Lyons Katy, Hei Man Alison Hau, Booth David, Rena Graham
Division of Cellular and Systems Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, United Kingdom.
School of Life Sciences, University of Dundee, Dundee, United Kingdom.
JMIR Form Res. 2024 Jun 10;8:e53806. doi: 10.2196/53806.
Sedentary behavior (SB) is one of the largest contributing factors increasing the risk of developing noncommunicable diseases, including cardiovascular disease and type 2 diabetes. Guidelines from the World Health Organization for physical activity suggest the substitution of SB with light physical activity. The Apple Watch contains a health metric known as the stand hour (SH). The SH is intended to record standing with movement for at least 1 minute per hour; however, the activity measured during the determination of the SH is unclear.
In this cross-sectional study, we analyzed the algorithm used to determine time spent standing per hour. To do this, we investigated activity measurements also recorded on Apple Watches that influence the recording of an SH. We also aimed to estimate the values of any significant SH predictors in the recording of a SH.
The cross-sectional study used anonymized data obtained in August 2022 from 20 healthy individuals gathered via convenience sampling. Apple Watch data were extracted from the Apple Health app through the use of a third-party app. Appropriate statistical models were fitted to analyze SH predictors.
Our findings show that active energy (AE) and step count (SC) measurements influence the recording of an SH. Comparing when an SH is recorded with when an SH is not recorded, we found a significant difference in the mean and median AE and SC. Above a threshold of 97.5 steps or 100 kJ of energy, it became much more likely that an SH would be recorded when each predictor was analyzed as a separate entity.
The findings of this study reveal the pivotal role of AE and SC measurements in the algorithm underlying the SH recording; however, our findings also suggest that a recording of an SH is influenced by more than one factor. Irrespective of the internal validity of the SH metric, it is representative of light physical activity and might, therefore, have use in encouraging individuals through various means, for example, notifications, to reduce their levels of SB.
久坐行为（SB）是增加包括心血管疾病和2型糖尿病在内的非传染性疾病发病风险的最大促成因素之一。世界卫生组织关于身体活动的指南建议用轻度身体活动替代久坐行为。苹果手表包含一项名为站立小时数（SH）的健康指标。站立小时数旨在记录每小时至少有1分钟的站立并伴有活动；然而，在确定站立小时数期间所测量的活动并不明确。
在这项横断面研究中，我们分析了用于确定每小时站立时间的算法。为此，我们调查了苹果手表上记录的也会影响站立小时数记录的活动测量值。我们还旨在估计在记录站立小时数时任何显著的站立小时数预测因素的值。
这项横断面研究使用了2022年8月通过便利抽样从20名健康个体收集的匿名数据。苹果手表数据通过使用第三方应用程序从苹果健康应用中提取。采用适当的统计模型来分析站立小时数预测因素。
我们的研究结果表明，活动能量（AE）和步数（SC）测量会影响站立小时数的记录。比较记录站立小时数的情况和未记录站立小时数的情况，我们发现平均和中位数活动能量及步数存在显著差异。当将每个预测因素作为单独个体进行分析时，超过97.5步或100千焦能量的阈值时，记录站立小时数的可能性就会大大增加。
本研究结果揭示了活动能量和步数测量在站立小时数记录基础算法中的关键作用；然而，我们的研究结果还表明，站立小时数的记录受到不止一个因素的影响。无论站立小时数指标的内部有效性如何，它都代表轻度身体活动，因此可能有助于通过各种方式（例如通知）鼓励个体减少久坐行为水平。
