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. Author manuscript; available in PMC: 2024 Apr 22.
Published in final edited form as: J Aging Health. 2017 Nov 1;31(4):595–610. doi: 10.1177/0898264317738828

Can Cognitive Speed of Processing Training Improve Everyday Functioning among Older Adults with Psychometrically-Defined Mild Cognitive Impairment?

Elise G Valdés 1,2, Ross Andel 1,3, Jennifer J Lister 1,4, Alyssa Gamaldo 1,5, Jerri D Edwards 4,6
PMCID: PMC11034754  NIHMSID: NIHMS1722389  PMID: 29254421

Abstract

Objective:

The aim of these secondary analyses was to examine cognitive speed of processing training (SPT) gains in cognitive and everyday functioning among older adults with psychometrically-defined mild cognitive impairment (MCI).

Method:

A subgroup of participants from the Staying Keen in Later Life study (SKILL) with psychometrically-defined MCI (n =49) were randomized to either the SPT intervention or an active control group of cognitive stimulation. Outcome measures included the Useful Field of View (UFOV), Road Sign Test, and Timed Instrumental Activities of Daily Living (IADL) test. A 2x2 repeated measures MANOVA revealed an overall effect of training, indicated by a significant group (SPT vs. control) by time (baseline vs. post-test) interaction.

Results:

Effect sizes were large for improved UFOV, small for the Road Sign test, and medium for Timed IADL.

Discussion:

Results indicate that further investigation of cognitive intervention strategies to improve everyday functioning in patients with MCI is warranted.

Keywords: activities of daily living, mild cognitive impairment, cognitive training

Mild cognitive impairment (MCI) is a transitional stage between normal aging and dementia (Petersen et al., 1999). It is a heterogeneous condition that varies in etiology, clinical presentation, and prognosis. Individuals with MCI show cognitive declines greater than would be expected by age and education. Further, while individuals with MCI are able to maintain functional independence, they have more trouble performing complex everyday tasks than healthy individuals (Brown, Devanand, Liu, & Caccappolo, 2011).

As there is limited evidence for efficacy of pharmacological treatments in MCI (Cooper, Li, Lyketsos, & Livingston, 2013), interest has turned towards cognitive interventions. Cognitive training is one type of cognitive intervention that aims to improve specific domains of cognitive function either by repeated practice (i.e., process-based training) or direct instruction of strategies (i.e., strategy-based training). Meta-analyses of cognitive training in MCI show encouraging results (Hill et al., 2016; Teixeira et al., 2012). Process-based cognitive training programs involve perceptual practice of specific fluid abilities (e.g., attention, speed of processing). This type of cognitive training shows benefits among persons with MCI in domains such as speed of processing, visual attention, global cognition, and verbal memory (Rosen, Sugiura, Kramer, Whitfield-Gabrieli, & Gabrieli, 2011; Valdés, O’Connor, & Edwards, 2012). A few interventions using strategy-based training have also shown some success in MCI populations, (e.g., Belleville et al., 2006; Hampstead, Sathian, Moore, Nalisnick, & Stringer, 2008), particularly when the strategy-based training is preceded by process-based training (Belleville et al., 2006). However, most strategy-based training has not shown success among those with MCI, (e.g., Rapp, Brenes, & Marsh, 2002; Troyer, Murphy, Anderson, Moscovitch, & Craik, 2008). The goal of the current study was to investigate the efficacy of cognitive speed of processing training (SPT) to improve cognitive and everyday functioning among older adults with psychometrically-defined MCI.

