| 1. |
- Aeddula, Omsri, 1993-
(författare)
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Navigating Data Challenges: AI-Driven Decision Support for Product-Service System Development
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Solution providers are transitioning from product-centric models to service-oriented solutions. This shift has led to the rise of Product-Service Systems (PSS), which offer a holistic approach by integrating physical products with associated services. However, the inherent complexity and collaborative nature of PSS development present a significant challenge: information gathering, analysis, and knowledge building. This is further amplified in the early stages of PSS development due to data challenges such as uncertainty, ambiguity, and complexity. This complicates informed decision-making, potentially leading to the risk of sub-optimal outcomes and impacting the success of final offerings.This research proposes an AI-powered data analysis approach to address these data challenges and augment the decision-making process of PSS development. The focus is on supporting early-stage decision-making, as decisions made at this stage greatly impact the success of final solutions. The research investigates how data can be utilized and visualized to extract actionable insights, ultimately facilitating informed decision-making.The presented research demonstrates that AI-powered data analysis effectively supports informed decision-making in early-stage PSS development. By extracting actionable insights from complex data, handling data limitations, and enabling informed strategic decisions, knowledge sharing, and collaboration are facilitated among stakeholders. Furthermore, integrating AI with visualization tools fosters knowledge building and a deeper understanding of system behavior, ultimately leading to more successful PSS solutions. The efficacy of AI-powered data analysis for handling diverse data types across application domains is demonstrated, potentially leading to benefits such as a deeper understanding of system behavior and proactive solution strategies. These advancements contribute to developing decision support systems specifically for PSS development.Overall, this research demonstrates the efficacy of AI-powered data analysis in overcoming data challenges and empowering decision-makers in early-stage PSS development. This translates to more informed choices, leading to the creation of successful and efficient PSS solutions.
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| 2. |
- Aeddula, Omsri, 1993-, et al.
(författare)
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AI-driven Ossification Assessment in Knee MRI : A Product-Service System Development for Informed Clinical Decision-Making
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background: Traditionally, assessing the degree of ossification in the epiphyseal plate for growth plate development relies on manual evaluation, which can be inefficient due to the complexities of the distal femoral epiphysis anatomy. Existing methods lack efficient detection techniques.Method: This study proposes an AI-based decision support system, designed within a product-service system (PSS) framework, to automate ossification assessment and detection of the distal femoral epiphysis in knee magnetic resonance imaging (MRI) data. The system leverages advanced machine learning techniques, specifically two Convolutional Neural Networks (CNNs), combined with computer vision techniques. This intelligent system analyzes MRI slices to predict the optimal slice for analysis and identify variations in the degree of ossification within individual datasets.Results: The proposed method's effectiveness is demonstrated using a set of T2-weighted gradient echo grayscale knee MRI data. The system successfully detects the complex anatomy of the distal femoral epiphysis, revealing variations in the degree of ossification ranging from completely closed/open to fully open/closed regions.Conclusions: This study presents a robust and efficient AI-based method, integrated within a PSS framework, for measuring the degree of ossification in the distal femoral epiphysis. This approach automates ossification assessment, providing valuable insights for clinical decision-making by clinicians and forensic practitioners. The PSS framework ensures seamless integration of the AI technology into existing workflows.
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| 3. |
- Christiansen, Line, 1986-
(författare)
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Using Mobile Health Technology to Support Health-related Quality of Life : From the Perspective of Older Adults with Cognitive Impairment
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The prevalence of cognitive impairment and illness increases with age. For older adults, maintaining or improving health-related quality of life (HRQoL) in the early stages of cognitive impairment is important to prevent consequences related to the progression of the condition. This thesis aims to identify factors affecting HRQoL and describe how mHealth technology can support HRQoL in older adults with cognitive impairment.Four studies were conducted using quantitative and qualitative approaches. A cross-sectional design was used to identify factors affecting older adults’ HRQoL (Study I) and investigate the relationship between mHealth technology use and self-rated quality of life (QoL) (Study III). A phenomenographical design was used to describe variations in older adults’ perceptions of mHealth technology and its impact on HRQoL (Study II). A prospective longitudinal design was used to examine older adults’ HRQoL changes over time (Study IV).Participants were selected from two longitudinal population studies using a purposive sampling strategy to include those aged 55 years and above with mild cognitive impairment or mild dementia. Data were obtained from questionnaires and semi-structured interviews. Data from the quantitative studies were analysed using statistical analysis, including descriptive and comparative analysis and regression analysis, while data from the qualitative study were examined using phenomenographical analysis in consecutive steps.The results showed that most older adults experienced good HRQoL with regard to both physical and mental health. The likelihood of having good-to-excellent QoL increased with age and was higher among males and those with higher education levels. Those diagnosed with dementia reported poorer HRQoL. Factors associated with low HRQoL included dependency in activities of daily living, receiving informal care and feelings of loneliness and pain. The use of mHealth technology was perceived as supportive in maintaining social interactions and facilitating independent living. The technology literacy levels among the study sample varied significantly. Those who reported having moderate-to-high technical skills and using the internet regularly via mHealth technology had higher odds of experiencing good-to-excellent QoL. No significant changes were observed in the older adults’ HRQoL over time in relation to the non-use and use of a customized mHealth application.The indicators of HRQoL are clinically relevant for the secondary prevention of dementia to help maintain good HRQoL in older adults with incipient cognitive impairment. The technology-related differences reflect the risk of digital exclusion. To improve preconditions for being digitally involved in society, societal initiatives that aim to empower the technology literacy level of older adults are needed.
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| 4. |
- Eivazzadeh, Shahryar, 1975-
(författare)
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Evaluating Success Factors of Health Information Systems
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Health information systems are our technological response to the growing demand for health care. However, their success in their mission can be challenging due to the complexity of evaluating technological interventions in health care. In the series of studies compiled in this dissertation, we looked at the evaluation of these systems. We focused on the evaluation of factors that lead to success, where success is indicated by user satisfaction and can be induced by both intervention-specific and individual-specific factors.Study 1 developed a method, called UVON, to elicit and organise the user-demanded qualities in the outcomes of the health information system intervention. Through the application of the UVON method in the FI-STAR project, an EU project which developed and deployed seven e-health applications in seven member countries, ten categories of quality and their subcategories were identified. These qualities formed two questionnaires, specific to the patient and health professional users. Through the questionnaires, the patients and health-professionals users evaluated and graded both the occurrence of those demanded qualities in the project outcomes and their general satisfaction.Study 2 analysed the survey results to find out which of those ten qualities have the highest impact on satisfaction or can predict it better. Two partial least squares structural equation modelling (PLS-SEM) models were constructed, for the patient and health professionals, based on the Unified eValuation using ONtology (UVON) and survey outputs. The models showed that effectiveness is an important quality in creating satisfaction for both user groups. Besides, affordability for the health professionals and efficiency plus safety for the patients were the most influential. A satisfaction index is also introduced for simple and fast inferring of the changes in the outcome qualities.Study 5 recruited outputs and learnings from studies 1 and 2 to design a system that partially automates the process of evaluating success factors in health information systems, making it continuous and real-time, and replacing hard-to-run surveys with automatically captured indicators and analytics.Study 3 focused on individual-specific factors in using health information systems, particularly the technophilia personality trait. A short six-items instrument, called TechPH, was designed to measure technophilia in users, tuned for older users. The study recruited empirical data from the Swedish National Study on Aging and Care (SNAC) project. Two factors, labelled techAnxiety and techEnthusiams, are identified by the factor analysis method. A TechPH score was introduced as a scalar measurement of technophilia.Study 4 elicited and discussed the ethical challenges of evaluating and researching health information systems. Both a scoping review and a novel systematic postulation approach were recruited to identify twenty ethical challenges. The identified ethical challenges were discussed and mapped into a three-dimensional space of evaluation stages, demanded qualities, and major involving entities (stakeholder and artefacts), which fosters further postulation of ethical challenges.
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| 5. |
- Flyborg, Johan, et al.
(författare)
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Results of objective brushing data recorded from a powered toothbrush used by elderly individuals with mild cognitive impairment related to values for oral health
- 2024
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Ingår i: Clinical Oral Investigations. - : Springer Nature. - 1432-6981 .- 1436-3771. ; 28:1
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: The study aimed to investigate how the objective use of a powered toothbrush in frequency and duration affects plaque index, bleeding on probing, and periodontal pocket depth ≥ 4 mm in elderly individuals with MCI. A second aim was to compare the objective results with the participants’ self-estimated brush use.Materials and methods: Objective brush usage data was extracted from the participants’ powered toothbrushes and related to the oral health variables plaque index, bleeding on probing, and periodontal pocket depth ≥ 4 mm. Furthermore, the objective usage data was compared with the participants’ self-reported brush usage reported in a questionnaire at baseline and 6- and 12-month examination.Results: Out of a screened sample of 213 individuals, 170 fulfilled the 12-month visit. The principal findings are that despite the objective values registered for frequency and duration being lower than the recommended and less than the instructed, using powered toothbrushes after instruction and information led to improved values for PI, BOP, and PPD ≥ 4 mm in the group of elderly with MIC.Conclusions: Despite lower brush frequency and duration than the generally recommended, using a powered toothbrush improved oral health. The objective brush data recorded from the powered toothbrush correlates poorly with the self-estimated brush use.Clinical relevance: Using objective brush data can become one of the factors in the collaboration to preserve and improve oral health in older people with mild cognitive impairment. Trial registration: ClinicalTrials.gov Identifier: NCT05941611, retrospectively registered 11/07/2023.
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| 6. |
- Aeddula, Omsri, 1993-, et al.
