Characterizing the Synergistic Effects of Physical and Cognitive Training on Attention and Working Memory
Phase: NA
Conditions: Healthy Adults
Sponsor: University of California, San Francisco

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Study Protocol Approved by UCSF Ethics Committee 
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IRB  10-04957  
  
 
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A video game to enhance cognitive health in older adults, children, and adolescents 
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“Characterizing the Synergistic Effects of Physical and Cognitive Training on Attention and Working Memory” 
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June 18 2022 
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Study Protocol Approved by Ethics Committee 
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Recruitment Criteria 
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1. Males and Females, aged 55-80 
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2. Inclusion criteria 
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a. English speaking 
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b. Normal or corrected to normal vision and hearing 
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c. Ability to complete cognitive tasks 
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d. Ability to cooperate and comply with all study procedures 
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e. General health/physical fitness allowing safe participation with BBT physical components 
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f. Be within 2 standard deviations (SD) of age-matched controls on all five of the 
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neuropsychological composite scores.  
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3. Exclusion criteria 
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a. Neurological or psychiatric disorders 
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b. History of stroke  
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c. Prior head trauma 
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d. Psychiatric illness 
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e. Psychotropic medications 
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f. Hormonal medications 
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g. Cardiovascular medications 
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h. Physical or mental conditions that interfere with daily activities 
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Two or more composite scores exceeded 1.5 SDs. 
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Procedures 
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1. Screening for general inclusion/exclusion criteria 
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2. Consenting 
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3. Baseline outcome data collection 
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4. Cognitive control training intervention period 
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5. Return visit to UCSF for post-intervention outcome data collection  
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6. Return to UCSF for 1-year outcome data collection 
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Measures administered 
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1. CPT 
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2. Working Memory Task 
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3. Frontal Theta Power 
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4. Filter Assessment 
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5. Frontal Theta ITC 
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6. Diastolic Blood Pressure 
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Time commitment 
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Consenting: 30 minutes based on individual reading speed and comprehension level 
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Outcome assessments at baseline, 2-month, and 1 year time points: 2 hours 
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Intervention training: 25-35 minutes 3-5x/week for 6-8weeks, depending on intervention arm assigned 
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Introduction 
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The augmentation of deficient attention abilities is especially of interest for older adults, given well 
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documented age-related declines in these abilities1-9, which are exacerbated in the presence of 
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interference2,10-13. Over the last decade there has been a surge of research aimed at improving cognitive 
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abilities by harnessing neuroplasticity via cognitive training13-17. Cognitive training as a whole has come under 
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warranted scrutiny given consistent shortcomings in study design and outcomes: i) minimal extension of 
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benefits beyond the training interventions themselves, ii) lack of appropriate control groups, and iii) absence 
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of follow-up testing to assess sustainability of observed effects18-20.  However, there have been several notable 
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examples of cognitive interventions enhancing non-trained cognitive abilities in older adults5,21-23, with these 
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findings aligning with systemic reviews supporting the utility of cognitive interventions in the older adult 
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population24-26.  
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In contrast to the uncertainty surrounding the benefits of cognitive training, the utility of physical fitness 
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interventions for older adults’ general health has been well established. Fitness-based interventions have also been 
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shown to benefit cognitive control abilities27-32, with such findings hinting at the possibility that combining cognitive and 
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physical training approaches may lead to greater cognitive benefits than either approach alone (although not supported 
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to date33-36). The use of combined cognitive and physical interventions are especially compelling as a strategy to 
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maintain both cognitive and physical health37-39, given that such a pairing can be a time- and resource-effective approach 
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for addressing multiple risk factors in older adults. Indeed, combined training has been shown to result in greater 
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participant enjoyment compared to either sequential training40 or physical exercise alone41. However, while the typical 
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protocol of alternating between physical and cognitive training has shown to have some cognitive benefits34,35,41-43, this 
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cumbersome tactic requires a multitude of human and material resources. Alternatively, one resource-effective 
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approach in this arena has been the use of exergames; however, it remains unclear how effective such approaches are 
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at improving cognitive and physical outcomes in aging populations44-49, leaving an open question of their overall utility. 
