Abstract
BackgroundWearable 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.MethodsSenior 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.ResultsAfter 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.ConclusionsResults 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 registrationhttp://www.clinicaltrials.govNCT02043834.Electronic supplementary materialThe online version of this article (doi:10.1186/1743-0003-11-164) contains supplementary material, which is available to authorized users.
Highlights
Aging has a detrimental effect on postural control as a consequence of general age-related deterioration of sensory and neuromuscular control mechanisms and/or specific pathologies [1]
Sensor-based training programs may have several advantages compared to conventional exercises including interactive environments responsive to the user’s action, feedback for motor skill acquisition, incorporation of gaming features, and targeted intervention incorporating guided home exercising without the expense of a personal trainer [5,6,7]
Results of this study suggest that wearable sensor systems may help to translate laboratory-based balance training regimes into community settings, this needs to be validated in a larger study directly comparing the proposed system with other exercise interventions
Summary
Aging has a detrimental effect on postural control as a consequence of general age-related deterioration of sensory and neuromuscular control mechanisms and/or specific pathologies [1]. Impaired postural control can have serious consequences regarding physical functioning and is a predictor for falls in older adults [2]. Sensor-based training programs may have several advantages compared to conventional exercises including interactive environments responsive to the user’s action, feedback for motor skill acquisition, incorporation of gaming features, and targeted intervention incorporating guided home exercising without the expense of a personal trainer [5,6,7]. 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
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