Computerized Cognitive and Skills Training in Older People with Mild Cognitive Impairment: Using Ecological Momentary Assessment to Index Treatment Related Changes in Real-World Performance of Technology-Dependent Functional Tasks

Abstract

Objectives:

Cognitive and functional skills training improves skills and cognitive test performance, but the true test of efficacy is real-world transfer. We trained participants with mild cognitive impairment (MCI) or normal cognition (NC) for up to 12 weeks on six technology-related skills using remote computerized functional skills assessment and training (FUNSAT^™) software. Using ecological momentary assessment (EMA), we measured real-world performance of the technology-related skills over 6 months and related EMA-identified changes in performance to training gains.

Design.

Randomized clinical trial with post-training follow-up.

Setting:

14 Community centers in New York City and Miami.

Participants.

Older adults with normal cognition (n=72) or well-defined MCI (n = 92), ranging in age from 60-90, primarily female, and racially and ethnically diverse.

Intervention:

Computerized cognitive and skills training.

Measurements.

EMA surveys measuring trained and untrained functional skills 3 or more days per week for 6 months and training gains from baseline to end of training.

Results:

Training gains in completion times across all 6 tasks were significant (p<.001) for both samples, with effect sizes more than 1.0 SD for all tasks. EMA surveys detected increases in performance for both trained (p<.03) and untrained (p<.001) technology-related skills for both samples. Training gains in completion times predicted increases in performance of both trained and untrained technology-related skills (all p<.001).

Conclusions.

Computerized training produces increases in real-world performance of important technology-related skills. These gains continued after the end of training, with greater gains in MCI participants.

Introduction

The older population will grow significantly in the future. In 2040¹ there will be 80 million people older than age 65 in the US, double that of 2000. With these projections, the prevalence of dementia and precursor conditions such as mild cognitive impairment (MCI) are global concerns. MCI is defined by cognitive functioning between normal and dementia, reflecting a decline from previous functioning. Amnestic MCI (a-MCI²) has been found to predict increased risk for developing Alzheimer’s Disease (AD)³.

In this increasingly technology-driven world, older adults are disadvantaged by deficits in technological skills ⁴. Many are challenged in navigating the internet to perform financial tasks such as online shopping and banking, or health management tasks like managing insurance (e.g., Medicare) or appointments and refilling prescriptions^5–9. The continuous updating of technology and decreases in access to alternative strategies (i.e., closing bank branches; elimination of paper prescriptions) compounds these challenges.

Even with cognitive decline and limited prior experience, evidence suggests that older individuals retain cognitive plasticity, benefitting from computerized training. Meta-analyses have shown benefits of computerized cognitive training (CCT) in healthy older individuals and those with MCI^10–11. Our studies have shown that older adults can improve significantly in simulations of everyday tasks, such as ATM and internet banking, and develop mastery of trained tasks, as well as near to transfer to improvements in cognitive performance^12–13.

A critical question regarding computerized training is the extent to which training generalizes to real-world activities¹⁴. CCT has been shown to improve previously acquired skills, such as driving¹⁵, but not acquisition of novel skills¹⁶, which may require targeted training. There are several levels of improvement associated with computerized skills training: 1) Gains in the trained tasks¹², 2) near transfer to related tasks like neurocognitive performance¹³ and far transfer to untrained functional capacity tasks, and 3) real-world transfer, performing newly trained skills in everyday settings. The Functional Skills Assessment and Training Program (FUNSAT^™) has demonstrated two levels of gains in previous studies.^12–13 The current study evaluated transfer of training to the real world.

Specifically, an updated version of the FUNSAT^™ program was developed and tested in a randomized clinical trial (RCT) remotely delivering cognitive and functional skills training, targeting 6 technology-based activities of daily living, in older adults with NC and MCI. This trial has three different pre-planned outcomes (NCT046779441) presented separately. Improvements in performance on the training simulations in errors and time to completion, Czaja et al.¹⁷ was the designated primary paper, near transfer to cognitive performance and far transfer to untrained functional capacity measures (Chirino et al., in preparation) the second, and this paper, presenting real world transfer of the technology related skills assessed with Ecological momentary assessment (EMA),¹⁸ is the third.

