525666 research-article2014

JAHXXX10.1177/0898264314525666Journal of Aging and HealthEllis et al.

Article

Effects of Cognitive Speed of Processing Training Among Older Adults With Heart Failure

Journal of Aging and Health 2014, Vol. 26(4) 600­–615 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0898264314525666 jah.sagepub.com

Michelle L. Ellis, MA1, Jerri D. Edwards, PhD1, Lindsay Peterson, MS1, Rosalyn Roker, MA1, and Ponrathi Athilingam, PhD1

Abstract Objective: Cognitive deficits pose serious problems for older adults with heart failure (HF). Cognitive speed of processing training improves cognition among older adults but has not been examined among older adults with HF. Method: Data from the ACTIVE study were used to examine the effects of cognitive speed of processing training on cognitive and functional performance among older adults with HF. Results: Of the 54 participants included in the analyses, 23 who were randomized to cognitive training performed significantly better on a composite of everyday speed of processing from pre- to post-training compared with 31 participants who were randomized to 2 the control group, F(1, 51) = 28.67, p ≤ .001, ηp = .360. Discussion: Results indicate that speed of processing training may improve everyday cognitive performance among older adults with HF. Future studies should investigate the longitudinal effects of cognitive training with HF patients. Keywords cognition, heart failure, speed of processing

1University

of South Florida, Tampa, USA

Corresponding Author: Jerri D. Edwards, PhD, School of Aging Studies, University of South Florida, Tampa, FL 33612, USA. Email: [email protected]

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Heart failure (HF) is a complex medical condition and a significant public health problem. It currently affects more than 5.7 million Americans and that number is expected to rise to 8.7 million by 2030 (Go et al., 2013). HF is becoming more prevalent as the population of older adults grows and cardiac diagnosis and care improve. For every 1,000 people aged 65 and above, 10 new HF cases are identified every year (Go et al., 2013). While lifesaving care is advancing, these patients experience a diminished quality of life, including fatigue, depression, and functional and cognitive impairments (Alosco, Spitznagel, Cohen, et al., 2012; Bekelman et al., 2007; Gure et al., 2012; Pressler et al., 2010). Research shows that cognitive impairment risk is 4 times higher for older adults with HF, compared with those without HF (Sauvé, Lewis, Blankenbiller, Rickabaugh, & Pressler, 2009). Previous studies have found that cognitive speed of processing training among healthy older adults improves cognition (Ball et al., 2002; Wolinsky, Vander Weg, Howren, Jones, & Dotson, 2013) as well as everyday function (Ball, Edwards, Ross, & McGwin, 2010; Ball, Edwards, & Ross, 2007; Edwards, Myers et al., 2009; Edwards, Wadley, et al., 2005; Roenker, Cissell, Ball, Wadley, & Edwards, 2003). The purpose of the present study is to examine the effects of cognitive speed of processing training among older adults with HF. HF is considered a clinical syndrome that affects the brain through the reduction of the heart’s ventricular function, which leads to inadequate blood flow to the tissues (Heckman et al., 2007; Hoth, Poppas, Moser, Paul, & Cohen, 2008). The syndrome affects cognition in several domains, including memory, executive functioning, and speed of processing (Festa et al., 2011; Pressler et al., 2010; Riegel et al., 2002; Sauvé et al., 2009). Cognitive deficits in older adults with HF are associated with declines in function (Alosco, Spitznagel, Cohen, et al., 2012) and decision making (Dickson, Tkacs, & Riegel, 2007). Older adults with HF and cognitive deficits are more likely to report difficulty driving (Alosco, Spitznagel, Cohen, et al., 2012) or to quit driving (Edwards et al., 2008), which can lead to further health declines (Edwards, Lunsman, Perkins, Rebok, & Roth, 2009). HF patients with cognitive deficits are less likely to adhere to medication regiment and less likely to make appropriate self-care decisions (Alosco, Spitznagel, van Dulmen, et al., 2012; Dickson, Lee, & Riegel, 2011; Hawkins et al., 2012), which may increase hospital readmission rates and mortality (Ekman, Fagerberg, & Skoog, 2001; Fitzgerald et al., 2011). According to Cameron and colleagues (2010), patients with HF have self-care and decision-making difficulties even in cases of mild cognitive impairment. Overall, older adults can experience reductions in their decision-making abilities, but research shows that such difficulties are more specifically due to declines in certain cognitive domains, such as speed of processing (Henninger,

