Accepted for/Published in: JMIR Formative Research
Date Submitted: Apr 8, 2021
Date Accepted: Dec 30, 2021
Analysability of photoplethysmographic smartwatch data by the Preventicus Heartbeats algorithm during everyday life: Feasibility Study
ABSTRACT
Background:
Continuous heart rate monitoring via mobile health technologies based on photoplethysmography (PPG) has great potential for the early detection of sustained cardiac arrhythmias such as atrial fibrillation. However, PPG measurements are impaired by motion artifacts. Thus, the purpose of this investigation was to evaluate the analysability of smartwatch-derived PPG data during everyday life and to determine the relationship between analysability and the activity level of the participant.
Objective:
The purpose of this investigation was to evaluate the analysability of smartwatch-derived PPG data during everyday life and to determine the relationship between analysability and the activity level of the participant.
Methods:
Forty-one (♀: 19 / ♂: 22) cardiological healthy adults (age: 19-79 years) continuously wore a smartwatch equipped with a PPG sensor and a three-dimensional accelerometer (Cardio Watch 287, Manufacture Modules Technologies SA., Geneva, Switzerland) for a period of 24 hours that represented their individual daily routine. For each participant, smartwatch data were analysed on a one-minute-basis by an algorithm designed for heart rhythm analysis (Preventicus Heartbeats, Preventicus GmbH, Jena, Germany). As outcomes the percentage of analysable data (PAD) and the mean acceleration (ACC) were calculated. To map changes of ACC and PAD in the process of a day, the 24-hour period was divided into eight sub-intervals comprising three hours each.
Results:
The univariate analysis of variance proofed a large effect (ηp² > 0.6, P < 0.001) of the time interval on ACC and PAD. Parameter PAD ranged between 34 and 100% and averaged to 71.5% for the whole day which is equivalent to a period of 17.2 hours. Between midnight and 6 am the mean values were highest for PAD (> 94%) and lowest for ACC (< 6 · 10-3 m/s²), respectively. Regardless of the daytime the correlation between PAD and ACC was on a large level (r = -0.64). A linear regression analysis for the averaged data resulted in an almost perfect coefficient of determination (r² = 0.99).
Conclusions:
This study showed a large relationship between the activity level and the analysability of smartwatch-derived PPG data. Given the high yield of analysable data during the night continuous arrhythmia screening seems particularly effective during sleep phases.
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