Background

The aim of our study was to conduct an ad hoc data collection in healthy adults with the intention of extracting individual profiles to study the ability to effectively monitor one's health by extracting relevant indicators. As “each patient is a unique case”, AMISIA (Defi CNRS AUTON project) proposes an integrated approach, combining medical health devices, information technology, and human factors to provide patients, health care actors and family caregivers with both the best incentives and a high degree of monitoring.

Method

We conducted a data collection experiment in Limoges with 61 participants at the Limoges University. Data were biographic elements, socio-economic profiles, cognitive performance (Corsi test results), a psychological battery (anxiety, fatigue, sleep), posture and gait measurement with 4 Imus and a Wii-balance board, and finally physical activity during a week at home (Armband sensors).

Results

For the Corsi virtual walking test, the median memory span for Group A was significantly less (p<0.001) than for Group B. Step count and active energy expenditure were significantly higher in Group B (p<0.05). A multiple regression analysis showed that gender, active energy expenditure, fatigue and tendency to play video games account for 41% of the memory span variance.

Conclusion

We have shown that encouraging physical activity can be based on the knowledge of many parameters, such as weight, age, gender and other bio-psycho-social parameters that must also be included in the model.

中文翻译：

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多维数据采集，作为二级预防创伤和中风患者自主性丧失的第一步：AMISIA试点研究

背景

我们研究的目的是在健康成年人中进行临时数据收集，目的是提取个人资料，以研究通过提取相关指标来有效监测个人健康的能力。由于“每个患者都是一个独特的案例”，AMISIA（Defi CNRS AUTON项目）提出了一种综合方法，将医疗保健设备，信息技术和人为因素相结合，为患者，医疗保健参与者和家庭护理人员提供了最佳的激励措施和措施。高度监控。

方法

我们在利摩日（Limoges）进行了数据收集实验，有61名参与者在利摩日大学（Limoges University）进行了实验。数据包括传记元素，社会经济状况，认知表现（Corsi测试结果），心理状况（焦虑，疲劳，睡眠），4 Imus和Wii平衡板的姿势和步态测量，以及一周内的体育锻炼在家里（波段传感器）。

结果

对于Corsi虚拟步行测试，A组的中位记忆跨度显着小于B组（p <0.001）。B组的步数和有功能量消耗显着高于B组（p <0.05）。多元回归分析显示，性别，有功能量消耗，疲劳和玩电子游戏的倾向占记忆跨度差异的41％。

结论

我们已经表明，鼓励体育锻炼可以基于许多参数的知识，例如体重，年龄，性别和其他也必须包括在模型中的生物心理社会参数。