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Madden, & Huettel, 2010). Speed of processing diminishes with age (Ball, Owsley, Sloane, Roenker, & Bruni, 1993; Birren, Woods, & Williams, 1980; Goode et al., 1998) and is further compromised among people with HF (Kindermann et al., 2012; Vogels et al., 2007). Speed of processing is not only important for decision making, but is also associated with performance of instrumental activities of daily living, including driving (Ball et al., 2006; Owsley et al., 1998; Owsley, Sloane, McGwin, & Ball, 2002). Speed of processing, however, is a dynamic cognitive function that can improve with cognitive training (Ball et al., 2002; Ball et al., 2007; Wolinsky et al., 2013). The Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study was a multi-site randomized controlled trial that examined the effects of cognitive training in older adults. The purpose was to determine whether cognitive interventions could affect cognitively based measures of daily functioning related to living independently (Jobe et al., 2001). The study examined speed of processing training, which, unlike other cognitive training, includes process-based practice of visual exercises. The ultimate goal of training is to differentiate more complex information in shorter periods of time (Ball et al., 2007). The primary outcome of speed of processing training is the computer-administered Useful Field of View (UFOV1) measure (Jobe et al., 2001). The UFOV test measures the speed at which one can process multiple stimuli across a visual field (Edwards, Vance, et al., 2005). Scores are strongly related to one’s cognitive speed of processing abilities (Edwards, Vance, et al., 2005; Lunsman et al., 2008). A recent study showed that older adults with HF perform poorly on this test (Alwerdt, Edwards, Athilingam, O’Connor, & Valdes, 2013). Cognitive speed of processing training improves UFOV performance (Ball et al., 2002; Ball et al., 2007; Wolinsky et al., 2013). Furthermore, speed of processing training transfers to real-world abilities, such as quickly and accurately finding a telephone number, reading the directions on a medicine container, and reacting to road signs (Ball et al., 2007; Edwards, Wadley, et al., 2005; Roenker et al., 2003). Other research has demonstrated that speed of processing training results in lower rates of at-fault motor vehicle collisions among older drivers (Ball et al., 2010). Additional benefits from training include protection from health-related quality of life declines (Wolinsky et al., 2006) and against depressive symptoms across a 5-year period (Wolinsky et al., 2009). Research is limited on whether cognitive interventions will benefit older adults with HF, but a recent preliminary study shows the potential exists to improve their memory (Pressler et al., 2011). A related pilot study by Pressler and colleagues (2013) indicated that health care resource use is lower among HF patients who have undergone cognitive training, although the differences

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between the training and control groups in this study were not statistically significant. Given the recent finding that impaired UFOV performance is evident in older adults with HF (Alwerdt et al., 2013), these secondary data analyses will test the hypothesis that cognitive speed of processing training improves cognitive and everyday function among older adults with HF.

