Validation of the Health Assessment Tool (HAT) based on four aging cohorts from the Swedish National study on Aging and Care.

BACKGROUND: As global aging accelerates, routinely assessing the functional status and morbidity burden of older patients becomes paramount. The aim of this study is to assess the validity of the comprehensive clinical and functional Health Assessment Tool (HAT) based on four cohorts of older adults (60 + years) from the Swedish National study on Aging and Care (SNAC) spanning urban, suburban, and rural areas. METHODS: The HAT integrates five health indicators (gait speed, global cognition, number of chronic diseases, and basic and instrumental activities of daily living), providing an individual-level score between 0 and 10. The tool was constructed using nominal response models, first separately for each cohort and then in a harmonized dataset. Outcomes included all-cause mortality over a maximum follow-up of 16 years and unplanned hospital admissions over a maximum of 3 years of follow-up. The predictive capacity was assessed through the area under the curve (AUC) using logistic regressions. For time to death, Cox regressions were performed, and Harrell's C-indices were reported. Results from the four cohorts were pooled using individual participant data meta-analysis and compared with those from the harmonized dataset. RESULTS: The HAT demonstrated high predictive capacity across all cohorts as well as in the harmonized dataset. In the harmonized dataset, the AUC was 0.84 (95% CI 0.81-0.87) for 1-year mortality, 0.81 (95% CI 0.80-0.83) for 3-year mortality, 0.80 (95% CI 0.79-0.82) for 5-year mortality, 0.69 (95% CI 0.67-0.70) for 1-year unplanned admissions, and 0.69 (95% CI 0.68-0.70) for 3-year unplanned admissions. The Harrell's C for time-to-death throughout 16 years of follow-up was 0.75 (95% CI 0.74-0.75). CONCLUSIONS: The HAT is a highly predictive, clinically intuitive, and externally valid instrument with potential for better addressing older adults' health needs and optimizing risk stratification at the population level.

Magnetic resonance and diffusion tensor imaging of the adolescent rabbit growth plate of the knee.

PURPOSE: To assess the ability of MRI-DTI to evaluate growth plate morphology and activity compared with that of histomorphometry and micro-CT in rabbits. METHODS: The hind limbs of female rabbits aged 16, 20, and 24 wk (n = 4 per age group) were studied using a 9.4T MRI scanner with a multi-gradient echo 3D sequence and DTI in 14 directions (b-value = 984 s/mm2 ). After MRI, the right and left hind limb were processed for histological analysis and micro-CT, respectively. The Wilcoxon signed-rank test was used to evaluate the height and volume of the growth plate. Intraclass correlation and Pearson correlation coefficient were used to evaluate the association between DTI metrics and age. RESULTS: The growth plate height and volume were similar for all modalities at each time point and age. Age was correlated with all tractography and DTI metrics in both the femur and tibia. A correlation was also observed between all the metrics at both sites. Tract number and volume declined with age; however, tract length did not show any changes. The fractional anisotropy color map showed lateral diffusion centrally in the growth plate and perpendicular diffusion in the hypertrophic zone, as verified by histology and micro-CT. CONCLUSION: MRI-DTI may be useful for evaluating the growth plates.

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review.

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.

Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.

Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations.

Self-perceived oral health and orofacial appearance in an adult population, 60 years of age.

OBJECTIVE: The study aimed to compare self-perceived oral health and orofacial appearance in three different cohorts of 60-year-old individuals. METHOD: A cross-sectional design, based on data obtained from a questionnaire used in the Swedish National Study of Aging and Care. The sample comprised 478 individuals, from baseline, 2001-2003 (n = 191), 2007-2009 (n = 218) and 2014-2015 (n = 69). Comparisons were made within and between the cohorts, with bivariate analysis and Fisher's exact test. Statistical significance was considered at p < 0.05. RESULTS: The result showed that a low number of the participants reported self-perceived problems with oral health. Of the problems reported, a higher proportion in cohort 2014-2015 (39.3%) experienced problems with bleeding gums. The experience of bleeding gums increased between the cohorts 2001-2003 and 2014-2015 (p = 0.040) and between 2007-2009 and 2014-2015 (p = 0.017). The prevalence of discomfort with sensitive teeth was experienced in 7%-32%. Twice as many women compared to men experienced discomfort in all cohorts (no significant differences between the cohorts). Satisfaction with dental appearance was experienced in 75%-84%. Twice as many women compared to men were dissatisfied with their dental appearance in 2001-2003 (p = 0.011) and with discoloured teeth (p = 0.020). No significant differences could be seen between the cohorts regarding discomfort with dental appearance or discoloured teeth. CONCLUSION: The 60-year-olds irrespective of birth cohort, perceived their oral health and orofacial appearance as satisfactory.