(författare)
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A Solution with Bluetooth Low Energy Technology to Support Oral Healthcare Decisions for improving Oral Hygiene
- 2021
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Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450389846 ; , s. 134-139
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Konferensbidrag (refereegranskat)abstract
- The advent of powered toothbrushes and associated mobile health applications provides an opportunity to collect and monitor the data, however collecting reliable and standardized data from large populations has been associated with efforts from the participants and researchers. Finding a way to collect data autonomously and without the need for cooperation imparts the potential to build large knowledge banks. A solution with Bluetooth low energy technology is designed to pair a powered toothbrush with a single-core processor to collect raw data in a real-time scenario, eliminating the manual transfer of powered toothbrush data with mobile health applications. Associating powered toothbrush with a single-core processor is believed to provide reliable and comprehensible data of toothbrush use and propensities can be a guide to improve individual exhortation and general plans on oral hygiene quantifies that can prompt improved oral wellbeing. The method makes a case for an expanded chance to plan assistant capacities to protect or improve factors that influence oral wellbeing in individuals with mild cognitive impairment. The proposed framework assists with determining various parameters, which makes it adaptable and conceivable to execute in various oral care contexts
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| 7. |
- Aeddula, Omsri, 1993-
(författare)
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Data-Driven Decision Support Systems for Product Development - A Data Exploration Study Using Machine Learning
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Modern product development is a complex chain of events and decisions. The ongoing digital transformation of society, increasing demands in innovative solutions puts pressure on organizations to maintain, or increase competitiveness. As a consequence, a major challenge in the product development is the search for information, analysis, and the build of knowledge. This is even more challenging when the design element comprises complex structural hierarchy and limited data generation capabilities. This challenge is even more pronounced in the conceptual stage of product development where information is scarce, vague, and potentially conflicting. The ability to conduct exploration of high-level useful information using a machine learning approach in the conceptual design stage would hence enhance be of importance to support the design decision-makers, where the decisions made at this stage impact the success of overall product development process.The thesis aims to investigate the conceptual stage of product development, proposing methods and tools in order to support the decision-making process by the building of data-driven decision support systems. The study highlights how the data can be utilized and visualized to extract useful information in design exploration studies at the conceptual stage of product development. The ability to build data-driven decision support systems in the early phases facilitates more informed decisions.The thesis presents initial descriptive study findings from the empirical studies, showing the capabilities of the machine learning approaches in extracting useful information, and building data-driven decision support systems. The thesis initially describes how the linear regression model and artificial neural networks extract useful information in design exploration, providing support for the decision-makers to understand the consequences of the design choices through cause-and-effect relationships on a detailed level. Furthermore, the presented approach also provides input to a novel visualization construct intended to enhance comprehensibility within cross-functional design teams. The thesis further studies how the data can be augmented and analyzed to extract the necessary information from an existing design element to support the decision-making process in an oral healthcare context.
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| 8. |
- Berner, Jessica, et al.
(författare)
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Five-factor model, technology enthusiasm and technology anxiety
- 2023
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Ingår i: Digital Health. - : Sage Publications. - 2055-2076. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Older adults need to participate in the digital society, as societal and personal changes and what they do with the remaining time that they have in their older years has an undeniable effect on motivation, cognition and emotion. Changes in personality traits were investigated in older adults over the period 2019–2021. Technology enthusiasm and technology anxiety are attitudes that affect the relationship to the technology used. The changes in the score of technology enthusiasm and technology anxiety were the dependent variables. They were investigated with personality traits, age, gender, education, whether someone lives alone, cognitive function, digital social participation (DSP) and health literacy as predictors of the outcome. The Edwards-Nunnally index and logistic regression were used. The results indicated that DSP, lower age, lower neuroticism and higher education were indicative of less technology anxiety. High DSP and high extraversion are indicative of technology enthusiasm. DSP and attitude towards technology seem to be key in getting older adults to stay active online.
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| 9. |
- Eivazzadeh, Shahryar, 1975-, et al.
(författare)
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Design of a Semi-Automated and Continuous Evaluation System : Customized for Application in e-Health
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background and ObjectivesSurvey-based evaluation of a system, such as measuring user’s satisfaction or patient-reported outcomes, entails a set of burdens that limits the feasibility, frequency, extendability, and continuity of the evaluation. Automating the evaluation process, that is reducing the burden of evaluators in questionnaire curation or minimizing the need for explicit user attention when collecting their attitudes, can make the evaluation more feasible, repeatable, extendible, continuous, and even flexible for improvement. An automated evaluation process can be enhanced to include features, such as the ability to handle heterogeneity in evaluation cases. Here, we represent the design of a system that makes it possible to have a semi-automated evaluation system. The design is presented and partially implemented in the context of health information systems, but it can be applied to other contexts of information system usages as well.MethodThe system was divided into four components. We followed a design research methodology to design the system, where each component reached a certain level of maturity. Already implemented and validated methods from previous studies were embedded within components, while they were extended with improved automation proposals or new features.ResultsA system was designed, comprised of four major components: Evaluation Aspects Elicitation, User Survey, Benchmark Path Model, and Alternative Metrics Replacement. All components have the essential maturity of identification of the problem, identification of solution objectives, and the overall design. In the overall design, the primary flow, process-entities, data-entities, and events for each component are identified and illustrated. Parts of some components have been already verified and demonstrated in real-world cases.ConclusionA system can be developed to minimize human burden, both for the evaluators and respondants, in survey-based evaluation. This system automates finding items to evaluate, creating questionnaire based on those items, surveying the users' attitude about those items, modeling the relations between the evaluation items, and incrementally changing the model to rely on automatically collected metrics, usually implicit indicators, collected from the users, instead of requiring their explicit expression of their attitudes. The system provides the possibility of minimal human burden, frequent repetition, continuity and real-time reporting, incremental upgrades regarding environmental changes, proper handling of heterogeneity, and a higher degree of objectivity.
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| 10. |
- Eivazzadeh, Shahryar, 1975-, et al.
(författare)
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Most Influential Qualities in Creating Satisfaction Among the Users of Health Information Systems : Study in Seven European Union Countries
- 2018
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Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 6:4
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Tidskriftsartikel (refereegranskat)abstract
- Background:Several models suggest how the qualities of a product or service influence user satisfaction. Models, such as the Customer Satisfaction Index (CSI), Technology Acceptance Model (TAM), and Delone and McLean Information Systems Success (D&M IS), demonstrate those relations and have been used in the context of health information systems.Objective:We want to investigate which qualities foster greater satisfaction among patient and professional users. In addition, we are interested in knowing to what extent improvement in those qualities can explain user satisfaction and if this makes user satisfaction a proxy indicator of those qualities.Methods:The Unified eValuation using ONtology (UVON) method was utilised to construct an ontology of the required qualities for seven e-health applications being developed in the FI-STAR project, a European Union (EU) project in e-health. The e-health applications were deployed across seven EU countries. The ontology included and unified the required qualities of those systems together with the aspects suggested by the Model for ASsessment of Telemedicine applications (MAST) evaluation framework. Two similar questionnaires, for 87 patient users and 31 health professional users, were elicited from the ontology. In the questionnaires, user was asked if the system has improved the specified qualities and if the user was satisfied with the system. The results were analysed using Kendall correlation coefficients matrices, incorporating the quality and satisfaction aspects. For the next step, two Partial Least Squares Structural Equation Modelling (PLS-SEM) path models were developed using the quality and satisfaction measure variables and the latent construct variables that were suggested by the UVON method.Results:Most of the quality aspects grouped by the UVON method are highly correlated. Strong correlations in each group suggest that the grouped qualities can be measures which reflect a latent quality construct. The PLS-SEM path analysis for the patients reveals that the effectiveness, safety, and efficiency of treatment provided by the system are the most influential qualities in achieving and predicting user satisfaction. For the professional users, effectiveness and affordability are the most influential. The parameters of the PLS-SEM that are calculated allow for the measurement of a user satisfaction index similar to CSI for similar health information systems.Conclusions:For both patients and professionals, the effectiveness of systems highly contributes to their satisfaction. Patients care about improvements in safety and efficiency, while professionals care about improvements in the affordability of treatments with health information systems. User satisfaction is reflected more in the users' evaluation of system output and fulfilment of expectations, but slightly less in how far the system is from ideal. Investigating satisfaction scores can be a simple, fast way to infer if the system has improved the abovementioned qualities in treatment and care.
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| 11. |
- Flyborg, Johan, et al.
(författare)
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The long-term effect on oral health and quality of life using a powered toothbrush in individuals with mild cognitive impairment. An intervention trial
- 2024
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Ingår i: Special Care in Dentistry. - : John Wiley & Sons. - 0275-1879 .- 1754-4505.
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Tidskriftsartikel (refereegranskat)abstract
- Background: The number of older individuals with mild cognitive impairment and neurocognitive diseases is increasing, which may rapidly deteriorate oral health and Quality of life. Therefore, removing dental biofilm is essential for maintaining good oral health. The present study aimed to investigate whether introducing a powered toothbrush reduces the presence of dental plaque, bleeding on probing, and periodontal pockets ≥4 mm, leading to maintained or improved oral health and improved Quality of life in a group of older individuals with mild cognitive impairment. Methods: Two hundred and thirteen individuals aged 55 or older living without official home care with a Mini–Mental State Examination (MMSE) score between 20 and 28 and a history of memory problems in the previous 6 months were recruited and screened for the study. The individuals received a powered toothbrush and thorough instructions on how to use it. Clinical oral examinations, Quality of life examinations, and MMSE tests were conducted at baseline, 6, 12, and 24 months. The intervention group was compared to control groups at baseline and 24-month examination. It was divided into an MMSE high group with a score of more than 26 and an MMSE low group with a score of 26 and lower or decreasing two steps or more for 12 months. Results: PI, BOP, and PPD≥4 mm improved continuously in both MMSE groups during the 24 months of the study. The values for QoL-AD deteriorated over time, while the oral health-related Quality of life did not show any statistically significant changes. Conclusions: Introducing a powered toothbrush improved PI, BOP, and PPD≥4 mm over 24 months, even among individuals with low or declining MMSE scores. Improved oral health is associated with a preserved OHR-QoL. © 2024 The Author(s). Special Care in Dentistry published by Special Care Dentistry Association and Wiley Periodicals LLC.
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| 12. |
- Idrisoglu, Alper, et al.
(författare)
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Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders : Systematic Literature Review
- 2023
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Ingår i: Journal of Medical Internet Research. - : JMIR Publications. - 1438-8871. ; 25
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Forskningsöversikt (refereegranskat)abstract
- BACKGROUND: Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. OBJECTIVE: This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. METHODS: This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. RESULTS: In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. CONCLUSIONS: This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.
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| 13. |
- Idrisoglu, Alper, et al.