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We have previously demonstrated that cognitive interventions using closed-loop, adaptive algorithms50 
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designed to target specific cognitive abilities in older adults can improve task performance and neural measures of 
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cognition12,23,51-53, with some evidence of these effects persisting well after the intervention period51,54. These 
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interventions, delivered as engaging video games, have shown positive effects not only with older adults, but in diverse 
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populations55-58, with previous work highlighting the possibility of returning performance on measures of cognitive 
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control to young adult level.5,51  Here we created a novel exergame intervention for older adults (Body-Brain Trainer, 
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BBT; Figure 1) that requires participants to perform full-body, physical movements in response to cognitive challenges 
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that engage different cognitive control domains50,59. As in our previous work51,60,61, we integrated real-time adaptivity 
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using closed loop mechanics for each cognitive ability being challenged. Given the movement demands of BBT, we also 
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implemented an adaptive physical fitness challenge by using real-time heart rate data to titrate game play for a 
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personalized and integrated training experience across both cognitive and physical domains (see Methods and 
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Supplementary Materials for more details on the BBT software).  
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In the present study, we sought to evaluate the primary question of whether BBT could improve measures of 
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attention and physical fitness in older adults beyond that of an expectancy-matched, active placebo control group. To 
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the best of our knowledge, this design is noteworthy given that the use of expectancy matching62 has been absent in all 
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previous efforts examining cognitive benefits from combined approaches, including those studies designed to evidence 
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potential synergistic effects through mechanistic control groups (see Supplemental Materials for a brief treatise 
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describing our rationale for utilizing this particular control group). We also interrogated the following secondary 
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questions: what is the neural mechanism underlying positive cognitive effects, do any observed cognitive improvements 
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persist one year later without booster training sessions, does BBT result in older adults achieving comparable levels to 
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young adults on our primary outcome measure, and does this intervention affect other measures of cognitive control 
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(working memory).  
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METHODS 
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Participants:  
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The study was approved by the Committee on Human Research at the University of California, San Francisco, and 
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retrospectively registered on ISRCTN registry [ISRCTN [66423499] to distinguish the goals and approach taken here from 
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those of the ‘parent’ registration (clinicaltrials.gov submission NCT03032796). This study was designed to gauge the 
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feasibility and potential efficacy of using the BBT intervention compared to an expectancy matched active placebo 
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group, for a subsequent mechanistic trial exploring synergistic effects of a combined intervention versus individual 
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components alone (as described in the ‘parent’ clinicaltrials.gov submission). For continuity and cohesion, we used the 
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same primary (CPT) and secondary (Working Memory Task) outcome measures listed in the parent clinicaltrials.gov 
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registration. Furthermore, here we also examined a subset of the exploratory outcomes listed in the parent registration 
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(blood pressure, event-related spectral perturbation derived from EEG) as well as two new exploratory measures (Filter 
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Task, Limit of Stability Assessment). 49 healthy, older adult (OA) individuals consented to participate in this study. OAs 
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were randomly assigned (block size of 5) to a training group (body-brain trainer; BBT; N=24, mean age=68.8 +/- 5.9, 13 
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females) or an active, expectancy-matched control group (mind-body trainer; MBT; N=25, mean age=68.20 +/- 6.75, 13 
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females). All participants were from the San Francisco Bay Area and recruited through online and newspaper 
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advertisements. All participants had normal or corrected-to-normal vision, had no history of stroke, traumatic brain 
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injury, or psychiatric illness, were not taking psychotropic, hormonal, or cardiovascular medications, and did not have 
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any physical or mental conditions that may interfere with their daily activities (e.g., migraine headaches, substance 
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abuse, neuropathy. Similar to our previous work, all participants reported playing less than 2-hours of video games per 
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month, and completed a general health questionnaire reviewed by the study team assessing each individual’s current 
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state of physical fitness to ensure that they could safely engage with the physical aspects of the BBT platform. All 
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participants gave written informed consent, and were paid $15/hour for their in-lab and at-home participation. All 
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participants were encouraged to not change any aspects of their daily routine (e.g. to change exercise habits) for the 
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duration of the study. 
