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. Author manuscript; available in PMC: 2021 Jul 16.
Published in final edited form as: J Aging Health. 2014 Mar 28;26(4):600–615. doi: 10.1177/0898264314525666

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

Michelle Ellis 1, Jerri D Edwards 1, Lindsay Peterson 1, Rosalyn Roker 1, Ponrathi Athilingam 1
PMCID: PMC8285066  NIHMSID: NIHMS1722392  PMID: 24681975

Abstract

Cognitive deficits pose serious problems for older adults, and may affect 25 to 70% persons with heart failure (HF). Cognitive speed of processing training improves speed of processing among older adults, but research is limited on whether such interventions will benefit older adults with HF. Data from the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study were used to examine the effects of cognitive speed of processing training across three performance measures among older adults with heart failure, Useful Field of View (UFOV), complex reaction time (CRT), and Timed Instrumental Activities of Daily Living (TIADL). Of the 54 participants included in analyses, 23 who were randomized to the cognitive training group performed significantly better in speed of processing (UFOV) from pre-to-post training compared to 31 participants who were randomized to the control group, F(1, 51)= 19.72, p=<.001, partial η2 =.279. The results indicate that speed of processing training improves UFOV 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

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 age 65 and older, 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 (Bekelman et al., 2007; Pressler et al., 2010; Alosco et al., 2012; Gure et al., 2012). Research shows that cognitive impairment risk is four times higher for older adults with HF, compared to those without HF (Sauvé, Lewis, Blankenbiller, Rickabaugh, & Pressler, 2009). Previous studies have found that cognitive speed of processing training with older adults improves speed of processing (Ball et al, 2002; Wolinsky, Vander Weg, Howren, Jones, & Dotson, 2013) as well as everyday function, such as driving and reading directions on a medicine container (Roenker, Cissell, Ball, Wadley & Edwards, 2003; Edwards, Wadley et al., 2005; Ball et al., 2007; Edwards, Myers et al., 2009; Ball, Edwards, Ross, & McGwin, 2010). 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; Suave et al., 2009; Riegel et al., 2002). Cognitive deficits in older adults with HF are associated with declines in function (Alosco, Spitznagel, Cohen et al., 2012) and decision making (Dickson, Tzacs, & 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 regimen which may increase hospital readmission and mortality (Fitzgerald et al., 2011) and less likely to make appropriate self-care decision (Dickson, Lee, & Riegel, 2011; Alosco, Spitznagel, van Dulmen et al., 2012; Hawkins et al., 2012). According to Cameron and colleagues (2010), patients with HF have self-care and decision-making difficulties even in cases of mild cognitive impairment (MCI).

Older adults can experience reductions in their decision-making abilities, but research shows that such difficulties are due more specifically to declines in certain cognitive domains, such as speed of processing (Henninger, Madden, & Huettel, 2010). Speed of processing is a cognitive domain that diminishes with age (Birren, Woods & Williams, 1980; Ball, Owsley, Sloane, Roenker, & Bruni, 1993; Goode et al., 1998) and is further compromised among people with HF (Vogels et al., 2007; Kindermann et al., 2012). Decline in speed of processing increases the risks for at-fault vehicle crashes (Ball et al., 2006; Owsley et al., 1998) and for falls (Sims et al., 2001). Speed of processing is also associated with instrumental activities of daily living (Owsley, Sloane, McGwin, & Ball, 2002; Edwards et al., 2005). Speed of processing, however, is a dynamic cognitive function that can improve with cognitive training (Ball et al., 2002; Ball, Edwards, & Ross, 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 training 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, Edwards & Ross, 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), relying on both visual sensory function and cognitive abilities. Scores are strongly related to one’s cognitive speed of processing abilities (Edwards, Vance et al., 2005; Lunsman et al., 2008).