(författare)
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COPDVD : Automated Classification of Chronic Obstructive Pulmonary Disease on a New Developed and Evaluated Voice Dataset
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background: Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systematical effects, e.g., heart failure or voice distortion. However, the systematic effects of COPD might provide valuable information for early detection. In other words, symptoms caused by systematic effects could be helpful to detect the condition in its early stages.Objective: The proposed study aims to: (i) investigate whether the voice features extracted from the vowel "A" phonation carry information that can be predictive of COPD by employing Machine Learning (ML); and (ii) develop a voice dataset based on the evaluation of the features.Methods: Forty-eight participants were recruited from the pool of research clinic visitors at Blekinge Institute of Technology (BTH) in Sweden between January 2022 and May 2023. A dataset consisting of 1246 recordings from 48 participants was gathered. The collection of voice recordings containing the vowel "A" phonation commenced following an information and consent meeting with each participant using the VoiceDiagnistic application. The collected voice data was subjected to silence segment removal, feature extraction of baseline acoustic features, and Mel Frequency Cepstrum Coefficients (MFCC). Sociodemographic data was also collected from the participants. Three ML models were investigated for the binary classification of COPD and healthy controls: Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB). A nested k-fold cross-validation approach was employed. Additionally, the hyperparameters were optimized using grid-search on each ML model. For best performance assessment, accuracy, F1-score, precision, and recall metrics were computed. Afterward, we further examined the best classifier by utilizing the Area Under the Curve (AUC), Average Precision (AP), and SHapley Additive exPlanations (SHAP) feature importance measures. Results: The classifiers RF, SVM, and CB achieved a maximum accuracy of 77%, 69%, and 78% on the test set and 93%, 78% and 97% on the validation set, respectively. The CB classifier outperformed RF and SVM. After further investigation of the best-performing classifier, CB demonstrated the highest performance, producing an AUC of 82% and AP of 76%. In addition to age and gender, the mean values of baseline acoustic and MFCC features demonstrate high importance and deterministic characteristics for classification performance in both test and validation sets, though in varied order. Conclusion: This study concludes that the vowel "A" recordings contain information that can be captured by the CatBoost classifier with high accuracy for the classification of COPD. Additionally, baseline acoustic and MFCC features, in conjunction with age and gender information, can be employed for classification purposes and benefit healthcare for decision support in COPD diagnosis. Lastly, we believe that the newly developed voice dataset will be a valuable resource to researchers within the domain.
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| 14. |
- Idrisoglu, Alper, et al.
(författare)
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COPDVD : Automated classification of chronic obstructive pulmonary disease on a new collected and evaluated voice dataset
- 2024
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier. - 0933-3657 .- 1873-2860. ; 156
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundChronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systematical effects, e.g., heart failure or voice distortion. However, the systematic effects of COPD might provide valuable information for early detection. In other words, symptoms caused by systematic effects could be helpful to detect the condition in its early stages.ObjectiveThe proposed study aims to explore whether the voice features extracted from the vowel “a” utterance carry any information that can be predictive of COPD by employing Machine Learning (ML) on a newly collected voice dataset.MethodsForty-eight participants were recruited from the pool of research clinic visitors at Blekinge Institute of Technology (BTH) in Sweden between January 2022 and May 2023. A dataset consisting of 1246 recordings from 48 participants was gathered. The collection of voice recordings containing the vowel “a” utterance commenced following an information and consent meeting with each participant using the VoiceDiagnostic application. The collected voice data was subjected to silence segment removal, feature extraction of baseline acoustic features, and Mel Frequency Cepstrum Coefficients (MFCC). Sociodemographic data was also collected from the participants. Three ML models were investigated for the binary classification of COPD and healthy controls: Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB). A nested k-fold cross-validation approach was employed. Additionally, the hyperparameters were optimized using grid-search on each ML model. For best performance assessment, accuracy, F1-score, precision, and recall metrics were computed. Afterward, we further examined the best classifier by utilizing the Area Under the Curve (AUC), Average Precision (AP), and SHapley Additive exPlanations (SHAP) feature-importance measures.ResultsThe classifiers RF, SVM, and CB achieved a maximum accuracy of 77 %, 69 %, and 78 % on the test set and 93 %, 78 % and 97 % on the validation set, respectively. The CB classifier outperformed RF and SVM. After further investigation of the best-performing classifier, CB demonstrated the highest performance, producing an AUC of 82 % and AP of 76 %. In addition to age and gender, the mean values of baseline acoustic and MFCC features demonstrate high importance and deterministic characteristics for classification performance in both test and validation sets, though in varied order.ConclusionThis study concludes that the utterance of vowel “a” recordings contain information that can be captured by the CatBoost classifier with high accuracy for the classification of COPD. Additionally, baseline acoustic and MFCC features, in conjunction with age and gender information, can be employed for classification purposes and benefit healthcare for decision support in COPD diagnosis.
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| 15. |
- Javeed, Ashir, 1989-, et al.
(författare)
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Breaking barriers : a statistical and machine learning-based hybrid system for predicting dementia
- 2023
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Ingår i: Frontiers in Bioengineering and Biotechnology. - : Frontiers Media S.A.. - 2296-4185. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Dementia is a condition (a collection of related signs and symptoms) that causes a continuing deterioration in cognitive function, and millions of people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining dementia rely primarily on clinical examinations, analyzing medical records, and administering cognitive and neuropsychological testing. However, these methods are time-consuming and costly in terms of treatment. Therefore, this study aims to present a noninvasive method for the early prediction of dementia so that preventive steps should be taken to avoid dementia. Methods: We developed a hybrid diagnostic system based on statistical and machine learning (ML) methods that used patient electronic health records to predict dementia. The dataset used for this study was obtained from the Swedish National Study on Aging and Care (SNAC), with a sample size of 43040 and 75 features. The newly constructed diagnostic extracts a subset of useful features from the dataset through a statistical method (F-score). For the classification, we developed an ensemble voting classifier based on five different ML models: decision tree (DT), naive Bayes (NB), logistic regression (LR), support vector machines (SVM), and random forest (RF). To address the problem of ML model overfitting, we used a cross-validation approach to evaluate the performance of the proposed diagnostic system. Various assessment measures, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and Matthew’s correlation coefficient (MCC), were used to thoroughly validate the devised diagnostic system’s efficiency. Results: According to the experimental results, the proposed diagnostic method achieved the best accuracy of 98.25%, as well as sensitivity of 97.44%, specificity of 95.744%, and MCC of 0.7535. Discussion: The effectiveness of the proposed diagnostic approach is compared to various cutting-edge feature selection techniques and baseline ML models. From experimental results, it is evident that the proposed diagnostic system outperformed the prior feature selection strategies and baseline ML models regarding accuracy.
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| 16. |
- Javeed, Ashir, 1989-, et al.
(författare)
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Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
- 2023
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 13:8
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Tidskriftsartikel (refereegranskat)abstract
- Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental purposes of the proposed model consisted of 55 features with a total of 368 samples. We found that the classes in the dataset were highly imbalanced. To avoid the problem of bias in the machine learning model, we used the synthetic minority oversampling technique (SMOTE). After balancing the classes in the dataset, the newly proposed system employed a (Formula presented.) statistical model to rank the features from the dataset. The highest-ranked features were fed into an optimized random forest (RF) model for classification. The hyperparameters of the RF classifier were optimized using a grid search algorithm. The performance of the newly proposed model ((Formula presented.) _RF) was validated using several evaluation measures, including accuracy, sensitivity, specificity, F1 score, and a receiver operating characteristic (ROC) curve. With only 10 features from the dataset, the proposed model (Formula presented.) _RF achieved the highest accuracy of 94.59%. The proposed model (Formula presented.) _RF improved the performance of the standard RF model by 5.5%. Moreover, the proposed model (Formula presented.) _RF was compared with other state-of-the-art machine learning models. The experimental results show that the newly proposed decision support system outperforms the other machine learning systems using the same feature selection module ((Formula presented.)).
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| 17. |
- Javeed, Ashir, 1989-, et al.
(författare)
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Optimizing Depression Prediction in Older Adults : A Comparative Study of Feature Extraction and Machine Learning Models
- 2024
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Ingår i: International Conference on Control, Automation and Diagnosis, ICCAD 2024. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350361025
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Konferensbidrag (refereegranskat)abstract
- Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K-nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE. © 2024 IEEE.
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| 18. |
- Javeed, Ashir, 1989-, et al.
(författare)
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Predictive Power of XGBoost_BiLSTM Model : A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data
- 2023
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Ingår i: International Journal of Computational Intelligence Systems. - : Springer Nature. - 1875-6891 .- 1875-6883. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors.
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| 19. |
- Lilje, Stina
(författare)
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Aspects of musculoskeletal pain interfering with normal life and naprapathic manual therapy from a health technology assessment perspective
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- IntroductionMusculoskeletal pain is one of the most common reasons for seeking health care. If a patient’s disorders remain after conventional primary care, a referral to secondary care is often made, yet many referrals on the waiting lists concern patients who are not in need of surgery. Manual therapy has a lot of “proved experience” but is not routine in the Swedish national health care system today. There is a lack of scientific evidence for its treatment and cost effects.AimsThe overall aim of this thesis was to increase the knowledge of musculoskeletal pain that interferes with normal life. Specific aims were to investigate if musculoskeletal pain in older adults is associated with heavy physical and negative psychosocial workloads through life, and to deepen the knowledge of the treatment and cost effects of naprapathic manual therapy (NMT), and of older adults' experiences of reminders of home exercises through mHealth.MethodsStudy I is a cross sectional study (n=641) that investigates associations between musculoskeletal pain interfering with normal life in older adults and physical and psychological loads through life. Study II is a randomised controlled trial (n=78) that compares NMT with standard orthopaedic care for “low priority” orthopaedic outpatients. Study III (n=1) is a case study that describes the treatment effects of NMT in a patient diagnosed with adhesive capsulitis. Study IV is a cost consequence analysis (n=78), where the costs and the health economic gains in study II were analyzed. Study V is a qualitative interview study (n=8) exploring older adults’ experiences of text messages as reminders of home exercises after NMT.Results The results in Study I were that psychosocial and physical workloads are associated with musculoskeletal pain that interferes with normal life in older adults. NMT for low priority orthopaedic outpatients yielded larger improvements in pain, physical function and perceived recovery compared with standard orthopaedic care (Study II). NMT for the acromio-clavicular joint, for adhesive capsulitis resulted in significant pain relief and perceived recovery, decreased sleeping disorders and medication (Study III). The health gains for naprapathy were higher compared with standard orthopaedic care, and the costs significantly lower (Study IV). Study V concluded that the use of SMS:s as reminders of home exercises after NMT were appreciated by the patients, and stimulated them to practice memorising and to create.Conclusion This thesis suggests that pain in older adults is associated with heavy physical and negative psychosocial workloads through life. NMT may be cost effective for low priority orthopaedic outpatients of working age with musculoskeletal disorders that are not likely to benefit from orthopaedic surgery, and was effective in a patient diagnosed with adhesive capsulitis. mHealth used to remind older adults of home exercises stimulates the patients to create own routines for continued compliance.
