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Bengtsson, D., Stenling, A., Nygren, J. M., Ntoumanis, N. & Ivarsson, A. (2024). The effects of interpersonal development programmes with sport coaches and parents on youth athlete outcomes: A systematic review and meta-analysis. Psychology of Sport And Exercise, 70, Article ID 102558.
Open this publication in new window or tab >>The effects of interpersonal development programmes with sport coaches and parents on youth athlete outcomes: A systematic review and meta-analysis
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2024 (English)In: Psychology of Sport And Exercise, ISSN 1469-0292, E-ISSN 1878-5476, Vol. 70, article id 102558Article, review/survey (Refereed) Published
Abstract [en]

Interpersonal coach-and parent development programmes (CDP and PDP, respectively), have the goal to foster positive youth sport experiences through high-quality relations between coaches, parents, and youth athletes. In this paper we systematically reviewed the extant literature and estimate the overall magnitude of such programmes and how they can inform future interventions. Specifically, we aimed to: (a) conduct a systematic review on the literature of interpersonal CDPs and PDPs within the youth sport context; (b) examine the effects of such interventions on youth athlete outcomes via a meta-analysis. English written peer-reviewed publications and grey literature was identified through electronic search in databases and manual searches of reference lists. By utilising a priori criteria for inclusion and exclusion, 33 studies describing interpersonal CDPs, and PDPs were identified in the systematic review. Studies that presented required data for estimation of Hedge's g effect sizes were included in the meta-analysis (k = 27). By and large, the included studies used a quasi-experimental design (58%), sampled from team sports (79%), and reported several delivery methods (e.g., workshops, audio feedback, observations, peer group discussions) and outcome measures (e.g., anxiety, autonomous motivation, self-confidence). Some interventions were based on the same delivery protocols (e.g., Coach Effectiveness Training, Mastery Approach to Coaching) or theoretical frameworks (e.g., Achievement Goal Theory, Self-Determination Theory). The meta-analysis showed statistically significant small, and medium, effect sizes on a subsample of youth athlete outcomes (e.g., task-related climate, fun and enjoyment, anxiety), indicating that coach interpersonal skills can contribute to positive youth sport experiences. Theory-based interpersonal CDPs and PDPs are recommended to expand the knowledge in this field of research. © 2023 The Authors

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2024
Keywords
Behaviour change, Design, Effectiveness, Intervention, Youth sport
National Category
Sport and Fitness Sciences
Identifiers
urn:nbn:se:hh:diva-52683 (URN)10.1016/j.psychsport.2023.102558 (DOI)001127782400001 ()37993028 (PubMedID)2-s2.0-85183648023 (Scopus ID)
Note

Funding: The Swedish Ice Hockey Association

Available from: 2024-02-15 Created: 2024-02-15 Last updated: 2024-02-15Bibliographically approved
Nilsen, P., Svedberg, P., Neher, M., Nair, M., Larsson, I., Petersson, L. & Nygren, J. M. (2023). A Framework to Guide Implementation of AI in Health Care: Protocol for a Cocreation Research Project. JMIR Research Protocols, 12, Article ID e50216.
Open this publication in new window or tab >>A Framework to Guide Implementation of AI in Health Care: Protocol for a Cocreation Research Project
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2023 (English)In: JMIR Research Protocols, E-ISSN 1929-0748, Vol. 12, article id e50216Article in journal (Refereed) Published
Abstract [en]

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).