Cognitive speed of processing training improves UFOV performance (Ball et al., 2002; Ball et al., 2007; Wolinsky et al., 2013). Further, 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 (Roenker et al., 2003; Edwards, Wadley et al., 2005; Ball et al., 2007). Other research has associated speed of processing training with lower rates of at-fault motor vehicle collisions (Ball, Edwards, Ross, & McGwin, 2010), in addition to protection from health-related quality of life declines (Wolinsky et al., 2006) and against depressive symptoms over five years (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 study by Pressler and colleagues (2013) indicates that health care resource use is lower among HF patients who have undergone cognitive training, although the differences between the training and control groups in this study were not statistically significant. Recent research further shows that impaired UFOV performance is evident in older adults with HF (Alwerdt, Edwards, Athilingam, O’Connor, & Valdes, in press). These secondary data analyses will test the hypothesis that cognitive speed of processing training improves cognitive speed of processing and everyday function among older adults with HF, who are at particular risk of the negative consequences of functional and cognitive deficits.

Method

Participants

Data were obtained from the ACTIVE study which are deidentified 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 on 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 (e.g., recent stroke) or mortality (e.g., 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; h) availability during 5-year span of study (Jobe et al., 2001).

The current study focused on ACTIVE participants who responded in baseline assessments to a yes/no question concerning whether a doctor or nurse had diagnosed them with congestive heart failure (N=2,802). Data were excluded from participants who answered no to the heart failure question (n=2,638), had missing data (n=6), refused to answer (n=4), or indicated they did not know (n=16). The current study further narrowed the participants by training group, excluding those randomized into the memory and reasoning training conditions (n=84). This created a group of 64 eligible participants with HF randomized to speed training or control groups. Their average age was 75 years (SD=6.171), 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 percent, and all were either White (75%) or Black (25%).

Measures

Please refer to Jobe et al. for details and rationalization of ACTIVE measures.

Mini Mental State Exam.

The MMSE is a widely used screening for cognitive statusin 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 22 or higher were included in the ACTIVE study.

Useful Field of View Test.

The UFOV measures cognitive speed of processing function through visual attention tasks (Ball et al., 1993; Goode et al., 1998; Edwards, Vance et al., 2005; Edwards et al., 2006). The testrequires 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 subtest were administered in the present study through a touch PC to evaluate stimulus identification alone, divided attention, selective attention, and selective attention in conjunction with same/different discriminations. Participants identify 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 is compiled from the four subtests performances (Edwards, Vance et al., 2005). Further detail on the UFOV can be found elsewhere (Edwards, Vance et al., 2005).

Depressive Symptoms.

The Center for Epidemiologic Studies Depression Scale (CES-D) is a validated and reliable measure for detecting depression symptoms in the general population, r=.57 (Radloff, 1977). The CES-D is a 12 item Likert-scaled self-reported instrument for assessing symptoms of depression. A score of 0 indicates no depressive symptoms and a score of 36 indicates all depressive symptoms are present.

Timed Instrumental Activities of Daily Living.

TIADLs 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 making correct change from a group of coins (Owsley, Sloane, McGwin, & Ball, 2002). The tasks are designed to simulate activities of daily living in a laboratory setting and completion time in seconds is recorded. TIADLs were assessed four times during the study period including baseline and post-training. The TIADL is a reliable measure for assessing cognitive functioning in older adults, r=.68 (Owsley et al., 2002).

Complex Reaction Time.

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 time taken to complete the task and accuracy of responses were recorded. The present analyses averaged the two baseline and post-training CRT scores for comparison. Reliability for the 2-test CRT has been reported at r=.45 and r=.56 (Ball & Owsley, 2000).

Far Visual Acuity.

Far visual acuity was assessed using a Good-Lite model 600A light box with the Early Treatment Diabetic Retinopathy Shot (ETDRS) chart using standard procedures. Participants read the chart from a 10-foot distance wearing corrective lenses if necessary. Scores were assigned on a scale of 0 to 90 based on the number of letters that were correctly identified (0 is equivalent to a Snellen score of 20/125, and 90 to a Snellen score of 20/16), with higher scores indicating better far visual acuity (Jobe et al., 2001).

