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. Generally speaking, the elderly are not well-disposed to technologies and have limited experience; these factors contribute towards limiting the widespread acceptance of technology. It is therefore important to evaluate the potential success of technologies prior to their deployment. MATERIALS AND METHODS: The research described in this paper builds upon our previous work on modelling adoption of assistive technology, in the form of cognitive prosthetics such as reminder apps and aims at identifying a refined sub-set of features which offer improved accuracy in predicting technology adoption. Consequently, in this paper, an adoption model is built using a set of features extracted from a user's background to minimise the likelihood of non-adoption. The work is based on analysis of data from the Cache County Study on Memory and Aging (CCSMA) with 31 features covering a range of age, gender, education and details of health condition. In the process of modelling adoption, feature selection and feature reduction is carried out followed by identifying the best classification models. FINDINGS: With the reduced set of labelled features the technology adoption model built achieved an average prediction accuracy of 92.48% when tested on 173 participants. CONCLUSIONS: We conclude that modelling user adoption from a range of parameters such as physical, environmental and social perspectives is beneficial in recommending a technology to a particular user based on their profile.

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. Therefore, it is important to identify factors that contribute to maternal BLL and minimise them to reduce the transfer to the foetus. Literacy and awareness related to its impact are low and the clinical establishment for biological monitoring of blood lead level (BLL) is low, costly, and time-consuming. A significant contribution to an infant's BLL load is caused by maternal lead transfer during pregnancy. This acts as the first pathway to the infant's lead exposure. The social and demographic information that includes lifestyle and environmental factors are key to maternal lead exposure. Results: We propose a novel approach to build a computational model framework that can predict lead toxicity levels in maternal blood using a set of sociodemographic features. To illustrate our proposed approach, maternal data comprising socio-demographic features and blood samples from the pregnant woman is collected, analysed, and modelled. The computational model is built that learns from the maternal data and then predicts lead level in a pregnant woman using a set of questionnaires that relate to the maternal's social and demographic information as the first point of testing. The range of features identified in the built models can estimate the underlying function and provide an understanding of the toxicity level. Following feature selection methods, the 12-feature set obtained from the Boruta algorithm gave better prediction results (kNN = 76.84%, DT = 74.70%, and NN = 73.99%). Conclusion: The built prediction model can be beneficial in improving the point of care and hence reducing the cost and the risk involved. It is envisaged that in future, the proposed methodology will become a part of a screening process to assist healthcare experts at the point of evaluating the lead toxicity level in pregnant women. Women screened positive could be given a range of facilities including preliminary counselling to being referred to the health centre for further diagnosis. Steps could be taken to reduce maternal lead exposure; hence, it could also be possible to mitigate the infant's lead exposure by reducing transfer from the pregnant woman.

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. Studies were classified by telemedicine class; store-and-forward (SAF), real-time consultation (RTC) and telecare (TC), and study stage. Studies were scanned for evaluation metric reporting, i.e. clinical outcomes, satisfaction, patient quality and cost measures. FINDINGS: Evaluation metric use was compared among telemedicine classes, and between pilot and routine use stages. Diagnostic accuracy was reported significantly more frequently in pilots for RTC and TC. Cost measures were more frequently reported in routine use for TC. Clinical effectiveness and hospital attendance were better reported in routine use for SAF. Comparison also revealed different evaluation strategies. In pilots, SAF favoured diagnostic accuracy, compared to RTC and TC. TC preferred clinical effectiveness evaluations and TC more frequently assessed patient satisfaction. Cost was only reported in less than 20 per cent of studies, but most frequently in RTC. Routine use led to increased reporting of all metrics, except diagnostic accuracy. Clinical effectiveness reporting increased significantly with routine use for RTC and SAF, but declined for TC. ORIGINALITY/VALUE: Clinical outcomes and patient satisfaction were reported frequently in telemedicine studies, but reporting of other performance metrics was rare. Understanding current trends in metric reporting will facilitate better design of future telemedicine evaluations.