Place, publisher, year, edition, pages
Toronto: JMIR Publications, 2023
Keywords
artificial intelligence, AI, health care, implementation, process models, frameworks, framework, process model
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Research subject
Health Innovation, IDC
Identifiers
urn:nbn:se:hh:diva-52291 (URN)10.2196/50216 (DOI)001115572400003 ()37938896 (PubMedID)2-s2.0-85178219393 (Scopus ID)
Funder
Knowledge FoundationVinnova
Available from: 2023-12-22 Created: 2023-12-22 Last updated: 2024-01-17Bibliographically approved
Pinheiro Sant'Anna, A. & Nygren, J. M. (2023). A Pragmatic Mapping of Perceptions and Use of Digital Information Systems in Primary Care in Sweden: Survey Study. Interactive Journal of Medical Research, 12(1), Article ID e49973.
Open this publication in new window or tab >>A Pragmatic Mapping of Perceptions and Use of Digital Information Systems in Primary Care in Sweden: Survey Study
2023 (English)In: Interactive Journal of Medical Research, E-ISSN 1929-073X, Vol. 12, no 1, article id e49973Article in journal (Refereed) Published
Abstract [en]

Background: Electronic health records and IT infrastructure in primary care allow for digital documentation and access to information, which can be used to guide evidence-based care and monitor patient safety and quality of care. Quality indicators specified by regulatory authorities can be automatically computed and presented to primary care staff. However, the implementation of digital information systems (DIS) in health care can be challenging, and understanding factors such as relative advantage, compatibility, complexity, trialability, and observability is needed to improve the success and rate of adoption and diffusion.

Objective: This study aims to explore how DIS are used and perceived by health care professionals in primary care.

Methods: This study used quantitative assessment to gather survey data on the use and potential of DIS in health care in Sweden from the perspectives of primary care personnel in various roles. The digital questionnaire was designed to be short and contained 3 sections covering respondent characteristics, current use of platforms, and perceptions of decision support tools. Data were analyzed using descriptive statistics, nonparametric hypothesis testing, ordinal coefficient α, and confirmatory factor analysis.

Results: The study collected responses from participants across 10 regions of Sweden, comprising 31.9% (n=22) from private clinics and 68.1% (n=47) from public clinics. Participants included administrators (18/69, 26.1%), a medical strategist (1/69, 1.4%), and physicians (50/69, 72.5%). Usage frequency varied as follows: 11.6% (n=8) used DIS weekly, 24.6% (n=17) monthly, 27.5% (n=19) a few times a year, 26.1% (n=18) very rarely, and 10.1% (n=7) lacked access. Administrators used DIS more frequently than physicians (P=.005). DIS use centered on quality improvement and identifying high-risk patients, with differences by role. Physicians were more inclined to use DIS out of curiosity (P=.01). Participants desired DIS for patient follow-up, lifestyle guidance, treatment suggestions, reminders, and shared decision-making. Administrators favored predictive analysis (P<.001), while physicians resisted immediate patient identification (P=.03). The 5 innovation attributes showed high internal consistency (α>.7). These factors explained 78.5% of questionnaire variance, relating to complexity, competitive advantage, compatibility, trialability, and observability. Factors 2, 3, and 4 predicted intention to use DIS, with factor 2 alone achieving the best accuracy (root-mean-square=0.513).

Conclusions: Administrators and physicians exhibited role-based DIS use patterns highlighting the need for tailored approaches to promote DIS adoption. The study reveals a link between positive perceptions and intention to use DIS, emphasizing the significance of considering all factors for successful health care integration. The results suggest various directions for future studies. These include refining the trialability and observability questions for increased reliability and validity, investigating a larger sample with more specific target groups to improve generalization, and exploring the relevance of different groups' perspectives and needs in relation to decisions about and use of DIS. ©Anita Sant’Anna, Jens Nygren. 

Place, publisher, year, edition, pages
Toronto, ON: JMIR Publications, 2023
Keywords
digital information systems, implementation, primary care, health care professionals, information system, information systems, usability, adoption, perception, perceptions, technology use, perspective, perspectives
National Category
Information Systems, Social aspects
Research subject
Health Innovation, IDC
Identifiers
urn:nbn:se:hh:diva-52233 (URN)10.2196/49973 (DOI)001103963800001 ()37878357 (PubMedID)
Note

Funding: This study was partially funded by CSAM Carmona AB and Halmstad University. 