Heart Failure.

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 function in a diverse sample of community dwelling adults age 65 and older (n=2832) who were living independently of formal care (Jobe et al., 2001). After completing all baseline testing (including assessments of cognitive and functional abilities), the study participants were randomly assigned to one of three distinct cognitive training groups (memory training, reasoning training, or speed of processing training) or a no-contact control group. Follow-up assessments of cognitive and functional abilities were conducted at two months, one, two, three and five years. (Jobe et al., 2001).

Analyses

The Chi-square Tests of Independence were calculated to determine whether differences existed between the speed and control groups in gender and race. Multivariate analysis of variance (MANOVA) was used to compare the speed of processing and control groups by age, years of education, total CESD score, and total MMSE at baseline. A MANOVA design was used to examine the effects of speed of processing training on TIADL, CRT, and UFOV scores from pre- to post-testing.

Results

Eligible participants for the present study reported heart failure at baseline, were randomly assigned to either the speed of processing training group or control group, and completed testing after training n=57. Of those eligible, the composite depression score for one participant was missing and post-training UFOV scores were missing from two participants, leaving 54 participants to be used in the present analyses. Summary statistics for eligible participants in the speed of processing training group and control group can be found in Table 2.

Table 2.

Summary Statistics for Eligible Participants Completing Post-Test by Intervention Group

Speed of Training Group Control Group
n=23 n=31
Characteristic M SD M SD
Age 75.39 6.39 75.1 6.26
Far Visual Acuity 66.74 9.18 70.60 12.83
CESD 4.74 5.73 5.94 4.58
MMSE 28.00 1.67 27.39 1.68
UFOV baseline 1029.13 311.53 1085.32 269.93
UFOV post training 669.61 300.41 951.09 235.67
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Note. CESD =Center for Epidemiological Studies Depression Score n=33; UFOV= Useful Field of View Test; MMSE = Mini Mental State Exam

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 gender did not differ significantly between the groups, X2 (1, n=54) = 3.78, p =.052. No differences in race were detected between conditions, X2 (1, n=54) = .979, p =.322.

A MANOVA was conducted to compare the two groups on the variables of age, education, total MMSE scores, and total CESD at baseline. There was no statistically significant effect of group, Wilks’ Λ= .911, F(4, 49)=1.195, p=.325, partial η2=.089. Participants did not differ between groups on the variables of age, education, total MMSE scores, at total CESD at baseline.

A MANOVA was calculated to examine whether the pre- and post-test performance on UFOV, CRT or TIADL changed differently for participants in the speed of processing training group compared to participants in the control group. Results did not show a significant main effect of group, Wilks’ Λ =.879, F(3, 49)=2.25, p=.094, partial η2=.121 but there was a significant main effect of time, Wilks’ Λ =.360, F(3, 49)=29.1, p=<.001, partial η2=.640 and a significant group × time interaction, Wilks’ Λ =.703, F(3, 49)=6.91, p=.001, partial η2=.297. Further univariate analysis found a significant main effect of group for UFOV, F(1, 51)=5.42, p=.024, partial η2=.096; a significant main effect of time, F(1, 51)=90.03, p=<.001, partial η2=.638; and a significant group × time interaction, F(1, 51)= 19.72, p=<.001, partial η2 =.279. Participants in the speed of processing training group performed better on the UFOV from pre- to post-training as compared to participants in the control group(see Figure 1).There was no significant main effect of group for CRT scores, F(1, 51)=<1, p=.385, partial η2=.015, but there was a significant main effect of time, F(1,51)=5.31, p=.025, partial η2=.094. No significant group × time interaction was found, F(1,51)=.160, p=.691, partial η2=.003. Participants in the speed of processing training group did not perform significantly different as compared to participants in the control group from pre- to post- training. Additionally there was not a significant main effect of group for TIADL scores, F(1, 51)=<1, p=.816, partial η2=.001; no significant main effect of time, F(1, 51)=<1, p=.753, partial η2=.002, and no significant group × time interaction, F(1,51)=.025, p=.874, partial η2=<.001. There were no significant differences in TIADL performance between the speed of processioning training group and the control group from pre- to post-training.

