Development of an Open-source and Lightweight Sensor Recording Software System for Conducting Biomedical Research: Technical Report.

BACKGROUND: Digital sensing devices have become an increasingly important component of modern biomedical research, as they help provide objective insights into individuals' everyday behavior in terms of changes in motor and nonmotor symptoms. However, there are significant barriers to the adoption of sensor-enhanced biomedical solutions in terms of both technical expertise and associated costs. The currently available solutions neither allow easy integration of custom sensing devices nor offer a practicable methodology in cases of limited resources. This has become particularly relevant, given the need for real-time sensor data that could help lower health care costs by reducing the frequency of clinical assessments performed by specialists and improve access to health assessments (eg, for people living in remote areas or older adults living at home). OBJECTIVE: The objective of this paper is to detail the end-to-end development of a novel sensor recording software system that supports the integration of heterogeneous sensor technologies, runs as an on-demand service on consumer-grade hardware to build sensor systems, and can be easily used to reliably record longitudinal sensor measurements in research settings. METHODS: The proposed software system is based on a server-client architecture, consisting of multiple self-contained microservices that communicated with each other (eg, the web server transfers data to a database instance) and were implemented as Docker containers. The design of the software is based on state-of-the-art open-source technologies (eg, Node.js or MongoDB), which fulfill nonfunctional requirements and reduce associated costs. A series of programs to facilitate the use of the software were documented. To demonstrate performance, the software was tested in 3 studies (2 gait studies and 1 behavioral study assessing activities of daily living) that ran between 2 and 225 days, with a total of 114 participants. We used descriptive statistics to evaluate longitudinal measurements for reliability, error rates, throughput rates, latency, and usability (with the System Usability Scale [SUS] and the Post-Study System Usability Questionnaire [PSSUQ]). RESULTS: Three qualitative features (event annotation program, sample delay analysis program, and monitoring dashboard) were elaborated and realized as integrated programs. Our quantitative findings demonstrate that the system operates reliably on consumer-grade hardware, even across multiple months (>420 days), providing high throughput (2000 requests per second) with a low latency and error rate (<0.002%). In addition, the results of the usability tests indicate that the system is effective, efficient, and satisfactory to use (mean usability ratings for the SUS and PSSUQ were 89.5 and 1.62, respectively). CONCLUSIONS: Overall, this sensor recording software could be leveraged to test sensor devices, as well as to develop and validate algorithms that are able to extract digital measures (eg, gait parameters or actigraphy). The proposed software could help significantly reduce barriers related to sensor-enhanced biomedical research and allow researchers to focus on the research questions at hand rather than on developing recording technologies.

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.

An Instrumented Apartment to Monitor Human Behavior: A Pilot Case Study in the NeuroTec Loft.

For patients suffering from neurodegenerative disorders, the behavior and activities of daily living are an indicator of a change in health status, and home-monitoring over a prolonged period of time by unobtrusive sensors is a promising technology to foster independent living and maintain quality of life. The aim of this pilot case study was the development of a multi-sensor system in an apartment to unobtrusively monitor patients at home during the day and night. The developed system is based on unobtrusive sensors using basic technologies and gold-standard medical devices measuring physiological (e.g., mobile electrocardiogram), movement (e.g., motion tracking system), and environmental parameters (e.g., temperature). The system was evaluated during one session by a healthy 32-year-old male, and results showed that the sensor system measured accurately during the participant's stay. Furthermore, the participant did not report any negative experiences. Overall, the multi-sensor system has great potential to bridge the gap between laboratories and older adults' homes and thus for a deep and novel understanding of human behavioral and neurological disorders. Finally, this new understanding could be utilized to develop new algorithms and sensor systems to address problems and increase the quality of life of our aging society and patients with neurological disorders.

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.

Contactless Gait Assessment in Home-like Environments.