Available from: 2023-12-15 Created: 2023-12-15 Last updated: 2023-12-18Bibliographically approved
Nair, M., Andersson, J., Nygren, J. M. & Lundgren, L. E. (2023). Barriers and Enablers for Implementation of an Artificial Intelligence–Based Decision Support Tool to Reduce the Risk of Readmission of Patients With Heart Failure: Stakeholder Interviews. JMIR Formative Research, 7, Article ID e47335.
Open this publication in new window or tab >>Barriers and Enablers for Implementation of an Artificial Intelligence–Based Decision Support Tool to Reduce the Risk of Readmission of Patients With Heart Failure: Stakeholder Interviews
2023 (English)In: JMIR Formative Research, E-ISSN 2561-326X, Vol. 7, article id e47335Article in journal (Refereed) Published
Abstract [en]

Background: Artificial intelligence (AI) applications in health care are expected to provide value for health care organizations, professionals, and patients. However, the implementation of such systems should be carefully planned and organized in order to ensure quality, safety, and acceptance. The gathered view of different stakeholders is a great source of information to understand the barriers and enablers for implementation in a specific context.

Objective: This study aimed to understand the context and stakeholder perspectives related to the future implementation of a clinical decision support system for predicting readmissions of patients with heart failure. The study was part of a larger project involving model development, interface design, and implementation planning of the system.

Methods: Interviews were held with 12 stakeholders from the regional and municipal health care organizations to gather their views on the potential effects implementation of such a decision support system could have as well as barriers and enablers for implementation. Data were analyzed based on the categories defined in the nonadoption, abandonment, scale-up, spread, sustainability (NASSS) framework.

Results: Stakeholders had in general a positive attitude and curiosity toward AI-based decision support systems, and mentioned several barriers and enablers based on the experiences of previous implementations of information technology systems. Central aspects to consider for the proposed clinical decision support system were design aspects, access to information throughout the care process, and integration into the clinical workflow. The implementation of such a system could lead to a number of effects related to both clinical outcomes as well as resource allocation, which are all important to address in the planning of implementation. Stakeholders saw, however, value in several aspects of implementing such system, emphasizing the increased quality of life for those patients who can avoid being hospitalized.

Conclusions: Several ideas were put forward on how the proposed AI system would potentially affect and provide value for patients, professionals, and the organization, and implementation aspects were important parts of that. A successful system can help clinicians to prioritize the need for different types of treatments but also be used for planning purposes within the hospital. However, the system needs not only technological and clinical precision but also a carefully planned implementation process. Such a process should take into consideration the aspects related to all the categories in the NASSS framework. This study further highlighted the importance to study stakeholder needs early in the process of development, design, and implementation of decision support systems, as the data revealed new information on the potential use of the system and the placement of the application in the care process. © The Author(s) 2023.

Place, publisher, year, edition, pages
Toronto, ON: JMIR Publications, 2023
Keywords
implementation, AI systems, health care, interviews, decision support tool, readmission prediction, heart failure, digital tool
National Category
Medical and Health Sciences Health Care Service and Management, Health Policy and Services and Health Economy
Research subject
Health Innovation; Health Innovation, Information driven care
Identifiers
urn:nbn:se:hh:diva-51707 (URN)10.2196/47335 (DOI)37610799 (PubMedID)2-s2.0-85170696488 (Scopus ID)
Projects
CAISR Health
Funder
Knowledge Foundation
Available from: 2023-09-26 Created: 2023-09-26 Last updated: 2023-11-24Bibliographically approved
Petersson, L., Svedberg, P., Nygren, J. M. & Larsson, I. (2023). Developing an ethical model for guidance the implementation of AI in healthcare. In: Nordic Health Promotion Research Conference 2023 Abstracts: . Paper presented at Nordic Health Promotion Research Conference 2023. Halmstad
Open this publication in new window or tab >>Developing an ethical model for guidance the implementation of AI in healthcare
2023 (English)In: Nordic Health Promotion Research Conference 2023 Abstracts, Halmstad, 2023Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Background: Artificial intelligence (AI) is predicted to improve healthcare, increase efficiency, save time andresources. However, research shows an urgent need to develop guidance to ensure that the use of AI in healthcare isethically 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 wereconducted with 18 healthcare professionals from two emergency departments in Sweden where the county council hasdeveloped an AI application to predict the risk for unexpected mortality within 30 days after visiting an emergencydepartment. 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 theimplementation of AI. In relation to virtue ethics, moral considerations in relation to the use of AI were mentioned. Inrelation to deontology, considerations were mentioned on actions performed based on information acquired from thetechnology and adherence to specific duties, roles and responsibilities. In relation to consequentialism, considerationsabout how to provide better resources more rapidly in an equal way and how the technology can be adjusted to eachpatients’ individual needs and preferences in order to support decisions, self-determination, and actions that are in thepatients best interest.

