Comparing the effectiveness of two theory-based strategies to promote cognitive training adherence.

This study compared the effectiveness of two theory-based strategies to promote cognitive training adherence among older adults (Mage = 70 years, SD = 4.42, range = 64-84). Strategies incorporated either (a) elements of implementation intention formation or (b) positive message framing, both of which have been found to promote adherence to health behaviors in other domains. Participants (N = 120) were asked to engage in technology-based cognitive training at home comprised of seven gamified neuropsychological tasks. In Phase 1 (structured), participants were provided a schedule that required engagement in 1 hr of cognitive training 5 days each week over 2 months. In Phase 2 (unstructured), participants were instructed to engage with the intervention as much as they desired for 1 month. Contrary to expectations, neither the implementation intention nor positive message framing produced greater adherence relative to control as measured by the total number of training sessions completed in each phase. However, exploratory analysis indicated a greater likelihood of intervention engagement for participants assigned to the implementation intention condition on many days of the intervention, though the trajectory of engagement decline was similar for all three groups. Measures of cognition, attitudes/personality, and technology proficiency also did not predict adherence over either phase. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Ultra-brief Assessment of Working Memory Capacity: Ambulatory Assessment Study Using Smartphones.

BACKGROUND: The development of mobile technology with substantial computing power (ie, smartphones) has enabled the adaptation of performance-based cognitive assessments to remote administration and novel intensive longitudinal study designs (eg, measurement burst designs). Although an "ambulatory" cognitive assessment paradigm may provide new research opportunities, the adaptation of conventional measures to a mobile format conducive to intensive repeated measurement involves balancing measurement precision, administration time, and procedural consistency. OBJECTIVE: Across 3 studies, we adapted "complex span" tests of working memory capacity (WMC) for ultra-brief, smartphone-based administration and examined their reliability, sufficiency, and associations with full-length, laboratory-based computerized administrations. METHODS: In a laboratory-based setting, study 1 examined associations between ultra-brief smartphone adaptations of the operation span, symmetry span, and rotation span tasks and full-length computerized versions. In study 2, we conducted a 4-day ecological momentary assessment (EMA) study (4 assessments per day), where we examined the reliability of ultra-brief, ambulatory administrations of each task. In study 3, we conducted a 7-day EMA study (5 assessments per day) involving the ultra-brief rotation span task, where we examined reliability in the absence of extensive onboarding and training. RESULTS: Measurement models in study 1 suggest that comparable estimates of latent WMC can be recovered from ultra-brief complex span task performance on smartphones. Significant correlations between the ultra-brief tasks and respective full-length versions were observed in study 1 and 2, ranging from r=0.4 to r=0.57. Results of study 2 and study 3 suggest that reliable between-person estimates of operation span, symmetry span, rotation span, and latent WMC can be obtained in 2-3 ultra-brief administrations (equivalent to <1 day of testing in an EMA study design). The results of study 3 replicated our findings, showing that reliable between-person estimates of rotation span may be obtained in as few as 2 ultra-brief administrations in the absence of extensive onboarding and training. In addition, the modification of task parameterization for study 3 improved the estimates of reliability of within-person change. CONCLUSIONS: Ultra-brief administration of complex span tasks on smartphones in a measurement burst design can generate highly reliable cross-sectional estimates of WMC. Considerations for future mobile cognitive assessment designs and parameterizations are discussed.

To BYOD or not: Are device latencies important for bring-your-own-device (BYOD) smartphone cognitive testing?

Studies using remote cognitive testing must make a critical decision: whether to allow participants to use their own devices or to provide participants with a study-specific device. Bring-your-own-device (BYOD) studies have several advantages including increased accessibility, potential for larger sample sizes, and reduced participant burden. However, BYOD studies offer little control over device performance characteristics that could potentially influence results. In particular, response times measured by each device not only include the participant's true response time, but also latencies of the device itself. The present study investigated two prominent sources of device latencies that pose significant risks to data quality: device display output latency and touchscreen input latency. We comprehensively tested 26 popular smartphones ranging in price from < $100 to $1000+ running either Android or iOS to determine if hardware and operating system differences led to appreciable device latency variability. To accomplish this, a custom-built device called the Latency and Timing Assessment Robot (LaTARbot) measured device display output and capacitive touchscreen input latencies. We found considerable variability across smartphones in display and touch latencies which, if unaccounted for, could be misattributed as individual or group differences in response times. Specifically, total device (sum of display and touch) latencies ranged from 35 to 140 ms. We offer recommendations to researchers to increase the precision of data collection and analysis in the context of remote BYOD studies.

