130 research outputs found

    A Mobile App For Delirium Screening

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    Objective: The objective of this study is to describe the algorithm and technical implementation of a mobile app that uses adaptive testing to assess an efficient mobile app for the diagnosis of delirium. Materials and Methods: The app was used as part of a NIH-funded project to assess the feasibility, effectiveness, administration time, and costs of the 2-step delirium identification protocol when performed by physicians and nurses, and certified nursing assistants (CNA). The cohort included 535 hospitalized patients aged 79.7 (SDĀ¼6.6) years enrolled at 2 different sites. Each patient was assessed on 2 consecutive days by the research associate who performed the reference delirium assessment. Thereafter, physicians, nurses, and CNAs performed adaptive delirium assessments using the app. Qualitative data to assess the experience of administering the 2-step protocol, and the app usability were also collected and analyzed from 50 physicians, 189 nurses, and 83 CNAs. We used extensible hypertext markup language (XHTML) and JavaScript to develop the app for the iOSā€“based iPad. The App was linked to Research Electronic Data Capture (REDCap), a relational database system, via a REDCap application programming interface (API) that sent and received data from/to the app. The data from REDCap were sent to the Statistical Analysis System for statistical analysis. Results: The app graphical interface was successfully implemented by XHTML and JavaScript. The API facilitated the instant updating and retrieval of delirium status data between REDCap and the app. Clinicians performed 881 delirium assessments using the app for 535 patients. The transmission of data between the app and the REDCap system showed no errors. Qualitative data indicated that the users were enthusiastic about using the app with no negative comments, 82% positive comments, and 18% suggestions of improvement. Delirium administration time for the 2-step protocol showed similar total time between nurses and physicians (103.9 vs 106.5 seconds). Weekly enrollment reports of the app data were generated for study tracking purposes, and the data are being used for statistical analyses for publications. Discussion: The app developed using iOS could be easily converted to other operating systems such as Android and could be linked to other relational databases beside REDCap, such as electronic health records to facilitate better data retrieval and updating of patientā€™s delirium status

    Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

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    Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

    Studies involving people with dementia and touchscreen technology: a literature review

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    Background: Devices using touchscreen interfaces such as tablets and smartphones have been highlighted as potentially suitable for people with dementia due to their intuitive and simple control method. This population experience a lack of meaningful, engaging activities, yet the potential use of the touchscreen format to address this issue has not been fully realized. Objective: To identify and synthesize the existing body of literature involving the use of touchscreen technology and people with dementia in order to guide future research in this area. Methods: A systematized review of studies in the English language was conducted, where a touchscreen interface was used with human participants with dementia. Results: A total of 45 articles met the inclusion criteria. Four questions were addressed concerning (1) the context of use, (2) reasons behind the selection of the technology, (3) details of the hardware and software, and (4) whether independent use by people with dementia was evidenced. Conclusions: This review presents an emerging body of evidence demonstrating that people with dementia are able to independently use touchscreen technology. The intuitive control method and adaptability of modern devices has driven the selection of this technology in studies. However, its primary use to date has been as a method to deliver assessments and screening tests or to provide an assistive function or cognitive rehabilitation. Building on the finding that people with dementia are able to use touchscreen technology and which design features facilitate this, more use could be made to deliver independent activities for meaningful occupation, entertainment, and fun

    The development of a web-based app employing machine learning for delirium prevention in long-term care facilities in South Korea