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| 20. |
- Anderberg, Peter, et al.
(författare)
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A Novel Instrument for Measuring Older People's Attitudes Toward Technology (TechPH) : Development and Validation
- 2019
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Ingår i: Journal of Medical Internet Research. - : JMIR PUBLICATIONS, INC. - 1438-8871. ; 21:5
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Tidskriftsartikel (refereegranskat)abstract
- Background: The use of health technology by older people is coming increasingly in focus with the demographic changes. Health information technology is generally perceived as an important factor in enabling increased quality of life and reducing the cost of care for this group. Age-appropriate design and facilitation of technology adoption are important to ensure functionality and removal of various barriers to usage. Development of assessment tools and instruments for evaluating older persons' technology adoption and usage as well as measuring the effects of the interventions are of high priority. Both usability and acceptance of a specific technology or service are important factors in evaluating the impact of a health information technology intervention. Psychometric measures are seldom included in evaluations of health technology. However, basic attitudes and sentiments toward technology (eg, technophilia) could be argued to influence both the level of satisfaction with the technology itself as well as the perception of the health intervention outcome. Objective: The purpose of this study is to develop a reduced and refined instrument for measuring older people's attitudes and enthusiasm for technology based on relevant existing instruments for measuring technophilia A requirement of the new instrument is that it should be short and simple to make it usable for evaluation of health technology for older people. Methods: Initial items for the TechPH questionnaire were drawn from a content analysis of relevant existing technophilia measure instruments. An exploratory factor analysis was conducted in a random selection of persons aged 65 years or older (N=374) on eight initial items. The scale was reduced to six items, and the internal consistency and reliability of the scale were examined. Further validation was made by a confirmatory factor analysis (CFA). Results: The exploratory factor analysis resulted in two factors. These factors were analyzed and labeled techEnthusiasm and techAnxiety. They demonstrated relatively good internal consistency (Cronbach alpha=.72 and .68, respectively). The factors were confirmed in the CFA and showed good model fit (chi(2)(8)=21.2, chi(2)/df=2.65, comparative fit index=0.97, adjusted goodness-of-fit index=0.95, root mean square error of approximation=0.067, standardized root mean square residual=0.036). Conclusions: The construed TechPH score showed expected relations to external real-world criteria, and the two factors showed interesting internal relations. Different technophilia personality traits distinguish clusters with different behaviors of adaptation as well as usage of new technology. Whether there is an independent association with the TechPH score against outcomes in health technology projects needs to be shown in further studies. The instrument must also be validated in different contexts, such as other countries.
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| 21. |
- Anderberg, Peter, et al.
(författare)
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An instrument for measuring social participation to examine older adults' use of the internet as a social platform : Development and validation study
- 2021
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Ingår i: JMIR Aging. - : JMIR Publications Inc.. - 2561-7605. ; 4:2
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Tidskriftsartikel (refereegranskat)abstract
- Background: Older people's use of the internet is increasingly coming into focus with the demographic changes of a growing older population. Research reports several benefits of older people's internet use and highlights problems such as various forms of inequality in use within the group. There is a need for consistent measurements to follow the development and use of the internet in this group and to be able to compare groups both within and between countries, as well as follow the changes over time. Objective: The aim of this study was to create an instrument to measure an older person's perception of the benefits of their online social participation, unconnected to specific applications and services. The instrument to measure internet social participation proposed in this paper builds on social participation factors and is a multidimensional construct incorporating both social relations and societal connectedness. Methods: A short instrument for measuring social participation over the internet was created. An exploratory factor analysis (EFA) was conducted in a random selection of persons aged 65 years or older (n=193) on 10 initial items. Further validation was made by confirmatory factor analysis (CFA) in the remaining group (n=193). Results: A 1-factor solution for the social internet score was decided upon after exploratory factor analysis (EFA; based on a random sample of half the data set). None of the questionnaire items were excluded based on the EFA, as they all had high loadings, the lowest being 0.61. The Cronbach α coefficient was.92. The 1-factor solution explained 55% of the variance. CFA was performed and included all 10 questionnaire items in a 1-factor solution. Indices of goodness of fit of the model showed room for improvement. Removal of 4 questions in a stepwise procedure resulted in a 6-item model (χ26=13.985; χ2/degrees of freedom=1.554; comparative fit index=0.992; root mean square error of approximation=0.054; standardized root mean square residual=0.025). Conclusions: The proposed instrument can be used to measure digital social participation and coherence with society. The factor analysis is based on a sufficient sample of the general population of older adults in Sweden, and overall the instrument performed as expected. © Peter Anderberg, Linda Abrahamsson, Johan Sanmartin Berglund. Originally published in JMIR Aging (https://aging.jmir.org),17.05.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Aging, is properly cited. The complete bibliographic information, a link to the original publication on https://aging.jmir.org, as well as this copyright and license information must be included.
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| 22. |
- Anderberg, Peter
(författare)
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FACE : Disabled People, Technology and Internet
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is based on the Internet experiences of people withsignificant mobility/physical impairments who are proficient andexperienced computer users in their computer world but havelimitations in mobility that severely restrict their functioning inthe physical world. The Internet functioning of this group isanalysed by means of the factors attitude, control and enabling,with the main focus on what is achievable when all accessproblems such as unadapted interfaces, beginners’ difficulties andthe digital divide are overcome. If the virtual world is fullyavailable but the real world is not – what are the effects onlearning, self image, communities of practice, sense of coherence,power and control? What are the effects on peer-to-peer learningand co-operation? Independent living concepts and theoriesmanifest themselves throughout the thesis, most obviously,perhaps, in the selection of issues that are studied and in theperspectives.The theoretical background and concepts are those of disabilitystudies, with a social model and independent living perspective,and with strong influences from rehabilitation engineering anddesign.Throughout the thesis elaborations and clarifications of thepossibilities of interplay and co-existence between rehabilitationengineering and design and disability studies are made. Differentaspects of function design and technology are examined from anexpanded view on functioning, where technology is put in anindividual and social context with the FACE (Function – Attitude, Control, Enabling) tool.
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| 23. |
- Anderberg, Peter, et al.
(författare)
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Older people’s use and nonuse of the internet in Sweden
- 2020
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI. - 1661-7827 .- 1660-4601. ; 17:23, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- The use of the internet has considerably increased over recent years, and the importance of internet use has also grown as services have gone online. Sweden is largely an information society like other countries with high reported use amongst European countries. In line with digitalization development, society is also changing, and many activities and services today take place on the internet. This development could potentially lead to those older persons who do not use the internet or do not follow the development of services on the internet finding it difficult to take part in information and activities that no longer occur in the physical world. This has led to a digital divide between groups, where the older generations (60+), in particular, have been affected. In a large study of Sweden’s adult population in 2019, 95 percent of the overall population was said to be internet users, and the corresponding number for users over 66 years of age was 84%. This study shows that the numbers reported about older peoples’ internet use, most likely, are vastly overestimated and that real use is significantly lower, especially among the oldest age groups. We report that 62.4% of the study subjects are internet users and that this number most likely also is an overestimation. When looking at nonresponders to the questionnaire, we find that they display characteristics generally attributed to non-use, such as lower education, lower household economy, and lower cognitive functioning.
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| 24. |
- Anderberg, Peter, et al.
(författare)
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The Effects of the Digital Platform Support Monitoring and Reminder Technology for Mild Dementia (SMART4MD) for People With Mild Cognitive Impairment and Their Informal Carers : Protocol for a Pilot Randomized Controlled Trial
- 2019
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Ingår i: JMIR Research Protocols. - : JMIR PUBLICATIONS, INC. - 1929-0748. ; 8:6
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Tidskriftsartikel (refereegranskat)abstract
- Background: Many countries are witnessing a trend of growth in the number and proportion of older adults within the total population. In Europe, population aging has had and will continue to have major social and economic consequences. This is a fundamentally positive development where the added life span is of great benefit for both the individual and the society. Yet, the risk for the individual to contract noncommunicable diseases and disability increases with age. This may adversely affect the individual's ability to live his or her life in the way that is desired. Cognitive conditions constitute a group of chronic diseases that predominantly affects older people. Recent technology advancements can help support the day-to-day living activities at home for people with cognitive impairments. Objective: A digital platform (Support Monitoring and Reminder for Mild Dementia; SMART4MD) is created to improve or maintain the quality of life for people with mild cognitive impairment (PwMCI) and their carers. The platform will provide reminders, information, and memory support in everyday life, with the purpose of giving structure and lowering stress. In the trial, we will include participants with a diagnosed neurocognitive disorder as well as persons with an undiagnosed subjective memory problem and cognitive impairment, that is, 20 to 28 points on the Mini-Mental State Examination. Methods: A pragmatic, multicenter RCT is being conducted in Spain, Sweden, and Belgium. The targets for recruitment are 1200 dyads-split into an intervention group and a control group that are in usual care. Intervention group participants will be provided with a data-enabled computer tablet with the SMART4MD app. Its core functionalities, intended to be used daily at home, are based on reminders, cognitive supporting activities, and sharing health information. Results: Inclusion of participants started in December 2017, and recruitment is expected to end in February 2019. Furthermore, there will be 3 follow-up visits at 6, 12, and 18 months after the baseline visit. Conclusions: This RCT is expected to offer benefits at several levels including in-depth knowledge of the possibilities of introducing a holistic multilayered information and communication technology solution for this group. SMART4MD has been developed in a process involving the structured participation of PwMCI, their informal carers, and clinicians. The adoption of SMART4MD faces the challenge of this age group's relative unfamiliarity with digital devices and services. However, this challenge can also be an opportunity for developing a digital device tailored to a group at risk of digital exclusion. This research responds to the wider call for the development of digital devices which are accessible and affordable to older people and this full scale RCT can hopefully serve as a model for further studies in this field.
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| 25. |
- Andersson, Ewa K., 1972-, et al.
(författare)
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Self-Reported eHealth literacy among nursing students in Sweden and Poland : The eNursEd cross-sectional multicentre study
- 2023
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Ingår i: Health Informatics Journal. - : Sage Publications. - 1460-4582 .- 1741-2811. ; 29:4
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Tidskriftsartikel (refereegranskat)abstract
- This study aimed to provide an understanding of nursing students’ self-reported eHealth literacy in Sweden and Poland. This cross-sectional multicentre study collected data via a questionnaire in three universities in Sweden and Poland. Descriptive statistics, the Spearman’s Rank Correlation Coefficient, Mann–Whitney U, and Kruskal–Wallis tests were used to analyse different data types. Age (in the Polish sample), semester, perceived computer or laptop skills, and frequency of health-related Internet searches were associated with eHealth literacy. No gender differences were evidenced in regard to the eHealth literacy. Regarding attitudes about eHealth, students generally agreed on the importance of eHealth and technical aspects of their education. The importance of integrating eHealth literacy skills in the curricula and the need to encourage the improvement of these skills for both students and personnel are highlighted, as is the importance of identifying students with lacking computer skills.