Deep learning-based predictions of older adults' adherence to cognitive training to support training efficacy.

As the population ages, the number of older adults experiencing mild cognitive impairment (MCI), Alzheimer's disease, and other forms of dementia will increase dramatically over the next few decades. Unfortunately, cognitive changes associated with these conditions threaten independence and quality of life. To address this, researchers have developed promising cognitive training interventions to help prevent or reverse cognitive decline and cognitive impairment. However, the promise of these interventions will not be realized unless older adults regularly engage with them over the long term, and like many health behaviors, adherence to cognitive training interventions can often be poor. To maximize training benefits, it would be useful to be able to predict when adherence lapses for each individual, so that support systems can be personalized to bolster adherence and intervention engagement at optimal time points. The current research uses data from a technology-based cognitive intervention study to recognize patterns in participants' adherence levels and predict their future adherence to the training program. We leveraged the feature learning capabilities of deep neural networks to predict patterns of adherence for a given participant, based on their past behavior. A separate, personalized model was trained for each participant to capture individualistic features of adherence. We posed the adherence prediction as a binary classification problem and exploited multivariate time series analysis using an adaptive window size for model training. Further, data augmentation techniques were used to overcome the challenge of limited training data and enhance the size of the dataset. To the best of our knowledge, this is the first research effort to use advanced machine learning techniques to predict older adults' daily adherence to cognitive training programs. Experimental evaluations corroborated the promise and potential of deep learning models for adherence prediction, which furnished highest mean F-scores of 75.5, 75.5, and 74.6% for the Convolution Neural Network (CNN), Long Short-Term Memory (LSTM) network, and CNN-LSTM models respectively.

A Machine-Learning Based Approach for Predicting Older Adults' Adherence to Technology-Based Cognitive Training.

Adequate adherence is a necessary condition for success with any intervention, including for computerized cognitive training designed to mitigate age-related cognitive decline. Tailored prompting systems offer promise for promoting adherence and facilitating intervention success. However, developing adherence support systems capable of just-in-time adaptive reminders requires understanding the factors that predict adherence, particularly an imminent adherence lapse. In this study we built machine learning models to predict participants' adherence at different levels (overall and weekly) using data collected from a previous cognitive training intervention. We then built machine learning models to predict adherence using a variety of baseline measures (demographic, attitudinal, and cognitive ability variables), as well as deep learning models to predict the next week's adherence using variables derived from training interactions in the previous week. Logistic regression models with selected baseline variables were able to predict overall adherence with moderate accuracy (AUROC: 0.71), while some recurrent neural network models were able to predict weekly adherence with high accuracy (AUROC: 0.84-0.86) based on daily interactions. Analysis of the post hoc explanation of machine learning models revealed that general self-efficacy, objective memory measures, and technology self-efficacy were most predictive of participants' overall adherence, while time of training, sessions played, and game outcomes were predictive of the next week's adherence. Machine-learning based approaches revealed that both individual difference characteristics and previous intervention interactions provide useful information for predicting adherence, and these insights can provide initial clues as to who to target with adherence support strategies and when to provide support. This information will inform the development of a technology-based, just-in-time adherence support systems.

The where and when of COVID-19: Using ecological and Twitter-based assessments to examine impacts in a temporal and community context.

In March 2020, residents of the Bronx, New York experienced one of the first significant community COVID-19 outbreaks in the United States. Focusing on intensive longitudinal data from 78 Bronx-based older adults, we used a multi-method approach to (1) examine 2019 to early pandemic (February-June 2020) changes in momentary psychological well-being of Einstein Aging Study (EAS) participants and (2) to contextualize these changes with community distress scores collected from public Twitter posts posted in Bronx County. We found increases in mean loneliness from 2019 to 2020; and participants that were higher in neuroticism had greater increases in thought unpleasantness and feeling depressed. Twitter-based Bronx community scores of anxiety, depressivity, and negatively-valenced affect showed elevated levels in 2020 weeks relative to 2019. Integration of EAS participant data and community data showed week-to-week fluctuations across 2019 and 2020. Results highlight how community-level data can characterize a rapidly changing environment to supplement individual-level data at no additional burden to individual participants.

