Alex Bahar-Fuchs, PhD,1–3,
*, Marjolein E.A. Barendse, MSC,1
Rachel Bloom, MA,2,4
Ramit Ravona-Springer, MD,2,5
Anthony Heymann, MD,5,6
Hai Dabush, BA,2
Lior Bar, BA,2
Shirel Slater-Barkan, BA,2
Yuri Rassovsky, PhD,4,7,8
and Michal Schnaider Beeri, PhD2,9
1
Academic Unit for Psychiatry of Old Age, Department of Psychiatry, University of Melbourne, Victoria, Australia. 2
Joseph Sagol
Neuroscience Center, Sheba Medical Center, Ramat Gan, Israel. 3
Center for Research on Aging, Health, and Wellbeing, Research School
of Population Health, Australian National University, Canberra, Australian Capital Territory, Australia. 4
Department of Psychology, Bar-Ilan
University, Ramat Gan. 5
Sackler School of Medicine, Tel-Aviv University. 6
Maccabi Healthcare Services, Tel-Aviv. 7
Leslie and Susan Gonda
(Goldschmied) Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel. 8
Department of Psychiatry and Biobehavioral
Sciences, University of California, Los Angeles (UCLA). 9
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York.
*Address correspondence to: Alex Bahar-Fuchs, PhD, Academic Unit for Psychiatry of Old Age, Building 5, 34-54 Poplar Road, Parkville, Victoria
3052, Australia. E-mail: alex.bahar@unimelb.edu.au
Received: September 8, 2018; Editorial Decision Date: March 4, 2019
Decision Editor: Anne Newman, MD, MPH
Abstract
Background: To evaluate the effects of adaptive and tailored computerized cognitive training on cognition and disease self-management in
older adults with diabetes.
Methods: This was a single-blind trial. Eighty-four community-dwelling older adults with diabetes were randomized into a tailored and
adaptive computerized cognitive training or a generic, non-tailored or adaptive computerized cognitive training condition. Both groups trained
for 8 weeks on the commercially available CogniFit program and were supported by a range of behavior change techniques. Participants in
each condition were further randomized into a global or cognition-specific self-efficacy intervention, or to a no self-efficacy condition. The
primary outcome was global cognition immediately following the intervention. Secondary outcomes included diabetes self-management, metamemory, mood, and self-efficacy. Assessments were conducted at baseline, immediately after the training, and at a 6-month follow-up.
Results: Adherence and retention were lower in the generic computerized cognitive training condition, but the self-efficacy intervention was not associated
with adherence. Moderate improvements in performance on a global cognitive composite at the posttreatment assessments were observed in both
cognitive training conditions, with further small improvement observed at the 6-month follow-up. Results for diabetes self-management showed a modest
improvement on self-rated diabetes care for both intervention conditions following the treatment, which was maintained at the 6-month follow-up.
Conclusions: Our findings suggest that older adults at higher dementia risk due to diabetes can show improvements in both cognition and
disease self-management following home-based multidomain computerized cognitive training. These findings also suggest that adaptive
difficulty and individual task tailoring may not be critical components of such interventions.
Trial registration: NCT02709629.
Keywords: Self-management, Self-efficacy, Mild cognitive impairment, Cognition.
Prevention of dementia is a global health priority, and according to
an increasingly dominant view, the elimination, reduced exposure to,
and better management of several common modifiable risk factors
can lead to the prevention of more than a third of all dementia cases
(1). A relationship between chronic metabolic conditions and cognitive ageing is firmly established with population studies repeatedly

demonstrating a link between type 2 diabetes and increased risk of
cognitive decline (2,3), conversion of mild cognitive impairment to
dementia (4), and development of dementia-spectrum disorders in
general (5–7). Diabetes-related biological processes have been implicated in the genesis and maintenance of the pathophysiological
mechanisms that give rise to neurodegenerative diseases, including
Alzheimer’s disease and vascular dementia (8–11).
