TIHM: An open dataset for remote healthcare monitoring in dementia.

Dementia is a progressive condition that affects cognitive and functional abilities. There is a need for reliable and continuous health monitoring of People Living with Dementia (PLWD) to improve their quality of life and support their independent living. Healthcare services often focus on addressing and treating already established health conditions that affect PLWD. Managing these conditions continuously can inform better decision-making earlier for higher-quality care management for PLWD. The Technology Integrated Health Management (TIHM) project developed a new digital platform to routinely collect longitudinal, observational, and measurement data, within the home and apply machine learning and analytical models for the detection and prediction of adverse health events affecting the well-being of PLWD. This work describes the TIHM dataset collected during the second phase (i.e., feasibility study) of the TIHM project. The data was collected from homes of 56 PLWD and associated with events and clinical observations (daily activity, physiological monitoring, and labels for health-related conditions). The study recorded an average of 50 days of data per participant, totalling 2803 days.

Home monitoring of daily living activities and prediction of agitation risk in a cohort of people living with dementia.

BACKGROUND: People living with dementia (PLWD) have an increased susceptibility to developing adverse physical and psychological events. Internet of Things (IoT) technologies provides new ways to remotely monitor patients within the comfort of their homes, particularly important for the timely delivery of appropriate healthcare. Presented here is data collated as part of the on-going UK Dementia Research Institute's Care Research and Technology Centre cohort and Technology Integrated Health Management (TIHM) study. There are two main aims to this work: first, to investigate the effect of the COVID-19 quarantine on the performance of daily living activities of PLWD, on which there is currently little research; and second, to create a simple classification model capable of effectively predicting agitation risk in PLWD, allowing for the generation of alerts with actionable information by which to prevent such outcomes. METHOD: A within-subject, date-matched study was conducted on daily living activity data using the first COVID-19 quarantine as a natural experiment. Supervised machine learning approaches were then applied to combined physiological and environmental data to create two simple classification models: a single marker model trained using ambient temperature as a feature, and a multi-marker model using ambient temperature, body temperature, movement, and entropy as features. RESULT: There are 102 PLWD total included in the dataset, with all patients having an established diagnosis of dementia, but with ranging types and severity. The COVID-19 study was carried out on a sub-group of 21 patient households. In 2020, PLWD had a significant increase in daily household activity (p = 1.40e-08), one-way repeated measures ANOVA). Moreover, there was a significant interaction between the pandemic quarantine and patient gender on night-time bed-occupancy duration (p = 3.00e-02, two-way mixed-effect ANOVA). On evaluating the models using 10-fold cross validation, both the single and multi-marker model were shown to balance precision and recall well, having F1-scores of 0.80 and 0.66, respectively. CONCLUSION: Remote monitoring technologies provide a continuous and reliable way of monitoring patient day-to-day wellbeing. The application of statistical analyses and machine learning algorithms to combined physiological and environmental data has huge potential to positively impact the delivery of healthcare for PLWD.

Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia.

Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers.

Amisulpride for very late-onset schizophrenia-like psychosis: the ATLAS three-arm RCT.

BACKGROUND: Very late-onset (aged ≥ 60 years) schizophrenia-like psychosis (VLOSLP) occurs frequently but no placebo-controlled, randomised trials have assessed the efficacy or risks of antipsychotic treatment. Most patients are not prescribed treatment. OBJECTIVES: The study investigated whether or not low-dose amisulpride is superior to placebo in reducing psychosis symptoms over 12 weeks and if any benefit is maintained by continuing treatment thereafter. Treatment safety and cost-effectiveness were also investigated. DESIGN: Three-arm, parallel-group, placebo-controlled, double-blind, randomised controlled trial. Participants who received at least one dose of study treatment were included in the intention-to-treat analyses. SETTING: Secondary care specialist old age psychiatry services in 25 NHS mental health trusts in England and Scotland. PARTICIPANTS: Patients meeting diagnostic criteria for VLOSLP and scoring > 30 points on the Brief Psychiatric Rating Scale (BPRS). INTERVENTION: Participants were randomly assigned to three arms in a two-stage trial: (1) 100 mg of amisulpride in both stages, (2) amisulpride then placebo and (3) placebo then amisulpride. Treatment duration was 12 weeks in stage 1 and 24 weeks (later reduced to 12) in stage 2. Participants, investigators and outcome assessors were blind to treatment allocation. MAIN OUTCOME MEASURES: Primary outcomes were psychosis symptoms assessed by the BPRS and trial treatment discontinuation for non-efficacy. Secondary outcomes were extrapyramidal symptoms measured with the Simpson-Angus Scale, quality of life measured with the World Health Organization's quality-of-life scale, and cost-effectiveness measured with NHS, social care and carer work loss costs and EuroQol-5 Dimensions. RESULTS: A total of 101 participants were randomised. Ninety-two (91%) participants took the trial medication, 59 (64%) completed stage 1 and 33 (56%) completed stage 2 treatment. Despite suboptimal compliance, improvements in BPRS scores at 12 weeks were 7.7 points (95% CI 3.8 to 11.5 points) greater with amisulpride than with placebo (11.9 vs. 4.2 points; p = 0.0002). In stage 2, BPRS scores improved by 1.1 point in those who continued with amisulpride but deteriorated by 5.2 points in those who switched from amisulpride to placebo, a difference of 6.3 points (95% CI 0.9 to 11.7 points; p = 0.024). Fewer participants allocated to the amisulpride group stopped treatment because of non-efficacy in stages 1 (p = 0.01) and 2 (p = 0.031). The number of patients stopping because of extrapyramidal symptoms and other side effects did not differ significantly between groups. Amisulpride treatment in the base-case analyses was associated with non-significant reductions in combined NHS, social care and unpaid carer costs and non-significant reductions in quality-adjusted life-years (QALYs) in both stages. Including patients who were intensive users of inpatient services in sensitivity analyses did not change the QALY result but resulted in placebo dominance in stage 1 and significant reductions in NHS/social care (95% CI -£8923 to -£122) and societal costs (95% CI -£8985 to -£153) for those continuing with amisulpride. LIMITATIONS: The original recruitment target of 300 participants was not achieved and compliance with trial medication was highly variable. CONCLUSIONS: Low-dose amisulpride is effective and well tolerated as a treatment for VLOSLP, with benefits maintained by prolonging treatment. Potential adverse events include clinically significant extrapyramidal symptoms and falls. FUTURE WORK: Trials should examine the longer-term effectiveness and safety of antipsychotic treatment in this patient group, and assess interventions to improve their appreciation of potential benefits of antipsychotic treatment and compliance with prescribed medication. TRIAL REGISTRATION: Current Controlled Trials ISRCTN45593573 and EudraCT2010-022184-35. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 22, No. 67. See the NIHR Journals Library website for further project information.

Technology integrated health management for dementia.

Pioneering advances have been made in Internet of Things technologies (IoT) in healthcare. This article describes the development and testing of a bespoke IoT system for dementia care. Technology integrated health management (TIHM) for dementia is part of the NHS England National Test Bed Programme and has involved trailing the deployment of network enabled devices combined with artificial intelligence to improve outcomes for people with dementia and their carers. TIHM uses machine learning and complex algorithms to detect and predict early signs of ill health. The premise is if changes in a person's health or routine can be identified early on, support can be targeted at the point of need to prevent the development of more serious complications.

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques.

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients' routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.