive Abilities ranges from 0 to 32. § Activity Maintenance ratio ranges from 0 to 1. a Adjusted for age, gender, education, and number of chronic diseases. ⁎ Statistically significant at p b 0.05.
frequent engagement in cognitive abilities per 1 log-unit decrease in visual function in retired individuals. 4. Discussion This study concerning visual function and cognitive activity engagement in middle-aged and elderly individuals revealed that contrast sensitivity (but not near visual acuity) plays a role in engagement in higher cognition tasks. Neither near visual acuity nor contrast sensitivity appear to influence overall cognitive activity engagement or activity maintenance as a single determinant. Among retired individuals, the combination of near visual acuity and contrast sensitivity (visual function score) was significantly associated with cognitive activity engagement and the frequency of activity engagement. Whereas visual acuity tests the ability of the eye to discern detail, contrast sensitivity tests the ability to differentiate objects form their background; both capabilities are necessary to master daily life. It is reasonable to assume that good contrast sensitivity is important for painting and other tasks that involve the discrimination of subtle shades of gray, which are included in the FCAS questionnaire. Contrast sensitivity has been linked to normal aging (Owsley et al., 1983; Ross et al., 1985) and several areas of cognition (See et al., 2011; Toner et al., 2012; Norton et al., 2009). We can only speculate about an underlying biological mechanism for our findings on contrast sensitivity and its relationship to cognitive activity engagement; the availability of dopamine, a neurotransmitter important for both light adaptation in the retina and cognitive function in the brain (Witkovsky, 2004; Volkow et al., 1998; Kaasinen and Rinne, 2002), might play a role. Although this possibility was not examined in the present study, it should be investigated in future research. Our results on contrast sensitivity and engagement in higher cognitive abilities are in line with those of other studies that have established an association between contrast sensitivity and cognitive function tasks regarding higher visual sensory processing demands, cognitive screening and memory measures (Risacher et al., 2013; Cronin-Golomb et al., 2007). Few studies have examined the relationship between visual acuity and mental activity engagement in older adults (Kiely et al., 2013; Tsang et al., 2013). The present study did not find an association between near visual acuity and the engagement in cognitive activities. This finding differs from that of a longitudinal study that identified low visual acuity and low mental activity as risk factors for mild cognitive impairment (Tsang et al., 2013). The same study also found that better visual acuity and engaging in higher mental activity recovered
mild cognitive impairment to normal cognitive function (Tsang et al., 2013). However, in our study, few participants had poor acuity levels, which might explain the different findings. When the activities ceased are considered, one can observe that the top two activities are those requiring two or more people to perform, which might have been ceased because of a lack of social contact. A study on preserving factors for cognitive function showed a significant negative correlation between overall loneliness and cognitive function (Fung et al., 2011). Based on our study, we can only speculate whether ceasing these activities is because of vision problems or other reasons, such as overall or visual fatigue or a change in social environment, i.e., loss of friends. We were unable to adjust for these factors. The strengths of the present study are that participants were recruited from the general population, independent of cognitive or visual status, and the good participation rate of 61.2% (120/196) in an elderly population. Furthermore, we evaluated two visual concepts both separately and together and investigated their relationship and activity engagement. In addition, a 25-item questionnaire was used to gain insight into cognitive activities performed and ceased by middle-aged and elderly individuals. To compensate for the time constraints imposed by the number of activities an individual can accomplish in any given period, a subgroup analysis was performed by restricting the analysis to those individuals who were retired, based on the assumption that they have more time available to engage in leisure activities than those still working a job or engaging as a volunteer. A potential limitation is that most participants had a good cognitive status, resulting in little differentiation in cognitive functioning. However, the ethical considerations required participants to understand the content of the informed consent. The cross-sectional study design hampered evaluating the change in cognitive activity behavior over a longer period because visual functioning and cognitive activities were not included in the baseline measurements of the longitudinal PROFIELstudy. However, the FCAS includes the Cognitive Activity Maintenance ratio, which functions as an indicator of change in health or cognitive status. In our study population, the Cognitive Activity Maintenance ratio was very low, indicating little change in cognitively challenging activities in the year prior to data collection. The results of the present study suggest that visual function should be considered when evaluating challenging cognitive tasks, especially tasks with higher processing demands. Furthermore, the results of the present study indicate that visual function tests should not be limited to the commonly used visual acuity but should be extended to measure contrast sensitivity as well. Further research is needed to investigate the
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111
influence of contrast sensitivity on cognitive activity engagement in individuals with more severe cognitive deficiencies and to evaluate whether, for example, improving contrast causes increased cognitive engagement. In addition, studies on cognitive activity engagement should consider changes in the social environment.