Gait analysis is an important part of assessments for a variety of health conditions, specifically neurodegenerative diseases. Currently, most methods for gait assessment are based on manual scoring of certain tasks or restrictive technologies. We present an unobtrusive sensor system based on light detection and ranging sensor technology for use in home-like environments. In our evaluation, we compared six different gait parameters, based on recordings from 25 different people performing eight different walks each, resulting in 200 unique measurements. We compared the proposed sensor system against two state-of-the art technologies, a pressure mat and a set of inertial measurement unit sensors. In addition to test usability and long-term measurement, multi-hour recordings were conducted. Our evaluation showed very high correlation (r>0.95) with the gold standards across all assessed gait parameters except for cycle time (r=0.91). Similarly, the coefficient of determination was high (R2>0.9) for all gait parameters except cycle time. The highest correlation was achieved for stride length and velocity (r≥0.98,R2≥0.95). Furthermore, the multi-hour recordings did not show the systematic drift of measurements over time. Overall, the unobtrusive gait measurement system allows for contactless, highly accurate long- and short-term assessments of gait in home-like environments.

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.

Isometric Strength Measures are Superior to the Timed Up and Go Test for Fall Prediction in Older Adults: Results from a Prospective Cohort Study.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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).

Evaluation of 1-Year in-Home Monitoring Technology by Home-Dwelling Older Adults, Family Caregivers, and Nurses.

Introduction: Population aging is increasing the needs and costs of healthcare. Both frailty and the chronic diseases affecting older people reduce their ability to live independently. However, most older people prefer to age in their own homes. New development of in-home monitoring can play a role in staying independent, active, and healthy for older people. This 12-month observational study aimed to evaluate a new in-home monitoring system among home-dwelling older adults (OA), their family caregivers (FC), and nurses for the support of home care. Methods: The in-home monitoring system evaluated in this study continuously monitored OA's daily activities (e.g., mobility, sleep habits, fridge visits, door events) by ambient sensor system (DomoCare®) and health-related events by wearable sensors (Activity tracker, ECG). In the case of deviations in daily activities, alerts were transmitted to nurses via email. Using specific questionnaires, the opinions of 13 OA, 13 FC, and 20 nurses were collected at the end of 12-months follow-up focusing on user experience and the impact of in-home monitoring on home care services. Results: The majority of OA, FC, and nurses considered that in-home sensors can help with staying at home, improving home care and quality of life, preventing domestic accidents, and reducing family stress. The opinion tended to be more frequently favorable toward ambient sensors (76%; 95% CI: 61-87%) than toward wearable sensors (Activity tracker: 65%; 95% CI: 50-79%); ECG: 60%; 95% CI: 45-75%). On average, OA (74%; 95% CI: 46-95%) and FC (70%; 95% CI: 39-91%) tended to be more enthusiastic than nurses (60%; 95% CI: 36-81%). Some barriers reported by nurses were a fear of weakening of the relationship with OA and lack of time. Discussion/Conclusion: Overall, the opinions of OA, FC, and nurses were positively related to in-home sensors, with nurses being less enthusiastic about their use in clinical practice.

A Simple Two-Dimensional Location Embedding for Passive Infrared Motion-Sensing based Home Monitoring Applications.

Pervasive computing based home-monitoring has attracted increasing interest over the past years, especially regarding applications in the growing population of older adults. Applications include safety, monitoring chronic conditions like dementia, or providing preventive information about changes in health and behavior. Commonly used components of such systems are inexpensive and low-power passive infrared motion sensing units, usually placed in distinct locations of an older adult's apartment. To efficiently analyse the resulting data the majority of procedures expect the resulting sensor data to be encoded in a vector space. However, most common vector space encodings are based on orthogonal representations of the sensor locations and thus lead to loss of information as the sensors are placed in a 3D-space. In this work we introduce an embedding of sensor-locations in a 2D-space based on multidimensional scaling, without knowledge of the physical position of the sensors. We evaluate this embedding, using two different algorithms and compare it to commonly used baselines in different tasks. All evaluations are carried out on a real-world home-monitoring data-set.

Real-World Consumer-Grade Sensor Signal Alignment Procedure Applied to High-Noise ECG to BCG Signal Synchronization.

In recent years, consumer-grade sensors that measure health relevant physiological signals have become widely available and are increasingly used by consumers and researchers alike. While this allows for multiple novel, potentially highly beneficial, large-scale health monitoring applications, quality of these data streams is oftentimes suboptimal. This makes alignment of different high-frequency data streams from multiple, non-connected sensors, a difficult task. In this work we describe a noise-robust framework to align high-frequency signals from different sensors, that share some underlying characteristic, obtained in a free-living, non-clinical, home environment. We demonstrate the approach on the basis of a single-lead, medical-grade, mobile electrocardiography device and a consumer-grade sleep sensor that allows for ballistocardiography. Both commercially available sensors measure the physiological process of a heartbeat. We show, on the basis of real-world data with multiple people and sensors, that the two highly noisy and sometimes dissimilar signals could in most cases be aligned with considerable precision. As a result, we could reduce mean heartbeat peak-to-peak difference by 58.1% on average and increase signal correlation by 0.40 on average.

