Cognition, hallucination severity and hallucination-specific insight in neurodegenerative disorders and eye disease.

Introduction: Hallucinations occur across neurodegenerative disorders, with increasing severity, poorer cognition and impaired hallucination-specific insight associated with worse outcomes and faster disease progression. It remains unclear how changes in cognition, temporal aspects of hallucinations, hallucination-specific insight and distress relate to each other.Methods: Extant samples of patients experiencing visual hallucinations were included in the analyses: Parkinson's Disease (n = 103), Parkinson's Disease Dementia (n = 41), Dementia with Lewy Bodies (n = 27) and Eye Disease (n = 113). We explored the relationship between factors of interest with Spearman's correlations and random-effect linear models.Results: Spearman's correlation analyses at the whole-group level showed that higher hallucination-specific insight was related to higher MMSE score (rs = 0.39, p < 0.001) and less severe hallucinations (rs = -0.28, p < .01). Linear mixed-models controlling for diagnostic group showed that insight was related to higher MMSE (p < .001), to hallucination severity (p = 0.003), and to VH duration (p = 0.04). Interestingly, insight was linked to the distress component but not the frequency component of severity. No significant relationship was found between MMSE and hallucination severity in these analyses.Conclusion: Our findings highlight the importance of hallucination-specific insight, distress and duration across groups. A better understanding of the role these factors play in VH may help with the development of future therapeutic interventions trans-diagnostically.

Visual hallucinations in neurological and ophthalmological disease: pathophysiology and management.

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.

Re-fixation and perseveration patterns in neglect patients during free visual exploration.

The literature suggests that neglect patients not only show impairments in directing attention toward the left, contralesional space, but also present with perseverative behavior. Moreover, previous studies described re-fixations during visual search tasks, and interpreted this finding as an impairment of spatial working memory. The aim of the present study was to study re-fixations and perseverations (i.e., recurrent re-fixations to same locations) during free visual exploration, a task with high ecological validity. We hypothesized that: (1) neglect patient would perform re-fixations more frequently than healthy controls within the right hemispace; and, (2) the re-fixation behavior of neglect patients would be characterized by perseverative fixations. To test these hypotheses, we assessed 22 neglect patients and 23 healthy controls, measuring their eye movements during free exploration of naturalistic pictures. The results showed that neglect patients tend to re-fixate locations within the ipsilesional hemispace when they freely explore naturalistic pictures. Importantly, the saliency of discrete locations within the pictures has a stronger influence on fixation behavior within the contralesional than within the ipsilesional hemispace in neglect patients. Finally, the results indicated that, for re-fixations, saliency plays a more important role within the contralesional than the ipsilesional hemispace. Moreover, we found evidence that re-fixation behavior of neglect patients is characterized by frequent recurrent re-fixations back to the same spatial locations which may be interpreted as perseverations. Hence, with the present study, we could better elucidate the mechanism leading to re-fixations and perseverative behavior during free visual exploration in neglect patients.

Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults.

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.

Long-Term Home-Monitoring Sensor Technology in Patients with Parkinson's Disease-Acceptance and Adherence.

Parkinson's disease (PD) is characterized by a highly individual disease-profile as well as fluctuating symptoms. Consequently, 24-h home monitoring in a real-world environment would be an ideal solution for precise symptom diagnostics. In recent years, small lightweight sensors which have assisted in objective, reliable analysis of motor symptoms have attracted a lot of attention. While technical advances are important, patient acceptance of such new systems is just as crucial to increase long-term adherence. So far, there has been a lack of long-term evaluations of PD-patient sensor adherence and acceptance. In a pilot study of PD patients (N = 4), adherence (wearing time) and acceptance (questionnaires) of a multi-part sensor set was evaluated over a 4-week timespan. The evaluated sensor set consisted of 3 body-worn sensors and 7 at-home installed ambient sensors. After one month of continuous monitoring, the overall system usability scale (SUS)-questionnaire score was 71.5%, with an average acceptance score of 87% for the body-worn sensors and 100% for the ambient sensors. On average, sensors were worn 15 h and 4 min per day. All patients reported strong preferences of the sensor set over manual self-reporting methods. Our results coincide with measured high adherence and acceptance rate of similar short-term studies and extend them to long-term monitoring.

