CancerWEARABLESNCT07030998
Wearable Technology and Machine Learning for Early Detection and Risk Assessment of Unacceptable Toxicities in a Paediatric Oncology Cohort
An observational study of Cancer and Infection, run by Murdoch Childrens Research Institute at 1 site in Australia.
Trial summary
Data collection study to establish a predictive model of infection observed during childhood cancer therapy using data captured by wearable technology. Read more about this study
- Who is it for?
- Ages 5 to 18 years
- Conditions the study is for
- Cancer, Infection, Digital Health
- What is being tested?
- Wearable Device
- Type of treatment
- Medical device
- How many people
- 150 participants
- Where
- 1 siteAustralia
- Study dates
- 15 Oct 2025to Dec 2027
- How long will I take part?
- 4 Weeks
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 5 to 18 years
- All sexes
- No healthy volunteers
- Child, Adult
Study population: Patients aged 5-18 years currently receiving treatment for cancer at The Royal Children's Hospital
Who may be able to join
- 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).
Who cannot join
- <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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Australia1 site, 1 recruiting
- Recruiting
The Royal Children's Hospital
Parkville, Victoria
Lane Collier, 03 9936 6324, lane.collier@mcri.edu.au
A/Prof Rachel Conyers, 03 9345 5522, rachel.conyers@rch.org.au
About this study
Official title: WEARABLES: Wearable Technology and Machine Learning for Early Detection and Risk Assessment of Unacceptable Toxicities in a Paediatric Oncology Cohort
Data collection study to establish a predictive model of infection observed during childhood cancer therapy using data captured by wearable technology.
Read the 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
- Being tested
- Wearable Device
- Keywords
- Cancer, Paediatric, Infection, Treatment Toxicity, Wearable Device
- Medical subject headings
- Neoplasms, Infections
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Wearable DeviceDevice
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
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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
The acceptability of using wearable devices in children receiving cancer therapies as determined at study completion using the Theoretical Framework of Acceptability (TFA).
Measured: Day 29
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jun 2025
- Study start15 Oct 2025
- Primary completionAug 2027 (estimated)
- Study completionDec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 150 participants (estimated)
- Target duration
- 4 Weeks
Sponsor and investigators
- Lead sponsor
- Murdoch Childrens Research Institute
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07030998
- Sponsor's protocol ID
- 111206
- Acronym
- WEARABLES
- Status last verified
- Oct 2025
- First submitted
- 15 May 2025
- Last updated
- 17 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The de-identified data set collected for this trial will be available 12 months after publication of the primary outcome. The study protocol, analysis plan and consent forms will also be available. The data may be obtained from the Murdoch Children's Research Institute by emailing MCTC@mcri.edu.au.
Study at a glance
From the registry record, last updated by the sponsor on 17 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- WEARABLES: Wearable Technology and Machine Learning for Early Detection and Risk Assessment of Unacceptable Toxicities in a Paediatric Oncology Cohort
- ClinicalTrials.gov ID
- NCT07030998
- Acronym / sponsor ID
- WEARABLES / 111206
- Conditions
- Cancer, Infection, Digital Health, Wearable Devices
- Keywords
- Cancer, Paediatric, Infection, Treatment Toxicity, Wearable Device
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 150 participants (estimated)
- Lead sponsor
- Murdoch Childrens Research InstituteUniversity, hospital or other
- Study start
- 15 Oct 2025 (actual)
- Primary completion
- Aug 2027 (estimated)
- Study completion
- Dec 2027 (estimated)
- First posted
- 22 Jun 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Target follow-up
- 4 Weeks
Who may qualify
- Ages
- 5 to 18 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
- Study population
- Patients aged 5-18 years currently receiving treatment for cancer at The Royal Children's Hospital
Key inclusion criteria (8 listed)
- 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)
Key exclusion criteria (4 listed)
- <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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Wearable DeviceWearable 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 | Device | – |
Primary outcome
Changes in cardiac electrical activity patterns on Electrocardiogram (ECG)
Time frame: Baseline (Day 1), Day 8, Day 15, Day 22, Day 29
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.
Changes in physical activity
Time frame: Baseline (Day 1) and every 15 minutes until Study completion at Day 29
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.
Changes in heart rate
Time frame: Baseline (Day 1) and every 15 minutes until Study completion at Day 29
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.
Changes in heart rhythm
Time frame: Baseline (Day 1) and every 15 minutes until Study completion at Day 29
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.
Changes in blood oxygen saturation
Time frame: Baseline (Day 1) and every 15 minutes until Study completion at Day 29
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.
Changes in respiratory rate
Time frame: Baseline (Day 1) and every 15 minutes until Study completion at Day 29
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.
Infection-Related Hospital Admission
Time frame: Baseline (Day 1), Day 8, Day 15, Day 22, Day 29
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.
1 secondary outcome
The acceptability of using (and not using) wearable devices in children receiving cancer therapies
Time frame: Day 29
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Nov 2025.
- Individual participant dataSharing planned: Yes. The de-identified data set collected for this trial will be available 12 months after publication of the primary outcome. The study protocol, analysis plan and consent forms will also be available. The data may be obtained from the Murdoch Children's Research Institute by emailing MCTC@mcri.edu.au.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cancer and Infection, run by Murdoch Childrens Research Institute at 1 site in Australia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 17 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Australia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Lane Collier (03 9936 6324, lane.collier@mcri.edu.au), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Changes in cardiac electrical activity patterns on Electrocardiogram (ECG) (time frame: Baseline (Day 1), Day 8, Day 15, Day 22, Day 29). The study lists 7 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Nov 2025.