To Örebro University

Background: The COVID-19 pandemic compelled governments worldwide to adopt diverse public health and economic policies. However, the relative efficiency of these interventions across European countries has not been comprehensively evaluated. This study assessed the efficiency of COVID-19 policy responses in 27 European countries of the Organization for Economic Co-operation and Development (OECD), with the aim of identifying factors associated with effective pandemic management. The time period of this study is from January 1, 2020 to December 31, 2022.

Methods: A data envelopment analysis (DEA) framework was applied to evaluate the efficiency of national responses. Policy inputs, including healthcare resources, economic support, and stringency indices, were assessed against outputs reflecting adverse outcomes, namely COVID-19 cases, mortality, and virus transmission. Data were obtained from the Oxford COVID-19 Government Response Tracker and other publicly available international databases.

Results: Marked heterogeneity in policy efficiency was observed. Countries that implemented early, stringent, and adaptive measures demonstrated superior efficiency scores. Italy, Greece, and Austria consistently ranked among the most efficient, while nations with delayed or inflexible strategies performed less effectively despite comparable resource availability. Importantly, policy flexibility and dynamic adjustment to epidemiological trends emerged as critical determinants of sustained efficiency.

Conclusions: This study provides a robust comparative evaluation of COVID-19 policy efficiency across European OECD countries. The findings emphasize that timeliness of interventions, adaptability of policy measures, and judicious resource allocation were more influential than absolute resource capacity. Incorporating efficiency assessments into pandemic preparedness strategies may enhance resilience and inform evidence-based decision-making for future global health emergencies.

OBJECTIVES: European countries implemented highly diverse mitigation policies during the COVID-19 pandemic, ranging from strict nationwide lockdowns to more voluntary approaches. This study aims to understanding how timing, stringency, and comprehensiveness of policy responses influenced epidemic trajectories by applying machine learning-based counterfactual analysis.

METHODS: Data from 27 European OECD countries between January 2020 and December 2022 were analysed. Daily epidemiological data, including COVID-19 cases, deaths, effective reproduction number were linked with government response indicators, demographics, vaccination, testing, and mobility data. Temporal Fusion Transformer (TFT) models were applied for multi-horizon time series forecasting by capturing nonlinear relationships between policy response indicators and COVID-19 outcome variables. Simulations were conducted under eight hypothetical response scenarios, ranging from the loosest to the strictest policy bundles. The impacts of policy responses on COVID-19 outcomes were assessed by comparing the counterfactual scenarios with the factual outcomes.

RESULTS: TFT models achieved excellent predictive accuracy (percentage mean absolute error <10%). The strictest responses were associated with reduced daily COVID-19 cases by over 10% in several countries, notably Hungary, Switzerland, and Turkey, while the loosest responses were associated with increased incidence by 10-20%, with the highest value observed in Poland. Associations with mortality were smaller and heterogeneous, with potentially maximum reductions of 7-10% in Belgium, Portugal, and Switzerland under strict scenarios. Over early relaxation from the restrictions might lead to outcomes as adverse as those under continuously loose policies. Feature importance analyses highlighted restrictions on mobility and gatherings, vaccination, testing, and fiscal measures as dominant drivers, alongside country-level factors such as age structure and chronic disease burden.

CONCLUSIONS: The TFT-based machine learning framework demonstrated favourable feasibility and interpretability, reinforcing its value in guiding policy decisions. Comprehensive, multi-domain interventions outperformed partial or short-lived restrictions. Lifting measures before achieving sufficient immunity and epidemic control posed substantial risks. Stringent policies reduced transmission, but their impact on mortality was constrained, which might be due to demographic and systemic vulnerabilities. Adaptive, data-driven strategies integrating epidemiology, policy, and structural context are essential to strengthen policy responses against future pandemics.

Background and objective: Machine learning (ML) models that use data captured from Inertial Measurement Units (IMUs) are widely applied in the clinical management of Parkinson's disease (PD). However, performance and interpretability of these models can be influenced by the selection of input features, especially when dealing with multi-dimensional data. This study investigates the performance of Lasso regularization in improving performance and simplicity of supervised and unsupervised ML models using IMUs data.