Visual Hallucinations in Eye Disease and Lewy Body Disease.

OBJECTIVES: Visual hallucinations (VH) most commonly occur in eye disease (ED), Parkinson disease (PD), and Lewy body dementia (LBD). The phenomenology of VH is likely to carry important information about the brain areas within the visual system generating them. METHODS: Data from five controlled cross-sectional VH studies (164 controls, 135 ED, 156 PD, 79 [PDD 48 + DLB 31] LBD) were combined and analyzed. The prevalence, phenomenology, frequency, duration, and contents of VH were compared across diseases and sex. RESULTS: Simple VH were most common in ED patients (ED 65% versus LBD 22% versus PD 9%, χ(2) = 31.43, df = 2, p < 0.001), whereas complex VH were more common in LBD (LBD 76% versus ED 38%, versus PD 28%, χ(2) = 96.80, df = 2, p < 0.001). The phenomenology of complex VH was different across diseases and sex. ED patients reported more "flowers" (ED 21% versus LBD 6% versus PD 0%, χ(2) = 10.04, df = 2, p = 0.005) and "body parts" (ED 40% versus LBD 17% versus PD 13%, χ(2) = 11.14, df = 2, p = 0.004); in contrast, LBD patients reported "people" (LBD 85% versus ED 67% versus PD 63%, χ(2) = 6.20, df = 2, p = 0.045) and "animals/insects" (LBD 50% versus PD 42% versus ED 21%, χ(2) = 9.76, df = 2, p = 0.008). Men reported more "machines" (13% versus 2%, χ(2) = 6.94, df = 1, p = 0.008), whereas women reported more "family members/children" (48% versus 29%, χ(2) = 5.10, df = 1, p = 0.024). CONCLUSIONS: The phenomenology of VH is likely related to disease-specific dysfunctions within the visual system and to past, personal experiences.

Patient and Informant Views on Visual Hallucinations in Parkinson Disease.

OBJECTIVE: Visual hallucinations (VHs) are a very personal experience, and it is not clear whether information about them is best provided by informants or patients. Some patients may not share their hallucinatory experiences with caregivers to avoid distress or for fear of being labeled insane, and others do not have informants at all, which limits the use of informant-based questionnaires. The aim of this study was to compare patient and caregiver views about VHs in Parkinson disease (PD), using the North-East Visual Hallucinations Interview (NEVHI). METHODS: Fifty-nine PD patient-informant pairs were included. PD patients and informants were interviewed separately about VHs using the NEVHI. Informants were additionally interviewed using the four-item version of the Neuropsychiatric Inventory. Inter-reliability and concurrent validity of the different measures were compared. RESULTS: VHs were more commonly reported by patients than informants. The inter-rater agreement between NEVHI-patient and NEVHI-informant was moderate for complex VHs (Cohen's kappa = 0.44; 95% confidence interval [CI]: 0.13-0.75; t = 3.43, df = 58, p = 0.001) and feeling of presence (Cohen's kappa = 0.35; 95% CI: 0.00-0.70; t = 2.75, df = 58, p = 0.006), but agreement was poor for illusions (Cohen's kappa = 0.25; 95% CI: -0.07-0.57; t = 2.36, df = 58, p = 0.018) and passage hallucinations (Cohen's kappa = 0.16; 95% CI: -0.04-0.36; t = 2.26, df = 58, p = 0.024). CONCLUSION: When assessing VHs in PD patients, it is best to rely on patient information, because not all patients share the details of their hallucinations with their caregivers.

Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers.

