Use of Wearable Inertial Sensor in the Assessment of Timed-Up-and-Go Test: Influence of Device Placement on Temporal Variable Estimation

Association Between Depressive Symptoms, Cognitive Status, and the Dual-Task Performance Index in Older Adults: A Cross-Sectional Study

The Performance Index (P-Index) is a measure for evaluating mobility-related dual-task performance in older adults. The identification of specific clinicodemographic factors predictive of P-Index scores, however, remains unclear. This cross-sectional study analyzed data from 120 community-dwelling older adults (average age 71.3 ± 11.23 years) to explore clinicodemographic variables that influence P-Index scores during the instrumented timed up and go test. Unadjusted analyses suggested several factors, including age, gender, body mass index, Mini-Mental Status Examination scores, functional reach test performance, history of falls, ethnicity, Geriatric Depression Scale scores, alcohol consumption, and educational levels, as potential predictors of P-Index. However, adjusted multinomial multiple regression analysis revealed Geriatric Depression Scale and Mini-Mental Status Examination scores as the exclusive independent predictors of P-Index classifications, segmented into high, intermediate, or low (percentiles ≤ 25, 26-74, or ≥ 75, respectively). A significant association was observed between the manifestation of depressive symptoms, lower Mini-Mental Status Examination scores, and reduced cognitive-motor performance. The findings implicate depressive symptoms and low cognitive performance as substantial impediments to optimal dual-task mobility within this cohort. Further studies are warranted to examine the efficacy of cognitive stimulation and antidepressant therapy, in augmenting mobility-related dual-task performance among older adults.

Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease

Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.

Validity of an android device for assessing mobility in people with chronic stroke and hemiparesis: a cross-sectional study

BACKGROUND

Incorporating instrument measurements into clinical assessments can improve the accuracy of results when assessing mobility related to activities of daily living. This can assist clinicians in making evidence-based decisions. In this context, kinematic measures are considered essential for the assessment of sensorimotor recovery after stroke. The aim of this study was to assess the validity of using an Android device to evaluate kinematic data during the performance of a standardized mobility test in people with chronic stroke and hemiparesis.

METHODS

This is a cross-sectional study including 36 individuals with chronic stroke and hemiparesis and 33 age-matched healthy subjects. A simple smartphone attached to the lumbar spine with an elastic band was used to measure participants' kinematics during a standardized mobility test by using the inertial sensor embedded in it. This test includes postural control, walking, turning and sitting down, and standing up. Differences between stroke and non-stroke participants in the kinematic parameters obtained after data sensor processing were studied, as well as in the total execution and reaction times. Also, the relationship between the kinematic parameters and the community ambulation ability, degree of disability and functional mobility of individuals with stroke was studied.

RESULTS

Compared to controls, participants with chronic stroke showed a larger medial-lateral displacement (p = 0.022) in bipedal stance, a higher medial-lateral range (p < 0.001) and a lower cranio-caudal range (p = 0.024) when walking, and lower turn-to-sit power (p = 0.001), turn-to-sit jerk (p = 0.026) and sit-to-stand jerk (p = 0.001) when assessing turn-to-sit-to-stand. Medial-lateral range and total execution time significantly correlated with all the clinical tests (p < 0.005), and resulted significantly different between independent and limited community ambulation patients (p = 0.042 and p = 0.006, respectively) as well as stroke participants with significant disability or slight/moderate disability (p = 0.024 and p = 0.041, respectively).

CONCLUSION

This study reports a valid, single, quick and easy-to-use test for assessing kinematic parameters in chronic stroke survivors by using a standardized mobility test with a smartphone. This measurement could provide valid clinical information on reaction time and kinematic parameters of postural control and gait, which can help in planning better intervention approaches.

The 180° Turn Phase of the Timed Up and Go Test Better Predicts History of Falls in the Oldest-Old When Compared With the Full Test: A Case-Control Study

The 180° turn phase of the test may better differentiate the oldest-old regarding their history of falls. This is a case-control study designed to detect the ability of the 180° turn timed up and go (TUG) phase to detect a history of falls in the oldest-old. Sixty people aged 85 years and older were assessed in their homes. The single-task and dual-task TUG tests were performed using an inertial sensor (G-Walk). Sociodemographic data, physical activity levels, mental status, depressive symptoms, concern for falls occurrence, number of medicines in use, self-perception of balance, and the functional reach test were also assessed. The logistic regressions revealed the 180° turn phase of both the single-task and dual-task TUG was almost three times better than the full TUG test to detect a history of falls, thus providing insights that can be used to better assess functional mobility in the oldest-old.

Quantitative analysis of voluntary movement and anticipatory postural adjustments: a functional approach

Daily living activities and tasks like standing forward reaching present complex Anticipatory Postural Adjustments (APAs), and an objective, repeatable, subject- and task-dependent procedure to detect Voluntary Movements (VM) and APAs onsets is still missing. This paper proposes a new approach to the VMs study, based on a functional mechanical interpretation of the movement performing, which allows defining kinematic and dynamic APAs. A protocol for the identification of VMs and APAs onsets in the reaching movement is presented. Acquired data on 9 healthy young subjects enable a preliminary validation of this method suitability as support for an objective quantification of APAs.