scholarly journals Consensus on the Definition of Advanced Parkinson’s Disease: A Neurologists-Based Delphi Study (CEPA Study)

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Maria-Rosario Luquin ◽  
Jaime Kulisevsky ◽  
Pablo Martinez-Martin ◽  
Pablo Mir ◽  
Eduardo S. Tolosa
Keyword(s):  
Activities Of Daily Living ◽  
Movement Disorders ◽  
Delphi Study ◽  
Daily Living ◽  
Ad Hoc ◽  
Motor Fluctuations ◽  
Key Factors ◽  
Advanced Parkinson’S Disease ◽  
Definition Of

To date, no consensus exists on the key factors for diagnosing advanced Parkinson disease (APD). To obtain consensus on the definition of APD, we performed a prospective, multicenter, Spanish nationwide, 3-round Delphi study (CEPA study). An ad hoc questionnaire was designed with 33 questions concerning the relevance of several clinical features for APD diagnosis. In the first-round, 240 neurologists of the Spanish Movement Disorders Group participated in the study. The results obtained were incorporated into the questionnaire and both, results and questionnaire, were sent out to and fulfilled by 26 experts in Movement Disorders. Review of results from the second-round led to a classification of symptoms as indicative of “definitive,” “probable,” and “possible” APD. This classification was confirmed by 149 previous participating neurologists in a third-round, where 92% completely or very much agreed with the classification. Definitive symptoms of APD included disability requiring help for the activities of daily living, presence of motor fluctuations with limitations to perform basic activities of daily living without help, severe dysphagia, recurrent falls, and dementia. These results will help neurologists to identify some key factors in APD diagnosis, thus allowing users to categorize the patients for a homogeneous recognition of this condition.

Analysis of Functional and Cognitive Impairment in Institutionalized Individuals with Movement Disorders

2019 ◽  
Vol 19 (7) ◽  
pp. 1022-1031 ◽  
Author(s):  
Paula D. Cebrián ◽  
Omar Cauli
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Activities Of Daily Living ◽  
Cognitive Performance ◽  
Functional Impairment ◽  
Movement Disorders ◽  
Neurological Disorders ◽  
Daily Living ◽  
Severe Cognitive Impairment

Background: Many neurological disorders lead to institutionalization and can be accompanied in their advanced stages by functional impairment, and progressive loss of mobility, and cognitive alterations. Objective: We analyzed the relationship between functional impairment and cognitive performance and its related subdomains in individuals with Parkinson’s disease, Alzheimer’s disease accompanied by motor dysfunction, and with other neurological disorders characterized by both motor and cognitive problems. Methods: All participants lived in nursing homes (Valencia, Spain) and underwent cognitive evaluation with the Mini-Mental State Examination; functional assessment of independence in activities of daily living using the Barthel score and Katz index; and assessment of mobility with the elderly mobility scale. Results: The mean age of the subjects was 82.8 ± 0.6 years, 47% of the sample included individuals with Parkinson’s disease, and 48 % of the sample presented severe cognitive impairment. Direct significant relationships were found between the level of cognitive impairment and functional capacity (p < 0.01) and mobility (p < 0.05). Among the different domains, memory impairment was not associated with altered activities of daily living or mobility. The functional impairment and the risk of severe cognitive impairment were significantly (p<0.05) higher in female compared to male patients. Among comorbidities, overweight/obesity and diabetes were significantly (p < 0.05) associated with poor cognitive performance in those individuals with mild/moderate cognitive impairment. Conclusion: In institutionalized individuals with movement disorders there is an association between functional and cognitive impairment. Reduction of over-weight and proper control of diabetes may represent novel targets for improving cognitive function at such early stages.

scholarly journals A Modular Mobile Robotic Platform to Assist People with Different Degrees of Disability

2021 ◽  
Vol 11 (15) ◽  
pp. 7130
Author(s):  
Jose M. Catalan ◽  
Andrea Blanco ◽  
Arturo Bertomeu-Motos ◽  
Jose V. Garcia-Perez ◽  
Miguel Almonacid ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Occupational Therapists ◽  
Living Room ◽  
Key Factors ◽  
Robotic Platform ◽  
Robotic Exoskeleton ◽  
Simulated Home Environment ◽  
Wheel Chair ◽  
The Subject