BACKGROUND: Activities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data. METHODS: A wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL. RESULTS: Out of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB. CONCLUSIONS: The performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.

Age-dependent visual exploration during simulated day- and night driving on a motorway: a cross-sectional study.

BACKGROUND: Central and peripheral vision is needed for object detection. Previous research has shown that visual target detection is affected by age. In addition, light conditions also influence visual exploration. The aim of the study was to investigate the effects of age and different light conditions on visual exploration behavior and on driving performance during simulated driving. METHODS: A fixed-base simulator with 180 degree field of view was used to simulate a motorway route under daylight and night conditions to test 29 young subjects (25-40 years) and 27 older subjects (65-78 years). Drivers' eye fixations were analyzed and assigned to regions of interests (ROI) such as street, road signs, car ahead, environment, rear view mirror, side mirror left, side mirror right, incoming car, parked car, road repair. In addition, lane-keeping and driving speed were analyzed as a measure of driving performance. RESULTS: Older drivers had longer fixations on the task relevant ROI, but had a lower frequency of checking mirrors when compared to younger drivers. In both age groups, night driving led to a less fixations on the mirror. At the performance level, older drivers showed more variation in driving speed and lane-keeping behavior, which was especially prominent at night. In younger drivers, night driving had no impact on driving speed or lane-keeping behavior. CONCLUSIONS: Older drivers' visual exploration behavior are more fixed on the task relevant ROI, especially at night, when driving performance becomes more heterogeneous than in younger drivers.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data.

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.

A web-based non-intrusive ambient system to measure and classify activities of daily living.

BACKGROUND: The number of older adults in the global population is increasing. This demographic shift leads to an increasing prevalence of age-associated disorders, such as Alzheimer's disease and other types of dementia. With the progression of the disease, the risk for institutional care increases, which contrasts with the desire of most patients to stay in their home environment. Despite doctors' and caregivers' awareness of the patient's cognitive status, they are often uncertain about its consequences on activities of daily living (ADL). To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline. The occurrence, performance, and duration of different ADL are important indicators of functional ability. The patient's ability to cope with these activities is traditionally assessed with questionnaires, which has disadvantages (eg, lack of reliability and sensitivity). Several groups have proposed sensor-based systems to recognize and quantify these activities in the patient's home. Combined with Web technology, these systems can inform caregivers about their patients in real-time (e.g., via smartphone). OBJECTIVE: We hypothesize that a non-intrusive system, which does not use body-mounted sensors, video-based imaging, and microphone recordings would be better suited for use in dementia patients. Since it does not require patient's attention and compliance, such a system might be well accepted by patients. We present a passive, Web-based, non-intrusive, assistive technology system that recognizes and classifies ADL. METHODS: The components of this novel assistive technology system were wireless sensors distributed in every room of the participant's home and a central computer unit (CCU). The environmental data were acquired for 20 days (per participant) and then stored and processed on the CCU. In consultation with medical experts, eight ADL were classified. RESULTS: In this study, 10 healthy participants (6 women, 4 men; mean age 48.8 years; SD 20.0 years; age range 28-79 years) were included. For explorative purposes, one female Alzheimer patient (Montreal Cognitive Assessment score=23, Timed Up and Go=19.8 seconds, Trail Making Test A=84.3 seconds, Trail Making Test B=146 seconds) was measured in parallel with the healthy subjects. In total, 1317 ADL were performed by the participants, 1211 ADL were classified correctly, and 106 ADL were missed. This led to an overall sensitivity of 91.27% and a specificity of 92.52%. Each subject performed an average of 134.8 ADL (SD 75). CONCLUSIONS: The non-intrusive wireless sensor system can acquire environmental data essential for the classification of activities of daily living. By analyzing retrieved data, it is possible to distinguish and assign data patterns to subjects' specific activities and to identify eight different activities in daily living. The Web-based technology allows the system to improve care and provides valuable information about the patient in real-time.

