Assessing the feasibility of using smartwatches for low burden, high temporal density capture of longitudinal health data.

纵向健康数据使研究人员和临床医生能够以横断面数据无法实现的方式探索疾病的原因、进展和治疗方法。前瞻性纵向队列研究可以在群体水平上进行研究，而N-of-1研究可以在个体层面上进行探索。在这两种情况下，记录数据的频率与参与者的负担之间存在权衡：较高的时间密度提供了对详细健康模式的更深入了解，但给参与者带来了更大的负担。一段时间以来，研究人员一直在使用智能手机进行低成本、高时间密度的数据采集。基于智能手机的生态瞬间评估（EMA）每天多次提示参与者在自由生活的条件下回答有关他们健康的问题。这种方法减少了回忆偏差和错误，提高了生态有效性，并且可以提供非常高的时间密度数据。然而，当捕获高时间密度数据时，智能手机EMA可能具有高度破坏性。虽然我们认为智能手机总是在身边，但平均只有50%的时间智能手机在我们触手可及的范围内。这意味着参与者需要从他们当前的任务中解脱出来。此外，由于智能手机不一定是随身携带的，因此需要听觉提示，而不是不那么分散注意力的触觉提示。这些干扰导致较低的依从率，并且在纵向队列研究中存在问题，因为增加的负担是参与者参与的一个问题。智能手表使我们能够保留EMA的好处，同时减少干扰。因为它们是戴在手腕上的，所以它们永远都够不着，所以访问设备所需的时间大大减少了。还可以使用侵入性较小的触觉提示。基于微交互的EMA （uEMA）的最新发展进一步减少了干扰：请求被简化为一个问题，带有一组有限的答案，可以通过一次点击来响应。最近的试点测试发现，当这些元素结合起来形成基于智能手表的uEMA时，与基于智能手机的EMA相比，在4周的时间内，智能手表系统具有更好的合规性（82% vs 64%），完成性（92% vs 67%）和更低的中断水平（38% vs 53%）。虽然这项试点工作支持更广泛地使用基于智能手表的数据收集，并且这些技术在公共卫生研究中的潜力已得到承认5，但它们尚未在此背景下使用。在本奖学金提出的可行性工作是必不可少的，使这些方法的吸收，帮助研究人员了解；这些方法在较长时间内的表现如何，特定群体是否比其他人更容易接受它们，它们的可行性是否因所收集的健康数据类型而异，以及在单个智能手表设备中结合多种健康数据收集方法的可行性。该研究金还将探讨如何最好地分析用这些方法获取的数据，这可能在理解健康和疾病的演变方面产生重大变化，从而更好地理解因果关系，更早地进行干预，并最终改善健康状况。这项工作的性质是探索如何利用新的数字技术来获取卫生数据，这与MRC和HDR英国在数字技术和卫生信息学领域的优先事项高度一致。从个人发展的角度来看，该奖学金将为我提供必要的时间和资源，让我专注于建立我在数字表型领域的技能和形象，使我更接近我的目标，成为一名完全独立的研究人员，领导一个享誉国际的卓越数字表型团队。参考文献1. dey, a，…（2011）. ubicomp:163-172。2.拿破仑,D…(2012)。Psychol评估24:713-20.3.Lucas P,…(2013)。[j] .中国医学杂志，2016,(13):564.]CHI-EA: 2767 - 277

Longitudinal health data enable researchers and clinicians to explore the causes, progression and treatments of diseases in ways that are not possible with cross-sectional data. Prospective longitudinal cohort studies enable these to be studied at the population level, while N-of-1 studies enable their exploration in individuals. In both cases, there is a trade-off between how frequently data are recorded, and the burden to the participant: higher temporal density provides greater insights into detailed patterns of health but places a higher burden on participants. For some time, researchers have used smartphones for low-cost, high temporal density data capture. Smartphone-based ecological momentary assessment (EMA) prompts participants several times a day in free-living conditions to answer questions about their health. This approach reduces recall biases and errors, increases ecological validity, and can provide very high temporal density data. However, when capturing high temporal density data, smartphone EMA can be highly disruptive. While we perceive smartphones as always nearby, on average smartphones are only within reach ~50% of the time1. This means participants need to disengage from their current tasks to respond. Also, as the smartphone is not necessarily on the body, audible rather than less distracting haptic prompts are required. These disruptions lead to poor compliance rates2, and are problematic in longitudinal cohort studies as increased burden is a concern for participant engagement3.Smartwatches enable us to retain the benefits of EMA, while reducing disruption. As they are worn on the wrist, they are never beyond reach, so time taken to access the device is significantly reduced. Less intrusive haptic prompts can also be used. The recent development of microinteraction-based EMA (uEMA) goes further in reducing disruption: requests are reduced to a single question with a limited set of answers that can be responded to with a single tap. Recent pilot testing4 found that when these elements were combined to form smartwatch-based uEMA, compared with smartphone-base EMA over a 4-week period, the smartwatch system had better compliance (82% v 64%), completion (92% v 67%), and lower levels of disruption (38% v 53%).While this pilot work is supportive of the wider use of smartwatch-based data collection, and the potential for these techniques in public health research has been acknowledged5, they have yet to be used in this context. The feasibility work proposed in this fellowship is essential in enabling the uptake of these methods, helping researchers understand; how these methods perform over longer periods, whether specific groups are more likely than others to find them acceptable, whether their feasibility varies by the type of health data collected, and the feasibility of combining multiple health data collection approaches in a single smartwatch device. The fellowship will also explore how best to analyse the data capture with these methods, which could make a step change in understanding the evolution of health and disease, enabling better causal understanding, earlier intervention and ultimately health improvements. The nature of this work, exploring how new digital technologies can be used to capture health data, aligns strongly with MRC and HDR UK priorities in the areas of Digital Technologies and Informatics for Health.From a personal development perspective, the fellowship will provide the time and resources necessary for me to focus on building my skills and profile in the field of digital phenotyping, moving me closer to my goal of becoming a fully independent researcher leading a digital phenotyping team with an international reputation for excellence.References1.Dey,A,...(2011).UbiComp:163-172. 2.Courvoisier,D...(2012).Psychol Assess,24:713-20.3.Lucas,P,...(2013).BMC Med Res Method,13:564.Intille,S,...(2016).UbiComp:1124-11285.China,G,...(2017).CHI-EA: 2767-277