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| 26. |
- Behrens, Anders, et al.
(författare)
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CoGNIT Automated Tablet Computer Cognitive Testing in Patients With Mild Cognitive Impairment : Feasibility Study
- 2022
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Ingår i: JMIR Formative Research. - : JMIR Publications Inc.. - 2561-326X. ; 6:3
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Tidskriftsartikel (refereegranskat)abstract
- Background: Early diagnosis of cognitive disorders is becoming increasingly important. Limited resources for specialist assessment and an increasing demographical challenge warrants the need for efficient methods of evaluation. In response, CoGNIT, a tablet app for automatic, standardized, and efficient assessment of cognitive function, was developed. Included tests span the cognitive domains regarded as important for assessment in a general memory clinic (memory, language, psychomotor speed, executive function, attention, visuospatial ability, manual dexterity, and symptoms of depression). Objective: The aim of this study was to assess the feasibility of automatic cognitive testing with CoGNIT in older patients with symptoms of mild cognitive impairment (MCI). Methods: Patients older than 55 years with symptoms of MCI (n=36) were recruited at the research clinic at the Blekinge Institute of Technology (BTH), Karlskrona, Sweden. A research nurse administered the Mini-Mental State Exam (MMSE) and the CoGNIT app on a tablet computer. Technical and testing issues were documented. Results: The test battery was completed by all 36 patients. One test, the four-finger-tapping test, was performed incorrectly by 42% of the patients. Issues regarding clarity of instructions were found in 2 tests (block design test and the one finger-tapping test). Minor software bugs were identified. Conclusions: The overall feasibility of automatic cognitive testing with the CoGNIT app in patients with symptoms of MCI was good. The study highlighted tests that did not function optimally. The four-finger-tapping test will be discarded, and minor improvements to the software will be added before further studies and deployment in the clinic. © 2022 JMIR Publications Inc.. All right reserved.
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| 27. |
- Behrens, Anders, et al.
(författare)
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Sleep disturbance predicts worse cognitive performance in subsequent years : A longitudinal population-based cohort study
- 2023
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Ingår i: Archives of gerontology and geriatrics (Print). - : Elsevier. - 0167-4943 .- 1872-6976. ; 106
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Tidskriftsartikel (refereegranskat)abstract
- Background: Poor sleep is a potential modifiable risk factor for later life development cognitive impairment. The aim of this study is to examine if subjective measures of sleep duration and sleep disturbance predict future cognitive decline in a population-based cohort of 60, 66, 72 and 78-year-olds with a maximal follow up time of 18 years. Methods: This study included participants from the Swedish National Study on Ageing and Care – Blekinge, with assessments 2001–2021. A cohort of 60 (n = 478), 66 (n = 623), 72 (n = 662) and 78 (n = 548) year-olds, were assessed at baseline and every 6 years until 78 years of age. Longitudinal associations between sleep disturbance (sleep scale), self-reported sleep duration and cognitive tests (Mini Mental State Examination and the Clock drawing test) were examined together with typical confounders (sex, education level, hypertension, hyperlipidemia, smoking status, physical inactivity and depression). Results: There was an association between sleep disturbance at age 60 and worse cognitive function at ages 60, 66 and 72 years in fully adjusted models. The association was attenuated after bootstrap-analysis for the 72-year-olds. The items of the sleep scale most predictive of later life cognition regarded nightly awakenings, pain and itching and daytime naps. Long sleep was predictive of future worse cognitive function. Conclusion: Sleep disturbance was associated with worse future cognitive performance for the 60-year-olds, which suggests poor sleep being a risk factor for later life cognitive decline. Questions regarding long sleep, waking during the night, pain and itching and daytime naps should be further explored in future research and may be targets for intervention.
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| 28. |
- Berner, Jessica, et al.
(författare)
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A cross-national and longitudinal study on predictors in starting and stopping Internet use (2001-2013) by Swedish and Dutch older adults 66 years and above
- 2016
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Ingår i: Gerontechnology. - : International Society for Gerontechnology. - 1569-1101 .- 1569-111X. ; 14:3, s. 157-168
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Tidskriftsartikel (refereegranskat)abstract
- Background The Internet and information communication technology is today considered as a means to sustain active and healthy aging, and to provide better care for the aging population. There is an increase in prevalence in older adults using the Internet, however many are still not using the Internet. This study therefore, investigated predictors in starting and stopping Internet use by older adults between 2001-2013 in Sweden and the Netherlands. These represent currently two of the highest older adult Internet users in Europe. The aim of this study was to examine, first, if there was a different starting and stopping rate in Sweden and the Netherlands; second, if the predictors age, gender, education, rural/urban living, living alone/not, cognition and functional limitations have different effects in either country. Methods A cross-national and longitudinal design was chosen. Data was used from the Longitudinal Aging study Amsterdam (LASA) and the Swedish National Study on Aging and Care (SNAC). Cox regression analyses were done to test the predictors over time with starting or stopping Internet use. An interaction term ‘variable*country’ was then considered for each variable, if significant, leading to a stratification into a multivariate model per country. Results More older adults started use in the Netherlands (19%); lower in age, normal cognitive functioning, living alone, fewer functional limitations and lower education were predictive of starting. In Sweden fewer started (10.3%), where being female was the only significant predictor of starting use. Both countries did not have many people stopping use; in the Netherlands (3%) they were younger in age and living urban, whereas in Sweden (1.7%), they had lower cognitive functioning. Conclusion Results indicate that there are differences between countries in starting use. These differences can possibly be explained by the early adoption of the Internet in Sweden. The new findings that the older adults living alone and lower educated are now going online, are positive regarding the theme of active aging. For those stopping use, the differences are more country-specific. More research is needed in order to understand better what an older adult was using the Internet for and why they stop. © 2016. Gerontechnology. All Rights Reserved.
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| 29. |
- Berner, Jessica, et al.
(författare)
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Maintaining cognitive function with internet use : a two-country, six-year longitudinal study
- 2019
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Ingår i: International psychogeriatrics. - : Cambridge University Press. - 1041-6102 .- 1741-203X. ; 31:7, s. 929-936
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: Maintaining good cognitive function with aging may be aided by technology such as computers, tablets, and their applications. Little research so far has investigated whether internet use helps to maintain cognitive function over time.Design: Two population-based studies with a longitudinal design from 2001/2003 (T1) to 2007/2010 (T2).Setting: Sweden and the Netherlands.Participants: Older adults aged 66 years and above from the Swedish National Study on Ageing and Care (N = 2,564) and from the Longitudinal Aging Study Amsterdam (N = 683).Measurements: Internet use was self-reported. Using the scores from the Mini-Mental State Examination (MMSE) from T1 and T2, both a difference score and a significant change index was calculated. Linear and logistic regression analysis were performed with difference score and significant change index, respectively, as the dependent variable and internet use as the independent variable, and adjusted for sex, education, age, living situation, and functional limitations. Using a meta-analytic approach, summary coefficients were calculated across both studies.Results: Internet use at baseline was 26.4% in Sweden and 13.3% in the Netherlands. Significant cognitive decline over six years amounted to 9.2% in Sweden and 17.0% in the Netherlands. Considering the difference score, the summary linear regression coefficient for internet use was-0.32 (95% CI:-0.62,-0.02). Considering the significant change index, the summary odds ratio for internet use was 0.54 (95% CI: 0.37, 0.78).Conclusions: The results suggest that internet use might play a role in maintaining cognitive functioning. Further research into the specific activities that older adults are doing on the internet may shine light on this issue.
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| 30. |
- Berner, Jessica, et al.
(författare)
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Technology anxiety and technology enthusiasm versus digital ageism
- 2022
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Ingår i: Gerontechnology. - : International Society for Gerontechnology (ISG). - 1569-1101 .- 1569-111X. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Europe has called attention to the importance of the e-inclusion of older adults. Society is indicating that the developers, websites, and devices are causing age bias in technology. This affects living independently, the values of ethical principles associated with an older person, and digital ageism: which is an age-related bias in artificial intelligence systems. Objective: This research attempts to investigate the instrument technology anxiety and enthusiasm, and assistive technology devices during the period 2019- 2021. This instrument may be a way to redress misconceptions about digital ageism. The assistive technology device that we will investigate in this study is the adoption of a service that is designed for online health consultations. Method: The participants are part of the longitudinal Swedish National Study on Aging and Care. Technology anxiety and technology enthusiasm are two factors, which aim to measure technophilia (vs technophobia) in older adults. The age range is 63 -99 years of age in 2019 T1 and 66 -101 in 2021 T2. Wilcoxon rank test was conducted to investigate technology enthusiasm, technology anxiety, and how they changed with time. An Edwards Nunnally index was then calculated for both variables to observe a significant change in score from T1 to T2. Mann Whitney U test was used to investigate the variables sex and health status with technology anxiety & technology enthusiasm in T1 & T2. Age, Cognitive function MMSE, and digital social participation were investigated through a Kruskall-Wallis test. A logistic regression was conducted with the significant variable. Results: Between 2019-2021, change in technology enthusiasm was based on less digital social participation (OR: 0.608; CI 95%: 0.476- 0.792). Technology anxiety was significantly higher due to age (OR: 1.086, CI 95%: 1.035-1.139) and less digital social participation (OR: 0.684; CI 95%: 0.522- 0.895). The want for online healthcare consultations was popular but usage was low. Conclusion: Staying active on- line and participating digitally may be a way to reduce digital ageism. However, digital ageism is a complex phenomenon, which requires different solutions in order to include older people and reduce an inaccurate categorisation of this group in the digital society.
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| 31. |
- Berner, Jessica, et al.
