Objective: This study, part of a broader project on digital life stories in dementia care, aimed to describe interactions and communication between healthcare professionals and people living with dementia in daycare settings when using digital life stories as a complement to or replacement for written ones.

Methods: With a focused ethnographic approach, 27 observations were conducted in two daycare centres in two municipalities in southern Sweden. The data were analysed in consecutive steps, according to Hammersley and Atkinson.

Results: The observer-identified findings are reported in the following themes: Collaborative engagement; Relational communication, with sub-themes of Verbal communication with visual aids and Non-verbal communication; and Social togetherness.

Conclusion: Digital life stories promote symmetrical, emotionally meaningful interactions by using pictures and profile content to elicit reminiscence and social connection. Interaction quality varies with technological familiarity and group dynamics, underscoring the need for adaptable use. Short, flexible sessions support fluctuating attention, energy, and emotional responses in people living with dementia while enabling spontaneous engagement. Effective implementation requires brief training for healthcare professionals covering technical use, communication strategies, person-centred approaches, and ethical considerations. Portable access and family involvement further strengthen continuity and reduce barriers to integrating digital life stories into routine care.

Background: Support, Monitoring, and Reminder Technology for Mild Dementia (SMART4MD), a customized tablet app, was developed to improve or maintain the quality of life of people with mild cognitive impairment (MCI) and their informal caregivers.

Objective: This study conducts an 18-month economic evaluation of the SMART4MD app, in addition to standard care, compared with standard care alone in Sweden and Spain, from a health care provider perspective.

Methods: In a pragmatic randomized controlled trial, people with MCI and their informal caregivers were randomized to the intervention and control groups. Health care costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs) were measured in 345 Swedish peoplewith MCI and their informal caregivers, and in 347 Spanish peoplewith MCI.

Results: The analysis showed higher incremental costs and lower QALYs for Swedish people with MCI than for controls, whereas higher incremental costs and higher QALYs were observed for Spanish people with MCI. The intervention was not found to be cost-effective for Swedish informal caregivers, with an ICER of & euro;78,000/QALY (& euro;1=US $1.16).

Conclusions: The differing findings regarding cost-effectiveness for people with MCI in Sweden and Spain highlight the need for further research with extended follow-up, ideally involving a larger sample size and conducted across different national contexts.

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

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.