In people with diabetes, effective disease self-management—a daily
regimen encompassing such behaviors as adherence to medication
intake, to appropriate dietary and exercise guidelines, blood glucose
monitoring, foot care, and regular health care visits—is of vital importance in the prevention of diabetes-related complications, including
cognitive and functional decline (12,13). However, subtle cognitive
dysfunction in older adults with diabetes is common and associated
with worse diabetes self-management (12,14). Therefore, the maintenance of cognitive abilities in older adults with diabetes by means of
effective cognition-oriented treatments may play an important role in
the prevention of diabetes-related complications and the associated
downward spiral. Importantly, evidence supports the hypothesis that
psychosocial behaviors may play an important role in attenuating
the association between cognition and pathophysiological changes in
older age (15). Cognitive training (CT), the guided practice on tasks
targeting specific cognitive abilities and processes, is associated with
improved performance on untrained cognitive measures in cognitively
healthy older adults (16), as well as in people at risk of dementia due
to mild cognitive impairment (17), but by the time mild-to-moderate
dementia is diagnosed, CT appears to be of little benefit (18). It has
therefore been suggested that CT may be offered to older adults at
risk of dementia due to mild cognitive impairment in the efforts to
prevent or delay the onset of dementia (19). An active debate remains,
however, on the extent and limits of transfer of gains from CT to more
distal outcomes (20–22), and whether certain CT elements (eg, adaptive task difficulty, personal tailoring) are related to gains is not clear.
In the first report concerning CT for persons with diabetes, Whitelock
and colleagues (23) have recently found that adults with type 2 diabetes improved on a measure of visuospatial attention following working memory training. However, to date there has been no investigation
of the potential of multidomain CT to improve global cognition or
diabetes self-management outcomes in older adults at higher dementia risk due to diabetes. Further, self-efficacy (SE), a person’s belief
that they can achieve a goal, has been associated with diabetes selfmanagement (24), but whether or not augmenting CT with techniques
to boost SE leads to improved treatment adherence or outcomes has
not been investigated. We therefore conducted a randomized controlled trial with the primary aims of comparing the effects of tailored
and adaptive multidomain CT with a simplified, nonadaptive and
“generic” CT in relation to (i) global cognition (primary outcome) and
(ii) diabetes self-management (secondary outcome), in older adults
with diabetes. Secondary aims included the effects of the intervention
on performance in specific cognitive domains, self-reported mood and
memory ability, activities of daily living, and the impact of a secondary SE intervention on training adherence or outcomes. The detailed
rationale, along with the complete methods, has been published separately, (25) and therefore, only key design features are summarized in
the following sections.
Methods
The trial was conducted in the metropolitan Tel-Aviv area, with
recruitment commencing in October 2015, and follow-up completed
by September 2017. The trial was approved by the ethical review
boards at Sheba Medical Center (SMC-0573-13) and at Maccabi
Health Services (MHS 25/2014) and was retrospectively registered in ClinicalTrials.gov (NCT02709629). Differences between
the current article and the published protocol (25) are listed in the
Supplementary Material.
Participants
Eighty-five community dwelling older adults (M age 71.45 years,
SD = 4.85; 51 male) enrolled in the study. They were recruited
through media advertising, diabetes education groups, fliers distributed in local health centers, and through a large observational study
(the Israel Diabetes and Cognitive Decline study (26)). Participants
were required to have a diagnosis of type 2 diabetes but no diagnosis
of dementia or Alzheimer’s disease. See the trial protocol (25) for
sample size calculations, a complete list of eligibility criteria, and
screening procedure. All participants provided written informed consent, and all procedures were approved by the institutional review
boards of Sheba Medical Center and of Maccabi Health Services.
Interventions
Following baseline assessment, randomization software was used to
assign participants to a tailored and adaptive computerized cognitive training (TA-CCT) or a generic computerized cognitive training
condition (G-CCT) at a 1:1 ratio by an independent researcher who
concealed the results of the randomization from the investigators.
