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Population-level physical activity surveillance in young people: are accelerometer-based measures ready for prime time?
International Journal of Behavioral Nutrition and Physical Activity ( IF 8.7 ) Pub Date : 2020-03-18 , DOI: 10.1186/s12966-020-00929-4
Stewart G Trost 1
Affiliation  

With the promotion of physical activity in young people, an established global health priority [1], it is critically important for governments and public health agencies to have a clear understanding of the proportion of children and adolescents meeting guidelines for physical activity and sedentary behaviour. Historically, the assessment of physical activity in large scale population-level surveillance systems has been limited to self-report measures. However, among youth, self-report methods are subject to significant social desirability and recall bias [2, 3]. Younger children, in particular, have difficulty recalling their past behaviour accurately; and struggle to understand the concepts of physical activity frequency, intensity, duration, and type [4]. Proxy self-reports completed by parents or caregivers are one solution, but this method is also subject to recall bias since respondents can only report on the time they are in contact with the child [2, 3]. In light of the limitations of self-report methods, device based physical activity measures such as accelerometers have become the preferred method in studies involving children and young people [2, 5].

Despite the ubiquitous use of accelerometer-based motion sensors in physical activity studies involving children and adolescents, the application of accelerometers in population-level physical activity surveillance systems has been seriously questioned [6]. Citing methodological limitations related to questionable validity, between-monitor variability, multiple sets of conflicting intensity-based cut-points, and bias resulting from monitoring non-compliance, Pedisic and Bauman [6] concluded that without appropriate standardisation protocols, the adoption of accelerometers for large-scale physical activity surveillance was premature. They further concluded that accelerometer-based physical activity measures should not substitute, but only supplement self-report information systems; and that self-report methods should be the primary assessment tool in physical activity surveillance systems.

The newly published paper by Steene-Johannessen et al. [7] goes a long way to dispel Pedisic and Bauman’s notion that accelerometer-based measures are not yet ready for “prime time” when it comes to population-level physical activity surveillance. Based on the reanalysis of accelerometer data from more than 47,000 young people from 30 different studies across 18 European countries, the authors present what could be argued as the most detailed and comprehensive descriptions of physical activity and sedentary behaviour levels in European children and adolescents to date. The results indicate that approximately two-thirds of European children and adolescents do not meet the daily 60 min moderate-to-vigorous physical activity (MVPA) recommendation, with youth from southern European countries being at significantly greater risk for inactivity than those residing in central or northern Europe. The findings are concerning to say the least and underscore the need for effective policies and programs to promote physical activity and reduce sedentary behaviour in children and young people.

Through the implementation of a carefully conceived accelerometer data processing protocol, Steene-Johannessen and colleagues [7] mitigate many of the concerns identified in the Pedisic and Bauman review [6]. The analysis was delimited to studies using an ActiGraph accelerometer on the hip, epoch length was standardised to 60 s, data from midnight to 6:00 am was excluded to minimise any bias associated with between-study variation in monitoring protocols and minimal wear time criteria, and point estimates of MVPA and sedentary behaviour were based on a single set of cut-point values, regardless of child age. Nevertheless, the numerous study limitations described in the discussion section indicate that not all the methodological limitations identified in the Pedisic and Bauman review were addressed adequately.

A significant limitation of the Steene-Johannessen study is the reliance on proprietary count-based metrics - making it impossible to integrate data from studies deploying different brands of accelerometers. The solution to this problem is to discontinue the practice of calculating and reporting count-based metrics and applying the currently available methods and metrics based on the raw acceleration signal [8,9,10]. It is acknowledged that Steene-Johannessen included data from studies that were conducted long before raw accelerometer data from the ActiGraph could be readily accessed, limiting their ability to use this approach. However, if future accelerometer data pooling projects are to provide legitimate between-study and cross-country comparability, the more than two decade old practice of applying cut-points to processed activity counts must be phased out.