Oral health status among 60-year-old individuals born in 1941-1943 and 1954-1955 and 81-year-old individuals born in 1922-1924 and 1933-1934, respectively: a cross-sectional study.

OBJECTIVE: This study aimed to analyze the oral health status of four different birth cohorts: two cohorts of 60-year-olds born in 1941-1943 and 1954-1955 and 2 cohorts of 81-year-olds born in 1920-1922 and 1933-1934. MATERIAL AND METHODS: The study was based on data from an ongoing longitudinal population project, The Swedish National Study on Aging and Care (SNAC). Oral health status was repeatedly examined clinically and radiographically in 2001-2003 and 2014-2015, including 60- and 81-year-olds, in total 412 individuals. Statistical analyses were performed using independent-samples t test and Pearson's χ2 test. RESULTS: More individuals were dentate in 2014-2015 compared to 2001-2003 in the two age groups: 60 and 81 years (p < 0.001 for both). The mean number of teeth increased in the 60-year-olds from 24.2 to 27.0 and in the 81-year-olds from 14.3 to 20.2. The numbers of at least one intact tooth increased for both age groups (p < 0.001 and p < 0.004, respectively). In the age groups 81 years, there was an increase in having at least one PPD ≥ 6 mm (p < 0.016) and bone loss ≥ 5 mm (p < 0.029) between the two examinations. No such differences were found in the age groups of 60 years. CONCLUSION: Over 13 years, oral health improved for both 60- and 81-year-old age groups. The most significant changes were in the 81-year-olds where oral health had improved except for periodontal status. CLINICAL RELEVANCE: More natural teeth and impaired periodontal status potentially impact oral health and should increase focus on preventive and supportive dental care in older individuals.

Periodontitis related to cardiovascular events and mortality: a long-time longitudinal study.

OBJECTIVE: The present study assessed if individuals ≥ 60 years of age with periodontitis are more likely to develop stroke or ischemic heart diseases, or at a higher risk of death for 17 years. MATERIAL AND METHODS: At baseline individuals ≥ 60 received a dental examination including a panoramic radiograph. Periodontitis was defined as having ≥ 30% sites with ≥ 5-mm distance from the cementoenamel junction to the marginal bone level. Medical records were annually reviewed from 2001 to 2018. Findings from the medical records identifying an ICD-10 code of stroke and ischemic heart diseases or death were registered. RESULTS: Associations between periodontitis and incidence of ischemic heart disease were found in this 17-year follow-up study in all individuals 60-93 years (HR: 1.5, CI: 1.1-2.1, p = 0.017), in women (HR: 2.1, CI: 1.3-3.4, p = 0.002), and in individuals 78-96 years (HR: 1.7, CI: 1.0-2.6, p = 0.033). Periodontitis was associated with mortality in all individuals (HR: 1.4, CI: 1.2-1.8, p = 0.002), specifically in men (HR: 1.5, CI: 1.1-1.9, p = 0.006) or in ages 60-72 years (HR: 2.2, CI: 1.5-3.2, p = 0.000). Periodontitis was more prevalent among men (OR: 1.8, CI: 1.3-2.4, p = 0.000). CONCLUSIONS: Individuals with periodontitis have an increased risk for future events of ischemic heart diseases and death. CLINICAL RELEVANCE: Improving periodontal health in older individuals may reduce overall mortality and ischemic heart diseases. Both dental and medical professionals should be aware of the associations and ultimately cooperate.

A Novel Instrument for Measuring Older People's Attitudes Toward Technology (TechPH): Development and Validation.

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 (χ28=21.2, χ2/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.

Implementation of a web-based national child health-care programme in a local context: A complex facilitator role.

