© 2015 American Psychological Association 0882-7974/15/$ 12.00 http://dx.doi .org/10.1037/a0039121
Psychology and Aging 2015, Vol. 30, No. 2, 348-355
Does Positivity Operate When the Stakes Are High? Health Status and Decision Making Among Older Adults Tammy English
Laura L. Carstensen
Washington University in St. Louis
Stanford University
Research and theory suggest that emotional goals are increasingly prioritized with age. Related empirical work has shown that, compared with younger adults, older adults attend to and remember positive information more than negative information. This age-related positivity effect has been eliminated in experiments that have explicitly demanded processing of both positive and negative information. In the present study, we explored whether a reduction of the preference for positive information over negative information appears when the material being reviewed holds personal relevance for the individual. Older participants whose health varied from poor to very good reviewed written material prior to making decisions about health related and non-health-related issues. As predicted, older adults in relatively poor health (compared with those in relatively good health) showed less positivity in review of information while making health-related decisions. In contrast, positivity emerged regardless of health status for decisions that were unrelated to health. Across decision contexts, those individuals who focused more on positive information than negative information reported better postdecisional mood and greater decision satisfaction. Results are consistent with the theoretical argument that the age-related positivity effect reflects goal-directed cognitive processing and, furthermore, suggests that personal relevance and contextual factors determine whether positivity emerges. Keywords: affect, heath care decisions, positivity effect, socioemotional selectivity theory
Prior research suggests that when older people do engage in decision making, they tend to gather less information before mak ing a decision and process information in a relatively biased fashion, focusing more on positive than negative information (Lockenhoff & Carstensen, 2007). Motivational factors are especially likely to play a role in age differences in contexts in which gathering information generates negative affect. According to socioemotional selectivity theory, emotional goals are increasingly prioritized later in life (Carstensen, 2006). Consistent with this motivational shift, an age-related positivity effect has been documented showing that older adults process a greater proportion of positive relative to negative material compared with younger adults (Mather & Carstensen, 2005). Theoretically, the positivity effect reflects goal-directed cognitive processing (Reed & Carstensen, 2012). A recent meta-analysis based on 100 studies observed the age-related positivity effect to be robust and reliable and to operate in theoretically specified ways (Reed, Chan, & Mikels, 2014). That is, the positivity effect is eliminated under experimental conditions in which informational goals are ex plicitly primed (e.g., Lockenhoff & Carstensen, 2007) or when cognitive processing is constrained (Mather & Knight, 2005). Findings such as these support the contention that age-related positivity results from a top-down, controlled process and sug gests that older adults can process negative and positive mate rial similarly to younger adults. Although focusing relatively more on positive information may promote emotional satisfaction, information processing that selec tively focuses on positive information may have negative conse quences in some contexts. Notably, the age-related positivity effect has been observed in the context of decision making, such as when
Decision making surrounding physical health is often cogni tively and emotionally taxing (Luce, Payne, & Bettman, 2000). The process requires gathering information and making trade-offs, such as choosing between two imperfect health care plans or doctors when neither option satisfies all preferences. Choices that demand consideration of unpleasant information are likely to be especially challenging because negative emotions are elicited while acquiring potentially useful information (Luce, 1998; Trope, Ferguson, & Raghunathan, 2001). There is a large body of work suggesting that decision making may be particularly unpleasant for older people, who approach health-related decisions differently than do younger adults (for a review, see Mather, 2006). Older adults, for example, are more likely to avoid making health-related decisions, often delegating the decision to others, such as their doctor or a family member.
This article was published Online First April 20, 2015. Tammy English, Department of Psychology, Washington University in St. Louis; Laura L. Carstensen, Department of Psychology, Stanford Uni versity. This research was supported by National Institute on Aging Grants R37-AG008816 and F32-AG034783. The content is solely the responsi bility of the authors and does not necessarily represent the official views of the National Institute on Aging or the National Institutes of Health. We express our appreciation to Jessica Barnes for her extensive assistance with data collection. Correspondence concerning this article should be addressed to Tammy English, Department of Psychology, Washington University in St. Louis, 1 Brookings Dr., St. Louis, MO 63130-4899. E-mail: [email protected]
348
HEALTH AND POSITIVITY EFFECT
choosing consumer products (Kim, Healey, Goldstein, Hasher, & Wiprzycka, 2008) and when making hypothetical decisions about health care (Lockenhoff & Carstensen, 2007, 2008; Mather, Knight, & McCaffrey, 2005), raising concern that older adults may, at times, make poor decisions because they avoid relevant, but negative, information. Evidence that decision quality is com promised by positivity has not been systematically examined, however (e.g., Finucane et ah, 2002; Yates, & Patalano, 1999). The question also remains whether older adults naturally reduce their focus on positive material under conditions in which attention to negative material is adaptive. There is reason to expect that this may be the case. Hess, Queen, and Ennis (2013) found that age differences in information search were reduced for decisions that are personally relevant. In partic ular, older adults were more likely to engage in systematic search strategies when faced with a decision about prescription drug plans (higher in relevance to older adults) compared with a decision about wireless phone plans (lower in relevance for older adults). These findings suggest that older adults may be more likely to engage in systematic, effortful processing of information for de cisions that are personally relevant. To our knowledge, prior research has been based on healthy people making decisions, leaving open answers to questions about whether findings generalize to older adults in relatively poor health. Advancing the understanding of how individuals in poor health make health-related choices is important because their de cisions may have more immediate and serious consequences. Pre sumably, prioritizing emotionally positive information over nega tive information reflects selective cognitive processing in the service of emotional goals. If so, people in poor health may adaptively adjust information review for decisions that have health-related consequences if they are focused on other, healthrelated goals in this context. To the extent that focusing dispro portionately on positive information may have detrimental effects on decision quality, relatively unhealthy adults may review infor mation in a more balanced manner to improve their health out comes. Older adults in relatively good health might not make this adjustment to information review if their default emotion-focused goals continue to take priority.
