Measuring and Increasing the Brain Health Span across Adulthood: A Public Health Imperative

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This study examines 3-year outcomes from 3,966 adults (ages 19–94) in the BrainHealth Project, an online initiative integrating the BrainHealth Index (BHI) with cognitive training, lifestyle modules, and coaching. The BHI, assessed biannually, provides a multidimensional measure across factors of Clarity (cognitive function), Connectedness (social and purpose-driven engagement), and Emotional Balance (mental well-being). Results demonstrate sustained improvements in overall BHI and component factors, independent of baseline scores. Higher engagement with training tools – strategy-based learning, coaching, and brain-healthy habits – was associated with the greatest gains, underscoring the role of self-agency in brain health optimization. Improvements were observed across demographic groups, suggesting benefit regardless of age, gender, or education level. Findings support the potential for scalable, technology-driven interventions to help reduce years of cognitive decline while maximizing brain performance across the lifespan. Future efforts should focus on improving demographic diversity and retention strategies as well as integrating precision brain health approaches into public health initiatives. Health sciences/Health care/Public health Biological sciences/Neuroscience/Cognitive ageing Biological sciences/Neuroscience/Cognitive neuroscience/Cognitive control Health sciences/Health care/Disease prevention/Lifestyle modification Health sciences/Health care/Disease prevention/Preventive medicine brain health cognitive training digital health health behaviors precision care prevention Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In recent years, brain health has emerged as a major public health priority, driven by growing global efforts to prevent brain-based disease [ 1 , 2 ]. This attention is catalyzed by accumulating evidence of multiple modifiable risk factors for dementia, as highlighted in the 2024 Lancet Commission report [ 3 ]. While addressing risk factors is important, taking proactive steps to extend and strengthen brain health at every age may be even more impactful [ 4 ]. The need to promote healthy brain performance is underscored by evidence that neural and cognitive functions in healthy adults begin to decline as early as the late twenties, even in the absence of injury or disease [ 5 , 6 ]. In some scientific circles, it has even been postulated that brain aging itself might be considered a disease process [ 7 – 9 ]. We propose that the more consequential question to address is whether holistic brain health can be measured and improved over time, aimed at countering the insidious loss of neural and cognitive health. Extensive evidence of neuroplasticity shows that decline is not inevitable; instead, it foregrounds the possibility that extending the human brain health span is achievable [ 10 , 11 ]. A recent report on research priorities from the National Academies of Sciences, Engineering, and Medicine [ 12 ] noted that accelerating progress in understanding resilience to brain-based diseases such as dementia will require the dual approach of 1) quantifying brain health across the lifespan and 2) developing multi-pronged interventional approaches, ranging from individualized/precision medicine to scalable public health promotion strategies. Over the past one-and-a-half centuries, the human lifespan has doubled due to advancements in aspects such as heart health, physical and dietary health, the mitigation of infectious diseases, and improved living conditions [ 13 ]. There is now urgency to apply similar efforts toward extending the brain health span to ensure that continued gains in longevity are matched by prolonged cognitive vitality and peak brain function. Although various definitions of brain health exist, a growing consensus is emerging. Brain health encompasses a state of optimal neural plasticity, cognitive function, well-being, and connectedness to people and to purpose [ 4 , 14 ]. While ‘connectedness’ is a relatively recent addition to this definition, researchers and public health officials underscore its critical role in brain health [ 15 , 16 ], positioning socialization and purpose as fundamental factors in enabling individuals to reach their fuller potential at any age [ 17 ]. Thus, brain health is about harnessing cognitive, social, and well-being capacities to thrive in one’s daily context. Building on this, we define brain health span as the length of time an individual can maintain, enhance, or regain their overall brain health without experiencing continual decline in function. Effectively measuring brain health span requires a holistic approach that captures the combined contribution of key factors – cognitive, social, mental health/well-being, and lifestyle – on changing brain health status over time. Traditional screening tools and single-domain measures offer limited insights. They typically identify deficits and fail to reflect the broader interactions across factors that shape brain health and thus do little to improve proactive care [ 18 ]. A wellness-driven holistic brain health metric is essential, one that not only tracks functional changes but that also recognizes the brain’s potential for positive growth as well as allowing for the detection of early decline [ 19 ]. Ideally, such a metric would also be dynamic, allowing individuals to chart and monitor personal changes in brain health – both gains and losses – over time. Recent advances include lifestyle-based composite measures, such as the Healthy Lifestyle Score [ 20 ] and the Lifestyle for Brain Health (LIBRA) Score [ 21 , 22 ], which assess modifiable risk factors such as diet, physical activity, and social engagement to promote brain health and reduce dementia risk. These are welcome advances; nonetheless, they remain largely deficit-oriented, primarily designed for at-risk or older populations, and offer limited personalized guidance for fostering lifelong brain health practices. To effectively measure brain health span while addressing the need for a holistic, wellness-driven, and dynamic/repeatable approach, our team developed and tested the BrainHealth Index (BHI) [ 23 , 24 ]. This multidimensional composite metric captures the interdependence of key brain health components and visualizes how the components contribute to increasing or decreasing overall brain performance. The BHI enables both individuals and care providers to track personal brain health trajectories starting in early adulthood and continuing throughout life. Unlike traditional assessments that compare individuals against normative databases – which may or may not be representative for that individual – the BHI was designed as a personalized benchmark for improvement rather than a diagnostic classifier. Hence the numerical index given to people is largely interpreted in comparison to their own progress and not in comparison to others. This approach is similar to that of indices of physical fitness such as VO 2 max, used in physical health promotion [ 25 ], which often serve as targets for enhancement. This proactive, optimization-based approach to brain health assessment shifts the focus from deficits to growth, removing the fear and stigma of being told that one’s cognitive abilities are declining or are low compared to others. Instead, individuals are empowered to take ownership of their brain health, engaging with targeted strategies and practices to strengthen and sustain their brain health span, whatever their starting level. The seeming paradox in the statement that individuals may “have brain health without neural health” (M. D’Esposito, personal communication, January 28, 2025) is resolved by defining the health of the brain as its holistic capacity to improve in areas of clarity, connectedness, and emotional balance in the real world, despite dysfunction of certain neural circuits. This permits a growth-perspective on brain health, encouraging continual gains in overall brain health trajectory, even in the context of compromised neural health, such as observed in concussion, stroke, chemotherapy-induced brain decline, depression, and other issues. The potential to enhance brain health is grounded in well-established research on neuroplasticity, demonstrating that the human brain retains its capacity to adapt and change throughout life. This body of evidence counters outdated beliefs that brain function is fixed at a young age and instead underscores the brain’s ability to be continuously shaped by experience [ 10 , 11 ]. Such findings reinforce the opportunity to improve cognitive, social, and mental health functioning at any age through intentional habits and practices that promote an extended brain health span [ 26 ]. This lifelong capacity to build brain health calls into question the long-standing assumption that overall brain decline with aging is inevitable. Rather than an unavoidable trajectory of loss, brain health can be actively cultivated. Just as the Framingham study informed a host of preventive approaches and health-promoting behaviors for cardiovascular function, large-scale longitudinal research is needed to establish proactive approaches for brain health [ 27 , 28 ]. In response to this need, the BrainHealth Project was launched in 2020 to address whether scalable, population-level assessments and interventions can harness neuroplasticity to enhance and sustain brain performance over time [ 14 ]. The BrainHealth Project aims to recruit 100,000 generally healthy adults, ranging from age 18 to late adulthood, to track brain health trajectories (brain health span) using the BrainHealth Index. Participants engage with coaching-enhanced online training modules designed to strengthen executive functions through adoption of cognitive strategies, further applied to social, emotional and lifestyle factors - including sleep, physical activity, and daily habits. The BrainHealth Project is purposefully a single-arm trial, motivated by extant prior evidence from randomized controlled trials reporting significant gains in neural, cognitive, and/or emotional well-being domains in healthy adults as well as those with brain compromise following the strategy-based training when compared against active control interventions [ 29 – 39 ]. The overarching goal is to determine how proactive engagement with these tools can support and optimize brain function, whatever an individual’s starting level. An initial study established the feasibility of using a scalable online platform and repeatable, data-driven BrainHealth Index to measure individualized brain health trajectories as well as to deliver strategy-based executive function training, coaching, and healthy habit-building tools to promote gains [ 23 ]. Results from 180 healthy adults spanning ages 18–87 demonstrated the approach’s effectiveness, with 75% of participants exhibiting notable gain in their overall BrainHealth Index (BHI) at 3-month follow-up, independent of age, gender, or education level. Importantly, the study highlighted a positive relationship between engagement in online training and BHI gains. This study provided promising evidence that adults of all ages can complete the online BHI, engage in proactive brain health strategies, and adopt brain-healthy habits in their life contexts through an online platform delivery, achieving measurable improvement in brain health and performance. Methods Study Design The BrainHealth Project was designed as a prospective, longitudinal study, functioning as an interventional, open-label, single-arm clinical trial (NCT04869111). Following the initial pilot phase [ 23 ], recruitment for the longitudinal study began and will continue over at least 10 years of study. Overall BrainHealth Project recruitment and study procedures are still ongoing. Primary procedures for the study are conducted online through a desktop computer or the BrainHealth® app (the latter as of February 2024, available for both iPhone and Android users). At study enrollment and every 6 months thereafter, participants complete a BrainHealth Index (BHI) assessment. With each BHI completed, participants receive a high-level overview of their performance and can track their results over time. Participants can also schedule a personalized coaching session (every 3 months) to receive additional feedback and/or to address individual brain health goals. Throughout the study duration, participants are encouraged to utilize the training in their online account; these consist of interactive micro-learnings related to optimizing brain health and overall well-being, beginning with a strategy-based cognitive training, SMART (Strategic Memory Advanced Reasoning Tactics) [ 40 , 41 ] and including subsequent modules on sleep, stress, and practices that can support overall brain health in daily life. We collect and analyze participants’ engagement with their study account (e.g., frequency of logins, training completed, resources accessed, etc.) alongside the BHI outcomes. This allows us to determine how utilization of the online brain health tools may influence participants’ outcomes. All study activities involving the BrainHealth Project and BrainHealth Platform were approved and conducted in accordance with the standards provided by The University of Texas at Dallas Institutional Review Board. See the full online study protocol for an elaborated description of study procedures [ 14 ]. Study Objectives The current BrainHealth Project study reports on a large prospective sample of generally healthy adults followed over an interval of 3 years, drawn from the ongoing longitudinal study. The primary aim was to examine the impact of participation on holistic brain health, as measured by changes in the multidimensional BrainHealth Index (BHI), with biannual testing points every 6 months (Time 1 – Time 7). Specifically, the major questions addressed by this longitudinal study were: Could individuals across the adult lifespan improve brain health and maintain these gains over a 3-year period, given access to online brain health tools? How were changes reflected across time on the holistic measure (BHI) as well as on the three contributing brain health factors (Clarity, Connectedness, Emotional Balance)? Who was able to demonstrate changes from the brain health training protocols as measured by the BHI, in terms of age, education, gender and starting level (BHI at baseline)? What was the role of utilization – as demonstrated by completion of ongoing micro trainings, daily brain health habits, virtual coaching sessions, and/or accessing brain health resources – in achieving or failing to achieve gains on the BrainHealth Index? Participants The study targets generally healthy adults aged 18 years and older who are fluent in English, have regular access to an internet connection and device, and are able to hear and read information presented on a computer screen or mobile device. Recruitment is largely through word of mouth, online postings, social media, and advertisements. Participants in this online study do not receive any payment or monetary incentive for participation. Study exclusionary criteria are based on specific health conditions, including diagnosed neurodegenerative disease; a history of stroke, concussion, or brain injury that currently impairs their ability to function at their reported prior level (e.g., inability to carry out daily responsibilities); or a diagnosis of autism spectrum disorder without independent-level functioning. Other diagnoses, such as those representing other medical or psychiatric conditions and/or learning disorders, are captured through self-report in the context of ongoing study assessment timepoints and are not exclusionary, provided individuals are able to continue study participation without undue stress or frustration. Participants can discontinue their participation at any point during the 10-year study time period. See Table 1 for a breakdown of participant demographics across this 3-year longitudinal sample. A total of 4,190 participants were recruited. Of those, 202 dropped out of the study, and 10 participants were excluded from the analysis due to invalid/no age entered. As for gender, non-binary gender participants were not included in the analysis due to the low sample size ( n = 12) which is not enough for statistically meaningful results. The final dataset comprised 3,966 participants, which were included in the statistical analyses. Dropoff in assessment completion at each testing timepoint ranged from 37%-46% attrition over the 3-year study period. Table 1 Participant characteristics ( n = 3,966) Demographic variables M ( SD ) Range Age (years) 63.62 (12.84) 19–94 N % Gender Female 3024 76.25 Male 942 23.75 Race White 3662 89.78 Asian 167 4.09 Black or African American 91 2.23 Native American / Alaska Native 44 1.08 Native Hawaiian / Pacific Islander 12 0.29 Other 103 2.53 Hispanic or Latino Yes 197 4.97 No 3769 95.03 Education Pre Bachelor 532 13.41 Bachelor 1436 36.21 Post Bachelor 1998 50.38 Annual household income Less than $ 20,000 134 3.38 $ 20,000 - $ 39,999 283 7.14 $ 40,000 - $ 59,999 409 10.31 $ 60,000 - $ 99,999 898 22.64 $ 100,000 - $ 250,000 1497 37.75 More than $ 250k 496 12.51 Not Available/Prefer not to answer 249 6.28 - INSERT TABLE 1 - Study Procedures Primary Outcome: BrainHealth Index (BHI) The BrainHealth Index (BHI) provides a holistic, multidimensional assessment of brain health and performance. By integrating core measures of complex cognition with both novel and established assessments spanning daily life/lifestyle, mental health, and social domains, the BHI allows for both independent analysis of individual measures as well as a holistic view of their combined impact on overall function. Participants complete an online battery (average of 60–70 minutes to complete, in total) that includes cognitive performance tasks and self-report questionnaires. The BHI yields four separate scores, shared with participants through their BrainHealth Platform dashboard: a global BHI score and scores for its three validated factors: 1) Clarity (cognitive health – readiness to reason through complex situations and create new solutions via executive functions, memory, and supporting functions such as sleep), 2) Connectedness (social health – perceived connection to people and purpose), and 3) Emotional Balance (emotional health – steadiness in adversity while remaining productive via reduced anxiety, enhanced mood, and decreased stress). These scores are based on a factor analysis of changes scores from measures shown in Table 2 . The composite score captures the interdependency of brain health dimensions, leveraging machine learning analytics rather than presupposed components. Furthermore, the