Technology Supported Geriatric Assessment

Self Cite

One of the most common assessments for the mobility of older people is the Timed Up and Go test (TUG). Due to its sensitivity regarding the indication of Parkinson’s disease (PD) or increased fall risk in elderly people, this assessment test becomes increasingly relevant, should be automated and should become applicable for unsupervised self-assessments to enable regular examinations of the functional status. With Inertial Measurement Units (IMU) being well suited for automated analyses, we evaluate an IMU-based analysis-system, which automatically detects the TUG execution via machine learning and calculates the test duration. as well as the duration of its single components. The complete TUG was classified with an accuracy of 96% via a rule-based model in a study with 157 participants aged over 70 years. A comparison between the TUG durations determined by IMU and criterion standard measurements (stopwatch and automated/ambient TUG (aTUG) system) showed significant correlations of 0.97 and 0.99, respectively. The classification of the instrumented TUG (iTUG)-components achieved accuracies over 96%, as well. Additionally, the system’s suitability for self-assessments was investigated within a semi-unsupervised situation where a similar movement sequence to the TUG was executed. This preliminary analysis confirmed that the self-selected speed correlates moderately with the speed in the test situation, but differed significantly from each other.

Section: Resultsmentioning

confidence: 99%

“…These tests were measured in a conventional way by medical professionals and additionally with ambient and wearable technology. More details are described in [ 20 ]. The study has been approved by the appropriate ethics committees (ethical vote: Hannover Medical School, No.…”

Section: Methodsmentioning

confidence: 99%

Towards an Automated Unsupervised Mobility Assessment for Older People Based on Inertial TUG Measurements

Hellmers

Izadpanah

Dasenbrock

et al. 2018

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“…At its current development stage, the proposed application can also work as grounds for additional evolutions, not only to further enhance how it supports CGA, for instance improving how it enables patient management by integration with wider patient registries (along with the authorizations and additional security it requires [ 82 ]), but also adding to how the clinician can obtain relevant patient information. In this regard, the progressive inclusion of a self-assessment [ 83 ] component and sensing technology [ 84 ] might bring additional objectivity and information to the clinical assessment.…”

Section: Discussionmentioning

confidence: 99%

Geriatric Helper: An mHealth Application to Support Comprehensive Geriatric Assessment

Silva

Felgueiras

Oliveira

2018

The Comprehensive Geriatric Assessment (CGA) is a multidisciplinary diagnosis approach that considers several dimensions of fragility in older adults to develop an individualized plan to improve their overall health. Despite the evidence of its positive impact, CGA is still applied by a reduced number of professionals in geriatric care in many countries, mostly using a paper-based approach. In this context, we collaborate with clinicians to bring CGA to the attention of more healthcare professionals and to enable its easier application in clinical settings by proposing a mobile application, Geriatric Helper, to act as a pocket guide that is easy to update remotely with up-to-date information, and that acts as a tool for conducting CGA. This approach reduces the time spent on retrieving the scales documentation, the overhead of calculating the results, and works as a source of information for non-specialists. Geriatric Helper is a tool for the health professionals developed considering an iterative, User-Centred Design approach, with extensive contributions from a broad set of users including domain experts, resulting in a highly usable and accepted system. Geriatric Helper is currently being tested in Portuguese healthcare units allowing for any clinician to apply the otherwise experts-limited geriatric assessment.

“…The Machine-Learning (ML) Model was trained on an annotated dataset of a previous study covering movements of older adults over 70 years while conducting functional assessments in a supervised setting [28]. The data of the training set were recorded with the same IMU.…”

Section: Machine Learning Model For Movement Classificationmentioning

confidence: 99%

Measurement System for Unsupervised Standardized Assessment of Timed “Up & Go” and Five Times Sit to Stand Test in the Community—A Validity Study

Fudickar

Hellmers

Lau

et al. 2020

Self Cite

Comprehensive and repetitive assessments are needed to detect physical changes in an older population to prevent functional decline at the earliest possible stage and to initiate preventive interventions. Established instruments like the Timed “Up & Go” (TUG) Test and the Sit-to-Stand Test (SST) require a trained person (e.g., physiotherapist) to assess physical performance. More often, these tests are only applied to a selected group of persons already functionally impaired and not to those who are at potential risk of functional decline. The article introduces the Unsupervised Screening System (USS) for unsupervised self-assessments by older adults and evaluates its validity for the TUG and SST. The USS included ambient and wearable movement sensors to measure the user’s test performance. Sensor datasets of the USS’s light barriers and Inertial Measurement Units (IMU) were analyzed for 91 users aged 73 to 89 years compared to conventional stopwatch measurement. A significant correlation coefficient of 0.89 for the TUG test and of 0.73 for the SST were confirmed among USS’s light barriers. Correspondingly, for the inertial data-based measures, a high and significant correlation of 0.78 for the TUG test and of 0.87 for SST were also found. The USS was a validated and reliable tool to assess TUG and SST.