AIM: The aim of this study was to investigate child health-care coordinators' experiences of being a facilitator for the implementation of a new national child health-care programme in the form of a web-based national guide. METHODS: The study was based on eight remote, online focus groups, using Skype for Business. A qualitative content analysis was performed. RESULTS: The analysis generated three categories: adapt to a local context, transition challenges and led by strong incentives. There were eight subcategories. In the latent analysis, the theme 'Being a facilitator: a complex role' was formed to express the child health-care coordinators' experiences. CONCLUSIONS: Facilitating a national guideline or decision support in a local context is a complex task that requires an advocating and mediating role. For successful implementation, guidelines and decision support, such as a web-based guide and the new child health-care programme, must match professional consensus and needs and be seen as relevant by all. Participation in the development and a strong bottom-up approach was important, making the web-based guide and the programme relevant to whom it is intended to serve, and for successful implementation. The study contributes valuable knowledge when planning to implement a national web-based decision support and policy programme in a local health-care context.

Periodontitis, tooth loss and cognitive functions among older adults.

OBJECTIVE: This study aims to evaluate the potential association between periodontitis, the number of teeth and cognitive functions in a cohort of older adults in Sweden. MATERIAL AND METHODS: In total, 775 individuals from 60 to 99 years of age were selected for the study. A clinical and radiographic examination was performed. The number of teeth and prevalence of periodontal pockets and bone loss was calculated and categorised. Cognitive functions were assessed using the Mini-Mental State Examination (MMSE) and clock test. The education level was obtained from a questionnaire. Data were analysed using chi-square tests and multivariate logistic regression. RESULTS: Age and gender were associated with the prevalence of bone loss. Age and education were associated with lower number of teeth. Gender was also associated with the presence of pockets. The multivariate logistic regression analysis demonstrated a statistically significant association between prevalence of bone loss, the number of teeth and the outcome on MMSE test. This association remained even after adjustment for age, education and gender. Tooth loss was also associated with lower outcome on clock test. Presence of periodontal pockets ≥ 5 mm was not associated with cognitive test outcome. CONCLUSIONS: A history of periodontitis and tooth loss may be of importance for cognitive functions among older adults. CLINICAL RELEVANCE: Diseases with and inflammatory profile may have an impact on cognitive decline.

Better platelet function, less fibrinolysis and less hemolysis in re-transfused residual pump blood with the Ringer's chase technique - a randomized pilot study.

INTRODUCTION: Residual pump blood from the cardiopulmonary bypass (CPB) circuit is often collected into an infusion bag (IB) and re-transfused. An alternative is to chase the residual blood into the circulation through the arterial cannula with Ringer's acetate. Our aim was to assess possible differences in hemostatic blood quality between these two techniques. METHODS: Forty adult patients undergoing elective coronary artery bypass graft surgery with CPB were randomized to receive the residual pump blood by either an IB or through the Ringer's chase (RC) technique. Platelet activation and function (impedance aggregometry), coagulation and hemolysis variables were assessed in the re-transfused blood and in the patients before, during and after surgery. Results are presented as median (25-75 quartiles). RESULTS: Total hemoglobin and platelet levels in the re-transfused blood were comparable with the two methods, as were soluble platelet activation markers P-selectin and soluble glycoprotein VI (GPVI). Platelet aggregation (U) in the IB blood was significantly lower compared to the RC blood, with the agonists adenosine diphosphate (ADP) 24 (10-32) vs 46 (33-65), p<0.01, thrombin receptor activating peptide (TRAP) 50 (29-73) vs 69 (51-92), p=0.04 and collagen 24 (17-28) vs 34 (26-59), p<0.01. The IB blood had higher amounts of free hemoglobin (mg/L) (1086 (891-1717) vs 591(517-646), p<0.01) and D-dimer 0.60 (0.33-0.98) vs 0.3 (0.3-0.48), p<0.01. Other coagulation variables showed no difference between the groups. CONCLUSIONS: The handling of blood after CPB increases hemolysis, impairs platelet function and activates coagulation and fibrinolysis. The RC technique preserved the blood better than the commonly used IB technique.

Nurses' Use of a Web-Based National Guide for Child Health Care.