In the Pedisic and Bauman review [6], the existence of multiple sets of conflicting intensity-based cut-points was identified as a major methodological weakness limiting the application of accelerometers in population-level surveillance studies. Steene-Johannessen and colleagues partially address this issue by applying, in a standardised manner, an intensity-based cut-point with established evidence of validity in school-aged youth [11, 12]. However, there is growing recognition that the relationship between accelerometer counts and energy expenditure is highly dependent on the activities included the calibration study; and that cut-points derived from a single regression model or Receiver Operating Characteristic curve cannot adequately characterise physical activity intensity across a wide range of physical activities [13]. In an independent evaluation of ActiGraph cut-points for youth, the Evenson thresholds were found to have the least physical activity intensity classification error of all the cut-points tested [12]. However, it is important to note that the Evenson cut-points still misclassified MVPA as light-intensity physical activity 20% of the time, and that light intensity physical activities were misclassified as sedentary at least 40% of the time [12]. Moreover, given that the relationship between activity counts and energy expenditure in children under five differs substantially to that observed in adolescent youth [14], the application of the Evenson cut-points in children aged 2- to 5-years by Steene-Johannessen must be questioned.

Over the last decade, there has been a shift from count-based thresholds to machine learning activity classification and energy expenditure estimation algorithms based on features extracted from raw accelerometer signals [15]. When applied to youth, machine learning approaches have shown to provide more accurate predictions of physical activity intensity [13, 16]. Moreover, in contrast to cut-point methods, which only estimate time spend in MVPA, physical activity classification models can predict time spent in specific activity types (e.g., walking, running, dancing, cycling) or broader activity classes (e.g., active games or sports) [13, 16]. This enables researchers in the public health and exercise sciences to explore a greater variety of physical activity metrics as well as examine age-related differences in movement behaviours that are not confounded by developmental differences in the relationship between accelerometer counts and energy expenditure.

To date, the uptake of machine learning methods by public health researchers has been slow, primarily because of the need to collect and process large quantities of raw accelerometer signal using specialised software; and partly because of concerns that machine learning models trained on laboratory-based activities trials do not generalise well to free living scenarios [17]. As machine learning accelerometer data processing methods evolve and the required computer platforms enabling public health researchers to apply machine learning methods become available, it is anticipated that future physical activity surveillance studies will address the aforementioned limitations of cut-point methods by implementing potentially more accurate and versatile machine learning accelerometer data processing methods.

In their review, Pedisic and Bauman [6] identify between-study variations in monitoring protocols and non-compliance (non-wear) as key methodological issues limiting the use of accelerometer in physical activity surveillance studies. In their study, Steene-Johannessen and colleagues [7] partially address this limitation by excluding all accelerometer data recorded from midnight to 6:00 am and excluding monitoring days with less than eight hours of valid wear time. While these decision rules are not without precedent, it is likely that at a significant percentage of participants were awake and/or wearing the monitor during the excluded time periods. Furthermore, as noted by the authors, the inclusion of studies implementing 24 h monitoring protocols may have led to an overestimation of sedentary time, given the longer daily wear periods and the misclassification of sleep as sedentary time. It can be argued that the issue of non-wear is a legacy issue from objective monitoring studies requiring participants to wear an accelerometer on the hip during the waking hours. Because hip mounted accelerometers are typically placed on snug fitting elastic belts and worn over clothing, non-compliance and insufficient wear time were frequent problems in these studies. To improve compliance and minimise missing data due to non-wear, more and more studies are implementing continuous 24-h monitoring protocols with wrist mounted accelerometers. Wrist mounted accelerometers are easier to wear for extended periods and allow investigators to evaluate compliance with more contemporary 24-h movement guidelines which require concurrent monitoring of physical activity, sedentary behaviour, and sleep [18]. With wrist mounted accelerometers, it is critically important that researchers compute physical activity metrics using more sophisticated accelerometer data processing methods [16, 19]. Simple cut-point approaches applied to either processed activity counts or raw acceleration signal (i.e., Euclidean Norm Minus One - ENMO) provide misleading estimates of movement behaviour because they do not account for upper limb movements during sedentary or stationary light-intensity activities [20, 21]. In investigations where sitting time is of primary interest, assessments of posture with thigh mounted accelerometers, alone or in combination with other placements, should be considered [22, 23].

