International Journal of Telemedicine and Applications最新文献

Aging in place is a notion which supports the independent living of older adults at their own place of residence for as long as possible. To support this alternative living which can be in contrast to various other types of assisted living options, modes of monitoring technology need to be explored and studied in order to determine a balance between the preservation of privacy and adequacy of sensed information for better estimation and visualization of movements and activities. In this paper, we explore such monitoring paradigm on how a network of RGB-D sensors can be utilized for this purpose. This type of sensor offers both visual and depth sensing modalities from the scene where the information can be fused and coded for better protection of privacy. For this purpose, we introduce the novel notion of passive observer. This observer is only triggered by detecting the absence of movements of older adults in the scene. This is accomplished by classifying and localizing objects in the monitoring scene from both before and after the detection of movements. A deep learning tool is utilized for visual classification of known objects in the physical scene followed by virtual reality reconstructing of the scene where the shape and location of objects are recreated. Such reconstruction can be used as a visual summary in order to identify objects which were handled by an older adult in-between observation. The simplified virtual scene can be used, for example, by caregivers or monitoring personnel in order to assist in detecting any anomalies. This virtual visualization can offer a high level of privacy protection without having any direct visual access to the monitoring scene. In addition, using the scene graph representation, an automatic decision-making tool is proposed where spatial relationships between the objects can be used to estimate the expected activities. The results of this paper are demonstrated through two case studies.

This paper describes the design and implementation of a software system to improve the management of diabetes using a machine learning approach and to demonstrate and evaluate its effectiveness in controlling diabetes. The proposed approach for this management system handles the various factors that affect the health of people with diabetes by combining multiple artificial intelligence algorithms. The proposed framework factors the diabetes management problem into subgoals: building a Tensorflow neural network model for food classification; thus, it allows users to upload an image to determine if a meal is recommended for consumption; implementing K-Nearest Neighbour (KNN) algorithm to recommend meals; using cognitive sciences to build a diabetes question and answer chatbot; tracking user activity, user geolocation, and generating pdfs of logged blood sugar readings. The food recognition model was evaluated with cross-entropy metrics that support validation using Neural networks with a backpropagation algorithm. The model learned features of the images fed from local Ghanaian dishes with specific nutritional value and essence in managing diabetics and provided accurate image classification with given labels and corresponding accuracy. The model achieved specified goals by predicting with high accuracy, labels of new images. The food recognition and classification model achieved over 95% accuracy levels for specific calorie intakes. The performance of the meal recommender model and question and answer chatbot was tested with a designed cross-platform user-friendly interface using Cordova and Ionic Frameworks for software development for both mobile and web applications. The system recommended meals to meet the calorific needs of users successfully using KNN (with k = 5) and answered questions asked in a human-like way. The implemented system would solve the problem of managing activity, dieting recommendations, and medication notification of diabetics.

Introduction: False alarm reduction is an important challenge in self-care, whereas one of the most important false alarm causes in the cardiology domain is electrodes misplacements in ECG recordings, the main investigations to perform for early and pervasive detection of cardiovascular diseases. In this context, we present and assess a new method for electrode reversals identification for Mason-Likar based 3D ECG recording systems which are especially convenient to use in self-care and allow to achieve, as previously reported, high computerized ischemia detection accuracy.

Methods: We mathematically simulate the effect of the six pairwise reversals of the LA, RA, LL, and C2 electrodes on the three ECG leads I, II, and V2. Our approach then consists in performing serial comparisons of the newly recorded 3D ECG and of the six derived ECGs simulating an electrode reversal with a standard, 12-lead reference ECG by means of the CAVIAR software. We further use a scoring method to compare these analysis results and then apply a decision tree model to extract the most relevant measurements in a learning set of 121 patients recorded in ICU.

Results: The comparison of the seven sets of serial analysis results from the learning set resulted in the determination of a composite criteria involving four measurements of spatial orientation changes of QRS and T and providing a reversal identification accuracy of 100%. Almost the same results, with 99.99% of sensitivity and 100% of specificity, were obtained in two test sets from 90 patients, composed of 2098 and 2036 representative ECG beats respectively recorded during PTCA balloon inflation, a procedure which mimics ischemia, and before PTCA for control.

Conclusion: Personalized automatic detection of ECG electrode cable interchanges can reach almost the maximal accuracy of 100% in self-care, and can be performed in almost real time.

Background: Advances in information and communication technologies (ICTs) represent a growing platform for the expansion of healthcare related services, but there is little information on how generational differences might account for distinct patterns of use and interest for ICTs. Our study aims to achieve a better understanding on how generational cohorts might influence the use and preferences for ICTs among patients with obstructive lung diseases in Latin America.

Materials and methods: We conducted an anonymous cross-sectional survey-based study, involving 968 patients with obstructive lung diseases (OLD) in Latin America. Patients rated their frequency of use and preferences of ICTs through a modified version of the Michigan Questionnaire. Chi-square test for association and adjusted regression analyses were performed.

Results: Of all, 63.6% of participants had Internet access. Younger generations, in particular Generation Z and Millennials, had the highest rate of Internet access and smartphone ownership, as well as of overall frequency of ICT use. Web-based Internet was found to be the main source to seek information about the disease (36.9%) across all generational cohorts. Generation Z and Millennials presented the highest odds to be interested in using Twitter (OR 31.79 and 8.86) for receiving health-related information, and email (OR 4.87 and 4.86) as the preferred way to ask physicians information related to their disease through ICTs.

Conclusion: Generational cohorts influence the use and preferences for ICTs among patients with obstructive lung diseases. Younger generational cohorts were associated with higher access to the Internet and smartphone ownership, as well as higher interest for using ICTs to receive and ask for health-related information.