Notable limitations in cognitive intervention research in MCI exist including inadequate control groups (Hampstead et al., 2008) and inappropriate statistical analyses (Kurz, Leucht, & Lautenschlager, 2011). A debate in the field is whether or not cognitive training only improves the exercises practiced during training, or whether the benefits of training generalize, or transfer, to other similar cognitive abilities (i.e., near transfer). Further, whether cognitive gains generalize to positively affect functioning in everyday life is also debated (i.e., far transfer). Despite claims that cognitive training does not transfer to everyday functioning (Ratner & Atkinson, 2015; Simons et al., 2016), very little research has examined everyday functioning as an outcome of cognitive training (Kelly et al., 2014), and even fewer have examined everyday functioning as an outcome of cognitive training in MCI. However, evidence among cognitively healthy older adults suggests SPT transfers to everyday functioning (e.g., Edwards, Wadley, et al., 2005; Willis et al., 2006), and one recent study suggests SPT improves instrumental activities of daily living among those with MCI (Lin et al., 2016). As functional impairment due to cognitive decline is the hallmark of dementia (Ratner & Atkinson, 2015), any intervention that delays cognitive and functional decline could potentially also delay the onset of dementia.

SPT may be a particularly beneficial type of training for individuals with MCI because it is a process-based, perceptual training technique that is adaptive. According to Lovden and colleagues’ (2010) model of adult plasticity, a cognitive intervention must be sufficiently challenging to induce changes in cognition. As SPT is self-paced and becomes progressively more challenging as performance improves, it may be particularly suited to individuals with a heterogenous condition like MCI, because each individual will be training at their optimal level. Further, the information degradation hypothesis (Schneider & Pichora-Fuller, 2000) posits that errors in the initial sensory/perceptual processing cause difficulties with downstream information processing. Visual perceptual processing is impaired in MCI (Bublak et al., 2011). If initial perceptual processing can be improved, via SPT, information processing and hence cognitive functioning should also improve. Finally, process-based techniques may be more efficacious in MCI than strategy-based techniques because process-based techniques do not rely on learning, remembering, or applying strategies.

The Staying Keen in Later Life (SKILL) study included a randomized clinical trial to evaluate the impact of cognitive speed of processing training (SPT- a process-based, cognitive training program involving perceptual practice) on cognitive and functional performance of older adults. Previous work using the SKILL data demonstrated that participants randomized to cognitive SPT showed significantly better Useful Field of View (UFOV) performance as well as transfer to improved everyday functional performance relative to controls (Edwards, Wadley, et al., 2005). The current study expands this previous work by investigating if these training gains are evident among individuals with psychometrically-defined MCI.

We examined two hypotheses. First, we hypothesized that, among those with psychometrically-defined MCI, individuals randomized to the cognitive SPT group would perform significantly better on UFOV than similar individuals randomized to the active control group. Second, we hypothesized that, among those with psychometrically-defined MCI, individuals randomized to the cognitive SPT group would perform significantly better on assessments of near transfer, measured by the Road Sign Test, and a far transfer measure, everyday functioning, assessed by the Timed Instrumental Activities of Daily Living (IADL) Test, than similar individuals randomized to the active control group.

Methods

SKILL Study Overview

The SKILL study examined the relationships among sensory, cognitive, and functional abilities in older adults in phase 1, while phase 2 was a randomized clinical trial to evaluate the impact of SPT on cognitive and functional performance among those with speed of processing impairments. Participants were community dwelling older adults recruited from Bowling Green, KY, Birmingham, AL and surrounding areas. As detailed by Harrison-Bush and colleagues (2015), 1052 participants age 60 and older were screened for inclusion criteria of visual acuity of 20/80 or better. Of those screened (see Figure 1), 158 did not complete baseline: 134 refused further participation and 24 were ineligible. A total of 894 participants had adequate vision and attended a baseline evaluation: 890 of these individuals had sufficient data for psychometric MCI classification for the purpose of these analyses (four participants were missing baseline data on digit symbol copy). To be included in phase 2 of SKILL and thereby be randomized to SPT or control, participants were further required to have (a) contrast sensitivity ≥ 1.35, (b) adequate hearing (pure tone average of 40 dB or better), (c) intact cognitive status (Mini Mental State Exam; MMSE ≥ 24), and (d) a speed of processing deficit (UFOV subtest 3 and 4 combined score of ≥ 800 or subtest 2 score ≥ 150). All participants who met the inclusion criteria (n = 226) who completed a baseline visit were randomized to either the intervention group (n=120) or an active control group (n=106). The cognitive assessments were repeated post-intervention or after an equivalent delay (Edwards, Wadley, et al., 2005). Data from all participants were collected in compliance with the Institutional Review Boards at University of Alabama at Birmingham and Western Kentucky University. For this secondary data analytic study, we were interested in a sub-set of these participants who met criteria for psychometrically-defined MCI.