The findings of the Steene-Johannessen study highlight the long-standing methodological issue of how to operationalise compliance with physical activity guidelines in accelerometer-based studies [24]. Steene-Johannessen and colleagues [7] operationalise meeting guidelines as accumulating an average of > 60 min of MVPA across all valid monitoring days; but other studies have applied more stringent criteria by requiring participants to accumulate ≥60 min on every monitoring day [25]. It can be argued that, based on the wording of the guidelines, children are required to reach the target of 60 min of MVPA on each and every day. If this is the case, then significantly more than two-thirds of European children and adolescents are insufficiently active for health benefit. Alternatives to the “average over all days” and “all days” methods are “the most days” methods (e.g., meeting the 60 min recommendation on > 50% of monitoring days) and the “Child x Day” method used by Cooper at colleagues [25] in which the percentage of valid monitoring days with ≥60 min of MVPA is calculated and interpreted as the probability that a randomly selected child on a randomly selected day met the guideline [24]. It is acknowledged that all methods have advantages and disadvantages. However, the difficulty associated with comparing prevalence estimates from studies applying different approaches highlights the need for consensus on how physical activity guidelines for young people should be operationised.

The article by Steene-Johannessen and colleagues [7] highlights the utility of accelerometer-based measures in physical activity surveillance studies involving children and adolescents. The findings are significant and identify the promotion of physical activity as a priority concern for public health authorities across Europe. Yet, it is important to acknowledge that the accelerometer data processing methods applied in this study have been implemented with only minor modifications for more than two decades, despite significant advances in wearable sensor technology and artificial intelligence over the same time period. If accelerometer-based measures of physical activity and sedentary behaviour are to be accepted as best practice methodology in large scale population-level surveillance studies, public health researchers must be willing to adopt more contemporary monitoring protocols and apply new accelerometer data processing methods.

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  1. Institute of Health and Biomedical Innovation at QLD Centre for Children’s Health Research, Queensland University of Technology, Level 6, 62 Graham Street, South Brisbane, QLD, 4101, Australia
    • Stewart G Trost
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Trost, S.G. Population-level physical activity surveillance in young people: are accelerometer-based measures ready for prime time?. Int J Behav Nutr Phys Act 17, 28 (2020). https://doi.org/10.1186/s12966-020-00929-4

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中文翻译：
年轻人的人口级体育活动监测：基于加速度计的措施是否已准备就绪？

随着青少年体育活动的发展，这是已确立的全球卫生重点[1]，对于政府和公共卫生机构来说，清楚了解符合体育锻炼和久坐行为准则的儿童和青少年的比例至关重要。从历史上看，在大规模人口级别的监视系统中对体育活动的评估仅限于自我报告措施。然而，在年轻人中，自我报告的方法受到社会的强烈期望和回忆偏见[2，3]。尤其是年幼的孩子，很难准确地回忆起他们过去的行为；努力理解体育活动的频率，强度，持续时间和类型的概念[4]。父母或照顾者完成的代理人自我报告是一种解决方案，但是这种方法也容易引起回忆偏见，因为受访者只能报告他们与孩子接触的时间[2，3]。鉴于自我报告方法的局限性，基于设备的身体活动量度（如加速度计）已成为涉及儿童和年轻人的研究中的首选方法[2，5]。

