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Implications of Data Extraction and Processing of Electronic Health Records for Epidemiological Research: Observational Study

Implications of Data Extraction and Processing of Electronic Health Records for Epidemiological Research: Observational Study

According to Austin [36], absolute values of the SD of 0.2, 0.5, and 0.8 correspond to small, medium, and large differences, respectively [37]. Remaining cautious to some extent, and in order to disclose small differences, differences with absolute values of the SD>0.2 were considered to be significant. All analyses were performed using R (version 4.2.3; R Foundation for Statistical Computing) and RStudio (version 2022.02.1+461 “Prairie Trillium”; R Foundation for Statistical Computing).

Melissa H J van Essen, Robin Twickler, Yvette M Weesie, Ilgin G Arslan, Feikje Groenhof, Lilian L Peters, Isabelle Bos, Robert A Verheij

J Med Internet Res 2025;27:e64628

Smartphone Ecological Momentary Assessment and Wearable Activity Tracking in Pediatric Depression: Cohort Study

Smartphone Ecological Momentary Assessment and Wearable Activity Tracking in Pediatric Depression: Cohort Study

Data in the JSON files were converted to csv using a combination of standard and pandas [51] libraries in Py Charm [52] (Python version 3.9; Python Software Foundation) and rearranged for analysis using tidyr [53] and dplyr [54] in R [55]. Following others’ work [56] in the field, we extracted numerous sleep features from the data collected using the Fitbit device. These are related to sleep architecture, quality, and stability (Table S2 in Multimedia Appendix 1 contains details or individual metrics).

Jimena Unzueta Saavedra, Emma A Deaso, Margot Austin, Laura Cadavid, Rachel Kraff, Emma E M Knowles

JMIR Form Res 2025;9:e66187