Within the CT conditions, participants were further randomized into
a global SE, CT SE, or a no SE condition. Participants and those
completing outcome assessments were blind to condition allocation. Participants in both training conditions trained at their home
on a commercially available multidomain computerized cognitive
training (CCT) platform (CogniFit). All participants also received
psychoeducation and a range of behavior change techniques (BCTs)
were used to optimize adherence and perseverance to the CCT intervention. These behavioral components were developed using a taxonomy of BCTs and the associated Theoretical Domains Framework
outlined by Michie and colleagues as a guide (27). Details of all
BCTs can be found in the protocol (25).
CCT conditions
The TA-CCT condition differed from the G-CCT condition in three
key features. (i) Individual tailoring: Participants in the TA-CCT condition were assigned specific tasks based on their cognitive profile of
strengths and weaknesses, established at baseline through a computerized assessment built-in to the training platform, and continuously updated over the training period. In contrast, task allocation
in the G-CCT condition was similar for all participants, irrespective of their cognitive profiles. (ii) Adaptive difficulty level: In the
TA-CCT condition, task difficulty changed in response to actual performance levels to adjust and maintain levels of challenge, whereas
in the G-CCT condition task difficulty across sessions remained
fixed. (iii) Session-based feedback: Participants in the TA-CCT condition viewed their performance feedback at the end of each session,
whereas participants in the G-CCT condition could only see their
baseline and end-of-training scores.
Participants were instructed to train three times per week over
an 8-week period, and to complete two training sessions on each
training day, with a session lasting 10–15 minutes (total of 48 sessions). Each session included a unique combination of three types
of tasks reflecting a range of cognitive abilities. Three months following the completion of the main training phase, participants who

Supplementary Table 1. Participants in the TA-CCT condition were
slightly older than those in the G-CCT condition (t(78.68) = 2.11,
p = .038), but they did not differ on any other demographic characteristics or cognitive outcomes at baseline. There were also no differences on any demographic characteristics or baseline cognition
between participants who completed all three assessments and those
who did not complete post-intervention and/or follow-up.
Participants in the TA-CCT and G-CCT conditions were comparable in terms of overall compliance with the prescribed intervention, with 68% and 71%, respectively completing at least 80% of
the total prescribed sessions (n = 48). However, participants in the
TA-CCT condition spent more time training relative to the G-CCT
condition (t(79) = 2.67, p = .009). No differences were found
between the SE conditions in the total time spent training.
Cognitive Outcomes
Across training conditions, global cognition and both memory
composite scores improved following the intervention, with further improvements observed at the follow-up assessment. The nonmemory composite increased from pre- to posttraining, which was
retained at follow-up. However, these improvements were overall of
similar magnitude in the two training conditions (Figure 2), and in
the three SE conditions. The changes in global cognition from baseline to post-intervention and follow-up were still significant when the
analysis was repeated with training time as a covariate (baseline to
post-intervention: β = 0.22, stderr (standard error) = 0.04, p < .001; post-intervention to follow-up: β = 0.10, stderr = 0.04, p = .007). There was also an effect of total training time: participants who spent more time training had higher global cognition scores across all assessment points (β = 0.0005, stderr = 0.0002, p = .02). The interaction between assessment occasion and total training time was not significant, that is, a higher “dosage” was not associated with greater improvements in global cognition. Beta estimates, associated standard errors, and effect sizes of these results are shown in Table 2. Fifteen people were defined as having lower baseline global cognition (z ≤ −0.5) with the rest (n = 68) defined as having averagehigh baseline global cognition (z > −0.5). Participants with lower
baseline global cognition showed a greater improvement at the posttreatment assessment relative to those with average-high baseline
cognition (Cohen’s d = 1.072), whereas participants with averagehigh baseline cognition improved to a greater extent between the
post-intervention and follow-up assessments (Cohen’s d = 0.367).
No interaction was observed between baseline cognition, training
condition, and assessment occasion.
Secondary Outcomes
Findings regarding diabetes self-management can be found later.
Results concerning other secondary outcomes (meta-memory, mood
outcomes, SE, activities of daily living and caregiver burden, and
distress) are provided in the Supplementary Material.