Methods: Data were collected using IMUs placed on the wrists and ankles of 19 patients (14 males and 5 females) with advanced PD (mean years with disease of 10 years). Participants performed different motor tests, and three movement disorder specialists rated the severity of motor states (Off and dyskinesia) on a Treatment Response Scale (TRS). Sensor data were processed, and features were reduced by Lasso regularization and used as inputs to Support Vector Machines (SVM) for classification and regression. In addition, clustering methods were employed to align the sensor data to clinical labels.

Results: Linear SVM correctly classified Off motor state from treatment-induced dyskinesia state with an accuracy of 93.9 % and 93.3 %, respectively. The correlation coefficient between the predicted TRS score derived by gaussian SVM and mean TRS score of the three specialists was 0.91. The clusters derived by the clustering algorithms separated well the instances when the patients were in Off and dyskinesia motor states.

Conclusions: Using Lasso regularization as a feature selection method coupled with ML models yielded good predictive performance when fusing multi-sensor and -activity data from IMUs. This approach can be used as a tool to objectively assess PD motor states and improve the management of the disease by individualizing treatments.

BACKGROUND: There is a knowledge gap regarding the effectiveness and utility of various preventive interventions during the COVID-19 pandemic. In this study, we aimed to evaluate the cost-effectiveness of various COVID-19 preventive interventions, including non-medical interventions (NMIs) and vaccination programs, using real-world data across different demographic and socioeconomic contexts worldwide.

METHODS: We searched Medline, Cochrane Library, Embase, and Web of Science Core Collection from December 2019 to March 2024. We identified 75 studies which compared 34 COVID-19 preventive interventions. We conducted a network meta-analysis to assess the incremental net benefits (INB) of these interventions from both societal and health care system perspectives. We adjusted purchasing power parity (PPP) and standardised willingness to pay (WTP) to enhance the comparability of cost-effectiveness across different economic levels. We performed sensitivity and subgroup analyses to examine the robustness of the results.

RESULTS: Movement restrictions and expanding testing emerged as the most cost-effective strategies from a societal perspective, with WTP-standardised INB values of USD 21 050 and USD 11 144. In contrast, combinations of NMIs with vaccination were less cost-effective, particularly in high-income regions. From a health care system perspective, vaccination plus distancing and test, trace, and isolate strategy were highly cost-effective, while masking requirements were less economically viable. The effectiveness of interventions varied significantly across different economic contexts, underlining the necessity for region-specific strategies.

CONCLUSIONS: In this study, we highlight significant variations in the cost-effectiveness of COVID-19 preventive interventions. Tailoring strategies to specific regional economic and infrastructural conditions is crucial. Continuous evaluation and adaptation of these strategies are essential for effective management of ongoing and future public health threats.

REGISTRATION: PROSPERO: CRD42023385169.

Upper limb motor impairments require consistent assessment and rehabilitation across diverse patient populations including stroke, brain injuries, Parkinson’s disease, among others. However, traditional approaches are often constrained by cost, limited access, therapist availability, and lack of scalability. In this position paper, we argue that a modular, smartphone-based system that leverages augmented reality (AR) and artificial intelligence (AI) represents a transformative opportunity to guide motor tasks, collect movement data, and deliver personalized feedback. The system architecture includes a client-side AR app with gamified motor tasks, a cloud backend with machine learning capabilities, and a web-based clinician dashboard for review and human-in-the-loop data labeling. Representative AR tasks target specific motor functions such as reach, coordination, and postural control, enabling fine-grained tracking of upper limb dysfunctions and empowerment of users to take control of their recovery while supporting clinicians with valuable data to track the performance and progress of their patients. By utilizing devices already in patients’ hands, AR, and AI, this approach supports remote, low-cost, and extensible rehabilitation—bringing scalable digital therapy closer to everyday use by democratizing access to care and improving outcomes of people living with upper limb impairments.