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    Background Long-term care facilities (LCFs) in South Korea have limited knowledge of and capability to care for patients with delirium. They also often lack an electronic medical record system. These barriers hinder systematic approaches to delirium monitoring and intervention. Therefore, this study aims to develop a web-based app for delirium prevention in LCFs and analyse its feasibility and usability. Methods The app was developed based on the validity of the AI prediction model algorithm. A total of 173 participants were selected from LCFs to participate in a study to determine the predictive risk factors for delerium. The app was developed in five phases: (1) the identification of risk factors and preventive intervention strategies from a review of evidence-based literature, (2) the iterative design of the app and components of delirium prevention, (3) the development of a delirium prediction algorithm and cloud platform, (4) a pilot test and validation conducted with 33 patients living in a LCF, and (5) an evaluation of the usability and feasibility of the app, completed by nurses (Main users). Results A web-based app was developed to predict high risk of delirium and apply preventive interventions accordingly. Moreover, its validity, usability, and feasibility were confirmed after app development. By employing machine learning, the app can predict the degree of delirium risk and issue a warning alarm. Therefore, it can be used to support clinical decision-making, help initiate the assessment of delirium, and assist in applying preventive interventions. Conclusions This web-based app is evidence-based and can be easily mobilised to support care for patients with delirium in LCFs. This app can improve the recognition of delirium and predict the degree of delirium risk, thereby helping develop initiatives for delirium prevention and providing interventions. Moreover, this app can be extended to predict various risk factors of LCF and apply preventive interventions. Its use can ultimately improve patient safety and quality of care. Ā© 2022, The Author(s).1

    Categorizing Health Outcomes and Efficacy of mHealth Apps for Persons With Cognitive Impairment: A Systematic Review

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    Background Use of mobile health (mHealth) apps is growing at an exponential rate in the United States and around the world. Mild cognitive impairment (MCI), Alzheimer disease, and related dementias are a global health problem. Numerous mHealth interventions exist for this population, yet the effect of these interventions on health has not been systematically described. Objective The aim of this study is to catalog the types of health outcomes used to measure effectiveness of mHealth interventions and assess which mHealth interventions have been shown to improve the health of persons with MCI, Alzheimer disease, and dementia. Methods We searched 13 databases, including Ovid MEDLINE, PubMed, EMBASE, the full Cochrane Library, CINAHL, PsycINFO, Ei Compendex, IEEE Xplore, Applied Science & Technology Source, Scopus, Web of Science, ClinicalTrials.gov, and Google Scholar from inception through May 2017 for mHealth studies involving persons with cognitive impairment that were evaluated using at least one quantitative health outcome. Proceedings of the Annual ACM Conferences on Human Factors in Computing Systems, the ACM User Interface Software and Technology Symposium, and the IEEE International Symposium on Wearable Computers were searched in the ACM Digital Library from 2012 to 2016. A hand search of JMIR Publications journals was also completed in July 2017. Results After removal of duplicates, our initial search returned 3955 records. Of these articles, 24 met final inclusion criteria as studies involving mHealth interventions that measured at least one quantitative health outcome for persons with MCI, Alzheimer disease, and dementia. Common quantitative health outcomes included cognition, function, mood, and quality of life. We found that 21.2% (101/476) of the fully reviewed articles were excluded because of a lack of health outcomes. The health outcomes selected were observed to be inconsistent between studies. For those studies with quantitative health outcomes, more than half (58%) reported postintervention improvements in outcomes. Conclusions Results showed that many mHealth app interventions targeting those with cognitive impairment lack quantitative health outcomes as a part of their evaluation process and that there is a lack of consensus as to which outcomes to use. The majority of mHealth app interventions that incorporated health outcomes into their evaluation noted improvements in the health of persons with MCI, Alzheimer disease, and dementia. However, these studies were of low quality, leading to a grade C level of evidence. Clarification of the benefits of mHealth interventions for people with cognitive impairment requires more randomized controlled trials, larger numbers of participants, and trial designs that minimize bias. Trial Registration PROSPERO Registration: PROSPERO 2016:CRD42016033846; http://www.crd.york.ac.uk/PROSPERO/ display_record.asp?ID=CRD42016033846 (Archived by WebCite at http://www.webcitation.org/6sjjwnv1M

    Interpersonal APProach to Dementia: An iPad-Based Program for Caregiver Education and Decreasing Problem Behaviors in Older Adults with Cognitive Impairments

