27,523 research outputs found
Predictors of Post Prandial Glucose Level in Diabetic Elderly
Post prandial glucose (PPG) level describes the speed of glucose absorption after 2 hours of macronutrient consumption. By knowing this, we could get the big picture of insulin regulation function and macronutrient metabolism in our body. In elderly, age-related slower glucose metabolism leads to diabetes mellitus (DM) in older age. This study aimed to analyze the predictors of PPG level in diabetics elderly which consist of functional status, self-care activity, sleep quality, and stress level. Cross-sectional study design was applied in this study. There were 45 diabetic elderly participated by filling in study instruments. Pearson and Spearman Rank correlation test were used in data analysis (α<.05). Results showed that most respondents were female elderly, 60-74 years old, had DM for 1-5 years with no family history, and only 33.33% respondents reported regular consumption of oral anti diabetes (OAD). Hypertension was found to be frequent comorbidity. Statistical analysis results showed that functional status, self-care activity, sleep quality, and stress level were not significantly correlated with PPG level in diabetic elderly (all p>α), therefore these variables could not be PPG level predictors. Other factors may play a more important role in predicting PPG level in diabetic elderly
How will the Internet of Things enable Augmented Personalized Health?
Internet-of-Things (IoT) is profoundly redefining the way we create, consume,
and share information. Health aficionados and citizens are increasingly using
IoT technologies to track their sleep, food intake, activity, vital body
signals, and other physiological observations. This is complemented by IoT
systems that continuously collect health-related data from the environment and
inside the living quarters. Together, these have created an opportunity for a
new generation of healthcare solutions. However, interpreting data to
understand an individual's health is challenging. It is usually necessary to
look at that individual's clinical record and behavioral information, as well
as social and environmental information affecting that individual. Interpreting
how well a patient is doing also requires looking at his adherence to
respective health objectives, application of relevant clinical knowledge and
the desired outcomes.
We resort to the vision of Augmented Personalized Healthcare (APH) to exploit
the extensive variety of relevant data and medical knowledge using Artificial
Intelligence (AI) techniques to extend and enhance human health to presents
various stages of augmented health management strategies: self-monitoring,
self-appraisal, self-management, intervention, and disease progress tracking
and prediction. kHealth technology, a specific incarnation of APH, and its
application to Asthma and other diseases are used to provide illustrations and
discuss alternatives for technology-assisted health management. Several
prominent efforts involving IoT and patient-generated health data (PGHD) with
respect converting multimodal data into actionable information (big data to
smart data) are also identified. Roles of three components in an evidence-based
semantic perception approach- Contextualization, Abstraction, and
Personalization are discussed
East Midlands Research into Ageing Network (EMRAN) Discussion Paper Series
Academic geriatric medicine in Leicester
.
There has never been a better time to consider joining us. We have recently appointed a
Professor in Geriatric Medicine, alongside Tom Robinson in stroke and Victoria Haunton,
who has just joined as a Senior Lecturer in Geriatric Medicine. We have fantastic
opportunities to support students in their academic pursuits through a well-established
intercalated BSc programme, and routes on through such as ACF posts, and a successful
track-record in delivering higher degrees leading to ACL post. We collaborate strongly
with Health Sciences, including academic primary care. See below for more detail on our
existing academic set-up.
Leicester Academy for the Study of Ageing
We are also collaborating on a grander scale, through a joint academic venture focusing
on ageing, the ‘Leicester Academy for the Study of Ageing’ (LASA), which involves the
local health service providers (acute and community), De Montfort University; University
of Leicester; Leicester City Council; Leicestershire County Council and Leicester Age UK.
Professors Jayne Brown and Simon Conroy jointly Chair LASA and have recently been
joined by two further Chairs, Professors Kay de Vries and Bertha Ochieng. Karen
Harrison Dening has also recently been appointed an Honorary Chair.
LASA aims to improve outcomes for older people and those that care for them that takes
a person-centred, whole system perspective. Our research will take a global perspective,
but will seek to maximise benefits for the people of Leicester, Leicestershire and Rutland,
including building capacity. We are undertaking applied, translational, interdisciplinary
research, focused on older people, which will deliver research outcomes that address
domains from: physical/medical; functional ability, cognitive/psychological; social or
environmental factors. LASA also seeks to support commissioners and providers alike for
advice on how to improve care for older people, whether by research, education or
service delivery. Examples of recent research projects include: ‘Local History Café’
project specifically undertaking an evaluation on loneliness and social isolation; ‘Better
Visits’ project focused on improving visiting for family members of people with dementia
resident in care homes; and a study on health issues for older LGBT people in Leicester.
