Snir Shachar, Chen Yupeng, Yechezkel Matan, Patalon Tal, Shmueli Erez, Brandeau Margaret L, Yamin Dan
Industrial Engineering Department, Tel Aviv University, Tel Aviv, Israel.
Department of Management Science and Engineering, Stanford University, Stanford, CA, USA.
Lancet Reg Health Eur. 2024 May 16;42:100934. doi: 10.1016/j.lanepe.2024.100934. eCollection 2024 Jul.
Limited knowledge exists regarding behavioral and biomarker shifts during the period from respiratory infection exposure to testing decisions (the diagnostic decision period), a key phase affecting transmission dynamics and public health strategy development. This study aims to examine the changes in behavior and biomarkers during the diagnostic decision period for COVID-19, influenza, and group A streptococcus (GAS).
We analyzed data from a two-year prospective cohort study involving 4795 participants in Israel, incorporating smartwatch data, self-reported symptoms, and medical records. Our analysis focused on three critical phases: the digital incubation period (from exposure to physiological anomalies detected by smartwatches), the symptomatic incubation period (from exposure to onset of symptoms), and the diagnostic decision period for influenza, COVID-19, and GAS.
The delay between initial symptom reporting and testing was 39 [95% confidence interval (CI): 34-45] hours for influenza, 53 [95% CI: 49-58] hours for COVID-19, and 38 [95% CI: 32-46] hours for GAS, with 73 [95% CI: 67-78] hours from anomalies in heart measures to symptom onset for influenza, 23 [95% CI: 18-27] hours for COVID-19, and 62 [95% CI: 54-68] hours for GAS. Analyzing the entire course of infection of each individual, the greatest changes in heart rates were detected 67.6 [95% CI: 62.8-72.5] hours prior to testing for influenza, 64.1 [95% CI: 61.4-66.7] hours prior for COVID-19, and 58.2 [95% CI: 52.1-64.2] hours prior for GAS. In contrast, the greatest reduction in physical activities and social contacts occurred after testing.
These findings highlight the delayed response of patients in seeking medical attention and reducing social contacts and demonstrate the transformative potential of smartwatches for identifying infection and enabling timely public health interventions.
This work was supported by the European Research Council, project #949850, the Israel Science Foundation (ISF), grant No. 3409/19, within the Israel Precision Medicine Partnership program, and a Koret Foundation gift for Smart Cities and Digital Living.
从呼吸道感染暴露到做出检测决定（诊断决策期）这一阶段，关于行为和生物标志物变化的了解有限，而这一关键阶段会影响传播动态和公共卫生策略的制定。本研究旨在探究新型冠状病毒肺炎（COVID-19）、流感和A组链球菌（GAS）在诊断决策期的行为和生物标志物变化。
我们分析了以色列一项为期两年的前瞻性队列研究中的数据，该研究纳入了4795名参与者，数据包括智能手表数据、自我报告症状和医疗记录。我们的分析聚焦于三个关键阶段：数字潜伏期（从暴露到智能手表检测到生理异常）、症状潜伏期（从暴露到症状出现）以及流感、COVID-19和GAS的诊断决策期。
流感从最初症状报告到检测的延迟时间为39[95%置信区间（CI）：34 - 45]小时，COVID-19为53[95%CI：49 - 58]小时，GAS为38[95%CI：32 - 46]小时；流感从心脏指标异常到症状出现的时间为73[95%CI：67 - 78]小时，COVID-19为23[95%CI：18 - 27]小时，GAS为62[95%CI：54 - 68]小时。分析每个个体的整个感染过程，流感在检测前67.6[95%CI：62.8 - 72.5]小时心率变化最大，COVID-19在检测前64.1[95%CI：61.4 - 66.7]小时，GAS在检测前58.2[95%CI：52.1 - 64.2]小时。相比之下，体力活动和社交接触在检测后减少最多。
这些发现凸显了患者在寻求医疗救治和减少社交接触方面的延迟反应，并证明了智能手表在识别感染和实现及时的公共卫生干预方面的变革潜力。
本研究得到了欧洲研究理事会（项目编号#949850）、以色列科学基金会（ISF，资助编号3409/19，属于以色列精准医学合作项目）以及科雷特基金会对智慧城市和数字生活的捐赠支持。