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. CONCLUSIONS: Multi-state modelling is an appropriate means of analysing data on geriatric patient care and can reveal underlying patterns of differential effects, some of which may not be apparent from more routine data processing.

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. OBJECTIVE: The objective of this study was to reduce the future risk of developing AD, while in the short term promoting vascular health, through behavior change. METHODS: The study was an interventional randomized controlled trial consisting of subjects who were randomly assigned into either treatment (n=102) or control group (n=42). Outcome measures included various blood-based biomarkers, anthropometric measures, and behaviors related to AD risk. The treatment group was provided with a bespoke "Gray Matters" mobile phone app designed to encourage and facilitate behavior change. The app presented evidence-based educational material relating to AD risk and prevention strategies, facilitated self-reporting of behaviors across 6 behavioral domains, and presented feedback on the user's performance, calculated from reported behaviors against recommended guidelines. RESULTS: This paper explores the rationale for a mobile phone-led intervention and details the app's effect on behavior change and subsequent clinical outcomes. Via the app, the average participant submitted 7.3 (SD 3.2) behavioral logs/day (n=122,719). Analysis of these logs against primary outcome measures revealed that participants who improved their high-density lipoprotein cholesterol levels during the study duration answered a statistically significant higher number of questions per day (mean 8.30, SD 2.29) than those with no improvement (mean 6.52, SD 3.612), t97.74=-3.051, P=.003. Participants who decreased their body mass index (BMI) performed significantly better in attaining their recommended daily goals (mean 56.21 SD 30.4%) than those who increased their BMI (mean 40.12 SD 29.1%), t80 = -2.449, P=.017. In total, 69.2% (n=18) of those who achieved a mean performance percentage of 60% or higher, across all domains, reduced their BMI during the study, whereas 60.7% (n=34) who did not, increased their BMI. One-way analysis of variance of systolic blood pressure category changes showed a significant correlation between reported efforts to reduce stress and category change as a whole, P=.035. An exit survey highlighted that respondents (n=83) reported that the app motivated them to perform physical activity (85.4%) and make healthier food choices (87.5%). CONCLUSIONS: In this study, the ubiquitous nature of the mobile phone excelled as a delivery platform for the intervention, enabling the dissemination of educational intervention material while simultaneously monitoring and encouraging positive behavior change, resulting in desirable clinical effects. Sustained effort to maintain the achieved behaviors is expected to mitigate future AD risk. TRIAL REGISTRATION: ClinicalTrails.gov NCT02290912; https://clinicaltrials.gov/ct2/show/NCT02290912 (Archived by WebCite at http://www.webcitation.org/6ictUEwnm).

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. This paper presents the use of the Mobile App Rating Scale (MARS) to assess the quality of the Grey Matters app, a cross platform app to deliver health education material and track behavior change across multi-domains with the aim of reducing the risk of developing Alzheimer's disease. The Gray Matters app shows promising results following reviews from 5 Expert raters, achieving a mean overall MARS score of 4.45 ± 0.14. Future work will involve undertaking of a detailed content analysis of behavior change apps to identify common themes and features which may lead to the successful facilitation of sustained behavior change.

Technology adoption and prediction tools for everyday technologies aimed at people with dementia.

A wide range of assistive technologies have been developed to support the elderly population with the goal of promoting independent living. The adoption of these technology based solutions is, however, critical to their overarching success. In our previous research we addressed the significance of modelling user adoption to reminding technologies based on a range of physical, environmental and social factors. In our current work we build upon our initial modeling through considering a wider range of computational approaches and identify a reduced set of relevant features that can aid the medical professionals to make an informed choice of whether to recommend the technology or not. The adoption models produced were evaluated on a multi-criterion basis: in terms of prediction performance, robustness and bias in relation to two types of errors. The effects of data imbalance on prediction performance was also considered. With handling the imbalance in the dataset, a 16 feature-subset was evaluated consisting of 173 instances, resulting in the ability to differentiate between adopters and non-adopters with an overall accuracy of 99.42 %.

