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: 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 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.

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. © 2022

Background: Early diagnosis of cognitive disorders is becoming increasingly important. Limited resources for specialist assessment and an increasing demographical challenge warrants the need for efficient methods of evaluation. In response, CoGNIT, a tablet app for automatic, standardized, and efficient assessment of cognitive function, was developed. Included tests span the cognitive domains regarded as important for assessment in a general memory clinic (memory, language, psychomotor speed, executive function, attention, visuospatial ability, manual dexterity, and symptoms of depression). Objective: The aim of this study was to assess the feasibility of automatic cognitive testing with CoGNIT in older patients with symptoms of mild cognitive impairment (MCI). Methods: Patients older than 55 years with symptoms of MCI (n=36) were recruited at the research clinic at the Blekinge Institute of Technology (BTH), Karlskrona, Sweden. A research nurse administered the Mini-Mental State Exam (MMSE) and the CoGNIT app on a tablet computer. Technical and testing issues were documented. Results: The test battery was completed by all 36 patients. One test, the four-finger-tapping test, was performed incorrectly by 42% of the patients. Issues regarding clarity of instructions were found in 2 tests (block design test and the one finger-tapping test). Minor software bugs were identified. Conclusions: The overall feasibility of automatic cognitive testing with the CoGNIT app in patients with symptoms of MCI was good. The study highlighted tests that did not function optimally. The four-finger-tapping test will be discarded, and minor improvements to the software will be added before further studies and deployment in the clinic. © 2022 JMIR Publications Inc.. All right reserved.

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, and 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 logistic regression.

Results: Our study found that older adults (60 years+) have a good mean sleep health score of 7.40 (SD = 2.03). We found that males showed significantly better sleep satisfaction (66.5%), timing (90.8%), and duration (78.4%) than females, while females have significantly better alertness during the day (59.4%). 60 - 74 years old experienced significantly better alertness and sleep duration than those over 85 years. Technology Enthusiasm (TE) and digital social participation (DSP) are linked to better sleep, while technology anxiety (TA) is associated with poorer sleep health. Frequent internet use and engaging with screens before bedtime is significantly associated with better sleep health. The most common digital determinants of sleep dimensions were TA and DSP.

Conclusions: The study highlights both positive and negative effects of technology use on sleep health and encourages further research on this among older adults.