Modern EMA is conducted with real-time surveys on digital devices and can be augmented by passive measurement¹⁹. EMA offers benefits compared to other assessments, including capture of variability in experiences and activities associated with location, social context, stress, and mood. EMA strategies also improve reliability by densely sampling behaviors that have validity challenges with dispersed assessments, such as retrospective recall²⁰ or informant reports²¹, while increasing statistical power²². EMA can capture wide ranging data across populations. Although EMA requires adherence, adherence rates for EMA data can be 80% in both healthy and neuropsychiatric populations²³. There are several design features (e.g., momentary compensation and predictable sampling) that increase adherence²⁴, although there are daily upper limits²⁵.

We present EMA data regarding changes in real-world performance of trained and untrained technology-related functional skills. We also examined the extent to which training gains on the primary outcome for the simulations ( time to completion) predicted changes in real-world technology-related skills performance.

We hypothesized that:

1. Real world performance of trained technology-related skills would increase in frequency across both MCI and NC samples.

2. Increases in performance of untrained technology-related skills such as using the internet would also be detected.

3. Participants with greater training gains would manifest greater real-world transfer on both trained and untrained skills.

Methods

Overall Study Design

This was a randomized trial conducted at community centers in South Florida (n=4) and New York City (n=10). Following screening, orientation to the study, and an in-person baseline assessment with one of three fixed difficulty assessments of six functional tasks , participants self-administered up to 12 weeks of computerized training at home, with in-person follow-up assessments. The WCG IRB approved the study and all participants provided signed informed consent.

Participants

The sample included English or Spanish-speaking adults over the age of 60 who lived in the community, had at least 20/60 vision, were able to read a computer screen, and had adequate manual dexterity to use a touch-screen. Males and females were recruited, without restrictions on racial or ethnic status. MCI status was ascertained with a neuropsychological assessment using the Jak-Bondi criteria²⁶. Participants were designated as having normal cognition or one of three different MCI subtypes: Amnestic (impairments on two or more memory tests but no other domains); Non-Amnestic (impairments on two non-memory cognitive domains, but no more than one memory domain); Multi-domain: (two or more impairments on both memory and other domains). Normative standards were used to evaluate performance, with impairment defined as 1.0 SD below normative standards.

Exclusion criteria included a MOCA score of <18 and a reading score, in their commonly spoken language, at less than a 6^th grade level. Other exclusions included the inability to undergo assessments in in either English or Spanish, receiving a similar intervention in the past 12 months, a diagnosis of a serious psychiatric condition apart from major depression, a previous medical history of brain disease such as CVA, seizures, tumor, or significant traumatic brain injury with extended loss of consciousness.

Cognitive Assessments

Cognitive assessments were used to collect data for the performance-based MCI criteria. All assessments were performed in the participants’ preferred language (English or Spanish).

Montreal Cognitive Assessment (MOCA)²⁷.

This test examines cognitive performance with scores ranging from 0-30. Assessments were performed by certified bilingual raters.

Reading Performance.

The literacy level of English speakers was examined with the Wide Range Achievement Test (WRAT)²⁸, 3^rd edition. Spanish speakers were assessed with the Woodcock-Munoz Language Survey, 3^rd edition²⁹.

Wechsler Memory Scale- revised, Logical Memory I and II (Anna Thompson Story).

Participants were read the story and asked for immediate recall, followed by 20 -minute delayed recall filled with other non-verbal assessments.

Brief Assessment of Cognition (BAC): App version.

The BAC measures domains of cognition known to be related to everyday functioning³⁰. The BAC App³¹ delivers the same assessments with cloud-connected tablet delivery for ease of administration and standardization.

The cognitive domains assessed include:

Verbal Memory:

5-trial word list learning test.

Digit Sequencing:

Verbal working memory task.

Token Motor Task:

Measures motor speed and manual dexterity.

Verbal Fluency:

Measures category fluency (animals) and letter fluency (F and S).

Symbol Coding:

Processing speed task, measuring coding performance.

Tower of London:

Executive functioning task, measures problem solving.