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Neuropsychological Battery  
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Prior to experimental testing, all participants were evaluated on 3 measures probing for cognitive impairments and 
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depression (the Montreal Cognitive Assessment (MoCA110; minimum score of 26; the Geriatric Depression Scale (GDS)111; 
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the PHQ-9112) as well as nine neuropsychological tests. These nine tests were subdivided into related domains and 
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composite scores of each were calculated for each of the following: 
 
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1. Immediate Memory – consisted of the five immediate recall trials from the California Verbal Learning Test 
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(CVLT-II) 113 
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2. Delayed Memory – the long delay free and cued recall trials as well as the Recognition measure from the CVLT-II 
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3. Processing Speed – Digit Symbol task (citation), Executive Composite-DKEFS Trails Condition 2 (numbers only) 114 
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4. Cognitive Flexibility (Task Switching) – DKEFS Trails Condition 4 (number-letter switch) 
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5. Fluency - Verbal Fluency (Animals 115, D- Words (MoCA)  
 
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Color vision deficiency was assessed with Ishihara’s Tests for Colour Deficiency116. To be included in the study, all 
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individuals were required to be within 2 standard deviations (SD) of age-matched controls on all five of the composite 
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scores. Participants were also excluded if two or more composite scores exceeded 1.5 SDs. This procedure provided a 
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thorough characterization of the cognitive status of each OA participant in multiple domains while simultaneously 
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ensuring that their cognitive faculties were comparable to that of their age-matched peers. All participants tested within 
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two SDs of the normative values established for each of these measures.  
 
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Study Design:  
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For those participants randomized to the BBT group, they were asked to come to Neuroscape at UCSF 3 days per week, 1 
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hour per visit (36min of on-task training time per session, 24 minutes allocated for warm up/cool down/breaks) for 8 
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weeks (24 hours total, 14.4 hours of actual training time). Each of these visits were accompanied by an onsite trainer to 
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facilitate the training experience for the participant and ensure that training was being completed in a safe manner. For 
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those participants randomized to the MBT group, they were loaned an iPad tablet (9.7 inch screen size; 1024 x 768 
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screen resolution) for their training session following their ‘Pre-training’ assessment, and were instructed to train with 
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their assigned task at home for six weeks, 5-days per week, with 30-minute training sessions per day, for a maximum of 
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thirty 30-minute training sessions (15 hours of training total). All MBT participants were instructed to train sitting down 
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with the tablet on a flat surface, such as a table, in a location with minimal external distraction.   
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We used a study design that involved several layers of blinding: i) data was collected by a group of research 
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associates who were blinded to the identity of the intervention group, ii) data analysis (which was anonymized to 
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conceal intervention group identity) was performed by a different group of researchers, and iii) participants were 
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blinded to the group assignment following randomization procedure, which was done prior to the first participant visit 
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to the lab. Blinding began at the point of recruitment, where all participants were informed that they were being 
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recruited for a study designed to test the efficacy of software interventions for improving cognition in a variety of 
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domains. Neither the BBT nor the MBT participants were aware of the other group or the task that they trained with. 
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Both groups were administered the same instructions and brief overview of the goals of the study, namely to determine 
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if the training game could improve cognitive abilities. Thus, all participants were told that they were part of an active 
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intervention to improve their cognitive abilities (see below for details on how we established matched expectancy117). 
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Finally, one study coordinator was informed of the treatment assignments, as their role was solely to provide technical 
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and other support during the training.  
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All participants reported to our UCSF Neuroscape laboratories prior to training (‘Pre-training’ session) and 
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following the completion (‘Post-training’ session) of training (1-week grace period from start/end of training) to compete 
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a battery of cognitive and physical outcome measures to assess training-related changes. Participants were invited to 
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return to the laboratory 1 year after their post-training outcome assessment to evidence which, if any, positive training 
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effects persisted in the cognitive domain.  Over the course of the study, 3 participants in the BBT group and 2 
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participants in the MBT group voluntarily withdrew from the study, resulting in the complete pre- and post-training data 
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sets from 24 BBT participants and 25 MBT participants. One additional participant had a non-training related adverse 
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event which caused them to withdraw from the study prior to be randomized to a group. We were unable to collect EEG 
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data from three participants at pre-training due to equipment malfunctions, and, due to technical issues with 
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photodiodes, we were unable to time-lock the EEG to the event onsets for several other participants (n= 4 at pre-
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training, n= 4 at post-training). There was one participant who did not return for their post physical assessment. 