Conclusions: Our findings provide an ethical model demonstrating the relevance of virtue, deontology andconsequentialism when AI are to be implemented in practice.

Place, publisher, year, edition, pages
Halmstad: , 2023
Keywords
Artificial intelligence, ethic, healthcare professionals, implementation, qualitative method
National Category
Social Sciences
Research subject
Health Innovation; Health Innovation, IDC
Identifiers
urn:nbn:se:hh:diva-52342 (URN)
Conference
Nordic Health Promotion Research Conference 2023
Funder
Halmstad University
Available from: 2023-12-28 Created: 2023-12-28 Last updated: 2023-12-28
Petersson, L., Vincent, K., Svedberg, P., Nygren, J. M. & Larsson, I. (2023). Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice. Digital Health, 9
Open this publication in new window or tab >>Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice
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2023 (English)In: Digital Health, E-ISSN 2055-2076, Vol. 9Article in journal (Refereed) Published
Abstract [en]

Background: Artificial intelligence (AI) is predicted to be a solution for improving healthcare, increasing efficiency, and saving time and recourses. A lack of ethical principles for the use of AI in practice has been highlighted by several stakeholders due to the recent attention given to it. Research has shown an urgent need for more knowledge regarding the ethical implications of AI applications in healthcare. However, fundamental ethical principles may not be sufficient to describe ethical concerns associated with implementing AI applications.

Objective: The aim of this study is twofold, (1) to use the implementation of AI applications to predict patient mortality in emergency departments as a setting to explore healthcare professionals’ perspectives on ethical issues in relation to ethical principles and (2) to develop a model to guide ethical considerations in AI implementation in healthcare based on ethical theory.

Methods: Semi-structured interviews were conducted with 18 participants. The abductive approach used to analyze the empirical data consisted of four steps alternating between inductive and deductive analyses. Results: Our findings provide an ethical model demonstrating the need to address six ethical principles (autonomy, beneficence, non-maleficence, justice, explicability, and professional governance) in relation to ethical theories defined as virtue, deontology, and consequentialism when AI applications are to be implemented in clinical practice.

Conclusions: Ethical aspects of AI applications are broader than the prima facie principles of medical ethics and the principle of explicability. Ethical aspects thus need to be viewed from a broader perspective to cover different situations that healthcare professionals, in general, and physicians, in particular, may face when using AI applications in clinical practice. © The Author(s) 2023.

