The majority of patients benefit from psychotherapeutic treatment. However, many fail to maintain their treatment gains following discharge. In a controlled study, we investigated the effectiveness of internet chat groups in preventing relapse following inpatient treatment. One hundred and fifty-two patients were assessed with the Longitudinal Follow-up Evaluation (LIFE) 1 year after discharge from the hospital. Kaplan Meier survival analyses showed that significantly fewer chat participants (22.2%) than control participants (46.5%) experienced a relapse. Additional analyses yielded a significant difference in the relapse rates of chat and control participants depending on their utilization of outpatient treatment after discharge. The results confirm that technology-enhanced interventions are effective in maintaining treatment gains. Implications of the findings for health care provision are discussed.
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http://dx.doi.org/10.1080/10503307.2010.547530 | DOI Listing |
BMC Health Serv Res
January 2025
Department of Psychiatry, Faculty of Medicine, Recep Tayyip Erdogan University, Rize, Turkey.
Background: Many variables may affect approaches of psychiatrists to methamphetamine-associated psychotic disorder (MAP) treatment. This study was aimed to reach adult psychiatrists actively practicing in Turkey through an internet-based survey and to determine their practices and attitudes to MAP treatment.
Methods: In this internet-based study, participants were divided into three groups based on their answers: Those who do not follow-up any MAP patient were group 1 (n = 78), partially involved in the treatment process of at least one patient diagnosed with MAP were group 2 (n = 128), completely involved in the treatment process of at least one patient diagnosed with MAP were group 3 (n = 202).
J Med Internet Res
December 2024
Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Background: Primary hypertension (PH) poses significant risks to children and adolescents. Few prediction models for the risk of PH in children and adolescents currently exist, posing a challenge for doctors in making informed clinical decisions.
Objective: This study aimed to investigate the incidence and risk factors of PH in Chinese children and adolescents.
J Med Internet Res
December 2024
University Clinic for Interdisciplinary Orthopedic Pathways (UCOP), Elective Surgery Center, Silkeborg Regional Hospital, Silkeborg, Denmark.
Background: Access to clear and comprehensible health information is crucial for patient empowerment, leading to improved self-care, adherence to treatment plans, and overall health outcomes. Traditional methods of information delivery, such as written documents and oral communication, often result in poor memorization and comprehension. Recent innovations, such as animation videos, have shown promise in enhancing patient understanding, but comprehensive investigations into their effectiveness across various health care settings are lacking.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China (Hong Kong).
Background: Obesity could compromise people's health and elevate the risk of numerous severe chronic conditions and premature mortality. Young adults are at high risk of adopting unhealthy lifestyles related to overweight and obesity, as they are at a phase marked by several significant life milestones that have been linked to weight gain. They gain weight rapidly and excess adiposity mostly accrues, compared with middle-aged and older adults when weight stabilizes or even decreases.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé - LIMICS, Inserm, Université Sorbonne Paris-Nord, Sorbonne Université, Paris, France.
Background: Artificial intelligence (AI) applied to real-world data (RWD; eg, electronic health care records) has been identified as a potentially promising technical paradigm for the pharmacovigilance field. There are several instances of AI approaches applied to RWD; however, most studies focus on unstructured RWD (conducting natural language processing on various data sources, eg, clinical notes, social media, and blogs). Hence, it is essential to investigate how AI is currently applied to structured RWD in pharmacovigilance and how new approaches could enrich the existing methodology.
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