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. 2021 Mar 16:9:641754.
doi: 10.3389/fpubh.2021.641754. eCollection 2021.

A Novel BrainHealth Index Prototype Improved by Telehealth-Delivered Training During COVID-19

Sandra Bond Chapman  1 , Julie M Fratantoni  1 , Ian H Robertson  1   2 , Mark D'Esposito  3 , Geoffrey S F Ling  4 , Jennifer Zientz  1 , Stacy Vernon  1 , Erin Venza  1 , Lori G Cook  1 , Aaron Tate  1 , Jeffrey S Spence  1
Affiliations

A Novel BrainHealth Index Prototype Improved by Telehealth-Delivered Training During COVID-19

Sandra Bond Chapman et al. Front Public Health. .

Abstract

Introduction: Brain health is neglected in public health, receiving attention after something goes wrong. Neuroplasticity research illustrates that preventive steps strengthen the brain's component systems; however, this information is not widely known. Actionable steps are needed to scale proven population-level interventions. Objectives: This pilot tested two main objectives: (1) the feasibility/ease of use of an online platform to measure brain health, deliver training, and offer virtual coaching to healthy adults and (2) to develop a data driven index of brain health. Methods: 180 participants, ages 18-87, enrolled in this 12-week pilot. Participants took a BrainHealth Index™ (BHI), a composite of assessments encompassing cognition, well-being, daily-life and social, pre-post training. Participants engaged in online training with three coaching sessions. We assessed changes in BHI, effects of training utilization and demographics, contributions of sub-domain measures to the BHI and development of a factor analytic structure of latent BrainHealth constructs. Results: The results indicated that 75% of participants showed at least a 5-point gain on their BHI which did not depend on age, education, or gender. The contribution to these gains were from all sub-domains, including stress, anxiety and resilience, even though training focused largely on cognition. Some individuals improved due to increased resilience and decreased anxiety, whereas others improved due to increased innovation and social engagement. Larger gains depended on module utilization, especially strategy training. An exploratory factor analytic solution to the correlation matrix of online assessments identified three latent constructs. Discussion/Conclusion: This pilot study demonstrated the efficacy of an online platform to assess changes on a composite BrainHealth Index and efficacy in delivering training modules and coaching. We found that adults, college age to late life, were motivated to learn about their brain and engage in virtual-training with coaching to improve their brain health. This effort intends to scale up to thousands, thus the pilot data, tested by an impending imaging pilot, will be utilized in ongoing machine learning (ML) algorithms to develop a precision brain health model. This pilot is a first step in scaling evidence-based brain health protocols to reach individuals and positively affect public health globally.

Keywords: brain health; digital health; mental health; neuroplasticity; pandemic; personalized care; prevention; resilience.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Participant recruitment and retention.
Figure 2
Figure 2
Study timeline.
Figure 3
Figure 3
BrainHealth component wheel.
Figure 4
Figure 4
Preliminary BrainHealth Index at baseline for age, education, and gender. The BHI distributions were similar across these demographic categories.
Figure 5
Figure 5
(A) Change in the preliminary BrainHealth Index after 3 months with a mean gain of 10.3 units. (B) Regression of the change in the BrainHealth Index on age by gender (shown in gray scale) shows that gains do not depend on either of these attributes.
Figure 6
Figure 6
Regression of the change in the BrainHealth Index on the number of cumulative training modules completed by the participant. The first seven modules constitute interventional training; the remaining two are informational only.
Figure 7
Figure 7
Boxplots of change measures for each of the components of the BrainHealth wheel. Each measure was scaled for presentation on a common axis. *The measures for sleep, depression, anxiety, and stress are shown with opposite sign.
Figure 8
Figure 8
Correlation matrix for the measures from the BrainHealth wheel domains, obtained by online assessments. Across-domain correlations are relatively strong and contribute to latent factors of brain health that incorporate these extended relationships.
Figure 9
Figure 9
Eigenvalue plot showing unadjusted eigenvalues (in red) and adjusted eigenvalues (in black and white). The factor solution was based on three retained (three adjusted eigenvalues greater than zero). Adjustments were based on the 95th percentile of a random eigenvalue distribution (in blue).
Figure 10
Figure 10
Two case studies.
See this image and copyright information in PMC

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