Tseng Yi-Ju, Olson Karen L, Bloch Danielle, Mandl Kenneth D
Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
NPJ Digit Med. 2023 Sep 20;6(1):175. doi: 10.1038/s41746-023-00917-5.
Participatory surveillance systems crowdsource individual reports to rapidly assess population health phenomena. The value of these systems increases when more people join and persistently contribute. We examine the level of and factors associated with engagement in participatory surveillance among a retrospective, national-scale cohort of individuals using smartphone-connected thermometers with a companion app that allows them to report demographic and symptom information. Between January 1, 2020 and October 29, 2022, 1,325,845 participants took 20,617,435 temperature readings, yielding 3,529,377 episodes of consecutive readings. There were 1,735,805 (49.2%) episodes with self-reported symptoms (including reports of no symptoms). Compared to before the pandemic, participants were more likely to report their symptoms during pandemic waves, especially after the winter wave began (September 13, 2020) (OR across pandemic periods range from 3.0 to 4.0). Further, symptoms were more likely to be reported during febrile episodes (OR = 2.6, 95% CI = 2.6-2.6), and for new participants, during their first episode (OR = 2.4, 95% CI = 2.4-2.5). Compared with participants aged 50-65 years old, participants over 65 years were less likely to report their symptoms (OR = 0.3, 95% CI = 0.3-0.3). Participants in a household with both adults and children (OR = 1.6 [1.6-1.7]) were more likely to report symptoms. We find that the use of smart thermometers with companion apps facilitates the collection of data on a large, national scale, and provides real time insight into transmissible disease phenomena. Nearly half of individuals using these devices are willing to report their symptoms after taking their temperature, although participation varies among individuals and over pandemic stages.
参与式监测系统通过众包个人报告来快速评估人群健康现象。当更多人加入并持续贡献时，这些系统的价值就会增加。我们使用与智能手机相连的温度计及一款允许用户报告人口统计学和症状信息的配套应用程序，对一个全国规模的回顾性队列中的个体参与式监测的参与程度及相关因素进行了研究。在2020年1月1日至2022年10月29日期间，1325845名参与者进行了20617435次体温测量，产生了3529377次连续测量事件。有1735805次（49.2%）事件有自我报告的症状（包括无症状报告）。与疫情前相比，参与者在疫情期间更有可能报告他们的症状，尤其是在冬季疫情波开始后（2020年9月13日）（整个疫情期间的比值比范围为3.0至4.0）。此外，在发热期间症状更有可能被报告（比值比=2.6，95%置信区间=2.6 - 2.6），对于新参与者，在他们的第一次事件期间（比值比=2.4，95%置信区间=2.4 - 2.5）。与50 - 65岁的参与者相比，65岁以上的参与者报告症状的可能性较小（比值比=0.3，95%置信区间=0.3 - 0.3）。有成人和儿童的家庭中的参与者（比值比=1.6[1.6 - 1.7]）报告症状的可能性更大。我们发现，使用带有配套应用程序的智能温度计有助于在全国范围内大规模收集数据，并提供对传染病现象的实时洞察。使用这些设备的近一半个体在测量体温后愿意报告他们的症状，尽管个体之间以及在疫情不同阶段的参与情况有所不同。