Method Participants Data were obtained from the ACTIVE study, which are de-identified and publically available (Tennstedt et al., 2010). ACTIVE spanned 10 years and was a randomized, controlled, multi-site clinical trial that examined the impact of cognitive training among older adults living independently within the community (Ball et al., 2002; Tennstedt et al., 2010). Eligibility for participation in ACTIVE was based on the following criteria at initial screening: (a) age 65 or older, (b) score of ≥23 on the Mini-Mental State Examination (MMSE), (c) score of 20/50 or better on a visual acuity, (d) no medical conditions that would result in severe cognitive or functional decline (i.e., recent stroke) or mortality (i.e., cancer) during the 5-year study, (e) no recent cognitive training, (f) no communication difficulties when speaking to an interviewer, (g) willingness to participate in testing and training sessions, and (h) availability during 5-year span of study (Jobe et al., 2001). ACTIVE included 2,802 participants, of whom 2,776 responded at baseline to a yes/no question concerning whether a doctor or nurse had diagnosed them with congestive HF. Twenty-six had missing data, of whom n = 4 refused to answer, and n = 16 indicated they did not know. Data were excluded from participants who answered no to the HF question (n = 2,638). The current study further narrowed the 138 participants with HF by training group, excluding those randomized into the memory and reasoning training conditions (n = 74; see procedure below for details). This created a group of 64 eligible participants with HF randomized to either the speed training or nocontact control groups. Their average age was 75 years (SD = 6.17), and their average education level was 13.5 years (SD = 2.69), ranging from grade 6 to the doctoral level. Women made up the majority of these eligible participants, 68.8%, and all were either White (75%) or Black (25%).

Measures Please refer to Jobe et al. (2001) for further details and rationalization of the ACTIVE study measures.

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Everyday speed of processing composite. An everyday speed of processing composite variable was calculated as an average of the following equally weighted measures: Timed Instrumental Activities of Daily Living (TIADL), Complex Reaction Time (CRT), and the UFOV test as previously detailed (Ball et al., 2002). TIADL.  TIADL were assessed by observing participants perform activities such as finding items on a shelf, reading directions on medication containers, reading food can ingredients, finding a telephone number in a phonebook, and counting correct change from a handful of coins (Owsley et al., 2002). The tasks are designed to simulate activities of daily living in a laboratory setting and completion time in seconds is recorded. The TIADL is a reliable measure for assessing everyday functioning in older adults, r = .68 (Owsley et al., 2002). CRT. CRT, a measure of everyday processing speed (Ball & Owsley, 2000), was assessed by computer-based tasks. Participants were instructed to respond to an instructional road sign, by either clicking or moving their mouse (Jobe et al., 2001). The average time taken to accurately complete the task across trials was recorded. Reliability for the CRT has been reported at r = .45 to .56 (Ball & Owsley, 2000). UFOV test.  The UFOV measures cognitive speed of processing function through visual attention tasks (Ball et al., 1993; Edwards, Vance, et al., 2005; Goode et al., 1998). The test requires a non-verbal, instantaneous processing of information while discriminating between a main task and peripheral stimuli. Past studies have shown test–retest reliability, r = .884 (Edwards, Vance, et al., 2005). Four subtests were administered through a touch PC to evaluate stimulus identification alone, divided attention, selective attention, and selective attention in conjunction with same/different discriminations. Participants identified targets at display durations ranging from 16.67 to 500 ms, and scores reflect the display time for 75% accuracy within each subtest. A composite score was compiled from the four subtests performances (Edwards, Vance, et al., 2005). Further details on the UFOV can be found elsewhere (Edwards, Vance, et al., 2005). Speed of processing training.  The focus of cognitive speed of processing training was to improve performance speed for visual attention tasks. The training exercises involved visual targets presented via computer. The visual attention training exercises progressively increased in difficulty through systematic reductions in stimulus duration (Ball et al., 2007; Jobe et

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al., 2001). The visual attention training tasks were similar to the subtests of the UFOV with three basic levels of complexity. However, in training sessions 5 through 10, difficulty and display speed of the exercises was adapted to individual performance. Task difficulty was modified by changing the main task to require either stimulus detection, stimulus identification, or stimulus discrimination, and by altering the location of the peripheral target. These techniques produced at least 18 different tasks that were presented at 10 different display speeds (ranging between 20 and 400 ms). For further details, see Ball et al. (2007). MMSE.  The MMSE was explored as a potential covariate. It is a widely used screening for cognitive status in clinical settings (Ball et al., 2002; Jobe et al., 2001; Cockrell & Folstein, 2002). The measure evaluates language, orientation to time and place, attention and calculation, word registration and recall, and visual construction (Folstein, Folstein, & McHugh, 1975; Cockrell & Folstein, 2002). The MMSE has a maximum score of 30 points and participants with scores of 23 or higher were included in the ACTIVE study. HF.  Participants were defined as having HF based on their responses at baseline to a yes/no question concerning whether a doctor or nurse had ever diagnosed them with HF. Thus, HF was self-reported.

