There are about 8563 clinical studies being (or have been) conducted in Sweden. The country of the clinical trial is determined by the location of where the clinical research is being studied. Most studies are often held in multiple locations & countries.
Physical activity (PA) is one of the few behaviors that individuals can change on their own, incurring minimal costs while simultaneously yielding significant health benefits. Over the past decade, new methods have been developed to measure both physical activity and associated health outcomes, such as blood pressure. Notably, there has been an explosive development of so-called wearables, including smartwatches and activity trackers. Wearables are equipped with multiple sensors that measure various aspects of PA, such as steps and heart rate, as well as cardiovascular health indicators like blood pressure and oxygen saturation. Therefore, wearables can be viewed as Swiss army knives with many tools in one instrument. They are highly popular in the fitness industry, but their role in healthcare is appropriately limited. However, most wearables on the market have several disadvantages that make them unsuitable for use, even among healthy individuals. Several studies have revealed that they do not produce reliable or valid data for metrics like pulse, steps, and PA-related energy expenditure. Furthermore, they are primarily designed for the fitness market, not for use within healthcare systems or as support for behavior change, and they have not been transparently evaluated. Additionally, the algorithms translating signals from sensors into interpretable outcomes are often trade secrets. Worse still, they are updated and modified at irregular intervals, making it challenging to compare outcomes over time. Other significant limitations include questionable patient confidentiality, as data is often uploaded to companies' cloud services. While research monitors are more flexible and transparent compared to commercial wearables, they lack essential features for daily use that are crucial in healthcare environments, such as the ability to communicate with the user. Currently, both commercial and research monitors cannot assess PA on an individual level, as they only utilize a limited portion of the rich data collected. Therefore, it is not surprising that their implementation in clinical care remains a challenge. Given the plethora of new products entering the market without documented validity, it is crucial to provide consumers, patients, healthcare professionals, and researchers with a transparent, evidence-based wearable. Against this backdrop, an interdisciplinary research group with the ambitious goal of developing and testing a high-functioning wearable tailored for use in healthcare-an e-physiotherapist (as opposed to commercial wearables targeting the fitness market-an "e-personal trainer") have been formed. In this project, the focus is on measuring PA, blood pressure, and energy consumption, as they represent some of the most significant risk factors for mortality and morbidity, namely inactivity, hypertension, and obesity. The overall goal of this project is to develop and validate AI-based algorithms for individually measuring various aspects of physical activity (PA), heart rate, energy expenditure, and blood pressure in laboratory settings as well as in everyday conditions. These algorithms represent a significant advancement compared to previous methods. In the case of PA metrics from accelerometry, current approaches rely on cut-points (threshold values) to define the intensity of PA. These cut-points are absolute, and individual variations in biology and biomechanics increase the risk of serious misclassification. To estimate intensity using heart rate, it is well-known that both resting heart rate and maximum heart rate are relative, requiring individual calibration for accurate measurements-essential even for accelerometry if one aims to measure PA on an individual level, a step not commonly taken today. Furthermore, heart rate is influenced by factors beyond PA, such as emotions and medication. To address these issues, combining information from accelerometry (biomechanics) and heart rate (physiological response), enhancing the ability to identify individual intensity and energy expenditure of PA. In this project, artificial intelligence (AI) and machine learning (ML) will be employed to analyze the collected data and predict the intensity of PA. If the proposed method demonstrates the ability to measure PA and blood pressure at an individual level, the project will proceed. Our intention is to use AI/ML to combine PA information with blood pressure data, creating a self-learning system capable of suggesting an appropriate dose of PA to optimize blood pressure. This approach has not been studied yet, likely due to the complexity of obtaining and analyzing these data. However, the technology, processing power, and analysis tools are now available, making it timely to investigate its feasibility.
Disturbed sleep occurs in almost all patients in psychiatric inpatient care, and although it is well known that comorbid sleep disorders in depression often persist after treatment of depression and also increase the risk of new depressive episodes, the availability of effective, evidence-based treatments for sleep disorders in hospitalised patients is very limited. The overall goal of the current project is to translate, adapt and evaluate an acute psychological sleep treatment based on cognitive behavioural therapy for insomnia (CBT-I) for patients hospitalized with depression and comorbid sleep problems in the specialized psychiatric inpatient care in the Stockholm Region. The main hypothesis for the study is that acute psychological sleep stabilization (APS) reduces self-reported sleep complains compared to care as usual reinforced with sleep hygiene advice, and secondary hypotheses are that APS also leads to reduced depressive symptoms and earlier discharge. The project includes a pilot study, which will be followed by a randomized, controlled trial of APS compared to care as usual with structured sleep hygiene (minimal active control) and treatment effect is evaluated every three days during the hospital stay and 1,2,4 and 12 weeks after randomization. APS will be performed by existing staff in the department with the support of a psychologist.