(författare)
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Technology anxiety and technology enthusiasm versus digital ageism
- 2022
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Ingår i: Gerontechnology. - : International Society for Gerontechnology. - 1569-1101 .- 1569-111X. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Europe has called attention to the importance of the e-inclusion of older adults. Society is indicating that the developers, websites, and devices are causing age bias in technology. This affects living independently, the values of ethical principles associated with an older person, and digital ageism: which is an age-related bias in artificial intelligence systems. Objective: This research attempts to investigate the instrument technology anxiety and enthusiasm, and assistive technology devices during the period 2019-2021. This instrument may be a way to redress misconceptions about digital ageism. The assistive technology device that we will investigate in this study is the adoption of a service that is designed for online health consultations. Method: The participants are part of the longitudinal Swedish National Study on Aging and Care. Technology anxiety and technology enthusiasm are two factors, which aim to measure technophilia (vs technophobia) in older adults. The age range is 63 -99 years of age in 2019 T1 and 66 -101 in 2021 T2. Wilcoxon rank test was conducted to investigate technology enthusiasm, technology anxiety, and how they changed with time. An Edwards Nunnally index was then calculated for both variables to observe a significant change in score from T1 to T2. Mann Whitney U test was used to investigate the variables sex and health status with technology anxiety & technology enthusiasm in T1 & T2. Age, Cognitive function MMSE, and digital social participation were investigated through a Kruskall-Wallis test. A logistic regression was conducted with the significant variable. Results: Between 2019-2021, change in technology enthusiasm was based on less digital social participation (OR: 0.608; CI 95%: 0.476-0.792). Technology anxiety was significantly higher due to age (OR: 1.086, CI 95%: 1.035-1.139) and less digital social participation (OR: 0.684; CI 95%: 0.522-0.895). The want for online healthcare consultations was popular but usage was low. Conclusion: Staying active online and participating digitally may be a way to reduce digital ageism. However, digital ageism is a complex phenomenon, which requires different solutions in order to include older people and reduce an inaccurate categorisation of this group in the digital society © 2022,Gerontechnology. All Rights Reserved.
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| 32. |
- Christiansen, Line, et al.
(författare)
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Associations Between Mobile Health Technology use and Self-rated Quality of Life : A Cross-sectional Study on Older Adults with Cognitive Impairment
- 2021
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Ingår i: Gerontology and geriatric medicine. - : Sage Publications. - 2333-7214. ; 7, s. 1-8
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Tidskriftsartikel (refereegranskat)abstract
- Background: Quality of life (QoL) is affected even at early stages in older adults with cognitive impairment. The use of mobile health (mHealth) technology can offer support in daily life and improve the physical and mental health of older adults. However, a clarification of how mHealth technology can be used to support the QoL of older adults with cognitive impairment is needed. Objective: To investigate factors affecting mHealth technology use in relation to self-rated QoL among older adults with cognitive impairment. Methods: A cross-sectional research design was used to analyse mHealth technology use and QoL in 1,082 older participants. Baseline data were used from a multi-centered randomized controlled trial including QoL, measured by the Quality of Life in Alzheimer’s Disease (QoL-AD) Scale, as the outcome variable. Data were analyzed using logistic regression models. Results: Having moderately or high technical skills in using mHealth technology and using the internet via mHealth technology on a daily or weekly basis was associated with good to excellent QoL in older adults with cognitive impairment. Conclusions: The variation in technical skills and internet use among the participants can be interpreted as an obstacle for mHealth technology to support QoL. © The Author(s) 2021.
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| 33. |
- Christiansen, Line, et al.
(författare)
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Health-related quality of life and related factors among a sample of older people with cognitive impairment
- 2019
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Ingår i: Nursing Open. - : Wiley-Blackwell Publishing Inc.. - 2054-1058. ; 6:3, s. 849-859
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Tidskriftsartikel (refereegranskat)abstract
- Aim: This study aimed to identify factors affecting health-related quality of life (HRQoL) of older adults with cognitive impairment and to describe the association of these factors with different components of HRQoL. Design: A cross-sectional, descriptive research design was used. Methods: Data were collected from 247 individuals aged 60 years and older from a Swedish longitudinal cohort study. The Short-Form Health Survey-12 (SF-12) and EuroQol (EQ-5D) were used to assess HRQoL. The data were analysed using descriptive and comparative statistics. Results: The present study identified several factors that influenced HRQoL of older adults with cognitive impairment. The results of a multiple logistic regression analysis revealed that the following factors were associated with physical and mental HRQoL: dependency in activities of daily living (ADL), receiving informal care and feelings of loneliness and pain. © 2019 The Authors. Nursing Open published by John Wiley & Sons Ltd.
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| 34. |
- Christiansen, Line, et al.
(författare)
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Using Mobile Health and the Impact on Health-Related Quality of Life : Perceptions of Older Adults with Cognitive Impairment
- 2020
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI. - 1661-7827 .- 1660-4601. ; 17:8
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Tidskriftsartikel (refereegranskat)abstract
- Digital health technologies such as mobile health (mHealth) are considered to have the potential to support the needs of older adults with cognitive impairment. However, the evidence for improving health with the use of mHealth applications is of limited quality. Few studies have reported on the consequences of technology use concerning the older adults' quality of life. The purpose of this study was to describe perceptions of mHealth and its impact on health-related quality of life (HRQoL) among older adults with cognitive impairment. The study was conducted using a qualitative design with a phenomenographic approach. A total of 18 older participants with cognitive impairment were interviewed. The interviews were analyzed in order to apply phenomenography in a home-care context. The results showed variations in the older adults' perceptions that were comprised within three categories of description; Require technology literacy, Maintain social interaction, and Facilitate independent living. In conclusion, the development and design of mHealth technologies need to be tailored based on older adults´ needs in order to be understood and perceived as useful in a home-care context. For mHealth to support HRQoL, healthcare should be provided in a way that encourages various forms of communication and interaction.
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| 35. |
- Dallora Moraes, Ana Luiza, et al.
(författare)
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Age assessment of youth and young adults using magnetic resonance imaging of the knee : A deep learning approach
- 2019
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Ingår i: JMIR Medical Informatics. - : JMIR PUBLICATIONS. - 2291-9694. ; 7:4, s. 419-436
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Tidskriftsartikel (refereegranskat)abstract
- Background: Bone age assessment (BAA) is an important tool for diagnosis and in determining the time of treatment in a number of pediatric clinical scenarios, as well as in legal settings where it is used to estimate the chronological age of an individual where valid documents are lacking. Traditional methods for BAA suffer from drawbacks, such as exposing juveniles to radiation, intra- and interrater variability, and the time spent on the assessment. The employment of automated methods such as deep learning and the use of magnetic resonance imaging (MRI) can address these drawbacks and improve the assessment of age. Objective: The aim of this paper is to propose an automated approach for age assessment of youth and young adults in the age range when the length growth ceases and growth zones are closed (14-21 years of age) by employing deep learning using MRI of the knee. Methods: This study carried out MRI examinations of the knee of 402 volunteer subjects-221 males (55.0%) and 181 (45.0%) females-aged 14-21 years. The method comprised two convolutional neural network (CNN) models: the first one selected the most informative images of an MRI sequence, concerning age-assessment purposes; these were then used in the second module, which was responsible for the age estimation. Different CNN architectures were tested, both training from scratch and employing transfer learning. Results: The CNN architecture that provided the best results was GoogLeNet pretrained on the ImageNet database. The proposed method was able to assess the age of male subjects in the range of 14-20.5 years, with a mean absolute error (MAE) of 0.793 years, and of female subjects in the range of 14-19.5 years, with an MAE of 0.988 years. Regarding the classification of minors-with the threshold of 18 years of age-an accuracy of 98.1% for male subjects and 95.0% for female subjects was achieved. Conclusions: The proposed method was able to assess the age of youth and young adults from 14 to 20.5 years of age for male subjects and 14 to 19.5 years of age for female subjects in a fully automated manner, without the use of ionizing radiation, addressing the drawbacks of traditional methods. © 2019 Journal of Medical Internet Research. All rights reserved.
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| 36. |
- Dallora Moraes, Ana Luiza, et al.
(författare)
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Bone age assessment with various machine learning techniques : A systematic literature review and meta-analysis
- 2019
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Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 14:7
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Forskningsöversikt (refereegranskat)abstract
- Background The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment for the diagnosis of pediatric endocrinology, orthodontics and orthopedic disorders, and legal environment in what concerns if an individual is a minor or not when there is a lack of documents. Being a time-consuming activity that can be prone to inter- and intra-rater variability, the use of methods which can automate it, like Machine Learning techniques, is of value. Objective The goal of this paper is to present the state of the art evidence, trends and gaps in the research related to bone age assessment studies that make use of Machine Learning techniques. Method A systematic literature review was carried out, starting with the writing of the protocol, followed by searches on three databases: Pubmed, Scopus and Web of Science to identify the relevant evidence related to bone age assessment using Machine Learning techniques. One round of backward snowballing was performed to find additional studies. A quality assessment was performed on the selected studies to check for bias and low quality studies, which were removed. Data was extracted from the included studies to build summary tables. Lastly, a meta-analysis was performed on the performances of the selected studies. Results 26 studies constituted the final set of included studies. Most of them proposed automatic systems for bone age assessment and investigated methods for bone age assessment based on hand and wrist radiographs. The samples used in the studies were mostly comprehensive or bordered the age of 18, and the data origin was in most of cases from United States and West Europe. Few studies explored ethnic differences. Conclusions There is a clear focus of the research on bone age assessment methods based on radiographs whilst other types of medical imaging without radiation exposure (e.g. magnetic resonance imaging) are not much explored in the literature. Also, socioeconomic and other aspects that could influence in bone age were not addressed in the literature. Finally, studies that make use of more than one region of interest for bone age assessment are scarce. Copyright: © 2019 Dallora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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| 37. |
- Ehn, Bodil, et al.
(författare)
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The process of opting for female permanent contraception : A qualitative study of women's experiences in Sweden
- 2021
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Ingår i: Contraception. - : Elsevier. - 0010-7824 .- 1879-0518. ; 103:1, s. 48-52
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: We aimed to explore Swedish women's decision-making experiences regarding permanent contraception. Study design: In this study, we included 17 women aged 30–48 who were scheduled to undergo female permanent contraceptive procedures. We conducted semistructured interviews using two broad open-ended questions. We analyzed these data using systematic text condensation based on the principles of psychological phenomenological analysis. Results: The interviewees experienced no counseling or support from health care workers regarding permanent contraception until they specifically asked for it. Participants reported that they themselves place the responsibility of permanent contraception solely on women. Consequently, our participants described feeling hesitancy and ambivalence in the process of deciding to have the procedure. Once the decision was made and the women were on the waiting lists for surgery, they experienced relief and empowerment. Conclusions: Our findings suggest that health care providers in Sweden miss opportunities to support patient-centered decision-making regarding permanent contraception. This study indicates that women make deliberate and considered decisions regarding permanent contraception and are best positioned to know when the procedure should take place in their reproductive lives. Implication statements: Health care professionals should discuss permanent contraception as an option with all women desiring contraception to allow them to decide if that method is right for them. © 2020 Elsevier Inc.
