Daily function outcomes in adults with mild cognitive impairment due to Alzheimer's disease after two years of processing speed training versus a control training protocol

Abstract

Background

Cognitive processing speed is integral to everyday activities and can be improved with training in persons with mild cognitive impairment (MCI). However, whether this training maintains everyday abilities is not known.

Objective

We aimed to determine whether everyday functions key to independence could be preserved with two years of processing speed training.

Methods

In a randomized controlled trial, we objectively evaluated a processing speed training protocol compared to a control training protocol, in 103 persons with MCI (n = 90) or very mild dementia (n = 13) due to Alzheimer's disease (AD). Each protocol involved serial assessments, laboratory training, and home training over a two-year period. We accounted for APOE ε4 carrier status and MRI-based neurodegeneration conducted at baseline. Outcomes were longitudinal changes in performance-based Instrumental Activities of Daily Living (IADLs), community mobility, and on-road driving. We used linear mixed models to evaluate changes in these outcomes over time.

Results

Changes in IADL function, driving, and community mobility did not differ by training assignment. Greater baseline neurodegeneration predicted larger declines in all functional outcomes (p values < 0.001).

Conclusions

In persons with MCI or very mild dementia, processing speed training was no more effective for maintaining everyday functions than training involving common computer activities and games that do not target processing speed. Greater baseline neurodegeneration predicted worse performance over time on all measures of function.

Keywords

Alzheimer's disease cognitive training driving instrumental activities of daily living mild cognitive impairment neuroimaging processing speed SPARE-AD

Introduction

Processing speed is critical for performing cognitively demanding everyday activities. In persons with mild cognitive impairment (MCI) due to Alzheimer's disease (AD) or with very mild dementia, processing speed is strongly associated with driving skills, financial abilities, other instrumental activities of daily living (IADLs), and community mobility.¹ An evidence-based processing speed training (PST) protocol has been associated with both cognitive and functional gains in cognitively normal older adults² and with cognitive gains on specific tests in persons with MCI.³

The Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study, a controlled trial of three training protocols (memory, speed of processing, and reasoning) in 2832 older adults, showed that participants improved in the targeted cognitive domains⁴ and sustained these improvements over time.⁵ Retrospective analyses demonstrated, in a subsample of memory-impaired individuals, the capacity to derive durable cognitive benefits from speed of processing training and reasoning training, but not memory training.⁶ Additional research using the ACTIVE cohort found that in contrast to cognitive gains, self-reported IADL function worsened more over time in persons with retrospectively defined MCI than in cognitively normal adults.⁷ In that study, persons of all MCI subtypes (amnestic, non-amnestic, multi-domain and single-domain) reported significantly steeper rates of decline in function than cognitively normal peers.

We previously reported greater declines in both speed and accuracy of objectively measured IADL performance in a sample of persons with clinically diagnosed MCI relative to cognitively normal participants.⁸ Using on-road driving evaluations and performance-based measures of financial abilities and other cognitively demanding activities, declines were greater in persons with MCI. Thus, these results were concordant with previous research that used only subjective reports of function.

These findings suggested that a trial of processing speed training in a new sample of participants with MCI due to AD or with very mild dementia was warranted. Memory impairment is the primary early deficit in the majority of these individuals, but memory training has not improved memory in prior research.⁶ However, processing speed, which is highly related to IADL function, was shown to be amenable to training in persons with memory impairment.⁶ We reasoned that if processing speed can be improved in individuals with memory impairment, everyday activities crucial to independent functioning might be maintained. Maintaining these functions could effectively delay progression to dementia, which by definition includes dependence in daily activities.

We aimed to determine whether everyday functions key to independence could be preserved with two years of processing speed training. We evaluated training outcomes objectively in a randomized controlled trial of a processing speed training protocol (PST) in adults with MCI or mild dementia over two years of training. We compared an enriched version of PST to a closely matched control protocol (Internet Navigation Training—INT and Mentally Stimulating Activities–MST) that did not target processing speed. We also accounted for contributions of AD risk biomarkers measured at baseline, including APOE ε4 carrier status and brain neurodegeneration, using an MRI-based measure of AD signature region atrophy called the Spatial Pattern of Abnormality for Recognition of Early AD (SPARE-AD).

Methods

Participants and procedures

From 2014–2019 we conducted a randomized controlled trial of PST among persons with expert-adjudicated MCI or very mild dementia, in which AD was determined to be the primary or contributing etiology based on the National Institute on Aging-Alzheimer's Association clinical criteria⁹ at baseline. Recruitment took place continuously during the first three years of the 5-year study; each participant was involved in study activities for two years. All protocol activities were approved by the Institutional Review Boards of the institutions conducting the research and were in accordance with the Declaration of Helsinki.

Neurology, Neuropsychology, and Geriatrics clinics at the University of Alabama at Birmingham (UAB) identified patients who had received or maintained a clinical diagnosis of MCI within one year prior to being approached for permission to be screened for the study. The study screening visit (Baseline Visit 1) included the informed consent interview as well as medical history, a full battery of neuropsychological testing, and informant report of participants’ IADL functioning. Informants (spouses, partners, adult children) identified by the participants were queried via in-person interviews conducted by the study coordinator using the Functional Assessment Questionnaire (FAQ).¹⁰

The data obtained at the screening visit were used by a panel of expert study investigators (DG, DM, VW, MC) who adjudicated each case to establish the participant's current diagnosis.

Each participant's data were randomly assigned to two adjudicators. Cases of disagreement regarding diagnosis were reviewed by the entire panel in regularly scheduled meetings, and the majority opinion was then recorded as the diagnosis. Exclusion criteria, determined during the adjudication process, included normal cognition, advanced dementia, stroke within the past two years, and psychiatric conditions that could primarily account for cognitive decline. The adjudication procedure was repeated annually for each participant using the same criteria with data collected at the annual study visits. Performance-based measures of function, mobility, and driving were obtained at all subsequent study visits. As these measures constituted the primary outcomes of interest, they were not available to study adjudicators at any time point.

A total of 103 participants qualified and enrolled in the study. The final sample included persons who were not current drivers (n = 4); these participants completed all procedures except the on-road driving evaluation. We also included in the final sample persons who had contraindications for MRI (n = 17); these participants were included in all other protocol procedures. Persons on memory medications (donepezil, rivastigmine, memantine, or galantamine; n = 76) were also included, as were persons using online “brain training” programs such as Lumosity (n = 8).