three factors have been validated by neural metrics (namely, predictive hemodynamic response functions) obtained from a study cohort undergoing functional neuroimaging in conjunction with BHI assessment timepoints during their study course [ 42 ]. Table 2 Measures included in the BrainHealth Index Measure Assessment Instrument Strategic Attention Visual Selective Learning Task [ 70 ] Abstraction Proverb Interpretation Task (developed at the Center for BrainHealth) Reasoning Synthesis Interpretation Memory Strategic Cognitive Inventory (formerly Test of Strategic Learning - TOSL) [ 71 ] Condensed synopsis of complex text (~ 550-word narrative) Fluency of take-home messages/interpretations from text Memory for text details (free and cued/elaborated recall) Innovation Fluency of high-level Interpretations from Picture Interpretation Task (developed at the Center for BrainHealth, modeled after semantic verbal fluency task, adapted from [ 72 ]) Processing Speed Coding Task, modified from the Digit-Symbol Verification Task (DSVT) [ 73 ] Sleep Pittsburgh Sleep Quality Index (PSQI) [ 74 ] Compassion Questionnaire adapted from the Light Triad Scale [ 75 , 76 ] Mood Depression Anxiety Stress Depression Anxiety Stress Scale (DASS-21) [ 77 ] Meaningful Activities/Purpose Engagement in Meaningful Activities Survey (EMAS) [ 78 ] Happiness Oxford Happiness Questionnaire (OHQ) [ 79 ] Social Support Social Support Survey Index [ 80 ] Resilience Connor-Davidson Resilience Scale [ 81 ] Life Satisfaction Quality of Life Scale [ 82 ] Social Engagement Social BrainHealth Scale (developed at the Center for BrainHealth) Growth Mindset BrainHealth Appraisal Questionnaire (developed at the Center for BrainHealth) Fitness Metabolic Equivalents: Cardiorespiratory Fitness (CFEQ) [ 83 ] For the cognitive performance measures, the novel tasks included assessment of complex thinking abilities such as reasoning, abstraction, mental flexibility, and strategy, using randomized, alternate-stimuli versions across timepoints. Self-report questionnaires for the other domains evaluate aspects of emotional well-being, quality of life, purpose, happiness, resilience, social support, fitness, and sleep, utilizing empirically validated tools where available. See Table 2 for a full list of measures included in the BHI. - INSERT TABLE 2 - To enhance accessibility, assessments can be completed in shorter segments (with progress saved) over a two-week period at baseline and every 6 months thereafter. Individual scores are graphed to visualize progress over time, helping participants understand their brain health holistically (composite score) while identifying pathways for improvement (factor scores). Unlike traditional measures, the BHI evaluates growth against the individual’s own baseline, supporting a highly personalized approach. Brain Health Training: Modules, Habits, and Resources Upon completion of the baseline BHI assessment, participants access self-paced training modules, habits, and resources centered on the topic of brain health and holistic practices that can support overall brain health. Core Training . Participants access micro-learning content, delivered in 5-10-minute daily-available units incorporating videos, knowledge checks, and personal application activities. The initial modules provide training in evidence-based cognitive strategies from the Strategic Memory Advanced Reasoning Tactics (SMART) protocol. Developed by researchers at the Center for BrainHealth of the University of Texas at Dallas, SMART focuses on improving executive functions through training of nine strategies supporting three core cognitive skill areas: 1) strategic attention (e.g., reducing information intake, prioritizing focus, and allowing brain downtime), 2) integrated reasoning (e.g., synthesizing information and applying abstracted ideas), and 3) innovation (e.g., considering multiple perspectives and possibilities, generating novel solutions). These strategies draw upon frontally-mediated brain networks and can be applied to everyday activities and responsibilities in support of cognitive, emotional, and social well-being [ 39 , 40 ]. SMART has also been shown through previous randomized trials to promote neural changes such as enhanced functional connectivity, cerebral blood flow, and neural efficiency [ 29 , 31 , 33 ]. Following SMART, training modules address stress management and resilience-building practices as well as sleep hygiene, integrating brain strategies with healthy lifestyle practices. Extended Training . After completing the core training modules, participants access monthly themed micro-learning modules designed to reinforce the core training concepts and extend their application to broader areas of real-life application. Topics cover a broad range of applications, including aspects such as decision-making, gratitude practices, curiosity building, and fostering relationships, to name a few. Modules include interactive learning components, various media modalities, opportunities for personal reflection, and learnings about relevant brain-behavior relationships. Habits . Each module is accompanied by a selection of specific and actionable habits to incorporate training concepts into daily routines. Just as modules range in focus from cognitive strategies to wellness or lifestyle practices, so do the habits. If a training segment focuses on single tasking (versus multi-tasking), a corresponding habit could be “Set aside 30- to 60-minute blocks of focused, uninterrupted time for your most important tasks today.” The BrainHealth Platform allows participants to receive daily habit reminders, track habit streaks, and earn digital badges. Resources . A curated collection of publicly available educational content, including research, media articles, and video lectures, etc., that relate to holistic brain health is regularly updated to support ongoing learning and engagement. Brain Health Coaching Throughout the study, participants can engage with one-on-one or group coaching sessions. Participants can schedule quarterly (every 3 months) 20-minute videoconference sessions with study personnel who serve as brain health coaches. In these one-on-one sessions, coaches provide personalized feedback on BHI results, guide strategy application, and/or assist with goal setting. Individual session summaries are saved in participants’ dashboards for easy reference. In addition, study participants have access to monthly virtual group coaching sessions. These 45-minute group sessions use a workshop-style format, where coaches guide participants through personally relevant activities. These sessions aim to: 1) reinforce training concepts, 2) help participants develop specific next steps for applying the concepts in daily life, and 3) foster a shared sense of learning and community. Statistical Analyses For the primary outcome measure, the BrainHealth Index (BHI), we calculated means and standard errors for the general pattern of longitudinal change from baseline to 3-year assessments along with change statistics for each 6-month testing point interval. To assess whether longitudinal trajectories over three years depended on initial baseline level, four levels of baseline measurements were defined by quartiles. Average gradients beyond the baseline means were fitted by weighted regression, where weights were determined by the reciprocal of the variances of the means, and gradients were compared over the four baseline levels using F -statistics. In addition to long-term change, we were interested in determining whether utilization influenced the initial change from baseline and whether age, gender, or education level influenced potential differences among utilization groups. Utilization was defined by the completion rates of training and daily habits, participation in coaching calls, utilization of resources, and overall engagement with the platform. The distribution of the number of items for each variable was divided into quintiles from which ranks were assigned, and a utilization score was calculated as a sum of ranks. These ranged from 0 to 12. Finally, we defined levels of utilization by splitting the rank sums into the lower 25% (Low), middle 50% (Modest) and the upper 25% (High). Index measures were dependent variables in a linear mixed effects model which included effects of time (T1/baseline and T2/6-months), utilization group (low, modest, high – defined above), age (continuous variable), gender (female, male), and all interactions involving time, group and age; all interactions involving time, group and gender; and all interactions involving time, group, and education. We modeled the error term as a variance component structure, a within-subject variance and a between-subject variance, since measurements within subjects are positively correlated. Our primary interest was the time/utilization interaction and whether age or gender influenced this interaction (i.e., both 3-way interactions). A second tier of analyses, intended to assess the effect of utilization on index measures, considered only the sample of Low-level utilizers from the model described above. Over the course of six months (between T2/6-months and T3/12-months) some increased their engagement with the platform, switching to either modest or high utilization levels. From these new group designations (i.e., Low->Low; Low->Modest; and Low->High), we ran the same mixed effects model as above and tested the time/utilization-change interaction between T1 and T3. All interaction contrasts from both models were assessed using t -statistics, and p -values were Bonferroni-adjusted. Results Longitudinal changes in brain health indices As seen in Fig. 1 , the general pattern of longitudinal change for the overall BrainHealth Index (BHI) score is one of monotonically increasing change throughout the 3-year study period, with significant growth from one testing interval to the next. When comparing baseline and 3-year timepoints (T7-T1), the 79.9-point overall gain was also highly significant ( t = 17.7, p < 0.0001, effect size = 2.06). Figure 2 shows that, with only a few exceptions, this general pattern of growth is seen regardless of initial baseline level. This was represented across the overall BHI score [Figure 2 (A)] as well as for the three factors of Connectedness [Figure 2 (B)], Emotional Balance [Figure 2 (C)], and Clarity [Figure 2 (C)]. For baseline levels in the lowest quartile, the longitudinal gradient is greater than that in the other baseline levels (BHI: F = 7.8, p = .002; Connectedness: F = 8.0, p = .002; Emotional Balance: F = 19.6, p < .001; Clarity: F = 2.9, p = .068) based on 3 degree-of-freedom F -statistics. However, to make sure that these gradients could not be solely explained by regression to the mean, we removed the lowest quartile gradient and compared the gradients of the upper three baseline quartiles with 2 degree-of-freedom F statistics. With the exception of Emotional Balance, we see that they all have comparable gradients (BHI: F = 2.0, p = .163; Connectedness: F = 2.8, p = .092; Emotional Balance: F = 9.7, p = .002; Clarity: F = .53, p = .597). Impact of utilization See Table 3 for results from the linear mixed effects model. Utilization had an impact on degree of change across all index measures. Figure 3 shows graded change across the three levels of utilization, with minimal change in those with low level of utilization and greatest change in those with high level of utilization. This interaction is significant for all indices (see Table 3 ). Notably, this general pattern of graded change as a function of utilization did not depend on gender or on age, as there were no significant three-way interaction effects between time, group, and gender or between time, group, and age on any of the scores. This suggests that changes in these scores across the utilization groups were not influenced by gender or age. Education had a small effect on this pattern, such that the modest utilization group had small increases over education levels. Table 3 Linear mixed effects model results BHI Clarity Emotional Balance Connectedness Effects ndf ddf F-statistic p-value F-statistic p-value F-statistic p-value F-statistic p-value Time:Gender 1 3951 0.28 .59 0.77 .38 0.05 .82 0.22 .64 Time:Age 1 3951 39.22 < .001*** 35.17 < .001*** 12.09 < .001*** 17.44 < .001*** Time:Group 2 3951 14.40 < .001*** 3.83 .022* 5.04 .007** 20.33 < .001*** Time:Group:Gender 2 3951 2.02 .13 0.28 .76 1.35 .26 2.55 .08 Time:Group:Age 1 3951 0.13 .87 0.06 .94 0.28 .76 0.91 .40 Time:Group:Education 4 3951 2.45 .044* 1.04 .38 1.70 .15 1.83 0.12 Time:Group change (T1 to T3) 2 477 6.52 .002** 5.78 .003** 0.97 0.38 6.00 .003** Notes: Group = utilization category; * p < .05, ** p < .01, *** p < .001 - INSERT Table 3 - Post-hoc analysis using pairwise comparison (see Table 4 ) showed that, compared to low utilization, high utilization led to a significantly greater change between Time 1 and Time 2 (6 months) in the overall BHI score, as well as in the scores for Clarity, Emotional balance, and Connectedness. Modest utilization resulted in a significantly greater change in the overall BHI score and the Connectedness score, compared to low utilization. High utilization also led to significantly greater BHI score change than modest utilization. These results suggested that score changes from T1 to T2 varied depending on the level of utilization. The mean score changes across the board are lowest for low utilization and highest for high utilization. The overall BHI and Connectedness scores exhibited more dramatic score differences among the groups than those for Clarity and Emotional balance (see Table 4 ). Table 4 Results from post-hoc analysis using pairwise comparison, including utilization group contrasts of T2-T1 change (6 months) and low utilizers’ T3-T1 change (1 year) BHI Clarity Emotional Balance Connectedness Utilization group Contrasts of T2 – T1 Change t-statistic Bonferroni-adjusted p-value effect size t-statistic Bonferroni-adjusted p-value effect size t-statistic Bonferroni-adjusted p-value effect size t-statistic Bonferroni-adjusted p-value effect size High – Low 5.36 < .001*** 0.46 2.76 .017* 0.24 3.02 .008** 0.26 6.19 < .001*** 0.53 Modest - Low 2.79 .016* 0.19 1.43 .39 0.10 0.57 .92 0.04 4.28 < .001*** 0.29 High - Modest 3.40 .002** 0.27 1.76 .22 0.14 2.75 .018* 0.22 3.04 .007** 0.24 Utilization Group change Contrasts of T3 – T1 Change t-statistic Bonferroni-adjusted p-value effect size t-statistic Bonferroni-adjusted p-value effect size t- statistic Bonferroni-adjusted p-value effect size t- statistic Bonferroni-adjusted p-value effect size Low_High – Low_Low 2.64 .025* 0.29 1.86 .018* 0.18 1.39 .42 0.15 2.51 .037* 0.28 Low Modest – Low_Low 3.30 .003** 0.23 3.35 .003** 0.24 0.48 .95 0.03 3.18 .005** 0.22 Low_High – Low_Modest 0.58 .92 0.06 -0.28 .99 0.03 1.14 .59 0.12 0.51 .94 0.05 Note: All p -values are Bonferroni-adjusted; * p < .05, ** p < .01, *** p < .001 Tables 3 and 4 also show the effect of utilization change over time for those who were initially low-level utilizers. Those who switched to higher-level utilization over the following six months had higher mean changes on the indices through one year (see also Fig. 4 ). Conversely, those who remained low-level utilizers had no change on their brain health indices through one year. - INSERT Table 4 - Four Case Illustrations Figure 5 presents four brief case examples which highlight the dynamic nature of individual brain health trajectories and demonstrate how the brain health factors can guide individuals in setting goals to maintain an overall Index score. By linking these insights to personalized goals, the cases illustrate the utility of the BrainHealth Index in promoting specific, actionable strategies for sustained growth and optimization across varying life contexts. In particular, these cases underscore the distinctive insights derived from the BrainHealth Index, enabling a precision approach to brain health. Discussion This study addresses the major question of whether brain health can be improved over a 3-year period through engagement with a scalable online training platform. The results show that this is indeed the case for the study’s participants, signaling a shift from a prevailing narrative of inevitable, progressive brain decline to one of potential growth and development across the adult lifespan. This research examined three key questions. First, we investigated whether the online-delivered BrainHealth Index (BHI), offered every six months, effectively tracks changes in brain health – both gains and losses – over a 3-year interval. Intrinsic to this question, we examined the effectiveness of online-delivered brain health tools, centered around strategy-based executive function training (SMART), in promoting brain health across adults of all ages. Second, we examined the characteristics of participants who demonstrated brain health gains versus those who did not, considering baseline brain health score, age, education, and gender. Finally, we examined the role of utilization in optimizing BHI performance over time. The findings are summarized below, along with their implications for extending healthy brain function. Taken together, these findings inform 1) what is possible in terms of measuring and enhancing brain health, 2) who is likely to benefit from this proactive brain health approach, 3) how improvements were likely achieved, and 4) why brain health promotion is a promising pathway to support overall health outcomes. What can measure and promote increase in brain health span First, this study demonstrated that the online BrainHealth Index is sensitive to measuring and tracking changes (gains and losses) in brain health trajectory over time. In a large prospective sample of adults ages 19–94, the majority of participants who took advantage of accessing and engaging with the brain health tools and strategies showed significant gains over three years. The data suggest the BHI was informative in evaluating the efficacy of ongoing online delivery of micro-learning brain health modules, with comparable results to those previously reported [ 23 ] as well as those achieved through previous studies utilizing in-person training delivery [ 29 – 35 ]. Additionally, the BHI showed viability in capturing the complex, multidimensional nature of brain health [ 18 ], with upward trajectories observed in both the holistic score as well as its three contributing factor scores (Clarity, Connectedness, and Emotional Balance). These trends were visualized at the group level (Figs. 1 and 2 ) as well as illustrated more dynamically at an individual level (Fig. 5 ), supporting its use in precision brain health. Both the composite BHI and its factors provided valuable insights regarding the course of change. These measures showed sensitivity to charting