109
because of visual deficiencies (i.e. contrast issues) rather than cognitive abilities. Additional research is needed to evaluate whether contrast sensitivity should be included in routine visual assessments when evaluating engagement in cognitive activities.
Acknowledgments 5. Conclusions The present study found an association between contrast sensitivity and engagement in higher cognitive ability tasks among middle-aged and elderly individuals. Near visual acuity does not seem to play a role in cognitively stimulating task engagement. This result is particularly important for health care providers when evaluating test outcomes of complex cognitive activities because performance might be decreased
The PROFIEL-study was funded by ZonMw, The Netherlands' organization for health research and development, under grant number 60-61900-98-146. The funding organization played no role in the design and performance of the study, data collection, management, analysis and interpretation of the data or in the preparation, review, or approval of the manuscript. The VF-PROFIEL extension study was self-funded.
Appendix A
Fig. A.1a. Florida Cognitive Activity Scale scores as a function of near visual acuity.
Fig. A.1b. Florida Cognitive Activity Scale scores as a function of contrast sensitivity.
110
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111
Fig. A.1c. Florida Cognitive Activity Scale scores as a function of the visual function compound score.
References Alexandre Tda, S., Corona, L.P., Nunes, D.P., Santos, J.L., Duarte, Y.A., Lebrao, M.L., 2014. Disability in instrumental activities of daily living among older adults: gender differences. Rev. Saude Publica 48, 379–389. Anstey, K.J., Lord, S.R., Hennessy, M., Mitchell, P., Mill, K., von Sanden, C., 2006. The effect of cataract surgery on neuropsychological test performance: a randomized controlled trial. J. Int. Neuropsychol. Soc. 12, 632–639. Barber, A.J., 2003. A new view of diabetic retinopathy: a neurodegenerative disease of the eye. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 27, 283–290. Bosma, H., van Boxtel, M.P., Ponds, R.W., et al., 2002. Engaged lifestyle and cognitive function in middle and old-aged, non-demented persons: a reciprocal association? Z. Gerontol. Geriatr. 35, 575–581. Cronin-Golomb, A., Gilmore, G.C., Neargarder, S., Morrison, S.R., Laudate, T.M., 2007. Enhanced stimulus strength improves visual cognition in aging and Alzheimer's disease. Cortex 43, 952–966. den Ouden, M.E., Schuurmans, M.J., Mueller-Schotte, S., van der Schouw, Y.T., 2013. Identification of high-risk individuals for the development of disability in activities of daily living. A ten-year follow-up study. Exp. Gerontol. 48, 437–443. dos Santos, N.A., Andrade, S.M., 2012. Visual contrast sensitivity in patients with impairment of functional independence after stroke. BMC Neurol. 12 (90-2377-12-90). Dotson, V.M., Schinka, J.A., Brown, L.M., Mortimer, J.A., Borenstein, A.R., 2008. Characteristics of the Florida cognitive activities scale in older African Americans. Assessment 15, 72–77. Elliott, A.F., McGwin Jr., G., Kline, L.B., Owsley, C., 2015. Vision impairment among older adults residing in subsidized housing communities. The Gerontologist 55 (Suppl. 1), S108–S117. Elyashiv, S.M., Shabtai, E.L., Belkin, M., 2014. Correlation between visual acuity and cognitive functions. Br. J. Ophthalmol. 98, 129–132. 