Relative effectiveness of general versus specific cognitive training for aging adults.

In the present study, we examined three experimental cognitive interventions, two targeted at training general cognitive abilities and one targeted at training specific instrumental activities of daily living (IADL) abilities, along with one active control group to compare benefits of these interventions beyond expectation effects, in a group of older adults (N = 230). Those engaged in general training did so with either the web-based brain game suite BrainHQ or the strategy video game Rise of Nations, while those trained on IADL skills completed instructional programs on driving and fraud awareness. Active control participants completed sets of puzzles. Comparing baseline and postintervention data across conditions, none of the preregistered primary outcome measures demonstrated a significant interaction between session and intervention condition, indicating no differential benefits. Analysis of expectation effects showed differences between intervention groups consistent with the type of training. Those in the IADL training condition did demonstrate superior knowledge for specific trained information (driving and finances). Twelve months after training, significant interactions between session and intervention were present in the primary measure of fraud detection, as well as the secondary measures of the letter sets task and Rey's Auditory Verbal Learning Test. However, the specific source of these interactions was difficult to discern. At 1-year follow-up those in the IADL condition did not maintain superior knowledge of driving and finances gained through training, as was present immediately postintervention. Hence, the interventions, when compared to an active control condition, failed to show general or specific transfer in a meaningful or consistent way. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Variability in Cognitive Performance on Mobile Devices Is Sensitive to Mild Cognitive Impairment: Results From the Einstein Aging Study.

Background and Objective: Within-person variability in cognitive performance has emerged as a promising indicator of cognitive health with potential to distinguish normative and pathological cognitive aging. We use a smartphone-based digital health approach with ecological momentary assessments (EMA) to examine differences in variability in performance among older adults with mild cognitive impairment (MCI) and those who were cognitively unimpaired (CU). Method: A sample of 311 systematically recruited, community-dwelling older adults from the Einstein Aging Study (Mean age = 77.46 years, SD = 4.86, Range = 70-90; 67% Female; 45% Non-Hispanic White, 40% Non-Hispanic Black) completed neuropsychological testing, neurological assessments, and self-reported questionnaires. One hundred individuals met Jak/Bondi criteria for MCI. All participants performed mobile cognitive tests of processing speed, visual short-term memory binding, and spatial working memory on a smartphone device up to six times daily for 16 days, yielding up to 96 assessments per person. We employed heterogeneous variance multilevel models using log-linear prediction of residual variance to simultaneously assess cognitive status differences in mean performance, within-day variability, and day-to-day variability. We further tested whether these differences were robust to the influence of environmental contexts under which assessments were performed. Results: Individuals with MCI exhibited greater within-day variability than those who were CU on ambulatory assessments that measure processing speed (p < 0.001) and visual short-term memory binding (p < 0.001) performance but not spatial working memory. Cognitive status differences in day-to-day variability were present only for the measure of processing speed. Associations between cognitive status and within-day variability in performance were robust to adjustment for sociodemographic and contextual variables. Conclusion: Our smartphone-based digital health approach facilitates the ambulatory assessment of cognitive performance in older adults and the capacity to differentiate individuals with MCI from those who were CU. Results suggest variability in mobile cognitive performance is sensitive to MCI and exhibits dissociative patterns by timescale and cognitive domain. Variability in processing speed and visual short-term memory binding performance may provide specific detection of MCI. The 16-day smartphone-based EMA measurement burst offers novel opportunity to leverage digital technology to measure performance variability across frequent assessments for studying cognitive health and identifying early clinical manifestations of cognitive impairment.

Investigating message framing to improve adherence to technology-based cognitive interventions.

