NCT07030998

Wearable Technology and Machine Learning for Early Detection and Risk Assessment of Unacceptable Toxicities in a Paediatric Oncology Cohort

WEARABLES: Wearable Technology and Machine Learning for Early Detection and Risk Assessment of Unacceptable Toxicities in a Paediatric Oncology Cohort

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 150 (estimated)
• Sponsor: Murdoch Childrens Research Institute · Academic / Other
• Sex: All
• Age: 5 Years – 18 Years
• Healthy volunteers: Not accepted

Summary

Data collection study to establish a predictive model of infection observed during childhood cancer therapy using data captured by wearable technology.

Detailed description

Rationale: Development in treatment for childhood cancers has improved remarkably with the 5-year survival rate now exceeding 80% in developed countries. However, these treatments are not without their adverse effects. The international Childhood Cancer Survivor study revealed that 62.3% of survivors had at least one chronic health condition and 27.5% had a severe or life-threatening condition as a direct result of their cancer treatment (DOI: 10.1056/NEJMsa060185). One of the adverse events experienced by 90% of children treated for cancer is infection. Septic shock, the most severe of infection outcomes, is characterized by life-threatening organ dysfunction, is the most and carriers a mortality rate of 41 to 46% (DOI: 10.1016/j.jped.2023.01.001). Beyond mortality, delayed first antibiotic administration (\> 1 hour from fever onset \>38 degrees) is associated with intensive care admissions, prolonged hospital stays, and adverse outcomes. Fluctuations in physiology can precede fever onset by 72 hours in patients with infection. This may provide a window for early detection of infection via wearable technology. The WEARABLES study will combine wearable technology with machine learning to develop an infection prediction model to allow earlier detection and reduce the suffering of children with cancer. Trial Design: This is a non-interventional silent pilot trial to establish a predictive model for infection observed during childhood cancer therapy using data passively captured via wearables. The study will be conducted in patients (5-18 years) with a new cancer diagnosis, currently receiving treatment at The Royal Children's Hospital, and have access to an iPhone (either themselves as an adolescent and young adult or via their parents/guardian). Once consented the wearable device will be paired to the patients or parent/guardians phone, and the WEARABLES app will be downloaded onto the phone. Once the device has been set up correctly, the wearable device will collect a range of vital signs for the duration of the study (4 weeks), and a weekly survey will be sent to check for symptoms and/or hospital admissions for infection. At the end of the 4 weeks, participants will receive a final survey to evaluate the feasibility of using a wearable device for toxicity detection. No further involvement will be asked of participants for this pilot trial. All data collected will be utilized to develop a machine learning model for sepsis/infection before being prospectively validated in a second trial.

Conditions

• Cancer
• Infection
• Digital Health
• Wearable Devices

Interventions

Type	Name	Description
DEVICE	Wearable Device	Wearable device to collect the following health metrics directly from participants for the duration of the study (4 weeks). Health metrics are collected every 15 minutes, except for the ECG which will be collected once per week. Data points: * ECG data (Once per week) * Exercise time * Body Temperature * Heart Rate * Irregular Heart Rhythm * Blood Oxygen Saturation * Respiratory Rate

Timeline

• Start date: 2025-10-15
• Primary completion: 2027-08-01
• Completion: 2027-12-01
• First posted: 2025-06-22
• Last updated: 2025-11-17

Locations

1 site across 1 country: Australia