Abstract
Digital cognitive training may improve cognition in people with mild cognitive impairment (MCI); however, the effect on functionality remains poorly defined. The Canadian Occupational Performance Measure (COPM) is a valid and consistent instrument for evaluating the performance of activities of daily living in this population. This study used the COPM to investigate the effects of digital cognitive training on functionality in individuals with MCI. We recruited participants aged 60 or older with MCI to a double-blinded, randomized, stepped wedge clinical trial of digital cognitive training compared to an active control group of commercial computer games. Participants were evaluated for functionality and cognition before and after 10 h of intervention. Ten hours of digital cognitive training improved functionality, measured by COPM performance, compared to the active control group. Learning over trials also improved significantly after 10 h of digital cognitive training, as compared to the active control group. Ten hours of digital cognitive training improved functionality in MCI. More sensitive tools, such as COPM, should be used to evaluate the effect of therapeutic interventions for functionality in MCI.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11357-024-01464-x.
Keywords: Cognition, Cognitive training, Functionality
Introduction
Mild cognitive impairment (MCI) is a state of cognitive functioning between the cognitive capacities associated with normal aging and the early stages of dementia [1]. Diagnostic criteria include complaints of cognitive changes, confirmed by evidence of performance below expectations in one or more cognitive domains. Despite the impairments in cognition, functional independence in daily activities is maintained, although they may experience prolonged task completion, reduced efficiency, and increased errors compared to their previous performance [2]. While the cognitive deficits in MCI do not interfere with their independence in Activities of Daily Living (ADL), these activities are carried out with additional effort [3]. This stage represents a critical window for interventions designed to prevent or delay further decline. From a global perspective, measures commonly used to assess ADL may not be sensitive in detecting early functional changes associated with MCI [4]. Conventional measures often consist of questionnaires, which may overlook some particular difficulties, resulting in the omission of crucial data [4, 5]. In contrast, the Canadian Occupational Performance Measure (COPM) is one of the few individualized scales designed to identify difficulties in ADL. By assessing functionality through a client’s self-perception across three key areas of daily life: Self-care, Productivity, and Leisure, the COPM offers a comprehensive understanding of individual challenges. This assessment involves a semi-structured interview where clients identify activities that are currently difficult to perform satisfactorily and then rate their performance and satisfaction with each activity. Notably, this measure has demonstrated its efficacy in accurately identifying occupational performance. In Brazil, validation studies have confirmed the COPM’s validity and consistency in evaluating ADL performance among individuals with mild cognitive impairment (MCI) [6, 7].
Impairments in executive functions have been associated with the impairments in functionality in MCI [8–10]. In MCI, better executive functions have been associated with better performance in ADL and more complex tasks [8]. Thus, the decline in executive function may be a determining factor in the decline of functionality in MCI, standing out as a relevant target for rehabilitation interventions.
Digital cognitive training emerges as a promising brain-plasticity-based intervention to enhance cognition in MCI. The effort demanded by digital cognitive training results in an increase in neural capacity or in the efficiency with which existing brain resources are used [11]. This approach generally focuses on improving general cognitive processes such as processing speed, attention, and working memory, with the premise that these improvements will transfer to real-world cognitive gains [12]. A large body of studies has applied digital cognitive training to MCI, and systematic reviews and meta-analyses concluded that digital cognitive training benefits cognition in this population [13–15].
However, the effect of digital cognitive training on functionality remains poorly defined. Specifically, most previous studies focusing on digital cognitive training in older adults either did not evaluate functionality as an outcome or failed to identify statistically significant results concerning this variable when measured through questionnaires assessing activities of daily living [13–15].
Interestingly, although individuals with MCI may not exhibit marked functional impairments, cognitive remediation holds the potential for enhancing functional performance. A meta-analysis by [16] demonstrated that even brief cognitive remediation interventions can benefit functional abilities in this population. The findings emphasize the importance of targeting cognitive domains to indirectly enhance functional outcomes, especially as cognitive decline progressively affects daily living with the progression of the condition. Given the potential benefits of digital cognitive training for cognition in MCI, in this study, we sought to investigate the effects of training in functionality evaluated using a more sensitive tool. Our hypothesis is that training with digital exercises focused on executive function will improve the functionality of the participants. Thus, we asked the following questions: (1) Does digital cognitive training improve cognition in Brazilians with MCI? and (2) Does digital cognitive training improve functionality, evaluated using COPM, in MCI?