Diabetes self-management
Self- and informant-reported diabetes self-management were
strongly correlated at baseline (r = .68, p < .01). No association was found between self- or informant-reported diabetes management and cognitive performance at baseline or at subsequent time points (all ps > .05). Across conditions, self-reported diabetes-management
improved at the post-intervention assessment, and this was maintained at the follow-up assessment (Figure 1 and Table 2). No
interactions between assessment occasion and either training or SE
condition were observed. No change in informant-reported diabetes
management was found at the different assessment occasions, and
there were no interactions with any of the training or SE conditions.
Defining low baseline diabetes self-management as at least 1 SD
below the total sample mean (ie, ≤5.49) on the DSMQ, 13 participants
had low self-management (TA-CCT = 7, G-CCT = 6). A Linear Mixed
Model with baseline self-management (low vs average-high) as a predictor showed an effect of both assessment occasion and baseline selfmanagement on self-management outcomes, but no interaction. That
is, participants with low self-management scores at baseline showed
similar improvement in self-management as those with average-high
baseline self-management scores and continued scoring lower than the
rest of the participants at post-intervention and follow-up.
Predictors of Treatment Response
Of the 75 participants who completed the post intervention assessment, 13 (TA-CCT = 6, G-CCT = 7) were found to have a “clinically
meaningful” improvement in cognition (improvement of 0.5 SD or
more in the global cognitive composite post-intervention). A logistic
regression with gender, age, education, adherence, baseline cognition,
and baseline diabetes self-management showed that better global
cognition at baseline was associated with being a nonrespondent
(β = −1.65, stderr = 0.76, p = .03), whereas being a female was associated with being a respondent (β = −1.90, stderr = 0.87, p = .03). No
differences were found between respondents and nonrespondents on
diabetes self-management outcomes, and the association between
posttreatment change on global cognition and change in diabetes
self-management was weak and nonsignificant (r = −.09, ns).
Sixteen participants (TA-CCT = 8, G-CCT = 8) had a “clinically
meaningful” improvement in diabetes self-management (improvement of 1 SD or more in self-reported DSMQ). A logistic regression with gender, age, education, adherence, baseline cognition, and
baseline diabetes self-management showed that lower baseline selfmanagement (β = −0.63, stderr = 0.28, p = .03) and more years of
education (β = 0.28, stderr = 0.12, p = .03) were associated with
being a respondent on the self-management outcome.
Discussion
This is the first report of the effects of multidomain CCT on cognitive
and noncognitive outcomes in older adults at higher risk of dementia
due to diabetes. We also investigated the effects of a secondary SE
intervention in relation to adherence and disease self-management outcomes. Our findings join a growing body of evidence that
has found benefits of CCT for cognitive outcomes in older adults
without dementia (17). Specifically, scores on a global cognitive composite, as well as performance in memory and non-memory cognitive
domains improved following CCT, and benefits were either maintained or further improved at the 6-month follow-up evaluation.
Beyond objectively measured cognitive performance, participants in
the current trial also reported greater use of everyday memory strategies at the end of the intervention period, and by the final follow-up
assessment at 6-month post-intervention, participants have also reported fewer mood-related symptoms, particularly related to anxiety, relative to the study baseline possibly explaining the continued
objective improvements 6 months after the end of the intervention
(see Supplementary Material).