Robotics to support elderly people in living independently and to assist disabled people in carrying out the activities of daily living independently have demonstrated good results. Basically, there are two approaches: one of them is based on mobile robot assistants, such as Care-O-bot, PR2, and Tiago, among others; the other one is the use of an external robotic arm or a robotic exoskeleton fixed or mounted on a wheelchair. In this paper, a modular mobile robotic platform to assist moderately and severely impaired people based on an upper limb robotic exoskeleton mounted on a robotized wheel chair is presented. This mobile robotic platform can be customized for each user’s needs by exploiting its modularity. Finally, experimental results in a simulated home environment with a living room and a kitchen area, in order to simulate the interaction of the user with different elements of a home, are presented. In this experiment, a subject suffering from multiple sclerosis performed different activities of daily living (ADLs) using the platform in front of a group of clinicians composed of nurses, doctors, and occupational therapists. After that, the subject and the clinicians replied to a usability questionnaire. The results were quite good, but two key factors arose that need to be improved: the complexity and the cumbersome aspect of the platform.

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

2021 ◽  
Vol 18 (24) ◽  
pp. 13235
Author(s):  
Lee-Nam Kwon ◽  
Dong-Hun Yang ◽  
Myung-Gwon Hwang ◽  
Soo-Jin Lim ◽  
Young-Kuk Kim ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Normal Control ◽  
Daily Living ◽  
Smart Home ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Evaluation Tool ◽  
Dementia Patients ◽  
Diagnosis Of Dementia

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.

A review of instrumental ADL assessments for use with elderly people

1998 ◽  
Vol 8 (1) ◽  
pp. 65-71 ◽  
Author(s):  
Gillian Ward ◽  
Carol Jagger ◽  
William Harper
Keyword(s):  
Activities Of Daily Living ◽  
Standardized Tests ◽  
Elderly People ◽  
Daily Living ◽  
Self Care ◽  
Primary Level ◽  
Instrumental Activities ◽  
Hierarchical Nature ◽  
The 1960S ◽  
Definition Of

The concept of formal or standardized tests for assessing function came to the fore in the 1960s. Katz et al. acknowledged the hierarchical nature of activities of daily living (ADL) such as eating, continence, transferring, going to the toilet, dressing and bathing in his ’Index of ADL’ and by 1968 ‘ADL’ was an accepted Index Medicus category. The definition of instrumental activities of daily living (IADL) began in 1969 with the work of Lawton and Brody who presented two scales to assess function which recognized the different degrees of complexity required for performing functional tasks. The first scale, taking life maintenance and activities essential for self-care as the primary level, was called the Physical Self-Maintenance Scale.

scholarly journals Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

2015 ◽  
Vol 14 (1) ◽  
Author(s):  
Prabitha Urwyler ◽  
Luca Rampa ◽  
Reto Stucki ◽  
Marcel Büchler ◽  
René Müri ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Healthy Subjects ◽  
Ad Hoc

scholarly journals Classification of Activities of Daily living using Smart Phones

2019 ◽  
Vol 8 (3) ◽  
pp. 2984-2988
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Human Life ◽  
Smart Phone ◽  
Smart Phones ◽  
Machine Learning Techniques ◽  
Accelerometer Sensor ◽  
Learning Techniques ◽  
Direct Input

Smart phones have become an integral part of everyday human life. These phones are packed with various sensors for different purposes. Most of them are used for understanding the environment in which the user uses the phone so that the device could respond rapidly. Indirectly the phone extracts context information of the users like the activity performed using accelerometer and gyroscope sensors. This information can be used for a variety of applications like home automation, smart environment, etc to perform automatic changes to the environment without direct input from the user. This paper deals with the classification of activities of daily living like walking, jogging, sitting, standing, upstairs and downstairs using the data collected from accelerometer sensor within the smart phone. A comparative analysis has been performed on different machine learning techniques for activity classification.

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