Place, publisher, year, edition, pages
London: Sage Publications, 2023
Keywords
Artificial intelligence, codes of ethics, emergency department, ethical theory, healthcare, healthcare professionals, implementation, qualitative research
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Research subject
Health Innovation; Health Innovation, IDC
Identifiers
urn:nbn:se:hh:diva-51854 (URN)10.1177/20552076231206588 (DOI)001078896200001 ()37829612 (PubMedID)2-s2.0-85173652948 (Scopus ID)
Funder
Vinnova, 2019-04526Knowledge Foundation, 20200208 01H
Available from: 2023-11-08 Created: 2023-11-08 Last updated: 2023-11-09Bibliographically approved
Petersson, L., Vincent, K., Svedberg, P., Nygren, J. M. & Larsson, I. (2023). Ethical Perspectives on Implementing AI to Predict Mortality Risk in Emergency Department Patients: A Qualitative Study. In: Maria Hägglund; Madeleine Blusi; Stefano Bonacina; Lina Nilsson; Inge Cort Madsen; Sylvia Pelayo; Anne Moen; Arriel Benis; Lars Lindsköld; Parisis Gallos (Ed.), Caring is sharing - exploiting the value in data for health and innovation: Proceedings of MIE 2023. Paper presented at 33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE 2023, Gothenburg, 22-25 May, 2023 (pp. 676-677). Amsterdam: IOS Press, 302
Open this publication in new window or tab >>Ethical Perspectives on Implementing AI to Predict Mortality Risk in Emergency Department Patients: A Qualitative Study
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2023 (English)In: Caring is sharing - exploiting the value in data for health and innovation: Proceedings of MIE 2023 / [ed] Maria Hägglund; Madeleine Blusi; Stefano Bonacina; Lina Nilsson; Inge Cort Madsen; Sylvia Pelayo; Anne Moen; Arriel Benis; Lars Lindsköld; Parisis Gallos, Amsterdam: IOS Press, 2023, Vol. 302, p. 676-677Conference paper, Published paper (Refereed)
Abstract [en]

Artificial intelligence (AI) is predicted to improve health care, increase efficiency and save time and recourses, especially in the context of emergency care where many critical decisions are made. Research shows the urgent need to develop principles and guidance to ensure ethical AI use in healthcare. This study aimed to explore healthcare professionals' perceptions of the ethical aspects of implementing an AI application to predict the mortality risk of patients in emergency departments. The analysis used an abductive qualitative content analysis based on the principles of medical ethics (autonomy, beneficence, non-maleficence, and justice), the principle of explicability, and the new principle of professional governance, that emerged from the analysis. In the analysis, two conflicts and/or considerations emerged tied to each ethical principle elucidating healthcare professionals' perceptions of the ethical aspects of implementing the AI application in emergency departments. The results were related to aspects of sharing information from the AI application, resources versus demands, providing equal care, using AI as a support system, trustworthiness to AI, AI-based knowledge, professional knowledge versus AI-based information, and conflict of interests in the healthcare system. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2023
Series
Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365 ; 302
Keywords
AI applications, ethical aspects, healthcare professionals, qualitative study
National Category
Nursing
Research subject
Health Innovation; Health Innovation, IDC
Identifiers
urn:nbn:se:hh:diva-51747 (URN)10.3233/SHTI230234 (DOI)001071432900176 ()37203776 (PubMedID)2-s2.0-85159761458 (Scopus ID)978-1-64368-388-1 (ISBN)978-1-64368-389-8 (ISBN)
Conference
33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE 2023, Gothenburg, 22-25 May, 2023
Available from: 2023-11-17 Created: 2023-11-17 Last updated: 2023-11-20Bibliographically approved
Petersson, L., Svedberg, P., Nygren, J. M. & Larsson, I. (2023). Expected values of implementing AI in healthcare – A Qualitative study. In: Nordic Health Promotion Research Conference 2023: Abstracts. Paper presented at The 10th Nordic Health Promotion Research Conference "Sustainability and the impact on health and well-being", Halmstad, Sweden, 14-16 June, 2023. Halmstad
Open this publication in new window or tab >>Expected values of implementing AI in healthcare – A Qualitative study
2023 (English)In: Nordic Health Promotion Research Conference 2023: Abstracts, Halmstad, 2023Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Background: Artificial intelligence (AI) is often presented as a technology that will change healthcare and be useful inclinical work in disease prediction, diagnosis, and precision health. More knowledge is needed regarding the value of AI applications based on the perspectives of healthcare leaders to understand their roles as gatekeepers and facilitatorsfor successful implementation.

The purpose of the study: To explore healthcare leaders’ perceptions of the value of AI applications in clinical work.

Methods: The study had an explorative qualitative approach. Individual interviews were conducted from October2020 to May 2021 with 26 healthcare leaders with different experiences in implementing AI in clinical practice in acounty council in Sweden. Inductive qualitative content analysis was used, and eight sub-categories and threecategories emerged.

