Lyme borreliosis (LB), the most common European tick-borne disease, can manifest as an erythema migrans (EM) rash or as disseminated LB. The prospective Burden of Lyme Disease (BOLD) study evaluated the frequency of LB clinical manifestations, including signs, symptoms, and treatment patterns in 14 healthcare practices in endemic regions of six European countries: the Czech Republic, Germany, Poland, Slovakia, Slovenia, and Sweden. Between April 2021 and December 2022, patients with suspected LB were evaluated using predefined case definitions that were applied by investigators to identify medically attended LB cases. Enrolled cases were interviewed about their symptoms. Among the 797 LB cases, 615 (77.2%) had EM and 182 (22.8%) had disseminated disease; 154 of the disseminated cases had Lyme arthritis (LA), five had Lyme neuroborreliosis, and three had Lyme carditis. Geographically, the proportion of disseminated disease varied by country, from 1.1% in Slovenia to 78.0% in Slovakia. Overall, 76.3% of all LB cases in Slovakia were LA. Antibiotic use varied by country, although every country prescribed doxycycline. The frequency of LB manifestations varied substantially between countries. EM was the most common manifestation in all countries except Slovakia, where LA was most common. This study underscores the need for improved prevention strategies.

Background: Evening technology use (ETU) has been associated with sleep disturbances, often attributed to blue light exposure and cognitive arousal. However, most of the existing evidence focuses on younger populations and relies primarily on subjective measures. As older adults increasingly engage with both passive and active technology use, it is important to investigate how ETU impacts objective sleep. Currently, there is also a limited understanding of how particular evening digital activities, especially active versus passive engagement, affect objective sleep in older adults.

Objective: This study aims to investigate the impact of exposure to ETU on both objective and subjective sleep outcomes in older adults.

Methods: This is a randomized crossover trial involving approximately 55 adults aged 60-75 years from the ongoing Swedish National Study on Aging and Care-Blekinge. Each participant will undergo 3 one-week intervention periods: active ETU, passive ETU, and a nondigital activity (book reading), with one-week washout periods in between. The order of interventions will be randomized. Sleep will be assessed using a home-based electroencephalography device (MUSE headband) and daily self-reports. Primary outcomes are sleep onset latency and wake after sleep onset. Secondary outcomes include objective measures such as total sleep time, sleep efficiency, and time spent in REM, deep, and light sleep, subjective sleep quality, adherence, and perception of the intervention and comfort of using the objective measurement tool, that is, the electroencephalography headband. Linear mixed-effects models (with fixed effects for condition and period and a random participant intercept) were used to analyze crossover effects on sleep outcomes.

Results: Participant recruitment and data collection began in the fall of 2025 and will continue through summer 2026 or until the target sample size is reached. Data collection is scheduled to be completed by spring 2027. Results will include participant flow, baseline characteristics, adherence data, and comparative analyses of the 3 intervention conditions. Within-subject statistical models will be used to evaluate differences in sleep outcomes and investigate the associations between ETU and sleep quality.

Conclusions: This crossover study will clarify how active and passive ETU, compared with a nondigital activity, relate to objective sleep in older adults. Findings will inform simple, practical recommendations for technology use before bed in late life. 

Introduction: As digital engagement becomes integral to society, understanding the association between technology use and sleep health in older adults is important.

Objective: This study examined sleep health and its relationship with technology use before bedtime and midnight in a population-based cohort aged 60 years and older.

Methods: We conducted a cross-sectional analysis of 436 older adults (2023) from the Swedish National Study on Aging and Care, Blekinge (SNAC-B). Participants completed questionnaires on health status, sleep, internet use, screen use before bedtime (SUBB), and Midnight screen use (MSU). Sleep health was measured using the SATED instrument. Statistical analyses included chi2 tests, T-tests, and Linear regression.

Results: Older adults had a mean sleep health score of 7.40 (SD = 2.03). Internet users and those who use the internet frequently had significantly higher sleep health scores than non-users (p < 0.005). Daily SUBB was associated with a better sleep health score (7.70) compared to no SUBB (7.10). SUBB was positively associated with sleep health, with significant effects in both unadjusted (B = 0.64, p = 0.003) and adjusted models (B = 0.512, p = 0.013). MSU, however, showed a non-significant negative association in both unadjusted (B = -0.512, p = 0.258) and adjusted models (B = -0.678, p = 0.117). Health status was the strongest predictor across all models (B = 0.595, p < 0.001).

Conclusions: This study underscores the nuanced effects of technology use on sleep health among older adults, emphasizing the importance of health status. Further research is warranted to explore these relationships. 