Methods
Participants
The study was approved by the ethics committee of the Federal University of Rio de Janeiro, Institute of Psychiatry (4.315.008), and pre-registered at ClinicalTrials.gov (NCT03911765). We followed the Consolidated Standards of Reporting Trials (CONSORT) checklist to ensure comprehensive and transparent design and reporting of the trial [17]. Participants signed a written consent after being informed about the study procedures. Participants were recruited through referrals from professionals and advertising on social media and contacted by the research group via email or text message. Participants were included in the study if they (1) were ≥ 60 years old, (2) had normal or corrected vision and hearing, (3) had access to the internet, and (4) were diagnosed with MCI according to DSM 5. Participants were excluded if they (1) were diagnosed with dementia, (2) were dependent on daily living activities, and (3) had major medical disorders that precluded participation in the study.
All participants underwent a comprehensive evaluation, including clinical history, as well as assessments of cognition, functionality, and symptoms of anxiety and depression. The clinical diagnosis was established according to the DSM-5 criteria and confirmed by a board-certified psychiatrist (R.P.).
Study design
The study used a randomized, double-blinded, stepped wedge design [18]. After enrollment in the study, participants were assessed at baseline and randomly assigned to the Digital Cognitive Training Group (CT) or the active Computer Games Control Group (CG) using a hierarchical stratification by gender, age, years of education, TADLQ, and MoCA score. The randomization process was carried out by a researcher who was not involved with the assessments. After 10 h of intervention, both groups were reassessed. Participants were blind to group assignments. The research team performing the assessments and the researcher who performed the data analysis had no access to the participants’ group assignments. Data was collected from April 2019 to June 2023.
Group conditions
The CT group engaged in digital cognitive training exercises primarily focused on enhancing executive functions. We designed a cognitive training routine, using the exercises Divided Attention, Eye For Detail, Target Tracker, Juggle Factor, Double Decision, Right Turn, Mental Map, and Optic Flow, from the Brazilian version of the BrainHQ program (Posit Science®). During each training session, participants completed 15 min of four different exercises, and participants were advised to train at least twice a week. These exercises dynamically adjusted their speed and stimulus type in response to the participant’s performance. Task difficulty systematically increased as performance improved, and an algorithm was utilized to maintain individual success rates in the exercises at approximately 85%. Correct performance was incentivized through engaging visual and auditory enhancements and the accumulation of stars.
The CG was established to align with the experimental group in terms of duration but did not incorporate adaptive features based on participants’ performance. The CG group performed computer-based games, which included The Birds Game, Let’s Clean Up, Bubble Poke, Word Search, Memory Game, Puzzle, Dominoes, Smarty Bubbles, and Spot the Difference. Participants were advised to play for 1 h per day, at least twice a week.
Participants played (CG) or trained (CT) at homes either on their own or study-provided computers or tablets. Subjects in both groups were monitored remotely, through video call, by a designated research assistant who offered support on issues such as difficulties with the computer or training compliance. Groups were comparable in terms of their interaction with the research team and their contact with the computer.
Measurements
The assessments conducted included evaluations of cognition, functionality, and symptoms of anxiety and depression. To assess cognition, we used the following: Rey Auditory Verbal Learning Test (RAVLT) [19] Stroop test [20], semantic (animals) verbal fluency test [21], Montreal Cognitive Assessment (MOCA) [22, 23]. To assess neuropsychiatric symptoms we applied the following: Geriatric Depression Scale 15 (GDS) [24] and Geriatric Anxiety Inventory (GAI) [25]. Finally AQ18 , to evaluate functionality, we administered the following: Technology Activities of Daily Living Questionnaire (TADLQ) [26] and Canadian Occupational Performance Measure (COPM) [7].
