Unwrapping phase contrast MRI by iterative graph cuts.

PURPOSE: To develop and evaluate a phase unwrapping method for cine phase contrast MRI based on graph cuts. METHODS: A proposed Iterative Graph Cuts method was evaluated in 10 cardiac patients with two-dimensional flow quantification which was repeated at low venc settings to provoke wrapping. The images were also unwrapped by a path-following method (ROMEO), and a Laplacian-based method (LP). Net flow was quantified using semi-automatic vessel segmentation. High venc images were also wrapped retrospectively to asses the residual amount of wrapped voxels. RESULTS: The absolute net flow error after unwrapping at venc = 100 cm/s was 1.8 mL, which was 0.83 mL smaller than for LP. The repeatability error at high venc without unwrapping was 2.5 mL. The error at venc = 50 cm/s was 7.5 mL, which was 8.2 mL smaller than for ROMEO and 5.7 mL smaller than for LP. For retrospectively wrapped images with synthetic venc of 100/50/25 cm/s, the residual amount of wrapped voxels was 0.00/0.12/0.79%, which was 0.09/0.26/8.0 percentage points smaller than for LP. With synthetic venc of 25 cm/s, omitting magnitude information resulted in 3.2 percentage points more wrapped voxels, and only spatial/temporal unwrapping resulted in 4.6/21 percentage points more wrapped voxels compared to spatiotemporal unwrapping. CONCLUSION: Iterative Graph Cuts enables unwrapping of cine phase contrast MRI with very small errors, except for at extreme blood velocities, with equal or better performance compared to ROMEO and LP. The use of magnitude information and spatiotemporal unwrapping is recommended.

Self-perceived oral health and orofacial appearance in older adults - an 18-year follow-up study in Karlskrona, Sweden.

OBJECTIVES: To analyze whether self-perceived oral health and orofacial appearance change with increasing age. METHODS: This longitudinal study is based on data from a questionnaire used in the Swedish National Study of Aging and Care. The sample comprises 160 participants 60 years of age at baseline 2001-2003. The same participants were re-examined at 66-, 72-, and 78 years of age. To analyze whether perceptions of oral health and orofacial appearance changed with increasing age, Cochran's Q test was conducted. Statistical significance was considered at p ≤ 0.05, and the calculated value Q must be equal to or greater than the critical chi-square value (Q ≥ 7.82). Significance values have been adjusted for the Bonferroni correction for multiple tests. RESULTS: Self-perceived mouth dryness, both day (Q = 7.94) and night (Q = 23.41), increased over the 18-year follow-up. When divided by gender, significant differences were only seen for mouth dryness at nighttime. A decrease in sensitive teeth was perceived with increasing age, and an increase in self-perceived satisfaction with dental appearance, and a decrease in self-perceived problems with dental gaps between the ages of 60 and 78. These changes were, however, not statistically significant. Men experienced a higher proportion of discomfort with discolored teeth at age 78 than at 60 (Q = 9.09). CONCLUSIONS: Self-perceived oral health and orofacial appearance were relatively stable, with few changes over an 18-year follow-up.

Lot-to-lot immunogenicity consistency of the respiratory syncytial virus prefusion F protein vaccine in older adults.