dynamic trajectories of upward growth (Case #1), general longer-term maintenance (Case #2), episodic declines in particular contributing subcomponents (Case #3), and/or a downward trend (Case #4). By allowing users to track and chart their personal brain health trends through an accessible platform, the BHI can serve as a motivational tool to implement simple steps into their everyday life activities to potentially detect and regain declining capacities at the earliest time possible. Our team is continuing to refine and validate the BrainHealth Index with neural markers from functional magnetic resonance imaging (fMRI) analyses of associated changes, including markers that show promise in predicting performance gains on the behavioral measures comprising the BrainHealth Index [ 42 , 43 ]. The BrainHealth Index’s utility extends beyond individual tracking; it offers a promising metric for evaluating brain health interventions. Preliminary investigations are underway to evaluate the sensitivity of the Index and/or some of its subcomponents in measuring gains following other intervention protocols that may improve brain health, including other cognitive training protocols such as BrainHQ (Posit Science, 2024), medical interventions such as Hormone Replacement Therapy, and lifestyle interventions such as physical fitness training. Future efforts should continue exploring its potential applications in medical, mental health, and preventive care contexts. One paramount question to address is what aspects contributed to the observed brain health gains. The present findings demonstrate that sustained improvements were achievable, regardless of an individual’s starting level, when given continual and easy access to brain health training, content, and practices. Participants in each quartile, based on baseline BHI, showed incremental gains over time, without evidence of ceiling effects. We propose that the strategy-based executive function training (SMART) equipped participants with metacognitive tools that could be applied to support meaningful, real-life demands to facilitate ongoing improvements. SMART has shown to promote generalized gains by engaging higher cognitive functions applicable across various contexts [ 30 , 31 , 35 , 38 , 40 , 44 ]. Evidence from SMART and similar programs demonstrate broad benefits, including enhanced executive function, psychological well-being, and neural connectivity, particularly within the brain’s frontal networks, across both clinical and non-clinical populations [ 29 – 38 , 40 ]. Strategy-based interventions stand out for their ability to generalize to multiple domains of daily function [ 45 , 46 ], whereas bottom-up approaches, such as speed-of-processing training, show domain-specific gains [ 47 ]. These findings highlight the potential for integrating top-down and bottom-up approaches to tailor brain health interventions to individual needs and optimize outcomes across various life stages. Who benefits from the proactive brain health protocols Notably, overall brain health gains in this study cohort were independent of age, education level, gender, or starting level of brain health performance, yielding two somewhat unexpected findings. First, we found that younger people generally benefited as much as older participants. This challenged a commonly held perspective that older individuals would likely benefit more, given that they have typically experienced more decline, and young adults would have little to gain. This reinforces the idea that adults can optimize their brain health at any age and supports the recommendation to begin proactive brain health practices early in adulthood and continue throughout the adult lifespan. As with physical health, it is easier to preserve and build brain function than to regain lost ground after substantial decline has occurred. Second, we found that those in the lowest BHI performance quartile at baseline made greater gains over time than those in the upper quartiles (Fig. 2 ). While it is likely that this finding may partly represent statistical regression to the mean, it is clear that lower baseline brain health can be improved at least as much as higher baseline levels. Many would say that this is intuitive – given that generally those with the greatest room for improvement often show the biggest changes. However, there is a common misconception that lower performance from the onset is somehow fixed in nature and thus linked to poorer long-term outcomes and limited ability to close the gap with higher performers. The results of greater gains in lower performers over the three years speaks to the likely contribution of the brain’s inherent neuroplasticity and its capacity to continuously improve and be strengthened, given proper practices (i.e., experiential neuroplasticity) [ 11 , 48 ]. Moreover, we did not see that the highest BHI performance quartile made the least gains, which would be predicted by regression to the mean. The gradients of the upper three quartiles were statistically similar. The only exception to this is for the factor of Emotional Balance, which we attribute to being more limited in continued growth potential. We were not surprised by the evidence that females and males showed comparable gains. Nor was the finding of no significant educational effect unforeseen, given that we had a relatively well-educated group, with less than 15% having lower than a bachelor’s-level education. These gender and education findings are also consistent with prior findings from our first study cohort [ 23 ]. The relatively high education of our group highlights one major limitation of the present study, in that the results may not generalize to less educated individuals. We are actively working to recruit more participants from less educated backgrounds. In sum, our results lend support to the universal applicability of proactive strategy-based executive function training to promote brain health. The comparable gains across age groups and education levels represented in our sample suggest that key demographic factors which often predict disparities in health outcomes and/or responsiveness to health-focused interventions may not face the same barriers when it comes to increasing brain health span [ 49 ]. From a public health perspective, these findings reinforce the potential for brain health strategies to benefit diverse populations, regardless of starting level. How can brain health gains be achieved Given the single-arm design of the study, the first hypothesis to explain the observed results must be that the improvements in brain health were simply practice effects, with repeated exposure to the BrainHealth Index leading to better performance over time. While practice can enhance performance, this cannot account for the observed improvements for two reasons. First, participants with generally low utilization who repeatedly completed the Index did not show significant gains. Next, we have shown in previous research that taking the BrainHealth Index multiple times without engaging in the training did not yield similar improvements to those in the current study [ 23 ]. A second hypothesis is that participants improved simply due to non-specific factors, such as the mere attention given to them and that their expectations were to improve brain health, regardless of specific training. This explanation is countered by evidence from prior clinical trials of our cognitive training where participants in active control conditions (e.g., psychoeducation or physical exercise training) did not improve on measures from the major Clarity factor of the BHI when given equal attention [ 29 , 31 ]. These findings reinforce the conclusion that active skill- and habit-building, rather than passive expectations, drive meaningful brain health gains. A third potential mechanism is that of a direct effect of specific metacognitive strategies and associated habits in allowing participants to meet their daily life demands, combined with implementing advice about nurturing social connectedness and maintaining emotional balance. The four case studies (Fig. 5 ) give numerous personal examples of such a mechanism. More generally, higher utilization entailed consistent engagement with the online tools – assessments, coaching sessions, strategy-based micro-learnings, and habit-tracking – and was associated with greater gains in brain health over the 3-year follow-up period. These tools were designed for “bite-size” delivery, requiring minimal time (no more than 15 minutes within a single day), to motivate such continued use and optimize overall learning and application. Encouragingly, utilization was not necessarily static: among participants with low utilization in the first 6-month cycle (n = 507), 63.12% went on to increase their engagement over time (i.e., shifted to a higher utilization category). This increase in utilization further supports the strategy-and-habit-learning hypothesis rather than a more non-specific global expectation mechanism to explain the improvements in brain health. A final hypothesis concerns the possibility that learning strategies to control one’s brain function and brain health makes participants feel more empowered and in control – in other words, they develop greater self-agency. This hypothesis is entirely compatible with the previous one – indeed we consider both the utilization and implementation of specific strategies and habits, leading in turn to increased feelings of self-agency – and continuing in a mutually reinforcing cycle – as being the most likely mechanisms underlying the observed 3-year improvements in brain health. Research shows that simply inducing a person to think abstractly increases their sense of power [ 50 ]. Central to our training are metacognitive techniques such as ‘zooming out’ from a particular situation or problem to widen one’s perspective to take a more abstract view of it. This is a technique used, for example, by Case 3 above (Fig. 5 ). Metacognitive strategies are the essence of abstract thinking, and we hypothesize that engaging in these strategies increases empowerment and hence a sense of self-agency. We therefore interpret the observed strong relationships between utilization and improvements in brain health as being due to the mutual reinforcement of utilization of key strategies and habits, leading to an increasing self-agency. As such, increases in self-agency works to further boost utilization, as shown by the low utilizers shifting to higher levels of utilization. By this argument, utilization is a form of self-agency in action. The executive function skills learned during the SMART training – which have been shown to improve brain health in previous, smaller randomized controlled trials [ 29 – 35 , 40 ] - give participants self-agency over their own attention, thought, and emotion processes. This sense of control over their own brain processes should increase self-agency more generally. In support of this hypothesis, research shows that cognitive training increases self-agency in older people. In a recent study of over 12,000 older U.S. adults found that individuals with greater sense of control at the onset of the study had improved physical health outcomes over the study’s 4-year follow-up period [ 51 ]. Moreover, a stronger sense of control was associated with greater engagement in health-promoting behaviors as well as higher outcomes in multiple aspects of well-being and social connectedness [ 51 ]. Simply knowing which actions to take – through awareness and education for example – however, is insufficient to drive the lasting behavior change necessary to reduce risks and promote health [ 52 , 53 ]. We propose that a key component of self-agency is its link to utilization of potent metacognitive strategies for controlling brain processes – what we have called self-agency in action [ 54 , 55 ]. This ability to translate intention into action is intrinsically linked to executive function, a set of cognitive processes that enables individuals to regulate thoughts, actions, and emotions in pursuit of goals [ 56 ], and which is trained in the current project’s online SMART program. Importantly, executive functions can be strengthened through training and, as our data suggest, can result in improved brain health over an extended period of time [ 57 , 58 ]. These findings motivate further research and clinical efforts to explore strategies for fostering self-agency in brain health promotion. We propose that individuals are more likely to show sustained improvement when they 1) perceive that they can impact their brain health, 2) are empowered to take action, 3) equipped with executive function tools and 4) have ready access to technology-driven nudges to continually reinforce and practice brain health strategies and habits. Importantly, low utilization of the brain health tools could not be explained by lower baseline performance. Indeed, those in the lowest baseline quartile manifested the highest gradient in enhancing their brain health span. Further research must unpack the triadic relationship between executive function, strategy-and-habit utilization, and self-agency so that the potency of brain health training can be further increased. Just as has been shown in physical health, individual self-agency in symbiotic relationship with executive function and utilization, is likely a pivotal factor in maintaining and improving brain health, fostering autonomy and resilience in navigating daily challenges. Why brain health promotion is imperative The primary health significance of this study is its demonstration that the complex construct of “brain health” can both be longitudinally measured and enhanced, along with its contributing factors, across the adult lifespan, in independently functioning individuals who are relatively healthy or may have some degree of compromised brain health. The current findings motivate efforts to integrate brain health metrics into clinical and research settings to advance health promotion strategies. In sum, the BrainHealth Index shows promise as a novel, validated, change-sensitive tool capable of tracking both growth and decline in brain health over time, filling a critical void in available brain health measures. Not only is the BrainHealth Index holistic with its contributing subfactors, but it is also scalable and wellness driven. Until now, most efforts to assess brain health have employed measures designed primarily for diagnosis or deficit detection, relying on normative comparisons and threshold-based criteria. While such measures are essential for identifying impairments, they fail to recognize that brain health is more than just the absence of brain compromise. Additionally, existing assessments tend to be screening-level scales or measure isolated pillars of brain health such as cognition [ 18 , 19 , 59 , 60 ], socialization [ 61 , 62 ], mental health [ 63 ], physical fitness [ 64 , 65 ], or sleep [ 66 ] – without capturing how these components work together to support overall brain health. This fragmented approach overlooks the dynamic and interrelated nature of brain health, whereas our findings suggest that incremental changes (gains or losses) in the personal brain-behavioral profile can be regularly monitored and managed using a platform that can be accessed remotely through online technology, including mobile apps. Results from this study demonstrated the utility of a holistic, wellness-driven brain health measurement that is repeatable over time and actionable. The present findings advance our understanding of brain health promotion in two significant ways: 1) demonstrating the potential of a strategy-based executive function approach to facilitate behavior change and promote generalized, sustained benefits across multiple life domains and throughout adulthood and 2) underscoring the importance of leveraging accessible, tele-delivered tools to motivate self-agency of action and facilitate tailored application. Beyond its impact on brain health, this study highlights the broader implications for overall health promotion. For instance, prior work with a large longitudinal sample (n = 10,855) of working-age adults in Finland demonstrated that not only can improved health behaviors promote gains in subjective well-being, but that this can be a bidirectional relationship, such that enhanced well-being can also further reinforce long-term health behaviors [ 67 ]. Optimizing brain health may serve as a foundation for holistic, lifelong health improvements. Limitations and future directions This study has a number of limitations that should be considered when interpreting the findings. First, we acknowledge this is a single-arm interventional study versus a randomized control trial. At this stage of science, we propose that prior evidence supports that all participants should have access to the intervention protocols since the intervention does no harm and has shown to benefit most. Specifically, prior randomized clinical trials (with combined totals of > 100 participants) using the SMART intervention have yielded findings demonstrating its effectiveness in promoting the types of gains observed, including evidence of corresponding neural changes [ 29 – 38 ]. The BrainHealth Project’s approach allows a way to address the duration of the gains or detect early declines at an individual level. Additionally, while a large study cohort has been enrolled to date, demographic diversity has been somewhat limited, most notably in racial, ethnic, and education level representation, which may limit the generalizability of the current findings to broader populations. Another limitation is the inclusion of some self-reported data comprising the BrainHealth Index assessment. Self-report is subject to reporting bias. Moreover, the current collected data lack comprehensive information regarding participants’ medical histories and concurrent treatments that may impact an individual’s brain health trajectory. Efforts are underway to expand and enhance recruitment strategies to better reflect overall population demographics (age, sex/gender, race/ethnicity, level of education) as well as incorporate objective metrics of physical activity and sleep (e.g., wearable devices), medical history, and expand ongoing investigation linking behavioral outcomes to neural changes through neuroimaging or other biomarkers. Furthermore, as with many online longitudinal studies, study attrition is a limitation – averaging 43% for this study cohort over the three years. This represents a comparable rate to what has been shown in previous longitudinal, population-based studies involving internet-based/eHealth platforms [ 68 ]. Increased attrition and lack of adherence have been longstanding barriers to successful health-behavior interventions, in particular [ 69 ]. Efforts to enhance engagement and