Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach
Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these device...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/6/1818 |
| _version_ | 1797239407518941184 |
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| author | Patrick Kasl Lauryn Keeler Bruce Wendy Hartogensis Subhasis Dasgupta Leena S. Pandya Stephan Dilchert Frederick M. Hecht Amarnath Gupta Ilkay Altintas Ashley E. Mason Benjamin L. Smarr |
| author_facet | Patrick Kasl Lauryn Keeler Bruce Wendy Hartogensis Subhasis Dasgupta Leena S. Pandya Stephan Dilchert Frederick M. Hecht Amarnath Gupta Ilkay Altintas Ashley E. Mason Benjamin L. Smarr |
| author_sort | Patrick Kasl |
| collection | DOAJ |
| description | Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants’ wearable device data and participants’ responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants’ fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks. |
| first_indexed | 2024-04-24T17:51:03Z |
| format | Article |
| id | doaj.art-d927fad5d1ad4384afa085c8a2e7b1ec |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T17:51:03Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d927fad5d1ad4384afa085c8a2e7b1ec2024-03-27T14:03:52ZengMDPI AGSensors1424-82202024-03-01246181810.3390/s24061818Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection ApproachPatrick Kasl0Lauryn Keeler Bruce1Wendy Hartogensis2Subhasis Dasgupta3Leena S. Pandya4Stephan Dilchert5Frederick M. Hecht6Amarnath Gupta7Ilkay Altintas8Ashley E. Mason9Benjamin L. Smarr10Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, San Diego, CA 92093-0021, USAUC San Diego Health Department of Biomedical Informatics, University of California San Diego, San Diego, CA 92093-0021, USAUCSF Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA 92093-0021, USASan Diego Supercomputer Center, University of California San Diego, San Diego, CA 92093-0021, USAUCSF Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA 92093-0021, USADepartment of Management, Zicklin School of Business, Baruch College, The City University of New York, New York, NY 10010, USAUCSF Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA 92093-0021, USASan Diego Supercomputer Center, University of California San Diego, San Diego, CA 92093-0021, USASan Diego Supercomputer Center, University of California San Diego, San Diego, CA 92093-0021, USAUCSF Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA 92093-0021, USAShu Chien-Gene Lay Department of Bioengineering, University of California San Diego, San Diego, CA 92093-0021, USACommercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants’ wearable device data and participants’ responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants’ fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.https://www.mdpi.com/1424-8220/24/6/1818wearablessyndromic surveillanceillness detection |
| spellingShingle | Patrick Kasl Lauryn Keeler Bruce Wendy Hartogensis Subhasis Dasgupta Leena S. Pandya Stephan Dilchert Frederick M. Hecht Amarnath Gupta Ilkay Altintas Ashley E. Mason Benjamin L. Smarr Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach Sensors wearables syndromic surveillance illness detection |
| title | Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach |
| title_full | Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach |
| title_fullStr | Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach |
| title_full_unstemmed | Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach |
| title_short | Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach |
| title_sort | utilizing wearable device data for syndromic surveillance a fever detection approach |
| topic | wearables syndromic surveillance illness detection |
| url | https://www.mdpi.com/1424-8220/24/6/1818 |
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