General Procedures

All participants completed the baseline fixed difficulty (Form A) assessments in person. MCI participants were randomized into FUNSAT^™ only or FUNSAT^™ + CCT, stratified by site, with NC participants receiving FUNSAT^™ only in order to develop a normative understanding of training gains. FUNSAT^™ training targeted all 6 functional tasks, 2 hours per week, for up to 12 weeks or mastery. In each session, participants trained for up to 60 minutes and were instructed to practice as many tasks as possible, completing each task twice consecutively. As participants progressed in training, they retrained only on tasks not previously mastered. Those in the combined FUNSAT^™ + CCT training group received a 3-week, twice weekly one-hour burst of CCT, after which time they trained for up to 9 weeks on the FUNSAT^™. At the end of the 12-week training period, or after mastery of all 6 training tasks, participants were reassessed with form B of the fixed difficulty assessments on the six tasks with later follow-ups occurring out to approximately 6 months. See Figure 1 for a full depiction of the study flow.

FIGURE 1.

FLOW CHART FOR STUDY ACTIVITIES AND ASSESSMENTS

Participants were paid $2.00 for each EMA survey answered, with a momentary signal providing a running total of their expected compensation. They were also compensated $30.00 for each in-person assessment. Participants received a $15.00 bonus payment for each task upon mastery.

Training Procedures

FUNSAT^™ Program

The skills trained in the third generation of the FUNSAT^™ program are the same as previous versions of the software, including: operating an ATM and a ticket kiosk, internet banking, utilizing a pharmacy website for online shopping and prescription refill, navigating a telephone voice menu for prescription refills, and managing medication (comprehending medication labels and organizing medication). (Figure 2). The six tasks were presented in a multi-media format that included upgraded graphic representations, text, and voice, and had 3-6 subtasks with sequential demands. For example, for the telephone refill task, participants called the pharmacy (using a simulated mobile phone keypad), refilled different prescriptions (pill bottles appeared on the screen), chose a delivery preference, and requested a pick-up time and date. Data on completion time and errors were collected in real time. Consistent with other technology based functional capacity assessments³², completion time included only the time that the participant was actively engaging in the tasks The FUNSAT software automatically progoresses to the next question if more than four errors are made on any item in a subtask (e.g., choosing the wrong account in the ATM task). Error feedback is delivered by repetition of the original instructions in a pop-up window.

FIGURE 2.

IMAGES OF SIX DIFFERENT FUNCTIONAL TASKS TRAINING FINANCES, TRANSPORTATION, MEDICATION MANAGEMENT, AND ON-LINE SHOPPING.

FUNSAT^™ training was delivered on a touch-screen device (Chrome Book) in a cloud-based format. All participants had the option of accessing the internet via a provided hotspot or through their own Wi-Fi connection. FUNSAT^™ training uses an adaptive protocol, with immediate feedback and graduated instruction, with increases in corrective information provided following errors. If the participant entered the wrong PIN in the ATM task in their first attempt, they would receive the following feedback, repeating the original instruction, “Try Again! Your ATM PIN is 1234.” If they repeated the error for a second time, feedback was “Try Again! Remember, your PIN is 1234. Please enter 1234.” Feedback for a third error was “Try Again! Press 1, then press 2, then press 3, and then press 4. Then press ENTER.” If they made a fourth error, the four keys lit up in sequence, with the participant instructed to touch them. Successful mastery of a subtask (e.g., Entering a PIN) was defined as performing that subtask with no errors once or twice consecutively with a maximum of a single error, with each of the 6 tasks considered mastered when all individual subtasks were mastered. When the participant returned to train after not mastering a subtask, only the non-mastered items were re-trained. After all 6 tasks were mastered, training was complete and follow-up assessments were initiated.

Computerized Cognitive Training

As described above the MCI participants were randomized to FUNSAT^™ training alone or combined FUNSAT^™ + CCT. The CCT procedure was Brain HQ “Double Decision”. The task was chosen due to the significant benefits from processing speed training reported in the ACTIVE and related trials^33–34 and in our previous study. The program consisted of two concurrent tasks: identifying one of two centrally presented items (Car vs. Truck) and the location of a simultaneous peripheral stimulus that differs from 7 others in a semi-circular array. In line with previous studies with Brain HQ³⁵, participants were also allowed to train up to 20% of their sessions on an additional task, “Hawkeye”, to provide variety.