The Present Study We postulate that although emotional goals are chronically activated in older adults, goals likely shift when the stakes are high. In such situations, an evenhanded search of information may better serve goals related to decision making. In the present study we tested the hypothesis that positivity would be reduced among older adults in relatively poorer health when reviewing material related to health. We did not, however, expect health status to moderate positivity when reviewing material about non-healthrelated decisions: Relatively healthy older adults and those in relatively poor health were expected to display similar levels of positivity in that context. Notably, the age-related positivity effect is conceptualized as an age by valence interaction, such that older adults focus more on positive material than negative material compared with relatively younger adults. To test our hypotheses about the positivity effect, we computed an index of attention to positive relative to negative material then tested whether physical health moderates the effect of age when making decisions that are
349
health related and non-health related. We expected an interaction between age and physical health for health related decisions, but only a main effect of age for non-health-related decisions. Because there are equivocal findings about the potential affec tive consequences of the age-related positivity effect (Isaacowitz & Blanchard-Fields, 2012), we also examined mood and decision satisfaction. We hypothesized that individuals who focused rela tively more on positive information than negative information during review of the choice options would report enhanced mood after making their decisions and would report more satisfaction with their decisions.
Method Participants The sample comprised 134 patients recruited from a low-income clinic (67% women) ranging in age from 60 to 92 years (M = 72.46, SD = 7.15). The sample was racially and economically diverse. Sixty-five percent of patients self-identified as European American, 27% identified as African American, 4% identified as Asian American, 4% identified as American-Indian, and 3% iden tified as Hispanic. In terms of self-reported socioeconomic status, 58% self-identified as lower income, 26% self-identified as lower middle income, 14% self-identified as middle income, and 2% self-identified as upper middle or upper income. Education ranged from 8 to 26 years (M = 15.61, SD = 2.98).
Procedure After providing informed consent, participants completed health and baseline mood measures. They were then presented with the decision task that contained four hypothetical decisions, two health-related (health plan and physician) and two non-healthrelated (car and neighbor). Decision types were presented in blocks and counterbalanced across participants. For each decision, participants were presented with a grid on a computer screen that contained information about four options (labeled with the letters A, B, C, or D) that were each described in terms of the same five characteristics (i.e., a grid with 20 cells of information). For example, the health plan decision grid contained a row for each of the plan options (“Plan A,” “Plan B,” “Plan C,” and “Plan D”) and a column for each of the following character istics: preventative care, after-hours care, prescription drugs, ap pointment availability, and consumer satisfaction (see Figure 1 for a depiction of the health plan decision grid and the car decision grid). They were told to search the grid until they were ready to choose one of the options. To make the decision more emotionally challenging, all options were described as average on a global index of quality (e.g., consumer satisfaction) and had two positive characteristics (one “good” and one “very good”) and two negative characteristics (one “poor” and one “very poor”). In addition, the importance of each characteristic was highlighted before the in formation was presented (e.g., “Preventive care coverage is im portant because it may help to catch health problems early or avoid them altogether”). The cell content (e.g., very good) was concealed until participants clicked on the cell. The cells were shaded to indicate their emotional valence, such that white cells contained positive information (i.e., good or very good), gray cells
ENGLISH AND CARSTENSEN
350 A Preventive Care
A fter-H ours Care
Prescription D rugs
A ppointm ent A vailability
C onsum er Satisfaction
Plan A
very poor
very good
good
Poor
average
Plan B
good
poor
very poor
very good
average
P la n C
very good
very poor
poor
Good
average
Plan D
poor
good
very good
very poor
average
B Safety R ecord
Fuel E conom y
Riding C om fort
M aintenance C ost
C onsum er Satisfaction
C ar A
very poor
very good
good
Poor
average
C arB
good
poor
very poor
very good
average
C arC
very good
very poor
poor
Good
average
C arD
poor
good
very good
very poor
average
Figure I. Decision grids for the health plan choice (Panel A) and car choice (Panel B), with the content of the cells revealed.
contained neutral information (i.e., average), and dark cells con tained negative information (i.e., poor or very poor). After choos ing one of the four options, participants indicated their current emotional experience and decision satisfaction. After the decision task, participants completed a cognitive bat tery and demographic questionnaire. The study session took place in a quiet room at the health clinic where participants received their primary care.
