Hanover Medical School, Peter L. Reichertz Institute for Medical Informatics, Carl-Neuberg-Str. 1, Hanover, 30625, Germany.
J Med Syst. 2016 Jan;40(1):29. doi: 10.1007/s10916-015-0383-3. Epub 2015 Nov 7.
The increasing use of wearable actimetry devices in cohort studies can provide a deep and objective insight in physical activity (PA) patterns. For reliable and reproducible pattern recognition, and to minimize the influence of specific device characteristics, there is a need for a generic method to identify relevant PA events in sensor data sets on the basis of comprehensive features such as PA duration and intensity. The objectives of this paper are to present a method to identify universal event detection thresholds for such parameters, and to attempt to find stable meta-clusters of PA behaviour. PA events of 5, 10, 20 and 30 min with low, medium and high intensity thresholds found in literature and intensity deciles were computed for a random sample (N = 100) of the NHANES 2005-06 accelerometer data set (N = 7457). On the basis of all combinations of the above, activity events were detected, and parameters mean duration, mean intensity and event regularity were computed. Results were clustered using x-Means clustering and visualized for 5-, 10-, 20-, and 30-min events. Stable clustering results are obtained with intensity thresholds up to the 8th decile and for event durations up to 10 min. Two stable meta-clusters were detected: 'irregularly active' (intensity at 52nd percentile) and 'regularly active' (intensity at 42nd percentile). Distinct generic thresholds could be identified and are proposed. They may prove useful for further investigations of similar actimetry data sets, minimising the influence of specific device characteristics. The results also confirm that distinct PA event patterns - including event regularity - can be identified using wearable sensor devices, especially when regarding low-intensity, short-term activities which do not correspond to current PA recommendations. Further research is necessary to evaluate actual associations between sensor-based PA parameters and health outcome. The author identified generic intensity and duration thresholds for analysing objective PA data from wearable devices. This may contribute to further analyses of PA patterns along with their relations with health outcome parameters.
可穿戴活动记录仪在队列研究中的使用越来越多，可以深入、客观地了解身体活动 (PA) 模式。为了进行可靠和可重复的模式识别，并最大程度地减少特定设备特征的影响，需要有一种通用的方法来根据活动持续时间和强度等综合特征，从传感器数据集中识别相关的 PA 事件。本文的目的是提出一种识别此类参数通用事件检测阈值的方法，并尝试找到稳定的 PA 行为元聚类。在文献中找到并计算了强度阈值为低、中、高的 5、10、20 和 30 分钟 PA 事件，以及强度十分位数，对 NHANES 2005-06 加速度计数据集（N=7457）的随机样本（N=100）进行了计算。基于上述所有组合，检测到活动事件，并计算了参数的平均持续时间、平均强度和事件规律性。使用 x-Means 聚类对结果进行聚类，并为 5、10、20 和 30 分钟的事件进行可视化。使用高达第 8 十分位数的强度阈值和长达 10 分钟的事件持续时间，可以获得稳定的聚类结果。检测到两个稳定的元聚类：“不规则活动”（强度为第 52 百分位）和“规则活动”（强度为第 42 百分位）。可以识别出明确的通用阈值，并提出了建议。它们可能对进一步研究类似的活动记录仪数据集有用，从而最大程度地减少特定设备特征的影响。结果还证实，使用可穿戴传感器设备可以识别出明显的 PA 事件模式，包括事件规律性，尤其是在涉及不符合当前 PA 建议的低强度、短时间活动时。有必要进行进一步的研究来评估基于传感器的 PA 参数与健康结果之间的实际关联。作者为分析可穿戴设备的客观 PA 数据确定了通用的强度和持续时间阈值。这可能有助于进一步分析 PA 模式及其与健康结果参数的关系。