Written informed consent was provided at Visit 1 and repeated annually. We incorporated the Competency Assessment Checklist for Research Informed Consent (^© Daniel Marson, JD, PhD, UAB 2002; MCI Study version 1 July 2004) in consent discussions.

Each eligible participant attended Baseline Visit 2, which involved objective testing of IADLs, saliva sampling, an on-road driving evaluation, and a structural brain MRI, followed by randomization to one of two training protocols, using simple 1:1 random assignment. We did not stratify randomization by any demographic parameter or by adjudicated diagnoses. Participants then were scheduled to engage in 8–10 h of lab-based training in PST or internet navigation training (INT) over a six-week period, followed by two years of home-based training using iPads provided by the study and supportive home visits.

Neuropsychological and functional testing were repeated after lab training and again at two annual visits during the home training phase. Primary outcomes were longitudinal changes in composite measures of cognitive processing speed and performance-based IADLs, community mobility, and on-road driving evaluations.

Figure 1 outlines the study phases from recruitment to enrollment, randomization, training, and follow-up assessments.

Figure 1.

Recruitment, enrollment, and randomization flowchart.

Measures

Composites

Prior to initiation of research, we defined composite measures representing processing speed (PSC), mobility (MC), and performance-based IADL function (functional composite—FC). We chose to use composite measures in order to avoid test-specific effects and as a data reduction strategy to preserve statistical power. Component tests (Table 1) were selected based on prior usage as exemplars of the constructs of interest. Even so, we acknowledge that none of these tests is purely a measure of only one domain or construct. For example, part-B of the Trail Making test involves not only processing speed but executive ability to control task switching as well. Test descriptions and composite construction have been described previously.¹ In brief, we converted component test scores to Z-scores using published age- and education-appropriate norms. Tests with raw scores representing time were reverse coded so that in all cases, higher scores represent better performance. We then averaged Z-scores within each domain to produce a single value for each composite at each testing occasion.

Table 1.

Composites of performance-based function and component tests.

Composite	Component Tests
Processing Speed Composite (PSC)^a	Wechsler Adult Intelligence Scale, fourth edition, Coding subtest¹¹ Consortium to Establish a Registry for Alzheimer's Disease Semantic Fluency (animals) subtest¹² Benton Controlled Oral Word Association test, Letter Fluency subtest¹³ Trail Making Test-part B¹⁴ Useful Field of View (UFOV^®)¹⁵ subtest 2
Functional Composite (FC)	Timed Instrumental Activities of Daily Living¹⁶ Financial Capacity Instrument-Short Form¹⁷ UFOV^® total score¹⁸
Mobility Composite (MC)	University of Alabama at Birmingham Life Space Assessment¹⁹ Road Sign Test⁴

We also used a partial PSC excluding only UFOV^® subtest 2 in models evaluating predictors of changes in the FC, because the FC includes the UFOV^® total score as an index of driving-related competencies¹⁸

To avoid potential overlap in measures within the composites, we also created a partial PSC that excluded UFOV^® subtest 2 (processing speed/divided attention) for models evaluating predictors of the FC, because the FC included the UFOV^® total score as a representative of driving-related competencies.⁴

Driving

A 45-min, fair weather driving assessment was conducted in urban and suburban settings in Birmingham, AL, by the UAB Driving Assessment Clinic (e.g.,²⁰). An occupational therapist (OTR/L) who was a certified driving rehabilitation specialist sat in the front passenger seat and instructed the participant, and a back seat coder (CO) evaluated skills and safety at 50 pre-determined points during the drive. Global ratings were recorded independently by each rater, using a 5-point Likert scale: 5 = optimal; 4 = few minor flaws/satisfactory; 3 = unsatisfactory but not unsafe; 2 = unsafe; 1 = drive terminated. Raters were masked to participants’ specific diagnoses and training assignments. The kappa value for interrater agreement was 0.86. The two ratings were averaged to form each participant's global driving performance score.

Genetic and neuroimaging markers of AD risk

Participants’ saliva (∼2 ml) was collected at baseline using Oragene DNA collection kits. Assays were performed in the laboratory of the UAB Department of Epidemiology. For the current analyses, only APOE ε4 carrier status was used. Structural MRI scans were performed on a single Philips 3 Tesla Achieva scanner; acquired sequences are described elsewhere²¹ but included isotropic 1 mm T1 images. Scans were de-identified and transmitted to the MRI Reading Center at the University of Pennsylvania. We calculated a machine-learning based index of AD-related atrophy (SPARE-AD) using T1-weighted images.²² SPARE-AD scores quantify AD-like brain patterns, with higher positive scores representing brains that are more AD-like than normal. SPARE-AD scores were strongly correlated with processing speed and daily functioning at baseline.¹

Training protocols

Both laboratory training protocols used in this study are outlined in standardized training manuals and have been used successfully in prior research (e.g., PST,⁴ INT²³).

Processing speed training modifications

We modified the standard PST protocol in two ways. First, in the ACTIVE sample, lab-based PST was administered to cognitively normal older adults in small groups over 8–10 sessions. Although durable training gains were detected in participants who had brief annual sessions of booster training, incidence of dementia over a 5-year period did not differ between training and control groups.²⁴ Thus, for our study among persons with MCI, we tested an enriched cognitive training, with the lab portion administered one-to-one by a certified trainer. Second, we followed lab training with home training, using a modified home-training paradigm that closely mirrored the standard lab protocol. We previously found that cognitively normal older adults who underwent 8 −10 h of home-based PST administered by videotape improved their processing speed significantly more than untrained and INT-trained control groups, with gains 74% as great as those in trainer-facilitated training in the lab^.²⁵ This research provided proof of concept that people can improve processing speed at home. For persons with MCI, we reasoned that extending the period of self-administered home training would provide additional support necessary for potential benefit.

Laboratory training procedures

The same trainer conducted PST and INT sessions. Participants who were randomly assigned to either PST or INT attended sessions of the same duration and frequency: 6–10 individual training sessions of 60–90 min over a 6-wk period. All participants completed 8 to 10 h of laboratory training, the standard for a full training regimen.

