Clinical application of the experimental ADL test for patients with cognitive impairment: pilot study

Associations Between Sleep Duration and Activity of Daily Living Disability Among Older Adults in China: Cross-Sectional Study

Background

China has the largest elderly population globally; the growth rate of the aged tendency of the population was higher than that of Western countries. Given the distinctions in historical, ethnic, and economic status as well as socio-cultural background, Chinese adults had different sleep patterns compared with adults in other countries. Considering the heavy disease burden caused by activities of daily living (ADL) disability, we conducted a cross-sectional analysis using data from the China Health and Retirement Longitudinal Study (CHARLS) to test the hypothesis that individuals with short and longer sleep duration are more likely to have ADL disability.

Objective

ADL disability is a common condition affecting the quality of life among older people. This study aimed to explore the associations between sleep duration and ADL disability among middle-aged and older adults in China.

Methods

This cross-sectional study used data from 17,607 participants from the 2018 CHARLS (from 2018 to 2020), an ongoing representative survey of adults aged 45 years or older and their spouses. Self-reported sleep duration per night was obtained from face-to-face interviews. The ADL was measured using a 6-item summary assessed with an ADL scale that included eating, dressing, getting into or out of bed, bathing, using the toilet, and continence. Multiple generalized linear regression models-adjusted for age, sex, education, marital status, tobacco and alcohol use, depression, place of residence, sensory impairment, self-reported health status, life satisfaction, daytime napping, chronic disease condition, and sample weights-were used.

Results

Data were analyzed from 17,607 participants, of whom 8375 (47.6%) were men. The mean (SD) age was 62.7 (10.0) years. Individuals with 4 hours or less (odds ratio [OR] 1.91, 95% CI 1.60-2.27; P<.001), 5 hours (OR 1.33, 95% CI 1.09-1.62; P=.006), 9 hours (OR 1.48, 95% CI 1.13-1.93; P<.001), and 10 hours or more (OR 1.88, 95% CI 1.47-2.14; P<.001) of sleep per night had a higher risk of ADL disability than those in the reference group (7 hours per night) after adjusting for several covariates. Restricted cubic splines analysis suggested a U-shaped association between sleep duration and ADL disability. When sleep duration fell below 7 hours, an increased sleep duration was associated with a significantly low risk of ADL disability, which was negatively correlated with sleep duration until it fell below 7 hours (OR 0.83, 95% CI 0.79-0.87; P<.001). When sleep duration exceeded 7 hours, the risk of ADL disability would increase facing prolonged sleep duration (OR 1.19, 95% CI 1.12-1.27; P<.001). ADL disability should be monitored in individuals with insufficient (≤4 or 5 hours per night) or excessive (9 or ≥10 hours per night) sleep duration.

Unlabelled

In this study, a U-shaped association between sleep duration and ADL disability was found. Future longitudinal studies are needed to establish temporality and examine the mechanisms of the associations between sleep duration and ADL disability.

A scoping review of different monitoring-technology devices in caring for older adults with cognitive impairment

Various monitoring technologies are being developed to prevent potential complications among older adults with cognitive impairment and improve their cognitive function. This scoping review identified gaps in the development of monitoring-technology devices for cognitive health status and highlights areas that require further inquiry. This study used the Joanna Briggs Institute (JBI) and the PRISMA extension for the checklist for scoping reviews using the eligibility criteria recommended by Population, Concept, and Context (PCC) framework. The study population included adults aged 65 years and above, while the concept and context are monitoring-technology devices utilized in detecting and caring for an older adult with cognitive impairment. Three electronic databases (Medline, Scopus, and Web of Science) were searched, and a total of 21 articles met the selection criteria. Several innovative technology-based devices for screening, assessing, detecting, and monitoring the interventions for older adult cognitive impairment and for family caregivers to ensure the continuity of care were established. Monitoring devices are useful in promoting older adult safety, improving their quality of life by enabling them to live independently for a longer period, and improving their mental wellbeing to help reduce the burden on caregivers by providing them with information concerning the activities of older adults. Moreover, studies have shown that older adults and their caregivers can learn to use these devices effectively and comfortably with proper education and training. The results of this study provide crucial insights into innovative technologies that can be used to assess cognitive health among older adults, which could substantially improve their mental health, and this baseline information can be used for supporting public health policy and enhancing their quality of life.

Newly self-administered two-step tool for screening cognitive function in an ageing Chinese population: an exploratory cross-sectional study

Background

Early screening of cognitive function is critical to dementia treatment and care. However, traditional tests require face-to-face administration and are often limited by implementation costs and biases.

