Psychology · Social Psychology
A study of 240 adults from Western and East Asian cultural contexts found that selfies and self-descriptions together were more useful for modeling self-reported body satisfaction than either type of material alone. The associations differed between the groups, but the study did not show that social media behavior causes body satisfaction or provide a clinical assessment.
Interactive engagement with trauma-related online content was associated with more depressive, anxiety and posttraumatic stress symptoms after a community trauma. Passive viewing of graphic material was not associated with higher distress and was linked to lower symptom prevalence.
An umbrella review of 22 systematic reviews found highly credible evidence of mental-health disparities affecting LGBTQIA+ populations. Compared with cisgender heterosexual people, lesbian, gay and bisexual populations had higher risks of depressive disorders and suicidal behavior, while bisexual people had higher odds of anxiety symptoms and non-suicidal self-injury than lesbian and gay people.
In a German study, 23.6% of young people were classified as having at-risk social media use and 4.9% as having pathological use. Loneliness, rising feelings of exclusion and heavy social media use were associated with higher odds, while family cohesion, physical activity and better sleep were associated with lower odds.
Rural U.S. counties expressed higher life satisfaction, while urban counties expressed greater momentary happiness, according to an analysis of 2.6 billion geolocated tweets from 2014 to 2022. The findings suggest that where people live may relate differently to reflective judgments about life and to day-to-day feelings.
A study found that increases in online mentions of the Benadryl Challenge were followed by a significant rise in adolescent-related calls to US poison centers about diphenhydramine. Those calls remained elevated for more than 25 days after the first spike in online mentions.
In a national US study, adolescents and young adults exposed to a suicide attempt within their social networks had higher odds of a later suicide attempt and higher depressive symptoms over time. The exposure was not linked to the onset of suicidal thoughts.
A review of recent studies finds that generative AI tools may make mental-health support more accessible and provide personalized help around the clock. It also identifies risks including emotional reliance, misinformation, privacy concerns, bias and limited clinical validation.
Across 11 U.S. surveys involving 183,000 adults, higher social media use was consistently associated with self-reported depression. The association in the researchers’ models shifted from neutral to positive at about 150 minutes of daily use, highlighting a question for future longitudinal and experimental studies.
An AI model predicted suicidal thoughts and behaviors more accurately than traditional statistical methods among a group of 3,901 Taiwanese university students. The researchers say its interpretable design could support screening, but the study does not show whether using the model improves care or prevents suicide-related outcomes.
In a randomized study of 388 university students in China, an AI counseling agent improved perceived rapport and willingness to disclose. It recognized general risks better, but was less reliable with crisis-level disclosures, pointing to a supporting role alongside trained counselors.
A study of female university students living in dormitories in Baghdad links social media use to worse mental health, including more anxiety, depression, and social isolation. The researchers report that longer daily use and entertainment or social comparison content were associated with these mental health indicators.
A study in Scientific Reports tested an AI system that predicts suicide-related behaviour within 30 days of an emergency department visit using structured triage information available at admission. The best-performing model, a LightGBM system, showed AUROC 0.88 and good recall on an independent test set, and highlight likely drivers such as prior psychiatric history, age, and physiological variables.
A national U.S. survey found that 24% of adults use general-purpose AI chatbots for mental health. Researchers estimate that 14–18 million U.S. adults may use these tools for mental health in 2024, and that users report poorer mental health and greater difficulty accessing traditional care.
Generative AI chatbots are increasingly used by young people for mental health support, but their safety for suicide-risk situations is not well understood. In a review, researchers found that existing studies often test chatbots with simple, one-turn questions and that results worsen when risk is indirect or gradually revealed.
