Risk and early signs of PTSD in people indirectly exposed to October 7 events
Yamin, D.; Lev-Ari, S.; Mofaz, M.; Elias, R.; Spiegel, D.; Yechezkel, M.; L. Brandeau, M.; Shmueli, E.
Show abstract
The coordinated terrorist attacks on October 7, 2023, resulted in catastrophic atrocities, and marked the beginning of the 2023 Israel-Hamas war. The overwhelming coverage by mainstream and social media, characterized by extreme details and graphic images, vividly transported viewers to the horrifying scene. It remains unclear to what extent such indirect exposure influences the occurrence of stress, anxiety, and post-traumatic symptoms. We analyzed data from a three-year prospective study in which 4,797 participants received smartwatches and completed daily questionnaires, supplemented by a nationwide clinical survey with 2,536 participants. Among the participants not directly exposed, we estimated PTSD prevalence to be 22.9-36.0% and moderate to severe anxiety prevalence to be 22.9-55.32%, with 752,057 daily questionnaires before and after October 7 further indicating higher stress levels than those reported in previous events, including political disputes, the COVID-19 pandemic, and past armed conflicts. The occurrences of PTSD and anxiety are well explained by increased and persistent news consumption, and especially by the availability of gory videos on social media. Continuous monitoring of participants via smartwatches and daily questionnaires further revealed considerable differences in stress, mood, step counts, sleep quality, and duration in the first week after the October 7 events among those who later developed PTSD. This study demonstrates the unprecedented amplifying effect of mass media on mental health in terror and war settings and highlights the potential of continuous monitoring for early detection and prompt treatment of those in need.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Stress-related emotional and behavioural impact following the first COVID-19 outbreak peak 95%
- Altered gene expression and PTSD symptom dimensions in World Trade Center responders 93%
- Understanding the comorbidity between posttraumatic stress severity and coronary artery disease using genome-wide information and electronic health records 92%
Similar papers in this journal
- Potential Neurocognitive Biomarkers for Post Traumatic Stress Disorder (PTSD) Severity in Recent Trauma Survivors 93%
- Emotional Adaptation During A Crisis: Decline in Anxiety and Depression After the Initial Weeks of COVID-19 in the United States 93%
- Stress-induced change in salience network coupling prospectively predicts trauma-related symptoms 92%
Similar papers in this journal
- Heterogeneity in COVID-19 Pandemic-Induced Lifestyle Stressors and Predicts Future Mental Health in Adults and Children in the US and UK 92%
- The impact of the initial and 2 nd national COVID-19 lockdown on mental health in young people with and without pre-existing depressive symptoms 90%
- Divergent transcriptomic profiles in depressed individuals with hyper- and hypophagia implicating inflammatory status 90%
Similar papers in this journal
- Long-term Trajectories of Depressive Symptoms in Deployed Military Personnel: A 10-year prospective study 91%
- The Genetic Relationships Between Post-Traumatic Stress Disorder and Its Corresponding Neural Circuit Structures 90%
- Screening for Post-Traumatic Stress Disorder following Childbirth using the Peritraumatic Distress Inventory 90%
Similar papers in this journal
- Cross-continental environmental and genome-wide association study on children and adolescent anxiety and depression 91%
- Mental Health Impact of COVID-19: A global study of risk and resilience factors 91%
- Patients with affective disorders profit most from telemedical treatment: Evidence from a naturalistic patient cohort during the COVID-19 pandemic 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.