Marques Diogo Luís, Neiva Henrique Pereira, Pires Ivan Miguel, Marinho Daniel Almeida, Marques Mário Cardoso
Department of Sport Sciences, University of Beira Interior, Covilhã, Portugal.
Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, Covilhã, Portugal.
Data Brief. 2020 Sep 23;33:106328. doi: 10.1016/j.dib.2020.106328. eCollection 2020 Dec.
The sit-to-stand test is commonly used by clinicians and researchers to analyze the functional capacity of older adults. The test consists to stand up and sit down from a chair and can be applied either in function of a predetermined number of repetitions to be completed or according to a specific time. The most common tool used by the evaluators is the chronometer, due to its low cost and ease of use. However, this tool may miss some important data throughout the test, such as the stand-up time and the total time of each repetition, as well as other kinematic and kinetic variables. Therefore, it is necessary to develop new cheap and affordable tools to capture these data with reliability. In this perspective, the development of mobile applications can be a valid and reliable alternative for the automatic calculation of different variables with sensors' data, including acceleration, velocity, force, power, and others. Thus, in this paper, we present a dataset related to the acquisition of the accelerometer data from a commodity smartphone for the measurement of different variables during the sit-to-stand test with institutionalized older adults. Forty participants (20 men and 20 women, 78.9 ± 8.6 years old, 71.7 ± 15.0 kg, 1.57 ± 0.1 m) from five community-dwelling centers (Centro de Dia e Apoio Domiciliário de Alcongosta, Lar Nossa Senhora de Fátima, Centro Comunitário das Minas da Panasqueira, Lar da Misericórdia, and Lar da Aldeia de Joanes) from Fundão, in Portugal, volunteered to participate in the data acquisition. A mobile phone was attached to the waist of the participants to capture the data during the sit-to-stand test. Then, seated in an armless chair with the arms crossed over the chest, the participants stood up and sat down in a chair six times. The stand-up action was ordered by an acoustic signal emitted by the mobile application. All data were acquired with the mobile application, and the outcome measures were the reaction time, total time, stand-up time and movement time. This paper describes the procedures to acquire the data. These data can be reused for testing machine learning or other methods for the evaluation of neuromuscular function in older adults during the sit-to-stand test.
坐立试验被临床医生和研究人员广泛用于分析老年人的功能能力。该试验要求从椅子上站起和坐下，可根据预定要完成的重复次数或特定时间进行。评估人员最常用的工具是计时器，因其成本低且易于使用。然而，该工具在整个测试过程中可能会遗漏一些重要数据，如站立时间、每次重复的总时间以及其他运动学和动力学变量。因此，有必要开发新的廉价且经济实惠的工具来可靠地获取这些数据。从这个角度来看，移动应用程序的开发可以成为一种有效且可靠的替代方案，用于利用传感器数据自动计算不同变量，包括加速度、速度、力、功率等。因此，在本文中，我们展示了一个数据集，该数据集与从商用智能手机获取加速度计数据有关，用于测量机构养老的老年人在坐立试验期间的不同变量。来自葡萄牙丰沙尔五个社区日间照料中心（阿尔孔戈斯塔家庭日间照料中心、我们的法蒂玛圣母养老院、帕纳斯凯拉矿社区中心、仁慈之家养老院和若阿内斯村养老院）的40名参与者（20名男性和20名女性，年龄78.9±8.6岁，体重71.7±15.0千克，身高1.57±0.1米）自愿参与数据采集。在坐立试验期间，将一部手机系在参与者的腰部以采集数据。然后，参与者双臂交叉抱在胸前，坐在无扶手椅子上，站起并坐下六次。站立动作由移动应用程序发出的声音信号指示。所有数据均通过移动应用程序采集，结果测量指标为反应时间、总时间、站立时间和动作时间。本文描述了获取数据的过程。这些数据可用于测试机器学习或其他方法，以评估老年人在坐立试验期间的神经肌肉功能。