The calculation of the MoCA subdomains was based on the MoCA index [27], excluding the number of words recalled in delayed-free recall, as this data was not collected. Therefore, memory score was obtained through delayed recall (ranging from 0 to 5 points); executive functions were assessed through trail-making, clock drawing, digit span, letter A tapping, serial 7 subtraction, letter fluency, and abstraction (ranging from 0 to 13); visuospatial abilities were measured by performance on the bed copy, clock drawing, and naming tasks (score ranging from 0 to 7); language was evaluated through the tasks naming, sentence repetition, and letter fluency (score ranging from 0 to 6); orientation was measured by considering all items related to orientation (score ranging from 0 to 6); and attention was assessed based on performance in the tasks digit span, letter A tapping, serial 7 subtraction, and sentence repetition (score ranging from 0 to 8). Processing speed and inhibitory control refer to the Stroop test, card 1, and card 3, respectively. Finally, through the RAVLT, we obtained long-term memory (A7) which refers to the number of words recalled after a 20 to 30-min delay (score ranging from 0 to 15), short-term memory (A6) to the number of words recalled after the interference list (score ranging from 0 to 15), recognition to the sum of all correct answers (list A and non-list A words correctly identified) minus 35 (total distractors) (score ranging from 0 to 15), and learning over trials to the sum of words recalled across the five trials minus five times on the 1st trial (score ranging from 0 to 75).
Statistical analysis
We used T tests to assess differences in socio-demographic, cognitive, and functional status at the baseline assessment. Data normality was verified using the Shapiro–Wilk test. The distributions of the measures were evaluated for normalcy and missing values. Winsorized means were calculated for five variables (GDS, TADLQ, processing speed, inhibitory control, and learning over trials). Missing data were replaced with the last observed value from the same subject.
To determine the effects of digital cognitive training, we used the mixed-model analysis of covariance, including baseline scores and education as covariates. We used one-way repeated measures-analysis of variance (RM-ANOVA) in the post hoc analysis. The main outcome was a change in the COPM functionality scores, and secondary outcomes were changes in cognition, functionality scores, and depression and anxiety symptoms. We applied Bonferroni correction in all statistical analyses involving multiple comparisons. Depending on the distribution of the scores, Spearman’s rho correlation or Pearson’s r was used to test the associations. Multiple regression analysis with backward stepwise modeling was conducted to determine which changes in cognitive variables could explain the variation in COPM performance. We used g*power software to calculate the sample size, GraphPad Prism to create the figures and SPSS software for statistical analysis. Based on the study conducted by [28], which employed digital cognitive training in MCI, we expected an effect size of 0.29 in the change in MoCA. Then, a sample of 42 participants attained 95% statistical power.
Results
Participants
We assessed 106 participants; 34 were deemed ineligible, and 6 withdrew before the intervention. The remaining 66 were randomly assigned to either the CG group (n = 35) or the CT group (n = 31). In the CG group, 6 participants withdrew, leaving 29 completers, while in the CT group, 5 participants withdrew, resulting in 26 completers. The attrition rates were 17.14% for the CG and 16.13% for the CT group. The study flow diagram is shown in Fig. 1.
Fig. 1.
Study flow diagram
Sample characterization
Participants were predominantly composed of women (with 13.41 years of education (SD = 4.92) and a mean age of 74.50 (SD = 6.67)). Table 1 shows the detailed demographic characteristics of the total sample and by group, comparing groups concerning demographic and clinical variables. There were no significant differences between the groups. In the measures of functionality, the COPM mean score was 5.65 (1.95) indicating moderate functional impairment, while the TADLQ showed a mean score of 15.49 (10.34) indicating none to mild functional impairment. Also, we conducted a correlation analysis to assess whether the functionality measures scores were associated. The results showed no association between COPM performance and TADQL, neither at baseline (r = 0.14, p = 0.34) nor in the change after intervention (r = 0.02, p = 0.86).
Table 1.