Tablet-Based Puzzle Game Intervention for Cognitive Function and Well-Being in Healthy Adults: Pilot Feasibility Randomized Controlled Trial.

Background: 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). Objective: 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. Methods: 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. Results: 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. Conclusions: 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.

A Sensor-Driven Visit Detection System in Older Adults' Homes: Towards Digital Late-Life Depression Marker Extraction.

Modern sensor technology is increasingly used in older adults to not only provide additional safety but also to monitor health status, often by means of sensor derived digital measures or biomarkers. Social isolation is a known risk factor for late-life depression, and a potential component of social-isolation is the lack of home visits. Therefore, home visits may serve as a digital measure for social isolation and late-life depression. Late-life depression is a common mental and emotional disorder in the growing population of older adults. The disorder, if untreated, can significantly decrease quality of life and, amongst other effects, leads to increased mortality. Late-life depression often goes undiagnosed due to associated stigma and the incorrect assumption that it is a normal part of ageing. In this work, we propose a visit detection system that generalizes well to previously unseen apartments - which may differ largely in layout, sensor placement, and size from apartments found in the semi-annotated training dataset. We find that by using a self-training-based domain adaptation strategy, a robust system to extract home visit information can be built (ROC AUC = 0.773). We further show that the resulting visit information correlates well with the common geriatric depression scale screening tool ( ρ = -0.87, p = 0.001), providing further support for the idea of utilizing the extracted information as a potential digital measure or even as a digital biomarker to monitor the risk of late-life depression.

A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust.

Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person's activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.

Clinical outcome measures in dementia with Lewy bodies trials: critique and recommendations.

The selection of appropriate outcome measures is fundamental to the design of any successful clinical trial. Although dementia with Lewy bodies (DLB) is one of the most common neurodegenerative conditions, assessment of therapeutic benefit in clinical trials often relies on tools developed for other conditions, such as Alzheimer's or Parkinson's disease. These may not be sufficiently valid or sensitive to treatment changes in DLB, decreasing their utility. In this review, we discuss the limitations and strengths of selected available tools used to measure DLB-associated outcomes in clinical trials and highlight the potential roles for more specific objective measures. We emphasize that the existing outcome measures require validation in the DLB population and that DLB-specific outcomes need to be developed. Finally, we highlight how the selection of outcome measures may vary between symptomatic and disease-modifying therapy trials.

The role of dyadic cognitive report and subjective cognitive decline in early ADRD clinical research and trials: Current knowledge, gaps, and recommendations.

Efficient identification of cognitive decline and Alzheimer's disease (AD) risk in early stages of the AD disease continuum is a critical unmet need. Subjective cognitive decline is increasingly recognized as an early symptomatic stage of AD. Dyadic cognitive report, including subjective cognitive complaints (SCC) from a participant and an informant/study partner who knows the participant well, represents an accurate, reliable, and efficient source of data for assessing risk. However, the separate and combined contributions of self- and study partner report, and the dynamic relationship between the two, remains unclear. The Subjective Cognitive Decline Professional Interest Area within the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment convened a working group focused on dyadic patterns of subjective report. Group members identified aspects of dyadic-report information important to the AD research field, gaps in knowledge, and recommendations. By reviewing existing data on this topic, we found evidence that dyadic measures are associated with objective measures of cognition and provide unique information in preclinical and prodromal AD about disease stage and progression and AD biomarker status. External factors including dyad (participant-study partner pair) relationship and sociocultural factors contribute to these associations. We recommend greater dyad report use in research settings to identify AD risk. Priority areas for future research include (1) elucidation of the contributions of demographic and sociocultural factors, dyad type, and dyad relationship to dyad report; (2) exploration of agreement and discordance between self- and study partner report across the AD syndromic and disease continuum; (3) identification of domains (e.g., memory, executive function, neuropsychiatric) that predict AD risk outcomes and differentiate cognitive impairment due to AD from other impairment; (4) development of best practices for study partner engagement; (5) exploration of study partner report as AD clinical trial endpoints; (6) continued development, validation, and optimization, of study partner report instruments tailored to the goals of the research and population.