BACKGROUND: Parkinson disease (PD) is a complex, noncurable, and progressive neurological disease affecting different areas of the human nervous system. PD is associated with both motor and nonmotor symptoms, which negatively affect patients' quality of life and may cause changes in socialization such as intentional social withdrawal. This may further lead to social isolation and loneliness. The use of information and communication technology (ICT) plays an important role in managing social isolation and loneliness. Currently, there is a lack of research focusing on designing and developing ICT solutions that specifically address social isolation and loneliness among people living with PD.

OBJECTIVE: This study addresses this gap by investigating barriers and social needs in the context of social isolation, loneliness, and technology use among people living with PD. The insights gained can inform the development of effective ICT solutions, which can address social isolation and loneliness and improve the quality of life for people living with PD.

METHODS: A qualitative study with 2 phases of data collection were conducted. During the first phase, 9 health care professionals and 16 people living with PD were interviewed to understand how PD affects social life and technology use. During the second phase, 2 focus groups were conducted with 4 people living with PD in each group to gather insights into their needs and identify ways to manage social isolation and loneliness. Thematic analysis was used to analyze both data sets and identify key themes.

RESULTS: The results showed that the barriers experienced by people living with PD due to PD such as "fatigue," "psychological conditions," "social stigma," and "medication side effects" affect their social life. People living with PD also experience difficulties using a keyboard and mouse, remembering passwords, and navigating complex applications due to their PD-related physical and cognitive limitations. To manage their social isolation and loneliness, people living with PD suggested having a simple and easy-to-use solution, allowing them to participate in a digital community based on their interests, communicate with others, and receive recommendations for social events.

CONCLUSIONS: The new ICT solutions focusing on social isolation and loneliness among people living with PD should consider the barriers restricting user's social activities and technology use. Given the wide range of needs and barriers experienced by people living with PD, it is more suitable to adopt user-centered design approaches that emphasize the active participation of end users in the design process. Importantly, any ICT solution designed for people living with PD should not encourage internet addiction, which will further contribute to the person's withdrawal from society.

Persons with Parkinson’s disease (PwPs) may have difficulty participating in social activities due to motor and non-motor symptoms that may lead to social isolation and loneliness. This paper addresses how to manage social isolation and loneliness among PwPs using digital solutions. Information and Communication Technologies (ICT) have the potential to address social isolation and loneliness, but there are no current solutions that specifically target these issues among PwPs. In this paper, we present an ongoing project based on design science research (DSR) combined with a user-centered approach to identify challenges, requirements, and design objectives. The empirical work includes data from interviews and focus groups with PwPs and healthcare professionals. Based on the empirical material, we formulated design principles on identified challenges and requirements, which were instantiated into a high-fidelity prototype. This initial cycle serves as a foundation for ongoing improvements and evaluations in a continuous DSR process.

BACKGROUND: Advancements in science and various technologies have resulted in people having access to better health care, a good quality of life, and better economic situations, enabling humans to live longer than ever before. Research shows that the problems of loneliness and social isolation are common among older adults, affecting psychological and physical health. Information and communication technology (ICT) plays an important role in alleviating social isolation and loneliness.

OBJECTIVE: The aim of this review is to explore ICT solutions for reducing social isolation or loneliness among older adults, the purpose of ICT solutions, and the evaluation focus of these solutions. This study particularly focuses on customized ICT solutions that either are designed from scratch or are modifications of existing off-the-shelf products that cater to the needs of older adults.

METHODS: A scoping literature review was conducted. A search across 7 databases, including ScienceDirect, Association for Computing Machinery, PubMed, IEEE Xplore, PsycINFO, Scopus, and Web of Science, was performed, targeting ICT solutions for reducing and managing social isolation and loneliness among older adults. Articles published in English from 2010 to 2020 were extracted and analyzed.