Participant characteristics
| Total (n = 55) | Computer Games Control Group (n = 29) | Digital Cognitive Training Group (n = 26) | p-value | |
|---|---|---|---|---|
| Age, years | 74.67 (7.62) | 74.24 (8.27) | 75.15 (6.96) | 0.66 |
| Female, % | 67.3 | 65.5 | 69.2 | 0.76 |
| Marital status, % | 0.84 | |||
| Single | 10.9 | 13.8 | 7.7 | |
| Married | 40.0 | 37.9 | 42.3 | |
| Divorced | 16.4 | 13.8 | 19.2 | |
| Widowed | 32.7 | 34.5 | 30.8 | |
| Education, years | 13.44 (4.96) | 13.07 (4.85) | 13.85 (5.14) | 0.56 |
| Physical activity, times per week | 1.55 (2.03) | 1.55 (2.01) | 1.54 (2.10) | 0.98 |
| Number of falls, past year | 0.25 (0.77) | 0.28 (0.96) | 0.23 (0.51) | 0.83 |
| Previous contact with computer, % | 0.66 | |||
| None | 23.6 | 24.1 | 23.1 | |
| Low | 41.8 | 41.4 | 42.3 | |
| Medium | 21.8 | 17.2 | 26.9 | |
| High | 12.7 | 17.2 | 7.7 | |
| COPM performance | 5.65 (1.95) | 5.64 (1.64) | 5.66 (2.29) | 0.97 |
| COPM satisfaction | 6.59 (2.54) | 6.69 (2.48) | 6.48 (2.65) | 0.77 |
| TADLQ score | 15.49 (10.34) | 15.17 (10.67) | 15.87 (10.17) | 0.81 |
| MoCA score (0–30) | 20.73 (1.77) | 20.62 (1.72) | 20.85 (1.87) | 0.61 |
| GDS score (0–15) | 3.62 (2.60) | 3.52 (2.79) | 3.73 (2.42) | 0.76 |
| GAI score (0–20) | 5.73 (4.55) | 5.31 (4.90) | 6.23 (4.15) | 0.49 |
Results are given as mean (standard deviation). MoCA Montreal cognitive assessment, GDS Geriatric Depression Scale, GAI Geriatric Anxiety Inventory
Effect of digital cognitive training
Initially, we examined the effect of 10 h of digital cognitive training on the main study outcome, i.e., functionality evaluated using COPM measure. Digital cognitive training led to a 21% improvement in COPM performance in participants with MCI. Mixed-model ANCOVA using the baseline scores and education as covariates revealed a global effect of time over 10 h of training in COPM performance (F(1,45) = 7.07, p = 0.01, d = 0.79) with a significant group-by-time interaction (F(1,45) = 4.35, p = 0.04, d = 0.62) (Fig. 2). Post hoc one-way RM-ANOVA for the two groups separately revealed significant improvement over time in COPM performance in the digital cognitive training group (F(1,20) = 14.1, p = 0.00, d = 1.68), but not in the control group (F(1,23) = 0.47, p = 0.49, d = 0.28) (Fig. 2). There were no changes in COPM satisfaction after 10 h of digital cognitive training. Additionally, 10 h of digital cognitive training had no effect on the TADLQ measure of functionality (Table 2).
Fig. 2.
A Ten hours of digital cognitive training improved functionality, measured by COPM, compared to Computer Games Control Group, mixed-model ANCOVA using the baseline scores and education as covariates revealed a global effect of time over 10 h of training in COPM performance (F(1,45) = 7.07, p = 0.01, d = 0.79) with a significant group-by-time interaction (F(1,45) = 4.35, p = 0.04, d = 0.62). Post hoc one-way RM-ANOVA for the two groups separately revealed significant improvement over time in COPM performance in the digital cognitive training group (F(1,20) = 14.1, p = 0.00, d = 1.68), but not in the control group (F(1,23) = 0.47, p = 0.49, d = 0.28). Score = COPM, Canadian Occupational Performance Measure. B Ten hours of digital cognitive training improved learning over trials compared to Computer Games Control Group, mixed-model ANCOVA using the baseline scores and education as a covariate revealed a global effect of time over 10 h of training in learning over trials (F(1,30) = 12.41, p = 00.0, d = 1.28) with a significant group-by-time interaction (F(1,30) = 7.05, p = 0.01, d = 0.96). Post hoc one-way RM-ANOVA for the two groups separately revealed significant improvement over time in learning over trials in the digital cognitive training group (F(1,13) = 11.23, p = 0.00, d = 1.86), but not in the computer games control condition (F1,15) = 1.22, p = 0.28, d = 0.56). Score = Rey Auditory Verbal Learning Test (RAVLT), learning over trials
Table 2.