Processing speed training

Computer-administered tasks that are used to evaluate Useful Field of View¹⁵ are the basis for PST. Touch-screen technology allows computer novices to participate as easily as those familiar with computers. Training involves detection, localization, and discrimination of briefly displayed stimuli (17–500 ms) using tasks with varying demands on visual attention. Cognitive processing speed is defined as response accuracy, in milliseconds, at a given display duration, irrespective of motor speed. Practice continues until the participant can perform a task correctly 75% of the time at a given duration; training then progresses to more complex tasks. Task complexity is modified by holding display duration constant (at or slightly above the trainee's processing speed threshold), and gradually increasing the complexity of a central task, a peripheral task, or both through a variety of methods tailored to the ability of the participant. Individuals practice tasks at customized levels of difficulty until mastery is achieved.

Internet navigation training

INT has been used as an optimal control task for the PST intervention.²³ INT was developed to provide comparable levels of time and social contact components involved in PST, equivalent exposure to computers, and the opportunity to improve a skill. The protocol is face valid as a mentally stimulating activity and consists of three levels: 1) using a computer 2) internet search engine training, and 3) search engine proficiency tasks. Prior internet experience is assessed and dictates the level at which training will begin for each participant. Like PST, training involves verbal instruction followed by practice. Each task introduced during INT is practiced and mastered prior to the next task. As participants master skills, the difficulty of assignments is increased. While INT shares several characteristics with PST, the protocol is not designed to target cognitive processing speed.

Home training procedures. At the last session of lab-based training, participants in each arm were introduced to continuation training on iPads. Each training arm continued training consistent with their initial training assignment, at home, with study-distributed iPads and supportive home visits twice annually by the same trainer for two years. At the first home visit, the trainer issued each participant an iPad pre-loaded with training activities consistent with either PST or INT. Internet access was not required for either home-training paradigm.

PST home training consisted of selected modules of Brain HQ (^©2021, Posit Science) based on the PST paradigm.²⁶ The program includes ongoing determination of the user's level of ability and ongoing feedback regarding the user's progress. The user is guided by the program toward activities at or just above their level of complex attention. The study trainer instructed the participant in the use of this program and observed each participant's use at the first home visit for one hour. Participants were instructed to engage in practice serially among 40 speed activities for 1 h per week at a regular day and time chosen by the participant. Monthly phone calls from the trainer assessed problems and compliance, and the biannual home visits for 2 years provided additional support. At these subsequent visits, the trainer collected participants’ training logs, uploaded each participant's hours of program use to a secure portal, and observed the participant conducting a 15-min training session at their current level of ability as a compliance check or refresher training.

INT participants transitioned to mentally stimulating activities (MSA) selected from publicly available apps. Activities were crossword puzzles, word search, sudoku, solitaire, checkers, puzzles, and mazes, none of which places demands upon or “trains” processing speed. All other procedures (i.e., one hour per week of use on a day and time selected by the participant), biannual home visits, monthly phone calls and compliance checks were identical to those used in PST.

Participant satisfaction

At participants’ final study testing sessions, we administered an Exit Interview to query satisfaction with specific components of the study and overall, using a Likert scale ranging from 1 (not at all satisfied) to 5 (very satisfied) for each item.

Statistical analyses

Participant characteristics

Baseline characteristics of participants were described using means and standard deviations (SD) for continuous variables and frequencies (percentages) for categorical variables.

Primary analysis: longitudinal training effects

Distributions of participants’ performance on each composite at each time point were visually inspected and were tested for normality using the Shapiro-Wilk normality test.

We conducted longitudinal linear mixed effects models (random coefficients models)²⁷ evaluating changes over time in the composite Z-score outcomes by training arm, and raw score ratings of global driving performance, using an intent-to-treat paradigm. This statistical approach allows all randomized participants’ data to contribute to modeling, whether or not longitudinal testing visits were missed. Time was modeled as a continuous variable rather than categorizing by visit occasions.

We examined four outcomes: changes over time in the full and partial PSC, the FC, the MC, and the global driving score. We estimated the impact of training assignment, APOE ε4, and SPARE-AD on these outcomes. Because Z-scores for tests within composites of processing speed, function, and mobility were based on published normative values for age and education, we did not further adjust for these demographics in our models. The interaction of training group by follow-up time was the primary parameter of interest for each outcome. Results were considered statistically significant at p < 0.05.

Supplemental analyses

We conducted additional analyses to address questions that might help in interpreting the primary results. Hochberg correction for multiple comparisons was used to reduce the impact of multiple comparisons.

Adjudicated diagnoses

Although the distributions did not differ statistically, there were fewer MCI-amnestic single domain (ASD) and more MCI-amnestic multi-domain (AMD) in the PST group than the INT group. We therefore estimated the impact of these two baseline diagnoses on the full and partial PSC, FC, MC and Global Driving Score in mixed models, including interactions of diagnosis by training arm, by follow-up time, and by both training arm and follow-up time.

Participation in MRI

We used t-tests to compare the baseline characteristics of participants who underwent study imaging to those who were not MRI-eligible. We also compared the characteristics of the MRI participants only, by training assignment.

Compliance and satisfaction

We used Kruskal-Wallis test statistics to examine compliance by training group, using average and median hours spent in training per week over the course of each participant's study involvement. We also conducted t-tests to examine differences by training group in participant satisfaction items from the Exit Interview, including satisfaction with the overall program and ease of training, as well as enjoyment and motivation to continue similar activities after completing the study.

Transitions in diagnosis over time

We evaluated whether there were differences by training arm in diagnosis changes over time, using adjudicators’ classifications at baseline and at each participant's final study visit (Year 1 for those who subsequently dropped out or died, Year 2 for all others). Trajectories were coded as follows: Stable (no change from baseline diagnosis), Improved (from MCI to normal, or from MCI multi-domain to MCI single-domain), or Declined (from MCI single-domain to multi-domain, from MCI to dementia).

Results

We examined density plots of score distributions for each composite at each time point, as well as results of Shapiro-Wilk normality tests. Participants’ PST and the MC scores were normally distributed until the Year 2 assessment, while the FC scores and Global Driving ratings were not normally distributed at any time point (Supplemental Figure 1).

Participant characteristics

Table 2 displays characteristics of the participants overall and by training arm. Random assignment resulted in equivalent groups with respect to age, distribution of sex and race, neuropsychological test performance, APOE ε4 carrier status, pre-specified neuroimaging parameters, and distribution of diagnoses on the MCI spectrum.

Table 2.

Baseline characteristics of participants overall and by training group.

