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

ABSTRACT: 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.

SUBMITTER: Enshaeifar S

PROVIDER: S-EPMC6333356 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Enshaeifar Shirin S Zoha Ahmed A Skillman Severin S Markides Andreas A Acton Sahr Thomas ST Elsaleh Tarek T Kenny Mark M Rostill Helen H Nilforooshan Ramin R Barnaghi Payam P

PloS one 20190115 1

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 ...[more]

PMID: 30645599

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Project description:Objectives:To evaluate application of the Barthel Index (BI) in assessing basic activities of daily living (ADL) of patients with dementia using Rasch analysis. Design:A multi-country cross-sectional study. Setting and Participants:Nineteen long-term care facilities located in China, Japan, South Korea, and Thailand. A total of 644 patients with dementia were included. Methods:Unidimensionality, global and item fit, local dependence, person-item targeting, threshold disordering, and differential item functioning (DIF) were examined. Negative correlations between scores for DIF items and Neuropsychiatric Inventory Nursing Home version (NPI-NH) were evaluated. Results:Item reliability (1.0) and person reliability (.88) were acceptable. The Rasch dimension explained 72.9% of the variance (Eigenvalue = 27), while the first contrast explained 6.6% (Eigenvalue = 2.4). The "mobility" was misfitting to the Rasch model (infit mean square = 1.86). The overall difficulty of the BI exceeded patients' ability (person location = -2.27 logits). The "stairs climbing" and "mobility" showed narrow category thresholds (< 1.4 logits). The location of "controlling bladder" and "toilet use" overlapped. Removing "stairs climbing", collapsing categories with narrow threshold widths in "mobility", and combining "controlling bowel" and "controlling bladder" into one item, improved unidimensionality, and item fit of the scale. Only three items ("grooming", "dressing", and "toilet use") were free from DIF across countries. The scores for "feeding" were negatively related to scores for "disinhibition" (r = -0.46, P < 0.01), and scores for "controlling bowel" were negatively related to scores for "disinhibition" (r = -0.44, P < 0.01), "agitation" (r = -0.32, P < 0.05), and "aggression" (r = -0.27, P < 0.01) in Japanese samples. Conclusions and Implications:The performance of the BI for assessing patients with dementia might be compromised by misfit items, person-item mistargeting, measurement gaps, redundant items, narrow threshold width, and item bias. Mobility ability might not be helpful for determining capability of basic ADL in the patients. Comparisons of BI scores between countries should be undertaken with caution due to item bias. Neuropsychiatric symptoms might interact with basic ADL abilities of the patients. We will not suggest using the instrument in patients with dementia, without future refining to improve its performance.

Project description:BackgroundThe prevalence of dementia is expected to increase dramatically. Due to a lack of pharmacological treatment options for people with dementia, non-pharmacological treatments such as exercise programs have been recommended to improve cognition, activities of daily living, and neuropsychiatric symptoms. However, inconsistent results have been reported across different trials, mainly because of the high heterogeneity of exercise modalities. Thus, this systematic review aims to answer the questions whether exercise programs improve cognition, activities of daily living as well as neuropsychiatric symptoms in community-dwelling people with dementia.MethodsEight databases were searched for articles published between 2016 and 2021 (ALOIS, CENTRAL, CINAHL, Embase, MEDLINE, PsycINFO, PubMed, Web of Science). Randomized controlled trials evaluating the effects of any type of physical activity on cognition, activities of daily living, or neuropsychiatric symptoms in community-dwelling people with a formal diagnosis of dementia were included in this systematic review. Two authors independently assessed eligibility and quality of the studies. The methodology was in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines.ResultsEight publications covering seven trials were included in this review with the majority investigating either a combination of strength and aerobic exercise or aerobic exercise alone. This review revealed that there is no clear evidence for the beneficial effects of exercise on cognition. None of the included trials found an impact on activities of daily living. Although different randomized controlled trials reported inconsistent results, one trial indicated that especially aerobic exercise may improve neuropsychiatric symptoms.ConclusionOur systematic review did not confirm the impact of exercise on cognition and activities of daily living in community-dwelling people with dementia. The results suggested that aerobic exercise might be effective to reduce neuropsychiatric symptoms. Well-designed trials including only community-dwelling people with a formal diagnosis of dementia, large samples, long-term follow-ups, and detailed description of adherence to the intervention are needed to improve the scientific evidence on the best type of exercise modality.Trial registrationPROSPERO, CRD42021246598 .