EMA Surveys

Surveys were delivered daily to the participants from an application on the training device at 5 PM, with instructions to answer at least 3 times per week. Surveys began on the first day of training and continued until the end of final follow-up period . Participants were compensated for answering surveys. The surveys included check-box questions across 4 screens regarding participants’ engagement in any of the trained skills since the previous survey, with participants also asked if they had performed any technology-related skills that were untrained by FUNSAT^™ such as using the internet to look up information or taking digital photos. To examine shifts from in-person to technology related activities, we also asked if, when refilling a prescription, they used the phone or internet (trained skills) or went in person. Surveys remained open for one hour. Surveys began on the first day of at home training and continued until the participants fully completed the protocol (up to 6 months), with participants who completed training earlier answering fewer surveys. Participants who did not answer a survey during a one-week period were contacted by study coordinators. Each survey was timestamped to facilitate organization of data in relation to the first training session. Supplementary Table 1 includes the items in the EMA Survey.

Data Analyses

EMA survey responses were aggregated within surveys into total trained and untrained skills performed. Gains in completion time from the baseline assessment to the final training session on all 6 tasks were calculated. The primary results of the training study are submitted for publication, but we performed a non-overlapping examination of the changes from baseline across cognitive status and training condition. All training gain change scores from baseline were tested for significance with paired t-tests. Each individual’s change score for each task was standardized based on the standard deviation of the baseline scores in the entire sample (change score/overall SD). The six standard scores for the individual tasks averaged to create an aggregate standard (z) change score for each participant.

We compared differences in training gains by condition in MCI participants with t-tests, as well as examining training gains across cognitive status, in order to determine if these two factors should be used as covariates.

Analyses of the time course of the EMA responses were performed with Mixed Model Repeated Measures Analysis of variance (MMRM) using the SPSS v28 Generalized Linear Models (GLM) module. In these analyses, we created a random subject intercept and interpreted models whose fit improved on the null (intercept-only) model. We used month (1-6) as the repeated-measures factor, defining day 1 as the first day of training. We used full information maximum likelihood procedures, because participants answered different numbers of surveys within each one-month period. The test statistic was the Wald Chi-square, with an unstructured covariance matrix.

Analyses were performed separately for trained and untrained tasks. Any significant models were repeated, first incorporating participants’ composite training gains as a between subjects factor and then adding baseline cognitive status as another between-subjects factor. After this model, we added total training sessions as a covariate. When we reached the final model, we repeated the analyses with race, ethnicity, and location as additional covariates.

Results

Demographic information is presented in Table 1 and the CONSORT diagram is presented in Figure 3. There were 4074 surveys answered over the 6-month period. There were 164 participants who were scheduled to complete surveys and 155 participants who answered one or more surveys (95%). Participants with MCI provided 2038 survey responses and those with NC provided 2036, suggesting lower response rates on the part of MCI participants (22 per participant) than NC (28 per participant). Month- by-month EMA survey responses are presented in Supplemental figure 1.

Table 1.

Demographic and Descriptive Information on Participants

	Mild Cognitive Impairment		Normal Cognition
		N=92	N=72
	M	SD	M	SD	t	p
Age	71.68	6.40	71.17	6.36	−.50	.65
MOCA score	22.45	3.21	27.15	1.37	11.50	<.001
Years of Education	13.29	3.94	15.56	2.52	4.18	<.001

	Mild Cognitive Impairment		Normal Cognition
		N=92	N=72
Site		N(%)	N(%)	X2	p
	Miami	38 (41%)	32 (43%)	1.21	.55
	NY	54 (59%)	40 (57%)
Sex
	Male	18 (20%)	7 (8%)	6.00	.05
	Female	74 (80%)	65 (92%)
Race
	White	36 (36%)	34 (47%)	7.95	.44
	Black	28 (30%)	14 (18%)
	Other/	28 (30%)	24 (35%)
	More than 1/None
Ethnicity
	Latinx	52 (57%)	34 (46%)	8.20	.04
	Non Latinx	40 (43%)	38 (54%)
Training Language
	English	50 (55%)	48 (67%)	2.64	.27
	Spanish	42 (35%)	24 (33%)
MCI Classification
	Amnestic	14 (15%)
	Multi-Domain	38 (41%)
	Non-Amnestic	40 (43%)

FIGURE 3.