	All(N = 103)	PST(N = 53)	INT/MSA(N = 50)	p	N
Age (y)	73.5 (7.37)	73.3 (7.73)	73.7 (7.05)	0.786	103
Women (self-report)	54 (52.4%)	28 (52.8%)	26 (52.0%)	1.000	103
Race (self-report)				0.324	103
Black or African American	9 (8.74%)	6 (11.3%)	3 (6.00%)
Asian	2 (1.94%)	0 (0.00%)	2 (4.00%)
White	92 (89.3%)	47 (88.7%)	45 (90.0%)
Education (y)	15.6 (2.73)	15.3 (2.76)	15.9 (2.70)	0.319	103
CES-D^a raw score	9.42 (7.47)	10.3 (8.64)	8.48 (5.93)	0.213	103
FAQ^b score	4.16 (3.56)	4.08 (3.78)	4.24 (3.36)	0.819	99
DRS-2^c raw score	130 (7.98)	129 (8.76)	131 (6.88)	0.124	101
On memory medications	76 (73.1%)	37 (71.2%)	37 (75.5%)	0.850	103
APOE ε4^d carrier	60 (58.3%)	32 (60.4%)	28 (56.0%)	0.802	103
SNP^e score	0.84 (0.15)	0.86 (0.13)	0.83 (0.16)	0.319	103
LH-adj^f (%)	25.6 (3.36)	25.7 (3.31)	25.4 (3.44)	0.720	86
WML volume^g(% of WM volume)	0.89 (1.01)	0.70 (0.68)	1.08 (1.24)	0.082	86
SPARE-AD score^h	1.28 (1.69)	1.52 (1.81)	1.04 (1.53)	0.194	86
Composite Z-scores
Processing Speed Composite- fullⁱ	-0.71 (0.82)	-0.75 (0.77)	-0.66 (0.88)	0.550	103
Processing Speed Composite- partial^j	-0.66 (0.83)	-0.69 (0.75)	-0.62 (0.90)	0.702	103
Functional Composite^k	-0.89 (0.96)	-0.99 (0.96)	-0.79 (0.96)	0.292	103
Mobility Composite^l	-1.53 (1.19)	-1.63 (1.29)	-1.42 (1.09)	0.374	103
Global Driving Score^m	3.97 (0.96)	3.93 (1.07)	4.02 (0.83)	0.643	99
Diagnosisⁿ				0.578	103
pMCI	9 (8.74%)	5 (9.43%)	4 (8.00%)
ASD	30 (29.1%)	11 (20.8%)	19 (38.0%)
AMD	45 (43.7%)	26 (49.1%)	19 (38.0%)
NASD	3 (2.91%)	2 (3.77%)	1 (2.00%)
NAMD	1 (0.97%)	1 (1.89%)	0 (0.00%)
Dementia	13 (12.6%)	7 (13.2%)	6 (12.0%)
CI-CC	2 (1.94%)	1 (1.89%)	1 (2.00%)

CES-D, Center for Epidemiologic Studies Depression Scale, range 0–60. Higher scores indicate more depressive symptoms.

FAQ, Functional Assessment Questionnaire, administered to informant, range 0–30. Higher scores indicate more difficulty, assistance or dependence in everyday instrumental activities.

DRS-2, Mattis Dementia Rating Scale v. 2, range 0–144. Higher scores indicate better cognitive function.

APOE ε4, apolipoprotein ε4 allele. Positive cases are carriers of 1 or both ε4 alleles.

SNP score, product of the odds ratios for AD for 10 SNPs from www.alzgene.org (mutant vs. ancestral). The SNPs are CLU-rs11136000, PICALM1-rs3851179, PICALM1-rs541458, TNF-α-rs1800629, and six SORL1 SNPs (rs668387, rs689021, rs641120, rs3824968, rs2282649, and rs1010159).

Left hippocampal volume expressed as percentage of total intracranial volume. The parameter estimate for LH-adjusted has been multiplied by 1000 for the ease of reading.

WML, Total White Matter Lesion volume, expressed as the volume of abnormal white matter as percentage of total white matter.

SPARE-AD, Spatial Pattern of Abnormality for Recognition of Early AD a multivariate metric of global cerebral atrophy.

Processing Speed Composite, Full is the mean of: 1) WAIS-IV Coding Z-score 2) Semantic fluency Z-score 3) COWA Z-score 4) Trails B Z score (truncated at −3) 5) UFOV subtest 2 (divided attention) Z-score (normalized, reversed and truncated at −3). Higher scores represent better processing speed.

Processing Speed Composite, Partial is the mean of: 1) WAIS-IV Coding Z-score 2) Animal fluency Z-score 3) COWA Z-score 4) Trails B Z score (truncated at −3). Higher scores represent better processing speed.

Functional Composite is the mean of: 1) TIADL Z-score (mean of 5 domain Z scores for completion times, reversed and truncated at −3) 2) FCI total Z-score (FCI total → Age-adjusted scaled score → age/education-adjusted scaled score → Z score) 3) UFOV Z-score (UFOV total score → normalized and reversed, truncated at −3). Higher scores represent better everyday IADL function.

Mobility Composite is the mean of: 1) Life space Z-score 2) Road Signs Test Z-score. Higher scores represent better mobility and greater road awareness.

Global Driving Score is derived from the on-road driving assessment and is the mean of: 1) Front seat coder rating and back seat coder ratings, range 1–5. Higher scores indicate better driving skills.

ⁿ

Diagnosis adjudicated at baseline: pMCI—possible MCI (evidence of decline without frank impairment); ASD—Amnestic Single Domain MCI; AMD—Amnestic Multi Domain MCI; NASD—Non Amnestic Single Domain MCI; NAMD—Non Amnestic Multi Domain MCI; Dementia (very mild dementia in which Alzheimer's Disease is presumed to be a contributing etiology); CI-CC—Cognitive Impairment, Cannot Classify.

Primary outcomes

Models of composite outcomes were estimated with planned adjustment for baseline SPARE-AD scores and APOE ε4 carrier status (Table 3). Models excluding these biomarkers, which included the additional 17 participants who did not undergo MRI, replicated the adjusted models’ results with respect to the direction and significance levels for the interaction of training arm by follow-up time over two years (data not shown). In the fully adjusted models, the interaction of training arm by follow-up time revealed small but significant differences in performance change only on the partial PSC. The PST arm had more decline in scores than the control training arm (p < 0.05) on this partial PSC, which excluded subtest 2 of the UFOV^® measure—the foundation on which PST training was based. Also contrary to our hypotheses, there was no difference between training arms in changes on the FC, the MC, or Global Driving scores. Greater neurodegeneration, as measured by SPARE-AD scores, was negatively associated with all composite scores and Global Driving scores at baseline (p values < 0.001) and predicted larger declines over time on the FC (p < 0.05) and the full and partial PSC (p values < 0.001). APOE ε4 carrier status did not significantly predict change on any outcome after adjusting for other covariates.