Procedure ACTIVE was a multi-site randomized controlled trial that examined cognitive and everyday function in a diverse sample of community dwelling adults aged 65 and older (N = 2,832) who were living independently of formal care (Jobe et al., 2001). Older adults who were at risk for functional declines, but had not experienced them, were recruited across six different sites including Birmingham, Alabama; Boston, Massachusetts; Indianapolis, Indiana; Baltimore, Maryland; State College, Pennsylvania; and Detroit, Michigan. After completing all baseline testing (including assessments of cognitive and functional abilities), the study participants were randomly assigned (using a computer randomization program) to one of three distinct cognitive training groups (memory training, reasoning training, or speed of processing training) or a no-contact control group. Certified trainers conducted small-group (3-5 participants) training in ten 60- to 75-min sessions. Training sessions were administered across 6 weeks. Follow-up assessments of cognitive and functional abilities were conducted at 2 months, 1-, 2-, 3-, 5-, and 10-years between 1998 and 2008 (Jobe et al., 2001).

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Analyses Given that randomization in ACTIVE was not stratified by HF status, the speed of processing training and no-contact control groups were compared to determine if covariates should be included in analyses. Chi-square Tests of Independence were calculated to determine whether differences existed between the groups in gender or race. Independent-samples t tests were used to compare the speed of processing and control groups by age, years of education, and total MMSE at baseline. These variables were selected as potential covariates given that prior research found education, MMSE, and age were correlated with speed of processing training gains (Ball et al., 2007). Repeated-measures MANCOVA was used to examine the effects of speed of processing training on the everyday speed composite score from pre- to posttesting, controlling for any baseline differences as covariates.

Results Participants were included in analyses if they reported HF at baseline, were randomly assigned to either the speed of processing training group or nocontact control group, and completed post-testing (n = 56). Eight of the 64 eligible participants did not complete post-test. Of those who completed posttest, UFOV scores were missing from 2 participants, leaving 54 participants to be used in the present analyses. Summary statistics for the analytic sample by condition can be found in Table 1. The Chi-Square Test of Independence was used to determine if participants in the speed of processing training group differed from those in the control group by gender or race. Results indicated that neither gender χ2(1, N = 54) = 3.78, p = .052, nor race χ2(1, N = 54) = .979, p = .322, differed significantly between the groups. Independent-samples t tests were conducted to compare the two groups on the potential covariates of age, education, and MMSE scores. No significant differences were indicated between the speed and control conditions in age, t(52) = 0.17, p = .866, d = .04, or MMSE scores, t(52) = 1.32, p = .192, d = .43. A significant difference for years of education existed between conditions, t(52) = 2.067, p = .044, with the speed training group averaging 14.13 years (SD = 2.96) and the control group averaging 12.61 years of education (SD = 2.43), an effect size (d) = .57. Thus, education was used as a covariate in subsequent analyses. A repeated-measures MANCOVA was calculated to examine pre- to posttest performance on the everyday speed of processing composite comparing the speed of processing training and no-contact control groups while

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Ellis et al. Table 1.  Summary Statistics of Analytic Sample by Intervention Group. Speed of training group (n = 23)

Control group (n = 31)

 

Characteristic

M (n)

SD (%)

M (n)

SD (%)

p

Age Education* MMSE Gender  Female  Male Race  White  Black

75.39 14.13 28.00

6.39 2.96 1.67

75.1 12.61 27.39

6.26 2.43 1.68

(12) (11)

(52) (48)

(24) (7)

(77) (23)

(19) (4)

(83) (17)

(22) (9)

(71) (29)

0.866 0.044 0.192 0.052     0.322    

Note. MMSE = Mini-Mental State Exam. *p < .05.