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. In this paper, a predictive adoption model for a mobile phone-based video streaming system, developed for people with dementia, is presented. Taking into consideration characteristics related to a person's ability, living arrangements, and preferences, this paper discusses the development of predictive models, which were based on a number of carefully selected data mining algorithms for classification. For each, the learning on different relevant features for technology adoption has been tested, in conjunction with handling the imbalance of available data for output classes. Given our focus on providing predictive tools that could be used and interpreted by healthcare professionals, models with ease-of-use, intuitive understanding, and clear decision making processes are preferred. Predictive models have, therefore, been evaluated on a multi-criterion basis: in terms of their prediction performance, robustness, bias with regard to two types of errors and usability. Overall, the model derived from incorporating a k-Nearest-Neighbour algorithm using seven features was found to be the optimal classifier of assistive technology adoption for people with dementia (prediction accuracy 0.84 ± 0.0242).

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. Therefore it is of merit to establish the individual parameters that characterize an adopter and non-adopter, to better target future interventions and their deployment. To aid the collection of this data a smartphone app was developed for persons with dementia. It has been designed as both a reminder application to help those with dementia accommodate their forgetfulness and a data collection tool to log usage and compliance with reminders. The app has been evaluated by a pre-pilot cohort (n=9) and was found to have a mean reminder acknowledgement of 73.09%.

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., once daily), the expense of using observers to label activities, and the intrusion that would be caused by the presence of observers over long time periods. It is important, therefore, to make as much use of any labeled data that are collected, however, incomplete these data may be. In this paper, we propose an algorithm for learning behavioral patterns for multi-inhabitants living in a single smart home environment, by making full use of all limited labeled activities, including incomplete data resulting from unreliable low-level sensors in this environment. Through maximum-likelihood estimation, using Expectation-Maximization, we build a model that captures both environmental uncertainties from sensor readings and user uncertainties, including variations in how individuals carry out activities. Our algorithm outperforms models that cannot handle data incompleteness, with increasing performance gains as incompleteness increases. The approach also enables the impact of particular sensors to be assessed and can thus inform sensor maintenance and deployment.

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. The requirements were subsequently mapped onto current technologies as part of the technical design process. In this paper we outline the development and second round of evaluations of a prototype as part of a three-phase iterative development cycle. The evaluation was undertaken with 6 participants aged between 60 and 73 years of age. The results of the evaluation demonstrate the potential role that technology can play in the promotion of activity regimes for the older population.

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. A validation study has been conducted in a lab-based environment with 10 healthy young participants. The work presented discusses the implementation of the approach adopted, data analysis of the results attained along with outlining future developments of this approach.

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.

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. The results show why 10-15% bed emptiness is necessary to maintain service efficiency and demonstrate how unstaffed beds can serve to provide a more responsive and cost effective service. Further work is needed to test the validity and applicability of the model.

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. This paper examines previously developed models for patient length of stay in hospital and describes the recently developed conditional phase-type distribution (C-Ph) to model patient duration of stay in relation to explanatory patient variables. The Clinics data set was used to demonstrate the C-Ph methodology. The resulting model highlighted a strong relationship between Barthel grade, patient outcome and length of stay showing various groups of patient behaviour. The patients who stay in hospital for a very long time are usually those that consume the largest amount of hospital resources. These have been identified as the patients whose resulting outcome is transfer. Overall, the majority of transfer patients spend a considerably longer period of time in hospital compared to patients who die or are discharged home. The C-Ph model has the potential for considering costs where different costs are attached to the various phases or subgroups of patients and the anticipated cost of care estimated in advance. It is hoped that such a method will lead to the successful identification of the most cost effective case-mix management of the hospital ward.