Table 3.

Longitudinal linear mixed models of composite outcomes.

	Functional Composite	Processing SpeedComposite	Partial Processing SpeedComposite	Global Driving Score	Mobility Composite
Predictors	Estimates	Estimates	Estimates	Estimates	Estimates
Follow-up time	0.07(F = 0.611, p = 0.437)	0.04(F = 0.026, p = 0.873)	0.03(F = 2.899, p = 0.093)	−0.09(F = 0.493, p = 0.484)	−0.06(F = 0.046, p = 0.831)
Training-PST^a	0.05(F = 0.044, p = 0.834)	0.03(F = 0.022, p = 0.882)	−0.04(F = 0.042, p = 0.838)	0.11(F = 0.441, p = 0.508)	0.22(F = 0.957, p = 0.331)
SPARE-AD^b	−0.27 ***(F = 15.985, p < .001)	−0.16 **(F = 9.982, p = 0.002)	−0.15 **(F = 8.081, p = 0.006)	−0.20 ***(F = 17.100, p < .001)	−0.28 ***(F = 18.205, p < .001)
APOE ε4-Yes^c	−0.04(F = 0.033, p = 0.857)	−0.08(F = 0.231, p = 0.632)	−0.07(F = 0.149, p = 0.700)	−0.02(F = 0.016, p = 0.899)	0.10(F = 0.193, p = 0.662)
Time*Training-PST	−0.03(F = 0.091, p = 0.764)	−0.09(F = 3.351, p = 0.071)	−0.17 **(F = 10.411, p = 0.002)	0.08(F = 0.374, p = 0.542)	0.08(F = 0.238, p = 0.627)
Time*SPARE-AD	−0.08 *(F = 5.782, p = 0.019)	−0.06 ***(F = 15.415, p < .001)	−0.06 ***(F = 13.814, p < .001)	−0.05(F = 1.614, p = 0.206)	−0.04(F = 0.844, p = 0.362)
N	86	86	86	83	86
Observations	302	316	315	285	294
Marginal R² / Conditional R²	0.194 / 0.810	0.173 / 0.910	0.177 / 0.925	0.257 / NA	0.162 / 0.685

Estimates that include follow-up time represent annual change.

PST—Processing Speed Training (Estimates displayed are for PST; Internet Navigation Training/Mentally Stimulating Activities is the reference group)

SPARE-AD— Spatial Pattern of Abnormality for Recognition of Early AD, a multivariate metric of global cerebral atrophy

APOE ε4, apolipoprotein ε4 carrier. Positive cases are carriers of 1 or 2 ε4 alleles.

* p < 0.05; ** p < 0.01; *** p < 0.001

Composite and driving score trajectories by training arm and time are displayed in Figure 2.

Figure 2.

Longitudinal trajectories for composite and driving outcomes by training group.

Results of supplemental analyses

Diagnoses: MCI-ASD versus MCI-AMD

Our initial model examining potential differences in training outcomes by baseline diagnosis of MCI-ASD versus MCI-AMD included two- and three-way interactions of diagnosis, training group, and follow-up time. The three-way interaction was not significant and was removed from the model. The remaining results showed that irrespective of training arm, participants with ASD performed significantly better overall than those with AMD on the PSC (p < 0.001) and the partial PSC (p < 0.001), as well as the MC (p < 0.05), but not on the FC or Global Driving scores (all p values > 0.05) (Supplemental Table 1). There was no interaction of diagnosis by training group on any outcome, indicating that training outcomes were not explained by diagnoses of ASD versus AMD.

Participation in MRI

Only MRI participants were included in the final study models that included the SPARE-AD predictor. We found that participants who underwent MRI (n = 86) did not differ from those who were not eligible for MRI (n = 17) in any demographic characteristic. Furthermore, the MRI participants did not differ by their assigned training arms on any of these characteristics (all p values > 0.10; data not shown).

Compliance and satisfaction with training

Participants in the PST group spent an average of 30 min per week (SD 58) and median of 16.4 min (Inter quartile range [IQR] 4.6–31.5) in PST home training. INT/MSA participants spent an average of 120 min per week (SD 139) and median of 58.9 min (IQR 23.8–169.5) in MSA home training (p < 0.001). There were no differences by training arm in participants’ ratings of satisfaction with the program, ease of training, enjoyment, or motivation to continue such activities (p values >0.10, data not shown). Ratings obtained on these items ranged from 4.3–4.7 on the 5-point Likert scale.

Transitions in diagnosis over time

There was no difference between the two training arms regarding diagnosis changes over time (p = 0.33), as determined at each participant's final study visit. Among the 90 participants with MCI at baseline, 34 declined (i.e., from single domain to multi-domain MCI, or from either MCI category to dementia), 11 improved, and 39 remained stable; 6 participants had no follow-up. No participant with a baseline diagnosis of dementia improved to MCI or normal at their final visit.

Discussion

Main finding

In a rigorous, randomized controlled trial among persons on the continuum of MCI to very mild dementia, processing speed training was no more effective for maintaining important everyday functions, including driving and handling finances, than training in common mentally stimulating activities that neither require processing speed nor target improvement of cognitive processing speed. Thus, our hypothesis that processing speed training would produce superior functional outcomes was not supported. Individuals with greater degrees of brain neurodegeneration at baseline performed worse over two years on several measures of function, irrespective of training type.

Relationship to prior research

With respect to processing speed outcomes, our immediate PST lab training results are similar to those of Lin and colleagues,²⁸ in which investigators randomly assigned participants with amnestic MCI to six weeks of visual processing speed/ attention training or a control condition. The intervention group had greater immediate improvements on the UFOV task, similar to our PST arm's initial gains on this task after lab training. However, the intervention group in that study maintained UFOV gains at 6-month follow-up,²⁸ whereas our study found no durable improvements in processing speed over 2 years. Had we focused on test-specific changes, we would have examined only test-specific improvements. Instead, we focused on outcomes consisting of changes in everyday function. These outcomes represent an important step in the field of cognitive training. The impact of these functional activities on one's daily life, particularly in the context of MCI or AD, is arguably more meaningful than a narrow focus on a trained cognitive task.

