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Trial details imported from ClinicalTrials.gov
For full trial details, please see the original record at
https://clinicaltrials.gov/study/NCT07030998
Registration number
NCT07030998
Ethics application status
Date submitted
15/05/2025
Date registered
22/06/2025
Date last updated
2/07/2025
Titles & IDs
Public title
Wearable Technology and Machine Learning for Early Detection and Risk Assessment of Unacceptable Toxicities in a Paediatric Oncology Cohort
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Scientific title
WEARABLES: Wearable Technology and Machine Learning for Early Detection and Risk Assessment of Unacceptable Toxicities in a Paediatric Oncology Cohort
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Secondary ID [1]
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111206
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Universal Trial Number (UTN)
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Trial acronym
WEARABLES
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Linked study record
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Health condition
Health condition(s) or problem(s) studied:
Cancer
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Infection
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Digital Health
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Wearable Devices
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Condition category
Condition code
Infection
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Studies of infection and infectious agents
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Infection
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Other infectious diseases
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Intervention/exposure
Study type
Observational [Patient Registry]
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Patient registry
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Target follow-up duration
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Target follow-up type
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Description of intervention(s) / exposure
Treatment: Devices - Wearable Device
Treatment: Devices: 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
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Intervention code [1]
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Treatment: Devices
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Comparator / control treatment
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Control group
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Outcomes
Primary outcome [1]
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Changes in cardiac electrical activity patterns on Electrocardiogram (ECG)
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Assessment method [1]
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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.
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Timepoint [1]
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Baseline (Day 1), Day 8, Day 15, Day 22, Day 29
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Primary outcome [2]
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Changes in physical activity
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Assessment method [2]
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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.
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Timepoint [2]
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Baseline (Day 1) and every 15 minutes until Study completion at Day 29
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Primary outcome [3]
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Changes in heart rate
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Assessment method [3]
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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.
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Timepoint [3]
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Baseline (Day 1) and every 15 minutes until Study completion at Day 29
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Primary outcome [4]
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Changes in heart rhythm
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Assessment method [4]
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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.
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Timepoint [4]
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Baseline (Day 1) and every 15 minutes until Study completion at Day 29
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Primary outcome [5]
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Changes in blood oxygen saturation
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Assessment method [5]
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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.
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Timepoint [5]
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Baseline (Day 1) and every 15 minutes until Study completion at Day 29
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Primary outcome [6]
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Changes in respiratory rate
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Assessment method [6]
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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.
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Timepoint [6]
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Baseline (Day 1) and every 15 minutes until Study completion at Day 29
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Primary outcome [7]
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Infection-Related Hospital Admission
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Assessment method [7]
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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.
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Timepoint [7]
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Baseline (Day 1), Day 8, Day 15, Day 22, Day 29
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Secondary outcome [1]
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The acceptability of using (and not using) wearable devices in children receiving cancer therapies
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Assessment method [1]
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The acceptability of using wearable devices in children receiving cancer therapies as determined at study completion using the Theoretical Framework of Acceptability (TFA).
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Timepoint [1]
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Day 29
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Eligibility
Key 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).
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Minimum age
5
Years
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Maximum age
18
Years
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Sex
Both males and females
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Can healthy volunteers participate?
No
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Key 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.
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Study design
Purpose
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Duration
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Selection
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Timing
Prospective
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Statistical methods / analysis
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Recruitment
Recruitment status
Not yet recruiting
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Data analysis
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Reason for early stopping/withdrawal
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Other reasons
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Date of first participant enrolment
Anticipated
1/08/2025
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Actual
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Date of last participant enrolment
Anticipated
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Actual
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Date of last data collection
Anticipated
1/12/2027
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Actual
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Sample size
Target
150
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Accrual to date
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Final
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Recruitment in Australia
Recruitment state(s)
VIC
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Recruitment hospital [1]
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The Royal Children's Hospital - Parkville
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Recruitment postcode(s) [1]
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- Parkville
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Funding & Sponsors
Primary sponsor type
Other
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Name
Murdoch Childrens Research Institute
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Address
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Country
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Ethics approval
Ethics application status
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Summary
Brief summary
Data collection study to establish a predictive model of infection observed during childhood cancer therapy using data captured by wearable technology.
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Trial website
https://clinicaltrials.gov/study/NCT07030998
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Trial related presentations / publications
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Public notes
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Contacts
Principal investigator
Name
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Address
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Country
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Phone
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Fax
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Email
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Contact person for public queries
Name
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Lane Collier
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Address
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Country
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Phone
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03 9936 6324
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Fax
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Email
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[email protected]
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Contact person for scientific queries
Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
Yes
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What data in particular will be shared?
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
[email protected]
.
Supporting document/s available: Study protocol, Statistical analysis plan (SAP), Informed consent form (ICF)
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When will data be available (start and end dates)?
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
[email protected]
.
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.
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Available to whom?
The data may be obtained from the Murdoch Children's Research Institute by emailing
[email protected]
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Available for what types of analyses?
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How or where can data be obtained?
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What supporting documents are/will be available?
No Supporting Document Provided
Results publications and other study-related documents
No documents have been uploaded by study researchers.
Results not provided in
https://clinicaltrials.gov/study/NCT07030998
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