Background: Previous phase 3 studies showed that the AS01E-adjuvanted respiratory syncytial virus (RSV) prefusion F protein-based vaccine for older adults (RSVPreF3 OA) is well tolerated and efficacious in preventing RSV-associated lower respiratory tract disease in adults ≥ 60 years of age. This study evaluated lot-to-lot immunogenicity consistency, reactogenicity, and safety of three RSVPreF3 OA lots. Methods: This phase 3, multicenter, double-blind study randomized (1:1:1) participants ≥ 60 years of age to receive one of three RSVPreF3 OA lots. Serum RSVPreF3-binding immunoglobulin G (IgG) concentration was assessed at baseline and 30 days post-vaccination. Lot-to-lot consistency was demonstrated if the two-sided 95 % confidence intervals (CIs) of the RSVPreF3-binding IgG geometric mean concentration (GMC) ratios between each lot pair at 30 days post-vaccination were within 0.67 and 1.50. Solicited adverse events (AEs) within four days, unsolicited AEs within 30 days, and serious AEs (SAEs) and potential immune-mediated diseases within six months post-vaccination were recorded. Results: A total of 757 participants received RSVPreF3 OA, of whom 708 were included in the per-protocol set (234, 237, and 237 participants for each lot). Lot-to-lot consistency was demonstrated: GMC ratios were 1.06 (95 % CI: 0.94-1.21), 0.92 (0.81-1.04), and 0.87 (0.77-0.99) between the lot pairs (lot 1/2; 1/3; 2/3). For the three lots, the RSVPreF3-binding IgG concentration increased 11.84-, 11.29-, and 12.46-fold post-vaccination compared to baseline. The reporting rates of solicited and unsolicited AEs, SAEs, and potential immune-mediated diseases were balanced between lots. Twenty-one participants reported SAEs; one of these-a case of atrial fibrillation-was considered by the investigator as vaccine-related. SAEs with a fatal outcome were reported for four participants, none of which were considered by the investigator as vaccine-related. Conclusion: This study demonstrated lot-to-lot immunogenicity consistency of three RSVPreF3 OA vaccine lots and indicated that the vaccine had an acceptable safety profile.ClinicalTrials.gov: NCT05059301.

A mathematical model for temporal cerebral blood flow response to acetazolamide evaluated in patients with Moyamoya disease.

BACKGROUND: Paired cerebral blood flow (CBF) measurement is usually acquired before and after vasoactive stimulus to estimate cerebrovascular reserve (CVR). However, CVR may be confounded because of variations in time-to-maximum CBF response (tmax) following acetazolamide injection. With a mathematical model, CVR can be calculated insensitive to variations in tmax, and a model offers the possibility to calculate additional model-derived parameters. A model that describes the temporal CBF response following a vasodilating acetazolamide injection is proposed and evaluated. METHODS: A bi-exponential model was adopted and fitted to four CBF measurements acquired using arterial spin labelling before and initialised at 5, 15 and 25 min after acetazolamide injection in a total of fifteen patients with Moyamoya disease. Curve fitting was performed using a non-linear least squares method with a priori constraints based on simulations. RESULTS: Goodness of fit (mean absolute error) varied between 0.30 and 0.62 ml·100 g-1·min-1. Model-derived CVR was significantly higher compared to static CVR measures. Maximum CBF increase occurred earlier in healthy- compared to diseased vascular regions. CONCLUSIONS: The proposed mathematical model offers the possibility to calculate CVR insensitive to variations in time to maximum CBF response which gives a more detailed characterisation of CVR compared to static CVR measures. Although the mathematical model adapts generally well to this dataset of patients with MMD it should be considered as experimental; hence, further studies in healthy populations and other patient cohorts are warranted.

Effect of MRI acquisition parameters on accuracy and precision of phase-contrast measurements in a small-lumen vessel phantom.

