Chiu Ya-Lan, Tsai Yi-Ju, Lin Chueh-Ho, Hou You-Ruei, Sung Wen-Hsu
National Yang-Ming University, Taipei, Taiwan.
National Cheng Kung University, Tainan, Taiwan.
Comput Methods Programs Biomed. 2017 Feb;139:191-195. doi: 10.1016/j.cmpb.2016.11.005. Epub 2016 Nov 11.
BACKGROUND: Ankle sprain is the most common sports-related injury, and approximately 80% of patients studied suffered recurrent sprains. These repeated ankle injuries could cause chronic ankle instability, a decrease in sports performance, and a decrease in postural control ability. At the present time, smartphones have become very popular and powerful devices, and smartphone applications (apps) that have been shown to have good validity have been designed to measure human body motion. However, the app focusing on ankle function assessment and rehabilitation is still not widely used and has very limited functions. The purpose of this study is to evaluate the feasibility of smartphone-based systems in the assessment of postural control ability for patients with chronic ankle instability. METHODS: Fifteen physically active adults (6 male, 9 female; aged = 23.4 ± 5.28 years; height = 167.13 ± 7.3 cm; weight = 62.06 ± 10.82 kg; BMI = 22.08 ± 2.57 kg/ m) were recruited, and these participants had at least one leg that was evaluated as scoring lower than 27 points according to the Cumberland Ankle Instability Tool (CAIT). The smartphone used in the study was ASUS Zenfone 2, and an app developed using MIT App Inventor was used to record built-in accelerometer data during the assessment process. Subjects were asked to perform single leg stance for 20 s in eyes-open and eyes-closed conditions with each leg. The smartphone was fixed in an upright position on the middle of the shin, using an exercise armband, with the screen facing forward. The average of recorded acceleration data was used to represent the postural control performance, and higher values indicated more instability. Data were analyzed with a paired t-test with SPSS 17.0, and the statistical significance was set as alpha <0.05. RESULTS: A significant difference was found between CAIT scores from the healthier leg and injured leg (healthier leg 23.07 ± 3.80 vs. injured leg 18.27 ± 3.92, p < 0.001). Significant differences were also found between the scores for the healthier leg and injured leg during both eyes-open and eyes-closed conditions (eyes-open: healthier leg 0.051 ± 0.018 vs. injured leg 0.072 ± 0.034, p = 0.027; eyes-closed: healthier leg 0.100 ± 0.031 vs. injured leg 0.123 ± 0.038, p = 0.001, unit: m/s). Significant differences were also found between eyes-open and eyes-closed conditions during both single leg standing with healthier leg and injured leg (healthier leg: eyes-open 0.051 ± 0.018 vs. eyes-closed 0.100 ± 0.031, p < 0.001; injured leg: eyes-open 0.072 ± 0.034 vs. eyes-closed 0.123 ± 0.038, p = 0.001, unit: m/s). The results demonstrate that the smartphone software can be used to discriminate between the different performances of the healthier leg and injured leg, and also between eyes-open and eyes-closed conditions. CONCLUSION: The smartphone may have the potential to be a convenient, easy-to-use, and feasible tool for the assessment of postural control ability on subjects with chronic ankle instability.
背景:踝关节扭伤是最常见的与运动相关的损伤,在接受研究的患者中,约80%会出现反复扭伤。这些反复的踝关节损伤可能导致慢性踝关节不稳定、运动表现下降以及姿势控制能力下降。目前,智能手机已成为非常流行且功能强大的设备,并且已设计出经证明具有良好效度的智能手机应用程序(应用)来测量人体运动。然而,专注于踝关节功能评估和康复的应用仍未得到广泛使用,且功能非常有限。本研究的目的是评估基于智能手机的系统在评估慢性踝关节不稳定患者姿势控制能力方面的可行性。 方法:招募了15名身体活跃的成年人(6名男性,9名女性;年龄 = 23.4 ± 5.28岁;身高 = 167.13 ± 7.3厘米;体重 = 62.06 ± 10.82千克;BMI = 22.08 ± 2.57千克/平方米),这些参与者至少有一条腿根据坎伯兰踝关节不稳定工具(CAIT)评估得分低于27分。研究中使用的智能手机是华硕Zenfone 2,并且使用麻省理工学院应用发明家开发的一个应用在评估过程中记录内置加速度计数据。要求受试者每条腿在睁眼和闭眼条件下进行单腿站立20秒。智能手机使用运动臂带固定在小腿中部的直立位置,屏幕朝前。记录的加速度数据的平均值用于表示姿势控制表现,值越高表明不稳定程度越高。使用SPSS 17.0进行配对t检验分析数据,统计学显著性设定为α < 0.05。 结果:发现健康腿和受伤腿的CAIT得分之间存在显著差异(健康腿23.07 ± 3.80 vs. 受伤腿18.27 ± 3.92,p < 0.001)。在睁眼和闭眼条件下,健康腿和受伤腿的得分之间也存在显著差异(睁眼:健康腿0.051 ± 0.018 vs. 受伤腿0.072 ± 0.034,p = 0.027;闭眼:健康腿0.100 ± 0.031 vs. 受伤腿0.123 ± 0.038,p = 0.001,单位:米/秒)。在健康腿和受伤腿单腿站立时,睁眼和闭眼条件之间也存在显著差异(健康腿:睁眼0.051 ± 0.018 vs. 闭眼0.100 ± 0.031,p < 0.001;受伤腿:睁眼0.072 ± 0.034 vs. 闭眼0.123 ± 0.038,p =
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