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| 38. |
- Eivazzadeh, Shahryar, 1975-, et al.
(författare)
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Ethical Challenges of Evaluating Health Information Systems
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundEvaluating and researching health information systems are interventions of their kind and might lead to ethical complexities and challenges. Most of those challenges are inherited from the more general fields of research and evaluation, health studies, and information systems studies. Beyond those challenges, this field has its particular traits, regarding the involved stakeholders, required values or qualities, or the process which can raise field-specific or context-specific ethical challenges.ObjectivesThis paper reports and discusses some of the challenges of evaluating and researching health information systems by taking a systematic approach in finding, postulating, and analyzing them.MethodThrough a scoping review, a set of ethical challenges, regarding the evaluation and research of health information systems, were extracted. From the same set of articles, the acting entities, including stakeholders and artefacts, were identified. From a sample of seven cases of health information systems, a set of demanded impact qualities were extracted. From the literature, the evaluation stages were elicited. The acting entities, required qualities, and the evaluation stages were combined to create a three-dimensional space. The space contained the ethical challenges extracted from the scoping review and helped to postulate more items.ResultsThe final list of identified items contains 20 possible ethical challenges that can be caused or raised by evaluating or researching health information systems and technologies. The ethical challenges are discussed, based on their probable stage of occurrence. The three-dimensional space and the method of populating it is proposed as an effective method in similar cases of discovering ethical challenges.ConclusionEvaluating or researching health information systems can raise ethical challenges, that we have identified 20 of them in this article. All the challenges were discussed, such as the actual value of evaluation, breach of privacy, risks for safety, problems with usability and accessibility, conflict of interests, problems with the informed consent, and miscommunication. The novel approach for elicitation of the ethical challenges introduced in this article might be applied in other similar studies.
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| 39. |
- Ferati, Mexhid, et al.
(författare)
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Tackling the Sustainability of Digital Aging Innovations Through Design Thinking and Systems Thinking Perspectives
- 2021
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Ingår i: ICT for Health, Accessibility and Wellbeing. - Cham : Springer. - 9783030942083 - 9783030942090 ; , s. 179-184
-
Bokkapitel (refereegranskat)abstract
- The digitalization of society brings many opportunities and challenges, especially on how we organize the welfare society in the future. This becomes especially pertinent as we are heading toward a global increase of older people, which will strain healthcare and bring the challenge of building sustainable solutions. In this paper, we argue that the unsustainable solutions within healthcare are due to them being defined and ‘solved’ with a single approach or approaches used in silos. We advocate that a more sustainable solution could be achieved by combining systems thinking and design thinking perspectives throughout the entire process—from problem definition to solution offering. A benefit of such combined perspectives is the ability to develop a shared context among all stakeholders, which helps uncover unique tacit knowledge from their experience. This will serve as a solid foundation to generate unconventional ideas that will lead to sustainable and satisfactory solutions.
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| 40. |
- Flyborg, Johan, et al.
(författare)
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Measurement of body temperature in the oral cavity with a temperature sensor integrated with a powered toothbrush
- 2023
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Ingår i: SN Applied Sciences. - : Springer Nature Switzerland AG. - 2523-3963 .- 2523-3971. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a method for collecting core body temperature data via a temperature sensor integrated into a powered toothbrush. The purpose is to facilitate the collection of temperature data without any extended effort from the user. Twelve participants use a powered toothbrush with a temperature sensor mounted on the brush head twice daily for two months. The obtained values are compared with those from a conventional fever thermometer approved for intraoral use. The results show that the temperature sensor–integrated powered toothbrush can measure the core body temperature and provide values comparable to those provided by a traditional oral thermometer. The use of the device can facilitate disease monitoring, fertility control, and security solutions for the elderly. © 2022, The Author(s).
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| 41. |
- Flyborg, Johan, et al.
(författare)
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Use of a powered toothbrush to improve oral health in individuals with mild cognitive impairment
- 2023
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Ingår i: Gerodontology. - : John Wiley & Sons. - 0734-0664 .- 1741-2358. ; 40:1, s. 74-82
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Tidskriftsartikel (refereegranskat)abstract
- ObjectivesThe aim of the study is to investigate whether the use of a powered toothbrush could maintain oral health by reducing the dental plaque (PI), bleeding on probing (BOP), and periodontal pocket depth (PPD) ≥4 mm in a group of individuals with MCI and also if changes in oral health affect various aspects of quality of life.BackgroundPeople with cognitive impairment tend to have poor oral hygiene and poorer Quality of life. In the present study, the participants were asked to use a powered toothbrush for at least 2 min morning and evening and no restrictions were given against the use of other oral care products. The participant survey conducted at each examination demonstrated that 61.2% of participants at baseline claimed to have experience of using a powered toothbrush, 95.4% at 6 months and 95% after 12 months. At the same time, the use of manual toothbrushes dropped from 73.3% to 44.7% from baseline to the 12-month check-up. This shows that several participants continue to use the manual toothbrush in parallel with the powered toothbrush, but that there is a shift towards increased use of the powered toothbrush. Removal of dental biofilm is essential for maintaining good oral health. We investigated whether using a powered toothbrush reduces the presence of dental plaque, bleeding on probing and periodontal pockets ≥4 mm in a group of older individuals with mild cognitive impairment.Materials and methodsTwo hundred and thirteen individuals with the mean age of 75.3 years living without official home care and with a Mini-Mental State Examination (MMSE) score between 20 and 28 and a history of memory problems in the previous six months were recruited from the Swedish site of a multicenter project, Support Monitoring And Reminder Technology for Mild Dementia (SMART4MD) and screened for the study. The individuals received a powered toothbrush and thorough instructions on how to use it. Clinical oral examinations and MMSE tests were conducted at baseline, 6 and 12 months.ResultsOne hundred seventy participants, 36.5% women and 63.5% men, completed a 12-month follow-up. The use of a powered toothbrush resulted, for the entire group, in a significant decrease in plaque index from 41% at baseline to 31.5% after 12 months (P < .000). Within the same time frame, the values for bleeding on probing changed from 15.1% to 9.9% (P < .000) and the percentage of probing pocket depths ≥4 mm from 11.5% to 8.2% (P < .004). The observed improvements in the Oral Health Impact Profile 14 correlate with the clinical improvements of oral health.ConclusionThe use of a powered toothbrush was associated with a reduction of PI, BOP and PPD over 12 months even among individuals with low or declining MMSE score. An adequately used powered toothbrush maintain factors that affect oral health and oral health-related Quality of Life in people with mild cognitive impairment.
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| 42. |
- Frögren, Joakim, et al.
(författare)
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Designing a model app for older persons with cognitive impairment : insights from a usability perspective
- 2018
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Ingår i: Gerontechnology. - : International Society for Gerontechnology. - 1569-1101 .- 1569-111X. ; 17, s. 80-
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Tidskriftsartikel (refereegranskat)abstract
- Purpose Research indicates that health-oriented applications on mobile units such as smartphones and PDAs, so called mHealth applications, can be useful to support older persons with cognitive impairment and their informal caregivers1. However, several studies suggest that a prerequisite for older persons to start using computer-based technology is that it offers individual customization according to personal preference 2,3,4. In the ongoing Horizon 2020 project SMART4MD (Support, Monitoring And Reminder Technology for older persons with Mild Dementia), an health-oriented model app has been developed through a user-centered process involving stakeholders in six European countries and with an emphasis on customization to allow for the various needs of older persons with cognitive impairment and their informal caregivers. The aim of this study is to gain insights about the specific needs of the target group and success factors related to the user-centered design process. Method Within the frames of the SMART4MD project, an initial Feasibility study was conducted in two countries (Spain and Sweden) simultaneously, in which in total nineteen persons with cognitive impairment aged 66-93, and their respective informal caregivers, performed a taskbased usability test of the SMART4MD model app individually in a clinical setting, followed by a four-week testing of the app in their home environment. Finally, a usability evaluation was done through individual structured interviews. Results & Discussion The result indicates that less exposure to similar technology affects both ability and self-esteem when confronted with the model app, and that evaluating usability with the target group using standard forms within usability testing requires pre-cautions. © 2018 International Society for Gerontechnology.
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| 43. |
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| 44. |
- Ghani, Zartashia, 1980-, et al.
(författare)
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Short Term Economic Evaluation of the Digital Platform "Support, Monitoring and Reminder Technology for Mild Dementia" (SMART4MD) for People with Mild Cognitive Impairment and their Informal Caregivers
- 2022
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Ingår i: Journal of Alzheimer's Disease. - : IOS Press. - 1387-2877 .- 1875-8908. ; 86:4, s. 1629-1641
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Tidskriftsartikel (refereegranskat)abstract
- Background: A randomized controlled trial of the SMART4MD tablet application was conducted for persons with mild cognitive impairment (PwMCI) and their informal caregivers to improve or maintain quality of life. Objective: The objective was to conduct economic evaluation of SMART4MD compared to standard care in Sweden from a healthcare provider perspective based on a 6-month follow-up period. Methods: Three hundred forty-five dyads were enrolled: 173 dyads in the intervention group and 172 in standard care. The primary outcome measures for PwMCI and informal caregivers were quality-adjusted life years (QALY). The results are presented as incremental cost-effectiveness ratios, and confidence intervals are calculated using non-parametric bootstrap procedure. Results: For PwMCI, the mean difference in total costs between intervention and standard care was (sic)12 (95%CI: -2090 to 2115) (US$ =(sic) 1.19) and the mean QALY change was -0.004 (95%CI: -0.009 to 0.002). For informal caregivers, the cost difference was -(sic)539 (95%CI: -2624 to 1545) and 0.003 (95%CI: -0.002 to 0.008) for QALY. The difference in cost and QALY for PwMCI and informal caregivers combined was -(sic)527 (95%CI: -3621 to 2568) and -0.001 (95%CI: -0.008 to 0.006). Although generally insignificant differences, this indicates that SMART4MD, compared to standard care was: 1) more costly and less effective for PwMCI, 2) less costly and more effective for informal caregivers, and 3) less costly and less effective for PwMCI and informal caregivers combined. Conclusion: The cost-effectiveness of SMART4MD over 6 months is inconclusive, although the intervention might be more beneficial for informal caregivers than PwMCI.
