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We followed the methods by Yu et al [38] to extract some features from the same Fitbit and survey data, and engineered extra features to accommodate our objectives.
Raw data from Fitbit included minute-by-minute steps and heart rate as well as the start time, end time, duration, and efficiency of each sleep period. From raw heart rate data, we computed their average, SD, and sample entropy.
JMIR Form Res 2025;9:e65000
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These studies have used the following metrics to evaluate the well-being prediction performance: accuracy in classification and mean absolute error (MAE) in regression models, calculated as follows:
Yu et al [13] used passive mobile phone data (eg, phone calls, SMS text messages, screen usage) and wearable sensor data (electrodermal activity, skin temperature, body accelerometer) from 243 college students to develop a long short-time transfer learning neural network model that could predict the students’ next-day
JMIR Res Protoc 2021;10(3):e24799
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