Project description:Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V?o2) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V?o2 kinetics). This study evaluated aerobic system dynamics based on predicted V?o2 data obtained from wearable sensors during unsupervised activities of daily living (?ADL). Thirteen healthy men performed a laboratory-controlled moderate exercise protocol and were monitored for ?6 h/day for 4 days (?ADL data). Variables derived from hip accelerometer (ACCHIP), heart rate monitor, and respiratory bands during ?ADL were extracted and processed by a validated random forest regression model to predict V?o2. The aerobic system analysis was based on the frequency-domain analysis of ACCHIP and predicted V?o2 data obtained during ?ADL. Optimal samples for frequency domain analysis (constrained to ?0.01 Hz) were selected when ACCHIP was higher than 0.05 g at a given frequency (i.e., participants were active). The temporal characteristics of predicted V?o2 data during ?ADL correlated with the temporal characteristics of measured V?o2 data during laboratory-controlled protocol ([Formula: see text]?=?0.82, P < 0.001, n?=?13). In conclusion, aerobic system dynamics can be investigated during unsupervised activities of daily living by wearable sensors. Although speculative, these algorithms have the potential to be incorporated into wearable systems for early detection of changes in health status in realistic environments by detecting changes in aerobic response dynamics. NEW & NOTEWORTHY The early detection of subclinical aerobic system impairments might be indicative of impaired physiological reserves that impact the capacity for physical activity. This study is the first to use wearable sensors in unsupervised activities of daily living in combination with novel machine learning algorithms to investigate the aerobic system dynamics with the potential to contribute to models of functional health status and guide future individualized health care in the normal population.

Project description:BackgroundThe everyday practical capabilities of dementia patients have a direct influence on a patient's independence and thus on the person's quality of life and on the amount of care needed. These capabilities are therefore important as therapeutic goals and are also important from a health-economic point of view. To date, no economical and valid performance test is available. The E-ADL-Test developed by Grä?el et al. in 2009 is a short performance test that has, however, only been validated on a small sample thus far. The objective of the present study is to re-validate the E-ADL-Test and explore possibilities for further development.MethodsThe data were obtained from an RCT with a sample of 139 dementia patients in 5 nursing homes in Bavaria (Germany). The internal consistency was calculated as a measure of reliability. An item analysis was performed for the sample and subgroups with various degrees of dementia. Criterion and construct validity were tested based on five hypotheses. For validation, the residents' capabilities were examined using the Barthel-Index (BI), the Nurses' Observation Scale for Geriatric Patients (NOSGER), the Alzheimer's Disease Assessment Scale (ADAS), and the Mini-Mental Status Examination (MMSE).ResultsThe internal consistency was .68 for the sample and .73 for the subgroup with severe dementia. The item analysis yielded good difficulty indices and discrimination power for moderate and severe dementia. The tasks were found to be too easy for mild dementia. The predictive criterion-related validity was confirmed by a correlation of r = .54 with the care level after 22 months and significant mean differences in the E-ADL-Test between persons with and without an increase in the care level. A differentiated correlation profile supported the three hypotheses on construct validity.ConclusionsThe E-ADL-Test in its current form is a valid and reliable instrument for assessing the ADL capabilities of patients with moderate and severe dementia. More difficult items should be developed for use with mild dementia.Trial registrationhttp://www.isrctn.com Identifier: ISRCTN87391496.

Project description:Background and purposeScreening tests for dementia such as the Mini Mental State Examination (MMSE) and the Montreal Cognitive Assessment are widely used, but there are drawbacks to their efficient use. There remains a need for a brief and easy method of assessing the activities of daily living (ADL) that can be administered to elderly individuals by healthcare workers. We have therefore developed a new scale named the Simple Observation Checklist for Activities of Daily Living (SOC-ADL).MethodsWe developed the SOC-ADL scale as a team of experts engaged in caring for individuals with dementia. This scale comprises eight items and was designed based on the Korean instrumental activities of daily living (K-IADL) scale and the Barthel activities of daily living scale (Barthel Index). The new scale was validated by enrolling 176 patients with cognitive dysfunction across 6 centers. Confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) were performed. We assessed its concurrent validity by performing comparisons with the Korean-MMSE, Clinical Dementia Rating, Clinical Dementia Rating-Sum of Boxes, K-IADL, and Barthel Index, and its criterion validity by performing comparisons between mild cognitive impairment (MCI) and dementia. We also used Cronbach's alpha to assess the interitem reliability. The appropriate cutoff values were determined by analyzing receiver operating characteristic curves, including the areas underneath them.ResultsEFA extracted one factor and CFA revealed that all of the model fits exceeded the minimum acceptable criteria. The SOC-ADL scores were strongly correlated with those of the other tools for dementia and could be used to differentiate MCI from dementia. Cronbach's alpha values indicated that the results were reliable. The optimal cutoff value of the SOC-ADL for discriminating dementia from MCI was 3 points, which provided a sensitivity and specificity of 74.5% and 75.7%, respectively.ConclusionsOur results demonstrate that the SOC-ADL is a valid and reliable tool for differentiating dementia from MCI based on an assessment of ADL. This new tool can be used for screening ADL in elderly subjects who have difficulty communicating, and to increase the efficiency of dementia screening at the population level.

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

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

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