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| 2. |
- Brorsson, Sofia, et al.
(författare)
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Maximal strength in one leg squat correlates with acceleration capacity and agility
- 2010
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Konferensbidrag (refereegranskat)abstract
- INTRODUCTION: In many intermittent team sports capacities such as speed, agility and explosiveness are important for performance and are evaluated by sprint-, agility- and strength tests. Earlier studies have shown strong correlations between strength in the two leg squat exercise and sprint performance in various distances but not in sprint performance in agility. Studies evaluating squat strength predominantly perform tests on two legs even when they test athletes involved in intermittent sports where sprinting and agility are common features. Thus, the purpose of this study was to investigate the correlation between maximal strength in a one leg squat (Bulgarian split squat) and acceleration capacity in various sprint distances and agility.METHODS: The test group consisted of 19 men (mean age 24 ± 2 years ) with experience in intermittent team sports. Acceleration capacity was assessed by sprint tests at 5, 10 and 20 meters and agility was evaluated using the zigzag agility test. The timing was made using photocells (Muscle lab,Ergotest Technology,Norway). The Bulgarian split squat was performed in a smith machine with the barbell on the shoulders to a depth of 110 degrees between tibia and femur.RESULTS: The results show significant correlation between maximal strength in the Bulgarian split squat and sprint capacity in the 5 and 10 meter sprint test (Rp= -0,56; p<0.01) as well as the agility test. Maximal strength relative to bodyweight showed significant correlation with the 5 and 20 meter sprint (Rp=-0,62; p< 0,01) as well as the agility test. The zigzag agility test also showed significant correlation between all distances in the sprint tests (p<0.01).CONCLUSION: The results from this study show that there maximal strength in one leg correlate significantly with both acceleration capacity and agility. Implementing one leg exercises in the strength and conditioning routine can be useful for athletes in intermittent sports wanting to improve agility and short sprinting capacity. Further implications is that the Bulgarian split squat could be a more functional test for agility performance than the squat on two legs which predominantly is being used today.
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| 4. |
- Hagel, Sofia, et al.
(författare)
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Which patients improve the most after arthritis rehabilitation? A study of predictors in patients with inflamatory arthritis in northern Europe, the STAR-ETIC collaboration
- 2014
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Ingår i: Journal of Rehabilitation Medicine. - Uppsala : Foundation of Rehabilitation Information. - 1650-1977 .- 1651-2081. ; 46:3, s. 250-257
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: To study health-related quality of life (HRQoL) in arthritis rehabilitation performed by multidisciplinary teams in patients with chronic inflammatory arthritis. Predictors of change in health-related quality of life and the proportion of patients with clinical improvement were investigated.DESIGN: Multicentre prospective observational study in 4 European countries.METHODS: HRQoL was measured with the European Quality 5 Dimensions (EQ-5D) and the Short Form 36 Health Survey (SF-36) in 731 patients who underwent multidisciplinary rehabilitation. Potential predictors were physical functioning (Health Assessment Questionnaire (HAQ)), self-efficacy (Arthritis Self Efficacy Scale (ASES)), psychological health (Hopkins Symptom Check List (HSCL-25)), pain/fatigue (numeric rating scales (NRS)), age, sex, diagnosis, comorbidity, education, clinical setting and change of medication during rehabilitation. Analysis of covariance (ANCOVA) was used to assess for potential predictors and interactions. The minimal important differences for HRQoL were analysed.RESULTS: Reporting worse function (b 0.05, p = 0.01), less psychological well-being (b 0.09, p = 0.000), and experiencing more pain (b 0.03, p = 0.000) or fatigue (b 0.02, p = 0.000) at admission predicted improved HRQoL. Change in medication during rehabilitation (b 0.08, p = 0.013) was associated with greater improvement in HRQoL. These EQ-5D findings were supported by SF-36 findings. Positive minimal important differences were noted in 46% (EQ-5D) and 23-47% (SF-36 subscales) of the patients.CONCLUSION: Patients with more severe symptoms experienced the largest gain in HRQoL post-intervention. The results of this study are of value for selecting the right patients for rheumatological team rehabilitation. © 2014 The Authors
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| 5. |
- Nair, Monika, 1985-, et al.