Findings: The value of AI applications in clinical care was described in terms of expected benefits for patients as toolssupporting person-centered information and individualized self-management. The expected benefits for healthcareprofessionals included decision-support in diagnostics, risk assessments, and treatment recommendations but alsoproviding warning systems and second opinions in clinical work. On an organizational level, the benefits comprisedpatient safety and decision-support in prioritizing healthcare resources in and across healthcare organizations.

Conclusions: The healthcare leaders perceived that AI applications would provide value on different levels inhealthcare for patients, healthcare professionals, and organizations. Across these levels, the implementation of AI cansupport person-centeredness, patient self-management, quality of care, patient safety, and resource optimization.

Place, publisher, year, edition, pages
Halmstad: , 2023
Keywords
AI applications, healthcare leaders, qualitative study, value
National Category
Health Sciences
Research subject
Health Innovation; Health Innovation, IDC
Identifiers
urn:nbn:se:hh:diva-52343 (URN)
Conference
The 10th Nordic Health Promotion Research Conference "Sustainability and the impact on health and well-being", Halmstad, Sweden, 14-16 June, 2023
Funder
Halmstad University
Available from: 2023-12-28 Created: 2023-12-28 Last updated: 2024-01-02Bibliographically approved
Lönn, M., Aili, K., Svedberg, P., Nygren, J. M., Jarbin, H. & Larsson, I. (2023). Experiences of Using Weighted Blankets among Children with ADHD and Sleeping Difficulties. Occupational Therapy International, 2023, 1-12, Article ID 1945290.
Open this publication in new window or tab >>Experiences of Using Weighted Blankets among Children with ADHD and Sleeping Difficulties
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2023 (English)In: Occupational Therapy International, ISSN 0966-7903, E-ISSN 1557-0703, Vol. 2023, p. 1-12, article id 1945290Article in journal (Refereed) Published
Abstract [en]

Introduction. Sleeping difficulties are common in children with attention deficit hyperactivity disorder (ADHD). A sleep intervention with weighted blankets was designed to increase current understanding of using weighted blankets to target children’s individual needs in connection with sleep and daytime functioning. Aim. To explore how children with ADHD and sleeping difficulties experience the use of weighted blankets. Methods. An explorative qualitative design in which 26 children with ADHD and sleeping difficulties, 6-15 years old, were interviewed about a sleep intervention with weighted blankets. Four categories emerged from qualitative content analysis. Results. Children’s experiences revealed that the use of weighted blankets 1) requires a commitment, by adjusting according to needs and preferences and adapting to the environment; 2) improves emotional regulation by feeling calm and feeling safe; 3) changes sleeping patterns by creating new routines for sleep and improving sleep quality; and 4) promotes everyday participation by promoting daily function and balancing activity and sleep. Conclusions. Using weighted blankets promoted children’s management of daily life with ADHD and sleeping difficulties. Occupational therapists can improve the assessment and delivery of weighted blankets tailored to individual needs based on increased knowledge from the children themselves. Copyright © 2023 Maria Lönn et al.

Place, publisher, year, edition, pages
Oxford: John Wiley & Sons, 2023
National Category
Psychiatry
Identifiers
urn:nbn:se:hh:diva-50117 (URN)10.1155/2023/1945290 (DOI)000938743200001 ()36824380 (PubMedID)2-s2.0-85148774514 (Scopus ID)
Available from: 2023-03-27 Created: 2023-03-27 Last updated: 2023-03-27Bibliographically approved
Petersson, L., Svedberg, P., Nygren, J. M. & Larsson, I. (2023). Healthcare Leaders' Perceptions of the Usefulness of AI Applications in Clinical Work: A Qualitative Study. In: Caring is sharing - exploiting the value in data for health and innovation: 33rd Medical Informatics Europe Conference, MIE2023, Gothenburg, Sweden, 22-25 May. Paper presented at 33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023. Gothenburg, Sweden, 22-25 May, 2023 (pp. 678-679). Amsterdam: IOS Press, 302
Open this publication in new window or tab >>Healthcare Leaders' Perceptions of the Usefulness of AI Applications in Clinical Work: A Qualitative Study
2023 (English)In: Caring is sharing - exploiting the value in data for health and innovation: 33rd Medical Informatics Europe Conference, MIE2023, Gothenburg, Sweden, 22-25 May, Amsterdam: IOS Press, 2023, Vol. 302, p. 678-679Conference paper, Published paper (Refereed)
Abstract [en]

