Adjustment Disorders Are Uniquely Suited for eHealth Interventions: Concept and Case Study

Background: Adjustment disorders (also known as mental distress in response to a stressor) are among the most frequently diagnosed mental disorders in psychiatry and clinical psychology worldwide. They are also commonly diagnosed in clients engaging in deliberate self-harm and in those consulting general practitioners. However, their reputation in research-oriented mental health remains weak since they are largely underresearched. This may change when the International Statistical Classification of Diseases-11 (ICD-11) by the World Health Organization is introduced, including a new conceptualization of adjustment disorders as a stress-response disorder with positively defined core symptoms. Objective: This paper provides an overview of evidence-based interventions for adjustment disorders. Methods: We reviewed the new ICD-11 concept of adjustment disorder and discuss the the rationale and case study of an unguided self-help protocol for burglary victims with adjustment disorder, and its possible implementation as an eHealth intervention. Results: Overall, the treatment with the self-help manual reduced symptoms of adjustment disorder, namely preoccupation and failure to adapt, as well as symptoms of depression, anxiety, and stress. Conclusions: E-mental health options are considered uniquely suited for offering early intervention after the experiences of stressful life events that potentially trigger adjustment disorders.

A feasibility study for the provision of electronic healthcare tools and services in areas of Greece, Cyprus and Italy

Background Through this paper, we present the initial steps for the creation of an integrated platform for the provision of a series of eHealth tools and services to both citizens and travelers in isolated areas of thesoutheast Mediterranean, and on board ships travelling across it. The platform was created through an INTERREG IIIB ARCHIMED project called INTERMED. Methods The support of primary healthcare, home care and the continuous education of physicians are the three major issues that the proposed platform is trying to facilitate. The proposed system is based on state-of-the-art telemedicine systems and is able to provide the following healthcare services: i) Telecollaboration and teleconsultation services between remotely located healthcare providers, ii) telemedicine services in emergencies, iii) home telecare services for "at risk" citizens such as the elderly and patients with chronic diseases, and iv) eLearning services for the continuous training through seminars of both healthcare personnel (physicians, nurses etc) and persons supporting "at risk" citizens. These systems support data transmission over simple phone lines, internet connections, integrated services digital network/digital subscriber lines, satellite links, mobile networks (GPRS/3G), and wireless local area networks. The data corresponds, among others, to voice, vital biosignals, still medical images, video, and data used by eLearning applications. The proposed platform comprises several systems, each supporting different services. These were integrated using a common data storage and exchange scheme in order to achieve system interoperability in terms of software, language and national characteristics. Results The platform has been installed and evaluated in different rural and urban sites in Greece, Cyprus and Italy. The evaluation was mainly related to technical issues and user satisfaction. The selected sites are, among others, rural health centers, ambulances, homes of "at-risk" citizens, and a ferry. Conclusions The results proved the functionality and utilization of the platform in various rural places in Greece, Cyprus and Italy. However, further actions are needed to enable the local healthcare systems and the different population groups to be familiarized with, and use in their everyday lives, mature technological solutions for the provision of healthcare services.

Speech perception benefits of internet versus conventional telephony for hearing-impaired individuals

Telephone communication is a challenge for many hearing-impaired individuals. One important technical reason for this difficulty is the restricted frequency range (0.3-3.4 kHz) of conventional landline telephones. Internet telephony (voice over Internet protocol [VoIP]) is transmitted with a larger frequency range (0.1-8 kHz) and therefore includes more frequencies relevant to speech perception. According to a recently published, laboratory-based study, the theoretical advantage of ideal VoIP conditions over conventional telephone quality has translated into improved speech perception by hearing-impaired individuals. However, the speech perception benefits of nonideal VoIP network conditions, which may occur in daily life, have not been explored. VoIP use cannot be recommended to hearing-impaired individuals before its potential under more realistic conditions has been examined.

Development and implementation of a web-enabled 3D consultation tool for breast augmentation surgery based on 3D-image reconstruction of 2D pictures

Producing a rich, personalized Web-based consultation tool for plastic surgeons and patients is challenging.