CONSORT DIAGRAM FOR PATIENT FLOW IN THE STUDY

MCI participants had significantly less education and lower MOCA scores than NC participants but did not differ in age. There were no site, race, or training language differences across MCI status. There were slightly more Latinx participants and slightly more male participants in the MCI group than in the NC group. In terms of mastery, 128 (79%) of the cases mastered all six tests and trained less than 12 weeks, averaging 6.52 (SD=5.94) training sessions per task compared to those who did not master all six tasks of 7.70 (SD=6.21)

Table 2 presents the training gains in time to completion for the total sample, including baseline time and changes in time to completion from baseline to the final training session. As shown in the table, performance all 6 tasks improved significantly with training, with the smallest effect size compared to no gains (Cohen’s d) being over 1.0. We also examined the differences in the 6 change scores across cognitive status, finding that the MCI participants had significantly larger training gains in seconds from baseline to the final training session: (all t>3.18, all p<.001;df=154) , although their performance at baseline was worse across all six tasks, (all t>3.58, all p<.001; df=154). There were no training condition differences in changes in completion time from baseline to the final training session in the MCI participants, all t<1.45, all p>.15; df=91. Cognitive status, but not training condition, was entered as between-subjects factors in the subsequent MMRM analyses.

Table 2.

Baseline Completion Times and Training Gains Across Simulations:Presented in Seconds.

Task	Baseline		Improvement From Baseline t-Test for changes Compared to 0 To Final Training Session
	M	SD	M	SD	t	p	d
Ticket Kiosk	1068.07	470.23	470.52	410.94	14.57	<.001	1.15
ATM banking	1541.46	863.82	700.59	626.91	14.09	<.001	1.14
Medication Management	1152.46	704.04	542.64	437.54	14.96	<.001	1.11
Telephone Voice Menu	825.29	380.35	342.37	325.77	13.34	<.001	1.06
Pharmacy Website	1592.31	868.22	917.11	728.98	15.96	<.001	1.26
On-Line Banking	1357.14	739.32	680.44	604.42	13.93	<.001	1.13
Composite	0.00	1.00	−0.94	1.05	14.21	<.001	1.14

Table 3 presents the results of the MMRM analyses for time effects for the entire sample on trained and untrained tasks and Figure 4 presents the growth curves for the scores. As can be seen in the Table, there were significant increases in performance for trained and untrained skills in the whole sample. When training gains were entered as a between-subjects covariate for trained tasks, the overall results were still significant and there was an association between training gains and changes in functional performance, despite the effect of month no longer being significant. Cognitive status was associated with changes in frequency of performance of both trained and untrained tasks. Like training gains, real-world functional performance increased more over time for participants with MCI.

Table 3.

Mixed Model Repeated Measures Analyses of the Effects of Time Post Training Start on Trained and UnTrained Technology Related Everyday Activities Measured with Ecological Momentary Assessment

Total Sample
	Trained Activities			Untrained Activities
	X2	df	p	X2	df	p
Omnibus	12.36	5	.03	22.70	5	<.001
Intercept	3969.23	1	<.001	1195.27	1	<.001
Month	12.38	5	.03	22.86	5	<.001
Total Sample, Training Gains as a Between-Subjects Fixed Covariate
	Trained Activities			Untrained Activities
	X2	df	p	X2	df	p
Omnibus	77.39	6	<.001	22.79	1	<.001
Intercept	140.84	1	<.001	395.08	1	<.001
Month	8.77	5	0.11	9.60	5	.08
Composite	25.15	1	<.001	242.74	1	<.001
Training Gains
Full Training Ggains Model with Cognitive Status Added as a Between-Subjects Fixed Covariate
	X2	df	p	X2	df	p
CognitiveStatus Status	38.11	1	<.001	108.21	1	<.001

FIGURE 4.

GROWTH CURVES FOR CHANGES IN TRAINED AND UNTRAINED TECHNOLOGY-RELATED FUNCTIONAL SKILLS OVER 6 MONTHS FOR BOTH PARTICIPANT SAMPLES

We then added several potential covariates to the full model with these results presented in Table 4. We found that fewer total training sessions and mastery of all six tasks added to the prediction of changes in both trained and untrained activities and that race and ethnic status were unassociated with changes in performance. Location was associated only with untrained activities and at a much lower level of significance.

Table 4.