Aims

This study aimed to assess whether the Thoven Cognitive Self-Assessment (TCSA), a novel, innovative two-step touchscreen-based cognition assessment tool, could identify early cognitive impairment due to dementia in older adults.

Methods

The TCSA was administered to 61 healthy controls (HCs), 46 participants with mild cognitive impairment (MCI) and 44 participants diagnosed with dementia recruited from Shanghai. Two outcome measures were generated from the TCSA test: the TCSA_{primary task} score and the TCSA_{secondary task} score.

Results

The total average scores in the control group for the TCSA_{primary task} and TCSA_{secondary task} were significantly higher than those in the MCI and dementia groups (TCSA_{primary task}: HCs vs MCI group vs dementia group, 8.58±1.76 vs 5.40±2.67 vs 2.74±2.11, F=75.40, p<0.001; TCSA_{secondary task}: HCs vs MCI group vs dementia group, 23.02±3.31 vs 17.95±4.93 vs 11.93±5.50, F=76.46, p<0.001). Moreover, receiver operating characteristic analysis showed that a score below 7.5 for the TCSA_{primary task} and a score below 22.5 for the TCSA_{secondary task} were indicators of MCI.

Conclusions

The TCSA appears to be efficacious for the detection of cognitive impairment in older adults. It demonstrates the potential for large-scale cognition screening in community service settings.

Development of an Internet of Things Technology Platform (the NEX System) to Support Older Adults to Live Independently: Protocol for a Development and Usability Study

Background

In a rapidly aging population, new and efficient ways of providing health and social support to older adults are required that not only preserve independence but also maintain quality of life and safety.

Objective

The NEX project aims to develop an integrated Internet of Things system coupled with artificial intelligence to offer unobtrusive health and wellness monitoring to support older adults living independently in their home environment. The primary objective of this study is to develop and evaluate the technical performance and user acceptability of the NEX system. The secondary objective is to apply machine learning algorithms to the data collected via the NEX system to identify and eventually predict changes in the routines of older adults in their own home environment.

Methods

The NEX project commenced in December 2019 and is expected to be completed by August 2022. Mixed methods research (web-based surveys and focus groups) was conducted with 426 participants, including older adults (aged ≥60 years), family caregivers, health care professionals, and home care workers, to inform the development of the NEX system (phase 1). The primary outcome will be evaluated in 2 successive trials (the Friendly trial [phase 2] and the Action Research Cycle trial [phase 3]). The secondary objective will be explored in the Action Research Cycle trial (phase 3). For the Friendly trial, 7 older adult participants aged ≥60 years and living alone in their own homes for a 10-week period were enrolled. A total of 30 older adult participants aged ≥60 years and living alone in their own homes will be recruited for a 10-week data collection period (phase 3).

Results

Phase 1 of the project (n=426) was completed in December 2020, and phase 2 (n=7 participants for a 10-week pilot study) was completed in September 2021. The expected completion date for the third project phase (30 participants for the 10-week usability study) is June 2022.

Conclusions

The NEX project has considered the specific everyday needs of older adults and other stakeholders, which have contributed to the design of the integrated system. The innovation of the NEX system lies in the use of Internet of Things technologies and artificial intelligence to identify and predict changes in the routines of older adults. The findings of this project will contribute to the eHealth research agenda, focusing on the improvement of health care provision and patient support in home and community environments.

International Registered Report Identifier (IRRID)

DERR1-10.2196/35277

Automated Classification of Normal Control and Early-Stage Dementia Based on Activities of Daily Living (ADL) Data Acquired from Smart Home Environment

With the global trend toward an aging population, the increasing number of dementia patients and elderly living alone has emerged as a serious social issue in South Korea. The assessment of activities of daily living (ADL) is essential for diagnosing dementia. However, since the assessment is based on the ADL questionnaire, it relies on subjective judgment and lacks objectivity. Seven healthy seniors and six with early-stage dementia participated in the study to obtain ADL data. The derived ADL features were generated by smart home sensors. Statistical methods and machine learning techniques were employed to develop a model for auto-classifying the normal controls and early-stage dementia patients. The proposed approach verified the developed model as an objective ADL evaluation tool for the diagnosis of dementia. A random forest algorithm was used to compare a personalized model and a non-personalized model. The comparison result verified that the accuracy (91.20%) of the personalized model was higher than that (84.54%) of the non-personalized model. This indicates that the cognitive ability-based personalization showed encouraging performance in the classification of normal control and early-stage dementia and it is expected that the findings of this study will serve as important basic data for the objective diagnosis of dementia.