Our results suggesting a lack of apparent benefits are not unprecedented in the field of cognitive training, both in the contexts of normal aging, MCI,²⁹ and dementia.³⁰ Indeed, some research has found superior cognitive and quality of life results in active control groups (e.g., a non-specific computer training relative to cognition-specific computer training).³¹ Our study found better compliance and subsequent preservation of some functions in our active control group. Other processing speed intervention studies that have used BrainHQ modules in persons with MCI or subjective cognitive decline have produced mixed results with respect to cognitive outcomes, ranging from no significant effects³² to test-specific effects³³ and effects on global cognition.³⁴ Our research extends these findings using an existing processing speed training protocol delivered with the additional support of one-to-one training and home visits over two years. We also focused on practical outcomes that are key to independent functioning rather than on cognitive outcomes.

Persons with multi-domain MCI had worse functional performance at baseline than those with single-domain MCI, but longitudinal changes in function did not differ significantly according to either initial diagnosis or training type. Persons with more severe AD-like neurodegeneration declined more over time in processing speed and IADL function. The consistent effect of baseline brain atrophy on functional decline did not differ by training arm. A prior study of cognitive training found a trend toward an association between neurodegeneration and fewer improvements or greater declines in cognitive outcomes.³⁴ Our research shows that one's baseline degree of AD-like neurodegeneration also reliably predicts declines over time in important daily activity outcomes integral to independence.

APOE ε4 carrier status affected neither training outcomes nor rate of functional decline. This finding may be due to lack of power to detect decline associated with this particular biomarker³⁵ or due to APOE ε4 genotype being associated with risk of AD onset rather than progression in MCI or early AD.³⁶ Furthermore, It is likely that APOE ε4 carrier status overlaps with other factors such as SPARE-AD scores and was not significantly associated with functional decline for that reason.

Annual rates of progression from MCI to dementia have been reported to range from 17–20% in clinical samples whose MCI is attributed to prodromal AD.³⁷ In other research, 15–20% of persons with MCI have been found to have improvement in cognitive function 1–2 years after diagnosis.³⁸ In our sample of persons with MCI at baseline, collapsed across training conditions, 38% of participants declined to either multi-domain MCI or dementia over two years, while 12% improved in diagnosis and 43% remained stable. The similarity of our disease progression outcomes over two years with previously published annual estimates suggests that our training protocols did little to alter expected rates of progression or reversion of diagnoses.

Compliance and satisfaction

Unexpectedly, we found rather stark differences by training arm in compliance with home training. This difference occurred despite monthly supportive phone calls and biannual home visits by the study trainer in both training arms. PST participants were less likely to spend a full hour per week in their home training activities, while INT/MSA participants averaged two hours per week in their training activities. In accordance with our strict protocol, we did not lend extra support to individuals in either training arm.

Despite compliance differences, there were no differences by training type on any index of self-reported program enjoyment and satisfaction. Anecdotally, participants in both training arms expressed high self-efficacy even in the absence of symptom improvement. However, the study trainer observed that participants in the PST arm had more difficulty with PST activities than those in the INT/MSA arm did with their assigned activities. Task difficulty and lower compliance may account for our finding that PST was not superior to INT/MSA with respect to functional outcomes. While unanticipated, this finding suggests that home training in this particular PST paradigm may require further modification and additional support for persons with clinically relevant cognitive impairment.

Study strengths and limitations

To our knowledge, this is the first study that has evaluated intensive one-on-one processing speed training followed by two years of home training and home visits in persons with adjudicated MCI or early dementia due to AD. Including MRI, serial testing, and on-road driving evaluations in a sample of persons with well-defined MCI is a strength of this research. Importantly, this is the first trial to specify longitudinal changes in crucial, objectively measured IADLs, driving, and mobility as the primary outcomes of interest.

We did not compare outcomes among participants who were using acetylcholinesterase inhibitors to participants who were not.³⁹ Had we conducted a post hoc analysis in this regard, the variety of memory medications participants were using would likely muddy interpretation. In addition, any medication-related differences in training outcomes, if found, would beg the question of possible confounding by the indications for which these medications were prescribed.

We included an important but limited set of easily obtained biomarkers (saliva-based APOE, MRI-based neurodegeneration) as potential contributors to or confounders of training outcomes. However, there certainly are many potential biological attributes or physiological processes of scientific interest in determining which persons might benefit from training. For example, future research might evaluate pre-to- post-training changes in biomarkers of oxidative stress⁴⁰ or brain-derived neurotrophic factor⁴¹ as potential moderators or mediators relevant to outcomes.

Because our sample consisted of clinically referred participants in the southern U.S., our results may not generalize to community samples of persons with MCI, persons in other regions of the U.S., or to persons with MCI without AD as a contributing etiology. However, our research was more broadly inclusive than many AD trials, as we did not exclude persons on memory medications or with vascular risk factors, nor did we require MRI eligibility.

As noted, home compliance in the PST arm was suboptimal. This finding highlights the lack of feasibility of home training with this particular protocol in a clinically impaired population. Finally, we did not include a no-contact control condition that might have clarified whether cognitive training in general results in better functional outcomes than no training.

Conclusions

Cognitive training among persons with clinical impairment is controversial. Indeed, an extensive review of the cognitive training literature concluded that there is little evidence that “brain training” in general augments performance of cognitively demanding activities in everyday life.⁴²

Our findings suggest that home-based PST training should be simplified for persons with MCI or dementia and/or combined with a more diverse set of mentally stimulating activities to enhance engagement. As with medication trials, this specific cognitive training might hold more promise for improving or maintaining daily function in persons in preclinical stages of disease or with a family history of dementia but little evidence of neurodegeneration. Combining cognitive training with physical activity interventions may also lead to better outcomes than cognitive training alone for maintaining function.⁴³ There is some evidence that virtual reality training, in which daily IADL tasks are simulated, can provide some direct IADL benefits in the context of MCI and dementia.⁴⁴

The lack of demonstrable gains in daily function with this specific training protocol, evaluated objectively in a rigorous trial among persons with MCI or very mild dementia, is an important addition to the cognitive training literature.