Cognitive performance. Cognitive functioning was assessed with four instruments (all of which have been normed for older adults): the Wechsler Vocabulary Subtest (Wechsler, 1997), the Wechsler Digit Symbol Test (Wechsler, 1997), the Wechsler Digit Span Test (Wechsler, 1997), and the Category Naming Task (Lindenberger, Mayr, & Kliegl, 1993). The Wechsler Vocabulary subtest assesses verbal intelligence by having participants provide definitions for words presented in both written and spoken form. The Wechsler Digit Symbol Test assesses visual-motor speed by having participants match as many symbols with letters as they can
Measures Physical health. Participants completed a health question naire (Hultsch, Hammer, & Small, 1993) that assessed multiple facets of physical health, including self-rated overall health, illness episodes, instrumental health, chronic illnesses, and number of medications. An index of physical health was computed by aver aging across these five dimensions (a = .81). There was substan tial variability across individuals in their health status (see Table 1 for descriptive statistics). Decision satisfaction. After making each decision, partici pants completed a 6-item decision satisfaction scale (Sainfort & Booske, 2000). An example item is “I am satisfied with my decision”. Across decisions, average a = .79 (range = .7 4 - .81). Emotion experience. Before the decision task and after each decision, participants indicated how much they were feeling each of six positive emotions {happy, excited, proud, calm, content, peaceful) and six negative emotions {anxious, irritated, frustrated, concerned, sad, bored), on a 7-point scale ranging from 1 {not at all) to 7 {very much). Positive and negative emotion composites were created by averaging across emotion ratings at baseline and after each of the four decisions (i.e., postdecisional emotion); average alpha for positive emotion = .87 (range = .82-89) and negative emotion = .81 (range = .74—.84).
Table 1 Descriptive Statistics fo r Predictor Variables Range
Variable
M
SD
Age Physical health Subjective health Illness episode Instrumental health Chronic illness Medications Cognitive ability Verbal fluency Digit symbol Digit span Vocabulary Baseline emotion (positive— negative)
72.46
7.15
60-92
2.02 3.93 5.27 4.99 3.19
0.84 2.42 5.65 4.12 2.15
1-4 0-12 0-22 0-25 0-11
18.57 40.08 15.43 50.08 2.75
5.60 12.24 4.78 13.68 1.64
6-36 14-76 7-28 10-66 -2 .95-6.00
Note. Physical health components indicate scores on subscales of a comprehensive self-report health questionnaire (Hultsch, Hammer, & Small. 1993). Fluency indicates scores on Category Naming Task (Lin denberger, Mayr, & Kliegl, 1993). Digit symbol, digit span, and vocabu lary indicate scores on the respective Wechsler tests (Wechsler, 1997). Baseline emotion reflects composite score of 6 positive states minus 6 negative states, each rated on 7-point scales.
HEALTH AND POSITIVITY EFFECT
in 90 s. The Digit Span Test assesses working memory by having participants repeat numerical strings frontward and backward. The Category Naming Task assesses verbal fluency by having partic ipants name as many animals as they can in 90 s.