Potential of Ambient Sensor Systems for Early Detection of Health Problems in Older Adults.

Background: Home monitoring sensor systems are increasingly used to monitor seniors in their apartments for detection of emergency situations. The aim of this study was to deliver a proof-of-concept for the use of multimodal sensor systems with pervasive computing technology for the detection of clinically relevant health problems over longer time periods. Methods: Data were collected with a longitudinal home monitoring study in Switzerland (StrongAge Cohort Study) in a cohort of 24 old and oldest-old, community-dwelling adults over a period of 1 to 2 years. Physical activity in the apartment, toilet visits, refrigerator use, and entrance door openings were quantified using a commercially available passive infrared motion sensing system (Domosafety S.A., Switzerland). Heart rate, respiration rate, and sleep quality were recorded with the commercially available EMFIT QS bed sensor device (Emfit Ltd., Finland). Vital signs and contextual data were collected using a wearable sensor on the upper arm (Everion, Biovotion, Switzerland). Sensor data were correlated with health-related data collected from the weekly visits of the seniors by health professionals, including information about physical, psychological, cognitive, and behavior status, health problems, diseases, medication, and medical diagnoses. Results: Twenty of the 24 recruited participants (age 88.9 ± 7.5 years, 79% females) completed the study; two participants had to stop their study participation because of severe health deterioration, whereas two participants died during the course of the study. A history of chronic disease was present in 12/24 seniors, including heart failure, heart rhythm disturbances, pulmonary embolism, severe insulin-dependent diabetes, and Parkinson's disease. In total, 242,232 person-hours were recorded. During the monitoring period, 963 health status records were reported and repeated clinical assessments of aging-relevant indicators and outcomes were performed. Several episodes of health deterioration, including heart failure worsening and heart rhythm disturbances, could be captured by sensor signals from different sources. Conclusions: Our results indicate that monitoring of seniors with a multimodal sensor and pervasive computing system over longer time periods is feasible and well-accepted, with a great potential for detection of health deterioration. Further studies are necessary to evaluate the full range of the clinical potential of these findings.

Development and Evaluation of Maze-Like Puzzle Games to Assess Cognitive and Motor Function in Aging and Neurodegenerative Diseases.

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, n = 18), older (64-79 years, n = 14), and oldest adults (86-98 years, n = 14), and patients with Parkinson's (59-76 years, n = 4) and Huntington's disease (HD; 35-66 years, n = 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.

Wearable Based Calibration of Contactless In-home Motion Sensors for Physical Activity Monitoring in Community-Dwelling Older Adults.

Passive infrared motion sensors are commonly used in telemonitoring applications to monitor older community-dwelling adults at risk. One possible use case is quantification of in-home physical activity, a key factor and potential digital biomarker for healthy and independent aging. A major disadvantage of passive infrared sensors is their lack of performance and comparability in physical activity quantification. In this work, we calibrate passive infrared motion sensors for in-home physical activity quantification with simultaneously acquired data from wearable accelerometers and use the data to find a suitable correlation between in-home and out-of-home physical activity. We use data from 20 community-dwelling older adults that were simultaneously provided with wireless passive infrared motion sensors in their homes, and a wearable accelerometer for at least 60 days. We applied multiple calibration algorithms and evaluated results based on several statistical and clinical metrics. We found that using even relatively small amounts of wearable based ground-truth data over 7-14 days, passive infrared based wireless sensor systems can be calibrated to give largely better estimates of older adults' daily physical activity. This increase in performance translates directly to stronger correlations of measured physical activity levels with a variety of age relevant health indicators and outcomes known to be associated with physical activity.

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.

[New healthcare technologies to support home care of older people]. / De nouvelles technologies au service du maintien à domicile des personnes âgées.

