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.

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.

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.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Aphasia is the loss or impairment of language functions and affects every day 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, the training must be adapted individually and dynamically to the patient needs. An important factor for a successful aphasia therapy is dose frequency. Computer-based applications allow patients to train independently with high intensity at home. But most of the newest tele-rehabilitation applications focus on one linguistic modality, whereas there is currently a lack of validated tele-rehabilitation systems that focus on multimodal linguistic modalities. Objective: The aim of this project was to develop a multimodal system that enables aphasic patients to train at home using language-related tasks autonomously and also allows therapists to remotely assign new tasks and track the patient’s progress as well as to create new individual exercises. Methods: The system consists of two main parts. First, the patient’s interface, which allows the patient to exercise. Second, the therapist’s interface, which allows the therapist to allocate new exercises to the patient and supervise the progress of the patient. The system has been evaluated by therapists and patients by validated questionnaires in terms of usability and motivation. Results: A total of 11 speech and language therapists (age, MD = 28, SD =7) and 15 patients (age, MD = 53, SD =10) with diagnosed aphasia participated in this study. Patients rated the Bern Aphasia App in terms of usability (scale zero to hundred) as excellent (score above 70, 95% CI 85.9 to 100.0, t (14) = 2.16, P = .024) and therapists as good (score above 85, 95% CI 70.5 to 100.0, t (10) = 2.21, P = .026). Furthermore, patients highly enjoyed (scale zero to six) solving the exercises (score above 3, 95% CI 3.1 to 6.0, t (4) = 2.58, P = .031). Conclusions: Based on the questionnaire scores, the developed system is well accepted and simple to use by patients and therapists. Furthermore, the new system allows patients with different types of aphasia to train with a high intensity independently at home. Thus, the novel system has potential for treatment of aphasic patients as a supplement to face-to-face therapy.

A multidisciplinary journal on gaming and gamification for health education/promotion, teaching and social change.

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 ( $\rho =-0.87$ , 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.