Application of Eye Tracking in Puzzle Games for Adjunct Cognitive Markers: Pilot Observational Study in Older Adults.

BACKGROUND: Recent studies suggest that computerized puzzle games are enjoyable, easy to play, and engage attentional, visuospatial, and executive functions. They may help mediate impairments seen in cognitive decline in addition to being an assessment tool. Eye tracking provides a quantitative and qualitative analysis of gaze, which is highly useful in understanding visual search behavior. OBJECTIVE: The goal of the research was to test the feasibility of eye tracking during a puzzle game and develop adjunct markers for cognitive performance using eye-tracking metrics. METHODS: A desktop version of the Match-3 puzzle game with 15 difficulty levels was developed using Unity 3D (Unity Technologies). The goal of the Match-3 puzzle was to find configurations (target patterns) that could be turned into a row of 3 identical game objects (tiles) by swapping 2 adjacent tiles. Difficulty levels were created by manipulating the puzzle board size (all combinations of width and height from 4 to 8) and the number of unique tiles on the puzzle board (from 4 to 8). Each level consisted of 4 boards (ie, target patterns to match) with one target pattern each. In this study, the desktop version was presented on a laptop computer setup with eye tracking. Healthy older subjects were recruited to play a full set of 15 puzzle levels. A paper-pencil-based assessment battery was administered prior to the Match-3 game. The gaze behavior of all participants was recorded during the game. Correlation analyses were performed on eye-tracking data correcting for age to examine if gaze behavior pertains to target patterns and distractor patterns and changes with puzzle board size (set size). Additionally, correlations between cognitive performance and eye movement metrics were calculated. RESULTS: A total of 13 healthy older subjects (mean age 70.67 [SD 4.75] years; range 63 to 80 years) participated in this study. In total, 3 training and 12 test levels were played by the participants. Eye tracking recorded 672 fixations in total, 525 fixations on distractor patterns and 99 fixations on target patterns. Significant correlations were found between executive functions (Trail Making Test B) and number of fixations on distractor patterns (P=.01) and average fixations (P=.005). CONCLUSIONS: Overall, this study shows that eye tracking in puzzle games can act as a supplemental source of data for cognitive performance. The relationship between a paper-pencil test for executive functions and fixations confirms that both are related to the same cognitive processes. Therefore, eye movement metrics might be used as an adjunct marker for cognitive abilities like executive functions. However, further research is needed to evaluate the potential of the various eye movement metrics in combination with puzzle games as visual search and attentional marker.

Case Report: Ambient Sensor Signals as Digital Biomarkers for Early Signs of Heart Failure Decompensation.

Home monitoring systems are increasingly used to monitor seniors in their apartments for detection of emergency situations. More recently, multimodal ambient sensor systems are also used to monitor digital biomarkers to detect clinically relevant health problems over longer time periods. Clinical signs of HF decompensation including increase of heart rate and respiration rate, decreased physical activity, reduced gait speed, increasing toilet use at night and deterioration of sleep quality have a great potential to be detected by non-intrusive contactless ambient sensor systems and negative changes of these parameters may be used to prevent further deterioration and hospitalization for HF decompensation. This is to our knowledge the first report about the potential of an affordable, contactless, and unobtrusive ambient sensor system for the detection of early signs of HF decompensation based on data with prospective data acquisition and retrospective correlation of the data with clinical events in a 91 year old senior with a serious heart problem over 1 year. The ambient sensor system detected an increase of respiration rate, heart rate, toilet use at night, toss, and turns in bed and a decrease of physical activity weeks before the decompensation. In view of the rapidly increasing prevalence of HF and the related costs for the health care systems and the societies, the real potential of our approach should be evaluated in larger populations of HF patients.

Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study.

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.