(författare)
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Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records : Protocol for a Quasi-Experimental Study for Impact Assessment
- 2024
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Ingår i: JMIR Research Protocols. - Toronto, ON : JMIR Publications. - 1929-0748. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Care for patients with heart failure (HF) causes a substantial load on health care systems where a prominent challenge is the elevated rate of readmissions within 30 days following initial discharge. Clinical professionals face high levels of uncertainty and subjectivity in the decision-making process on the optimal timing of discharge. Unwanted hospital stays generate costs and cause stress to patients and potentially have an impact on care outcomes. Recent studies have aimed to mitigate the uncertainty by developing and testing risk assessment tools and predictive models to identify patients at risk of readmission, often using novel methods such as machine learning (ML).Objective: This study aims to investigate how a developed clinical decision support (CDS) tool alters the decision-making processes of health care professionals in the specific context of discharging patients with HF, and if so, in which ways. Additionally, the aim is to capture the experiences of health care practitioners as they engage with the system’s outputs to analyze usability aspects and obtain insights related to future implementation.Methods: A quasi-experimental design with randomized crossover assessment will be conducted with health care professionals on HF patients’ scenarios in a region located in the South of Sweden. In total, 12 physicians and nurses will be randomized into control and test groups. The groups shall be provided with 20 scenarios of purposefully sampled patients. The clinicians will be asked to take decisions on the next action regarding a patient. The test group will be provided with the 10 scenarios containing patient data from electronic health records and an outcome from an ML-based CDS model on the risk level for readmission of the same patients. The control group will have 10 other scenarios without the CDS model output and containing only the patients’ data from electronic medical records. The groups will switch roles for the next 10 scenarios. This study will collect data through interviews and observations. The key outcome measures are decision consistency, decision quality, work efficiency, perceived benefits of using the CDS model, reliability, validity, and confidence in the CDS model outcome, integrability in the routine workflow, ease of use, and intention to use. This study will be carried out in collaboration with Cambio Healthcare Systems.Results: The project is part of the Center for Applied Intelligent Systems Research Health research profile, funded by the Knowledge Foundation (2021-2028). Ethical approval for this study was granted by the Swedish ethical review authority (2022-07287-02). The recruitment process of the clinicians and the patient scenario selection will start in September 2023 and last till March 2024.Conclusions: This study protocol will contribute to the development of future formative evaluation studies to test ML models with clinical professionals. © 2024 JMIR Publications Inc.. All rights reserved.
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- Neher, Margit, 1959-, et al.
(författare)
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Innovation in healthcare : leadership perceptions about the innovation characteristics of artificial intelligence—a qualitative interview study with healthcare leaders in Sweden
- 2023
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Ingår i: Implementation Science Communications. - London : BioMed Central (BMC). - 2662-2211. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Background: Despite the extensive hopes and expectations for value creation resulting from the implementation of artificial intelligence (AI) applications in healthcare, research has predominantly been technology-centric rather than focused on the many changes that are required in clinical practice for the technology to be successfully implemented. The importance of leaders in the successful implementation of innovations in healthcare is well recognised, yet their perspectives on the specific innovation characteristics of AI are still unknown. The aim of this study was therefore to explore the perceptions of leaders in healthcare concerning the innovation characteristics of AI intended to be implemented into their organisation.Methods: The study had a deductive qualitative design, using constructs from the innovation domain in the Consolidated Framework for Implementation Research (CFIR). Interviews were conducted with 26 leaders in healthcare.Results: Participants perceived that AI could provide relative advantages when it came to care management, supporting clinical decisions, and the early detection of disease and risk of disease. The development of AI in the organisation itself was perceived as the main current innovation source. The evidence base behind AI technology was questioned, in relation to its transparency, potential quality improvement, and safety risks. Although the participants acknowledged AI to be superior to human action in terms of effectiveness and precision in some situations, they also expressed uncertainty about the adaptability and trialability of AI. Complexities such as the characteristics of the technology, the lack of conceptual consensus about AI, and the need for a variety of implementation strategies to accomplish transformative change in practice were identified, as were uncertainties about the costs involved in AI implementation.Conclusion: Healthcare leaders not only saw potential in the technology and its use in practice, but also felt that AI’s opacity limits its evidence strength and that complexities in relation to AI itself and its implementation influence its current use in healthcare practice. More research is needed based on actual experiences using AI applications in real-world situations and their impact on clinical practice. New theories, models, and frameworks may need to be developed to meet challenges related to the implementation of AI in healthcare. © 2023, The Author(s).
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| 7. |
- Nilsen, Per, 1960-, et al.
(författare)
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A Framework to Guide Implementation of AI in Health Care : Protocol for a Cocreation Research Project
- 2023
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Ingår i: JMIR Research Protocols. - Toronto : JMIR Publications. - 1929-0748. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Background: Artificial intelligence (AI) has the potential in health care to transform patient care and administrative processes, yet health care has been slow to adopt AI due to many types of barriers. Implementation science has shown the importance of structured implementation processes to overcome implementation barriers. However, there is a lack of knowledge and tools to guide such processes when implementing AI-based applications in health care.Objective: The aim of this protocol is to describe the development, testing, and evaluation of a framework, “Artificial Intelligence-Quality Implementation Framework” (AI-QIF), intended to guide decisions and activities related to the implementation of various AI-based applications in health care.Methods: The paper outlines the development of an AI implementation framework for broad use in health care based on the Quality Implementation Framework (QIF). QIF is a process model developed in implementation science. The model guides the user to consider implementation-related issues in a step-by-step design and plan and perform activities that support implementation. This framework was chosen for its adaptability, usability, broad scope, and detailed guidance concerning important activities and considerations for successful implementation. The development will proceed in 5 phases with primarily qualitative methods being used. The process starts with phase I, in which an AI-adapted version of QIF is created (AI-QIF). Phase II will produce a digital mockup of the AI-QIF. Phase III will involve the development of a prototype of the AI-QIF with an intuitive user interface. Phase IV is dedicated to usability testing of the prototype in health care environments. Phase V will focus on evaluating the usability and effectiveness of the AI-QIF. Cocreation is a guiding principle for the project and is an important aspect in 4 of the 5 development phases. The cocreation process will enable the use of both on research-based and practice-based knowledge.Results: The project is being conducted within the frame of a larger research program, with the overall objective of developing theoretically and empirically informed frameworks to support AI implementation in routine health care. The program was launched in 2021 and has carried out numerous research activities. The development of AI-QIF as a tool to guide the implementation of AI-based applications in health care will draw on knowledge and experience acquired from these activities. The framework is being developed over 2 years, from January 2023 to December 2024. It is under continuous development and refinement.Conclusions: The development of the AI implementation framework, AI-QIF, described in this study protocol aims to facilitate the implementation of AI-based applications in health care based on the premise that implementation processes benefit from being well-prepared and structured. The framework will be coproduced to enhance its relevance, validity, usefulness, and potential value for application in practice. © 2023 The Author(s).
