Interactive balance training integrating sensor-based visual feedback of movement performance: a pilot study in older adults.

BACKGROUND: Wearable sensor technology can accurately measure body motion and provide incentive feedback during exercising. The aim of this pilot study was to evaluate the effectiveness and user experience of a balance training program in older adults integrating data from wearable sensors into a human-computer interface designed for interactive training. METHODS: Senior living community residents (mean age 84.6) with confirmed fall risk were randomized to an intervention (IG, n = 17) or control group (CG, n = 16). The IG underwent 4 weeks (twice a week) of balance training including weight shifting and virtual obstacle crossing tasks with visual/auditory real-time joint movement feedback using wearable sensors. The CG received no intervention. Outcome measures included changes in center of mass (CoM) sway, ankle and hip joint sway measured during eyes open (EO) and eyes closed (EC) balance test at baseline and post-intervention. Ankle-hip postural coordination was quantified by a reciprocal compensatory index (RCI). Physical performance was quantified by the Alternate-Step-Test (AST), Timed-up-and-go (TUG), and gait assessment. User experience was measured by a standardized questionnaire. RESULTS: After the intervention sway of CoM, hip, and ankle were reduced in the IG compared to the CG during both EO and EC condition (p = .007-.042). Improvement was obtained for AST (p = .037), TUG (p = .024), fast gait speed (p = . 010), but not normal gait speed (p = .264). Effect sizes were moderate for all outcomes. RCI did not change significantly. Users expressed a positive training experience including fun, safety, and helpfulness of sensor-feedback. CONCLUSIONS: Results of this proof-of-concept study suggest that older adults at risk of falling can benefit from the balance training program. Study findings may help to inform future exercise interventions integrating wearable sensors for guided game-based training in home- and community environments. Future studies should evaluate the added value of the proposed sensor-based training paradigm compared to traditional balance training programs and commercial exergames. TRIAL REGISTRATION: http://www.clinicaltrials.govNCT02043834.

Deviations from the diffusion tensor model as revealed by contour plot visualization using high angular resolution diffusion-weighted imaging (HARDI).

The standard diffusion tensor model is limited in its ability to provide detailed information about multidirectional fiber architecture in human white matter. Additional directional acquisition of diffusivity properties with high angular resolution diffusion-weighted imaging (HARDI) acquisition schemes may deliver more information on areas with fiber crossings than standard DTI. However, representation of the additional information and the rating and visualization of fiber complexity is challenging. We used projection 2D-plots in combination with a HARDI acquisition scheme of 129 diffusion directions and compared the spherical diffusion variance index (SDI) with the relative anisotropy index (RAI). In normal controls, white matter areas with unidirectional fiber arrangement and areas with more complex fiber composition were identified with this approach. HARDI confirms and can visualize deviations from the tensor-like representation, thereby providing information on fiber structure complexity, which may be of considerable interest for clinical studies.