Can a novel web-based computer test predict poor simulated driving performance? A pilot study with healthy and cognitive-impaired participants

BACKGROUND Driving a car is a complex instrumental activity of daily living and driving performance is very sensitive to cognitive impairment. The assessment of driving-relevant cognition in older drivers is challenging and requires reliable and valid tests with good sensitivity and specificity to predict safe driving. Driving simulators can be used to test fitness to drive. Several studies have found strong correlation between driving simulator performance and on-the-road driving. However, access to driving simulators is restricted to specialists and simulators are too expensive, large, and complex to allow easy access to older drivers or physicians advising them. An easily accessible, Web-based, cognitive screening test could offer a solution to this problem. The World Wide Web allows easy dissemination of the test software and implementation of the scoring algorithm on a central server, allowing generation of a dynamically growing database with normative values and ensures that all users have access to the same up-to-date normative values. OBJECTIVE In this pilot study, we present the novel Web-based Bern Cognitive Screening Test (wBCST) and investigate whether it can predict poor simulated driving performance in healthy and cognitive-impaired participants. METHODS The wBCST performance and simulated driving performance have been analyzed in 26 healthy younger and 44 healthy older participants as well as in 10 older participants with cognitive impairment. Correlations between the two tests were calculated. Also, simulated driving performance was used to group the participants into good performers (n=70) and poor performers (n=10). A receiver-operating characteristic analysis was calculated to determine sensitivity and specificity of the wBCST in predicting simulated driving performance. RESULTS The mean wBCST score of the participants with poor simulated driving performance was reduced by 52%, compared to participants with good simulated driving performance (P<.001). The area under the receiver-operating characteristic curve was 0.80 with a 95% confidence interval 0.68-0.92. CONCLUSIONS When selecting a 75% test score as the cutoff, the novel test has 83% sensitivity, 70% specificity, and 81% efficiency, which are good values for a screening test. Overall, in this pilot study, the novel Web-based computer test appears to be a promising tool for supporting clinicians in fitness-to-drive assessments of older drivers. The Web-based distribution and scoring on a central computer will facilitate further evaluation of the novel test setup. We expect that in the near future, Web-based computer tests will become a valid and reliable tool for clinicians, for example, when assessing fitness to drive in older drivers.

The Virtual Skeleton Database: An Open Access Repository for Biomedical Research and Collaboration

Background: Statistical shape models are widely used in biomedical research. They are routinely implemented for automatic image segmentation or object identification in medical images. In these fields, however, the acquisition of the large training datasets, required to develop these models, is usually a time-consuming process. Even after this effort, the collections of datasets are often lost or mishandled resulting in replication of work. Objective: To solve these problems, the Virtual Skeleton Database (VSD) is proposed as a centralized storage system where the data necessary to build statistical shape models can be stored and shared. Methods: The VSD provides an online repository system tailored to the needs of the medical research community. The processing of the most common image file types, a statistical shape model framework, and an ontology-based search provide the generic tools to store, exchange, and retrieve digital medical datasets. The hosted data are accessible to the community, and collaborative research catalyzes their productivity. Results: To illustrate the need for an online repository for medical research, three exemplary projects of the VSD are presented: (1) an international collaboration to achieve improvement in cochlear surgery and implant optimization, (2) a population-based analysis of femoral fracture risk between genders, and (3) an online application developed for the evaluation and comparison of the segmentation of brain tumors. Conclusions: The VSD is a novel system for scientific collaboration for the medical image community with a data-centric concept and semantically driven search option for anatomical structures. The repository has been proven to be a useful tool for collaborative model building, as a resource for biomechanical population studies, or to enhance segmentation algorithms.

Mobile competence network for community health workers in rural Myanmar: a supportive information system that facilitates the improvement of health services.

The goal of this project is the development of international cooperation for fostering solutions to provide better access to basic healthcare services.

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.

Improving Pediatric Basic Life Support Performance Through Blended Learning With Web-Based Virtual Patients: Randomized Controlled Trial

BACKGROUND E-learning and blended learning approaches gain more and more popularity in emergency medicine curricula. So far, little data is available on the impact of such approaches on procedural learning and skill acquisition and their comparison with traditional approaches. OBJECTIVE This study investigated the impact of a blended learning approach, including Web-based virtual patients (VPs) and standard pediatric basic life support (PBLS) training, on procedural knowledge, objective performance, and self-assessment. METHODS A total of 57 medical students were randomly assigned to an intervention group (n=30) and a control group (n=27). Both groups received paper handouts in preparation of simulation-based PBLS training. The intervention group additionally completed two Web-based VPs with embedded video clips. Measurements were taken at randomization (t0), after the preparation period (t1), and after hands-on training (t2). Clinical decision-making skills and procedural knowledge were assessed at t0 and t1. PBLS performance was scored regarding adherence to the correct algorithm, conformance to temporal demands, and the quality of procedural steps at t1 and t2. Participants' self-assessments were recorded in all three measurements. RESULTS Procedural knowledge of the intervention group was significantly superior to that of the control group at t1. At t2, the intervention group showed significantly better adherence to the algorithm and temporal demands, and better procedural quality of PBLS in objective measures than did the control group. These aspects differed between the groups even at t1 (after VPs, prior to practical training). Self-assessments differed significantly only at t1 in favor of the intervention group. CONCLUSIONS Training with VPs combined with hands-on training improves PBLS performance as judged by objective measures.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.