controlling for education. Results showed a significant main effect of group, 2 F(1, 51) = 6.06, p = .017, ηp = .106, but there was no main effect of education, F(1, 51) = .017, p = .896, η2 < .001. There was a significant main effect 2 of time, Wilks’s Λ =.914, F(1, 51) = 4.79, p = .033, ηp = .086, and a significant Group × Time interaction, Wilks’s Λ = .640, F(1, 51) = 28.68, p ≤ .001, η2p = .360. There was no significant Education × Time interaction, Wilks’s Λ 2 = .995, F(1, 51) = .258, p = .613, ηp = .005. Participants in the speed of processing training group performed better on the everyday speed of processing composite from pre- to post-training as compared with participants in the no-contact control group (see Figure 1). Years of education did not significantly affect training gains.

Discussion This study examined the effects of speed of processing training on cognitive and everyday function as measured by an everyday speed of processing composite comprised of UFOV, CRT, and TIADL performance among older adults with HF. We hypothesized that cognitive speed of processing training would improve their performance. Results supported this hypothesis, showing that participants with HF randomized to the speed of processing training group significantly improved their everyday speed of processing from pre- to post-training compared with the no-contact control group. Many studies have shown that training improves UFOV performance (e.g., Ball et al., 2002; Edwards, Wadley, et al., 2005; Roenker et al., 2003; Wolinsky et al., 2013).

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Everyday Speed of Processing Composite

4 3 2 1 0

Speed group Control group

-1 -2 -3 -4 1

Time

2

Figure 1.  Everyday speed of processing composite performance from baseline to post by intervention group.

Another study also found that speed of processing training enhances CRT performance administered in a driving simulator (Roenker et al., 2003). In two studies finding that training enhances TIADL performance, participants were selected for UFOV impairment at baseline and the study samples were larger than those in the present study (Ball et al., 2007; Edwards, Wadley, et al., 2005). Cognitive training research among older adults with HF has been limited, but one study has shown that such interventions have the potential to improve memory (Pressler et al., 2013; Pressler et al., 2011). The present study builds on previous research, showing the potential of speed of processing training among older adults with HF. Previous studies have clearly documented the prevalence of cognitive impairment in older adults with HF and its negative consequences. Older adults with cognitive speed of processing deficits and those with HF are at greater risk of ceasing to drive (Edwards et al., 2008), which can further endanger their health (Edwards, Lunsman, et al., 2009). Prior studies among older adults in general have found that cognitive speed of processing training prolongs safe driving mobility (Ball et al., 2010; Edwards, Myers, et al., 2009) and protects against decline in health-related quality of life (Wolinsky et al., 2006). Cognitive difficulties can also impede the ability of HF patients to adhere to treatment regimens and make necessary medical care decisions (Alosco, Spitznagel, Cohen, et al., 2012; Cameron et al., 2010; Dickson et al.,

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2011; Ekman et al., 2001). The improvement in everyday speed of processing performance for HF participants after cognitive training demonstrates that such training has the potential to improve their health and quality of life, but determining this would require further research. It is encouraging that among older adults without HF cognitive speed of processing training has been shown to buffer health-related and functional decline 3 to 5 years after completion (Edwards, Myers, et al., 2009; Wolinsky et al., 2006). The longitudinal effects of cognitive speed of processing training among older adults with HF is deserving of further investigation. There are several limitations to this study. First, secondary data from the ACTIVE study were used for this investigation. There are advantages to using secondary data in that it is less expensive, saves time, and offers a generally pre-established degree of validity and reliability, but the data were not collected specifically for the research question in hand. Because of this, HF was identified for participants through self-report instead of clinical measurements, which could better verify the presence of HF and allow for stratification based on degree of HF. Participants were not stratified by HF status when randomized to treatment conditions, therefore this cannot be considered an efficacy analysis. Data concerning HF diagnosis related to clinical variables (e.g., ejection fraction), the American Heart Association, or the New York Heart Association Classification were unavailable. The effect of these influences on cognitive function may not be the same. For instance, evidence supports that performance in visuo-spatial intelligence and memory between the II to IV New York Association classes is worse (Incalzi et al., 2003). To ensure baseline cognitive performance had no impact on training gains we conducted a sensitivity analysis. Regardless of baseline cognitive performance, those in the cognitive speed of processing training group experienced significant improvement relative to controls. Other limitations include a lack of information on participants’ use of medical interventions to treat their HF. Our sample size was small with only 54 individuals. Nevertheless, these results are an important first step in exploring the effects of cognitive speed of processing training among older adults with HF. There are other limitations to the ACTIVE study. The sample includes only Black and White participants, which limits the generalizability of results to more diverse populations. The control condition was no-contact. Although an attention control group is preferable, prior research has found cognitive speed of processing training to be effective relative to social- and computer-contact control conditions (Edwards, Wadley, et al., 2005; Wolinsky et al., 2013). A criticism of speed of processing training is that it is “training to the test,” given the similarity of the exercises to the UFOV. This was addressed by Wolinsky et al. (2013), who showed in a randomized