BACKGROUND: Phase-contrast magnetic resonance imaging (PC-MRI) quantifies blood flow and velocity noninvasively. Challenges arise in neurovascular disorders due to small vessels. We evaluated the impact of voxel size, number of signal averages (NSA), and velocity encoding (VENC) on PC-MRI measurement accuracy and precision in a small-lumen vessel phantom. METHODS: We constructed an in vitro model with a constant flow rate using a 2.2-mm inner diameter plastic tube. A reservoir with a weight scale and timer was used as standard reference. Gradient-echo T1 weighted PC-MRI sequence was performed on a 3-T scanner with varying voxel size (2.5, 5.0, 7.5 mm3), NSA (1, 2, 3), and VENC (200, 300, 400 cm/s). We repeated measurements nine times per setting, calculating mean flow rate, maximum velocity, and least detectable difference (LDD). RESULTS: PC-MRI flow measurements were higher than standard reference values (mean ranging from 7.3 to 9.5 mL/s compared with 6.6 mL/s). Decreased voxel size improved accuracy, reducing flow rate measurements from 9.5 to 7.3 mL/s. The LDD for flow rate and velocity varied between 1 and 5%. The LDD for flow rate decreased with increased voxel size and NSA (p = 0.033 and 0.042). The LDD for velocity decreased with increased voxel size (p < 10-16). No change was observed when VENC varied. CONCLUSIONS: PC-MRI overestimated flow. However, it has high precision in a small-vessel phantom with constant flow rate. Improved accuracy was obtained with increasing spatial resolution (smaller voxels). Improved precision was obtained with increasing signal-to-noise ratio (larger voxels and/or higher NSA). RELEVANCE STATEMENT: Phase-contrast MRI is clinically used in large vessels. To further investigate the possibility of using phase-contrast MRI for smaller intracranial vessels in neurovascular disorders, we need to understand how acquisition parameters affect phase-contrast MRI-measured flow rate and velocity in small vessels. KEY POINTS: • PC-MRI measures flow and velocity in a small lumen phantom with high precision but overestimates flow rate. • The precision of PC-MRI measurements matches the precision of standard reference for flow rate measurements. • Optimizing PC-MRI settings can enhance accuracy and precision in flow rate and velocity measurements.

Prediction of dementia based on older adults' sleep disturbances using machine learning.

BACKGROUND: The most common degenerative condition in older adults is dementia, which can be predicted using a number of indicators and whose progression can be slowed down. One of the indicators of an increased risk of dementia is sleep disturbances. This study aims to examine if machine learning can predict dementia and which sleep disturbance factors impact dementia. METHODS: This study uses five machine learning algorithms (gradient boosting, logistic regression, gaussian naive Bayes, random forest and support vector machine) and data on the older population (60+) in Sweden from the Swedish National Study on Ageing and Care - Blekinge (n=4175). Each algorithm uses 10-fold stratified cross-validation to obtain the results, which consist of the Brier score for checking accuracy and the feature importance for examining the factors which impact dementia. The algorithms use 16 features which are on personal and sleep disturbance factors. RESULTS: Logistic regression found an association between dementia and sleep disturbances. However, it is slight for the features in the study. Gradient boosting was the most accurate algorithm with 92.9% accuracy, 0.926 f1-score, 0.974 ROC AUC and 0.056 Brier score. The significant factors were different in each machine learning algorithm. If the person sleeps more than two hours during the day, their sex, education level, age, waking up during the night and if the person snores are the variables that most consistently have the highest feature importance in all algorithms. CONCLUSION: There is an association between sleep disturbances and dementia, which machine learning algorithms can predict. Furthermore, the risk factors for dementia are different across the algorithms, but sleep disturbances can predict dementia.

Association of systemic anticholinergic medication use and accelerated decrease in lung function in older adults.

Older adults are frequently exposed to medicines with systemic anticholinergic properties, which are linked to increased risk of negative health outcomes. The association between systemic anticholinergics and lung function has not been reported. The aim of this study was to investigate if exposure to systemic anticholinergics influences lung function in older adults. Participants of the southernmost centres of the Swedish National study on Aging and Care (SNAC) were followed from 2001 to 2021. In total, 2936 subjects (2253 from Good Aging in Skåne and 683 from SNAC-B) were included. An extensive medical examination including spirometry assessments was performed during the study visits. The systemic anticholinergic burden was described using the anticholinergic cognitive burden scale. The effect of new use of systemic anticholinergics on the annual change in forced expiratory volume (FEV1s) was estimated using mixed models. During follow-up, 802 (27.3%) participants were exposed to at least one systemic anticholinergic medicine. On average, the FEV1s of participants without systemic anticholinergic exposure decreased 37.2 ml/year (95% CI [33.8; 40.6]) while participants with low and high exposure lose 47.2 ml/year (95% CI [42.4; 52.0]) and 43.7 ml/year (95% CI [25.4; 62.0]). A novel association between new use of medicines with systemic anticholinergic properties and accelerated decrease in lung function in older adults was found. The accelerated decrease is comparable to that observed in smokers. Studies are needed to further explore this potential side effect of systemic anticholinergics.