long-term participant retention, such as more personalized follow-ups and community-building strategies, are underway. Conclusion Given the brain’s central role in shaping who we are and how we function, these results emphasize the potential gains to the human brain health span achieved through promoting brain health from young adulthood to older ages with personalized and participatory practices. Science is clear that the time is urgent to expand medical practice and public health approaches to move away from a predominant brain disease focus to a public health imperative that delivers brain health protocols. Such an approach will enable more people to realize their full potential over their life course, regardless of the presence or absence of brain issues. Health care providers can be on the forefront of brain health promotion by 1) adopting proven ways to measure growth in brain skills whatever one’s starting point and by 2) guiding their patients to protocols which will most enhance their brain skills with simple strategies, requiring self-agency of action, as there is no “magic brain pill.” This effort parallels the advancements made in heart health over the past six decades, with the goal of optimizing each individual’s peak brain health span to match increasing longevity. Achieving this will require utilization of technology that provides a way to scale and advance access to precision brain health to measure, monitor, and nudge healthier behaviors. Finally, we propose that the potential societal and economic benefits of advancing brain health are significant. Proactively building stronger brain health and performance not only promotes individual well-being but also offers the prospect of reduced healthcare costs and increased productivity. As such, future research should explore the broader impact of scalable, precision brain health approaches on societal outcomes. Brain health promotion can not only extend the brain health span but can also support healthier, more fulfilling lives across the lifespan. This achievement will be possible if we make brain health a public health imperative – focusing on neuroplasticity, self-agency, and proactive growth while shifting from a reactive, deficit-based model. Our future depends on extending our brain health spans. Declarations Data availability The data sets used and analyzed in this study are available from the corresponding author upon reasonable request. Acknowledgements We express sincere gratitude to the coaches of the BrainHealth Project for guiding and encouraging participants with exceptional care and skill: Tandra Allen, Katie Hinds, Janet Koslovsky, Sarah Laane, Marco Lopez, Kalyn Potter, Audette Rackley, Colleen Ryan, Stacy Vernon, Jennifer Zientz. Additionally, we must thank our skilled technology, data management, and support team, including Margaret Chaplin, Sonal Jain, Bryan Vosburg, Haider Naeem, Sameena Shaik, Dinesh Sharma, Mahanaz Attila, Radi Tawfiq, and the team at Dialexa for their tremendous work on the online platform. Finally, we are extremely grateful to the participants who choose to be pioneers in brain health and invest their time in this research. Funding The BrainHealth Project is currently funded by private philanthropy, including Sammons Enterprises, Inc. and the Hoglund Foundation. Author Contributions LC collected and scored data, interpreted results, and wrote and revised the manuscript. JSS performed analyses, interpreted results, and wrote and edited the manuscript. ZC performed analyses, interpreted results, and contributed to the manuscript. EEV assisted with the design and content of the training protocol, collected and scored data, and contributed to the manuscript. AT supervised and contributed to the development of the online platform and training modules and collected data. IHR contributed content to the online training modules, advised on some of the measures comprising the BrainHealth Index, interpreted results, and contributed to the manuscript. MD’E and JW provided critical revision of the manuscript. GSFL assisted with study design. SBC designed the study, interpreted results, and wrote and edited the manuscript. All authors reviewed and approved the submitted version. Competing Interests The system and method for precision brain health assessment is patent pending. 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Neuropsychopharmacology 37 , 43–76 (2012). https://doi.org/10.1038/npp.2011.251. Ross, C. E., Masters, R. K. & Hummer, R. A. Education and the gender gaps in health and mortality. Demography 49 , 1157–1183 (2012). https://doi.org/10.1007/s13524-012-0130-z. Smith, P. K., Wigboldus, D. H. J. & Dijksterhuis, A. J. Exp. Soc. Psychol. 44 , 378–385 (2008). https://doi.org/10.1016/j.jesp.2006.12.005. Hong, J. H. et al. The role of control beliefs and health behaviors in shaping well-being in adulthood. Prev. Med. 149 , 106612 (2021). https://doi.org/10.1016/j.ypmed.2021.106612. Arlinghaus, K. R. & Johnston, C. A. Advocating for behavior change with education. Am. J. Lifestyle Med. 12 , 113–116 (2017). Kelly, M. P. & Barker, M. Why is changing health-related behaviour so difficult? Public Health 136 , 109–116 (2016). Lachman, M. E. & Weaver, S. L. The sense of control as a moderator of social class differences in health and well-being. J. Pers. Soc. Psychol. 74 , 763–773 (1998). Skinner, E. A. A guide to constructs of control. J. Pers. Soc. Psychol. 71 , 549–570 (1996). Lezak, M. D. Neuropsychological Assessment 3rd edn (Oxford Univ. Press, 1995). Diamond, A. Executive functions. Annu. Rev. Psychol. 64 , 135–168 (2013). https://doi.org/10.1146/annurev-psych-113011-143750. Shields, G. S., Moons, W. G. & Slavich, G. M. Better executive function under stress mitigates the effects of recent life stress exposure on health in young adults. Stress 20 , 75–85 (2017). https://doi.org/10.1080/10253890.2017.1286322. Ball, K. et al. Effects of cognitive training interventions with older adults: A randomized controlled trial. JAMA 288 , 2271–2281 (2002). https://doi.org/10.1001/jama.288.18.2271. Park, D. C. et al. The impact of sustained engagement on cognitive function in older adults: The Synapse Project. Psychol. Sci. 25 , 103–112 (2014). https://doi.org/10.1177/0956797613499592. Hughes, M. E., Waite, L. J., Hawkley, L. C. & Cacioppo, J. T. A short scale for measuring loneliness in large surveys: Results from two population-based studies. Res. Aging 26 , 655–672 (2004). https://doi.org/10.1177/0164027504268574. Park, S., Kwon, E. & Lee, H. Life course trajectories of later-life cognitive functions: Does social engagement in old age matter? Int. J. Environ. Res. Public Health 14 , 393 (2017). https://doi.org/10.3390/ijerph14040393. Ibanez, A. & Zimmer, E. R. Time to synergize mental health with brain health. Nat. Ment. Health 1 , 441–443 (2023). https://doi.org/10.1038/s44220-023-00086-0. Gu, Y. et al. Assessment of leisure time physical activity and brain health in a multiethnic cohort of older adults. JAMA Netw. Open 3 , e2026506 (2020). https://doi.org/10.1001/jamanetworkopen.2020.26506. de Bruijn, R. F. A. G. et al. The association between physical activity and dementia in an elderly population: The Rotterdam Study. Eur. J. Epidemiol. 28 , 277–283 (2013). https://doi.org/10.1007/s10654-013-9773-3. Gottesman, R. F. et al. Impact of sleep disorders and disturbed sleep on brain health: A scientific statement from the American Heart Association. Stroke 55 , e61–e76 (2024). https://doi.org/10.1161/STR.0000000000000453. Stenlund, S., Koivumaa-Honkanen, H. & Sillanmäki, L. Changed health behavior improves subjective well-being and vice versa in a follow-up of 9 years. Health Qual. Life Outcomes 20 , 66 (2022). https://doi.org/10.1186/s12955-022-01972-4. Eysenbach, G. The law of attrition. J. Med. Internet Res. 7 , e11 (2005). https://doi.org/10.2196/jmir.7.1.e11. Middleton, K. R., Anton, S. D. & Perri, M. G. Long-term adherence to health behavior change. Am. J. Lifestyle Med. 7 , 395–404 (2013). Hanten, G. et al. Development of verbal selective learning. Dev. Neuropsychol. 32 , 585–596 (2007). https://doi.org/10.1080/87565640701361112. Vas, A. K., Chapman, S. B. & Cook, L. G. Language impairments in traumatic brain injury: A window into complex cognitive performance. In Handbook of Clinical Neurology: Traumatic Brain Injury Part II (eds. Salazar, A. & Grafman, J.) 497–510 (Elsevier, 2015). Lezak, M. D., Howieson, D. B., Loring, D. W. & Fischer, J. S. Neuropsychological Assessment (Oxford Univ. Press, 2004). Rypma, B. et al. Neural correlates of cognitive efficiency. Neuroimage 33 , 969–979 (2006). https://doi.org/10.1016/j.neuroimage.2006.05.065. Buysse, D. J. et al. The Pittsburgh Sleep Quality Index (PSQI): A new instrument for psychiatric research and practice. Psychiatry Res. 28 , 193–213 (1989). https://doi.org/10.1016/0165-1781(89)90047-4. Johnson, L. K. D. The Light Triad Scale: Developing and validating a preliminary measure of prosocial orientation. Master’s thesis, The University of Western Ontario (2018). https://ir.lib.uwo.ca/etd/5515. Strauss, C. et al. What is compassion and how can we measure it? A review of definitions and measures. Clin. Psychol. Rev. 47 , 15–27 (2016). https://doi.org/10.1016/j.cpr.2016.05.004. Lovibond, P. F. & Lovibond, S. H. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav. Res. Ther. 33 , 335–343 (1995). https://doi.org/10.1016/0005-7967(94)00075-u. Eakman, A. M. Convergent validity of the engagement in meaningful activities survey in a college sample. OTJR 30 , 23–32 (2011). https://doi.org/10.3928/15394492-20100122-02. Hills, P. & Argyle, M. The Oxford Happiness Questionnaire: A compact scale for the measurement of psychological well-being. Pers. Individ. Differ. 33 , 1073–1082 (2002). https://doi.org/10.1016/S0191-8869(01)00213-6. Sherbourne, C. D. & Stewart, A. L. The MOS social support survey. Soc. Sci. Med. 32 , 705–714 (1991). https://doi.org/10.1016/0277-9536(91)90150-b. Connor, K. M. & Davidson, J. R. T. Development of a new resilience scale: The Connor-Davidson Resilience Scale (CD-RISC). Depress. Anxiety 18 , 76–82 (2003). https://doi.org/10.1002/da.10113. Burckhardt, C. S. & Anderson, K. L. The Quality of Life Scale (QOLS): Reliability, validity, and utilization. Health Qual. Life Outcomes 1 , 60 (2003). https://doi.org/10.1186/1477-7525-1-60. Jurca, R. et al. Assessing cardiorespiratory fitness without performing exercise testing. Am. J. Prev. Med. 29 , 185–193 (2005). https://doi.org/10.1016/j.amepre.2005.06.004. Additional Declarations Yes there is potential Competing Interest. The system and method for precision brain health assessment is patent pending. Specifically, the Board of Regents for The University of Texas System have applied for a patent (Application Number: WO2025029876A2) that includes the BrainHealth Index and the online platform. The status is currently pending. Inventors are (including several of the study authors): Aaron M. Tate, Julie Fratantoni, Stephen B. White, Sandra Chapman, Jeff Spence, Jennifer Zientz, Erin Venza, Lori Cook. 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Cook","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie3QuwrCMBSA4VMK7XLU9ZRKfQWLgw4+TFw6ubg4OVQKnbzM4uUZfIRIoS6ia0fdK3RyEcRjB8HB2tEhP4S0IR8kAVCp/rAqD/n6INMHQOjU82UsIMaboNR85KkUySMSZYk5vUgYdR1rkbbP6YiwNpfa+RoWENw3JcRey7b77ngVE1IidHddRMgDvkTU2zAJ0OCDJWDYlVLEOjB5EDZO0ryXIktCN6iEhE0pDL2QYAxS8F2siTdYrGcMk15grY7fSc0M9SzjF6N9tM3SW9dxTtEuS4ffSZ74/NX8H/tVKpVK9asnSqZI99GGq5sAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-2601-8167","institution":"The University of Texas at Dallas","correspondingAuthor":true,"prefix":"","firstName":"Lori","middleName":"","lastName":"Cook","suffix":""},{"id":497238215,"identity":"6bce018f-12fc-4e88-8a06-ea3bae13278d","order_by":1,"name":"Jeffrey Spence","email":"","orcid":"","institution":"The University of Texas at 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Dallas","correspondingAuthor":false,"prefix":"","firstName":"Aaron","middleName":"","lastName":"Tate","suffix":""},{"id":497238219,"identity":"6a03579d-0d0d-4d4b-8d33-1bf260814def","order_by":5,"name":"Ian Robertson","email":"","orcid":"https://orcid.org/0000-0001-8637-561X","institution":"Global Brain Health Institute (GBHI), University of California, San Francisco, US; and Trinity College Dublin, Dublin, Ireland","correspondingAuthor":false,"prefix":"","firstName":"Ian","middleName":"","lastName":"Robertson","suffix":""},{"id":497238220,"identity":"b10182fe-ad26-434f-9903-36971a903dec","order_by":6,"name":"Mark D'Esposito","email":"","orcid":"https://orcid.org/0000-0002-3462-006X","institution":"University of California at Berkeley","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"D'Esposito","suffix":""},{"id":497238221,"identity":"652de702-8ce3-473f-9e1e-6d7f7a842c28","order_by":7,"name":"Geoffrey 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20:41:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6264411/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6264411/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88596450,"identity":"719da782-65ca-48a2-8341-d912c97b2ba4","added_by":"auto","created_at":"2025-08-08 06:58:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36145,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Trajectory of the overall BrainHealth Index (BHI) score over the 3-year study period for the study sample as a whole, with (b) corresponding change statistics listed by testing point intervals (e.g., T2-T1 = Time 2/0.5 years – Time 1/Baseline/0 years)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6264411/v1/afbf8a0db33401d0818c1111.jpg"},{"id":88596681,"identity":"0d2f1d45-3f15-477a-9d14-5edb4e5db8c9","added_by":"auto","created_at":"2025-08-08 07:06:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64381,"visible":true,"origin":"","legend":"\u003cp\u003eTrajectories of brain health indices over the 3-year study period, plotted by quartiles of initial baseline levels, including overall BHI (A), Connectedness (B), Emotional Balance (C), and Clarity (D).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6264411/v1/33e78331db0177de768b606c.jpg"},{"id":88596680,"identity":"145c1f8b-6451-4feb-9cad-d5f909d75c96","added_by":"auto","created_at":"2025-08-08 07:06:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33120,"visible":true,"origin":"","legend":"\u003cp\u003eMagnitude of score changes from Time 1 (T1) to Time 2 (T2) assessments across all brain health indices, by utilization groups (low, modest, high)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6264411/v1/b7c0b5d18608b67553523966.jpg"},{"id":88596683,"identity":"27f80895-a530-46dd-bbe7-055d744e9601","added_by":"auto","created_at":"2025-08-08 07:06:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31983,"visible":true,"origin":"","legend":"\u003cp\u003eMagnitude of score changes from Time 1 (T1) to Time 3 (T3) assessments across all brain health indices, considering only initially low-level utilizers\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6264411/v1/e4962c4012c0f54abeb6b75f.jpg"},{"id":88596459,"identity":"9dbf912a-c8f6-452e-a43f-caafe15b8202","added_by":"auto","created_at":"2025-08-08 06:58:36","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":270958,"visible":true,"origin":"","legend":"\u003cp\u003eCase illustrations\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6264411/v1/bd8d7372d44f05f69b69934b.jpg"},{"id":92261284,"identity":"7c1bbe75-2f89-498e-9f88-14d5024256a8","added_by":"auto","created_at":"2025-09-26 12:43:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1661737,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6264411/v1/10a30d96-223d-47e0-ba9c-241681705260.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nThe system and method for precision brain health assessment is patent pending. Specifically, the Board of Regents for The University of Texas System have applied for a patent (Application Number: WO2025029876A2) that includes the BrainHealth Index and the online platform. The status is currently pending. Inventors are (including several of the study authors): Aaron M. Tate, Julie Fratantoni, Stephen B. White, Sandra Chapman, Jeff Spence, Jennifer Zientz, Erin Venza, Lori Cook.","formattedTitle":"Measuring and Increasing the Brain Health Span across Adulthood: A Public Health Imperative","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, brain health has emerged as a major public health priority, driven by growing global efforts to prevent brain-based disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This attention is catalyzed by accumulating evidence of multiple modifiable risk factors for dementia, as highlighted in the 2024 Lancet Commission report [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While addressing risk factors is important, taking proactive steps to extend and strengthen brain health at every age may be even more impactful [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The need to promote healthy brain performance is underscored by evidence that neural and cognitive functions in healthy adults begin to decline as early as the late twenties, even in the absence of injury or disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In some scientific circles, it has even been postulated that brain aging itself might be considered a disease process [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe propose that the more consequential question to address is whether holistic brain health can be measured and improved over time, aimed at countering the insidious loss of neural and cognitive health. Extensive evidence of neuroplasticity shows that decline is not inevitable; instead, it foregrounds the possibility that extending the human brain health span is achievable [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A recent report on research priorities from the National Academies of Sciences, Engineering, and Medicine [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] noted that accelerating progress in understanding resilience to brain-based diseases such as dementia will require the dual approach of 1) quantifying brain health across the lifespan and 2) developing multi-pronged interventional approaches, ranging from individualized/precision medicine to scalable public health promotion strategies. Over the past one-and-a-half centuries, the human lifespan has doubled due to advancements in aspects such as heart health, physical and dietary health, the mitigation of infectious diseases, and improved living conditions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. There is now urgency to apply similar efforts toward extending the brain health span to ensure that continued gains in longevity are matched by prolonged cognitive vitality and peak brain function.