Data collection study to establish a predictive model of infection observed during childhood cancer therapy using data captured by wearable technology.
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
<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.
Patients aged 5-18 years currently receiving treatment for cancer at The Royal Children's Hospital
Case-only
Prospective
Wearable Device
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
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.
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.
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.
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.
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).
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