Naomi Burg

Advisors

Hamid Moradi (M.Sc.), Malte Ollenschläger (M.Sc.), Prof. Dr. Björn Eskofier

Duration

12 / 2022 – 05 / 2023

Abstract

Functional independence in activities of daily living (ADL) is an important indicator of the rehabilitation process in geriatric patients.[1]. Professional caregivers can assess the need for care and disease progression by measuring the autonomy of ADL. The underlying predictive nature of ADL has already been proven effective in the 1980s for actions like admission to a nursing home, use of paid home care, use of hospital and physician services, living arrangements, and mortality[1]. Rosenberg et al. showed that ADL indicators such as frailty and health status predict death, nursing home transfer, and hospital admission more strongly than medical diagnoses [2]. The most prevalent score to assess ADL is the Barthel index (BI). BI contains ten sub-scores, including gait-related items such as chair transfer, ambulation, and stair climbing. The non-gaitrelated items include feeding, bathing, dressing, and bladder control [3, 4]. Geriatric patients with Sarcopenia- loss of muscle mass and strength in the elderly- are a group of patients who require repetitive BI evaluation [5]. However, caregivers cannot follow up on the BI’s assessment periodically and accurately after the discharge at home.

Timely admissions to nursing homes or hospitals could be achieved by recognizing deterioration in a patient’s ADL remotely, regularly, and accurately [2]. Thus, fatal or life-endangering injuries could be prevented, and a better quality of life could be established. Moreover, deeper insights into patients’ health conditions at home would be possible [6]. A practical solution for remote assessment of BI could be using instrumented gait analysis.

Chang et al. discovered that gait-related BI subscores correlate to gait speed and stance time. Their implemented linear regression analysis revealed a weak correlation with R2 values of 0.096 to 0.181. Therefore, their method is not robust for the BI prediction from the mentioned gait parameters [7]. Potter et al. showed that the assessment of gait speed allows one to predict whether a patient is dependent on one or more activities of daily living, but they did not establish a predictive model for the BI using gait parameters [8]. Friedrich et al. illustrated the possibility of predicting clinical scores, such as the Timed Up & Go test, from wearable sensors. Nonetheless, their analysis was based on a small cohort of 20 patients. Consequently, their model could not be generalized and applied to other patients[9]. Including other parameters and a larger cohort might result in a better outcome.

This study investigates the predictability between the gait-dependent sub-scores of BI, namely ambulation, chair transfer, and stair climbing with the total score of BI. The student will also examine the spatio-temporal gait parameters extracted from the IMU data to estimate the three mentioned sub-scores and, respectively, the overall BI.

This study will answer how accurate IMU instrumented standardized gait tests and the calculated gait parameters can determine the independence in ADL.

References

[1] Joshua M Wiener u. a. “Measuring the activities of daily living: Comparisons across national surveys”. In: Journal of gerontology 45.6 (1990), S229–S237.
[2] Ted Rosenberg u. a. “Using frailty and quality of life measures in clinical care of the elderly in Canada to predict death, nursing home transfer and hospitalisation-the frailty and ageing cohort study”. In: BMJ open 9.11 (2019), e032712.
[3] C. Collin u. a. “The Barthel ADL Index: A reliability study”. In: International Disability Studies 10.2 (1988). PMID: 3403500, S. 61–63. doi: 10.3109/09638288809164103. eprint: https://doi.org/10.3109/09638288809164103. url: https://doi.org/10.3109/09638288809164103.
[4] Terence J Quinn, Peter Langhorne und David J Stott. “Barthel index for stroke trials: development, properties, and application”. In: Stroke 42.4 (2011), S. 1146–1151.
[5] Dolores Sánchez-Rodríguez u. a. “Sarcopenia, physical rehabilitation and functional outcomes of patients in a subacute geriatric care unit”. In: Archives of Gerontology and Geriatrics 59.1 (2014), S. 39–43. issn: 0167-4943. doi: https://doi.org/10.1016/j.archger.2014.02.009. url: https://www.sciencedirect.com/science/article/pii/S0167494314000272.
[6] Francesco Landi u. a. “Sarcopenia as a risk factor for falls in elderly individuals: Results from the ilSIRENTE study”. In: Clinical Nutrition 31.5 (2012), S. 652–658. issn: 0261-5614. doi: https://doi.org/10.1016/j.clnu.2012.02.007. url: https://www.sciencedirect.com/science/article/pii/S0261561412000362.
[7] Min Cheol Chang u. a. “The parameters of gait analysis related to ambulatory and balance functions in hemiplegic stroke patients: a gait analysis study”. In: BMC neurology 21.1 (2021), S. 1–8.
[8] Jan M Potter, Alan L Evans und George Duncan. “Gait speed and activities of daily living function in geriatric patients”. In: Archives of physical medicine and rehabilitation 76.11 (1995), S. 997–999.
[9] Björn Friedrich u. a. “A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data”. In: Healthcare 9.2 (2021). issn: 2227-9032. doi: 10.3390/healthcare9020149. url: https://www.mdpi.com/2227-9032/9/2/149.