Since 2015, Uppsala Tjej- och Transjour has run school sessions for secondary school pupils in Uppsala municipality, from year 9 to year 2. The school session is a 120-minute workshop and focuses on increasing knowledge and changing attitudes about consent, reciprocity and sexual violence. The intervention aims to increase participants' knowledge about consent, reciprocity, sex, sexuality and sexual violence in order to create positive attitudinal changes around gender and sexual violence and influence behaviour. The purpose of the study is to evaluate the effect of Uppsala Tjej- och Transjour's school intervention on young people's knowledge, attitudes and behaviour regarding consent, reciprocity and sexual violence. A cluster randomised controlled trial with 16 clusters (89 participants in each cluster) in each arm, a total of 32 clusters. Schools are randomised after baseline measurement to receive the intervention in autumn 2023 or spring 2024 (waiting list). Data collection is done through a questionnaire at two measurement points. A baseline measurement before the intervention (T1) and measurement two (T2) 6 months later. There are still few violence prevention programmes in Sweden that have been evaluated for effectiveness, and several programmes come from the USA. This study is based on a Swedish material and constitutes an important contribution to the development of more effective methods for violence prevention and increased insights into reciprocity and consent.
This is an open-label study, in which all participants receives an active treatment with repetitive transcranial magnetic stimulation (rTMS) according to clinical protocol. The aim with this pilotstudy is to investigate the feasibility to perform a trial of low-frequency rTMS on treatment-resistant depression in adolescents. The study includes adolescents 13-19 years old, with average to severe depression.
Continuation study to provide continued access to latozinemab for participants who have previously participated in a latozinemab study
The overall objective of this study is to confirm that ctDNA detected after curative intended treatment for PDAC is a marker of residual disease and for risk-of-recurrence, and applicable in clinical practice. Primary objective To confirm that ctDNA analyses performed after PDAC treatment can identify patients with a high risk-of-recurrence. Specifically, we want to determine the association between disease-free survival (DFS) and ctDNA detection status after (1) curative-intended surgery and (2) adjuvant chemotherapy.
In this study an artificial intelligence (AI) tool for skin cancer diagnosis is implemented in a teleldermatoscopy platform. The aim is to study the effects on clinician diagnostic accuracy, management decisions, and confidence. Furthermore, this prospective randomized study investigates the role of human factors in determining clinician reliance on AI tools and the consequent accuracy in a real-world setting.
The project is focused on the detailed study of structural genomic variants (SVs). Such genetic mutations are in fact alterations in the DNA molecule structure and include copy number variants, inversions and translocations. A single event may affect many genes as well as regulatory regions and the specific phenotypic consequences will depend on the location, genetic content and type of SV. Many times, the specific disease-causing mechanism is not known. Here, we plan to study the molecular genetic behavior of structural variants as well as the underlying mutational mechanisms involved. First, we will use genome sequencing to pinpoint the chromosomal breakpoints at the nucleotide level, characterize the genomic architecture at the breakpoints and study the relationship between structural variants and SNVs. Second, we will study how structural variants impact gene expression. Finally, we will functionally explore the disease mechanisms in vivo using zebrafish and in vitro using primary patient cells and induced pluripotent stem cells. Our studies will focus on the origin, structure and impact of structural variation on human disease. The results will directly lead to a higher mutation detection rate in genetic diagnostics. Through a better understanding of disease mechanisms our findings will also assist in the development of novel biomarkers and therapeutic strategies for patients with rare genetic disorders.
Chronic diseases such as cardiovascular disease and diabetes type 2 are major causes of death worldwide. Preventive interventions can be delivered through primary care, as this is the first-line healthcare with which a considerable proportion of the population comes into contact every year. The goal of this cluster-randomized trial is to compare the effects of a Health Dialogue Intervention (HDI) to Opportunistic Screening (OS) in primary care among middle-aged adults with low socioeconomic status. The main questions it aims to answer are: - What is the short-term change in cardiovascular risk factors, lifestyle behaviors, and perceived quality-of-life among participants offered HDI, as compared to participants offered OS? - What is the long-term risk of ischemic heart disease, stroke, type 2 diabetes, and death due to cardiovascular disease or type 2 diabetes, among participants offered HDI, as compared to participants offered OS?
The study investigates long-term opioid treatment in patients with chronic non-cancer pain (CNCP). The study aims to prospectively identify predictive factors for work ability and for developing opioid use disorder (according to DSM-5) as well as predictive factors for pain, activity, and health-related quality of life. It is hypothesized that certain biopsychosocial factors mapped in this study predict patterns of opioid use and the risk for developing OUD for patients with CNCP on long-term opioid therapy. Further, it is hypothesized that certain biopsychosocial factors mapped in this study predict the chance of improved work ability and other treatment benefits of long term opioid therapy in patients with CNCP.