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| 8. |
- Nilsen, Per, 1960-, et al.
(författare)
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Towards evidence-based practice 2.0 : leveraging artificial intelligence in healthcare
- 2024
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Ingår i: Frontiers in Health Services. - Lausanne : Frontiers Media S.A.. - 2813-0146. ; 4
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Forskningsöversikt (refereegranskat)abstract
- Background: Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this.Challenges with the three components of EBP: Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources.AI assistance for the three components of EBP: AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue.Conclusion: This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare. ©2024 The Authors
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| 9. |
- Petersson, Lena, 1968-, et al.
(författare)
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Challenges to implementing artificial intelligence in healthcare : a qualitative interview study with healthcare leaders in Sweden
- 2022
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Ingår i: BMC Health Services Research. - London : BioMed Central (BMC). - 1472-6963. ; 22
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Tidskriftsartikel (refereegranskat)abstract
- Background: Artificial intelligence (AI) for healthcare presents potential solutions to some of the challenges faced by health systems around the world. However, it is well established in implementation and innovation research that novel technologies are often resisted by healthcare leaders, which contributes to their slow and variable uptake. Although research on various stakeholders’ perspectives on AI implementation has been undertaken, very few studies have investigated leaders’ perspectives on the issue of AI implementation in healthcare. It is essential to understand the perspectives of healthcare leaders, because they have a key role in the implementation process of new technologies in healthcare. The aim of this study was to explore challenges perceived by leaders in a regional Swedish healthcare setting concerning the implementation of AI in healthcare.Methods: The study takes an explorative qualitative approach. Individual, semi-structured interviews were conducted from October 2020 to May 2021 with 26 healthcare leaders. The analysis was performed using qualitative content analysis, with an inductive approach.Results: The analysis yielded three categories, representing three types of challenge perceived to be linked with the implementation of AI in healthcare: 1) Conditions external to the healthcare system; 2) Capacity for strategic change management; 3) Transformation of healthcare professions and healthcare practice.Conclusions: In conclusion, healthcare leaders highlighted several implementation challenges in relation to AI within and beyond the healthcare system in general and their organisations in particular. The challenges comprised conditions external to the healthcare system, internal capacity for strategic change management, along with transformation of healthcare professions and healthcare practice. The results point to the need to develop implementation strategies across healthcare organisations to address challenges to AI-specific capacity building. Laws and policies are needed to regulate the design and execution of effective AI implementation strategies. There is a need to invest time and resources in implementation processes, with collaboration across healthcare, county councils, and industry partnerships. © The Author(s) 2022.
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| 10. |
- Petersson, Lena, 1968-, et al.
(författare)
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Developing an ethical model for guidance the implementation of AI in healthcare
- 2023
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Ingår i: 10th Nordic Health Promotion Research Conference 2023. Sustainability and the impact on health and well-being. - Halmstad : Halmstad University Press. - 9789189587410 ; , s. 84-84
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Konferensbidrag (refereegranskat)abstract
- Background: Artificial intelligence (AI) is predicted to improve healthcare, increase efficiency, save time and resources. However, research shows an urgent need to develop guidance to ensure that the use of AI in healthcare is ethically acceptable.Purpose: To develop an ethical model to support AI implementation in practice.Methods: The study used an explorative and empirically driven qualitative design. Individual interviews were conducted with 18 healthcare professionals from two emergency departments in Sweden where the county council has developed an AI application to predict the risk for unexpected mortality within 30 days after visiting an emergency department. A deductive analysis based on ethical theory i.e virtue, deontology and consequentialism, was used.Findings: The developed model shows how the healthcare professionals use ethical reasoning in relation to the implementation of AI. In relation to virtue ethics, moral considerations in relation to the use of AI were mentioned. In relation to deontology, considerations were mentioned on actions performed based on information acquired from the technology and adherence to specific duties, roles and responsibilities. In relation to consequentialism, considerations about how to provide better resources more rapidly in an equal way and how the technology can be adjusted to each patients’ individual needs and preferences in order to support decisions, self-determination, and actions that are in the patients best interest.Conclusions: Our findings provide an ethical model demonstrating the relevance of virtue, deontology and consequentialism when AI are to be implemented in practice.
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