Supplemental Material

sj-docx-1-alz-10.1177_13872877251351341 - Supplemental material for Daily function outcomes in adults with mild cognitive impairment due to Alzheimer's disease after two years of processing speed training versus a control training protocol

Supplemental material, sj-docx-1-alz-10.1177_13872877251351341 for Daily function outcomes in adults with mild cognitive impairment due to Alzheimer's disease after two years of processing speed training versus a control training protocol by Virginia G Wadley, Yue Zhang, Tyler Bull, Cheyanne Barba, Yvonne Bolaji, R Nick Bryan, Michael Crowe, Lisa Desiderio, Guray Erus, David S Geldmacher, Rodney Go, Caroline L Lassen-Greene, Olga A Mamaeva, Daniel C Marson, Marianne McLaughlin, Ilya M Nasrallah, Cynthia Owsley, Jesse Passler, Rodney T Perry, Giovanna Pilonieta, Kayla A Steward, Andrea Wood and Richard E Kennedy in Journal of Alzheimer's Disease

Footnotes

Acknowledgements

We gratefully acknowledge our participants and their family members for their contributions to this research.

ORCID iDs

Virginia G Wadley

Yvonne Bolaji

Giovanna Pilonieta

Ethical considerations

This study received ethical approval from the University of Alabama at Birmingham IRB (approval #140714001) on August 06 2014.

Consent to participate

The study was approved by the University of Alabama at Birmingham IRB (Protocol # 140714001) on August 6, 2014 and received continuing review annually prior to study completion. All participants provided written informed consent prior to participating and continuing written informed consent at annual study visits.

Author contributions

Virginia G Wadley: Conceptualization; Funding acquisition; Investigation; Methodology; Project administration; Writing - original draft.

Yue Zhang: Formal analysis; Writing - review & editing.

Tyler Bull: Conceptualization; Data curation; Methodology; Validation; Writing - review & editing.

Cheyanne Barba: Methodology; Validation; Writing - review & editing.

Yvonne Bolaji: Conceptualization; Methodology; Writing - review & editing.

R Nick Bryan: Conceptualization; Investigation; Methodology; Writing - review & editing.

Michael Crowe: Conceptualization; Investigation; Writing - review & editing.

Lisa Desiderio: Conceptualization; Methodology; Project administration; Writing - review & editing.

Guray Erus: Conceptualization; Methodology; Writing - review & editing.

David S Geldmacher: Investigation; Methodology; Writing - review & editing.

Rodney Go: Conceptualization; Investigation; Writing - review & editing.

Caroline L Lassen-Greene: Conceptualization; Methodology; Validation; Writing - review & editing.

Olga A Mamaeva: Data curation; Investigation; Methodology; Writing - review & editing.

Daniel C Marson: Investigation; Methodology; Writing - review & editing.

Marianne McLaughlin: Methodology; Project administration; Writing - review & editing.

Ilya M Nasrallah: Conceptualization; Investigation; Methodology; Resources; Software; Writing - review & editing.

Cynthia Owsley: Conceptualization; Investigation; Methodology; Resources; Writing - review & editing.

Jesse Passler: Data curation; Methodology; Validation; Writing - review & editing.

Rodney T Perry: Conceptualization; Data curation; Investigation; Methodology; Resources; Writing - review & editing.

Giovanna Pilonieta: Methodology; Project administration; Writing - review & editing.

Kayla A Steward: Conceptualization; Data curation; Methodology; Validation; Writing - review & editing.

Andrea Wood: Data curation; Methodology; Validation; Writing - review & editing.

Richard E Kennedy: Conceptualization; Data curation; Formal analysis; Investigatio; Methodology; Software; Validation; Writing - review & editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute on Aging (Grant No. NIA: R01 AG045154, P30 AG022838) and the National Eye Institute (NEI: P30EY03039) at the National Institutes of Health, as well as by Research to Prevent Blindness and the EyeSight Foundation of Alabama. Posit Science granted access to Processing Speed modules of Brain HQ free of charge for research purposes. None of these funding sources was involved in study design; collection, analysis or interpretation of data; in writing or reviewing the report; or in the decision to submit the article for publication.

Declaration of conflicting interests

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

Data availability

The data supporting the findings of this study are available on request from authors Drs. Richard E. Kennedy and Yue Zhang.

Supplemental material

Supplemental material for this article is available online.

References

Wadley

Bull

Zhang

, et al. Cognitive processing speed is strongly related to driving skills, financial abilities, and other instrumental activities of daily living in persons with MCI and mild dementia. J Gerontol A Biol Sci Med Sci 2021; 76: 1829–1838.

Willis

Tennstedt

Marsiske

, et al. Long-term effects of cognitive training on everyday functional outcomes in older adults. JAMA 2006; 296: 2805–2814.

Rosen

Sugiura

Kramer

, et al. Cognitive training changes hippocampal function in mild cognitive impairment: a pilot study. J Alzheimers Dis 2011; 26: 349–357.

Ball

Berch

Helmers

, et al. Effects of cognitive training interventions with older adults: a randomized controlled trial. JAMA 2002; 288: 2271–2281.

Rebok

Ball

Guey

, et al. Ten-year effects of the advanced cognitive training for independent and vital elderly cognitive training trial on cognition and everyday functioning in older adults. J Am Geriatr Soc 2014; 62: 16–24.

Unverzagt

Kasten

Johnson

, et al. Effect of memory impairment on training outcomes in ACTIVE. J Int Neuropsychol Soc 2007; 13: 953–960.

Wadley

Crowe

Marsiske

, et al. Changes in everyday function in individuals with psychometrically defined mild cognitive impairment in the advanced cognitive training for independent and vital elderly study. J Am Geriatr Soc 2007; 55: 1192–1198.

Lassen-Greene

Steward

Okonkwo

, et al. Mild cognitive impairment and changes in everyday function over time: the importance of evaluating both speed and accuracy. J Geriatr Psychiatry Neurol 2017; 30: 220–227.

Carrillo

Dean

Nicolas

, et al. Revisiting the framework of the national institute on aging-Alzheimer's association diagnostic criteria. Alzheimers Dement 2013; 9: 594–601.

10.

Pfeffer

Kurosaki

Harrah Jr

, et al. Measurement of functional activities in older adults in the community. J Gerontol 1982; 37: 323–329.

11.

Wechsler

. Wechsler Adult Intelligence Scale. 4th ed. San Antonio, TX: Pearson, 2008.