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    Overall Problem More than 10 million family members care for an older adult with dementia in the community. Due to the progressive nature of dementia, a high burden of care is placed on caregivers. Adult day centers offer respite, but while providing families temporary relief from caregiving responsibilities, they do not emphasize interventions for individuals with dementia. Agitation, a symptom of dementia, can affect both the older adult and others around them negatively. When the caregivers of older adults with dementia receive proper education about interacting with and caring for someone with this progressive disease, the quality of life of the older adults and caregivers improve. Aim and Purpose The aim of this Capstone project was to create a program for older adults with dementia and their caregivers. The program was designed to decrease older adultsā€™ agitation and provide education to caregivers about appropriate strategies for managing agitation and utilizing effectivecommunication. The Capstone project incorporated technology through the use of applications (apps) on an iPad to deliver both interventions and education. Methods Repeated measures included the Agitated Behavior Scale and observation of nonverbal satisfaction. A revised Learning About Dementia: Test Questions and Family Education Questionnaire were used as pre/posttest and posttest only measures, respectively. Sample A total of five older adults with dementia participated in the agitation component of the program. For the educational component of the program, a total of eight family caregivers received educational information, but data was only collected from six of the participants. Implementation The Interpersonal APProach to Dementia program was conducted at Community LIFE: East End, an adult day center in Wilkinsburg, Pennsylvania. Four apps were utilized for the agitation component of the program. For the educational component, meetings or email correspondence with caregivers occurred to discuss important information about dementia, including common symptoms and how to interact with individuals with this diagnosis. Key Findings The long-term effects of the apps on decreasing agitation were not statistically significant. However, multiple factors, including small sample size and consistent ABS scores for participants, were taken into consideration. Caregiversā€™ knowledge about dementia increased significantly after program participation

    Functional assessment in older people

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    I-care-an interaction system for the individual activation of people with dementia

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    I-CARE is a hand-held activation system that allows professional and informal caregivers to cognitively and socially activate people with dementia in joint activation sessions without special training or expertise. I-CARE consists of an easy-to-use tablet application that presents activation content and a server-based backend system that securely manages the contents and events of activation sessions. It tracks various sources of explicit and implicit feedback from user interactions and different sensors to estimate which content is successful in activating individual users. Over the course of use, I-CAREā€™s recommendation system learns about the individual needs and resources of its users and automatically personalizes the activation content. In addition, information about past sessions can be retrieved such that activations seamlessly build on previous sessions while eligible stakeholders are informed about the current state of care and daily form of their protegees. In addition, caregivers can connect with supervisors and professionals through the I-CARE remote calling feature, to get activation sessions tracked in real time via audio and video support. In this way, I-CARE provides technical support for a decentralized and spontaneous formation of ad hoc activation groups and fosters tight engagement of the social network and caring community. By these means, I-CARE promotes new care infrastructures in the community and the neighborhood as well as relieves professional and informal caregivers

    Mobile health assessments of geriatric elements in older patients with atrial fibrillation: The Mobile SAGE-AF Study (M-SAGE)

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    Background Geriatric conditions (eg, cognitive impairment, frailty) are increasingly recognized for their impact on clinical and quality-of-life outcomes in older patients with cardiovascular disease, but are not systematically assessed in the context of clinical visits owing to time constraints. Objective To examine feasibility of remote monitoring of the physical, cognitive, and psychosocial status of older adults with atrial fibrillation (AF) via a novel smartphone app over 6 months. Methods Forty participants with AF and eligible for anticoagulation therapy (CHA2DS2VASc ā‰„2) enrolled in an ongoing cohort study participated in a mobile health pilot study. A 6-component geriatric assessment, including validated measures of frailty, cognitive function, social support, depressive symptoms, vision, and hearing, was deployed via a smartphone app and 6-minute walk test was completed using a Fitbit. Adherence to mobile assessments was examined over 6 months. Results Participants were an average of 71 years old (range 65ā€“86 years) and 38% were women. At 1 month, 75% (30/40) of participants completed the app-based geriatric assessment and 63% (25/40) completed the 6-minute walk test. At 6 months, 52% (15/29) completed the geriatric assessment and 28% (8/29) completed the walk test. There were no differences in demographic, clinical, or psychosocial factors between participants who completed the surveys at 6 months and those who did not. Participants, on average, required less than 10 minutes of telephone support over the 6-month period. Conclusion It is feasible, among smartphone users, to use a mobile health app and wearable activity monitor to conduct serial geriatric assessments in older patients with AF for up to 6 months
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