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Intervention Descriptions: 
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BBT paradigm:  BBT is comprised of three modules, with each targeting a different aspect of cognitive control: visual 
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search tasks for attention (with increasing distraction), spatial span/multiple object tracking tasks for working memory, 
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and a task-switching paradigm targeting goal management/cognitive flexibility abilities. There are also three different 
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tasks with ascending difficulty within each module, such that advancing to the next level engages a fresh challenge while 
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maintaining interest (for example, a change from a spatial span condition to a multiple object tracking condition with 
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working memory demands). Comparable to our previous work using cognitive measures alone51,60,61, here we integrate 
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real-time adaptivity for both the cognitive and physical aspects of the gameplay. For each cognitive task, difficulty scales 
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on a trial-by-trial basis, with a correct trial performed within a thresholding-determined response window leading to 
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shorter response window by 10msec, and an incorrect trial leading to a lengthening of the response window by 30msec 
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(thus, a 1”up”/3”down” staircase). These cognitive adaptive algorithms are designed to assure participants remain at an 
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~80% rate of accuracy, a level that is not too easy nor too hard, so that it is enjoyable and engaging. For the physical 
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training, difficulty is tied to the demands associated with the distance an individual must travel for a given response and 
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the amount of time allocated to complete this response. These movement-related aspects are directly responsive to 
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whether heart rate is below/within/above a predetermined heart rate window to ensure a moderately intense workout 
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that does not impede the ability to perform the cognitive task. For example, if one is playing the game below their 
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assigned heart rate range, the software will automatically increase the distance that the participant must move to 
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respond with their hands/feet on each trial until their heart rate is within the specified range. Training sessions are 
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linked, such that the next session begins at the level attained at the end of the previous session. Participants are 
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provided two types of feedback: 1) real-time feedback – indicating whether the participant successfully detected or 
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classified the target and 2) punctuated feedback – participants advance through a series of “levels” that are reported at 
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the beginning and end of each run.  
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BBT Module Descriptions 
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(i) BBT Attention. This module demands an active scan of the screen in search of a target, much like traditional visual 
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search tasks118. This module involves a constantly evolving amount of cued information as well as number of 
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incongruent distracting elements, such that participants experience less cued information while experiencing more and 
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more distracting elements as they advance. Participants are required to quickly identify the direction of a probe target 
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that is facing at a right angle (up, down, left, right), and are aided by the presence of directional cue indicating in which 
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location of the screen the target will appear amongst distracting elements. Responses are made by reaching their hands 
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to indicate the direction of the probe, with the additional physical challenge of running in place if the target is up or 
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down. Prior to each level, participants completed a thresholding session to determine the optimal starting point from 
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both a cognitive and physical perspective. After completing their initial 7 training sessions, participants advance to LEVEL 
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2 of this module which entailed facing a greater challenge: here they encountered an increase in the number and 
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salience of distracting elements, including the presence of congruent distractors, as based on their performance on the 
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previous trial. After completing 14 training sessions, participants moved on to LEVEL 3 of this module: here participants 
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performed the same task as before, but now without the aid of a directional cue. Participants only receive game points 
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when they correctly perform a given trial faster than the predetermined, personalized threshold determined at the 
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beginning of each level to optimize the attentional engagement.  
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(ii) BBT Working Memory. This module engages spatial working memory resources similar to the Corsi block task119-121, 
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requiring individuals to memorize an additional stimulus following two consecutive correct responses, with two 
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consecutive incorrect trials leading to one element being subtracted. Participants memorize the location of objects on 
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screen followed by a 5-7 second delay period during which the participants perform a directed physical movement, with 
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a correct response leading to a greater number of potential targets to be memorized on the next trial (and vice versa). 
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Responses are made with both hands and feet by reaching/kicking targets, with additional physical challenges (making a 
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woodchopping motion) occurring during the delay period. Prior to each level, participants completed a thresholding 
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session to determine the optimal starting point from both a cognitive and physical perspective. After completing 7 
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training sessions, participants are asked to also memorize and report the sequential order in which the targets originally 
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appeared on the screen (LEVEL 2), thus increasing the spatial working memory load. After 14 training sessions, 
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participants perform a working memory/multiple object tracking task that requires memorizing and tracking the targets 
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as they become invisible and move amongst a sea of moving objects (LEVEL 3).  Participants receive game points when 
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they correctly complete a working memory trial faster than a predetermined, personalized threshold so as to challenge 
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the underlying cognitive working memory circuitry. 