The ageing of the Swiss population is increasing the healthcare needs and costs. Both frailty and chronic diseases affecting older people reduce their ability to live independently. However, the vast majority of older people want to continue living at home, while having a quality of life and receiving the best healthcare services. In this context, new connected healthcare technologies can be a relevant solution to facilitate home care of older people. In this article, we present the issues related to these technologies and, more particularly, to what extent they could contribute to home care of older people and be a benefit for patients and family caregivers, but also for physicians and other healthcare professionals. Finally, the fears and risks associated with these technologies, and the importance of scientifically assessing their usefulness are discussed.

Le vieillissement de la population suisse augmente les besoins et les coûts liés à la santé. La fragilité et les maladies chroniques touchant les personnes âgées diminuent leur capacité à vivre de façon autonome. Cependant, la grande majorité d'entre eux souhaitent continuer à vivre chez eux, tout en ayant une qualité de vie et en recevant les meilleures prestations de soins. Dans ce contexte, de nouvelles technologies de santé connectée peuvent être une solution pertinente pour faciliter le maintien à domicile des personnes âgées. Nous présentons, dans cet article, les enjeux en lien avec ces technologies et, plus particulièrement, dans quelle mesure pourraient-elles contribuer au maintien à domicile des personnes âgées et être une plus-value pour les patients et les proches, mais aussi pour les médecins et les autres professionnels de santé. Enfin, les craintes et les risques associés à ces technologies et l'importance d'évaluer scientifiquement leur utilité sont discutés.

Validity of pervasive computing based continuous physical activity assessment in community-dwelling old and oldest-old.

In older adults, physical activity is crucial for healthy aging and associated with numerous health indicators and outcomes. Regular assessments of physical activity can help detect early health-related changes and manage physical activity targeted interventions. The quantification of physical activity, however, is difficult as commonly used self-reported measures are biased and rather unprecise point in time measurements. Modern alternatives are commonly based on wearable technologies which are accurate but suffer from usability and compliance issues. In this study, we assessed the potential of an unobtrusive ambient-sensor based system for continuous, long-term physical activity quantification. Towards this goal, we analysed one year of longitudinal sensor- and medical-records stemming from thirteen community-dwelling old and oldest old subjects. Based on the sensor data the daily number of room-transitions as well as the raw sensor activity were calculated. We did find the number of room-transitions, and to some degree also the raw sensor activity, to capture numerous known associations of physical activity with cognitive, well-being and motor health indicators and outcomes. The results of this study indicate that such low-cost unobtrusive ambient-sensor systems can provide an adequate approximation of older adults' overall physical activity, sufficient to capture relevant associations with health indicators and outcomes.

Search and Match Task: Development of a Taskified Match-3 Puzzle Game to Assess and Practice Visual Search.

BACKGROUND: Visual search declines with aging, dementia, and brain injury and is linked to limitations in everyday activities. Recent studies suggest that visual search can be improved with practice using computerized visual search tasks and puzzle video games. For practical use, it is important that visual search ability can be assessed and practiced in a controlled and adaptive way. However, commercial puzzle video games make it hard to control task difficulty, and there are little means to collect performance data. OBJECTIVE: The aim of this study was to develop and initially validate the search and match task (SMT) that combines an enjoyable tile-matching match-3 puzzle video game with features of the visual search paradigm (taskified game). The SMT was designed as a single-target visual search task that allows control over task difficulty variables and collection of performance data. METHODS: The SMT is played on a grid-based (width × height) puzzle board, filled with different types of colored polygons. A wide range of difficulty levels was generated by combinations of 3 task variables over a range from 4 to 8 including height and width of the puzzle board (set size) and the numbers of tile types (distractor heterogeneity). For each difficulty level, large numbers of playable trials were pregenerated using Python. Each trial consists of 4 consecutive puzzle boards, where the goal of the task is to find a target tile configuration (search) on the puzzle board and swap 2 adjacent tiles to create a line of 3 identical tiles (match). For each puzzle board, there is exactly 1 possible match (single target search). In a user study with 28 young adults (aged 18 to 31 years), 13 older (aged 64 to 79 years) and 11 oldest (aged 86 to 98 years) adults played the long (young and older adults) or short version (oldest adults) of the difficulty levels of the SMT. Participants rated their perception and the usability of the task and completed neuropsychological tests that measure cognitive domains engaged by the puzzle game. RESULTS: Results from the user study indicate that the target search time is associated with set size, distractor heterogeneity, and age. Results further indicate that search performance is associated with general cognitive ability, selective and divided attention, visual search, and visuospatial and pattern recognition ability. CONCLUSIONS: Overall, this study shows that an everyday puzzle game-based task can be experimentally controlled, is enjoyable and user-friendly, and permits data collection to assess visual search and cognitive abilities. Further research is needed to evaluate the potential of the SMT game to assess and practice visual search ability in an enjoyable and adaptive way. A PsychoPy version of the SMT is freely available for researchers.

