Elderly caregiver: knowledge, attitudes and practices about falls and its prevention

From basic care to beyond: A Q methodology study into the English communication needs among Thai caregivers of foreign older adults

A shift toward the aging population worldwide brings about a growing demand of caregivers, who can communicate effectively with their care recipients. Using Q methodology, this study investigates the English communication needs among Thai caregivers of foreign older adults, aiming to profile the specific tasks that necessitate effective intercultural communication. Data were collected through card-sorting task and follow-up interviews. The findings show that caregiver's target tasks can be classified into hands-on nurturers, emotional supporters, and trusted companions. The hands-on nurturers focused on tasks requiring direct physical care and day-to-day assistance, emphasizing the role of English in activities such as bathing and aiding with hygiene. The emotional supporters recognized the importance of English in providing psychological and emotional comfort. Trusted companions placed value on English for fostering social connections, engaging in leisurely activities, and facilitating casual exchanges. This study highlights Thai caregivers' multifaceted roles, stressing the necessity for comprehensive English training for intercultural communication in caregiving.

Household Need and Unmet Need for Caregivers of Older Persons in Thailand

The need for caregivers is a crucial issue in Thailand. This research examined levels and trends of household needs and unmet needs for caregivers of older persons and explored potential factors associated with these needs. The analysis utilized data from the Survey of Older Persons in Thailand 2007, 2011, 2014, and 2017. The household need for a caregiver of older persons was defined as a household with one or more older people who needed a caregiver to help them perform basic activities of daily living. The unmet need for a caregiver referred to households where at least one older person in the household needed care but did not receive it. Findings illustrated the increasing levels and trends of household needs as well as unmet needs over time. In terms of potential determinants, older person households in Bangkok and households with higher socioeconomic status were more likely to be the household need for caregivers. In contrast, those households in the Northeastern, the poorest region, were more likely to be the unmet need household. These findings are indicative of the rising demand for long-term care services in Thailand. However, it is vital to consider unmet household needs, especially in the worse-off area, when designing national policies.

A model for predicting fall risks of hospitalized elderly in Taiwan-A machine learning approach based on both electronic health records and comprehensive geriatric assessment

Backgrounds

Falls are currently one of the important safety issues of elderly inpatients. Falls can lead to their injury, reduced mobility and comorbidity. In hospitals, it may cause medical disputes and staff guilty feelings and anxiety. We aimed to predict fall risks among hospitalized elderly patients using an approach of artificial intelligence.

Materials and methods

Our working hypothesis was that if hospitalized elderly patients have multiple risk factors, their incidence of falls is higher. Artificial intelligence was then used to predict the incidence of falls of these patients. We enrolled those elderly patients aged >65 years old and were admitted to the geriatric ward during 2018 and 2019, at a single medical center in central Taiwan. We collected 21 physiological and clinical data of these patients from their electronic health records (EHR) with their comprehensive geriatric assessment (CGA). Data included demographic information, vital signs, visual ability, hearing ability, previous medication, and activity of daily living. We separated data from a total of 1,101 patients into 3 datasets: (a) training dataset, (b) testing dataset and (c) validation dataset. To predict incidence of falls, we applied 6 models: (a) Deep neural network (DNN), (b) machine learning algorithm extreme Gradient Boosting (XGBoost), (c) Light Gradient Boosting Machine (LightGBM), (d) Random Forest, (e) Stochastic Gradient Descent (SGD) and (f) logistic regression.

Results

From modeling data of 1,101 elderly patients, we found that machine learning algorithm XGBoost, LightGBM, Random forest, SGD and logistic regression were successfully trained. Finally, machine learning algorithm XGBoost achieved 73.2% accuracy.

Conclusion

This is the first machine-learning based study using both EHR and CGA to predict fall risks of elderly. Multiple risk factors of falls in hospitalized elderly patients can be put into a machine learning model to predict future falls for early planned actions. Future studies should be focused on the model fitting and accuracy of data analysis.

Factors Associated with the Acceptance of New Technologies for Ageing in Place by People over 64 Years of Age

Background: In the context of growing population ageing, technologies aimed at helping people age in place play a fundamental role. Acceptance of the implementation of technological solutions can be defined as the intention to use a technology or the effective use of it. Approaches based on the technology acceptance model (TAM) have been shown to have good predictive power for pre-implementation attitudes towards new technologies. Objective: To analyze the degree of acceptability of the use of new technologies for ageing in place and the factors associated with greater acceptance in people older than 64 years. Methodology: A descriptive cross-sectional study was carried out. Sociodemographic, clinical and environmental variables, architectural barriers, social risk and quality of life, degree of autonomy, morbidity, and risk of falls were collected in a population sample over 64 years of age in a large region of western Spain. The degree of acceptance of the use of technologies was measured through a scale based on the TAM. Results: Of the 293 people included in the study, 36.2% exhibited a high acceptability of new technologies, 28.3% exhibited a medium acceptability, and 35.5% exhibited a low acceptability. Of all the factors, age, education level, and living alone were significantly associated with high acceptance in the adjusted analyses. Conclusions: Younger age, a higher education level, and living alone are factors associated with a greater degree of acceptance of the use of technologies for ageing in place.

Factors associated with risk, perception and knowledge of falls in elderly people

OBJECTIVE

To verify the factors associated with risk, perception, and knowledge of falls; and pain among older adults.

METHOD

A cross-sectional study carried out in the Emergency Service of a teaching hospital in the city of São Paulo between September 2019 and March 2020. We selected 197 older adults aged 65 and over, who were not disoriented or confused, of both genders. The instruments Awareness Questionnaire on the Risk of Falls, Morse Fall Scale and Numerical Pain Scales were applied. Mann-Whitney and Kruskal-Wallis tests were used.

RESULTS

Interviewees with a high risk of falls in older adults (p = 0.0041); those with a support network had a lower perception and knowledge about the risk of falls (p = 0.0025) and lower percentage of severe pain (p = 0.0033).

CONCLUSION

Factors associated with risk, perception and knowledge of falls and pain among older adults were age, family income, number of dependents, caregiver, support network, hypertension, impaired walking, antihypertensive medication, lipid-lowering medication, level of education, comorbidities and religion.