Clinical trial · Observational
Wearable Technology and Machine Learning for Early Detection and Risk Assessment of Unacceptable Toxicities in a Paediatric Oncology Cohort
WEARABLES: Wearable Technology and Machine Learning for Early Detection and Risk Assessment of Unacceptable Toxicities in a Paediatric Oncology Cohort
NCT07030998CI-TRIAL-00097273WEARABLESrecruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Data collection study to establish a predictive model of infection observed during childhood cancer therapy using data captured by wearable technology.
Conditions
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cancer | Malignant Neoplasm | ALIAS | 0.90 |
| Digital Health | — | UNRESOLVED | — |
| Infection | — | UNRESOLVED | — |
| Wearable Devices | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Wearable Device | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (7)
- measure
- Changes in cardiac electrical activity patterns on Electrocardiogram (ECG)
- timeFrame
- Baseline (Day 1), Day 8, Day 15, Day 22, Day 29
- description
- 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.
- measure
- Changes in physical activity
- timeFrame
- Baseline (Day 1) and every 15 minutes until Study completion at Day 29
- description
- 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.
- measure
- Changes in heart rate
- timeFrame
- Baseline (Day 1) and every 15 minutes until Study completion at Day 29
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 5 Years
- Maximum age
- 18 Years
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available
No reference posted for this study.