Results Data Reduction and Analysis Plan For each decision, we computed an index of positivity in review (Lockenhoff & Carstensen, 2007). The number of negative cells viewed (i.e., “poor” or “very poor”) was subtracted from the number of positive cells viewed (i.e., “good” or “very good”) and divided by the total number of emotional cells viewed (positive and negative cells). Positive values indicate a greater focus on positive features; negative values indicate a greater focus on neg ative features; and zero indicates comparable focus on positive and negative information. The total number of cells visited and the number of repeat visits to cells (i.e., number of cells viewed multiple times) were also calculated for each decision. On average, people visited about 31 cells before making a decision (health decisions: M = 31.28, SD = 22.75; non-health decisions: M = 30.45, SD = 22.30), with about half of the cells being repeat visits (health: M = 16.74, SD = 19.11; non-health: M = 15.89, SD = 19.10). Overall, a similar number of positive and negative cells were viewed: about 13 positive cell views (health decisions: M = 13.75, SD = 10.53; non-health decisions: M = 12.93, SD = 10.27) and 13 negative cell views (health decisions: M = 12.81, SD = 10.80; non-health decisions: M = 13.06, SD = 10.47). The posi tivity index scores ranged from 1 to —1 for health decisions (M = .07, SD = .39) and non-health decisions (M = .00, SD = .38). Analyses were conducted using multilevel modeling (MLM) in the linear mixed models function in SPSS. We ran a two-level model, in which decisions were nested within persons. Positivity in review, as well as the amount of information viewed (total number of cells and number of repeat visits), was predicted from decision type (0 = health decisions, 1 = non-health decisions), age, health, and their interactions. Results are reported as unstandardized MLM coefficients in Table 2. In addition, postdecisional mood and
351
decision satisfaction were predicted from positivity, age, decision type, and their interactions. Results are reported as unstandardized MLM coefficients in Table 3. The proportion of within- and between-person variance explained by each model was estimated by subtracting the variance in the conditional model (with level 1 and level 2 predictors included) from the variance in the baseline model (viz., intercept only) and then dividing by the variance in the baseline model (Raudenbush & Bryk, 2002). Semipartial R2 values were computed as estimates of effect size (Edwards, Muller, Wolfinger, Qaqish, & Schabenberger, 2008). Cognitive ability and baseline mood were included as person level covariates in follow-up analyses because age and health have been shown to be associated with these variables. To simplify these models, we created single indices of baseline mood and cognitive ability. A baseline mood index was created by subtract ing individuals’ negative emotion composite score from their positive emotion composite score (Carstensen et al., 2011). An index of cognitive performance was created by z scoring partici pants scores for each of the four cognitive measures then averaging across them (a = .78). In the current sample, age was associated with lower cognitive ability (r = —.32, p < .001) but was not significantly correlated with baseline mood (r = .04, p = .619), and health was associated with greater cognitive ability (r = .29. p = .001) and being in a better mood at baseline (r = .21; p = .014). There was not a significant association between age and physical health (r = -.1 2 , p = .173). Preliminary analyses revealed that order of the decision blocks (i.e., health first vs. non-health first) did not influence the core findings so it is not discussed further.
Does Health Status Moderate Age-Related Positivity? First, we examined whether health status moderated age-related positivity in attention during the decision task. There was a main effect of age such that relatively older adults showed a greater focus on positive information than did younger older adults (y = 0.071, SE = .030, p = .018, semipartial R2 = 0.028). The inter action between age and health was also significant reflecting the fact that positivity in attention was reduced for older adults in
Table 2 Results o f Multilevel Modeling Predicting Aspects o f Information Seeking as a Function o f Age, Physical Health, and Decision Type Variable Intercept Age Physical Health Decision Type Age X Health Age X Decision Type Health X Decision Type Decision Type X Age X Health
Positivity in review 0.084 0.071 -0.001 -0.083 0.091 -0.015 -0.029 -0 .0 9 6
(.028)** (.030)* (.039) (.025)** (.039)* (.026) (.034) (.035)**
Amount of information
Repeat Visits
31.199(1.724)** -0.543(1.819) -0.714(1.370) -0.556(1.299) -0 .8 5 2 (2.399) 1.743 (1.370) -0.147(1.784) 3.026(1.807)
16.662(1.450)** -0.671 (1.531) 0.105 (1.989) -0.852(1.156) -0 .8 7 8 (2.016) 1.642(1.231) -0 .8 3 0 (2.016) 2.554(1.627)
Note. Unstandardized estimates are presented with standard errors in parentheses. Age and physical health are z scored. Health index was keyed such that higher scores indicate better physical health. Decision type was coded 0 for health-related decision and 1 for non-health-related decisions. Positivity indicates proportion of positive relative to negative information reviewed. Amount of information indicates the total number of cells reviewed (including repeat visits to the same cell). Repeat visits indicates the number of cells viewed that had already been viewed previously. * p < .05. * > < . 0 1 .