A cognitive intervention study was conducted with the purpose of exploring methods to improve adherence to a technology-based cognitive intervention and uncover individual differences that predict adherence (N = 120). The study was divided into two phases: Phase 1, in which participants were asked to follow a prescribed schedule of training that involved gamified neuropsychological tests administered via tablet, and Phase 2, in which participants were asked to play as frequently as they wished. Positive- and negative-framed messages about brain health were delivered via the software program, and measures of cognition, technology proficiency, self-efficacy, technology attitudes, and belief in the benefits of cognitive training were collected. Generalized linear mixed-effects models revealed that positive-framed messages encouraged greater adherence over negative-framed messages, but this effect was restricted to Phase 2 of the study in the absence of social pressure. Measures of memory and self-efficacy demonstrated some, but limited, ability to predict individual differences in adherence. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A New Tool for Assessing Older Adults' Wireless Network Proficiency: The Wireless Network Proficiency Questionnaire.

Interacting with information communication technologies (ICTs) often requires proficiency with wireless networks. For older adults especially, lacking proficiency can lock them out of the many benefits afforded by these devices. In an older adult sample (N = 203), we assessed reliability and validated a set of tools to assess wireless network proficiency: the Wireless Network Proficiency Questionnaire (WNPQ; 19 questions) and a short-form (WNPQ-8; eight questions). The WNPQ and WNPQ-8 were found to be reliable and valid, positively related to computer proficiency (r = .63 for both) and mobile device proficiency (r = .75, r = .74), and negatively related to age (r = -.21, r = -.23). WNPQ scores predicted whether participants could report their Wi-Fi credentials (odd ratio = 1.17-1.73). We conclude that the WNPQ may serve as a useful tool for facilitating wireless network training and assessing proficiency in research.

Aging: Older Adults' Driving Behavior Using Longitudinal and Lateral Warning Systems.

OBJECTIVE: This study assessed older drivers' driving behavior when using longitudinal and lateral vehicle warning systems together. BACKGROUND: Advanced driver assistance systems (ADAS) can benefit drivers of all ages. Previous research with younger to middle-aged samples suggests that safety benefits are not necessarily additive with additional ADAS. Increases in following distance associated with the use of forward collision warning (FCW) decreased when drivers also used lane departure warning (LDW), likely due to attending to the LDW more than the FCW. METHOD: The current study used a driving simulator to provide 128 older drivers experience with FCW and/or LDW system(s) during a ~25-min drive to gauge their usage's effects on driving performance and subjective workload. RESULTS: There were no significant differences found in headway distance between older drivers who used different combinations of FCW and LDW systems, but those who used an FCW system showed significantly longer time-to-collision (TTC) when approaching the critical event than those who did not. Users of LDW systems did not show reductions in standard deviation of lane position. Analyses of subjective workload measures showed no significant differences between conditions. CONCLUSION: Findings suggest that FCW could increase older drivers' TTC over the course of a drive. Contrary to previous findings in younger samples, concurrent use of FCW and LDW systems did not adversely affect older drivers' longitudinal driving performance and subjective workload. APPLICATION: Potential applications of this research include the assessment of older drivers' use of vehicle warning systems and their effects on subjective workload.

The spatial dynamics of mouse-tracking reveal that attention capture is stimulus-driven rather than contingent upon top-down goals.

There has been a longstanding debate over why stimuli capture attention. Some argue that capture is driven by stimulus salience, while others believe that capture only occurs when the features of a stimulus match what we are searching for. This debate has recently focused on attentional disengagement, with the stimulus-driven camp claiming that all salient stimuli capture attention but attention is quickly disengaged from items dissimilar from our target, producing little cost in terms of response time. We used mouse-tracking to examine the spatial effect of cues that either matched or mismatched an observer's target. Experiment 1 demonstrated that a cue mismatching the feature defining the target initially produced a spatial effect that was rapidly resolved, consistent with quick disengagement. Experiment 2 was a preregistered replication with double the sample size that replicated the results of Experiment 1. Overall, computer mouse-tracking provided a direct observation of attentional disengagement, supporting stimulus-driven capture. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Intervention Comparative Effectiveness for Adult Cognitive Training (ICE-ACT) Trial: Rationale, design, and baseline characteristics.