Figure 1.

Figure 1.

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Participant flow diagram

Analytic Sample.

Among the participants randomized, 49 participants met the criteria for psychometric MCI at baseline and had complete data on all outcomes at baseline and post-test. Of those who met the MCI criteria, 24 participants were randomized to SPT and 25 to the control group. This sample size is powered to detect Cohen’s d effect sizes of 0.53 or larger, for a repeated measures MANOVA within-between interaction. See Table 1 for descriptive statistics.

Table 1.

Baseline descriptive statistics for participants with psychometrically-defined mild cognitive impairment by intervention group.

Control Group
 SPT Group
 Variable M (%) SD M (%) SD d
Age 76.09 5.55 74.26 5.25 --
Years of Education 13.80 2.06 12.79 2.93 --
Female 32.0% -- 45.8% -- --
Caucasian 84.0% -- 83.3% -- --
Baseline UFOV performance 1212.12 266.49 1210.45 242.23 --
Baseline RST performance 2.62 1.37 2.30 0.80 --
Baseline Timed IADL performance 0.05 0.43 0.02 0.55 --
Post-Test UFOV performance 1017.00 212.92 722.50 212.68 1.10
Post-Test RST performance 2.72 2.33 2.06 1.04 0.39
Post-Test Timed IADL performance 0.17 0.77 −0.03 0.68 0.25
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Notes: An active control group was comparison. M=mean, SD=standard deviation, UFOV=Useful Field of View Test, SPT = Speed of Processing Training, MCI = mild cognitive impairment, RST = Road Sign Test, Timed IADL =Timed Instrumental Activities of Daily Living. Overall N = 49, SPT Group n = 24, Control Group n = 25. Cohen’s d effect sizes were calculated for the group by time interaction for each outcome as [(SPT mean at post-test – active control group at post-test) – (SPT mean at baseline – active control group at baseline)]/SD of the control group at baseline.

MCI Classification

MCI was determined using a psychometric algorithm per prior research (e.g., Crowe et al., 2006; O’Connor, Edwards, Wadley, & Crowe, 2010; Valdés et al., 2012; Wadley et al., 2007). SKILL participants’ baseline cognitive scores were standardized and summed to create composites for speed of processing, memory, and executive function based on factor analyses. Detailed descriptions of each task can be found elsewhere (Edwards, Wadley, et al., 2005) or in Supplement 2. The speed of processing composite consisted of the Letter Comparison, Pattern Comparison, and Digit Symbol Copy and Substitution tests. The memory composite consisted of the Spatial Span and Digit Span tasks, and the Hopkins Verbal Learning Test. The executive functioning composite consisted of Trails A and B and the Stroop task.

Participants with scores at or below the 7th percentile on any of the composites (speed of processing, memory, executive functioning or any combination thereof) were classified as psychometric MCI. The 7th percentile is equal to 1.5 SDs in a normal distribution, which is the traditional recommended cutoff for an MCI clinical diagnosis (Loewenstein et al., 2006). Because prior research found no difference in SPT gains among subgroups of MCI (e.g., amnestic vs. non-amnestic; Valdés et al., 2012), subgroup analyses were not performed.

Outcome Measures

UFOV Test.

The UFOV is a computerized test that measures cognitive speed of processing for visual attention tasks, which includes four subtests that progressively increase in complexity (Edwards, Vance, et al., 2005). In each subtest, visual targets (cars and trucks) are shown on the computer screen at display durations ranging from 16 to 500 ms. Scores for each subtest represent the fastest display durations at which the participant responded correctly 75% of the time. Per standard procedure, scores were combined into a composite with a possible range of 64 to 2,000 ms, with higher scores indicating worse performance. The UFOV has high test-retest reliability (r = .74-.81)(Edwards, Vance, et al., 2005).

