STROKE999 for Quick Stroke Screening (original) (raw)
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Journal of clinical neurology (Seoul, Korea), 2015
Recent advances in information technology have created opportunities for advances in the management of stroke. The objective of this study was to test the feasibility of using a smartphone software application (app) for the management of vascular risk factors in patients with stroke. This prospective clinical trial developed a smartphone app, the 'Korea University Health Monitoring System for Stroke: KUHMS₂,' for use by patients with stroke. During a 6-month follow-up period, its feasibility was assessed by measuring the changes in their vascular risk-factor profiles and the number of days per patient with data registration into the app. The effect of the app on the achievement rate of risk-factor targets was assessed by classifying subjects into compliant and noncompliant groups. At the end of the trial, data on 48 patients were analyzed. The number of days on which data were registered into the app was 60.42±50.17 (mean±standard deviation). Among predefined vascular risk f...
Yonsei Medical Journal, 2014
The benefits of thrombolytic treatment are time-dependent. We developed a smartphone application that aids stroke patient self-screening and hospital selection, and may also decrease hospital arrival time. Materials and Methods: The application was developed for iPhone and Android smartphones. Map data for the application were adopted from the open map. For hospital registration, a web page (http://stroke119.org) was developed using PHP and MySQL. Results: The Stroke 119 application includes a stroke screening tool and real-time information on nearby hospitals that provide thrombolytic treatment. It also provides information on stroke symptoms, thrombolytic treatment, and prescribed actions when stroke is suspected. The stroke screening tool was adopted from the Cincinnati Prehospital Stroke Scale and is displayed in a cartoon format. If the user taps a cartoon image that represents abnormal findings, a pop-up window shows that the user may be having a stroke, informs the user what to do, and directs the user to call emergency services. Information on nearby hospitals is provided in map and list views, incorporating proximity to the user's location using a Global Positioning System (a builtin function of smartphones). Users can search for a hospital according to specialty and treatment levels. We also developed a web page for hospitals to register in the system. Neurology training hospitals and hospitals that provide acute stroke care in Korea were invited to register. Seventy-seven hospitals had completed registration. Conclusion: This application may be useful for reducing hospital arrival times for thrombolytic candidates.
Mobile Technology for Primary Stroke Prevention
Stroke, 2019
Background and Purpose— Feasibility of utilizing the Stroke Riskometer App (App) to improve stroke awareness and modify stroke risk behaviors was assessed to inform a full randomized controlled trial. Methods— A parallel, open-label, 2-arm prospective, proof-of-concept pilot randomized controlled trial. Participants were randomized to usual care/control or App intervention group and assessed at baseline, 3, and 6 months. The App measures stroke risk and provides information on management of risk factors. Participants were aged >19 years with at least 2 modifiable stroke risk factors identified, no prior stroke, and owned a smartphone. Results— Fifty participants (24 control, 26 App) were recruited from 148 eligible participants. Retention in the trial was 87%. Mean cardiovascular health (Life’s Simple 7) improved by 0.36 (95% CI, −2.10 to 1.38) in the App group compared with 0.01 (95% CI, −1.34 to 1.32) in controls ( P =0.6733). Conclusions— These findings support a full randomiz...
The Stroke Riskometer TM App: Validation of a data collection tool and stroke risk predictor
International Journal of Stroke, 2014
The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the 'mass' approach), the 'high risk' approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer TM , has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods. Methods 752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke Riskometer TM ) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R 2 statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm. Results The Stroke Riskometer TM performed well against the FSRS five-year AUROC for both males (FSRS = 75·0% (95% CI 72·3%-77·6%), Stroke Riskometer TM = 74·0(95% CI 71·3%-76·7%) and females [FSRS = 70·3% (95% CI 67·9%-72·8%, Stroke Riskometer TM = 71·5% (95% CI 69·0%-73·9%)], and better than QStroke [males -59·7% (95% CI 57·3%-62·0%) and comparable to females = 71·1% (95% CI 69·0%-73·1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0·51-0·56, D-statistic ranging from 0·01-0·12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data (P < 0·006). Conclusions The Stroke Riskometer TM is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer TM will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors.