Effect of digital cognitive training
| Measurements (score range) | Computer Games Control Group (n = 29) | Digital Cognitive Training Group (n = 26) | Main effect of time, F (p) | Interaction group time, F (p) | Cohen’s d | ||
|---|---|---|---|---|---|---|---|
| Cognition | Baseline | 10 h digital games | Baseline | 10 h Digital Cognitive Training |
|||
| MoCA (0–30) | 20.62 (1.72) | 21.31 (2.92) | 20.85 (1.87) | 21.73 (3.05) | 0.90 (0.34) | 0.05 (0.81) | 0.06 |
| Executive functions (0–13) | 7.66 (2.05) | 7.72 (2.03) | 7.88 (2.35) | 8.23 (2.25) | 6.32 (0.01) | 0.50 (0.48) | 0.20 |
| Memory (0–5) | 1.55 (1.47) | 1.79 (1.69) | 1.35 (1.64) | 1.50 (1.63) | 2.45 (0.12) | 0.26 (0.60) | 0.14 |
| Language (0–6) | 4.55 (1.02) | 4.59 (1.01) | 4.58 (0.85) | 4.73 (1.04) | 19.30 (0.00) | 0.12 (0.72) | 0.08 |
| Visuospatial abilities (0–7) | 4.93 (1.22) | 5.21 (1.20) | 5.38 (1.20) | 5.27 (1.25) | 17.15 (0.00) | 0.15 (0.69) | 0.10 |
| Attention (0–8) | 5.69 (1.46) | 5.69 (1.60) | 5.73 (1.56) | 06.08 (1.44) | 9.44 (0.00) | 0.77 (0.38) | 0.24 |
| Orientation (0–6) | 5.34 (1.04) | 5.34 (1.17) | 5.19 (0.89) | 5.65 (0.56) | 32.34 (0.00) | 2.99 (0.09) | 0.48 |
| Processing speed, seconds | 25.51 (9.76) | 21.40 (5.04) | 21.66 (7.68) | 20.64 (5.86) | 45.61 (0.00) | 0.65 (0.42) | 0.22 |
| Inhibitory control, seconds | 51.51 (23.21) | 43.58 (12.92) | 46.15 (16.18) | 42.49 (12.48) | 32.71 (0.00) | 0.27 (0.60) | 0.59 |
| Long-term memory (0–15) | 5.82 (3.01) | 5.36 (3.72) | 5.45 (2.68) | 6.27 (2.78) | 0.70 (0.40) | 2.78 (0.10) | 0.52 |
| Short-term memory (0–15) | 5.70 (3.08) | 5.65 (2.99) | 5.68 (2.78) | 6.05 (2.43) | 7.93 (0.00) | 0.44 (0.50) | 0.21 |
| Recognition (0–15) | 10.68 (3.79) | 11.41 (2.32) | 11.64 (2.25) | 11.45 (2.84) | 26.01 (0.00) | 0.054 (0.81) | 0.06 |
| Learning over trials (0–70) | 13.76 (6.19) | 11.49 (7.52) | 11.51 (5.50) | 15.74 (5.54) | 12.41 (00.0) | 7.05 (0.01) | 0.96 |
| Verbal fluency, number of words | 14.97 (3.65) | 14.69 (4.55) | 15.00 (4.88) | 16.08 (4.48) | 10.60 (0.00) | 1.69 (0.19) | 0.36 |
| Functionality | |||||||
| COPM, performance (1–10) | 5.64 (1.64) | 5.72 (2.23) | 5.66 (2.29) | 6.87 (1.72) | 7.07 (0.01) | 4.35 (0.04) | 0.62 |
| COPM, satisfaction (1–10) | 6.69 (2.48) | 7.04 (2.50) | 6.48 (2.65) | 7.61 (1.85) | 1.51 (0.22) | 0.86 (0.35) | 0.27 |
| TADQL (0–100) | 15.17 (10.67) | 13.69 (9.66) | 15.87 (10.17) | 14.96 (8.39) | 0.22 (0.63) | 0.46 (0.50) | 0.20 |
| Neuropsychiatric symptoms | |||||||
| GDS (0–15) | 3.39 (2.33) | 3.38 (2.11) | 3.72 (2.23) | 3.69 (2.03) | 7.35 (0.00) | 0.12 (0.72) | 0.08 |
| GAI (0–20) | 5.31 (4.90) | 4.92 (4.90) | 6.39 (4.13) | 5.39 (3.96) | 0.07 (0.79) | 0.19 (0.66) | 0.12 |
Results are given as mean (standard deviation). MoCA Montreal cognitive assessment, COPM Canadian Occupational Performance Measure, TADLQ Technology Activities of Daily Living Questionnaire, GDS Geriatric Depression Scale, GAI Geriatric Anxiety Inventory
Then, we evaluated the effect of 10 h of digital cognitive training on cognition. Digital cognitive training improved learning over trials by 36.75% compared to the group engaged in computer games. Mixed-model ANCOVA using the baseline scores and education as covariates revealed a global effect of time over 10 h of training in learning over trials (F(1,30) = 12.41, p = 00.0, d = 1.28) with a significant group-by-time interaction (F(1,30) = 7.05, p = 0.01, d = 0.96). Post hoc one-way RM-ANOVA for the two groups separately revealed significant improvement over time in learning over trials in the digital cognitive training group (F(1,13) = 11.23, p = 0.00, d = 1.86), but not in the computer games control condition (F1,15) = 1.22, p = 0.28, d = 0.56). Mixed-model ANCOVA using the baseline cognition and education as covariates did not reveal any significant group-by-time interaction in other cognitive domains. Therefore, we observed that after 10 h of training, there was an enhancement in learning over trials, but we did not find improvements in the other cognitive domains when comparing digital cognitive training and the active control group.
