Explore the vast range of titles available.

After prolonged stay in an intensive care unit (ICU) patients often complain about cognitive impairments that affect health-related quality of life after discharge. The aim of this proof-of-concept study was to test the feasibility and effects of controlled visual and acoustic stimulation in a virtual reality (VR) setup in the ICU. The VR setup consisted of a head-mounted display in combination with an eye tracker and sensors to assess vital signs. The stimulation consisted of videos featuring natural scenes and was tested in 37 healthy participants in the ICU. The VR stimulation led to a reduction of heart rate (p = 0. 049) and blood pressure (p = 0.044). Fixation/saccade ratio (p < 0.001) was increased when a visual target was presented superimposed on the videos (reduced search activity), reflecting enhanced visual processing. Overall, the VR stimulation had a relaxing effect as shown in vital markers of physical stress and participants explored less when attending the target. Our study indicates that VR stimulation in ICU settings is feasible and beneficial for critically ill patients.

Visual hallucinations are common in older people and are especially associated with ophthalmological and neurological disorders, including dementia and Parkinson’s disease. Uncertainties remain whether there is a single underlying mechanism for visual hallucinations or they have different disease-dependent causes. However, irrespective of mechanism, visual hallucinations are difficult to treat. The National Institute for Health Research (NIHR) funded a research programme to investigate visual hallucinations in the key and high burden areas of eye disease, dementia and Parkinson’s disease, culminating in a workshop to develop a unified framework for their clinical management. Here we summarise the evidence base, current practice and consensus guidelines that emerged from the workshop.Irrespective of clinical condition, case ascertainment strategies are required to overcome reporting stigma. Once hallucinations are identified, physical, cognitive and ophthalmological health should be reviewed, with education and self-help techniques provided. Not all hallucinations require intervention but for those that are clinically significant, current evidence supports pharmacological modification of cholinergic, GABAergic, serotonergic or dopaminergic systems, or reduction of cortical excitability. A broad treatment perspective is needed, including carer support. Despite their frequency and clinical significance, there is a paucity of randomised, placebo-controlled clinical trial evidence where the primary outcome is an improvement in visual hallucinations. Key areas for future research include the development of valid and reliable assessment tools for use in mechanistic studies and clinical trials, transdiagnostic studies of shared and distinct mechanisms and when and how to treat visual hallucinations.

Abstract Hallucinations can occur in different sensory modalities, both simultaneously and serially in time. They have typically been studied in clinical populations as phenomena occurring in a single sensory modality. Hallucinatory experiences occurring in multiple sensory systems—multimodal hallucinations (MMHs)—are more prevalent than previously thought and may have greater adverse impact than unimodal ones, but they remain relatively underresearched. Here, we review and discuss: (1) the definition and categorization of both serial and simultaneous MMHs, (2) available assessment tools and how they can be improved, and (3) the explanatory power that current hallucination theories have for MMHs. Overall, we suggest that current models need to be updated or developed to account for MMHs and to inform research into the underlying processes of such hallucinatory phenomena. We make recommendations for future research and for clinical practice, including the need for service user involvement and for better assessment tools that can reliably measure MMHs and distinguish them from other related phenomena.

Smart homes for the aging population have recently started attracting the attention of the research community. The “health state” of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor “AIDE-MOI” was developed. “AIDE-MOI” senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse > 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was split-up into one-minute chunks for labelling as “fall” or “non-fall”. The sensitivity and specificity of the threshold-based algorithm improved significantly from 27.3% to 80.0% and 99.9957% (0.43) to 99.9978% (0.17 false alarms per week and subject), respectively.

There is currently a need for engaging, user-friendly, and repeatable tasks for assessment of cognitive and motor function in aging and neurodegenerative diseases. This study evaluated the feasibility of a maze-like Numberlink puzzle game in assessing differences in game-based measures of cognition and motor function due to age and neurodegenerative diseases. Fifty-five participants, including young (18-31 years, = 18), older (64-79 years, = 14), and oldest adults (86-98 years, = 14), and patients with Parkinson's (59-76 years, = 4) and Huntington's disease (HD; 35-66 years, = 5) played different difficulty levels of the Numberlink puzzle game and completed usability questionnaires and tests for psychomotor, attentional, visuospatial, and constructional and executive function. Analyses of Numberlink game-based cognitive (solving time and errors) and motor [mean velocity and movement direction changes (MDC)] performance metrics revealed statistically significant differences between age groups and between patients with HD and older adults. However, patients with Parkinson's disease (PD) did not differ from older adults. Correlational analyses showed significant associations between game-based performance and movement metrics and performance on neuropsychological tests for psychomotor, attentional, visuospatial, and constructional and executive function. Furthermore, varying characteristics of the Numberlink puzzle game succeeded in creating graded difficulty levels. Findings from this study support recent suggestions that data from a maze-like puzzle game provide potential \"digital biomarkers\" to assess changes in psychomotor, visuoconstructional, and executive function related to aging and neurodegeneration. In particular, game-based movement measures from the maze-like puzzle Numberlink games are promising as a tool to monitor the progression of motor impairment in neurodegenerative diseases. Further studies are needed to more comprehensively establish the cognitive validity and test-retest reliability of using Numberlink puzzles as a valid cognitive assessment tool.