尽管在涉及儿童和青少年的体育活动研究中普遍使用基于加速度计的运动传感器，但加速度计在人口级体育活动监测系统中的应用受到了严重质疑[6]。Pedisic和Bauman引用了与可疑的有效性，显示器之间的可变性，基于强度的相互冲突的多套切割点以及由于监视不合规而导致的偏差有关的方法学限制，得出结论，Pedisic和Bauman [6]得出结论，如果没有适当的标准化协议，就会采用加速度计进行大规模体育锻炼的监视还为时过早。他们进一步得出结论，基于加速度计的体育锻炼措施不应替代，而只能补充自我报告信息系统。

Steene-Johannessen等人新发表的论文。[7]消除了Pedisic和Bauman的观点，即基于加速度计的措施尚未准备好进行人群一级体育活动监测的“黄金时间”。根据对来自18个欧洲国家/地区的30项不同研究的47,000多名年轻人进行的加速度计数据的重新分析，作者提出了迄今为止可能最有力和最全面的描述，描述了欧洲儿童和青少年的体育活动和久坐行为水平。结果表明，大约三分之二的欧洲儿童和青少年不符合每天60分钟的中度到剧烈体育锻炼（MVPA）的建议，与居住在中欧或北欧的年轻人相比，来自欧洲南部国家的年轻人处于不活动状态的风险明显更高。这项发现至少可以说是最重要的，并且强调了需要有效的政策和计划来促进儿童和年轻人的体育锻炼并减少久坐的行为。

通过实施精心构思的加速度计数据处理协议，Steene-Johannessen及其同事[7]减轻了Pedisic和Bauman评论[6]中确定的许多问题。该分析仅限于在臀部使用ActiGraph加速度计进行的研究，历时长度标准化为60 s，排除了从午夜到6:00 am的数据，以最大程度地减少与研究方案之间的研究差异和最小的穿着时间标准带来的偏差，而MVPA和久坐行为的点估计是基于一组割点值，而不考虑儿童的年龄。然而，讨论部分描述的众多研究局限性表明，Pedisic和Bauman综述中确定的所有方法学局限性并未得到适当解决。

Steene-Johannessen研究的一个重大局限性是对专有的基于计数的度量的依赖-使得无法整合来自部署不同品牌加速度计的研究中的数据。该问题的解决方案是停止基于原始加速度信号[8,9,10]的计算和报告基于计数的度量，并应用当前可用的方法和度量的做法。公认的是，Steene-Johannessen包含的研究数据是在很容易获得ActiGraph的原始加速度计数据之前进行的，这限制了他们使用这种方法的能力。但是，如果将来的加速度计数据池项目要提供合法的研究之间和跨国可比性，

在Pedisic和Bauman的综述中[6]，存在多个基于强度的相互冲突的切点的存在，这是限制加速度计在人群水平监测研究中应用的主要方法学缺陷。Steene-Johannessen和他的同事通过以标准化的方式应用基于强度的切入点来部分解决这个问题，该切入点具有已确定的学龄儿童有效性的证据[11，12]。但是，人们越来越认识到，加速度计计数与能量消耗之间的关系高度依赖于包括校准研究在内的活动。而且从单个回归模型或“接收者工作特征”曲线得出的切入点不能充分表征各种体育活动中的体育活动强度[13]。在针对青年的ActiGraph切点的独立评估中，发现Evenson阈值在所有测试的切点中具有最小的体育活动强度分类误差[12]。然而，重要的是要注意，Evenson临界点仍然有20％的时间将MVPA误分类为光强度的体育活动，并且至少40％的时间将光强度的体育活动分类为久坐[12]。此外，鉴于五岁以下儿童的活动计数与能量消耗之间的关系与青春期青年中观察到的有很大不同[14]，Steene-Johannessen必须在2至5岁的儿童中应用Evenson临界点。受到质疑。在所有测试的切入点中，Evenson阈值的体育活动强度分类误差最小[12]。然而，重要的是要注意，Evenson临界点仍然有20％的时间将MVPA误分类为光强度的体育活动，并且至少40％的时间将光强度的体育活动分类为久坐[12]。此外，鉴于五岁以下儿童的活动计数与能量消耗之间的关系与青春期青年中观察到的有很大不同[14]，Steene-Johannessen必须在2至5岁的儿童中应用Evenson临界点。受到质疑。在所有测试的切入点中，Evenson阈值的体育活动强度分类误差最小[12]。然而，重要的是要注意，Evenson临界点仍然有20％的时间将MVPA误分类为光强度的体育活动，并且至少40％的时间将光强度的体育活动分类为久坐[12]。此外，鉴于五岁以下儿童的活动计数与能量消耗之间的关系与青春期青年中观察到的有很大不同[14]，Steene-Johannessen必须在2至5岁的儿童中应用Evenson临界点。受到质疑。重要的是要注意，Evenson临界点仍然有20％的时间将MVPA误分类为光强度的体育活动，而至少40％的时间将光强度的体育活动分类为久坐[12]。此外，鉴于五岁以下儿童的活动计数与能量消耗之间的关系与青春期青年中观察到的有很大不同[14]，Steene-Johannessen必须在2至5岁的儿童中应用Evenson临界点。受到质疑。重要的是要注意，Evenson临界点仍然有20％的时间将MVPA误分类为光强度的体育活动，而至少40％的时间将光强度的体育活动分类为久坐[12]。此外，鉴于五岁以下儿童的活动计数与能量消耗之间的关系与青春期青年[14]的观察有很大不同，Steene-Johannessen必须在2至5岁的儿童中应用Evenson临界点。受到质疑。