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. J. Psychiatr. Res. 12, 189–198. Fung, A.W., Leung, G.T., Lam, L.C., 2011. Modulating factors that preserve cognitive function in healthy ageing. East Asian Arch. Psychiatry 21, 152–156. Gaynes, B.I., Shah, R., Leurgans, S., Bennett, D., 2013. Neuroticism modifies the association of vision impairment and cognition among community-dwelling older adults. Neuroepidemiology 40, 142–146. Gray, C.S., Karimova, G., Hildreth, A.J., Crabtree, L., Allen, D., O'connell, J.E., 2006. Recovery of visual and functional disability following cataract surgery in older people: Sunderland cataract study. J. Cataract Refract. Surg. 32, 60–66. Grodstein, F., Chen, J., Hankinson, S.E., 2003. Cataract extraction and cognitive function in older women. Epidemiology 14, 493–497. Hall, T.A., McGwin Jr., G., Owsley, C., 2005. Effect of cataract surgery on cognitive function in older adults. J. Am. Geriatr. Soc. 53, 2140–2144. Hunt, L.A., Bassi, C.J., 2010. Near-vision acuity levels and performance on neuropsychological assessments used in occupational therapy. Am. J. Occup. Ther. 64, 105–113. Ishii, K., Kabata, T., Oshika, T., 2008. The impact of cataract surgery on cognitive impairment and depressive mental status in elderly patients. Am J. Ophthalmol. 146, 404–409. Jefferis, J.M., Collerton, J., Taylor, J.P., et al., 2012. The impact of visual impairment on MiniMental State Examination Scores in the Newcastle 85+ study. Age Ageing 41, 565–568. Kaasinen, V., Rinne, J.O., 2002. Functional imaging studies of dopamine system and cognition in normal aging and Parkinson's disease. Neurosci. Biobehav. Rev. 26, 785–793. Kiely, K.M., Anstey, K.J., Luszcz, M.A., 2013. Dual sensory loss and depressive symptoms: the importance of hearing, daily functioning, and activity engagement. Front. Hum. Neurosci. 7, 837.
Lebrun, C.E., van der Schouw, Y.T., Bak, A.A., et al., 2002. Arterial stiffness in postmenopausal women: determinants of pulse wave velocity. J. Hypertens. 20, 2165–2172. Lebrun, C.E., van der Schouw, Y.T., de Jong, F.H., Pols, H.A., Grobbee, D.E., Lamberts, S.W., 2005. Endogenous oestrogens are related to cognition in healthy elderly women. Clin. Endocrinol. 63, 50–55. Lin, M.Y., Gutierrez, P.R., Stone, K.L., et al., 2004. Vision impairment and combined vision and hearing impairment predict cognitive and functional decline in older women. J. Am. Geriatr. Soc. 52, 1996–2002. Mitchell, A.J., 2009. A meta-analysis of the accuracy of the mini-mental state examination in the detection of dementia and mild cognitive impairment. J. Psychiatr. Res. 43, 411–431. Mueller-Schotte, S., van der Schouw, Y.T., Schuurmans, M.J., 2015. Ocular straylight: a determinant of quality of life in the elderly? Gerontol. Geriatr. Med. 1. Muller, M., Grobbee, D.E., Aleman, A., Bots, M., van der Schouw, Y.T., 2007. Cardiovascular disease and cognitive performance in middle-aged and elderly men. Atherosclerosis 190, 143–149. Norton, D., McBain, R., Chen, Y., 2009. Reduced ability to detect facial configuration in middle-aged and elderly individuals: associations with spatiotemporal visual processing. J. Gerontol. B Psychol. Sci. Soc. Sci. 64, 328–334. O'Bryant, S.E., Humphreys, J.D., Smith, G.E., et al., 2008. Detecting dementia with the minimental state examination in highly educated individuals. Arch. Neurol. 65, 963–967. Ong, S.Y., Cheung, C.Y., Li, X., et al., 2012. Visual impairment, age-related eye diseases, and cognitive function: the Singapore Malay eye study. Arch. Ophthalmol. 