Age-related perceptual and cognitive declines are associated with difficulties performing everyday tasks required to remain independent. Encouraging improvements in cognitive abilities have been shown for various short-term interventions but there is little evidence for direct impact on independence. This project compares the effect of broad and directed (narrow) technology-based training on basic perceptual and cognitive abilities in older adults and on the performance of simulated tasks of daily living including driving and fraud avoidance. Participants (N = 230, Mean age = 72) were randomly assigned to one of four training conditions: broad training using either (1) a web-based brain game suite, Brain HQ, or (2) a strategy video game, Rise of Nations, or to directed training for (3) Instrumental Activities of Daily Living (IADL) training using web-based programs for both driving and fraud avoidance training, or (4) to an active control condition of puzzle solving. Training took approximately 15-20 h for each intervention condition across four weeks. Before training began, participants received baseline ability tests of perception, attention, memory, cognition, and IADL, including a driving simulator test for hazard perception, and a financial fraud recognition test. They were tested again on these measures following training completion (post-test). A one-year follow-up from training completion is also scheduled. The baseline results support that randomization was successful across the intervention conditions. We discuss challenges and potential solutions for using technology-based interventions with older adults. We also discuss how the current trial addressed methodological limitations of previous intervention studies. TRIAL REGISTRATION NUMBER: NCT03141281.

Exploring Older Adults' Video Game Use in the PRISM Computer System.

BACKGROUND AND OBJECTIVE: As part of the PRISM (Personal Reminder Information & Social Management) randomized field trial, a large group of older adults (N = 150) received a computer system in their home that presented them with the opportunity to play eleven different video games. While researchers have often assessed older adults' gaming preferences and habits through survey data and focus groups, this trial represented a unique opportunity to study gaming behavior "in the wild" over an entire year. RESEARCH DESIGN AND METHODS: We present an exploration of game usage data, individual differences in game preferences and gaming habits, and individual difference predictors of game use. RESULTS: Although few individual difference variables consistently predicted game use and preferences, there were clear favorites among the different games, and results demonstrate that given the opportunity and training many older adults may become active and long-term gamers. DISCUSSION AND IMPLICATIONS: Findings have implications for designing video games that older adults enjoy, supporting enjoyable and meaningful interactions with video games across the life span, and for designing cognitive, social, and health interventions involving games.

Attention capture, processing speed, and inattentional blindness.

Previous theories of inattentional blindness (IB; a phenomenon of observers failing to notice a salient but unexpected event when attention is already occupied) have suggested that an unexpected object reaches conscious awareness when: 1) the location of the unexpected object and attention align, and 2) the unexpected object undergoes sufficient processing. Moreover, it is expected that the same factors that influence the allocation of observers' attention in attention capture studies influence what reaches conscious awareness in IB studies. We explored the degree to which individual differences in susceptibility to attention capture and processing speed are important predictors of IB. One hundred forty-six participants (from Study 1 of Roque, Wright, & Boot, 2016) completed four classic attention (implicit) capture tasks designed to assess stimulus-driven and contingent capture. Following the completion of these capture tasks, participants completed a sustained IB (multiple object tracking) task where an unexpected event appeared during the final critical trial. Indices of stimulus-driven and contingent capture were derived from the capture tasks, and a measure of processing speed was derived from aggregating reaction times from the three speed-based capture tasks. Surprisingly, results of logistic regression analyses revealed no relationship between measures of implicit and explicit capture (noticing the unexpected event). However, consistent with the a priori hypothesis, processing speed did predict IB. Findings suggest that attention capture is unrelated to the noticing of an unexpected stimulus, but efficient encoding and recognition of a stimulus is an important factor.

Hand proximity effects are fragile: a useful null result.