\u003c/p\u003e\u003cp\u003eAlthough various definitions of brain health exist, a growing consensus is emerging. \u003cem\u003eBrain health\u003c/em\u003e encompasses a state of optimal neural plasticity, cognitive function, well-being, and connectedness to people and to purpose [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While \u0026lsquo;connectedness\u0026rsquo; is a relatively recent addition to this definition, researchers and public health officials underscore its critical role in brain health [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], positioning socialization and purpose as fundamental factors in enabling individuals to reach their fuller potential at any age [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Thus, brain health is about harnessing cognitive, social, and well-being capacities to thrive in one\u0026rsquo;s daily context. Building on this, we define \u003cem\u003ebrain health span\u003c/em\u003e as the length of time an individual can maintain, enhance, or regain their overall brain health without experiencing continual decline in function.\u003c/p\u003e\u003cp\u003eEffectively measuring \u003cem\u003ebrain health span\u003c/em\u003e requires a holistic approach that captures the combined contribution of key factors \u0026ndash; cognitive, social, mental health/well-being, and lifestyle \u0026ndash; on changing brain health status over time. Traditional screening tools and single-domain measures offer limited insights. They typically identify deficits and fail to reflect the broader interactions across factors that shape brain health and thus do little to improve proactive care [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A wellness-driven holistic brain health metric is essential, one that not only tracks functional changes but that also recognizes the brain\u0026rsquo;s potential for positive growth as well as allowing for the detection of early decline [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Ideally, such a metric would also be dynamic, allowing individuals to chart and monitor personal changes in brain health \u0026ndash; both gains and losses \u0026ndash; over time.\u003c/p\u003e\u003cp\u003eRecent advances include lifestyle-based composite measures, such as the Healthy Lifestyle Score [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and the Lifestyle for Brain Health (LIBRA) Score [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which assess modifiable risk factors such as diet, physical activity, and social engagement to promote brain health and reduce dementia risk. These are welcome advances; nonetheless, they remain largely deficit-oriented, primarily designed for at-risk or older populations, and offer limited personalized guidance for fostering lifelong brain health practices.\u003c/p\u003e\u003cp\u003eTo effectively measure \u003cem\u003ebrain health span\u003c/em\u003e while addressing the need for a holistic, wellness-driven, and dynamic/repeatable approach, our team developed and tested the BrainHealth Index (BHI) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This multidimensional composite metric captures the interdependence of key brain health components and visualizes how the components contribute to increasing or decreasing overall brain performance. The BHI enables both individuals and care providers to track personal brain health trajectories starting in early adulthood and continuing throughout life.\u003c/p\u003e\u003cp\u003eUnlike traditional assessments that compare individuals against normative databases \u0026ndash; which may or may not be representative for that individual \u0026ndash; the BHI was designed as a personalized benchmark for improvement rather than a diagnostic classifier. Hence the numerical index given to people is largely interpreted in comparison to \u003cem\u003etheir own\u003c/em\u003e progress and not in comparison to others. This approach is similar to that of indices of physical fitness such as VO\u003csub\u003e2\u003c/sub\u003emax, used in physical health promotion [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which often serve as targets for enhancement. This proactive, optimization-based approach to brain health assessment shifts the focus from deficits to growth, removing the fear and stigma of being told that one\u0026rsquo;s cognitive abilities are declining or are low compared to others. Instead, individuals are empowered to take ownership of their brain health, engaging with targeted strategies and practices to strengthen and sustain their brain health span, whatever their starting level. The seeming paradox in the statement that individuals may \u0026ldquo;have brain health without neural health\u0026rdquo; (M. D\u0026rsquo;Esposito, personal communication, January 28, 2025) is resolved by defining the health of the brain as its holistic capacity to improve in areas of clarity, connectedness, and emotional balance in the real world, despite dysfunction of certain neural circuits. This permits a growth-perspective on brain health, encouraging continual gains in overall brain health trajectory, even in the context of compromised neural health, such as observed in concussion, stroke, chemotherapy-induced brain decline, depression, and other issues.\u003c/p\u003e\u003cp\u003eThe potential to enhance brain health is grounded in well-established research on neuroplasticity, demonstrating that the human brain retains its capacity to adapt and change throughout life. This body of evidence counters outdated beliefs that brain function is fixed at a young age and instead underscores the brain\u0026rsquo;s ability to be continuously shaped by experience [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Such findings reinforce the opportunity to \u003cem\u003eimprove\u003c/em\u003e cognitive, social, and mental health functioning at any age through intentional habits and practices that promote an extended brain health span [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This lifelong capacity to build brain health calls into question the long-standing assumption that overall brain decline with aging is inevitable. Rather than an unavoidable trajectory of loss, brain health can be actively cultivated.\u003c/p\u003e\u003cp\u003eJust as the Framingham study informed a host of preventive approaches and health-promoting behaviors for cardiovascular function, large-scale longitudinal research is needed to establish proactive approaches for brain health [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In response to this need, the BrainHealth Project was launched in 2020 to address whether scalable, population-level assessments and interventions can harness neuroplasticity to enhance and sustain brain performance over time [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The BrainHealth Project aims to recruit 100,000 generally healthy adults, ranging from age 18 to late adulthood, to track brain health trajectories (brain health span) using the BrainHealth Index. Participants engage with coaching-enhanced online training modules designed to strengthen executive functions through adoption of cognitive strategies, further applied to social, emotional and lifestyle factors - including sleep, physical activity, and daily habits. The BrainHealth Project is purposefully a single-arm trial, motivated by extant prior evidence from randomized controlled trials reporting significant gains in neural, cognitive, and/or emotional well-being domains in healthy adults as well as those with brain compromise following the strategy-based training when compared against active control interventions [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The overarching goal is to determine how proactive engagement with these tools can support and optimize brain function, whatever an individual\u0026rsquo;s starting level.\u003c/p\u003e\u003cp\u003eAn initial study established the feasibility of using a scalable online platform and repeatable, data-driven BrainHealth Index to measure individualized brain health trajectories as well as to deliver strategy-based executive function training, coaching, and healthy habit-building tools to promote gains [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Results from 180 healthy adults spanning ages 18\u0026ndash;87 demonstrated the approach\u0026rsquo;s effectiveness, with 75% of participants exhibiting notable gain in their overall BrainHealth Index (BHI) at 3-month follow-up, independent of age, gender, or education level. Importantly, the study highlighted a positive relationship between engagement in online training and BHI gains. This study provided promising evidence that adults of all ages can complete the online BHI, engage in proactive brain health strategies, and adopt brain-healthy habits in their life contexts through an online platform delivery, achieving measurable improvement in brain health and performance.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eThe BrainHealth Project was designed as a prospective, longitudinal study, functioning as an interventional, open-label, single-arm clinical trial (NCT04869111). Following the initial pilot phase [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], recruitment for the longitudinal study began and will continue over at least 10 years of study. Overall BrainHealth Project recruitment and study procedures are still ongoing.\u003c/p\u003e\u003cp\u003ePrimary procedures for the study are conducted online through a desktop computer or the BrainHealth® app (the latter as of February 2024, available for both iPhone and Android users). At study enrollment and every 6 months thereafter, participants complete a BrainHealth Index (BHI) assessment. With each BHI completed, participants receive a high-level overview of their performance and can track their results over time. Participants can also schedule a personalized coaching session (every 3 months) to receive additional feedback and/or to address individual brain health goals.\u003c/p\u003e\u003cp\u003eThroughout the study duration, participants are encouraged to utilize the training in their online account; these consist of interactive micro-learnings related to optimizing brain health and overall well-being, beginning with a strategy-based cognitive training, SMART (Strategic Memory Advanced Reasoning Tactics) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and including subsequent modules on sleep, stress, and practices that can support overall brain health in daily life. We collect and analyze participants’ engagement with their study account (e.g., frequency of logins, training completed, resources accessed, etc.) alongside the BHI outcomes. This allows us to determine how utilization of the online brain health tools may influence participants’ outcomes. All study activities involving the BrainHealth Project and BrainHealth Platform were approved and conducted in accordance with the standards provided by The University of Texas at Dallas Institutional Review Board. See the full online study protocol for an elaborated description of study procedures [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eStudy Objectives\u003c/h3\u003e\n\u003cp\u003eThe current BrainHealth Project study reports on a large prospective sample of generally healthy adults followed over an interval of 3 years, drawn from the ongoing longitudinal study. The primary aim was to examine the impact of participation on holistic brain health, as measured by changes in the multidimensional BrainHealth Index (BHI), with biannual testing points every 6 months (Time 1 – Time 7). Specifically, the major questions addressed by this longitudinal study were:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCould individuals across the adult lifespan improve brain health and maintain these gains over a 3-year period, given access to online brain health tools? How were changes reflected across time on the holistic measure (BHI) as well as on the three contributing brain health factors (Clarity, Connectedness, Emotional Balance)?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWho was able to demonstrate changes from the brain health training protocols as measured by the BHI, in terms of age, education, gender and starting level (BHI at baseline)?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat was the role of utilization – as demonstrated by completion of ongoing micro trainings, daily brain health habits, virtual coaching sessions, and/or accessing brain health resources – in achieving or failing to achieve gains on the BrainHealth Index?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe study targets generally healthy adults aged 18 years and older who are fluent in English, have regular access to an internet connection and device, and are able to hear and read information presented on a computer screen or mobile device. Recruitment is largely through word of mouth, online postings, social media, and advertisements. Participants in this online study do not receive any payment or monetary incentive for participation. Study exclusionary criteria are based on specific health conditions, including diagnosed neurodegenerative disease; a history of stroke, concussion, or brain injury that currently impairs their ability to function at their reported prior level (e.g., inability to carry out daily responsibilities); or a diagnosis of autism spectrum disorder without independent-level functioning. Other diagnoses, such as those representing other medical or psychiatric conditions and/or learning disorders, are captured through self-report in the context of ongoing study assessment timepoints and are not exclusionary, provided individuals are able to continue study participation without undue stress or frustration. Participants can discontinue their participation at any point during the 10-year study time period. See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for a breakdown of participant demographics across this 3-year longitudinal sample. A total of 4,190 participants were recruited. Of those, 202 dropped out of the study, and 10 participants were excluded from the analysis due to invalid/no age entered. As for gender, non-binary gender participants were not included in the analysis due to the low sample size (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12) which is not enough for statistically meaningful results. The final dataset comprised 3,966 participants, which were included in the statistical analyses. Dropoff in assessment completion at each testing timepoint ranged from 37%-46% attrition over the 3-year study period.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipant characteristics (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3,966)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.62 (12.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19\u0026ndash;94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack or African American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNative American / Alaska Native\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNative Hawaiian / Pacific Islander\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic or Latino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre Bachelor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost Bachelor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual household income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than \u003cspan\u003e$\u003c/span\u003e20,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e20,000 - \u003cspan\u003e$\u003c/span\u003e39,999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e40,000 - \u003cspan\u003e$\u003c/span\u003e59,999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e60,000 - \u003cspan\u003e$\u003c/span\u003e99,999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e100,000 - \u003cspan\u003e$\u003c/span\u003e250,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than \u003cspan\u003e$\u003c/span\u003e250k\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Available/Prefer not to answer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e-\tINSERT TABLE 1 -\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStudy Procedures\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003ePrimary Outcome: BrainHealth Index (BHI)\u003c/h2\u003e\u003cp\u003eThe BrainHealth Index (BHI) provides a holistic, multidimensional assessment of brain health and performance. By integrating core measures of complex cognition with both novel and established assessments spanning daily life/lifestyle, mental health, and social domains, the BHI allows for both independent analysis of individual measures as well as a holistic view of their combined impact on overall function. Participants complete an online battery (average of 60\u0026ndash;70 minutes to complete, in total) that includes cognitive performance tasks and self-report questionnaires.