Wearables and machine learning to spot infections early in children and teens with cancer
WEARABLES: Wearable Technology and Machine Learning for Early Detection and Risk Assessment of Unacceptable Toxicities in a Paediatric Oncology Cohort
This project will try to use data from wearable devices and machine learning to detect infections earlier in children and adolescents receiving cancer treatment.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 150 (estimated) |
| Ages | 5 Years to 18 Years |
| Sex | All |
| Sponsor | Murdoch Childrens Research Institute Academic / other |
| Locations | 1 site (Parkville, Victoria) |
| Trial ID | NCT07030998 on ClinicalTrials.gov |
What this trial studies
This is a non‑interventional, silent pilot that passively collects wearable data from children and adolescents aged 5–18 undergoing cancer therapy at The Royal Children's Hospital. Participants wear an Apple Watch during waking hours for about 4 weeks while the study app on a compatible iPhone records physiological signals and activity. The collected data will be used to train machine learning models to identify patterns that may precede infection or fever by up to 72 hours. No treatments are assigned by the study; the goal is to build and validate a predictive model for future clinical use.
Who should consider this trial
Good fit: Children and young people aged 5–18 receiving cancer treatment at The Royal Children's Hospital who can wear an Apple Watch for four weeks and have an iPhone 8 or later are ideal candidates.
Not a fit: Children under 5, those unable or unwilling to wear the Apple Watch, those without a compatible iPhone, or patients not receiving care at the enrolling hospital will not be eligible and are unlikely to benefit from this project.
Why it matters
Potential benefit: If successful, earlier detection could allow faster treatment and reduce severe infection-related complications and hospital stays.
How similar studies have performed: Preliminary work and pilot studies have shown that wearable signals can precede fever onset, but using machine learning specifically for infection prediction in pediatric oncology remains largely experimental.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Paediatric, adolescent or young adult diagnosis of cancer AND receiving therapy placing them at risk of infection * Receiving cancer treatment at The Royal Children's Hospital * Patients aged 5-18 years at time of the eligibility screening * If aged \< 16 years, parent or guardian able to provide consent * iPhone 8 or later (iOS must be up to date/updated at time of enrolment) * At least 10MB of iPhone storage for WEARABLES app and data collection. * Willing and able to wear a wearable device for a period of 4 weeks (during waking hours). * Consent to data being shared to the WEARABLES app (owned by the research team). Exclusion Criteria: * \<5 years of age. * \<16 years of age without guardian or parent consent. * Aged 16-18 and unable to provide consent. * Participant did not consent to wearing Apple Watch for a period of 4 weeks.
Where this trial is running
Parkville, Victoria
- The Royal Children's Hospital — Parkville, Victoria, Australia (Recruiting)
Study contacts
- Study coordinator: Lane Collier
- Email: lane.collier@mcri.edu.au
- Phone: 03 9936 6324
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.