Participation by design: Integrating a social ecological approach with universal design to increase participation and add value for consumers.

BACKGROUND: Physical activity is an essential component of a healthy lifestyle. Health clubs encourage sustained healthy lifestyles but are still largely not accessible to people with disabilities. Cost is a barrier for accessibility enhancements. HYPOTHESIS: We postulate that: (A) universal design coupled with a social ecological approach improves measured accessibility compared with existing fitness facilities constructed since the adoption of the ADA; (B) increased accessibility coupled with an environment friendly to people with disabilities attracts more participants to a YMCA than predicted by traditional industry market research producing a recovery of the cost of increased accessibility; and (C) attitudes of facility members toward people with disabilities may improve if an accessible facility facilitates more personal interactions between people with and without disabilities. METHODS: Accessibility is measured with the Accessibility Instruments Measuring Fitness and Recreation Environments (AIMFREE). Cost recovery is determined by comparing excess membership revenue to the cost of universal design elements beyond regulatory requirements, and attitudes toward people with disabilities are measured with the Attitudes Toward Disabled Persons Scale. RESULTS: AIMFREE scores were significantly higher than comparison facilities in all areas except for equipment, parking, training, and programs. Excess revenue exceeded the extra cost of accessibility enhancements and attitudes toward people with disabilities did not change. CONCLUSIONS: Universal design coupled with a social ecological approach improves accessibility in fitness facilities and results in a reasonable payback time. Attitudes toward people with disabilities did not change in a YMCA designed to accommodate people with disabilities.

Data-science ready, multisite, human diffusion MRI white-matter-tract statistics.

The white matter tracts in the living human brain are critical for healthy function, and the diffusion MRI measured in these tracts is correlated with diverse behavioral measures. The technical skills required to analyze diffusion MRI data are complex: data acquisition requires MRI sequence development and acquisition expertise, analyzing raw-data into meaningful summary statistics requires computational neuroimaging and neuroanatomy expertise. The human white matter study field will advance faster if the tract summaries are available in plain data-science-ready format for non-diffusion MRI experts, such as statisticians, computer graphic researchers or data scientists in general. Here, we share a curated and processed dataset from three different MRI centers in a format that is data-science ready. The multisite data we share include measures of within and between MRI center variation in white-matter-tract diffusion measurements. Along with the dataset description and summary statistics, we describe the state-of-the-art computational system that guarantees reproducibility and provenance from the original scanner output.

Replication and generalization in applied neuroimaging.

There is much interest in translating neuroimaging findings into meaningful clinical diagnostics. The goal of scientific discoveries differs from clinical diagnostics. Scientific discoveries must replicate under a specific set of conditions; to translate to the clinic we must show that findings using purpose-built scientific instruments will be observable in clinical populations and instruments. Here we describe and evaluate data and computational methods designed to translate a scientific observation to a clinical setting. Using diffusion weighted imaging (DWI), Wahl et al. (2010) observed that across subjects the mean fractional anisotropy (FA) of homologous pairs of tracts is highly correlated. We hypothesize that this is a fundamental biological trait that should be present in most healthy participants, and deviations from this assessment may be a useful diagnostic metric. Using this metric as an illustration of our methods, we analyzed six pairs of homologous white matter tracts in nine different DWI datasets with 44 subjects each. Considering the original FA measurement as a baseline, we show that the new metric is between 2 and 4 times more precise when used in a clinical context. Our framework to translate research findings into clinical practice can be applied, in principle, to other neuroimaging results.

Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging.

Here, we describe a quantitative neuroimaging method to estimate the macromolecular tissue volume (MTV), a fundamental measure of brain anatomy. By making measurements over a range of field strengths and scan parameters, we tested the key assumptions and the robustness of the method. The measurements confirm that a consistent quantitative estimate of MTV can be obtained across a range of scanners. MTV estimates are sufficiently precise to enable a comparison between data obtained from an individual subject with control population data. We describe two applications. First, we show that MTV estimates can be combined with T1 and diffusion measurements to augment our understanding of the tissue properties. Second, we show that MTV provides a sensitive measure of disease status in individual patients with multiple sclerosis. The MTV maps are obtained using short clinically appropriate scans that can reveal how tissue changes influence behavior and cognition.