\u003c/p\u003e\u003cp\u003eThe BHI yields four separate scores, shared with participants through their BrainHealth Platform dashboard: a global BHI score and scores for its three validated factors: 1) Clarity (cognitive health \u0026ndash; readiness to reason through complex situations and create new solutions via executive functions, memory, and supporting functions such as sleep), 2) Connectedness (social health \u0026ndash; perceived connection to people and purpose), and 3) Emotional Balance (emotional health \u0026ndash; steadiness in adversity while remaining productive via reduced anxiety, enhanced mood, and decreased stress). These scores are based on a factor analysis of changes scores from measures shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The composite score captures the interdependency of brain health dimensions, leveraging machine learning analytics rather than presupposed components. Furthermore, the three factors have been validated by neural metrics (namely, predictive hemodynamic response functions) obtained from a study cohort undergoing functional neuroimaging in conjunction with BHI assessment timepoints during their study course [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasures included in the BrainHealth Index\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAssessment Instrument\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrategic Attention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVisual Selective Learning Task [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbstraction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProverb Interpretation Task (developed at the Center for BrainHealth)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReasoning\u003c/p\u003e\u003cp\u003e\u003cem\u003eSynthesis\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eInterpretation\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eMemory\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrategic Cognitive Inventory (formerly Test of Strategic Learning - TOSL) [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eCondensed synopsis of complex text (~\u0026thinsp;550-word narrative)\u003c/p\u003e\u003cp\u003eFluency of take-home messages/interpretations from text\u003c/p\u003e\u003cp\u003eMemory for text details (free and cued/elaborated recall)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluency of high-level Interpretations from Picture Interpretation Task (developed at the Center for BrainHealth, modeled after semantic verbal fluency task, adapted from [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e])\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProcessing Speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoding Task, modified from the Digit-Symbol Verification Task (DSVT) [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePittsburgh Sleep Quality Index (PSQI) [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompassion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuestionnaire adapted from the Light Triad Scale [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMood\u003c/p\u003e\u003cp\u003e\u003cem\u003eDepression\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eAnxiety\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eStress\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepression Anxiety Stress Scale (DASS-21) [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeaningful Activities/Purpose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEngagement in Meaningful Activities Survey (EMAS) [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHappiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOxford Happiness Questionnaire (OHQ) [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Support Survey Index [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResilience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConnor-Davidson Resilience Scale [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuality of Life Scale [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Engagement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial BrainHealth Scale (developed at the Center for BrainHealth)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrowth Mindset\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrainHealth Appraisal Questionnaire (developed at the Center for BrainHealth)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetabolic Equivalents: Cardiorespiratory Fitness (CFEQ) [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor the cognitive performance measures, the novel tasks included assessment of complex thinking abilities such as reasoning, abstraction, mental flexibility, and strategy, using randomized, alternate-stimuli versions across timepoints. Self-report questionnaires for the other domains evaluate aspects of emotional well-being, quality of life, purpose, happiness, resilience, social support, fitness, and sleep, utilizing empirically validated tools where available. See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for a full list of measures included in the BHI.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003e-\tINSERT TABLE 2 -\u003c/h3\u003e\n\u003cp\u003eTo enhance accessibility, assessments can be completed in shorter segments (with progress saved) over a two-week period at baseline and every 6 months thereafter. Individual scores are graphed to visualize progress over time, helping participants understand their brain health holistically (composite score) while identifying pathways for improvement (factor scores). Unlike traditional measures, the BHI evaluates growth against the individual\u0026rsquo;s own baseline, supporting a highly personalized approach.\u003c/p\u003e\n\u003ch3\u003eBrain Health Training: Modules, Habits, and Resources\u003c/h3\u003e\n\u003cp\u003eUpon completion of the baseline BHI assessment, participants access self-paced training modules, habits, and resources centered on the topic of brain health and holistic practices that can support overall brain health.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCore Training\u003c/b\u003e. Participants access micro-learning content, delivered in 5-10-minute daily-available units incorporating videos, knowledge checks, and personal application activities. The initial modules provide training in evidence-based cognitive strategies from the Strategic Memory Advanced Reasoning Tactics (SMART) protocol. Developed by researchers at the Center for BrainHealth of the University of Texas at Dallas, SMART focuses on improving executive functions through training of nine strategies supporting three core cognitive skill areas: 1) strategic attention (e.g., reducing information intake, prioritizing focus, and allowing brain downtime), 2) integrated reasoning (e.g., synthesizing information and applying abstracted ideas), and 3) innovation (e.g., considering multiple perspectives and possibilities, generating novel solutions). These strategies draw upon frontally-mediated brain networks and can be applied to everyday activities and responsibilities in support of cognitive, emotional, and social well-being [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. SMART has also been shown through previous randomized trials to promote neural changes such as enhanced functional connectivity, cerebral blood flow, and neural efficiency [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Following SMART, training modules address stress management and resilience-building practices as well as sleep hygiene, integrating brain strategies with healthy lifestyle practices.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExtended Training\u003c/b\u003e. After completing the core training modules, participants access monthly themed micro-learning modules designed to reinforce the core training concepts and extend their application to broader areas of real-life application. Topics cover a broad range of applications, including aspects such as decision-making, gratitude practices, curiosity building, and fostering relationships, to name a few. Modules include interactive learning components, various media modalities, opportunities for personal reflection, and learnings about relevant brain-behavior relationships.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHabits\u003c/b\u003e. Each module is accompanied by a selection of specific and actionable habits to incorporate training concepts into daily routines. Just as modules range in focus from cognitive strategies to wellness or lifestyle practices, so do the habits. If a training segment focuses on single tasking (versus multi-tasking), a corresponding habit could be \u0026ldquo;Set aside 30- to 60-minute blocks of focused, uninterrupted time for your most important tasks today.\u0026rdquo; The BrainHealth Platform allows participants to receive daily habit reminders, track habit streaks, and earn digital badges.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResources\u003c/b\u003e. A curated collection of publicly available educational content, including research, media articles, and video lectures, etc., that relate to holistic brain health is regularly updated to support ongoing learning and engagement.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eBrain Health Coaching\u003c/h2\u003e\u003cp\u003e Throughout the study, participants can engage with one-on-one or group coaching sessions. Participants can schedule quarterly (every 3 months) 20-minute videoconference sessions with study personnel who serve as brain health coaches. In these one-on-one sessions, coaches provide personalized feedback on BHI results, guide strategy application, and/or assist with goal setting. Individual session summaries are saved in participants\u0026rsquo; dashboards for easy reference. In addition, study participants have access to monthly virtual group coaching sessions. These 45-minute group sessions use a workshop-style format, where coaches guide participants through personally relevant activities. These sessions aim to: 1) reinforce training concepts, 2) help participants develop specific next steps for applying the concepts in daily life, and 3) foster a shared sense of learning and community.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analyses\u003c/h2\u003e\u003cp\u003eFor the primary outcome measure, the BrainHealth Index (BHI), we calculated means and standard errors for the general pattern of longitudinal change from baseline to 3-year assessments along with change statistics for each 6-month testing point interval. To assess whether longitudinal trajectories over three years depended on initial baseline level, four levels of baseline measurements were defined by quartiles. Average gradients beyond the baseline means were fitted by weighted regression, where weights were determined by the reciprocal of the variances of the means, and gradients were compared over the four baseline levels using \u003cem\u003eF\u003c/em\u003e-statistics.\u003c/p\u003e\u003cp\u003eIn addition to long-term change, we were interested in determining whether utilization influenced the initial change from baseline and whether age, gender, or education level influenced potential differences among utilization groups. Utilization was defined by the completion rates of training and daily habits, participation in coaching calls, utilization of resources, and overall engagement with the platform. The distribution of the number of items for each variable was divided into quintiles from which ranks were assigned, and a utilization score was calculated as a sum of ranks. These ranged from 0 to 12. Finally, we defined levels of utilization by splitting the rank sums into the lower 25% (Low), middle 50% (Modest) and the upper 25% (High).\u003c/p\u003e\u003cp\u003eIndex measures were dependent variables in a linear mixed effects model which included effects of time (T1/baseline and T2/6-months), utilization group (low, modest, high \u0026ndash; defined above), age (continuous variable), gender (female, male), and all interactions involving time, group and age; all interactions involving time, group and gender; and all interactions involving time, group, and education. We modeled the error term as a variance component structure, a within-subject variance and a between-subject variance, since measurements within subjects are positively correlated. Our primary interest was the time/utilization interaction and whether age or gender influenced this interaction (i.e., both 3-way interactions).\u003c/p\u003e\u003cp\u003eA second tier of analyses, intended to assess the effect of utilization on index measures, considered only the sample of Low-level utilizers from the model described above. Over the course of six months (between T2/6-months and T3/12-months) some increased their engagement with the platform, switching to either modest or high utilization levels. From these new group designations (i.e., Low-\u0026gt;Low; Low-\u0026gt;Modest; and Low-\u0026gt;High), we ran the same mixed effects model as above and tested the time/utilization-change interaction between T1 and T3. All interaction contrasts from both models were assessed using \u003cem\u003et\u003c/em\u003e-statistics, and \u003cem\u003ep\u003c/em\u003e-values were Bonferroni-adjusted.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLongitudinal changes in brain health indices\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the general pattern of longitudinal change for the overall BrainHealth Index (BHI) score is one of monotonically increasing change throughout the 3-year study period, with significant growth from one testing interval to the next. When comparing baseline and 3-year timepoints (T7-T1), the 79.9-point overall gain was also highly significant (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, effect size\u0026thinsp;=\u0026thinsp;2.06). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that, with only a few exceptions, this general pattern of growth is seen regardless of initial baseline level. This was represented across the overall BHI score [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(A)] as well as for the three factors of Connectedness [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(B)], Emotional Balance [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(C)], and Clarity [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(C)].\u003c/p\u003e\u003cp\u003eFor baseline levels in the lowest quartile, the longitudinal gradient is greater than that in the other baseline levels (BHI: \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002; Connectedness: \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002; Emotional Balance: \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; Clarity: \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.068) based on 3 degree-of-freedom \u003cem\u003eF\u003c/em\u003e-statistics. However, to make sure that these gradients could not be solely explained by regression to the mean, we removed the lowest quartile gradient and compared the gradients of the upper three baseline quartiles with 2 degree-of-freedom \u003cem\u003eF\u003c/em\u003e statistics. With the exception of Emotional Balance, we see that they all have comparable gradients (BHI: \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.163; Connectedness: \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.092; Emotional Balance: \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002; Clarity: \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.597).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eImpact of utilization\u003c/h2\u003e\u003cp\u003eSee Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for results from the linear mixed effects model. Utilization had an impact on degree of change across all index measures. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows graded change across the three levels of utilization, with minimal change in those with low level of utilization and greatest change in those with high level of utilization. This interaction is significant for all indices (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, this general pattern of graded change as a function of utilization did not depend on gender or on age, as there were no significant three-way interaction effects between time, group, and gender or between time, group, and age on any of the scores. This suggests that changes in these scores across the utilization groups were not influenced by gender or age. Education had a small effect on this pattern, such that the modest utilization group had small increases over education levels.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLinear mixed effects model results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eBHI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eClarity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eEmotional Balance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eConnectedness\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003endf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eddf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eF-statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eF-statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eF-statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eF-statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime:Gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime:Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime:Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.022*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.007**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime:Group:Gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime:Group:Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime:Group:Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.044*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime:Group change\u003c/p\u003e\u003cp\u003e(T1 to T3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.002**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.003**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.003**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eNotes: Group\u0026thinsp;=\u0026thinsp;utilization category; * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e- INSERT Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e -\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePost-hoc analysis using pairwise comparison (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that, compared to low utilization, high utilization led to a significantly greater change between Time 1 and Time 2 (6 months) in the overall BHI score, as well as in the scores for Clarity, Emotional balance, and Connectedness. Modest utilization resulted in a significantly greater change in the overall BHI score and the Connectedness score, compared to low utilization. High utilization also led to significantly greater BHI score change than modest utilization. These results suggested that score changes from T1 to T2 varied depending on the level of utilization. The mean score changes across the board are lowest for low utilization and highest for high utilization. The overall BHI and Connectedness scores exhibited more dramatic score differences among the groups than those for Clarity and Emotional balance (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults from post-hoc analysis using pairwise comparison, including utilization group contrasts of T2-T1 change (6 months) and low utilizers\u0026rsquo; T3-T1 change (1 year)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"24\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eBHI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u003cp\u003eClarity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c19\" namest=\"c14\"\u003e\u003cp\u003eEmotional Balance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c24\" namest=\"c20\"\u003e\u003cp\u003eConnectedness\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUtilization group Contrasts of T2 \u0026ndash; T1 Change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003et-statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u003cem\u003eBonferroni-adjusted p-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eeffect size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e\u003cem\u003et-statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e\u003cem\u003eBonferroni-adjusted p-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eeffect size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cem\u003et-statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u003cem\u003eBonferroni-adjusted p-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e\u003cp\u003eeffect size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e\u003cem\u003et-statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e\u003cp\u003e\u003cem\u003eBonferroni-adjusted p-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e\u003cp\u003eeffect size\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHigh \u0026ndash; Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e5.