在过去的十年中，已经从基于计数的阈值转变为基于从原始加速度计信号中提取的特征的机器学习活动分类和能量消耗估算算法[15]。当应用于青年时，机器学习方法已经显示出可以提供对体育活动强度的更准确的预测[13，16]。此外，与仅估算MVPA花费时间的切入点方法相反，体育活动分类模型可以预测在特定活动类型（例如，步行，跑步，跳舞，骑自行车）或更广泛的活动类别（例如，活动游戏）中花费的时间或运动）[13，16]。

迄今为止，公共卫生研究人员对机器学习方法的采用一直很缓慢，这主要是因为需要使用专用软件来收集和处理大量原始加速度计信号；部分是因为担心在基于实验室的活动试验中训练的机器学习模型不能很好地推广到自由生活场景[17]。随着机器学习加速度计数据处理方法的发展以及使公共卫生研究人员能够应用机器学习方法的所需计算机平台的出现，预计未来的体育锻炼监视研究将通过实现可能更准确，更准确的方法来解决上述切入点方法的局限性。通用的机器学习加速度计数据处理方法。

在他们的综述中，Pedisic和Bauman [6]将研究方案之间的研究差异和不遵守（不磨损）确定为限制在身体活动监测研究中使用加速度计的关键方法学问题。在他们的研究中，Steene-Johannessen及其同事[7]通过排除从午夜到凌晨6:00记录的所有加速度计数据，并排除了有效佩戴时间少于八小时的监测日，部分地解决了这一限制。尽管这些决策规则并非没有先例，但很可能有很大一部分参与者在排除的时间段内都醒着和/或戴着显示器。此外，正如作者所指出的那样，纳入实施24小时监视协议的研究可能导致对久坐时间的高估，考虑到较长的日常穿着时间以及将睡眠误认为是久坐时间。可以认为，不佩戴问题是客观监测研究中的遗留问题，该研究要求参与者在清醒时间内在臀部上佩戴加速度计。由于髋部安装的加速度计通常放置在贴身的松紧带上并穿着在衣服上，因此不合规和佩戴时间不足是这些研究中的常见问题。为了提高合规性并最大程度地减少由于不佩戴而导致的数据丢失，越来越多的研究正在使用腕上安装的加速度计实施连续的24小时监测协议。腕上安装的加速度计很容易长时间佩戴，并允许研究者评估是否需要同时监测身体活动，久坐行为和睡眠的更现代的24小时运动指导原则[18]。对于腕上安装的加速度计，研究人员使用更复杂的加速度计数据处理方法计算体力活动指标至关重要[16，19]。适用于处理后的活动计数或原始加速度信号的简单的切入点方法（即欧几里得标准减一-ENMO）提供了对运动行为的误导性估计，因为它们没有考虑到久坐或静止的光强度活动中的上肢运动[20] ，21]。在以就座时间为主要关注点的调查中，