Placing one's hands near an object has been reported to enhance visual processing in a number of ways. We explored whether hand proximity confers an advantage when applied to complex visual search. In one experiment, participants indicated the presence or absence of a target item in a baggage x-ray image by pressing response boxes located at the edge of a tablet computer screen, requiring them to grip the display between their hands. Alternatively, they responded using a mouse held within their lap. Contrary to expectations, hand position did not influence search performance. In a second experiment, participants used their finger to trace along the x-ray image while searching. In addition to any effect of hand proximity it was predicted that this strategy would encourage a more systematic search strategy. Participants inspected bags longer using this strategy, but this did not translate into improved target detection. A third experiment attempted to replicate the near-hands advantage in a change detection paradigm featuring simple stimuli (Tseng and Bridgeman, Experimental Brain Research 209:257-269, 2011), and the same equipment and hand positions as Experiment 1, but was unable to do so. One possibility is that the grip posture associated with holding a tablet is not conducive to producing a near-hands advantage. A final experiment tested this hypothesis with a direct replication of Tseng and Bridgeman, in which participants responded to stimuli presented on a CRT monitor using keys attached to the side of the monitor. Still, no near-hands advantage was observed. Our results suggest that the near-hands advantage may be sensitive to small differences in procedure, a finding that has important implications for harnessing the near-hands advantage to produce better performance in applied contexts.

A New Tool for Assessing Mobile Device Proficiency in Older Adults: The Mobile Device Proficiency Questionnaire.

Mobile device proficiency is increasingly required to participate in society. Unfortunately, there still exists a digital divide between younger and older adults, especially with respect to mobile devices (i.e., tablet computers and smartphones). Training is an important goal to ensure that older adults can reap the benefits of these devices. However, efficient/effective training depends on the ability to gauge current proficiency levels. We developed a new scale to accurately assess the mobile device proficiency of older adults: the Mobile Device Proficiency Questionnaire (MDPQ). We present and validate the MDPQ and a short 16-question version of the MDPQ (MDPQ-16). The MDPQ, its subscales, and the MDPQ-16 were found to be highly reliable and valid measures of mobile device proficiency in a large sample. We conclude that the MDPQ and MDPQ-16 may serve as useful tools for facilitating mobile device training of older adults and measuring mobile device proficiency for research purposes.

Changing similarity: Stable and flexible modulations of psychological dimensions.

Successfully categorizing objects requires discriminating between relevant and irrelevant dimensions (e.g., shape, color). Categorization can lead to changes in the visual system that stretch psychological space, making relevant dimensions more distinct and irrelevant dimensions more similar. These changes are known as dimensional modulation (DM) and they can be both stable and flexible in nature. The current study examined the interaction between stable DM and flexible DM, as well as the time course of relative changes in similarity. Using a two-dimensional space of cars, participants learned to categorize the space and then completed a target identification task during EEG recording. We found that attention, operationally defined as the selection negativity, was sensitive to category-relevance and appeared to selectively enhance previously irrelevant differences in the service of a target detection task. In contrast, we found that late decisional stages, operationally defined as the P3 b, were less sensitive to relevance and instead more sensitive to the number of morphsteps that separated targets from non-targets. Thus, it appears that relative similarity between targets and non-targets dynamically changed over the time course of individual decisions. Similarity between exemplars was greater along the irrelevant than the relevant dimension early on in the time course but a compensatory allocation of attention led to similarity being optimized among all dimensions for later stages. This finding is important because it 1) provides a new source of converging evidence for stable DM and 2) links a neural measure of attentional modulation with facilitation of an unpracticed, but task-relevant perceptual dimension.

Evidence for Narrow Transfer after Short-Term Cognitive Training in Older Adults.

The degree to which "brain training" can improve general cognition, resulting in improved performance on tasks dissimilar from the trained tasks (transfer of training), is a controversial topic. Here, we tested the degree to which cognitive training, in the form of gamified training activities that have demonstrated some degree of success in the past, might result in broad transfer. Sixty older adults were randomly assigned to a gamified cognitive training intervention or to an active control condition that involved playing word and number puzzle games. Participants were provided with tablet computers and asked to engage in their assigned training for 30 45-min training sessions over the course of 1 month. Although intervention adherence was acceptable, little evidence for transfer was observed except for the performance of one task that most resembled the gamified cognitive training: There was a trend for greater improvement on a version of the corsi block tapping task for the cognitive training group relative to the control group. This task was very similar to one of the training games. Results suggest that participants were learning specific skills and strategies from game training that influenced their performance on a similar task. However, even this near-transfer effect was weak. Although the results were not positive with respect to broad transfer of training, longer duration studies with larger samples and the addition of a retention period are necessary before the benefit of this specific intervention can be ruled out.