What is the clinical evidence from this study?

Study design: Other. Population: Systemic inflammation following influenza exposure (n=55). Intervention: Machine learning prediction models using multimodal wearable biosensors vs. Symptom-based detection. Primary outcome: Prediction of systemic inflammatory surges (24-h tolerance prediction window) (ROC-AUC 0.89, 95% CI 0.87-0.90).

What does this research mean for the field?

Machine learning models using wearable biosensor data can predict systemic inflammatory surges following influenza exposure with high accuracy (ROC-AUC of 0.89), outperforming symptom-based detection. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

July 1, 2025The Lancet Digital Health12 citationsOpen Access

Development of machine learning prediction models for systemic inflammatory response following controlled exposure to a live attenuated influenza vaccine in healthy adults using multimodal wearable biosensors in Canada: a single-centre, prospective controlled trial

AHAmir HadidMcGill University EMEmily G. McDonaldMcGill University Health Centre QDQianggang DingMila - Quebec Artificial Intelligence Institute

Key Result

Machine learning models using wearable biosensor data predicted systemic inflammatory surges following influenza exposure with a ROC-AUC of 0.89 (95% CI 0.87-0.90), outperforming symptom-based detection.

Structured PICO

Does continuous monitoring with multimodal wearable biosensors and machine learning improve the prediction of systemic inflammation following controlled exposure to a live attenuated influenza vaccine compared to symptom-based detection in healthy adults?

Population

55 healthy adults aged 18-59 years undergoing controlled exposure to a live attenuated influenza vaccine, monitored with multimodal wearable biosensors for 12 days.

Intervention

Continuous monitoring with multimodal wearable biosensors (Oura ring, Biobeat watch, Astroskin-Hexoskin shirt) and machine learning algorithms to predict systemic inflammation following controlled exposure to a live attenuated influenza vaccine

Comparator

Symptom-based detection

Outcome

Prediction of systemic inflammatory surges (measured by ROC-AUC)surrogate

Machine learning models using physiological data from wearable biosensors can accurately predict systemic inflammation following a low-grade influenza challenge, outperforming symptom-based detection.

Main Result

Effect estimate: ROC-AUC 0.89 (95% CI 0.87-0.90)

Absolute Event Rate: 0.89% vs 0.79%

Abstract

BACKGROUND: Presymptomatic or asymptomatic immune system signals and subclinical physiological changes might provide a more objective measure of early viral upper respiratory tract infections (VRTIs) compared with symptom-based detection. We aimed to use multimodal wearable sensors, host-response biomarkers, and machine learning to predict systemic inflammation following controlled exposure to a live attenuated influenza vaccine, without relying on symptoms. METHODS: WE SENSE study is a single-centre (McGill University Health Center, Montreal, QC, Canada), prospective controlled trial that recruited healthy adults aged 18-59 years who had not received or were not planning to receive the seasonal influenza vaccine or any other vaccine during the study period. We excluded participants with any infectious symptoms within 7 days before screening. We collected physiological and activity data (eg, heart rate, breathing rate, and acceleration) through continuous monitoring with a smart ring (Oura ring Gen 2, Oura Oy, Finland), smart watch (Biobeat watch, Biobeat Technologies, Israel), and smart shirt (Astroskin-Hexoskin shirt, Hexoskin, Canada) along with high temporal resolution systemic inflammatory biomarker mapping over 12 days (7 days before inoculation and 5 days after). We frequently tested participants both before and after inoculation via PCR for respiratory pathogens, and monitored them via apps for symptoms and free-text annotations. Machine learning algorithms predicting systemic inflammatory surges were trained (35 participants), validated (ten participants), and tested (ten participants) using gradient-boosting techniques. FINDINGS: Between Dec 10, 2021, and Feb, 28, 2022, we enrolled 56 participants, of whom 55 had available data; all 55 participants continuously wore the Oura ring, 54 participants wore the Astroskin-Hexoskin shirt, and 50 wore the Biobeat watch. 27 (49%) participants were female and 28 (51%) were male; 31 (56%) participants were White, eight (15%) were Asian, four (7%) were Black, two (4%) were Latino or Hispanic, and ten (18%) did not disclose. We used model 2, which included handpicked features from the Oura ring night-time data, as the candidate model because it was built on the lowest number of features (more practical). This model predicted inflammatory surges with receiver operating characteristic area under the curve (ROC-AUC) of 0·73 (95% CI 0·71-0·74) for real-time prediction and 0·89 (0·87-0·90) for a 24-h tolerance prediction window (24h-tol) using night-time data from the Oura ring. Incorporating both night-time and daytime data from the Astroskin-Hexoskin shirt yielded ROC-AUC values of 0·73 (0·71-0·75) for real-time and 0·91 (0·90-0·92) for 24h-tol along with improved precision (ie, specificity 0·83, 0·79-0·87 and F1 score 0·65, 0·58-0·71). The model based on symptoms alone had lower performance, with ROC-AUC values of 0·66 (0·63-0·68) for real-time and 0·79 (0·77-0·82) for 24h-tol. INTERPRETATION: Systemic inflammatory biomarkers coupled with physiological data from wearable biosensors provided rich and objective data from which to train machine learning algorithms to predict systemic inflammation from a low-grade influenza challenge. This approach outperformed symptom-based detection and has the potential to improve detection of VRTIs such as influenza and decrease time to detection, even among asymptomatic people. FUNDING: The Canadian Institutes of Health Research.

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