Figure 1.

Figure 1.

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Results of a MANOVA Interaction Group by Time

Discussion

This study examined the effects of cognitive speed of processing training on cognitive and everyday function as measured by UFOV, CRT, and TIADL performance among older adults with HF. We hypothesized that cognitive speed of processing training would improve their performance. Results partially supported this hypothesis, showing that participants randomized to the speed of processing training group significantly improved their cognition from pre- to post-training as measured by the UFOV, compared to the control group. No training effects were evident for CRT or TIADL performance. Other studies have found that speed of processing training enhances CRT performance, but only in driving simulations (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 in the present study (Edwards, Wadley et al., 2005, Ball et al., 2007).Recent research shows that UFOV deficits are evident in older adults with HF (Alwerdt et al., in press). Previous research also has documented that cognitive speed of processing training can improve UFOV scores in older adults (Ball et al., 2002; Ball et al., 2007; Wolinsky et al., 2013). Research is limited on whether cognitive training will help older adults with HF, but studies have shown that such interventions have the potential to improve their memory (Pressler et al., 2011; Pressler et al., 2013). 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 HF and cognitive deficits are at greater risk of ceasing to drive (Edwards et al., 2008), which can further endanger their health (Edwards, Lunsman et al., 2009). Cognitive difficulties also can impede the ability of HF patients to adhere to treatment regimens and make necessary medical care decisions (Ekman et al., 2001; Cameron et al., 2010; Dickson et al., 2011; Alosco, Spitznagel, Cohen et al., 2012).

Speed of processing is a cognitive domain that is vital in decision making and other processes. The improvement in UFOV performance for HF participants after speed of processing training demonstrates that such training has the potential to improve their health and quality of life. It is also encouraging that cognitive speed of processing training has been shown to buffer cognitive decline three to five years after completion (Wolinsky et al., 2006; Edwards et al., 2009).

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-reports instead of clinical measurements, which could better verify the presence of HF and allow for stratification based on degree of HF. Information relative to the onset of HF would be beneficial in explaining the trajectory of cognitive decline. 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 impact 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 (Antonelli Incalzi et al., 2003). There was a lack of information on participants’ use of medical interventions to treat their HF. Our sample size was small. With only 54 individuals completing the post-test, including 23 randomized to the speed of processing training group. The ACTIVE study only includes Black and White participants, which limits the generalizability of results to more diverse populations. Nonetheless, the ACTIVE data is beneficial for examining the effects of speed of processing in HF.

This to our knowledge is the only study to document the effects of cognitive speed of processing training among HF patients and the potential of that training to improve their health. Cognitive speed of processing training also has well-documented benefits to everyday function, and future research should further explore this relationship with 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 worsening HF through training to improve function. This research also demonstrates the need for inquiry into the benefits of cognitive screening of HF patients. 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 could improve cognitive functioning in a large segment of the U.S. population, improving public health by extending healthy life years.

Table 1.

Individual Characteristics as a Percentage of the Sample for the ACTIVE Control and Speed Training Group

Control Group Speed Training Group
n=37 n=27
Characteristic M or (n) SD or (%) M or (n) SD or (%)
Age 75.43 6.01 74.37 6.45
Sex
Female (29.00) (78.40) (15.00) (55.60)
Male (8.00) (21.60) (12.00) (44.40)
Race
White (27.00) (73.00) (21.00) (77.80)
Black (10.00) (27.00) (6.00) (22.20)
Years of Education 12.57 2.35 14.33 2.83
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Footnotes

1

UFOV is a registered trademark of Visual Awareness Inc.

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