Smart clothing that can measure electrocardiogram (ECG) signals and monitor the health status of people meets the needs of our increasingly aging society. However, the conventional measurement of ECG signals is complicated and its electrodes can cause irritation to the skin, which makes the conventional measurement method unsuitable for applications in smart clothing. In this paper, a novel wearable measurement of ECG signals is proposed. There are only three ECG textile electrodes knitted into the fabric of smart clothing. The acquired ECG signals can be transmitted to a smartphone via Bluetooth, and they can also be sent out to a PC terminal by a smartphone via WiFi or Internet. To get more significant ECG signals, the ECG differential signal between two electrodes is calculated based on a spherical volume conductor model, and the best positions on the surface of a human body for two textile electrodes to measure ECG signals are simulated by using the body-surface potential mapping (BSPM) data. The results show that position 12 in the lower right and position 11 in the upper left of the human body are the best for the two electrodes to measure ECG signals, and the presented wearable measurement can obtain good performance when one person is under the conditions of sleeping and jogging.

Purpose: The study aims to describe a novel method of utilization of the eyeSmart EMR (electronic medical record) app. It describes the demographic distribution, clinical presentation, query requested by the vision technician, and management advised to the patients by using "eyeSmart EMR" app from the vision centers located across a three-tier eye care network in India.

Methods: This is a retrospective review of all patients who required a tele-ophthalmology consultation from January 2017 to August 2018. The demographic, clinical details, and the impact of teleophthalmology using eyeSmart app, in the vision centers of a three-tier eye care network, was analyzed in detail.

Results: A total of 15,001 tele-ophthalmology consults were included which is from January 2017 to August 2018. The mean age was 38 ± 19 years and male to female ratio was 1 : 2. Video calls were performed for 6191 (41.27%) consults and the impact was measured. Additional clinical information was received in 65.61% consults through video call. Medical management was advised in 47.07% of patients and 30.30% were referred to higher centers for medical intervention and 0.59% were referred for surgical intervention, 16.23% were prescribed glasses. No intervention required for 0.69% of cases. Hence nil intervention was advised.

Conclusion: The combination of using tablet and video calls with the help of eyeSmart EMR app is a novel method in teleophthalmology. It helps in connecting the patients at rural areas and the ophthalmologists in higher centers. The use of technology plays a vital role in the appropriate medical management of the patient.

Purpose: In relation to this research, only a few studies have been carried out around the world. However, in Saudi Arabia, there have been no investigations into this subject. In this sense, the objective of this study was to investigate the physicians' attitudes towards the use of social media for professional purposes in Saudi Arabia.

Methods: A cross-sectional study was conducted among 235 physicians from different regions of Saudi Arabia. The data were collected by means of a survey. The survey questionnaire was distributed using the WhatsApp application. Descriptive statistics were used to analyze the results.

Results: The most common social media used by the respondents was Facebook, and the majority of the participants agreed that social media improved their knowledge and skills. However, most of the participants did not interact with patients using these tools, did not feel comfortable conducting an online consultation, and believed that social media affected the choice of the healthcare provider. Similarly, 30.6% of the respondents opined that it is not appropriate to search online information about patients, and 44.3% of them considered that patients would not trust the medical advice if a physician obtained the information from a website.

Conclusion: The results showed that the majority of the respondents considered that social media improved the knowledge and abilities of physicians. In addition, the respondents reflected that there were ethical issues that must be taken into account when using social media and more than half of them did not interact with their patients employing these tools. The outcomes of this research will help to develop programs directed at the physicians in Saudi Arabia to enhance their knowledge, professional skills, medicine practice, patient-doctor interaction, and handle the risks involved in the use of social media.

Background: Stroke is a leading cause of disability and requires continued care after hospital discharge. Mobile-based interventions are suitable to reduce the cost of stroke rehabilitation and facilitate self-management among stroke survivors. However, before attempting to use mobile-based home exercise program, it is crucial to recognize the readiness of stroke survivors and their caregivers to opt for such interventions.

Objective: To assess the acceptability and attitude towards a mobile-based home exercise program among stroke survivors and their primary caregivers.

Methods: A cross-sectional study was conducted among 102 participants to understand their attitude and acceptability towards mobile-based home exercise program. A validated 10-item questionnaire was adapted for the study. The questions which assessed the attitude were rated on a three-point Likert scale, with three denoting agree and one denoting disagree. The acceptability was assessed by their willingness to opt for a mobile-based home program services. A Chi-square analysis and cross-tabulation were performed to test differences between caregivers and patients. A logistic regression was performed to determine the effects of age, gender, and mobile phone on acceptability.

Results: Ninety-two percent of caregivers and 90% of patients showed willingness to opt for mobile-based intervention. Majority of the participants showed a positive attitude towards this mode of treatment. There was no difference in the attitude noted among caregivers and patients (p>0.05) towards mobile-based intervention.

Conclusion: The stroke survivors and caregivers welcomed the concept of mobile-based home exercise program even in a low-resource settings, but further studies to understand treatment and cost-effectiveness of this technology among the stroke survivors would lead to better implementation.

Purpose: This study describes the development of an attachment for a smart tablet to stream live video information of an eye examination through the slit lamp.

Methods: A tablet attachment was developed that enables the live streaming of video from the slit lamp from the rural vision centers of the LVPEI network. A video streaming solution like Skype Lite was used to transmit the same. The eyeSmart app was utilized for the documentation of the clinical information of the patients.

Results: A tablet attachment of eyeSmart Cyclops was developed and piloted in 3 vision centers of the LVPEI network.

Conclusion: The use of real-time video transmission illustrates a novel teleophthalmology solution in low resource settings to screen rural populations. The ability to transmit live video enables gathering more information than static images.