Results of objective brushing data recorded from a powered toothbrush used by elderly individuals with mild cognitive impairment related to values for oral health.

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.

Loneliness in Relation to Social Factors and Self-Reported Health Among Older Adults: A Cross-Sectional Study.

BACKGROUND: Loneliness is described as a public health problem and can be both a consequence of aging and a cause of ill health. Lonely older adults tend to have difficulties making new social connections, essential in reducing loneliness. Loneliness often varies over time, but established loneliness tends to persist. Maintaining good health is fundamental throughout the life course. Social connections change with aging, which can contribute to loneliness. AIM: This study aimed to investigate loneliness in relation to social factors and self-reported health among older adults. METHOD: A cross-sectional research design was used based on data from the Swedish National Study on Aging and Care, Blekinge (SNAC-B), from February 2019 to April 2021. Statistical analysis consisted of descriptive and inferential analysis. RESULTS: Of n = 394 participants, 31.7% (n = 125) stated loneliness. Close emotional connections were necessary for less loneliness. Loneliness was more common among those who did not live with their spouse or partner and met more rarely. Furthermore, seeing grandchildren and neighbors less often increased loneliness, and a more extensive social network decreased loneliness. CONCLUSION: This study underlined the importance of social connections and having someone to share a close, emotional connection with to reduce loneliness.

Five-factor model, technology enthusiasm and technology anxiety.

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.

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.

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.

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.

DTI assessment of the maturing growth plate of the knee in adolescents and young adults.

PURPOSE: To assess the growth plates of the knee in a healthy population of young adults and adolescents using DTI, and to correlate the findings with chronological age and skeletal maturation. METHODS: A prospective, cross-sectional study to assess the tibial and femoral growth plates with DTI in 155 healthy volunteers aged between 14.0 and 21 years old. Echo-planar DTI with 15 directions and b value of 0 and 600 s/mm2 was performed on a 3 T whole-body scanner. RESULTS: A relationship was observed between chronological age and most DTI metrics (fractional anisotropy, mean diffusivity, and radial diffusivity), tract length and volume. (No significant relationship could be seen for axonal diffusivity and tract length.) Subdivision according to skeletal maturation showed the greatest tract lengths and volumes seen in stage 4b and not 4a. The intra-observer agreement was significant (P = 0.01) for all the measured variables, but agreement varied (femur 0.53 - 0.98; tibia 0.58 - 0.98). Spearman's correlation showed a significant correlation for age (P = 0.05; P = 0.01) as well as for the fractional anisotropy value within all variables in both femur and tibia. Tract number and volume had a similar correlation with most variables, especially the DTI metrics, and would seem to be interchangeable. CONCLUSION: The current study indicates that DTI metrics could be a tool to assess the skeletal maturation process of the growth plate and its activity. Tractography seems promising to assess the activity of the growth plate in a younger population but must be used with caution in the more mature growth plate.

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.

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.

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 [Formula: see text] years, and having a higher education are associated with high eHealth literacy ([Formula: see text]). eHealth literacy is significantly correlated ([Formula: see text]=0.12, p-value=0.002) and associated with perceived health status ([Formula: see text]=0.39, p-value=0.008). It is also significantly correlated ([Formula: see text]=-0.12, p-value=0.001) and associated with psychological distress ([Formula: see text]=-0.14, p-value=0.002). The interaction of eHealth literacy and good perceived health status reduced psychological distress ([Formula: see text]=-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.

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.

Use of a powered toothbrush to improve oral health in individuals with mild cognitive impairment.

OBJECTIVES: The 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. BACKGROUND: People 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 METHODS: Two 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. RESULTS: One 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. CONCLUSION: The 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.

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.