Road Sign Test.

The Road Sign Test is a measure of complex reaction time that was administered by computer. Participants are instructed to use a mouse to quickly react to changes in displays of road signs (Edwards, Wadley, et al., 2005). Multiple road signs appear simultaneously (either three or six) and each condition contains 12 trials. Time from stimulus presentation to correct participant response is recorded. The score for the Road Sign Test is the average of the participant’s reaction time across both conditions. Test-retest reliability of the Road Sign Test is r = .56 (Ball et al., 2002).

Timed Instrumental Activities of Daily Living.

The Timed IADL Test involves timed performance of five tasks encountered in daily life, for which faster and more efficient completion would likely result in better outcomes than slower and/or less efficient task completion (Edwards, Wadley, et al., 2005). The previously validated tasks (Owsley, Sloane, McGwin, & Ball, 2002) utilize real-world stimuli and represent five IADL domains, including communication (finding a telephone number in a phone book), finance (making change), cooking (reading the first three ingredients on a can of food), shopping (finding two items on a shelf of packaged foods) and medicine (reading the directions on a medicine bottle label). Task scores were generated by combining the completion time and error code for each task per standard procedure. Task scores were combined into a single composite Timed IADL measure by taking the average of z scores computed for each of the five tasks. Test-retest reliability of the Timed IADL is r = .85 (Owsley et al., 2002).

Both the intervention and the active control groups were completed over five weeks and involved 10 one-hour training sessions that began with a 10-15 minute discussion of topics relevant to the training condition and ended with 45-50 minutes of individual practice exercises on a computer guided by a trainer. The two groups were identical except for the topics of discussion and types of exercises practiced on the computer.

The SPT group practiced computerized tasks at increasingly complex levels of difficulty (adaptive to the individual user) with central visual targets alone or combinations of central and peripheral targets at decreasing (faster) presentation speeds. Per a standard protocol (Edwards, Wadley, et al., 2005), the difficulty of the task was adapted by gradually increasing the complexity of the central target, the peripheral target, or both while the display speed was held constant. Once a participant mastered a particular task at 75% correct, the display speed was decreased, a process which repeated until a task was mastered at a pre-specified criterion. Then the complexity of the task was increased and the process was repeated. Thus, difficulty (complexity and speed) was adapted to the participant’s skill level until mastery was achieved through practice. The goal of this training is to increase the amount and complexity of information that can be quickly processed.

The active control group practiced computer skills such as an introduction to computer hardware, how to use a mouse, how to acquire and use an e-mail account, and how to access and use web-pages.

Analyses

MANOVA and Chi-square analyses were conducted to determine if there were significant differences between the SPT and control groups on descriptive characteristics: age, race, sex, and education. A 2 (SPT vs. control) x 2 (baseline vs. post-test) repeated measures MANOVA was conducted to determine if there was a significant effect of training (i.e., group by time interaction) with UFOV, Road Sign Test, and Timed IADL, as outcomes. Cohen’s d effect sizes were calculated for the group by time interaction for each outcome as ([SPT mean at post-test –control group at post-test] –[SPT mean at baseline –control group at baseline])/SD of the control group at baseline.

Results

Baseline Group Differences

Overall, there were no significant baseline differences between the conditions on any descriptive characteristics, Wilks λ=.92, F(5,43)=.79, p=.56. At baseline, the participants in the SPT and control groups did not differ significantly in terms of age, F(1,47)=1.40, p=.24; education, F(1,47)=1.95, p=.17; UFOV, F(1,47)<1, p=.98; Road Sign Test, F(1,47)=1.02, p=.32; or Timed IADL, F(1,47)<1, p=.85;. Chi-square indicated training groups did not differ significantly in terms of sex, χ2(1)=.99, p=.32, or race, χ2(1)<.01, p=.95 . Those with MCI who were excluded due to missing data were compared to the analytic sample. There were no significant differences, see Supplement 1.