ENGLISH AND CARSTENSEN
352
Table 3 Results o f Multilevel Modeling Predicting Postdecisional Mood and Decision Satisfaction as a Function o f Positivity in Attention, Age, and Decision Type Variable Intercept Positivity Age Decision Type Positivity X Age Positivity X Decision Type Age X Decision Type Positivity X Age X Decision Type
Positive emotion 4.159 0.268 0.109 0.064 -0 .0 0 2
(.121)** (,147)+ (.122) (.063) (.133) - 0.0002 (.181) -0 .0 6 7 (.065) -0.037 (.181)
Negative emotion 2.174 -0 .4 6 2 0.118 -0.001 -0 .4 0 4 0.0004 0.067 -0 .0 5 4
(.100)** (.125)** (. 101) (.054) (.112)** (.154) (.055) (.153)
Satisfaction 3.579 0.254 0.002 -0.0002 0.046 -0 .1 5 2 -0 .1 0 8 0.015
(.059) (.109)* (.059) (.050) (.010) (.142) (.052)* (.141)
Note. Unstandardized estimates are presented with standard errors in parentheses. Positivity indicates propor tion of positive relative to negative information reviewed in the decision task (0 = equal review of positive and negative information). Decision type was coded 0 for health-related decision and 1 for non-health-related decisions. Age is z scored. > < .1 0 . > < .0 5 . *>
Psychology and Aging 2015, Vol. 30, No. 2, 348-355
Does Positivity Operate When the Stakes Are High? Health Status and Decision Making Among Older Adults Tammy English
Laura L. Carstensen
Washington University in St. Louis
Stanford University
Research and theory suggest that emotional goals are increasingly prioritized with age. Related empirical work has shown that, compared with younger adults, older adults attend to and remember positive information more than negative information. This age-related positivity effect has been eliminated in experiments that have explicitly demanded processing of both positive and negative information. In the present study, we explored whether a reduction of the preference for positive information over negative information appears when the material being reviewed holds personal relevance for the individual. Older participants whose health varied from poor to very good reviewed written material prior to making decisions about health related and non-health-related issues. As predicted, older adults in relatively poor health (compared with those in relatively good health) showed less positivity in review of information while making health-related decisions. In contrast, positivity emerged regardless of health status for decisions that were unrelated to health. Across decision contexts, those individuals who focused more on positive information than negative information reported better postdecisional mood and greater decision satisfaction. Results are consistent with the theoretical argument that the age-related positivity effect reflects goal-directed cognitive processing and, furthermore, suggests that personal relevance and contextual factors determine whether positivity emerges. Keywords: affect, heath care decisions, positivity effect, socioemotional selectivity theory
Prior research suggests that when older people do engage in decision making, they tend to gather less information before mak ing a decision and process information in a relatively biased fashion, focusing more on positive than negative information (Lockenhoff & Carstensen, 2007). Motivational factors are especially likely to play a role in age differences in contexts in which gathering information generates negative affect. According to socioemotional selectivity theory, emotional goals are increasingly prioritized later in life (Carstensen, 2006). Consistent with this motivational shift, an age-related positivity effect has been documented showing that older adults process a greater proportion of positive relative to negative material compared with younger adults (Mather & Carstensen, 2005). Theoretically, the positivity effect reflects goal-directed cognitive processing (Reed & Carstensen, 2012). A recent meta-analysis based on 100 studies observed the age-related positivity effect to be robust and reliable and to operate in theoretically specified ways (Reed, Chan, & Mikels, 2014). That is, the positivity effect is eliminated under experimental conditions in which informational goals are ex plicitly primed (e.g., Lockenhoff & Carstensen, 2007) or when cognitive processing is constrained (Mather & Knight, 2005). Findings such as these support the contention that age-related positivity results from a top-down, controlled process and sug gests that older adults can process negative and positive mate rial similarly to younger adults. Although focusing relatively more on positive information may promote emotional satisfaction, information processing that selec tively focuses on positive information may have negative conse quences in some contexts. Notably, the age-related positivity effect has been observed in the context of decision making, such as when
Decision making surrounding physical health is often cogni tively and emotionally taxing (Luce, Payne, & Bettman, 2000). The process requires gathering information and making trade-offs, such as choosing between two imperfect health care plans or doctors when neither option satisfies all preferences. Choices that demand consideration of unpleasant information are likely to be especially challenging because negative emotions are elicited while acquiring potentially useful information (Luce, 1998; Trope, Ferguson, & Raghunathan, 2001). There is a large body of work suggesting that decision making may be particularly unpleasant for older people, who approach health-related decisions differently than do younger adults (for a review, see Mather, 2006). Older adults, for example, are more likely to avoid making health-related decisions, often delegating the decision to others, such as their doctor or a family member.
This article was published Online First April 20, 2015. Tammy English, Department of Psychology, Washington University in St. Louis; Laura L. Carstensen, Department of Psychology, Stanford Uni versity. This research was supported by National Institute on Aging Grants R37-AG008816 and F32-AG034783. The content is solely the responsi bility of the authors and does not necessarily represent the official views of the National Institute on Aging or the National Institutes of Health. We express our appreciation to Jessica Barnes for her extensive assistance with data collection. Correspondence concerning this article should be addressed to Tammy English, Department of Psychology, Washington University in St. Louis, 1 Brookings Dr., St. Louis, MO 63130-4899. E-mail: [email protected]
348
HEALTH AND POSITIVITY EFFECT
choosing consumer products (Kim, Healey, Goldstein, Hasher, & Wiprzycka, 2008) and when making hypothetical decisions about health care (Lockenhoff & Carstensen, 2007, 2008; Mather, Knight, & McCaffrey, 2005), raising concern that older adults may, at times, make poor decisions because they avoid relevant, but negative, information. Evidence that decision quality is com promised by positivity has not been systematically examined, however (e.g., Finucane et ah, 2002; Yates, & Patalano, 1999). The question also remains whether older adults naturally reduce their focus on positive material under conditions in which attention to negative material is adaptive. There is reason to expect that this may be the case. Hess, Queen, and Ennis (2013) found that age differences in information search were reduced for decisions that are personally relevant. In partic ular, older adults were more likely to engage in systematic search strategies when faced with a decision about prescription drug plans (higher in relevance to older adults) compared with a decision about wireless phone plans (lower in relevance for older adults). These findings suggest that older adults may be more likely to engage in systematic, effortful processing of information for de cisions that are personally relevant. To our knowledge, prior research has been based on healthy people making decisions, leaving open answers to questions about whether findings generalize to older adults in relatively poor health. Advancing the understanding of how individuals in poor health make health-related choices is important because their de cisions may have more immediate and serious consequences. Pre sumably, prioritizing emotionally positive information over nega tive information reflects selective cognitive processing in the service of emotional goals. If so, people in poor health may adaptively adjust information review for decisions that have health-related consequences if they are focused on other, healthrelated goals in this context. To the extent that focusing dispro portionately on positive information may have detrimental effects on decision quality, relatively unhealthy adults may review infor mation in a more balanced manner to improve their health out comes. Older adults in relatively good health might not make this adjustment to information review if their default emotion-focused goals continue to take priority.
