Prediction and detection of side effects severity following COVID-19 and influenza vaccinations: utilizing smartwatches and smartphones
Abstract Vaccines stand out as one of the most effective tools in our arsenal for reducing morbidity and mortality. Nonetheless, public hesitancy towards vaccination often stems from concerns about potential side effects, which can vary from person to person. As of now, there are no automated system...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-56561-w |
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| author | Yosi Levi Margaret L. Brandeau Erez Shmueli Dan Yamin |
| author_facet | Yosi Levi Margaret L. Brandeau Erez Shmueli Dan Yamin |
| author_sort | Yosi Levi |
| collection | DOAJ |
| description | Abstract Vaccines stand out as one of the most effective tools in our arsenal for reducing morbidity and mortality. Nonetheless, public hesitancy towards vaccination often stems from concerns about potential side effects, which can vary from person to person. As of now, there are no automated systems available to proactively warn against potential side effects or gauge their severity following vaccination. We have developed machine learning (ML) models designed to predict and detect the severity of post-vaccination side effects. Our study involved 2111 participants who had received at least one dose of either a COVID-19 or influenza vaccine. Each participant was equipped with a Garmin Vivosmart 4 smartwatch and was required to complete a daily self-reported questionnaire regarding local and systemic reactions through a dedicated mobile application. Our XGBoost models yielded an area under the receiver operating characteristic curve (AUROC) of 0.69 and 0.74 in predicting and detecting moderate to severe side effects, respectively. These predictions were primarily based on variables such as vaccine type (influenza vs. COVID-19), the individual's history of side effects from previous vaccines, and specific data collected from the smartwatches prior to vaccine administration, including resting heart rate, heart rate, and heart rate variability. In conclusion, our findings suggest that wearable devices can provide an objective and continuous method for predicting and monitoring moderate to severe vaccine side effects. This technology has the potential to improve clinical trials by automating the classification of vaccine severity. |
| first_indexed | 2024-04-24T23:07:18Z |
| format | Article |
| id | doaj.art-b257643361b94f31b1a813ccf4f80846 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T23:07:18Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b257643361b94f31b1a813ccf4f808462024-03-17T12:26:44ZengNature PortfolioScientific Reports2045-23222024-03-0114111110.1038/s41598-024-56561-wPrediction and detection of side effects severity following COVID-19 and influenza vaccinations: utilizing smartwatches and smartphonesYosi Levi0Margaret L. Brandeau1Erez Shmueli2Dan Yamin3Department of Industrial Engineering, Tel-Aviv UniversityDepartment of Management Science and Engineering, Stanford UniversityDepartment of Industrial Engineering, Tel-Aviv UniversityDepartment of Industrial Engineering, Tel-Aviv UniversityAbstract Vaccines stand out as one of the most effective tools in our arsenal for reducing morbidity and mortality. Nonetheless, public hesitancy towards vaccination often stems from concerns about potential side effects, which can vary from person to person. As of now, there are no automated systems available to proactively warn against potential side effects or gauge their severity following vaccination. We have developed machine learning (ML) models designed to predict and detect the severity of post-vaccination side effects. Our study involved 2111 participants who had received at least one dose of either a COVID-19 or influenza vaccine. Each participant was equipped with a Garmin Vivosmart 4 smartwatch and was required to complete a daily self-reported questionnaire regarding local and systemic reactions through a dedicated mobile application. Our XGBoost models yielded an area under the receiver operating characteristic curve (AUROC) of 0.69 and 0.74 in predicting and detecting moderate to severe side effects, respectively. These predictions were primarily based on variables such as vaccine type (influenza vs. COVID-19), the individual's history of side effects from previous vaccines, and specific data collected from the smartwatches prior to vaccine administration, including resting heart rate, heart rate, and heart rate variability. In conclusion, our findings suggest that wearable devices can provide an objective and continuous method for predicting and monitoring moderate to severe vaccine side effects. This technology has the potential to improve clinical trials by automating the classification of vaccine severity.https://doi.org/10.1038/s41598-024-56561-wCOVID-19 vaccineBNT162b2Side effectsVaccine safetyWearable sensorsSmartwatches |
| spellingShingle | Yosi Levi Margaret L. Brandeau Erez Shmueli Dan Yamin Prediction and detection of side effects severity following COVID-19 and influenza vaccinations: utilizing smartwatches and smartphones Scientific Reports COVID-19 vaccine BNT162b2 Side effects Vaccine safety Wearable sensors Smartwatches |
| title | Prediction and detection of side effects severity following COVID-19 and influenza vaccinations: utilizing smartwatches and smartphones |
| title_full | Prediction and detection of side effects severity following COVID-19 and influenza vaccinations: utilizing smartwatches and smartphones |
| title_fullStr | Prediction and detection of side effects severity following COVID-19 and influenza vaccinations: utilizing smartwatches and smartphones |
| title_full_unstemmed | Prediction and detection of side effects severity following COVID-19 and influenza vaccinations: utilizing smartwatches and smartphones |
| title_short | Prediction and detection of side effects severity following COVID-19 and influenza vaccinations: utilizing smartwatches and smartphones |
| title_sort | prediction and detection of side effects severity following covid 19 and influenza vaccinations utilizing smartwatches and smartphones |
| topic | COVID-19 vaccine BNT162b2 Side effects Vaccine safety Wearable sensors Smartwatches |
| url | https://doi.org/10.1038/s41598-024-56561-w |
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