Background: Population aging is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased health care expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults. Objective: In this work we aim to evaluate which contactlessly measurable sleep parameter is best suited to monitor perceived and actual health status changes in older adults. Methods: We analyzed real-world longitudinal (up to 1 year) data from 37 community-dwelling older adults including more than 6000 nights of measured sleep. Sleep parameters were recorded by a pressure sensor placed beneath the mattress, and corresponding health status information was acquired through weekly questionnaires and reports by health care personnel. A total of 20 sleep parameters were analyzed, including common sleep metrics such as sleep efficiency, sleep onset delay, and sleep stages but also vital signs in the form of heart and breathing rate as well as movements in bed. Association with self-reported health, evaluated by EuroQol visual analog scale (EQ-VAS) ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to objective, real-world health incidents was investigated through manual retrospective case-by-case analysis. Results: Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed—measured by the number toss-and-turn events—as the most predictive sleep parameter (t score=–0.435, P value [adj]=<.001). Case-by-case analysis further substantiated this finding, showing that increases in number of body movements could often be explained by reported health incidents. Real world incidents included heart failure, hypertension, abdominal tumor, seasonal flu, gastrointestinal problems, and urinary tract infection. Conclusions: Our results suggest that nightly body movements in bed could potentially be a highly relevant as well as easy to interpret and derive digital biomarker to monitor a wide range of health deteriorations in older adults. As such, it could help in detecting health deteriorations early on and provide timelier, more personalized, and precise treatment options.

Isometric strength measures and timed up and go (TUG) tests are both recognized as valuable tools for fall prediction in older adults. However, results from direct comparison of these two tests are lacking. We aimed to assess the potential of isometric strength measures and the different modalities of the TUG test to detect individuals at risk of falling. This is a prospective cohort study including 24 community-dwelling older adults (≥65 years, 19 females, 88±7 years). Participants performed three variations of the TUG test (standard, counting and holding a full cup) and three isometric strength tests (handgrip, knee extension and hip flexion) at several time points (at baseline and every ~6 weeks) during a one-year follow-up. The association between these tests and the incidence of falls during the follow-up was assessed. Twelve participants out of 24 participants experienced falls during the follow-up. Fallers showed a significantly lower handgrip strength (-5.7 kg, 95% confidence interval: -10.4 to -1.1, p=0.019) and knee extension strength (-4.9 kg, -9.6 to -0.2, p=0.042) at follow-up, while no significant differences were found for any TUG variation. Handgrip and knee extension strength measures - particularly when assessed regularly over time - have the potential to serve as a simple and easy tool for detecting individuals at risk of falling as compared to functional mobility measures (ie, TUG test).

Promoting cognitive health is key to maintaining cognitive and everyday functions and preventing the risk of cognitive impairment or dementia. Existing scientific evidence shows the benefits of various training modalities on cognition. One way to promote cognitive health is through engagement in cognitive activities (eg, board and video games). This study aims to investigate the benefits of dynamic adaptive casual puzzle games on cognitive function and well-being in healthy adults and older people. A total of 12 adults and older people (female participants: n=6; mean age 58.92, SD 10.28 years; range 46-75 years) were included in this pilot randomized controlled trial. This study used a crossover design with two phases (8 weeks each) and three measurement waves (pretest, midtest, and posttest). The participants were randomly allocated either to the control or experimental group. In the control group, participants read newspapers between the pre- and midtest, then switched to cognitive training with puzzle games. In the experimental group, the interventions were reversed. Baseline measurements (pretest) were collected before the intervention. The interventions were delivered on tablet computers and took place unsupervised at participants' homes. The outcome measures included global cognitive function, higher cognitive function, and emotional well-being at 3 time points (pretest, midtest, and posttest) using standardized neuropsychological tests. The participants showed improvements in their visual attention and visuospatial measures after the puzzle game intervention. The study showed that digital games are a feasible way to train cognition in healthy adults and older people. The algorithm-based dynamic adaption allows accommodations for persons with different cognitive levels of skill. The results of the study will guide future prevention efforts and trials in high-risk populations.

IntroductionAphasia is a common language disorder acquired after stroke that reduces the quality of life of affected patients. The impairment is frequently accompanied by a deficit in cognitive functions. The state-of-the-art therapy is speech and language therapy but recent findings highlight positive effects of high-frequency therapy. Telerehabilitation has the potential to enable high-frequency therapy for patients at home. This study investigates the effects of high-frequency telerehabilitation speech and language therapy (teleSLT) on language functions in outpatients with aphasia compared with telerehabilitative cognitive training. We hypothesise that patients training with high-frequency teleSLT will show higher improvement in language functions and quality of life compared with patients with high-frequency tele-rehabilitative cognitive training (teleCT).Methods and analysisThis study is a randomised controlled, evaluator-blinded multicentre superiority trial comparing the outcomes following either high-frequency teleSLT or teleCT. A total of 100 outpatients with aphasia will be recruited and assigned in a 1:1 ratio stratified by trial site and severity of impairment to one of two parallel groups. Both groups will train over a period of 4 weeks for 2 hours per day. Patients in the experimental condition will devote 80% of their training time to teleSLT and the remaining 20% (24 min/day) to teleCT, vice versa for patients in the control condition. The primary outcome measure is the understandability of verbal communication on the Amsterdam Nijmegen Everyday Language Test and secondary outcome measures are intelligibility of the verbal communication, impairment of receptive and expressive language functions, confrontation naming. Other outcomes measures are quality of life and acceptance (usability and subjective experience) of the teleSLT system.Ethics and disseminationThis study is approved by the Ethics Committee Bern (ID 2016-01577). Results will be submitted to a peer-reviewed journal.Trial registration numberNCT03228264.