Statistical Tests for Training Session, Mastery of All Tasks, Race, Ethnicity, and Location Added to the Previous Full Models Including Cognitive Status

	Trained Activities			Untrained Activities
	X2	df	p	X2	df	p
Training Sessions	87.98	1	<.001	92.22	1	<.001
Mastery of All Tasks	67.64	1	<.001	34.47	1	<.001
Race	0.43	1	0.51	0.42	1	0.51
Ethnicity	2.94	1	0.86	2.15	1	0.14
Location	0.36	1	0.45	5.84	1	0.016

In a final descriptive analysis, we examined changes in the proportion of surveys reporting refilling a prescription at a pharmacy. The overall model was significant, X²(5)=12.19, p=.032, with the effect of month also significant, X²(5)=12.21, p=.032. The proportion of surveys answered affirmatively dropped from a baseline of .15 to a low of .09 at month 6. When training gains were added as a covariate, the effect of training gains was also significant, X²(1)=13.29, p<.001, predicting a decrease in in-person refills.

Discussion

Consistent with studies of in person training^12–13, we found substantial (d>1.0) improvements in time to completion of training tasks with both NC and MCI participants receiving benefit. In terms of our hypotheses, we detected statistically significant increases in performance of both trained and untrained technology-related functional skills. In terms of our third hypothesis, training gains were a predictor of increases in performance of technology-related functional skills, suggesting that real-world transfer is not explainable by increased exposure to technology.

There are several important features of these findings. This is the first study to our knowledge reporting transfer of computerized training to real-world task performance in participants with MCI. Remote training was associated with gains that were consistent with gains from in person training in previous studies. Adherence to EMA assessments was excellent, with 95% of participants answering surveys and modest differences in the average number of surveys answered across cognitive status. These findings suggest that fully remote training is feasible, efficacious, and associated with changes in real-world functioning across cognitive status. Our MCI participants had more training gains and increases in technology related activities (albeit starting out lower), suggesting that computerized training can lead to real-world transfer in cognitively impaired participants. Further, over a third of our participants were assessed and trained in Spanish, suggesting that training gains have cross-cultural relevance.

Both cognitive status subsamples contained substantial racial and ethnic minorities and their levels of education were comparatively low for a long and complex training protocol. In contrast, in the ACTIVE trial, participants receiving processing speed training were 75% white, had 13.7 years of education, and a mean Mini-Mental State Examination score of 27³⁶. The ACTIVE intervention was considerably shorter than our intervention (maximum of 14 hours), but recipients of speed training in ACTIVE manifested persistence of cognitive gains up to 10 years after training³⁷ with reduced risk for development of dementia³⁸. Importantly, sustained gains in performance of previously acquired complex skills, such as driving,¹⁶ were greater in the training groups than controls. In our study, participants with MCI were found to increase the frequency of performance of tasks that they rarely performed in month 1, suggesting acquisition of novel skills.

There are some limitations in this study. Our EMA sampling density was less than other studies. We excluded participants who may have had dementia, so findings may not generalize to more impaired populations. We could not stratify our MCI participants on subtype and the samples are too small to make meaningful comparisons across subtypes and training conditions. That said, to select representative samples, we did take all comers at each of the research sites. There were fewer surveys completed in months 5-6 than before, but this is largely because the fastest learners mastered all the tasks more rapidly.

A final critical finding was the shift toward using technology for medication refills, rather than in person visits. This shift occurred concurrently with the elimination of pandemic restrictions at the study locations, arguing against the idea that outside restrictions were the operative factor in choice of refill strategy. Even in the context of reduced restrictions, our participants shifted toward technology. If there is a recurrence of a pandemic, the ability to rapidly learn, deploy, and utilize technology related skills could be a protective factor, not only against infection, but also for sustaining independence and managing money and deliveries. More broadly, technological competence equals independence and showing that real-world gains are possible with remote training in participants with MCI has the potential to reduce person and caregiver burden in a variety of domains.

Highlights.

• What is the primary question addressed by this study?

  The study addressed the question of whether a fully remote cognitive and functional skills training program improved real-world performance of technology-based tasks, measured with daily ecological momentary assessment (EMA) in older adults with normal cognition and mild cognitive impairments.

• What is the main finding of this study?

  EMA assessments demonstrated statistically significant increases in activity of both trained and untrained technology-related activities. Moreover, the degree of training gains was found to be a significant predictor of real-world performance in domains of technology utilization.

• What is the meaning of the finding?

  These findings provide compelling evidence that a fully remote functional skills training program is both practical and efficacious, as well as providing benefits for real-world functioning. This training program has the potential to empower individuals, regardless of their cognitive status or geographical location, by enabling them to maintain or optimize their autonomy as they age.