Importantly, the current trial was also the first to examine the
effects of CCT on subjectively reported disease self-management, a
cardinal feature of long-term outcomes in people with diabetes, and
participants reported a modest improvement in self-management
behaviors, which was maintained 6 months later. Importantly, this
finding was limited to participants’ self-report but was not replicated in the analyses of the informant-reported diabetes management. Although informant-reported outcomes regarding the primary
participant may have greater importance and reliability in some domains (eg, mood and behavior), informant-report diabetes outcomes
are likely to be less reliable in the context of this study, as some
informants did not reside with the primary participant or did not
engage with them with a level of frequency to allow them to confidently rate some of the self-management behaviors, and indeed in
many cases informants left some questions un-answered or stated
having low confidence in their responses. Notwithstanding this
limitation, the correlation between the self and informant-report
self-management behaviors was strong (r = .68), indicating high
general agreement between the observations of the primary participants and the informants. We found no evidence in our analyses
that improvement in self-reported diabetes management was more
likely among participants who have shown a “clinically meaningful”
improvement in global cognition (defined as an improvement of at
least SD = 0.5) or that change in global cognition and in self-report
diabetes management were related. Hence, we are unable to draw
a clear link between improved cognition and improved diabetes
self-management at this point. However, in exploratory analyses
(data not shown), in which a more lenient criterion for cognitive
improvement was used (score greater than the posttreatment mean

change of participants who completed less than 20% of the prescribed training, n = 4, M = 0.17), a small-to-moderate, but statistically nonsignificant effect (d = 0.39, t(67) = −1.5, p = .1) was observed
suggesting that participants who showed at least modest improvement in global cognitive ability (n = 45, M = 7.4, SD = 1.7) reported
better diabetes self-management posttraining relative to participants
who did not show an improvement in global cognition (n = 24,
M = 6.8, SD = 1.3). Given the modest improvements in self-management reported by participants, the question of whether changes in
cognition moderate CCT-induced improvements in self-management
in diabetes remains to be adequately addressed in a larger trial.
In this trial, we compared outcomes between two types of CCT,
one which was tailored to participants’ cognitive profile and in
which difficulty levels were adaptive, and a more G-CCT approach,
in which tasks were not tailored and difficulty levels were fixed.
Participants who were assigned to the tailored and adaptive condition spent more time training and were more likely to remain in the
study, possibly reflecting greater motivation associated with maintaining a level of challenge. However, except for greater use of everyday memory strategies reported by participants in the TA-CCT
condition, we found no evidence for an additional benefit associated
with training in the tailored and adaptive condition in relation to
objective cognitive outcomes. This finding is in keeping with other
studies that compared adaptive and nonadaptive CT conditions and
found similar degree of overall cognitive improvement on both conditions in healthy older adults (31) and people with mild cognitive
impairment (32), but contrasts with the findings from other trials,
for example by Bahar-Fuchs and colleagues (30), in which tailored
and adaptive training was superior to generic training. The reasons
for this inconsistent finding are unclear, but more head-to-head trials
comparing tailored and adaptive to generic forms of CT within the
same population are clearly needed to assess the degree to which
these elements are critical for improvement on the trained and
transfer tasks. It is, however, both interesting and encouraging that,
across training conditions, participants with lower cognitive abilities at baseline improved at a greater rate than other participants by
the end of intervention period and were more likely to demonstrate
a “clinically meaningful” improvement in overall cognition. This
observation, if replicated, has important implications as it suggests
that CT may be initially even more beneficial to those whose cognitive abilities are relatively low at the start of the intervention.
Importantly, we found no differences between participants
assigned to the three SE conditions, either in relation to adherence to
the intervention, or in relation to measured outcomes. One possible
explanation for this observation could be that the sample recruited
for this study was overall well educated and comprised mostly highfunctioning individuals, who appear to have had relatively high levels of SE to begin with, making the detection of any improvement
more difficult. Importantly, our SE intervention was exploratory, and
our study was underpowered in relation to the analyses of interactions of time, training condition, and SE condition.
Limitations
We did not include a “treatment as usual” condition based on the
accumulated evidence from several systematic reviews that have supported the efficacy of CT for cognitive outcomes in older adults without dementia (17,33) and the suggestion that the field should move
on to trials focused on understanding of mechanism of action and
head-to-head comparisons of different treatments, rather than focus
merely on “efficacy” (16,34). Our trial design impedes ruling out
the possibility that the observed improvements in cognition in both
conditions are better explained by practice effects alone. Although
retest and practice effects have likely contributed to the changes
observed in participants’ cognitive performance, several observations support the possibility that intervention-attributable improvements have also been observed, at least in part. First, as already
noted, numerous studies have already demonstrated the efficacy of
CCT in comparison to both passive and active control conditions.