Therapist-Guided Tablet-Based Telerehabilitation for Patients With Aphasia: Proof-of-Concept and Usability Study.

BACKGROUND: Aphasia is the loss or impairment of language functions and affects everyday social life. The disorder leads to the inability to understand and be understood in both written and verbal communication and affects the linguistic modalities of auditory comprehension, verbal expression, reading, and writing. Due to heterogeneity of the impairment, therapy must be adapted individually and dynamically to patient needs. An important factor for successful aphasia therapy is dose and intensity of therapy. Tablet computer-based apps are a promising treatment method that allows patients to train independently at home, is well accepted, and is known to be beneficial for patients. In addition, it has been shown to ease the burden of therapists. OBJECTIVE: The aim of this project was to develop an adaptive multimodal system that enables aphasic patients to train at home using language-related tasks autonomously, allows therapists to remotely assign individualized tasks in an easy and time-efficient manner, and tracks the patient's progress as well as creation of new individual exercises. METHODS: The system consists of two main parts: (1) the patient's interface, which allows the patient to exercise, and (2) the therapist's interface, which allows the therapist to assign new exercises to the patient and supervise the patient's progress. The pool of exercises is based on a hierarchical language structure. Using questionnaires, therapists and patients evaluated the system in terms of usability (ie, System Usability Scale) and motivation (ie, adapted Intrinsic Motivation Inventory). RESULTS: A total of 11 speech and language therapists (age: mean 28, SD 7 years) and 15 patients (age: mean 53, SD 10 years) diagnosed with aphasia participated in this study. Patients rated the Bern Aphasia App in terms of usability (scale 0-100) as excellent (score >70; Z=-1.90; P=.03) and therapists rated the app as good (score >85; Z=-1.75; P=.04). Furthermore, patients enjoyed (scale 0-6) solving the exercises (score>3; mean 3.5, SD 0.40; Z=-1.66; P=.049). CONCLUSIONS: Based on the questionnaire scores, the system is well accepted and simple to use for patients and therapists. Furthermore, the new tablet computer-based app and the hierarchical language exercise structure allow patients with different types of aphasia to train with different doses and intensities independently at home. Thus, the novel system has potential for treatment of patients with aphasia as a supplement to face-to-face therapy.

Accuracy and Calibration of Computational Approaches for Inpatient Mortality Predictive Modeling.

Electronic Health Record (EHR) data can be a key resource for decision-making support in clinical practice in the "big data" era. The complete database from early 2012 to late 2015 involving hospital admissions to Inselspital Bern, the largest Swiss University Hospital, was used in this study, involving over 100,000 admissions. Age, sex, and initial laboratory test results were the features/variables of interest for each admission, the outcome being inpatient mortality. Computational decision support systems were utilized for the calculation of the risk of inpatient mortality. We assessed the recently proposed Acute Laboratory Risk of Mortality Score (ALaRMS) model, and further built generalized linear models, generalized estimating equations, artificial neural networks, and decision tree systems for the predictive modeling of the risk of inpatient mortality. The Area Under the ROC Curve (AUC) for ALaRMS marginally corresponded to the anticipated accuracy (AUC = 0.858). Penalized logistic regression methodology provided a better result (AUC = 0.872). Decision tree and neural network-based methodology provided even higher predictive performance (up to AUC = 0.912 and 0.906, respectively). Additionally, decision tree-based methods can efficiently handle Electronic Health Record (EHR) data that have a significant amount of missing records (in up to >50% of the studied features) eliminating the need for imputation in order to have complete data. In conclusion, we show that statistical learning methodology can provide superior predictive performance in comparison to existing methods and can also be production ready. Statistical modeling procedures provided unbiased, well-calibrated models that can be efficient decision support tools for predicting inpatient mortality and assigning preventive measures.