Steene-Johannessen研究的结果突出了长期存在的方法论问题，即如何在基于加速度计的研究中实现对身体活动指南的依从性[24]。Steene-Johannessen及其同事[7]在所有有效的监测日内平均积累了超过60分钟的MVPA来实施会议准则。但是其他研究则通过要求参与者在每个监测日累积≥60分钟来应用更严格的标准[25]。可以说，根据指南的措词，要求儿童每天达到60分钟MVPA的目标。如果真是这样，那么欧洲三分之二的儿童和青少年中，有三分之二的人没有充分的运动来获得健康利益。“全天平均”和“全天”方法的替代方法是“最多的天”方法（例如，在监视天数的50％以上满足60分钟建议）和Cooper在2007年使用的“儿童x天”方法。同事[25]，其中计算了≥60分钟的MVPA的有效监视天数，并将其解释为随机选择的孩子在随机选择的日期达到准则的概率[24]。公认的是，所有方法都有优点和缺点。但是，比较采用不同方法进行的研究的患病率估算值所带来的困难凸显了需要就如何实施年轻人的体育锻炼指南达成共识。50％的监测天数）和Cooper在同事中使用的“ Child x Day”方法[25]，其中计算出≥60分钟MVPA的有效监测天数的百分比，并将其解释为随机选择的孩子在MVPA上的概率。随机选择的日期符合准则[24]。公认的是，所有方法都有优点和缺点。但是，比较采用不同方法进行的研究的患病率估算值所带来的困难凸显了需要就如何实施年轻人的体育锻炼指南达成共识。50％的监测天数）和Cooper在同事中使用的“ Child x Day”方法[25]，其中计算出≥60分钟MVPA的有效监测天数的百分比，并将其解释为随机选择的孩子在MVPA上的概率。随机选择的日期符合准则[24]。公认的是，所有方法都有优点和缺点。但是，比较采用不同方法进行的研究的患病率估算值所带来的困难凸显了需要就如何实施年轻人的体育锻炼指南达成共识。公认的是，所有方法都有优点和缺点。但是，比较采用不同方法进行的研究的患病率估算值所带来的困难凸显了需要就如何实施年轻人的体育锻炼指南达成共识。公认的是，所有方法都有优点和缺点。但是，比较采用不同方法进行的研究的患病率估计值所带来的困难凸显了需要就如何实施年轻人的体育锻炼指南达成共识。

Steene-Johannessen及其同事[7]的文章强调了基于加速度计的措施在涉及儿童和青少年的体育活动监视研究中的效用。这些发现具有重要意义，并将促进体育锻炼确定为整个欧洲公共卫生当局的首要任务。然而，重要的是要承认，尽管可穿戴传感器技术和人工智能在同一时期取得了重大进步，但本研究中使用的加速度计数据处理方法仅进行了很小的修改就已经实施了二十多年。如果将基于加速度计的身体活动和久坐行为量度作为大规模人群监测研究的最佳方法，

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  1. 昆士兰科技大学QLD儿童健康研究中心的健康与生物医学创新研究所，昆士兰州南布里斯班，格雷厄姆街62号6楼，澳大利亚昆士兰州4101，澳大利亚
    • 斯图尔特·G·斯特罗斯特
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Trost，SG年轻人中的人口级体育活动监测：基于加速度计的措施是否已准备就绪？诠释J Behav营养学物理学法 17， 28（2020）。https://doi.org/10.1186/s12966-020-00929-4

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  • DOI ：https ：//doi.org/10.1186/s12966-020-00929-4
更新日期：2020-04-22
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