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| 45. |
- Ghani, Zartashia, 1980-, et al.
(författare)
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The Cost-Effectiveness of Mobile Health (mHealth) Interventions for Older Adults : Systematic Review
- 2020
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI. - 1661-7827 .- 1660-4601. ; 17:15
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Forskningsöversikt (refereegranskat)abstract
- The objective of this study was to critically assess and review empirical evidence on the cost-effectiveness of Mobile Health (mHealth) interventions for older adults. We systematically searched databases such as Pubmed, Scopus, and Cumulative Index to Nursing and Allied Literature (CINAHL) for peer-reviewed economic evaluations published in English from 2007 to 2018. We extracted data on methods and empirical evidence (costs, effects, incremental cost-effectiveness ratio) and assessed if this evidence supported the reported findings in terms of cost-effectiveness. The consolidated health economic evaluation reporting standards (CHEERS) checklist was used to assess the reporting quality of the included studies. Eleven studies were identified and categorized into two groups: complex smartphone communication and simple text-based communication. Substantial heterogeneity among the studies in terms of methodological approaches and types of intervention was observed. The cost-effectiveness of complex smartphone communication interventions cannot be judged due to lack of information. Limited evidence of cost-effectiveness was found for interventions related to simple text-based communications. Comprehensive economic evaluation studies are warranted to assess the cost-effectiveness of mHealth interventions designed for older adults.
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| 46. |
- Ghazi, Sarah Nauman, 1989-, et al.
(författare)
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Psychological Health and Digital Social Participation of the Older Adults during the COVID-19 Pandemic in Blekinge, Sweden—An Exploratory Study
- 2022
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI. - 1661-7827 .- 1660-4601. ; 19:6
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Tidskriftsartikel (refereegranskat)abstract
- COVID-19 has affected the psychological health of older adults directly and indirectly through recommendations of social distancing and isolation. Using the internet or digital tools to participate in society, one might mitigate the effects of COVID-19 on psychological health. This study explores the social participation of older adults through internet use as a social platform during COVID-19 and its relationship with various psychological health aspects. In this study, we used the survey as a research method, and we collected data through telephonic interviews; and online and paper-based questionnaires. The results showed an association of digital social participation with age and feeling lack of company. Furthermore, in addition, to the increase in internet use in older adults in Sweden during COVID-19, we conclude that digital social participation is essential to maintain psychological health in older adults.
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| 47. |
- Ghazi, Sarah Nauman, 1989-, et al.
(författare)
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The prevalence of eHealth literacy and its relationship with perceived health status and psychological distress during Covid-19 : a cross-sectional study of older adults in Blekinge, Sweden
- 2023
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Ingår i: BMC Geriatrics. - : BioMed Central (BMC). - 1471-2318. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- Background and aims: eHealth literacy is important as it influences health-promoting behaviors and health. The ability to use eHealth resources is essential to maintaining health, especially during COVID-19 when both physical and psychological health were affected. This study aimed to assess the prevalence of eHealth literacy and its association with psychological distress and perceived health status among older adults in Blekinge, Sweden. Furthermore, this study aimed to assess if perceived health status influences the association between eHealth literacy and psychological distress. Methods: This cross-sectional study (October 2021-December 2021) included 678 older adults’ as participants of the Swedish National Study on Aging and Care, Blekinge (SNAC-B). These participants were sent questionnaires about their use of Information and Communications Technology (ICT) during the COVID-19 pandemic. In this study, we conducted the statistical analysis using the Kruskal-Wallis one-way analysis of variance, Kendall’s tau-b rank correlation, and multiple linear regression. Results: We found that 68.4% of the participants had moderate to high levels of eHealth literacy in the population. Being female, age < 75 years, and having a higher education are associated with high eHealth literacy (p< 0.05). eHealth literacy is significantly correlated (τ=0.12, p-value=0.002) and associated with perceived health status (β=0.39, p-value=0.008). It is also significantly correlated (τ=-0.12, p-value=0.001) and associated with psychological distress (β=-0.14, p-value=0.002). The interaction of eHealth literacy and good perceived health status reduced psychological distress (β=-0.30, p-value=0.002). Conclusions: In our cross-sectional study, we found that the point prevalence of eHealth literacy among older adults living in Blekinge, Sweden is moderate to high, which is a positive finding. However, there are still differences among older adults based on factors such as being female, younger than 75 years, highly educated, in good health, and without psychological distress. The results indicated that psychological distress could be mitigated during the pandemic by increasing eHealth literacy and maintaining good health status.
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| 48. |
- Guzman-Parra, Jose, et al.
(författare)
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Attitudes and use of information and communication technologies in older adults with mild cognitive impairment or early stages of dementia and their caregivers : cross-sectional study
- 2020
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Ingår i: Journal of Medical Internet Research. - : JMIR Publications. - 1438-8871. ; 22:6
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Information and communication technologies are promising tools to increase the quality of life of people with dementia or mild cognitive impairment and that of their caregivers. However, there are barriers to their use associated with sociodemographic factors and negative attitudes, as well as inadequate knowledge about technologies. OBJECTIVE: The aim of this study was to analyze technophilia (attitudes toward new technologies) and the use of smartphones and tablets along with associated factors in people with dementia/mild cognitive impairment and their caregivers. METHODS: Data from the first visit of the Support Monitoring and Reminder for Mild Dementia (SMART4MD) randomized multicenter clinical trial were used for this analysis. Data were obtained from two European countries, Spain and Sweden, and from three centers: Consorci Sanitari de Terrassa (Catalonia, Spain), Servicio Andaluz de Salud (Andalusia, Spain), and the Blekinge Institute of Technology (Sweden). Participants with a score between 20 and 28 in the Mini Mental State Examination, with memory problems (for more than 6 months), and who were over the age of 55 years were included in the study, along with their caregivers. The bivariate Chi square and Mann-Whitney tests, and multivariate linear and logistic regression models were used for statistical analysis. RESULTS: A total of 1086 dyads were included (N=2172). Overall, 299 (27.53%) of people with dementia/mild cognitive impairment had a diagnosis of dementia. In addition, 588 (54.14%) of people with dementia/mild cognitive impairment reported using a smartphone almost every day, and 106 (9.76%) used specific apps or software to support their memory. Among the caregivers, 839 (77.26%) used smartphones and tablets almost every day, and 181 (16.67%) used specific apps or software to support their memory. The people with dementia/mild cognitive impairment showed a lower level of technophilia in comparison to that of their caregivers after adjusting for confounders (B=0.074, P=.02) with differences in technology enthusiasm (B=0.360, P<.001), but not in technology anxiety (B=-0.042, P=.37). Technophilia was associated with lower age (B=-0.009, P=.004), male gender (B=-0.160, P<.001), higher education level (P=.01), living arrangement (living with children vs single; B=-2.538, P=.01), country of residence (Sweden vs Spain; B=0.256, P<.001), lower depression (B=-0.046, P<.001), and better health status (B=0.004, P<.001) in people with dementia/mild cognitive impairment. Among caregivers, technophilia was associated with comparable sociodemographic factors (except for living arrangement), along with a lower caregiver burden (B=-0.005, P=.04) and better quality of life (B=0.348, P<.001). CONCLUSIONS: Technophilia was associated with a better quality of life and sociodemographic variables in people with dementia/mild cognitive impairment and caregivers, suggesting potential barriers for technological interventions. People with dementia/mild cognitive impairment frequently use smartphones and tablets, but the use of specific apps or software to support memory is limited. Interventions using these technologies are needed to overcome barriers in this population related to sociodemographic characteristics and the lack of enthusiasm for new technologies. TRIAL REGISTRATION: ClinicalTrials.gov NCT03325699; https://clinicaltrials.gov/ct2/show/NCT03325699. ©Jose Guzman-Parra, Pilar Barnestein-Fonseca, Gloria Guerrero-Pertiñez, Peter Anderberg, Luis Jimenez-Fernandez, Esperanza Valero-Moreno, Jessica Marian Goodman-Casanova, Antonio Cuesta-Vargas, Maite Garolera, Maria Quintana, Rebeca I García-Betances, Evi Lemmens, Johan Sanmartin Berglund, Fermin Mayoral-Cleries.
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| 49. |
- Javeed, Ashir, 1989-, et al.
(författare)
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An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning
- 2022
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Ingår i: Life. - : MDPI. - 2075-1729. ; 12:7, s. 1-18
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Tidskriftsartikel (refereegranskat)abstract
- Dementia is a neurological condition that primarily affects older adults and there is stillno cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 yearsbefore the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchershave presented several methods for early detection of dementia based on symptoms. However,these techniques suffer from two major flaws. The first issue is the bias of ML models caused byimbalanced classes in the dataset. Past research did not address this issue well and did not takepreventative precautions. Different ML models were developed to illustrate this bias. To alleviate theproblem of bias, we deployed a synthetic minority oversampling technique (SMOTE) to balance thetraining process of the proposed ML model. The second issue is the poor classification accuracy ofML models, which leads to a limited clinical significance. To improve dementia prediction accuracy,we proposed an intelligent learning system that is a hybrid of an autoencoder and adaptive boostmodel. The autoencoder is used to extract relevant features from the feature space and the Adaboostmodel is deployed for the classification of dementia by using an extracted subset of features. Thehyperparameters of the Adaboost model are fine-tuned using a grid search algorithm. Experimentalfindings reveal that the suggested learning system outperforms eleven similar systems which wereproposed in the literature. Furthermore, it was also observed that the proposed learning systemimproves the strength of the conventional Adaboost model by 9.8% and reduces its time complexity.Lastly, the proposed learning system achieved classification accuracy of 90.23%, sensitivity of 98.00%and specificity of 96.65%.
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| 50. |
- Javeed, Ashir, 1989-, et al.
(författare)
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Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification
- 2023
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Ingår i: Biomedicines. - : MDPI. - 2227-9059. ; 11:2
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Tidskriftsartikel (refereegranskat)abstract
- Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using dementia symptoms. However, these methods have fundamental limitations, such as low accuracy and bias in machine learning (ML) models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive synthetic sampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extraction techniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM) using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM are calibrated by employing the grid search approach. It is evident from the experimental results that the newly pr oposed model (FEB-SVM) improves the dementia prediction accuracy of the conventional SVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testing accuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of 86.59, F1-score of 89.12%, and Matthew’s correlation coefficient (MCC) of 0.4987. Moreover, the newly proposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.
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