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e.017*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e.008**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e6.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eModest - Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e.016*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHigh - Modest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e3.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e.002**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e.018*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e\u003cp\u003e.007**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUtilization Group change Contrasts of T3 \u0026ndash; T1 Change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003et-statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cem\u003eBonferroni-adjusted p-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eeffect size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003et-statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e\u003cem\u003eBonferroni-adjusted p-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eeffect size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e\u003cem\u003et- statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e\u003cem\u003eBonferroni-adjusted p-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eeffect size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c21\" namest=\"c19\"\u003e\u003cp\u003e\u003cem\u003et- statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u003cp\u003e\u003cem\u003eBonferroni-adjusted p-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c24\"\u003e\u003cp\u003eeffect size\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLow_High \u0026ndash; Low_Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e.025*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.018*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c21\" namest=\"c19\"\u003e\u003cp\u003e2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u003cp\u003e.037*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c24\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLow Modest \u0026ndash; Low_Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e.003**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.003**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c21\" namest=\"c19\"\u003e\u003cp\u003e3.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u003cp\u003e.005**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c24\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLow_High \u0026ndash; Low_Modest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c21\" namest=\"c19\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u003cp\u003e.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c24\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"24\"\u003eNote: All \u003cem\u003ep\u003c/em\u003e-values are Bonferroni-adjusted; * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e also show the effect of utilization change over time for those who were initially low-level utilizers. Those who switched to higher-level utilization over the following six months had higher mean changes on the indices through one year (see also Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Conversely, those who remained low-level utilizers had no change on their brain health indices through one year.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e- INSERT Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e -\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eFour Case Illustrations\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents four brief case examples which highlight the dynamic nature of individual brain health trajectories and demonstrate how the brain health factors can guide individuals in setting goals to maintain an overall Index score. By linking these insights to personalized goals, the cases illustrate the utility of the BrainHealth Index in promoting specific, actionable strategies for sustained growth and optimization across varying life contexts. In particular, these cases underscore the distinctive insights derived from the BrainHealth Index, enabling a precision approach to brain health.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study addresses the major question of whether brain health can be improved over a 3-year period through engagement with a scalable online training platform. The results show that this is indeed the case for the study\u0026rsquo;s participants, signaling a shift from a prevailing narrative of inevitable, progressive brain decline to one of potential growth and development across the adult lifespan.\u003c/p\u003e\u003cp\u003eThis research examined three key questions. First, we investigated whether the online-delivered BrainHealth Index (BHI), offered every six months, effectively tracks changes in brain health \u0026ndash; both gains and losses \u0026ndash; over a 3-year interval. Intrinsic to this question, we examined the effectiveness of online-delivered brain health tools, centered around strategy-based executive function training (SMART), in promoting brain health across adults of all ages. Second, we examined the characteristics of participants who demonstrated brain health gains versus those who did not, considering baseline brain health score, age, education, and gender. Finally, we examined the role of utilization in optimizing BHI performance over time. The findings are summarized below, along with their implications for extending healthy brain function. Taken together, these findings inform 1) \u003cem\u003ewhat\u003c/em\u003e is possible in terms of measuring and enhancing brain health, 2) \u003cem\u003ewho\u003c/em\u003e is likely to benefit from this proactive brain health approach, 3) \u003cem\u003ehow\u003c/em\u003e improvements were likely achieved, and 4) \u003cem\u003ewhy\u003c/em\u003e brain health promotion is a promising pathway to support overall health outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhat\u003c/b\u003e \u003cb\u003ecan measure and promote increase in brain health span\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFirst, this study demonstrated that the online BrainHealth Index is sensitive to measuring and tracking changes (gains and losses) in brain health trajectory over time. In a large prospective sample of adults ages 19\u0026ndash;94, the majority of participants who took advantage of accessing and engaging with the brain health tools and strategies showed significant gains over three years. The data suggest the BHI was informative in evaluating the efficacy of ongoing \u003cem\u003eonline\u003c/em\u003e delivery of micro-learning brain health modules, with comparable results to those previously reported [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] as well as those achieved through previous studies utilizing \u003cem\u003ein-person\u003c/em\u003e training delivery [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, the BHI showed viability in capturing the complex, multidimensional nature of brain health [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], with upward trajectories observed in both the holistic score as well as its three contributing factor scores (Clarity, Connectedness, and Emotional Balance). These trends were visualized at the group level (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) as well as illustrated more dynamically at an individual level (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), supporting its use in precision brain health.\u003c/p\u003e\u003cp\u003eBoth the composite BHI and its factors provided valuable insights regarding the course of change. These measures showed sensitivity to charting dynamic trajectories of upward growth (Case #1), general longer-term maintenance (Case #2), episodic declines in particular contributing subcomponents (Case #3), and/or a downward trend (Case #4). By allowing users to track and chart their personal brain health trends through an accessible platform, the BHI can serve as a motivational tool to implement simple steps into their everyday life activities to potentially detect and regain declining capacities at the earliest time possible. Our team is continuing to refine and validate the BrainHealth Index with neural markers from functional magnetic resonance imaging (fMRI) analyses of associated changes, including markers that show promise in predicting performance gains on the behavioral measures comprising the BrainHealth Index [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe BrainHealth Index\u0026rsquo;s utility extends beyond individual tracking; it offers a promising metric for evaluating brain health interventions. Preliminary investigations are underway to evaluate the sensitivity of the Index and/or some of its subcomponents in measuring gains following other intervention protocols that may improve brain health, including other cognitive training protocols such as BrainHQ (Posit Science, 2024), medical interventions such as Hormone Replacement Therapy, and lifestyle interventions such as physical fitness training. Future efforts should continue exploring its potential applications in medical, mental health, and preventive care contexts.\u003c/p\u003e\u003cp\u003eOne paramount question to address is what aspects contributed to the observed brain health gains. The present findings demonstrate that sustained improvements were achievable, regardless of an individual\u0026rsquo;s starting level, when given continual and easy access to brain health training, content, and practices. Participants in each quartile, based on baseline BHI, showed incremental gains over time, without evidence of ceiling effects. We propose that the strategy-based executive function training (SMART) equipped participants with metacognitive tools that could be applied to support meaningful, real-life demands to facilitate ongoing improvements. SMART has shown to promote generalized gains by engaging higher cognitive functions applicable across various contexts [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Evidence from SMART and similar programs demonstrate broad benefits, including enhanced executive function, psychological well-being, and neural connectivity, particularly within the brain\u0026rsquo;s frontal networks, across both clinical and non-clinical populations [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Strategy-based interventions stand out for their ability to generalize to multiple domains of daily function [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], whereas bottom-up approaches, such as speed-of-processing training, show domain-specific gains [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These findings highlight the potential for integrating top-down and bottom-up approaches to tailor brain health interventions to individual needs and optimize outcomes across various life stages.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWho\u003c/b\u003e \u003cb\u003ebenefits from the proactive brain health protocols\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNotably, overall brain health gains in this study cohort were independent of age, education level, gender, or starting level of brain health performance, yielding two somewhat unexpected findings. First, we found that younger people generally benefited as much as older participants. This challenged a commonly held perspective that older individuals would likely benefit more, given that they have typically experienced more decline, and young adults would have little to gain. This reinforces the idea that adults can optimize their brain health at any age and supports the recommendation to begin proactive brain health practices early in adulthood and continue throughout the adult lifespan. As with physical health, it is easier to preserve and build brain function than to regain lost ground after substantial decline has occurred.\u003c/p\u003e\u003cp\u003eSecond, we found that those in the lowest BHI performance quartile at baseline made greater gains over time than those in the upper quartiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). While it is likely that this finding may partly represent statistical regression to the mean, it is clear that lower baseline brain health can be improved at least as much as higher baseline levels. Many would say that this is intuitive \u0026ndash; given that generally those with the greatest room for improvement often show the biggest changes. However, there is a common misconception that lower performance from the onset is somehow fixed in nature and thus linked to poorer long-term outcomes and limited ability to close the gap with higher performers. The results of greater gains in lower performers over the three years speaks to the likely contribution of the brain\u0026rsquo;s inherent neuroplasticity and its capacity to continuously improve and be strengthened, given proper practices (i.e., experiential neuroplasticity) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Moreover, we did not see that the highest BHI performance quartile made the least gains, which would be predicted by regression to the mean. The gradients of the upper three quartiles were statistically similar. The only exception to this is for the factor of Emotional Balance, which we attribute to being more limited in continued growth potential.\u003c/p\u003e\u003cp\u003eWe were not surprised by the evidence that females and males showed comparable gains. Nor was the finding of no significant educational effect unforeseen, given that we had a relatively well-educated group, with less than 15% having lower than a bachelor\u0026rsquo;s-level education. These gender and education findings are also consistent with prior findings from our first study cohort [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The relatively high education of our group highlights one major limitation of the present study, in that the results may not generalize to less educated individuals. We are actively working to recruit more participants from less educated backgrounds.\u003c/p\u003e\u003cp\u003eIn sum, our results lend support to the universal applicability of proactive strategy-based executive function training to promote brain health. The comparable gains across age groups and education levels represented in our sample suggest that key demographic factors which often predict disparities in health outcomes and/or responsiveness to health-focused interventions may not face the same barriers when it comes to increasing brain health span [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. From a public health perspective, these findings reinforce the potential for brain health strategies to benefit diverse populations, regardless of starting level.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHow\u003c/b\u003e \u003cb\u003ecan brain health gains be achieved\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven the single-arm design of the study, the first hypothesis to explain the observed results must be that the improvements in brain health were simply practice effects, with repeated exposure to the BrainHealth Index leading to better performance over time. While practice can enhance performance, this cannot account for the observed improvements for two reasons. First, participants with generally low utilization who repeatedly completed the Index did \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003enot\u003c/span\u003e show significant gains. Next, we have shown in previous research that taking the BrainHealth Index multiple times without engaging in the training did not yield similar improvements to those in the current study [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA second hypothesis is that participants improved simply due to non-specific factors, such as the mere attention given to them and that their expectations were to improve brain health, regardless of specific training. This explanation is countered by evidence from prior clinical trials of our cognitive training where participants in active control conditions (e.g., psychoeducation or physical exercise training) did not improve on measures from the major Clarity factor of the BHI when given equal attention [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These findings reinforce the conclusion that active skill- and habit-building, rather than passive expectations, drive meaningful brain health gains.\u003c/p\u003e\u003cp\u003eA third potential mechanism is that of a direct effect of specific metacognitive strategies and associated habits in allowing participants to meet their daily life demands, combined with implementing advice about nurturing social connectedness and maintaining emotional balance. The four case studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) give numerous personal examples of such a mechanism. More generally, higher utilization entailed consistent engagement with the online tools \u0026ndash; assessments, coaching sessions, strategy-based micro-learnings, and habit-tracking \u0026ndash; and was associated with greater gains in brain health over the 3-year follow-up period. These tools were designed for \u0026ldquo;bite-size\u0026rdquo; delivery, requiring minimal time (no more than 15 minutes within a single day), to motivate such continued use and optimize overall learning and application.\u003c/p\u003e\u003cp\u003eEncouragingly, utilization was not necessarily static: among participants with low utilization in the first 6-month cycle (n\u0026thinsp;=\u0026thinsp;507), 63.12% went on to increase their engagement over time (i.e., shifted to a higher utilization category). This increase in utilization further supports the strategy-and-habit-learning hypothesis rather than a more non-specific global expectation mechanism to explain the improvements in brain health.\u003c/p\u003e\u003cp\u003eA final hypothesis concerns the possibility that learning strategies to control one\u0026rsquo;s brain function and brain health makes participants feel more \u003cem\u003eempowered\u003c/em\u003e and \u003cem\u003ein control\u003c/em\u003e \u0026ndash; in other words, they develop greater \u003cem\u003eself-agency.