RESULTS: From the review of 39 articles, we identified 5 different purposes of customized ICT solutions focusing on reducing social isolation and loneliness. These were social communication, social participation, a sense of belonging, companionship, and feelings of being seen. The mapping of purposes of ICT solutions with problems found among older adults indicates that increasing social communication and social participation can help reduce social isolation problems, whereas fulfilling emotional relationships and feeling valued can reduce feelings of loneliness. In terms of customized ICT solution types, we found the following seven different categories: social network, messaging services, video chat, virtual spaces or classrooms with messaging capabilities, robotics, games, and content creation and management. Most of the included studies (30/39, 77%) evaluated the usability and acceptance aspects, and few studies (11/39, 28%) focused on loneliness or social isolation outcomes.

CONCLUSIONS: This review highlights the importance of discussing and managing social isolation and loneliness as different but related concepts and emphasizes the need for future research to use suitable outcome measures for evaluating ICT solutions based on the problem. Even though a wide range of customized ICT solutions have been developed, future studies need to explore the recent emerging technologies, such as the Internet of Things and augmented or virtual reality, to tackle social isolation and loneliness among older adults. Furthermore, future studies should consider evaluating social isolation or loneliness while developing customized ICT solutions to provide more robust data on the effectiveness of the solutions.

Valid assessment of pain is essential in daily clinical practice to enhance the quality of care for the patients and to avoid the risk of addiction to strong analgesics. The aim of this paper is to find a method for objective and quantitative evaluation of pain using multiple physiological markers. Data was obtained from healthy volunteers exposed to thermal and ischemic stimuli. Twelve subjects were recruited and their physiological data including skin conductance, heart rate, and skin temperature were collected via a wrist-worn sensor together with their selfreported pain on a visual analogue scale (VAS). Statistically significant differences (p< 0.01) were found between physiological scores obtained with the wearable sensor before and during the thermal test. Test-retest reliability of sensor-based measures was good during the thermal test with intraclass correlation coefficients ranging from 0.22 to 0.89. These results support the idea that a multi-sensor wearable device can objectively measure physiological reactions in the subjects due to experimentally induced pain, which could be used for daily clinical practice and as an endpoint in clinical studies. Nevertheless, the results indicate a need for further investigation of the method in real-life pain settings.

BACKGROUND: Parkinson disease (PD) is a chronic degenerative disorder that causes progressive neurological deterioration with profound effects on the affected individual's quality of life. Therefore, there is an urgent need to improve patient empowerment and clinical decision support in PD care. Home-based disease monitoring is an emerging information technology with the potential to transform the care of patients with chronic illnesses. Its acceptance and role in PD care need to be elucidated both among patients and caregivers.

OBJECTIVE: Our main objective was to develop a novel home-based monitoring system (named EMPARK) with patient and clinician interface to improve patient empowerment and clinical care in PD.

METHODS: We used elements of design science research and user-centered design for requirement elicitation and subsequent information and communications technology (ICT) development. Functionalities of the interfaces were the subject of user-centric multistep evaluation complemented by semantic analysis of the recorded end-user reactions. The ICT structure of EMPARK was evaluated using the ICT for patient empowerment model.

RESULTS: Software and hardware system architecture for the collection and calculation of relevant parameters of disease management via home monitoring were established. Here, we describe the patient interface and the functional characteristics and evaluation of a novel clinician interface. In accordance with our previous findings with regard to the patient interface, our current results indicate an overall high utility and user acceptance of the clinician interface. Special characteristics of EMPARK in key areas of interest emerged from end-user evaluations, with clear potential for future system development and deployment in daily clinical practice. Evaluation through the principles of ICT for patient empowerment model, along with prior findings from patient interface evaluation, suggests that EMPARK has the potential to empower patients with PD.

CONCLUSIONS: The EMPARK system is a novel home monitoring system for providing patients with PD and the care team with feedback on longitudinal disease activities. User-centric development and evaluation of the system indicated high user acceptance and usability. The EMPARK infrastructure would empower patients and could be used for future applications in daily care and research.