背景:跌倒风险评估很复杂。根据目前的科学证据,多因素方法,包括对物理性能的分析,步态参数,以及外在和内在的风险因素,强烈推荐。基于智能手机的应用程序旨在评估个人跌倒风险,并使用先前列出的决定因素将多个跌倒风险因素结合到一个综合指标中。
目的:本研究对设计的跌倒风险评分进行了描述性评估,并根据实际数据对应用程序的辨别能力进行了分析。
方法:回顾性分析242名老年人的匿名数据。数据是在2018年6月至2019年5月之间使用跌倒风险评估应用程序收集的。首先,我们提供了基础数据集的描述性统计分析.随后,多学习模型(Logistic回归,高斯朴素贝叶斯,梯度提升,支持向量分类,和随机森林回归)在数据集上进行训练,以获得最佳决策边界。受试者工作曲线及其相应的曲线下面积(AUC)和灵敏度是用于评估跌倒风险评分区分跌倒者和非跌倒者能力的主要性能指标。为了完整起见,特异性,精度,并为每个模型提供了总体准确性。
结果:在242名平均年龄为84.6岁(SD6.7)的参与者中,139(57.4%)报告之前没有下跌(非下跌),而103(42.5%)报告了先前的下跌(下跌)。平均跌倒风险为29.5点(SD12.4)。Logistic回归模型的性能指标为AUC=0.9,灵敏度=100%,特异性=52%,准确度=73%。高斯朴素贝叶斯模型的性能指标为AUC=0.9,灵敏度=100%,特异性=52%,准确度=73%。梯度提升模型的性能指标为AUC=0.85,灵敏度=88%,特异性=62%,准确度=73%。支持向量分类模型的性能指标为AUC=0.84,灵敏度=88%,特异性=67%,准确度=76%。随机森林模型的性能指标为AUC=0.84,灵敏度=88%,特异性=57%,准确率=70%。
结论:提供数据集的描述性统计作为比较和参考值。跌倒风险评分表现出很高的辨别能力,可以区分跌倒者和非跌倒者,与评估的学习模型无关。这些模型的平均AUC为0.86,平均灵敏度为93%,平均特异性为58%。平均总体准确率为73%。因此,跌倒风险应用程序有可能支持看护者轻松进行有效的跌倒风险评估。跌倒风险评分的前瞻性准确性将在前瞻性试验中得到进一步验证。
BACKGROUND: Fall-risk assessment is complex. Based on current scientific evidence, a multifactorial approach, including the analysis of physical performance, gait parameters, and both extrinsic and intrinsic risk factors, is highly recommended. A smartphone-based app was designed to assess the individual risk of falling with a score that combines multiple fall-risk factors into one comprehensive metric using the previously listed determinants.
OBJECTIVE: This study provides a descriptive evaluation of the designed fall-risk score as well as an analysis of the app\'s discriminative ability based on real-world data.
METHODS: Anonymous data from 242 seniors was analyzed retrospectively. Data was collected between June 2018 and May 2019 using the fall-risk assessment app. First, we provided a descriptive statistical analysis of the underlying dataset. Subsequently, multiple learning models (Logistic Regression, Gaussian Naive Bayes, Gradient Boosting, Support Vector Classification, and Random Forest Regression) were trained on the dataset to obtain optimal decision boundaries. The receiver operating curve with its corresponding area under the curve (AUC) and sensitivity were the primary performance metrics utilized to assess the fall-risk score\'s ability to discriminate fallers from nonfallers. For the sake of completeness, specificity, precision, and overall accuracy were also provided for each model.
RESULTS: Out of 242 participants with a mean age of 84.6 years old (SD 6.7), 139 (57.4%) reported no previous falls (nonfaller), while 103 (42.5%) reported a previous fall (faller). The average fall risk was 29.5 points (SD 12.4). The performance metrics for the Logistic Regression Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gaussian Naive Bayes Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gradient Boosting Model were AUC=0.85, sensitivity=88%, specificity=62%, and accuracy=73%. The performance metrics for the Support Vector Classification Model were AUC=0.84, sensitivity=88%, specificity=67%, and accuracy=76%. The performance metrics for the Random Forest Model were AUC=0.84, sensitivity=88%, specificity=57%, and accuracy=70%.
CONCLUSIONS: Descriptive statistics for the dataset were provided as comparison and reference values. The fall-risk score exhibited a high discriminative ability to distinguish fallers from nonfallers, irrespective of the learning model evaluated. The models had an average AUC of 0.86, an average sensitivity of 93%, and an average specificity of 58%. Average overall accuracy was 73%. Thus, the fall-risk app has the potential to support caretakers in easily conducting a valid fall-risk assessment. The fall-risk score\'s prospective accuracy will be further validated in a prospective trial.