Smartphone App in Stroke Management: A Narrative Updated Review
Journal of Stroke
The spread of smartphones and mobile-Health (m-health) has progressively changed clinical practice, implementing access to medical knowledge and communication between doctors and patients. Dedicated software called Applications (or Apps), assists the practitioners in the various phases of clinical practice, from diagnosis to follow-up and therapy management. The impact of this technology is even more important in diseases such as stroke, which are characterized by a complex management that includes several moments: primary prevention, acute phase management, rehabilitation, and secondary prevention. This review aims to evaluate and summarize the available literature on Apps for the clinical management of stroke. We described their potential and weaknesses, discussing potential room for improvement. Medline databases were interrogated for studies concerning guideline-based decision support Apps for stroke management and other medical scenarios from 2007 (introduction of the first iPh...
CIN: Computers, Informatics, Nursing, 2020
Cerebrovascular accident is a serious public health problem and requires the attention of professionals who can detect, diagnose, and provide care in a timely fashion. A quantitative quasi-experimental study was conducted using a mobile app called mSmartAVC for clinical evaluation of nursing care at the bedside. The study aimed at measuring the knowledge of nurses and nursing students in the detection and care of cerebrovascular accident. In this study, a total of 115 nurses from health services in the South of Brazil and 35 nursing students of a community university participated. The stages focused on development, modeling of clinical cases, problem-based learning, pretest (before) app use, and posttest (after) use of the app. The results of the pretest and posttest corrections showed a substantial statistical difference (P < .001), indicating a significant knowledge gain after the use of the app, particularly in terms of the detection scales and interpretation of the imaging te...
Neuroepidemiology
Background: Stroke is considered the second leading cause of mortality and disability worldwide. The increasing burden of stroke is strong evidence that currently used primary prevention strategies are not sufficiently effective. The Stroke Riskometer™ application (app) represents a new stroke prevention strategy distinctly different from the conventional high-cardiovascular disease risk approach. Objective: This proposed study aims to evaluate the effectiveness of the Stroke Riskometer™ app in improving stroke awareness and stroke risk probability amongst the adult population in Malaysia. Methods: A non-blinded, parallel-group cluster-randomized controlled trial with a 1:1 allocation ratio will be implemented in Kelantan, Malaysia. Two groups with a sample size of 66 in each group will be recruited. The intervention group will be equipped with the Stroke Riskometer™ app and informational leaflets, while the control group will be provided with standard management, including informat...
2019
Background: Mobile health (mHealth) technologies hold great promise in improving the delivery of high-quality health care services. Yet, there has been little research so far applying mHealth technologies in the context of delivering stroke care in resource-limited rural regions. Objective: This study aimed to introduce the design and development of an mHealth system targeting primary health care providers and to ascertain its feasibility in supporting the delivery of a System-Integrated techNology-Enabled Model of cAre (SINEMA) service for strengthening secondary prevention of stroke in rural China. Methods: The SINEMA mHealth system was designed by a multidisciplinary team comprising public health researchers, neurologists, and information and communication technology experts. The iterative co-design and development of the mHealth system involved the following 5 steps: (1) assessing the needs of relevant end users through in-depth interviews of stakeholders, (2) designing the functional modules and evidence-based care content, (3) designing and building the system and user interface, (4) improving and enhancing the system through a 3-month pilot test in 4 villages, and (5) finalizing the system and deploying it in field trial, and finally, evaluating its feasibility through a survey of the dominant user group. Results: From the in-depth interviews of 49 relevant stakeholders, we found that village doctors had limited capacity in caring for village-dwelling stroke patients in rural areas. Primary health care workers demonstrated real needs in receiving appropriate training and support from the mHealth system as well as great interests in using the mHealth technologies and tools. Using these findings, we designed a multifaceted mHealth system with 7 functional modules by following the iterative user-centered design and software development approach. The mHealth system, aimed at 3 different types of users (village doctors, town physicians, and county managers), was developed and utilized in a cluster-randomized controlled trial by 25 village doctors in a resource-limited county in rural China to manage 637 stroke patients between July 2017 and July 2018. In the end, a survey on the usability and functions of the mHealth system among village doctors (the dominant group of users, response rate=96%, 24/25) revealed that most of them were satisfied with the essential functions provided (71%) and were keen to continue using it (92%) after the study. Conclusions: The mHealth system was feasible for assisting primary health care providers in rural China in delivering the SINEMA service on the secondary prevention of stroke. Further research and initiatives in scaling up the SINEMA approach and this mHealth system to other resource-limited regions in China and beyond will likely enhance the quality and accessibility of essential secondary prevention among stroke patients.