Effect of SPT

Overall, there was a significant main effect of time, Wilks λ=.25, F(3,45)=45.56, p<.001, pη2=.75, but not training group, Wilks λ=.88, F(3, 45)=2.04, p=.12, indicating that there was a difference over time on the combined outcome measures. There was a significant training group x time interaction, Wilks λ=.63, F(3, 45)=8.79, p<.001, indicating that the groups differed over time in their performance on the outcome measures. Subsequent univariate ANOVAs were conducted to further describe these results. See Figure 2.

Figure 2.

Figure 2.

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SPT – Cognitive Speed of Processing Training. Control group was an active control group. A) Useful Field of View (UFOV) performance from pre- to post-training. B) Road Sign Test performance from pre- to post-training. C) Timed IADL performance from pre- to post-training. Smaller scores across all tests indicate better performance.

UFOV.

For UFOV, there were significant main effects of time, F(1,47)=141.10, p<.001, and training group, F(1, 47)=5.97, p=.02, and a significant training group x time interaction, F(1, 47)=25.93, p<.001, Cohen’s d= 1.10. Results indicated that SPT enhanced UFOV performance relative to controls with a large effect size.

Road Sign Test.

For the Road Sign Test, there were no significant effects of time, F(1,47)=0.13, p=.72, training group, F(1, 47)=1.64, p=.21, or training group x time interaction, F(1, 47)=0.72, p=.40, Cohen’s d=0.25. The small effect size suggests that SPT may potentially enhance Road Sign Test performance relative to controls.

Timed IADL.

For Timed IADL, there were no significant effects of time, F(1,47)=0.10, p=.76, training group, F(1, 47)=0.61, p=.44, or group x time interaction, F(1, 47)=0.62, p=.43, Cohen’s d=0.39. The medium effect size suggests that SPT may potentially enhance Timed IADL performance relative to controls.

Discussion

We examined the potential efficacy of SPT among those with psychometrically-defined MCI. Our results indicated that individuals randomized to SPT performed significantly better on UFOV compared to an active control group. Although no statistically significant differences between SPT and control groups were observed for transfer measures (Road Sign Test, d=0.25; Timed IADL, d=0.39), effect sizes were in the direction of improvement. Our sample was only sufficiently powered to detect significant effects of d=0.53 or larger.

Our results that cognitive SPT improves UFOV performance are consistent with research among both healthy older adults (e.g., Ball et al., 2002; Ball, Edwards, & Ross, 2007), and those with MCI (Lin et al., 2016; Valdés et al., 2012). The Cohen’s d effect size for improved UFOV found in the current study (d=1.10) is slightly smaller than the effect size (d= 1.41) seen in the overall SKILL sample, which included psychometric MCI, but was primarily comprised of older adults with normal cognition (Edwards, Wadley, et al., 2005). This suggests that those with MCI show benefits on UFOV performance from SPT with similar effects as healthy older adults. The UFOV training effect size observed in this study is slightly larger than the effect sizes (d’s = 0.61-0.96) seen in other SPT studies (Ball et al., 2007). The larger effect sizes observed in the SKILL study are likely due to differences in the amount of adaptive training received. Studies such as SKILL (Edwards, Wadley, et al., 2005) and Accelerate (Vance et al., 2007) used only adaptive SPT in which training exercises are tailored to the individual’s ongoing performance. These studies show larger effect sizes (SKILL d = 1.10-1.41; Accelerate d = 0.96) than studies that have used partially adaptive SPT: d=.61 (Edwards et al., 2002), ACTIVE d=.72 (Ball et al., 2002), or non-adaptive home-based SPT d=.63 (Wadley et al., 2006). To further support this assertion, the participants with MCI in Lin and colleagues’ (2016) study of SPT showed improved UFOV effect sizes similar to healthy older adults (d=1.30).