\u003c/em\u003e This hypothesis is entirely compatible with the previous one \u0026ndash; indeed we consider both the utilization and implementation of specific strategies and habits, leading in turn to increased feelings of self-agency \u0026ndash; and continuing in a mutually reinforcing cycle \u0026ndash; as being the most likely mechanisms underlying the observed 3-year improvements in brain health. Research shows that simply inducing a person to think abstractly increases their sense of power [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Central to our training are metacognitive techniques such as \u0026lsquo;zooming out\u0026rsquo; from a particular situation or problem to widen one\u0026rsquo;s perspective to take a more abstract view of it. This is a technique used, for example, by Case 3 above (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Metacognitive strategies are the essence of abstract thinking, and we hypothesize that engaging in these strategies increases empowerment and hence a sense of self-agency.\u003c/p\u003e\u003cp\u003eWe therefore interpret the observed strong relationships between utilization and improvements in brain health as being due to the mutual reinforcement of utilization of key strategies and habits, leading to an increasing self-agency. As such, increases in self-agency works to further boost utilization, as shown by the low utilizers shifting to higher levels of utilization. By this argument, utilization is a form of self-agency in action. The executive function skills learned during the SMART training \u0026ndash; which have been shown to improve brain health in previous, smaller randomized controlled trials [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] - give participants self-agency over their own attention, thought, and emotion processes. This sense of control over their own brain processes should increase self-agency more generally.\u003c/p\u003e\u003cp\u003eIn support of this hypothesis, research shows that cognitive training increases self-agency in older people. In a recent study of over 12,000 older U.S. adults found that individuals with greater sense of control at the onset of the study had improved physical health outcomes over the study\u0026rsquo;s 4-year follow-up period [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Moreover, a stronger sense of control was associated with greater engagement in health-promoting behaviors as well as higher outcomes in multiple aspects of well-being and social connectedness [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSimply knowing which actions to take \u0026ndash; through awareness and education for example \u0026ndash; however, is insufficient to drive the lasting behavior change necessary to reduce risks and promote health [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. We propose that a key component of self-agency is its link to utilization of potent metacognitive strategies for controlling brain processes \u0026ndash; what we have called \u003cem\u003eself-agency in action\u003c/em\u003e [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This ability to translate intention into action is intrinsically linked to executive function, a set of cognitive processes that enables individuals to regulate thoughts, actions, and emotions in pursuit of goals [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], and which is trained in the current project\u0026rsquo;s online SMART program. Importantly, executive functions can be strengthened through training and, as our data suggest, can result in improved brain health over an extended period of time [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese findings motivate further research and clinical efforts to explore strategies for fostering self-agency in brain health promotion. We propose that individuals are more likely to show sustained improvement when they 1) perceive that they can impact their brain health, 2) are empowered to take action, 3) equipped with executive function tools and 4) have ready access to technology-driven nudges to continually reinforce and practice brain health strategies and habits. Importantly, low utilization of the brain health tools could not be explained by lower baseline performance. Indeed, those in the lowest baseline quartile manifested the highest gradient in enhancing their brain health span. Further research must unpack the triadic relationship between executive function, strategy-and-habit utilization, and self-agency so that the potency of brain health training can be further increased. Just as has been shown in physical health, individual self-agency in symbiotic relationship with executive function and utilization, is likely a pivotal factor in maintaining and improving brain health, fostering autonomy and resilience in navigating daily challenges.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhy\u003c/b\u003e \u003cb\u003ebrain health promotion is imperative\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary health significance of this study is its demonstration that the complex construct of \u0026ldquo;brain health\u0026rdquo; can both be longitudinally measured and enhanced, along with its contributing factors, across the adult lifespan, in independently functioning individuals who are relatively healthy or may have some degree of compromised brain health. The current findings motivate efforts to integrate brain health metrics into clinical and research settings to advance health promotion strategies. In sum, the BrainHealth Index shows promise as a novel, validated, change-sensitive tool capable of tracking both growth and decline in brain health over time, filling a critical void in available brain health measures. Not only is the BrainHealth Index holistic with its contributing subfactors, but it is also scalable and wellness driven.\u003c/p\u003e\u003cp\u003eUntil now, most efforts to assess brain health have employed measures designed primarily for diagnosis or deficit detection, relying on normative comparisons and threshold-based criteria. While such measures are essential for identifying impairments, they fail to recognize that brain health is more than just the absence of brain compromise. Additionally, existing assessments tend to be screening-level scales or measure isolated pillars of brain health such as cognition [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], socialization [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], mental health [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], physical fitness [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], or sleep [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] \u0026ndash; without capturing how these components work together to support overall brain health. This fragmented approach overlooks the dynamic and interrelated nature of brain health, whereas our findings suggest that incremental changes (gains or losses) in the personal brain-behavioral profile can be regularly monitored and managed using a platform that can be accessed remotely through online technology, including mobile apps.\u003c/p\u003e\u003cp\u003eResults from this study demonstrated the utility of a holistic, wellness-driven brain health measurement that is repeatable over time and actionable. The present findings advance our understanding of brain health \u003cem\u003epromotion\u003c/em\u003e in two significant ways: 1) demonstrating the potential of a strategy-based executive function approach to facilitate behavior change and promote generalized, sustained benefits across multiple life domains and throughout adulthood and 2) underscoring the importance of leveraging accessible, tele-delivered tools to motivate self-agency of action and facilitate tailored application.\u003c/p\u003e\u003cp\u003eBeyond its impact on brain health, this study highlights the broader implications for overall health promotion. For instance, prior work with a large longitudinal sample (n\u0026thinsp;=\u0026thinsp;10,855) of working-age adults in Finland demonstrated that not only can improved health behaviors promote gains in subjective well-being, but that this can be a bidirectional relationship, such that enhanced well-being can also further reinforce long-term health behaviors [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Optimizing brain health may serve as a foundation for holistic, lifelong health improvements.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and future directions\u003c/h2\u003e\u003cp\u003eThis study has a number of limitations that should be considered when interpreting the findings. First, we acknowledge this is a single-arm interventional study versus a randomized control trial. At this stage of science, we propose that prior evidence supports that all participants should have access to the intervention protocols since the intervention does no harm and has shown to benefit most. Specifically, prior randomized clinical trials (with combined totals of \u0026gt;\u0026thinsp;100 participants) using the SMART intervention have yielded findings demonstrating its effectiveness in promoting the types of gains observed, including evidence of corresponding neural changes [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The BrainHealth Project\u0026rsquo;s approach allows a way to address the duration of the gains or detect early declines at an individual level. Additionally, while a large study cohort has been enrolled to date, demographic diversity has been somewhat limited, most notably in racial, ethnic, and education level representation, which may limit the generalizability of the current findings to broader populations. Another limitation is the inclusion of some self-reported data comprising the BrainHealth Index assessment. Self-report is subject to reporting bias. Moreover, the current collected data lack comprehensive information regarding participants\u0026rsquo; medical histories and concurrent treatments that may impact an individual\u0026rsquo;s brain health trajectory. Efforts are underway to expand and enhance recruitment strategies to better reflect overall population demographics (age, sex/gender, race/ethnicity, level of education) as well as incorporate objective metrics of physical activity and sleep (e.g., wearable devices), medical history, and expand ongoing investigation linking behavioral outcomes to neural changes through neuroimaging or other biomarkers. Furthermore, as with many online longitudinal studies, study attrition is a limitation \u0026ndash; averaging 43% for this study cohort over the three years. This represents a comparable rate to what has been shown in previous longitudinal, population-based studies involving internet-based/eHealth platforms [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Increased attrition and lack of adherence have been longstanding barriers to successful health-behavior interventions, in particular [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Efforts to enhance engagement and long-term participant retention, such as more personalized follow-ups and community-building strategies, are underway.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGiven the brain’s central role in shaping who we are and how we function, these results emphasize the potential gains to the human brain health span achieved through promoting brain health from young adulthood to older ages with personalized and participatory practices. Science is clear that the time is urgent to expand medical practice and public health approaches to move away from a predominant brain disease focus to a public health imperative that delivers brain health protocols. Such an approach will enable more people to realize their full potential over their life course, regardless of the presence or absence of brain issues. Health care providers can be on the forefront of brain health promotion by 1) adopting proven ways to \u003cem\u003emeasure\u003c/em\u003e growth in brain skills whatever one’s starting point and by 2) guiding their patients to protocols which will most \u003cem\u003eenhance\u003c/em\u003e their brain skills with simple strategies, requiring self-agency of action, as there is no “magic brain pill.” This effort parallels the advancements made in heart health over the past six decades, with the goal of optimizing each individual’s peak brain health span to match increasing longevity. Achieving this will require utilization of technology that provides a way to scale and advance access to precision brain health to measure, monitor, and nudge healthier behaviors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, we propose that the potential societal and economic benefits of advancing brain health are significant. Proactively building stronger brain health and performance not only promotes individual well-being but also offers the prospect of reduced healthcare costs and increased productivity. As such, future research should explore the broader impact of scalable, precision brain health approaches on societal outcomes. Brain health promotion can not only extend the brain health span but can also support healthier, more fulfilling lives across the lifespan. This achievement will be possible if we make brain health a public health imperative – focusing on neuroplasticity, self-agency, and proactive growth while shifting from a reactive, deficit-based model. Our future depends on extending our brain health spans.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets used and analyzed in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express sincere gratitude to the coaches of the BrainHealth Project for guiding and encouraging participants with exceptional care and skill: Tandra Allen, Katie Hinds, Janet Koslovsky, Sarah Laane, Marco Lopez, Kalyn Potter, Audette Rackley, Colleen Ryan, Stacy Vernon, Jennifer Zientz. Additionally, we must thank our skilled technology, data management, and support team, including Margaret Chaplin, Sonal Jain, Bryan Vosburg, Haider Naeem, Sameena Shaik, Dinesh Sharma, Mahanaz Attila, Radi Tawfiq, and the team at Dialexa for their tremendous work on the online platform. Finally, we are extremely grateful to the participants who choose to be pioneers in brain health and invest their time in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe BrainHealth Project is currently funded by private philanthropy, including Sammons Enterprises, Inc. and the Hoglund Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLC collected and scored data, interpreted results, and wrote and revised the manuscript. JSS performed analyses, interpreted results, and wrote and edited the manuscript. ZC performed analyses, interpreted results, and contributed to the manuscript. EEV assisted with the design and content of the training protocol, collected and scored data, and contributed to the manuscript. AT supervised and contributed to the development of the online platform and training modules and collected data. IHR contributed content to the online training modules, advised on some of the measures comprising the BrainHealth Index, interpreted results, and contributed to the manuscript. MD’E and JW provided critical revision of the manuscript. GSFL assisted with study design. SBC designed the study, interpreted results, and wrote and edited the manuscript. All authors reviewed and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe system and method for precision brain health assessment is patent pending. Specifically, the Board of Regents for The University of Texas System have applied for a patent (Application Number: WO2025029876A2) that includes the BrainHealth Index and the online platform. The status is currently pending. Inventors are (including several of the study authors): \u0026nbsp;Aaron M. Tate, Julie Fratantoni, Stephen B. White, Sandra Chapman, Jeff Spence, Jennifer Zientz, Erin Venza, Lori Cook.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHachinski, V. \u0026amp; Gorelick, P. B. Brain health as a global priority. \u003cem\u003eJ. Neurol. Sci.\u003c/em\u003e\u003cstrong\u003e434\u003c/strong\u003e, 120166 (2022). https://doi.org/10.1016/j.jns.2022.120166. \u003c/li\u003e\n\u003cli\u003eRost, N. 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Med.\u003c/em\u003e\u003cstrong\u003e29\u003c/strong\u003e, 185\u0026ndash;193 (2005). https://doi.org/10.1016/j.amepre.2005.06.004. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"brain health, cognitive training, digital health, health behaviors, precision care, prevention","lastPublishedDoi":"10.21203/rs.3.rs-6264411/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6264411/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExtending \u003cem\u003ebrain health\u003c/em\u003e span \u0026ndash; maintaining or improving cognitive, social, and emotional well-being \u0026ndash; is critical to aligning health span with lifespan. This study examines 3-year outcomes from 3,966 adults (ages 19\u0026ndash;94) in the BrainHealth Project, an online initiative integrating the BrainHealth Index (BHI) with cognitive training, lifestyle modules, and coaching. The BHI, assessed biannually, provides a multidimensional measure across factors of Clarity (cognitive function), Connectedness (social and purpose-driven engagement), and Emotional Balance (mental well-being).\u003c/p\u003e\u003cp\u003eResults demonstrate sustained improvements in overall BHI and component factors, independent of baseline scores. Higher engagement with training tools \u0026ndash; strategy-based learning, coaching, and brain-healthy habits \u0026ndash; was associated with the greatest gains, underscoring the role of self-agency in brain health optimization. Improvements were observed across demographic groups, suggesting benefit regardless of age, gender, or education level.\u003c/p\u003e\u003cp\u003eFindings support the potential for scalable, technology-driven interventions to help reduce years of cognitive decline while maximizing brain performance across the lifespan. Future efforts should focus on improving demographic diversity and retention strategies as well as integrating precision brain health approaches into public health initiatives.\u003c/p\u003e","manuscriptTitle":"Measuring and Increasing the Brain Health Span across Adulthood: A Public Health Imperative","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 06:58:31","doi":"10.21203/rs.3.rs-6264411/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e45c84a6-2a06-4ac5-888a-4b93b25d58c1","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52821670,"name":"Health sciences/Health care/Public health"},{"id":52821671,"name":"Biological sciences/Neuroscience/Cognitive ageing"},{"id":52821672,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Cognitive control"},{"id":52821673,"name":"Health sciences/Health care/Disease prevention/Lifestyle modification"},{"id":52821674,"name":"Health sciences/Health care/Disease prevention/Preventive medicine"}],"tags":[],"updatedAt":"2025-11-23T05:05:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-08 06:58:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6264411","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6264411","identity":"rs-6264411","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}