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(iii) BBT Task Switching. This module challenges cognitive flexibility resources by requiring participants to rapidly switch 
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their focus based on distinct rules, much like a traditional task-switching paradigm122-124. Here a morphing algorithm is 
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used to titrate the perceptual similarity of the target presented, such that a correct trial makes a subsequent exemplar 
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morph more similar to the probe presented (and vice versa). Participants are presented with exemplar objects along 
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with a target, and move their hands to the target object that is most similar to the exemplar presented. For example, 
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when a greenish-blue target appears, participants decide whether the image is more GREEN or more BLUE. The target 
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changes its degree of likeness to each exemplar following each trial, with each correct response morphing the probe 
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towards an indistinguishable 50/50 ratio of each exemplar (and vice versa). Prior to each level, participants completed a 
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thresholding session to determine the optimal starting point from both a cognitive and physical perspective. After 
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completing 7 training sessions, the presented probes now have features that integrate two rule bases (Level 2, e.g. both 
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Color and Shape, so a BLUE square), creating greater cognitive demands, similar to interference generated by a Stroop 
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task. Finally, after 14 training sessions, participants perform the same task, but the exemplars now spawn in random 
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locations across the screen, heightening the cognitive demands further by requiring visual search (LEVEL 3). Participants 
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receive game points when they perform a trial as fast or faster than a predetermined, personalized threshold to 
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pressure underlying goal-management circuitry. 
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BBT cognitive and physical training calibration 
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For each cognitive task, difficulty was initially determined through a pre-training assessment to determine an optimal 
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training threshold, then scaled on a trial-by-trial basis throughout the training experience. The associated physical 
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challenge for each cognitive task was driven via motion capture of one’s hands and feet, where the distance that an 
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individual had to reach/kick to respond during the cognitive task was directly tied to a participant’s predetermined 
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optimal training heart rate. For example, if an individual’s heart rate was below their training window, then the distance 
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an individual had to traverse to respond on a given trial increased.  
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This continuous cognitive scaling occurred in parallel with an adaptive cardiovascular challenge, with a pre-
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training VO2 max assessment determining an optimal training window with respect to one’s heart rate using 
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indirect calorimetry125 to calculate a more precise level of cardiovascular fitness for each individual. More 
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specifically, this involved participants completing a treadmill graded exercise VO2 Max test to subsequently 
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determine an individual’s maximal heart rate, which subsequently was used to determine the heart rate 
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percentiles (60-70% of one’s max HR, 70-80% of one’s max HR, etc.) at which participants would train at during 
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the BBT training experience. This predetermined heart rate window was used as a baseline comparator against 
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one’s current heart rate, with the responses needed for a given cognitive task scaled to be closer or further 
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away than a previous trial without impeding the ability to perform the cognitive task.  
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MBT battery: 
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 To mitigate any potential placebo effects brought on by participant expectations, we identified a suitable active 
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control condition based on participant predictions of potential training-related gains as in our previous work58. 
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We identified a set of three commercially available iOS apps (Supplementary Figure 1) that were matched to 
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our BBT program in terms of expectation of improvement on our cognitive outcome measures (see below for 
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description of the statistical selection process).  Those apps were a language learning app (Duolingo; 
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www.duolingo.com), a Tai Chi app (Tai Chi Step by Step; www.imoblife.net), and a logic games app (100 Logic 
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Games; www.andreasabbatini.com/LogicGames.aspx). For Duolingo, participants were given a choice of which 
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language they wanted to learn from those available on the app. Within the app, we set a 10 min training time 
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per day. During training, the app takes users through a series of modules that increase in difficulty and are only 
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unlocked sequentially following completion of an earlier module. Modules are organized topically (e.g., Food, 
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Animals, Phrases, etc.) and each module contains listening, speaking, vocabulary, and translation tasks and 
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culminates with a topic quiz. At the end of each lesson the app provides a progress report showing learning 
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“streaks” and the accumulation of “lingots” (Duolingo currency). These feedback features are meant to keep 
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participants motivated. For Tai Chi, users simply open the app and select from a series of modules that provide 
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detailed and easy-to-follow instructions on how to perform many basic, intermediate, and advanced Tai Chi 
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movements and is geared toward beginners with no Tai Chi experience. Each description can be read or listened 
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to and is accompanied by an animation. Users were instructed to then practice the exercise themselves several 
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times after each lesson. The logic games app is comprised of a series of “puzzle sets” that revolve around a 
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particular theme and which get progressively more difficult as people advance. The puzzles are similar to the 
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more well-known Sudoku puzzles, but provide a more engaging experience with colorful icons, unique rule sets 
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for each theme, and increasing difficulty. For each puzzle, users are given a task (e.g., plant trees according to 
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specific rules), a time limit, and several hints that they can unlock. Participants were instructed to spend 
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approximately 10 min with each app each training day (5 days per week for 6 weeks). For Duolingo, the time 
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was set internally in the app. For the other two apps, participants self-timed their training, and they recorded 
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their time on a training log. All participants completed the MBT intervention at home using an iPad Mini 2 (iOS 
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version 8.2; Apple) that was supplied by the UCSF Neuroscape Center. On the day they were given their iPad to 
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begin training, participants were also provided an instructional binder with instructions on how to play, a 
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calendar for recording notes and comments throughout their training experience, and were given e-mail support 
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throughout the intervention period. Throughout the intervention period, technical support for the iPads and all 
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software was provided via e-mail, phone, and in-person contact, when needed. 