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controlled trial that speed of processing training improves performance on the Trail Making Test, the Symbol Digit Modalities Test, and the Stroop test. Furthermore, the transfer of cognitive speed of processing training to improved everyday functional and health-related outcomes lessens this concern (Ball et al., 2010; Edwards, Wadley, et al., 2005; Roenker et al., 2003; Wolinsky et al., 2006, 2009). Anguera et al. (2013) found that multi-task training using an adaptive version of a video game (NeuroRacer) improved cognitive performance among older adults without HF. The cognitive outcome, multi-tasking, was measured by how quickly a person could respond to the appearance of a sign when a green circle was present (similar to our CRT test) while simultaneously navigating a joy stick to maintain lane position of a simulated car. The authors asserted that interference was a vital feature of the training approach. Similarly, recent analyses showed that improvements in divided attention (i.e., multitasking) were key to transfer of speed of processing training to improved everyday functional performance (Edwards, Ruva, O’Brien, Haley, & Lister, 2013). To our knowledge, the present study is the first to document the effects of cognitive speed of processing training among HF patients. Cognitive speed of processing training also has well-documented benefits to everyday function (driving in particular), but it remains unclear if training can enhance everyday function among older adults with HF. Future research should further explore this relationship among individuals with HF. It would be of interest to determine if cognitive speed of processing training improves everyday function among participants in the early phases of HF, particularly in light of recent research showing the association between functional impairment and HF risk (Bowling et al., 2012). This raises the possibility of preventing HF progression through training to improve everyday function. Recent research has found that while cognitive impairment is common in older adults with HF, it is not frequently documented by physicians following hospitalization (Dodson, Truong, Towle, Kerins, & Chaudhry, 2013). In this vein, greater awareness of the prevalence of cognitive impairment among older adults with HF and the use of cognitive training with this population has potential to improve cognitive functioning in a large segment of the U.S. population, improving public health by extending healthy life years. Acknowledgments The authors would like to acknowledge and thank the entire ACTIVE (Advanced Cognitive Training for Independent and Vital Elderly) team as well as the staff and students of the Western Kentucky University Vision Laboratory, University of Alabama at Birmingham, Translational Center for Research on Aging and Mobility, and the University of Alabama at Huntsville Cognitive Aging Laboratory.

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From June to August 2008, Dr. Edwards worked as a limited consultant to Posit Science, who currently markets the Useful Field of View (UFOV) test and speed of processing training software (now called Insight).

Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The ACTIVE study was funded by the National Institutes of Health with grants to the following institutions: Hebrew Senior Life (U01 NR04507), Indiana University School of Medicine (U01 NR04508), Johns Hopkins University (U01 AG14260), New England Research Institutes (Data Coordinating Center) (U01 AG14282), Pennsylvania State University (U01 AG14263), University of Alabama at Birmingham (U01 AG14289), University of Florida/Wayne State University (U01 AG014276), National Institutes of Health, and National Institute on Aging. Dr. Jerri D. Edwards was supported as co-investigator.

Note 1.

UFOV (Useful Field of View) is a registered trademark of Visual Awareness Inc.

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