Journal of Health and Biomedical Informatics

Introduction: Due to the relatively high rate of psychological problems among Dementia caregivers, they need necessary information about this disease and methods of care giving. This study aimed to improve caregivers’ awareness on this disease in order to improve their performance and the quality of patient care and to reduce their stress through the provision of web-based health information.
Methods: This clinical trial used intervention and control groups. The sample size was calculated as 25 in each group (total=50). First, subjects were selected through purposive sampling and were randomly divided into the test and control groups. Then, a pre-test was carried out using knowledge and care scale questionnaires. In the next step, through a site designed for this purpose, health information related to the disease was provided to careers in 12 sessions within two months. After the end of the intervention, post-test was performed in both groups and the results were analyzed using descriptive and inferential statistics.
Results: According to the obtained results, mean score of caregiving burden showed no significant change in the control group and it, even, showed a little increase (0.53), while, in the test group, it decreased by 13.58 points.
Conclusion: It can be concluded that the provision of web-based health information is effective in reducing caregiving burden of Dementia caregivers.
 
 

Introduction: The number of elderly people who need help in their daily routines is increasing rapidly. Dementia is one of the most important causes of disability in elderly people and its outbreak has been a major burden on human societies. The purpose of this research was using intelligent home technology to monitor elderly behaviors, identify abnormal behaviors, and discover the initial signs of dementia before the onset of the disease. Early diagnosis of dementia at an early stage can lead to a high improvement in its treatment and delay the disease.
Method: In this applied, descriptive-analytic study, the abnormal behavior and early symptoms of dementia were identified using machine learning techniques.  The kmedoide algorithm was used to analyze abnormal behaviors and to assess the quality of sleep as the primary symptoms of dementia, the valid PSQI questionnaire was used. Matlab 2012 was used for implementation.
Results: The results in the abnormal behavioral section indicated that clustering algorithms have high efficacy in detecting abnormal behavior in smart home, and also results in early symptom examinations led to poor sleep recognition in the PSQI as a primary symptom of dementia.
Conclusion: The behavior of the elderly, their abnormal behavior and early signs of diseases such as dementia can be recognized using the technology of the system under the supervision of the smart home.