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Because these apps were commercial apps and not designed to send data to our server, we took additional 
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steps to track compliance in the MBT group. On the day they were given their iPad to begin training, participants 
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were also provided an instructional binder with instructions on how to play, a calendar for recording notes and 
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comments throughout their training experience, and were given e-mail support throughout the treatment 
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period. They were instructed to try to split their time equally among the three apps. Upon completion of the 
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training, researchers confirmed the participants’ logged training times. 
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Behavioral Cognitive Control Assessments:   
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Attention without distraction. A custom continuous performance task (CPT) designed in Presentation 
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(http://neurobs.com) was used to assess vigilance without distraction. This tool is a modified version of a well-
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validated vigilance task (Figure 2a), the Test of Variables of Attention (TOVA), that we have used as an outcome 
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measure in previous intervention studies. As our metric of interest, we focused on the ex-gaussian tau of 
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response times, a non-parametric measure of distribution skewing that quantifies attentional lapses by 
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examining the distribution of long response times. Several studies have demonstrated a clear advantage of this 
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measure over traditional measures of response time (although we report on traditional metrics of response 
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time and response time variance in the Supplemental Materials) given that it explains a greater proportion of 
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variance without needing to employ data trimming techniques126-128. Importantly, this measure has been shown 
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to reflect performance inconsistencies that are especially present in both healthy older adults and those with 
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mild cognitive impairment129, and has been used to support neural noise130 and dedifferentiation131-133 theories 
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of cognitive aging. 
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For the present study, we adapted the task for use with EEG recordings, which requires many trials with 
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an actual response, and also facilitates the examination of ex-gaussian tau, a non-parametric measure of 
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distribution skewing of response times134-137. In this task, participants maintain fixation on a central crosshairs 
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and grey squares are shown on a black background at the top or bottom of the field of view. Stimuli are 
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presented frequently at the top of the screen as a 4:1 ratio of targets to nontargets and participants are 
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instructed to only respond to these stimuli. Participants completed 2 blocks of 125 trials with 100 targets per 
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block, yielding 200 total targets and 50 non-target trials.  
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Working memory fidelity task.  We used a delayed recognition working memory paradigm designed to measure changes 
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in participants’ ability to maintain an accurate mental representation of items in working memory either in presence or 
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absence of distracting or interfering information. We have used versions of this task in numerous previous 
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studies11,51,56,78. To summarize briefly, this paradigm consisted of three different conditions that were presented in 
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blocks: 1) no distraction (ND), 2) Ignore distractor (ID, distractor was present, but participants were informed that the 
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distractor was to be ignored), and 3) Attend Distractor (AD, participants were required to make a judgment about the 
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interfering stimulus). Each run was preceded by an instruction slide informing the participant which condition they 
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would be performing. Each trial began with the presentation of a face displayed for 800 ms, followed by a delay period 
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(3 s), the presentation of a face stimulus as a distractor in the ID and AD conditions (800 ms), a second delay period (3 s), 
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and the presentation of a face probe (1 s). The participants were instructed to make a match/nonmatch button press 
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response at the probe as quickly as possible, without sacrificing accuracy. This was followed by a self-paced intertrial 
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interval (ITI). The experiment was programmed in E-Prime (https://pstnet.com/products/e-prime/) and the stimuli were 
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presented on a CRT monitor.  
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Basic response time task. Here we administered a basic response time (BRT) task as a measure of simple motoric 
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response time to ensure that any differences observed between groups were not due to differences in basic motoric 
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quickness. Basic motor speed was assessed in the form of a simple target-detection task during their pre-training 
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assessment, to ensure that any training-related enhancements in performance were not attributed to a general motoric 
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speed increase, but rather to enhancements in cognitive control processes. On this task participants pressed a keyboard 
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button as quickly as possible upon the appearance of a circle at the center of the screen (50 trials). Average response 
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time (the time between the target appearance and button press) was assayed as a unit of basic motor speed 138.  