The Present Study We postulate that although emotional goals are chronically activated in older adults, goals likely shift when the stakes are high. In such situations, an evenhanded search of information may better serve goals related to decision making. In the present study we tested the hypothesis that positivity would be reduced among older adults in relatively poorer health when reviewing material related to health. We did not, however, expect health status to moderate positivity when reviewing material about non-healthrelated decisions: Relatively healthy older adults and those in relatively poor health were expected to display similar levels of positivity in that context. Notably, the age-related positivity effect is conceptualized as an age by valence interaction, such that older adults focus more on positive material than negative material compared with relatively younger adults. To test our hypotheses about the positivity effect, we computed an index of attention to positive relative to negative material then tested whether physical health moderates the effect of age when making decisions that are
349
health related and non-health related. We expected an interaction between age and physical health for health related decisions, but only a main effect of age for non-health-related decisions. Because there are equivocal findings about the potential affec tive consequences of the age-related positivity effect (Isaacowitz & Blanchard-Fields, 2012), we also examined mood and decision satisfaction. We hypothesized that individuals who focused rela tively more on positive information than negative information during review of the choice options would report enhanced mood after making their decisions and would report more satisfaction with their decisions.
Method Participants The sample comprised 134 patients recruited from a low-income clinic (67% women) ranging in age from 60 to 92 years (M = 72.46, SD = 7.15). The sample was racially and economically diverse. Sixty-five percent of patients self-identified as European American, 27% identified as African American, 4% identified as Asian American, 4% identified as American-Indian, and 3% iden tified as Hispanic. In terms of self-reported socioeconomic status, 58% self-identified as lower income, 26% self-identified as lower middle income, 14% self-identified as middle income, and 2% self-identified as upper middle or upper income. Education ranged from 8 to 26 years (M = 15.61, SD = 2.98).
Procedure After providing informed consent, participants completed health and baseline mood measures. They were then presented with the decision task that contained four hypothetical decisions, two health-related (health plan and physician) and two non-healthrelated (car and neighbor). Decision types were presented in blocks and counterbalanced across participants. For each decision, participants were presented with a grid on a computer screen that contained information about four options (labeled with the letters A, B, C, or D) that were each described in terms of the same five characteristics (i.e., a grid with 20 cells of information). For example, the health plan decision grid contained a row for each of the plan options (“Plan A,” “Plan B,” “Plan C,” and “Plan D”) and a column for each of the following character istics: preventative care, after-hours care, prescription drugs, ap pointment availability, and consumer satisfaction (see Figure 1 for a depiction of the health plan decision grid and the car decision grid). They were told to search the grid until they were ready to choose one of the options. To make the decision more emotionally challenging, all options were described as average on a global index of quality (e.g., consumer satisfaction) and had two positive characteristics (one “good” and one “very good”) and two negative characteristics (one “poor” and one “very poor”). In addition, the importance of each characteristic was highlighted before the in formation was presented (e.g., “Preventive care coverage is im portant because it may help to catch health problems early or avoid them altogether”). The cell content (e.g., very good) was concealed until participants clicked on the cell. The cells were shaded to indicate their emotional valence, such that white cells contained positive information (i.e., good or very good), gray cells
ENGLISH AND CARSTENSEN
350 A Preventive Care
A fter-H ours Care
Prescription D rugs
A ppointm ent A vailability
C onsum er Satisfaction
Plan A
very poor
very good
good
Poor
average
Plan B
good
poor
very poor
very good
average
P la n C
very good
very poor
poor
Good
average
Plan D
poor
good
very good
very poor
average
B Safety R ecord
Fuel E conom y
Riding C om fort
M aintenance C ost
C onsum er Satisfaction
C ar A
very poor
very good
good
Poor
average
C arB
good
poor
very poor
very good
average
C arC
very good
very poor
poor
Good
average
C arD
poor
good
very good
very poor
average
Figure I. Decision grids for the health plan choice (Panel A) and car choice (Panel B), with the content of the cells revealed.
contained neutral information (i.e., average), and dark cells con tained negative information (i.e., poor or very poor). After choos ing one of the four options, participants indicated their current emotional experience and decision satisfaction. After the decision task, participants completed a cognitive bat tery and demographic questionnaire. The study session took place in a quiet room at the health clinic where participants received their primary care.