Patients with dementia experience a steady deterioration in cognitive function that increases mortality and impairments. Moreover, dementia is also anticipated to increase significantly in prevalence as the world's population ages, placing a strain on healthcare systems throughout the globe. Hence, early identification and prediction of dementia are essential due to timely treatments, enhanced patient care, and the potential for preventative measures. Therefore, the aim of this project is to construct a diagnostic system that leverages patient electronic medical data to predict dementia as well as dementia risk factors. We developed a novel variable selection method (VSM) based on data mining techniques to accomplish this goal by selecting the most relevant variables from the dataset that contribute to the onset of dementia in older people. We employed a random forest (RF) model to classify dementia, healthy subjects, and the hyperparameters of the selected RF model were adjusted using a random search approach. The proposed diagnostic system is based on two components that hybridize as a single system; therefore, we named it the VSM_RF model. We obtained the dataset from the Swedish National Study on Aging and Care (SNAC) to verify the reliability and accuracy of the proposed VSM_RF model. The three SNAC locations collectively yielded 8191 data observations, each including 75 variables. Numerous validation metrics, including accuracy, balance accuracy, sensitivity, specificity, and Matthew's correlation coefficient, were deployed to thoroughly assess the efficiency of the proposed VSM_RF model. Only six out of the 75 variables were used to achieve the maximum accuracy, along with balance accuracy of 98.00% and 97.29%, respectively. 

Introduction: Blood-based biomarkers for Alzheimer's disease (AD) have the potential to improve diagnostic accessibility, but their clinical interpretation requires understanding of variability and biological influences.

Methods: We repeatedly sampled blood from 57 adults referred for lumbar puncture as part of a cognitive evaluation at a memory clinic. We measured serum phosphorylated- tau-181 (s-p-tau181) and plasma amyloid beta (Aβ)42/40 ratio (p-Aβ42/Aβ40) and evaluated the impact of renal and blood–brain barrier (BBB) function.

Results: Test–retest analysis revealed large variability of s-p-tau181 and small for p-Aβ42/Aβ40. Markers of renal function and BBB integrity significantly influenced s-p-tau181 levels, whereas p-Aβ42/Aβ40 was not affected.

Discussion: This study emphasizes the need for caution when interpreting longitudinal changes in s-p-tau181. Inter-individual variability is to a large degree due to susceptibility to biological influences where a novel association with integrity of BBB function were identified. These results have implications for the clinical application of blood-based biomarkers in AD diagnostics and monitoring.

Highlights: Blood phosphorylated- tau-181 (p-tau181) shows high test–retest variability in memory clinic patients. Blood amyloid beta (Aβ)42/Aβ40 ratio is stable but has poor diagnostic accuracy. Renal function and blood–brain barrier (BBB) integrity affect blood p-tau181 levels. Caution is needed when interpreting longitudinal changes in blood p-tau181. Renal and BBB disorders should be considered when assessing blood p-tau181. 

Introduction: Exploring the association between technology use and sleep health in older adults is important as digital engagement becomes integrated into society.

Objective: This study aimed to examine sleep health and its association with technology use in a population-based cohort of 60 years and older.

Methods: This cross-sectional, population-based study (2023) included 436 older adults from the Swedish National Study on Aging and Care, Blekinge (SNAC-B) population. These participants were sent questionnaires about their sleep, internet usage, Digital Social Participation (DSP), Technology Anxiety (TA), Technology Enthusiasm (TE), and use of information and communication technology. We used a multidimensional instrument, SATED, to measure sleep health. In this study, we conducted statistical analyses using the chi2 test, T-test, Pearson correlation, and backward linear and logistic regression.

Results: Our study found that older adults (60 years+) have a mean sleep health score of 7.40 (SD = 2.03). TE (,) and DSP (,) were positively associated with better sleep health, while TA (,) was negatively associated. Frequent internet users(M = 7.6) and engaging with screens before bedtime (M = 7.7) had higher sleep health scores compared to non-frequent users (M = 6.90,) and none or seldom engagement with screens before bedtime (M = 7.10,) respectively. Linear regression showed TE positively associated (= 0.241,) while TA negatively associated (= -0.220,) with sleep health. DSP was found to be a predictor of better satisfaction (OR: 1.32,), efficiency (OR: 1.16,), and duration of sleep (OR:1.16,). Lower TA predicted better satisfaction (OR: 0.81,), timing (OR: 0.74,), and efficiency (OR:0.78,) of sleep. Older adults who use technology one hour before sleep have better sleep timing (OR: 3.003,), while those who do use mobile phones with a screen during the awake period after sleep onset have poor sleep timing (OR:0.016,).

Conclusions: DSP and TE support better sleep health, while TA negatively impacts sleep satisfaction, timing, and efficiency. Encouraging positive digital engagement and minimizing technology-related stress may promote healthier sleep in older adults. 

Background: In addition to impairing the lung function, chronic obstructive pulmonary disease (COPD) also affects phonatory characteristics. Recent research highlights the potential of voice as a digital biomarker to support clinical decision-making. While machine learning (ML) can detect disease patterns from acoustic features, clinical relevance requires understanding the relationship between the disorder and acoustic features.

Objective: This study investigates both statistical and clinical significance using Baseline Acoustic (BLA) and Mel-Frequency Cepstral Coefficient (MFCC) features with focusing on individuals with COPD and healthy controls (HC).