Further analyses showed a significant global effect of time but not in the cognitive domains of language, visuospatial abilities, attention, orientation, processing speed, inhibitory control, short-term memory, recognition, and verbal fluency (Table 3). The detailed post hoc analysis is described in the supplementary Table 2.
Table 3.
Changes in cognition predict changes in COPM performance
| Covariates | Coef | CI | p-value |
|---|---|---|---|
| Change in processing speed | − .034 | .043 to − .067 | .043 |
| Change in verbal fluency | .246 | .030 to .462 | .028 |
COPM performance Canadian Occupational Performance Measure, CI confidence interval
Finally, we performed a multiple regression analysis to determine which changes in cognitive domains could explain the improvement in COPM performance. Using the backward elimination method, we initially included changes in short-term memory, recognition, learning and learning over trials, verbal fluency, processing speed, and inhibitory control in the analysis. Subsequently, the least significant variables were removed from the model. The results indicated that changes in processing speed and verbal fluency were significant predictors of the improvements in COPM performance (RMSE = 1.34, r = 0.60; p = 0.02).
Discussion
This study demonstrated that digital cognitive training led to improvements in participants with MCI in functionality, measured by COPM. The improvement in functionality, measured by the COPM after training, can be attributed to the measure’s sensitivity in assessing self-perceived functional performance. This is particularly relevant for individuals with MCI, as they may experience a slight decline in efficiency despite maintaining functional independence [2]. A recent meta-analysis of digital cognitive training for MCI, published by Cochrane in 2019 [29], showed that only three studies measured functional performance. Fiatarone Singh (2014) [30] and [31] utilized the BAYER-Activities of Daily Living (B-ADL) scale to measure daily function, while [32] employed the Activities of Daily Living-Function scale. Despite these efforts, none of the studies analyzed found significant changes in this variable. Our results highlight the importance of using sensitive tools, such as COPM, to assess self-perceived functional performance, especially in MCI, where traditional measures may overlook subtle changes.
In people with MCI, the improvement observed in self-perceived functional performance evaluated using COPM may be associated with less effort to perform daily activities, although this was not directly measured in the present study. The amplitude of changes in COPM considered clinically relevant depends on the population, the type of occupational performance issues, and the intervention context [33–35]. Interestingly, the amplitude of change we observed in the present study is similar to what was found in a study using cognitive rehabilitation for people with early-stage Alzheimer’s disease [36].
Further analysis demonstrated that changes in verbal fluency and processing—both components of executive functions [37]—were significant predictors of improvements in COPM performance. These findings suggest that enhancements after training in executive functions contribute to gains in functionality reported in COPM performance. Accordingly, the integrity of executive functions has been associated with better performance in daily activities and more complex tasks [8]. This result underscores the important role of executive functions in enhancing individuals’ perceived functionality. Although no significant improvements in executive functions were observed when comparing the intervention group to the control group, our predictive analysis revealed an association between individual gains in executive function and improvements in COPM performance.
The lack of significant change in the TADLQ after training may be attributed to the specific nature of the challenges faced by individuals with MCI. At baseline, the TADLQ indicated none to mild functional performance decline, and after the training, the measure did not detect changes in functionality. In this context, it is important to develop and use more sensitive and reliable measures to better detect early functional changes [38]. COPM may be more effective in revealing significant changes in perceived functionality. Similarly to our study, another study that applied a cognitive intervention in older adults with MCI also found significant changes in COPM scores, while no significant changes were observed in the Bayer-Activities of Daily Living scale, which is a standardized questionnaire [39]. The COPM has been recognized for its sensitivity in detecting changes in health-related functional status, particularly when compared to conventional assessment methods [40].
