Automated lead toxicity prediction using computational modelling framework.

Background: Lead, an environmental toxicant, accounts for 0.6% of the global burden of disease, with the highest burden in developing countries. Lead poisoning is very much preventable with adequate and timely action.

Assessing app quality through expert peer review: a case study from the gray matters study.

Health apps focused on inciting behavior change are becoming increasingly popular. Nevertheless, many lack underlying evidence base, scientific credibility and have limited clinical effectiveness. It is therefore important that apps are well-informed, scientifically credible, peer reviewed and evidence based.

Modelling assistive technology adoption for people with dementia.

Purpose: Assistive technologies have been identified as a potential solution for the provision of elderly care. Such technologies have in general the capacity to enhance the quality of life and increase the level of independence among their users. Nevertheless, the acceptance of these technologies is crucial to their success.

The Empowering Role of Mobile Apps in Behavior Change Interventions: The Gray Matters Randomized Controlled Trial.

Background: Health education and behavior change programs targeting specific risk factors have demonstrated their effectiveness in reducing the development of future diseases. Alzheimer disease (AD) shares many of the same risk factors, most of which can be addressed via behavior change. It is therefore theorized that a behavior change intervention targeting these risk factors would likely result in favorable rates of AD prevention.

A smartphone application to evaluate technology adoption and usage in persons with dementia.

Dementia affects a proportionally large number of the older population, presenting a set of symptoms that cause cognitive decline and negatively affect quality of life. Technology offers an assistive role for some of these symptoms, specifically in addressing forgetfulness. Current works have explored the benefits of reminding technology, which whilst useful is only effective for those who adopt the technology.

A predictive model for assistive technology adoption for people with dementia.

Assistive technology has the potential to enhance the level of independence of people with dementia, thereby increasing the possibility of supporting home-based care. In general, people with dementia are reluctant to change; therefore, it is important that suitable assistive technologies are selected for them. Consequently, the development of predictive models that are able to determine a person's potential to adopt a particular technology is desirable.

Evaluation of a technology enabled garment for older walkers.

Walking is often cited as the best form of activity for persons over the age of 60. In this paper we outline the development and evaluation of a smart garment system that aims to monitor the wearer's wellbeing and activity regimes during walking activities. Functional requirements were ascertained using a combination of questionnaires and two workshops with a target cohort.

Trends in telemedicine assessment indicate neglect of key criteria for predicting success.

Purpose: This innovative analysis aims to quantify the use of evaluation criteria in telemedicine and to identify current trends in metric adoption. The focus is to determine the frequency of actual performance metric reporting in telemedicine evaluation, in contrast to systematic reviews where assessment of study quality is the goal.

Design/methodology/approach: Automated literature search identified telemedicine studies reporting quantitative performance metrics.

Probabilistic learning from incomplete data for recognition of activities of daily living in smart homes.

Learning behavioral patterns for activities of daily living in a smart home environment can be challenged by the limited number of training data that may be available. This may be due to the infrequent repetition of routine activities (e.g.

Assessing task compliance following mobile phone-based video reminders.

In the development of technology for people with mild dementia it is essential to achieve a combination of the features which provide both support and monitoring along with the ability to offer a level of personalization. Reminding support by means of personalized video reminders portraying a relative or friend combined with sensors to assess whether the requested task was performed lends itself as an ideal combination to achieve this aim. This study assesses the potential of using low cost, off the shelf sensors combined with a mobile phone-based video reminding system to assess compliance with task completion.

Using a multi-state model to enhance understanding of geriatric patient care.

Objectives: To use multi-state Markov chain modelling to analyse data on geriatric patient care, and to make comparisons between male and female patients.

Methods: Estimation, from observed data, of covariate (age of patient and date of admission to hospital or community care) dependent parameters of statistical models for time in care and subsequent events.

Results: Differential effects of these covariates shown on the parameters of the models for female and male patients, where these parameters can be interpreted as affecting different features of the distributions of time in care.

Estimating the costs for a group of geriatric patients using the Coxian phase-type distribution.

The length of stay in hospital of geriatric patients may be modelled using the Coxian phase-type distribution. This paper examines previous methods which have been used to model health-care costs and presents a new methodology to estimate the costs for a cohort of patients for their duration of stay in hospital, assuming there are no further admissions. The model, applied to 1392 patients admitted into the geriatric ward of a local hospital in Northern Ireland, between 2002 and 2003, should be beneficial to hospital managers, as future decisions and policy changes could be tested on the model to investigate their influence on costs before the decisions were carried out on a real ward.

Using Coxian phase-type distributions to identify patient characteristics for duration of stay in hospital.

Coxian phase-type distributions are a special type of Markov model that describes duration until an event occurs in terms of a process consisting of a sequence of latent phases. This paper considers the use of Coxian phase-type distributions for modelling patient duration of stay for the elderly in hospital and investigates the potential for using the resulting distribution as a classifying variable to identify common characteristics between different groups of patients according to their (anticipated) length of stay in hospital. The identification of common characteristics for patient length of stay groups would offer hospital managers and clinicians possible insights into the overall management and bed allocation of the hospital wards.

Addressing bed costs for the elderly: a new methodology for modelling patient outcomes and length of stay.

The proportion of elderly in the population has dramatically increased and will continue to do so for at least the next 50 years. Medical resources throughout the world are feeling the added strain of the increasing proportion of elderly in the population. The effective care of elderly patients in hospitals may be enhanced by accurately modelling the length of stay of the patients in hospital and the associated costs involved.

Using a queueing model to help plan bed allocation in a department of geriatric medicine.

By integrating queuing theory and compartmental models of flow we demonstrate how changing admission rates, length of stay and bed allocation influence bed occupancy, emptiness and rejection in departments of geriatric medicine. By extending the model to include waiting beds, we show how the provision of extra, emergency use, unstaffed, back up beds could improve performance while controlling costs. The model is applicable to all lengths of stay, admission rates and bed allocations.