12.

Morris

Heyman

Mohs

, et al. The consortium to establish a registry for Alzheimer’s disease (CERAD) part I. Clinical and neuropsychological assessment of Alzheimer's disease. Neurology 1989; 39: 1159–1165.

13.

Ruff

Light

Parker

, et al. Benton controlled oral word association test: reliability and updated norms. Arch Clin Neuropsychol 1996; 11: 329–338.

14.

Reitan

Wolfson

. The Halstead–Reitan Neuropsychological Test Battery: Therapy and clinical interpretation. Tucson, AZ: Neuropsychological Press, 1985.

15.

Edwards

Vance

Wadley

, et al. Reliability and validity of useful field of view test scores as administered by personal computer. J Clin Exp Neuropsychol 2005; 27: 529–543.

16.

Owsley

Sloane

McGwin Jr

, et al. Timed instrumental activities of daily living tasks: relationship to cognitive function and everyday performance assessments in older adults. Gerontology 2002; 48: 254–265.

17.

Gerstenecker

Eakin

Triebel

, et al. Age and education corrected older adult normative data for a short form version of the Financial Capacity Instrument. Psychol Assess 2016; 28: 737–749.

18.

Owsley

Ball

Sloane

, et al. Visual processing impairment and risk of motor vehicle crash among older adults. JAMA 1998; 279: 1083–1088.

19.

Baker

Bodner

Allman

. Measuring Life-Space mobility in community-dwelling older adults. J Am Geriatr Soc 2003; 51: 1610–1614.

20.

Wadley

Okonkwo

Crowe

, et al. Mild cognitive impairment and everyday function: an investigation of driving performance. J Geriatr Psychiatry Neurol 2009; 22: 87–94.

21.

Steward

Kennedy

Erus

, et al. Poor awareness of IADL deficits is associated with reduced regional brain volume in older adults with cognitive impairment. Neuropsychologia 2019; 129: 372–378.

22.

Fan

Batmanghelich

Clark

, et al. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 2008; 39: 1731–1743.

23.

Vance

Dawson

Wadley

, et al. The Accelerate study: the longitudinal effect of speed of processing training on cognitive performance of older adults. Rehabil Psychol 2007; 52: 89–96.

24.

Unverzagt

Guey

Jones

, et al. ACTIVE Cognitive training and rates of incident dementia. J Int Neuropsychol Soc 2012; 18: 669–677.

25.

Wadley

Benz

Ball

, et al. Development and evaluation of home-based speed of processing training for older adults. Arch Phys Med Rehabil 2006; 87: 757–763.

26.

Delahunt

Ball

Roenker

, et al. Evolution of useful field of view training. San Francisco: Posit Science, 2009.

27.

Brown

Prescott

. Applied mixed models in medicine (statistics in practice). 3rd ed. Chichester, UK: Wiley, 2015.

28.

Lin

Tao

Chen

, et al. Processing speed and attention training modifies autonomic flexibility: a mechanistic intervention study. Neuroimage 2020; 213: 116730.

29.

Vermeij

Claassen

Dautzenberg

, et al. Transfer and maintenance effects of online working-memory training in normal ageing and mild cognitive impairment. Neuropsychol Rehabil 2016; 26: 783–809.

30.

Kallio

Ohman

Hietanen

, et al. Effect of cognitive training on cognition and quality of life of older persons with dementia. Randomized controlled trial. J Am Geriatr Soc 2018; 66: 664–670.

31.

Park

. Does cognition-specific computer training have better clinical outcomes than non-specific computer training? A single-blind, randomized controlled trial. Clin Rehabil 2018; 32: 213–222.

32.

Barnes

Yaffe

Belfor

, et al. Computer-based cognitive training for mild cognitive impairment: results from a pilot randomized, controlled trial. Alzheimer Dis Assoc Disord 2009; 23: 205–210.

33.

McMaster

Kim

Clare

, et al. Lifestyle risk factors and cognitive outcomes from the multidomain dementia risk reduction randomized controlled trial, Body Brain Life for Cognitive Decline (BBL-CD). J Am Ger Soc 2020; 68: 2629–2637.

34.

Bamidis

Fissler

Papageorgiou

, et al. Gains in cognition through combined cognitive and physical training: the role of training dosage and severity of neurocognitive disorder. Front Aging Neurosci 2015; 7: 152.

35.

Stone

Molony

Suver

, et al. Apoe genotyping as a progression-rate biomarker in phase II disease-modification trials for Alzheimer’s disease. Pharmacogenomics J 2010; 10: 161–164.

36.

Suzuki

Hirakawa

Ihara

, et al. Effect of apolipoprotein E ε4 allele on the progression of cognitive decline in the early stage of Alzheimer’s disease. Alzheimers Dement (N Y) 2020; 6: e12007.

37.

Plassman

Langa

Fisher

, et al. Prevalence of cognitive impairment without dementia in the United States. Ann Intern Med 2008; 148: 427–434.

38.

Koepsell

Monsell

. Reversion from mild cognitive impairment to normal or near-normal cognition: risk factors and prognosis. Neurology 2012; 79: 1591–1598.

39.

Giacconi

Giuli

Casoli

, et al. Acetylcholinesterase inhibitors in Alzheimer’s disease influence zinc and copper homeostasis. J Trace Elem Med Biol 2019; 55: 58–63.

40.

Fattoretti

Malavolta

Fabbietti

, et al. Oxidative stress in elderly with different cognitive status: my Mind project. J Alzheimers Dis 2018; 63: 1405–1414.

41.

Balietti

Giuli

Casoli

, et al.

Is brain-derived neurotrophic factor a useful biomarker to monitor mild cognitive impairment patients?

Rejuvenation Res 2020; 23: 411–419.

42.

Simons

Boot

Charness

, et al.

Do “Brain-Training” programs work?

Psychol Sci Public Interest 2016; 17: 103–186.

43.

Sokolov

Collignon

Bieler-Aeschlimann

. Serious video games and virtual reality for prevention and neurorehabilitation of cognitive decline because of aging and neurodegeneration. Curr Opin Neurol 2020; 33: 239–248.

44.

Son

Park

. Ecological effects of VR-based cognitive training on ADL and IADL in MCI and AD patients: a systematic review and meta-analysis. Int J Environ Res Public Health 2022; 19: 15875.

Supplementary Material

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