Method: Acoustic features derived from Swedish utterances of the vowel [a:], recorded via mobile phones from 48 age-matched participants (24 COPD, 24 HC; equal gender distribution), were analyzed. To reduce bias from varying recording counts, features were aggregated by averaging 10 randomly selected recordings per participant over 100 iterations. Vowel articulation was visualized in the vowel quadrilateral space using F1 (tongue height) and F2 (tongue advancement). Group differences were assessed using the Shapiro-Wilk test, Mann-Whitney U test (α = 0.05), Benjamini-Hochberg (BH) and Bonferroni corrections, Permutational Multivariate Analysis of Variance (PERMANOVA) test, and Cliff's Delta (δ).

Results: Of 101 features, 29 remained significant after BH correction and one after Bonferroni. Multivariate testing (p = 0.019) showed group separation. Additionally, 34 features demonstrated large effect sizes, suggesting potential as digital biomarkers.

Conclusion: Voice data recorded via mobile phones capture meaningful acoustic differences associated with COPD. These findings support the integration of voice-based assessments into eHealth platforms for noninvasive COPD screening and monitoring, which is pending further validation on larger populations.

Clinical Trial: NCT06705647

Background Previous literature indicate retinal hyperspectral imaging as a non-invasive method with the potential for identifying amyloid-beta (Aβ) protein deposits. Current diagnostic methods, such as cerebrospinal fluid analysis or positron emission tomography, are costly, invasive, and non-scalable. Hyperspectral imaging offers a potentially accessible alternative for early detection of Alzheimer’s disease. The aim of this study is to investigate the potential of retinal hyperspectral imaging in identifying Aβ-positive patients within a clinical cohort from a memory clinic.

Methods A prospective cross-sectional cohort study was conducted between January 2023 and May 2024 at a single memory clinic in Sweden. The study recruited 57 patients (35 Aβ-positive and 22 Aβ-negative) who underwent lumbar puncture as part of their diagnostic workup for cognitive complaints. Retinal hyperspectral images were captured from all participants at the time of their lumbar puncture and again 2–4 weeks later. Data was collected from five anatomical regions of the retina (Superior 1, Superior 2, Inferior 1, Inferior 2, and the center of the Fovea).The main outcome was the Aβ status (Aβ-positive or Aβ-negative). Catboost machine learning models were trained on hyperspectral imaging data to predict Aβ status. A nested cross-validation approach was used to train and evaluate classification models. Performance metrics included area under the curve (AUC), accuracy, sensitivity, and specificity.

Results The best-performing model used the combination of regions Superior 1, Superior 2, and center of the fovea, achieving a mean AUC of 0.77 (0.05), mean accuracy of 0.66 (0.03), and mean sensitivity of 0.73 (0.13) and mean specificity of 0.55 (0.12). Performance was consistent across outer folds. Models using all five regions or less informative combinations yielded lower and more variable results.

Conclusions Retinal hyperspectral imaging combined with the Catboost algorithm demonstrated significant potential as a non-invasive biomarker for detecting Alzheimer’s disease in a consecutive clinical cohort. Further studies should validate these findings in larger, more diverse populations and explore the integration of hyperspectral imaging with other diagnostic modalities. Limited sample size and imaging constraints highlight the need for validation in diverse clinical settings.

Trial registration ClinicalTrials.gov, ID: NCT05604183 (registration date: 2022-10-27).

Inadequate sleep in older adults is linked to health issues such as frailty, cognitive impairment, and cardiovascular disorders. Maintaining regular sleep patterns is important for healthy aging, making effective sleep monitoring essential. While polysomnography (PSG) is the gold standard for diagnosing sleep disorders, its regular use in home settings is limited. Alternative objective monitoring methods in the home can offer insights into natural sleep patterns and factors affecting them without the limitations of PSG.

This scoping review aims to examine current technologies, sensors, and sleep parameters used for home-based sleep monitoring in older adults. It also aims to explore various predictors and outcomes associated with sleep to understand the factors of sleep monitoring at home. 

We identified 54 relevant articles using PubMed, Scopus, Web of Science, and an AI tool (Research Rabbit), with 48 studies using wearable technologies and eight studies using non-wearable technologies. Further, six types of sensors were utilized. The most common technology employed was actigraphy wearables, while ballistocardiography and electroencephalography were less common. The most frequent objective parameters of sleep measured were Total Sleep Time (TST), Wakeup After Sleep Onset (WASO), and Sleep Efficiency (SE), with only six studies evaluating sleep architecture in terms of sleep stages. Additionally, six categories of predictors and outcomes associated with sleep were analyzed, including Health-related, Environmental, Interventional, Behavioral, Time and Place, and Social associations. These associations correlate with TST, WASO, and SE and include in-bed behaviors, exterior housing conditions, aerobic exercise, living place, relationship status, and seasonal thermal environments.