Continuous Temperature Monitoring via Wearable Devices for Fever or Infection Management in Acute Care Hospitals:...

Infections are a major health concern in hospitalized patients. Fever is an early sign of infection, making temperature monitoring essential for infection surveillance. Wearable devices are increasingly being explored for continuous temperature monitoring in acute care hospitals, but how temperature data from wearables are monitored, presented, and used to support fever or infection management in clinical practice remains poorly understood.

This study aims to map existing evidence on the use of temperature data from wearables for fever or infection management in acute care hospitals, focusing on wearables' characteristics; data transmission, storage, and presentation strategies; data preprocessing and analytic approaches; and the maturity of wearables' clinical integration.

We searched MEDLINE, Embase, Web of Science, CINAHL, and IEEE Xplore for publications from January 2013 to July 2026 using the population, concept, context (PCC) framework. Primary research studies using wearables to monitor patients' body temperature in acute care hospitals for fever or infection management, published in English or Chinese, were eligible. Temperature data analyses were classified according to the descriptive, diagnostic, predictive, and prescriptive analytics framework. The clinical integration of wearables was assessed using a tailored maturity framework modified from the World Health Organization's stages of maturity for digital health interventions.

We included 29 publications from 26 studies, published between 2018 and 2026. Eighteen wearable devices were identified, monitoring temperature at the axilla, chest, wrist, or upper arm. Continuous data streams were predominantly transmitted in real time, while 5 studies used non-real-time batch uploads, periodic synchronization, or device-memory downloads. Only 7 studies presented wearable temperature data in clinical settings, displaying data on mobile devices and/or centralized monitoring stations; only 1 study reported enabling alerts through electronic health records (EHRs). Invalid sensor data filtering methods varied across studies, such as physiological thresholds, firmware quality scores, and statistical outlier detection. Temperature data were most frequently analyzed by descriptive analytics (n=19) to depict the frequency, timing, and duration of fever episodes, followed by diagnostic analytics (n=6) to identify risk factors or distinguish causes of fever, and predictive analytics (n=9) to forecast impending fever or infections. None of the included studies used prescriptive analytics. For clinical integration, most studies were at the clinical validation stage (n=9) or clinical research stage (n=17), corresponding to an early stage of maturity; 3 studies reached the routine clinical practice stage, and none progressed to multicenter implementation or full integration stages.

While promising, the clinical integration of wearables remains at an early stage of maturity. Critical gaps exist in translating temperature data into actionable clinical insights, with limited data presentation, poor EHR interoperability, and underdeveloped analytic approaches. Future research should prioritize standardized data cleaning frameworks, workflow integration, and clinical interpretation to facilitate the active use of wearable data in clinical decision-making.

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RR2-10.1136/bmjopen-2025-103630.