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Complex visual discrimination amid distractions. For this exploratory measure of attention in the presence of variable 
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distraction (considered exploratory given that it was not described in the parent clinicaltrial.gov submission), we used a 
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custom Filter Task139 (Supplemental Figure 7a) to assess how well participants were able to identify targets in the 
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presence of task-irrelevant information58,140,141. The experiment was programmed in MATLAB’s psychophysics toolbox 
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(http://psychtoolbox.org/) and the stimuli were presented on a CRT monitor. In this task, participants must attend to an 
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array of different numbers of items (either 1 or 3 red rectangles) with or without the presence of 2 visual distractors (2 
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blue or green rectangles): set size 1 no distractors (1 total item), set size 1 with distractors (3 total items), set size 3 no 
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distractors (3 total items), set size 3 with distractors (5 total items). Half of each of the trials for each condition began 
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with a cue indicating the participant should attend to either the left or the right side of the screen. The procedure for 
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each trial began with a 750 ms fixation cross following by a right/left cue (200ms) and then a 300 ms blank ISI. Next, a 
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sample set from one of the four conditions was shown for 200ms followed by a 900 ms blank delay and then a probe set 
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containing the same number of red rectangles as in the sample in either the same orientation or with a single rectangle 
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of altered orientation (50% of each). The probe screen remained visible until participants responded with a “Yes” or 
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“No” button press indicating whether or not one of the attended rectangles changed orientation. Participants 
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completed 8 blocks of 80 trials, yielding 160 trials per condition. Participants were instructed to respond as fast as 
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possible without sacrificing accuracy. As in our previous work58, we focused on Response Time Variability (RTV) as our 
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measure of interest here. 
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92 
 
 
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Neural Cognitive Control Assessments:   
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EEG Recordings. Neurophysiological data were recorded during each cognitive control assessment using an 
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active two head cap (Cortech Solutions) with a BioSemiActiveTwo 64-channel EEG acquisition system in 
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conjunction with BioSemiActiView software (Cortech Solutions). Signals were amplified and digitized at 1024 Hz 
97 
with a 16-bit resolution. Anti-aliasing filters were used and data were band-pass filtered between 0.01–100 Hz 
98 
during data acquisition. For each EEG recording session, a 1 × 1-inch white box was flashed for 10 ms at one of 
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the corners on the stimulus presentation monitor at the start of each trial. A photodiode 
00 
(http://www.gtec.at/Products/Hardware-and-Accessories/g.TRIGbox-Specs-Features) captured this change in 
01 
luminance to facilitate precise time-locking of the neural activity associated with each sign event. During the 
02 
experiment, these corners were covered with tape to prevent participants from being distracted by the flashing 
03 
light. 
04 
 
05 
EEG Preprocessing. Preprocessing was conducted using the EEGLAB software142. Noisy channels were identified 
06 
upon initial visual inspection, were removed from the data, and interpolated using a spherical spline 
07 

13 
 
interpolation, using the average signal of the surrounding channels to reconstruct the data in the removed 
08 
channel. The data were then down-sampled to 1024 Hz to reduce the computational demand without losing 
09 
any important information in the data. A finite impulse response filter with a high-pass cutoff of 1 Hz was applied 
10 
to remove drift, and then a low-pass filter at 40 Hz was applied to remove high frequency noise. Ocular 
11 
correction was performed by using ICA to isolate and remove activity induced by eye-blinks and lateral eye 
12 
movements from the signal. The data were then re-referenced to the average signal of all channels. Epochs of -
13 
1000 ms to +1000 ms were generated for each stimulus type for subsequent analyses. Epochs containing 
14 
excessive peak-to-peak deflections (±100 µV) were removed.  