Artificial intelligence (AI) is often presented as a technology that changes healthcare and is useful in clinical work in disease prediction, diagnosis, treatment effectiveness, and precision health. This study aimed to explore healthcare leaders' perceptions of the usefulness of AI applications in clinical work. The study was based on qualitative content analysis. Individual interviews were conducted with 26 healthcare leaders. The usefulness of AI applications in clinical care was described in terms of expected benefits for 1) patients as supporting individualized self-management and person-centered information support tools 2) healthcare professionals in terms of providing decision-support in diagnostics, risk assessments, treatment recommendations, warning systems, and as a new colleague supporting the clinical work, and 3) organizations as providing patient safety and decision-support in prioritizing healthcare resources in organizing healthcare.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2023
Series
Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365 ; 302
Keywords
AI-based applications, healthcare leaders, qualitative study, usefulness
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Research subject
Health Innovation, IDC
Identifiers
urn:nbn:se:hh:diva-51972 (URN)10.3233/SHTI230235 (DOI)001071432900177 ()37203777 (PubMedID)2-s2.0-85159767799 (Scopus ID)
Conference
33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023. Gothenburg, Sweden, 22-25 May, 2023
Available from: 2023-11-13 Created: 2023-11-13 Last updated: 2023-11-14Bibliographically approved
Projects
Peer support intervention for improved mental health in children [2012-00027_Formas]; Halmstad University; Publications
Einberg, E.-L., Nygren, J., Svedberg, P. & Enskär, K. (2016). ‘Through my eyes’: health-promoting factors described by photographs taken by children with experience of cancer treatment. Child Care Health and Development, 42(1), 76-86
Automatic Idea Detection: Implementing artificial intelligence in medical technology innovation (AID); Halmstad UniversityEvaluation of health effects and cost effectiveness from a sleep intervention with weight blankets in children with ADHD and sleep problems [2021-00664_Forte]; Halmstad University; Publications
Lönn, M., Svedberg, P., Nygren, J. M., Jarbin, H., Aili, K. & Larsson, I. (2023). The efficacy of weighted blankets for sleep in children with attention-deficit/hyperactivity disorder—A randomized controlled crossover trial. Journal of Sleep Research, Article ID e13990. Harris, U., Svedberg, P., Aili, K., Nygren, J. M. & Larsson, I. (2022). Parents’ Experiences of Direct and Indirect Implications of Sleep Quality on the Health of Children with ADHD: A Qualitative Study. International Journal of Environmental Research and Public Health, 19(22), Article ID 15099.
Health Data Sweden [1083629]; Implementing Artificial Intelligence (AI): Exploring how AI changes information and knowledge practices in healthcare [2022-05406_VR]; Halmstad University; Publications
Petersson, L., Steerling, E., Neher, M., Larsson, I., Nygren, J. M., Svedberg, P. & Nilsen, P. (2023). Implementering av artificiell intelligens (AI): Ett projekt om hur AI förändrar information och kunskapspraktiker i hälso- och sjukvården. In: : . Paper presented at FALF 2023 - Forum för arbetslivsforskning. Apeloig, A. (2023). Stakeholders’ perceptions on potential barriers and facilitators of implementing technology based on Artificial Intelligence for predicting and preventing mental illness among young adults: – a qualitative study applying the NASSS framework. (Student paper). Högskolan i Halmstad
Social capital for identification and support of young people's mental Health;
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ORCID iD: ORCID iD iconorcid.org/0000-0002-3576-2393

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