Clinical Geriatric Medicine in Leicester
We have developed a service which recognises the complexity of managing frail older
people at the interface (acute care, emergency care and links with community services).
There are presently 17 consultant geriatricians supported by existing multidisciplinary
teams, including the largest complement of Advance Nurse Practitioners in the country.
Together we deliver Comprehensive Geriatric Assessment to frail older people with
urgent care needs in acute and community settings.
The acute and emergency frailty units – Leicester Royal Infirmary
This development aims at delivering Comprehensive Geriatric Assessment to frail older
people in the acute setting. Patients are screened for frailty in the Emergency
Department and then undergo a multidisciplinary assessment including a consultant
geriatrician, before being triaged to the most appropriate setting. This might include
admission to in-patient care in the acute or community setting, intermediate care
(residential or home based), or occasionally other specialist care (e.g. cardiorespiratory).
Our new emergency department is the county’s first frail friendly build and includes
fantastic facilities aimed at promoting early recovering and reducing the risk of hospital
associated harms.
There is also a daily liaison service jointly run with the psychogeriatricians (FOPAL); we
have been examining geriatric outreach to oncology and surgery as part of an NIHR
funded study.
We are home to the Acute Frailty Network, and those interested in service developments
at the national scale would be welcome to get involved.
Orthogeriatrics
There are now dedicated hip fracture wards and joint care with anaesthetists,
orthopaedic surgeons and geriatricians. There are also consultants in metabolic bone
disease that run clinics.
Community work
Community work will consist of reviewing patients in clinic who have been triaged to
return to the community setting following an acute assessment described above.
Additionally, primary care colleagues refer to outpatients for sub-acute reviews. You will
work closely with local GPs with support from consultants to deliver post-acute, subacute,
intermediate and rehabilitation care services.
Stroke Medicine
24/7 thrombolysis and TIA services. The latter is considered one of the best in the UK
and along with the high standard of vascular surgery locally means one of the best
performances regarding carotid intervention
Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal
Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal
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Trends in virtual reality technologies for the learning patient
NextMed convened the Medicine Meets Virtual Reality 22 (MMVR 22) conference in 2016. Since 1992, the conference has brought together a diverse group of researchers to share creative solutions for the evolving challenge of integrating virtual reality tools into medical education. Virtual reality (VR) and its enabling technologies utilize hardware and software to simulate environments and encounters where users can interact and learn. The MMVR 22 symposium proceedings contain projects that support a variety of learners: medical students, practitioners, soldiers, and patients. This report will contemplate the trends in virtual reality technologies for patients navigating their medical and healthcare learning. The learning patient seeks more than intervention; they seek prevention. From virtual humans and environments to motion sensors and haptic devices, patients are surrounded by increasingly rich and transformative data-driven tools. Applied data enables VR applications to simulate experience, predict health outcomes, and motivate new behavior. The MMVR 22 presents investigations into the usability of wearable devices, the efficacy of avatar inclusion, and the viability of multi-player gaming. With increasing need for individualized and scalable programming, only committed open source efforts will align instructional designers, technology integrators, trainers, and clinicians. Curriculum and InstructionCurriculum and Instructio
CoachAI: A Conversational Agent Assisted Health Coaching Platform
Poor lifestyle represents a health risk factor and is the leading cause of
morbidity and chronic conditions. The impact of poor lifestyle can be
significantly altered by individual behavior change. Although the current shift
in healthcare towards a long lasting modifiable behavior, however, with
increasing caregiver workload and individuals' continuous needs of care, there
is a need to ease caregiver's work while ensuring continuous interaction with
users. This paper describes the design and validation of CoachAI, a
conversational agent assisted health coaching system to support health
intervention delivery to individuals and groups. CoachAI instantiates a text
based healthcare chatbot system that bridges the remote human coach and the
users. This research provides three main contributions to the preventive
healthcare and healthy lifestyle promotion: (1) it presents the conversational
agent to aid the caregiver; (2) it aims to decrease caregiver's workload and
enhance care given to users, by handling (automating) repetitive caregiver
tasks; and (3) it presents a domain independent mobile health conversational
agent for health intervention delivery. We will discuss our approach and
analyze the results of a one month validation study on physical activity,
healthy diet and stress management
Is the timed-up and go test feasible in mobile devices? A systematic review
The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.info:eu-repo/semantics/publishedVersio
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