Sensor-based balance training with motion feedback in people with mild cognitive impairment.

Michael Schwenk, Marwan Sabbagh, Ivy Lin, Pharah Morgan, Gurtej S Grewal, Jane Mohler, David W Coon, Bijan Najafi
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引用次数: 52

Abstract

Some individuals with mild cognitive impairment (MCI) experience not only cognitive deficits but also a decline in motor function, including postural balance. This pilot study sought to estimate the feasibility, user experience, and effects of a novel sensor-based balance training program. Patients with amnestic MCI (mean age 78.2 yr) were randomized to an intervention group (IG, n = 12) or control group (CG, n = 10). The IG underwent balance training (4 wk, twice a week) that included weight shifting and virtual obstacle crossing. Real-time visual/audio lower-limb motion feedback was provided from wearable sensors. The CG received no training. User experience was measured by a questionnaire. Postintervention effects on balance (center of mass sway during standing with eyes open [EO] and eyes closed), gait (speed, variability), cognition, and fear of falling were measured. Eleven participants (92%) completed the training and expressed fun, safety, and helpfulness of sensor feedback. Sway (EO, p = 0.04) and fear of falling (p = 0.02) were reduced in the IG compared to the CG. Changes in other measures were nonsignificant. Results suggest that the sensor-based training paradigm is well accepted in the target population and beneficial for improving postural control. Future studies should evaluate the added value of the sensor-based training compared to traditional training.

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基于传感器的运动反馈平衡训练在轻度认知障碍患者中的应用。
一些轻度认知障碍(MCI)患者不仅会出现认知缺陷,还会出现运动功能下降,包括姿势平衡。本初步研究旨在评估一种新型基于传感器的平衡训练方案的可行性、用户体验和效果。遗忘型轻度认知损伤患者(平均年龄78.2岁)随机分为干预组(IG, n = 12)和对照组(CG, n = 10)。大鼠接受平衡训练(4周,每周2次),包括体重转移和虚拟障碍穿越。可穿戴传感器提供实时视觉/音频下肢运动反馈。CG没有接受过任何训练。用户体验是通过问卷调查来衡量的。测量了干预后对平衡(睁眼和闭眼站立时的重心摆动)、步态(速度、变异性)、认知和对跌倒的恐惧的影响。11名参与者(92%)完成了培训,并表达了传感器反馈的乐趣、安全性和有用性。与CG相比,IG组的摇摆(EO, p = 0.04)和害怕摔倒(p = 0.02)减少。其他指标的变化不显著。结果表明,基于传感器的训练模式在目标人群中被广泛接受,有利于改善姿势控制。未来的研究应该评估基于传感器的训练与传统训练相比的附加价值。
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