| Autor: |
Guntuku SC; Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.; Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA., Gaulton JS; Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.; Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Seltzer EK; Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.; Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA., Asch DA; Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.; Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA., Srinivas SK; Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Ungar LH; Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.; Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, USA., Mancheno C; Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA., Klinger EV; Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.; Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA., Merchant RM; Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.; Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA. |
| Abstrakt: |
We sought to evaluate whether there was variability in language used on social media across different time points of pregnancy (before, during, and after pregnancy, as well as by trimester and parity). Consenting patients shared access to their individual Facebook posts and electronic medical records. Random forest models trained on Facebook posts could differentiate first trimester of pregnancy from 3 months before pregnancy (F1 score = .63) and from a random 3-month time period (F1 score = .64). Posts during pregnancy were more likely to include themes about family (β = .22), food craving (β = .14), and date/times (β = .13), while posts 3 months prior to pregnancy included themes about social life (β = .30), sleep (β = .31), and curse words (β = .27), and 3 months post-pregnancy included themes of gratitude (β = .17), health appointments (β = .21), and religiosity (β = .18). Users who were pregnant for the first time were more likely to post about lack of sleep (β = .15), activities of daily living (β = .09), and communication (β = .08) compared with those who were pregnant after having a child who posted about others' birthdays (β = .16) and life events (.12). A better understanding about social media timelines can provide insight into lifestyle choices that are specific to pregnancy. |