Second, effect size of the change in global cognition across conditions in this study (d = 0.495), as well as in the G-CCT condition
(d = 0.83, data not shown) was significantly larger than the effect
size associated with passive (d = 0.12; CI = 0.08–0.16) or active
(d = 0.18; CI = 0.12–0.24) control conditions, as shown in a recent
meta-analysis (35). Third, we attempted to estimate the approximate
improvement that might be expected from practice or retest alone in
the current study by examining the mean improvement in cognitive
composites in participants who did not adhere even to a minimal
dose of the prescribed intervention (ie, <20% of the total prescribed sessions, n = 4). The effect estimate in this small group on the global cognitive composite at the post-intervention assessment was Cohen’s d = 0.17, significantly lower than the mean improvement observed in the rest of the sample (Cohen’s d = 0.49). Further studies comparing TA-CCT to generic forms are required to confirm our hypothesis that G-CCT is an active treatment condition associated with greater benefits than those associated with active control or placebo conditions. Another limitation is related to characteristics of our sample, namely, the inclusion of predominantly highly functioning, welleducated, and motivated community-dwelling older adults with diabetes. This self-selection may have led to both positive and negative bias in our findings. For example, participants in this study might be more motivated and compliant in completing the training than the general diabetic population, limiting the generalizability of training effects if implemented on a wider scale. Nonetheless, our findings concerning the effect of training on cognition and self-management are encouraging considering this limitation, and effects may prove to be even stronger in lower functioning individuals. This possibility is also supported by our finding of a greater improvement in cognition by the end of treatment among those who had lower baseline cognition. Lastly, although our sample was sufficiently powered to detect intervention-related differences between conditions on the main outcomes, it was insufficiently powered to detect more complex relationships, such as three-level interactions between training conditions, SE conditions, and assessment occasions. Finally, surrogate biological or physical health outcomes, such as hemoglobin A1c levels were not directly measured in this trial, although we plan on obtaining data from routine medical examinations from the partnering HMO (MHS) for further analyses and findings from these analyses will be reported in the future. Further work is required to examine the possible predictive or moderating role of diabetesrelated health indicators on treatment outcome, as well as transfer of trained skills to everyday cognitive functioning, factors associated with treatment adherence and participant retention, and maintenance of benefits beyond the 6 months follow-up. Conclusions Older adults at higher dementia risk due to diabetes may benefit in the short to intermediate terms from home-based multidomain CCT in relation to their cognition, self-reported diabetes management, self-reported use of memory strategies, and symptoms of anxiety. A tailored and adaptive as well as a more generic version of the training seem to produce comparable results. Further work is required to better rule-out practice effects, establish or enable transfer of trained skills to day-to-day life, and understand the role of intervention parameters such as adaptive task difficulty and personal task tailoring in improving adherence. CCT represents an intervention that is relatively easy to implement in a range of community and clinical settings at relatively low cost, and given the encouraging findings in relation to cognition from large meta-analyses, future work exploring possible implementation strategies and barriers is warranted. Supplementary Material Supplementary data are available at The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences online. Funding This work was supported by Maccabi Health Services (MHS; grant no. 25860 to M.S.B.). The funding source played no role in the design and implementation of the trial, analysis and interpretation of the data, or preparation of the manuscript. The CCT platform was donated by CogniFit. CogniFit or its employees played no role in the design and implementation of the trial, analysis and interpretation of the data, or preparation of the manuscript. R.B. was supported by the Vice-Chancellor Award from Bar Ilan University, Israel. A.B-F. was supported by an Australian National Health and Medical Research Council fellowship (grant no. 1072688). M.S.B. was supported by the National Institute on Aging (grant no. R01-AG-034087). A.H. is an employee of MHS who provided funding for this study. The authors declare that they have no competing interests. Acknowledgements The authors wish to thank the participants who took part in this study and their helpful informants. 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