Is Cognitive Training Worth It? Exploring Individuals' Willingness to Engage in Cognitive Training

Motivation to Engage in Aging Research: Are There Typologies and Predictors?

BACKGROUND AND OBJECTIVES

Study recruitment and retention of older adults in research studies is a major challenge. Enhancing understanding of individual differences in motivations to participate, and predictors of motivators, can serve the dual aims of facilitating the recruitment and retention of older adults, benefiting study validity, economy, and power.

RESEARCH DESIGN AND METHODS

Older adults (N = 472) past and potential participants were surveyed about motivations to participate in research, demographic, and individual difference measures (e.g., health status, cognitive difficulties). Latent class and clustering analyses explored motivation typologies, followed by regression models predicting individual motivators and typologies.

RESULTS

Older adults endorsed a diversity of research motivations, some of which could be predicted by individual difference measures (e.g., older participants were more motivated by the desire to learn new technology, participants without a college education were more motivated by financial compensation, and participants with greater self-reported cognitive problems were more likely to participate to gain cognitive benefit). Clustering analysis revealed 4 motivation typologies: brain health advocates, research helpers, fun seekers, and multiple motivation enthusiasts. Cognitive difficulties, age, employment status, and previous participation predicted membership in these categories.

DISCUSSION AND IMPLICATIONS

Results provide an understanding of different participant motivations beyond differences between younger and older adults and begin to identify different classes of older adults motivated to participate in research studies. Results can provide guidance for targeted recruitment and retention strategies based on individual differences in stated or predicted motivations.

Parallel randomized controlled feasibility trials of the "Active Brains" digital intervention to protect cognitive health in adults aged 60-85

Introduction

Multidomain interventions to address modifiable risk factors for dementia are promising, but require more cost-effective, scalable delivery. This study investigated the feasibility of the "Active Brains" digital behavior change intervention and its trial procedures.

Materials and methods

Active Brains aims to reduce cognitive decline by promoting physical activity, healthy eating, and online cognitive training. We conducted 12-month parallel-design randomized controlled feasibility trials of "Active Brains" amongst "lower cognitive scoring" (n = 180) and "higher cognitive scoring" (n = 180) adults aged 60-85.

Results

We collected 67.2 and 76.1% of our 12-month primary outcome (Baddeley verbal reasoning task) data for the "lower cognitive score" and "higher cognitive score" groups, respectively. Usage of "Active Brains" indicated overall feasibility and satisfactory engagement with the physical activity intervention content (which did not require sustained online engagement), but engagement with online cognitive training was limited. Uptake of the additional brief telephone support appeared to be higher in the "lower cognitive score" trial. Preliminary descriptive trends in the primary outcome data might indicate a protective effect of Active Brains against cognitive decline, but further investigation in fully-powered trials is required to answer this definitively.

Discussion

Whilst initial uptake and engagement with the online intervention was modest, it was in line with typical usage of other digital behavior change interventions, and early indications from the descriptive analysis of the primary outcome and behavioral data suggest that further exploration of the potential protective benefits of Active Brains are warranted. The study also identified minor modifications to procedures, particularly to improve online primary-outcome completion. Further investigation of Active Brains will now seek to determine its efficacy in protecting cognitive performance amongst adults aged 60-85 with varied levels of existing cognitive performance.

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.

Everything Comes at a Price: Considerations in Modeling Effort-Based Choice

When observing human behavior, one of the key factors determining choice is effort. It is often assumed that people prefer an easier course of action when the alternative yields the same benefits. However, recent research demonstrates that this is not always the case: effort is not always costly and can also add value. A promising avenue to study effort-based choice is to utilize formal decision models that enable quantitative modeling. In this paper, we aim to present an overview of the current approaches to modeling effort-based choice and discuss some considerations that stem from theoretical and practical issues (present and previous) in studies on the role of effort, focusing on the connections and discrepancies between formal models and the findings from the body of empirical research. Considering that effort can, in some circumstances, act as a cost and as a benefit, reconciling these discrepancies is a practical and theoretical challenge that can ultimately lead to better predictions and increased model validity. Our review identifies and discusses these discrepancies providing direction for future empirical research.