Rikshandboken i Barnhälsovård is a Swedish Web-based guide for child healthcare, providing quality-ensured guidelines and support contributing to equality in child healthcare among all children. In 2015, a new child healthcare program was implemented and made available in this Web-based guide. The aim of this study was to investigate how child healthcare nurses use Rikshandboken i Barnhälsovård and factors affecting its use. The study was a comprehensive Web survey of 2376 child healthcare nurses in Sweden answered by 1309. Statistical processing was performed using descriptive and analytical methods. Rikshandboken i Barnhälsovård was widely used by the respondents, but regional differences and number of years in the profession affected the use. Almost all nurses were satisfied with the usability, content, and design and felt that a national guide for child healthcare is important. This indicates that an established Web-based national guide is an appropriate setting when a new national program is implemented. In order to achieve an equal and equitable child healthcare, it is essential that all nurses use the national guide to provide evidence-based practice. The value of main child healthcare units as regional facilitators in the innovation process of Rikshandboken i Barnhälsovård should not be underestimated.

Sleep disturbance predicts worse cognitive performance in subsequent years: A longitudinal population-based cohort study.

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.

Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia.

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.

Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification.

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.

Antibodies against Porphyromonas gingivalis in serum and saliva and their association with rheumatoid arthritis and periodontitis. Data from two rheumatoid arthritis cohorts in Sweden.

Background: Periodontitis and oral pathogenic bacteria can contribute to the development of rheumatoid arthritis (RA). A connection between serum antibodies to Porphyromonas gingivalis (P. gingivalis) and RA has been established, but data on saliva antibodies to P. gingivalis in RA are lacking. We evaluated antibodies to P. gingivalis in serum and saliva in two Swedish RA studies as well as their association with RA, periodontitis, antibodies to citrullinated proteins (ACPA), and RA disease activity. Methods: The SARA (secretory antibodies in RA) study includes 196 patients with RA and 101 healthy controls. The Karlskrona RA study includes 132 patients with RA ≥ 61 years of age, who underwent dental examination. Serum Immunoglobulin G (IgG) and Immunoglobulin A (IgA) antibodies and saliva IgA antibodies to the P. gingivalis-specific Arg-specific gingipain B (RgpB) were measured in patients with RA and controls. Results: The level of saliva IgA anti-RgpB antibodies was significantly higher among patients with RA than among healthy controls in multivariate analysis adjusted for age, gender, smoking, and IgG ACPA (p = 0.022). Saliva IgA anti-RgpB antibodies were associated with RA disease activity in multivariate analysis (p = 0.036). Anti-RgpB antibodies were not associated with periodontitis or serum IgG ACPA. Conclusion: Patients with RA had higher levels of saliva IgA anti-RgpB antibodies than healthy controls. Saliva IgA anti-RgpB antibodies may be associated with RA disease activity but were not associated with periodontitis or serum IgG ACPA. Our results indicate a local production of IgA anti-RgpB in the salivary glands that is not accompanied by systemic antibody production.

CoGNIT Automated Tablet Computer Cognitive Testing in Patients With Mild Cognitive Impairment: Feasibility Study.

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.

An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning.

Dementia is a neurological condition that primarily affects older adults and there is still no cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 years before the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchers have 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 by imbalanced classes in the dataset. Past research did not address this issue well and did not take preventative precautions. Different ML models were developed to illustrate this bias. To alleviate the problem of bias, we deployed a synthetic minority oversampling technique (SMOTE) to balance the training process of the proposed ML model. The second issue is the poor classification accuracy of ML 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 boost model. The autoencoder is used to extract relevant features from the feature space and the Adaboost model is deployed for the classification of dementia by using an extracted subset of features. The hyperparameters of the Adaboost model are fine-tuned using a grid search algorithm. Experimental findings reveal that the suggested learning system outperforms eleven similar systems which were proposed in the literature. Furthermore, it was also observed that the proposed learning system improves 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%.

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.

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 € 12 (95% CI: -2090 to 2115) (US$ = € 1.19) and the mean QALY change was -0.004 (95% CI: -0.009 to 0.002). For informal caregivers, the cost difference was - € 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 -€ 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.

Psychological Health and Digital Social Participation of the Older Adults during the COVID-19 Pandemic in Blekinge, Sweden-An Exploratory Study.

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.