Research among healthy older adults has shown that cognitive SPT transfers to improved everyday function such as IADL performance (Edwards et al., 2002; Edwards, Wadley, et al., 2005), fewer depressive symptoms (Wolinsky et al., 2009), and improvements in self-rated health (Wolinsky et al., 2010). Rebok and colleagues (2014) showed that those randomized to SPT showed the least functional decline over 10 years. Research with the overall SKILL sample showed transfer to improved Timed IADL performance (Edwards, Wadley, et al., 2005). The effect size among the MCI subsample for Timed IADL, d=0.39, is actually larger than the effect size obtained in the overall SKILL sample (Timed IADL d=0.29), which included both individuals with and without psychometrically defined MCI. Thus, the lack of significant differences in the current subsample is likely due to inadequate power. It may also be that individuals with MCI need more hours of training to see transfer benefits. Lin and colleagues’ (2016) work supports this idea, as their study demonstrated that 24 hours of SPT among MCI participants led to effect size improvements of Cohen’s d = 1.39 for Timed IADL.

Strengths, Limitations, and Implications

A primary limitation is the secondary analyses of a select subsample. Another limitation is that MCI status was only defined using baseline cognitive performance, which could potentially be problematic given the unstable nature of MCI that has been reported in the literature (e.g., Busse, Hensel, Guhne, Angermeyer, & Riedel-Heller, 2006). However, the psychometric approach to defining MCI has been validated by previous research (Albert et al., 2011; Crowe et al., 2006; Valdés et al., 2012). Future research should attempt to replicate these results in a sample with a clinical diagnosis of MCI. Nonetheless, these data are valuable given the potential clinical implications of improving functional performance in MCI, which could potentially delay dementia onset.

Similar to previous research, our results suggest that cognitive SPT can improve cognitive functioning in those with MCI with a relatively large effect size, suggesting that some cognitive plasticity is maintained. The results showed medium effect sizes for potential transfer to everyday functional performance. However, the sample was only adequately powered to detect effect sizes of 0.53 or larger, another limitation of the current study. Further, as the training is adaptive, some participants may process through the training further and faster than others. Thus, it would be interesting to examine if differences in the level of training completed may affect performance outcomes. Unfortunately, individual training data were not available for the SKILL study, but future research should explore this possibility.

A major strength of this study is that it is one of the first to investigate everyday functional performance as an outcome of cognitive training among those with MCI. There is abundant evidence to suggest that cognitive training improves the cognitive abilities trained among healthy older adults (e.g., Kelly et al., 2014) and emerging evidence among those with MCI (Li et al., 2011; Teixeira et al., 2012). Despite the importance of demonstrating real world benefits of training, transfer to everyday abilities is relatively under-studied among healthy older adults (Kelly et al., 2014) and even less so among those with MCI. Unlike other studies of cognitive intervention, a strength is that SPT was compared to an active control condition that involved cognitively-stimulating activity.

Overall, these results and prior research suggest that process based, adaptive cognitive training may produce larger gains and be more likely to transfer than other approaches, particularly among individuals with MCI. Future research should further investigate the effects of adaptive SPT on everyday functional performance among those with clinically confirmed MCI in a well-powered randomized clinical trial.

Supplementary Material

1

Acknowledgements

The authors wish to acknowledge Dr. Karlene K. Ball, who was awarded the MERIT grant to conduct the SKILL study, and the investigators of SKILL, Drs. Daniel Roenker, Lesley Ross, David Roth, Virginia Wadley, and David Vance.

Funding

This research was supported in part by the National Institutes of Health/National Institute on Aging [5 R37 AG05739-16], and in part by the National Institutes of Health/National Institute on Aging [5 R36 AG049889-01].

Conflicts of Interest

From June to August 2008, Dr. Edwards worked as a limited consultant to Posit Science who, currently markets the speed of processing training software (now called BrainHQ Double Decision). Dr. Edwards currently serves on the data safety and monitoring board of NIH grants awarded to employees of Posit Science. The other authors report no conflicts to disclosure.

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