Keogh Alison, Johnston William, Ashton Mitchell, Sett Niladri, Mullan Ronan, Donnelly Seamas, Dorn Jonas F, Calvo Francesc, Mac Namee Brian, Caulfield Brian
Insight Centre of Data Analytics, University College, Dublin, Ireland.
UCD School of Public Health, Physiotherapy and Sports Science, University College, Dublin, Ireland.
Digit Biomark. 2020 Nov 26;4(Suppl 1):87-99. doi: 10.1159/000512044. eCollection 2020 Winter.
Data derived from wearable activity trackers may provide important clinical insights into disease progression and response to intervention, but only if clinicians can interpret it in a meaningful manner. Longitudinal activity data can be visually presented in multiple ways, but research has failed to explore how clinicians interact with and interpret these visualisations. In response, this study developed a variety of visualisations to understand whether alternative data presentation strategies can provide clinicians with meaningful insights into patient's physical activity patterns.
To explore clinicians' opinions on different visualisations of actigraphy data.
Four visualisations (stacked bar chart, clustered bar chart, linear heatmap and radial heatmap) were created using Matplotlib and Seaborn Python libraries. A focus group was conducted with 14 clinicians across 2 hospitals. Focus groups were audio-recorded, transcribed and analysed using inductive thematic analysis.
Three major themes were identified: (1) the importance of context, (2) interpreting the visualisations and (3) applying visualisations to clinical practice. Although clinicians saw the potential value in the visualisations, they expressed a need for further contextual information to gain clinical benefits from them. Allied health professionals preferred more granular, temporal information compared to doctors. Specifically, physiotherapists favoured heatmaps, whereas the remaining members of the team favoured stacked bar charts. Overall, heatmaps were considered more difficult to interpret.
The current lack of contextual data provided by wearables hampers their use in clinical practice. Clinicians favour data presented in a familiar format and yet desire multi-faceted filtering. Future research should implement user-centred design processes to identify ways in which all clinical needs can be met, potentially using an interactive system that caters for multiple levels of granularity. Irrespective of how data is displayed, unless clinicians can apply it in a manner that best supports their role, the potential of this data cannot be fully realised.
可穿戴活动追踪器获取的数据可能为疾病进展和干预反应提供重要的临床见解，但前提是临床医生能够以有意义的方式对其进行解读。纵向活动数据可以通过多种方式直观呈现，但研究尚未探讨临床医生如何与这些可视化数据进行交互以及如何解读它们。为此，本研究开发了多种可视化方式，以了解替代数据呈现策略是否能为临床医生提供有关患者身体活动模式的有意义见解。
探讨临床医生对活动记录仪数据不同可视化方式的看法。
使用Matplotlib和Seaborn Python库创建了四种可视化方式（堆叠条形图、聚类条形图、线性热图和径向热图）。对两家医院的14名临床医生进行了焦点小组访谈。焦点小组访谈进行了录音、转录，并采用归纳主题分析法进行分析。
确定了三个主要主题：（1）背景的重要性，（2）解读可视化数据，（3）将可视化数据应用于临床实践。尽管临床医生看到了这些可视化数据的潜在价值，但他们表示需要更多背景信息才能从中获得临床益处。与医生相比，专职医疗人员更喜欢更详细的时间信息。具体而言，物理治疗师更喜欢热图，而团队中的其他成员更喜欢堆叠条形图。总体而言，热图被认为更难解读。
目前可穿戴设备缺乏背景数据，这阻碍了它们在临床实践中的应用。临床医生喜欢以熟悉的格式呈现的数据，但也希望进行多方面的筛选。未来的研究应采用以用户为中心的设计流程，以确定满足所有临床需求的方法，可能会使用一个能适应多个粒度级别的交互式系统。无论数据如何显示，除非临床医生能够以最能支持其工作的方式应用它，否则这些数据的潜力就无法完全实现。