15 
 
16 
Midline Frontal Theta Analyses. Midline frontal theta (4-7Hz) power (mft Power) has been implicated in 
17 
sustained attention abilities 51,143-145, including correlating with RTV across the lifespan146, that can evidence the 
18 
engagement of the prefrontal cognitive control processes during a given task. This particular marker has also 
19 
demonstrated a level of sensitivity in revealing changes following a digital intervention in multiple 
20 
studies23,51,54,55,58. Time series were created by resolving 4-40 Hz activity using a fast Fourier transform (FFT) in 
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EEGLAB in epochs from -1000 to +1000msec. Midline frontal theta total power analyses (evoked power + 
22 
induced power) across trials from the vigilance task was conducted by resolving 4–40 Hz activity using a fast 
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Fourier transform in EEGLAB, with these values subsequently referenced to a -900 to -700 pre-stimulus baseline 
24 
(thus relative power (dB)). After mft relative power was computed, we specifically interrogated a predetermined 
25 
cluster of frontal electrodes (Fz, FPz, AF3, AF4, and AFz) at the time window of maximum power, as this is the 
26 
same approach that we have previously utilized in several intervention studies interrogating mft power 
27 
changes51,54,55,58.  
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29 
Physical outcome measures:  
30 
To capture changes in physical fitness due to training during the study, all participants underwent a 
31 
comprehensive physical outcomes assessment in addition to the cognitive assessments described above. Our 
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targeted outcome measures were a senior-specific measure of stability indicative of fall risk76,77 as well as a 
33 
physiological measure of fitness and health (diastolic blood pressure)74. Our decision to focus on diastolic (rather 
34 
than systolic) blood pressure was based on diastolic blood pressure being a proportionally larger contributor to 
35 
mean arterial pressure (MAP)147, with MAP being an important predictor of cardiovascular disease148-150. 
36 
Descriptions of each exploratory fitness measure evaluated, including systolic blood pressure, are described in 
37 

14 
 
the Supplemental Materials; note that these measures  came from the Senior Fitness Test (SFT) to assess global 
38 
physical performance151.  
39 
 
40 
Training-related expectancy ratings:  
41 
In an effort to identify an expectancy matched placebo versus the BBT intervention, we first compared 
42 
expectancy on 10 different apps to BBT in a sample of 261 participants (Female=135, mean age=33.5). Each 
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participant was randomly assigned to view a video of BBT or one of 10 potential apps (n= 15–30 individuals 
44 
per app) which we did not hypothesize would improve cognitive abilities. They were then asked to rate the 
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extent to which the presented intervention would lead to improvements on these outcomes, using a Likert 
46 
scale measuring 1 (no improvement) to 7 (a lot of improvement). Following this first round of testing, we 
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selected the three apps that had the highest expectancy matching scores, and subsequently collected new 
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expectancy data in a sample of 76 young adults to assess the level of expectancy involving the combination of 
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these three apps (n=37) versus BBT (n=39). Here we found no differences in participant expectations of 
50 
improvement on our CPT task (t74= 0.24, P = 0.62, 95% CI: −1.3 to 0.77) or on our filter task (t74= 0.55, P = 0.16, 
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95% CI: −0.30 to 1.8), suggesting that we had comparable expectancy on this battery of control apps as 
52 
compared to the BBT intervention with respect to our cognitive control outcome measures.   
53 
 
To determine if these expectancy findings held with older adults, we had 91 older adults complete this same 
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expectancy survey. These older adults were shown a recording of either the BBT (n=46, mean age=64.4, Female=28) or 
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the MBT (n=45, mean age= 63.2, Female=31) interventions and subsequently shown a video describing our cognitive 
56 
outcome measures. We observed no significant difference between interventions in terms of participant expectations of 
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improvement on the CPT (independent t-test: t89 =-1.84, p=0.069, with the MBT intervention showing a trend towards 
58 
having greater expectation of improving on this measure than the BBT group) and the Filter tasks (independent t-test: t89 
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=-0.682, p=0.50). To ensure that these findings extended to the present study, we asked all BBT and MBT participants to 
60 
complete a comparable version of this survey after they had completed their first few days of training on their assigned 
61 
intervention to see if their experience led to different expectancy-based perceptions. This was motivated especially by 
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the fact that the MBT training experience was distinct from the BBT training experience in that they completed their 
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training at home on a tablet by themselves, whereas the BBT group trained at a laboratory on a large screen TV in the 
64 
presence of a trainer. Once again, we observed no significant difference between interventions in terms of participant 
65 
expectations of improvement on the CPT (independent t-test: t36 =1.26, p=0.21, MBT mean=5.0, BBT mean=5.4) and the 
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Filter tasks (independent t-test: t36 =0.64, p=0.53, MBT mean=5.0, BBT mean=5.3). 
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