Mobile Applications for Stroke Prevention: A Survey of Physicians’ Perspectives
Journal of Mobile Technology in Medicine, 2017
Background: Little is known about the prevalence and nature of mobile application adoption in clinical practice. Aims: To explore current and potential mobile application use in primary care physicians (PCPs) for stroke prevention. Do PCPs recommend, use, or discuss mobile health applications for stroke preventative measures? Methods: Current PCPs in the New York City area specializing in Internal Medicine, Ob/Gyn, and Family Medicine were surveyed in person. The survey consisted of demographic questions and 11 questions on mobile application use. Results: Of the 86 physicians surveyed (53% female; mean age 37 years, SD 12), 74% (95% CI 65%, 84%) reported using mobile applications in patient care, whether for their own use or in recommending to patients. Experience was the most important determining factor, with 82% of physicians with less than 3 years practice experience using mobile apps, 78% of physicians with 3 to 10 years, 60% of physicians with 11 to 20 years, and 58% of physicians with greater than 20 years experience (p=0.045). Physicians reported using mobile applications to manage stroke risk factors 25% (95% CI 16%, 35%) of the time, while 77% (95% CI 68%, 86%) expressed interest in new apps to help their patients manage these risks. Lastly, 41% (95% CI 30%, 51%) of physicians surveyed strongly agreed that mobile applications are useful in providing patient care, while 49% (95% CI 38%, 59%) simply agreed and 0% disagreed. Conclusions: Most urban PCPs we surveyed believe that mobile applications belong in healthcare, with one in four using them to manage stroke risk factors.
JMIR mHealth and uHealth
Background: Mobile health (mHealth) technologies hold great promise in improving the delivery of high-quality health care services. Yet, there has been little research so far applying mHealth technologies in the context of delivering stroke care in resource-limited rural regions. Objective: This study aimed to introduce the design and development of an mHealth system targeting primary health care providers and to ascertain its feasibility in supporting the delivery of a System-Integrated techNology-Enabled Model of cAre (SINEMA) service for strengthening secondary prevention of stroke in rural China. Methods: The SINEMA mHealth system was designed by a multidisciplinary team comprising public health researchers, neurologists, and information and communication technology experts. The iterative co-design and development of the mHealth system involved the following 5 steps: (1) assessing the needs of relevant end users through in-depth interviews of stakeholders, (2) designing the functional modules and evidence-based care content, (3) designing and building the system and user interface, (4) improving and enhancing the system through a 3-month pilot test in 4 villages, and (5) finalizing the system and deploying it in field trial, and finally, evaluating its feasibility through a survey of the dominant user group. Results: From the in-depth interviews of 49 relevant stakeholders, we found that village doctors had limited capacity in caring for village-dwelling stroke patients in rural areas. Primary health care workers demonstrated real needs in receiving appropriate training and support from the mHealth system as well as great interests in using the mHealth technologies and tools. Using these findings, we designed a multifaceted mHealth system with 7 functional modules by following the iterative user-centered design and software development approach. The mHealth system, aimed at 3 different types of users (village doctors, town physicians, and county managers), was developed and utilized in a cluster-randomized controlled trial by 25 village doctors in a resource-limited county in rural China to manage 637 stroke patients between July 2017 and July 2018. In the end, a survey on the usability and functions of the mHealth system among village doctors (the dominant group of users, response rate=96%, 24/25) revealed that most of them were satisfied with the essential functions provided (71%) and were keen to continue using it (92%) after the study. Conclusions: The mHealth system was feasible for assisting primary health care providers in rural China in delivering the SINEMA service on the secondary prevention of stroke. Further research and initiatives in scaling up the SINEMA approach and this mHealth system to other resource-limited regions in China and beyond will likely enhance the quality and accessibility of essential secondary prevention among stroke patients.