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. 

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. 

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: As the older population grows, so does the prevalence of cognitive impairment, emphasizing the importance of early diagnosis. The Mini-Mental State Examination (MMSE) is vital in identifying cognitive impairment. It is known that degraded oral health correlates with MMSE scores <= 26.

Objective: This study aims to explore the potential of using machine learning (ML) technologies using oral health and demographic examination data to predict the probability of having MMSE scores of 30 or <= 26 in Swedish individuals older than 60 years.

Methods: The study had a cross-sectional design. Baseline data from 2 longitudinal oral health and ongoing general health studies involving individuals older than 60 years were entered into ML models, including random forest, support vector machine, and CatBoost (CB) to classify MMSE scores as either 30 or <= 26, distinguishing between MMSE of 30 and MMSE <= 26 groups. Nested cross-validation (nCV) was used to mitigate overfitting. The best performance-giving model was further investigated for feature importance using Shapley additive explanation summary plots to easily visualize the contribution of each feature to the prediction output. The sample consisted of 693 individuals (350 females and 343 males).

Results: All CB, random forest, and support vector machine models achieved high classification accuracies. However, CB exhibited superior performance with an average accuracy of 80.6% on the model using 3 x 3 nCV and surpassed the performance of other models. The Shapley additive explanation summary plot illustrates the impact of factors on the model's predictions, such as age, Plaque Index, probing pocket depth, a feeling of dry mouth, level of education, and use of dental hygiene tools for approximal cleaning.

Conclusions: The oral health parameters and demographic data used as inputs for ML classifiers contain sufficient information to differentiate between MMSE scores <= 26 and 30. This study suggests oral health parameters and ML techniques could offer a potential tool for screening MMSE scores for individuals aged 60 years and older. 

The higher education systems of Sweden and Pakistan differ significantly in structure, teaching practices, and educational culture, creating unique challenges for Pakistani teachers and students transitioning to Swedish universities. This study provides a comparative analysis of these higher education systems, focusing on their structures and teaching practices. Insights are drawn from interviews with Pakistani teachers who have experience studying and teaching in both countries.

These interviews offer a comparative view of teaching practices, assessment methods, and institutional cultures. The teachers share the challenges they faced when transitioning from teaching in Pakistan to Sweden and the strategies they employed to overcome these challenges. Additionally, the study highlights the challenges Pakistani students face in Swedish universities, such as cultural integration and academic adjustments. The teachers also recommended strategies to overcome the challenges students face during their studies in Swedish universities.

Comparing different education systems from structural aspects [1] or stakeholders' viewpoints [2] is not a new concept. Previous studies have compared Swedish and Pakistani educational systems [3, 4]; however, the unique context of identifying challenges specific to Pakistani teachers and students was not found in the literature. By identifying these challenges and strategies, this study aims to improve the educational experiences of Pakistani teachers and students in Sweden, fostering better academic integration and success. The findings offer practical insights and strategies based on the experiences of teachers who have navigated this transition. These recommendations provide valuable guidance for both individuals and institutions, with many strategies being implementable at the individual level. These insights facilitate smoother transitions, promote cultural integration, and support academic success in diverse educational environments.

 Doctoral supervision is a challenging process, and supervisors use multiple supervision styles to ensure the success of their doctoral students in their research and education. To provide valuable insights into the effectiveness of these supervision practices and help identify areas for improvement, it is important to understand how doctoral students perceive different aspects of supervision. This study aims to explore the perspectives of doctoral students regarding the supervision practices of their supervisors at the Blekinge Institute of Technology (BTH). The study employs a deductive approach with a cross-sectional study design. A survey questionnaire was sent to the doctoral students at BTH. The survey included questions on the five facets of supervision based on the theoretical framework by Halse and Malfroy [1]: learning alliance, habits of mind, scholarly expertise, technè, and contextual expertise. The data collected was quantitative, and descriptive analysis was performed. The total number of respondents (N) was 51 (53.12%) out of 96 invited participants. Results indicated that while most students reported effective learning alliance, significant challenges were noted in fostering teamwork, providing constructive feedback, and offering technical guidance. Specific departments, such as the Department of Computer Science (DIDA) and the Department of Health (TIHA), reported more substantial challenges across multiple facets of supervision. In conclusion, while the overall perception of supervision at BTH is positive, there is a clear need for targeted interventions to address the identified weaknesses and enhance the quality of doctoral education. This information can help strengthen the quality of the doctoral programs at BTH.

Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K-nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE. © 2024 IEEE.

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 (). 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.

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 <75<75 years, and having a higher education are associated with high eHealth literacy (𝑝<0.05p<0.05). eHealth literacy is significantly correlated (𝜏τ=0.12, p-value=0.002) and associated with perceived health status (𝛽β=0.39, p-value=0.008). It is also significantly correlated (𝜏τ=-0.12, p-value=0.001) and associated with psychological distress (𝛽β=-0.14, p-value=0.002). The interaction of eHealth literacy and good perceived health status reduced psychological distress (𝛽β=-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.