The observed absence of individual association between COPM and TADQL scores may suggest that they assess different constructs of functionality. The TADLQ functions as an informant-based assessment of functional abilities. In contrast, the COPM is distinguished as a tool that accurately identifies difficulties in functionality from the individual’s perspective.
Our results showed a significant improvement in learning over trials after digital cognitive training compared to computer games. This metric reflects the progression of learning accounting for initial performance, offering a clearer picture of information acquired over successive trials [41] and has proven especially useful in identifying cognitive impairments in individuals with MCI [42]. A higher value obtained in the learning over trials indicates a greater capacity for learning across trials, isolating the initial memory retention ability measured on the first trial. This measure has been used in other tests that assess verbal learning, [41, 42] where it was noted that scores on this metric were lower in participants with MCI, AD, [41] hippocampal atrophy, β-amyloid burden, and apolipoprotein ε4 carrier status [42] compared to those with normal cognition.
The effect of neuroplasticity induced by digital cognitive training was observed in studies using the diffusion tensor imaging. These studies have demonstrated that skills training over several weeks promotes changes in the properties of white matter, reflecting changes in myelin and packaging density [43, 44]. Additionally, memory training has been linked to increased activation and connectivity in various brain regions, such as the frontal, temporal, and occipital lobes, as well as the hippocampus, in both cognitively healthy older adults and those with MCI. However, the exact extent of these changes in individuals is still not fully understood [45, 46].
This study has some limitations, including the fact that the participants were older adults with access to technological resources and the Internet, which is not a representative sample of the Brazilian population as a whole. To mitigate this limitation, we provided tablets to participants living in Rio de Janeiro and offered support through video calls. Furthermore, our study was conducted remotely with self-report measures, which may have limitations in capturing some relevant information. While online assessment may omit important details, the inclusion of ecological assessments could offer valuable insights into participants’ real-life environments. However, due to the constraints imposed by the COVID-19 pandemic and social distancing measures, we were unable to incorporate alternative assessment methods. We recommend that future studies incorporate objective and physiological measures alongside self-reported assessments to provide a more comprehensive view of both functional and cognitive outcomes in MCI populations.
Another limitation of this study is that we did not differentiate between MCI subtypes, such as amnestic and non-amnestic or single and multiple domain impairments. While this broader approach allows us to explore cognitive interventions in MCI populations, it may overlook differences in how various subtypes respond. Therefore, our findings should be interpreted cautiously, and generalizations must be made carefully due to the diversity within the MCI population. Future research with more specific samples is needed to better understand the effects of interventions on different MCI subtypes.
Despite the extensive remote support provided during the training, including device loans and technical assistance, future studies should incorporate a pre-education component for participants with limited technological experience. A pre-education phase would ensure that all participants acquire the necessary skills to navigate digital platforms effectively, thus enhancing their overall experience. This addition would not only improve the accessibility of cognitive training but also allow for a more comprehensive evaluation of the intervention’s effectiveness.
Our study highlights the potential benefits of digital cognitive training for individuals with MCI. We found improvements in both functionality and cognition after just 10 h of training, suggesting the effectiveness of this intervention. Overall, our findings provide valuable information about the potential of digital cognitive training as a non-pharmacological intervention for improving functional and cognitive outcomes in individuals with MCI.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We are grateful to the participants for their involvement in this study.
Funding
This work was supported by grants from the Global Brain Health Institute, Alzheimer’s Association, Atlantic Institutes, Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). R.P. is an Atlantic Fellow of the Global Brain Health Institute. C.M.C was supported by fellowships from CNPq. The cognitive training software used in this study and all technical support were provided to us free of charge by Posit Science, Inc.
Data availability
The data are available upon request to the researcher for replication purposes, including access to analytic methods and materials. The studies reported in the manuscript were pre-registered at ClinicalTrials.gov under the identifier NCT03911765.
Declarations
Competing interests
Dr. Panizzutti is the founder of NeuroForma LTDA, a company with a financial interest in cognitive training. The remaining authors have no conflict of interest to disclose.
Footnotes
Publisher's Note
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Data Availability Statement
The data are available upon request to the researcher for replication purposes, including access to analytic methods and materials. The studies reported in the manuscript were pre-registered at ClinicalTrials.gov under the identifier NCT03911765.