Cognitive performance. Cognitive functioning was assessed with four instruments (all of which have been normed for older adults): the Wechsler Vocabulary Subtest (Wechsler, 1997), the Wechsler Digit Symbol Test (Wechsler, 1997), the Wechsler Digit Span Test (Wechsler, 1997), and the Category Naming Task (Lindenberger, Mayr, & Kliegl, 1993). The Wechsler Vocabulary subtest assesses verbal intelligence by having participants provide definitions for words presented in both written and spoken form. The Wechsler Digit Symbol Test assesses visual-motor speed by having participants match as many symbols with letters as they can
Measures Physical health. Participants completed a health question naire (Hultsch, Hammer, & Small, 1993) that assessed multiple facets of physical health, including self-rated overall health, illness episodes, instrumental health, chronic illnesses, and number of medications. An index of physical health was computed by aver aging across these five dimensions (a = .81). There was substan tial variability across individuals in their health status (see Table 1 for descriptive statistics). Decision satisfaction. After making each decision, partici pants completed a 6-item decision satisfaction scale (Sainfort & Booske, 2000). An example item is “I am satisfied with my decision”. Across decisions, average a = .79 (range = .7 4 - .81). Emotion experience. Before the decision task and after each decision, participants indicated how much they were feeling each of six positive emotions {happy, excited, proud, calm, content, peaceful) and six negative emotions {anxious, irritated, frustrated, concerned, sad, bored), on a 7-point scale ranging from 1 {not at all) to 7 {very much). Positive and negative emotion composites were created by averaging across emotion ratings at baseline and after each of the four decisions (i.e., postdecisional emotion); average alpha for positive emotion = .87 (range = .82-89) and negative emotion = .81 (range = .74—.84).
Table 1 Descriptive Statistics fo r Predictor Variables Range
Variable
M
SD
Age Physical health Subjective health Illness episode Instrumental health Chronic illness Medications Cognitive ability Verbal fluency Digit symbol Digit span Vocabulary Baseline emotion (positive— negative)
72.46
7.15
60-92
2.02 3.93 5.27 4.99 3.19
0.84 2.42 5.65 4.12 2.15
1-4 0-12 0-22 0-25 0-11
18.57 40.08 15.43 50.08 2.75
5.60 12.24 4.78 13.68 1.64
6-36 14-76 7-28 10-66 -2 .95-6.00
Note. Physical health components indicate scores on subscales of a comprehensive self-report health questionnaire (Hultsch, Hammer, & Small. 1993). Fluency indicates scores on Category Naming Task (Lin denberger, Mayr, & Kliegl, 1993). Digit symbol, digit span, and vocabu lary indicate scores on the respective Wechsler tests (Wechsler, 1997). Baseline emotion reflects composite score of 6 positive states minus 6 negative states, each rated on 7-point scales.
HEALTH AND POSITIVITY EFFECT
in 90 s. The Digit Span Test assesses working memory by having participants repeat numerical strings frontward and backward. The Category Naming Task assesses verbal fluency by having partic ipants name as many animals as they can in 90 s.