130, 895–900. Ong, S.Y., Ikram, M.K., Haaland, B.A., et al., 2013. Myopia and cognitive dysfunction: the Singapore Malay eye study. Invest. Ophthalmol. Vis. Sci. 54, 799–803. Owsley, C., Sekuler, R., Siemsen, D., 1983. Contrast sensitivity throughout adulthood. Vis. Res. 23, 689–699. Pillai, J.A., Hall, C.B., Dickson, D.W., Buschke, H., Lipton, R.B., Verghese, J., 2011. Association of crossword puzzle participation with memory decline in persons who develop dementia. J. Int. Neuropsychol. Soc. 17, 1006–1013. Risacher, S.L., Wudunn, D., Pepin, S.M., et al., 2013. Visual contrast sensitivity in Alzheimer's disease, mild cognitive impairment, and older adults with cognitive complaints. Neurobiol. Aging 34, 1133–1144. Rog, L.A., Park, L.Q., Harvey, D.J., Huang, C.J., Mackin, S., Farias, S.T., 2014. The independent contributions of cognitive impairment and neuropsychiatric symptoms to everyday function in older adults. Clin. Neuropsychol. 28, 215–236. Rogers, M.A., Langa, K.M., 2010. Untreated poor vision: a contributing factor to late-life dementia. Am. J. Epidemiol. 171, 728–735. Ross, J.E., Clarke, D.D., Bron, A.J., 1985. Effect of age on contrast sensitivity function: uniocular and binocular findings. Br. J. Ophthalmol. 69, 51–56. Sachdev, P.S., Lipnicki, D.M., Crawford, J., et al., 2012. Risk profiles of subtypes of mild cognitive impairment: the Sydney memory and ageing study. J. Am. Geriatr. Soc. 60, 24–33. Schinka, J.A., McBride, A., Vanderploeg, R.D., Tennyson, K., Borenstein, A.R., Mortimer, J.A., 2005. Florida Cognitive Activities Scale: initial development and validation. J. Int. Neuropsychol. Soc. 11, 108–116. Schinka, J.A., Raj, A., Loewenstein, D.A., Small, B.J., Duara, R., Potter, H., 2010. Crossvalidation of the Florida Cognitive Activities Scale (FCAS) in an Alzheimer's disease research center sample. J. Geriatr. Psychiatry Neurol. 23, 9–14. Schneck, M.E., Haegerstom-Portnoy, G., Lott, L.A., Brabyn, J.A., 2010. Monocular vs. binocular measurement of spatial vision in elders. Optom. Vis. Sci. 87, 526–531. See, A.Y., Anstey, K.J., Wood, J.M., 2011. Simulated cataract and low contrast stimuli impair cognitive performance in older adults: implications for neuropsychological assessment and everyday function. Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn. 18, 1–21. Tamura, H., Tsukamoto, H., Mukai, S., et al., 2004. Improvement in cognitive impairment after cataract surgery in elderly patients. J. Cataract Refract. Surg. 30, 598–602.
S. Mueller-Schotte et al. / Experimental Gerontology 82 (2016) 104–111 Toner, C.K., Reese, B.E., Neargarder, S., Riedel, T.M., Gilmore, G.C., Cronin-Golomb, A., 2012. Vision-fair neuropsychological assessment in normal aging, Parkinson's disease and Alzheimer's disease. Psychol. Aging 27, 785–790. Treiber, K.A., Carlson, M.C., Corcoran, C., et al., 2011. Cognitive stimulation and cognitive and functional decline in Alzheimer's disease: the cache county dementia progression study. J. Gerontol. B Psychol. Sci. Soc. Sci. 66, 416–425. Tsang, R.S., Sachdev, P.S., Reppermund, S., et al., 2013. Sydney memory and ageing study: an epidemiological cohort study of brain ageing and dementia. Int. Rev. Psychiatry 25, 711–725. UNESCO. Institute of Statistics, 2011. International Classification of Education ISCED. (Available at:) http://www.uis.unesco.org/Education/Documents/isced-2011-en.pdf (Accessed January 14, 2014).
111
Volkow, N.D., Gur, R.C., Wang, G.J., et al., 1998. Association between decline in brain dopamine activity with age and cognitive and motor impairment in healthy individuals. Am. J. Psychiatry 155, 344–349. Wilson, R.S., Bennett, D.A., Bienias, J.L., CF, M.d.L., MC, M., DA, E., 2003. Cognitive activity and cognitive decline in a biracial community population. Neurology 61, 812–816. Witkovsky, P., 2004. Dopamine and retinal function. Doc. Ophthalmol. 108, 17–40. Wood, J., Chaparro, A., Anstey, K., et al., 2010. Simulated visual impairment leads to cognitive slowing in older adults. Optom. Vis. Sci. 87, 1037–1043.