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

NCT07030998 | Recruiting

• 1 other identifier: 111206
• Study Type: observational
• Target: 150
• Locations: 1 country
• Sites: 1

Brief Summary

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

Conditions

Cancer; Infection; Digital Health; Wearable Devices

Keywords

Cancer; Paediatric; Infection; Treatment Toxicity; Wearable Device

Outcome Measures

Primary Outcomes (7)

• Changes in cardiac electrical activity patterns on Electrocardiogram (ECG)

  ECG data will be collected once per week over a 4-week period for each participant to identify changes in cardiac electrical activity that may be associated with early infection. These data will be used as input features for a machine learning model aimed at predicting infection risk in children receiving cancer treatment.

  Baseline (Day 1), Day 8, Day 15, Day 22, Day 29

• Changes in physical activity

  Exercise data will be collected every 15 minutes over a 4-week period for each participant to identify changes that may correlate with early signs of infection. These data points will be used as input features for a machine learning model aimed at predicting infection risk in children receiving cancer treatment.

  Baseline (Day 1) and every 15 minutes until Study completion at Day 29

• Changes in heart rate

  Heart rate data will be collected every 15 minutes over a 4-week period for each participant to identify changes that may be indicative of infection. These data points will be used as input features for a machine learning model aimed at predicting infection risk in children receiving cancer treatment.

  Baseline (Day 1) and every 15 minutes until Study completion at Day 29

• Changes in heart rhythm

  Detection of irregular heart rhythms that may reflect early signs of infection. These data points will be used as input features for a machine learning model aimed at predicting infection risk in children receiving cancer treatment.

  Baseline (Day 1) and every 15 minutes until Study completion at Day 29

• Changes in blood oxygen saturation

  Blood oxygen saturation data will be collected every 15 minutes over a 4-week period for each participant to identify changes that may be indicative of infection. These data points will be used as input features for a machine learning model aimed at predicting infection risk in children receiving cancer treatment.

  Baseline (Day 1) and every 15 minutes until Study completion at Day 29

• Changes in respiratory rate

  Respiratory rate data will be collected every 15 minutes over a 4-week period for each participant to identify changes that may be indicative of infection. These data points will be used as input features for a machine learning model aimed at predicting infection risk in children receiving cancer treatment.

  Baseline (Day 1) and every 15 minutes until Study completion at Day 29

• Infection-Related Hospital Admission

  An infection survey will be completed by patients once per week over a 4-week period to identify confirmed episodes of infections requiring admission to hospital. This data will be used to determine when patients are controls (non-infectious) vs cases (infectious), and used as an input feature for a machine learning model.

  Baseline (Day 1), Day 8, Day 15, Day 22, Day 29

Secondary Outcomes (1)

• The acceptability of using (and not using) wearable devices in children receiving cancer therapies

  Day 29

Interventions

Wearable Device 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

Eligibility Criteria

• Age: 5 Years - 18 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Patients aged 5-18 years currently receiving treatment for cancer at The Royal Children's Hospital

You may qualify if:

• 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).

You may not qualify if:

• \<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.

Sponsors & Collaborators

• Murdoch Childrens Research Institute: lead

Study Sites (1)

The Royal Children's Hospital

Parkville, Victoria, Australia

RECRUITING

MeSH Terms

Conditions

Neoplasms; Infections

Interventions

Wearable Electronic Devices

Intervention Hierarchy (Ancestors)

Electrical Equipment and Supplies; Equipment and Supplies

Central Study Contacts

Lane Collier

CONTACT

03 9936 6324; lane.collier@mcri.edu.au

A/Prof Rachel Conyers

CONTACT

03 9345 5522; rachel.conyers@rch.org.au

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Target Duration: 4 Weeks
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 15, 2025
• First Posted: June 22, 2025
• Study Start: October 15, 2025
• Primary Completion (Estimated): August 1, 2027
• Study Completion (Estimated): December 1, 2027
• Last Updated: November 17, 2025

Record last verified: 2025-10

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF
• Time Frame: The de-identified data set collected for this trial will be available 12 months after publication of the primary outcome. The data may be obtained from the Murdoch Children's Research Institute by emailing MCTC@mcri.edu.au. Prior to accessing any data, the following would be required: a data access agreement must be signed by all relevant parties, the investigators of the study must see and approve the analysis plan describing how the data will be analyzed. There must also be an agreement around appropriate acknowledgement in any future publications.
• Access Criteria: The data may be obtained from the Murdoch Children's Research Institute by emailing MCTC@mcri.edu.au.

Locations

AU (1)