To assess the ability of a machine learning algorithm to accurately detect fussing and crying time in infants using accelerometery data collected from a wearable device, compared to the Barr's parent-/caregiver-completed behaviour diary.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
DEVICE_FEASIBILITY
Masking
NONE
Enrollment
13
Accelerometer device
Atlantia Clinical Trials
Cork, Ireland
RECRUITINGDevice feasibility: Comparison of device-generated versus diary-reported crying and fussing time data
Part 1: Comparison of daily crying and fussing time data generated by accelerometer and machine-learning versus crying and fussing time data reported by parental Barrs diary
Time frame: 4 days
Device feasibility: Comparison of device-generated versus diary-reported crying and fussing time data
Part 2: Comparison of daily crying and fussing time data generated by accelerometer and machine-learning versus crying and fussing time data reported by parental Barrs diary
Time frame: 7 days
Device feasibility: Comparison of device-generated versus diary-reported crying time data
Part 2: Comparison of daily crying time data generated by accelerometer and machine-learning versus crying time data reported by parental Barrs diary
Time frame: 7 days
Device feasibility: Comparison of device-generated versus diary-reported fussing time data
Part 2: Comparison of daily fussing time data generated by accelerometer and machine-learning versus fussing time data reported by parental Barrs diary
Time frame: 7 days
Device feasibility: Comparison of device-generated versus diary-reported sleeping time data
Part 2: Comparison of daily sleeping time data generated by accelerometer and machine-learning versus sleeping time data reported by parental Barrs diary
Time frame: 7 days
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