Feasibility of a Home-Based Task-Switching Training in Middle-Aged Caregivers

The current study aimed at investigating feasibility of a self-administered task-switching training in a middle-aged working population. Eighty-one caregivers (41–62 years old) were instructed to train at home 8 times either within a 7- or 14-day interval. Only 56.7% performed more than 50% of the instructed number of training sessions. However, compliant caregivers (who completed more than 4 training sessions) showed significant training gains and transfer to an untrained task-switching task. Although transfer effects to other cognitive tasks were not found, trained participants tended to report fewer everyday memory failures than a control group. In conclusion, the implementation of a home-based task-switching training in everyday life of caregivers is possible. However, there is only limited evidence for generalization of results of previous laboratory studies. Adherence and transfer to other cognitive tasks are discussed as important challenges in conveying laboratory findings into real life.

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).

Exploring Individuals' Willingness to Engage in Interventions to Improve Cognitive Health and Prolong Late-Life Independence: An Extension of Harrell, Kmetz, and Boot (2019)

Interventions designed to preserve cognition have become increasingly popular as our population ages. In exploring intervention effectiveness, however, researchers have often overlooked a potentially important factor: willingness to engage. Recent findings from Harrell, Kmetz, Boot (2019) showed that perceived cognitive deficits and perceived training efficacy were significant predictors of willingness to engage in a brain training intervention designed to preserve cognition. However, they did not explore another potentially important factor: anticipated intervention enjoyment. In the current study, younger, middle-aged, and older adults (N = 169) completed surveys that assessed their willingness to engage in different types of training (aerobic exercise, brain, meditation) to improve cognition and the extent that factors such as health, perceived cognitive deficits, belief in training efficacy, and personality contributed to willingness to engage. Participants reported being least willing to engage in meditation training and meditation training was rated by participants as the least likely to improve cognition. Anticipated training enjoyment was the overriding factor that predicted willingness. These findings provide additional insights into why, and for how long, individuals may be willing to engage in training to prolong independence and have implications for understanding intervention adoption and adherence.

Applying the Health Belief Model to Quantify and Investigate Expectations for Computerized Cognitive Training

Despite the demonstrated benefits of computerized cognitive training for older adults, little is known about the determinants of training behavior. We developed and tested scales to quantify expectations about such training, examine whether expectations predicted training adherence, and explore if training expectations changed from pre- to post-training. Participants (N=219) were healthy older adults aged 55-96 years (M=75.36, SD=9.39), enrolled in four studies investigating Dakim, Insight, or Posit Science Brain Fitness computerized cognitive training programs. Instruments were adapted from existing health behavior scales: Self Efficacy for Cognitive Training, Outcome Expectations for Cognitive Training, Perceived Susceptibility to Cognitive Decline, Dementia or Alzheimer's Disease, and Perceived Severity of Cognitive Decline, Dementia or Alzheimer's Disease. Participants completed scales at baseline (N=219) and post-training (n=173). Eight composites were derived from factor analyses. Adherence rates were high (M=81%), but none of the composites predicted training adherence. There was an overall significant effect of time, Wilks' λ=.843, F(8, 114)=2.65, p=.010, partial η ² =.157, a significant overall effect of training group, Wilks' λ=.770, F(16, 228)=1.99, p=.015, partial η ² =.123, and an overall significant group x time interaction, Wilks' λ=.728, F(16, 226)=2.44, p=.002, partial η ² =.147. Significant effects of time were found for expected psychological outcomes and self-efficacy. Post-training, participants more strongly agreed that training was enjoyable and increased their sense of accomplishment. Changes in self-efficacy for cognitive training varied by program, improvingfor Dakim- and declining for the more challenging Brain Fitness- and InSight participants. These newly devised scales may be useful for examining cognitive training behaviors. However, more work is needed to understand factors that influence older adults' enrollment in and adherence to cognitive training.