Results Data Reduction and Analysis Plan For each decision, we computed an index of positivity in review (Lockenhoff & Carstensen, 2007). The number of negative cells viewed (i.e., “poor” or “very poor”) was subtracted from the number of positive cells viewed (i.e., “good” or “very good”) and divided by the total number of emotional cells viewed (positive and negative cells). Positive values indicate a greater focus on positive features; negative values indicate a greater focus on neg ative features; and zero indicates comparable focus on positive and negative information. The total number of cells visited and the number of repeat visits to cells (i.e., number of cells viewed multiple times) were also calculated for each decision. On average, people visited about 31 cells before making a decision (health decisions: M = 31.28, SD = 22.75; non-health decisions: M = 30.45, SD = 22.30), with about half of the cells being repeat visits (health: M = 16.74, SD = 19.11; non-health: M = 15.89, SD = 19.10). Overall, a similar number of positive and negative cells were viewed: about 13 positive cell views (health decisions: M = 13.75, SD = 10.53; non-health decisions: M = 12.93, SD = 10.27) and 13 negative cell views (health decisions: M = 12.81, SD = 10.80; non-health decisions: M = 13.06, SD = 10.47). The posi tivity index scores ranged from 1 to —1 for health decisions (M = .07, SD = .39) and non-health decisions (M = .00, SD = .38). Analyses were conducted using multilevel modeling (MLM) in the linear mixed models function in SPSS. We ran a two-level model, in which decisions were nested within persons. Positivity in review, as well as the amount of information viewed (total number of cells and number of repeat visits), was predicted from decision type (0 = health decisions, 1 = non-health decisions), age, health, and their interactions. Results are reported as unstandardized MLM coefficients in Table 2. In addition, postdecisional mood and
351
decision satisfaction were predicted from positivity, age, decision type, and their interactions. Results are reported as unstandardized MLM coefficients in Table 3. The proportion of within- and between-person variance explained by each model was estimated by subtracting the variance in the conditional model (with level 1 and level 2 predictors included) from the variance in the baseline model (viz., intercept only) and then dividing by the variance in the baseline model (Raudenbush & Bryk, 2002). Semipartial R2 values were computed as estimates of effect size (Edwards, Muller, Wolfinger, Qaqish, & Schabenberger, 2008). Cognitive ability and baseline mood were included as person level covariates in follow-up analyses because age and health have been shown to be associated with these variables. To simplify these models, we created single indices of baseline mood and cognitive ability. A baseline mood index was created by subtract ing individuals’ negative emotion composite score from their positive emotion composite score (Carstensen et al., 2011). An index of cognitive performance was created by z scoring partici pants scores for each of the four cognitive measures then averaging across them (a = .78). In the current sample, age was associated with lower cognitive ability (r = —.32, p < .001) but was not significantly correlated with baseline mood (r = .04, p = .619), and health was associated with greater cognitive ability (r = .29. p = .001) and being in a better mood at baseline (r = .21; p = .014). There was not a significant association between age and physical health (r = -.1 2 , p = .173). Preliminary analyses revealed that order of the decision blocks (i.e., health first vs. non-health first) did not influence the core findings so it is not discussed further.
Does Health Status Moderate Age-Related Positivity? First, we examined whether health status moderated age-related positivity in attention during the decision task. There was a main effect of age such that relatively older adults showed a greater focus on positive information than did younger older adults (y = 0.071, SE = .030, p = .018, semipartial R2 = 0.028). The inter action between age and health was also significant reflecting the fact that positivity in attention was reduced for older adults in
Table 2 Results o f Multilevel Modeling Predicting Aspects o f Information Seeking as a Function o f Age, Physical Health, and Decision Type Variable Intercept Age Physical Health Decision Type Age X Health Age X Decision Type Health X Decision Type Decision Type X Age X Health
Positivity in review 0.084 0.071 -0.001 -0.083 0.091 -0.015 -0.029 -0 .0 9 6
(.028)** (.030)* (.039) (.025)** (.039)* (.026) (.034) (.035)**
Amount of information
Repeat Visits
31.199(1.724)** -0.543(1.819) -0.714(1.370) -0.556(1.299) -0 .8 5 2 (2.399) 1.743 (1.370) -0.147(1.784) 3.026(1.807)
16.662(1.450)** -0.671 (1.531) 0.105 (1.989) -0.852(1.156) -0 .8 7 8 (2.016) 1.642(1.231) -0 .8 3 0 (2.016) 2.554(1.627)
Note. Unstandardized estimates are presented with standard errors in parentheses. Age and physical health are z scored. Health index was keyed such that higher scores indicate better physical health. Decision type was coded 0 for health-related decision and 1 for non-health-related decisions. Positivity indicates proportion of positive relative to negative information reviewed. Amount of information indicates the total number of cells reviewed (including repeat visits to the same cell). Repeat visits indicates the number of cells viewed that had already been viewed previously. * p < .05. * > < . 0 1 .
ENGLISH AND CARSTENSEN
352
Table 3 Results o f Multilevel Modeling Predicting Postdecisional Mood and Decision Satisfaction as a Function o f Positivity in Attention, Age, and Decision Type Variable Intercept Positivity Age Decision Type Positivity X Age Positivity X Decision Type Age X Decision Type Positivity X Age X Decision Type
Positive emotion 4.159 0.268 0.109 0.064 -0 .0 0 2
(.121)** (,147)+ (.122) (.063) (.133) - 0.0002 (.181) -0 .0 6 7 (.065) -0.037 (.181)
Negative emotion 2.174 -0 .4 6 2 0.118 -0.001 -0 .4 0 4 0.0004 0.067 -0 .0 5 4
(.100)** (.125)** (. 101) (.054) (.112)** (.154) (.055) (.153)
Satisfaction 3.579 0.254 0.002 -0.0002 0.046 -0 .1 5 2 -0 .1 0 8 0.015
(.059) (.109)* (.059) (.050) (.010) (.142) (.052)* (.141)
Note. Unstandardized estimates are presented with standard errors in parentheses. Positivity indicates propor tion of positive relative to negative information reviewed in the decision task (0 = equal review of positive and negative information). Decision type was coded 0 for health-related decision and 1 for non-health-related decisions. Age is z scored. > < .1 0 . > < .0 5 . *>
