Migraine can cause different pain activity patterns. This cross-sectional study examines the relationship between pain activity patterns and physical and psychological aspects and sleep quality in women with migraine. Women diagnosed with migraine (n = 129) were reached through social media and announcements. Outcome measures were Pattern of Activity Measure-Pain (POAM-P) (avoidance, overdoing, pacing), Migraine Disability Assessment Scale (MIDAS), International Physical Activity Questionnaire-Short Form (IPAQ-SF), Depression Anxiety Stress Scale-21 (DASS-21), the Pittsburgh Sleep Quality Index (PSQI). While there was a negative correlation between the POAMP-avoidance and the IPAQ-SF rho = -0.178), there were positive correlations between the POAMP-avoidance and the MIDAS (rho = 0.454), the DASS-21-depression (rho = 0.413), the DASS-21-anxiety (rho = 0.321), and the DASS-21-stress (rho = 0.446). There were positive correlations between the POAMP-overdoing, and the DASS-21-depression (rho = 0.229), the DASS-21-stress (rho = 0.207), and the PSQI (rho = 0.217). There were also positive correlations between the POAMP-pacing and the MIDAS (rho = 0.283), the DASS-21-depression (rho = 0.250), and the DASS-21-anxiety (rho = 0.213) (p < .05). Pain activity patterns in women with migraines were associated with their disability, physical activity, psychological state, and sleep quality. Determining treatment based on pain activity patterns can improve migraine treatment outcomes.

Is there a correlation between the severity of symptoms and vitamin D levels in pregnancy with hyperemesis gravidarum? - Turkish Journal of Obstetrics and Gynecology

Aim: In outbreaks, public concern is reflected in search behavior. Examining the health literacy of the population and predicting before the diagnosis of cases may benefit the outbreak management. This study aims to evaluate the association of search behavior with the number of new confirmed cases in the affected countries by the Coronavirus disease 2019 (COVID-19) pandemic. This retrospective study is based on monitoring search behavior with an infodemiology and infoveillance approach. Materials and Method: Google TrendsTM was used to investigate Internet search behavior related to COVID-19 for 10 countries from February 15, 2020, to November 10, 2020. Spearman’s rank correlation and time-lag correlation were used to determine the correlation with a delay of -30 days to +30 days between public interest and new daily confirmed cases. Results: The level of COVID-19-related interest peaked about 33 days before the first peak in the number of cases. The correlation gradually decreased in seven countries towards the peak of cases. Spearman's rank correlations between Google searches and the number of new confirmed cases showed a negative correlation in Argentina, Brazil, India, and the United Kingdom (p<0.001), and a positive correlation in Italy, Turkey (p<0.001), and Russia (p=0.017). Eight countries had negative correlations in the increasing phase (p<0.001), and eight countries had strong to moderate positive correlations in the decreasing phase (p<0.001). Conclusion: The findings showed that searches on Google TrendsTM increased before new cases in the countries. Amaç: Salgınlarda, halkın endişesi İnternet arama davranışına yansır. Nüfusun sağlık okuryazarlığının incelenmesi ve vakalara tanı konulmadan önce tahmin yapılması ülkelerin salgın yönetimine fayda sağlayabilir. Bu çalışma Koronavirüs hastalığı 2019 (COVID-19) pandemisinden etkilenen ülkelerdeki doğrulanmış yeni vaka sayısı ile arama davranışı arasındaki ilişkiyi değerlendirmeyi amaçlamaktadır. Bu retrospektif çalışma, arama davranışının bir infodemiyoloji ve bilgi gözetimi yaklaşımıyla izlenmesine dayanmaktadır. Gereç ve Yöntem: On ülke için 15 Şubat 2020 tarihinden 10 Kasım 2020 tarihine kadar COVID-19 ile ilgili İnternet arama davranışını araştırmak için Google TrendsTM kullanıldı. Halkın ilgisi ile günlük doğrulanmış yeni vakalar arasındaki -30 gün ila +30 gün gecikmeli korelasyonu belirlemek için Spearman rank korelasyonu ve zaman gecikmesi korelasyonu kullanıldı. Bulgular: COVID-19 ile ilgi düzeyi, vaka sayısındaki ilk zirveden yaklaşık 33 gün önce zirve yaptı. Korelasyon yedi ülkede vakaların zirvesine doğru kademeli olarak azaldı. Google aramaları ile doğrulanmış yeni vaka sayısı arasındaki Spearman sıralama korelasyonları; Arjantin, Brezilya, Hindistan ve Birleşik Krallık'ta negatif bir korelasyon (p<0.001) ve İtalya, Türkiye (p<0.001) ve Rusya'da pozitif bir korelasyon gösterdi. (p=0.017). Sekiz ülke artan fazda negatif korelasyon vardı (p<0.001), sekiz ülke ise azalan fazda güçlü ila orta derecede pozitif korelasyon vardı (p<0.001). Sonuç: Bulgular, ülkelerdeki yeni vakalardan önce Google TrendsTM'deki aramaların arttığını gösterdi.