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Hip-Specific Patient-Reported Outcomes Strongly Correlate With Pain and Physical Function Metrics but not Mental Health Before and After Hip Arthroscopy for Femoroacetabular Impingement Syndrome.

Authors: David WB, Sang L, Sampson H, Brumm Z, Wong SE, Zhang AL
Journal: Arthroscopy, sports medicine, and rehabilitation
mental health psychology open access

Abstract

Pregnancy and postpartum care encompasses medical and nursing services during pregnancy, childbirth, and the postpartum period, with the aim of ensuring maternal and infant health. During this critical period, women undergo not only profound physiological changes but also face considerable psychological stress and emotional challenges. World Health Organization reports about 300,000 annual maternal deaths from pregnancy- or childbirth-related complications, about 80% of which are preventable with timely, adequate care. About 10%–13% of women during pregnancy and postpartum are affected globally, with higher rates in developing regions. Previous studies have shown that personalized care has a positive impact on pregnancy and postpartum outcomes, including improved maternal satisfaction and psychological well-being, enhanced adherence to prenatal and postpartum care, and better clinical outcomes such as early identification and management of postpartum complications, reduced hospital readmissions, and improved breastfeeding. At present, pregnancy and postpartum care mainly consists of face-to-face, telecommunication counseling, home care by nurses, group seminars, and so on. However, these traditional care models face several limitations, including shortages of health care personnel, uneven regional distribution of services, and inadequate timeliness in doctor–patient communication. These challenges hinder continuous, personalized health support in pregnant and postpartum care. As an emerging digital health technology, chatbots have demonstrated considerable potential in pregnancy and postpartum care. They are mostly powered by rule-based artificial intelligence (AI) and utilize natural language processing (NLP) to interact with users conversationally. Characterized by their high feasibility, scalability, and relatively low cost, chatbots offer a practical solution for expanding access to health care services, particularly in resource-constrained settings. Previous studies have highlighted their effectiveness in various domains, including enhanced chronic disease management, better health promotion, increased access to patient education, and improved patient self-management capabilities.